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Characterization of metabolically distinct muscle resident fibro-adipogenic subpopulations reveals a potentially exploitable mechanism of skeletal muscle aging and disease
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Characterization of metabolically distinct muscle resident fibro-adipogenic subpopulations reveals a potentially exploitable mechanism of skeletal muscle aging and disease
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Content
Characterization of Metabolically Distinct Muscle
Resident Fibro-Adipogenic Subpopulations Reveals
a Potentially Exploitable Mechanism of Skeletal
Muscle Aging and Disease
by
Maxwell B. Ederer
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
(DEVELOPMENT, STEM CELLS AND REGENERATIVE MEDICINE)
December 2020
Copyright 2020 Maxwell Ederer
ii
Acknowledgments
I would first like to thank my mentor, Joe Rodgers, for sticking with me through the
rollercoaster ride that was my time as a graduate student, especially at the end with the COVID-
19 virus and all the challenges that came with it. I would not have been able to get through
everything without his patience and diligence. In his lab, I was given the independence to pursue
all the angles of my project.
I would also like to thank all my lab mates in the Rodgers lab, especially Sanjana Ahsan,
Andrew Chareunsouk, Manmeet Raval, and Rajiv Tiwari for always being there for me, having
excellent discussions, and being exceptional co-workers; and Deep Sidhpura and XiangYue Hu,
with whom I worked directly, for being such hard workers and for putting up with my tangents. I
am also grateful for my committee members, Gage Crump and Francesca Mariani for working
with me through my project and giving me good advice along the way, and the USC Keck
School of Medicine Department of Stem Cell Biology and Regenerative Medicine as a whole for
all the support and inspiration. Also, thanks to Cristy Lytal and Andy McMahon for helping me
with the final push of this thesis.
Thank you to all my friends for helping me stay sane, especially Lars St. Pierre, Aaron
Stavely, and Daniel Mullen. I hope we have many more adventures ahead. Special thanks to
Steven Bloom for pushing me at the beginning of my graduate school career and to Tuo Shi for
pushing me at the end of it. I would never have finished this thesis without Tuo’s steady
insistence.
Finally, I would like to thank my family for being awesome and loving and just the
absolute best. I feel immeasurably blessed to be related to such wonderful people.
iii
Table of Contents
Acknowledgments ii
List of Figures v
Abstract vi
Introduction 1
Results 5
Fibro-adipogenic progenitors can be isolated via FACS and show a
bimodal MitoTracker Deep Red fluorescent signature. 5
Functional differences between MTDR
HI
and MTDR
LO
FAP
sub-populations in vitro. 9
FAPs can change their metabolic phenotypes. 14
FAP sub-populations shift in response to injury. 17
FAP sub-populations can be identified in situ and are associated
with scar tissue. 20
MTDR
LO
FAPs are associated with impaired regeneration. 24
Discussion 27
Supplemental Figures and Tables 31
Further Discussion of Other Parts of My Project 37
Exploring the effects of changing FAP culture conditions 37
Different rates of apoptosis in FAP sub-populations 42
Validating and comparing MitoTracker Deep Red to other
mitochondrial and superoxide stains 44
Adipose derived stem cells 46
Attempts at in vivo MTDR staining 48
Cell and mitosis tracking 49
Materials and Methods 53
Isolating FAPs from mus musculus 53
Plating and culturing cells 56
Performing Expectation Maximization of Gaussian Mixture 56
Making ECM coated chamber slides 61
MitoTracker Deep Red (MTDR) Staining 62
Quantification of per-cell staining intensities in CellProfiler 63
Basic immunostaining 65
Mito Stress Test 66
EdU incorporation assay 67
Oil Red O staining with immunohistochemistry 68
Quantification of full image fluorescence using CellProfiler 69
iv
Transplantation of GFP+ cells into recipient mice 70
Bulk Differential Expression RNA-seq 72
Basic procedure for mouse surgeries 76
Sciatic nerve injury 77
Synergistic ablation of gastrocnemius muscle 78
Barium chloride and glycerol injury 79
Freezing injury 80
Bibliography 81
v
List of Figures
Figure 1: Fibro-adipogenic progenitors can be isolated via FACS and show
a bimodal MitoTracker Deep Red fluorescent signature. 8
Figure 2: Functional differences between MTDR
HI
and MTDR
LO
FAP
sub-populations in vitro. 12
Figure 3: FAPs can change their metabolic phenotypes. 16
Figure 4: FAP sub-populations shift in response to injury. 19
Figure 5: FAP sub-populations can be identified in situ and are associated
with scar tissue. 22
Figure 6: MTDR
LO
FAPs are associated with impaired regeneration and age. 26
Supplemental Figures:
Supplemental Figure 1: Supplemental Figure 1: FAP sub-populations
do not change in proportion during isolation 31
Supplemental Figure 2: Some injury models do not induce a change
in MTDR
LO
proportion 32
Supplemental Figure 3: MTDR
LO
and MTDR
HI
FAPs have
differential gene expression 33
Table 1: Top 20 upregulated genes in MTDR
HI
cells 34
Table 2: Top 20 upregulated genes in MTDR
LO
cells 35
Table 3: Components of the basal Lamina 36
Figure I: Secreted Factors induce changes in differentiation to
MTDRHI FAPs in vitro 40
Figure II: DMH1 inhibits MTDRHI FAPs from differentiating into
adipocytes. 41
Figure III: FAP sub-populations show no difference to apoptosis
in response to hydrogen peroxide 43
Figure IV: MTDR costained with MTO and MTG 45
Figure V: FAPs have similar MTDR staining patterns to cells
found in Subcutaneous Adipse Tissue 47
Figure VI: Tracking and analyzing MuSCs using timelapse microscopy 52
vi
Abstract
Skeletal muscle is a highly robust and regenerative tissue. However, certain injuries,
diseases, and conditions, such as overuse, muscular dystrophy, and aging, are known to increase
fibrosis in the muscle. Muscle fibrosis and scarring replace normally functional, muscle tissue
with excess deposits of connective tissue. This leads to losses in strength and flexibility. There is
evidence that a normally beneficial population of muscle resident mesenchymal stem cells,
Fibro/Adipogenic Progenitors (FAPs), are largely responsible for muscle scarring and fibrosis
pathologies. The role of FAPs and the mechanism by which they function in normal and
pathologic conditions is poorly understood. Here, I describe the identification and
characterization of two subpopulations of FAPs in mouse skeletal muscle that have differential
functional roles in muscle fibrosis. These two sub-populations can be distinguished based on
differential levels of mitochondrial activity and staining by the mitochondrial specific dye,
MitoTracker Deep Red (MTDR). Young, healthy mice have FAPs comprised of, on average,
28% MTDR
LO
FAPs and 72% MTDR
HI
FAPs. During an injury response, these proportions
shift, but return to normal with injury resolution. However, the proportion of MTDR
LO
FAPs
increases with age as well as in response to fibrosis-causing injury. Transplantation of MTDR
LO
,
but not MTDR
HI
, FAPs into an injured muscle accordingly leads to poor regeneration and greater
accumulation of extracellular matrix. In vitro, MTDR
LO
FAPs preferentially differentiate into
fibroblasts, whereas MTDR
HI
FAPs preferentially differentiate into adipocytes. Collectively, My
data suggests that these inter-convertible FAP sub-populations have differential roles in muscle
fibrosis and aging. The shift towards a greater proportion of MTDR
LO
FAPs is potentially an
underlying cause of age-associated muscle fibrosis. This implies that altering the proportions of
MTDR
LO
and MTDR
HI
subpopulations could accordingly alter the risk of fibrosis. An
understanding of how FAP subpopulations are regulated could lead to treatments for muscle
fibrosis.
Introduction
Fibro-adipogenic progenitors (FAPs) are named for their propensity to differentiate into
fibroblasts, myofibroblasts, and adipocytes. FAPs are mesenchymal stromal cells that reside in
between skeletal muscle fibers, but outside of the thin layer of connective tissue called the
endomysium that surrounds each individual fiber (Biferali, Proietti, Mozzetta, & Madaro, 2019);
(Uezumi, Fukada, Yamamoto, Takeda, & Tsuchida, 2010); (Joe et al., 2010). FAPs have been
characterized as CD31
-
/CD45
-
/α7 integrin
-
/PDGFRα
+
/Sca-1
+
/CD34
+
and are readily isolated via
Fluorescence Activated Cell Sorting (FACS), which has enabled us to learn about their key
characteristics and behaviors (Judson, Low, Eisner, & Rossi, 2017; L. Liu, Cheung, Charville, &
Rando, 2015). A heterogeneous population of cells, FAPs exist in a spectrum of states in
response to various signals from the rest of the muscle (Stuelsatz, Shearer, & Yablonka-Reuveni,
2014). That state is highly dependent on the microenvironment (Dunn et al., 2019). In
homeostasis, FAPs are quiescent and secrete important factors for maintaining the extracellular
matrix (ECM) architecture of the skeletal muscle niche (Chapman, Mukund, Subramaniam,
Brenner, & Lieber, 2017; Dunn et al., 2019; Reggio, Rosina, Palma, et al., 2020; Schmidt,
Schüler, Hüttner, von Eyss, & von Maltzahn, 2019).
FAPs are one of the many different cell types involved in the dynamic interplay of
skeletal muscle regeneration (Juban et al., 2018); (Wosczyna & Rando, 2018). When a muscle is
injured, a robust, acute inflammatory response starts the regenerative process. (Peake, Neubauer,
Della Gatta, & Nosaka, 2017). FAPs and fibroblasts proliferate and give rise to a transient FAP
population (Murphy, Lawson, Mathew, Hutcheson, & Kardon, 2011); (Malecova et al., 2018),
which supports myoblast proliferation, muscle fiber fusion, and regeneration (Boppart, De Lisio,
Zou, & Huntsman, 2013) and is essential for revascularization, restructuring, clonal expansion,
2
and the removal of pro-fibrotic FAPs (Santini et al., 2020a). During the resolution phase of
skeletal muscle regeneration, transient FAPs undergo apoptosis in response to tumor necrosis
factor (TNF) signals from macrophages and are cleared by monocytes , preventing post-injury
fibrosis (Lemos et al., 2015).
The microenvironment ultimately controls whether FAPs facilitate the activation of other
stem cells or lay down fibrotic extracellular matrix(X. Liu et al., 2016); (Dunn et al., 2019).
Chronic inflammation, muscular dystrophies, injuries, and aging can all shift FAPs toward a
lingering, fibrogenic fate (Mann et al., 2011). TGFβ signaling via SMAD2 has been found to
inhibit apoptosis in FAPs, increasing risk of fibrosis (Lemos et al., 2015). However, fibrosis has
also been shown to be caused by connective tissue growth factor (CTGF) independently of TGFβ
(Rebolledo et al., 2019), by inhibition of STAT3 and IL-6 (Madaro et al., 2018), and by intronic
polyadenylation of PDGFRα (Contreras & Brandan, 2017).
Normally, inflammation is good for FAPs: Il-15 increases FAP proliferation and reduces
adipogenesis through JAK/STAT (Kang et al., 2018); and IL4 and IL13 help FAPs facilitate
muscle regeneration and clear up debris (Heredia et al., 2013). However, chronic inflammation
can lead to fibrosis, an aberrant overproduction of ECM proteins, leading to a long lasting scar
and eventually to tissue dysfunction (Mann et al., 2011). Chronic inflammation also causes FAPs
to accumulate, resist apoptosis (Saito, Chikenji, Matsumura, Nakano, & Fujimiya, 2020), and
assume a fibrotic or adipogenic phenotype (Muñoz-Cánoves & Serrano, 2015) due to
insensitivity to Notch inhibition (Marinkovic et al., 2019).
During muscular dystrophies, FAPs persist in high numbers (Malecova et al., 2018). Wnt
signaling is much lower in dystrophic FAPs, causing dysregulation of the surrounding tissue
(Reggio, Rosina, Palma, et al., 2020). Other injury types, such as denervation, can also induce
3
adverse phenotypes, such as accumulation of FAPs (Kopinke, Roberson, & Reiter, 2017);
(Madaro et al., 2018).
As we age, FAPs also lose effectiveness (Ancel, Mashinchian, & Feige, 2019) and are
more likely to differentiate into fibroblasts and adipocytes instead of remaining FAPs.
Correspondingly, older muscle becomes more and more likely to develop fibrotic and fatty
tissues, which impair function and lower a person’s quality of life (Brioche, Pagano, Py, &
Chopard, 2016). Aging FAPs secrete fewer supporting factors, such as WISP1. These are
important for satellite cell expansion and commitment (Lukjanenko et al., 2019). Potential
therapies other studies have indicated include metabolic alterations such as enhancing expression
of G6PDH (Brioche et al., 2016), decreasing caloric intake, or pushing FAPs toward fatty acid
oxidation(Reggio, Rosina, Krahmer, et al., 2020).
FAPs are also thought to be the major and potentially sole contributor to intramuscular
adipose tissue (IMAT) (Arrighi et al., 2015). The canonical Wnt/ B-catenin axis (Reggio, Rosina,
Palma, et al., 2020; Schmidt et al., 2019), the hormone β-klotho (Phelps, Stuelsatz, & Yablonka-
Reuveni, 2016), Gli1 (Moratal, Arrighi, Dechesne, & Dani, 2019), and annexin A2 in the ECM
(Hogarth et al., 2019) have all been shown to induce adipogenesis in FAPs. PDGFRα signaling
increases fibrotic potential at the cost of adipogenic potential by inducing FAP proliferation and
differentiation (Iwayama et al., 2015b; Reggio, Rosina, Palma, et al., 2020). Inhibition of
PDGFRα decreases fibrosis but impairs myogenesis (Fiore et al., 2016). Fibrosis and
adipogenesis are modulated dynamically (Kopinke et al., 2017; Lombardi et al., 2016). With age,
Desert Hedgehog (Dhh) signaling decreases and inhibits adipogenesis by changing the matrix
metalloproteinases expressed (Kopinke et al., 2017). Inactivity can reduce intramuscular adipose
tissue formation during healing (Pagano et al., 2015). An ischemic injury can make FAPs
4
respond to chronic inflammation as if it were acute, modulating FAPs to favor tissue repair over
fibrosis (Santini et al., 2020a).
Given their power to promote either healthy or unhealthy skeletal muscles, FAPs present
an excellent case as a therapy or as a target for therapy. Inducing FAP senescence can change the
inflammatory environment from chronic and pro-fibrotic to acute and pro-regenerative. (Saito et
al., 2020) Transplantation of young FAPs rescues the ability of HDAC inhibitors to increase
fiber size in old mdx mice (Mozzetta et al., 2013). A high fat diet can shift FAP metabolism
toward oxidative phosphorylation while lowering proliferation and fibrosis. This shift can lower
muscle necrosis and mitigate a dystrophic muscle phenotype (Reggio, Rosina, Krahmer, et al.,
2020). In addition, FAPs have similar functions in cardiac muscle (Lombardi et al., 2016),
adipose tissue (Arrighi et al., 2015), neural crest derived adipose tissue (Lemos et al., 2012), and
tendons (Harvey, Flamenco, & Fan, 2019a), meaning that therapies designed to keep skeletal
muscle healthy might also work in other tissues.
In this study, I look at the population-scale cellular dynamics of two metabolically
distinct sub-populations of FAPs: one with a high metabolic rate; and the other with a low
metabolic rate and a strong association with fibrosis. These metabolically distinct FAP sub-
populations correspond to sub-populations expressing high and low levels of PDGFRα
(Contreras et al., 2019), and have different fate preferences and expression profiles. I show that
these sub-populations are interconvertible, and that the proportions of each are governed by the
local microenvironment. Further, these sub-populations are dynamic and change in response to
injury, but eventually return to homeostatic levels. Shifting the balance between these two sub-
populations of FAPs can change the outcome of injury, serving as a potential therapeutic target.
5
Results
Fibro-adipogenic progenitors can be isolated via FACS and show a bimodal MitoTracker Deep
Red fluorescent signature.
To isolate fibro-adipogenic progenitors (FAPs), I used a FACS isolation protocol adapted
from Liu et al. and Judson et al. (Judson et al., 2017; L. Liu et al., 2015), selecting for single live
(PI negative) CD31
-
, CD45
-
, integrin α7
-
, Sca-1
+
cells from collagenase and dispase digested
muscle tissue (Fig 1A, details in Material and Methods). This selection process gated out cells
from endothelial, hematopoietic, and skeletal muscle stem cell (satellite cell) lineages,
respectively, and selected for parenchymal stem cells that stained positive for SCA-1. In the
skeletal muscle compartment, FAPs match these selection criteria. To test whether this
population matched the FAP markers and profile described by others, I performed fluorescent
immunohistochemistry (IHC), using primary antibodies recognizing PDGFRα, the cell surface
marker most commonly used to identify FAPs (Uezumi et al., 2014). I isolated fresh FAPs
immediately after sacrificing each animal, so that isolated cells were as close to their quiescent,
homeostatic state as possible.
During the staining step of the FACS protocol, I stained for mitochondria as well as for
positive and negative markers for FAPs, because mitochondrial content is important for the
activation of some stem cells (Raval et al., 2020). Mitotracker Deep Red (MTDR) is a small
molecule cell stain that fluoresces when there is an active mitochondrial membrane potential,
indicating active mitochondrial content. I was surprised to find that the fluorescence distribution
of MTDR staining in FAPs was bimodal (Fig. 1B), This finding was consistent across all mice
that I sorted for FAPs. As the FAP population in skeletal muscle is known to be heterogeneous
6
(Gatto, Puri, & Malecova, 2017), I set out to query if and how the two sub-populations of FAPs
represented by the two peaks are functionally relevant to skeletal muscle regeneration and repair.
To isolate cells from each of these sub-populations, I gated using the MTDR fluorescence
of FAPs during FACS. I divided the distribution at its lowest point and then made gates that
excluded 5% of the parent population on either side (Fig 1B). This method enabled us to achieve
purer sorts of both sub-populations and less contamination of one sub-population by the other.
To quantify the proportions of the sub-populations represented by the two peaks in the
MTDR distribution, I applied a statistical method called Expectation Maximization (EM) of
Gaussian Mixture Models (GMM). EM of GMM is a probabilistically grounded soft clustering
method useful if the clusters overlap and if the parameters—mean and variance—are unknown.
It starts with two random Gaussians (mean and variance) and assigns each data point to one of
the means. EM calculates the probability that a data point is from one distribution or the other. It
uses those probabilities to better fit the means and variances to the data and recalculate. I iterated
this process until the two Gaussians fit the data as closely as possible (Fig 1C). Fitting the
Gaussians meant that I knew the means and variances of both distributions. I then used this
information to calculate the proportion of the total population represented by each sub-
population. I consistently found that FAPs isolated from 3 month old animals adopted a
distribution where 27.7 ± 3.7% of FAPs displayed low staining intensity for MTDR (MTDR
LO
)
and 72.3 ± 3.7% had a high staining intensity (MTDR
HI
) (Fig. 1D). EM of GMM provided an
accurate method to distinguish the proportion of FAPs belonging to the MTDR
HI
sub-population
and the proportion belonging to the MTDR
LO
sub-population.
After FACS, I plated and cultured these two populations and found that the differences in
MTDR staining were readily apparent immediately after isolation (freshly isolated (FI)) (Fig.
7
1E), and even in FAPs stained 24 hours after isolation (Fig. 1F). Greater than 95% of both
MTDR
LO
and MTDR
HI
cells stained positive for PDGFRa, a marker of FAPs (Fig. 1G, 1H),
confirming that these are FAP subpopulations. Collectively, these data show that FAPs can be
divided into two populations based on MTDR staining intensity.
After confirming that I had two identifiably distinct FAP sub-populations, I investigated
how the MTDR
HI
and MTDR
LO
sub-populations were different from one another. Other studies
from the Rodgers Lab have shown that differences in MTDR staining and mitochondrial activity
are indicative of differences in activity when activated from quiescence (Raval et al., 2020). My
first thought was that the bimodal distribution of mitochondrial staining was indicative of a new
population quickly arising from these multipotent progenitors. The digestion and preparation for
FACS can take up to four hours. To test whether I was seeing a transient phenomenon caused by
the digestion process, I checked if the staining changed in the time it took to digest the cells. I
did the normal muscle digestion in preparation for FACS, but I waited to stain portions of the
sample to see if a longer time before staining and analysis had any effect on the distribution of
MTDR staining in the cells. In short, there was no difference between the distributions,
indicating that the cells do not change phenotype immediately in response to activation, and that
the two phenotypes are indicative of heterogeneity within the quiescent FAP population (Supp.
Fig 1A).
8
Figure 1: Fibro-adipogenic progenitors can be isolated via FACS and show a bimodal MitoTracker Deep Red
fluorescent signature.
A) FACS plots showing the gating sequence to isolate live FAPs. FAPs are characterized as single cells, which are
CD31
-
, CD45
-
, Intα7
-
, SCA-1
+
and stain negative for propidium iodide as they are alive.
B) FAPs have a bimodal MTDR fluorescent distribution. FACS histogram of Mitotracker Deep Red fluorescence of
FAPs showing gating method to isolate each sub-population.
C) FACS histogram from B with overlaid gaussian distributions calculated via Expectation Maximization of
Gaussian Mixture models (EM of GMM) a soft clustering method.
D) Proportions of each calculated sub-population predicted by EM of GMM. Mean calculated from n = 12 FACS
experiments with mice ranging in age from 60-180 days old. Each experiment had 100,000 total events. Student’s T
test p = 3.9x10
-19
.
E) Fluorescent microscopy of FAPs isolated according to sub-population. MTDR
LO
(left) MTDR
HI
(right). Cells
pictured were allowed to adhere to chamber slides for either 1 hour (top) or 24 hours (bottom). Scale Bar = 10 µm
F) Quantification of mean MTDR fluorescence of freshly isolated FAPs from MTDR
LO
and MTDR
HI
sub-
populations. N = 12 FACS experiments. Student’s T test p = 5.8x10
-16
G) Fluorescent microscopy of FAPs using antibodies against PDGFRα (orange) with DAPI (blue). Cells have been
allowed to adhere for 24 hours. Scale Bar = 10 µm.
H) Nearly all isolated FAPs from both MTDR
LO
and MTDR
HI
sub-populations are PDGFRα
+
. N = 463 MTDR
HI
cells, 662 MTDR
LO
cells. Quantification of PDGFRα fluorescence from Fig 1G, using per cell measurements of a
CellProfiler pipeline. Two sample Z- test p = 0.0823
9
Functional differences between MTDR
HI
and MTDR
LO
FAP sub-populations in vitro.
Next, I tested if MTDR intensity reflected functional metabolic differences between
MTDR
HI
and MTDR
LO
subpopulations. I did this by analyzing Oxygen Consumption Rate
(OCR) and ExtraCellular Acidification Rate (ECAR) of MTDR
LO
and MTDR
HI
subpopulations
using a Seahorse bioanalyzer. I found that the MTDR
HI
subpopulation had both a higher basal
OCR and a higher basal ECAR (Fig 2A, 2B). These data indicate a higher level of both
mitochondrial respiration and glycolysis in the MTDR
HI
sub-population, and that MTDR staining
is reflective of differences in the metabolic activity of these FAP subpopulations.
Differences in a quiescent stem cell population can cause major differences in when that
population activates (i.e., enters the cell cycle) when a tissue is undergoing regeneration and
repair. Previous work had shown that changes in mitochondrial activity of MuSCs, at the time of
isolation, strongly correlated with speed by which they activate (Raval et al., 2020; Rodgers et
al., 2014). Lemos at al. (2015) have also shown that during skeletal muscle regeneration, the
FAP population goes through a massive proliferation event followed by a massive apoptotic
event. I wanted to see if MTDR
HI
and MTDR
LO
FAPs proliferated in the same proportions and at
the same rate. The implications from other projects in the Rodgers lab (Raval et al., 2020) and
my result that MTDR
HI
FAPs have greater metabolic rate (Fig. 2A) led me to hypothesize that
activated MTDR
HI
FAPs would proliferate faster than MTDR
LO
FAPs. To determine if the
MTDR
LO
and MTDR
HI
populations had differences in activation potential, I measured the
dynamics with which they incorporated EdU nucleotide as a measure of entry into the cell cyle
(Fig. 2C). I stained freshly isolated FAPs, which I let attach for an hour, and FAPs cultured for
24, 48, and 72 hours in DMEM+20%FBS. In response to being taken out of the skeletal muscle
niche, both sub-populations activated and started to proliferate.
10
24 hours after isolation 0.92% of MTDR
LO
and 0.75% MTDR
HI
FAPs had incorporated EdU
(Fig. 2D). After 48 and 72 hours in culture there was a progressive increase in the percentage of
FAPs that incorporated EdU, however there were no statistically significant differences in the
percentage of EdU
+
MTDR
LO
and MTDR
HI
FAPs at any time point (Fig. 2D).
I also wanted to determine if one of the sub-populations could represent cells in vivo that
were already proliferating or actively cycling. I injected 50 mg/kg EdU intraperitoneally and
digested and sorted for FAPs the next day. I then plated the cells, allowing them to attach for an
hour, then stained for EdU. Virtually none of the cells plated were EdU
+
(Fig. 2D), confirming
that FAPs exist in a quiescent state in homeostatic muscle, and that neither of the two sub-
populations represent a subset of actively cycling cells. Collectively, these results suggest that
the MTDR
LO
and MTDR
HI
FAP have similar cycling properties in normal conditions in vivo and
following isolation.
With previous reports of FAPs being multipotent (Arrighi et al., 2015; Dunn et al., 2019;
Joe et al., 2010; Moratal et al., 2019; Uezumi et al., 2010) and my own RNA-seq and Seahorse
experiments indicating functional differences between MTDR
HI
and MTDR
LO
FAPs, I looked at
how these two sub-populations differentiate in vitro. FAPs from each sub-population were
cultured in DMEM + 20% FBS at 50 thousand cells per well on an 8-well chamber slide coated
with ECM. They grew to confluence by day 5 and started differentiating. At two weeks, I stained
them with an antibody for α-smooth muscle actin (αSMA) and with Oil Red O to look at
differentiation into myofibroblasts and adipocytes (Fig. 2E). I observed a high frequency of cells
that adopted an adipocyte morphology and stained positive for Oil Red O in culture of MTDR
HI
FAPs (Fig. 2E). I found that few MTDR
LO
FAPs adopted characteristics of adipocytes.
However, the majority of MTDR
LO
FAPs displayed characteristics of fibroblasts: they stained
11
positive for α-smooth muscle actin (αSMA) and had a fibroblast-like morphology (Fig. 2E). I
also observed these fibroblast-like cells in cultures of MTDR
HI
FAPs, albeit with much lower
frequency than in cultures of MTDR
LO
FAPs (Fig. 2E). To quantify the fluorescence, I randomly
took seven images from across a plate and calculated the average total fluorescence across all
seven images from the 488 channel (αSMA) and the 645 channel (ORO) (Fig. 2F, 2G).
12
13
Figure 2: Functional differences between MTDR
HI
and MTDR
LO
FAP sub-populations in vitro
A) Agilent Seahorse Mito Stress Test showing differences in Oxygen Consumption Rate between the two FAP sub-
populations. Final cell counts were taken just prior to assay, and measurements were normalized to 10,000 cells.
After the first 4 timepoint measurements, oligomycin is injected to 1.5 µM. After timepoint 8, FCCP is injected to
1.5 µM, and after timepoint 12, Rotenone and Antimycin are both injected to 0.5 µM. N = 3. Student’s T test on the
grand mean of the first 4 measurements (basal OCR) p = 1.7x10
-7
.
B) Quantification of extracellular acidification rate, denoting glycolytic respiration during the first 4 (basal)
measurements. Final cell counts were taken just prior to assay, and measurements were normalized to 10,000 cells.
N = 3 wells, 4 timepoints. Student’s T test p = 8.9x10
-7
.
C) Experimental setup showing process for EdU staining.
D) EdU incorporation in FAP sub-populations. MTDR
HI
and MTDR
LO
FAP sub-populations were isolated and
cultured in excess EdU. At days 1, 2, and 3, the cells were then stained for EdU and imaged. Resultant images were
hand counted and quantified. For the in vivo measurement, mice were injected with 50 mg/kg EdU. FAPs were then
isolated and stained for EdU. N = 3 for all timepoints in vivo, 48, 72 hrs. N = 7 at 24 hrs. Student T test p > 0.05 for
all timepoints.
E) Immunofluorescent and Oil Red O staining on MTDR
HI
and MTDR
LO
cells cultured in DMEM+20%FBS for 2
weeks. Stains shown are DAPI (blue), anti-αSMA (gray), Oil Red O (orange), and merge (far right). Scale bar = 50
μm.
F) The MTDR
LO
sub-population has greater staining against αSMA than the MTDR
HI
sub-population. Quantification
is average fluorescence of 7 scenes per sub-population, taken randomly from culture, measured via Cellprofiler
Pipeline. Student’s T test p = 0.025
G) The MTDR
HI
sub-population has greater Oil Red O staining than the MTDR
LO
sub-population. Quantification is
average fluorescence of 7 scenes per sub-population taken randomly from culture, measured via Cellprofiler
pipeline. Student’s T test p = 0.015
14
FAPs can change their metabolic phenotypes.
The average MTDR fluorescence of MTDR
LO
and MTDR
HI
FAPs, by definition, is
different at the time of isolation. I tested if the MTDR fluorescence signature of cultured FAPs
remains distinct between the MTDR
LO
and MTDR
HI
subpopulations over 72 hours in vitro. I
analyzed fluorescent imaging on a per cell basis. To do this, I ran my images through a
CellProfiler pipeline designed to use DAPI staining to identify individual cells and MTDR
staining to expand the area around each nucleus to include pixels above a negative threshold and
within a defined distance. With the cytoplasmic area defined, the pipeline takes the average pixel
fluorescence data for each cell. After 24 hours in culture, the average fluorescence per cell is still
different between the two subpopulations (Fig. 3A). However, by 72 hours, the average
fluorescence per cell is no longer statistically significantly different between the two
subpopulations (Fig. 3A). This finding indicates that individual FAPs in vitro are capable of
changing their metabolic phenotype as indicated by MTDR staining, and that it takes them days
to do so.
To determine if FAPs can change MTDR phenotype in vivo, I used an injury transplant
model (Fig. 3B). I transplanted cells from a transgenic donor mouse into a congenic recipient
and then reanalyzed the MTDR distribution within transplanted cells. I performed intramuscular
injection of BaCl2 into the Tibialis Anterior muscle to induce an injury. Two days after injury, I
isolated 250k MTDR
LO
and 250k MTDR
HI
FAPs from mice that constitutively express eGFP
under the ubiquitin C promotor (Ubi-GFP) (https://www.jax.org/strain/004353) and transplanted
them into the recipient mice where the TA muscle had been subject to injury (Fig. 3B). Two
weeks after transplantation I assessed engraftment by dissecting recipient muscle and quantifying
GFP
+
, SCA-1
+
, CD31
-
, CD45
-
, Inta7
-
FAPs (Fig. 3C). All recipient mice were found to have
15
GFP
+
cells when muscle was digested and analyzed via FACS. I found no significant differences
in engraftment between the MTDR
LO
and MTDR
HI
populations (Fig. 3D). My analysis consisted
of looking both at the MTDR profile of transplanted cells via FACS as well as identifying cells
in situ in flash frozen skeletal muscle sections. FAPs, therefore, are engraftable, can survive
apoptosis, and can maintain their identities as FAPs. They are also capable of surviving the
massive apoptotic event following injury resolution. This bodes well for the development of
future cell therapies involving FAPs, since these hardy cells survive and maintain their
population.
When sorting for GFP
+
FAPs in non-GFP recipient mice, I find GFP
+
MTDR
HI
and GFP
+
MTDR
LO
sub-populations in proportions comparable to the native MTDR
HI
and MTDR
LO
FAP
subpopulations (Fig. 3E, 3F). GFP
+
MTDR
HI
and GFP
+
MTDR
LO
FAP sub-populations in
recipient mice much more closely resemble native proportions than do FAPs purified from one
subpopulation when isolated for transplantation (Fig. 3E, 3F). This implies that individual FAPs
adjust their phenotype in response to their environment and possibly in response to signaling
from the FAP population as a whole.
16
Figure 3: FAPs can change their metabolic phenotypes.
A) Average MTDR fluorescence of cultured FAPs from MTDR
HI
and MTDR
LO
sub-populations, cultured 1, 2, and 3
days post isolation. Values are normalized to 1. Fluorescence is measured on a per cell basis via CellProfiler
pipeline. By day 3, MTDR fluorescence is indistinguishable between the two cultures. Student’s T test p = 2.2 x10
-22
(24 hrs), p = 1.15x10
-13
(48 hrs), p = 0.310 (72 hrs)
B) Experimental setup of GFP
+
FAP transplantation experiment
C) FACS gates showing GFP
+
FAPs
D) The percentage of FAPs in donor mice that stain positive for GFP. Neither sub-population engrafts at a higher
rate than the other. N = 3 mice. Student’s T test p = 0.22
E) Proportion of MTDR
LO
sub-population in donor (GFP
+
) FAPs both pre- and post- transplantation, as well as total
resident FAPs from recipient mice. Donor cells were sorted to be MTDR
LO
. Pre-transplant donor FAPs have
significantly different proportions than donor FAPs post-transplant (Tukey’s HSD p = 8.30x10
-5
) and from recipient
native FAP population (Tukey’ HSD p = 3.78x10
-5
). The difference between post-transplant FAPs and the recipient
native population is not statistically significant (Tukey’s HSD p = 0.3167). N = 3. One-way ANOVA p = 2.96x10
-5
.
F) Proportion of MTDR
LO
sub-population in donor (GFP
+
) FAPs both pre- and post- transplantation, as well as total
resident FAPs from recipient mice. Donor cells were sorted to be MTDR
HI
. Pre-transplant donor FAPs are
significantly different from donor FAPs post-transplant (Tukey’s HSD p = 0.04953) and from recipient native FAP
population (p = 0.04986). The difference between post-transplant FAPs and the recipient native population is not
statistically significant (p = .99998). N = 3. One-way ANOVA: p = 0.02937
17
FAP sub-populations shift in response to injury.
Previous work has shown that muscle injury activates FAPs and that they are critical for
efficient muscle repair (Fiore et al., 2016; Wosczyna et al., 2019). I wanted to examine how our
FAP sub-populations responded to injury and then activates stem cells in the skeletal muscle
compartment. My injury model of choice was, again, a 1.2% barium chloride injection. In this
model, fully regenerated tissue is almost indistinguishable from uninjured muscle, and I get even,
widespread tissue damage. I performed muscle injuries on mice on subsequent days, then sorted
for FAPs and analyzed MTDR staining as describe prior (Fig. 4A). This provided snapshots of
the MTDR fluorescence distribution throughout the healing process. The proportions of FAP
subpopulations on day 14, following injury resolution, are the same as the proportions of FAPs
from an uninjured TA indicating a return to homeostatic levels (Fig. 4B). Similar to the in vitro
experiments, the two sub-populations started with differential MTDR fluorescence, but became
indistinguishable after day 2 (Fig. 4C). However, as healing concluded, the FAPs reconstituted
their pre-injury MTDR proportions, and the two sub-populations re-emerged once more. The
results from this experiment suggest that the mitochondrial phenotype of FAPs can and does
change in vivo, but that the proportions of the two FAP sub-populations return to a homeostatic
set point as the injury response resolves.
To test whether this homeostatic set point was a response to environmental stimuli, I set
out to change the microenvironment of skeletal muscle and determine if this caused a shift in the
proportions of the sub-populations. I therefore decided to try different injury models that cause
major changes within the muscle and to analyze MTDR at two weeks post injury. Using the
standard barium chloride injection as a control, I tested four additional injury models to induce
different states in the muscle environment. Synergistic ablation is a model in which excision of a
18
muscle (in this case the gastrocnemius) causes hypertrophy in its agonist (the Soleus). Resection
of the sciatic nerve denervates the leg muscles and causes a state of atrophy. Injection of 50%
glycerol causes intramuscular adipose tissue formation, and freeze injury causes intramuscular
fibrosis. As with the BaCl2 injection, I saw no difference in MTDR
HI
and MTDR
LO
FAP
proportions between uninjured muscle and resolved muscle from the glycerol injection,
synergistic ablation, or nerve resection (Supp. Fig. 2). However, in the freeze injury model, the
proportion of MTDR
LO
FAPs was significantly higher than in matched uninjured controls (Fig
4D). This proportional increase is long-lasting and, like fibrosis, does not resolve. I observed a
higher MTDR
LO
sub-population even two months following freeze injury (Fig. 4E). This result
strongly implies that fibrotic ECM and MTDR
LO
FAPs are linked.
19
Figure 4: FAP sub-populations shift in response to injury.
A) Experimental setup of barium chloride injury timecourse
B) No difference in the calculated proportion of MTDR
LO
sub-population from uninjured contralateral leg and a
barium chloride injured leg 14 days post injury. N = 4 mice, Student’s T Test p = 0.412.
C) FACS histograms of MTDR fluorescence from FAPs isolated from injured tibialis anteriors over the course of 2
weeks.
D) Legs receiving a freeze injury have a higher proportion of MTDR
LO
FAPs 14 days post injury when compared to
an uninjured leg. N = 3 mice, Student’s T Test p = 0.00037.
E) Legs receiving a freeze injury have a higher proportion of MTDR
LO
FAPs 2 months post injury when compared
to an uninjured leg. N = 3 mice, Student’s T Test p = 0.0011.
20
FAP sub-populations can be identified in situ and are associated with scar tissue.
MTDR does not provide good staining of FAPs when injected intravenously or
intramuscularly, and it cannot stain frozen muscle sections because it requires an active
mitochondrial membrane potential. I therefore isolated cells from each sub-population and
performed Differential Expression Bulk RNAseq to analyze differences in gene expression and
to find candidates I could use to identify sub-populations in situ.
While Bulk RNA-seq does not give us the cellular resolution of single cell RNA-seq, it
does allow for deep reads of defined sub-populations. I found 267 genes more highly regulated in
MTDR
LO
FAPs and 255 genes more highly regulated in MTDR
HI
FAPs. Gene Ontology analysis
gave us a glimpse of potential targets for future studies. From the genes upregulated in the
MTDR
LO
cells, I found terms mostly relating to ion channel complexes, regulation of other cells,
and ECM (Supp. Fig. 3A). Modulation of ion channels within the mitochondrial membrane can
alter the inner membrane potential. This controls how quickly the flow of H+ ions flow through
ATPases and is also important for the binding of the MTDR stain. As mentioned previously,
MTDR requires an active membrane potential to bind to its target. Regulation of ion
concentrations within the mitochondria would be a good place to start exploring if and how to
pharmaceutically alter the proportions of FAP sub-populations. The list of genes upregulated in
the MTDR
LO
sub-population also included inhibitors of major signaling pathways such as
GREM2, a BMP inhibitor; WIF1, SFRP2, and SFRP4, Wnt signaling regulators; ISM1, an
angiogenesis inhibitor; CCDC3, a TNF inhibitor; and PAPPA2, an IGF inhibitor. This was in
contrast to the MTDR
HI
sub-population, where SMAD protein signal transduction was a top hit
in gene ontology, and BMP1-7 were all upregulated. In addition, the MTDR
HI
upregulated gene
list contained all components of the skeletal muscle basal lamina (Supp. Table 3). These results
21
suggest that MTDR
HI
cells are more involved in ECM production, and MTDR
LO
cells are more
regulatory.
The most differentially regulated genes expressed were Lamβ1, which codes for the β1
laminin subunit and was highly expressed in MTDR
HI
FAPs, and Grem2, which codes for a
BMP antagonist and was highly expressed in MTDR
LO
FAPs. These were the two genes for
which we bought RNAscope probes. RNAscope uses an RNA probe with several fluorophore
attachment sites to amplify fluorescence signal. I found that cells with a higher than average
fluorescence from Lamβ1 and lower than average fluorescence from Grem2 were from the
MTDR
HI
sorted sub-population 89% of the time, whereas cells with a lower than average Lamβ1
fluorescence and greater than average Grem2 fluorescence were from the MTDR
LO
sorted sub-
population 95% of the time (Fig. 5B). Some cells in both sub-populations stained ambiguously,
so even though my analysis method correctly identified cells from either sub-population, it did
not identify every cell from each sub-population (Supp Fig 3B).
To analyze the distribution of MTDR
LO
and MTDR
HI
FAPs in situ, I used RNAscope for
Grem2 and Lam 1 expression respectively as a proxy for identifying these subpopulations on
cryosections from both uninjured muscle and muscle which had received a freeze injury. I used a
Cellprofiler pipeline to identify cells from each sub-population (Fig. 5C, 5D) and overlaid
positional data onto Hematoxylin and Eosin stained serial sections. Grem2 and Lam 1 high
expressing FAPs seem to be interspersed throughout uninjured muscle, showing no localization
preference (Fig. 5E). In fibrotic muscle, however, the fibrotic areas had many more FAPs in
general, including more Grem2 expressing FAPs (Fig. 5F). These results support the idea that
FAPs are associated with fibrotic tissue and that perhaps close association and a higher
proportion of Grem2+, MTDR
LO
FAPs are important for scar formation and maintenance.
22
23
Figure 5: FAP sub-populations can be identified in situ and are associated with scar tissue.
A) Volcano plot showing differentially expressed genes from bulk RNAseq of MTDR
LO
FAPs and MTDR
HI
FAPs.
Gray denotes genes more highly expressed in MTDR
LO
FAPs p<0.05 and Log 2Fold Change >1. Orange denotes
genes more highly expressed in MTDR
HI
FAPs p<0.05 and Log 2Fold Change >1. The two most differentially
expressed genes were GREM2 and LAMβ1.
B) RNAscope fluorescence of isolated FAPs in vitro. MTDRHI FAPs have greater LamB1 staining, whereas
MTDRLO FAPs have greater GREM2 staining. Blue = DAPI, green = GREM2, Orange = LAMB1, Magenta =
PDRFRa. Scale Bar = 5 µm
C) MTDR
HI
and MTDR
LO
FAPs from uninjured muscle cryosections. RNAscope with probes to GREM2 (green)
(highly expressed in MTDR
LO
FAPs), LAMβ1 (orange, highly expressed in MTDR
HI
FAPs), PDGFRα (magenta,
highly expressed in all FAPs), and DAPI (blue). Scale Bar = 5 µm
D) MTDR
HI
and MTDR
LO
FAPs from freeze injured muscle cryosections. RNAscope with probes to GREM2
(green) (highly expressed in MTDR
LO
FAPs), LAMβ1 (orange, highly expressed in MTDR
HI
FAPs), PDGFRα
(magenta, highly expressed in all FAPs), and DAPI (blue). Scale Bar = 5 µm
E) H&E stained Serial section of uninjured tibialis anterior muscle section analyzed for RNAscope overlaid with
positional data of predicted MTDR
HI
(orange) and MTDR
LO
(gray) cells. Scale Bar = 150 µm
F) H&E stained Serial section of freeze injured, fibrotic tibialis anterior muscle section analyzed for RNAscope
overlaid with positional data of predicted MTDR
HI
(orange) and MTDR
LO
(gray) cells. Scale Bar = 150 µm
24
MTDR
LO
FAPs are associated with impaired regeneration.
My next transplantation experiments illustrated that the MTDR
LO
sub-population is
associated with scar tissue and worse healing. I transplanted MTDR
LO
and MTDR
HI
FAPs into
the injured tibialis anterior (TA) to see if artificially increasing the proportion of one sub-
population or the other would change the outcome of regeneration (Fig 6A). In these
experiments, I pre-injured mouse TAs with barium chloride to prime the area for transplantation.
Two days later, I isolated FAPs from each sub-population from donor mice. As quickly as
possible, I transplanted about 250 thousand cells into the experimental leg, and the same volume
of saline into the contralateral leg. CFDA-SE, a cell permeable fluorescent dye, was added to the
cell suspensions, and tattoo ink was coated on the outside of the transplantation needle in order
to visualize where the cells were placed and to be sure that the correct muscle received cells. The
recipient mice were then allowed to heal for two weeks. The mice were then sacrificed and their
TA muscles flash frozen, sectioned, and stained with antibodies recognizing Laminin α2. I
visually saw tattoo ink in the muscle during sectioning and visualized CFDA-SE in the same
regions as well as in blood vessels (Fig 6B). The tattoo ink and CFDA-SE verified that the
muscle received FAPs. I imaged the whole muscle section and used the laminin staining as a
basis to create image masks, which could be used to calculate the total cross-sectional area of the
tibialis anterior, the area taken up by muscle fibers, and the interfibrillar area. Almost all fibers
had centrally located nuclei, indicating regeneration. TA muscles that received MTDR
LO
cells
showed poor regeneration, with greater interfibrillar space, and a lower percentage of the area
taken up by muscle fibers (Fig 6C). TA muscles receiving MTDR
HI
cells showed excellent
healing with no difference in muscle mass from control legs (Fig 6C). The analysis of this
experiment considered the muscle fibers from the entire tibialis anterior, not just the affected
25
area. The results indicate that the presence of a higher proportion of MTDR
LO
FAPs inhibit
proper regeneration.
To validate previous experiments (Figure 5F), that the shift in sub-population
proportions was localized to the site of injury, and that the FAPs were associated with fibrotic
areas within the muscle, I performed a freezing injury on the lateral gastrocnemius of one leg of
the mouse. At two weeks post injury, I sacrificed the mouse, cut each gastrocnemius into medial
and lateral halves, and digested them in preparation for FACS. After sorting, I analyzed MTDR
fluorescence and compared the proportions of the two sub-populations. I found that similar to
full-muscle freezing injuries, the proportion of MTDR
LO
cells increased. However, this
difference was only observed in the lateral muscle halves, the ones that had been frozen (Fig.
6D). This confirms what I learned from my RNAscope experiments: that FAPs cluster in scar
tissue. Not only do they cluster there, but there is also a significantly higher proportion of
MTDR
LO
FAPs, showing the importance of the MTDR
LO
sub-population to the scarring process.
Throughout this project, I found that a higher proportion of MTDR
LO
vs. MTDR
HI
cells
correlates with increased ECM deposition following injury. With age, skeletal muscle is more
likely to scar, with very young mice having almost no scarring at all (Mann et al., 2011). I was
therefore unsurprised when compiling data regarding the proportions of MTDR
HI
and MTDR
LO
sub-populations from mice of different ages. The proportion of FAPs that stain MTDR
LO
increases logarithmically with age (Fig. 6E) thus a higher MTDR
LO
sub-population correlates
well with the increased propensity for scarring in aging individuals. As we learn more about the
mechanisms of this imperfect repair process, we will improve our ability to affect a shift towards
a more complete process of regeneration.
26
Figure 6: MTDR
LO
FAPs are associated with impaired regeneration.
A) Tibialis anterior sections from muscles with MTDR
LO
FAPs transplanted (left) and MTDR
HI
FAPs transplanted
(right). CFDA-SA is from the injection site (green), and Laminin α2 (red) surrounds muscle fibers. DAPI (blue)
stains nuclei. Scale Bar = 150 µm
B) Injured area of tibialis anterior, where MTDR
LO
FAPs (left) and MTDR
HI
FAPs (right) have been injected.
Laminin α2 (red), CFDA-SE (green), DAPI (blue)
C) Muscles with MTDR
LO
FAPs transplanted in have significantly more space between muscle fibers than control
muscle injected with PBS (p = 0.049). Muscles with MTDR
HI
FAPs transplanted in show no difference in space
between muscle fibers than control muscle injected with PBS (p = 0.661). A lower score indicates more interfibrillar
space and greater fibrosis. N = 5. Paired T test used to control for surgery date and age of mice.
D) Freeze injury only induces fibrosis and higher MTDR
LO
proportion locally. Left lateral leg received freezing
injury. Right leg is uninjured control. N = 5. ANOVA p = 0.0046. Tukeys HSD LL – LM p = 0.0043. LL-RL p =
0.0627. LL-RM p = 0.0171. LM -RL p = 0.550. LM-RM p = 0.9030. RL-RM p = 0.9092
E) Mouse age correlated with MTDR
LO
proportion, calculated by EM of GMM from FACS data. Each dot
represents a mouse. N = 35, R
2
= 0.6696
27
Discussion
Skeletal muscle normally regenerates completely. However, injury can cause fibrosis,
which takes up functional space and makes muscles stiffer. Most fibrosis has been attributed to
ECM production by FAPs, a normally quiescent stem cell population that plays a supportive role
in muscle regeneration. As FAPs are a heterogeneous population in both mice and humans
(Contreras et al 2017), I have been studying how differences in this cell population affect
regeneration and the degree of fibrosis. My results indicate that the FAP population is made up
of two functionally distinct sub-populations which can be identified with MTDR, a
mitochondrial stain. MTDR
LO
FAPs contribute to worse regenerative outcomes and play an
inhibitory role, whereas MTDR
HI
FAPs contribute to the ECM and play a supportive role.
I studied FAPs using FACS to analyze staining patterns in populations of cells and to
isolate them for in vitro analysis and for transplantation. I looked at FAP staining, metabolism,
and differentiation in vitro and analyzed muscle cryosections to identify functional differences in
FAP sub-populations. I investigated cells in their native context as much as possible and
analyzed cells as quickly as possible after isolation to ensure that my results were not artifacts of
culture or protocols.
I found that there are two FAP sub-populations, which stain differentially for MTDR and
display differences in metabolism, differentiation, and RNA expression profiles. FAPs can
change phenotypes but take days to do so. MTDR
HI
and MTDR
LO
proportions shift with age and
in response to fibrosis. Transplantation of MTDR
LO
FAPs causes impaired regeneration with a
lower fibrillar fractional area, more fibrosis, and disorganized ECM. Higher proportions of
28
MTDR
LO
FAPs in a local area are associated with fibrotic tissue. Age is also correlated with
higher MTDR
LO
proportions.
Though both sub-populations are interconvertible, each has distinct signaling properties
and ECM production. The intrinsic differences between these sub-populations may be what
causes these differences in differentiation potential. There is a need for further research into
signaling pathways, as well as which environmental factors control the balance between
MTDR
LO
and MTDR
HI
FAPs.
My results suggest a mechanism whereby aging increases the proportion of MTDR
LO
FAPs, which increases the risk of muscular fibrosis following injury. This also implies that
fibrotic injury might increase the chances of future fibrosis by shifting the homeostatic
proportions of the FAP sub-populations. This loop might be part of why it is so difficult to get
rid of scar tissue, which might be making its own microenvironment and increasing the chances
of its own expansion.
FAPs make up 8-12% of mononuclear cells in skeletal muscle. High in numbers as well
as ECM and signaling protein secretions, FAPs play a large role in maintaining the skeletal
muscle niche. MTDR
LO
FAPs are higher in proportion in scar tissue than in healthy skeletal
muscle. As such, it’s reasonable to posit that MTDR
LO
FAPs play a role in actively maintaining
intramuscular scar tissue. Accordingly, MTDR
LO
FAPs make up the lowest proportion of FAPs
in young, fibrosis-resistant mice.
As an obvious next step, it would be useful to query which signals change FAPs of one
sub-population into FAPs of the other. This knowledge would inform efforts to control fibrotic
risk following large injuries or surgeries, and to modulate fibrosis in pathologies such as
29
muscular dystrophies. Mesenchymal stromal cells similar to FAPS are also present in almost
every tissue. Future therapies involving FAPs could be translatable to fibrosis in other organs as
well, underscoring the usefulness of further research on this cell population.
My efforts have focused mostly on FAPs during homeostasis or following injury
resolution, due to the fact that we lose definition of our sub-populations during regeneration.
However, other recent studies in the field indicate that a transient FAP population emerges
during regeneration. Malecova et al. (2018) suggest that this transient population is VCAM
HI
and
disappears with injury resolution. How these transient cells are different from FAPs and how
they function during regeneration are prime questions in the field.
Although both sub-populations express PDGFRα, this gene showed up in my RNA-seq
data as more highly expressed in MTDR
HI
cells. This finding is interesting, because Contreras
and his colleagues (2019) presented a bimodal PDGFRα fluorescence histogram very similar to
my MTDR fluorescence histogram. Their results indicate a higher PDGFRα
LO
proportion in
more fibrotic conditions, which agrees with my data. This becomes even more interesting in light
of the widespread disagreement in the field agree about the role of PDGF signaling in fibrosis
(Contreras & Brandan, 2017; Contreras et al., 2019; Contreras, Rebolledo, Oyarzun, Olguin, &
Brandan, 2016; Harvey, Flamenco, & Fan, 2019b; Iwayama et al., 2015a; Rebolledo et al., 2019;
Santini et al., 2020b), indicating a more complicated role.
My results also agree with more recent findings about the importance of metabolism to
FAP physiology (Reggio et al. 2020). Reggio and his colleagues have shown that altering
metabolism alters adipogenic potential, and I have observed a similar phenomenon in my cell
cultures, where MTDR
HI
cells spontaneously differentiate into adipocytes more readily than
MTDR
LO
cells. This also provides evidence that intramuscular adipose tissue (IMAT) deposition
30
is different than fibrosis, but that the two are not mutually exclusive. Many studies (Farup,
Madaro, Puri, & Mikkelsen, 2015; Hogarth et al., 2019; Juban et al., 2018; Madaro et al., 2018;
Malecova et al., 2018; Mann et al., 2011; Marinkovic et al., 2019; Moratal et al., 2019) have
shown that both fibrosis and adipogenesis are present in muscular dystrophies, but not in healthy
muscle. Clearly, there is more to unravel here.
An understanding of how to manipulate FAP subpopulation proportions could lead to a
useful treatment for muscle fibrosis before surgery or after large injuries with high scarring
potential. Knowledge of the subtleties of this cell population may even facilitate the growth and
implantation of new replacement muscle.
31
Supplemental Figures and Tables
Supplemental Figure 1: FAP sub-populations do not change in proportion during isolation
A) FACS histogram showing MTDR fluorescence of FAPs 0 – 4 hours post digestion
B) Quantification of average fluorescence of MTDR
LO
and MTDR
HI
cells, showing no difference in fluorescence
between timepoints
32
Supplemental Figure 2: Some injury models do not induce a change in MTDR
LO
proportion
A) Legs receiving a synergistic ablation injury have no difference in proportion of MTDR
LO
FAPs 14 days post
injury when compared to an uninjured leg. N = 7 mice, Student’s T Test p = 0.269.
B) Legs receiving a sciatic nerve resection injury have no difference in proportion of MTDR
LO
FAPs 14 days post
injury when compared to an uninjured leg. N = 3 mice, Student’s T Test p = 0.175.
C) Legs receiving a 50% glycerol injection injury have no difference in proportion of MTDR
LO
FAPs 14 days post
injury when compared to an uninjured leg. N = 4 mice, Student’s T Test p = 0.626.
33
Supplemental Figure 3: MTDR
LO
and MTDR
HI
FAPs have differential gene expression
A) GO terms for MTDR
LO
FAPs (right—gray) and MTDR
HI
FAPs (left – orange) from the top 10 hits from each
category
B) Quantification of images represented in 5B. LAMβ1 and GREM2 fluorescence are per cell average values
calculated via CellProfiler pipeline. MTDR
LO
(gray) and MTDR
HI
(orange) were isolated and allowed to adhere
overnight. Linear regression was done on all data points. Cells with greater than average Lamβ1 fluorescence and
above the linear regression (red outline) predict MTDR
HI
FAPs with 85.4% accuracy. Cells with greater than
average GREM2 fluorescence and below the linear regression (black outline) predict MTDR
LO
FAPs with 95.8%
accuracy. N = 158 cells, 24 predicted MTDR
LO
, 23 of which were actually MTDR
LO
; 41 cells predicted MTDR
HI
, 35
of which were actually MTDR
HI
.
C) Quantification of images represented in Fig 5G. LAMβ1 and GREM2 are per cell average values calculated via
CellProfiler pipeline. Cells analyzed had greater than average PDGFRα RNAscope fluorescence. Cells with greater
than average LamB1 and above the linear regression predict MTDR
HI
cells (Orange). Cells with greater than average
Grem2 and below the linear regression predict MTDR
LO
cells (Gray).
34
Table 1: Top 20 upregulated genes in MTDR
HI
cells
Symbol Entrez Gene Name Hi Lo
Expression
log ratio
Expr p-
value
LAMB1 laminin subunit beta 1 9981.028 1567.2 2.573 4.5E-43
RET ret proto-oncogene 1229.294 198.13 2.517 4.35E-35
SORL1 sortilin related receptor 1 1030.766 241.89 2.012 8.35E-27
Cmah
cytidine monophospho-N-
acetylneuraminic acid hydroxylase 751.792 176.31 1.991 2.04E-21
HSD11B1
hydroxysteroid 11-beta
dehydrogenase 1 2122.73 376.07 2.326 1.49E-20
NID2 nidogen 2 3522.974 1039.9 1.695 1.42E-19
MME membrane metalloendopeptidase 2385.425 494.45 2.129 4.64E-19
LIFR LIF receptor alpha 4104.624 1246.8 1.655 9.89E-19
ENPP2
ectonucleotide
pyrophosphatase/phosphodiesterase
2 5246.577 929.38 2.307 2.19E-18
PCSK6
proprotein convertase
subtilisin/kexin type 6 10726.007 2800.1 1.845 3.62E-18
VTN vitronectin 759.464 183.21 1.939 3.96E-18
VWA1
von Willebrand factor A domain
containing 1 930.139 258.19 1.765 7.57E-18
TEK TEK receptor tyrosine kinase 3058.47 888.56 1.707 1.66E-17
SMOC2
SPARC related modular calcium
binding 2 24288.201 6306.9 1.846 4.08E-17
COLGALT2
collagen beta(1-
O)galactosyltransferase 2 908.006 247.94 1.782 4.49E-17
LAMC3 laminin subunit gamma 3 537.474 116.36 2.064 5.61E-17
CXCL14 C-X-C motif chemokine ligand 14 8969.976 2812.3 1.606 2.65E-16
CAPN6 calpain 6 562.662 112.38 2.14 2.78E-16
ALDH1A3
aldehyde dehydrogenase 1 family
member A3 622.786 231.21 1.386 4.07E-16
THY1 Thy-1 cell surface antigen 1826.924 530.66 1.699 6.38E-16
35
Table 2: Top 20 upregulated genes in MTDR
LO
cells
Symbol Entrez Gene Name Hi Lo
Expression log
ratio
Expr p-
value
GREM2 gremlin 2, DAN family BMP antagonist 131.938 1426.7 -3.137 2.24E-29
CGREF1 cell growth regulator with EF-hand domain 1 69.591 428.04 -2.464 4.85E-25
ISM1 isthmin 1 angiogenesis inhibitor 92.01 518.36 -2.349 8.24E-25
KCNMA1
potassium calcium-activated channel subfamily M
alpha 1 317.714 983.49 -1.583 4.91E-22
COMP cartilage oligomeric matrix protein 1144.995 3814.4 -1.68 1.85E-21
CCDC3 coiled-coil domain containing 3 164.113 542.32 -1.668 3.82E-21
PAPPA2 pappalysin 2 IGF regulator 258.696 1360.7 -2.24 4.4E-20
ABCC9 ATP binding cassette subfamily C member 9 280.973 1637.3 -2.338 5.92E-18
FBLN1 fibulin 1 1734.831 6652.6 -1.839 1.11E-16
SLC24A1 solute carrier family 24 member 1 230.359 864.49 -1.809 2.8E-16
LETM2
leucine zipper and EF-hand containing
transmembrane protein 2 1041.377 3231.2 -1.569 7.12E-16
CSMD1 CUB and Sushi multiple domains 1 160.88 656.04 -1.904 1.44E-15
WIF1 WNT inhibitory factor 1 98.136 507.34 -2.171 6.31E-15
SFRP2 secreted frizzled related protein 2 855.629 2809.3 -1.634 1.35E-14
PIK3R5 phosphoinositide-3-kinase regulatory subunit 5 102.88 422.42 -1.9 1.88E-14
TIAM2 T-cell lymphoma invasion and metastasis 2 349.232 1023.4 -1.489 4.13E-14
SFRP4 secreted frizzled related protein 4 1229.374 3422.8 -1.422 5.26E-14
EGFL6 EGF like domain multiple 6 64.064 235.24 -1.768 6.75E-14
CSF2RB
colony stimulating factor 2 receptor beta common
subunit 66.355 269.83 -1.879 8.75E-14
SLC1A2 solute carrier family 1 member 2 158.235 791.75 -2.115 8.95E-14
36
Table3: Components of the basal Lamina and MTDR
HI
expression data showing all components significantly
higher expressed in MTDR
HI
cells except LAMB2
Entrez Gene Name Symbol Hi Lo
Expression Log
Ratio
Expression p-
value
laminin subunit alpha 2 LAMA2 24645.18 9834.757 1.244 0.000000197
laminin subunit beta 1 LAMB1 9981.028 1567.243 2.573 4.5E-43
laminin subunit gamma 1 LAMC1 28820.2 9663.816 1.501 1.97E-12
laminin subunit alpha 4 LAMA4 11280.74 4736.664 1.183 0.000000186
laminin subunit beta 2 LAMB2 11832.72 10584.23 0.153 0.458
collagen type IV alpha 1 chain COL4A1 28838.63 12486.48 1.169 2.33E-11
collagen type IV alpha 2 chain COL4A2 17672 7749.919 1.148 3.78E-10
collagen type IV alpha 3 chain COL4A3 1002.769 438.463 1.118 0.00000322
collagen type IV alpha 4 chain COL4A4 1916.001 856.683 1.111 2.62E-08
collagen type IV alpha 5 chain COL4A5 1072.019 682.958 0.616 0.00458
collagen type VI alpha 1 chain COL6A1 21128.59 11253.65 0.871 0.0000128
collagen type VI alpha 2 chain COL6A2 25404.46 12275.06 1.011 6.13E-08
collagen type VI alpha 3 chain COL6A3 30209.24 14807.86 0.989 0.000000279
collagen type VI alpha 6 chain COL6A6 5826.43 1523.36 1.824 3.72E-15
heparan sulfate proteoglycan 2 HSPG2 41977.95 15303.71 1.406 9.83E-15
nidogen 1 NID1 36882.73 13630.14 1.36 1.17E-09
nidogen 2 NID2 3522.974 1039.896 1.695 1.42E-19
dystroglycan 1 DAG1 3797.6 2287.318 0.699 0.000569
37
Further Discussion of Other Parts of my Project
The following discussion sections include pieces of my work as a PhD student that may
not have worked, were not related to my main project on FAPs, or were not relevant to the main
corpus of the project.
Exploring the effects of changing FAP culture conditions
After finding that isolated FAPs from different sub-populations preferentially
differentiated into different cells types when allowed to come to confluence, I decided to try
experiments looking at differentiation in culture. My Bulk RNA-seq data, as previously
mentioned, showed that the two sub-populations of FAPs had some differentially expressed
genes, but were the same cell type. There were no genes that one sub-population expressed while
the other did not, merely higher or lower expression of the same genes. This, along with the
findings from the GFP+ FAP transplantation experiment, suggested that MTDR
LO
and MTDR
HI
FAPs are not different cell types, and that these phenotypes are even interconvertible. As a
reminder, GFP+ MTDR
LO
cells transplanted into a recipient injured mouse were able to
reconstitute both MTDR
LO
and MTDR
HI
FAPs in the recipient mouse. This was true of MTDR
HI
transplanted cells as well. Given the similarities between these two sub-populations, it was
surprising to me that there was such a pointed and obvious difference in differentiation in culture
under basic conditions for maintaining mesenchymal cells. I suspected that the difference was
likely due to cellular signaling (external stimulus), as most of the most differentially regulated
genes code for proteins exported out of the cells.
38
My hypothesis was that MTDR
HI
cells secrete more of a particular signal than MTDR
LO
cells. To test this, I isolated MTDR
LO
and MTDR
HI
FAPs and plated them at 80,000 cells per
chamber on an 8-well chamber slide coated in ECM. I compared FAP cultures of each
subpopulation where I changed the media three times per day against changing the media once
every three days. I imaged these cultures after 2 weeks and did not find much difference in the
MTDR
LO
cell cultures. However, I found that in MTDR
HI
cultures, adipocyte formation was
much lower in the wells that were changed three times per day (Fig I). Aside from this, cells also
seemed smaller and all oriented in the same direction.
This got me thinking about FAP differentiation as a response to extracellular signaling. I
discussed this with one of my co-workers, Xi Chen in Qi-Long Ying’s lab, and he suggested that
I try some cell signaling inhibitors. After looking back at my RNA-seq data, I found that several
BMP ligands and receptors were upregulated in MTDR
HI
cells. Ingenuity Pathway analysis also
suggested that the upregulation of several genes in my list could be due to FGF or MAPK
signaling, for which Chen had inhibitors.
For this experiment, I tested MTDR
LO
and MTDR
HI
FAPs in DMH1 BMP inhibitor
(Sigma Aldrich D8946), PD173074 FGFR1 inhibitor (Sigma Aldrich P2499), and PD032591
ERK/ MAPK inhibitor (Selleck S1036). I also tested a low dosage of oligomycin, an ATP
synthase inhibitor also used for Seahorse experiments. For each of these conditions, I changed
the media every day and cultured the entire time with the inhibitor present in each new batch of
media. FAPs can survive a Mito Stress Test Seahorse run, where oligomycin, as well as other
metabolic inhibitors, are introduced. However, I found that culturing FAPs with even half the
concentration of oligomycin (750 nM) will kill them all after a couple days. For most conditions,
I saw no change from my control. Cells survived, reach confluence, and started to differentiate.
39
Cells from the MTDR
HI
sub-population continued to prefer differentiation into lipid-droplet
containing cells—either adipocytes or pre-adipocytes. Arrighi et al. (2015) have shown that
adipocytes made from FAPs are more closely related to cells from white rather than brown fat.
The one condition where I did see a change was in the cultures with 5 µM DMH1, a
selective inhibitor of BMP Receptor 1 that is upstream of SMAD proteins. BMP is also a known
signal leading to the commitment of pluripotent stem cell lines into an adipocyte lineage (Schulz
& Tseng, 2009). Cultured MTDR
LO
cells generally commit to a fibroblast lineage, whereas
MTDR
HI
cells are much more likely to differentiate into adipocytic cells. When both sub-
populations were cultured with DMH1, however, the MTDR
HI
sub-population, similar to the
MTDR
LO
sub-population, differentiated into mostly fibroblasts, with very few lipid droplet
containing cells (Fig II).
I would have liked to do more experiments looking at soluble factors contributing to the
differentiation of the two sub-populations of FAPs. However, I ultimately decided to shift my
focus to in vivo experiments. Cultured cells are a fine model system, but often behave completely
differently than they would in vivo. They are receiving different cellular signals, are attached to
different substrates, and are receiving different amounts of oxygen. Other groups have also
looked at FAP differentiation in vitro and observed that they readily differentiate into fibroblasts,
myofibroblasts, and adipocytes—hence the name fibro-adipogenic progenitor (Joe et al., 2010;
Uezumi et al., 2010). However, as mesenchymal stem cells found in a mesenchymal tissue, FAPs
can also be pushed to differentiate into other mesenchymal cell types in vitro, including bone and
cartilage (Wosczyna, Biswas, Cogswell, & Goldhamer, 2012). These factors all played a part in
my decision to focus on the aspects of my project that might be more relevant in a living system.
40
Figure I: Secreted Factors induce changes in differentiation to MTDRHI FAPs in vitro
A) When culture media is changed 3 times per day, MTDRHI FAPs are less likely to differentiate into Adipocytes.
Upper panels show fluorescence microscopy of MTDRHI FAPs cultured for 2 weeks. DAPI (blue) PDGFRa (green)
HSAP47 (orange) Oil Red O (Magenta). Lower panels show a similar culture in brightfield. Scale Bar = 50 µm
41
Figure II: DMH1 inhibits MTDRHI FAPs from differentiating into adipocytes.
A) Brightfield Images of MTDRHI FAPs 2 weeks in culture with DMH1 (right) and without (left). Scale Bar = 50
µm.
42
Different rates of apoptosis in FAP sub-populations
In skeletal muscle satellite cells, greater MTDR staining is associated with an increase in
proliferation rate in response to injury (Raval et al., 2020). I found no difference in proliferation
rates between MTDR
LO
and MTDR
HI
FAP sub-populations (Fig 2D). Thus, lower mitochondrial
activity might indicate higher susceptibility to apoptosis. Alternatively, cells with high
metabolisms might be more susceptible to apoptosis because they produce more free radicals. To
test this, I introduced media with 500 μM hydrogen peroxide into my FAP cell cultures and
recorded a timelapse, imaging every 10 minutes for 4 days. I visualized apoptosis using
CellEvent Caspase3/7 detection substrate, as well as propidium iodide. Cells express Caspase 3/7
and fluoresce accordingly before dying and becoming porous enough to allow for the influx and
fluorescence of the propidium iodide. All cells that fluoresced green with Caspase3/7 fluoresced
red with propidium iodide soon after. To analyze the timelapse, I followed each cell on screen
and noted if and when it died. Unfortunately, there was too much variance both within and
between experiments to make any solid conclusions. This experimental design also had its
problems. As in all in vitro experiments, cells are likely to behave differently than they would in
vivo. The massive FAP apoptosis during the resolution of injury is almost certainly not mediated
by hydrogen peroxide. The experimental condition I chose is not very biologically relevant. This
might be an experiment to repeat after more mechanistic studies. It is currently unknown which
signal mediates apoptosis in the FAP population post injury resolution. If I were to investigate
this signal, the data might be different, cleaner, and more relevant. As it stands, neither MTDR
HI
nor MTDR
LO
FAPs show a greater susceptibility to hydrogen peroxide mediated apoptosis in
vitro (Fig. III). With this limited data, I decided to focus on other aspects of the two sub-
populations and how they affect regeneration.
43
Figure III: FAP sub-populations show no difference to apoptosis in response to hydrogen peroxide
A) Box and whisker plot showing the lifetime of FAPs cultured in hydrogen peroxide. Student’s T Test p > 0.05
44
Validating and comparing MitoTracker Deep Red to other MitoTracker Stains
When I first started investigating FAPs, I found that they have a bimodal fluorescence
profile when stained with Mitotracker Deep Red (Fig. 1B). This result was the seed for my thesis
project and implies two FAP sub-populations with differential staining properties. Mitotracker
Deep Red is a small molecule that requires an active mitochondrial membrane potential in order
to fluoresce. Differential staining, therefore, also implies functional differences. Our lab was
founded on the idea that the metabolism of a cell can determine its effectiveness during
regeneration. With all this in mind, I wanted to confirm that the staining I saw was not an artifact
of that specific stain. I also wanted to confirm that the differences I was seeing were functional
and not structural. More specifically, I wanted to see if any differences I observed were
associated with greater mitochondrial mass and/or cell size, or with mitochondrial output and
chemical processes.
There are several different colors of Mitotracker stain. Each is a little different: some will
only fluoresce in the presence of an active mitochondrial membrane, whereas others have less of
a need for active mitochondria and are better for measuring mitochondrial mass. MitoTracker™
Orange CMTMRos (Thermo Fisher M7510) is the MitoTracker most sensitive to an active
mitochondrial membrane, whereas MitoTracker™ Green FM (Thermo Fisher M7514) is a better
stain for experiments more focused on mitochondrial mass than activity. I wanted to use
MitoTracker Deep Red (MTDR), which is also good for visualizing mitochondrial activity and
much easier to fit into the FACS antibody/fluorophore combinations available to us.
I performed several FACS experiments, during which I stained FAPs with combinations
of MitoTracker Deep Red and either MitoTracker Green or MitoTracker Orange. As expected,
MTO and MTDR correlated nicely (Fig IVA). Both gave bimodal distributions, and cells that
45
stained high for one stain also stained high for the other. MTDR and MTO showed that there
were two sub-populations of FAPs with differentially active mitochondria. These differences
were functional and were also seen in the Mito Stress Seahorse Assay (Fig 2A, 2B).
Interestingly, MTG and MTDR correlated for the most part, but there was a group of cells that
stained high for MTG but low for MTDR (Fig. IVB). This result indicated that there were cells
with low mitochondrial activity, but high mitochondrial mass. Similarly, there was a group of
cells that stained low for MTDR, but high for MSR. This MTG
HI
/MTDR
LO
group was the same
as the MSR
HI
/MTDR
LO
group. These could be dying or stressed cells, as the experimental setup
prevented me from using PI or DAPI as dead cell stains. Alternatively, they could be cells
transitioning from one state to another. This experiment poses unanswered questions regarding
the nature of MTDR
LO
FAPs. It did show, however, that a cell can have many mitochondria, and
yet have low mitochondrial activity. I ultimately decided to continue grouping FAPs into the two
sub-populations indicated by MTDR staining because MTDR indicates functional mitochondria.
Figure IV: MTDR costained with MTO and MTG
A) FAPs costained with MTO and MTDR positively correlate.
B) FAPs costained with MTG and MTDR. A population of MTG
HI
MTDR
LO
FAPs emerges
46
Adipose derived stem cells
FAPs are a mesenchymal and stromal stem cell residing in skeletal muscles. In other
mesenchymal tissues, there are stromal cells with similar cellular signatures and multipotency.
One example is the adipose derived stem cell (ADSC), a type of stromal mesenchymal stem cell
found in adipose tissue that expresses many of the same cellular markers as FAPs. Out of
curiosity, I wanted to see if ADSCs have the same MTDR fluorescent signature as FAPs. During
another experiment where I dissected skeletal muscles for FACS, I dissected subcutaneous fat
pads from the mouse and processed these samples side by side with my skeletal muscle samples.
Interestingly, when stained with MTDR, ADSCs recapitulated the bimodal fluorescent signature
seen in FAPs (Fig. VA). This is not a property of all stem cells, as skeletal muscle satellite cells
do not have bimodal MTDR staining (Raval et al., 2020). When cultured, even MTDR
LO
ADSCs
differentiate readily into adipocytes (Fig VB), in contrast to FAPs. It would be interesting to see
if my conclusions about FAPs are applicable to ADSCs as well. If so, ADSCs could be a more
readily available cell population for developing future therapies to prevent fibrosis.
47
Figure V: FAPs have similar MTDR staining patterns to cells found in Subcutaneous Adipse Tissue
A) FACS plots of cells isolated from skeletal muscle (FAPS – Top) and subcutaneous fat (Adipose derived stem
cells- Bottom)
B) Brightfield Image of MTDRLo ADSCs after 2 weeks in culture. Scale bar = 50 µm.
48
Attempts at in vivo MTDR staining
I really wanted to visualize MTDR
HI
and MTDR
LO
FAPs in situ. Unfortunately, it is
necessary to freeze muscle sections prior to staining, and MTDR is a live cell stain. Skeletal
muscle is also quite a dense tissue and is basically impossible to clear for whole mount staining.
(Cabrera & Frevert, 2012) has a protocol for in vivo MTDR staining. I was hoping that if I could
distribute MTDR through the bloodstream, enough would get into the skeletal muscle for a
useable stain. Unfortunately, these attempts never resulted in anything close to my normal
fluorescence signature. When I attempted an in vivo stain and then sorted for FAPs, MTDR
fluorescence was basically nonexistent. I injected high amounts of MTDR into the tail vein (up
to 150 μg) and still saw nothing. The failure of this experiment to produce useable data incited us
to use RNAscope to visualize MTDR
LO
and MTDR
HI
FAPs in situ instead (Fig. 5).
49
Cell and mitosis tracking
My goal was to be able to easily find differences in the behavior of different groups of
cells. Specifically, I wanted to isolate satellite cells from an old mouse and satellite cells from a
young mouse, plate them, and see if I could observe and quantify differences between the two
populations. At first, I tried to do this with a software called CellProfiler, which did a good job of
taking images of many cells on a plate and identifying individual cells (Fig VIA). With this
pipeline, I could measure many different aspects of each individual cell. I could also follow cells
throughout a timelapse, although CellProfiler’s algorithm lost track of cells that touched other
cells or were not identified for a frame. To improve upon cell tracking and identification, I used
the output from the tracking algorithm in a Python script. At the same time, I began to implement
machine learning techniques to identify cells undergoing mitosis. My eventual objective was to
track and quantify how fast cells started to move, how quickly they grew in mass, and when they
underwent their first and second cell divisions. I also wanted to use the measurements from
CellProfiler to find principal components that would highlight differences between populations.
I made progress, but never quite got to the point where the data was clear through the
noise. When cells touch, they become virtually indistinguishable from each other, even by eye. I
circumvented this challenge my comparing the measurements of the cells before the merge and
after the merge, linking the lineages of cells before and after. However, this fell apart if three or
more cells touched each other at the same time. I was able to use the same technique if all three
separated in the same couple frames, but often, this was not the case. Often, one cell separated
from the group, while the other two remained adjacent for several frames. This three or more
body problem stumped us for several iterations of the code, making it very difficult to maintain
cell identities throughout the timelapse (Fig VI B).
50
The three body problem was further complicated by debris in the scene, which cells pick
up, carry for a little while, then put down again. Generally, I could discern between debris versus
and cells, because debris doesn’t move and has different visual properties (it looks different).
Unfortunately, when a cell picks up debris, it takes on many of its visual properties. A person can
tell that it is a cell carrying debris, but the computer cannot distinguish between the two. This
means that if a cell picks up debris, we know that it is still only one cell. However, if another cell
touches the first and picks up the piece of debris, that piece of debris may get transferred to the
second cell. This means that when the two cells split again, the second cell (now carrying the
debris) will look more like the first cell before the two cells touched (when the first cell was
carrying the debris). This further confuses cell lineage.
I was also interested in annotating and quantifying cell divisions. As a satellite cell begins
mitosis, it becomes spherical. Then, over the course of several minutes, it makes more of a
figure-8 shape as the cell begins cytokinesis. Finally, the cell splits into two daughter cells and
assumes a normal cellular appearance (Fig. VI C). Using machine learning, we can teach the
computer to recognize these steps, and annotate that cell and timepoint as a potential division.
Daughter cells also split relatively quickly post mitosis, which can improve the accuracy of
identifying mitoses. However, when another cell becomes involved, the situation becomes more
complicated. When two cells are touching, it is difficult to tell which one is undergoing mitosis.
It also makes tracking the lineage of cells moving away from the cluster almost impossible. Two
cells go in, and three come out. An even worse scenario is when two cells go in, two cells come
out, and then several frames later, another comes out (Fig. VI B).
Another challenge was that satellite cells move around a lot, and they would often move
off screen. Using data from cells that were on screen for the entire length of the timelapses,
51
which were two to three days long, yielded very few cells per scene for training our machine
learning algorithm. Often, cells would enter the scene late or leave early. I did not know if cells
entering the scenes were primary isolated cells or daughter cells arising post mitosis. For cells
that left early, I would not know whether they would undergo mitosis or come back into the
scene. This made it difficult to determine the average time of first mitosis for the whole
population, because I wasn’t sure when or if these cells ever divided, lowering the number of
useable data points.
Although I was able to pull some information from our timelapses (FigVI D), this project
ultimately became too complicated and had too many dependencies to continue, given that I was
a self-taught beginner working with one part-time computer science master’s student. We made a
lot of progress, but at the end of the day, the data we were getting were not clean enough to be
useable, and I decided to focus more on my wet lab work.
52
Figure VI: Tracking and analyzing MuSCs using timelapse microscopy
A) CellProfiler pipeline can identify cell edges from brightfield microscopy. Brightfield image overlaid with
identified object numbers (left) Cellular Areas identified by CellProfiler (right)
B) Schematic of the issues faced maintaining cell lineage when 3 cells come together and then separate over several
timepoints.
C) Brightfield of a single cell undergoing Cell division over 4 timepoints.
D) The change in cell area over 2000 minutes. Here we can see how a cell expands and contracts as it moves around
the culture dish
Materials and Methods
All bar graphs and error bars represent Mean and Standard Deviation, respectively.
Isolating FAPs from Mus musculus
First, I made a general wash solution: Ham's Nutrient Mixture F10 with L-glutamine
(Cytiva SH30025.01) +10% Horse Serum (Thermo Fisher Scientific 16050114) + 1% Penicillin-
Streptomycin (10,000 U/mL) (Thermo Fisher Scientific 15140122).
Wash media was kept warm (at 37°C) in a water or bead bath throughout the entire procedure. In
wash solution, I made a 2 mg/mL Collagenase, Type II (powder, Thermo Fisher Scientific
17101015) solution for primary chemical digestion. I kept it warm until ready for use. mice were
placed in a euthanasia chamber with carbon dioxide subsequently turned on at 20-30% chamber
53
volume per minute. When the mouse had not been breathing for several minutes, I performed a
cervical dislocation as a secondary euthanasia method.
I dissected out skeletal muscles by first removing skin and then removing muscles with
scalpel and tweezers. The final cell suspension was purer if I removed as much nerve and tendon
as possible. It is also recommended to bleed the mouse prior to dissection, as blood clots can
disrupt FACS. As muscles were dissected, I placed them in a Petri dish with the collagenase II
solution. Once the desired muscles were harvested, I diced them into approx. 1-2 mm pieces. I
then poured the solution with minced muscles into a 50 mL conical tube, making sure to scrape
out all remaining muscle pieces from the Petri dish into the conical tube with a cell scraper.
Next, samples were incubated at 37°C in a shaking incubator for 1 hour.
During this time, I prepared ECM coated chamber slides from poly-D-lysine coated
chamber slides. While muscles were digesting, I also prepared a collagenase II and dispase
(Dispase II, powder, Thermo Fisher Scientific, 17105041) solution. In 5 mL wash solution, I
added 8 mg collagenase II (200 U/mg) and 12 mg dispase (1.80 U/mg). Once muscles were done
incubating, I added wash media to the 50 mL conical tube until full. The sample was then
centrifuged in a bucket centrifuge at 500 x g for 5 minutes. I then suctioned out 45 mL of
supernatant, being careful not to disturb large pellet. The secondary digestion solution was then
added to the sample and mixed several times with serological autopipette. The sample then
incubated for another 30 minutes.
I then performed shear digestion by aspirating at least 10 times with a 10 mL syringe and
20G needle. If there were still too many large chunks, I would start with an 18G needle and
aspirate up and down until smooth then switch back to 20G needle. I tried not to make the
solution frothy by avoiding aspirating air. I then added wash solution until the conical tube was
54
full and centrifuged at 500x g for 5 minutes. I suctioned out 45 mL of wash solution, making
sure not to disturb pellet. I then resuspend the cell pellet in a total of 50 mL wash solution and
filtered it through a 40 µm cell filter into an empty 50 mL conical tube. The sample was then
centrifuged at 500xg for 5 minutes, and I suctioned off as much supernatant as possible without
disturbing cell pellet. The cell pellet was then resuspended in 1 mL Wash solution and transfer to
an Eppendorf tube.
I stained with:
5 μL BUV395 Rat Anti-Mouse Ly-6A/E Clone D7 (BD Biosciences 563990)
5 μL CD31 (PECAM-1) Monoclonal Antibody (390), PE, (Thermo Fisher Scientific
12-0311-81)
5 μL CD45 Monoclonal Antibody (clone 30-F11), PE, (Thermo Fisher Scientific 12-
0451-81)
15 μL ITGA7 Monoclonal Antibody (clone 334908), PE (Thermo Fisher Scientific
MA5-23608)
0.5 μL Propidium iodide, 95%, (Thermo Fisher Scientific AC440300250)
3 μL of 100 μM MitoTracker™ Deep Red FM (Thermo Fisher Scientific M22426)
I incubated the sample with the fluorophore conjugated antibodies and cell stains for 30 minutes
to 1 hour at 37°C. I then filled the Eppendorf with wash solution and centrifuged at 500 x g for 5
minutes, again suctioning out as much supernatant as possible without disturbing cell pellet. The
pellet was resuspended in warm culture media (DMEM/High Glucose with L-glutamine, sodium
pyruvate (Hyclone SH30243.FS) + 20% Fetal Bovine Serum (Thermo Fisher Scientific
26140079) + 1% Penicillin-Streptomycin (10,000 U/mL) (Thermo Fisher Scientific 15140122))
and filtered into 40 μm filter top 5 mL FACS tube on ice.
55
FACS was performed on ARIA I with 1.5 Neutral Density Filter, 70 μm sorting nozzle,
and the following lasers: 355nm (UV); 488nm (blue); 561nm (yellow-green); and 633nm (red). I
used a 400/30 BP for BUV channel on the 355 laser, 510/20 BP for GFP on the 488 laser (when
necessary), 582/15 BP for the PE channel on the 561 laser, 610/20 BP for the PI channel on 561
laser, and 660/20 BP for the MTDR channel on the 633 laser. The PI and PE channels do overlap
slightly, but this was not problematic as cells negative in both channels were sorted. FAPs are:
Ly6a (SCA-1)
+
CD31
-
CD45
-
integrin-α7
-
PI
-
or BUV
+
PE
-
PI
-
. I generally divided FAPs into
MTDR
HI
and MTDR
LO
fractions when sorting, to compare the two sub-populations. Cells are
sorted into two 5 mL FACS tubes each with 1 mL culture media (DMEM + 20% FBS).
Performing Expectation Maximization of Gaussian Mixture
To perform Expectation Maximization of Gaussian Mixture models, I exported channel
data from FACS using FloJo V10.6.1 and created an input file containing the experiment, date,
date of birth, age, sex, injury model, number of cells, and MTDR channel data as columns in a
CSV file. I then wrote and ran a Python script (see below), which creates and/or amends an
output file containing the input experimental data and weights (proportions), means, and
variances of each calculated MTDR sub-population. This output file allows for easy comparison
of different experiments. The Python script also uses a service called Plotly to create a histogram
with overlaid Gaussians of the input data. Colors and styles can be edited in script.
# -*- coding: utf-8 -*-
import plotly.graph_objects as go
import plotly.io as pio
import numpy as np
56
import csv
from sklearn import mixture
import math
channel_data = []
col = dict()
age = 0
injury = ""
date = ""
with open('input.csv', newline='') as csvfile:
csvreader = csv.reader(csvfile, delimiter=',', quotechar='|')
for counter, row in enumerate(csvreader):
if counter == 0:
for ref, label in enumerate(row):
col[label] = ref
elif counter == 1:
age = row[col['Age']]
injury = row[col['Injury']]
date = row[col['Date Sorted']]
experiment = row[col['Experiment']]
dob = row[col['DOB']]
totalnum = row[col['Number of events recorded']]
sex = row[col['sex']]
else:
intensity = row[col['APC-A']]
channel_data.append(intensity)
n = len(channel_data)
57
reshaped = np.array(channel_data).reshape(-1,1)
gmm = mixture.GaussianMixture(n_components=2, covariance_type='full').fit(reshaped)
predict_prob = gmm.predict_proba(reshaped)
mult_data = []
pred_data1 = []
pred_data2 = []
for counter, value in enumerate(channel_data):
[p1, p2] = predict_prob[counter]
p1 = int(round(10*p1))
p2 = int(round(10*p2))
for i in range(p1):
pred_data1.append(value)
mult_data.append(value)
for i in range(p2):
pred_data2.append(value)
mult_data.append(value)
m1 = gmm.means_[0][0]
m2 = gmm.means_[1][0]
v1 = gmm.covariances_[0][0][0]
v2 = gmm.covariances_[1][0][0]
w1 = gmm.weights_[0]
w2 = gmm.weights_[1]
binsize = 5
fig = go.Figure()
x = np.linspace(0, 1200, num = 1201)
y1 = [{'x': x, 'y': (10*w1*binsize*n*((math.e**((-1/2)*(((x-
m1)/math.sqrt(v1))**2)))/(math.sqrt(v1*2*math.pi))))}]
58
fig.add_trace(go.Scatter(
x = y1[0]['x'],
y = y1[0]['y'],
mode = 'lines',
name = 'lines',
line_color='rgb(0,0,0)',
line_width=4
))
y2 = [{'x': x, 'y': (10*w2*binsize*n*((math.e**((-1/2)*(((x-
m2)/math.sqrt(v2))**2)))/(math.sqrt(v2*2*math.pi))))}]
fig.add_trace(go.Scatter(
x = y2[0]['x'],
y = y2[0]['y'],
mode = 'lines',
name = 'lines',
line_color='rgb(0,0,0)',
line_width=4
))
fig.add_trace(go.Histogram(
x=pred_data1,
marker_color='#000000',
opacity=0.33,
xbins=dict(
start=0,
end=1200,
size= binsize),
autobinx = False,
))
59
fig.add_trace(go.Histogram(
x=pred_data2,
marker_color='#000000',
opacity=0.33,
xbins=dict(
start=0,
end=1200,
size= binsize),
autobinx = False
))
fig.add_trace(go.Histogram(
x=mult_data,
marker_color='#000000',
opacity=0.75,
xbins=dict(
start=0,
end=1200,
size= binsize),
autobinx = False
))
fig.update_layout(barmode='overlay',paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)')
fig.show()
pio.write_html(fig, file='GMM.html', auto_open=True)
log_prob = gmm.score_samples(reshaped)
prediction = gmm.predict(reshaped)
means = gmm.means_
aic = gmm.aic(reshaped)
60
bic = gmm.bic(reshaped)
prediction = gmm.predict(reshaped)
covariances = gmm.covariances_
precisions = gmm.precisions_
pc = gmm.precisions_cholesky_
weights = gmm.weights_
iterations = gmm.n_iter_
output = [experiment, date, dob, age, sex, injury, n, w1, w2, m1, m2, v1, v2, totalnum]
with open("output.csv", "a") as fp:
wr = csv.writer(fp, dialect='excel')
wr.writerow(output)
“””end of file”””
61
Making ECM coated chamber slides
I used 8–well chamber slides when culturing cells so I could analyze live cells in a
microscope mounted incubation chamber. I coated the chamber slides with Poly-D-Lysine
followed by ECM gel to facilitate cell adhesion to the glass slides.
Coating chamber slides with Poly-D-Lysine
To coat slides with Ploy-D-lysine, I diluted 0.7ml of Stock Poly-D-Lysine Hydrobromide
solution, 1 mg/mL (Millipore A-003-E ) in 6.3 mL sterile MilliQ water. I then coated each
chamber slide with the Poly-D-Lysine solution and let sit overnight. The next day, I aspirated the
remaining liquid and allowed to dry before coating with ECM gel.
Coating slides with ECM
ECM gel solidifies at room temperature, so it must be thawed at 4°C. I diluted ECM Gel
from Engelbreth-Holm Swarm murine sarcoma (Sigma E1270-10ml) 1:100 and Penicillin-
Streptomycin (10,000 U/mL) (Thermo Fisher Scientific 15140122) 1:100 in DMEM/High
Glucose with L-glutamine, sodium pyruvate (Hyclone SH30243.FS). I added 150 µL of this
solution to pre-chilled poly-D Lysine coated chamber slides, then allowed them to set in a 37°C
incubator. The remaining liquid was aspirated before cells were plated.
62
MitoTracker Deep Red (MTDR) staining
Once FAPs were sorted, I plated 80,000 cells in 500 μL culture media (DMEM/High Glucose
with L-glutamine, sodium pyruvate (Hyclone SH30243.FS) +20% Fetal Bovine Serum (Thermo
Fisher Scientific 26140079)) per well onto an 8-well chamber slide coated with ECM. I allowed
cells to adhere to the surface of the slide in incubator at 37°C for 1 hour or 24 hours. I prepared
staining media with MitoTracker™ Deep Red FM (Thermo Fisher Scientific M22426)(MTDR)
by diluting 1 mM stock MTDR in DMEM +20% FBS to a final working concentration of 300
nM MTDR. To stain the FAPs, I incubated them at 37°C for 20 minutes in warmed staining
media. I then washed 3 times with warm media, and fixed in fresh 4% PFA for 20 minutes. I then
washed 3x with PBS and incubated at room temp with PBS+0.25%Triton X-100 for 10 minutes.
Once the cells were permeabilized, I removed the chambers and mounted with ProLong™
Diamond Antifade Mountant with DAPI (Thermo Fisher Scientific P36971). Images were taken
on a Zeiss LSM 780 AxioObserver.Z1 in the Optical Imaging Facility at the Eli and Edythe
Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC.
63
Quantification of per-cell staining intensities in CellProfiler 3.1.9 (Kamentsky et al., 2011)
Reported intensities were the calculated mean intensity from each identified cell. Inputs
were single channel images including at least DAPI channel and one other (e.g., MTDR). The
pipeline used is:
Identify primary objects (nuclei, using the DAPI channel)
Identify secondary objects (the area around each nucleus, containing staining data)
64
Measure object intensity (using secondary objects)
Export data to spreadsheet
65
Basic immunostaining
I used the same basic protocol on 4% PFA fixed cells or muscle sections for all the following
antibodies:
Alpha-Smooth Muscle Actin Monoclonal Antibody (1A4), Thermo Fisher Scientific MA1-06110
MF20, Developmental Studies Hybridoma Bank
Perilipin-1 (D1D8) XP® Rabbit mAb #9349, Cell Signaling Technology 9349S
Anti-Laminin-2 (α-2 Chain) antibody, Rat monoclonal, Sigma Aldrich L0663
Anti-GFP antibody, Abcam (ab13970)
Mouse PDGF R alpha Antibody, R&D systems AF1062
Immunofluorescence-immunohistochemistry (IF-IHC)
After fixation and, I permeabilized the sample in PBS+ 0.3% TritonX100 (MilliporeSigma 9002-
93-1) for 30 minutes. I then washed the sample 3x in PBS and stained in a solution of PBS +
0.3% triton + 10% Donkey Serum (Millipore Sigma D9663) + 1:100 diluted primary antibody. I
left sample in primary antibody solution overnight at 4°C, then washed 3x with PBS. I then
added a solution of PBS + 0.3% triton + 10% Donkey Serum + 1:500 diluted secondary antibody
for 30 minutes. I then washed 3x in PBS, removed the chambers and mounted with ProLong™
Diamond Antifade Mountant with DAPI (Thermo Fisher Scientific P36971). Images were taken
on a Zeiss LSM 780 AxioObserver.Z1 in the Optical Imaging Facility at the Eli and Edythe
Broad CIRM Center for Regenerative Medicine and Stem Cell Research at USC.
66
Mito Stress Test
I used an Agilent Seahorse XFp to perform a Mito Stress Test according to the Agilent Seahorse
XFp Cell Mito Stress Test Kit User Guide:
https://www.agilent.com/cs/library/usermanuals/public/XFp_Cell_Mito_Stress_Test_Kit_User_
Guide.pdf
I plated 150,000 cells per ECM coated XFp culture well and allowed them to adhere to the plate
overnight before analysis.
67
EdU incorporation assay using Click-iT™ EdU Cell Proliferation Kit for Imaging, Alexa
Fluor™ 488 dye (Thermo Fisher Scientific C10337)
After isolating FAPs, I added EdU to the culture medium (DMEM +20% FBS+Pen/Strep) to a
final concentration of 0.05 mM EdU. If culturing for more than 24 hours, I replace the media
with new culture media containing 0.05 mM EdU. At the appropriate timepoints, I performed
EdU staining as described in Click-iT staining protocol.
68
Oil Red O staining with immunohistochemistry
I plated isolated FAPs onto an 8-well chamber slide at 80,000 cells per well and allowed to
adhere. I allowed cells to come to confluence and begin differentiating, changing media every
other day with fresh, warm, filtered DMEM+20% FBS. I found that FAPs plated at this density
generally reach confluence at around day 5, and one can see evidence of differentiation shortly
thereafter. Once cells are at a desired state, I removed media and fixed the sample in 4% PFA for
20 minutes. To prepare Oil Red O solution, I added 6 mL Oil Red O (Sigma Aldrich O1391) to 4
mL diH2O. I then filtered the solution through a 40 µL cell strainer to remove any large chunks. I
washed the sample 3x with PBS and stained with the prepared Oil Red O solution at room
temperature for 10 minutes, then washed 3x with PBS. I then proceeded to basic
immunostaining, mounted with DAPI, and imaged. Oil Red O fluoresces in orange and far red
channels, so if staining with immunofluorescence, I made sure to use fluorophores only in the
yellow-green and blue channels (405 and 488).
Images were taken on a Zeiss LSM 780 AxioObserver.Z1 in the Optical Imagine Facility
at the Eli and Edythe Broad CIRM Center for Regenerative Medicine and Stem Cell Research at
USC.
69
Quantification of full image fluorescence using CellProfiler (Kamentsky et al., 2011)
To quantify full image fluorescence, I imaged 7 separate scenes per condition and then
loaded single channel images into CellProfiler with the following pipeline.
Measure image intensity:
Export to spreadsheet:
70
Transplantation of GFP+ cells into recipient mice
Two days prior to transplantation, I perform barium chloride injuries on both tibialis
anteriors for recipient mice. This creates a suitable environment for FAPs to be engrafted into
recipient mice.
Mice used for FAPs for transplantation were C57BL/6-Tg(UBC-GFP)30Scha/J (strain:
004353) mice from Jackson laboratories. This mouse strain expresses GFP under the ubiquitin-C
promoter, meaning cells from all tissues express GFP. FAPs were isolated using the above FAP
isolation protocol. I also checked the GFP channel during FACS to be sure my FAPs were
indeed GFP
+
. I sorted MTDR+ FAPs into HI and LO groups in 5 mL FACS tubes. These tubes
were then centrifuged in a bucket centrifuge at 500x g for 5 minutes. The supernatant was poured
out, and FAPs were resuspended in 20 μL/ 200,00 cells of DMEM+ 20% FBS. FAPs were kept
on ice and immediately brought down to the mouse procedure room to be transplanted into adult
C57BL/6J mice. Recipient mice were anesthetized by 2-3% inhaled isofluorane. Once asleep,
eye drops were applied to the mouse, the anterior lower leg was shaved and disinfected 3 times
with both iodine and isopropanol. 25 µL of cell suspension was then drawn and injected via a
borosilicate syringe into the tibialis anterior. Prior to injection, the needle of the syringe was
dipped into turquoise tattoo ink. This allows us to visualize the injection site when the muscle is
analyzed later. As there is no incision, no stitches are required. Mice are then set up to recover
with 0.5 mg/kg buprenorphine delivered subcutaneously. The mice were allowed to recover in a
cage warmed with a shop light and given food and an antibiotic hydrogel. Mice were monitored
until alert and responsive, and moving around before being brought back to the vivarium
according to IACUC protocol.
71
For protocols where I planned to analyze muscle sections, I also added CFDA-SE
(Cayman Chemical 14456) to a final concentration of 10 μM to the injection media in order to
visualize the injection site intramuscularly.
For GFP mouse experiments, I allowed the mice to heal for 2 weeks, then proceeded to
FACSort FAPs using the isolation protocol and to analyze MTDR and GFP signals.
72
Bulk Differential Expression RNA-Seq
I isolated MTDR
HI
and MTDR
LO
FAPs from 4 different mice and immediately flash froze them
for Library prep and sequencing. Sample Extraction, Quality Control, Library Preparation, and
Sequencing were done at the USC Molecular Genomics Core. Once the data was returned, I
uploaded to and analyzed it using tools on the Galaxy platform (www.UseGalaxy.org). The tools
and parameters are as follows:
73
74
75
76
Basic procedure for mouse surgeries
Prior to surgery, animals were inspected for body condition and health. Animals that did not
display good body condition were not subject to surgery. Isoflurane was administered via
vaporize as an anesthetic. Animals were placed in a chamber and subjected to 2-3% isoflurane
until effect and maintained on 1.5-2% isoflurane. Anesthesia was verified by failure of the
animal to respond to a toe pinch. During anesthesia eye lubricant was placed into the eyes to
prevent drying. Slow release buprenorphorine (1 mg/kg, subcutaneous) was administered before
the surgical procedure to prevent wind up pain. All surgeries were performed using aseptic
technique. The surgical site on the animals were draped with sterile disposable drapes. Prior to
surgery, instruments were sterilized using an autoclave and stored in autoclave bags. During
surgery, animals were monitored for anesthesia, breathing, and body temperature. Between
animals, the instruments, if used, were cleaned with 70% isopropanol and sterilized in a hot bead
bath. Animals received post-op oral antibiotics via supplemental gel cup placed in the cage
(MediGel TMS, which contains Trimethoprim (0.025%) and Sulfamethoxazole (0.124%)) for 1
week following surgery.
Post-surgery
For the first 2 days after surgery, animals were monitored every 12 hours for signs of distress or
pain. Any remaining sutures were removed 7-10 days post surgery Animals were monitored
daily until day 14 or until endpoint of experiment. At any time, if an animal had indications of
pain or distress (reduced body condition), monitoring of that animal increased to every 12 hours.
77
Sciatic nerve injury
After induction of an appropriate level of general anesthesia, mice were placed on a sterilized
surface for surgery. The fur of a hindlimb region was trimmed with an electric razor. The area
was sterilized with an alternating 70% isopropanol and betadine preps with 3 total applications,
and the betadine was given time to dry in order to ensure sterilization of the field. Prior to
surgical incision, the depth of anesthesia was verified with a pinch to the hindlimb with a toothed
forcep. If there was no response, surgery would begin. A 15 scalpel blade was used to make a
small incision just posterior to the palpable mid-femur, and blunt dissection then exposed the
sciatic nerve. A 5-0 Prolene suture was applied 5 mm from the sciatic notch, and the nerve was
cut. A non-absorbable suture (5-0 Prolene, monofilament) was used to close the skin in
interrupted fashion. The animal was removed from anesthesia and warmed by a warming light or
pad during recovery from anesthesia. Once fully conscious, the animal was placed in its cage.
78
Synergistic ablation of gastrocnemius muscle
After induction of an appropriate level of general anesthesia, mice were placed on a sterilized
surface above a warming pad for surgery. The fur of a distal hindlimb region was trimmed with
an electric razor. The area was sterilized with an alternating 70% isopropanol and betadine preps
with 3 total applications, and the betadine was given time to dry in order to ensure sterilization of
the field. Prior to surgical incision, the depth of anesthesia was verified with a pinch to the
hindlimb with a toothed forcep. If there was no response, surgery would begin. The skin on the
posterior distal hindlimb was opened using iris scissors to expose the achilles tendon up to the
mid-belly of the gastrocnemius muscle, approximately a 1 cm opening. The fascia enclosing the
gastrocnemius muscle was carefully peeled away using forceps. The tendons for the
gastroncnemius and soleus muscles were severed just above the ankle using an 11 blade and the
muscles carefully lifted away from the limb. Distal half of the gastrocnemius was removed by
cutting the muscles with an 11 blade at the mid-belly region. The skin was thenclosed using 5-0
Prolene monofilament sutures. The animal was then be allowed to recover on a warming pad.
Once conscious, the animal was placed in its cage.
79
Barium chloride and glycerol injury
After induction of an appropriate level of general anesthesia, mice were placed on a sterilized
surface above a warming pad for surgery. The fur of a distal hindlimb region was trimmed with
an electric razor. The area was sterilized with an alternating 70% isopropanol and betadine preps
with 3 total applications, and the betadine was given time to dry in order to ensure sterilization of
the field. Prior to surgical incision, the depth of anesthesia was verified with a pinch to the
hindlimb with a toothed forcep. If there was no response, surgery would begin. 30 µL 1.2%
barium chloride (Barium chloride dihydrate (crystalline/certified ACS), (Fisher Chemical 10326-
27-9)) solution or 50% glycerol (Millipore Sigma 56-81-5) solution was injected via insulin
syringe into tibialis anterior muscle of mouse. The animal was then be allowed to recover on a
warming pad. Once conscious, the animal was placed in its cage.
80
Freezing injury
After induction of an appropriate level of general anesthesia, mice were placed on a sterilized
surface above a warming pad for surgery. The fur of a distal hindlimb region was trimmed with
an electric razor. The area was sterilized with an alternating 70% isopropanol and betadine preps
with 3 total applications, and the betadine was given time to dry in order to ensure sterilization of
the field. Prior to surgical incision, the depth of anesthesia was verified with a pinch to the
hindlimb with a toothed forcep. If there was no response, surgery would begin.
The skin on the posterior distal hindlimb or anterior distal hindlimb was opened using iris
scissors to expose the achilles tendon up to the mid-belly of the gastrocnemius muscle or tibialis
anterior muscle, approximately a 1 cm opening. A steel implement (2 mm diameter hex wrench)
was cooled on dry ice, then held against exposed muscle until color indicated freezing has
occurred (~8-10 seconds). The skin was then closed using 5-0 Prolene monofilament sutures.
The animal was then allowed to recover on a warming pad. Once conscious, the animal was
placed in its cage.
81
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Abstract (if available)
Abstract
Skeletal muscle is a highly robust and regenerative tissue. However, certain injuries, diseases, and conditions, such as overuse, muscular dystrophy, and aging, are known to increase fibrosis in the muscle. Muscle fibrosis and scarring replace normally functional, muscle tissue with excess deposits of connective tissue. This leads to losses in strength and flexibility. There is evidence that a normally beneficial population of muscle resident mesenchymal stem cells, Fibro/Adipogenic Progenitors (FAPs), are largely responsible for muscle scarring and fibrosis pathologies. The role of FAPs and the mechanism by which they function in normal and pathologic conditions is poorly understood. Here, I describe the identification and characterization of two subpopulations of FAPs in mouse skeletal muscle that have differential functional roles in muscle fibrosis. These two sub-populations can be distinguished based on differential levels of mitochondrial activity and staining by the mitochondrial specific dye, MitoTracker Deep Red (MTDR). Young, healthy mice have FAPs comprised of, on average, 28% MTDRᴸᴼ FAPs and 72% MTDRᴴᴵ FAPs. During an injury response, these proportions shift, but return to normal with injury resolution. However, the proportion of MTDRᴸᴼ FAPs increases with age as well as in response to fibrosis-causing injury. Transplantation of MTDRᴸᴼ, but not MTDRᴴᴵ, FAPs into an injured muscle accordingly leads to poor regeneration and greater accumulation of extracellular matrix. In vitro, MTDRᴸᴼ FAPs preferentially differentiate into fibroblasts, whereas MTDRᴴᴵ FAPs preferentially differentiate into adipocytes. Collectively, my data suggests that these inter-convertible FAP sub-populations have differential roles in muscle fibrosis and aging. The shift towards a greater proportion of MTDRᴸᴼ FAPs is potentially an underlying cause of age-associated muscle fibrosis. This implies that altering the proportions of MTDRᴸᴼ and MTDRᴴᴵ subpopulations could accordingly alter the risk of fibrosis. An understanding of how FAP subpopulations are regulated could lead to treatments for muscle fibrosis.
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Ederer, Maxwell Brendan
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Core Title
Characterization of metabolically distinct muscle resident fibro-adipogenic subpopulations reveals a potentially exploitable mechanism of skeletal muscle aging and disease
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Keck School of Medicine
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Doctor of Philosophy
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Development, Stem Cells and Regenerative Medicine
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09/18/2020
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08/26/2020
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aging,FACs,FAP,fibro-adopogenic progenitors,fibroblasts,fibrosis,flow cytometry,injury,mesenchymal stem cells,metabolism,MitoTracker,myofibroblasts,OAI-PMH Harvest,scar tissue,skeletal muscle,stem cell,sub-population,Transplantation
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), Crump, Gage (
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Tags
FACs
FAP
fibro-adopogenic progenitors
fibroblasts
fibrosis
flow cytometry
mesenchymal stem cells
metabolism
MitoTracker
myofibroblasts
scar tissue
skeletal muscle
stem cell
sub-population