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ROOT: a novel pharmacotranscritomic pipeline for rescuing age-associated functional decline of hippocampal adult neurogenesis

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ROOT: a novel pharmacotranscritomic pipeline for rescuing age-associated functional decline of hippocampal adult neurogenesis

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Content ROOT: A Novel Pharmacotranscritomic Pipeline for Rescuing Age-Associated Functional
Decline of Hippocampal Adult Neurogenesis
By
Maxwell M. Bay
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
(NEUROSCIENCE)
MAY 2022
Copyright 2022 Maxwell M. Bay
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Acknowledgements
I would first like to express my gratitude for my Ph.D. advisor, Dr. Michael Bonaguidi.
Michael is a clear, thorough thinker and tenacious scientist. His uncanny ability to weave
together discoveries from across different domains of biomedical research, and to form new
testable hypothesis, motivated so much of my work. Michael is always receptive to new ideas,
allowing for great creative latitude to explore new things. He fostered a collaborative
environment that led to healthy working relationships and friendships within the lab. Michael is a
kind and understanding mentor, and I will forever be grateful for his patience with me and my
particular (and sometimes frustrating) manner of conducing work.
Over my Ph.D., I leaned heavily on the expertise of others who complemented my
computational developments. Lei Peng, a fellow NGP student, is my good friend and a truly
invaluable colleague. She designed and performed, quite literally, all of the many, many drug
infusion experiments for the validation of ROOT. This work spanned years and took countless
hours. She is a smart, creative scientist and a very hard worker. We also collaborated together on
her primary doctoral work examining the effects of exercise on aging NSC transcriptomics,
which remains an exciting area of study. Dr. Albina Ibrayeva is another colleague to whom I owe
a great debt of gratitude. She very recently became an alumna of the Bonaguidi lab, so
congratulations to her on finishing her Ph.D.. In addition to being a good friend, she is a diligent
scientist and one of the most reliable colleagues I’ve ever had or will ever have. Frankly, we
worked together on more projects than I have space to list here. Albina always goes above and
beyond to ensure her work is timely and of good quality. I very much look forward to seeing
what the future holds for her.
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Next, I’d like to thank my dissertation committee members, Dr. Justin Ichida and Dr.
David Cobrinik. Justin’s advice over his years-long involvement in my Ph.D. helped shape the
bigger picture of my work and informed on the broader applications of ROOT to different
disease domains. David was a late addition to my committee but has been closely connected to
my doctoral work since the beginning of my time as a Ph.D. student. He clearly cares quite a lot
about fostering a friendly, welcoming, collaborative environment between labs. Joint efforts
between his lab and Bonaguidi lab produced a great deal of mutual growth, especially in the
areas of single cell RNA-sequencing and analysis.
Finally, I’d like to acknowledge the infinite support from my family. My parents raised
me to do what I love and allow my curiosity to guide me. My wife, Aliné, has been my rock, my
biggest cheerleader and my north star since the day I met her. She threw her full support behind
my circuitous academic journey, always trusting that I knew what I was doing, even and
especially at the low points. As much as anyone, this work was made possible because of her.

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Table of Contents
Acknowledgements ......................................................................................................................... ii
List of Figures ................................................................................................................................ vi
Abstract ......................................................................................................................................... vii
Chapter 1. Introduction, Background and Motivation .....................................................................1
1.1 Increased global life expectancy ......................................................................................................... 1
1.2 A need to promote healthy aging ........................................................................................................ 1
1.3 Stem cells ang aging ............................................................................................................................ 2
1.4 Adult hippocampal neurogenesis ........................................................................................................ 2
1.5 Age-associated dysfunction in adult neurogenesis .............................................................................. 3
1.6 Modulation of neurogenesis ................................................................................................................ 4
1.7 A need for high-dimensional characterization .................................................................................... 6
1.8 Technical advancements and presented works .................................................................................... 7
Chapter 2. Gene regulatory modules of facial regionalization in zebrafish.....................................8
2.1 Overview of contributions and summary of results ............................................................................ 8
Chapter 3. Coordinated expression programs across chondrogenesis and cartilage
development ...................................................................................................................................21
3.1 Overview of contributions and summary of results ......................................................................... 21
Chapter 4. Early stem cell aging in the mature brain .....................................................................38
4.1 Overview of contributions and summary of results ......................................................................... 38
Chapter 5. ROOT: A pharmacotranscriptomic pipeline for age-associated NSC dysfunction ......59
5.1 Contributions ..................................................................................................................................... 59
5.2 Introduction and summary................................................................................................................. 59
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5.3 Pseudotemporal alignment, repression point detection and dynamic time warp .............................. 61
5.4 Transition signature ........................................................................................................................... 62
5.5 Connecting to small molecules.......................................................................................................... 65
5.6 In vivo validation of ROOT predictions............................................................................................ 67
5.7 Sulindac Sulfone rejuvenates NSC function at cellular and behavioral levels ................................. 68
5.8 Method............................................................................................................................................... 71
5.9 Discussion ......................................................................................................................................... 78
Chapter 6. Summary, conclusions and future directions ...............................................................81
Bibliography ..................................................................................................................................84

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List of Figures
Figure 1. Identifying hidden state of age-related NSC dysfunction .............................................61
Figure 2. Single cell drug discovery for restoring NSC activity....................................................64
Figure 3. Motivation behind weighting scheme employed in reranking connectivity map ...........65
Figure 4. Validation of top ROOT candidates’ capacity to increase older NSC activity ..............67
Figure 5. Sulindac sulfone reverses NSC functional decline and improves cognition .................69
Figure 6. Sulindac sulfone is the only candidate the increases neurogenesis and restores
NSC function .................................................................................................................................70

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Abstract
As global life expectancy increases, so too does the proportion of age 65 and older
individuals. This represents a positive trend for human thriving, but also introduces new
challenges for society and the individual. With age, the incidence of disease increases
dramatically, leading to a c oncordant increase in healthcare costs, where neurological diseases
are disproportionately costly relative to incidence. There is therefore a powerful need to discover
new ways of promoting healthy aging.
Adult hippocampal neurogenesis, subserved by a population of neural stem cells in the
dentate gyrus, is an important component in normal cognitive function. Aging leads to
dysfunction among neural stem cells, characterized by dramatic increase in quiescence. This
process correlates strongly to decline in learning and memory.
Motivated by this, I developed ROOT: Revealing Origins and Ontological Targets.
ROOT is a novel pharmacotranscriptomic pipeline, applied to the problem of age-associated
functional decline of hippocampal adult neurogenesis in order to rescue it. ROOT leverages
single cell transcriptional information to derive the developmental timepoint in neurogenesis
over which NSCs in older hippocampi fail to transition, revealing the “transition signature” that
NSCs in younger hippocampi utilize to exit quiescence. The transition signature is then used to
generate a ranked list of candidate small molecule interventions. The top five compounds were
tested in aged mice, revealing three compounds which significantly improved neural stem cell
proliferation acutely. One compound, Sulindac Sulfone (SS), exhibited long-term improvements
in neurogenesis, stem cell activation and stem cell pool size. SS treatment animals also displayed
improved learning and memory against vehicle controls.
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Here I present an overview of the major bioinformatic developments I led and
contributed to across my tenure as a PhD student, organized in chronological order of
publication, ending with a chapter on ROOT, the primary focus of my time as Ph.D. student.
This work has been guided by a central theme of using and developing tools to better understand
the high-dimensional nature of stem cells in order to promote improved cellular function and
healthy aging.
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Chapter 1. Introduction, Background and Motivation
1.1. Increased global life expectancy
Global life expectancy has shown decade-over-decade increases for over a century (Max
Roser and Ritchie, 2013). Between 1990 and 2019, there was a recorded increase in life
expectancy from 64.2 years to 72.6 years, and is projected to continue to increase monotonically,
reaching 77.1 years by 2050 (ONU, 2019). As life expectancy increases, so too does the relative
proportion of older individuals. By 2050, 1 in 6 people in the world will be over age 65, up from
1 in 11 in 2019 (ONU, 2019). The number of individuals aged 80 years or over is projected to
triple, from 143 million in 2019 to 426 million in 2015. Although this represents a positive trend
in human thriving, it also presents new challenges to the individual and to society.

1.2. A need to promote healthy aging
With each additional year of life, the per person cost of personal health care increases,
growing rapidly after 65 (Hartman et al., 2022). Neurological diseases specifically represent an
ever-growing and disproportionately large fraction of total health care costs (Dieleman et al.,
2016). As the proportion of age 65 and older individuals grows, neurological diseases are
projected to massively increase in both incidence and cost across the population (Brookmeyer et
al., 2018; Dorsey et al., 2018). In the context of increasing longevity, the challenge is to discover
new ways of promoting healthy aging, reducing both incidence and cost of age-related diseases.

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1.3. Stem cells and aging
Adult somatic stem cells maintain and repair damaged tissue throughout life (Prentice,
2019; Tweedell, 2017). Progressive loss of local stem cell function is seen across tissues ,
although this process is temporally asynchronous across tissue compartments. Stem cell
dysfunction is primarily characterized by a decrease in stem cell pool size and an increase in
stem cell quiescence, i.e. a decrease in proliferation rate and resultant differentiated progenies
(Ahmed et al., 2017).
Increased quiescence can reduce molecular damage by reducing DNA replication,
metabolic activity and gene transcription, all processes that impact cellular aging, but this comes
at the cost of reduction in capacity for tissue maintenance and an increased vulnerability to tissue
damage (Tümpel and Rudolph, 2019). Indeed, stem cell disfunction is one of the main drivers of
cellular aging and aging disorders (Ahmed et al., 2017; Goodell and Rando, 2015; De Haan and
Lazare, 2018).

1.4. Adult hippocampal neurogenesis
The human brain is endowed with a stem cell niche in the dentate gyrus of the
hippocampus (Eriksson et al., 1998; Kumar et al., 2019; Moreno-Jiménez et al., 2021a). These
neural stem cells (NSCs) reside specifically within the subgranular zone, located between the
granule cell layer and the hilus of the dentate gyrus. Adult hippocampal NSCs are multipotent,
competent to produce both astroglia and granule neurons throughout the life of healthy adult
humans (Moreno-Jiménez et al., 2019, 2021b), though NSCs preferentially produce neuronal
progeny at roughly a 3:1 ratio. Newborn granule cells (GCs) migrate from the subgranular zone
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to the granule cell layer, where they undergo significant molecular change and axonal and
dendritic arborization, eventually synaptically integrating with the local network, serving as the
primary input structure to the hippocampus via the trisynaptic circuit (Abdissa, 2020; Andersen,
1975; Ming and Song, 2011).
Functionally, adult-born GCs serve an important role in mediating learning and memory.
Most research has focused on their crutial role in spatial and contextual memory (Abrous and
Wojtowicz, 2015; Alam et al., 2018; Baptista and Andrade, 2018; Seo et al., 2015), though there
is some evidence adult-born GCs play a broader role in cognition, including mood regulation
(Baptista and Andrade, 2018; Hill et al., 2015). A clear inverse correlation exists between
Alzheimer’s disease (AD) severity and quantity of neurogenesis observed, which has inspired
much research into a pathophysiological relationship (Moreno-Jiménez et al., 2019; Rodríguez
and Verkhratsky, 2011). Ablation and upregulation experiments have shown that GCs serve as
both pattern separators and pattern integrators (Deng et al., 2010) with different roles across their
post-mitotic cellular lifespan. This highlights the foundational role of adult neurogenesis:
functional plasticity. NSCs supply the dentate gyrus with new GCs, (Babcock et al., 2021; Lee et
al., 2012) which are critical for its maintenance and modulation, and ultimately critical for
subserving functional correlates of the dentate gyrus (Kuhn et al., 2018; Lledo et al., 2006).

1.5. Age-associated dysfunction in adult neurogenesis
Like every other stem cell compartment in the body, hippocampal NSCs lose function
over the lifespan of an individual. This is broadly characterized by a reduction in stem cell pool
size and a rapid increase in quiescence across the subgranular zone (Ibrayeva et al., 2021;
Klempin and Kempermann, 2007; Lee et al., 2012; Moreno-Jiménez et al., 2019; Ziebell et al.,
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2018). Unlike other stem cell compartments, NSCs in the dentate gyrus show signs of decreased
function very early, 4.5 months post-natal in mice (Ibrayeva et al., 2021). In AD patients, the rate
of observed age-associated neurogenesis decline is dramatically increased, even at the stage of
mild cognitive impairment (Moreno-Jiménez et al., 2019).
The cellular and molecular drivers underlying age-associated NSC quiescence remain an
area of active research, but appear to be highly multifactorial (Seib and Martin-Villalba, 2015a).
Hypothesized causal factors range from age-associated blood-brain barrier leakage (Lee et al.,
2012), to reduced local Notch signaling (Lugert et al., 2010), to abberant Abl expression,
(Ibrayeva et al., 2021), to evolving microglia function (Lee et al., 2012; Pérez-Rodríguez et al.,
2021) and more.

1.6. Modulation of neurogenesis
Adult neurogenesis is a highly dynamic process, which can be experimentally
manipulated a number of ways. Attempting to increase neurogenic output of NSCs, researchers
have explored a wide variety of interventions in model systems. Interventions can be broadly
grouped into exogeneous and endogenous categories (Kuipers et al., 2014). Exogeneous
interventions originate externally, involving direct external manipulation of control systems such
as proteomic, transcriptional or epigenetic regulation. Endogenous interventions harness and
augment existing capacities, generally through contextual manipulation of environmental and
social setting.
Voluntary exercise has been long been known to be a powerful positive regulator of
hippocampal adult neurogenesis (Brown et al., 2003; Llorens-Martín, 2020; Lucassen et al.,
2010; Van Praag et al., 1999; Trinchero et al., 2019; Zang et al., 2017). This burst of
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neurogenesis drives significant improvements in learning and memory (Diederich et al., 2017;
Inoue et al., 2015) and can partially rescue learning and memory loss in aging (Blackmore et al.,
2021; Van Praag et al., 2005), models of Fragile X syndrome (Pinar et al., 2018) and AD (Choi
et al., 2018). Voluntary exercise induces many biological changes across many systems,
however. Indeed, Choi et al (2018) showed that although ablation of neurogenesis was sufficient
to ameliorate the cognitive benefits of voluntary exercise in AD model mice, pharmacologically-
induced neurogenesis only phenocopied the effects of voluntary exercise when coupled with
artificial increases in BDNF levels. This indicates cognitive and learning benefits of voluntary
exercise are driven by more than improved neurogenesis levels alone.
Perhaps exogeneous interventions are the key to the discovery of promoting improved
adult hippocampal neurogenesis in aging and disease. By conditional inactivation of RBPJk
(component of Notch signaling) in mice, Ehm et al. (2010) observed a massive increase in
neurogenesis. However, this was followed by a permanent refractory period in neurogenesis, as a
consequence of almost complete depletion of the stem cell pool. Jones et al. (2015) conditionally
inactivated CHD7, essential for preventing replication, in adult mouse hippocampal NSCs, which
resulted in a transient increase in cell division, followed by a significant decline in neurogenesis
as a result of a premature loss of NSCs. Most experimental promoters of NSC activation are both
transient, and detrimental to the population size of the stem cell pool, which effectively ages the
tissue prematurely (Bonaguidi et al., 2011; Ehm et al., 2010; Mira et al., 2010; Renault et al.,
2009; Sierra et al., 2015; Zhang et al., 2019a).

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1.7. A need for high-dimensional characterization
Perhaps most importantly, experimental interventions to improve adult hippocampal
neurogenesis are not driven by a guiding central dogma. They are each typically informed by one
of many, largely non-overlapping causal factors in age-associated decline of adult hippocampal
neurogenesis. An upside of this field-wide approach is it has spurred a wide range of potential
avenues for intervention. However, this highly restrictive, often single-gene, single-target
philosophy of intervention may be the reason so many experimental interventions have promoted
neurogenesis but failed to save the stem cell pool depletion. There remains a need for a high-
dimensional, more holistic characterization of the cellular and molecular drivers of aging on
hippocampal neural stem cells. Development of therapies should consider more than just
individual candidate genes.
Single-cell RNA-sequencing technology, topological clustering, non-linear dimension
and umbrella tools like Seurat (Hao et al., 2021) have dramatically improved scalability and
accessibility, leading to a proliferation of transcriptomic research. Very recently, there has been
significant advancement in the related fields of pharmacotranscriptomics and in silico drug
discovery (Brogi et al., 2020; Cheng et al., 2019; Zheng and Wang, 2021).
Pharmacotranscriptomics joins transcriptome-wide information with potential interventions in
disease to generate candidates which promote healthy transcriptomes. Broadly, in silico drug
discovery leverages the enormous capacity of computational resources to yield pharmacological
insights from high-dimensional data.

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1.8. Technical advancements and presented works
During my tenure as a doctoral student, I have made use of and contributed to the
advancement of bioinformatic tools to address important questions in aging and development.
This work has been pursued with the philosophy that biology is fundamentally high-dimensional
and multifactorial, therefore the research into pathophysiology should also be high-dimensional
and multifactorial. Over the following four chapters, I will present published and unpublished
work which highlight key advancements made during my time as a graduate student.
Chapter 2 outlines a new method used to identify gene regulatory modules which
correlate spatial and/or temporal domains of zebrafish facial regionalization during development.
Chapter 2 details work in charactering chondrogenesis, wherein I used WGCNA to identify five
tissue-specific gene regulatory modules, differentially enriched across stages of development and
aging using in vivo human samples. I also show in vitro models of different timepoints properly
recapitulated of module enrichment observed in vivo. In chapter 3, I use single-cell RNA-
sequencing of young adult aging NSCs to tie age-associated transcriptional changes to changes
in differentiation dynamics with RNA Velocity (La Manno et al., 2018). I then use gene ontology
network structure and enriched terms to implicate Abl1 as an NSC aging factor, which my
coauthors validated in vivo. Chapter 4 details my primary doctoral work, in which I introduce a
novel pharmacotranscriptomic pipeline for rescuing age-associated functional decline of
hippocampal adult neurogenesis.
The dissertation ends with a summary of the presented works, emphasizing the common
network-based and high-dimensional methodology threaded between each of the chapters. I
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conclude with broader implications of ROOT, discussing the potential applications in other
disease contexts.

Chapter 2. Gene regulatory modules of facial regionalization in
Zebrafish
In this chapter, I present published work in collaboration with Dr. Amjad Askary and
colleagues at Eli and Edythe Broad Center for Regenerative Medicine & Stem Cell Research at
the USC. Facial skeletal patterning is driven by the coordination of many thousands of genes
across developmental time and structures. In the following work, we attempt to characterize the
gene expression regulatory networks responsible for facial skeletal patterning. This was achieved
by generating reporter lines of zebrafish, where known time and region specific marker genes
were used to create fluorescent reporter lines. These zebrafish were sacrificed and FACS sorted
to enrich for the marker genes of interest. The cell-type and expression diversity were exploited
to generate a weighted gene co-expression network analysis (WGCNA) (Langfelder and
Horvath, 2008). We created a novel method, called the TOM Driver Array, to assess the
importance of each sample in the maintaining the integrity of the gene network, and the strength
of each module. This helped us understand which collections of genes were most critical to each
region of facial skeletal pattern development. Below is the full published work.

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Chapter 3. Coordinated expression programs across chondrogenesis
and cartilage development
In this chapter, I present published work in collaboration with Dr. Gabriel Ferguson and
colleagues at the Department of Orthopaedic Surgery, Keck School of Medicine of USC, in
which I performed all transcriptomic analyses. In this work, we sought to elucidate the tissue-
specific gene expression patterns that define cellular identify and function in early human
cartilage development. Doing so would open new avenues for therapy development by informing
better in vitro model systems. Chondrocytes from in vivo tissue were profiled at four
developmental stages, as well as iPSC-derived chondrocytes at two stages of in vitro
differentiation. Bulk RNA sequencing was performed across all six groups, with three biological
replicates each. Gene-level hierarchical clustering revealed striking resemblance between early
iPSC-derived and embryonic chondrocytes, and a transcriptional profile in late iPSC-derived
chondrocytes that shared programs with all stages of chondrocyte maturation. This work serves
as a transcriptional roadmap and resource for future work in the design of future model systems.
Below is the full published work.

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Chapter 4. Early stem cell aging in the mature brain
In this chapter, I present published work in collaboration with Dr. Albina Ibrayeva and
colleagues at the Eli and Edythe Broad Center for Regenerative Medicine & Stem Cell Research
at the USC. I contributed considerably to this work, performing all sequencing alignment, QC,
post-processing and analyses and was involved at all stages of conceptualization. In this paper,
we aimed to pinpoint multiple factors that disrupt neural stem cell function in aging. scRNA-seq
was performed on 2-month-old and 4.5 month old NSCs. Using RNA Velocity, we noted
differentiation dynamics which suggested that NSCs in quiescence are more likely to move away
from activation in 4.5-month-old animals compared to 2-month-old animals. We also employed
topological networks, identifying a transcriptional program in quiescent NSCs from 4.5-month-
old mice that significantly enriched for hallmarks of molecular aging. This network structure also
revealed Abl1 as a target for functional restoration of aging NSCs.

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Chapter 5. ROOT: A novel pharmacotranscriptomic pipeline for
rescuing age-associated functional decline of hippocampal adult
neurogenesis
5.1. Contributions
This work represents a significant collaborative effort between colleagues within my
graduate lab, colleagues in Saul Villeda’s laboratory at UCSF and myself. I was responsible for
all computation, algorithm design and conceptualization of the pipeline. My lab-mate and fellow
graduate student Lei Peng performed the bulk of drug infusion and follow up histological
experiments and was assisted by another lab-mate, Dr. Albina Ibryeva. Behavior experiments
were performed by Dr. Adam Schroer in the lab of Dr. Saul Villeda in the Department of
Anatomy at UCSF.
5.2. Introduction and Summary
Rapid advances in single cell technology have enabled researchers to investigate complex
cellular processes with unprecedented precision. Recent Single Cell RNA-Sequencing work
investigating the molecular programs of adult hippocampal neurogenesis have carefully and
thoroughly revealed much of the endogenous program leading from stem cells to differentiating
neurons (Franjic et al., 2022; Hochgerner et al., 2018; Shin et al., 2015). As noted in chapter
section 1 section 5, early NSC quiescence leads to rapid decline in adult hippocampal
neurogenesis. This is associated with a marked decline in learning and memory. Experimental
genetic interventions designed to boost neurogenic output often prematurely deplete NSCs,
leading to an exaggerated premature aging.
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We sought to develop a single cell bioinformatic pipeline capable of implicating the
transcriptional transition signature younger NCSs express to transition out of quiescence that
older cells fail to engage, then connect that transition signature to small molecule intervention
compounds that serve as candidates to phenocopy younger NSC behavior in older NSCs. To that
end, we developed a system we call ROOT: Reveal Origins and Ontological Targets. ROOT was
applied to archival transcriptomes of NSCs from Shin et al. (2015), of both younger and older
mice, revealing the transition signature associated with NSCs exiting quiescence and generating
ranked small molecule interventions. To assess the fidelity of intervention predictions, we tested
five compounds in vivo for their ability to improve NSC activation acutely in older mice. Three
out of the five tested compounds significantly improved NSC activation. Over chronic timeline,
our lead compound, Sulindac Sulfone, showed improved neurogenesis, continued restoration of
NSC activation and no reduction in stem cell pool size. Finally, Sulindac Sulfone treated animals
also showed improved performance in behavioral tests of cognition.

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5.3. Pseudotemporal alignment, repression point detection and dynamic time
warp
We first employed Waterfall (Shin et al. 2015) to compute Pseudotime, aligning both
older and younger NSCs along the neurogenic trajectory. As aging itself impacts the
transcriptome, PT was computed independently for younger and older NSCs (Fig. 1c).
Figure 1 | Identifying hidden state of age-related neural stem cell dysfunction.
a, Hippocampal neural stem cells in older mammals are more quiescent than in younger animals, leading to fewer neurons. Red cells are
active. Blue cells are quiescent.
b, Conceptual framework for ROOT: Characterize expression program in young and old cells, discover activation bottleneck point in older
cells, expression program in younger cells associated with overcoming the block, tie the younger program to small molecule interventions.
c, Developmental trajectories of younger and older NSCs.
d, Derivation of activation bottleneck in older cells.
e, Dynamic time warp map between younger and older pseudotimes.
f, Visual representation of aligned trajectories.

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Independent computation was important because we did not want to introduce age-correlated
variance into dimension reduction. Pseudotime is meant to represent neurogenesis, not aging.
Age-associated reduction in hippocamp neurogenesis have long been known to originate
with dysfunction in NSCs themselves. (Encinas et al., 2011; Ibrayeva et al., 2021). This is
observed at the cellular level as a reduction in the stem cell pool size and a dramatic increase in
quiescence across cells. Evidence from single cell RNA-sequencing experiments in hippocampal
NSCs (Hochgerner et al., 2018; Ibrayeva et al., 2021; Shin et al., 2015) have revealed a
transcriptionally distinct subpopulation of quiescent NSCs, including one subpopulation that
immediately precedes the active NSC state. Inspired by this cellular and molecular phenomenon,
we developed the Repression Point Detection (RPD) algorithm (Fig. 1d), which identified the
point along pseudotime at which the older NSC population dropped off the fastest. This point,
the repression point, represents the moment in the neurogenic trajectory that cells transition from
pre-activation to activation. Detailed methodology are provided in the chapter 5.8.
As the transition out of quiescence is better facilitated in younger mice than older mice,
we used a high-dimensional Dynamic Time Warp (DTW) algorithm (Giorgino, 2009) to map the
repression point identified in older NSCs onto the complementary point along pseudotime in
younger NSCs. DTW alignment between older and younger NSC psuedotimes showed that the
earliest expression state in older NSCs maps onto roughly PT 20 while the latest expression state
maps onto PT 79 (Fig. 1e).

5.4. Transition signature
There does not appear to be one single pathogenic origin for age-associated NSC
dysfunction, rather a collision of causal factors (Ibrayeva et al., 2021; Okamoto et al., 2011; Seib
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and Martin-Villalba, 2015b; Zhang et al., 2019b) which all prevent NSCs from exiting
quiescence. Cell states can be mathematically characterized as high-dimensional attractor states
of a complex gene regulatory network (Huang et al., 2005), and nearly always require highly
coordinated gene interaction networks (Duan et al., 2019) to transition from one state to another.
This all suggests single-gene or single-target interventions in age-associated dysfunction
of hippocampal neurogenesis may be insufficient to properly restore function. Indeed,
experimental interventions targeting individual pathways and/or gene targets reliably deplete the
stem cell pool prematurely or otherwise fail to promote neurogenesis after an initial boost in
proliferation. (Bonaguidi et al., 2011; Ehm et al., 2010; Mira et al., 2010; Renault et al., 2009;
Sierra et al., 2015; Zhang et al., 2019a).
If not an individual gene, then what gene expression program enables younger NSCs to
transition out of quiescence, across the repression point, and into an active state more readily
than in younger animals? To answer this question, we developed the Transition Signature
Detection (TSD) algorithm. TSD first calculates a raster of on-off states for each gene using the
Baum-Welch algorithm (Rabiner, 1989), following the technique employed by Shin et al. (2015),
then filtered for genes which turn on at the identified repression point. TSD is explained in
greater detail in chapter 5.8. TSD was applied to the set of younger cells to determine the
transition signature genes. After filtration, TSD revealed that the TS of quiescent cells
transitioning out of quiescence contains 110 downregulated genes and 117 upregulated genes
(Fig. 2c).
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As the transition signature was
derived from younger cells, we were
curious if the same TS was engaged in
older NSCs to activate quiescent cells.
We collapsed the upregulated and
downregulated genes into a single set,
then computed eigengene expression
across cells in younger NSCs and
separately in older NSCs. In younger
NSCs, we see a sharp increase in
eigengene enrichment right at the block
point (Fig. 2d), as expected. Interestingly, eigengene expression of the TS across older NSC
pseudotime shows almost no relationship with the block point (Fig. 2e), suggesting the reason
Figure 2 | Single cell drug discovery for restoring
neural stem cell activity.
a, b, Representative downregulated and upregulated
TS genes (bold lines) against other down and
upregulated genes which aren’t associated with
activation bottleneck transition (translucent lines)
c, Smoothed heatmap of all TS genes.
d, Eigengene of transition signature across neural
stem cell activation in cells from younger animals.
e, Eigengene of transition signature in older animals
shows TS fails to be activated.
f, Up and Down components of the transition
signature are passed through Connectivity Map to
reveal primary connectedness of drug instances.
Connected drugs are then reprioritized using ROOT
algorithms to find highest probability candidates for
validation.
g, Amplitude matrix, indicating expression change as
a consequence of drug delivery on each gene from
the transition signature for random drugs.
h, Same as (g) but on prioritized drugs, which mirror
the transition signature better than randomly
selected drugs.

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cells have a difficult time transitioning out of
quiescence is they fail to properly coordinate
the expression of transition signature genes.

5.5. Connecting to small molecules
We hypothesize that inducing TS
expression would drive quiescent NSCs to
proliferate in older animals. Indeed, the idea of
pushing cells to express a particular signature
via small molecule interventions was a driving
concept behind the development of the
connectivity map (Lamb et al., 2006; Musa et
al., 2019).
Connectivity map is built upon a
massive database that characterizes the genome-
wide transcriptional changes associated with
treatment with 1,309 small molecules across
6,109 microarray experiments on immortalized
cell lines. The vast majority of experiments were on MCF7, PC3 and HL60 lines, representing
3,095, 1,741 and 1,229 experiments respectively. The connectivity map web tool provides an
interface for uploading bidirectional gene sets, which then connects to small molecules by their
ability to replicate the uploaded gene set. A ranked list of connected compounds is returned,
Figure 3 | Motivation behind weighting scheme employed in reranking
connectivity map out
a, Connected compounds by number of experiments and original connectivity
map ranking, highlighting tested compounds.
b, Connected compounds by mean connectivity score and -log 10p, highlighting
tested compounds.
c, Original and ROOT rankings, showing where each of the tested compounds
would have ranked under the original scheme.
d, Weighted ROOT scoring scheme
e, ROOT scores against rankings. Tested compounds have substantially higher
scores than rest of compounds.

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using a method outlined in Lamb et al. (2006) that ranks by p-value of the connectivity score
against a simulated null distribution. The ranked output is accompanied with additional statistical
information about the strength and reliability of connectivity between a compound and the input
signature.
We employed connectivity map by uploading the derived TS to generate connectivity
scores, between the transition signature and compounds. Connectivity map returned a ranked list
across all compounds, with accompanying statistical information. While connectivity map has
been widely successful (Musa et al., 2018), its essential noisiness (Segal et al., 2012), especially
in compounds for which few instances exist, can lead to a large number of false positives –
compounds with artificially low p-values (Lim and Pavlidis, 2021). In an effort to partially
overcome this, I inserted an additional ROOT scoring step, which considers number of
experiments, mean connectivity score, and the number of non-null instances (Fig. 3d). This
helped address the problem where compounds with weak connectivity scores but 2 or 3
additional replicates were significantly boosted by connectivity map’s default ranking scheme
(Fig. 3a,b).
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After computing the ROOT score and
reranking, 375 compounds showed some degree
of positive connectivity with the transition
signature. The top five, in order, were
Hippeastrine Hydrobromide,
Arachidonyltrifluoromethane, Carbimazole,
Imatinib, Sulindac Sulfone. Induction of the TS
across all five compounds was visualized against
randomly selected compounds (Fig. 2g-h).

5.6. In vivo validation of ROOT
predictions, 6-day acute paradigm
To examine the efficiency of ROOT’s in
silico prioritization, we first validated the top five
candidate compounds for ability to promote NSC
proliferation in 10-month-old C57BL/J6 mice
through 6-day osmotic pump
intracerebroventricular (ICV) infusion of vehicle
control or one of Hippeastrine Hydrobromide,
Arachidonyltrifluoromethane, Carbimazole,
Imatinib, Sulindac Sulfone experimental
compounds into the fimbria (Fig. 4a). Drug and
delivery method and concentrations were closely
Figure 4 | Validation of top ROOT candidates’ capacity to increase
older neural stem cell activity.
a, Middle-age (10 months) mice were i.c.v. infused with vehicle or one
of the top 5 ROOT candidate compounds for 6 days. Schematic
illustrates chronological order of surgery, drug infusion, and cellular
analysis.
b, Representative images of neural stem cell marker (Nestin, white) and
activation marker (Mcm2, red) across vehicle and candidate compounds.
Scale bar, 50 μm.
c, Neural stem cell activation rates of each candidate intervention
against vehicle control. We discovered three significant compounds. 10
month-old, n = 3 ~ 16 per group. Data presented by Nestin+Mcm2+
cells / Nestin+ cells in % and shown as observed values atop quartiles
[One-way ANOVA test (two-sided, Turkey’s post hoc test adjustment)].
d, Bar plot of hit rate odds ratios across HTS experiments and ROOT,
where base expectation was mean hit rate across sampled HTS
experiments.

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modeled after Pieper et al. (2010). ICV involves infusion directly into the central compartment of
the brain and for the purposes of this experiment, was superior to oral route by helping to bypass
the blood-brain barrier, allow for direct diffusion into hippocampal NSCs and avoids potential
confounding effects of distribution through the circulatory system.
Animals were then sacrificed, followed by excision of their brains, tissue sectioning and
immunostaining for DAPI, the NSC marker Nestin and the cell proliferation marker Mcm2.
Nestin+ cells were marked as NSCs while Nestin+Mcm2+ cells were marked as active NSCs.
NSC activation rate was defined as the proportion of Nestin+ cells that were double positive
Nestin+Mcm2+.
Surprisingly, three of the five tested compounds successfully improved NSC
proliferation. Sulindac Sulfone, Imatinib, and Arachidonyltrifluoromethane significantly
increased NSC activation rate against controls (Fig. 4b,c). Carbimizole and Hippeastrine
significantly decreased NSC activation rate against controls (Fig. 4b,c). Therefore, the target
enrichment (hit rate) of ROOT using single cell sequencing technology is 3/5 or 60%. In
comparison with the surveyed results from 15 high-throughput screenings (HTS) yielded roughly
1.02±0.82 % hit rates (Fig. 4d). Hence, ROOT dramatically improves candidate compound
prediction by roughly 60-fold compared to the traditional drug screenings. Assuming a random
hit rate of 1.02%, this represents a statistically significant enrichment for hits from ROOT 60%
(3/5) against random selection (~14/1,309), p < .0001.
5.7. Sulindac Sulfone rejuvenates NSC function, neurogenesis and
cognition in middle-aged mice. As NSCs are known for their limited self-renewal
capacity (Encinas et al., 2011; Ibrayeva et al., 2021) and their premature activation-dependent
depletion (Bonaguidi et al., 2011; Ehm et al., 2010; Mira et al., 2010; Renault et al., 2009; Sierra
69
et al., 2015; Zhang et al., 2019a),
analyses on NSC reactivating
compounds’ chronic effects on NSC homeostasis are in need. Contradictory to the NSC
“disposable” paradigm, our recent observation revealed an adaptive response of NSCs through
Imatinib: Imatinib can acutely reactivate NSCs in the middle-aged brain, and the once activated
NSCs will then rebound to deeper quiescence without premature depletion (Ibraveya, 2021). To
understand the influence of the three compounds that successfully improved NSC proliferation
over an acute timescale on NSC behavioral plasticity, i.c.v. drug-infused brains (Sulindac
Sulfone, Imatinib, and Arachidonyltrifluoromethane) versus artificial cerebrospinal fluid (aCSF)
vehicle control) of the 10-month-old mice were harvested 28 days after the surgical implantation
Figure 5 | Sulindac sulfone reverses neural stem
cell function and improves cognition in middle
aged mice.
a, Middle age (10 months) mice were i.c.v.
infused with Sulindac sulfone or aCSF control. 28
days after osmotic pump implantation, some
mice were sacrificed for histological analysis (n =
11~14 per group) while others underwent
cognitive tests (n = 14~15 per group). Schematic
illustrates chronological order of cognitive tests.
b, Left: Neurogenesis histological analysis stained
with anti-DCX (red) antibody; DAPI (blue). Scale
bar, 50 μm. n = 12~13 per group. Data presented
by number of DCX+ cells per mm
3
. Neural stem
cell population size and activation rate and
neurogenesis histological analysis co-stained
with anti-Nestin (white) and anti-Mcm2 (red)
antibodies; DAPI (blue). Scale bar, 50 μm. n =
11~14 per group.
c, Neurogenesis, quantified as Nestin+ cells per
mm
3

d, Activation rate, quantified as Nestin+Mcm2+
cells / Nestin+ cells in %
e, NSC pool size, quantified asNestin+ cells per
mm
3

f, g, Associative fear memory was assessed using
contextual (f) and cued (g) fear conditioning as
percent time spent freezing 24 hours after
training.
h, Object recognition memory was assessed by
NOR as percent of time spent exploring novel
object 48 hours after training.
Data are shown as observed values atop
quartiles; [independent samples t-test versus
50% in (h); Student’s t test in (c), (d), (e), (f), and
(g) (two-sided, no adjustment)].

70
(Fig. 5a). Surprisingly, Sulindac Sulfone increases
NSC numbers (Fig. 5c), and NSC activation rate
(Fig. 5d) compared to the controls. This indicates
that the NSC reactivation of Sulindac Sulfone can
persist, and that it does not come at the price of
premature NSC depletion. Neither Imatinib nor
Arachidonyltrifluoromethane treatment led to a
reduction in NSC pool size, but NSC activation
was significantly attenuated by both
compounds(Fig. 6b,c)
We further investigated the contribution of
Sulindac Sulfone in hippocampal neurogenesis at
day 28 (Fig. 5a). Remarkably, the number of
doublecortin (DCX)-positive new-born neurons
were increased in the Sulindac Sulfone treated
mice (Fig. 5e). This suggests that Sulindac Sulfone
reactivated NSCs can modulate neurogenesis in
middle-age mice, potentially contributing to the
enhancement of regenerative capacity in natural
aging brains. This effect is unique to Sulindac Sulfone, as both Imatnib nor
Arachidonyltrifluoromethane showed no significant change against aCSF condition.

Figure 6 | Sulindac Sulfone is the only candidate that both
increases neurogenesis and restores NSC function
Quantifications from Fig 5 generalized across the three
compounds that exhibited improved NSC activation in acute
condition.
a, Neurogenesis, quantified as Nestin+ cells per mm
3

b, Activation rate, quantified as Nestin+Mcm2+ cells / Nestin+
cells in %
c, NSC pool size, quantified asNestin+ cells per mm
3
Data are shown as observed values atop quartiles;
[independent samples t-test versus 50% in (h); Student’s t test
in (a), (b), and (c) (two-sided, no adjustment)].

71
Sulindac Sulfone effectively We also assessed the potential of Sulindac Sulfone on
redeeming age-related hippocampal-dependent learning and memory deficits. Contextual fear
conditioning and novel object recognition (NOR) paradigms were used on 10-month-old mice
with 7-day intracerebroventricular (ICV) infusion of Sulindac Sulfone or aCSF (Fig. 5a). Mice
receiving Sulindac Sulfone displayed increased freezing in contextual (Fig. 4f), but not cued
(Fig. 5g) memory test. Mice receiving Sulindac Sulfone also spent significantly more time with
the novel object (Fig. 5h). Collectively, these data denoted that Sulindac Sulfone reactivated
NSCs from deep quiescence, rescued neurogenesis exhaustion without NSC pool depletion, and
ameliorated cognitive function impairment in the middle-age hippocampus.

5.8. Method
Animals
C57BL/6 wildtype (The Jackson Laboratory) mouse line with mid-age (10-11 months) male and
female mouse cohorts was used. Mice were housed under specific pathogen-free conditions in a
12-h light-dark cycle with food and water provided ad libitum. All animal procedures were
performed in accordance with institutional guidelines approved by the Animal Care and Use
Committee at the University of Southern California (Protocol Number: 20287).

In Vivo Infusion of Small Molecule Drugs
The in vivo pharmacological candidate screening concentrations were chosen according to
Pieper et al., 2010. Sulindac sulfone (16805, CAYMAN chemical company), Imatinib
(CDS022173, Sigma), Arachidonyl Trifluoromethyl Ketone (A231, Sigma), Estradiol (E2758,
Sigma), Harmol Hydrochloride Monohydrate (sc-295137, ChemCruz), Hippeastrine
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Hyodrobromide (L6062, Latoxan), and Carbimazole (PHR1684, Sigma) were dissolved at 10
mM stock in DMSO as stock solutions. The working solution of the compounds were prepared at
1 mM concentration (except Estradiol at 0.1mM) in artificial cerebrospinal fluid (aCSF: 128 mM
NaCl/2.5 mM KCl/0.95 mM CaCl2/1.9 mM MgCl2); 10% DMSO in aCSF served as the vehicle
control. Mice were randomized for the treatments. The compound or vehicle was infused
intracerebroventricularly (i.c.v.) into the fimbria (-0.8 mm anterior, 0.75 mm lateral, and 2.5 mm
deep to Bregma) of the mouse through a surgically implanted cannula (Brain Infusion Kit 3,
Alzet, Cupertino, CA) attached with the osmotic minipump (1007D, Alzet, Cupertino, CA) at a
constant rate of 0.5 μl/hr for up to 7 days.
Minipumps were filled with working solutions of the compound/vehicle and primed in saline
(0.9% NaCl) for 12 hr at 37°C prior to the surgery. Mice were injected with non-steroidal anti-
inflammatory analgesic, Ketoprofen (5 mg/kg), prior to anesthesia. Mice were then anesthetized
with isoflurane (5% until recumbent, 2-3% maintenance) and fixed to a stereotaxic frame after
loss of the paw withdrawal reflex. The osmotic pump was implanted subcutaneously over the
scapulae and fitted to an intraven-tricular cannula. Following surgery, all mice were housed in
groups for the remainder of the experiment until designated sacrifice time.

Contextual fear conditioning.
In this task, mice learned to associate the environmental context (fear conditioning chamber)
with an aversive stimulus (mild foot shock; unconditioned stimulus, US) enabling testing for
hippocampal- dependent contextual fear conditioning. To assess amygdala-dependent cued fear
conditioning, the mild foot shock was paired with a light and tone cue (conditioned stimulus,
CS). Freezing behavior was used as a readout of conditioned fear. Specific training parameters
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are as follows: tone duration is 30 seconds; level is 70 dB, 2 kHz; shock duration is 2 seconds;
intensity is 0.6 mA. This intensity is not painful and can easily be tolerated but will generate an
unpleasant feeling. On the training day (day 1), each mouse was placed in a fear-conditioning
chamber and allowed to explore for 2 min before delivery of a 30- second tone (70 dB) and light,
ending with a 2-second foot shock (0.6 mA). Two minutes later, a second CS-US pair was
delivered. On the testing day (day 2), each mouse was first placed in the fear-conditioning
chamber containing the same exact context, but with no CS or foot shock. Freezing was analyzed
for 1–2 minutes. One hour later, the mice were placed in a new context containing a different
odor, cleaning solution, floor texture, chamber walls and shape. Animals could explore for 2
minutes before being re-exposed to the CS. Freezing was analyzed for 1–3 minutes using a
FreezeScan video tracking system and software (Cleversys, Inc).

Novel object recognition.
During the habituation phase (day 1), mice could freely explore an empty arena for 10 minutes.
During the training phase (day 2), two identical objects were placed in the habituated arena, and
mice could explore the objects for 5 minutes. For the testing phase (day 3), one object was
replaced with a novel object, and mice could explore the objects for 5 minutes. Time spent
exploring each object was quantified using the Smart Video Tracking Software (Panlab; Harvard
Apparatus). Two different sets of objects are used. To control for any inherent object preference,
half of the mice are exposed to object A as their novel object and half to object B. To control for
any potential object-independent location preference, the location of the novel object relative to
the trained object is also varied. The objects were chosen based on their ability to capture the
animal’s interest, independent of genetic background or age. To determine percent time with
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novel object, we calculate (Time with novel object)/(Time with Trained Object + Time with
Novel Object) * 100(50). In this preference index, 100% indicates full preference for the novel
object, and 0% indicates full preference for the trained object. A mouse with a value of 50%
would have spent equal time exploring both objects. Mice that did not explore both objects
during the training phase were excluded from analysis.

Tissue Processing
Mice were deeply anesthetized with isoflurane and then transcardially perfused with 4%
paraformaldehyde (PFA) in 0.1 M phosphate buffer (pH 7.4) after PBS. The brains were isolated
and postfixed overnight in 4% PFA, followed by incubation in 30% sucrose solution for a
subsequent 48 hours prior to sectioning. Frozen coronal sections in 45-µm thickness through the
entire dentate gyrus were then performed using a sliding microtome (SM2010R, Leica, Wetzlar,
Germany).The sections were then transferred to a cryoprotectant solution (27.3% sucrose, 45.5%
glycerol, 27.3% ethylene glycol) and stored at −20°C until processing for immunohistochemistry
staining.

Immunostaining, Confocal Imaging, and Processing
Every 8 coronal sections of the drug infused hemisphere were stereologically sampled
throughout the entire hippocampus (5~6 sections per dentate gyrus), mounted on SuperFrost Plus
slides (Thermo Scientific), dried overnight, rinsed in PBS, incubated in 0.01 mol/L citric buffer
(pH6.5) for 40 min at 95~98°C, and rinsed again in PBS. Subsequently, the sections were
incubated 2 overnights at 4°C with primary antibodies against the following antigens: Nestin
(1:500, goat, AF2736, R&D Systems), Mcm2 (1:500, mouse, 610701, BD Laboratories), and
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Doublecortin (DCX) (1:200, rabbit, ab18723, Abcam). Sections were then washed with PBS and
incubated in appropriate secondary antibodies conjugated to fluorophores (1:200, Jackson
Immunoresearch, West Grove, PA) with DAPI counterstaining (1:5000, 10236276001, Roche).
Stained sections were washed, air dried, and coverslipped with PVA/DABCO.
Images of the stained sections were acquired as a tiled z-stack across the area and the depth
containing the dentate gyrus region using a confocal microscope system (Axio.A1 Observer with
LSM700 Scanhead, Carl Zeiss, Germany) at 400X. Morphological and co-labeling analysis was
done using ZEN 2012 SP1 (black edition, Carl Zeiss, Germany).

Quantification of Neural Stem Cell Activation and Neurogenesis
Confocal images were used to identify the cell identity according to immunohistological and
morphological properties (Extended Data Fig. 3a) through coexpression of markers in the same
cell: Nestin+ only cells with radial glial branch as quiescent neural stem cells (qRGLs), RGL
morphological Nestin+/Mcm2+ cells as active neural stem cells (aRGLs), and DCX+ only cells
as neurogenesis. The neural stem cell activation rate percentages were calculated through
dividing the number of active neural stem cells by the number of total neural stem cells (both
Nestin+/Mcm2- and Nestin+/Mcm2+ cells). The total neural stem cell numbers and neurogenesis
data were quantified by cells / mm3.

Hierarchical Clustering
hclust base function in R was used to group cells with the Ward minimum variance method.
Clustering was performed independently on younger and older datasets. In both age groups, two
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super clusters were formed. Cluster assignment was used to broadly orient visualization of
downstream pseudotime analyses.

Pseudotime Calculation
Pseudotime was computed on younger and older single cell data sets independently, employing a
slightly modified version of Waterfall (Shin et al. 2015) to draw trajectories. We substituted the
existing k-means and minimum spanning tree step with a LOESS through PC1 and PC2. This
smoothed corners along the trajectory and removed the arbitrary hyperparameter k in k-means.
The loess base function in R was used with default degree and span. As with Waterfall,
orthogonal projections were drawn onto the LOESS trajectory, which was then flattened.
Pseudotime is defined as the fractional distance of any one point along the trajectory between the
two most distal points.

Repression Point Detection
Density estimation of cells across pseudotime was performed using the density() base function in
R. Lagged difference was then computed on the estimated density values to approximate the first
derivative of the density function. We defined the repression point as the pseudotime at which
the population drops off the fastest, which is equivalent to the global minimum of the first
derivative of the density function.

Dynamic Time Warp
Relational mapping between older and younger pseudotimes was computed via the Dynamic
Time Warp algorithm (Weste et al., 1983). The 5,000 highest variable genes were selected for
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template query mapping. To reduce noise, fitted values from LOESS regression of genes across
pseudotime was substituted for raw expression estimates. Both open.begin and open.end
parameters were assigned TRUE. The Rabiner-Juang step pattern was used (Giorgino, 2009).
Mappings for each index of pseudotime between young and old were returned.

Transition Signature Derivation
The repression point calculated in older cells was mapped onto younger cells through dynamic
time warp. We iterated across all detected genes, using gene expression as training data to fit the
parameters of a hidden Markov model comprised of two states, representing ON and OFF
expression. The Viterbi algorithm (Rabiner, 1989) was then used to decode expression into ON
and OFF states. A filter was then applied, selecting for two categories of genes. UP genes, which
were < 80% OFF prior to repression point and > 80% ON after repression point and DOWN
genes which were > 80% OFF prior to repression point and < 80% ON after repression point.

Connectivity Map and Reranking
Upregulated and downregulated components of the transition signature were passed through the
Connectivity Map 02 (https://portals.broadinstitute.org/cmap/), returning permuted results for
each of the 1,309 compounds. We took the weighted sum of min-max normalized connectivity,
exponentiated null rate and squared experiment number. These together gave a new priority
value, which were ordered into a new ranking.

78

Statistical Analysis
Data are reported as observed values atop quartiles. Statistical analyses were performed using
unpaired t-tests or One-Way analysis of variance (ANOVA) followed by post-hoc analyses with
the Turkey comparison as appropriate. All statistical analyses were performed using R software.
Statistical significance was defined as p<0.05.

5.9. Discussion and conclusions
Here we introduce ROOT, a single-cell pharmacotranscriptomic drug discovery pipeline,
and demonstrate its strong candidate enrichment efficiency. Further, we reveal that SS-induced
NSC capacity repair is neither transient, nor at the expense of NSC depletion. This represents a
paradigm shift, as previous work investigating genetic and small molecule interventions have
been limited by transience or NSC pool depletion and thus did not introduce sustainable
intervention candidates for NSC regeneration. In this work, we have shown that SS-induced
cellular restoration of NSCs corresponds to substantial improvements in DG-associated cognition
across multiple behavioral tests and NSC pool expansion for further cognitive reservoir as a
promising translational regenerative medicine against age-related dementia.
ROOT introduces a number of conceptual and algorithmic innovations to
pharmacotranscriptomics and single cell analysis. Repression point detection, transition signature
detection, and ROOT connectivity map scoring represent novel approaches to address significant
challenges in biomedical research. Repression point detection was inspired by the joint
observations of both increases in NSC quiescence across age and the quality of semi-discreteness
between adjacent cell states across NSC differentiation (Llorens-Bobadilla et al., 2015; Shin et
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al., 2015). If cells within each state were normally distributed about the state mean position in
pseudotime, then the point at which population drops off the fastest is given by the inflection
point. With repression point detection, we simply inferred the inflection point from kernel
density estimation. Transition signature detection builds upon repression detection, identifying
the ensemble (Huang et al., 2005) of genes responsible for transitioning across the repression
point. Together these techniques are germane to age-associated dysfunction across somatic stem
cells, where there remains a need to identify where state change bottlenecking is occurring in
somatic stem cells with age and program with which to transition across it (Brooks and Robbins,
2018; Koyuncu et al., 2015). Although the connectivity map has been used to great effect (Musa
et al., 2018), the often small number of biological replicates can lead to large numbers of false
positives and false negatives (Segal et al., 2012). In this work, it was critically important that we
examine as broad a sample of compounds as possible. ROOT scoring allows for this by weighing
heavily for mean connectivity score. This allows compounds with only a few biological
replicates to rank highly if the connectivity is strong enough.
This work was designed with and performed on archival scRNA-Seq data from Shin et al.
(2015). Although the underlying transcriptional data proved to be sufficient to make powerful
predictions, they are limited in known and potentially important ways. Principally, the
sequencing sampled only a relatively small survey of cells, was single-end (SE), not paired-end
(PE), and did not have insertion of unique molecular identifier (UMI) sequences in the gene
library preparation process to mitigate PCR over-amplification biases.
PE sequencing has the advantage of allowing for significantly increased confidence in
isoform mapping and identification (Hwang et al., 2018). Recent published work by Ibryeva et
al. (2021), presented in chapter 3, investigated hippocampal NSCs with scRNA-Seq at
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comparable timepoints to those explored in ROOT, but were generated with PE sequencing.
Application of ROOT to transcriptional data from Ibryeva et al. (2021) may reveal isoform-level
components TS, which have the potential to illuminate more specific signaling receptivity.
Very recent work in my graduate lab aimed to generate transcriptional profiles of young
and aged hippocampal NSCs with droplet-based10x Chromium library preparation (10X; 10X
Genomics, Pleasanton, CA). 10x Chromium allows for extreme high-throughput, single-cell gene
library preparation and sequencing, in the range of thousands to hundreds of thousands of cells.
10x chemistry also generates PE amplicons and includes a step to insert UMI sequences.
Sequencing for this present ROOT project yielded roughly 2.1 million reads per cells, while
recent 10x work yielded roughly 150 thousand reads per cell, which is a reduction of over 90%.
However, the 10x experiments contain over a thousand NSCs and downstream progeny. The
tradeoff between sequencing depth per cell and sampling breadth across cells can prove
worthwhile insights, revealing rare cell populations and states that are often missed with non
droplet-based methods (Wang et al., 2021). Application of ROOT to young and aged NSCs
processed through 10x could carry a number of potential benefits, including reduction of PCR-
noise through consideration of UMIs, PE-based mapping to reveal isoform-specific transcripts,
but most importantly, the expansion of sample set could provide a more faithful representation of
neurogenesis, revealing rare cell states that could ultimately lead to a more faithful derivation of
the TS.
Despite the sampling limitation of this study, ROOT proved to be a powerfully predictive
tool. I envision ROOT having broad application across many tissues and disease domains. The
key difference between historical differential expression-based connectivity mapping and ROOT
is the exploitation of pseudoemporal resolution to determine the true underlying molecular
81
program associated with disease. There is a quiet revolution in drug discovery, shifting from
single-target-based approaches and network-based approaches (Ferguson et al., 2018; Gerring et
al., 2021) which consider the essential high-dimensional nature of cell type and state changes
(Huang et al., 2005). ROOT’s design was inspired by the network-based approach to drug
discovery and is the reason why the TS is a set of hundreds of genes and not a few select targets.
The generalizability of the TS derivation technique also means ROOT has highly generalizable
application. With scRNA-Seq and ROOT, there’s the potential to generate intervention
candidates for quite literally any disease with a transcriptional dimension.

Chapter 6. Summary, Conclusions and Future Directions
Over the span of my graduate career, I have been very fortunate to collaborate with
researchers covering a wide breadth of topics in regenerative medicine. This work has been
guided by a central theme of using and developing tools to better understand the high-
dimensional nature of stem cells in order to promote improved cellular function and healthy
aging.
In collaboration with Dr. Askary, I designed TDA, a function for examining the
importance of any one sample or set of samples in maintaining the structural stability of a gene
regulatory network. This was used to identify key modules in facial skeletal patterning, and
highlight new marker genes for localized cell types. Although designed for the specific use case
in Zebrafish, it’s a highly generalizable function and can be used when characterizing any
transcriptional data.
My collaboration with Dr. Ferguson in his effort to characterize chondrogenesis
expression programs over the course of tissue ontogeny and through adulthood was primarily
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resource-building. However, it was important to perform this transcriptional work carefully.
Ultimately, we constructed a transcriptional roadmap, characterizing the transcriptome of each
stage of chondrogenesis and tissue differentiation. This serves as a guide for future
experimentalists who are interested in building model systems that are more transcriptionally
representative of true chondrocytes in developing limbs.
Published work in collaboration with Dr. Ibrayeva involved the development of expanded
RNA Velocity functionality, enabling us to examine age-group-level effects on differentiation
dynamics. Notably, we observed that the future expression states of non-active NSCs in older
mice were preferentially oriented in the direction of continued quiescence relative to NSCs from
younger animals. Differential expression analysis between older and younger NSCs followed by
GO term enrichment revealed an aging expression program that contained many of the hallmarks
of molecular aging. Further, deconstruction of the underlying aging GO term topology revealed
Abl1 was a key hub gene and aberrantly expressed in older mice.
ROOT represents the primary focus of my doctoral work. For this project, I built a
pharmacotranscriptomic pipeline designed to reveal the hidden repression point in NSC aging
and the transcriptional program associated with transitioning out of quiescence and ultimately tie
the transcriptional program to candidate intervention compounds to restore NSC function. Three
out of the five tested candidate compounds improved NSC activation after 7 days of treatment.
This represents a significantly improved hit rate over conventional high throughput screening
experiments.
Because the transition signature detection algorithm was designed to capture the full
ensemble of genes responsible for driving NSCs out of neurogenesis, we hypothesize that
delivery of candidate compounds will better phenocopy young neurogenesis in the neurogenesis
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of older animals than experimental interventions attempted historically. Indeed, in the 28-day
condition, we see persistent improvements in NSC activation, an increase in the total volume of
neurogenesis and importantly, no evidence of depletion of the stem cell pool. Finally, SS treated
animals exhibited dramatic improvements in behavioral tests of learning and memory.
In this thesis, I’ve outlined cellular and behavior validation of ROOT’s predictions, most
comprehensively on SS. However, both cellular behavior and cognitive effects of SS are high-
level, depending on underlying molecular factors that drive the biological system towards
improvement. ROOT prioritization assumes that small molecule intervention candidates will
induce expression of the transition signature and that this transcriptional program is what drives
improved cellular behavior, though this is not explicitly known. It is unlikely that any one
candidate comprehensively induces expression of the entire transition signature, and much more
likely that it only induces part of it. Investigating the expression induction by each compound
may reveal gene set correlations with cellular and behavioral phenotypes as well.
Mitigating or reversing aging effects has been very difficult, historically (Goodell and
Rando, 2015; Kennedy and Pennypacker, 2014; Trindade et al., 2013). This is likely because
aging itself is multifactorial, driven by a considerable number of coordinated causal factors. The
hallmarks of aging span scales and biophysical modalities, which means no one target of
intervention can serve as panacea. Mitigation or reversal strategies in aging that focus on only
one target are hamstrung by this.
The driving philosophy behind ROOT is that interventions which take a high-
dimensional or multifactorial approach give themselves more opportunities to succeed. Simple
differential expression can produce large lists of differentially expressed genes between healthy
and diseased tissue, and sometimes that alone is sufficient to characterize the set of targetable
84
genes. However, in the case of aging in adult hippocampal neurogenesis, we designed ROOT to
specifically focus on the transition signature, which is the expression program at the moment of
transition out of quiescence. In effect, ROOT enables one to focus characterization specifically
on the dysfunctional process, then target that process with small molecule interventions.
Although specific, the characterization is high-dimensional, considering the full ensemble of
genes associated with the dysfunctional process.
Because it is inherently general, I believe that ROOT holds the potential to address
cellular dysfunction and disease well beyond aging in NSCs. Single cell resolution enables
ROOT to detect aberrant cellular trajectories, characterize the dysfunctional expression
signature, then probe for interventions that reverse expression of the dysfunctional program.
With little to no algorithmic modifications, ROOT can be applied to address age-related
dysfunction in hematopoietic cells, mesenchymal stem cells, intestinal stem cells and satellite
cells, as there are many common aging phenotypes (Grajeda et al., 2021). More broadly,
ROOT’s targeted approach may also hold applications to any disease area in which the primary
affected tissue exhibits a strong transcriptional signature such as cancer (Uhlen et al., 2017) and
diabetes (Palmer and Kirkland, 2016).

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Abstract (if available)

Abstract As global life expectancy increases, so too does the proportion of age 65 and older individuals. This represents a positive trend for human thriving, but also introduces new challenges for society and the individual. With age, the incidence of disease increases dramatically, leading to a concordant increase in healthcare costs, where neurological diseases are disproportionately costly relative to incidence. There is therefore a powerful need to discover new ways of promoting healthy aging. Adult hippocampal neurogenesis, subserved by a population of neural stem cells in the dentate gyrus, is an important component in normal cognitive function. Aging leads to dysfunction among neural stem cells, characterized by dramatic increase in quiescence. This process correlates strongly to decline in learning and memory. Motivated by this, I developed ROOT: Revealing Origins and Ontological Targets. ROOT is a novel pharmacotranscriptomic pipeline, applied to the problem of age-associated functional decline of hippocampal adult neurogenesis in order to rescue it. ROOT leverages single cell transcriptional information to derive the developmental timepoint in neurogenesis over which NSCs in older hippocampi fail to transition, revealing the “transition signature” that NSCs in younger hippocampi utilize to exit quiescence. The transition signature is then used to generate a ranked list of candidate small molecule interventions. The top five compounds were tested in aged mice, revealing three compounds which significantly improved neural stem cell proliferation acutely. One compound, Sulindac Sulfone (SS), exhibited long-term improvements in neurogenesis, stem cell activation and stem cell pool size. SS treatment animals also displayed improved learning and memory against vehicle controls. Here I present an overview of the major bioinformatic developments I led and contributed to across my tenure as a PhD student, organized in chronological order of publication, ending with a chapter on ROOT, the primary focus of my time as Ph.D. student. This work has been guided by a central theme of using and developing tools to better understand the high-dimensional nature of stem cells in order to promote improved cellular function and healthy aging.

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Asset Metadata

Creator Bay, Maxwell Markus (author)

Core Title ROOT: a novel pharmacotranscritomic pipeline for rescuing age-associated functional decline of hippocampal adult neurogenesis

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Neuroscience

Degree Conferral Date 2022-05

Publication Date 05/03/2022

Defense Date 03/07/2022

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

Tag adult neurogenesis,aging,Computational Biology,drug discovery,neural stem cells,OAI-PMH Harvest,pharmacotranscriptomics,Regeneration,scRNA-seq

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Ichida, Justin (committee chair), Bonaguidi, Michael A. (committee member), Cobrinik, David E. (committee member)

Creator Email maxmbay@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111208392

Unique identifier UC111208392

Document Type Dissertation

Format application/pdf (imt)

Rights Bay, Maxwell Markus

Type texts

Source 20220503-usctheses-batch-937 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu