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RPE secretome for the treatment of retinal degeneration in the RCS rat

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RPE secretome for the treatment of retinal degeneration in the RCS rat

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Content Copyright 2023 Kabir Ahluwalia

RPE Secretome for the Treatment of Retinal Degeneration in the RCS Rat

by

Kabir Tylor Ahluwalia

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(CLINICAL AND EXPERIMENTAL THERAPEUTICS)

August 2023
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DEDICATION
I dedicate this thesis to my friends and family, whose love and support allowed me to make
it this far and achieve all I have. To Tyler, Khalil, and Jeff, you three will always be my best friends
and my chosen family, thank you for telling me I was not smart enough to do engineering. To my
siblings, Radhika, Maya, Landis, and Chris, through tough-love and sometimes compassion, you
taught me to be strong enough to get through this. To my newer siblings, Caleb, Yza, Vik, and
Radha, who gave me new perspectives on the world, you taught me how to tackle problems in
more than one way. To my parents of the last ten years, Sanjay and Anju, who live vigorously and
give selflessly, you taught me the happiness and freedom that comes from embodying dharma and
karma. To my dog Blue, who grew up in a research lab too and knows the struggles of lab-life as
well as anyone else, you taught me to not take life too seriously and to always take time to celebrate
the small victories. To my parents, Virender and Debbie, who raised me with the entirety of their
love, you are the reason I am who I am, you taught me to be strong, caring, curious, and that
nothing was impossible. To my wife, Palak, who deserves more than a few words, who has given
me more than I could ever repay, who has been by my side through every step, and who has
supported me every day, you complete me in every way and give me the drive to continuously
improve myself to try and keep up with you. And lastly, to my unborn son, affectionally called
Nimbu (lemon in Hindi), you gave me the kick I needed to write my dissertation. I love you, all,
but mostly Blue, and now Nimbu.

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ACKNOWLEDGEMENTS
I have received immense support and guidance throughout my PhD training. First, I
would like to thank my advisor, Dr. Stan Louie. I am fortunate to have an advisor who teaches me
a well-rounded view drug development and who always kept the goal centered on helping patients.
Especially during the writing of this dissertation, this would not have been possible without your
valuable guidance. I am grateful for everything you taught me in lab and beyond. Thank you to
my committee members, Dr. Enrique Cadenas, Dr. Liana Asatryan, and Dr. Curtis Okamoto. To
all of you, thank you for your guidance and for identifying areas of my research I needed to explore
deeper which helped me to develop as a scientist. More than that, thank you for your kind words
which encouraged me as a scientist and kept me going through the struggle. I would like to thank
Dr. Junji Watanabe for teaching me how to use all the equipment to do my research and for being
available late at night or on the weekends when things were going south.
Lastly, I want to thank all my lab mates who have supported me throughout this PhD. To
Dr. Isaac Asante and Eugene Zhou, you were the first members I met in the lab, gave me the most
unique insights, and always challenged my preconceived notions which helped me grow critically.
Not only that, but you also showed me what it looks like on the other side of the PhD, thank you
for being true inspirations. To Priyal, you are one of my closest friends in the lab, thank you for
letting me prank you as much as I did, and, as my last prank, thank you for taking over ordering.
To all of my lab mates, past and present, Hua, Tracey, Tiange, Andrew, Rita, Angela, Brandon,
Cindy, Cathy, Yahya, Malika, Darryl, Aditya, Kingsley, Mehri, Rachael, Catherine, and Dimitri,
I know I have not been in the lab as I have been drafting my dissertation, I am sorry to have left
all of you leaning and I am sad that I do not get to speak with you daily. In the future when I say,
“you had to be there,” you were the ones who were, and you are my “band of brothers.”

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TABLE OF CONTENTS
Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables ................................................................................................................................. vi
List of Figures ............................................................................................................................... vii
Abstract ........................................................................................................................................... x
Chapter 1: Introduction ................................................................................................................... 1
1.1 Structure and Function of the Retina ................................................................ 1
1.2 Retinal Degenerative Diseases .......................................................................... 3
1.3 Epidemiology of Visual Impairment ................................................................ 7
1.4 Risk Factors for Retinal Degeneration .............................................................. 9
1.5 Mechanisms of Retinal Degeneration ............................................................. 10
1.6 Royal College of Surgeons Rat ....................................................................... 21
1.7 Therapeutic Strategies for Retinal Degeneration ............................................ 25
1.8 The RPE Secretome ........................................................................................ 26
1.9 Hypothesis and Specific Aims ........................................................................ 27
1.10 Outline of the Dissertation ............................................................................ 29
1.11 References ..................................................................................................... 30
Chapter 2: Natural Progression And Novel Molecular Markers Of Retinal Degeneration
In The Rcs Rat .............................................................................................................................. 46
2.1 Introduction ..................................................................................................... 47
2.2 Methods........................................................................................................... 48
2.3 Results ............................................................................................................. 54
2.4 Discussion ....................................................................................................... 69
2.5 Funding Sources and Acknowledgements ...................................................... 75
2.6 References ....................................................................................................... 75
2.7 Supplementary Tables and Figures ................................................................. 80
Chapter 3: A profile of transcriptomic and lipidomic changes in the retinal dystrophic
RCS rat .......................................................................................................................................... 81
3.1 Introduction ..................................................................................................... 82
3.2 Methods........................................................................................................... 84
3.3 Results ............................................................................................................. 88
3.4 Discussion ..................................................................................................... 102
3.5 Funding Sources and Acknowledgements .................................................... 106
3.6 References ..................................................................................................... 106
3.7 Supplementary Tables and Figures ............................................................... 110

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Chapter 4: Polarized Rpe Secretome Reduces Oxidative Stress And Preserves
Photoreceptors In Retinal Dystrophic Rcs Rats .......................................................................... 118
4.1 Introduction ................................................................................................... 119
4.2 Methods......................................................................................................... 121
4.3 Results ........................................................................................................... 130
4.4 Discussion ..................................................................................................... 143
4.5 Author Contributions .................................................................................... 147
4.6 Funding Sources and Acknowledgements .................................................... 147
4.7 References ..................................................................................................... 148
4.8 Supplementary Tables and Figures ............................................................... 154
Chapter 5: Summary and Concluding Remarks .......................................................................... 160
5.1 References ..................................................................................................... 167
Complete References .................................................................................................................. 171

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LIST OF TABLES
Table 1.1 Comparison of Retinal Pathologies .............................................................................. 24
Table 2.1 Experimental Design ..................................................................................................... 49
Table 3.1 Experimental Design ..................................................................................................... 84
Table 3.2 Microglial Top Upregulated and Downregulated DEGs .............................................. 94
Table 3.3 Pathway Analysis Predicted Master Regulators ........................................................... 94
LIST OF SUPPLEMENTARY TABLES
Supplementary Table T2.1 RT-qPCR Primer Sequences ............................................................. 80
Supplementary Table T2.2 Western Blot Antibodies ................................................................... 80
Supplementary Table T2.3 Immunofluorescence Antibodies ...................................................... 80
Supplementary Table T3.1 Lipidomics MRM Table .................................................................. 116
Supplementary Table T3.2 RT-qPCR Primer Sequences ........................................................... 117
Supplementary Table T4.1 Sigma-Aldrich Primers .................................................................... 158
Supplementary Table T4.2 ARPE-19 Human Primers ............................................................... 158
Supplementary Table T4.3 Antibodies ....................................................................................... 159

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LIST OF FIGURES
Figure 1.1 The basic retinal structure. ............................................................................................ 3
Figure 1.2 Clinical manifestation of AMD from the early to late stage. ........................................ 5
Figure 1.3 Normal retina compared with nonproliferative diabetic retinopathy
(NPDR) with diabetic macular edema (DME). ................................................................... 7
Figure 1.4 Global Epidemiology of Visual Impairment. ................................................................ 9
Figure 1.5 Photoreceptor outer segment phagocytosis by RPE cells. ........................................... 22
Figure 2.1 Structural and function comparison of RCS and iRCS rats from p21 to
p90..................................................................................................................................... 55
Figure 2.2 Histology, OCT, and ERG Measurements in RCS rats. .............................................. 57
Figure 2.3 Longitudinal FA and FAF follow-up of an RCS rat at ages p21 to p90. .................... 59
Figure 2.4 Autofluorescence and immunostaining of RCS rats. .................................................. 60
Figure 2.5 Fold change of inflammation and oxidative-stress related gene
expression changes compared to p21. ............................................................................... 62
Figure 2.6 Representative immunofluorescence staining and quantitation of 4HNE
and MDA in RCS retinas throughout retinal degeneration. .............................................. 64
Figure 2.7 Representative immunofluorescence staining of PAD4 in RCS retinas
from p21 to p60. ................................................................................................................ 66
Figure 2.8 Citrullinated Histone H3 (CitH3) in RCS retinas throughout retinal
degeneration. ..................................................................................................................... 68
Figure 3.1 RNA-Seq differentially expressed genes in RCS rats. ................................................ 90
Figure 3.2 Top Effected Pathways in RCS Rat. ............................................................................ 93

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Figure 3.3 Lipoxygenase enzymes upstream of specialized pro-resolving lipid
mediators. .......................................................................................................................... 96
Figure 3.4 PCA-DA of RCS plasma lipidomics. .......................................................................... 98
Figure 3.5 Cytoscape analysis of DHA metabolism. .................................................................. 100
Figure 3.6 Correlation of retinal structure and function with lipidomics. .................................. 102
Figure 4.1 Diagram and timeline of polarized retinal pigmented epithelial soluble
factor (PRPE-SF) production .......................................................................................... 122
Figure 4.2 fRPCs treated with SF3 showed significant decrease in cell death ........................... 131
Figure 4.3 Rhodopsin and DAPI staining of fRPC ..................................................................... 132
Figure 4.4 Mean Fold change of fetal RPC gene expression after 24-hour incubation
compared to XV1 ............................................................................................................ 134
Figure 4.5 H&E, OCT, and ERG of p60 RCS retinas ................................................................ 136
Figure 4.6 Immunofluorescence images of p60 iRCS rat retinas treated with XV3
and SF3. .......................................................................................................................... 138
Figure 4.7 Effects of PRPE-SF on retinal reactive oxygen species. ........................................... 140
Figure 4.8 PAD4 immunofluorescence images of p60 iRCS rats .............................................. 142
Figure 5.1 Graphical summary of PRPE-SF effects. .................................................................. 167
LIST OF SUPPLEMENTARY
Supplementary Figure S3.1 Phototransduction pathway in p60 vs. p21 RCS rats. .................... 110
Supplementary Figure S3.2 Top Upregulated and Downregulated Pathways by Z-
Score. .............................................................................................................................. 111
Supplementary Figure S3.3 Canonical pathways analysis match between RCS and
human AMD. .................................................................................................................. 112

ix
Supplementary Figure S3.4 Principal component analysis of plasma and ocular
lipidomics. ....................................................................................................................... 113
Supplementary Figure S3.5 Graphs of identified PC loadings. .................................................. 114
Supplementary Figure S3.6 Cytoscape analysis of AA, DPA, and EPA. ................................... 115
Supplementary Figure S4.1 Grading Score System by OCT. ..................................................... 154
Supplementary Figure S4.2 Mean ± SEM Fold change of fetal RPC genetic
expression ....................................................................................................................... 155
Supplementary Figure S4.3 Scotopic b-wave amplitudes of XV3 and SF3 treated
and untreated eyes ........................................................................................................... 156
Supplementary Figure S4.4 Immunofluorescence staining and quantitation of
4HNE and MDA in RCS retinas ..................................................................................... 157

x
ABSTRACT
Retinal degenerative diseases, including age-related macular degeneration (AMD),
diabetic retinopathy (DR), and retinitis pigmentosa (RP), are significant challenges in
ophthalmology due to limited treatment options arising from complex etiologies. Novel
therapeutic approaches require accurate pre-clinical models for development. Although the Royal
College of Surgeons (RCS) rat is a popular retinal degeneration model, its molecular changes and
relevance to human pathology need further investigation.
This study conducted a comprehensive analysis of retinal degeneration in
immunocompetent and immunodeficient RCS rat models, using clinically relevant methodologies
such as electroretinography (ERG) and optical coherence tomography (OCT). Longitudinal
analyses found strong correlations between histology, OCT, and ERG, with scotopic a-wave as the
earliest indicator of retinal degeneration.
The study also investigated molecular changes related to inflammation and oxidative stress
mechanisms. Key findings include the upregulation of Gfap, Tnfa, and Nox1 genes, and age-
dependent increases in oxidative stress markers MDA and 4HNE. Microglial response emerged as
an early driver of pathological gene expression, with strong relationships between inflammation,
ROS gene expression, MDA and 4HNE levels, and retinal degeneration observed. Targeting
microglial response and associated pathways may offer therapeutic potential for retinal
degenerative diseases.
We performed a transcriptomic and lipidomic analysis of RCS rat retinal degeneration,
identifying cell-type-specific differentially expressed genes (DEGs) primarily associated with
microglial cells. Significant similarities between human AMD and RCS rat transcriptomic changes
further validate the model for therapeutic development. The pathogenic loss of Alox15 gene

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expression and inverse expression of Alox15 and Alox5 suggest a depletion of specialized pro-
resolving lipid mediators (SPMs), impairing inflammation shutdown. Correlations between SPM
loss and retinal function loss indicate potential plasma biomarkers for therapy development.
Stem cell-based therapies, particularly human embryonic stem cell-derived retinal pigment
epithelium (hESC-RPE), have shown promise for replacing damaged retinal cells and preserving
ocular function. We explored the therapeutic potential of stem cell-derived retinal pigment
epithelium soluble factors (PRPE-SF) for retinal degeneration treatment. PRPE-SF employs a
biomimetic membrane for RPE polarization and maturation, modulating secretome composition
towards a protective phenotype. Treatment with PRPE-SF promoted progenitor cell proliferation,
multipotency, and neuronal fate determination gene expression, enhanced photoreceptor
developmental gene expression, reduced cell death, preserved rhodopsin staining, and blocked pro-
apoptotic pathways. Additionally, PRPE-SF treatment preserved photoreceptors and delayed
retinal function decline through antioxidant mechanisms, reduced oxidative stress markers, and
inflammatory cell infiltration in the RCS retina.
This study demonstrates the potential of PRPE-SF as a promising therapeutic approach for
retinal degeneration, providing a step towards developing effective therapeutics for debilitating
retinal pathologies, such as AMD, DR, and RP. Further characterization and identification of the
most critical active components within PRPE-SF are necessary to meet regulatory requirements
and improve the reproducibility of an effective PRPE-SF product. Nevertheless, our findings
showcase PRPE-SF as a promising candidate for treating retinal pathologies, paving the way for
the development of effective therapeutics for these debilitating conditions and setting a precedent
for age-related and neurodegenerative disease therapeutic strategies.

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CHAPTER 1: INTRODUCTION
1.1 STRUCTURE AND FUNCTION OF THE RETINA
The retina is a layered structure composed of multiple specialized cell types which
coordinate the processing of incoming light to neuronal signals to produce vision (1). The retina
is divided into two major layers, the inner and outer retina. The cells are divided into four major
classes: neurons, glial cells, vascular cells, and the retinal pigment epithelium (RPE). The outer
retina consists of the photoreceptor neurons, the RPE, and the choroidal vasculature which is the
blood supply for the RPE and photoreceptors. The inner retina is composed of the other various
neurons including ganglion, amacrine, bipolar, and horizontal cells. Additionally, unless under
degeneration, glial astrocytes and microglia reside in the inner retina surrounding the retinal
vasculature which supplies the inner retina. Müller cells, a type of macroglia, are the only cell
which extends over both the inner and outer retina as it provides the main structural support for
the retina (Figure 1.1).
Phototransduction begins with the conversion of light to membrane potential changes in
the photoreceptor neurons, of which there are rod photoreceptors for low-light, non-color vision
and cone photoreceptors for bright-light, color vision (2). Cones are separated based on the opsin
proteins they possess which interact with separate wavelengths of light to produce the various
colors in vision. Humans and other primates are trichromatic having three distinct opsins which,
in essence, translates to red, green, and blue vision (3). Most other terrestrial mammals are only
dichromatic, but some species have more cones than humans. Many bird species have 5 classes of
cones, and some can even see ultraviolet light (4), while the stomatopod crustaceans, known as
mantis shrimps, are famous for having 12 photoreceptor types to produce extreme color acuity (5).

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The interneurons of the retina include bipolar cells, amacrine cells, and horizontal cells,
which modulate the signals transmitted between the photoreceptors and the retinal ganglion cells
(RGCs) (2). RGCs are the output neurons of the retina, and their axons form the optic nerve which
transmits visual information to the brain for processing (1). The retina is also protected by the
blood-retinal barrier (BRB), which maintains a selective permeability to protect the retina from
potentially harmful substances while allowing the exchange of nutrients and waste products. The
BRB provides retinal immune privilege to prevent systemic immune reactions that could
potentially impact visual function (6).
The retina glia contains astrocytes, microglia, and Müller cells which are the most
predominant representing about 90% of retinal glia (7). Additionally, Müller cells ensheath the
retinal neurons and blood vessels allowing them to survey the retina, support the structure and
function of the retina, perform metabolic exchange between the vasculature and neurons, as well
as respond to retinal injury (8). Astrocytes are almost exclusively confined to the RGC layer and
are highly associated with the distribution of blood vessels. Overall, astrocytes provide
neurotrophic support, mechanical support for axons, and maintenance of the BRB (9). Microglial
cells are tissue resident macrophages of the central nervous system (CNS) which provides the
immune activity of the immune-privileged retina (10). Microglia in the “resting” state are
distributed throughout the inner retina and have a ramified cell morphology, with a small cell body
and extended processes to monitor the environment. Upon activation from various stimuli, the
microglia will migrate, and their morphology will become more ameboid similar to macrophages
(11).
The RPE plays a critical role in the maintenance of vision, primarily through phagocytosis
of photoreceptor outer segments which are shed daily to allow photoreceptors to continuously

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interact with light and renew photoreceptor proteins (12). The RPE creates the barrier between the
photoreceptors and choroidal vasculature, being the primary source of nutrient and waste exchange
for photoreceptors (13, 14). In addition, the RPE serves several other roles: 1) maintenance of the
BRB of the outer retina, 2) absorption of light providing protection from oxidative stress and
improved visual acuity from reduced light-scattering, and 3) apical and basolateral secretion of
various neurotrophic factors to maintain the retinal microenvironment (15). Due to the distinct and
overlapping roles of different retinal cells, retinal pathologies have a wide range of etiologies but
also shared pathogenic themes.

Figure 1.1 The basic retinal structure. Adapted from Ding et al. (16)
1.2 RETINAL DEGENERATIVE DISEASES
There are several conditions which affect different aspects of the eye and result in vision
impairment. In the front of the eye, this includes cataracts caused by clouding of the lens, corneal
scarring associated with corneal opacity and trachoma infections, or uncorrected refractive error

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which is associated with the shape of the eye and cornea as well as ageing of the lens. In the back
of the eye, vision loss is caused by glaucoma resulting in degeneration of the optic nerve and retinal
degenerative conditions. The primary retinal degenerative conditions are age-related macular
degeneration (AMD), diabetic retinopathy (DR), and inherited retinal degeneration (IRD), a family
of diseases caused by over 250 genetic mutations (17).
AMD is a progressive disease and the impact on vision can vary from mild to severe.
Individuals with early AMD may not experience any significant vision loss, while those with
advanced AMD can experience significant central vision loss, leading to difficulty with daily
activities such as reading, driving, and recognizing faces (18). The primary clinical manifestation
of AMD is the formation of drusen deposits which are sub-RPE deposits (Figure 1.2). Early-stage
AMD is characterized by medium sized drusen with no impairment of vision, intermediate-stage
AMD is drusen size greater than 125 µm or there are abnormalities in the RPE, then late AMD is
characterized by atrophy of RPE and photoreceptor cells. There are two forms of late-stage AMD,
geographic atrophy (GA) and neovascular AMD (nAMD). GA is the loss of RPE and
photoreceptor cells resulting in loss of vision, while nAMD includes the atrophy of cells but
additionally includes the growth of choroidal blood vessels into the subretinal space. These new
blood vessels are extremely fragile resulting in leakage of fluid into the retina and rapid vision loss
(19).

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Figure 1.2 Clinical manifestation of AMD from the early to late stage. Drusen is the primary
manifestation which grows from early to intermediate stage. Late AMD, either geographic
atrophy (GA) or neovascular AMD, involves atrophy of the RPE and photoreceptors.
Neovascular AMD is distinct from GA with the growth of new blood vessels into the subretinal
space. Adapted from Ruan et al. (19)
Retinal degeneration secondary to diabetes is the second most common retinal degenerative
condition. The primary manifestation of DR is microvascular lesions in which there are two stages
of DR (Figure 1.3). The early stage is nonproliferative DR (NPDR) defined by microaneurysms,
retinal hemorrhages, intraretinal microvascular abnormalities, and venous caliber changes (20).
Similar to nAMD, the advanced proliferative DR (PDR) is characterized by preretinal
neovascularization which results in severe vision loss. A common feature of both NPDR and PDR
is diabetic macular edema (DME) and is the most common cause of vision loss in DR patients.
The group of IRDs are caused by a vast array of genetic mutations resulting in dysfunction
of photoreceptors or RPE. Stargardt disease is the most common macular dystrophy associated
with ABCA4 deficiency resulting in buildup of toxic byproducts from phototransduction (21).
These toxic compounds accumulate as lipofuscin in RPE cells resulting in RPE and subsequent
photoreceptor dysfunction and death and ultimately vision loss. Best disease is one
bestrophinopathy associated with genetic mutations of BEST1 and characterized by vitelliform

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lesions around the macula (22). BEST1 acts as an ion channel in RPE and BEST1 mutations result
in RPE dysfunction and lipofuscin accumulation (23).
Achromatopsia is the most common cone dysfunction syndrome from CNGB3 and
CNGA3 mutations which are cone-specific cyclic nucleotide-gated channels that participate in
phototransduction (24, 25). Diseases that directly affect phototransduction severely decrease visual
acuity and secondarily results in photoreceptor death likely through cGMP cytotoxicity (26). Of
all IRDs, rod-cone dystrophy, also known as retinitis pigmentosa (RP), is the most common
phenotype and is associated with over 100 genes (27). These genes include MERTK which is
critical for RPE phagocytosis of photoreceptors, PDE6B which is a rod-specific phototransduction
enzyme, and RPE65 which is an RPE protein involved in recycling retinol for phototransduction
(27).
As described above, retinal degeneration is primarily associated with photoreceptor and
RPE dysfunction which results in buildup of toxic metabolites and lipofuscin ultimately leading to
cellular death and vision loss. However, while photoreceptor and RPE dysfunction may be an
initiating source or degeneration, other cell types have been shown to play significant roles in
pathology including microglia, Müller cells, and vascular cells as described in Section 1.5.5 (28-
30).

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Figure 1.3 Normal retina compared with nonproliferative diabetic retinopathy (NPDR) with
diabetic macular edema (DME). NPDR is defined by microaneurysms, venous beading,
capillary degeneration, neovascularization, vascular lesions (cotton wool spots and exudate),
glial reactivity, neuronal and RPE damage, and accumulation of fluid in the retina. Adapted
from Duh et al. (20)
1.3 EPIDEMIOLOGY OF VISUAL IMPAIRMENT
As of 2020, there are an estimated 1.1 billion people living with vision impairment
worldwide and this number is expected to grow to 1.7 billion by 2050 (Figure 1.4A) (31). The vast
majority of this is associated with low- and middle-income countries with limited medical access.
Indeed, 64% of all people with vision loss live in South Asia, Southeast Asia, East Asia & Oceania
(Figure 1.4B). Additionally, vision loss is greatly associated with ageing, with 73% of people with
any vision loss being over the age of 50 (Figure 1.4C). The two largest causes of vision loss are
uncorrected refractive error and cataracts, resulting in an estimated 90% of all vision impairment
being preventable using spectacles or treatable through cataract surgery. The remaining 10%
require ongoing management and treatment including AMD, glaucoma, and DR which affect the

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retina and optic nerve (Figure 1.4D). In addition to these main causes of vision loss, rare diseases,
such as RP, are untreatable retinal diseases affecting about 1 in 2000 individuals (32).
AMD is the leading cause of irreversible blindness for individuals 50 and older in the
developed world (33). Early stages of AMD are difficult to account for due to lack of monitoring,
however, there was an estimated 195 million cases of any stage of AMD in 2020 with a predicted
increase to 288 million by 2040. Additionally, there is a higher prevalence of AMD in Europeans
compared to Asians or Africans (34). DR is a secondary condition of diabetes of which there was
an estimated 285 million people with diabetes in 2010 with over one-third having signs of DR
(35). Similar to AMD, Western communities had higher prevalence of DR compared to Asian
counterparts (36).
As vision loss affects all aspects of life, there is a strong economic burden associated with
retinal degeneration. Individuals with AMD have higher rates of unemployment, 31% in advanced
AMD and 44% in early AMD compared to 78% in healthy individuals. Additionally, the mean
wage is 30-38% lower in employed individuals with AMD resulting in an overall estimated loss
of $30 billion in annual GDP in the US (37). As well, the direct cost of AMD treatment was
approximately $525 million in 2004 (38). Overall, irreversible vision loss caused by retinal
degenerative conditions is highly prevalent and is growing due to an aging population. This is
associated with significant loss in quality of life and high economic burdens for individuals living
with these debilitating diseases.

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Figure 1.4 Global Epidemiology of Visual Impairment. The population of visual loss is expected
to increase significantly over time (A). Socioeconomics greatly impacts rates of visual
impairment with a majority of visual impairment coming from Asia and India (B). Age is the
greatest risk factor for visual impairment (C). Age-related macular degeneration, glaucoma, and
diabetic retinopathy are the most common causes of irreversible vision loss (D). Adapted from
the International Agency for the Prevention of Blindness Vision Atlas (IABP,
https://www.iapb.org/learn/vision-atlas/).
1.4 RISK FACTORS FOR RETINAL DEGENERATION
Several risk factors have been associated with the development and progression of retinal
degeneration including age, genetics, lifestyle, environmental exposures, and comorbidities. As
described previously, age is the most significant risk factor for all retinal degenerative conditions.
Smoking, alcohol consumption, hypertension, obesity, and high cholesterol have also been
identified as risk factors for AMD (39). DR is associated with uncontrolled diabetes, hypertension,
hyperlipidemia, BMI, and age (40, 41).
Genetic factors also play a role in the development of retinal degeneration. In AMD, the
strongest genetic risk factors include complement factor H (CFH), age-related maculopathy
susceptibility 2 (ARMS2), and high-temperature requirement A serine peptidase 1 (HTRA1) (42).

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CFH is a complement inhibitor, indicating the relevance of inflammation associated with
complement overactivation in AMD (43). ARMS2 is suggested to be involved in complement-
mediated clearance of cellular debris by retinal microglia (44). HTRA1 is a heat shock protease
which regulates angiogenesis and extracellular matrix deposition, core pathological changes
associated with AMD (45). A genome-wide association study (GWAS) identified 52 variants
across 34 loci associated with AMD (46). Pathway analysis of the 34 AMD loci demonstrated
significant enrichment for complement pathways, lipid transport and metabolism pathways, and
extracellular matrix pathways. Most genetic risk is shared between GA and nAMD. However,
matrix metallopeptidase 9 was identified as specific to nAMD. This suggests that patients at risk
of either forms of AMD can be separately screened for risk of developing neovascularization which
contributes to the majority of AMD associated blindness. While AMD and IRDs are primarily
associated with photoreceptor and RPE dysfunction and DR is associated with hyperglycemic
injury to retinal blood vessels, no single molecular mechanisms fully account for cellular atrophy.
Instead, a cascade of various molecular mechanisms contributes to progressive cellular
dysfunction and eventual cell death and loss of vision.
1.5 MECHANISMS OF RETINAL DEGENERATION
1.5.1 Age-related Macular Degeneration
A granular investigation of the cell biology underlying AMD can be categorized into five
concepts: aging changes, oxidative injuries, chronic inflammatory response, abnormal
extracellular matrix formation, and atrophy of the retina, retinal pigmented epithelium, and choroid
(47). The primary clinical sign of AMD is the presence of drusenoid deposits of lipoproteins below
the RPE. Drusen is thought to disrupt oxygen-carbon dioxide exchange, and hinder ocular
metabolism, leading to oxidative stress and tissue damage, which in turn activate inflammatory
processes (48, 49). The relationship of AMD and age, and considering the underlying cell biology,

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signals that the disease is likely a result of cumulative and cyclic damage. For example, reactive
oxygen species (ROS) can damage the retina, RPE, and the vasculature of the eye. Oxidation of
lipids in RPE membranes results in blebbing and accumulation of low-degradable debris in the
sub-RPE space. These oxidized lipids can initiate inflammatory responses such as recruiting
dendritic cells to the choroid (50, 51). In addition to lipids, proteins play a significant role in AMD
drusen. Particularly complement components which create positive feedback loops of
inflammatory cascades (52, 53). Recruited immune cells can secrete enzymes and cytokines which
damage cells, degrade the Bruch’s membrane (BM, the membrane separating the RPE from the
choroidal vasculature beneath), and enhance neovascularization. Resulting changes in the choroid
and BM, the controlling elements for nutrient and waste transport to and from the RPE, can
accentuate the disease state. Ultimately, the dysfunction and reduction in nutrients leads to the
atrophy of RPE cells and subsequent death of photoreceptors and resultant vision loss (47, 54-57).
1.5.2 Diabetic Retinopathy
DR is a common microvascular complication of diabetes mellitus with complex underlying
mechanisms including hyperglycemia, inflammation, oxidative stress, and the activation of
various signaling pathways (58). Hyperglycemia plays a central role in the development of DR, as
it contributes to the overproduction of advanced glycation end-products (AGEs) (59). AGEs are
formed by the non-enzymatic glycation of proteins, lipids, and nucleic acids, leading to the
formation of protein cross-links and the generation of ROS (60). AGEs activate intracellular
signaling pathways, such as the NFκB pathway, which promotes the expression of pro-
inflammatory cytokines and adhesion molecules, contributing to leukostasis and increased
vascular permeability (61).
Increased ROS production can damage cellular macromolecules, including proteins, lipids,
and DNA, leading to cellular dysfunction and death (62). The activation of antioxidant pathways,

12
such as the nuclear factor erythroid 2-related factor 2 (Nrf2) pathway, may counteract oxidative
stress in DR, but the overall balance between pro-oxidant and antioxidant forces is disturbed in
diabetic conditions (63). Various inflammatory cytokines, such as interleukin-1β (IL-1β), tumor
necrosis factor-alpha (TNF-α), and VEGF, contribute to the development of retinal microvascular
abnormalities (58). The activation of inflammatory pathways, such as the mitogen-activated
protein kinase (MAPK) and Janus kinase-signal transducer and activator of transcription (JAK-
STAT) pathways, leads to the upregulation of adhesion molecules, endothelial dysfunction, and
increased vascular permeability (64).
1.5.3 MERTK Retinitis Pigmentosa
Mutations in the MERTK gene are known to cause a form of RP (65). MERTK encodes a
receptor tyrosine kinase (RTK) which plays a critical role in the phagocytosis of photoreceptor
outer segments (POS) by RPE cells (66). MERTK is involved in the regulation of the daily
phagocytic process performed by RPE cells, removing the damaged and spent POS to allow for
their continuous renewal (67-69). Mutations in the MERTK gene disrupt the normal phagocytic
process in RPE cells, resulting in an accumulation of non-phagocytosed POS and debris, causing
inflammation, oxidative stress, and metabolic dysfunction in the RPE (70). Consequently, the
photoreceptor cells are unable to properly renew their outer segments, leading to their progressive
degeneration and the loss of vision observed in RP patients. Furthermore, MERTK dysfunction
may also disrupt the balance of pro-survival and pro-apoptotic signaling pathways, contributing to
photoreceptor cell death. For example, the loss of MERTK function can affect the activation of
the Akt and Erk signaling pathways, which are essential for cell survival (71). Overall, the RPE
dysfunction results in accumulation of oxidized debris which initiates inflammatory cascades, all
of which are contributing to the death of photoreceptors resulting in vision loss.

13
1.5.4 Oxidative Stress
The retina is particularly susceptible to oxidative stress due to its high oxygen
consumption, exposure to light, and abundant polyunsaturated fatty acids (PUFAs) (72). ROS are
highly reactive molecules, including superoxide anion, hydrogen peroxide, and hydroxyl radicals,
which can cause cellular damage by interacting with proteins, lipids, and nucleic acids (73). In the
retina, ROS can be generated through several mechanisms, such as mitochondrial dysfunction, the
activity of oxidases, and the photochemical reactions of the visual cycle (74). The damage induced
by ROS accumulation can lead to photoreceptor cell death, either through apoptosis or necrosis,
contributing to retinal degeneration (75).
The retina has a complex antioxidant defense system to counteract the harmful effects of
ROS, including enzymatic antioxidants, such as superoxide dismutase (SOD), catalase (CAT), and
glutathione peroxidase (GPx), as well as non-enzymatic antioxidants, like glutathione, vitamin E,
and vitamin C (76). However, in retinal degenerative conditions, the balance between ROS
production and antioxidant defenses is disrupted, leading to oxidative stress and cellular damage
(77). Nrf2 is a transcription factor that plays a key role in the induction of genes involved in the
cellular defense against oxidative stress. The decline in Nrf2 activity with aging has been shown
to lead to a decrease in glutathione synthesis, a major antioxidant in the retina, and to increase
oxidative stress in the RPE and photoreceptors, which can contribute to the development of AMD
(78). Abnormal levels of glutathione have been reported in patients with early AMD, suggesting a
potential role of glutathione deficiency in the pathogenesis of AMD (79). Therefore, targeting the
Nrf2 pathway may represent a promising therapeutic approach for the treatment of AMD,
particularly in early stages of the disease (80).
Oxidative stress can activate several signaling pathways that contribute to retinal
degeneration, such as the MAPK, NFκB, and NLRP3 inflammasome pathways (81). Activation of

14
these pathways can lead to the upregulation of pro-inflammatory cytokines and chemokines,
exacerbating inflammation and promoting the recruitment of immune cells to the retina, which
further contributes to photoreceptor cell death (82, 83).
Oxidative stress may impair cellular processes essential for photoreceptor function and
survival, such as the phagocytic activity of RPE cells and the maintenance of the BRB (84). This
can lead to the accumulation of debris and the disruption of nutrient and waste exchange, further
contributing to retinal degeneration (85). Oxidative stress also contributes to activation of
autophagy, a cellular process that degrades and recycles damaged organelles and macromolecules
(85). Under normal conditions, autophagy is a protective mechanism that maintains cellular
homeostasis. However, in the context of retinal degeneration, excessive oxidative stress can impair
autophagy, resulting in the accumulation of damaged organelles and protein aggregates, which
further contribute to the toxic microenvironment (86).
Oxidative stress can lead to the dysregulation of various growth factors and neurotrophic
factors, such as VEGF, brain-derived neurotrophic factor (BDNF), and pigment epithelial derived
factor (PEDF), which are essential for the survival and function of retinal cells (77). Imbalances
in these factors can exacerbate retinal degeneration by promoting angiogenesis, altering the BRB,
and impairing neuronal function. The RPE secretome, which encompasses a diverse array of
proteins, is crucial for maintaining retinal health and function. Recent studies have shed light on
how oxidative stress impacts the RPE secretome, influencing retinal function and contributing to
disease progression. Oxidative stress alters the composition of the RPE secretome by modulating
the expression and release of various growth factors, cytokines, and other molecules (87). Under
oxidative stress, RPE cells increase the production of pro-inflammatory cytokines such as IL-6,
IL-8, and monocyte chemoattractant protein-1 (MCP-1) (88). These cytokines can perpetuate

15
inflammation and promote the recruitment of immune cells, contributing to retinal pathogenesis.
This imbalance between pro-angiogenic and anti-angiogenic factors may contribute to
neovascularization observed in multiple retinal diseases. Furthermore, oxidative stress can disrupt
the RPE's ability to maintain the integrity of BRB by affecting the secretion of proteins involved
in tight junction formation and cell adhesion (89). This can result in increased permeability and
leakage, further contributing to retinal degeneration. As well, oxidative stress trigger epigenetic
modifications, such as DNA methylation and histone modifications, which may contribute to the
initiation and progression of retinal degenerative disorders by altering the transcriptional landscape
of retinal cells (90, 91).
The molecular mechanisms of oxidative stress in retinal degeneration are multifaceted,
involving the generation of ROS, the disruption of antioxidant defenses, the activation of various
signaling pathways, and the dysregulation of autophagy, growth factors, and epigenetic
modifications. A better understanding of these mechanisms is crucial for the development of novel
therapeutic strategies to prevent and treat retinal degenerative disorders.
1.5.5 Inflammation
Inflammation is known to play a critical role in the development and progression of retinal
degeneration. RPE and choroidal inflammation is initiated by the release of pro-inflammatory
cytokines and chemokines, which attract immune cells to the site of inflammation (57). The
inflammasome is a critical component of the innate immune system and is responsible for the
activation of inflammatory caspases. In AMD, the inflammasome is activated by osmotic stress,
leading to the induction of cyclooxygenase-2 (COX-2) and release of pro-inflammatory cytokines
in RPE cells (92). The complement system, a component of the innate immune system, has been
implicated in the pathogenesis of retinal degeneration in which dysregulation and overactivation

16
contributes to chronic inflammation, tissue damage, and retinal cell death (described further in
1.5.6)
Microglia are the primary immune cells in the retina, responsible for maintaining
homeostasis and regulating inflammation (93). In their resting state, microglia maintain retinal
homeostasis by surveying the environment, phagocytosing debris, and releasing neurotrophic
factors (11). In retinal degeneration, microglia become activated and polarized into pro-
inflammatory (M1) or anti-inflammatory (M2) phenotypes (94). M1 microglia release pro-
inflammatory cytokines, such as TNFα, IL-1β, and IL-6, as well as ROS and proteolytic enzymes
contributing to inflammation and tissue damage (95, 96). Müller cells undergo reactive gliosis, a
process characterized by changes in gene expression, hypertrophy, and the formation of a glial
scar (7). This reactive gliosis is thought to play both protective and detrimental roles in retinal
degeneration. On one hand, it contributes to neuroprotection by producing neurotrophic factors,
anti-inflammatory cytokines, and antioxidants. On the other hand, it can exacerbate damage
through the release of pro-inflammatory cytokines, excessive production of extracellular matrix
components, and disruption of the BRB (28). Both Müller cells and microglia are involved in the
formation and clearance of drusen, which are extracellular deposits commonly found in AMD (97).
Müller cells contribute to drusen formation by secreting lipoproteins and other molecules that
accumulate in the subretinal space, while activated microglia phagocytose drusen components to
facilitate their clearance (98). The interplay between Müller cells and microglia is essential in
modulating the inflammatory response during retinal degeneration. The release of pro-
inflammatory cytokines by activated microglia can induce reactive gliosis in Müller cells, which
in turn can further stimulate microglial activation, creating a vicious cycle of inflammation and
degeneration (99).

17
Extraretinal sources of inflammation have implicated neutrophils and macrophages in
retinal pathologies (100). Neutrophils play a critical role in the inflammatory response by releasing
ROS and cytokines that lead to tissue damage (95). Recent studies have shown that circulating
neutrophils in patients with nAMD are in an altered activation state (101). Additionally,
neutrophils have been shown to degrade the RPE barrier and promote choroidal neovascularization
(101-103). Macrophages are the primary immune cells found in the RPE and the choroid which
produce pro-inflammatory cytokines such as IL-1β, IL-6, and TNFα (57).
Overall, there is a complex array of inflammatory pathways occurring in several retinal
cells including Müller glia, microglia, and RPE as well as extraretinal vascular cells and immune
cells such as neutrophils and macrophages. Understanding initiation and resolution of
inflammation in the context of retinal degeneration can guide therapeutic strategies. For example,
pegcetacoplan was developed to inhibit complement activation as it is a primary inflammatory
pathway activated in retinal pathologies and mutations in the complement pathway are associated
with risk of retinal degeneration (104). Evaluation of pro-resolving strategies may also be
beneficial in retinal pathologies; however, dysfunction of these pathways has not been well
studied.
1.5.6 Complement Cascade
The complement cascade is an essential part of the immune system, playing a crucial role
in host defense against pathogens and clearance of cellular debris. The complement system is
comprised of three arms which provide an innate immune defense mechanism: 1) the classical
pathway in which antibodies bind to antigens; 2) the lectin pathway activated by carbohydrates
and oxidative stress and 3) the alternative pathway triggered by pathogens and cellular debris
(105). The dysregulation of the complement system has been implicated in the pathogenesis of
several retinal degenerative diseases, including AMD, DR, and RP (11, 81, 106).

18
In AMD, the complement system is thought to be involved in the formation and progression
of drusen deposits and choroidal neovascularization. Drusen deposits contain components of the
complement system, including C3, C5, and membrane attack complex (107). Genetic variations in
CFH and other complement-related genes have been associated with an increased risk of AMD
(108). The overactivation of the complement system in the retina leads to chronic inflammation,
which can damage the RPE, BM, and photoreceptors, ultimately leading to vision loss (109). As
well, the first drug approved for GA targets the complement pathway. In DR, the complement
system is activated by hyperglycemia-induced oxidative stress and AGEs (110). The increased
levels of complement activation products, such as C3a and C5a, contribute to the breakdown of
the BRB, retinal inflammation, and neovascularization, key features of DR (111). Additionally,
therapeutic strategies targeting the complement system have shown promise in animal models of
DR (112). In RP, complement activation has been reported in both human RP retinas and animal
models (113, 114). In a mouse model of RP, the genetic deletion of C3, a central component of the
complement cascade, resulted in reduced photoreceptor degeneration (115). This finding suggests
that targeting the complement system may be a potential therapeutic approach for treating RP.
Overall, dysregulation of the complement system contributes to chronic inflammation, tissue
damage, and vision loss. As shown by the recent approval of pegcetacoplan, targeting the
complement pathway is able to delay retinal degeneration.
1.5.7 Citrullination
Citrullination is a post-translational modification that involves the conversion of arginine
residues in proteins to citrulline by peptidylarginine deiminases (PADs), where the primary PAD
studied is PAD4 (116). Citrullination has been implicated in several inflammatory diseases,
including rheumatoid arthritis (RA) and multiple sclerosis (MS). There is also growing evidence
for the involvement of citrullination in retinal degeneration. Histone deamination has been shown

19
to occur in neutrophils in response to inflammatory stimuli and is thought to contribute to the
pathogenesis of autoimmune retinopathy (117). In addition, recent studies have implicated
neutrophils and their release of citrullinated proteins in the development of choroidal
neovascularization (102). Studies have demonstrated that increased citrullination and PAD
expression are found in the RPE cells of AMD patients (118, 119). Increased citrullination levels
and PAD4 expression have also been identified in RP patients contributing to disease associated
autoimmunity (120, 121). Recently, several studies have indicated that PAD4 is directly linked to
subretinal gliosis of Müller cells (122-124). There are limited studies investigating citrullination
in the retina and just a few investigating the role of PAD4 in the vasculature of the retina. Further
research into this mechanism could provide new therapeutic targets or biomarkers of disease
progression.
1.5.8 Lipids
Inflammation has been implicated in the pathogenesis of AMD, and recent studies have
highlighted the role of lipid metabolism in modulating immune responses and inflammation in this
disease (125, 126). Lipids are essential components of cell membranes and play critical roles in
cellular signaling and metabolism. Dysregulation of lipid metabolism has been shown to contribute
to the development of chronic inflammatory diseases, including AMD (51).
Lipidomics is a rapidly evolving field that allows for the comprehensive analysis of lipid
species within biological samples, providing insight into the lipid composition and metabolic
pathways in disease states (127). Recent lipidomic studies have identified several lipid species that
are dysregulated in AMD, including sphingolipids, phospholipids, and cholesterol esters (128-
130). These lipid species have been shown to play important roles in modulating inflammatory
responses in the retina, particularly in microglia and RPE cells (83, 131). Recent studies have
shown that specific lipid species, such as sphingosine-1-phosphate (S1P) and

20
lysophosphatidylcholine (LPC), can modulate microglial activation and polarization, influencing
the inflammatory response in the retina (132-135). Additionally, studies have shown that lipid
metabolism plays a critical role in RPE function, particularly in the regulation of the phagocytosis
of POS and the secretion of cytokines and chemokines (136). Dysregulation of specific lipid
species, such as oxysterols and ceramides, has been shown to impair RPE phagocytosis and
contribute to the development of inflammation in the retina (137, 138).
PUFAs, such as docosahexaenoic acid (DHA), are highly concentrated in photoreceptor
outer segments and play a crucial role in maintaining their structural integrity and function (129).
However, PUFAs are susceptible to lipid peroxidation, which generates toxic reactive aldehydes
that can cause cellular damage, inflammation, and cell death, contributing to retinal degeneration
(139). PUFAs can be metabolized into specialized pro-resolving mediators (SPMs) which play a
crucial role in controlling and resolving inflammation. SPMs include lipoxins, resolvins,
protectins, and maresins, which have been shown to possess anti-inflammatory, pro-resolving, and
tissue-protective properties (140, 141).
While there is no data on ocular and plasma changes in SPMs, recent studies have
suggested that SPMs may have potential therapeutic implications in retinal degenerative diseases.
Studies have demonstrated that SPMs, particularly resolvin D1 (RvD1) and lipoxin A4 (LXA4),
can exert anti-inflammatory and cytoprotective effects in RPE and microglial cells (142-145).
Moreover, treatment with RvD1 has been shown to suppress the formation of choroidal
neovascularization in an animal model of AMD, suggesting its potential therapeutic utility (146).
Studies have reported that SPMs, such as RvD1 and maresin 1, can counteract inflammation and
promote vascular repair in DR animal models (147, 148). SPMs have potential in mitigating
inflammation and promoting tissue repair in retinal degenerative diseases. Further research is

21
required to look at ocular and systemic levels of SPMs in these diseases. As well, mechanistic,
safety, and efficacy studies targeting SPMs are required to move these therapies towards a clinical
product.
1.6 ROYAL COLLEGE OF SURGEONS RAT
The Royal College of Surgeons (RCS) rat model is a MerTK mutant model which causes
RP in humans (70). Photoreceptors shed outer segments which are subsequently phagocytosed by
RPE cells. This serves to remove oxidative debris as well as restore the chromophore 11-cis-retinal
for phototransduction, which is why RPE function is essential to visual function (149). As shown
in Figure 1.5, POS are bound by extracellular phosphatidylserine-binding proteins (MFG-E8,
Gas6, and Protein S). Subsequently, these proteins activate phagocytic receptors on the RPE cells.
MFG-E8 stimulates the apical αvβ5 integrin receptors while Protein S and Gas6 bind MerTK
(150). In the RCS rat, the mutation in Mertk results in a truncated transcript that does not encode
a functional protein resulting in failure to phagocytose POS (70). This results in buildup of toxic
debris in the subretinal space, photoreceptor death, and loss of vision.

22

Figure 1.5 Photoreceptor outer segment phagocytosis by RPE cells. Adapted from Mao et al.
(150)
Several animal models of retinal pathologies exist; however, none fully recapitulate human
pathologies. The species include mice, rats, rabbits, pigs, and non-human primates and include
mechanisms of oxidative stress, inflammation, and lipid metabolism dysfunction. However, there
are no guidelines from regulatory agencies on the acceptable or preferred animal models for pre-
clinical studies in ophthalmology, complicating the choice of model selection. The development
of the CPCB-RPE1 implant, currently in clinical trials for GA, was based on the RCS rat model
(151-153). While this is a model of human RP, it shares many of the clinical manifestations of
AMD and DR. Combined with the knowledge that regulators have already accepted data from
RCS studies in the CPCB-RPE1, the RCS is a strong candidate for ongoing therapeutic
development for several forms of retinal degeneration.
The RCS rat model was compared to the phenotypic characterizations for AMD, DR, and
RP (Table 1.1). Thickening of BM is a natural phenomenon which occurs with age; however, one
study has indicated that AMD is associated with thicker BMs compared to age-matched controls
(154, 155). DR and RP have not been associated with thickening of BMs, however, there are

23
compositional changes associated with both diseases (156, 157). As well, the composition BM in
RCS rats has not been evaluated. Similarly, sub-RPE deposits, such as drusen, are observed in
AMD, and sub-RPE complement deposition is observed in DR, but the RCS rat model does not
show drusen formation (52, 158). Further investigation would be required to determine if there are
similar sub-RPE deposits in the RCS rat model. In contrast, sub-retinal debris is observed in each
pathology, however, the cause and composition vary. In AMD this is described as reticular
pseudodrusen which is compositionally different than sub-RPE drusen (159, 160). In DR, this is
in the form of hard exudate derived from vascular leakage (161). Lastly in RP and the RCS rat,
this is largely composed of photoreceptor outer segments (162, 163).
Complement activation is implicated in AMD, DR, and RP (106, 164, 165). While
complement signaling has not been extensively studied in the RCS rat model, one study indicated
RCS microglia utilized the complement pathway to drive synapse elimination. Similarly,
microglial activation is seen in AMD, DR, RP, and the RCS rat model, suggesting a similar
inflammatory response across these conditions (166, 167).
Intraretinal RPE migration is observed in AMD, DR, and RP (168-170). In the RCS rat,
RPE cells have been found to migrate into the subretinal space as well as migrate along vessels
towards the inner retina (171, 172). Increased autofluorescence is observed in each human
condition, the RCS rat also shows increased autofluorescence, which may indicate a similarity in
RPE dysfunction and accumulation of autofluorescence debris which is thought to be derived from
photoreceptors (173-176). Additionally, reduced electroretinogram responses is shared between
human pathologies and the RCS rat model due to the atrophy of photoreceptors and impaired visual
function in all conditions (177-180). In addition to photoreceptor loss, RPE dysfunction is
observed in each pathology and is the main cause of retinal degeneration in the RCS rat due to the

24
Mertk mutation, making it a relevant model for studying consequences of RPE dysfunction in
retinal diseases (65, 181-183). RGC loss is not necessarily observed in AMD but thinning of the
RGC layer is observed (184). In contrast, RGC loss is a prominent observation in DR and is also
observed in RP and the RCS rat, suggesting a common pathological feature (171, 181, 182, 185).
Lastly, vasculopathy is commonly observe in these retinal degenerations and the RCS model,
however, to varying degrees and locations (186-191).
Overall, the RCS rat model shares significant clinical features with AMD, DR, and RP.
AMD is unique compared to the other retinopathies due to the ubiquitous accumulation of drusen
and BM thickening, however, these are also associated with age. As such, while these are features
of AMD, they are a consequence of RPE dysfunction and immune activation which is shared with
the other retinopathies. While further investigation is needed to fully understand the similarities
and differences between the RCS rat model and human retinal diseases, retinal degeneration in the
RCS rat is well characterized which improves predictability and reproducibility in interventional
studies.
Table 1.1 Comparison of Retinal Pathologies
Feature AMD DR RP RCS References
Thickening of Bruch's membrane X

(156, 157, 192)
Sub-RPE deposits X X

(52, 158)
Sub-retinal deposits X X X X (160-163)
Complement signaling X X X X (106, 164, 165, 193)
Microglia/macrophage accumulation X X X X (166, 167)
RPE migration X X X X (168-171)
Increased autofluorescence X X X X (173-176)
Reduced electroretinograms X X X X (177-180)
RPE Dysfunction X X X X (65, 181-183)
RGC Loss

X X X (171, 181, 182, 184, 185)
Vasculopathy X X X X (186-191)

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1.7 THERAPEUTIC STRATEGIES FOR RETINAL DEGENERATION
There currently exists three FDA-approved therapies for the treatment of retinal
degeneration. For nAMD and DR, treatment options have only been available for limiting
neovascularization through anti-VEGF (brolucizumab, aflibercept, ranibizumab, pegaptanib)
treatments. The first treatment for GA was approved in 2023. This complement C3 inhibitor,
pegcetacoplan, slowed the progression of GA lesions but did not improve vision and was
associated with increased incidence of neovascularization (104, 194). For IRDs, only Leber
congenital amaurosis has an approved therapy, but the gene therapy did not halt retinal
degeneration (195, 196).
While not an approved therapy, the only potential recourse for non-neovascular AMD is
the use of high doses of antioxidants as demonstrated by the age-related eye disease studies
(AREDS and AREDS2). These studies demonstrated dietary supplements reduced the risk of
developing advanced AMD in high-risk persons (197-199). Other interventional strategies for
retinal pathologies have included:
• Statins to reduce cholesterol associated with drusen, however statistical significance was not
met (200)
• Metformin, an anti-diabetic medication, has been associated with reduced risk of developing
AMD, likely through its antioxidant and anti-inflammatory mechanisms in non-diabetic
patients (201, 202)
• Stem cell therapy directly replaces atrophied photoreceptor or RPE cells either through
delivery of cell suspension or cellular sheets (203). However, while the controlled
implantation of RPE sheets has reached Phase I/IIb clinical trials, the treatment is aimed at
late-stage AMD, excluding early events in retinal disease (204).

26
• Similar to anti-VEGF treatment, photocoagulation is used to remove and close off choroidal
neovascularization and commonly used in DR (205).
Therapeutic options for retinal degeneration are limited, and currently retinal degeneration
is not reversible. In addition, while the current options can halt some symptoms of disease, they
do not halt disease progression all together, and have even been associated with serious adverse
events. As the pathogenesis of retinal degeneration is better understood, new treatment modalities
are being examined including antioxidants, visual cycle modulators, anti-inflammatory agents,
complement inhibition, neuroprotective agents, and stem-cell therapies (199). The major
disadvantage of several therapies in development is that they are monotherapies and are unable to
efficiently treat the multiple dysfunctions associated with various retinal pathologies. The RPE has
long been speculated as a source of various neuroprotective factors. Characterization of the RPE
secretome has identified various proteins with neuroprotective properties in retinal degeneration
models (206-212). The following section will examine the role of RPE secretome in retinal
homeostasis and treatment.
1.8 THE RPE SECRETOME
The RPE secretome plays a crucial role in the maintenance of retinal health and function.
The RPE secretome is composed of various growth factors, cytokines, and other proteins that
regulate the homeostasis of the retinal microenvironment. RPE cells produce mitogenic factors for
retinal microvascular cells, release chemoattractants for astrocytes, synthesize, secrete, and
degrade insulin-like growth factor binding proteins, and express neurotrophic factors (15, 213-
215). In retinal degeneration, several pathogenic changes have been observed in the RPE
secretome. These include alterations in the levels of secreted factors such as PEDF, VEGF, and
TNF receptors (216). Chronic inflammation and oxidative stress are key drivers of RPE release of

27
pro-inflammatory cytokines which promote inflammation and contribute to RPE dysfunction and
photoreceptor cell death (57, 109). The loss of anti-angiogenic PEDF and increased angiogenic
VEGF contributes to BRB breakdown, increased vascular permeability, and pathologic
neovascularization in AMD and DR (77).
RPE cells can also secrete factors that promote the survival of photoreceptors and other
retinal cells. RPE-conditioned media has been shown to enhance photoreceptor cell survival,
neurite outgrowth, and differentiation in vitro (217). Additionally, RPE cells secrete factors that
can stimulate mesenchymal stem cell differentiation toward a functional RPE cell phenotype,
which may have potential applications in cell-based therapies for retinal degeneration (218).
Understanding the RPE secretome in retinal homeostasis and the pathogenic changes
associated with retinal degeneration can help identify potential therapeutic targets for these
diseases. For instance, targeting specific components of the RPE secretome, such as VEGF or
PEDF, has shown promise in the treatment of AMD and DR (219). Additionally, the modulation
of the RPE secretome by employing antioxidant or anti-inflammatory strategies may protect RPE
cells and photoreceptors from oxidative damage and inflammation, thereby preserving retinal
function in degenerative diseases (72). The development of therapeutic strategies targeting the
RPE secretome may help preserve retinal function and slow the progression of retinal
degeneration.
1.9 HYPOTHESIS AND SPECIFIC AIMS
Stem cell-based therapies have emerged as a promising approach to directly target
geographic atrophy (GA) and other retinal degenerative diseases by replacing atrophic tissue (220-
223). CPCB-RPE1, a subretinal implant composed of polarized human embryonic stem cell
(hESC)-derived RPE cells grown on ultrathin parylene membranes, was implanted in GA patients

28
during a phase I/IIa clinical trial, resulting in improvements in visual acuity (151, 152, 221). The
RPE secretome has been characterized, revealing numerous proteins with neuroprotective
properties in retinal degeneration models (206-212). However, the administration of these purified
components has not yet led to effective therapies in human trials (224, 225). During the
development of CPCB-RPE1, photoreceptor preservation was observed beyond the implant's
borders (226), suggesting that RPE cells secrete factors promoting neuronal survival (207, 227-
233). We hypothesize that an enriched secretome derived from healthy hESC-RPE cells may
restore the retinal microenvironment and promote photoreceptor preservation in the RCS rat retinal
degeneration model.
The primary focus of this project was to characterize the RCS rat model to establish
correlations between structural, functional, and molecular changes associated with retinal
degeneration. This approach will guide interventional analyses and provide deeper insights into
the molecular mechanisms underlying retinal degeneration. Building on the technological platform
developed for CPCB-RPE1, we subsequently evaluated the efficacy of polarized RPE soluble
factors (PRPE-SF) in preserving the structure and function of the retina and examined the
molecular mechanisms affected by PRPE-SF. This project culminated in the following specific
aims:
1. Characterize the structural and functional degeneration of the retina and associated
molecular changes in a longitudinal study of RCS rats.
2. Investigate the underlying mechanisms in the RCS rat model throughout retinal
degeneration using transcriptomic and lipidomic analysis.
3. Assess the efficacy of PRPE-SF in preserving retinal structure and visual function
in RCS rats.

29
By addressing these aims, this research contributes to the understanding of retinal
degeneration and the potential of stem cell-based therapies, such as PRPE-SF, in mitigating the
progression of retinal degenerative diseases.
1.10 OUTLINE OF THE DISSERTATION
This dissertation presents the characterization of the RCS model using clinically relevant
methods and molecular dissection in Chapter 2. Subsequently, larger gene expression and
lipidomic changes are evaluated in Chapter 3. Lastly, in Chapter 4, PRPE-SF is evaluated for the
ability to preserve retinal structure and function, as well as reduce molecular changes determined
in previous chapters.

30
1.11 REFERENCES
1. Forrester JV, et al. The Eye. Elsevier; 2016:269-337.e2.
2. Hoon M, et al. Functional architecture of the retina: development and disease. Prog Retin Eye
Res. 2014;42:44-84.
3. Nathans J, et al. Molecular Genetics of Human Color Vision: The Genes Encoding Blue,
Green, and Red Pigments. Science. 1986;232(4747):193-202.
4. Gerl EJ, and Morris MR. The Causes and Consequences of Color Vision. Evolution:
Education and Outreach. 2008;1(4):476-86.
5. Thoen HH, et al. A Different Form of Color Vision in Mantis Shrimp. Science.
2014;343(6169):411-3.
6. Armbrust KR, and Nussenblatt RB. Blood–Retinal Barrier, Immune Privilege, and
Autoimmunity. Elsevier Inc.; 2017.
7. Vecino E, et al. Glia-neuron interactions in the mammalian retina. Progress in Retinal and
Eye Research. 2016;51:1-40.
8. Hiscott PS, et al. Retinal and epiretinal glia - An immunohistochemical study. British Journal
of Ophthalmology. 1984;68(10):698-707.
9. Tao C, and Zhang X. Development of astrocytes in the vertebrate eye. Dev Dyn.
2014;243(12):1501-10.
10. Fernandes A, et al. Microglia and inflammation: conspiracy, controversy or control? Cellular
and Molecular Life Sciences. 2014;71(20):3969-85.
11. Silverman SM, and Wong WT. Microglia in the Retina: Roles in Development, Maturity, and
Disease. Annu Rev Vis Sci. 2018;4:45-77.
12. Yang S, et al. Functions and Diseases of the Retinal Pigment Epithelium. Front Pharmacol.
2021;12:727870.
13. Kim J, et al. Molecular Mechanisms of Retinal Pigment Epithelium Dysfunction in Age-
Related Macular Degeneration. Int J Mol Sci. 2021;22(22).
14. Sharma R, et al. Retinal Pigment Epithelium Replacement Therapy for Age-Related Macular
Degeneration: Are We There Yet? Annu Rev Pharmacol Toxicol. 2020;60:553-72.
15. Ishida K, et al. Expression of neurotrophic factors in cultured human retinal pigment epithelial
cells. Current Eye Research. 1997;16(2):96-101.
16. Shirley Ding SL, et al. Revisiting the role of erythropoietin for treatment of ocular disorders.
Eye. 2016;30(10):1293-309.

31
17. Chen T-C, et al. Genetic characteristics and epidemiology of inherited retinal degeneration in
Taiwan. npj Genomic Medicine. 2021;6(1):16.
18. Prevention CfDCa. Vision Health Initiative: Common Eye Disorders.
https://www.cdc.gov/visionhealth/basics/ced/index.html. Accessed February 25, 2023.
19. Ruan Y, et al. Age-Related Macular Degeneration: Role of Oxidative Stress and Blood
Vessels. Int J Mol Sci. 2021;22(3).
20. Duh EJ, et al. Diabetic retinopathy: current understanding, mechanisms, and treatment
strategies. JCI Insight. 2017;2(14).
21. Tanna P, et al. Stargardt disease: clinical features, molecular genetics, animal models and
therapeutic options. Br J Ophthalmol. 2017;101(1):25-30.
22. Singh Grewal S, et al. Bestrophinopathies: perspectives on clinical disease, Bestrophin-1
function and developing therapies. Ther Adv Ophthalmol. 2021;13:2515841421997191.
23. Marmorstein AD, et al. Bestrophin, the product of the Best vitelliform macular dystrophy gene
(VMD2), localizes to the basolateral plasma membrane of the retinal pigment
epithelium. Proceedings of the National Academy of Sciences. 2000;97(23):12758-63.
24. . In: Adam MP, Mirzaa GM, Pagon RA, Wallace SE, Bean LJH, Gripp KW, et al. eds.
GeneReviews(®). Seattle (WA): University of Washington, Seattle
Copyright © 1993-2023, University of Washington, Seattle. GeneReviews is a registered
trademark of the University of Washington, Seattle. All rights reserved.; 1993.
25. Khan NW, et al. CNGB3 Achromatopsia with Progressive Loss of Residual Cone Function
and Impaired Rod-Mediated Function. Investigative Ophthalmology & Visual Science.
2007;48(8):3864-71.
26. Carvalho LS, and Vandenberghe LH. Cham: Springer International Publishing; 2016:231-6.
27. Georgiou M, et al. Inherited retinal diseases: Therapeutics, clinical trials and end points-A
review. Clin Exp Ophthalmol. 2021;49(3):270-88.
28. Bringmann A, and Wiedemann P. Muller glial cells in retinal disease. Ophthalmologica.
2012;227(1):1-19.
29. Zeng H, et al. Microglial NADPH oxidase activation mediates rod cell death in the retinal
degeneration in rd mice. Neuroscience. 2014;275:54-61.
30. Jung Y-JJ, and Chung W-SS. Phagocytic Roles of Glial Cells in Healthy and Diseased Brains.
Biomol Ther. 2018;26(4):8-.

32
31. Bourne R, et al. Trends in prevalence of blindness and distance and near vision impairment
over 30 years: an analysis for the Global Burden of Disease Study. The Lancet Global Health.
2021;9(2):e130-e43.
32. Bourne RRA, et al. Causes of vision loss worldwide, 1990–2010: a systematic
analysis. The Lancet Global Health. 2013;1(6):e339-e49.
33. Velez-Montoya R, et al. Current knowledge and trends in age-related macular degeneration:
genetics, epidemiology, and prevention. Retina. 2014;34(3):423-41.
34. Wong WL, et al. Global prevalence of age-related macular degeneration and disease burden
projection for 2020 and 2040: A systematic review and meta-analysis. The Lancet Global
Health. 2014;2(2):e106-16.
35. Lee R, et al. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision
loss. Eye Vis (Lond). 2015;2:17.
36. Zhang X, et al. Prevalence of diabetic retinopathy in the United States, 2005-2008. Jama.
2010;304(6):649-56.
37. Brown MM, et al. Age-related macular degeneration: economic burden and value-based
medicine analysis. Canadian Journal of Ophthalmology. 2005;40(3):277-87.
38. Rein DB, et al. Forecasting age-related macular degeneration through the year 2050: the
potential impact of new treatments. Arch Ophthalmol. 2009;127(4):533-40.
39. Heesterbeek TJ, et al. Risk factors for progression of age-related macular degeneration.
Ophthalmic Physiol Opt. 2020;40(2):140-70.
40. Magliah SF, et al. The prevalence and risk factors of diabetic retinopathy in selected primary
care centers during the 3-year screening intervals. J Family Med Prim Care. 2018;7(5):975-
81.
41. Ting DSW, et al. Diabetic retinopathy: global prevalence, major risk factors, screening
practices and public health challenges: a review. Clinical & Experimental Ophthalmology.
2016;44(4):260-77.
42. DeAngelis MM, et al. Genetics of age-related macular degeneration (AMD). Hum Mol Genet.
2017;26(R2):R246.
43. Toomey CB, et al. Complement factor H in AMD: Bridging genetic associations and
pathobiology. Progress in Retinal and Eye Research. 2018;62(February):38-57.
44. Micklisch S, et al. Age-related macular degeneration associated polymorphism rs10490924 in
ARMS2 results in deficiency of a complement activator. Journal of Neuroinflammation.
2017;14(1):4.

33
45. Mohamad NA, et al. Association of HTRA1 and ARMS2 gene polymorphisms with response
to intravitreal ranibizumab among neovascular age-related macular degenerative subjects.
Human Genomics. 2019;13(1):13.
46. Fritsche LG, et al. A large genome-wide association study of age-related macular degeneration
highlights contributions of rare and common variants. Nature Genetics. 2016;48(2):134-43.
47. Zarbin MA. Current concepts in the pathogenesis of age-related macular degeneration. Arch
Ophthalmol. 2004;122(4):598-614.
48. Nita M, et al. Age-related macular degeneration in the aspect of chronic low-grade
inflammation (pathophysiological parainflammation). Mediators Inflamm.
2014;2014:930671.
49. Donoso LA, et al. The role of inflammation in the pathogenesis of age-related macular
degeneration. Surv Ophthalmol. 2006;51(2):137-52.
50. Wolter JR, and Falls HF. Bilateral confluent drusen. Arch Ophthalmol. 1962;68:219-26.
51. Curcio CA, et al. Accumulation of cholesterol with age in human Bruch's membrane. Invest
Ophthalmol Vis Sci. 2001;42(1):265-74.
52. Crabb JW, et al. Drusen proteome analysis: an approach to the etiology of age-related macular
degeneration. Proc Natl Acad Sci U S A. 2002;99(23):14682-7.
53. Janeway CA Jr TP, Walport M, et al. In: Science G ed. Immunobiology: The Immune System
in Health and Disease 5th edition. New York; 2001.
54. Armstrong RA, and Mousavi M. Overview of risk factors for age-related macular
degeneration (AMD). Journal of Stem Cells. 2015;10(3):171-91.
55. Barkana Y, and Belkin M. Neuroprotection in ophthalmology: A review. Brain Research
Bulletin. 2004;62(6):447-53.
56. Bressler NM, et al. Age-related macular degeneration. Survey of Ophthalmology.
1988;32(6):375-413.
57. Kauppinen A, et al. Inflammation and its role in age-related macular degeneration. Cellular
and Molecular Life Sciences. 2016;73(9):1765-86.
58. Antonetti DA. The neuroscience of diabetic retinopathy. Vis Neurosci. 2021;38:E001.
59. Brownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes.
2005;54(6):1615-25.
60. Giacco F, and Brownlee M. Oxidative stress and diabetic complications. Circ Res.
2010;107(9):1058-70.

34
61. Bierhaus A, et al. Understanding RAGE, the receptor for advanced glycation end products. J
Mol Med (Berl). 2005;83(11):876-86.
62. Wu H, et al. Targeting Oxidative Stress in Diabetic Complications: New Insights. J Diabetes
Res. 2018;2018:1909675.
63. Kowluru RA, and Mishra M. Oxidative stress, mitochondrial damage and diabetic retinopathy.
Biochim Biophys Acta. 2015;1852(11):2474-83.
64. Simo R, et al. Neurodegeneration in diabetic retinopathy: does it really matter? Diabetologia.
2018;61(9):1902-12.
65. Gal A, et al. Mutations in MERTK, the human orthologue of the RCS rat retinal dystrophy
gene, cause retinitis pigmentosa. Nature Genetics. 2000;26(3):270-1.
66. Nandrot EF, et al. Loss of synchronized retinal phagocytosis and age-related blindness in mice
lacking alphavbeta5 integrin. J Exp Med. 2004;200(12):1539-45.
67. Strauss O. The retinal pigment epithelium in visual function. Physiol Rev. 2005;85(3):845-81.
68. Almedawar S, et al. MERTK-Dependent Ensheathment of Photoreceptor Outer Segments by
Human Pluripotent Stem Cell-Derived Retinal Pigment Epithelium. Stem Cell Reports.
2020;14(3):374-89.
69. Burstyn-Cohen T, et al. Genetic dissection of TAM receptor-ligand interaction in retinal
pigment epithelial cell phagocytosis. Neuron. 2012;76(6):1123-32.
70. D'Cruz PM, et al. Mutation of the receptor tyrosine kinase gene Mertk in the retinal dystrophic
RCS rat. Hum Mol Genet. 2000;9(4):645-51.
71. Knubel KH, et al. MerTK inhibition is a novel therapeutic approach for glioblastoma
multiforme. Oncotarget. 2014;5(5):1338-51.
72. Beatty S, et al. The role of oxidative stress in the pathogenesis of age-related macular
degeneration. Surv Ophthalmol. 2000;45(2):115-34.
73. Jarrett SG, and Boulton ME. Consequences of oxidative stress in age-related macular
degeneration. Mol Aspects Med. 2012;33(4):399-417.
74. Yu DY, and Cringle SJ. Retinal degeneration and local oxygen metabolism. Exp Eye Res.
2005;80(6):745-51.
75. Simo R, and Hernandez C. Advances in the medical treatment of diabetic retinopathy.
Diabetes Care. 2009;32(8):1556-62.
76. Komeima K, et al. Antioxidants reduce cone cell death in a model of retinitis pigmentosa.
Proc Natl Acad Sci U S A. 2006;103(30):11300-5.

35
77. Campochiaro PA. Molecular pathogenesis of retinal and choroidal vascular diseases. Prog
Retin Eye Res. 2015;49:67-81.
78. Suh JH, et al. Decline in transcriptional activity of Nrf2 causes age-related loss of glutathione
synthesis, which is reversible with lipoic acid. Proceedings of the National Academy of
Sciences of the United States of America. 2004;101(10):3381-6.
79. Qin L, et al. Patients with early age-related macular degeneration exhibit signs of macro- and
micro-vascular disease and abnormal blood glutathione levels. Graefe's Archive for Clinical
and Experimental Ophthalmology. 2014;252(1):23-30.
80. Bellezza I. Oxidative stress in age-related macular degeneration: NRF2 as therapeutic target.
Frontiers in Pharmacology. 2018;9(NOV):1-7.
81. Anderson DH, et al. The pivotal role of the complement system in aging and age-related
macular degeneration: hypothesis re-visited. Prog Retin Eye Res. 2010;29(2):95-112.
82. Zhang H, et al. Oxidative stress response and Nrf2 signaling in aging. Free Radical Biology
and Medicine. 2015;88(Part B):314-36.
83. Hollyfield JG, et al. Oxidative damage-induced inflammation initiates age-related macular
degeneration. Nat Med. 2008;14(2):194-8.
84. Wang AL, et al. Autophagy and exosomes in the aged retinal pigment epithelium: possible
relevance to drusen formation and age-related macular degeneration. PLoS One.
2009;4(1):e4160.
85. Kaarniranta K, et al. Autophagy and heterophagy dysregulation leads to retinal pigment
epithelium dysfunction and development of age-related macular degeneration. Autophagy.
2013;9(7):973-84.
86. Frost LS, et al. Autophagy in the eye: implications for ocular cell health. Exp Eye Res.
2014;124:56-66.
87. Krohne TU, et al. Effects of lipid peroxidation products on lipofuscinogenesis and autophagy
in human retinal pigment epithelial cells. Exp Eye Res. 2010;90(3):465-71.
88. Wang H, et al. TNF-alpha mediates choroidal neovascularization by upregulating VEGF
expression in RPE through ROS-dependent beta-catenin activation. Mol Vis. 2016;22:116-28.
89. Klaassen I, et al. Molecular basis of the inner blood-retinal barrier and its breakdown in
diabetic macular edema and other pathological conditions. Prog Retin Eye Res. 2013;34:19-
48.
90. Xu XF, et al. Epigenetic regulation of the endothelial nitric oxide synthase gene in persistent
pulmonary hypertension of the newborn rat. J Hypertens. 2010;28(11):2227-35.

36
91. Moore SM, and Christoforidis JB. Advances in Ophthalmic Epigenetics and Implications for
Epigenetic Therapies: A Review. Genes (Basel). 2023;14(2).
92. Messerschmidt L, et al. Osmotic induction of cyclooxygenase-2 in RPE cells: Stimulation of
inflammasome activation. Mol Vis. 2019;25:329-44.
93. Karlstetter M, et al. Microglia in the healthy and degenerating retina: insights from novel
mouse models. Immunobiology. 2010;215(9-10):685-91.
94. Jin N, et al. Shaping the Microglia in Retinal Degenerative Diseases Using Stem Cell Therapy:
Practice and Prospects. Front Cell Dev Biol. 2021;9:741368.
95. Fan W, et al. Retinal microglia: Functions and diseases. Immunology. 2022;166(3):268-86.
96. Cherry JD, et al. Neuroinflammation and M2 microglia: the good, the bad, and the inflamed.
J Neuroinflammation. 2014;11:98.
97. Curcio CA, et al. The oil spill in ageing Bruch membrane. Br J Ophthalmol.
2011;95(12):1638-45.
98. Tarallo V, et al. DICER1 loss and Alu RNA induce age-related macular degeneration via the
NLRP3 inflammasome and MyD88. Cell. 2012;149(4):847-59.
99. Zhao L, et al. Microglial phagocytosis of living photoreceptors contributes to inherited retinal
degeneration. EMBO Mol Med. 2015;7(9):1179-97.
100. Barliya T, et al. Possible involvement of NETosis in inflammatory processes in the eye:
Evidence from a small cohort of patients. Mol Vis. 2017;23:922-32.
101. Krogh Nielsen M, et al. Altered activation state of circulating neutrophils in patients with
neovascular age-related macular degeneration. Immun Ageing. 2017;14:18.
102. Zhou J, et al. Neutrophils promote experimental choroidal neovascularization. Molecular
Vision. 2005;11(June):414-24.
103. Hinton DR, et al. Neutrophils compromise retinal pigment epithelial barrier integrity. Journal
of Biomedicine and Biotechnology. 2010;2010.
104. Liao DS, et al. Pegcetacoplan treatment for geographic atrophy due to age-related macular
degeneration: a plain language summary of the FILLY study. Immunotherapy.
2022;14(13):995-1006.
105. Sivaprasad S, and Chong NV. The complement system and age-related macular degeneration.
Eye (Lond). 2006;20(8):867-72.
106. Shahulhameed S, et al. A Systematic Investigation on Complement Pathway Activation in
Diabetic Retinopathy. Front Immunol. 2020;11:154.

37
107. Johnson LV, et al. Complement activation and inflammatory processes in Drusen formation
and age related macular degeneration. Exp Eye Res. 2001;73(6):887-96.
108. Hageman GS, et al. A common haplotype in the complement regulatory gene factor H
(HF1/CFH) predisposes individuals to age-related macular degeneration. Proc Natl Acad Sci
U S A. 2005;102(20):7227-32.
109. Ambati J, et al. Immunology of age-related macular degeneration. Nat Rev Immunol.
2013;13(6):438-51.
110. Koleva-Georgieva DN, et al. Serum inflammatory cytokines IL-1beta, IL-6, TNF-alpha and
VEGF have influence on the development of diabetic retinopathy. Folia Med (Plovdiv).
2011;53(2):44-50.
111. Donnelly R, et al. Protein kinase C inhibition and diabetic retinopathy: a shot in the dark at
translational research. Br J Ophthalmol. 2004;88(1):145-51.
112. Garcia-Ramirez M, et al. Interphotoreceptor retinoid-binding protein (IRBP) is downregulated
at early stages of diabetic retinopathy. Diabetologia. 2009;52(12):2633-41.
113. Walport MJ. Complement. First of two parts. N Engl J Med. 2001;344(14):1058-66.
114. Bhutto I, and Lutty G. Understanding age-related macular degeneration (AMD): relationships
between the photoreceptor/retinal pigment epithelium/Bruch's membrane/choriocapillaris
complex. Mol Aspects Med. 2012;33(4):295-317.
115. Walport MJ. Complement. Second of two parts. N Engl J Med. 2001;344(15):1140-4.
116. Rohrbach A, et al. Activation of PAD4 in NET formation. Frontiers in Immunology. 2012;3.
117. Neeli I, et al. Histone deimination as a response to inflammatory stimuli in neutrophils. J
Immunol. 2008;180(3):1895-902.
118. Bhattacharya SK, et al. Age-related reduction in retinal deimination levels in the F344BN rat.
Aging Cell. 2008;7(3):441-4.
119. Bonilha VL, et al. Retinal deimination and PAD2 levels in retinas from donors with age-
related macular degeneration (AMD). Exp Eye Res. 2013;111:71-8.
120. Iannaccone A, and Radic MZ. Increased Protein Citrullination as a Trigger for Resident
Immune System Activation, Intraretinal Inflammation, and Promotion of Anti-retinal
Autoimmunity: Intersecting Paths in Retinal Degenerations of Potential Therapeutic
Relevance. Adv Exp Med Biol. 2019;1185:175-9.
121. Acharya NK, et al. Neuronal PAD4 expression and protein citrullination: Possible role in
production of autoantibodies associated with neurodegenerative disease. Journal of
Autoimmunity. 2012;38(4):369-80.

38
122. Palko SI, et al. Peptidyl arginine deiminase 4 deficiency protects against subretinal fibrosis by
inhibiting Müller glial hypercitrullination. Journal of Neuroscience Research.
2023;101(4):464-79.
123. Wizeman JW, and Mohan R. Expression of peptidylarginine deiminase 4 in an alkali injury
model of retinal gliosis. Biochem Biophys Res Commun. 2017;487(1):134-9.
124. Palko SI, et al. Compartmentalized citrullination in Muller glial endfeet during retinal
degeneration. Proc Natl Acad Sci U S A. 2022;119(9).
125. Anderson DH, et al. A role for local inflammation in the formation of drusen in the aging eye.
Am J Ophthalmol. 2002;134(3):411-31.
126. Curcio CA, et al. Apolipoprotein B-containing lipoproteins in retinal aging and age-related
macular degeneration. J Lipid Res. 2010;51(3):451-67.
127. Fahy E, et al. Update of the LIPID MAPS comprehensive classification system for lipids. J
Lipid Res. 2009;50 Suppl(Suppl):S9-14.
128. Sparrow JR, et al. The bisretinoids of retinal pigment epithelium. Prog Retin Eye Res.
2012;31(2):121-35.
129. SanGiovanni JP, and Chew EY. The role of omega-3 long-chain polyunsaturated fatty acids
in health and disease of the retina. Prog Retin Eye Res. 2005;24(1):87-138.
130. Rotstein NP, et al. Protective effect of docosahexaenoic acid on oxidative stress-induced
apoptosis of retina photoreceptors. Invest Ophthalmol Vis Sci. 2003;44(5):2252-9.
131. Bettadahalli S, et al. Evidence on n-3 Fatty Acids and Oleic Acid Role in Retinal Inflammation
and Microvascular Integrity: Insight from a Hyperlipidemic Rat Model. Inflammation.
2020;43(3):868-77.
132. Terao R, and Kaneko H. Lipid Signaling in Ocular Neovascularization. International Journal
of Molecular Sciences. 2020;21(13):4758.
133. Mandal NA, et al. Role of Sphingosine 1-phosphate (S1P) in retinal structure and function-
lessons learned from mouse models. Investigative Ophthalmology & Visual Science.
2020;61(7):1124-.
134. Porter H, et al. Characterizing Sphingosine Kinases and Sphingosine 1-Phosphate Receptors
in the Mammalian Eye and Retina. Int J Mol Sci. 2018;19(12).
135. Skoura A, and Hla T. Regulation of vascular physiology and pathology by the S1P2 receptor
subtype. Cardiovasc Res. 2009;82(2):221-8.
136. Jun S, et al. The impact of lipids, lipid oxidation, and inflammation on AMD, and the potential
role of miRNAs on lipid metabolism in the RPE. Exp Eye Res. 2019;181:346-55.

39
137. Rodriguez IR, et al. 7-ketocholesterol accumulates in ocular tissues as a consequence of aging
and is present in high levels in drusen. Exp Eye Res. 2014;128:151-5.
138. Chen H, et al. Ceramide signaling in retinal degeneration. Adv Exp Med Biol. 2012;723:553-
8.
139. Rotstein NP, et al. Regulating survival and development in the retina: key roles for simple
sphingolipids. J Lipid Res. 2010;51(6):1247-62.
140. Serhan CN, and Levy BD. Resolvins in inflammation: emergence of the pro-resolving
superfamily of mediators. J Clin Invest. 2018;128(7):2657-69.
141. Serhan CN. Pro-resolving lipid mediators are leads for resolution physiology. Nature.
2014;510(7503):92-101.
142. Calandria JM, et al. Selective Survival Rescue in 15-Lipoxygenase-1-deficient Retinal
Pigment Epithelial Cells by the Novel Docosahexaenoic Acid-derived Mediator,
Neuroprotectin D1 *. Journal of Biological Chemistry. 2009;284(26):17877-82.
143. Xie T, et al. LXA4 protects against blue-light induced retinal degeneration in human A2E-
laden RPE cells and Balb-c mice. Ann Transl Med. 2021;9(15):1249.
144. Lu Z, et al. Lipoxin A4 delays the progression of retinal degeneration via the inhibition of
microglial overactivation. Biochem Biophys Res Commun. 2019;516(3):900-6.
145. Settimio R, et al. Resolvin D1 reduces the immunoinflammatory response of the rat eye
following uveitis. Mediators Inflamm. 2012;2012:318621.
146. Connor KM, et al. Increased dietary intake of ω-3-polyunsaturated fatty acids reduces
pathological retinal angiogenesis. Nature Medicine. 2007;13(7):868-73.
147. Yin Y, et al. Resolvin D1 inhibits inflammatory response in STZ-induced diabetic retinopathy
rats: Possible involvement of NLRP3 inflammasome and NF-κB signaling pathway. Mol Vis.
2017;23:242-50.
148. Saito-Sasaki N, et al. Maresin-1 and Inflammatory Disease. Int J Mol Sci. 2022;23(3).
149. Thumann G, et al. In: Ryan SJ, Hinton DR, Schachat AP, and Wilkinson CP eds. Retina
(Fourth Edition). Edinburgh: Mosby; 2006:137-52.
150. Mao Y, and Finnemann SC. Acute RhoA/Rho Kinase Inhibition Is Sufficient to Restore
Phagocytic Capacity to Retinal Pigment Epithelium Lacking the Engulfment Receptor
MerTK. Cells. 2021;10(8):1927.
151. Kashani AH, et al. One-Year Follow-Up in a Phase 1/2a Clinical Trial of an Allogeneic RPE
Cell Bioengineered Implant for Advanced Dry Age-Related Macular Degeneration.
Translational Vision Science & Technology. 2021;10(10):13-.

40
152. Kashani AH, et al. A bioengineered retinal pigment epithelial monolayer for advanced, dry
age-related macular degeneration. Science Translational Medicine. 2018;10(435):1-11.
153. Thomas BB, et al. A new immunodeficient retinal dystrophic rat model for transplantation
studies using human-derived cells. Graefe's Archive for Clinical and Experimental
Ophthalmology. 2018:2113-25.
154. Shao L, et al. Thickness of retinal pigment epithelium–Bruch’s membrane complex in adult
Chinese using optical coherence tomography. Eye. 2023;37(1):155-9.
155. Karampelas M, et al. Evaluation of retinal pigment epithelium-Bruch's membrane complex
thickness in dry age-related macular degeneration using optical coherence tomography. Br J
Ophthalmol. 2013;97(10):1256-61.
156. Schaal S, et al. Human Bruch's Membrane Proteome Alteration in Diabetic Retinopathy May
Reflect Cellular Pathologies in the Outer Retina. Investigative Ophthalmology & Visual
Science. 2010;51(13):5621-.
157. Hewitt AT, and Newsome DA. Altered synthesis of Bruch's membrane proteoglycans
associated with dominant retinitis pigmentosa. Curr Eye Res. 1985;4(3):169-74.
158. Fan S, et al. Extensive Sub-RPE Complement Deposition in a Nonhuman Primate Model of
Early-Stage Diabetic Retinopathy. Invest Ophthalmol Vis Sci. 2021;62(3):30.
159. Rabiolo A, et al. Spotlight on reticular pseudodrusen. Clin Ophthalmol. 2017;11:1707-18.
160. Monge M, et al. Subretinal drusenoid deposits: An update. Taiwan J Ophthalmol.
2022;12(2):138-46.
161. Otani T, and Kishi S. Tomographic findings of foveal hard exudates in diabetic macular
edema. Am J Ophthalmol. 2001;131(1):50-4.
162. Sun LW, et al. Assessing Photoreceptor Structure in Retinitis Pigmentosa and Usher
Syndrome. Investigative Ophthalmology & Visual Science. 2016;57(6):2428-42.
163. Lorach H, et al. Long-term Rescue of Photoreceptors in a Rodent Model of Retinitis
Pigmentosa Associated with MERTK Mutation. Sci Rep. 2018;8(1):11312.
164. Silverman SM, et al. C3- and CR3-dependent microglial clearance protects photoreceptors in
retinitis pigmentosa. J Exp Med. 2019;216(8):1925-43.
165. Armento A, et al. The complement system in age-related macular degeneration. Cell Mol Life
Sci. 2021;78(10):4487-505.
166. He J, et al. Disease-associated microglial activation prevents photoreceptor degeneration by
suppressing the accumulation of cell debris and neutrophils in degenerating rat retinas.
Theranostics. 2022;12(6):2687-706.

41
167. Wang SK, and Cepko CL. Targeting Microglia to Treat Degenerative Eye Diseases. Front
Immunol. 2022;13:843558.
168. Oh JK, et al. Differences in Intraretinal Pigment Migration Across Inherited Retinal
Dystrophies. Am J Ophthalmol. 2020;217:252-60.
169. Ho J, et al. Documentation of intraretinal retinal pigment epithelium migration via high-speed
ultrahigh-resolution optical coherence tomography. Ophthalmology. 2011;118(4):687-93.
170. Su C-C, et al. Lutein Inhibits the Migration of Retinal Pigment Epithelial Cells via Cytosolic
and Mitochondrial Akt Pathways (Lutein Inhibits RPE Cells Migration). International Journal
of Molecular Sciences. 2014;15(8):13755-67.
171. Villegas-Pérez MP, et al. Ganglion cell loss in RCS rat retina: A result of compression of
axons by contracting intraretinal vessels linked to the pigment epithelium. Journal of
Comparative Neurology. 1998;392(1):58-77.
172. Roque RS, and Caldwell RB. Pigment epithelial cell changes precede vascular
transformations in the dystrophic rat retina. Exp Eye Res. 1991;53(6):787-98.
173. Bubis E, et al. Blue Autofluorescence Fundus Imaging for Monitoring Retinal Degeneration
in Royal College of Surgeons Rats. Transl Vis Sci Technol. 2019;8(1):26.
174. Shen Y, et al. Fundus autofluorescence characteristics in patients with diabetic macular
edema. Chin Med J (Engl). 2014;127(8):1423-8.
175. Ly A, et al. Fundus Autofluorescence in Age-related Macular Degeneration. Optom Vis Sci.
2017;94(2):246-59.
176. Dysli C, et al. Fundus Autofluorescence Lifetime Patterns in Retinitis Pigmentosa.
Investigative Ophthalmology & Visual Science. 2018;59(5):1769-78.
177. McAnany JJ, et al. Clinical electroretinography in diabetic retinopathy: a review. Surv
Ophthalmol. 2022;67(3):712-22.
178. Fatih Cakir G, et al. In: Gregor B ed. Electroretinograms. Rijeka: IntechOpen; 2011:Ch. 6.
179. Forshaw TRJ, et al. Full-field electroretinography in age-related macular degeneration: an
overall retinal response. Acta Ophthalmologica. 2021;99(2):e253-e9.
180. Adachi K, et al. Optical Coherence Tomography of Retinal Degeneration in Royal College of
Surgeons Rats and Its Correlation with Morphology and Electroretinography. PLOS ONE.
2016;11(9):e0162835.
181. Natarajan S. Retinitis pigmentosa: a brief overview. Indian J Ophthalmol. 2011;59(5):343-6.
182. Yang S, et al. The cells involved in the pathological process of diabetic retinopathy.
Biomedicine & Pharmacotherapy. 2020;132:110818.

42
183. Hanus J, et al. RPE necroptosis in response to oxidative stress and in AMD. Ageing Res Rev.
2015;24(Pt B):286-98.
184. Camacho P, et al. Ganglion Cell Complex in Early and Intermediate Age-Related Macular
Degeneration: Evidence by SD-OCT Manual Segmentation. Ophthalmologica. 2017;238(1-
2):31-43.
185. Catalani E, and Cervia D. Diabetic retinopathy: a matter of retinal ganglion cell homeostasis.
Neural Regen Res. 2020;15(7):1253-4.
186. Jirarattanasopa P, et al. Choroidal Thickness, Vascular Hyperpermeability, and Complement
Factor H in Age-Related Macular Degeneration and Polypoidal Choroidal Vasculopathy.
Investigative Ophthalmology & Visual Science. 2012;53(7):3663-72.
187. Iovino C, et al. Evaluation of the Choroid in Eyes With Retinitis Pigmentosa and Cystoid
Macular Edema. Investigative Ophthalmology & Visual Science. 2019;60(15):5000-6.
188. Tan B, et al. Longitudinal Structural and Microvascular Observation in RCS Rat Eyes Using
Optical Coherence Tomography Angiography. Investigative Ophthalmology & Visual
Science. 2020;61(6):54-.
189. Adams AJ, and Bearse MA, Jr. Retinal neuropathy precedes vasculopathy in diabetes: a
function-based opportunity for early treatment intervention? Clin Exp Optom.
2012;95(3):256-65.
190. Fogle JA, et al. Retinitis Pigmentosa and Exudative Vasculopathy. Archives of
Ophthalmology. 1978;96(4):696-702.
191. Uliss AE, et al. Retinitis Pigmentosa and Retinal Neovascularization. Ophthalmology.
1986;93(12):1599-603.
192. Karampelas M, et al. Evaluation of retinal pigment epithelium–Bruch's membrane complex
thickness in dry age-related macular degeneration using optical coherence tomography.
British Journal of Ophthalmology. 2013;97(10):1256-61.
193. He J, et al. Microglia Mediate Synaptic Material Clearance at the Early Stage of Rats With
Retinitis Pigmentosa. Frontiers in Immunology. 2019;10.
194. Liao DS, et al. Complement C3 Inhibitor Pegcetacoplan for Geographic Atrophy Secondary
to Age-Related Macular Degeneration: A Randomized Phase 2 Trial. Ophthalmology.
2020;127(2):186-95.
195. Aoun M, et al. Inherited Retinal Diseases Due to RPE65 Variants: From Genetic Diagnostic
Management to Therapy. Int J Mol Sci. 2021;22(13).
196. Cideciyan AV, et al. Human retinal gene therapy for Leber congenital amaurosis shows
advancing retinal degeneration despite enduring visual improvement. Proc Natl Acad Sci U S
A. 2013;110(6):E517-25.

43
197. Aronow M, and Chew E. AREDS2: Perspectives, Recommendations, and Unanswered
Questions. 2015;25(3):186-90.
198. Chew EY, et al. Summary results and recommendations from the age-related eye disease
study. Archives of Ophthalmology. 2009;127(12):1678-9.
199. Meleth AD, et al. Treatment for atrophic macular degeneration. Current Opinion in
Ophthalmology. 2011;22(3):190-3.
200. Vavvas DG, et al. Regression of Some High-risk Features of Age-related Macular
Degeneration (AMD) in Patients Receiving Intensive Statin Treatment. EBioMedicine.
2016;5:198-203.
201. Barzilai N, et al. Metformin as a Tool to Target Aging. Cell Metab. 2016;23(6):1060-5.
202. Fang J, et al. Metformin alleviates human cellular aging by upregulating the endoplasmic
reticulum glutathione peroxidase 7. Aging Cell. 2018;17(4):e12765.
203. Baradaran-Rafii A, et al. Cell-based approaches towards treating age-related macular
degeneration. Cell Tissue Bank. 2020.
204. Rubner R, et al. Progress of clinical therapies for dry age-related macular degeneration. Int J
Ophthalmol. 2022;15(1):157-66.
205. Querques G, et al. Laser photocoagulation as treatment of non-exudative age-related macular
degeneration: state-of-the-art and future perspectives. Graefes Arch Clin Exp Ophthalmol.
2018;256(1):1-9.
206. Inanc Tekin M, et al. Brain-Derived Neurotrophic Factor in Patients With Age-Related
Macular Degeneration and Its Correlation With Retinal Layer Thicknesses. Invest Ophthalmol
Vis Sci. 2018;59(7):2833-40.
207. Kolomeyer AM, et al. Characterization of conditioned media collected from cultured adult
versus fetal retinal pigment epithelial cells. Investigative Ophthalmology and Visual Science.
2011;52(8):5973-86.
208. Mysona BA, et al. Nerve growth factor in diabetic retinopathy: beyond neurons. Expert Rev
Ophthalmol. 2014;9(2):99-107.
209. Meyer JG, et al. Proteome and Secretome Dynamics of Human Retinal Pigment Epithelium
in Response to Reactive Oxygen Species. Scientific Reports. 2019;9(1):1-12.
210. Yabe T, et al. Pigment epithelium-derived factor induces pro-survival genes through cyclic
AMP-responsive element binding protein and nuclear factor kappa B activation in rat cultured
cerebellar granule cells: Implication for its neuroprotective effect. Neuroscience.
2005;133(3):691-700.

44
211. LaVail MM, et al. Multiple growth factors, cytokines, and neurotrophins rescue
photoreceptors from the damaging effects of constant light. Proceedings of the National
Academy of Sciences. 1992;89(23):11249-53.
212. Yang PB, et al. Trophic factors GDNF and BDNF improve function of retinal sheet
transplants. Exp Eye Res. 2010;91(5):727-38.
213. Wong HC, et al. Retinal pigment epithelial cells produce mitogenic factors for retinal
microvascular cells in culture: A preliminary report. Eye (Basingstoke). 1987;1(6):754-6.
214. Rowen SL, and Glaser BM. Retinal Pigment Epithelial Cells Release a Chemoattractant For
Astrocytes. Archives of Ophthalmology. 1985;103(5):704-7.
215. Waldbillig RJ, et al. Monkey retinal pigment epithelial cells in vitro synthesize, secrete, and
degrade insulin-like growth factor binding proteins. Journal of Cellular Physiology.
1992;150(1):76-83.
216. Sippy BD, et al. Soluble tumor necrosis factor receptors are present in human vitreous and
shed by retinal pigment epithelial cells. Journal of Investigative Medicine. 1996;44(1):311-7.
217. Gaur VP, et al. RPE conditioned medium stimulates photoreceptor cell survival, neurite
outgrowth and differentiation in vitro. Experimental Eye Research. 1992;54(5):645-59.
218. Huang C, et al. Combination of retinal pigment epithelium cell-conditioned medium and
photoreceptor outer segments stimulate mesenchymal stem cell differentiation toward a
functional retinal pigment epithelium cell phenotype. Journal of Cellular Biochemistry.
2012;113(2):590-8.
219. Schmidt-Erfurth U, and Waldstein SM. A paradigm shift in imaging biomarkers in
neovascular age-related macular degeneration. Prog Retin Eye Res. 2016;50:1-24.
220. Nazari H, et al. Stem cell based therapies for age-related macular degeneration: The promises
and the challenges. Prog Retin Eye Res. 2015;48:1-39.
221. Kashani AH, et al. Survival of an HLA-mismatched, bioengineered RPE implant in dry age-
related macular degeneration. Stem Cell Reports. 2022;17(3):448-58.
222. Mandai M, et al. Autologous Induced Stem-Cell-Derived Retinal Cells for Macular
Degeneration. N Engl J Med. 2017;377(8):792-3.
223. da Cruz L, et al. Phase 1 clinical study of an embryonic stem cell-derived retinal pigment
epithelium patch in age-related macular degeneration. Nat Biotechnol. 2018;36(4):328-37.
224. Kolomeyer AM, and Zarbin MA. Trophic factors in the pathogenesis and therapy for retinal
degenerative diseases. Surv Ophthalmol. 2014;59(2):134-65.
225. Kanu LN, and Ciolino JB. Nerve Growth Factor as an Ocular Therapy: Applications,
Challenges, and Future Directions. Semin Ophthalmol. 2021;36(4):224-31.

45
226. Diniz B, et al. Subretinal Implantation of Retinal Pigment Epithelial Cells Derived From
Human Embryonic Stem Cells: Improved Survival When Implanted as a Monolayer.
Investigative Ophthalmology and Visual Science. 2013;54(7):5087-96.
227. Sheedlo HJ, et al. Effects of RPE-cell factors secreted from permselective fibers on retinal
cells in vitro. Brain Research. 1992;587(2):327-37.
228. Sheedlo HJ, et al. Effects of RPE age and culture conditions on support of photoreceptor cell
survival in transplanted RCS dystrophic rats. Experimental Eye Research. 1993;57(6):753-61.
229. Sheedlo HJ, et al. Retinal pigment epithelial cell support of photoreceptor survival in vitro. In
Vitro Cellular & Developmental Biology - Animal: Journal of the Society for In Vitro Biology.
1995;31(5):330-3.
230. Sheedlo HJ, and Turner JE. Influence of a retinal pigment epithelial cell factor(s) on rat retinal
progenitor cells. Developmental Brain Research. 1996;93(1-2):88-99.
231. Sheedlo HJ, and Turner JE. Effects of retinal pigment epithelial cell-secreted factors on
neonatal rat retinal explant progenitor cells. Journal of Neuroscience Research.
1996;44(6):519-31.
232. Sheedlo HJ, et al. A transformed neonatal rat retinal pigment epithelial cell line: Secreted
protein analysis and fibroblast growth factor and receptor expression. Current Eye Research.
1997;16(2):116-26.
233. Sheedlo HJ, et al. RPE secreted proteins and antibody influence photoreceptor cell survival
and maturation. Developmental Brain Research. 1998;107(1):57-69.

46
CHAPTER 2: NATURAL PROGRESSION AND NOVEL MOLECULAR
MARKERS OF RETINAL DEGENERATION IN THE RCS RAT
Kabir Ahluwalia
1
, Du Zhaodong
2,3
, Juan-Carlos Martinez-Camarillo
3,4
, Aditya Naik
1
, Biju
Thomas
3,4
, Mark S. Humayun
3,4
, Stan G. Louie
1,3*

1
Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA
90089, USA
2
Department of Ophthalmology, the Affiliated Hospital of Qingdao University, Qingdao, 266000, Shandong,
China
3
USC Ginsburg Institute of for Biomedical Therapeutics, University of Southern California, Los Angeles, CA
90033, USA USC

4
USC Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine, University of Southern
California, Los Angeles, CA 90033, USA
* Correspondence: Stan Louie; slouie@usc.edu; 1985 Zonal Avenue, Los Angeles, CA 90033; 323-442-3646

47

2.1 INTRODUCTION
Retinal degeneration animal models are indispensable tools for interrogating the
underlying molecular pathogenesis leading to disease manifestations. Accurate disease animal
models correlating with human disease clinical manifestations are critical for therapeutic
development such as age-related macular degeneration (AMD) and retinitis pigmentosa (RP). In
this context, the dystrophic Royal College of Surgeons (RCS) rat is one of the most commonly
utilized models for evaluating efficacy of new therapeutics to treat AMD or RP (153, 234-236).
The RCS rat has a Mertk gene mutation leading to retinal pigment epithelium (RPE) dysfunction
and subsequent photoreceptor degeneration. A major advantage of this model is that retinal
degeneration occurs reliably over time (70, 237), enabling both invasive and non-invasive
characterization of retinal morphological changes in relationship to retinal degeneration. Non-
invasive ocular coherence tomography (OCT) monitoring of retinal degeneration was found to
correlate with electroretinography (ERG) function (180, 238). These findings suggest progression
of RPE dysfunction corresponds with photoreceptor loss with concomitant morphological and
retinal function changes.
Although morphology and functional characterizations have been previously reported
(238-240), these reports have not correlated in vivo imaging data, visual function testing,
histological assessments, and molecular biomarkers. No longitudinal studies have correlated
molecular marker changes in relationship to retinal degeneration. In this study, we explored the
features and correlation of existing detection methods in order to establish a guideline on retinal
functional and morphological characterization in RCS rats with retinal degeneration using OCT,
ERG, fundus autofluorescence (FAF), fluorescein angiography (FA), and histology. In addition,
we compared these parameters between the RCS rat and the immunodeficient RCS (iRCS) rat

48
developed for cell transplantation research (153). Due to the central theme of inflammation and
oxidative stress in retinal degeneration, this report focuses on age associated changes in retinal
gene expression involved in these pathways to identify temporal patterns during retinal
degeneration and guide future therapeutic development.
2.2 METHODS
2.2.1 Animals
Animal experiments were conducted in full accordance with University of Southern
California (USC) Institutional Animal Care and Use Committee (IACUC)-approved protocols,
National Institutes of Health Guide for the Care and Use of Laboratory Animals and the ARVO
Statement for the Use of Animals in Ophthalmic and Vision Research. Dystrophic RCS rats were
obtained from Dr. Matthew LaVail (University of California, San Francisco, USA) which were
bred and propagated at the USC under an IACUC approved protocol. The iRCS rat breeding pair
were obtained from Dr. Biju B. Thomas (153). All the pups used for this study were their offspring
born in the USC vivarium. Rats were group housed under specific pathogen-free conditions and
had access to water and food ad libitum. All animals were housed in a temperature- and light-
controlled rooms with a 12-h light/dark cycle.
2.2.2 In vivo Experimental Design
The in vivo experimental design is displayed in Table 2.1. These experiments were
conducted between post-natal day (p) 21 and p90 at which the animal is completely blind.
Additionally, studies were conducted on both immunocompetent and immunodeficient RCS rats
to understand the impact immunodeficiency had on disease progression.

49
Table 2.1 Experimental Design
Post Natal Age (days) Analyses
p21 (21) ERG, OCT, Histology, RNA, Protein
p33 (30-35) ERG, OCT, Histology, RNA, Protein
p47 (45-49) ERG, OCT, Histology, RNA, Protein
p62 (60-63) ERG, OCT, Histology, RNA, Protein
p90 (90) ERG, OCT, Histology

2.2.3 Electroretinogram (ERG) Evaluation
Full-field ERG was evaluated using the HMsERG system (OcuScience, Las Vegas, NV)
as previously described at the following timepoints: p21, p35, p49, p60, and p90 (153). Prior to
each evaluation, animals were dark adapted overnight for 12 hours and prepared for testing under
a dim red light. Rats were anesthetized with a mixture of ketamine 80 mg/kg and xylazine
7.5 mg/kg given as an intraperitoneal injection. Pupils were dilated by topical instillation of 1%
tropicamide (Bausch & Lomb Inc., Tampa, FL) and 2.5% phenylephrine hydrochloride (Akorn
Pharmaceuticals, Lake Forest, IL). ERG was recorded from both eyes using ERG-Jet contact lens
electrodes (Fabrinal SA, Switzerland). Reference and ground electrodes were inserted into the
infraorbital (malar) area and between the ears respectively. The conductivity between the cornea
and recording electrodes was maintained by an optically clear ophthalmic gel. Scotopic testing
was conducted with flash stimuli intensities ranging from 1 to 25,000 millicandela (mcd) followed
by photopic testing (flash stimuli responses of 10 - 25,000 mcd). A 10-minute light adaptation
period was performed prior. Scotopic and photopic a-wave and b-wave amplitudes were analyzed.
2.2.4 Ocular Coherence Tomography (OCT) Evaluation
After ERG functional testing, spectral-domain OCT (SD-OCT) images were obtained also
by the diagnostic imaging platform (Spectralis HRA+OCT, Heidelberg Engineering Inc.,
Heidelberg, Germany). With the animals under anesthesia and their pupils fully dilated, multiple

50
horizontal linear scans were obtained at the central, nasal, and temporal retina. Total retina, inner
retina and outer retina (including an intraretinal segmentation of the outer nuclear layer (ONL))
thickness were measured at five points along the same horizontal line at nasal and temporal regions
from the optic nerve head (ONH). Measurements were averaged prior to statistical analysis. The
cornea was moist by a frequent application of balanced salt solution (BSS) (Alcon Laboratories,
Inc. Fort Worth, TX) during the entire imaging session.
2.2.5 Fluorescein Angiography (FA) and Fundus Autofluorescence (FAF)
Fundus autofluorescence (FAF) and fundus fluorescein angiography (FA) were performed
using the Heidelberg Spectralis HRA+OCT. FAF and FA imaging were used to evaluate
autofluorescence and retinal vascular changes, respectively (241). FA images were recorded after
intraperitoneal injection of sodium fluorescein (10%, 0.1 mL; Alcon).
2.2.6 Euthanasia and Tissue Collection
At end of study, rats were euthanized by intraperitoneal injection of 0.5 mL pentobarbitol
sodium 390 mg and phenytoin sodium 50 mg (Euthasol; Virbac AH, Inc., Fort Worth, TX). For
histology analysis, eyes were enucleated and fixed in Davidson’s solution. After 24 hours of
fixation, the Davidson’s solution was replaced with 70% ethanol and sent to the USC Ginsburg
Institute for Biomedical Therapeutics Core for paraffin embedding, sectioning, and hematoxylin
and eosin (H&E) staining. Anterior segment structures, including cornea, iris and lens were
removed and the posterior pole was exposed. The cut of the eye was through the optic nerve on its
sagittal plane. After dissection, all eyes were embedded in paraffin and cut in a microtome starting
from the center of the optic nerve. Serial sections of 5 µm in thickness were performed throughout
the entire eyeball. For RT-qPCR and Western Blot analysis, retinas were isolated from freshly
enucleated eyes and placed into 1mL RNAzol RT (Sigma Aldrich, St. Louis, MO) or 0.5 mL T-

51
PER Tissue Protein Extraction Reagent (Thermo Fisher Scientific, Waltham, MA), respectively,
and processed as described below.
2.2.7 Retinal RT-qPCR
Isolated Retinas were homogenized in RNAzol using a TissueLyser II (Qiagen LLC,
Germantown, MD). Total RNA was extracted following manufacturer’s instructions and
concentration was determined via the NanoDrop™ spectrophotometer (Thermo Fisher Scientific,
Waltham, MA). cDNA was prepared using the RevertAid™ First Strand cDNA Synthesis Kit
(Thermo Fisher Scientific, Waltham, MA) following manufacturer’s protocol. The RT-qPCR
master mix was prepared by mixing PowerUp™ SYBR™ Green Master Mix (Applied
Biosystems, Foster City, CA) and the forward and reverse primers. Primers were designed using
Primer-Blast from NCBI and sequences are listed in Supplementary Table T2.1 (242). Diluted
cDNA and master mix were pipetted into a 384-well plate using an Assist Plus Pipetting Robot
(INTEGRA Biosciences Corp., Hudson, NH) in triplicate. RT-qPCR was performed on
QuantStudio 12K Flex Real-Time PCR System (Applied Biosystems, Foster City, CA) with the
following run method: UDG activation at 50˚C for 2 minutes, followed by Dual-Lock DNA
polymerase at 95˚C for 2 minutes, then 40 cycles of denaturation at 95˚C for 15 seconds and
annealing/extension at 60˚C for 60 seconds, the final stage was a dissociation curve consisting of
ramping at 1.6˚C/second to 95˚C for 15 seconds, then 1.6˚C/second to 60˚C for 1 minute and
0.15˚C/second to 95˚C for 15 seconds. Data was collected and analyzed using the 2
−ΔΔC
T method
using GAPDH as the reference gene (243).
2.2.8 Retinal Western Blot Analysis
Isolated retinas were placed into 500 µL T-PER™ (Thermo Fisher Scientific, Waltham,
MA) supplemented with protease and phosphatase inhibitors (Pierce Biotechnology, Rockford,
IL) and homogenized using a TissueLyser II (Qiagen LLC, Germantown, MD). Tissue lysates

52
were prepared, and protein concentration was determined using Quick Start™ Bradford protein
assay with bovine serum albumin standards (Bio-Rad, Hercules, CA).
For western blots, 10 µg protein was resolved by sodium dodecyl sulfate-polyacrylamide
gel electrophoresis using a 4–20% Mini-PROTEAN® TGX™ gel (Bio-Rad, Hercules, CA) with
a 10-250 kDa protein ladder (Bio-rad). Protein was then transferred to polyvinylidene fluoride
(PVDF) membranes, blocked with 5% nonfat milk, and probed with primary antibodies overnight
at 4˚C. A list of antibodies can be found in Supplementary Table T2.2. Following primary
incubation, HRP-conjugated secondaries (1:10000 in 5% nonfat milk) were applied for 1 hour at
room temperature before incubating in Clarity ECL substrate (Bio-Rad, Hercules, CA) for 5
minutes at room temperature. Blots were imaged and densitometry analysis were performed on an
iBright FL1000 Gel/Cell Imager (Thermo Fisher). Data was double normalized with GAPDH as a
loading control and an internal standard (IS) to control for variability between blots by combining
samples together.
2.2.9 Immunofluorescence Staining
Eye tissue sections were first deparaffinized and rehydrated via immersion in a series of
xylene, ethanol, and PBS solutions. Heat-induced epitope retrieval was performed using 1x Citrate
Buffer (pH=6.0) in a pressure cooker. Following antigen retrieval, slides were moved onto a humid
chamber and washed three times with PBS. Tissue sections were then permeabilized with 0.3%
Triton X-100 in PBS for 10 minutes followed by 3 washes with PBS. Sections were blocked for
30 minutes with blocking buffer (PBS containing 2.5% normal goat serum (v/v)). The blocking
solution was replaced with 50 µL primary antibody diluted with blocking buffer at pre-determined
concentrations (Supplementary Table T2.3). Slides were incubated overnight at 4°C then washed
3 times with PBS. Following, slides were incubated for 45 minutes at room temperature with

53
secondary antibody diluted 1:500 with blocking buffer. After 3 washes with PBS, nuclear staining
was performed using 1 µg/mL DAPI in PBS for 10 minutes at room temperature. Slides were
washed three times and coverslips were mounted using VECTASHIELD Vibrance Antifade
Mounting Medium (Vector Laboratories, Newark, CA). Fluorescent images were taken on the
Olympus BX43 microscope using 40x magnification and analyzed using ImageJ described below.
2.2.10 ImageJ Quantification of Images
Surviving photoreceptors were determined in rats at the indicated post-natal ages. For
consistency, slides representing the central area of the retina, based on the presence of the optic
nerve as a landmark, were selected for enumeration. 40x images were acquired roughly 1 mm
superior of the optic nerve on an Olympus BX43 microscope with cellSens software (Olympus,
Tokyo, Japan). Four contiguous sections were imaged, and the cell count was averaged for each
eye measured. Retinal measurements were completed in ImageJ Fiji (ver. 2.3.0/1.53t) using a
semi-automated method (244). H&E color deconvolution is applied followed by thresholding
using the Phansalkar method. Iterations of “Fill”, “Open”, and “Close” are used to create a single
region of interest (ROI) for the ONL which is then adjusted manually using the brush tool. The
“watershed” operation is applied to deconvoluted H&E to create a binary map of the individual
photoreceptors within the ONL ROI. Finally, the photoreceptors are counted using “Analyze
Particles” with a size of 1 µm minimum area. The photoreceptor count/mm is calculated by
dividing the photoreceptor count by the image length (211.4 µm).
Immunofluorescence images were analyzed in ImageJ using 5 images per animal. ImageJ
analysis was performed by manually selecting the retinal layers. To account for influence of
autofluorescence associated with retinal degeneration, background subtraction using a 50-pixel
radius. For malondialdehyde (MDA), 4-hydroxynonenal (4HNE), and peptidyl arginine deiminase
4 (PAD4) and MDA staining, automatic thresholding using the Otsu method was applied (245).

54
For citrullinated histone H3 (CitH3) staining, automatic thresholding was performed using the
Triangle method (246). The percent area per retinal layer was measured and data was normalized
to the mean of p21 values.
2.2.11 Statistics
Statistical analysis was performed in GraphPad Prism 9 (Graphpad Software Inc., La Jolla,
CA). All graphs are plotted as mean ± standard error of the mean, unless otherwise noted in the
figure legend. Appropriate statistical analyses were performed for each data set. For comparing
more than two groups one-way/two-way ANOVA or Kruskall-Wallis with multiple comparison
correction based on the dataset. For genetic expression data, ΔΔCT was compared using two-way
ANOVA with multiple comparison correction. The number of animals and statistical test used in
an individual analysis is indicated in the figure legends.
2.3 RESULTS
2.3.1 Immune status of RCS rat does not affect retinal degeneration characteristics.
Inflammation has been associated with immune response in retinal degeneration (247), thus
structural and functional comparison between RCS and iRCS was performed (Figure 2.1). Retinal
morphology was correlated between OCT and histology examination (Figure 2.1A-J).
Longitudinal retinal morphology was evaluated using H&E photoreceptor counts and H&E/OCT
retinal layer thickness was comparable between RCS and iRCS (Figure 2.1K-S). Additionally,
ERG a-wave and b-wave amplitudes were not able to detect differences between RCS and iRCS
at any timepoint (Figure 2.1T-W).

55

Figure 2.1 Structural and function comparison of RCS and iRCS rats from p21 to
p90. H&E staining and OCT imaging corroborate changes in the retina (A-J).
Histology and OCT measurements are comparable between immunocompetent RCS
rats (black dots) and immunodeficient RCS (red dots) at each timepoint (K-W). 3.0
cd.s/m2 ERG amplitudes (% amplitude compared to baseline at p21) for scotopic a-
and b-wave (T-U) and photopic a- and b-wave (V-W) show no significant difference
between immune status at any timepoint. Data represented as mean ± SEM, two-way
ANOVA with Bonferonni’s correction. Histology: n=6 for each group; OCT:
immunocompetent RCS n=8, 7, 3 for p21, p35-p60, and p90, respectively and
immunodeficient RCS n=4 for each group; ERG: immunocompetent RCS n = 12, 8,
6, 12, 6 and immunodeficient RCS n=12, 10, 10, 8, 12 for p21, p35, p49, p60, and
p90 respectively. Scale bar = 100 µm

56
2.3.2 ERG, OCT, and histology correlate retinal degeneration in RCS rat.
Because the immune status of the RCS rat did not appear to influence the retinal
degeneration, the groups were combined for further structural, functional, and molecular analyses.
As shown previously in Figure 2.1A-J, histology confirmed the overall morphological changes
observed with OCT. All retinal layers thinned with RCS age. The outer retina and ONL showed
significant thinning at all timepoints while the inner retina only showed significant degeneration
at p35 (Figure 2.2A-D, L). OCT was able to detect significant thinning for all layers at each
timepoint (Figure 2.2E-H). OCT measurements cover a larger area of the retina and likely capture
more sensitive differences compared to histology. At p35, the ONL started to thin and by P47, it
degenerated significantly in both histology and OCT images. At p60, ONL appeared as about four
rows of cells and it was still visible vaguely in the OCT but was undetectable at p90. Although the
outer retina was still clearly seen in the retinal sections and OCT images at p60, it started to
disappear gradually from the region close to the optic nerve head.
ERG findings at 3.0 cd.s/m
2
are shown in Figure 2.2I-K. All waveforms were measurable
except photopic a-wave which had amplitudes of less than 5 µV at all timepoints (Figure 2.1V).
The characteristics of the decline were substantially different between the measured waveforms.
The scotopic a-wave dropped significantly in early stages with a 65% decrease in amplitude from
p21 to p35 and 25% decrease from p35 to p49 where it reached its minimum (Figure 2.2I). Scotopic
b-wave had a smooth decline with about 40% decrease between p21-p35 and p35-p49, as well as
a 20% decline from p49-p60 (Figure 2.2J). And photopic b-wave had a delayed decline with a 3%
decline from p21 to p35, a 61% decrease from p35 to p49, and 20% decline from p49 to p60
(Figure 2.2K). As photopic ERG is a measurement of the cone pathway, the delayed photopic b-
wave decline is consistent with literature showing that rods degenerate first in retinitis pigmentosa
(248).

57

Figure 2.2 Histology, OCT, and ERG Measurements in RCS rats. All
strucutral and functional measurements show progressive degeneration of
the retina as the RCS rat ages (A-L). All measurements show strong
correlation with RCS age and each other except for OCT Outer Retina (M).
Data represented as mean ± SEM, *P<0.05, **P<0.01, ***P<0.001,
****P<0.0001, one-way ANOVA with Bonferroni’s correction. Histology
n=12 for each group. OCT imaging: n= 12, 11, 7 for p21, p35-p60, and
p90, respectively. ERG: n = 24, 18, 16, 20, 18 for p21, p35, p49, p60, and
p90, respectively. Measurements were averaged for each timepoint, and
Pearson correlation was performed with p21, p35, p49, and p60 data.

58
Using the mean of each timepoint, Pearson correlation analysis was performed using p21-
p60 data (Figure 2.2M). All measurements except for OCT outer retina showed a strong correlation
with RCS age and with each other. In OCT, outer retinal thickness showed an increase at p35
before declining afterwards, this appears to be due to changes in autofluorescence of the outer
retina. Photoreceptor counts had a Pearson r of 0.98-0.99 for all H&E, OCT, except OCT outer
retinal thickness, and scotopic a- and b-wave measurements. Overall, histology, OCT, and ERG
show strong correlation with each other and the age of the RCS rat; these non-invasive methods
will be critical in therapeutic development for efficacy studies, humanely reducing the required
number of animals, and reducing associated costs of research.
2.3.3 FAF hypoautofluorescence correlates with vascular leakage and debris zone.
FA and FAF was used to monitor vascular and retinal changes in an RCS rat from p21 to
p90 (Figure 2.3). Vascular thinning was first observed at p47 (Figure 2.3C). At p61, irregular
patches of fluorescein leakage occurred close to the optic nerve head (ONH) (Figure 2.3D). At
p90, diffuse retinal fluorescein leakage was clearly visible (Figure 2.3E). FAF imaging showed
earlier signs of change with a homogeneous hyperautofluorescence at p30 followed by the
appearance of hypoautofluorescence areas surrounding the ONH (Figure 2.3F-J).
At p61, both hypo and hyperfluorescent changes (“mottled” pattern) are observed in FA
imaging and more extensively in FAF imaging (Figure 2.3D, I). These lesions continue to extend
from the ONH, reaching the peripheral retina by end of the study. At p61, numerous intense
hyperautofluorescent flecks were present within the hypoautofluorescent region with an increase
in number by the end of the study (p90). Interestingly, vascular leakage localized to
hypoautofluorescence lesions (Figure 2.3K-L), and the length of hypoautofluorescence measured
from ONH in the FAF image and the subretinal space without debris in the corresponding OCT
image (Figure 2.3L-N) showed a significant linear correlation (r=0.93, P<0.01) (Figure 2.3P).

59

Figure 2.3 Longitudinal FA and FAF follow-up of an RCS rat at ages p21 to p90. FA
demonstrated the vascular thinning (black asterisk) and fluorescein leakage (white
arrow) in the retina(A-E). FAF showed progressive changes in the FAF pattern in RCS
rats(F-J). From P61, numerous hyperautofluorescent flecks (black arrow) were present
within the hypoautofluorescent lesions (I-J). The area of fluorescein leakage
(hyperfluorescence) (K, white square) was consistent with FAF hypoautofluorescence
(L, white square) and focal loss of the debris zone in IR and OCT (M-N, white square).
Linear correlation between the length of hypoautofluorescence in FAF and the subretinal
space without debris zone in OCT (P).
2.3.4 OCT autofluorescence is derived from photoreceptor debris in the RCS model.
Retinal autofluorescence had peak emission wavelengths of 538nm and 570nm which was
confirmed to arise from the debris zone (DZ) in retinal sections (Figure 2.4A-B). Immunostaining
of CD68 demonstrated microglia presence in the OPL and ONL in early stages whereas GFAP
was strongly expressed in the inner retina ganglion cell layer (GCL) and less intensely throughout

60
the retina (Figure 2.4C-G). As expected, an increased expression on both markers was observed in
relation to loss of photoreceptor outer and inner segments. It has been proposed that microglial
and RPE cells contribute to the autofluorescence in FAF images (249). However, due to the RPE
phagocytosis defect in the RCS rat model the hyperautofluorescence is derived from the debris
zone. Additionally, the localization of Müller and microglial cells does not explain the diffuse
hyperautofluorescence in FAF imaging. However, the punctate hyperautofluorescent spots in p60
and p90 FAF (Figure 2.3I-J, black arrows) are likely microglial cells which have phagocytosed
retinal debris. This data contributes to the hypothesis that fundus autofluorescence in the DZ or
RPE is lipofuscin derived from photoreceptors (250).

Figure 2.4 Autofluorescence and immunostaining of RCS rats.
Spectral image of autofluorescence of a p60 RCS rat at 538nm (A)
and 570nm (B) emission wavelength. Immunofluorescence of RCS
rat retina at different ages using anti-CD68 antibody (green) and
anti-GFAP antibody (red) (C-G).

61
2.3.5 Inflammation and oxidative stress increase with retinal degeneration.
The changes in the FAF suggest that oxidative stress and inflammation may be key
components of retinal degeneration; however, investigation of gene expression changes associated
with these mechanisms in a longitudinal study of RCS rats has not been previously evaluated. Here
we compare retinal inflammation gene expression (Gfap, Tnfa, Tgfb1, and Nfkb1), ROS generating
genes from the NADPH oxidase (NOX) family (Nox1, Nox2, Nox4, and Cyba), and antioxidant
genes (Sod1, Sod2, Sod3, and Cat) throughout retinal degeneration in the RCS model (Figure 2.5).
Gene expression of inflammatory and ROS producing genes were upregulated early at p35
but peaked on p49 compared to p21. Gfap shows delayed increased expression until p49 but
continues to increase at p60. Tnfa expression increased 24-fold at p35 and 43-fold at p49 and
remains at that level at p60. Tgfb1, which can exert anti-inflammatory and pro-inflammatory
effects, showed a peak at p49, and subsequently reduced by p60. Upregulation of Tnfa and Tgfb1
corresponded with increased expression of ROS generating genes such as Nox1, Nox2, and Cyba.
The Nox4 isoform was lowly expressed and was not upregulated suggesting it does not participate
in the overall pathology. Nox1 was the primary isoform upregulated with a 23-fold and 73-fold
increase in expression at p35 and p49, respectively.
The cellular adaptive protective genes were also evaluated across the timepoints, where
increases in Sod1 and Sod2 isoforms were increased by 1.5 to 2-fold at the same timepoints (p35).
However, this level of gene expression changes may not be sufficient to manage the oxidative
stress. In addition, the extracellular Sod3 isoform does not show any upregulation at any timepoint
except for p60, suggesting the inadequate handling of extracellular oxidative stress may be driving
retinal disease progression. The pattern of expression clearly indicates a shift to a pro-
inflammatory and pro-oxidative microenvironment in the retina during degeneration.

62

Figure 2.5 Fold change of inflammation and oxidative-stress related gene expression changes
compared to p21. Left is categorical heatmap of all genes displayed with mean fold change.
Right is individual dot plots of fold changes for each gene with median displayed. ΔΔCT data
was analyzed using two-way ANOVA with Tukey’s multiple comparison,
†
P<0.05 compared
to p21,
ǂ
P<0.05 compared p35,
§
P<0.05 compared to p49, n=6 each group.
2.3.6 Oxidative products increase during retinal degeneration.
To further assess the oxidative damage associated with NOX gene expression, retinal lipid
peroxides MDA and 4HNE were investigated (Figure 2.6). These lipid peroxides are reactive
metabolites of polyunsaturated fatty acid (PUFA) oxidation and were increased with RCS
advancement in age. However, at the earliest timepoint, strong staining was already observed in
the GCL and the choroid suggesting early oxidative stress in the retinal vasculature (Figure 2.6A).
Additionally, the inner segments of the photoreceptors stain for 4HNE at p21. As degeneration of
the inner segments occurs, this stain is gradually lost.

63
Because autofluorescence increases with retinal degeneration, conservative background
subtraction and thresholding was applied to quantify the staining of MDA and 4HNE. While it
appears, several layers increase in staining, this conservative approach resulted in only the ONL
showing significant increased staining of MDA at p60 and 4HNE at p35-p60 (Figure 2.6B). MDA
appears primarily nuclear, and there does appear to be increased MDA staining per photoreceptor
nuclei seen in several rats at p35 as well as ectopic nuclei in the DZ at p49 (Figure 2.6A white
arrows). In contrast, 4HNE is primarily cytoplasmic or extracellular as it did not co-label with
DAPI. Additionally, 4HNE showed significant ONL increase at each subsequent timepoint.
Moreover, breaks are observed in the external limiting membrane (ELM) at p60 (Figure 2.6A
white triangles), and both increased staining of MDA and 4HNE are observed in these regions.
The upregulation of 4HNE at p35 corresponds to increased expression of ROS generating genes
like Nox1, Nox2, and Cyba. In addition, MDA and 4HNE can act as damage-associated molecular
patters (DAMPs) which activate pro-inflammatory signaling through Toll-like receptor 2/4 (TLRs)
(251), as such both mRNA and protein expression of TLR4 was upregulated with RCS age (Figure
2.6C). Additionally, the RNA-sensing TLR7 and DNA-sensing TLR9 were upregulated with age
(Figure 2.6D-E). TLR9, specifically, showed significant increased mRNA expression at each
timepoint and reached the highest increased protein expression of 15-fold at p60 compared to p21.

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Figure 2.6 Representative immunofluorescence staining and quantitation of 4HNE and MDA
in RCS retinas throughout retinal degeneration. Immunofluorescence at each time point with
DAPI (blue), MDA (red), 4HNE (green), and merged channels (A). MDA and 4HNE stain the
GCL, RPE/choroid complex, and photoreceptor inner segments (IS) at p21. Multiple MDA
+

nuclei are seen in the DZ at p49 (white arrow) and breaks of the external limiting membrane
are observed at p60 (white triangles). Quantification of % area of the ONL, normalized to p21,
shows significant increase in MDA at p60 and 4HNE at p35-p60 (B). MDA- and 4HNE-
adducts are damage associated molecular patterns and upregulate toll-like receptors (TLRs)
shown in RT-qPCR and densitometry of TLR4, TLR7, and TLR9 (C-E). Data represented as
mean ± SEM, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, one-way ANOVA with
Tukey’s correction, n=6 each group. Scale bars = 20 µm

65
2.3.7 Retinal citrullination correlates with degeneration.
Recently, PAD4 has been identified in Müller gliosis during retinal degeneration (122).
However, PAD4 is known to be a nuclear-targeting enzyme which citrullinates DNA. DAMP-
mediated TLR signaling results in NOX activation and ROS generation, resulting in PAD4
activation and nuclear citrullination (252, 253). As such, we evaluated the expression of PAD4
and its product CitH3 in RCS retinas throughout retinal degeneration (Figure 2.7 and Figure 2.8).
At p21, PAD4 is confined in the GCL and INL which is consistent with Müller cell expression.
PAD4 begins to expand into the outer retina with increased OPL staining by p35 (Figure 2.7A).
Strong staining is observed throughout the ONL by p49 with intense staining of the ELM and faint
staining below the ELM which is likely localized to Müller cell microvilli. Additionally, there is
choroidal staining of PAD4 at p49, suggesting both intra- and extra-retinal sources of PAD4. At
p60 the ONL staining was similar but there are clear break points in the ELM with PAD4 extending
into the DZ (Figure 2.7A white triangles), consistent with the recent findings that PAD4 is related
to Müller cells and reactive gliosis. ImageJ quantification confirmed these observations showing
significant increase in the ONL at p49 (Figure 2.7B).

66

Figure 2.7 Representative immunofluorescence staining of PAD4 in RCS retinas from p21 to
p60. 40x images of DAPI (blue) and PAD4 (red) (A). Early at p21 PAD4 is retained in the
ganglion cell layer (GCL) and inner nuclear layer (INL), by p49 strong expression is seen in
outer nuclear layer (ONL) up to external limiting membrane (ELM), and at p60 there is ELM
breakage (arrow heads) and PAD4 extending into the debris zone (DZ). ImageJ analysis show
significant ONL increase in PAD4 expression (B). Data represented as mean ± SEM, *P<0.05,
**P<0.01, ***P<0.001, ****P<0.0001, one-way ANOVA with Tukey’s correction, n=6 each
group. Scale bar = 50 µm
CitH3 is a byproduct of PAD4 activity and was analyzed in RCS retinas (Figure 2.8). At
p21, minimal detection of CitH3 was found in photoreceptor nuclei. By p35, there was significant
upregulation of CitH3 staining of photoreceptor nuclei and the intensity of the stain increased
further at p49. As PAD4 was observed at the earliest timepoint, it is likely that early activation of
basal levels of PAD4 between p21 and p35 results in CitH3 staining before significant
translocation of PAD4 into the ONL. Additionally, there is heterogeneity in the staining intensity
of individual photoreceptor nuclei at p35 and p49. The intensity of the stain may be an indication

67
of cells undergoing cell death which showed more intense staining at p49. Interestingly,
photoreceptor nuclei at p60 had limited staining. However, at both p49 and p60, CitH3 staining is
observed in the DZ, and CitH3 appears localized to photoreceptor inner segments (IS), which
suggests that mitochondrial DNA is also undergoing citrullination at these stages of the
degeneration.
These findings are confirmed in western blot analysis of retinal tissues in which CitH3 was
not observed at p21 but clearly detected at p35-p60 (Figure 2.8C-D). As opposed to the peak in
staining at p49, western blots suggest a continued accumulation of CitH3. Because there is no RPE
phagocytosis in the RCS rat, extracellular citrullinated DNA will continue to accumulate in the
DZ. Together, these findings provide evidence for a direct role of PAD4 in photoreceptor
degeneration and a potential therapeutic target for future consideration.

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Figure 2.8 Citrullinated Histone H3 (CitH3) in RCS retinas throughout retinal
degeneration. Immunofluorescence staining of retinas shows increase ONL
staining at p35 and p49 (A, B). At p49 and p60 CitH3 also strongly stains in the
debris zone (DZ) and localizes with photoreceptor inner segments (IS). Western
blot analysis of RCS retinas shows the expected band around 15kDa (C). At
p21, CitH3 was not detected by densitometry but shows an accumulation of
CitH3 from p35 to p60 after normalizing to GAPDH (D). Data represented as
mean ± SEM, *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, Kruskal-Wallis
with Dunn’s correction, n=6 each group. Scale bar = 20 µm

69
2.4 DISCUSSION
Retinal degenerative diseases, including AMD and RP, are the predominant causes of
blindness in adults worldwide (254). The RCS rat has been widely used as an animal model of
retinal degeneration and has been characterized using histology, OCT, and ERG (180). However,
no publication has evaluated all methodologies together in a single study. In addition, there is a
lack of information as to iRCS rat in comparisons with RCS. This set of information is critical to
assess the impact of therapeutic interventions such as biologics and stem-cell based therapeutics
in relations to disease progression. Additionally, there is a dearth of longitudinal studies evaluating
the underlying molecular mechanisms driving retinal degeneration in the RCS and iRCS models.
In this regard, we assessed changes in targeted gene expression and immunostaining of molecular
markers in relation to both morphological and non-invasive imaging that is critical for disease
dissection and therapeutic development.
This study provides a comprehensive assessment of the degeneration characteristics of
iRCS in comparison to immunocompetent RCS with functional and morphological
characterization by electrophysiological, imaging, and histological assessments. T-cell deficiency
in the iRCS model does not appear to alter the morphological retinal degeneration characteristics
or alter the molecular markers evaluated in this study. While the systemic immune system is
suggested to play a role in certain retinal pathologies (255), this study suggests that the adaptive
immune system, specifically T-cells, may not be as important in retinal degeneration of the
autosomal recessive RCS model.
It has been confirmed that in vivo methods like ERG and OCT can be used to characterize
the course of retinal degeneration and reduce the number of experimental animals used (256).
However, the most predictive test for tracking natural course is not clear. In agreement with

70
previous studies, this report confirms reduction in retinal thickness in OCT measurements of both
inner and outer retinal layers with histological findings (257, 258). As well. H&E staining provided
a standard for confirming the accuracy of these non-invasive testing methods, especially for OCT,
which is important for accurate assessment of therapeutic effects and safety in prospective therapy
studies. Our study also showed differences in the characteristics of the various ERG waveforms.
Both scotopic a- and b-wave amplitudes had significant reduction at p35 with a complete abolition
one month later. However, scotopic a-wave had steeper decline at p35 compared to scotopic b-
wave. It is not well delineated whether the change in the scotopic a- and b-wave in RCS rats is
proportional to the loss of the rod photoreceptors (259). In comparison, photopic b-wave remained
stable at p35, suggesting a preservation of the cone photoreceptor function. A decline of the b-
wave amplitude thereafter was expected due to the progression of the retinal degeneration with
gradual loss of cones secondary to oxidative stress after the death of rods (260). Overall, histology,
OCT, and ERG show strong correlation with each other and age of the RCS rat. This data can be
utilized to guide interventional studies in the future.
The attenuation of retinal vessels is considered to be one of the characteristic triads of RP
(261). In addition to the photoreceptor loss, secondary changes affecting the vasculature are
frequently observed using FA (240, 262). The ability to monitor the same retina serially makes
this approach a valuable tool for studying the dynamics of vascular change in the diseased retina.
Our study showed that the diameters of both arteries and veins were thinner at p49 than the
corresponding vessels at p35. Moreover, while the retinal vessels get thinner in early stages, a
fluorescein leakage pattern could be observed at p60, and its severity increased gradually. This
finding is in agreement with the outcomes published by Takeo et al. where they found that the dye
leaked could be observed since early stages and increased with degeneration (263). Although our

71
study was not able to assess the correlation of attenuation of retinal vessels and ERG findings, a
previous report demonstrated the amplitude of the ERG a-wave was significantly correlated with
retinal-choroidal blood flow (259).
Another prominent characteristic trait of RCS rats is the progressive changes of the
autofluorescence pattern. In this study, FAF showed a hypoautofluorescence pattern gradually
spread into the majority of the fundus with numerous hyperautofluorescent flecks at p90. A cross
analysis among FAF, FA, and OCT, showed a correlation between hypoautofluorescence, vascular
leakage, and subretinal debris loss. This is consistent with literature showing vascular atrophy in
early disease followed by subretinal neovascularization in the RCS rat at advanced stages of
degeneration (264). Given the correlation between hypoautofluorescent lesion and the
disappearance of the debris zone, the changes in the FAF pattern can be predictive of
morphological anomalies.
Macrophages and glial cells are recruited into the degenerating retina of the RCS rat (265,
266), thus we investigated whether these cells play a role in the characteristics of
hyperautofluorescence observed in RCS rats. The location of OCT hyperautofluorescence was
determined to originate from the within the DZ even though there is no doubt that fluorescence
intensity might reflect the magnitude of the lipofuscin accumulation inside of the RPE in other
models (267). In RCS rats, OCT/FAF hyperautofluorescence is mostly contributed by subretinal
debris whereas macrophages and components within the RPE have no major role. This
corroborates with the hypothesis that subretinal debris from photoreceptors is the major contributor
and source of autofluorescence observed in RPE lipofuscin (250).
After characterization of functional and structural changes associated with retinal
degeneration, we aimed to address longitudinal changes in molecular markers. We focused on

72
inflammation and oxidative stress mechanisms due to the large body of evidence suggesting they
are major contributors to retinal degeneration (268). In general, gene expression peaked or
plateaued at p49 which is paralleled with scotopic a-wave reaching its minimum at p49. The three
genes which showed the highest upregulation with RCS age were Gfap, Tnfa, and Nox1.
Gfap is a common marker for retinal damage expressed by glial cells. Müller glia
upregulate expression during formation of gliotic scars to fill retinal breaks and can be seen in the
DZ following photoreceptor cell death (269). We found that Gfap expression is not upregulated
until p49, after significant photoreceptor loss has occurred. While research has suggested that
reactive gliosis could be a pharmacological target for inhibition (270), it is clear in the RCS model
that gliosis is a secondary condition and inhibition would only slow late-stage neuronal
degeneration. Tnfa is a proinflammatory cytokine primarily expressed by microglia and monocytes
in the retina (271), it was highly expressed at p35 indicating an earlier response of microglia to
retinal stress compared to Müller cells. Nox1 is an isoform of the gp91phox catalytic subunit of
NOX. Similar to Tnfa, Nox1 shows significant upregulation at p35 and reaches its peak expression
at p49. While the isoforms Nox2 and Nox4 are considered relevant to other vascular pathologies
(272), Nox1 plays the primary role in the RCS model with minor roles of Nox2 and the Cyba
subunit. In a mouse model, Nox1 but not Nox2 or Nox4 knockout protected the retina against
oxygen-induced retinopathy and identified microglia as the source of hypoxia-induced ROS
generation and neovascularization (273). This study suggests that microglial response is a central
player in early phases of retinal degeneration through production of inflammatory signaling and
ROS generation.
Conversely, SOD-family genes only showed a mild upregulation, suggesting protective
antioxidant mechanisms are insufficient to manage the accumulating ROS. This unmanaged ROS

73
will begin to react with cellular components to form toxic metabolites such as MDA and 4HNE.
Both MDA and 4HNE showed a time-dependent increase in ONL staining, with 4HNE showing a
significant increase at p35, similar to Nox1 gene expression. This data suggests that the ONL is
the primary site for increased oxidative stress compared to other retinal layers. Additionally, MDA
and 4HNE are derived from PUFA oxidation, such as docosahexaenoic acid (DHA). The retina
contains the highest concentration of DHA in the body and decreased retinal DHA is a risk factor
in retinopathies (274). Lipid peroxides can propagate cellular damage through the formation of
DNA or protein adducts (275). We observed increased nuclear MDA in the photoreceptor nuclei
with more intense staining in photoreceptors closest to the DZ where oxidized outer segments are
accumulating due to a loss of RPE phagocytosis. Additionally, MDA is known to form adducts
with glial proteins such as GFAP, vimentin, and glutamine synthetase (276), and we observed
strong MDA staining of the GCL where Müller glia and astrocytes predominately localize. 4HNE
has been linked to protein modification in neurodegenerative diseases through altered energy
metabolism, mitochondrial dysfunction, insufficient antioxidant mechanisms, and is known to
induce TNF α and TGFβ synthesis (277, 278). We also observed an age-dependent increase in
TLR4, which produces ROS and inflammatory cytokines upon receptor binding of DAMPs such
as MDA and 4HNE adducts (279). Overall, there is a strong relationship between the changes in
inflammatory and ROS gene expression with MDA and 4HNE with subsequent retinal
degeneration. This information provides new insights into the timing of molecular changes in the
RCS model showing microglia to be an early driver of pathological gene expression likely driven
by DAMP signaling. While inhibition of DAMP signaling will not ameliorate the underlying RPE
defect, a microglial switch from pro-inflammatory to pro-resolution could significantly impact the
progression of the disease.

74
In addition to TLR4 which senses extracellular DAMPs, we observed increased TLR7 and
TLR9 gene and protein expression, which sense RNA and DNA DAMPs, respectively. TLR-
signaling in immune cells has been shown to results in ROS production and subsequent
extracellular trap (ET) formation through PAD4, PAD4 and ETs have been suggested to play a
role in retinal degeneration (100, 253, 280). However, investigation of PAD4 in the retina has
focused on citrullination of GFAP and the process of gliosis, but PAD4 is known to translocate to
the nucleus and citrullinate histones to induce DNA unwinding expulsion in ET formation (281).
In this regard, we examined the changes in retinal PAD4 and CitH3 throughout retinal
degeneration. Early at p21, PAD4 shows strong staining in the INL and begins translocating
towards the ONL with strong ONL staining at p49 and p60. As well, at p60 there is breakage of
the ELM and PAD4 extends into the DZ similar to gliosis which corresponds to the increased Gfap
expression at p49 and p60. Recently this was confirmed by Palko et al. by co-labeling GFAP and
PAD4 in a mouse model of neovascular AMD (122). Interestingly, photoreceptor CitH3
significantly increased at p35 prior to significant ONL PAD4 staining. This may be due to
increased activation of basal levels of calcium-dependent PAD4 prior to significant PAD4
upregulation, which is supported by research showing TNF α as well as NOX and mitochondrial
produced ROS are known initiators of PAD4-mediated DNA release (116). Aberrant
photoreceptor citrullination is strongly connected to the oxidative stress and inflammation
associated with early retinal changes, indicating a novel mechanism for treatment of retinal
degeneration.
In summary, this study provides the first systematic evaluation for retinal functional and
morphological characterization in both immunocompetent and immunodeficient RCS rats with
retinal degeneration. Among all modalities used, the scotopic a-wave is a key element to be

75
analyzed as it is the earliest indicator of retinal degeneration and its progression. Additionally, this
study provides molecular changes associated with retinal degeneration which can guide future
therapeutic strategies and development. Of particular interest will be pathways revolving around
TNF α and NOX1 inhibition and downstream changes in lipid content and citrullination. As the
microenvironment consisting of toxic lipid and cellular metabolites is a major contributor to
neuronal death, therapeutics that focus on restoring the microenvironment and resolution of
inflammatory and oxidative molecules are strong candidates for future development.
2.5 FUNDING SOURCES AND ACKNOWLEDGEMENTS
1. Unrestricted Grant to the Department of Ophthalmology from Research to Prevent Blindness,
New York, NY
2. Research reported in this publication was supported by the National Eye Institute of the
National Institutes of Health under Award Number P30EY029220. The content is solely the
responsibility of the authors and does not necessarily represent the official views of the
National Institutes of Health.
3. American Foundation for Pharmaceutical Education Pre-Doctoral Fellowship

2.6 REFERENCES
70. D'Cruz PM, et al. Mutation of the receptor tyrosine kinase gene Mertk in the retinal dystrophic
RCS rat. Hum Mol Genet. 2000;9(4):645-51.
100. Barliya T, et al. Possible involvement of NETosis in inflammatory processes in the eye:
Evidence from a small cohort of patients. Mol Vis. 2017;23:922-32.
116. Rohrbach A, et al. Activation of PAD4 in NET formation. Frontiers in Immunology. 2012;3.
122. Palko SI, et al. Peptidyl arginine deiminase 4 deficiency protects against subretinal fibrosis by
inhibiting Müller glial hypercitrullination. Journal of Neuroscience Research.
2023;101(4):464-79.
153. Thomas BB, et al. A new immunodeficient retinal dystrophic rat model for transplantation
studies using human-derived cells. Graefe's Archive for Clinical and Experimental
Ophthalmology. 2018:2113-25.
180. Adachi K, et al. Optical Coherence Tomography of Retinal Degeneration in Royal College of
Surgeons Rats and Its Correlation with Morphology and Electroretinography. PLOS ONE.
2016;11(9):e0162835.

76
234. Liu Y, et al. Long-term safety of human retinal progenitor cell transplantation in retinitis
pigmentosa patients. Stem Cell Res Ther. 2017;8(1):209-.
235. Carr A-J, et al. Protective effects of human iPS-derived retinal pigment epithelium cell
transplantation in the retinal dystrophic rat. PloS one. 2009;4(12):e8152-e.
236. Wang S, et al. Long-term vision rescue by human neural progenitors in a rat model of
photoreceptor degeneration. Investigative ophthalmology & visual science. 2008;49(7):3201-
6.
237. Shelby SJ, et al. MERTK signaling in the retinal pigment epithelium regulates the tyrosine
phosphorylation of GDP dissociation inhibitor alpha from the GDI/CHM family of RAB
GTPase effectors. Experimental eye research. 2015;140:28-40.
238. Ryals RC, et al. Long-term Characterization of Retinal Degeneration in Royal College of
Surgeons Rats Using Spectral-Domain Optical Coherence Tomography. Investigative
ophthalmology & visual science. 2017;58(3):1378-86.
239. Wang S, et al. Morphological changes in the Royal College of Surgeons rat retina during
photoreceptor degeneration and after cell-based therapy. The Journal of Comparative
Neurology. 2005;491(4):400-17.
240. Zambarakji HJ, et al. High resolution imaging of fluorescein patterns in RCS rat retinae and
their direct correlation with histology. Experimental eye research. 2006;82(1):164-71.
241. Zhao M, et al. Intravitreal Stanniocalcin-1 Enhances New Blood Vessel Growth in a Rat
Model of Laser-Induced Choroidal Neovascularization. Investigative Ophthalmology &
Visual Science. 2018;59(2):1125-33.
242. Ye J, et al. Primer-BLAST: a tool to design target-specific primers for polymerase chain
reaction. BMC Bioinformatics. 2012;13:134.
243. Livak KJ, and Schmittgen TD. Analysis of relative gene expression data using real-time
quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402-8.
244. Schindelin J, et al. Fiji: an open-source platform for biological-image analysis. Nature
Methods. 2012;9(7):676-82.
245. Otsu N. A threshold selection method from gray level histograms. IEEE Transactions on
Systems, Man, and Cybernetics. 1979;9:62-6.
246. Zack GW, et al. Automatic measurement of sister chromatid exchange frequency. Journal of
Histochemistry & Cytochemistry. 1977;25(7):741-53.
247. Van Lookeren Campagne M, et al. Mechanisms of age-related macular degeneration and
therapeutic opportunities. Journal of Pathology. 2014;232(2):151-64.

77
248. Narayan DS, et al. A review of the mechanisms of cone degeneration in retinitis pigmentosa.
Acta Ophthalmol. 2016;94(8):748-54.
249. Makabe K, et al. Microglia dynamics in retinitis pigmentosa model: formation of fundus
whitening and autofluorescence as an indicator of activity of retinal degeneration. Scientific
Reports. 2020;10(1):14700.
250. Sparrow JR, and Duncker T. Fundus Autofluorescence and RPE Lipofuscin in Age-Related
Macular Degeneration. Journal of Clinical Medicine. 2014;3(4):1302-21.
251. Binder CJ, et al. Innate sensing of oxidation-specific epitopes in health and disease. Nature
Reviews Immunology. 2016;16(8):485-97.
252. Chen T, et al. Receptor-Mediated NETosis on Neutrophils. Frontiers in Immunology.
2021;12.
253. Denning N-L, et al. DAMPs and NETs in Sepsis. Frontiers in Immunology. 2019;10.
254. Ben M'Barek K, et al. Developing Cell-Based Therapies for RPE-Associated Degenerative
Eye Diseases. Adv Exp Med Biol. 2019;1186:55-97.
255. Singh A, et al. Systemic frequencies of T helper 1 and T helper 17 cells in patients with age-
related macular degeneration: A case-control study. Scientific Reports. 2017;7(1):605.
256. Rosch S, et al. Evaluation of Retinal Function and Morphology of the Pink-Eyed Royal
College of Surgeons (RCS) Rat: A Comparative Study of in Vivo and in Vitro Methods. Curr
Eye Res. 2017;42(2):273-81.
257. Sauve Y, et al. Partial preservation of rod and cone ERG function following subretinal
injection of ARPE-19 cells in RCS rats. Vision Res. 2006;46(8-9):1459-72.
258. Pinilla I, et al. Contribution of rod and cone pathways to the dark-adapted electroretinogram
(ERG) b-wave following retinal degeneration in RCS rats. Vision Res. 2004;44(21):2467-74.
259. Zhang Y, et al. Decreased retinal-choroidal blood flow in retinitis pigmentosa as measured by
MRI. Doc Ophthalmol. 2013;126(3):187-97.
260. Lee SY, et al. N-Acetylcysteine promotes long-term survival of cones in a model of retinitis
pigmentosa. J Cell Physiol. 2011;226(7):1843-9.
261. Ma Y, et al. Quantitative analysis of retinal vessel attenuation in eyes with retinitis
pigmentosa. Invest Ophthalmol Vis Sci. 2012;53(7):4306-14.
262. Marano F, et al. Hereditary retinal dystrophies and choroidal neovascularization. Graefes Arch
Clin Exp Ophthalmol. 2000;238(9):760-4.

78
263. Satoh T, and Yamaguchi K. Ocular fundus abnormalities detected by fluorescein and
indocyanine green angiography in the Royal College of Surgeons dystrophic rat. Exp Anim.
2000;49(4):275-80.
264. Wang S, et al. Non-invasive stem cell therapy in a rat model for retinal degeneration and
vascular pathology. PLoS One. 2010;5(2):e9200.
265. Kyger M, et al. Autoimmune responses against photoreceptor antigens during retinal
degeneration and their role in macrophage recruitment into retinas of RCS rats. J
Neuroimmunol. 2013;254(1-2):91-100.
266. Xie J, et al. Olfactory Ensheathing Cells Grafted Into the Retina of RCS Rats Suppress
Inflammation by Down-Regulating the JAK/STAT Pathway. Front Cell Neurosci.
2019;13:341.
267. Sparrow JR, et al. Interpretations of fundus autofluorescence from studies of the bisretinoids
of the retina. Invest Ophthalmol Vis Sci. 2010;51(9):4351-7.
268. Hollyfield JG, et al. Retinal degenerative diseases. Cham: Springer International Publishing;
2005.
269. Fernández-Sánchez L, et al. Astrocytes and Müller Cell Alterations During Retinal
Degeneration in a Transgenic Rat Model of Retinitis Pigmentosa. Frontiers in Cellular
Neuroscience. 2015;9.
270. Sugasini D, et al. Efficient Enrichment of Retinal DHA with Dietary
Lysophosphatidylcholine-DHA: Potential Application for Retinopathies. Nutrients.
2020;12(10).
271. Nakazawa T, et al. Tumor necrosis factor-alpha mediates photoreceptor death in a rodent
model of retinal detachment. Invest Ophthalmol Vis Sci. 2011;52(3):1384-91.
272. Wingler K, et al. NOX1, 2, 4, 5: counting out oxidative stress. Br J Pharmacol.
2011;164(3):866-83.
273. Wilkinson-Berka JL, et al. NADPH oxidase, NOX1, mediates vascular injury in ischemic
retinopathy. Antioxid Redox Signal. 2014;20(17):2726-40.
274. Veneskoski M, et al. Specific recognition of malondialdehyde and malondialdehyde
acetaldehyde adducts on oxidized LDL and apoptotic cells by complement anaphylatoxin C3a.
Free Radic Biol Med. 2011;51(4):834-43.
275. Ayala A, et al. Lipid Peroxidation: Production, Metabolism, and Signaling Mechanisms of
Malondialdehyde and 4-Hydroxy-2-Nonenal. Oxidative Medicine and Cellular Longevity.
2014;2014:360438.
276. Zarkovic N, et al. Pathophysiological relevance of aldehydic protein modifications. J
Proteomics. 2013;92:239-47.

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277. 4-Hydroxy-2-Nonenal, a Reactive Product of Lipid Peroxidation, and Neurodegenerative
Diseases: A Toxic Combination Illuminated by Redox Proteomics Studies. Antioxidants &
Redox Signaling. 2012;17(11):1590-609.
278. Yang H-j, et al. 4-HNE induces proinflammatory cytokines of human retinal pigment
epithelial cells by promoting extracellular efflux of HSP70. Experimental Eye Research.
2019;188:107792.
279. Papac-Milicevic N, et al. Malondialdehyde Epitopes as Targets of Immunity and the
Implications for Atherosclerosis. Adv Immunol. 2016;131:1-59.
280. Chant SM, and Meyers-Elliott RH. Autoimmunity: A possible factor in the development of
retinal degeneration in the RCS rat. Clinical Immunology and Immunopathology.
1982;22(3):419-27.
281. El Shikh MEM, et al. Extracellular traps and PAD4 released by macrophages induce
citrullination and auto-antibody production in autoimmune arthritis. J Autoimmun.
2019;105:102297.

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2.7 SUPPLEMENTARY TABLES AND FIGURES
Supplementary Table T2.1 RT-qPCR Primer Sequences
Gene Forward (5’-3’) Reverse (5’-3’)
Cat TCACCTGAAGGACCCTGACA TCCATCTGGAATCCCTCGGT
Cyba GCAGGAGTGCTCATCTGTCT GTACTTCTGTCCACACCGCTC
Cybb (Nox2) TAGCACTTCACACGGCCATT ATATGGGTCCGAAGTCCCGA
Gapdh AGTGCCAGCCTCGTCTCATA GGTAACCAGGCGTCCGATAC
Gfap CAACCTCCAGATCCGAGAAACC GCATCTCCACCGTCTTTACCA
Nfkb1 TCCCGCCCCTTCTAAAACTC CTCCACCAGCTCTTTGATGGT
Nox1 CCTGAAGGATCCCATCAGAGA ACCAGCCAGTTTCCCATTGT
Nox4 TGTTGGGCCTAGGATTGTGT CTTCTGTGATCCGCGAAGGT
Sod1 AGGGCGTCATTCACTTCGAG CCCATGCTCGCCTTCAGTTA
Sod2 ACCGAGGAGAAGTACCACGA TGTGATTGATATGGCCCCCG
Sod3 ACACCTATGCACTCCACAGAC AGGGGATGCTAGGGGCTTAT
Tgfb1 GACTCTCCACCTGCAAGACC GGACTGGCGAGCCTTAGTTT
Tlr4 GATCTGAGCTTCAACCCCCT ATTGTCTCAATTTCACACCTGGA
Tlr7 GCCTTCAAGAAAGATGCCATT AGTTTGGTGGAGGAGAACAGAG
Tlr9 CTTCCATTTTCCATCATGGTTCTCT GCCATGAGGCTTCAGTTCAC
Alox5 ATGCCTTCCTACACTGTCACC GAGCCAATGAGGCTGAGGTAA

Supplementary Table T2.2 Western Blot Antibodies
Supplementary Table T2.3 Immunofluorescence Antibodies
Antibody Host Dilution Catalog Manufacturer
GFAP Rabbit 1:500 Z0334 Dako
CD68 Mouse 1:100 mca341r Bio-Rad
PAD4 Rabbit 1:100 17373-1-AP Proteintech
Cit-H3 Rabbit 1:1000 ab5103 Abcam
4-Hydroxynonenal Mouse 1:500 MAB3249 R&D Systems
Malondialdehyde Rabbit 1:1000 ab27642 Abcam
Mouse Secondary Goat 1:500 A32723 Thermo Fisher Scientific
Rabbit Secondary Goat 1:500 A11037 Thermo Fisher Scientific
Antibody Host Dilution Catalog Manufacturer
Cit-H3 rabbit 1:1000 ab5103 Abcam
GAPDH mouse 1:10000 60004-1-Ig Proteintech
Mouse Secondary Goat 1:10000 7076S Cell Signaling
Rabbit Secondary Goat 1:10000 7074S Cell Signaling

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CHAPTER 3: A PROFILE OF TRANSCRIPTOMIC AND LIPIDOMIC
CHANGES IN THE RETINAL DYSTROPHIC RCS RAT
Kabir Ahluwalia
1
, Brandon Ebright
1
, Aditya Naik
1
, Dante Dikeman
1
, Isaac Asante
2
Mark S.
Humayun
3,4
, Stan G. Louie
1,3*

1
Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California, Los Angeles, CA
90089, USA
2
ISAAC
3
USC Ginsburg Institute of for Biomedical Therapeutics, University of Southern California, Los Angeles, CA
90033, USA USC

4
USC Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine, University of Southern
California, Los Angeles, CA 90033, USA
* Correspondence: Stan Louie; slouie@usc.edu; 1985 Zonal Avenue, Los Angeles, CA 90033; 323-442-3646

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3.1 INTRODUCTION
Age-related macular degeneration (AMD), diabetic retinopathy (DR), and retinitis
pigmentosa (RP) are dystrophic retinal degenerative conditions which will ultimately lead to vision
loss (282). Recent studies have shown that at age 60 and above, more than 50% of individuals
residing in the US have some type of retinal disorder (283). More unfortunate, even with the high
prevalence of these conditions, there are limited treatment options. For both AMD and DR,
treatment options have only been available for limiting the more advanced stages of the diseases
through inhibiting neovascularization. As of 2023, the first treatment (complement C3 inhibitor;
pegcetacoplan) for geographic atrophy (GA), the advanced form of non-neovascular AMD, was
approved by the FDA. While this treatment slowed the progression of GA lesions, it did not
improve vision and it was associated with increased incidence of neovascularization (104, 194).
Up to 271 genes have been linked to inherited retinal diseases (IRDs), however the only gene
therapy available is for Leber congenital amaurosis (195). While vision was improved, the gene
therapy did not halt retinal degeneration (196). This current paradigm illustrates the dire need for
novel and effective interventions for the treatment of retinal degenerative conditions.
In order to develop effective therapeutics, insights to the molecular pathogenesis are
required. While cellular dissection of the molecular events given an important clue as how disease
is initiated and sustained, animal models able to recapitulate the molecular mechanisms are key to
evaluate novel and effective therapeutics. The Royal College of Surgeons (RCS) rat is a highly
utilized animal model for evaluating the effectiveness of therapeutics for retinal pathologies (65).
The RCS is a MerTK mutant model which results in inhibition of retinal pigment epithelium (RPE)
phagocytosis of photoreceptor outer segments, progressive cell atrophy, and loss of vision (70,
153). Animal models are critical for the development of efficacious therapeutics and their use

83
depends on their recapitulation linked to human disease in both clinical manifestations and
underlying dysfunctions. This well characterized rat model has predictable retinal degeneration
over time, but despite its wide utilization, there is still little known as to the molecular events
driving retinal dystrophy.
Transcriptomics has become popular for understanding the underlying molecular
mechanisms of various pathologies in both human tissues and animal models of disease, however,
transcriptomics of retinal degeneration animal models is sparse, as most studies have focused on
targeted profiling of genes and proteins. Particularly, non-interventional analyses of retinal
degeneration are limited. This study provides transcriptomic and metabolomic changes across time
in the RCS rat. In particular, this study focuses on transcriptomic changes of late-stage compared
to early-stage retinal degeneration in the RCS rat, similar to how many AMD transcriptomic
studies are conducted (284). We match our differentially expressed genes (DEGs) to specific cell
types based on other single-cell RNA-Seq studies. Additionally, we compare the analysis to human
transcriptomic studies to identify similarities with human disease.
We found a strong relationship between retinal degeneration and lipid metabolizing
enzymes which indicated a disproportionate balance of pro-inflammatory lipid mediators
compared to pro-resolving lipid mediators. This was corroborated by lipidomic analysis of both
ocular tissue and plasma showing moderate increases in pro-inflammatory arachidonic acid (AA)
metabolism but extreme shutdown of pro-resolving (DHA) and (DPA) metabolites. Several
metabolites in plasma lipidomics correlated with age associated changes in retinal structure and
function. Not only does this open a potential avenue for retinal degeneration treatment through
specialized pre-resolving lipid mediators (SPMs), but it also allows for a non-invasive, liquid
biopsy for retinal degeneration. Future studies can perform lipidomic analyses in other animal

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models of retinopathies and human patients to understand the lipidomic profile of these
degenerative conditions.
3.2 METHODS
3.2.1 Animals
Animal experiments were conducted in full accordance with University of Southern
California (USC) Institutional Animal Care and Use Committee (IACUC)-approved protocols,
National Institutes of Health Guide for the Care and Use of Laboratory Animals and the ARVO
Statement for the Use of Animals in Ophthalmic and Vision Research. Dystrophic RCS rats were
obtained from Dr. Matthew LaVail (University of California, San Francisco, USA) which were
bred and propagated at the USC under an IACUC approved protocol. The iRCS rat breeding pair
were obtained from Dr. Biju B Thomas (153). All the pups used for this study were their offspring
born in the USC vivarium. Rats were group housed under specific pathogen-free conditions and
had access to water and food ad libitum. All animals were housed in a temperature- and light-
controlled rooms with a 12-h light/dark cycle.
3.2.2 In vivo Experimental Design
The in vivo experimental design is displayed in Table 3.1. These experiments were
conducted between post-natal day (p) 21 and p60 at which the animal is completely blind. Studies
were conducted using n=3 immunocompetent and n=3 immunodeficient RCS rats which
previously showed no difference in retinal degeneration characteristics.
Table 3.1 Experimental Design
Post Natal Age (days) Analyses
p21 RNA-Seq, Lipidomics, RT-qPCR
p35 Lipidomics, RT-qPCR
p49 Lipidomics, RT-qPCR
p60 RNA-Seq, Lipidomics, RT-qPCR

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3.2.3 Euthanasia and Tissue Collection
At end of study, rats were euthanized by intraperitoneal injection of 0.5 mL pentobarbitol
sodium 390 mg and phenytoin sodium 50 mg (Euthasol; Virbac AH, Inc., Fort Worth, TX). For
RNA and protein analysis, retinas were isolated from freshly enucleated eyes and placed into 1mL
RNAzol RT (Sigma Aldrich, St. Louie, MO) or 0.5 mL T-PER Tissue Protein Extraction Reagent
(Thermo Fisher Scientific, Waltham, MA), respectively, and processed as described below. For
lipidomic analysis, eyes were snap frozen on dry ice following enucleation. Blood was collected
via cardiac puncture and collected via heparinized needles into tubes containing 1mM EDTA
(Sigma-Aldrich) and 2mM BNPP (Millipore Sigma, Burlington, MA). Plasma was isolated
following centrifugation at 2,000xg for 15 minutes at 4 °C and stored at -80°C.
3.2.4 Retinal Transcriptomics
Total RNA was sent to Azenta Life Sciences (South Plainfield, NJ) for transcriptomics
analysis. Sample quality control was performed using a Nanodrop 2000 (Thermo Fisher), Qubit
(Invitrogen), and TapeStation (Agilent) and all samples sequenced had an RNA integrity number
(RIN) over 8.0. Total RNA underwent polyA selection followed by mRNA sequencing analysis
using an HiSeq Illumina based system with 20-30 million 150 bp paired end reads per sample.
RNA-seq data analysis was performed by Azenta including read trimming, mapping, and
differential gene expression (DGE). Raw data quality was evaluated with FastQC. Reads were
trimmed with Trimmomatic and mapped to reference genome
(https://uswest.ensembl.org/Rattus_norvegicus/Info/Index) with STAR (285). Gene hits counts
were calculated with FeatureCounts and normalized and compared between groups using DESeq2.
Mapped genes were labeled with cell types using multiple publications (286-288). If publications
conflicted in cell type assignment, the majority assignment was used or no assignment was made
if there was a tie in cell type. Log2 fold change and Benjamini-Hochberg adjusted p-values for

86
DEGs were analyzed with the use of QIAGEN IPA (QIAGEN Inc.,
https://digitalinsights.qiagen.com/IPA) (289).
3.2.5 Ocular and Plasma Lipidomics
The lipidomics method was adapted from Ebright et al (290). Lipidomics was performed
on 100 μL of plasma or whole eye. To plasma, 1 mL cold methanol (MeOH) and 50 μL of internal
standard (IS) mix (50 ng/mL, Cayman Chemical, Ann Arbor, MI) in MeOH was added. IS mix is
composed of d5-RvD2, d8-5S-HETE, d4-PGE2, d5-LXA4, and d4-LTB4. For whole eye samples,
1 mL of cold MeOH and 100 μL of IS was added followed by TissueLyser II homogenization.
Samples were vortexed and centrifuged at 10,000 rpm for 8 minutes. The lipid-containing
methanolic layer was transferred to fresh tubes and diluted to 10% MeOH with HPLC-grade water.
Samples were purified using solid phase extraction (SPE) with a Strata X 33 μm Polymeric
Reverse Phase cartridge (Phenomenex, Torrance, CA) installed on a vacuum manifold. SPE
columns were conditioned by adding 1 mL of LCMS MeOH followed by 1 mL of HPLC water.
Samples in 10% MeOH were then run through the extraction column to allow sample binding and
then allowed to dry to remove excess water. Samples were eluted with 500 μL of LCMS-grade
MeOH and then dried under inert nitrogen gas. Finally, samples were then reconstituted to 50 μL
with 50% MeOH/water and transferred to LCMS micro-sampling vials. Lipids were separated
using an Agilent 1290 Infinity II LC System (Agilent, Santa Clara, CA) with Poroshell 120 EC-
C18 column (2.7 μm, 4.6 x 100mm, Agilent, Santa Clara, CA) and mass-spectrometry analysis
was performed QTRAP Sciex API6500+ (AB Sciex LLC, Framingham, MA). The aqueous mobile
phase was composed of HPLC-water + 0.01% formic acid (Sigma-Aldrich, St. Louis, MO), while
the organic mobile phase was composed of LCMS-MeOH + 0.01% formic acid. The mobile phase
gradient [time (%aqueous/%organic)] was programmed as: 0 min (80/20) – 0.1 min (50/50) – 2
min (50/50) – 11 min (20/80) – 14.5 min (20/80) – 14.6 min (2/98) – 20 min (2/98) – 20.1 min

87
(80/20) – 23 min (80/20). The flow rate was held at 0.5 mL/min, and the column temperature was
held at 40 °C. Resulting peaks were normalized to their respective deuterated internal standards,
eye weight was used to further normalize ocular tissue data. Normalized AUCs were log
transformed before statistical analysis. Each targeted analyte was identified using unique MRM
signatures (Supplementary Table T3.1) in negative mode (ESI-) in conjunction with internal
standards.
3.2.6 Retinal RT-qPCR
Isolated Retinas were homogenized in RNAzol RT (Sigma-Aldrich, St. Louis, MO) using
a TissueLyser II (Qiagen LLC, Germantown, MD). Total RNA was extracted following
manufacturer’s instructions and concentration was determine via the NanoDrop™
spectrophotometer (Thermo Fisher Scientific, Waltham, MA). cDNA was prepared using the
RevertAid™ First Strand cDNA Synthesis Kit (Thermo Fisher Scientific, Waltham, MA)
following manufacturer’s protocol. The RT-qPCR master mix was prepared by mixing
PowerUp™ SYBR™ Green Master Mix (Applied Biosystems, Foster City, CA) and the forward
and reverse primers. Primers were designed using Primer-Blast from NCBI and sequences are
listed in Supplementary Table T3.2 (242). Diluted cDNA and master mix were pipetted into a 384-
well plate using an Assist Plus Pipetting Robot (INTEGRA Biosciences Corp., Hudson, NH) in
triplicate. RT-qPCR was performed on QuantStudio 12K Flex Real-Time PCR System (Applied
Biosystems, Foster City, CA) with the following run method: UDG activation at 50˚C for 2
minutes, followed by Dual-Lock DNA polymerase at 95˚C for 2 minutes, then 40 cycles of
denature at 95˚C for 15 seconds and anneal/extend at 60˚C for 60 seconds, the final stage was a
dissociation curve consisting of ramping at 1.6˚C/second to 95˚C for 15 seconds, then
1.6˚C/second to 60˚C for 1 minute and 0.15˚C/second to 95˚C for 15 seconds. Data was collected
and analyzed using the 2
−ΔΔC
T method using GAPDH as the reference gene (243).

88
3.2.7 Statistics
Transcriptomics statistical analysis was performed by Azenta. Other statistical analysis
was performed in GraphPad Prism 9 (Graphpad Software Inc., La Jolla, CA). All graphs are plotted
as mean ± standard error of the mean, unless otherwise noted in the figure legend. Appropriate
statistical analyses were performed for each data set. For comparing more than two groups one-
way/two-way ANOVA or Kruskall-Wallis with multiple comparison correction based on the
dataset. For genetic expression data, ΔΔCT was compared using two-way ANOVA with multiple
comparison correction. The number of animals and statistical test used in an individual analysis is
indicated in the figure legends. For whole eye lipidomics, each eye was processed separately and
averaged for a single rat. Principal component analysis (PCA) was performed in GraphPad Prism
while PCA-discriminant analysis (PCA-DA) was performed in MarkerView (AB Sciex). Multiple
comparisons of lipid analytes were performed with two-way ANOVA with false-discovery
(q=0.05, two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli).
3.3 RESULTS
3.3.1 Identification of Differentially Expressed Genes (DEGs) and Cell Types
We performed RNA-Seq on whole-retina samples from RCS rats at p21 (n=6), prior to
retinal degeneration, and p60 (n=6), when significant photoreceptor degeneration has already
occurred (Chapter 2 Figure 2.2). Each timepoint contained immunocompetent RCS rats (n=3) and
immunodeficient RCS (iRCS) rats (n=3). Both age and immune status differentially expressed
genes (DEGs) were analyzed using an absolute fold change cutoff of 2 and adjusted p-value cutoff
of 0.05 (|Fold Change >2, padj<0.05). In RCS vs iRCS there were 25 and 2 significantly
downregulated DEGs and 9 and 24 significantly upregulated DEGs at p21 and p60, respectively
(Figure 3.1A-B). Pathway analysis did not identify pathway enrichment between RCS and iRCS.

89
In contrast, the analysis between p21 and p60 had 682 significantly downregulated and 671
significantly upregulated DEGs (Figure 3.1C).
Using published single-cell RNA-Seq studies, genes were labeled with a retinal cell type
if either a majority or no-conflict was found between publications. Overall, 6,671 genes were
labeled with a specific cell type or group (Figure 3.1D). As expected, when the data was filtered
for significant DEGs, the largest proportion of downregulated genes (60%) belonged to
photoreceptors which are decreasing in number throughout the disease pathology (Figure 3.1E).
Microglia, macroglia (Müller and astrocytes), and vascular cells were the highest proportion of
significantly upregulated genes with 40%, 31%, and 12%, respectively (Figure 3.1F). The full
dataset and the cell-type filtered data were analyzed for pathway enrichment using QIAGEN IPA.

90

Figure 3.1 RNA-Seq differentially expressed genes in RCS rats. Gene expression counts were
compared between RCS (n=3) and athymic RCS (iRCS, n=3) at p21 (A) and p60 (B) which
showed very few differentially expressed genes (DEGs). Comparing p21 (n=6) and p60 (n=6)
showed 682 significantly downregulated and 671 significantly upregulated DEGs (significant
DEG defined as absolute fold change >2, padj<0.05, Bejamini-Hochberg). Genes were mapped
to specific cell types based on single cell RNA-Seq data from literature (D). Photoreceptors
represented 60% of the cell-type specific downregulated DEGs (E), while microglia represented
40% and macroglia represented 31% of the upregulated DEGs (F).
3.3.2 Pathway Analysis of RCS Retinal Degeneration DEGs
The total DEGs and cell specific DEGs were analyzed for pathway enrichment in IPA. The
top pathways enriched in the total dataset were phagosome formation, TREM1 signaling,
phototransduction pathway, osteoarthritis pathway, neuroinflammation signaling pathway, and IL-
10 signaling. Outside of phototransduction, these top enriched pathways are inflammatory
response pathways and show upregulation of the pro-inflammatory pathways and down regulation
of the anti-inflammatory IL-10 pathway (Figure 3.2A). While no prediction was made on the

91
activation or inhibition of phototransduction, it is clear in the pathway view that phototransduction
is significantly downregulated as most genes in the pathway are significantly downregulated
(Supplementary Figure S3.1). In terms of z-score, the top upregulated pathways were FAK
signaling and S100 signaling, while the top downregulated pathways were PPAR signaling and
CLEAR signaling (Supplementary Figure S3.2). Both FAK and S100 signaling are inflammatory
pathways, in which FAK is involved with integrin cell adhesion and S100 are stress response
proteins, damage associated molecular patterns (DAMPs), which initiate inflammation through
AGER or TLR4 (291-294). PPAR and CLEAR signaling are regulators of inflammation, where
PPAR agonism by polyunsaturated fatty acids (PUFAs) results in anti-inflammatory effects and
CLEAR controls lysosomal biogenesis, autophagy, and cellular clearance (295-298).
In addition, the RCS p60 vs p21 analysis showed significant similarities to human age-
related macular degeneration RNA-Seq analyses. Overall, there is about a combined 50-60%
match between the RCS analysis and AMD analyses for enriched pathways, upstream regulators,
and causal networks. This includes the top identified pathways in the RCS, cytokine signaling,
S100 signaling, FAK signaling, phagosome formation, neuroinflammation, and TREM1 signaling
(Supplementary Figure S3.3), making the RCS rat a comparable animal model for human retinal
degeneration.
In accordance with the total dataset pathway analysis, the pathways enrichment of the most
significantly changed cell types, photoreceptors, macroglia, and microglia contained the top
enriched pathways identified. Unsurprisingly, photoreceptors primarily were enriched for the
phototransduction pathway (Figure 3.2B). The macroglial cells did not have overlap in the top 5
enriched pathways, instead they were enriched for activation of stem cell pluripotency,
myelination, FGF signaling, STAT3 signaling, and inhibition of WNT/β-catenin signaling (Figure

92
3.2C). This is in line with the physiological function of Müller cells which are activated in response
to retinal damage and formation of proliferative gliosis and gliotic scars (299). In animals such as
teleost fish, the Müller cells dedifferentiate and replace dying neurons, however, in humans this
has not been observed (300). Lastly, microglial pathway enrichment contained the remaining top
pathways of the total dataset which are primarily the activation of pro-inflammatory pathways and
inhibition of IL-10 signaling (Figure 3.2D).
These pathways colocalize around inflammatory signaling through the TNF receptors, toll-
like receptors (TLRs) Fc receptors, complement receptors, non-complement-receptor integrins,
lectins, and immunoglobulins. Consistent with the pathway enrichment, the top genes upregulated
in microglial cells included lectins (Clec5a and Clec7a), integrins (Icam1 and Itgam), MHCII, CSF
receptor, and interferon associated genes (Table 3.2). Several TLRs were also upregulated in the
dataset, which is consistent with our previous findings in the RCS rat. Interestingly, the most
significantly downregulated gene in the microglial dataset was Alox15, which encodes the 15-
lipoxygenase (15-LOX) enzyme involved in PUFA metabolism. 15-LOX is critical for the
formation of SPMs which control inflammatory resolution, suggesting that loss of 15-LOX could
contribute to the chronic and uncontrolled inflammation associated with retinal degeneration.

93

Figure 3.2 Top Effected Pathways in RCS Rat. QIAGEN IPA canonical pathways of RNA-Seq
DEGs using all DEGs (A) and cell-type specific DEGs (B-D) for p21 vs p60 RCS rats.
Photoreceptors and microglial cells are responsible for the top 5 pathways mapped in the total
data. Photoreceptors are responsible for the loss of phototransduction genes (3), while microglia
are responsible for the activation of inflammatory pathways phagosome formation (1), TREM1
signaling (2), and neuroinflammation (4), as well as the inhibition of the anti-inflammatory IL10
signaling (5).

94
Table 3.2 Microglial Top Upregulated and Downregulated DEGs
Top Upregulated Genes Top Downregulated Genes
Gene Log2(Fold Change) Adjusted p-value Gene Log2(Fold Change) Adjusted p-value
Clec5a 3.52 4.1E-15 Mbnl1 -1.53 1.4E-89
Clec7a 3.37 3.1E-08 Apoc4 -1.57 1.4E-04
Hla-dra 3.32 7.3E-15 G0s2 -2.06 2.7E-10
Csf2rb 3.21 1.5E-19 Ciita -2.11 1.1E-53
Icam1 3.12 2.3E-43 Slc31a2 -2.12 1.4E-132
Mx2 2.78 2.5E-17 Ankrd2 -2.19 6.6E-09
Itgam 2.71 3.8E-47 Capn3 -2.34 4.4E-77
Cd180 2.70 5.1E-35 Kif2c -2.61 4.0E-150
Galnt6 2.54 2.5E-10 C11orf98 -2.91 1.1E-275
Fcgr2b 2.48 3.5E-46 Alox15 -4.32 4.8E-24
3.3.3 Specialized Pro-Resolving Lipid Mediators as Upstream Regulators of Retinal
Degeneration in RCS Rats
After identifying Alox15 as the most downregulated gene in the microglial dataset,
upstream regulator analysis was performed to identify predicted changes in SPMs using the total
dataset as to avoid bias. Using the causal network analysis, 4 SPMs were predicted to be
significantly inhibited based on the observed gene expression changes in p60 vs p21 RCS rats
(Table 3.3). These included the DHA metabolites resolvin D2 (RvD2), maresin 1, and protectin
D1 (NPD1) and the AA metabolite lipoxin A4 (LXA4). PUFA metabolism is reliant on several
classes of enzymes including cytochrome P450, cyclooxygenase, epoxide hydrolase, and
lipoxygenase, however, lipoxygenases are critical in the biosynthesis of SPMs (301).
Table 3.3 Pathway Analysis Predicted Master Regulators
Master
Regulator
Predicted
Activation
Activation
z-score
p-value of
overlap
Network bias-
corrected p-
value
Genes in
Causal
network
Target-
connected
regulators
resolvin D2 Inhibited -6.492 2.42E-28 1.00E-04 359 42
7(R)-
maresin 1
Inhibited -5.318 1.19E-23 1.00E-04 154 8
protectin D1 Inhibited -3.889 2.64E-14 1.00E-04 81 7
lipoxin A4 Inhibited -3.501 1.88E-24 1.00E-04 204 20

95
In addition to Alox15, the Alox5AP gene showed a 3-fold increase in gene expression in
p60 rats compared to p21 in RNA-Seq data. In comparison, Alox15 showed a 20-fold decrease in
expression. The other detected LOX-isoforms, Alox12 and Aloxe3, did not show a significant
change in gene expression. Alox15 and Alox5 were confirmed by RT-qPCR at p21, p35, p49, and
p60 (Figure 3.3A). There is a significant change in both genes between p21 and p35, and between
p35 and p49 where the expression reaches its peak. Both genes reach ~20-fold change in gene
expression where Alox15 is decreased and Alox5 is increased compared to p21. This imbalance
of decreased Alox15 and increased Alox5 suggests that SPM biosynthesis will be significantly
disrupted and there would be accumulation of parent lipids, AA and DHA, which utilize Alox15
as the initiating lipoxygenase in SPM synthesis (Figure 3.3B). As shown in Figure 3.3C, the loss
of SPM upstream regulators is consistent with the downstream gene expression changes in the
RCS retinas including some of the top microglial genes (Clec7a and Icam1) and the second highest
upregulated gene in the entire dataset (Gfap). The DHA-metabolites primarily act as anti-
inflammatory molecules through inhibition of NFκB, so loss of SPMs results in activation
inflammatory cascades. In addition, maresin1 is shown to increase the expression of Alox15
described above, suggesting the loss of maresin1 early in the disease may contribute to the loss of
Alox15 expression. To better identify SPMs as potential biomarkers and targets of retinal
degeneration, targeted lipidomics was performed on ocular and plasma samples from RCS rats
between p21 and p60.

96

Figure 3.3 Lipoxygenase enzymes upstream of specialized pro-resolving lipid mediators. RT-
qPCR confirmed the loss of Alox15 and upregulation of Alox5 gene expression with increasing
age of RCS rats (A, two-way ANOVA with false-discovery, q=0.05, two-stage linear step-up
procedure of Benjamini, Krieger and Yekutieli). The change in these genes is suggested to lead
to depletion of specialized pro-resolving lipid mediators (SPMs, illustration of SPM synthesis
changes, B), such as lipoxin A4, resolving D2, protectin D1, and maresin 1 which were identified
as master regulators of transcriptional changes in retinal RNA-Seq of RCS rats (p60 compared
to p21, n=6 each group). IPA identified SPMs inhibit NFκB and MAPK signaling, and their loss
results in the upregulation of inflammatory genes in RNA-Seq dataset (C).
3.3.4 Principal Component Analysis of Ocular and Plasma Lipidomics
To characterize the lipidomics profiles associated with retinal degeneration, both ocular
and plasma samples of aged rats (p35, p49, and p60) were compared to p21 rats (n=3 each age

97
group). As previously demonstrated in Chapter 2 Figure 2.2, there is significant retinal structure
and function degeneration between these timepoints. Lipidomics analysis identified 54 unique
lipid analytes in plasma and 34 unique lipid analytes in ocular samples, for a total of 88 variables
used in principal component analysis (PCA) and PCA-discriminant analysis (PCA-DA). The initial
PCA revealed that top loadings for both principal components (PCs) were all plasma analytes
(Supplementary Figure S3.4). Subsequently, PCA-DA was applied to the plasma analytes which
revealed three distinct PCs which represented 41%, 36%, and 23% of the total variance of the data.
Graphing PC1 and PC2 revealed three distinct groupings of plasma samples (Figure 3.4A).
Samples with high PC1 scores belonged to p21 while low PC1 scores characterized p35-p60
samples. PC2 further separated the p35-p60 samples, in which low PC2 scores characterized p35
samples while high PC2 was associated with both p49 and p60 samples. Plotting the top PC1
loadings revealed associated p21 samples with DHA metabolites as well as the parent lipids AA,
DHA, DPA, and EPA (Figure 3.4B) Calculating fold change compared to p21 demonstrated that
p21 samples have significantly higher levels of all DHA metabolites and lower levels of parent
lipids DHA, DPA, and EPA (Figure 3.4C). Additionally, the identified DHA metabolites in PC1
loadings associated with p21 were all significant with the most dramatic declines in maresin1,
maresin2, and NPD1. Of the bottom loadings, DHA, DPA, and EPA were significantly different
with 4-, 6-, and 6-fold increases in p35-p60 samples compared to p21, respectively. Top loadings
in PC2, that had a PC1 score <0, were used to understand the relationship between p35 and p49-
p60. The top loads were AA metabolites, primarily increased DHET formation, while the bottom
loads were primarily EPA metabolites (Figure 3.4D). However, only RvE1 was significantly
higher at both p49 and p60 compared to p35. Additional scatter plots of lipids are in Supplementary
Figure S3.5.

98

Figure 3.4 PCA-DA of RCS plasma lipidomics. Plasma lipidomics were analyzed in
MarkerView (n=3 each age group). Plotting the principal components (PC) clearly separated
p21 with high PC1 scores and separated p35 from p49/p60 by PC2 (A). The top 5 highest and
lowest PC loadings were identified for both PCs (B). All highest PC1 loadings were DHA
metabolites which were significantly lower in p35-60 while the parent lipids were significantly
higher in p35-p60 (C). PC2 identified arachidonic acid metabolites (DHETs) and EPA
metabolites (HEPEs and RvEs), however, only RvE1 and 11,12 DHET showed some significant
differences. *q<0.05 compared to p21, †q<0.05 compared to p35, two-way ANOVA with false-
discovery, q=0.05, two-stage linear step-up procedure of Benjamini, Krieger and Yekutieli.
Together, this suggests that between p21 and p35-p60 there is significant loss of PUFA
metabolism and primarily DHA metabolism. DHA and DPA metabolism first require 12/15-LOX
enzymatic conversion to form downstream maresins, RvDs, and NPD1 (301), these findings are
consistent with the observed loss of Alox15 gene expression in p35-p60 retinas in RNA-Seq and
RT-qPCR data. Similarly, increased Ptgs2 (Cox2) and Alox5 expression results in higher AA and
EPA metabolism. The lipidomic changes were consistent with changes in associated retinal gene

99
expression with increased formation of DHETs and RvEs, however, these changes were not as
dramatic as DHA (Supplementary Figure S3.6). Plasma lipidomics demonstrated a significant and
consistent loss of DHA metabolism (Figure 3.5A-B). In contrast, while ocular tissues showed
moderate increase of DHA and loss of DHA metabolites (Figure 3.5C), the variability between
samples was higher and did not reach statistical significance.

100

Figure 3.5 Cytoscape analysis of DHA metabolism. Log(fold change) of p60 compared to p21
lipids (n=3 per group, two-way ANOVA with false-discovery, q=0.05, two-stage linear step-up
procedure of Benjamini, Krieger and Yekutieli) (A). DHA metabolism was mapped in cytoscape
to illustrate the relationship between Alox15 and lipidomic changes in plasma (B) and ocular
(C) samples, showing consistency between gene expression changes and inhibition of DHA
metabolism.
3.3.5 DHA and DPA Lipidome Changes Correlate with Retinal Degeneration
To determine the impact of DHA and DPA lipidome changes on retinal degeneration
characteristics, we evaluated the association between ocular and plasma lipids and retinal structure
and functional measurements (ocular coherence tomography (OCT), and electroretinography
(ERG) scotopic and photopic a- and b-wave amplitudes) of RCS rats at the same age. Using the
mean values for retinal measurements and log values of lipid concentrations, a Pearson correlation

101
matrix was produced to identify critical lipid mediators in retinal degeneration. Lipids which had
a significant Pearson correlation (p<0.05) were plotted against the correlated retinal measurement
(Figure 3.6). The top DHA metabolites identified in PCA-DA all had strong linear correlations
with scotopic a-wave measurements, which shows the most dramatic decline from baseline (p21).
In comparison, OCT ONL thickness and scotopic b-wave measurements, which show a gradual
decline, showed a linear correlation with primarily DPA metabolites. Lastly, photopic b-wave,
which shows a delayed decline, only correlated with ocular DPA RvD2. Overall, this suggests that
the loss of DHA metabolism occurs earlier in retinal degeneration followed by the loss of DPA
metabolism. Additionally, this indicates that plasma lipidome changes of DHA and DPA
metabolism may serve as biomarkers for retinal degenerative conditions. Future studies using
larger sample sizes will enable predictive regression to better identify metabolites to monitor
efficacy of therapeutic interventions.

102

Figure 3.6 Correlation of retinal structure and function with lipidomics. A Pearson correlation
between lipidomics and outer nuclear layer (ONL) thickness, and electroretinogram scotopic b-
and a-wave, and photopic b-wave was performed, lipids with p<0.05 were plotted against the
associated retinal measurement. Top DHA metabolites identified in PCA-DA all had strong
linear correlations with scotopic a-wave measurements.
3.4 DISCUSSION
Although the molecular events underlying retinal degeneration have been investigated for
decades, there is no therapeutic option available for the majority of retinal degenerative conditions
(302). Core themes have been identified for the various pathologies, such as inflammation and
oxidative stress, but anti-inflammatory and antioxidants have not been successful in treating retinal

103
degeneration and vision loss (247). In order to better develop novel therapeutic strategies, deeper
insights into the animal models utilized in the development process is critically necessary. Our
study provides some insights into the molecular events driving retinal degeneration in the RCS rat,
providing novel targets for future therapeutic development.
One key insight for narrowing the focus of future therapeutic development, is cell-type
specific association with significant DEGs. We were able to separate the significant DEGs using
single-cell transcript assignments determined in other studies, which revealed 60% of
downregulated DEGs are associated with photoreceptors and 40% and 31% of upregulated DEGs
are associated with microglia and macroglia, respectively. Comparing the pathway enrichment of
the total dataset to the cell-type specific analysis revealed that microglia are primarily responsible
for the pathogenic transcriptome changes associated with the RCS retinal degeneration. Consistent
with retinal degeneration in general, the enriched pathways were primarily inflammatory signaling
based. Only one other transcriptomic analysis of RCS has been previously conducted, however
this had an n=2 and compared RCS to Long Evans rats (303). Our analysis showed similar results
in activated pathways with an emphasis on activation of microglial cells. However, Jones et al.
only detected the downregulation of visual function pathways. In comparison, our study found
downregulation of IL10, PPAR, and CLEAR signaling. Together this indicated downregulation of
anti-inflammatory pathways and cellular clearance, which is likely to be controlled by microglial
cell activities. Additionally, macroglia-specific pathway analysis indicated activation of
pluripotency pathways. Zebrafish are known to have the ability to regenerate their retinal neurons
through Müller cell dedifferentiation via FGF, STAT3, and WNT signaling (300, 304). In our RCS
data, macroglial cells showed activated FGF and STAT3 signaling but inhibited WNT/β-catenin
signaling, suggesting an incomplete attempt to reenter the cell cycle which was also seen in the

104
transcriptomics of the rd10 retinitis pigmentosa mouse model (305). Future studies utilizing single-
cell RNA-Seq can further improve on the transcriptomic profiling of macroglia and microglia in
the RCS rat over time.
We found significant similarities between human AMD and RCS rat transcriptomic
changes associated with degeneration staging. While AMD has been previously thought to be due
to photoreceptor and RPE pathology (306), our results suggest that macroglia and microglia are
the primary sources of transcriptomic changes. This is consistent with the single-cell RNA-Seq
analysis of human AMD recently reported by Menon et al which identified cone photoreceptors,
macroglia, microglia, and vascular cells as the most predictive for AMD risk (287). This gives
credibility to the RCS rat model as an analog for human retinal pathologies in therapeutic
development.
In addition, we found significant downregulation of Alox15 and upregulation of
Alox5/Alox5AP gene expression, which is associated with microglial cells. These enzymes are
critical PUFA metabolizing enzymes, however, 12/15-LOX is required for SPM synthesis while
5-LOX also participates in synthesis of proinflammatory leukotrienes (307). The SPMs, RvD2,
maresin 1, NPD1, and LXA4 were also identified as inhibited master regulators of the observed
transcriptomic changes. These SPMs have also been shown to induce anti-inflammatory effects
through PPAR signaling (308-311).
The inverse expression of Alox15 and Alox5 suggests there would be a depletion of SPMs
resulting in loss of the ability to turn off inflammation. We confirmed this hypothesis via ocular
and plasma lipidomics in the RCS rats over time in which we found significant loss of RvDs,
NPD1, and maresins as well as accumulation of parent lipids DHA, DPA, and EPA. Interestingly,
plasma lipidomics showed significant changes in the lipidomic profile while ocular lipidomics

105
detected less analytes and showed higher variability. As this animal model is a MerTK mutant
model, clearance of the lipid debris, which is clearly visible during retinal dissection, may
contribute to variability of ocular results. We further identified that several SPMs significantly
correlated with loss of retinal structure and function, with the most significantly changed SPMs
correlating with the earliest marker of visual dysfunction, scotopic a-wave. These findings are
supported by literature which has demonstrated protective effects of NPD1 and RvDs in retinal
cell-damage models (307, 312, 313). As well, reduced serum and vitreous LXA4 has been
observed in diabetic retinopathy patients (314). Current lipidomic studies have focused on the
larger lipid classes in retinal degeneration (315, 316), however, this is the first study to provide
lipidomic analysis of SPMs in the plasma of a retinal degeneration model. As well, our data may
explain why exogenous application of DHA does not show beneficial outcomes in retinal
degeneration because there is significant inhibition of DHA metabolism to SPMs (317). However,
SPM lipidomic profiles and metabolizing enzymes should be further assessed in various models
to cement this hypothesis. Additionally, larger sample sizes in future studies can enable predictive
regression analysis to better determine which metabolites can be used as biomarkers for retinal
degeneration.
In conclusion, we demonstrate a comprehensive analysis of the transcriptomic and
lipidomic profile associated with retinal degeneration in the RCS rat. As suggested by several
studies, inflammation was a critical factor enriched in retinal degeneration and these pathways
were strongly associated with microglial cells. In addition, pathogenic loss of Alox15 gene
expression in the retina significantly contributes to retinal pathology through loss of pro-resolving
lipid mediators which was confirmed in plasma lipidomic analysis. Restoring healthy levels of
SPMs could resolve pathogenic inflammation in retinal degeneration and initiate reparative

106
pathways, however, the applicability to more models of retinal degeneration require further
assessment. More importantly, the loss of SPMs was correlated with loss of retinal function giving
rise to a potential plasma biomarker to guide future therapeutic development.
3.5 FUNDING SOURCES AND ACKNOWLEDGEMENTS
1. Unrestricted Grant to the Department of Ophthalmology from Research to Prevent Blindness,
New York, NY
2. Research reported in this publication was supported by the National Eye Institute of the
National Institutes of Health under Award Number P30EY029220. The content is solely the
responsibility of the authors and does not necessarily represent the official views of the
National Institutes of Health.
3. American Foundation for Pharmaceutical Education Pre-Doctoral Fellowship

3.6 REFERENCES
65. Gal A, et al. Mutations in MERTK, the human orthologue of the RCS rat retinal dystrophy
gene, cause retinitis pigmentosa. Nature Genetics. 2000;26(3):270-1.
70. D'Cruz PM, et al. Mutation of the receptor tyrosine kinase gene Mertk in the retinal dystrophic
RCS rat. Hum Mol Genet. 2000;9(4):645-51.
104. Liao DS, et al. Pegcetacoplan treatment for geographic atrophy due to age-related macular
degeneration: a plain language summary of the FILLY study. Immunotherapy.
2022;14(13):995-1006.
153. Thomas BB, et al. A new immunodeficient retinal dystrophic rat model for transplantation
studies using human-derived cells. Graefe's Archive for Clinical and Experimental
Ophthalmology. 2018:2113-25.
194. Liao DS, et al. Complement C3 Inhibitor Pegcetacoplan for Geographic Atrophy Secondary
to Age-Related Macular Degeneration: A Randomized Phase 2 Trial. Ophthalmology.
2020;127(2):186-95.
195. Aoun M, et al. Inherited Retinal Diseases Due to RPE65 Variants: From Genetic Diagnostic
Management to Therapy. Int J Mol Sci. 2021;22(13).
196. Cideciyan AV, et al. Human retinal gene therapy for Leber congenital amaurosis shows
advancing retinal degeneration despite enduring visual improvement. Proc Natl Acad Sci U S
A. 2013;110(6):E517-25.
242. Ye J, et al. Primer-BLAST: a tool to design target-specific primers for polymerase chain
reaction. BMC Bioinformatics. 2012;13:134.

107
243. Livak KJ, and Schmittgen TD. Analysis of relative gene expression data using real-time
quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402-8.
247. Van Lookeren Campagne M, et al. Mechanisms of age-related macular degeneration and
therapeutic opportunities. Journal of Pathology. 2014;232(2):151-64.
282. Massengill MT, et al. Cham: Springer International Publishing; 2018:185-91.
283. Thapa R, et al. Prevalence, Pattern and Risk Factors of Retinal Diseases Among an Elderly
Population in Nepal: The Bhaktapur Retina Study. Clin Ophthalmol. 2020;14:2109-18.
284. Ratnapriya R, et al. Retinal transcriptome and eQTL analyses identify genes associated with
age-related macular degeneration. Nat Genet. 2019;51(4):606-10.
285. Dobin A, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-
21.
286. Gamsiz ED, et al. Genome-wide transcriptome analysis in murine neural retina using high-
throughput RNA sequencing. Genomics. 2012;99(1):44-51.
287. Menon M, et al. Single-cell transcriptomic atlas of the human retina identifies cell types
associated with age-related macular degeneration. Nature Communications. 2019;10(1):4902.
288. Karademir D, et al. Single-cell RNA sequencing of the retina in a model of retinitis
pigmentosa reveals early responses to degeneration in rods and cones. BMC Biology.
2022;20(1):86.
289. Krämer A, et al. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics.
2013;30(4):523-30.
290. Ebright B, et al. Eicosanoid lipidome activation in post-mortem brain tissues of individuals
with APOE4 and Alzheimer’s dementia. Alzheimer's Research & Therapy. 2022;14(1):152.
291. De Nichilo MO, et al. De novo expression of pp125FAK in human macrophages regulates
CSK distribution and MAP kinase activation but does not affect focal contact structure.
Journal of Cellular Physiology. 1999;178(2):164-72.
292. Zhang X, et al. Focal adhesion kinase promotes phospholipase C-gamma1 activity. Proc Natl
Acad Sci U S A. 1999;96(16):9021-6.
293. Sattar Z, et al. The S100 Protein Family as Players and Therapeutic Targets in Pulmonary
Diseases. Pulmonary Medicine. 2021;2021:5488591.
294. Gonzalez LL, et al. Role of S100 proteins in health and disease. Biochimica et Biophysica
Acta (BBA) - Molecular Cell Research. 2020;1867(6):118677.
295. Bright JJ, et al. PPAR Regulation of Inflammatory Signaling in CNS Diseases. PPAR Res.
2008;2008:658520.

108
296. Palmieri M, et al. Characterization of the CLEAR network reveals an integrated control of
cellular clearance pathways. Human Molecular Genetics. 2011;20(19):3852-66.
297. Zhao Q, et al. Molecular Mechanisms of Lysosome and Nucleus Communication. Trends in
Biochemical Sciences. 2020;45(11):978-91.
298. Zhang W, et al. Regulation of TFEB activity and its potential as a therapeutic target against
kidney diseases. Cell Death Discovery. 2020;6(1):32.
299. Sardar Pasha SPB, et al. Retinal cell death dependent reactive proliferative gliosis in the
mouse retina. Scientific Reports. 2017;7(1):9517.
300. Goldman D. Müller glial cell reprogramming and retina regeneration. Nature Reviews
Neuroscience. 2014;15(7):431-42.
301. Dyall SC, et al. Polyunsaturated fatty acids and fatty acid-derived lipid mediators: Recent
advances in the understanding of their biosynthesis, structures, and functions. Prog Lipid Res.
2022;86:101165.
302. Nowak JZ. Age-related macular degeneration (AMD): Pathogenesis and therapy.
Pharmacological Reports. 2006;58(3):353-63.
303. Jones MK, et al. Gene expression changes in the retina following subretinal injection of human
neural progenitor cells into a rodent model for retinal degeneration. Mol Vis. 2016;22:472-90.
304. Wan J, et al. HB-EGF is necessary and sufficient for Müller glia dedifferentiation and retina
regeneration. Dev Cell. 2012;22(2):334-47.
305. Uren PJ, et al. A profile of transcriptomic changes in the rd10 mouse model of retinitis
pigmentosa. Mol Vis. 2014;20:1612-28.
306. Qi JH, et al. Expression of Sorsby's fundus dystrophy mutations in human retinal pigment
epithelial cells reduces matrix metalloproteinase inhibition and may promote angiogenesis. J
Biol Chem. 2002;277(16):13394-400.
307. Serhan CN, et al. Anti-inflammatory and proresolving lipid mediators. Annu Rev Pathol.
2008;3:279-312.
308. Liao Z, et al. Resolvin D1 attenuates inflammation in lipopolysaccharide-induced acute lung
injury through a process involving the PPARγ/NF-κB pathway. Respir Res. 2012;13(1):110.
309. Yao D, et al. Maresin 1 enhances osteogenic potential of mesenchymal stem cells by
modulating macrophage peroxisome proliferator-activated receptor-γ-mediated inflammation
resolution. Biomater Adv. 2022;141:213116.
310. Heras-Sandoval D, et al. Role of docosahexaenoic acid in the modulation of glial cells in
Alzheimer’s disease. Journal of Neuroinflammation. 2016;13(1):61.

109
311. Sobrado M, et al. Synthesis of lipoxin A4 by 5-lipoxygenase mediates PPARgamma-
dependent, neuroprotective effects of rosiglitazone in experimental stroke. J Neurosci.
2009;29(12):3875-84.
312. Tian H, et al. Resolvins E1 and D1 in Choroid-Retinal Endothelial Cells and Leukocytes:
Biosynthesis and Mechanisms of Anti-inflammatory Actions. Investigative Ophthalmology &
Visual Science. 2009;50(8):3613-20.
313. Chen JP, et al. Resolvin D2 prevents inflammation and oxidative stress in the retina of
streptozocin-induced diabetic mice. Int J Clin Exp Pathol. 2020;13(8):1986-94.
314. Wei J, and Gronert K. The role of pro-resolving lipid mediators in ocular diseases. Mol
Aspects Med. 2017;58:37-43.
315. Semba RD, et al. Serum lipids in adults with late age-related macular degeneration: a case-
control study. Lipids in Health and Disease. 2019;18(1):7.
316. Acar N, et al. Lipid Composition of the Human Eye: Are Red Blood Cells a Good Mirror of
Retinal and Optic Nerve Fatty Acids? PLOS ONE. 2012;7(4):e35102.
317. Souied EH, et al. Omega-3 Fatty Acids and Age-Related Macular Degeneration. Ophthalmic
Research. 2016;55(2):62-9.

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3.7 SUPPLEMENTARY TABLES AND FIGURES

Supplementary Figure S3.1 Phototransduction pathway in p60 vs. p21 RCS rats.

111

Supplementary Figure S3.2 Top Upregulated and Downregulated Pathways by Z-Score.

112

Supplementary Figure S3.3 Canonical pathways analysis match between RCS and human AMD.

113

Supplementary Figure S3.4 Principal component analysis of plasma and ocular lipidomics.

114
p21 p35 p49 p60
0
2
4
6
Maresin1
Log(CPS)
q < 0.0001
q < 0.0001
q < 0.0001
p21 p35 p49 p60
0
2
4
6
Maresin2
Log(CPS)
q < 0.0001
q < 0.0001
q < 0.0001
p21 p35 p49 p60
0
2
4
6
8
NPD1
Log(CPS)
q < 0.0001
q < 0.0001
q < 0.0001
p21 p35 p49 p60
0
1
2
3
4
5
RvD4
Log(CPS)
q < 0.0001
q < 0.0001
q < 0.0001
p21 p35 p49 p60
0
1
2
3
4
5
RvD6
Log(CPS)
q < 0.0001
q < 0.0001
q < 0.0001
p21 p35 p49 p60
0
2
4
6
8
AA
Log(CPS)
p21 p35 p49 p60
0
2
4
6
8
DHA
Log(CPS)
q = 0.0146
q = 0.0372
q = 0.0130
p21 p35 p49 p60
0
2
4
6
8
DPA
Log(CPS)
q = 0.0031
q = 0.0144
q = 0.0032
p21 p35 p49 p60
0
2
4
6
8
EPA
Log(CPS)
q = 0.0016
q = 0.0080
q = 0.0016
p21 p35 p49 p60
0
1
2
3
4
5
5,6 DHET
Log(CPS)
p21 p35 p49 p60
0
1
2
3
4
5
8,9 DHET
Log(CPS)
p21 p35 p49 p60
0
2
4
6
11,12 DHET
Log(CPS)
q = 0.0152
p21 p35 p49 p60
0
2
4
6
14,15 DHET
Log(CPS)
p21 p35 p49 p60
0
2
4
6
8
11-HETE
Log(CPS)
p21 p35 p49 p60
0
1
2
3
4
5
PGF2a
Log(CPS)
p21 p35 p49 p60
0
1
2
3
4
5
LXA4
Log(CPS)
p21 p35 p49 p60
0
1
2
3
4
5
11-HEPE
Log(CPS)
p21 p35 p49 p60
0
1
2
3
4
5
18-HEPE
Log(CPS)
p21 p35 p49 p60
0
1
2
3
4
RvE1
Log(CPS)
q < 0.0001
q < 0.0001
q = 0.0186
p21 p35 p49 p60
0
1
2
3
4
RvE3
Log(CPS)

Supplementary Figure S3.5 Graphs of identified PC loadings.

115

Supplementary Figure S3.6 Cytoscape analysis of AA, DPA, and EPA.

116
Supplementary Table T3.1 Lipidomics MRM Table
Compound Q1 (m/z) Q3 (m/z) RT (min) DP (V) CE (V) CXP (V) Internal Standard
d8-5S-HETE 327.3 116.1 17.5 -80 -17 -10 N/A
d4-LTB4 339.3 197.2 14.2 -80 -22 -13 N/A
d4-PGE2 355.3 193.2 11.3 -80 -25 -16 N/A
d5-LXA4 356.3 115.2 11.8 -80 -19 -14 N/A
d5-RvD2 380.3 141.2 11.3 -80 -23 -14 N/A
AA 303.3 259.1 18.7 -100 -16 -18 d8-5S-HETE
5-HETE 319.2 115.1 17.7 -80 -21 -12 d8-5S-HETE
12-HETE 319.2 179.1 17.6 -80 -21 -12 d8-5S-HETE
15-HETE 319.2 219.1 17.4 -80 -19 -12 d8-5S-HETE
LXA4 351.2 115.1 11.8 -80 -20 -13 d5-LXA4
LXB4 351.2 221.1 11.4 -80 -20 -13 d5-LXA4
LTB4 335.2 195.1 14 -80 -22 -13 d4-LTB4
20-COOH-LTB4 365.3 195.1 8.6 -80 -24 -15 d4-LTB4
PGD2 351.3 233.1 11.1 -80 -16 -15 d4-PGE2
PGE2 351.3 189.1 11 -80 -25 -14 d4-PGE2
PGF2a 353.3 193.1 11.7 -80 -34 -11 d4-PGE2
TXB2 369.3 169.1 10.6 -80 -22 -15 d4-PGE2
12-HHT 279.3 163.1 14.9 -80 -22 -13 d4-PGE2
5,6 EET 319.2 191.1 18 -80 -21 -12 d8-5S-HETE
8,9 EET 319.2 127.1 18 -80 -21 -12 d8-5S-HETE
11,12 EET 319.1 167.1 17.1 -80 -21 -12 d8-5S-HETE
14,15 EET 319.2 219.1 17.1 -80 -18 -12 d8-5S-HETE
5,6 DiHETrE 337.2 145.1 17 -80 -25 -12 d8-5S-HETE
8,9 DiHETrE 337.2 127.1 16.5 -80 -30 -12 d8-5S-HETE
11,12 DiHETrE 337.2 167.1 16 -80 -25 -12 d8-5S-HETE
14,15 DiHETrE 337.2 207.1 15.5 -80 -25 -12 d8-5S-HETE
EPA 301.3 257.1 17.9 -100 -16 -18 d8-5S-HETE
5-HEPE 317.2 115.1 17.6 -80 -18 -12 d8-5S-HETE
12-HEPE 317.2 179.1 16.8 -80 -19 -12 d8-5S-HETE
15-HEPE 317.2 219.1 16.4 -80 -18 -12 d8-5S-HETE
LXA5 349.2 115.1 10.6 -80 -20 -13 d5-LXA4
LXB5 349.2 221.1 10.3 -80 -20 -13 d5-LXA4
RvE2 333.3 253.1 12.5 -80 -20 -12 d5-RvD2

117
RvE3 33.33 201.2 13.9 -80 -20 -12 d5-RvD2
DHA 327.3 283.1 18.5 -100 -16 -18 d8-5S-HETE
7-HDHA 343.2 141.1 17.5 -80 -18 -15 d8-5S-HETE
14-HDHA 343.2 205.1 17.5 -80 -17 -14 d8-5S-HETE
17-HDHA 343.2 245.1 17.5 -80 -17 -14 d8-5S-HETE
RvD1 & 2 375.2 141.1 11.5 -80 -21 -13 d5-RvD2
RvD3 375.2 147.1 11.2 -80 -25 -13 d5-RvD2
RvD4 375.2 101.1 12.7 -80 -22 -16 d5-RvD2
RvD5 359.2 199.1 13.8 -80 -21 -13 d5-RvD2
RvD6 359.2 101.1 14.6 -80 -22 -16 d5-RvD2
NPD-1 359.2 153.1 13.6 -80 -21 -9 d5-RvD2
Maresin 1 359.2 221.1 13.8 -80 -20 -16 d5-RvD2
22-COOH-MaR1 389.3 221.1 9.9 -80 -24 -15 d5-RvD2
Maresin 2 359.1 221.2 14.9 -80 -20 -12 d5-RvD2
DPA 329.3 285.1 19 -100 -16 -18 d8-5S-HETE
DPA RvD1 377.2 215.1 11.6 -80 -26 -13 d5-RvD2

Supplementary Table T3.2 RT-qPCR Primer Sequences
Gene Forward (5’-3’) Reverse (5’-3’)
Nfkb TCCCGCCCCTTCTAAAACTC CTCCACCAGCTCTTTGATGGT
Tgfb1 GACTCTCCACCTGCAAGACC GGACTGGCGAGCCTTAGTTT
Pparg GAGTAGCCTGGGCTGCTTTT CTGATCACCAGCAGAGGTCC
Gapdh AGTGCCAGCCTCGTCTCATA GGTAACCAGGCGTCCGATAC
Tlr4 GATCTGAGCTTCAACCCCCT ATTGTCTCAATTTCACACCTGGA
Tlr7 GCCTTCAAGAAAGATGCCATT AGTTTGGTGGAGGAGAACAGAG
Tlr9 CTTCCATTTTCCATCATGGTTCTCT GCCATGAGGCTTCAGTTCAC
Alox5 ATGCCTTCCTACACTGTCACC GAGCCAATGAGGCTGAGGTAA
Alox15 CTGTGGTTGGTTGGACAGCA TTGAATTCTGCTTCCGAGTCCC
Prkn CTTGGAGAAGAGCAGTACAACA GACTTTCCTCTGGCCCTGTTC
Lc3a GACCGGCCTTTCAAGCA ATGATCACCGGGATCTTGC
Irgm1 AACTCTTCTGGATCAGGGTTTGA AGCAAGAAGGTCCTGTGTCTT
Lc3b CGCCGGAGCTTCGAACAAA ACTGGGATCTTGGTGGGGT

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CHAPTER 4: POLARIZED RPE SECRETOME REDUCES OXIDATIVE
STRESS AND PRESERVES PHOTORECEPTORS IN RETINAL DYSTROPHIC
RCS RATS
i

Kabir Ahluwalia
1
, Juan-Carlos Martinez-Camarillo
2,3
, Biju Thomas
2,3
, Aditya Naik
1
, Alejandra
Gonzalez-Calle
2,3
, Dimitrios Pollalis
2,3,4
, Jane Lebkowski
5
, Sun Young Lee
2,3,4
, Debbie Mitra
2
,
Stan G. Louie
1,2*
, Mark S. Humayun
2,3*

1
Mann School of Pharmacy and Pharmaceutical Sciences, University of Southern California,
Los Angeles, CA 90089, USA
2
USC Ginsburg Institute of for Biomedical Therapeutics, University of Southern California,
Los Angeles, CA 90033, USA USC

3
USC Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine, University
of Southern California, Los Angeles, CA 90033, USA
4
Department of Physiology & Neuroscience, Keck School of Medicine, University of Southern
California, Los Angeles, CA 90089, USA
5
Regenerative Patch Technologies LLC, Menlo Park, CA
* Correspondence: Stan Louie and Mark Humayun
SGL: slouie@usc.edu; 1985 Zonal Avenue, Los Angeles, CA 90033; 323-442-3646
Mark S. Humayun: humayun@med.usc.edu; 1450 San Pablo Street, Los Angeles, CA 90033;
323-865-3937

Conflict of interest statement: JL and MSH are founders of RPT.

i
Ahluwalia et al., submitted to The Journal of Clinical Investigation Insight

119
4.1 INTRODUCTION
Retinal dystrophies, like age-related macular degeneration (AMD), are the leading cause
of vision loss in industrialized countries. Globally, AMD is projected to reach 288 million cases
by 2040 (34). The molecular pathogenesis of AMD has included the accumulation of cellular
debris and oxidative stress triggering chronic inflammation, retinal cell atrophy, and ultimately
vision loss (247, 318). Currently, anti-vascular endothelial growth factor (VEGF) treatment is used
for neovascular AMD (nAMD), which accounts for 10-15% of AMD cases. Until recently, there
was no approved treatment for dry AMD (dAMD) and the only option to slow disease progression
was dietary supplements high in antioxidants (197, 198, 319-321). The first intraocular
administration of a complement inhibitor (i.e., C3 inhibitor; pegcetacoplan) was approved by the
FDA in February 2023 for the treatment of geographic atrophy (GA), the advanced form of dAMD.
Although pegcetacoplan can ameliorate GA progression, it is unable to halt or reverse vision loss
(194).
Due to the complex etiology of dAMD, investigational modalities have included
antioxidants, visual cycle modulators, mitochondrial modulators, anti-inflammatory agents,
complement inhibitors, neuroprotective agents, and stem cell therapies (199). Limitations
associated with monotherapeutic approaches are that they are unable to fully address the multiple
pathways promoting AMD development, which may account for limited effectiveness. Stem cell-
based therapies directly target GA and other retinal degenerative diseases by replacing atrophic
tissue (220-223). CPCB-RPE1, a subretinal implant containing polarized human embryonic stem
cell (hESC)-derived retinal pigment epithelium (RPE) grown on ultrathin parylene membranes,
was implanted in GA patients in a phase I/IIa clinical trial and showed improvement in visual
acuity (151, 152, 221).

120
The RPE has long been speculated as a source of several neuroprotective factors.
Characterization of the RPE secretome has identified several proteins with neuroprotective
properties in retinal degeneration models (206-212). Unfortunately, the administration of these
purified components has not led to an effective therapy in human trials (224, 225). During the
development of CPCB-RPE1, photoreceptor preservation was seen beyond the borders of the
implant (226). These findings suggest RPE secrete factors capable of promoting neuronal survival
(207, 227-233), and we propose to study the RPE secretome as a combination therapy for retinal
degenerative conditions.
Oxidative stress and inflammation are central to AMD pathogenesis, and it has been shown
they significantly alter the RPE secretome, resulting in increased secretion of pro-angiogenic
factors and decreased anti-inflammatory factors including complement factor H (CFH), a critical
inhibitor of complement activation and inflammation, and the strongest genetic risk for AMD
(322-327). Following the approval of pegcetacoplan, it can be surmised that the RPE secretome,
containing a complement inhibitor, is directly linked to disease pathology and treatment. Oxidative
damage to the RPE secretome results in a hostile microenvironment and we hypothesize that the
enriched secretome from healthy hESC-RPE can restore the retinal microenvironment and promote
photoreceptor preservation in retinal degeneration models. In this report, we demonstrate that
polarized RPE soluble factors (PRPE-SF), specifically concentrated PRPE-SF (SF3), promote fetal
retinal progenitor cell (fRPC) viability and survival. SF3 also delayed retinal degeneration in the
Royal College of Surgeons (RCS) rat model, which was associated with reduced oxidative stress
and inflammation. These findings support the conclusion that SF3 can alter the diseased retinal
microenvironment promoting preservation of photoreceptors and visual function, offering an
alternative therapeutic strategy for AMD and other complex retinopathies.

121
4.2 METHODS
4.2.1 hESC-RPE Cell Culture and PRPE-SF1 Production
PRPE-SF1 was produced at USC using hESC-RPE cells designated as intermediate cell
bank (ICB) manufactured at the City of Hope under cGMP using the methods established by the
CPCB (226, 328-331). The culture apparatus and PRPE-SF1 production scheme can be found in
Figure 4.1. Briefly, H9 hESCs were expanded in mTeSR1 medium (Stemcell Technologies,
Vancouver, BC, Canada). Media was replaced with X-VIVO10
TM
media for 12 weeks to drive
spontaneous RPE cell differentiation. Pigmented RPE-like cells were mechanically isolated
followed by dissociation with TrypLE (Life Technologies, Grand Island, NY). RPE-like cells were
cultured on human vitronectin (AMS Biotechnology, Lake Forest, CA)–coated plates with XVIVO
10 medium and frozen at passage two. hESC-derived RPE cells were seeded onto vitronectin
(Corning, Glendale, AZ) coated 10 mm x 20 mm parylene C membrane similar to the CPCB-RPE1
implant. The parylene C membrane has identical characteristics to CPCB-RPE1 as it is specially
designed to mimic the Bruch’s membrane with a 0.3 µm thickness supported on a 6.0 µm thick
mesh frame and diffusion regions for small molecules (manufactured at Leap Biomed Innovators,
LLC, Pasadena, CA.) (329). Parylene membranes were placed into 4-chamber wells (Corning Inc.,
Corning, NY) prior to hESC-RPE seeding at a final seeding density of 1.43x10
5
cells/cm
2
. Cells
were cultured with 1 mL XVIVO10 media and maintained at 37°C in a 5% CO2 incubator. Media
was exchanged every 4 days and replaced with 1mL of XVIVO10 media, PRPESF1 was collected
every 4 days starting on day 28 through 40. The collected PRPE-SF1 was pooled and filtered with
a 0.2 µm syringe filter system followed by 3-fold (3x) concentration, by volume, using Amicon
centrifugal filter devices (Millipore Sigma, Burlington, MA) with a 3 kD Ultracel regenerated
cellulose membrane. Both unconcentrated (SF1) and concentrated (SF3) PRPE-SF were stored at
-80°C until use. Secreted proteins were characterized using a quantitative 40 human growth factor

122
Array Q1 (RayBiotech Inc., Nacross, GA, USA) and PEDF and LIF determined by ELISA (Boster
Biological Technology, Pleaston, CA, USA).

Figure 4.1 Diagram and timeline of polarized retinal pigmented epithelial soluble factor (PRPE-
SF) production (A) with corresponding 4x and 20x images of PRPE (B). Human embryonic
stem cells (hESC) differentiated into RPE were plated onto a vitronectin coated ultrathin
parylene membranes and incubated in XVIVO10 medium. Significant morphological changes
occur during the first 15 days of cell culture resulting in polarization and cobblestone shape
expected of mature RPE. The following 12 days there is a rapid increase in pigmentation
signifying the maturation of the hESC-RPE cells. PRPE-SF (SF1) is collected, concentrated
(SF3) using a 3kDa filter, and stored at -80
o
C on days 27, 31, 35, and 39. Subsequently, SF1 and
SF3 are characterized in cells and the Royal College of Surgeons rat model of retinal
degeneration. Created with BioRender.com.
4.2.2 Fetal Retinal Progenitor Cell (RPC) and ARPE-19 Culture
The biological activity of PRPE-SF1 was characterized by using 18-20 weeks gestation of
human fRPC. Fetal donor eyes were obtained from Lion Eye Institute under appropriate USC
investigational review board approval. Following dissection, the retina was rinsed with PBS,
broken up by passing the cells through a 27-gauge needle. The cells were collected and pelleted
through centrifugation. The cells were washed with RPC media (DMEM/F12 [1:1], with 10%
knockout replacement serum (KRS; Invitrogen), 1 N2 supplement (Invitrogen), 1 B-27

123
(Invitrogen), 20 ng/mL FGF2 (R&D Systems), and 20 ng/mL EGF (R&D Systems)). Cells were
plated into Matrigel-coated 96-well plates, 48-well plates, and chamber slides and cultured for 24
hours. RPC media was removed after 24 hours and replaced with experimental media. After 24
hours, plates were used in cell viability assays, apoptosis assays, immunocytochemistry staining,
and RT-qPCR.
ARPE-19 cells were purchased from American Type Culture Collection (ATCC,
Manassas, VA and were maintained in culture media comprised of DMEM/F-12 (Corning Life
Sciences, Corning, NY) and 10% fetal bovine serum. Cell cultures maintained at 37°C in a 5%
CO2 incubator. ARPE-19 cell assay are described below.
4.2.3 ARPE-19 Cell Assays
The 2’,7’ –dichlorofluorescin diacetate (DCFDA) assay is an intracellular assay for non-
selectively detecting ROS. ARPE-19 cells were seeded into 96-well plates at a density of 1.25x10
5

cells/cm
2
in maintenance media. Following 24 hrs, cells were washed with HBSS (Cytiva,
Marlborough, MA) and media was replaced with 50 µL of experimental media diluted 1:1 with
fresh XVIVO10. After 24 hours, cells were washed with HBSS and media replaced with 25 µM
DCFDA (Sigma-Aldrich, St. Louis, MO) in HBSS and placed in CO2 incubator for 45-minutes
prior to measure fluorescence on Biotek Synergy H1 microplate reader (BioTek, Winooski,
Vermont) using 485 nm excitation and 535 nm emission wavelengths. Percent change was
calculated from blank-subtracted data using cells in HBSS with no DCFDA. Similarly, ARPE-19
were treated in 24-well plates (2x10
5
cells/well) for 24-hours and RNA was extracted as described
below and ROS generating and eliminating genes were probed.
4.2.4 Rhodopsin Staining
Following treatment and media removal, slides were fixed using 10% neutral buffered
formalin for 10 minutes. Antigen retrieval buffer (citrate buffer, pH 6.0) was diluted 100-fold using

124
distilled water, slides were placed in diluted buffer solution and placed in pressure cooker. Slides
were cooked at full pressure for 3 minutes then allowed to cool. The blocking buffer consisting of
5% BSA with 0.1% Triton X-100 was incubated for 30 minutes at RT in a humidified chamber.
Then rhodopsin antibody was diluted with 5% BSA to a dilution of 1:400 and applied to the
chamber overnight at 4 ℃. The slides were rinsed using PBS and secondary antibody, diluted with
5% BSA to a dilution of 1:100, was applied with an incubation for 1 hour at room temperature in
a humidified chamber. Slides were rinsed three times with PBS for 5 min each. Coverslips were
mounted with 2 drops of fluorescent enhance mounting medium with DAPI (VECTASHIELD
HardSet, Vector Labs, Newark, CA). Slides were imaged on Keyence BZ-X700 Microscope
(Keyence, Osaka, Japan).
4.2.5 TUNEL Assay
Apoptosis assays were performed using Promega’s DeadEnd™ Fluorometric TUNEL
System (Promega, Madison, WI) according to manufacturer’s protocol. Following incubation with
the various treatments, RPC cells were prefixed for 5 minutes with 10% formalin at room
temperature. Media was then removed and replaced with 100 µL of 10% formalin and incubated
for 10 minutes at RT. The cells were then washed twice with PBS for 5 minutes at RT. Cells were
permeabilized by adding 100 µL of 0.2% Triton X-100 for 5 minutes, and then washed twice with
PBS. Liquid was removed and 100 µL of equilibration buffer was added for 5 minutes at RT.
Reaction mix was then added to empty wells using 45 µL of equilibration buffer, 5 µL nucleotide
mix, and 1 µL rTdT enzyme. Equilibration buffer was replaced with 50 µL of reaction mix,
surrounding wells were filled with PBS, and plate was incubated at 37 °C for 1 hour protected
from light. Reaction mix was replaced with 1X SSC and incubated for 15 minutes at RT. The wells
were then washed with PBS three times for 5 minutes, and PBS was replaced with 1 µg/mL
propidium iodide in PBS for 15 minutes at RT, protected from light. Wells were then washed 3

125
times with deionized water and replaced with PBS for imaging. The plate was then imaged using
a Keyence BZ-X700 Microscope (Keyence, Osaka, Japan).
4.2.6 RPC RNA-Extraction and RT-qPCR
Following treatment, total RNA was isolated using a RNeasy Mini Kit (QIAGEN, Hilden,
Germany) following the manufacturer’s protocol. RNA concentration and purity were assessed
using a NanoDrop™ One (Thermo Fisher Scientific, Waltham, MA). Reverse transcription was
performed using a reverse transcription system (Promega, Madison, WI). 500 ng of total RNA was
used for transcription and the other components of the reaction mix were as described in the
manufacturer’s protocol. The samples were then heated at 95 ℃ for 5 minutes and then stored at -
20℃ for downstream qPCR. RT-qPCR was performed using 10 ng cDNA, 300 nM forward and
reverse primers, and 1x PowerUp SYBR Green Master Mix (Thermo Fisher Scientific, Waltham,
MA) in a 10µL reaction. Forward and reverse primers were designed by Sigma-Aldrich
(Supplementary Table T4.1) for fRPC studies. ARPE-19 primers were designed using Primer-
Blast from NCBI and sequences are listed in Supplementary Table T4.2. RT-qPCR was performed
on an Applied Biosystems Quantstudio 12K Flex (Thermo Fisher Scientific, Waltham, MA) with
SYBR fluorescence with ROX passive reference. The thermal cycling conditions followed the
standard cycling for primers with melting temperature greater than 60°C: UDG activation for 2
minutes at 50°C, Dual-Lock DNA polymerase for 2 minutes at 95°C, followed by 40 cycles of
denature for 15 seconds at 95°C then anneal/extend for 1 minute at 60°C. Melt curves were
performed by 1) ramping to 95°C at 1.6°C/second and hold for 15 seconds, 2) ramping to 60°C at
1.6°C/second and hold for 1 minute, and 3) ramp to 95°C at 0.15°C/second and hold for 15
seconds. Cycle thresholds and baselines were calculated using the automatic settings in the
Quantstudio 12K Flex Software. Fold change was calculated using the 2
-ΔΔCT
method (243).

126
4.2.7 Animals and Experimental Design
The immunodeficient Royal College of Surgeons (iRCS) rats breeding pair as previously
described (153). Rats were group housed under specific pathogen-free conditions and had access
to water and food ad libitum. At post-natal day 21 (±2 days), iRCS rats received 10 µL intravitreal
injections of SF1, SF3, XV1, XV3, or 20 μg/mL recombinant PEDF (Bio-Techne, Minneapolis,
MN) followed by general health and clinical ocular observations. This level of PEDF is equivalent
to the concentrations found in SF3. Prior to each IVT injection, functional and structural
assessment was performed by ERG and OCT, respectively. Animals received the second and third
serial injections every 14 days at p35 (+2 days) and p49 (+2 days). Before each IVT injection,
anesthesia was administered by abdominal injection of ketamine (37.5 mg/kg) and xylazine (5
mg/kg), topical anesthesia and pupil dilation was induced by 0.5% proparacaine hydrochloride
ophthalmic solution (Akorn, Inc., Lake Forest, IL) and 2.5% phenylephrine hydrochloride and
0.5% tropicamide (Akorn, Inc.), respectively.
4.2.8 Electroretinogram (ERG) Evaluation
For ERG assessment, animals were placed in dark adaptation overnight the day before the
functional testing. Under dark conditions and by using a dim red light, animals were anesthetized
as described above, along with application of pupil dilation and topical anesthesia eye drops. While
the animal is under anesthesia a heating table and monitor were used to monitor body temperature.
Reference and ground electrodes were inserted into the infraorbital (malar) area and between the
ears respectively. Scotopic testing was conducted with flash stimuli intensities ranging from 1 to
25,000 millicandela (mcd) was recorded from both eyes (HMsERG Rodent System, OcuScience,
NV). ERG raw data was collected and evaluated.

127
4.2.9 Ocular Coherence Tomography (OCT) Evaluation
Imaging scanning was performed at the end of the ERG session. Using the Spectralis OCT
(Heidelberg Engineering Inc, MA), the animal was placed over the animal-modified head support
used in patients. Balanced salt solution (BSS) was applied regularly to moist the cornea during the
imaging acquisition. A full set of b-scans was acquired from both sides of the optic nerve. Each
set of images includes a high-resolution b-scan and a volume scan composed of 30 b-scans from
the temporal and nasal sides of the optic nerve from both eyes. Morphological analysis of the retina
includes a grading score based on the outer retina changes as described in Supplementary Figure
S4.1. By using the Heidelberg Eye Explorer software, the retinal thickness, and ONL
characteristics were evaluated. After the completion of the OCT imaging session, animals were
injected with test articles.
4.2.10 Intravitreal Injection
Under sterile conditions, using a 30-gauge (G) needle, a scleral incision was performed
behind the limbus in the temporal superior quadrant of the left eye. Subsequently, intraocular
pressure was reduced by a puncture onto the anterior chamber through the periphery of the cornea
by a 30G needle as well. Then, 10 µl of the tested article was injected through the scleral incision
by using a 30G steel blunt needle attached to a 50µl Hamilton syringe. Once the completion of the
IVT injection, a clinical assessment of the posterior pole was done by clinical visualization through
the surgical microscope. A self-sealed healing of the sclera and the conjunctiva was observed, and
no sutures were needed. Topical application of antibiotic ointment was performed at the end of the
procedure.
4.2.11 Euthanasia and Tissue Collection
At p60, were euthanized by intraperitoneal injection of 0.5 mL pentobarbitol sodium 390
mg and phenytoin sodium 50 mg (Euthasol; Virbac AH, Inc., Fort Worth, TX) and their eyes were

128
enucleated and fixed in Davidson’s solution. After 24 hours of fixation, the Davidson’s solution
was replaced with 70% ethanol and sent to the USC Ginsburg Institute for Biomedical
Therapeutics Core for paraffin embedding, sectioning, and hematoxylin and eosin (H&E) staining.
Anterior segment structures, including cornea, iris and lens were removed and the posterior pole
was exposed. The cut of the eye was through the optic nerve on its sagittal plane. After dissection,
all eyes were embedded in paraffin and cut in a microtome starting from the center of the optic
nerve. Serial sections of 5 µm in thickness were performed throughout the entire eyeball.
Approximately 4 consecutive/serial retinal sections were placed on every glass slide.
4.2.12 Photoreceptor Counting
Surviving PRs were determined in rats treated with various test and control articles. Since
injection of both the test and control articles are not localized to one area of the retina, as they are
deployed within the vitreous cavity, slides representing the central area of the retina were selected
for enumeration. Specifically, the slide for enumeration was chosen based on the presence of the
optic nerve as a landmark. The preserved ONL cell numbers were enumerated from scanned
images of the sections. Photoreceptor counting was initiated 1 mm superior or inferior of the optic
nerve and continued for 1 mm. The “Nuclear V.09” algorithm (a nucleus counting algorithm)
provided in the Aperio ScanScope CS microscope software was used to enumerate the cells. Two
sections were counted for each animal and the results were averaged.
4.2.13 Immunofluorescence Staining
Eye tissue sections were first deparaffinized and rehydrated via immersion in a series of
xylene, ethanol, and PBS solutions. Heat-induced epitope retrieval was performed using 1x Citrate
Buffer (pH=6.0) in a pressure cooker. Following antigen retrieval, slides were moved onto a humid
chamber and washed three times with PBS. Tissues sections were then permeabilized with 0.3%
Triton X-100 in PBS for 10 minutes followed by 3 washes with PBS. Sections were blocked for

129
30 minutes with blocking buffer (PBS containing 2.5% normal goat serum (v/v)). The blocking
solution was replaced with 50 µL primary antibody diluted with blocking buffer at pre-determined
concentrations (Supplementary Table T4.3). Slides were incubated overnight at 4°C then washed
3 times with PBS. Following, slides were incubated for 45 minutes at room temperature with
secondary antibody diluted 1:500 with blocking buffer. After 3 washes with PBS, nuclear staining
was performed using 1 µg/mL DAPI in PBS for 10 minutes at room temperature. Slides were
washed three times and coverslips were mounted using VECTASHIELD Vibrance Antifade
Mounting Medium (Vector Laboratories, Newark, CA). Fluorescent images were taken on the
Olympus BX43 microscope using a 40x objective. Immunofluorescence images were analyzed in
ImageJ using 5 images per animal. ImageJ analysis was performed by manually selecting the
retinal layers combined with using the DAPI channel to refine nuclear layer regions of interest
with a histogram threshold of 50-255 on an 8-bit scale. For 4HNE and MDA staining, automatic
thresholding using the Phansalkar method was applied and then the percentage area per retinal
layer was measured. This was similarly applied for PAD4 in the ONL and outer segments. For
CitH3, histograms thresholds were set from 30-255 on an 8-bit scale and the percentage area within
the DAPI mask was measured. CD68 cell counts were performed on images with threshold set
from 30-255 on an 8-bit scale and particle analysis using 5-300 px
2
size range for the entire image
field.
4.2.14 Statistical Analysis
Statistical analysis was performed in GraphPad Prism 9 (Graphpad Software Inc., La Jolla,
CA). All graphs are plotted as mean ± standard error of the mean, unless otherwise noted in the
figure legend. Appropriate statistical analyses were performed for each data set including unpaired
t-tests for comparing two treatments. For comparing more than two groups one-way ANOVA with
Tukey’s correction, Welch’s ANOVA with Dunnett’s T3 correction, and Kruskal-Wallis with

130
Dunn’s correction were performed based on the dataset. For genetic expression data, ddCT was
compared using two-way ANOVA with Tukey’s correction. The statistical test used in an
individual analysis is indicated in the figure legends.
4.2.15 Study Approval
Regulatory approval for use of the hESC lines was obtained from the University of
Southern California Stem Cell Research Oversight Committee (SCRO). Animal experiments were
conducted in full accordance with University of Southern California (USC) Institutional Animal
Care and Use Committee (IACUC)-approved protocols, National Institutes of Health Guide for
the Care and Use of Laboratory Animals and the ARVO Statement for the Use of Animals in
Ophthalmic and Vision Research.
4.3 RESULTS
4.3.1 PRPE-SF increases fRPC cellular viability and rhodopsin expression.
Cellular apoptosis and proliferation of fRPC was compared among unconcentrated X-
VIVO10
TM
(XV1), 3X concentrated XV1 (XV3), 20 µg/mL pigmented epithelial derived factor
(PEDF), unconcentrated PRPE-SF (SF1) and concentrated SF1 (SF3). The concentration of PEDF
used reflects the levels found in SF3 as measured using ELISA. TUNEL staining was used to
quantify the number of apoptotic cells and propidium iodide used as the counter stain to measure
total cell area. The apoptotic/total cell ratio were then normalized to XV1 (Figure 4.2A). The
results showed that the SF3 treatment significantly reduced the apoptotic/total cell ratio, while no
reduction was observed with other treatments, including 20 µg/mL PEDF. In addition, both SF1
and SF3 improved cellular viability compared to XV1 and PEDF, as determined by the resazurin
assay (Figure 4.2B).

131

Figure 4.2 fRPCs treated with SF3 showed significant decrease in cell death compared
to all other treatments as determined via the apoptosis TUNEL assay following 24-hour
incubation (n=4 XV1, XV3, and SF1, n=5 PEDF and SF3) (A). Both SF1 and SF3
showed significantly improved cell viability compared to XV1 and PEDF, assessed by
resazurin metabolism following 24-hour incubation (n=4 each group) (B). Data
represented as mean ± SEM. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, one-way
ANOVA with Tukey’s correction.
The impact of SF1 and SF3 on photoreceptor survival was evaluated using rhodopsin
expression in fRPCs comparing XV1, XV3, and 20 µg/mL PEDF (Figure 4.3A-F). Freshly isolated
fRPCs displayed intense rhodopsin immunofluorescent staining (Figure 4.3A), however 24 hours
after treatment with XV1, XV3, PEDF, or SF1, the level of rhodopsin was minimally detectable
(Figure 4.3B-E). In contrast, SF3 treated fRPC maintained high levels of rhodopsin staining
(Figure 4.3F). Correspondingly, increased gene expression of rhodopsin and recoverin were
observed for both SF1 and SF3 treated fRPC (P<0.05) when compared to XV1 treatment (Figure
4.3G-H).

132

Figure 4.3 Rhodopsin and DAPI staining of fRPC immediately after isolation (A) and
after 24-hour incubation with XV1 (B), XV3 (C), PEDF (D), SF1 (E) and SF3 (F). RT-
qPCR shows SF1 and SF3 induce rhodopsin (G) and recoverin (H) gene expression. Data
represented as mean ± SEM. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, statistics
analyzed ddCT values with one-way ANOVA with Tukey’s correction using data from
n=3 independent experiments with triplicate samples in each experiment.
4.3.2 PRPE-SF induces gene expression in fRPC.
To characterize the biological activity(ies) associated with SF1 and SF3, impact of these
treatment on fRPC was evaluated using targeted gene expression (Figure 4.4A, Supplementary
Figure S4.2). Major constituents of SF1 and SF3, determined by microarray and ELISA, are
highlighted in Figure 4.4B showing NGF, BDNF, PEDF, LIF, TGFβ1, and BMP7 were all present
and were enriched with our processing. SF1 and SF3 were able to upregulate progenitor cell
proliferation, maturation, and differentiation associated gene expression.
To further dissect the molecular interactions, an ingenuity pathway analysis (IPA) was
undertaken. Factors such as PEDF, IGFBP3, and TGF β1 were detected in high concentrations in
both SF1 and SF3. TGF β1, a pleiotropic factor, can upregulate SOX2, HES1, MKI67 and DACH1
(Figure 4.4A&C). SF1 and SF3 both upregulated MKI67 expression coinciding with the ability
to promote fRPC viability (Figure 4.2). TGFβ1 can increase HES1 expression which regulates
cell division, gliogenesis, and maintaining tissue compartment boundaries (332). Other properties
ascribed to levels of TGFβ1 found in SF1 and SF3 include upregulation of DACH1, which has
been shown to promote transition from progenitor cells to neuronal precursor cells achieved

133
through cell cycle synchronization and interaction with cyclin D1 (333). In addition, SF1 and SF3
were able to upregulate SOX2 expression by 2.8 to 4.4-fold, respectively, which can in turn trigger
downstream upregulation of PAX6 and LHX2 expression. IPA also shows the contribution of
BMP7 to increase expression of PAX6 where there was 6.5- and 6.9-fold increased as compared
to fRPC treated with XV1. LHX2, a central factor coordinating optic cup development, is required
for BMP signaling and interacts with PAX6 to regulate SIX3 and SIX6 expression (334, 335). SF1
and SF3 upregulated DCX and NES, which are microtubule and cytoskeleton associated enzymes
which play a role in the migration of progenitors and the structural orientation of retinal layers
(336, 337).
While SF1 and SF3 promote progenitor cell proliferation, maturation, and differentiation
gene expression, it does not alter GFAP expression, a marker of astrocytes or Müller development
and reactivity (338). IPA suggests that upregulation of the transcription factors is consistent with
cells fated for the eye, proliferation of progenitor cells, and differentiation pathways (339-341).
While this is a targeted analysis, the relationships identified in IPA is evidence of critical
components to monitor in future manufacturing development for a clinical grade product.

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Figure 4.4 Mean Fold change of fetal RPC gene expression after 24-hour incubation
compared to XV1 (A). RT-qPCR shows SF1 and SF3 induce several eye-field
transcription factor genes relating to proliferation and retinal development. Mean ± SD
concentration of proteins in SF1 and SF3 determined by ELISA or microarray (B).
Relationships between transcription factors (TFs), mature retinal cell markers, and RPE
neurotrophic factors using IPA (C). TFs show a complex network of positive and
negative regulations on each other, and PRPE-SF components additionally show both
positive and negative regulations on TFs. Statistics analyzed ddCT values using two-
way ANOVA and Tukey’s multiple comparisons for data from n=3 independent
experiments with triplicate samples in each experiment, no comparisons were performed
for SIX3, SIX6, LHX2, and GFAP which only had n=2 experiments. (*P<0.05 compared
to XV1,
†
P<0.05 compared to XV3,
ǂ
P<0.05 compared to PEDF). Individual graphs can
be viewed in Supplementary Figure S4.2. Networks generated with QIAGEN IPA
(QIAGEN Inc., https://digitalinsights.qiagen.com/IPA).

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4.3.3 PRPE-SF preserves retinal structure and function in RCS rats.
The highest SF3/SF1 proteins are known to have antioxidant effects including NGF,
BDNF, PEDF, and LIF (342-345), suggesting that SF3 would have greater antioxidant effects and
efficacy in models of retinal degeneration. Experimental medias were administered via intravitreal
injection (IVT) on p21, p35, and p49 in athymic RCS (iRCS) rats, where on p60, eyes were
collected and subjected to molecular dissection. H&E staining revealed photoreceptor preservation
in iRCS rats treated with SF3, with a statistically significant increase in photoreceptor nuclei
counts when compared to XV1, XV3, and 20 µg/mL PEDF (Figure 4.5A-E, K). As well,
photoreceptor inner segments appeared to be more well organized in SF3 treated retinas as
compared to other treatments. This portion of the photoreceptor is where protein synthesis
machinery and mitochondria are localized. Photoreceptor preservation was also evaluated non-
invasively throughout the study using ocular coherence tomography (OCT) and confirmed with
histological findings on p60 (Figure 4.5F-J, L).
To investigate whether photoreceptor preservation translates to preservation of retinal
function, electroretinography (ERG) was performed prior to IVT injection at p21, p35, p49, and
p60 in XV3 and SF3 groups. The scotopic b-wave amplitudes, used to measure rod photoreceptor
response, showed preserved retinal function in SF3 treated retinas (Figure 4.5M-P, raw amplitudes
in Supplementary Figure S4.3). When compared to the baseline p21 values, XV3 and SF3 showed
about a 35% decline in amplitude at p35. However, SF3-treated retinas were able to preserve the
scotopic b-wave amplitude from p35 to p49 and p60, demonstrating the effectiveness of SF3 in
preserving retinal function.

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Figure 4.5 H&E, OCT, and ERG of p60 RCS retinas treated with XV1 (A, F), XV3 (B,
G), PEDF (C, H), SF1 (D, I), and SF3 (E, J). H&E-stained retinas show outer nuclear
layer (ONL) thickness correlates to OCT ONL (black arrow). The ONL was minimally
detected in all treatment groups except for SF3 (J) of p60 iRCS retinas. Photoreceptor
counts from H&E images (K, XV1 n=4, XV3 n=6, PEDF n=4, SF1 n=5, SF3 n=31) and
OCT grading scores (L, XV1 n=5, XV3 n=4, PEDF n=4, SF1 n=4, SF3 n=14) show SF3
treatment significantly preserves the ONL when compared to XV1 and XV3. Scotopic
b-wave amplitudes of XV3 and SF3 treated eyes were measured using flash intensities
of 3 cd/m
2
. Measured amplitudes (µV) were normalized to p21 baseline values (XV3 n=
8, SF3 n=16) (M-P). At p35 there was ~30-40% decrease in b-wave amplitude from
baseline (n=4 each group). SF3 shows significantly improved %amplitudes at both p49
(O, n=4 each group) and p60 (P, n=6 each group) compared to XV3. Data represented
as mean ± SEM. *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, Photoreceptor
counts: Welch’s ANOVA with Dunnett T3 correction; OCT Grading: Kruskal-Wallis
with Dunn’s correction, ERG: unpaired t-test. Scale bar is 100 µm.

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4.3.4 PRPE-SF reduces reactive oxygen species and reactive glial activation.
SF3 was able to preserve photoreceptor and retina structure while SF1 did not. This study
showed retinal degeneration was linked to the levels of oxidative stress, inflammation, and
activation of Müller and microglial cells. The in vivo administration of SF3 reduced the activation
of Müller and microglial cells, as indicated by decreased levels of GFAP (Figure 4.6A-B) and
CD68 (Figure 4.6C-G) staining, respectively. XV3 treated iRCS retinas showed evidence of gliosis
as indicated by strong GFAP staining as well as subretinal glial membranes below the outer nuclear
layer (ONL), which was reduced or not present in SF3-treated retinas. GFAP is commonly
upregulated in retinal degeneration models, where the reduction in GFAP may indicate reduced
gliosis (299). XV1 and XV3 treated iRCS retinas showed significant CD68
+
staining in the outer
segments, while retinas treated with PEDF and SF3 demonstrated a dramatic reduction in CD68
+

cells. However, the sample size was low and the reduction in CD68
+
cells was not found to be
statistically significant.

138

Figure 4.6 Immunofluorescence images of p60 iRCS rat retinas treated with XV3 and
SF3. XV3 treated retinas display significant gliosis as marked by GFAP staining
throughout the retina and below the ONL (white arrow) (A-B). Anti-CD68
immunofluorescence staining of p60 iRCS retinas treated with XV1 (C), XV3 (D), PEDF
(E), SF1 (F), and SF3 (G), n=3 each group. PEDF and SF3 shows, non-significant,
reduced CD68
+
cells (H). Blue: DAPI. Red: GFAP (A-B) or CD68 (C-G). Scale bar is
20 µm (A-B) and 100 µm (C-G).

139
The effects of SF3 treatment on the levels of ROS were also evaluated (Figure 4.7). In
ARPE-19 cells, DCFDA was used to measure the cytoplasmic levels of ROS following 24-hour
incubation of XV1, XV3, PEDF, SF1 and SF3. SF1 had little effect on ROS levels, while
concentrated SF3 was found to reduce ROS levels by approximately 20-30% as compared to XV3
(Figure 4.7A). ARPE-19 treated with decreased dilutions of SF3, showed alteration in genes
regulating ROS production and elimination (Figure 4.7B). Little to no changes in NADPH-oxidase
(NOX) expression of multiple isoforms were found. However, increased SOD2 expression was
seen. While SOD3 was also increased in a concentration dependent manner, the expression
changes did not reach significance. In addition, the glutathione pathway was also probed, and
changes were not found.
To determine whether these in vitro and molecular findings correspond to in vivo studies,
the effects of SF3 on oxidative products, such as malondialdehyde (MDA) and 4-hydroxy-2-
nonenal (4HNE), were also stained in treated iRCS retinas. Results showed that iRCS rats treated
with XV1 or SF1 had high levels of MDA and 4HNE in all retinal layers, while iRCS rats treated
with SF3 showed reduced retinal levels of MDA and 4HNE, particularly in the ONL and outer
segments (OS) (Figure 4.7C-I). The areas of MDA and 4HNE were measured as a percent of the
respective layer to account for increased retinal thickness in SF3 retinas. Staining showed that
MDA had a stronger nuclear staining, while 4HNE appeared to be cytoplasmic and extracellular,
suggesting separate roles for each lipid peroxide in retinal pathologies. Furthermore, SF3 treated
retinas showed 4HNE localized within preserved inner segments which links the ROS produced
by photoreceptor mitochondria to the 4HNE staining of the inner segments. A longitudinal study
was performed to verify that both MDA and 4HNE increase with the age of the iRCS rat
(Supplementary Figure S4.4). The results showed that MDA showed significant increased

140
expression in the ONL at p49 and p60, while 4HNE showed pan-retinal increases with the strongest
increases in the outer plexiform layer, ONL, OS, and RPE layers.

Figure 4.7 Effects of PRPE-SF on retinal reactive oxygen species. In vitro, SF3
significantly reduces DCFDA oxidation, a non-specific marker for reactive oxygen
species, in ARPE-19 cells following 24-hour incubation compared to all other treatments
(A) (one-way ANOVA with Tukey’s correction using data from 17 independent
experiments with triplicate samples in each experiment). 2x SF3 increases significantly
increases SOD2 expression following 24-hours of treatment (mean Log2(Fold
Change)>1, duplicates) (B). Immunofluorescence images of p60 iRCS rats treated with
XV1 (C, n=4), SF1 (D n=4), and SF3 (E n=8). Malondialdehyde (MDA; Red) show
strong nuclear staining while 4-hydroxynonenal (4HNE; Yellow) is primarily non-
nuclear. Due to preservation, SF3 images appear larger, but each image shows outer
nuclear layer (ONL) and outer segment (OS) at same magnification. SF3 treatment
shows preservation of inner segments (black arrows in E) which has strong staining for
4HNE. SF3 shows reduced photoreceptor nuclei staining of MDA and reduced ONL and
OS staining of 4HNE. ImageJ analysis shows a significant decrease in both MDA and
4HNE percent area in the ONL and OS (Kruskal-Wallis with Dunn’s multiple
comparisons) (F-I). *P<0.05, **P<0.01, ***P<0.001, ****P<0.0001. Blue: DAPI. Red:
MDA. Yellow: 4HNE. Scale bar is 20 µm.

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4.3.5 PRPE-SF reduces NETosis markers PAD4 and CitH3
In recent years, sterile neutrophil extracellular trap (NET) formation has emerged as a
crucial factor in retinal disease and is known to result from increased oxidative stress and play a
role in retinal autoimmunity (346). Since increased ROS production can lead to PAD4 activation
(116), the expression of PAD4 was evaluated in retinas of iRCS rats treated with XV1, PEDF,
SF1, and SF3 at p60 (Figure 4.8A-E). XV1 and PEDF treated retinas revealed high PAD4
expression present in all retinal layers, including the inner nuclear layer (INL), ONL, and OS.
However, retinas treated with SF3 exhibited reduced PAD4 expression, particularly in the ONL
and OS. These findings indicate the protective effect of SF3 in reducing PAD4 expression. In
addition, the presence of PAD4 in the choroid of retinas treated with XV1 and PEDF highlights
the presence of both intra- and extra-retinal sources of PAD4.
Retinas were also analyzed for citrullinated histones (CitH3) which is produced through
PAD4-mediated deimination. Results showed that retinas treated with XV1 had high levels of
CitH3 in various retinal layers including the ganglion cell layer (GCL), INL, ONL, RPE, and
choroid. However, retinas treated with SF3 demonstrated significantly reduced levels of CitH3
(Figure 4.8F-H). These findings provide evidence that reduction in PAD4 expression and
citrullination correspond with delayed retinal degeneration, highlighting the potential therapeutic
benefits of SF3 in retinal disease pertaining to autoimmunity.

142

Figure 4.8 PAD4 immunofluorescence images of p60 iRCS rats treated with XV1 (A, n=5),
PEDF (B, n=4), SF1 (C, n=4), and SF3 (D, n=5). PAD4 shows pan-retinal staining with
strong staining in the ganglion cell layer (GCL), inner nuclear layer (INL), outer nuclear
layer (ONL), outer segments (OS), retinal pigment epithelium (RPE), and choroid (Ch) in
XV1 and PEDF treated retinas. SF1 and SF3 treated retinas showed reduced staining
intensity, ImageJ analysis of shows SF3 treated retinas have significantly reduced PAD4
percent area of ONL compared to XV1 (E). (*P<0.05, one-way ANOVA with Tukey’s
multiple comparisons). Similar to 4HNE, light PAD4 staining is seen in preserved inner
segment region (IS) of SF3 treated retinas (D). Citrullinated Histone H3 (CitH3)
immunofluorescence images of iRCS rats at p60 treated with XV1 (F, n=5) and SF3 (G,
n=6). XV1 treated retinas show GCL, Ch, and extensive ONL staining with punctate debris
in the OS (white arrows). ImageJ analysis of ONL CitH3 shows SF3 treated retinas have
reduced ONL CitH3 expression compared to XV1 (H). (*P<0.05, unpaired t-test). Blue:
DAPI. Red: PAD4 or CitH3. Scale bars = 20 µm.

143
4.4 DISCUSSION
Retinal degenerative diseases are extremely limited in therapeutic options, whether AMD
or genetic such as retinitis pigmentosa. However, stem cell implantation has emerged as a
promising therapeutic approach for directly replacing atrophic cells (220). Previously, the
implantation of hESC-RPE showed that these cells were able to re-epithelialize the dystrophic
areas and extend preservation beyond the borders of the implant (152, 226). This effect may be
associated with the secretome elaborated by the implanted RPE that is capable of maintaining
ocular function, coinciding with reduce ocular oxidative stress and inflammation. The key aspects
of PRPE-SF are 1) PRPE-SF utilizes the biomimetic membrane to induce hESC-RPE to polarize
and mature, thus modulating the secretome towards a protective phenotype (347, 348), 2) PRPE-
SF does not use animal serum which will aid in clinical development to avoid immunogenicity,
and 3) PRPE-SF is a combination of neuroprotective factors which can address multiple
pathological mechanisms of retinal degeneration. This study examined the therapeutic potential of
SF3 for treatment of retinal degenerative conditions by promoting neuronal survival through
mitigating oxidative stress and inflammation.
The RPE is a source of neuroprotective factors, however, purified factors when given alone
have failed in clinical evaluation. Previous research demonstrated conditioned medium derived
from rat RPE was able to promote rat RPC survival and differentiation (217, 230, 231). Similarly,
human derived RPE conditioned media was able to reduce cellular death of porcine retinal explants
(207). Previously, we expanded on these findings showing that hESC-derived RPE conditioned
media can promote fRPC proliferation while inhibiting cell death (347). Here we further showed
PRPE-SF treatment of fRPCs was able to upregulate genes associated with progenitor cell
proliferation and multipotency through upregulation of MKI67, SOX2 and PAX6. As well, PRPE-

144
SF promoted eye fate determinants (DACH1 and LHX2) and promoted neuronal migration genes
(NES and DCX). The enriched secretome was able to upregulate photoreceptor developmental
genes, rhodopsin and recoverin, but did not affect glial genes like GFAP. These findings are
consistent with studies where RPE conditioned media preferentially differentiated rat retinal
explants and immortalized RPCs into photoreceptors (233, 349).
Upregulation of these genes suggests PRPE-SF may induce proliferation and
differentiation of latent progenitor cells towards neuronal fates, which is being explored in current
studies. Using upstream analysis and predictive modeling in IPA, there is a direct relationship
between PRPE neurotrophic factors and the effected transcription factor genes in our fRPC dataset.
However, SF3 showed a stronger effect on reduced cell death and preserved rhodopsin staining,
potentially through protective mechanisms rather than proliferation. Several growth factors in SF1
were proportionally higher in SF3. Proteins with high SF3/SF1 ratios, including NGF, BDNF,
PEDF, and LIF, are known to have protective effects for neurodegenerative disorders and likely
contribute to blockage of pro-apoptotic pathways (344, 345, 350). NGF and PEDF are particularly
interesting as these trophic factors have been individually investigated in clinical trials for retinal
conditions including NGF for retinitis pigmentosa, cystoid macular edema, and glaucoma, and
PEDF for macular degeneration (351-353).
A link between these neurotropic factors with oxidative stress led us to interrogate the
impact of SF3 in iRCS. IVT administration of SF3 starting on p21 and given bi-weekly till p60
was able to significantly preserve photoreceptors. More importantly, photoreceptor preservation
correlated with the preservation of scotopic b-wave amplitudes suggesting that SF3 can also
ameliorate and delay decline in retinal function. This study also suggests antioxidant mechanisms
of SF3 contribute to retinal preservation. SF3 was consistently able to reduce cytoplasmic ROS by

145
20% in ARPE-19 cells. Amongst the ROS generating and eliminating genes investigated, SF3
significantly increased SOD2 and marginally increased SOD3 in ARPE-19. Both SOD2 and SOD3
would increase the rate of superoxide elimination resulting in the observed ROS reduction. These
findings coincide with reduction of ROS in vivo, where reduced levels of MDA and 4NHE were
seen in SF3 treated iRCS retinas compared to XV1. The MDA reduction was particularly dramatic
in the ONL and OS, suggesting SF3 reduced oxidative stress in areas of photoreceptor
degeneration and subsequent microglia activation and translocation. The level of 4HNE, a
cytotoxic byproduct of arachidonic acid metabolism, was also lower in the ONL and OS layers of
iRCS treated with SF3. Both peroxides form protein and DNA adducts that can induce additional
inflammatory responses and perpetuate chronic inflammation as seen in retinodegenerative
diseases (247). Additionally, MDA accumulation has been linked to RPE dysfunction and VEGF
secretion in AMD (354). Microglia and macrophage cells infiltrate in response to these damage-
associated molecular patterns (DAMPs) released by damaged photoreceptors in an effort to clear
cellular debris and detoxify the local microenvironment (279). The reduction in ROS and oxidative
byproducts may partially explain why SF3 treatment had reduced infiltration of CD68
+
cells into
the retina.
In this study, ROS elevation was present in iRCS retinas corresponding to PAD4 activation
which is consistent with studies showing elevated intraretinal citrullination in human AMD donor
eyes (119). SF3 was able to reduce ONL and OS PAD4 staining coinciding with reduced CitH3.
Additionally, PAD4 and CitH3 choroidal staining was observed in XV1 treated retinas and reduced
with SF3. Choroidal neutrophils have been implicated in AMD and the degeneration of the RPE
barrier (103), which suggests there is retinal remodeling associated with sterile inflammation
initiated by aberrant oxidative stress and photoreceptor cell debris. Binet et al. suggested that

146
vascular endothelial cells in diseased blood vessels engaged in molecular pathways similar to those
in aging. Cellular damage ultimately led to cellular senescence-associated cytokine secretion,
recruiting neutrophils that can subsequently trigger NETs (355). While Binet et al. proposed that
NETs can facilitate elimination of diseased senescent vasculature as a protective mechanism,
chronic NETosis overactivation likely results in further destruction of the retinal vasculature. This
is corroborated in studies demonstrating NETosis contributes to the chronic inflammatory
microenvironment of colorectal tumors (356). In addition to infiltrating neutrophils in retinal
vasculature, Müller cells may be responsible for PAD4 expression and CitH3 in the ONL and OS
due to their known expression of PAD4 during reactive gliosis (124). While this mechanism
requires further exploration, SF3 treatment can ameliorate PAD4 and CitH3 staining in iRCS
which is paralleled by the reduction of 4HNE and MDA in treated eyes and retinal preservation.
This dataset shows clear concentration-dependent relationships with photoreceptor
preservation using both in vitro and in vivo models. Because SF1 and SF3 had similar effects on
viability and proliferation, the antioxidant and anti-inflammatory effects of SF3 are likely more
important than the mobilization of progenitor cells for prevention of retinal degeneration.
Additionally, the preservation of ERG only at p49 and p60 may be an additional indicator of the
primary mechanism of PRPE-SF. The RCS rat is a Mertk mutant model of retinal degeneration,
the primary cause of degeneration is the failure to phagocytose photoreceptor outer segments
which require daily renewal (70). Photoreceptors begin to die in response to the accumulating
debris and oxidative microenvironment. As shown with MDA and 4HNE staining, significant
staining is not observed until p49, and this accumulating oxidative stress will accelerate the
degeneration of the retina. Together, this suggests that the p49 ERG preservation is from the
protective effect of PRPE-SF against cytotoxic oxidation.

147
This study demonstrates the potential of PRPE-SF, in particular SF3, as a therapeutic
approach for the treatment of retinal degeneration. Taken together, the SF3 derived from PRPE
cells are able to 1) prevent retinal cell apoptosis, 2) reduce cellular ROS, and 3) reduce ocular
oxidative and inflammatory stress in the retinodegenerative RCS model. IPA predictive modeling
revealed a strong relationship between PRPE-SF and genes affected in this study, however,
additional work as to characterization of PRPE-SF will be necessary to meet regulatory demands.
And identification of the active component(s) most critical to modulating the hostile
microenvironment in retinodegenerative models will improve the ability to reproducibly
manufacture an effective PRPE-SF product. Most importantly, PRPE-SF showed preserved retinal
structure and function using clinically relevant methodologies. Development of products such as
PRPE-SF are extremely new, and until recently they were not feasible to safely develop for human
use due to technical limitations. With advancements in stem cell technology, PRPE-SF is a
promising candidate to treat retinal pathologies, such as dAMD and retinitis pigmentosa, providing
a step towards the development of an effective therapeutic for these debilitating conditions and
will hopefully set a precedent for age-related and neurodegenerative disease therapeutic strategies.
4.5 AUTHOR CONTRIBUTIONS
KA designed, performed, analyzed experiments, and wrote the manuscript. JCM and BT
designed, performed, analyzed experiments, and revised the manuscript. AN and DP performed
experiments and revised manuscript. JL, SYL, DM, SGL, and MSH gave conceptual advice,
designed experiments, and revised the manuscript.
4.6 FUNDING SOURCES AND ACKNOWLEDGEMENTS
4. Unrestricted Grant to the Department of Ophthalmology from Research to Prevent Blindness,
New York, NY
5. Research reported in this publication was supported by the National Eye Institute of the
National Institutes of Health under Award Number P30EY029220. The content is solely the
responsibility of the authors and does not necessarily represent the official views of the
National Institutes of Health.

148
6. The research was made possible by a grant from the California Institute for Regenerative
Medicine (Grant Number TRAN1-11532). The contents of this publication are solely the
responsibility of the authors and do not necessarily represent the official views of CIRM or
other agencies of the State of California.
7. The authors would like to thank Dr. Danhong Zhu for their contributions to this research. The
authors would like to acknowledge the late Dr. David Hinton who was a senior investigator in
this project. Dr. Hinton dedicated his life to developing treatments for retinal disorders and his
friendship and guidance will be greatly missed.

4.7 REFERENCES
34. Wong WL, et al. Global prevalence of age-related macular degeneration and disease burden
projection for 2020 and 2040: A systematic review and meta-analysis. The Lancet Global
Health. 2014;2(2):e106-16.
70. D'Cruz PM, et al. Mutation of the receptor tyrosine kinase gene Mertk in the retinal dystrophic
RCS rat. Hum Mol Genet. 2000;9(4):645-51.
103. Hinton DR, et al. Neutrophils compromise retinal pigment epithelial barrier integrity. Journal
of Biomedicine and Biotechnology. 2010;2010.
116. Rohrbach A, et al. Activation of PAD4 in NET formation. Frontiers in Immunology. 2012;3.
119. Bonilha VL, et al. Retinal deimination and PAD2 levels in retinas from donors with age-
related macular degeneration (AMD). Exp Eye Res. 2013;111:71-8.
124. Palko SI, et al. Compartmentalized citrullination in Muller glial endfeet during retinal
degeneration. Proc Natl Acad Sci U S A. 2022;119(9).
151. Kashani AH, et al. One-Year Follow-Up in a Phase 1/2a Clinical Trial of an Allogeneic RPE
Cell Bioengineered Implant for Advanced Dry Age-Related Macular Degeneration.
Translational Vision Science & Technology. 2021;10(10):13-.
152. Kashani AH, et al. A bioengineered retinal pigment epithelial monolayer for advanced, dry
age-related macular degeneration. Science Translational Medicine. 2018;10(435):1-11.
153. Thomas BB, et al. A new immunodeficient retinal dystrophic rat model for transplantation
studies using human-derived cells. Graefe's Archive for Clinical and Experimental
Ophthalmology. 2018:2113-25.
194. Liao DS, et al. Complement C3 Inhibitor Pegcetacoplan for Geographic Atrophy Secondary
to Age-Related Macular Degeneration: A Randomized Phase 2 Trial. Ophthalmology.
2020;127(2):186-95.
197. Aronow M, and Chew E. AREDS2: Perspectives, Recommendations, and Unanswered
Questions. 2015;25(3):186-90.

149
198. Chew EY, et al. Summary results and recommendations from the age-related eye disease
study. Archives of Ophthalmology. 2009;127(12):1678-9.
199. Meleth AD, et al. Treatment for atrophic macular degeneration. Current Opinion in
Ophthalmology. 2011;22(3):190-3.
206. Inanc Tekin M, et al. Brain-Derived Neurotrophic Factor in Patients With Age-Related
Macular Degeneration and Its Correlation With Retinal Layer Thicknesses. Invest Ophthalmol
Vis Sci. 2018;59(7):2833-40.
207. Kolomeyer AM, et al. Characterization of conditioned media collected from cultured adult
versus fetal retinal pigment epithelial cells. Investigative Ophthalmology and Visual Science.
2011;52(8):5973-86.
208. Mysona BA, et al. Nerve growth factor in diabetic retinopathy: beyond neurons. Expert Rev
Ophthalmol. 2014;9(2):99-107.
209. Meyer JG, et al. Proteome and Secretome Dynamics of Human Retinal Pigment Epithelium
in Response to Reactive Oxygen Species. Scientific Reports. 2019;9(1):1-12.
210. Yabe T, et al. Pigment epithelium-derived factor induces pro-survival genes through cyclic
AMP-responsive element binding protein and nuclear factor kappa B activation in rat cultured
cerebellar granule cells: Implication for its neuroprotective effect. Neuroscience.
2005;133(3):691-700.
211. LaVail MM, et al. Multiple growth factors, cytokines, and neurotrophins rescue
photoreceptors from the damaging effects of constant light. Proceedings of the National
Academy of Sciences. 1992;89(23):11249-53.
212. Yang PB, et al. Trophic factors GDNF and BDNF improve function of retinal sheet
transplants. Exp Eye Res. 2010;91(5):727-38.
217. Gaur VP, et al. RPE conditioned medium stimulates photoreceptor cell survival, neurite
outgrowth and differentiation in vitro. Experimental Eye Research. 1992;54(5):645-59.
220. Nazari H, et al. Stem cell based therapies for age-related macular degeneration: The promises
and the challenges. Prog Retin Eye Res. 2015;48:1-39.
221. Kashani AH, et al. Survival of an HLA-mismatched, bioengineered RPE implant in dry age-
related macular degeneration. Stem Cell Reports. 2022;17(3):448-58.
222. Mandai M, et al. Autologous Induced Stem-Cell-Derived Retinal Cells for Macular
Degeneration. N Engl J Med. 2017;377(8):792-3.
223. da Cruz L, et al. Phase 1 clinical study of an embryonic stem cell-derived retinal pigment
epithelium patch in age-related macular degeneration. Nat Biotechnol. 2018;36(4):328-37.

150
224. Kolomeyer AM, and Zarbin MA. Trophic factors in the pathogenesis and therapy for retinal
degenerative diseases. Surv Ophthalmol. 2014;59(2):134-65.
225. Kanu LN, and Ciolino JB. Nerve Growth Factor as an Ocular Therapy: Applications,
Challenges, and Future Directions. Semin Ophthalmol. 2021;36(4):224-31.
226. Diniz B, et al. Subretinal Implantation of Retinal Pigment Epithelial Cells Derived From
Human Embryonic Stem Cells: Improved Survival When Implanted as a Monolayer.
Investigative Ophthalmology and Visual Science. 2013;54(7):5087-96.
227. Sheedlo HJ, et al. Effects of RPE-cell factors secreted from permselective fibers on retinal
cells in vitro. Brain Research. 1992;587(2):327-37.
228. Sheedlo HJ, et al. Effects of RPE age and culture conditions on support of photoreceptor cell
survival in transplanted RCS dystrophic rats. Experimental Eye Research. 1993;57(6):753-61.
229. Sheedlo HJ, et al. Retinal pigment epithelial cell support of photoreceptor survival in vitro. In
Vitro Cellular & Developmental Biology - Animal: Journal of the Society for In Vitro Biology.
1995;31(5):330-3.
230. Sheedlo HJ, and Turner JE. Influence of a retinal pigment epithelial cell factor(s) on rat retinal
progenitor cells. Developmental Brain Research. 1996;93(1-2):88-99.
231. Sheedlo HJ, and Turner JE. Effects of retinal pigment epithelial cell-secreted factors on
neonatal rat retinal explant progenitor cells. Journal of Neuroscience Research.
1996;44(6):519-31.
232. Sheedlo HJ, et al. A transformed neonatal rat retinal pigment epithelial cell line: Secreted
protein analysis and fibroblast growth factor and receptor expression. Current Eye Research.
1997;16(2):116-26.
233. Sheedlo HJ, et al. RPE secreted proteins and antibody influence photoreceptor cell survival
and maturation. Developmental Brain Research. 1998;107(1):57-69.
243. Livak KJ, and Schmittgen TD. Analysis of relative gene expression data using real-time
quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402-8.
247. Van Lookeren Campagne M, et al. Mechanisms of age-related macular degeneration and
therapeutic opportunities. Journal of Pathology. 2014;232(2):151-64.
279. Papac-Milicevic N, et al. Malondialdehyde Epitopes as Targets of Immunity and the
Implications for Atherosclerosis. Adv Immunol. 2016;131:1-59.
299. Sardar Pasha SPB, et al. Retinal cell death dependent reactive proliferative gliosis in the
mouse retina. Scientific Reports. 2017;7(1):9517.
318. Rozing MP, et al. Age-related macular degeneration: A two-level model hypothesis. Progress
in Retinal and Eye Research. 2020(September 2018):100825-.

151
319. Rein DB, et al. Prevalence of Age-Related Macular Degeneration in the US in 2019. JAMA
Ophthalmol. 2022;140(12):1202-8.
320. Boyer DS, et al. The Pathophysiology of Geographic Atrophy Secondary to Age-Related
Macular Degeneration and the Complement Pathway as a Therapeutic Target. Retina.
2017;37(5):819-35.
321. Chandramohan A, et al. Visual Function Measures in Early and Intermediate Age-Related
Macular Degeneration. Retina. 2016;36(5):1021-31.
322. Landowski M, et al. Human complement factor H Y402H polymorphism causes an age-
related macular degeneration phenotype and lipoprotein dysregulation in mice. Proc Natl
Acad Sci U S A. 2019;116(9):3703-11.
323. Tong Y, and Wang S. Not All Stressors Are Equal: Mechanism of Stressors on RPE Cell
Degeneration. Front Cell Dev Biol. 2020;8:591067.
324. Chen L, et al. Oxidative stress differentially impacts apical and basolateral secretion of
angiogenic factors from human iPSC-derived retinal pigment epithelium cells. Scientific
Reports. 2022;12(1):12694.
325. Atienzar-Aroca S, et al. Oxidative stress in retinal pigment epithelium cells increases exosome
secretion and promotes angiogenesis in endothelial cells. J Cell Mol Med. 2016;20(8):1457-
66.
326. Sreekumar PG, et al. αB Crystallin Is Apically Secreted within Exosomes by Polarized Human
Retinal Pigment Epithelium and Provides Neuroprotection to Adjacent Cells. PLOS ONE.
2010;5(10):e12578.
327. Rabin DM, et al. Chronic oxidative stress upregulates Drusen-related protein expression in
adult human RPE stem cell-derived RPE cells: A novel culture model for dry AMD. Aging.
2012;5(1):51-66.
328. Pennington BO, et al. Defined culture of human embryonic stem cells and xeno-free
derivation of retinal pigmented epithelial cells on a novel, synthetic substrate. Stem Cells
Transl Med. 2015;4(2):165-77.
329. Hu Y, et al. A novel approach for subretinal implantation of ultrathin substrates containing
stem cell-derived retinal pigment epithelium monolayer. Ophthalmic Res. 2012;48(4):186-91.
330. Rowland TJ, et al. Differentiation of human pluripotent stem cells to retinal pigmented
epithelium in defined conditions using purified extracellular matrix proteins. J Tissue Eng
Regen Med. 2013;7(8):642-53.
331. Lu B, et al. Mesh-supported submicron parylene-C membranes for culturing retinal pigment
epithelial cells. Biomed Microdevices. 2012;14(4):659-67.

152
332. Gao F, et al. Hes1 is involved in the self-renewal and tumourigenicity of stem-like cancer cells
in colon cancer. Scientific Reports. 2014;4(1):3963.
333. Bras-Pereira C, et al. The retinal determination gene Dachshund restricts cell proliferation by
limiting the activity of the Homothorax-Yorkie complex. Development. 2015;142(8):1470-9.
334. Tetreault N, et al. The LIM homeobox transcription factor Lhx2 is required to specify the
retina field and synergistically cooperates with Pax6 for Six6 trans-activation. Dev Biol.
2009;327(2):541-50.
335. Yun S, et al. Lhx2 links the intrinsic and extrinsic factors that control optic cup formation.
Development. 2009;136(23):3895-906.
336. Lee JH, et al. Nestin expressing progenitor cells during establishment of the neural retina and
its vasculature. Anat Cell Biol. 2012;45(1):38-46.
337. Fernández-López B, et al. Spatiotemporal pattern of doublecortin expression in the retina of
the sea lamprey. Frontiers in Neuroanatomy. 2016;10(JAN):1-13.
338. Bernardos RL, et al. Late-stage neuronal progenitors in the retina are radial Muller glia that
function as retinal stem cells. J Neurosci. 2007;27(26):7028-40.
339. Lamba DA, et al. Generation, purification and transplantation of photoreceptors derived from
human induced pluripotent stem cells. PLoS One. 2010;5(1):e8763.
340. Klassen HJ, et al. Multipotent retinal progenitors express developmental markers, differentiate
into retinal neurons, and preserve light-mediated behavior. Invest Ophthalmol Vis Sci.
2004;45(11):4167-73.
341. Schmitt S, et al. Molecular characterization of human retinal progenitor cells. Invest
Ophthalmol Vis Sci. 2009;50(12):5901-8.
342. Salinas M, et al. Nerve Growth Factor Protects against 6-Hydroxydopamine-induced
Oxidative Stress by Increasing Expression of Heme Oxygenase-1 in a Phosphatidylinositol 3-
Kinase-dependent Manner*. Journal of Biological Chemistry. 2003;278(16):13898-904.
343. Hacioglu G, et al. Assessment of oxidative stress parameters of brain-derived neurotrophic
factor heterozygous mice in acute stress model. Iran J Basic Med Sci. 2016;19(4):388-93.
344. Dong S, et al. Leukemia inhibitory factor protects photoreceptor cone cells against oxidative
damage through activating JAK/STAT3 signaling. Ann Transl Med. 2021;9(2):152.
345. He X, et al. PEDF and its roles in physiological and pathological conditions: Implication in
diabetic and hypoxia-induced angiogenic diseases. Clinical Science. 2015;128(11):805-23.
346. Hollingsworth TJ, and Gross AK. Innate and Autoimmunity in the Pathogenesis of Inherited
Retinal Dystrophy. Cells. 2020;9(3):630-.

153
347. Zhu D, et al. Polarized Secretion of PEDF from Human Embryonic Stem Cell–Derived RPE
Promotes Retinal Progenitor Cell Survival. Investigative Opthalmology & Visual Science.
2011;52(3):1573-.
348. Danhong Zhu JH. Retinal Pigment Epithelial Cell Conditioned Medium Enhances the Yield
of RPE Cells Differentiated from Human Embryonic Stem Cells. Journal of Clinical &
Experimental Pathology. 2015;05(02).
349. Dutt K, et al. RPE-secreted factors: influence differentiation in human retinal cell line in dose-
and density-dependent manner. J Ocul Biol Dis Infor. 2010;3(4):144-60.
350. Kimura A, et al. Neuroprotection, Growth Factors and BDNF-TrkB Signalling in Retinal
Degeneration. Int J Mol Sci. 2016;17(9).
351. Raffaele OS, and S.p.A DF. https://ClinicalTrials.gov/show/NCT02609165; 2015.
352. GenVec. https://ClinicalTrials.gov/show/NCT00109499.
353. S.p.A DF, and Cromsource. https://ClinicalTrials.gov/show/NCT02855450; 2016.
354. Ye F, et al. Malondialdehyde induces autophagy dysfunction and VEGF secretion in the
retinal pigment epithelium in age-related macular degeneration. Free Radical Biology and
Medicine. 2016;94:121-34.
355. Binet F, et al. Neutrophil extracellular traps target senescent vasculature for tissue remodeling
in retinopathy. Science. 2020;369(6506).
356. Dong T, et al. NAP1051, a Lipoxin A4 Biomimetic Analogue, Demonstrates Antitumor
Activity Against the Tumor Microenvironment. Molecular Cancer Therapeutics.
2021;20(12):2384-97.

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4.8 SUPPLEMENTARY TABLES AND FIGURES

Supplementary Figure S4.1 Grading Score System by OCT. Grading system evaluated
morphological changes at the outer retina at the end of the study.

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Supplementary Figure S4.2 Mean ± SEM Fold change of fetal RPC genetic expression
after 24-hour incubation compared to XV1. RT-qPCR shows SF1 and SF3 induce several
transcription factor genes relating to proliferation and retinal development. Statistics
analyzed ddCT values with two-way ANOVA and Tukey’s multiple comparisons using
data from n=3 independent experiments with triplicate samples in each experiment, no
comparisons were performed for Six3, Six6, Lhx2, and Gfap which only had n=2
experiments. (*P<0.05 compared to XV1,
†
P<0.05 compared to XV3,
ǂ
P<0.05 compared
to PEDF).

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Supplementary Figure S4.3 Scotopic b-wave amplitudes of XV3 and SF3 treated and
untreated eyes were measured using flash intensities of 1-25,000 mCD. SF3 treated eyes
shows significantly improved %amplitudes at both p49 (O, n=4 each group) and p60 (P,
n=6 each group) compared to XV3 treated eyes. Data represented as mean ± SEM.
*P<0.05, **P<0.01, ***P<0.001, ****P<0.0001, unpaired t-test.

157

Supplementary Figure S4.4 Immunofluorescence staining and quantitation of 4HNE and
MDA in RCS retinas throughout retinal degeneration. Data was analyzed using two-way
ANOVA and Tukey’s multiple comparison, n=6 each group. 4HNE shows increased
staining in all retinal layers at p49 and p60 compared to p21 and increased intensity at
p60 in the OPL, ONL, DZ, and RPE layers compared to p49. MDA showed significant
increases at p49 and p60 in the ONL only. Comparisons between age with same immune
status: *P<0.05, compared with P21;
#
P<0.05, compared with P30;
◊
P<0.05, compared
with P45. DAPI (blue), MDA (red), 4HNE (yellow). Scale bar is 20 µm.

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Supplementary Table T4.1 Sigma-Aldrich Primers
Species Gene Gene ID Primer Pair
Human ACTB 60 1
Human DACH1 1602 1
Human DCX 1641 1
Human HES1 3280 1
Human PAX6 5080 1
Human SIX3 6496 1
Human SIX6 4990 1
Human SOX2 6657 1
Human MKI67 4288 1
Human NES 10763 1
Human LHX2 9355 1
Human GFAP 2670 1

Supplementary Table T4.2 ARPE-19 Human Primers
Gene Forward (5’-3’) Reverse (5’-3’)
CAT GAGGTTGAACAGATAGCCTTCG GCGGTGAGTGTCAGGATAGG
GSR CACAATAGAGGTCAGTGGGAAA AAATCCATCGCTGGTTATTCC
GPX1 GCAACCAGTTTGGGCATCAG CGTTCACCTCGCACTTCTC
GPX3 GCAGTATGCTGGCAAATACG CCAGAATGACCAGACCGAAT
SOD1 GCAGATGACTTGGGCAAAGG TGGGCGATCCCAATTACACC
SOD2 GCACTAGCAGCATGTTGAGC AGATACCCAAAACCGGAGC
SOD3 CTACTGTGTTCCTGCCTGCTC ACATGTCTCGGATCCACTCC
CYBB (NOX2) AACGAATTGTACGTGGGCAGA GAGGGTTTCCAGCAAACTGAG
NOX4 GCATGGCTGTGTCCTGGA GAGCCAGATGAACAGGCAGA
NOX5 CTATTGGACTCACCTGTCCTACC GGAAAAACAAGATTCCAGGCAC

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Supplementary Table T4.3 Antibodies
Antibody Host Dilution Catalog Manufacturer
Rhodopsin Mouse 1:400 ab3267 Abcam
GFAP Rabbit 1:500 Z0334 Dako
CD68 Mouse 1:100 mca341r Bio-Rad
PAD4 Rabbit 1:100 17373-1-AP Proteintech
CitH3 Rabbit 1:1000 ab5103 Abcam
4HNE Mouse 1:500 MAB3249 R&D Systems
MDA Rabbit 1:1000 ab27642 Abcam
Mouse Secondary Goat 1:500 A32723 Thermo Fisher Scientific
Rabbit Secondary Goat 1:500 A11037 Thermo Fisher Scientific

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CHAPTER 5: SUMMARY AND CONCLUDING REMARKS
Retinal degenerative diseases, such as age-related macular degeneration (AMD), diabetic
retinopathy (DR), and retinitis pigmentosa (RP), are leading causes of irreversible vision loss (31)
(32, 33). Despite significant research into these diseases, only three therapeutics have been
approved for any of these conditions and have limited effectiveness (104, 194-196). Retinal
degeneration results from complex combinations of dysfunctions with central themes in oxidative
stress and inflammation (47). Subsequently, conventional monotherapeutic approaches fail to
ameliorate the multiple affected pathways in retinal degeneration.
Stem cell implantation has emerged as a promising approach for directly replacing atrophic
cells and re-epithelializing dystrophic areas, as well as extending preservation beyond the borders
of the implant (220-223). The potential therapeutic benefits are partially attributed to the secretome
produced by the implanted RPE cells, which helps maintain ocular function while reducing
oxidative stress and inflammation (206-212). RPE cells are known to secrete neuroprotective
factors, but purified factors administered alone have failed in clinical trials. Polarized RPE soluble
factors (PRPE-SF) offer several advantages: 1) its biomimetic membrane aids in polarization and
maturation, modulating the secretome towards a protective phenotype (347, 348); 2) it does not
use animal serum, reducing immunogenicity risks; and 3) it combines neuroprotective factors that
address multiple pathological mechanisms of retinal degeneration. To develop novel therapeutic
strategies, pre-clinical animal models which represent clinical and molecular manifestations of the
disease are required. Although the Royal College of Surgeons (RCS) rat is a popular retinal
degeneration model, its molecular changes and relevance to human pathology need further
investigation. This dissertation was staged to contribute deeper insights into molecular

161
mechanisms of retinal degeneration, as well as explore the potential of using a protective secretome
product as a therapeutic strategy for complex degenerative conditions.
To address the gaps in knowledge in RCS characterization, we performed a comprehensive
evaluation of retinal degeneration in both immunocompetent RCS and immunodeficient (iRCS)
rats, focusing on functional and morphological characteristics and the molecular changes
associated with degeneration. The iRCS model was developed recently and is beneficial for the
development of biologic therapeutics as an athymic model to prevent immunogenicity. The RCS
rat has been extensively used as an animal model of retinal degeneration, with histology, optical
coherence tomography (OCT), and electroretinography (ERG) being employed to characterize the
model (180, 238). However, a single study that evaluates all methodologies together, a comparison
between RCS and iRCS, and molecular changes with retinal degeneration is lacking.
The current study reveals that T-cell deficiency in the iRCS model does not appear to affect
the morphological retinal degeneration characteristics or the molecular markers assessed. This
finding suggests that the adaptive immune system, specifically T-cells, may not be as important in
retinal degeneration of the autosomal recessive RCS model. Histological findings, OCT, and ERG
show a strong correlation with each other and the age of the RCS rat. Fluorescein angiography is
used to monitor the dynamics of vascular change in the diseased retina, revealing that both artery
and vein diameters are thinner at p49 than at p35, and a fluorescein leakage pattern is observed at
p60, with severity increasing gradually. Fundus autofluorescence patterns show progressive
hypoautofluorescence spreading into the majority of the fundus and numerous
hyperautofluorescent flecks appearing at p90 with a correlation between hypoautofluorescence,
vascular leakage, and subretinal debris loss. This accumulating subretinal debris further impairs

162
nutrient and debris exchange of the outer retina, including oxygen which is supplied by the choroid
and contributes to angiogenic signaling (354).
Macrophages and glial cells are recruited into the degenerating outer retina of the RCS rat.
These cells can promote neovascularization through secretion of CCL2, TNF α, IL1β, VEGF, and
MMPs, which has been seen in several animal and human retinal pathologies (95, 96). We
observed longitudinal changes in molecular markers of oxidative stress and inflammation, with
Gfap, Tnfa, and Nox1 showing the highest upregulation with RCS age. Furthermore, there is a
strong increase in malondialdehyde (MDA) and 4-hydroxynonenal (4HNE) in the outer retina
throughout retinal degeneration. Both peroxides form protein and DNA adducts that can induce
additional inflammatory responses and perpetuate chronic inflammation, as seen in retinal
degenerative diseases (247). Furthermore, MDA accumulation has been linked to RPE dysfunction
and VEGF secretion in AMD (354). Elevated ROS levels in RCS retinas corresponded to PAD4
translocation and increased photoreceptor citrullination, consistent with findings of increased
intraretinal citrullination in human AMD donor eyes (119). Subretinal debris including lipid
peroxides can serve as ligands for toll-like receptors (TLRs). Gene expression of multiple TLRs
was increased with RCS age and TLR signaling may contribute to retinal degeneration by
activating downstream signaling pathways, such as NFκB and mitogen-activated protein kinases,
which can further exacerbate inflammation and oxidative stress (357). This highlights the
importance of understanding the role of TLR signaling in retinal degeneration and its potential as
a therapeutic target for intervention strategies in the future. Overall, the results of this study
contribute significantly to the characterization of the RCS model and identify several targets for
future therapeutic development. However, this is a targeted approach to molecular dissection of

163
retinal degeneration in the RCS model and an unbiased approach could better reveal pathways and
targets for development.
To further elucidate the underlying molecular mechanisms of retinal degeneration in the
RCS rat, we preformed transcriptomic and lipidomic analyses of the model with the aim of
understanding disrupted pathways in retinal disease and identifying potential targets for future
therapeutic development. This study identified cell-type specific associations with differentially
expressed genes (DEGs), revealing that 60% of downregulated DEGs are associated with
photoreceptors, while 40% and 31% of upregulated DEGs are associated with microglia and
macroglia, respectively. Pathway enrichment analysis demonstrated that microglia are primarily
responsible for the pathogenic transcriptome changes in RCS retinal degeneration, with a focus on
inflammatory signaling pathways. In addition, downregulation of IL10, PPAR, and CLEAR
signaling pathways was observed, indicating the suppression of anti-inflammatory pathways and
cellular clearance, which is likely controlled by microglial cell activities. The RCS transcriptomic
profile showed significant similarities with RNA-Seq analysis of human AMD, supporting the
applicability of the RCS rat as an analog for human retinal pathologies.
Significant downregulation of Alox15 and upregulation of Alox5/Alox5AP gene
expression were observed and associated with microglial cells. The encoded enzymes participate
in lipid metabolism of polyunsaturated fatty acid (PUFA). The 12/15-LOX enzyme, encoded by
the Alox15 gene, is required for the synthesis of specialized pro-resolving lipid mediators (SPMs),
while 5-LOX, encoded by the Alox5 gene, also participates in the synthesis of pro-inflammatory
leukotrienes (307). The inverse expression of Alox15 and Alox5 suggests a depletion of SPMs in
the RCS rat model, resulting in a loss of the ability to resolve inflammation. We subsequently
analyzed ocular and plasma samples from the RCS for PUFA and their metabolites.

164
We found a depletion of SPMs in the RCS rat model, which can contribute to the inability
to resolve inflammation. We observed significant loss of SPMs such as resolvins (RvDs),
neuroprotectin D1 (NPD1), and maresins, as well as the accumulation of parent lipids, including
docosahexaenoic acid (DHA), docosapentaenoic acid (DPA), and eicosapentaenoic acid (EPA).
Several SPMs were found to significantly correlate with the loss of retinal structure and function,
with the most significantly changed SPMs correlating with the earliest marker of visual
dysfunction, the scotopic a-wave. These findings are supported by literature demonstrating the
protective effects of NPD1 and RvDs in retinal cell-damage models, as well as reduced serum and
vitreous LXA4 in diabetic retinopathy patients (307, 312-314). Current lipidomic studies have
focused on larger lipid classes in retinal degeneration; however, this is the first study to provide
lipidomic analysis of SPMs in the plasma of a retinal degeneration model. Our data may explain
why exogenous application of DHA does not show beneficial outcomes in retinal degeneration, as
there is significant inhibition of DHA metabolism to SPMs.
Overall, this study presents a comprehensive analysis of the transcriptomic and lipidomic
profiles associated with retinal degeneration in the RCS rat model. Inflammation emerged as a
critical factor enriched in retinal degeneration, and these pathways were strongly associated with
microglial cells. Furthermore, the pathogenic loss of Alox15 gene expression in the retina
significantly contributes to retinal pathology through the loss of SPMs. Restoring healthy levels
of SPMs could resolve pathogenic inflammation in retinal degeneration and initiate reparative
pathways; however, the applicability to more models of retinal degeneration requires further
investigation. More importantly, the loss of SPMs was correlated with the loss of retinal function,
suggesting the potential use of plasma biomarkers to guide future therapeutic development.

165
We investigated the therapeutic potential of PRPE-SF, in promoting neuronal survival and
mitigating oxidative stress and inflammation in vitro and in the RCS rat (Figure 5.1). Building on
previous findings, our study demonstrates that hESC-RPE conditioned media can promote retinal
progenitor cell proliferation while inhibiting cell death. Furthermore, PRPE-SF treatment
upregulates genes associated with progenitor cell proliferation and multipotency, including
MKI67, SOX2, and PAX6. It also promotes eye fate determinants (DACH1 and LHX2) and
neuronal migration genes (NES and DCX). These results suggest that PRPE-SF may induce
proliferation and differentiation of latent progenitor cells towards neuronal fates, which has been
suggested in other secretome analyses (217, 230, 231).
PRPE-SF demonstrated a concentration dependent effect reducing cell death and
preserving rhodopsin staining which may be attributed to several growth factors in PRPE-SF, such
as NGF, BDNF, PEDF, and LIF. These factors have known protective effects in neurodegenerative
disorders and likely contribute to the blockade of pro-apoptotic pathways (344, 345, 350).
Intravitreal (IVT) administration of PRPE-SF significantly preserved photoreceptors and scotopic
b-wave amplitudes with corresponding reduced activation of glial cells in RCS rats. Antioxidant
mechanisms of PRPE-SF were also observed, with a consistent 20% reduction in cytoplasmic
ROS, significantly increased SOD2, and marginally increased SOD3 in ARPE-19 cells. These
findings align with the in vivo reduction of ROS, as evidenced by lower levels MDA and 4HNE
in PRPE-SF-treated retinas, as well as reduced citrullination from PAD4 activity, which were
shown to increase with retinal degeneration as described previously. Müller cells may be
responsible for PAD4 expression and CitH3 in the ONL and OS due to their known expression of
PAD4 during reactive gliosis (119). While this mechanism requires further exploration, PRPE-SF

166
treatment can ameliorate PAD4 and CitH3 staining in iRCS, which parallels the reduction of 4HNE
and MDA in treated eyes and retinal preservation.
In conclusion, this study demonstrates the potential of PRPE-SF as a therapeutic approach
for retinal degenerative diseases. PRPE-SF promotes neuronal survival and mitigates oxidative
stress and inflammation in vitro and in vivo. The observed effects of PRPE-SF on progenitor cell
proliferation and differentiation, antioxidant activity, and the reduction of inflammatory markers
indicate that it targets multiple pathological mechanisms associated with retinal degeneration.
Notably, the preservation of retinal function in the RCS rat after PRPE-SF administration
highlights its potential for clinical translation. Future studies should focus on refining the
composition of PRPE-SF to enhance its therapeutic potential further, as well as optimizing the
delivery method for clinical use. It would be important to investigate the specific contributions of
the individual factors in PRPE-SF to its observed neuroprotective, antioxidant, and anti-
inflammatory properties. Additionally, examining the therapeutic effects of PRPE-SF in other
models of retinal degeneration would provide valuable insights into its applicability across a broad
range of retinal pathologies. Ultimately, a comprehensive understanding of the mechanisms
underlying the beneficial effects of PRPE-SF, could pave the way for the development of novel
and effective therapies for neurodegenerative diseases, improving the quality of life for millions
of people worldwide.

167

Figure 5.1 Graphical summary of PRPE-SF effects. Polarized retinal pigment epithelial soluble
factors (PRPE-SF) promotes cellular viability and photoreceptor preservation in vitro and
reduced photoreceptor death, improved visual function, and reduced oxidative stress, glial
reactivity, and destructive citrullination in the Royal College of Surgeons (RCS) rat model of
retinal degeneration.

5.1 REFERENCES
31. Bourne R, et al. Trends in prevalence of blindness and distance and near vision impairment
over 30 years: an analysis for the Global Burden of Disease Study. The Lancet Global Health.
2021;9(2):e130-e43.
32. Bourne RRA, et al. Causes of vision loss worldwide, 1990–2010: a systematic
analysis. The Lancet Global Health. 2013;1(6):e339-e49.
33. Velez-Montoya R, et al. Current knowledge and trends in age-related macular degeneration:
genetics, epidemiology, and prevention. Retina. 2014;34(3):423-41.
47. Zarbin MA. Current concepts in the pathogenesis of age-related macular degeneration. Arch
Ophthalmol. 2004;122(4):598-614.

168
95. Fan W, et al. Retinal microglia: Functions and diseases. Immunology. 2022;166(3):268-86.
96. Cherry JD, et al. Neuroinflammation and M2 microglia: the good, the bad, and the inflamed.
J Neuroinflammation. 2014;11:98.
104. Liao DS, et al. Pegcetacoplan treatment for geographic atrophy due to age-related macular
degeneration: a plain language summary of the FILLY study. Immunotherapy.
2022;14(13):995-1006.
119. Bonilha VL, et al. Retinal deimination and PAD2 levels in retinas from donors with age-
related macular degeneration (AMD). Exp Eye Res. 2013;111:71-8.
180. Adachi K, et al. Optical Coherence Tomography of Retinal Degeneration in Royal College of
Surgeons Rats and Its Correlation with Morphology and Electroretinography. PLOS ONE.
2016;11(9):e0162835.
194. Liao DS, et al. Complement C3 Inhibitor Pegcetacoplan for Geographic Atrophy Secondary
to Age-Related Macular Degeneration: A Randomized Phase 2 Trial. Ophthalmology.
2020;127(2):186-95.
195. Aoun M, et al. Inherited Retinal Diseases Due to RPE65 Variants: From Genetic Diagnostic
Management to Therapy. Int J Mol Sci. 2021;22(13).
196. Cideciyan AV, et al. Human retinal gene therapy for Leber congenital amaurosis shows
advancing retinal degeneration despite enduring visual improvement. Proc Natl Acad Sci U S
A. 2013;110(6):E517-25.
206. Inanc Tekin M, et al. Brain-Derived Neurotrophic Factor in Patients With Age-Related
Macular Degeneration and Its Correlation With Retinal Layer Thicknesses. Invest Ophthalmol
Vis Sci. 2018;59(7):2833-40.
207. Kolomeyer AM, et al. Characterization of conditioned media collected from cultured adult
versus fetal retinal pigment epithelial cells. Investigative Ophthalmology and Visual Science.
2011;52(8):5973-86.
208. Mysona BA, et al. Nerve growth factor in diabetic retinopathy: beyond neurons. Expert Rev
Ophthalmol. 2014;9(2):99-107.
209. Meyer JG, et al. Proteome and Secretome Dynamics of Human Retinal Pigment Epithelium
in Response to Reactive Oxygen Species. Scientific Reports. 2019;9(1):1-12.
210. Yabe T, et al. Pigment epithelium-derived factor induces pro-survival genes through cyclic
AMP-responsive element binding protein and nuclear factor kappa B activation in rat cultured
cerebellar granule cells: Implication for its neuroprotective effect. Neuroscience.
2005;133(3):691-700.

169
211. LaVail MM, et al. Multiple growth factors, cytokines, and neurotrophins rescue
photoreceptors from the damaging effects of constant light. Proceedings of the National
Academy of Sciences. 1992;89(23):11249-53.
212. Yang PB, et al. Trophic factors GDNF and BDNF improve function of retinal sheet
transplants. Exp Eye Res. 2010;91(5):727-38.
217. Gaur VP, et al. RPE conditioned medium stimulates photoreceptor cell survival, neurite
outgrowth and differentiation in vitro. Experimental Eye Research. 1992;54(5):645-59.
220. Nazari H, et al. Stem cell based therapies for age-related macular degeneration: The promises
and the challenges. Prog Retin Eye Res. 2015;48:1-39.
221. Kashani AH, et al. Survival of an HLA-mismatched, bioengineered RPE implant in dry age-
related macular degeneration. Stem Cell Reports. 2022;17(3):448-58.
222. Mandai M, et al. Autologous Induced Stem-Cell-Derived Retinal Cells for Macular
Degeneration. N Engl J Med. 2017;377(8):792-3.
223. da Cruz L, et al. Phase 1 clinical study of an embryonic stem cell-derived retinal pigment
epithelium patch in age-related macular degeneration. Nat Biotechnol. 2018;36(4):328-37.
230. Sheedlo HJ, and Turner JE. Influence of a retinal pigment epithelial cell factor(s) on rat retinal
progenitor cells. Developmental Brain Research. 1996;93(1-2):88-99.
231. Sheedlo HJ, and Turner JE. Effects of retinal pigment epithelial cell-secreted factors on
neonatal rat retinal explant progenitor cells. Journal of Neuroscience Research.
1996;44(6):519-31.
238. Ryals RC, et al. Long-term Characterization of Retinal Degeneration in Royal College of
Surgeons Rats Using Spectral-Domain Optical Coherence Tomography. Investigative
ophthalmology & visual science. 2017;58(3):1378-86.
247. Van Lookeren Campagne M, et al. Mechanisms of age-related macular degeneration and
therapeutic opportunities. Journal of Pathology. 2014;232(2):151-64.
307. Serhan CN, et al. Anti-inflammatory and proresolving lipid mediators. Annu Rev Pathol.
2008;3:279-312.
312. Tian H, et al. Resolvins E1 and D1 in Choroid-Retinal Endothelial Cells and Leukocytes:
Biosynthesis and Mechanisms of Anti-inflammatory Actions. Investigative Ophthalmology &
Visual Science. 2009;50(8):3613-20.
313. Chen JP, et al. Resolvin D2 prevents inflammation and oxidative stress in the retina of
streptozocin-induced diabetic mice. Int J Clin Exp Pathol. 2020;13(8):1986-94.
314. Wei J, and Gronert K. The role of pro-resolving lipid mediators in ocular diseases. Mol
Aspects Med. 2017;58:37-43.

170
344. Dong S, et al. Leukemia inhibitory factor protects photoreceptor cone cells against oxidative
damage through activating JAK/STAT3 signaling. Ann Transl Med. 2021;9(2):152.
345. He X, et al. PEDF and its roles in physiological and pathological conditions: Implication in
diabetic and hypoxia-induced angiogenic diseases. Clinical Science. 2015;128(11):805-23.
347. Zhu D, et al. Polarized Secretion of PEDF from Human Embryonic Stem Cell–Derived RPE
Promotes Retinal Progenitor Cell Survival. Investigative Opthalmology & Visual Science.
2011;52(3):1573-.
348. Danhong Zhu JH. Retinal Pigment Epithelial Cell Conditioned Medium Enhances the Yield
of RPE Cells Differentiated from Human Embryonic Stem Cells. Journal of Clinical &
Experimental Pathology. 2015;05(02).
350. Kimura A, et al. Neuroprotection, Growth Factors and BDNF-TrkB Signalling in Retinal
Degeneration. Int J Mol Sci. 2016;17(9).
354. Ye F, et al. Malondialdehyde induces autophagy dysfunction and VEGF secretion in the
retinal pigment epithelium in age-related macular degeneration. Free Radical Biology and
Medicine. 2016;94:121-34.
357. Mallavia B, et al. Mitochondrial DNA Stimulates TLR9-Dependent Neutrophil Extracellular
Trap Formation in Primary Graft Dysfunction. Am J Respir Cell Mol Biol. 2020;62(3):364-
72.

171
COMPLETE REFERENCES
1. Forrester JV, et al. The Eye. Elsevier; 2016:269-337.e2.
2. Hoon M, et al. Functional architecture of the retina: development and disease. Prog Retin Eye
Res. 2014;42:44-84.
3. Nathans J, et al. Molecular Genetics of Human Color Vision: The Genes Encoding Blue,
Green, and Red Pigments. Science. 1986;232(4747):193-202.
4. Gerl EJ, and Morris MR. The Causes and Consequences of Color Vision. Evolution:
Education and Outreach. 2008;1(4):476-86.
5. Thoen HH, et al. A Different Form of Color Vision in Mantis Shrimp. Science.
2014;343(6169):411-3.
6. Armbrust KR, and Nussenblatt RB. Blood–Retinal Barrier, Immune Privilege, and
Autoimmunity. Elsevier Inc.; 2017.
7. Vecino E, et al. Glia-neuron interactions in the mammalian retina. Progress in Retinal and
Eye Research. 2016;51:1-40.
8. Hiscott PS, et al. Retinal and epiretinal glia - An immunohistochemical study. British Journal
of Ophthalmology. 1984;68(10):698-707.
9. Tao C, and Zhang X. Development of astrocytes in the vertebrate eye. Dev Dyn.
2014;243(12):1501-10.
10. Fernandes A, et al. Microglia and inflammation: conspiracy, controversy or control? Cellular
and Molecular Life Sciences. 2014;71(20):3969-85.
11. Silverman SM, and Wong WT. Microglia in the Retina: Roles in Development, Maturity, and
Disease. Annu Rev Vis Sci. 2018;4:45-77.
12. Yang S, et al. Functions and Diseases of the Retinal Pigment Epithelium. Front Pharmacol.
2021;12:727870.
13. Kim J, et al. Molecular Mechanisms of Retinal Pigment Epithelium Dysfunction in Age-
Related Macular Degeneration. Int J Mol Sci. 2021;22(22).
14. Sharma R, et al. Retinal Pigment Epithelium Replacement Therapy for Age-Related Macular
Degeneration: Are We There Yet? Annu Rev Pharmacol Toxicol. 2020;60:553-72.
15. Ishida K, et al. Expression of neurotrophic factors in cultured human retinal pigment epithelial
cells. Current Eye Research. 1997;16(2):96-101.
16. Shirley Ding SL, et al. Revisiting the role of erythropoietin for treatment of ocular disorders.
Eye. 2016;30(10):1293-309.

172
17. Chen T-C, et al. Genetic characteristics and epidemiology of inherited retinal degeneration in
Taiwan. npj Genomic Medicine. 2021;6(1):16.
18. Prevention CfDCa. Vision Health Initiative: Common Eye Disorders.
https://www.cdc.gov/visionhealth/basics/ced/index.html. Accessed February 25, 2023.
19. Ruan Y, et al. Age-Related Macular Degeneration: Role of Oxidative Stress and Blood
Vessels. Int J Mol Sci. 2021;22(3).
20. Duh EJ, et al. Diabetic retinopathy: current understanding, mechanisms, and treatment
strategies. JCI Insight. 2017;2(14).
21. Tanna P, et al. Stargardt disease: clinical features, molecular genetics, animal models and
therapeutic options. Br J Ophthalmol. 2017;101(1):25-30.
22. Singh Grewal S, et al. Bestrophinopathies: perspectives on clinical disease, Bestrophin-1
function and developing therapies. Ther Adv Ophthalmol. 2021;13:2515841421997191.
23. Marmorstein AD, et al. Bestrophin, the product of the Best vitelliform macular dystrophy gene
(VMD2), localizes to the basolateral plasma membrane of the retinal pigment
epithelium. Proceedings of the National Academy of Sciences. 2000;97(23):12758-63.
24. . In: Adam MP, Mirzaa GM, Pagon RA, Wallace SE, Bean LJH, Gripp KW, et al. eds.
GeneReviews(®). Seattle (WA): University of Washington, Seattle
Copyright © 1993-2023, University of Washington, Seattle. GeneReviews is a registered
trademark of the University of Washington, Seattle. All rights reserved.; 1993.
25. Khan NW, et al. CNGB3 Achromatopsia with Progressive Loss of Residual Cone Function
and Impaired Rod-Mediated Function. Investigative Ophthalmology & Visual Science.
2007;48(8):3864-71.
26. Carvalho LS, and Vandenberghe LH. Cham: Springer International Publishing; 2016:231-6.
27. Georgiou M, et al. Inherited retinal diseases: Therapeutics, clinical trials and end points-A
review. Clin Exp Ophthalmol. 2021;49(3):270-88.
28. Bringmann A, and Wiedemann P. Muller glial cells in retinal disease. Ophthalmologica.
2012;227(1):1-19.
29. Zeng H, et al. Microglial NADPH oxidase activation mediates rod cell death in the retinal
degeneration in rd mice. Neuroscience. 2014;275:54-61.
30. Jung Y-JJ, and Chung W-SS. Phagocytic Roles of Glial Cells in Healthy and Diseased Brains.
Biomol Ther. 2018;26(4):8-.

173
31. Bourne R, et al. Trends in prevalence of blindness and distance and near vision impairment
over 30 years: an analysis for the Global Burden of Disease Study. The Lancet Global Health.
2021;9(2):e130-e43.
32. Bourne RRA, et al. Causes of vision loss worldwide, 1990–2010: a systematic
analysis. The Lancet Global Health. 2013;1(6):e339-e49.
33. Velez-Montoya R, et al. Current knowledge and trends in age-related macular degeneration:
genetics, epidemiology, and prevention. Retina. 2014;34(3):423-41.
34. Wong WL, et al. Global prevalence of age-related macular degeneration and disease burden
projection for 2020 and 2040: A systematic review and meta-analysis. The Lancet Global
Health. 2014;2(2):e106-16.
35. Lee R, et al. Epidemiology of diabetic retinopathy, diabetic macular edema and related vision
loss. Eye Vis (Lond). 2015;2:17.
36. Zhang X, et al. Prevalence of diabetic retinopathy in the United States, 2005-2008. Jama.
2010;304(6):649-56.
37. Brown MM, et al. Age-related macular degeneration: economic burden and value-based
medicine analysis. Canadian Journal of Ophthalmology. 2005;40(3):277-87.
38. Rein DB, et al. Forecasting age-related macular degeneration through the year 2050: the
potential impact of new treatments. Arch Ophthalmol. 2009;127(4):533-40.
39. Heesterbeek TJ, et al. Risk factors for progression of age-related macular degeneration.
Ophthalmic Physiol Opt. 2020;40(2):140-70.
40. Magliah SF, et al. The prevalence and risk factors of diabetic retinopathy in selected primary
care centers during the 3-year screening intervals. J Family Med Prim Care. 2018;7(5):975-
81.
41. Ting DSW, et al. Diabetic retinopathy: global prevalence, major risk factors, screening
practices and public health challenges: a review. Clinical & Experimental Ophthalmology.
2016;44(4):260-77.
42. DeAngelis MM, et al. Genetics of age-related macular degeneration (AMD). Hum Mol Genet.
2017;26(R2):R246.
43. Toomey CB, et al. Complement factor H in AMD: Bridging genetic associations and
pathobiology. Progress in Retinal and Eye Research. 2018;62(February):38-57.
44. Micklisch S, et al. Age-related macular degeneration associated polymorphism rs10490924 in
ARMS2 results in deficiency of a complement activator. Journal of Neuroinflammation.
2017;14(1):4.

174
45. Mohamad NA, et al. Association of HTRA1 and ARMS2 gene polymorphisms with response
to intravitreal ranibizumab among neovascular age-related macular degenerative subjects.
Human Genomics. 2019;13(1):13.
46. Fritsche LG, et al. A large genome-wide association study of age-related macular degeneration
highlights contributions of rare and common variants. Nature Genetics. 2016;48(2):134-43.
47. Zarbin MA. Current concepts in the pathogenesis of age-related macular degeneration. Arch
Ophthalmol. 2004;122(4):598-614.
48. Nita M, et al. Age-related macular degeneration in the aspect of chronic low-grade
inflammation (pathophysiological parainflammation). Mediators Inflamm.
2014;2014:930671.
49. Donoso LA, et al. The role of inflammation in the pathogenesis of age-related macular
degeneration. Surv Ophthalmol. 2006;51(2):137-52.
50. Wolter JR, and Falls HF. Bilateral confluent drusen. Arch Ophthalmol. 1962;68:219-26.
51. Curcio CA, et al. Accumulation of cholesterol with age in human Bruch's membrane. Invest
Ophthalmol Vis Sci. 2001;42(1):265-74.
52. Crabb JW, et al. Drusen proteome analysis: an approach to the etiology of age-related macular
degeneration. Proc Natl Acad Sci U S A. 2002;99(23):14682-7.
53. Janeway CA Jr TP, Walport M, et al. In: Science G ed. Immunobiology: The Immune System
in Health and Disease 5th edition. New York; 2001.
54. Armstrong RA, and Mousavi M. Overview of risk factors for age-related macular
degeneration (AMD). Journal of Stem Cells. 2015;10(3):171-91.
55. Barkana Y, and Belkin M. Neuroprotection in ophthalmology: A review. Brain Research
Bulletin. 2004;62(6):447-53.
56. Bressler NM, et al. Age-related macular degeneration. Survey of Ophthalmology.
1988;32(6):375-413.
57. Kauppinen A, et al. Inflammation and its role in age-related macular degeneration. Cellular
and Molecular Life Sciences. 2016;73(9):1765-86.
58. Antonetti DA. The neuroscience of diabetic retinopathy. Vis Neurosci. 2021;38:E001.
59. Brownlee M. The pathobiology of diabetic complications: a unifying mechanism. Diabetes.
2005;54(6):1615-25.
60. Giacco F, and Brownlee M. Oxidative stress and diabetic complications. Circ Res.
2010;107(9):1058-70.

175
61. Bierhaus A, et al. Understanding RAGE, the receptor for advanced glycation end products. J
Mol Med (Berl). 2005;83(11):876-86.
62. Wu H, et al. Targeting Oxidative Stress in Diabetic Complications: New Insights. J Diabetes
Res. 2018;2018:1909675.
63. Kowluru RA, and Mishra M. Oxidative stress, mitochondrial damage and diabetic retinopathy.
Biochim Biophys Acta. 2015;1852(11):2474-83.
64. Simo R, et al. Neurodegeneration in diabetic retinopathy: does it really matter? Diabetologia.
2018;61(9):1902-12.
65. Gal A, et al. Mutations in MERTK, the human orthologue of the RCS rat retinal dystrophy
gene, cause retinitis pigmentosa. Nature Genetics. 2000;26(3):270-1.
66. Nandrot EF, et al. Loss of synchronized retinal phagocytosis and age-related blindness in mice
lacking alphavbeta5 integrin. J Exp Med. 2004;200(12):1539-45.
67. Strauss O. The retinal pigment epithelium in visual function. Physiol Rev. 2005;85(3):845-81.
68. Almedawar S, et al. MERTK-Dependent Ensheathment of Photoreceptor Outer Segments by
Human Pluripotent Stem Cell-Derived Retinal Pigment Epithelium. Stem Cell Reports.
2020;14(3):374-89.
69. Burstyn-Cohen T, et al. Genetic dissection of TAM receptor-ligand interaction in retinal
pigment epithelial cell phagocytosis. Neuron. 2012;76(6):1123-32.
70. D'Cruz PM, et al. Mutation of the receptor tyrosine kinase gene Mertk in the retinal dystrophic
RCS rat. Hum Mol Genet. 2000;9(4):645-51.
71. Knubel KH, et al. MerTK inhibition is a novel therapeutic approach for glioblastoma
multiforme. Oncotarget. 2014;5(5):1338-51.
72. Beatty S, et al. The role of oxidative stress in the pathogenesis of age-related macular
degeneration. Surv Ophthalmol. 2000;45(2):115-34.
73. Jarrett SG, and Boulton ME. Consequences of oxidative stress in age-related macular
degeneration. Mol Aspects Med. 2012;33(4):399-417.
74. Yu DY, and Cringle SJ. Retinal degeneration and local oxygen metabolism. Exp Eye Res.
2005;80(6):745-51.
75. Simo R, and Hernandez C. Advances in the medical treatment of diabetic retinopathy.
Diabetes Care. 2009;32(8):1556-62.
76. Komeima K, et al. Antioxidants reduce cone cell death in a model of retinitis pigmentosa.
Proc Natl Acad Sci U S A. 2006;103(30):11300-5.

176
77. Campochiaro PA. Molecular pathogenesis of retinal and choroidal vascular diseases. Prog
Retin Eye Res. 2015;49:67-81.
78. Suh JH, et al. Decline in transcriptional activity of Nrf2 causes age-related loss of glutathione
synthesis, which is reversible with lipoic acid. Proceedings of the National Academy of
Sciences of the United States of America. 2004;101(10):3381-6.
79. Qin L, et al. Patients with early age-related macular degeneration exhibit signs of macro- and
micro-vascular disease and abnormal blood glutathione levels. Graefe's Archive for Clinical
and Experimental Ophthalmology. 2014;252(1):23-30.
80. Bellezza I. Oxidative stress in age-related macular degeneration: NRF2 as therapeutic target.
Frontiers in Pharmacology. 2018;9(NOV):1-7.
81. Anderson DH, et al. The pivotal role of the complement system in aging and age-related
macular degeneration: hypothesis re-visited. Prog Retin Eye Res. 2010;29(2):95-112.
82. Zhang H, et al. Oxidative stress response and Nrf2 signaling in aging. Free Radical Biology
and Medicine. 2015;88(Part B):314-36.
83. Hollyfield JG, et al. Oxidative damage-induced inflammation initiates age-related macular
degeneration. Nat Med. 2008;14(2):194-8.
84. Wang AL, et al. Autophagy and exosomes in the aged retinal pigment epithelium: possible
relevance to drusen formation and age-related macular degeneration. PLoS One.
2009;4(1):e4160.
85. Kaarniranta K, et al. Autophagy and heterophagy dysregulation leads to retinal pigment
epithelium dysfunction and development of age-related macular degeneration. Autophagy.
2013;9(7):973-84.
86. Frost LS, et al. Autophagy in the eye: implications for ocular cell health. Exp Eye Res.
2014;124:56-66.
87. Krohne TU, et al. Effects of lipid peroxidation products on lipofuscinogenesis and autophagy
in human retinal pigment epithelial cells. Exp Eye Res. 2010;90(3):465-71.
88. Wang H, et al. TNF-alpha mediates choroidal neovascularization by upregulating VEGF
expression in RPE through ROS-dependent beta-catenin activation. Mol Vis. 2016;22:116-28.
89. Klaassen I, et al. Molecular basis of the inner blood-retinal barrier and its breakdown in
diabetic macular edema and other pathological conditions. Prog Retin Eye Res. 2013;34:19-
48.
90. Xu XF, et al. Epigenetic regulation of the endothelial nitric oxide synthase gene in persistent
pulmonary hypertension of the newborn rat. J Hypertens. 2010;28(11):2227-35.

177
91. Moore SM, and Christoforidis JB. Advances in Ophthalmic Epigenetics and Implications for
Epigenetic Therapies: A Review. Genes (Basel). 2023;14(2).
92. Messerschmidt L, et al. Osmotic induction of cyclooxygenase-2 in RPE cells: Stimulation of
inflammasome activation. Mol Vis. 2019;25:329-44.
93. Karlstetter M, et al. Microglia in the healthy and degenerating retina: insights from novel
mouse models. Immunobiology. 2010;215(9-10):685-91.
94. Jin N, et al. Shaping the Microglia in Retinal Degenerative Diseases Using Stem Cell Therapy:
Practice and Prospects. Front Cell Dev Biol. 2021;9:741368.
95. Fan W, et al. Retinal microglia: Functions and diseases. Immunology. 2022;166(3):268-86.
96. Cherry JD, et al. Neuroinflammation and M2 microglia: the good, the bad, and the inflamed.
J Neuroinflammation. 2014;11:98.
97. Curcio CA, et al. The oil spill in ageing Bruch membrane. Br J Ophthalmol.
2011;95(12):1638-45.
98. Tarallo V, et al. DICER1 loss and Alu RNA induce age-related macular degeneration via the
NLRP3 inflammasome and MyD88. Cell. 2012;149(4):847-59.
99. Zhao L, et al. Microglial phagocytosis of living photoreceptors contributes to inherited retinal
degeneration. EMBO Mol Med. 2015;7(9):1179-97.
100. Barliya T, et al. Possible involvement of NETosis in inflammatory processes in the eye:
Evidence from a small cohort of patients. Mol Vis. 2017;23:922-32.
101. Krogh Nielsen M, et al. Altered activation state of circulating neutrophils in patients with
neovascular age-related macular degeneration. Immun Ageing. 2017;14:18.
102. Zhou J, et al. Neutrophils promote experimental choroidal neovascularization. Molecular
Vision. 2005;11(June):414-24.
103. Hinton DR, et al. Neutrophils compromise retinal pigment epithelial barrier integrity. Journal
of Biomedicine and Biotechnology. 2010;2010.
104. Liao DS, et al. Pegcetacoplan treatment for geographic atrophy due to age-related macular
degeneration: a plain language summary of the FILLY study. Immunotherapy.
2022;14(13):995-1006.
105. Sivaprasad S, and Chong NV. The complement system and age-related macular degeneration.
Eye (Lond). 2006;20(8):867-72.
106. Shahulhameed S, et al. A Systematic Investigation on Complement Pathway Activation in
Diabetic Retinopathy. Front Immunol. 2020;11:154.

178
107. Johnson LV, et al. Complement activation and inflammatory processes in Drusen formation
and age related macular degeneration. Exp Eye Res. 2001;73(6):887-96.
108. Hageman GS, et al. A common haplotype in the complement regulatory gene factor H
(HF1/CFH) predisposes individuals to age-related macular degeneration. Proc Natl Acad Sci
U S A. 2005;102(20):7227-32.
109. Ambati J, et al. Immunology of age-related macular degeneration. Nat Rev Immunol.
2013;13(6):438-51.
110. Koleva-Georgieva DN, et al. Serum inflammatory cytokines IL-1beta, IL-6, TNF-alpha and
VEGF have influence on the development of diabetic retinopathy. Folia Med (Plovdiv).
2011;53(2):44-50.
111. Donnelly R, et al. Protein kinase C inhibition and diabetic retinopathy: a shot in the dark at
translational research. Br J Ophthalmol. 2004;88(1):145-51.
112. Garcia-Ramirez M, et al. Interphotoreceptor retinoid-binding protein (IRBP) is downregulated
at early stages of diabetic retinopathy. Diabetologia. 2009;52(12):2633-41.
113. Walport MJ. Complement. First of two parts. N Engl J Med. 2001;344(14):1058-66.
114. Bhutto I, and Lutty G. Understanding age-related macular degeneration (AMD): relationships
between the photoreceptor/retinal pigment epithelium/Bruch's membrane/choriocapillaris
complex. Mol Aspects Med. 2012;33(4):295-317.
115. Walport MJ. Complement. Second of two parts. N Engl J Med. 2001;344(15):1140-4.
116. Rohrbach A, et al. Activation of PAD4 in NET formation. Frontiers in Immunology. 2012;3.
117. Neeli I, et al. Histone deimination as a response to inflammatory stimuli in neutrophils. J
Immunol. 2008;180(3):1895-902.
118. Bhattacharya SK, et al. Age-related reduction in retinal deimination levels in the F344BN rat.
Aging Cell. 2008;7(3):441-4.
119. Bonilha VL, et al. Retinal deimination and PAD2 levels in retinas from donors with age-
related macular degeneration (AMD). Exp Eye Res. 2013;111:71-8.
120. Iannaccone A, and Radic MZ. Increased Protein Citrullination as a Trigger for Resident
Immune System Activation, Intraretinal Inflammation, and Promotion of Anti-retinal
Autoimmunity: Intersecting Paths in Retinal Degenerations of Potential Therapeutic
Relevance. Adv Exp Med Biol. 2019;1185:175-9.
121. Acharya NK, et al. Neuronal PAD4 expression and protein citrullination: Possible role in
production of autoantibodies associated with neurodegenerative disease. Journal of
Autoimmunity. 2012;38(4):369-80.

179
122. Palko SI, et al. Peptidyl arginine deiminase 4 deficiency protects against subretinal fibrosis by
inhibiting Müller glial hypercitrullination. Journal of Neuroscience Research.
2023;101(4):464-79.
123. Wizeman JW, and Mohan R. Expression of peptidylarginine deiminase 4 in an alkali injury
model of retinal gliosis. Biochem Biophys Res Commun. 2017;487(1):134-9.
124. Palko SI, et al. Compartmentalized citrullination in Muller glial endfeet during retinal
degeneration. Proc Natl Acad Sci U S A. 2022;119(9).
125. Anderson DH, et al. A role for local inflammation in the formation of drusen in the aging eye.
Am J Ophthalmol. 2002;134(3):411-31.
126. Curcio CA, et al. Apolipoprotein B-containing lipoproteins in retinal aging and age-related
macular degeneration. J Lipid Res. 2010;51(3):451-67.
127. Fahy E, et al. Update of the LIPID MAPS comprehensive classification system for lipids. J
Lipid Res. 2009;50 Suppl(Suppl):S9-14.
128. Sparrow JR, et al. The bisretinoids of retinal pigment epithelium. Prog Retin Eye Res.
2012;31(2):121-35.
129. SanGiovanni JP, and Chew EY. The role of omega-3 long-chain polyunsaturated fatty acids
in health and disease of the retina. Prog Retin Eye Res. 2005;24(1):87-138.
130. Rotstein NP, et al. Protective effect of docosahexaenoic acid on oxidative stress-induced
apoptosis of retina photoreceptors. Invest Ophthalmol Vis Sci. 2003;44(5):2252-9.
131. Bettadahalli S, et al. Evidence on n-3 Fatty Acids and Oleic Acid Role in Retinal Inflammation
and Microvascular Integrity: Insight from a Hyperlipidemic Rat Model. Inflammation.
2020;43(3):868-77.
132. Terao R, and Kaneko H. Lipid Signaling in Ocular Neovascularization. International Journal
of Molecular Sciences. 2020;21(13):4758.
133. Mandal NA, et al. Role of Sphingosine 1-phosphate (S1P) in retinal structure and function-
lessons learned from mouse models. Investigative Ophthalmology & Visual Science.
2020;61(7):1124-.
134. Porter H, et al. Characterizing Sphingosine Kinases and Sphingosine 1-Phosphate Receptors
in the Mammalian Eye and Retina. Int J Mol Sci. 2018;19(12).
135. Skoura A, and Hla T. Regulation of vascular physiology and pathology by the S1P2 receptor
subtype. Cardiovasc Res. 2009;82(2):221-8.
136. Jun S, et al. The impact of lipids, lipid oxidation, and inflammation on AMD, and the potential
role of miRNAs on lipid metabolism in the RPE. Exp Eye Res. 2019;181:346-55.

180
137. Rodriguez IR, et al. 7-ketocholesterol accumulates in ocular tissues as a consequence of aging
and is present in high levels in drusen. Exp Eye Res. 2014;128:151-5.
138. Chen H, et al. Ceramide signaling in retinal degeneration. Adv Exp Med Biol. 2012;723:553-
8.
139. Rotstein NP, et al. Regulating survival and development in the retina: key roles for simple
sphingolipids. J Lipid Res. 2010;51(6):1247-62.
140. Serhan CN, and Levy BD. Resolvins in inflammation: emergence of the pro-resolving
superfamily of mediators. J Clin Invest. 2018;128(7):2657-69.
141. Serhan CN. Pro-resolving lipid mediators are leads for resolution physiology. Nature.
2014;510(7503):92-101.
142. Calandria JM, et al. Selective Survival Rescue in 15-Lipoxygenase-1-deficient Retinal
Pigment Epithelial Cells by the Novel Docosahexaenoic Acid-derived Mediator,
Neuroprotectin D1 *. Journal of Biological Chemistry. 2009;284(26):17877-82.
143. Xie T, et al. LXA4 protects against blue-light induced retinal degeneration in human A2E-
laden RPE cells and Balb-c mice. Ann Transl Med. 2021;9(15):1249.
144. Lu Z, et al. Lipoxin A4 delays the progression of retinal degeneration via the inhibition of
microglial overactivation. Biochem Biophys Res Commun. 2019;516(3):900-6.
145. Settimio R, et al. Resolvin D1 reduces the immunoinflammatory response of the rat eye
following uveitis. Mediators Inflamm. 2012;2012:318621.
146. Connor KM, et al. Increased dietary intake of ω-3-polyunsaturated fatty acids reduces
pathological retinal angiogenesis. Nature Medicine. 2007;13(7):868-73.
147. Yin Y, et al. Resolvin D1 inhibits inflammatory response in STZ-induced diabetic retinopathy
rats: Possible involvement of NLRP3 inflammasome and NF-κB signaling pathway. Mol Vis.
2017;23:242-50.
148. Saito-Sasaki N, et al. Maresin-1 and Inflammatory Disease. Int J Mol Sci. 2022;23(3).
149. Thumann G, et al. In: Ryan SJ, Hinton DR, Schachat AP, and Wilkinson CP eds. Retina
(Fourth Edition). Edinburgh: Mosby; 2006:137-52.
150. Mao Y, and Finnemann SC. Acute RhoA/Rho Kinase Inhibition Is Sufficient to Restore
Phagocytic Capacity to Retinal Pigment Epithelium Lacking the Engulfment Receptor
MerTK. Cells. 2021;10(8):1927.
151. Kashani AH, et al. One-Year Follow-Up in a Phase 1/2a Clinical Trial of an Allogeneic RPE
Cell Bioengineered Implant for Advanced Dry Age-Related Macular Degeneration.
Translational Vision Science & Technology. 2021;10(10):13-.

181
152. Kashani AH, et al. A bioengineered retinal pigment epithelial monolayer for advanced, dry
age-related macular degeneration. Science Translational Medicine. 2018;10(435):1-11.
153. Thomas BB, et al. A new immunodeficient retinal dystrophic rat model for transplantation
studies using human-derived cells. Graefe's Archive for Clinical and Experimental
Ophthalmology. 2018:2113-25.
154. Shao L, et al. Thickness of retinal pigment epithelium–Bruch’s membrane complex in adult
Chinese using optical coherence tomography. Eye. 2023;37(1):155-9.
155. Karampelas M, et al. Evaluation of retinal pigment epithelium-Bruch's membrane complex
thickness in dry age-related macular degeneration using optical coherence tomography. Br J
Ophthalmol. 2013;97(10):1256-61.
156. Schaal S, et al. Human Bruch's Membrane Proteome Alteration in Diabetic Retinopathy May
Reflect Cellular Pathologies in the Outer Retina. Investigative Ophthalmology & Visual
Science. 2010;51(13):5621-.
157. Hewitt AT, and Newsome DA. Altered synthesis of Bruch's membrane proteoglycans
associated with dominant retinitis pigmentosa. Curr Eye Res. 1985;4(3):169-74.
158. Fan S, et al. Extensive Sub-RPE Complement Deposition in a Nonhuman Primate Model of
Early-Stage Diabetic Retinopathy. Invest Ophthalmol Vis Sci. 2021;62(3):30.
159. Rabiolo A, et al. Spotlight on reticular pseudodrusen. Clin Ophthalmol. 2017;11:1707-18.
160. Monge M, et al. Subretinal drusenoid deposits: An update. Taiwan J Ophthalmol.
2022;12(2):138-46.
161. Otani T, and Kishi S. Tomographic findings of foveal hard exudates in diabetic macular
edema. Am J Ophthalmol. 2001;131(1):50-4.
162. Sun LW, et al. Assessing Photoreceptor Structure in Retinitis Pigmentosa and Usher
Syndrome. Investigative Ophthalmology & Visual Science. 2016;57(6):2428-42.
163. Lorach H, et al. Long-term Rescue of Photoreceptors in a Rodent Model of Retinitis
Pigmentosa Associated with MERTK Mutation. Sci Rep. 2018;8(1):11312.
164. Silverman SM, et al. C3- and CR3-dependent microglial clearance protects photoreceptors in
retinitis pigmentosa. J Exp Med. 2019;216(8):1925-43.
165. Armento A, et al. The complement system in age-related macular degeneration. Cell Mol Life
Sci. 2021;78(10):4487-505.
166. He J, et al. Disease-associated microglial activation prevents photoreceptor degeneration by
suppressing the accumulation of cell debris and neutrophils in degenerating rat retinas.
Theranostics. 2022;12(6):2687-706.

182
167. Wang SK, and Cepko CL. Targeting Microglia to Treat Degenerative Eye Diseases. Front
Immunol. 2022;13:843558.
168. Oh JK, et al. Differences in Intraretinal Pigment Migration Across Inherited Retinal
Dystrophies. Am J Ophthalmol. 2020;217:252-60.
169. Ho J, et al. Documentation of intraretinal retinal pigment epithelium migration via high-speed
ultrahigh-resolution optical coherence tomography. Ophthalmology. 2011;118(4):687-93.
170. Su C-C, et al. Lutein Inhibits the Migration of Retinal Pigment Epithelial Cells via Cytosolic
and Mitochondrial Akt Pathways (Lutein Inhibits RPE Cells Migration). International Journal
of Molecular Sciences. 2014;15(8):13755-67.
171. Villegas-Pérez MP, et al. Ganglion cell loss in RCS rat retina: A result of compression of
axons by contracting intraretinal vessels linked to the pigment epithelium. Journal of
Comparative Neurology. 1998;392(1):58-77.
172. Roque RS, and Caldwell RB. Pigment epithelial cell changes precede vascular
transformations in the dystrophic rat retina. Exp Eye Res. 1991;53(6):787-98.
173. Bubis E, et al. Blue Autofluorescence Fundus Imaging for Monitoring Retinal Degeneration
in Royal College of Surgeons Rats. Transl Vis Sci Technol. 2019;8(1):26.
174. Shen Y, et al. Fundus autofluorescence characteristics in patients with diabetic macular
edema. Chin Med J (Engl). 2014;127(8):1423-8.
175. Ly A, et al. Fundus Autofluorescence in Age-related Macular Degeneration. Optom Vis Sci.
2017;94(2):246-59.
176. Dysli C, et al. Fundus Autofluorescence Lifetime Patterns in Retinitis Pigmentosa.
Investigative Ophthalmology & Visual Science. 2018;59(5):1769-78.
177. McAnany JJ, et al. Clinical electroretinography in diabetic retinopathy: a review. Surv
Ophthalmol. 2022;67(3):712-22.
178. Fatih Cakir G, et al. In: Gregor B ed. Electroretinograms. Rijeka: IntechOpen; 2011:Ch. 6.
179. Forshaw TRJ, et al. Full-field electroretinography in age-related macular degeneration: an
overall retinal response. Acta Ophthalmologica. 2021;99(2):e253-e9.
180. Adachi K, et al. Optical Coherence Tomography of Retinal Degeneration in Royal College of
Surgeons Rats and Its Correlation with Morphology and Electroretinography. PLOS ONE.
2016;11(9):e0162835.
181. Natarajan S. Retinitis pigmentosa: a brief overview. Indian J Ophthalmol. 2011;59(5):343-6.
182. Yang S, et al. The cells involved in the pathological process of diabetic retinopathy.
Biomedicine & Pharmacotherapy. 2020;132:110818.

183
183. Hanus J, et al. RPE necroptosis in response to oxidative stress and in AMD. Ageing Res Rev.
2015;24(Pt B):286-98.
184. Camacho P, et al. Ganglion Cell Complex in Early and Intermediate Age-Related Macular
Degeneration: Evidence by SD-OCT Manual Segmentation. Ophthalmologica. 2017;238(1-
2):31-43.
185. Catalani E, and Cervia D. Diabetic retinopathy: a matter of retinal ganglion cell homeostasis.
Neural Regen Res. 2020;15(7):1253-4.
186. Jirarattanasopa P, et al. Choroidal Thickness, Vascular Hyperpermeability, and Complement
Factor H in Age-Related Macular Degeneration and Polypoidal Choroidal Vasculopathy.
Investigative Ophthalmology & Visual Science. 2012;53(7):3663-72.
187. Iovino C, et al. Evaluation of the Choroid in Eyes With Retinitis Pigmentosa and Cystoid
Macular Edema. Investigative Ophthalmology & Visual Science. 2019;60(15):5000-6.
188. Tan B, et al. Longitudinal Structural and Microvascular Observation in RCS Rat Eyes Using
Optical Coherence Tomography Angiography. Investigative Ophthalmology & Visual
Science. 2020;61(6):54-.
189. Adams AJ, and Bearse MA, Jr. Retinal neuropathy precedes vasculopathy in diabetes: a
function-based opportunity for early treatment intervention? Clin Exp Optom.
2012;95(3):256-65.
190. Fogle JA, et al. Retinitis Pigmentosa and Exudative Vasculopathy. Archives of
Ophthalmology. 1978;96(4):696-702.
191. Uliss AE, et al. Retinitis Pigmentosa and Retinal Neovascularization. Ophthalmology.
1986;93(12):1599-603.
192. Karampelas M, et al. Evaluation of retinal pigment epithelium–Bruch's membrane complex
thickness in dry age-related macular degeneration using optical coherence tomography.
British Journal of Ophthalmology. 2013;97(10):1256-61.
193. He J, et al. Microglia Mediate Synaptic Material Clearance at the Early Stage of Rats With
Retinitis Pigmentosa. Frontiers in Immunology. 2019;10.
194. Liao DS, et al. Complement C3 Inhibitor Pegcetacoplan for Geographic Atrophy Secondary
to Age-Related Macular Degeneration: A Randomized Phase 2 Trial. Ophthalmology.
2020;127(2):186-95.
195. Aoun M, et al. Inherited Retinal Diseases Due to RPE65 Variants: From Genetic Diagnostic
Management to Therapy. Int J Mol Sci. 2021;22(13).
196. Cideciyan AV, et al. Human retinal gene therapy for Leber congenital amaurosis shows
advancing retinal degeneration despite enduring visual improvement. Proc Natl Acad Sci U S
A. 2013;110(6):E517-25.

184
197. Aronow M, and Chew E. AREDS2: Perspectives, Recommendations, and Unanswered
Questions. 2015;25(3):186-90.
198. Chew EY, et al. Summary results and recommendations from the age-related eye disease
study. Archives of Ophthalmology. 2009;127(12):1678-9.
199. Meleth AD, et al. Treatment for atrophic macular degeneration. Current Opinion in
Ophthalmology. 2011;22(3):190-3.
200. Vavvas DG, et al. Regression of Some High-risk Features of Age-related Macular
Degeneration (AMD) in Patients Receiving Intensive Statin Treatment. EBioMedicine.
2016;5:198-203.
201. Barzilai N, et al. Metformin as a Tool to Target Aging. Cell Metab. 2016;23(6):1060-5.
202. Fang J, et al. Metformin alleviates human cellular aging by upregulating the endoplasmic
reticulum glutathione peroxidase 7. Aging Cell. 2018;17(4):e12765.
203. Baradaran-Rafii A, et al. Cell-based approaches towards treating age-related macular
degeneration. Cell Tissue Bank. 2020.
204. Rubner R, et al. Progress of clinical therapies for dry age-related macular degeneration. Int J
Ophthalmol. 2022;15(1):157-66.
205. Querques G, et al. Laser photocoagulation as treatment of non-exudative age-related macular
degeneration: state-of-the-art and future perspectives. Graefes Arch Clin Exp Ophthalmol.
2018;256(1):1-9.
206. Inanc Tekin M, et al. Brain-Derived Neurotrophic Factor in Patients With Age-Related
Macular Degeneration and Its Correlation With Retinal Layer Thicknesses. Invest Ophthalmol
Vis Sci. 2018;59(7):2833-40.
207. Kolomeyer AM, et al. Characterization of conditioned media collected from cultured adult
versus fetal retinal pigment epithelial cells. Investigative Ophthalmology and Visual Science.
2011;52(8):5973-86.
208. Mysona BA, et al. Nerve growth factor in diabetic retinopathy: beyond neurons. Expert Rev
Ophthalmol. 2014;9(2):99-107.
209. Meyer JG, et al. Proteome and Secretome Dynamics of Human Retinal Pigment Epithelium
in Response to Reactive Oxygen Species. Scientific Reports. 2019;9(1):1-12.
210. Yabe T, et al. Pigment epithelium-derived factor induces pro-survival genes through cyclic
AMP-responsive element binding protein and nuclear factor kappa B activation in rat cultured
cerebellar granule cells: Implication for its neuroprotective effect. Neuroscience.
2005;133(3):691-700.

185
211. LaVail MM, et al. Multiple growth factors, cytokines, and neurotrophins rescue
photoreceptors from the damaging effects of constant light. Proceedings of the National
Academy of Sciences. 1992;89(23):11249-53.
212. Yang PB, et al. Trophic factors GDNF and BDNF improve function of retinal sheet
transplants. Exp Eye Res. 2010;91(5):727-38.
213. Wong HC, et al. Retinal pigment epithelial cells produce mitogenic factors for retinal
microvascular cells in culture: A preliminary report. Eye (Basingstoke). 1987;1(6):754-6.
214. Rowen SL, and Glaser BM. Retinal Pigment Epithelial Cells Release a Chemoattractant For
Astrocytes. Archives of Ophthalmology. 1985;103(5):704-7.
215. Waldbillig RJ, et al. Monkey retinal pigment epithelial cells in vitro synthesize, secrete, and
degrade insulin-like growth factor binding proteins. Journal of Cellular Physiology.
1992;150(1):76-83.
216. Sippy BD, et al. Soluble tumor necrosis factor receptors are present in human vitreous and
shed by retinal pigment epithelial cells. Journal of Investigative Medicine. 1996;44(1):311-7.
217. Gaur VP, et al. RPE conditioned medium stimulates photoreceptor cell survival, neurite
outgrowth and differentiation in vitro. Experimental Eye Research. 1992;54(5):645-59.
218. Huang C, et al. Combination of retinal pigment epithelium cell-conditioned medium and
photoreceptor outer segments stimulate mesenchymal stem cell differentiation toward a
functional retinal pigment epithelium cell phenotype. Journal of Cellular Biochemistry.
2012;113(2):590-8.
219. Schmidt-Erfurth U, and Waldstein SM. A paradigm shift in imaging biomarkers in
neovascular age-related macular degeneration. Prog Retin Eye Res. 2016;50:1-24.
220. Nazari H, et al. Stem cell based therapies for age-related macular degeneration: The promises
and the challenges. Prog Retin Eye Res. 2015;48:1-39.
221. Kashani AH, et al. Survival of an HLA-mismatched, bioengineered RPE implant in dry age-
related macular degeneration. Stem Cell Reports. 2022;17(3):448-58.
222. Mandai M, et al. Autologous Induced Stem-Cell-Derived Retinal Cells for Macular
Degeneration. N Engl J Med. 2017;377(8):792-3.
223. da Cruz L, et al. Phase 1 clinical study of an embryonic stem cell-derived retinal pigment
epithelium patch in age-related macular degeneration. Nat Biotechnol. 2018;36(4):328-37.
224. Kolomeyer AM, and Zarbin MA. Trophic factors in the pathogenesis and therapy for retinal
degenerative diseases. Surv Ophthalmol. 2014;59(2):134-65.
225. Kanu LN, and Ciolino JB. Nerve Growth Factor as an Ocular Therapy: Applications,
Challenges, and Future Directions. Semin Ophthalmol. 2021;36(4):224-31.

186
226. Diniz B, et al. Subretinal Implantation of Retinal Pigment Epithelial Cells Derived From
Human Embryonic Stem Cells: Improved Survival When Implanted as a Monolayer.
Investigative Ophthalmology and Visual Science. 2013;54(7):5087-96.
227. Sheedlo HJ, et al. Effects of RPE-cell factors secreted from permselective fibers on retinal
cells in vitro. Brain Research. 1992;587(2):327-37.
228. Sheedlo HJ, et al. Effects of RPE age and culture conditions on support of photoreceptor cell
survival in transplanted RCS dystrophic rats. Experimental Eye Research. 1993;57(6):753-61.
229. Sheedlo HJ, et al. Retinal pigment epithelial cell support of photoreceptor survival in vitro. In
Vitro Cellular & Developmental Biology - Animal: Journal of the Society for In Vitro Biology.
1995;31(5):330-3.
230. Sheedlo HJ, and Turner JE. Influence of a retinal pigment epithelial cell factor(s) on rat retinal
progenitor cells. Developmental Brain Research. 1996;93(1-2):88-99.
231. Sheedlo HJ, and Turner JE. Effects of retinal pigment epithelial cell-secreted factors on
neonatal rat retinal explant progenitor cells. Journal of Neuroscience Research.
1996;44(6):519-31.
232. Sheedlo HJ, et al. A transformed neonatal rat retinal pigment epithelial cell line: Secreted
protein analysis and fibroblast growth factor and receptor expression. Current Eye Research.
1997;16(2):116-26.
233. Sheedlo HJ, et al. RPE secreted proteins and antibody influence photoreceptor cell survival
and maturation. Developmental Brain Research. 1998;107(1):57-69.
234. Liu Y, et al. Long-term safety of human retinal progenitor cell transplantation in retinitis
pigmentosa patients. Stem Cell Res Ther. 2017;8(1):209-.
235. Carr A-J, et al. Protective effects of human iPS-derived retinal pigment epithelium cell
transplantation in the retinal dystrophic rat. PloS one. 2009;4(12):e8152-e.
236. Wang S, et al. Long-term vision rescue by human neural progenitors in a rat model of
photoreceptor degeneration. Investigative ophthalmology & visual science. 2008;49(7):3201-
6.
237. Shelby SJ, et al. MERTK signaling in the retinal pigment epithelium regulates the tyrosine
phosphorylation of GDP dissociation inhibitor alpha from the GDI/CHM family of RAB
GTPase effectors. Experimental eye research. 2015;140:28-40.
238. Ryals RC, et al. Long-term Characterization of Retinal Degeneration in Royal College of
Surgeons Rats Using Spectral-Domain Optical Coherence Tomography. Investigative
ophthalmology & visual science. 2017;58(3):1378-86.

187
239. Wang S, et al. Morphological changes in the Royal College of Surgeons rat retina during
photoreceptor degeneration and after cell-based therapy. The Journal of Comparative
Neurology. 2005;491(4):400-17.
240. Zambarakji HJ, et al. High resolution imaging of fluorescein patterns in RCS rat retinae and
their direct correlation with histology. Experimental eye research. 2006;82(1):164-71.
241. Zhao M, et al. Intravitreal Stanniocalcin-1 Enhances New Blood Vessel Growth in a Rat
Model of Laser-Induced Choroidal Neovascularization. Investigative Ophthalmology &
Visual Science. 2018;59(2):1125-33.
242. Ye J, et al. Primer-BLAST: a tool to design target-specific primers for polymerase chain
reaction. BMC Bioinformatics. 2012;13:134.
243. Livak KJ, and Schmittgen TD. Analysis of relative gene expression data using real-time
quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001;25(4):402-8.
244. Schindelin J, et al. Fiji: an open-source platform for biological-image analysis. Nature
Methods. 2012;9(7):676-82.
245. Otsu N. A threshold selection method from gray level histograms. IEEE Transactions on
Systems, Man, and Cybernetics. 1979;9:62-6.
246. Zack GW, et al. Automatic measurement of sister chromatid exchange frequency. Journal of
Histochemistry & Cytochemistry. 1977;25(7):741-53.
247. Van Lookeren Campagne M, et al. Mechanisms of age-related macular degeneration and
therapeutic opportunities. Journal of Pathology. 2014;232(2):151-64.
248. Narayan DS, et al. A review of the mechanisms of cone degeneration in retinitis pigmentosa.
Acta Ophthalmol. 2016;94(8):748-54.
249. Makabe K, et al. Microglia dynamics in retinitis pigmentosa model: formation of fundus
whitening and autofluorescence as an indicator of activity of retinal degeneration. Scientific
Reports. 2020;10(1):14700.
250. Sparrow JR, and Duncker T. Fundus Autofluorescence and RPE Lipofuscin in Age-Related
Macular Degeneration. Journal of Clinical Medicine. 2014;3(4):1302-21.
251. Binder CJ, et al. Innate sensing of oxidation-specific epitopes in health and disease. Nature
Reviews Immunology. 2016;16(8):485-97.
252. Chen T, et al. Receptor-Mediated NETosis on Neutrophils. Frontiers in Immunology.
2021;12.
253. Denning N-L, et al. DAMPs and NETs in Sepsis. Frontiers in Immunology. 2019;10.

188
254. Ben M'Barek K, et al. Developing Cell-Based Therapies for RPE-Associated Degenerative
Eye Diseases. Adv Exp Med Biol. 2019;1186:55-97.
255. Singh A, et al. Systemic frequencies of T helper 1 and T helper 17 cells in patients with age-
related macular degeneration: A case-control study. Scientific Reports. 2017;7(1):605.
256. Rosch S, et al. Evaluation of Retinal Function and Morphology of the Pink-Eyed Royal
College of Surgeons (RCS) Rat: A Comparative Study of in Vivo and in Vitro Methods. Curr
Eye Res. 2017;42(2):273-81.
257. Sauve Y, et al. Partial preservation of rod and cone ERG function following subretinal
injection of ARPE-19 cells in RCS rats. Vision Res. 2006;46(8-9):1459-72.
258. Pinilla I, et al. Contribution of rod and cone pathways to the dark-adapted electroretinogram
(ERG) b-wave following retinal degeneration in RCS rats. Vision Res. 2004;44(21):2467-74.
259. Zhang Y, et al. Decreased retinal-choroidal blood flow in retinitis pigmentosa as measured by
MRI. Doc Ophthalmol. 2013;126(3):187-97.
260. Lee SY, et al. N-Acetylcysteine promotes long-term survival of cones in a model of retinitis
pigmentosa. J Cell Physiol. 2011;226(7):1843-9.
261. Ma Y, et al. Quantitative analysis of retinal vessel attenuation in eyes with retinitis
pigmentosa. Invest Ophthalmol Vis Sci. 2012;53(7):4306-14.
262. Marano F, et al. Hereditary retinal dystrophies and choroidal neovascularization. Graefes Arch
Clin Exp Ophthalmol. 2000;238(9):760-4.
263. Satoh T, and Yamaguchi K. Ocular fundus abnormalities detected by fluorescein and
indocyanine green angiography in the Royal College of Surgeons dystrophic rat. Exp Anim.
2000;49(4):275-80.
264. Wang S, et al. Non-invasive stem cell therapy in a rat model for retinal degeneration and
vascular pathology. PLoS One. 2010;5(2):e9200.
265. Kyger M, et al. Autoimmune responses against photoreceptor antigens during retinal
degeneration and their role in macrophage recruitment into retinas of RCS rats. J
Neuroimmunol. 2013;254(1-2):91-100.
266. Xie J, et al. Olfactory Ensheathing Cells Grafted Into the Retina of RCS Rats Suppress
Inflammation by Down-Regulating the JAK/STAT Pathway. Front Cell Neurosci.
2019;13:341.
267. Sparrow JR, et al. Interpretations of fundus autofluorescence from studies of the bisretinoids
of the retina. Invest Ophthalmol Vis Sci. 2010;51(9):4351-7.
268. Hollyfield JG, et al. Retinal degenerative diseases. Cham: Springer International Publishing;
2005.

189
269. Fernández-Sánchez L, et al. Astrocytes and Müller Cell Alterations During Retinal
Degeneration in a Transgenic Rat Model of Retinitis Pigmentosa. Frontiers in Cellular
Neuroscience. 2015;9.
270. Sugasini D, et al. Efficient Enrichment of Retinal DHA with Dietary
Lysophosphatidylcholine-DHA: Potential Application for Retinopathies. Nutrients.
2020;12(10).
271. Nakazawa T, et al. Tumor necrosis factor-alpha mediates photoreceptor death in a rodent
model of retinal detachment. Invest Ophthalmol Vis Sci. 2011;52(3):1384-91.
272. Wingler K, et al. NOX1, 2, 4, 5: counting out oxidative stress. Br J Pharmacol.
2011;164(3):866-83.
273. Wilkinson-Berka JL, et al. NADPH oxidase, NOX1, mediates vascular injury in ischemic
retinopathy. Antioxid Redox Signal. 2014;20(17):2726-40.
274. Veneskoski M, et al. Specific recognition of malondialdehyde and malondialdehyde
acetaldehyde adducts on oxidized LDL and apoptotic cells by complement anaphylatoxin C3a.
Free Radic Biol Med. 2011;51(4):834-43.
275. Ayala A, et al. Lipid Peroxidation: Production, Metabolism, and Signaling Mechanisms of
Malondialdehyde and 4-Hydroxy-2-Nonenal. Oxidative Medicine and Cellular Longevity.
2014;2014:360438.
276. Zarkovic N, et al. Pathophysiological relevance of aldehydic protein modifications. J
Proteomics. 2013;92:239-47.
277. 4-Hydroxy-2-Nonenal, a Reactive Product of Lipid Peroxidation, and Neurodegenerative
Diseases: A Toxic Combination Illuminated by Redox Proteomics Studies. Antioxidants &
Redox Signaling. 2012;17(11):1590-609.
278. Yang H-j, et al. 4-HNE induces proinflammatory cytokines of human retinal pigment
epithelial cells by promoting extracellular efflux of HSP70. Experimental Eye Research.
2019;188:107792.
279. Papac-Milicevic N, et al. Malondialdehyde Epitopes as Targets of Immunity and the
Implications for Atherosclerosis. Adv Immunol. 2016;131:1-59.
280. Chant SM, and Meyers-Elliott RH. Autoimmunity: A possible factor in the development of
retinal degeneration in the RCS rat. Clinical Immunology and Immunopathology.
1982;22(3):419-27.
281. El Shikh MEM, et al. Extracellular traps and PAD4 released by macrophages induce
citrullination and auto-antibody production in autoimmune arthritis. J Autoimmun.
2019;105:102297.
282. Massengill MT, et al. Cham: Springer International Publishing; 2018:185-91.

190
283. Thapa R, et al. Prevalence, Pattern and Risk Factors of Retinal Diseases Among an Elderly
Population in Nepal: The Bhaktapur Retina Study. Clin Ophthalmol. 2020;14:2109-18.
284. Ratnapriya R, et al. Retinal transcriptome and eQTL analyses identify genes associated with
age-related macular degeneration. Nat Genet. 2019;51(4):606-10.
285. Dobin A, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15-
21.
286. Gamsiz ED, et al. Genome-wide transcriptome analysis in murine neural retina using high-
throughput RNA sequencing. Genomics. 2012;99(1):44-51.
287. Menon M, et al. Single-cell transcriptomic atlas of the human retina identifies cell types
associated with age-related macular degeneration. Nature Communications. 2019;10(1):4902.
288. Karademir D, et al. Single-cell RNA sequencing of the retina in a model of retinitis
pigmentosa reveals early responses to degeneration in rods and cones. BMC Biology.
2022;20(1):86.
289. Krämer A, et al. Causal analysis approaches in Ingenuity Pathway Analysis. Bioinformatics.
2013;30(4):523-30.
290. Ebright B, et al. Eicosanoid lipidome activation in post-mortem brain tissues of individuals
with APOE4 and Alzheimer’s dementia. Alzheimer's Research & Therapy. 2022;14(1):152.
291. De Nichilo MO, et al. De novo expression of pp125FAK in human macrophages regulates
CSK distribution and MAP kinase activation but does not affect focal contact structure.
Journal of Cellular Physiology. 1999;178(2):164-72.
292. Zhang X, et al. Focal adhesion kinase promotes phospholipase C-gamma1 activity. Proc Natl
Acad Sci U S A. 1999;96(16):9021-6.
293. Sattar Z, et al. The S100 Protein Family as Players and Therapeutic Targets in Pulmonary
Diseases. Pulmonary Medicine. 2021;2021:5488591.
294. Gonzalez LL, et al. Role of S100 proteins in health and disease. Biochimica et Biophysica
Acta (BBA) - Molecular Cell Research. 2020;1867(6):118677.
295. Bright JJ, et al. PPAR Regulation of Inflammatory Signaling in CNS Diseases. PPAR Res.
2008;2008:658520.
296. Palmieri M, et al. Characterization of the CLEAR network reveals an integrated control of
cellular clearance pathways. Human Molecular Genetics. 2011;20(19):3852-66.
297. Zhao Q, et al. Molecular Mechanisms of Lysosome and Nucleus Communication. Trends in
Biochemical Sciences. 2020;45(11):978-91.

191
298. Zhang W, et al. Regulation of TFEB activity and its potential as a therapeutic target against
kidney diseases. Cell Death Discovery. 2020;6(1):32.
299. Sardar Pasha SPB, et al. Retinal cell death dependent reactive proliferative gliosis in the
mouse retina. Scientific Reports. 2017;7(1):9517.
300. Goldman D. Müller glial cell reprogramming and retina regeneration. Nature Reviews
Neuroscience. 2014;15(7):431-42.
301. Dyall SC, et al. Polyunsaturated fatty acids and fatty acid-derived lipid mediators: Recent
advances in the understanding of their biosynthesis, structures, and functions. Prog Lipid Res.
2022;86:101165.
302. Nowak JZ. Age-related macular degeneration (AMD): Pathogenesis and therapy.
Pharmacological Reports. 2006;58(3):353-63.
303. Jones MK, et al. Gene expression changes in the retina following subretinal injection of human
neural progenitor cells into a rodent model for retinal degeneration. Mol Vis. 2016;22:472-90.
304. Wan J, et al. HB-EGF is necessary and sufficient for Müller glia dedifferentiation and retina
regeneration. Dev Cell. 2012;22(2):334-47.
305. Uren PJ, et al. A profile of transcriptomic changes in the rd10 mouse model of retinitis
pigmentosa. Mol Vis. 2014;20:1612-28.
306. Qi JH, et al. Expression of Sorsby's fundus dystrophy mutations in human retinal pigment
epithelial cells reduces matrix metalloproteinase inhibition and may promote angiogenesis. J
Biol Chem. 2002;277(16):13394-400.
307. Serhan CN, et al. Anti-inflammatory and proresolving lipid mediators. Annu Rev Pathol.
2008;3:279-312.
308. Liao Z, et al. Resolvin D1 attenuates inflammation in lipopolysaccharide-induced acute lung
injury through a process involving the PPARγ/NF-κB pathway. Respir Res. 2012;13(1):110.
309. Yao D, et al. Maresin 1 enhances osteogenic potential of mesenchymal stem cells by
modulating macrophage peroxisome proliferator-activated receptor-γ-mediated inflammation
resolution. Biomater Adv. 2022;141:213116.
310. Heras-Sandoval D, et al. Role of docosahexaenoic acid in the modulation of glial cells in
Alzheimer’s disease. Journal of Neuroinflammation. 2016;13(1):61.
311. Sobrado M, et al. Synthesis of lipoxin A4 by 5-lipoxygenase mediates PPARgamma-
dependent, neuroprotective effects of rosiglitazone in experimental stroke. J Neurosci.
2009;29(12):3875-84.

192
312. Tian H, et al. Resolvins E1 and D1 in Choroid-Retinal Endothelial Cells and Leukocytes:
Biosynthesis and Mechanisms of Anti-inflammatory Actions. Investigative Ophthalmology &
Visual Science. 2009;50(8):3613-20.
313. Chen JP, et al. Resolvin D2 prevents inflammation and oxidative stress in the retina of
streptozocin-induced diabetic mice. Int J Clin Exp Pathol. 2020;13(8):1986-94.
314. Wei J, and Gronert K. The role of pro-resolving lipid mediators in ocular diseases. Mol
Aspects Med. 2017;58:37-43.
315. Semba RD, et al. Serum lipids in adults with late age-related macular degeneration: a case-
control study. Lipids in Health and Disease. 2019;18(1):7.
316. Acar N, et al. Lipid Composition of the Human Eye: Are Red Blood Cells a Good Mirror of
Retinal and Optic Nerve Fatty Acids? PLOS ONE. 2012;7(4):e35102.
317. Souied EH, et al. Omega-3 Fatty Acids and Age-Related Macular Degeneration. Ophthalmic
Research. 2016;55(2):62-9.
318. Rozing MP, et al. Age-related macular degeneration: A two-level model hypothesis. Progress
in Retinal and Eye Research. 2020(September 2018):100825-.
319. Rein DB, et al. Prevalence of Age-Related Macular Degeneration in the US in 2019. JAMA
Ophthalmol. 2022;140(12):1202-8.
320. Boyer DS, et al. The Pathophysiology of Geographic Atrophy Secondary to Age-Related
Macular Degeneration and the Complement Pathway as a Therapeutic Target. Retina.
2017;37(5):819-35.
321. Chandramohan A, et al. Visual Function Measures in Early and Intermediate Age-Related
Macular Degeneration. Retina. 2016;36(5):1021-31.
322. Landowski M, et al. Human complement factor H Y402H polymorphism causes an age-
related macular degeneration phenotype and lipoprotein dysregulation in mice. Proc Natl
Acad Sci U S A. 2019;116(9):3703-11.
323. Tong Y, and Wang S. Not All Stressors Are Equal: Mechanism of Stressors on RPE Cell
Degeneration. Front Cell Dev Biol. 2020;8:591067.
324. Chen L, et al. Oxidative stress differentially impacts apical and basolateral secretion of
angiogenic factors from human iPSC-derived retinal pigment epithelium cells. Scientific
Reports. 2022;12(1):12694.
325. Atienzar-Aroca S, et al. Oxidative stress in retinal pigment epithelium cells increases exosome
secretion and promotes angiogenesis in endothelial cells. J Cell Mol Med. 2016;20(8):1457-
66.

193
326. Sreekumar PG, et al. αB Crystallin Is Apically Secreted within Exosomes by Polarized Human
Retinal Pigment Epithelium and Provides Neuroprotection to Adjacent Cells. PLOS ONE.
2010;5(10):e12578.
327. Rabin DM, et al. Chronic oxidative stress upregulates Drusen-related protein expression in
adult human RPE stem cell-derived RPE cells: A novel culture model for dry AMD. Aging.
2012;5(1):51-66.
328. Pennington BO, et al. Defined culture of human embryonic stem cells and xeno-free
derivation of retinal pigmented epithelial cells on a novel, synthetic substrate. Stem Cells
Transl Med. 2015;4(2):165-77.
329. Hu Y, et al. A novel approach for subretinal implantation of ultrathin substrates containing
stem cell-derived retinal pigment epithelium monolayer. Ophthalmic Res. 2012;48(4):186-91.
330. Rowland TJ, et al. Differentiation of human pluripotent stem cells to retinal pigmented
epithelium in defined conditions using purified extracellular matrix proteins. J Tissue Eng
Regen Med. 2013;7(8):642-53.
331. Lu B, et al. Mesh-supported submicron parylene-C membranes for culturing retinal pigment
epithelial cells. Biomed Microdevices. 2012;14(4):659-67.
332. Gao F, et al. Hes1 is involved in the self-renewal and tumourigenicity of stem-like cancer cells
in colon cancer. Scientific Reports. 2014;4(1):3963.
333. Bras-Pereira C, et al. The retinal determination gene Dachshund restricts cell proliferation by
limiting the activity of the Homothorax-Yorkie complex. Development. 2015;142(8):1470-9.
334. Tetreault N, et al. The LIM homeobox transcription factor Lhx2 is required to specify the
retina field and synergistically cooperates with Pax6 for Six6 trans-activation. Dev Biol.
2009;327(2):541-50.
335. Yun S, et al. Lhx2 links the intrinsic and extrinsic factors that control optic cup formation.
Development. 2009;136(23):3895-906.
336. Lee JH, et al. Nestin expressing progenitor cells during establishment of the neural retina and
its vasculature. Anat Cell Biol. 2012;45(1):38-46.
337. Fernández-López B, et al. Spatiotemporal pattern of doublecortin expression in the retina of
the sea lamprey. Frontiers in Neuroanatomy. 2016;10(JAN):1-13.
338. Bernardos RL, et al. Late-stage neuronal progenitors in the retina are radial Muller glia that
function as retinal stem cells. J Neurosci. 2007;27(26):7028-40.
339. Lamba DA, et al. Generation, purification and transplantation of photoreceptors derived from
human induced pluripotent stem cells. PLoS One. 2010;5(1):e8763.

194
340. Klassen HJ, et al. Multipotent retinal progenitors express developmental markers, differentiate
into retinal neurons, and preserve light-mediated behavior. Invest Ophthalmol Vis Sci.
2004;45(11):4167-73.
341. Schmitt S, et al. Molecular characterization of human retinal progenitor cells. Invest
Ophthalmol Vis Sci. 2009;50(12):5901-8.
342. Salinas M, et al. Nerve Growth Factor Protects against 6-Hydroxydopamine-induced
Oxidative Stress by Increasing Expression of Heme Oxygenase-1 in a Phosphatidylinositol 3-
Kinase-dependent Manner*. Journal of Biological Chemistry. 2003;278(16):13898-904.
343. Hacioglu G, et al. Assessment of oxidative stress parameters of brain-derived neurotrophic
factor heterozygous mice in acute stress model. Iran J Basic Med Sci. 2016;19(4):388-93.
344. Dong S, et al. Leukemia inhibitory factor protects photoreceptor cone cells against oxidative
damage through activating JAK/STAT3 signaling. Ann Transl Med. 2021;9(2):152.
345. He X, et al. PEDF and its roles in physiological and pathological conditions: Implication in
diabetic and hypoxia-induced angiogenic diseases. Clinical Science. 2015;128(11):805-23.
346. Hollingsworth TJ, and Gross AK. Innate and Autoimmunity in the Pathogenesis of Inherited
Retinal Dystrophy. Cells. 2020;9(3):630-.
347. Zhu D, et al. Polarized Secretion of PEDF from Human Embryonic Stem Cell–Derived RPE
Promotes Retinal Progenitor Cell Survival. Investigative Opthalmology & Visual Science.
2011;52(3):1573-.
348. Danhong Zhu JH. Retinal Pigment Epithelial Cell Conditioned Medium Enhances the Yield
of RPE Cells Differentiated from Human Embryonic Stem Cells. Journal of Clinical &
Experimental Pathology. 2015;05(02).
349. Dutt K, et al. RPE-secreted factors: influence differentiation in human retinal cell line in dose-
and density-dependent manner. J Ocul Biol Dis Infor. 2010;3(4):144-60.
350. Kimura A, et al. Neuroprotection, Growth Factors and BDNF-TrkB Signalling in Retinal
Degeneration. Int J Mol Sci. 2016;17(9).
351. Raffaele OS, and S.p.A DF. https://ClinicalTrials.gov/show/NCT02609165; 2015.
352. GenVec. https://ClinicalTrials.gov/show/NCT00109499.
353. S.p.A DF, and Cromsource. https://ClinicalTrials.gov/show/NCT02855450; 2016.
354. Ye F, et al. Malondialdehyde induces autophagy dysfunction and VEGF secretion in the
retinal pigment epithelium in age-related macular degeneration. Free Radical Biology and
Medicine. 2016;94:121-34.

195
355. Binet F, et al. Neutrophil extracellular traps target senescent vasculature for tissue remodeling
in retinopathy. Science. 2020;369(6506).
356. Dong T, et al. NAP1051, a Lipoxin A4 Biomimetic Analogue, Demonstrates Antitumor
Activity Against the Tumor Microenvironment. Molecular Cancer Therapeutics.
2021;20(12):2384-97.
357. Mallavia B, et al. Mitochondrial DNA Stimulates TLR9-Dependent Neutrophil Extracellular
Trap Formation in Primary Graft Dysfunction. Am J Respir Cell Mol Biol. 2020;62(3):364-
72.

Abstract (if available)

Abstract Retinal degenerative diseases, including age-related macular degeneration (AMD), diabetic retinopathy (DR), and retinitis pigmentosa (RP), are significant challenges in ophthalmology due to limited treatment options arising from complex etiologies. Novel therapeutic approaches require accurate pre-clinical models for development. Although the Royal College of Surgeons (RCS) rat is a popular retinal degeneration model, its molecular changes and relevance to human pathology need further investigation.

This study conducted a comprehensive analysis of retinal degeneration in immunocompetent and immunodeficient RCS rat models, using clinically relevant methodologies such as electroretinography (ERG) and optical coherence tomography (OCT). Longitudinal analyses found strong correlations between histology, OCT, and ERG, with scotopic a-wave as the earliest indicator of retinal degeneration.

The study also investigated molecular changes related to inflammation and oxidative stress mechanisms. Key findings include the upregulation of Gfap, Tnfa, and Nox1 genes, and age-dependent increases in oxidative stress markers MDA and 4HNE. Microglial response emerged as an early driver of pathological gene expression, with strong relationships between inflammation, ROS gene expression, MDA and 4HNE levels, and retinal degeneration observed. Targeting microglial response and associated pathways may offer therapeutic potential for retinal degenerative diseases.

We performed a transcriptomic and lipidomic analysis of RCS rat retinal degeneration, identifying cell-type-specific differentially expressed genes (DEGs) primarily associated with microglial cells. Significant similarities between human AMD and RCS rat transcriptomic changes further validate the model for therapeutic development. The pathogenic loss of Alox15 gene expression and inverse expression of Alox15 and Alox5 suggest a depletion of specialized pro-resolving lipid mediators (SPMs), impairing inflammation shutdown. Correlations between SPM loss and retinal function loss indicate potential plasma biomarkers for therapy development.

Stem cell-based therapies, particularly human embryonic stem cell-derived retinal pigment epithelium (hESC-RPE), have shown promise for replacing damaged retinal cells and preserving ocular function. We explored the therapeutic potential of stem cell-derived retinal pigment epithelium soluble factors (PRPE-SF) for retinal degeneration treatment. PRPE-SF employs a biomimetic membrane for RPE polarization and maturation, modulating secretome composition towards a protective phenotype. Treatment with PRPE-SF promoted progenitor cell proliferation, multipotency, and neuronal fate determination gene expression, enhanced photoreceptor developmental gene expression, reduced cell death, preserved rhodopsin staining, and blocked pro-apoptotic pathways. Additionally, PRPE-SF treatment preserved photoreceptors and delayed retinal function decline through antioxidant mechanisms, reduced oxidative stress markers, and inflammatory cell infiltration in the RCS retina.

This study demonstrates the potential of PRPE-SF as a promising therapeutic approach for retinal degeneration, providing a step towards developing effective therapeutics for debilitating retinal pathologies, such as AMD, DR, and RP. Further characterization and identification of the most critical active components within PRPE-SF are necessary to meet regulatory requirements and improve the reproducibility of an effective PRPE-SF product. Nevertheless, our findings showcase PRPE-SF as a promising candidate for treating retinal pathologies, paving the way for the development of effective therapeutics for these debilitating conditions and setting a precedent for age-related and neurodegenerative disease therapeutic strategies.

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Asset Metadata

Creator Ahluwalia, Kabir Tylor (author)

Core Title RPE secretome for the treatment of retinal degeneration in the RCS rat

School School of Pharmacy

Degree Doctor of Philosophy

Degree Program Clinical and Experimental Therapeutics

Degree Conferral Date 2023-08

Publication Date 12/05/2024

Defense Date 05/02/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag age-related macular degeneration,OAI-PMH Harvest,retina,Retinal degeneration,secretome,stem cells

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Louie, Stan (committee chair), Asatryan, Liana (committee member), Cadenas, Enrique (committee member), Okamoto, Curtis (committee member)

Creator Email kabir.ahluwalia0@gmail.com,kahluwal@usc.edu

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Format theses (aat)

Rights Ahluwalia, Kabir Tylor

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