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Functional compensation between hematopoietic stem cell clones in vivo

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Functional compensation between hematopoietic stem cell clones in vivo

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Content Running head: COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 1

Functional Compensation between
Hematopoietic Stem Cell Clones In Vivo

By
Lisa Nguyen

A dissertation presented to the
Faculty of the University of Southern California Graduate School

In partial fulfillment of the
requirements for the degree
Doctor of Philosophy
Development, Stem Cell and Regenerative Medicine

December 2018
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Dedication
To my family.
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Acknowledgements
I express my sincere gratitude to my advisor Dr. Rong Lu for her continuous motivation and
patience throughout my journey in completing my doctorate degree. Her optimism during the
midst of obstacles has given me the strength to persevere. The door to Dr. Lu’s office was
always open when I needed help troubleshooting experiments or writing research grants.
I would also like to thank the rest of my committee members, Drs. Gage Crump, Paula Cannon,
Akil Merchant, and Justin Ichida, for their supportive guidance, insightful comments, and
difficult questions.
I thank my former and present fellow lab mates in the Lu Laboratory who have trained, assisted,
and accompanied me in my thesis research during the past 4 years.
Finally, I express my profound gratitude to my family and to my spouse, Andrew, for their
support throughout my years of study and researching.

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Abbreviations
5-FU 5-fluorauracil
CFU-S Colony forming unit, spleen
CLP Common lymphoid progenitor
CMP Common myeloid progenitor
DNA Deoxyribonucleic acid
DT Diphtheria toxin
FDA US Food and Drug Administration
Ebf1 Early B cell factor 1
EP Erythroid progenitor
Ery Erythrocyte
FACS Fluorescence activated cell sorting
GMP Granulocyte-macrophage progenitor
GO Gene ontology
GP Granulocyte progenitor
Gr Granulocyte
HSC Hematopoietic stem cell
HSPC Hematopoietic stem and progenitor cell
IL-7 Interleukin 7
IP Intraperitoneal
LT-HSC Long-term hematopoietic stem cell
MacP Macrophage progenitor
M-CSF Macrophage colony-stimulating factor
MEP Megakaryocyte-erythrocyte progenitor
Mkp Megakaryoid progenitor
MPP Multipotent progenitor
NK Natural killer
PBS Phosphate buffered saline
PCR Polymerase chain reaction
PI Primary immunodeficiencies
qPCR Quantitative polymerase chain reaction
RT-PCR Real time polymerase chain reaction
RNA Ribonucleic acid
RNA-seq Ribonucleic acid sequencing
SCF Stem cell factor
SCID Severe combined immunodeficiency
SOM Self-organizing mapping
STEM Short Time-series Expression Miner
ST-HSC Short-term hematopoietic stem cell
TAM Tamoxifen
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TPO Thrombopoietin
WBC White blood cell
WT Wildtype
vWF von Willebrand factor

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List of Publications
Nguyen L, Wang Z, Chowdhury AY, Chu, E, Eerdeng J, Jiang D, Lu R (2018) Functional
Compensation between Hematopoietic Stem Cell Clones In Vivo. EMBO Reports

COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 7

Table of contents
Dedication ........................................................................................................................................2
Acknowledgements ..........................................................................................................................3
Abbreviations ...................................................................................................................................4
List of publications ..........................................................................................................................6
Chapter I: Background ...................................................................................................................13
Clonal Origins of Hematopoiesis ...............................................................................................13
Functional and Phenotypic Characterization of Murine HSCs ..................................................14
The Hematopoietic Hierarchy ....................................................................................................16
HSC Heterogeneity in Adult Bone Marrow ...............................................................................18
Molecular Properties of HSC Subsets ........................................................................................20
Lineage Priming of HSCs ..........................................................................................................21
Regulation of HSCs by Intrinsic and Extrinsic Signals .............................................................22
Clonal Tracking Reveals HSC Heterogeneity ............................................................................24
HSC Transplantation Treatment for Hematopoietic Disorders ..................................................26
Chapter II: Functional Compensation between Hematopoietic Stem Cells In Vivo ......................28
Abstract ......................................................................................................................................28
Introduction ................................................................................................................................28
Results and Discussion ...............................................................................................................20
Methods ......................................................................................................................................39
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Mice ...................................................................................................................................39
Cell Isolation and transplantation ......................................................................................40
Blood sample collection and FACS analysis .....................................................................40
DNA barcode extraction and sequencing ..........................................................................42
RNA sequencing ................................................................................................................42
Statistical analysis ..............................................................................................................43
Data availability .........................................................................................................................43
Acknowledgements ....................................................................................................................43
Author contributions ..................................................................................................................44
Conflict of interest ......................................................................................................................44
Chapter III: Dynamics of Clonal compensation for Induced Lineage Deficiencies ......................77
Abstract ......................................................................................................................................77
Dynamics of compensation for induced loss of competitor HSCs .............................................78
Methods..............................................................................................................................80
Dynamics of compensation for induced monocyte deficiency ................................................. 80
Methods..............................................................................................................................83
Chapter IV: Phenotypic In Vivo Compound Screening Assay Identifies Compounds that Alter
Compensation
........................................................................................................................................................94
Abstract ......................................................................................................................................94
Identifying Candidate Compounds that Alter HSC Differentiation ...........................................95
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Methods..............................................................................................................................98
Verification of Candidate Compounds in a Non-Barcoded System ..........................................99
Methods..............................................................................................................................99
Discussion ....................................................................................................................................108
References ....................................................................................................................................110
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List of Figures
Figure 1.1 Properties of HSCs .......................................................................................................14
Figure 1.2 The hierarchy of hematopoiesis ....................................................................................18
Figure 1.3 HSC subsets have varying lineage outputs ...................................................................20
Figure 2.1 Wildtype (WT) HSCs compensate for the lymphopoietic deficiencies of co-
transplanted mutant HSCs in blood production ............................................................................45
Figure 2.2 Figure Highly expanded HSC clones increase their differentiation in response to the
lymphopoietic deficiencies of other HSCs ...................................................................................47
Figure 2.3 Persistence of lymphopoietic compensation in secondary transplantation .................49
Figure 2.4 Compensation for lymphopoietic deficiency is manifested as an increase in cell
numbers at the progenitor level......................................................................................................51
Figure 2.5 Differential gene expression of HSCs during lymphopoietic compensation ..............53
Expanded View Figure 2.1 Wildtype (WT) HSCs compensate for the lymphopoietic deficiencies
of co-transplanted mutant HSCs in blood production (Supplemental data for Figure 2.1) ...........55
Expanded View Figure 2.2 Highly expanded HSC clones increase their differentiation in
response to the lymphopoietic deficiencies of other HSCs (replicate experiment for Figure 2.2) 57
Expanded View Figure 2.3 A comprehensive list of diseases and biological functions that are
activated in WT HSCs co-transplanted with lineage deficient HSCs as compared to WT HSCs
co-transplanted with WT HSCs in the control group.....................................................................59
Expanded View Table 2.1 Gene expression signature of WT HSCs co-transplanted with NSG
HSCs as compared to WT HSCs co-transplanted with WT HSCs in the control group ...............61
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 11

Expanded View Table 2.2 Gene expression signature of WT HSCs co-transplanted with uMT
-/-

HSCs as compared to WT HSCs co-transplanted with WT HSCs in the control group ...............63
Appendix Figure S2.1 FACS plots and gating for hematopoietic stem cells (HSCs) ...................64
Appendix Figure S2.2 HSCs from NSG mice engraft similarly as those from WT mice .............65
Appendix Figure S2.3 FACS plots and gating for peripheral blood cells .....................................66
Appendix Figure S2.4 HSCs compensate for the lymphopoietic deficiencies of co-transplanted
HSCs in blood production (replicate experiment of Figure 2.1) ...................................................67
Appendix Figure S2.5 Schematic depicting genetic barcoding technology ..................................68
Appendix Figure S2.6 FACS plots and gating for hematopoietic progenitor populations in the
bone marrow ..................................................................................................................................69
Appendix Figure S2.7 Biological replicate samples are clustered together based on gene
expression ......................................................................................................................................70
Appendix Figure S2.8 Supplemental RNA sequencing graphs for Il10ra ....................................71
Appendix Table S2.1 Tables detailing p-values and fold changes of genes in Figure 2.5 ............73
Appendix Table S2.2 Antibodies used for flow cytometry sorting and analysis...........................75
Figure 3.1 Schematic depicting experimental set-up for understanding how individual HSCs
respond to loss of co-transplanted HSCs .......................................................................................84
Figure 3.2 WT HSCs compensate for loss of competitor HSCs ....................................................85
Figure 3.3 WT HSCs compensate for loss of competitor HSCs (replicate experiment of Figure
3.2) .................................................................................................................................................87
Figure 3.4 Schematic depicting experimental set-up for understanding how individual HSCs
respond to short-term induced monocyte deficiency .....................................................................89
Figure: 3.5 Schematic depicting experimental set-up for understanding how individual HSCs
respond to long-term induced monocyte deficiency ......................................................................89
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Figure 3.6 WT donor compensates for induced short-term monocyte deficiency .........................90
Figure 3.7 WT donor compensates for induced long-term monocyte deficiency..........................91
Figure 3.8 Subset of WT HSC clones consistently compensate for induced monocyte deficiency
........................................................................................................................................................92
Figure 3.9 Compensation for lymphopoietic deficiency is manifested as an increase in cell
numbers at the progenitor level......................................................................................................93
Figure 4.1 Schematic depicting experimental set-up for in vivo compound screen ....................101
Figure 4.2 Identifying compounds that alter differentiation .......................................................102
Figure 4.3 Schematic depicting experimental set-up for in vivo compound screen ...................103
Figure 4.4 Percentage of total reads of HSCs treated with with candidate compounds skew
differentiation ...............................................................................................................................104
Figure 4.5 Number of unique reads of HSCs treated with candidate compounds skew
differentiation ...............................................................................................................................105
Figure 4.6 Schematic depicting experimental set-up for verification of candidate compounds in
non-barcoded system ...................................................................................................................106
Figure 4.7 Verifying the effects of candidate compounds in non-barcoded experimental system
......................................................................................................................................................107

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Chapter I: Introduction
Clonal Origins of Hematopoiesis
Hematopoietic stem cells (HSCs) were first discovered many decades ago through a series of
seminal experiments designed and executed by James Till and Ernest McCulloch demonstrating
the ability of a subset of cells within the bone marrow to form macroscopic colonies upon
transplantation into the spleens of lethally irradiated recipient mice. They monitored the
hematopoietic reconstitution in the lethally irradiated recipient mice and observed that the donor-
derived spleen colonies post-transplantation were comprised of hematopoietic cells at various
differentiation stages. The founder cell that gave rise to the spleen colonies were initially termed
colony-forming units (CFU-S) (Till & McCulloch, 1961).
The clonal origins of hematopoiesis were formally established by chromosomal marking
techniques that used irradiation of donor bone marrow cells to generate genetically distinct CFU-
S (Becker, McCulloch, & Till, 1963; Siminovitch L., McCulloch E. A., & Till J. E., 1963). These
studies demonstrated that a rare subset of donor cells within the bone marrow can proliferate
extensively to form spleen colonies, and that marked colonies were derived from a single cell.
The CFU-S were originally assumed to be homogeneous with respect to their lineage distribution
and intrinsic capacity to self-renew.
The question of whether individual CFU-S had varying self-renewal potential was
addressed through functional analyses of bone marrow fractions separated on the basis of cell
size by sedimentation through gradients of fetal calf serum. These various fractions were tested
for their self-renewal potential using transplantation assays. The cell fraction possessing self-
renewal capacity sedimented at a slower rate in comparison to non-self-renewing CFU-S,
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 14

suggesting that there exists subsets of CFU-S with different capacities to self-renew and
differentiate (Magli, Iscove, & Odartchenko, 1982).
While CFU-S were observed to be myeloid lineage restricted, early chromosomal
marking studies showed that the same clones can produce both myeloid and lymphoid cells (Wu
A. M., Till J. E., Siminovitch L., & McCulloch E. A., 1967). In addition, studies using 5-
fluoreauracil (5-FU) to target cycling cells demonstrated that CFU-S can be eliminated without
consequence to repopulation after transplantation (Hodgson & Bradley, 1979). New CFU-S were
generated in secondary recipients of bone marrow cells from 5-FU treated mice were
transplanted into secondary recipients. These studies suggested the existence of a more primitive
multi-lineage cell type with self-renewal potential (Figure 1.1).

Figure 1.1. Properties of HSCs. HSCs are unique in their ability to self-renew (produce more
HSCs) and differentiate into all mature blood lineages.
Functional and Phenotypic Characterization of Murine HSCs
Such landmark studies revealed distinct subsets of murine bone marrow that could be identified
using density centrifugation and chromosomal marking. Additional studies later characterized
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prospective HSCs with the advent of monoclonal antibody production and multiparametric
fluorescence activated cell sorting (Hulett, Bonner, Barrett, & Herzenberg, 1969; Kohler &
Milstein, 1975). These techniques combined provided a novel way to distinguish different
hematopoietic cell populations based on their cell surface markers. Critical publications revealed
that Thy1 and Sca1 were the key cell surface markers that can distinguish repopulating
hematopoietic cells within the bone marrow (Christa E. Muller-Sieburg, Whitlock, & Weissman,
1986; Spangrude, Heimfeld, & Weissman, 1988). Cell surface markers that delineate mature cell
populations such as B220 (B cells) and Gr-1 (granulocytes) were also identified (Coffman &
Weissman, 1983; K. L. Holmes et al., 1986). This tracking method was particularly instrumental
for studying myeloid cell types as their turnover rates are high.
Multiparameter flow cytometry is the method of choice for accurately enumerating and
purifying hematopoietic cell populations. Several successive advances in software packages,
fluorochromes, and reagents for FACS have permitted this technology to analyze up to 20
fluorescence parameters (Chattopadhyay, Hogerkorp, & Roederer, 2008). Multiple mature cell
lineages can thus be tracked simultaneously over time after transplantation. Several cell surface
markers (Sca1, Kit, and Slam) were found to identify long-term HSCs (LT-HSC) with multi-
lineage potential and long-term reconstitution capacity of more than 16 weeks after
transplantation (Ikuta & Weissman, 1992; Kiel et al., 2005; Spangrude et al., 1988). Downstream
of LT-HSC, short-term HSCs (ST-HSC) and multipotent progenitors (MPP) generate detectable
engraftment for up to 6 and 3 weeks, respectively (Månsson et al., 2007). These are defined
based on their Mac1, CD34, and Flk2/Flt2 expression (Adolfsson et al., 2001; Christensen &
Weissman, 2001; Forsberg, Serwold, Kogan, Weissman, & Passegué, 2006; Sean J. Morrison,
Wright, & Weissman, 1997; Osawa, Hanada, Hamada, & Nakauchi, 1996). Single cell HSC
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 16

transplantation studies have since identified several murine subsets of HSCs with varying self-
renewal capacity (Benveniste et al., 2010; Benveniste, Cantin, Hyam, & Iscove, 2003).
The Hematopoietic Hierarchy
HSCs are responsible for the lifelong maintenance of hematopoiesis, producing approximately
10
12
blood cells are produced daily throughout the lifetime of a human being (Ogawa, 1993).
HSCs continuously replenish all types of blood and immune cells through a series of lineage-
restricted steps, resulting in more differentiated and developmentally limited cells. During
hematopoietic differentiation, HSCs give rise to intermediate progenitors with increasingly
restricted potentials (Figure 1.2). These progenitor cells are instrumental for expanding and
increasing the number of cells that can be produced by each HSC to minimize the proliferation-
induced damages in HSCs (Bryder, Rossi, & Weissman, 2006). HSC differentiation to MPP is
associated with a progressive loss of self-renewal capacity but retained multi-lineage potential
(Christensen & Weissman, 2001, p. 2; S. J. Morrison & Weissman, 1994).
The first functional model of hematopoietic hierarchy was outlined by several studies that
identified lineage restricted murine progenitors that segregate lymphoid versus myeloid
differentiation (Figure 1.1). Initially, the idea of lineage restricted progenitors was controversial
as the MPP was first proposed to directly give rise to mature myeloid and lymphoid cells (Sean J.
Morrison et al., 1997). This notion was challenged by studies using chromosomal and retroviral
marking based clonal tracking that suggest the existence of lineage restricted progenitors
(Abramson, Miller, & Phillips, 1977; Dick, Magli, Huszar, Phillips, & Bernstein, 1985;
Lemischka, Raulet, & Mulligan, 1986).
The common lymphoid progenitor (CLP), capable of clonally differentiating into all
mature lymphoid cell types including B, T, and natural killer cells, was first identified using IL-7
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 17

receptor expression (Kondo, Weissman, & Akashi, 1997). The clonogenic common myeloid
progenitor (CMP) was found to be capable of producing all myeloid cell progeny including
granulocytes, monocytes, erythrocytes, and megakaryocytes (Akashi, Traver, Miyamoto, &
Weissman, 2000). Bi-potent progenitors granulocyte-monocyte progenitors (GMP) and
megakaryocyte-erythroid progenitors (MEP) were identified further downstream in the myeloid
lineage (Akashi et al., 2000). CLP give rise to bi-potent early thymic progenitors (Gounari et al.,
2002; Moore & Zlotnik, 1995).
The pioneering studies discussed in this section suggested that 1) HSCs lose their ability
to self-renew as they differentiate, 2) that the myeloid and lymphoid lineages are absolutely
segregated after the MPP stage, and 3) that lineage specification occurs in a step-wise fashion
that is irreversible. This classical model of murine hematopoiesis has been challenged by other
groups that have shown that MPP subsets are lineage biased (Lai & Kondo, 2006; Månsson et
al., 2007; Eric M. Pietras et al., 2015). Additional studies have provided evidence for a
consortium of HSC subsets with distinct lineage biases, suggesting that lineage bias occurs at an
earlier stage in the hierarchy (Challen, Boles, Chambers, & Goodell, 2010; C. E. Muller-Sieburg,
2004; Sieburg et al., 2006).
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Figure 1.2. The hierarchy of hematopoiesis. HSCs reside at the top of the hematopoietic
hierarchy. As they differentiate into MPPs, their self-renewal properties are gradually lost.
Commitment towards either the myeloid or lymphoid lineage occur at the oligopotent progenitor
level. At the final differentiation stage, mature hematopoietic cells are produced.
HSC Heterogeneity in Adult Bone Marrow
FACS-purification allowed for in vivo functional evaluations of candidate HSC populations that
were previous impossible. Competitive primary and serial transplantations of HSCs isolated
based on the cell surface markers described above allowed for evaluation of the self-renewal and
lineage differentiation of HSCs in a challenged environment in vivo. Advancements in single
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 19

cell techniques have made it possible to discriminate functional patterns within sub-populations
of HSCs that have previously been obscured by population level blood output measurements.
Mouse single cell experiments have demonstrated extensive heterogeneity within the HSC
compartment with regards to lineage output and self-renewal capacity. Analysis of single HSCs
following transplantation into irradiated ckit receptor tyrosine mutant recipient mice showed that
there are at least 16 types of kinetic repopulation patterns over the course of 8 months following
clonal stem cell transplantation (Sieburg et al., 2006). HSCs purified from the bone marrow of
the primary recipient mice and transplanted into secondary recipient mice had the same
repopulation kinetics (Müller-Sieburg, Cho, Thoman, Adkins, & Sieburg, 2002). These data
suggested that may have an intrinsic pre-determined fate that can be propagated across serial
transplantations.
Studies measuring the mature hematopoietic cell lineage outputs in the peripheral blood
have shown that the level of reconstitution in the myeloid and lymphoid lineages was different
between individual HSC clones (Cho, Sieburg, & Muller-Sieburg, 2008; Ema et al., 2005;
Christa E. Muller-Sieburg, Sieburg, Bernitz, & Cattarossi, 2012). These studies suggest that
lineage bias can arise within the phenotypical HSC population as they commit to lineage fates
but still retain their HSC-defining HSC markers. The HSC subsets were classified as “myeloid-
biased”, “lymphoid-biased”, and “myeloid-lymphoid balanced” (Figure 1.3) (Dykstra et al.,
2007a). HSC subsets with high reconstitution abilities are also myeloid-biased, while lymphoid-
biased HSCs have lower reconstitution potential (Challen et al., 2010; Dykstra et al., 2007a;
Gekas & Graf, 2013; Oguro, Ding, & Morrison, 2013). More recently, other HSC subsets such as
“platelet-biased” have been described through transplantation of HSCs sorted for expression of
the von Willebrand factor (vWF) (Sanjuan-Pla et al., 2013). HSCs that express high levels of ckit
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 20

are also platelet-biased. The studies discussed above suggest that the lineage output of HSC
subsets may be primed intrinsically.

Figure 1.3. HSC subsets have varying lineage outputs.
Molecular Properties of HSC Subsets
Many groups have since sought to identify the molecular signatures associated with these
various HSC subsets. Combinations of sequencing and bioinformatics tools and programs
developed contributed extensively to understanding the heterogeneity of HSCs. Repopulation
assays of HSCs with varying expression levels of CD150, ckit, and Sca1 have indicated that
specific levels of these markers enrich for HSCs with different self-renewal capacity and
differentiation kinetics (Kent et al., 2009; Morita, Ema, & Nakauchi, 2010; van der Wath,
Wilson, Laurenti, Trumpp, & Liò, 2009; Wilson, Laurenti, & Trumpp, 2009). For instance, both
high and low ckit expression mark HSCs that can reconstitute irradiated recipients. However,
HSCs with intermediate ckit expression are highly proliferative after transplantation and can
efficiently repopulate within secondary recipients, while those with high ckit expression have
low expansion and repopulating capacity following transplantations (Grinenko et al., 2014; Shin,
Hu, Naramura, & Park, 2014). These studies suggest that varying levels of ckit expression mark
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different subsets of HSCs that are hierarchically organized with increasing levels of ckit
expression corresponding to functional repopulation and self-renewal characteristics. Several
other markers have been used to distinguish between subsets of HSCs that have varying
reconstitution potentials, including Hoxb5, CD150, ROS, CD41, Tie2, CD229, and CD61 (Chen
et al., 2016; Gekas & Graf, 2013, p. 150; Ito et al., 2016; Jang & Sharkis, 2007; Oguro et al.,
2013; Umemoto et al., 2008, p. 61).
Gene expression analyses demonstrated that responsiveness to signaling pathways or
factors in the microenvironment is another intrinsic molecular difference between HSC subsets.
Myeloid-biased or lymphoid-biased HSC subsets can be distinguished based on how they
respond to the IL-7 receptor (IL-7R). Myeloid-biased HSCs lose their ability to respond to
interleukin 7 (IL-7) due to the down-regulation of Il-7R (C. E. Muller-Sieburg, 2004). Compared
to myeloid-biased HSCs, myeloid-lymphoid balanced HSCs generally have higher expression of
lymphopoietic genes, including Pax5, Il7r, E2a, and Ikaros (Benz et al., 2012). Similarly, TGF-
beta induces differential responses in myeloid-biased and lymphoid-biased HSCs, promoting
myeloid differentiation n(Challen et al., 2010). Activation of the BMP signaling pathway in the
bone marrow is associated with lymphoid-biased HSCs (Crisan et al., 2015). Through the series
of studies described above, a spectrum of subcategories in the HSC pool was identified by the
multilineage potential and self-renewal ability of HSCs.
Lineage Priming of HSCs
Cell fate decisions in HSCs have been suggested to result from cross-antagonism of lineage-
specifying transcription factors (Graf & Enver, 2009). Studies using single cell multiplex real
time polymerase chain reaction (RT-PCR) have revealed that HSPCs express low levels of
lineage-restricted genes, suggesting that they are “primed” for differentiation (Brady et al., 1995;
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Hu et al., 1997). Genes of multiple lineages are co-expressed simultaneously in multipotent
hematopoietic cells, indicating that conflicting lineage specific programs are primed during a
process known as “multilineage priming” (Hu et al., 1997). Multilineage priming suggests the
presence of open chromatin at primed loci that enable flexibility of multipotent cells to commit
to multiple lineages. Lineage restricted progenitors tend to express lineage-affiliated genes
consistent with their differentiation potential (Akashi et al., 2000; Kondo et al., 1997).
The repertoire of genes expressed reflects the lineage bias of that cell, suggesting that
specification of cell fate occurs before lineage commitment. Additionally, the cell must resist
differentiation down the alternative lineage by turning off lineage inappropriate genes. For
instance, expression of the early B cell factor 1 (Ebf1) can be enforced in HSCs to restrict
lymphopoiesis to the B cell lineage (Zhang, Cotta, Stephan, deGuzman, & Klug, 2003). Paired
box 5 (Pax5) is co-expressed with Ebf1 to drive commitment by preventing expression of
alternative lineage-affiliated genes, and to thereby activate the B cell program (Zandi et al.,
2012). Single cell transplantation studies have shown that roughly 60% of purified HSCs,
marked by vWF expression, exhibit megakaryocyte priming with co-expression of both myeloid
and erythroid genes (Guo et al., 2013; Sanjuan-Pla et al., 2013). Single cell transplantations of
these vWF
+
HSCs resulted in production of platelets and myeloid cells. Differential priming is
therefore associated with stably biased subsets of HSCs.
Regulation of HSCs by Intrinsic and Extrinsic Signals
The fates that HSCs adopt are governed by the fine-tuned interplay of intrinsic and extrinsic
signals. Many intrinsic factors are involved in HSC regulation, including transcription factors,
transcriptional repressors, cell cycle regulators, and anti-apoptotic signals (Domen, Cheshier, &
Weissman, 2000; E. M. Pietras, Warr, & Passegue, 2011; Sauvageau, Iscove, & Humphries,
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 23

2004). Knockout mouse experiments have rigorously demonstrated the importance of nearly 200
genes in HSC function (L. Rossi et al., 2012). Ablation of these genes resulted in phenotypes
ranging from the demise of HSCs to loss of multilineage potential in HSCs (Iwasaki et al., 2005;
Lawrence et al., 2005).
Additionally, HSCs can be influenced by extrinsic signals such as growth factors and
developmental regulators. Under stress conditions such as infection or inflammation the
organism may need to generate new blood cells to adapt to the challenge (Takizawa, Boettcher,
& Manz, 2012). Hematopoietic lineage-specifying cytokines may alter the output of HSCs by
affecting survival, expansion and differentiation. Classical hematopoietic cytokines that are
frequently used in maintaining HSCs in vitro to provide survival and differentiation signals
include stem cell factor (SCF) and thrombopoietin (TPO) (Borge et al., 1996; Ema, Takano,
Sudo, & Nakauchi, 2000; Li & Johnson, 1994; Qian et al., 2007; Sitnicka, Ruscetti, Priestley,
Wolf, & Bartelmez, 1996; Yoshihara et al., 2007).
These cell autonomous and non-autonomous signals interact with each other in networks
to govern the differentiation activities of HSCs. Analysis of knockout mice experiments
revealed that cytokine signaling is not required for lineage commitment and instead, cytokines
act upon intrinsically committed progenitors to promote their growth and survival. Time-lapse
imaging showed that the macrophage colony-stimulating (M-CSF), a myeloid cytokine released
during infection and inflammation that can directly induce the myeloid master regulator PU.1
and instruct myeloid cell fate change in HSCs (Mossadegh-Keller et al., 2013; Rieger, Hoppe,
Smejkal, Eitelhuber, & Schroeder, 2009). Furthermore, single cell multiplex qPCR experiments
have shown that cells with PU.1 expression are multilineage primed, indicating an uncommitted
state prior to M-SCF exposure.
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These experiments suggest that cytokines may act to instruct lineage choice in HSCs, but
the ability of HSCs to respond depends on the intrinsically regulated expression of the relevant
receptor. The variable expression of receptors amongst individual HSCs, perhaps due to lineage
priming, may act to ensure a mixture of heterogeneous responsive and non-responsive HSCs,
preventing exhaustion of the HSC pool. Both extrinsic and intrinsic regulation of HSC lineage
commitment may therefore be required for balanced hematopoiesis.
Clonal Tracking Reveals HSC Heterogeneity
The discoveries that unique genetic mutations could be exploited for clonal analysis and that
retroviral vectors could introduce genetic material into HSCs, led to the advent of a method to
clonally track HSCs (Capel, Hawley, Covarrubias, Hawley, & Mintz, 1989; Dick et al., 1985;
Joyner, Keller, Phillips, & Bernstein, 1983; Kohn et al., 1995; Lemischka et al., 1986; Miller,
Eckner, Jolly, Friedmann, & Verma, 1984; Williams, Lemischka, Nathan, & Mulligan, 1984).
These clonal tracking studies demonstrated differences in clonal behavior of distinct HSCs.
Specifically, a high degree of diversity exists in the HSC pool such that contributions to lineages
and engraftment kinetics of individual HSC clones were striking different (Capel et al., 1989;
Kohn et al., 1995).
However, this insensitive approach assesses clonality by performing integration site
analysis using Southern blotting or polymerase chain reaction-based methods, which can only
detect dominant clones and cannot resolve highly polyclonal patterns or investigate low abundant
clones. Other methods incorporating PCR-based retrieval are more sensitive but have low
efficiency (Berry et al., 2012; Bushman et al., 2005; Schmidt et al., 2002; C. Wu et al., 2013). To
overcome the aforementioned limitations, our lab developed a clonal tracking method that
combines viral cellular labeling, high throughput sequencing, and DNA barcoding (Lu, Neff,
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 25

Quake, & Weissman, 2011a). This cellular tracking system allows us to capture the interactions
of hundreds of HSCs simultaneously. Specifically, this barcoding system allows quantitation of
cellular proliferation and differentiation at single cell resolution over long durations of time
while maintaining the integrity of the entire cell population.
The DNA barcode in this tracking system consists of a common 6-bp library ID at the 5´
end, followed by a random 27-bp cellular barcode. A lentiviral vector delivers barcodes from a
large library into a small number of cells at a titer low enough such that most cells receive a
single unique barcode. After transplantation, barcodes replicate with host cells in recipient mice.
The progeny cells of donor cells are harvested, and barcodes are recovered from their genomic
deoxyribonucleic acid (DNA) by polymerase chain reaction (PCR). Barcodes are identified and
quantified using high-throughput sequencing (Illumina GA II). The 6-bp library ID aids the
separation of the barcodes from the sequencing results. Identical 33-bp barcodes are combined
for further analysis.
Using this system, our lab discovered that individual HSCs differentially supply blood
cells and that their differentiation programs change with the transplantation dose (Brewer, Chu,
Chin, & Lu, 2016). In this study, the genetic barcoding technology was employed to track the
blood production of individual murine HSCs transplanted over a wide range of doses. The study
revealed that the majority of transplanted HSCs was not multi-lineage and in fact, was
“specialized” in producing only one or two lineages. The HSC differentiation programs change
depending on the transplantation dose such that high HSC transplantation doses increase the
number of short-term differentiating clones. Maximum blood production by a single HSC,
however, was unaffected by transplantation dose. Previous studies using limiting dilution assays
of HSC transplantation suggested that individual HSCs play equal roles and uniformly alter their
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 26

blood production in response to changes during hematopoiesis. However, work from our lab and
others has challenged this model by demonstrating the heterogeneity of HSC differentiation after
transplantation (Brewer et al., 2016; Lu et al., 2011a; C. Wu et al., 2014).
Clonal tracking studies of HSCs have relied upon irradiation mediated transplantation in
recipients to assess the lineage potential and self-renewal properties of HSCs, which may not
reflect normal HSC function during homeostasis. Recently, clonal tracking of HSCs during
native hematopoiesis was accomplished by using an inducible Sleeping Beauty transposase to
barcode hematopoietic stem and progenitor cells (HSPC) and the progeny blood cells were
analyzed by next-generation sequencing of the insertion sites at multiple time points after
labeling (Sun et al., 2014). Two general models that described how HSCs are recruited into
differentiation under physiological conditions have been proposed. In the “clonal succession
model”, small numbers of HSCs are sequentially recruited to enter the cell cycle to differentiate,
and are succeeded by a different group of HSCs upon exhaustion (Jordan & Lemischka, 1990;
Lemischka et al., 1986). On the other hand, the “clonal stability model” suggests that a single
group of HSCs continuously replenish the blood system (McKenzie, Gan, Doedens, Wang, &
Dick, 2006a; Prchal, 1996). The transposon-based cellular tracking study demonstrated that
clonal dynamics are consistent with the “clonal succession model” as dramatically different
clones appear to supply the blood at each measured time point (Sun et al., 2014). This study also
found that MPPs contributed to most of the mature cell populations and that less than 5% of HSC
clones are involved in homeostatic hematopoiesis. This discovery raised questions about the
identity of the cells that sustain homeostatic hematopoiesis and the regulatory systems that are
involved.
HSC Transplantation Treatment for Hematopoietic Disorders
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 27

The immune response is mounted by a diverse network of defenses that includes the rapid and
nonspecific innate immune response to initial infection, and later, the adaptive immune response
to the specific pathogen (Parkin & Cohen, 2001). The host function and resistance to infection
are maintained by these two arms in the immune system. Thus, disruption of the immune system
in recognizing, repelling, and eradicating pathogens may lead to immunodeficiency, severe
infections, and autoimmune disease.
Patients with primary immunodeficiency (PI) disorders have genetic defects of the
immune system that cause the inability to produce lymphocytes resulting in increased
susceptibility to infections (Cunningham-Rundles & Ponda, 2005). PI disorders were among the
first diseases in which HSC transplantation was attempted (Buckley et al., 1986; Gatti,
Meuwissen, Allen, Hong, & Good, 1968; O’Reilly et al., 1977; Parkman, Gelfand, Rosen,
Sanderson, & Hirschhorn, 1975; Reisner et al., 1983). These disorders can further be
characterized based on the cell lineages primarily affected. For instance, SCID patients have a
shared phenotype of NK, T- and B-cell deficiencies (Dvorak & Cowan, 2007). Given their role
in blood regeneration, HSC transplantation is the most prevalent curative treatment for many
blood diseases. Bone marrow from healthy donors are transplanted into conditioned
immunodeficient patients to correct the immune function.

COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 28

Chapter II: Functional Compensation between Hematopoietic Stem Cell Clones In Vivo
This work was published (EMBO Reports 2018).
Lisa Nguyen, Zheng Wang, Adnan Y. Chowdhury, Elizabeth Chu, Jiya Eerdeng, Du Jiang, Rong
Lu
Abstract
In most organ systems, regeneration is a coordinated effort that involves many stem cells, but
little is known about whether and how individual stem cells compensate for the differentiation
deficiencies of other stem cells. Functional compensation is critically important during disease
progression and treatment. Here, we show how individual hematopoietic stem cell (HSC) clones
heterogeneously compensate for the lymphopoietic deficiencies of other HSCs in a mouse. This
compensation rescues the overall blood supply and influences blood cell types outside of the
deficient lineages in distinct patterns. We find that highly differentiating HSC clones expand
their cell numbers at specific differentiation stages to compensate for the deficiencies of other
HSCs. Some of these clones continue to expand after transplantation into secondary recipients.
In addition, lymphopoietic compensation involves gene expression changes in HSCs that are
characterized by increased lymphoid priming, decreased myeloid priming and HSC self-renewal.
Our data illustrate how HSC clones coordinate to maintain the overall blood supply. Exploiting
the innate compensation capacity of stem cell networks may improve the prognosis and
treatment of many diseases.
Introduction
Hematopoietic stem cells (HSCs) are scattered throughout the body in dispersed bone marrow
niches and must coordinate to maintain a common pool of peripheral blood cells (Schofield,
1978). We have recently shown that HSCs can adapt their differentiation programs to the
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 29

presence of other HSCs at various transplantation doses to ensure overall balanced blood
production (Brewer et al., 2016). A similar coordination mechanism may allow HSCs to rescue
the functional deficiencies of other impaired HSCs within the same organism.
It is critically important to understand how the functional deficiencies of a subset of
HSCs impact the overall HSC network. Many hematopoietic diseases arise from either an
abnormal abundance (e.g., myeloproliferative disorder, thrombocytosis, leukocytosis, and
erythrocytosis) or an abnormal deficiency (e.g., myelodysplastic syndrome, neutropenia,
agranulocytosis, and thrombocytopenia) of certain blood cell types (Andrès et al., 2017; Corey &
Oyarbide, 2017; Thota & Gerds, 2017). The initial stages of these diseases may involve a latent
period during which a patient’s healthy HSCs can sufficiently compensate for the deficiencies of
diseased cells to ameliorate disease symptoms. In addition, bone marrow transplantation, the
primary treatment for many of these diseases, requires donor HSCs to adapt their differentiation
programs to the disease environment (Thomas, Lochte, Lu, & Ferrebee, 1957). Functional
compensation between HSCs, especially in the lymphoid lineages, may also take place during
aging, as lymphopoietic deficiencies often develop in the elderly (D. J. Rossi et al., 2005). Few
studies have attempted to understand the compensation capacity of regenerative networks. In this
study, we offer new insights into the compensation mechanisms at both the cellular and
molecular levels.
Previous studies using limiting dilution assays of HSC transplantation show that the
number of donor HSCs quantitatively determines the fraction of blood cells that they produce
(Bryder et al., 2006; Eaves et al., 1997; Purton & Scadden, 2007). These experiments suggest a
coordination model where individual HSCs play equal roles and uniformly alter their blood
production in response to changes in hematopoiesis. However, these assays analyze HSCs as a
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 30

population, and the differences between individual HSCs are hidden. Recent work from our
group and others has shown that HSC differentiation is heterogeneous at the clonal
level(Beerman et al., 2010; Benz et al., 2012; Dykstra et al., 2007a; Fehse & Roeder, 2008; Lu,
Neff, Quake, & Weissman, 2011b; McKenzie et al., 2006a; Sieburg et al., 2006; Weksberg,
Chambers, Boles, & Goodell, 2008a). For example, individual HSCs supply differential amounts
of blood cells in mice and in human patients (Dykstra et al., 2007a; Fehse & Roeder, 2008; Lu et
al., 2011b; McKenzie et al., 2006a; Weksberg et al., 2008a). Moreover, recent studies of native
hematopoiesis suggest that different blood cell types have distinct clonal origins(Sun et al.,
2014). These new findings raise the question of how the diverse differentiation programs of
individual HSCs are coordinated in the aftermath of functional disruptions. In this study, we use
a co-transplantation experimental model and high throughput genetic barcode tracking
technology to address this question. We show how wild type (WT) HSCs compensate for
genetically mutated HSCs that are deficient in supplying one or multiple types of lymphocytes.
Results and Discussion
To investigate how HSCs functionally compensate for the lineage deficiencies of other HSCs
within an organism, we co-transplanted WT HSCs and lineage-deficient HSCs into lethally
irradiated WT recipient mice (Fig 2.1A, Appendix Figs S2.1 and S2.2). In our experimental
models, we focused on B cell deficiency as well as B and T cell double deficiencies, because B
and T cells are the most abundant lymphocytes (Appendix Fig S2.3). B-deficient HSCs were
isolated from uMT
-/-
mice, and co-transplanted with WT HSCs at two different doses but at the
same ratio (1:3): 1,000 WT CD45.2 HSCs and 3,000 uMT
-/-
CD45.1 HSCs (Figs 2.1-2.5); 3,000
WT CD45.2 HSCs and 9,000 uMT
-/-
CD45.1 HSCs (Fig EV2.1-2.2, and Appendix Fig S2.4).
Results from the two doses are generally consistent (Fig EV2.1-2.2, and Appendix Fig S2.4),
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 31

suggesting that the transplantation dose plays a minor role in regulating the lymphopoietic
compensation. B and T double deficient HSCs were isolated from NSG and Rag2
-/-
γc
-/-
mice.
The genetic background of deficient HSCs plays a minor role in regulating lymphopoietic
compensation, as our results are generally consistent using the two different strains (Fig EV2.1-
2.2, and Appendix Fig S2.4). We co-transplanted 1,500 WT CD45.2 HSCs and 3,000 NSG or
Rag2
-/-
γc
-/-
CD45.1 HSCs per recipient (Appendix Fig S2). Recipients for all experiments were
CD45.1/ CD45.2 WT mice. They were lethally irradiated prior to the transplantation, and each
received 250,000 whole bone marrow cells (helper cells) that assist in blood production. For the
control groups, we co-transplanted HSCs from WT (CD45.1) and WT (CD45.2) donor mice at
the same doses as the deficient co-transplantation groups.
We examined the peripheral blood of the recipient mice at 6-8 months after
transplantation, when blood production has returned to a steady state and a stable group of HSC
clones continuously supplies blood cells over time (Jordan & Lemischka, 1990; Prchal, 1996;
Purton & Scadden, 2007) (Appendix Fig S2.3). We found that in the uMT
-/-
co-transplantation
groups, where deficient HSCs do not produce B cells (Fig 2.1B, Fig EV2.1B, and Appendix Fig
S2.4A), WT HSCs significantly oversupplied B cells (Fig 2.1D, Fig EV2.1D, and Appendix Fig
S2.4C) to maintain normal levels of total B cell production (Fig 2.1F, Fig EV2.1F, and Appendix
Fig S2.4E). In the Rag2
-/-
γc
-/-
and NSG co-transplantation groups, where deficient HSCs are
unable to produce B cells, CD4 T cells, and CD8 T cells (Fig 2.1C, Fig EV2.1C, and Appendix
Fig S2.4B), WT HSCs significantly oversupplied B cells, CD4 T cells, and CD8 T cells (Fig
2.1E, Fig EV2.1E, and Appendix Fig S2.4D) to maintain normal levels of total B and T cell
production (Fig 2.1G, Fig EV2.1G, and Appendix Fig S2.4F). These data suggest that WT HSCs
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 32

increase their differentiation specifically in the deficient lineages to maintain the balance of the
overall blood supply.
In addition, we found that the presence of WT HSCs also changed the differentiation of
lineage-deficient HSCs. uMT
-/-
mice have slightly higher levels of T cells (Fig EV2.1A), but
uMT
-/-
HSCs produced slightly fewer T cells in the co-transplantation group (Fig 2.1D). This T
cell reduction is statistically significant in a replicate experiment (Appendix Fig S2.4C), and
when data from both experiments are combined (Fig EV2.1D). WT HSCs compensated for the
reduction in B and T cells produced by uMT
-/-
HSCs such that the total B and T cell levels are
similar to the control group (Fig 2.1F, Fig EV2.1F, and Appendix Fig S2.4E).
Lymphopoietic compensation may originate from an increase in the number of
differentiating clones or from an elevated expansion in their differentiation. To distinguish
between these two possibilities, we used a genetic barcoding technique that we had developed to
track WT donor HSCs at the clonal level (Appendix Fig S2.5)(Lu et al., 2011b). Genetic
barcodes drawn from a large semi-random 33mer DNA barcode library were used to uniquely
label and track individual HSCs. We have verified that each barcode uniquely corresponds to a
distinct HSC with more than 95% confidence and that the lentiviral vectors deliver the barcodes
into quiescent HSCs without altering their properties (Lu et al., 2011b). DNA barcodes are
incorporated into the cellular genome and inherited by progeny cells along with regular genomic
DNA. The abundance of a genetic barcode in a cell population is proportional to the number of
progeny cells that the original barcoded cell produces. Barcodes are recovered by high-
throughput sequencing that reads millions of sequences from each sample and provides
quantitative results. We found that the total numbers of WT clones that supply the myeloid and
lymphoid lineages are similar between the control and deficient co-transplantation groups (Figs
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 33

2.2A-B and Figs EV2.2A-B), indicating that compensation does not originate from increases in
clone number.
To identify the HSC clones that respond to the lymphopoietic deficiencies, we
categorized WT clones based on the abundance of their genetic barcodes in each blood cell
population, and we called this “clonal abundance”. We found an increase in the number of clones
that produced high amounts of B cells in the uMT
-/-
co-transplantation groups, and high amounts
of B and T cells in the NSG and Rag2
-/-
γc
-/-
co-transplantation groups (Figs 2.2C-E and 2.2G-I,
Figs EV2.2C-E and EV2.2G-I). In addition, these expanding clones supplied significantly larger
amounts of lymphocytes in the deficient lineages (Figs 2.2K-L and Figs EV2.2K-L) that account
for the majority of the lymphopoietic compensation (Figs 2.1D-E, Figs EV2.1D-E, Appendix
Figs S2.4C-D). Taken together, these data suggest that lineage deficiency is primarily
compensated by clones that highly expand.
At the same time, we found significantly fewer clones that expanded their granulocyte
production in the presence of lymphopoietic deficient HSCs, suggesting that the myeloid
differentiation of dominant clones was compromised (Figs 2.2F and 2.2J). In the replicate
experiments, we found a similar reduction in the Rag2
-/-
γc
-/-
co-transplantation group (Fig
EV2.2J), but not in the uMT
-/-
co-transplantation group (Fig EV2F) possibly because of
differences in the transplantation dose.
To determine if the lymphopoietic compensation is determined at the HSC level, we
purified both WT and deficient HSCs from primary recipients and transplanted them into WT
secondary recipients (Fig 2.3A). In the secondary recipients, we found that WT donor HSCs
from the uMT
-/-
co-transplantation group continued to produce high levels of B cells (Fig 2.3B)
to compensate for the deficiency in B cell production of the uMT
-/-
HSCs (Fig 2.3C). Secondary
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 34

recipients displayed no significant differences in overall blood production (Fig 2.3D) and in total
clone number (Fig 2.3E). We found that significantly more clones expanded to produce B cells
and that significantly fewer clones expanded to produce granulocytes (Figs 2.3F, 2.3G and 2.3J).
These data are consistent with results from the primary transplantations (Fig 2.2 and Fig EV2.2).
To determine if compensating clones in the primary recipients remain compensating in
the secondary recipients, we compared the WT clones that expanded in the B cell lineage. We
found that the clones that had expanded in the B cell lineage in primary recipients were
significantly more likely to maintain their expansion in secondary recipients when uMT
-/-
HSCs
are present (Fig 2.3K). Since purified HSCs were the only cells transplanted into the secondary
recipients, our results suggest that lymphopoietic compensation may be determined and
memorized at the HSC level.
To identify the differentiation stage at which cellular compensation takes place, we
quantified the cell numbers of the total (Figs 2.4A-B) and WT donor derived stem and progenitor
cells (Figs 2.4C-D, Appendix Fig S2.6). We found significantly fewer total HSCs in both co-
transplantation groups (Figs 2.4A-B) and significantly fewer WT donor derived HSCs in the
NSG co-transplantation group (Fig 2.4D). Upon differentiation, HSCs first lose their self-
renewal potential and become multipotent progenitors (MPPs) that retain full differentiation
potential(S. J. Morrison & Weissman, 1994). We found significantly reduced numbers of total
MPPs in the NSG co-transplantation group and WT donor derived MPPs in both groups (Figs
2.4B-D). This is likely the result of reduced HSC number. Further downstream, MPPs
differentiate into the common lymphoid progenitor (CLP) and the common myeloid progenitor
(CMP) (Akashi et al., 2000; Kondo, Weissman, & Akashi, n.d.; Serwold, Richie Ehrlich, &
Weissman, 2009). In both co-transplantation groups, we found a significant increase in the
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 35

number of total and WT donor derived CLPs (Fig 2.4). As CLPs supply B and T cells, an
overproduction of CLPs to compensate for B cell deficiency will also increase T cell production.
This explains the overproduction of CD4 T and CD8 T cells observed in the uMT
-/-
co-
transplantation group (Fig EV 2.1B and Appendix Fig S2.4A). The uMT
-/-
co-transplantation
group also exhibited a reduction in the number of total and WT donor derived CMPs and
granulocyte macrophage progenitors (GMPs) (Figs 2.4A and 2.4C). Taken together, these data
suggest that compensation for lymphopoietic deficiencies arises from CLP expansion, which is
associated with compromised HSC self-renewal and reduced myelopoiesis.
While the lymphopoietic compensation is manifested as an increase in cell numbers at the
oligopotent progenitor level (Fig 2.4), it is possible that the compensation decision has already
been made at the HSC stage. This predestination may be accomplished by lineage priming,
where HSCs express a low level of key regulatory genes in specific lineages and poise
themselves to differentiate towards the corresponding lineages (Guo et al., 2013; Mercer et al.,
2011; Orkin, 2003). To determine if this mechanism had taken place during the lymphopoietic
compensation, we compared the gene expression profiles of WT phenotypic HSCs purified from
the bone marrow of mice co-transplanted with deficient HSCs with those from the control mice
(Appendix Fig S2.7). The purity of phenotypic HSCs (lineage (CD3, CD4, CD8, B220, Gr1,
Mac1, Ter119)-/ckit+/Sca1+/Flk2-/CD34-/CD150+) from previously transplanted bone marrow
may differ from the phenotypic HSCs from naïve bone marrow (Benz et al., 2012). We found
significant activation of genes involved in diseases, biological functions, and networks that are
related to lymphopoietic compensation (Fig EV2.3 and Tables EV2.1-2). We also found
significant activation of functions related to cell proliferation, hematological system
development, and cell-to-cell signaling and interaction (Fig EV2.3 and Tables EV2.1-2.2). These
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 36

data suggest that compensation for lymphopoietic deficiency may be communicated and
determined at the HSC level. 96 genes changed significantly in both co-transplantation groups
(Fig 2.5A). The two co-transplantation groups both activated genes involved in biological
functions that support compensation for lymphopoietic deficiencies, including quantity of
lymphocytes and proliferation of blood cells (Fig 2.5B). In the NSG co-transplantation group, we
found an enrichment for genes involved in natural killer (NK) cell proliferation, including Il7r
(Mercer et al., 2011, p. 7), Slamf6(N. Wu et al., 2016), and Axl (E.-M. Kim et al., 2017) (Fig 5B),
consistent with the previous findings that HSCs derived from NSG mice are unable to produce
NK cells (Coughlan et al., 2016; Shultz et al., 2005). We also identified a group of genes that
were important in lymphoid differentiation and were up regulated in both data sets, including
Pax5 (M. L. Holmes, Carotta, Corcoran, & Nutt, 2006, p. 5), Ptpro (M. Kim, Kim, & Jho, 2010),
and Bmf (Labi et al., 2008; Sidman, Paige, & Schreier, 1984) (Fig 2.5C and Appendix Table
S2.1A-B).
Biological functions related to the myeloid lineage, including myelopoiesis of bone
marrow, quantity of monocytes, and megakaryocytopoiesis, were inactivated in the WT HSCs
from the NSG co-transplantation group (Fig 2.5B). Il11ra signaling, which is involved in
expanding myeloid progenitors and megakaryocytes (Gordon & Whetton, 1996; Nandurkar,
Robb, & Begley, 1998), was down regulated in both data sets (Fig 2.5C and Appendix Table
S1A). In the NSG co-transplantation group where substantially more genes altered their
expression (Fig 2.5A), we found that key regulators of multiple stages of myelopoiesis, including
Gata1/2 (Iwasaki et al., n.d.), Hoxa10 (Magnusson, Brun, Miyake, et al., 2007), and Gfi1 (Hock
et al., 2004), were down regulated (Fig 2.5E and Appendix Table S2.1C). These data suggest that
the compensating HSCs had reduced myeloid priming.
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 37

We also found molecular evidence of reduced HSC self-renewal in the NSG co-
transplantation group. Genes that are known to promote HSC self-renewal, including Hoxb3
(Hong et al., 2014; Magnusson, Brun, Lawrence, & Karlsson, 2007, p. 3), Hoxb6 (Fischbach et
al., 2005, p. 6), and Gfi-1( Hock et al., 2004) were down regulated (Fig 2.5B). Genes involved in
differentiation such as Pax5 (M. L. Holmes et al., 2006, p. 5) and Ebf1 (Györy et al., 2012), and
in development such as Vpreb2(Shimizu, Mundt, Licence, Melchers, & Mårtensson, 2002) and
Zap70(Otsu et al., 2002), were up regulated (Fig 2.5B). These data are consistent with the
reduction in HSC numbers (Fig 2.4).
The lineage priming and reduction of self-renewal in HSCs during lymphopoietic
compensation suggest that HSCs are involved in lymphopoietic compensation. To identify the
genes that may mediate the HSC compensation, we predicted the upstream regulators of the
genes whose expression significantly changed in WT HSCs co-transplanted with lymphopoietic
deficient HSCs (Fig 2.5D and Appendix Fig S2.8B) (Krämer, Green, Pollard, & Tugendreich,
2014). This analysis identified interleukin 10 receptor alpha subunit (Il10ra) as the top candidate
(Fig 2.5D). Interestingly, Il10ra is predicted to be an active regulator in uMT
-/-
co-transplantation
group and a repressive regulator in NSG co-transplantation group (Fig 2.5D), consistent with the
changes of Il10ra expression (Appendix Fig S2.8A). Il10ra encodes a cytokine receptor that
mediates the Il10r signal to inhibit the synthesis of proinflammatory cytokines (Nagalakshmi,
Murphy, McClanahan, & de Waal Malefyt, 2004). Il10r signaling has both immunostimulatory
and immunosuppressive properties, depending on the presence of co-factors. For example, Il10r
immunostimulatory signaling promotes the survival, proliferation, and differentiation of B cells
(Mosmann, 1994). Conversely, Il10r signaling exerts immunosuppressive effects on CD4 T cells
by inhibiting the Cd28 and Icos co-stimulatory pathway (Akdis, Joss, Akdis, Faith, & Blaser,
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 38

2000). In our experiment, Il10ra was activated in the uMT
-/-
co-transplantation group, where
only the B cell lineage requires compensation. However, in the NSG co-transplantation group,
Il10ra was inactivated, and its downstream targets, Cd28 and Icos, were up regulated (Fig 2.5E
and Table S2.1B). These data suggest that Il10r signaling is differentially regulated to
compensate for the B lineage as opposed to both B and T lineages.
In summary, we have presented an experimental model suitable to understand how
individual HSC clones compensate for lymphopoietic deficiencies in vivo. By transplanting WT
and lineage compromised HSCs into a single recipient, the interactions between normal and
deficient hematopoietic stem and progenitor cells can be investigated. Similar approaches can be
used to study other tissue and organ systems and other disease models as well. Our data
demonstrate how the hematopoietic network operates in vivo as a robust and coordinated system.
The compensation capacities that we showed enable the hematopoietic network to tolerate partial
loss of function. We discovered that some WT clones highly expand and increase their
differentiation. They compensate for lymphopoietic deficiencies by specifically overproducing
undersupplied cell types. This suggests that individual hematopoietic clones heterogeneously
respond to lineage deficiencies in the blood. Some of these expanding clones persistently
produced high levels of B cells in secondary recipients, indicating sustained self-renewal and
compensation potential. The heterogeneity in compensation capacity may be essential for
maintaining robustness in blood regeneration and suggests that regeneration coordination is a
complex process.
Furthermore, we have provided insights on cellular and molecular mechanisms
underlying lymphopoietic compensation. We found an increase in cell numbers at the CLP level
as well as in other downstream lymphoid lineages. While cellular level compensation is
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 39

manifested at the oligopotent progenitor level, we have identified molecular changes at the stem
cell level. We have discovered molecular regulators and pathways in HSCs that are associated
with increased lymphopoiesis, as well as decreased myelopoiesis and HSC self-renewal. Our
data show a reduction in HSC and MPP numbers as well as a down-regulation of self-renewal
genes in compensating HSCs. This is consistent with previous findings in humans that early
lymphoid transcription factors antagonize human HSC self-renewal (Laurenti et al., 2013;
van Galen et al., 2014). Future studies can manipulate these regulatory molecules to improve the
efficacy of bone marrow transplantation and to develop new therapeutic strategies that exploit
the endogenous HSC compensation capacity. New approaches to prognosis may be developed by
monitoring the compensation activities of endogenous HSCs. A better understanding of stem and
progenitor cell interactions can help improve the treatment of many degenerative and age-related
diseases.
Methods
Mice. Mice were purchased from Jackson Laboratories(“Mouse Mutant Resource Website, The
Jackson Laboratory, Bar Harbor, Maine,” 2018). WT donor mice used in the co-transplantation
experiments were C57BL/6J (CD45.2). The lymphoid deficient donor mice were B6.129S2(B6)-
Ighm
tm1Cgn
/J (uMT
-/-
, CD45.1), NOD-scid IL2Rgamma
null
(NSG, CD45.1) and C;129S4-
Rag2
tm1.1Flv
Il2rg
tm1.1Flv
/J (Rag2
-/-
γc
-/-
, CD45.1). The recipient mice were off-springs of C57BL/6J
and B6.SJL-Ptprca Pepcb/BoyJ (CD45.1/ CD45.2). Both NSG and Rag2
-/-
γc
-/-
mice have similar
B and T cell developmental deficiencies. The Rag2
-/-
γc
-/-
mice have a pan deletion of Rag2 exon
3. NSG mice carry a mutation on the NOD/ShiLtJ genetic background: severe combined immune
deficiency (SCID). The SCID mutation is in the DNA repair complex protein Prkdc and renders
the mice B and T cell deficient. HSCs derived from Rag2
-/-
γc
-/-
and NSG mice lack the ability to
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 40

respond to Il7 and hence cannot produce lymphoid cell lineages. Although NSG mice are on a
NOD background and genetically distinct from the recipient B6 mice, the NSG donor cells were
not rejected. We transplanted 1,500 WT HSCs and 3,000 NSG HSCs into each recipient, and
found the expected ratio of 1:2 WT to NSG donor derived granulocytes (Appendix Fig S2.2). All
donor and recipient mice were 8-12 weeks old at the time of transplantation. Mice of both
genders were used without discrimination. Irradiation was performed on all recipient mice before
transplantation at 950 cGy. We examined 5-8 mice for each experimental group, and performed
biological replicates shown in appendix figures. Mice were bred and maintained at the Research
Animal Facility of the University of Southern California. Animal procedures were approved by
the Institutional Animal Care and Use Committee.
Cell Isolation and Transplantation. HSCs (lineage (CD3, CD4, CD8, B220, Gr1, Mac1,
Ter119)-/ckit+/Sca1+/Flk2-/CD34-/CD150+) were obtained from the crushed bones of donor
mice and isolated using FACS sorting with the FACS-Aria II (BD Biosciences, San Jose, CA)
after enrichment using CD117 microbeads (AutoMACS, Miltenyi Biotec, Auburn, CA)
(Appendix Fig S2.1). HSCs were infected for 15 hours with lentivirus carrying barcodes and then
transplanted via retro-orbital injection. HSC clonal labeling was performed as described
previously(Brewer et al., 2016; Lu et al., 2011b). In addition to the donor HSCs, we transplanted
each recipient mouse 250,000 whole bone marrow cells (helper cells) flushed from the femurs of
CD45.1/ CD45.2 mice. These helper cells contain HSCs and other hematopoietic progenitors that
assist in blood production. During secondary transplantation, HSCs were purified from the bone
marrow of primary recipients and transplanted into lethally irradiated secondary recipients.
Blood Sample Collection and FACS Analysis. Blood samples were collected into phosphate
buffered saline (PBS) containing 10 mM EDTA via a small transverse cut in the tail vein. To
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eliminate red blood cells, 2% dextran was added, and the remaining blood cells were treated with
ammonium-chloride-potassium lysis buffer on ice for 5 minutes to remove residual red blood
cells. After a 30-minute antibody incubation at 4° C, samples were suspended in PBS with 2%
FBS and 4,6-Diamidino-2-phenylindole to distinguish dead cells. Cells were stained by
antibodies and sorted using the FACS-Aria I and II cell sorters and separated into granulocytes,
B cells, CD4 T cells, and CD8 T cells (Appendix Fig S2.3). Antibodies were obtained from
eBioscience (currently Life Technologies/Thermo Fisher) and BioLegend as described
previously(Lu et al., 2011b) (Appendix Table S2.2). Donor cells were sorted based on the CD45
marker. The following cell surface markers were used to harvest hematopoietic populations:
Granulocyte: CD4-/CD8-/B220-/CD19-/Mac1+/Gr1+/side scatter
high
;
B cell: CD4-/CD8-/Gr1-/Mac1-/B220+/CD19+;
CD4 T cell: B220-/CD19-/Mac1-/Gr1-/TCRαβ+/CD4+/CD8-;
CD8 T cell: B220-/CD19-/Mac1-/Gr1-/TCRαβ+/CD4-/CD8+;
HSC (hematopoietic stem cells): lineage (CD3, CD4, CD8, B220, Gr1, Mac1, Ter119)-/
/ckit+/Sca1+/Flk2-/CD34-/CD150+
CLP: lineage (CD3, CD4, CD8, B220, Gr1, Mac1, Ter119)-/Flk2+/Il7rα+
GMP: lineage (CD3, CD4, CD8, B220, Gr1, Mac1, Ter119)-/ckit+/Sca1-/FcγR+/CD34+
MEP: lineage (CD3, CD4, CD8, B220, Gr1, Mac1, Ter119)-/ckit+/Sca1-/FcγR-/CD34-
CMP: lineage (CD3, CD4, CD8, B220, Gr1, Mac1, Ter119)-/ckit+/Sca1-/FcγR-/CD34+
Flk2+ MPP: lineage (CD3, CD4, CD8, B220, Gr1, Mac1, Ter119)-/ckit+/Sca1+/ Slamf1-
/ Flk2+
Flk2- MPP: lineage (CD3, CD4, CD8, B220, Gr1, Mac1, Ter119)-/ckit+/Sca1+/ Slamf1/
Flk2-/CD34+
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Flow cytometry data were analyzed using FlowJo software version 10.4.2 (Tree Start, Ashland,
OR) and Diva software 8.0.1(BD Biosciences, San Jose, CA).
DNA Barcode Extraction and Sequencing. Genomic DNA was extracted from sorted
hematopoietic cells and amplified using Phusion PCR master mix (Thermo Scientific, Waltham,
MA). The PCR reactions were halted once they had progressed halfway through the exponential
phase. PCR product was purified and analyzed using high-throughput sequencing. Sequencing
data were analyzed as previously described (Brewer et al., 2016; Lu et al., 2011b). We combined
sequencing data with FACS data to calculate the clonal abundance for each clone: Clonal
abundance =100%*(Each cell population (Gr, B, CD4T or CD8T cells) % WBCs) * (Donor %
Each cell population) * (GFP

% Donor cells) * (number of reads for each barcode) / (total reads
of all barcodes)
RNA Sequencing. RNA was isolated from 4,000 HSCs (lineage (CD3, CD4, CD8, B220, Gr1,
Mac1, Ter119)-/ckit+/Sca1+/Flk2-/CD34-/CD150+) (Appendix Fig S1) using the Zymo
Research (Irvine, CA) Quick-RNA MicroPrep. A separate mouse was used for each triplicate
sample. RNA was processed and sequenced by the University of Southern California Epigenome
Center and Children’s Hospital Los Angeles Genomics Core. Data was analyzed by Partek®
Flow® software, version 6.0 Copyright© (Partek Flow, 2017). Transcripts were filtered to
exclude those whose maximum raw read counts were less than or equal to 10 counts. We aligned
reads to a reference genome (mm10) using the Spliced Transcripts Alignment to a Reference
(STAR) algorithm. We quantified to transcriptome using the Partek E/M method. We generated
a list of differentially expressed genes with fold changes below -2 and greater than 2, and with p
values less than 0.05.
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The networks and functional analyses were performed using Ingenuity Pathway Analysis
(IPA) (Krämer et al., 2014). The biological functional analysis is based on expected causal
effects between genes, which are derived from the literature knowledge base of IPA. The
analysis examines genes in the data set that are known to affect biological functions. Functions
with fewer than 10 differentially expressed genes are excluded from the list. The threshold for all
biological functions bar graphs is –log (p-value) = 1.3, which is calculated using the Fisher’s
exact test right-tailed. Predictions of activation or inactivation of functions and pathways were
based on the z-score algorithm, which takes into account the causal relationships between genes
and biological functions and networks determined by the direction of effect.
Statistical analysis
Results were shown as mean + SEM, and statistical significance was determined by a two-tailed
and two-sample equal variance Student’s t-test for two-group comparisons. Significance in all
figures was indicated as follows: ns p>0.05, *p< 0.05, **p<0.01, and ***p<0.001.
Data availability
RNA sequencing data have been deposited at Annotare under accession E-MTAB-6776.
Acknowledgements
We thank USC Libraries Bioinformatics Service staff for assisting with data analysis. The
bioinformatics software and computing resources used in the analysis were funded by the USC
Office of Research and the Norris Medical Library. We thank A. Nogalska and Q. Liu for helpful
discussions and C. Lytal and Z. Huang for manuscript edits. We also thank Y. Chen, M. Li, and
T. Trecek for help with RNA sequencing analysis; A. Nogalska for laboratory management; L.
Barsky, J. Boyd, and B. Masinsin for FACS core management; and C. Nicolet for RNA
processing and high-throughput sequencing. This work is supported by NIH R00-HL113104,
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R01HL135292, R01HL138225 and P30CA014089. L.N. is supported by an NIH T32 Training
Grant and F31 HL134359. E.C. is supported by the Rose Hills Foundation Science and
Engineering Fellowship and the USC Provost’s Undergraduate Research Fellowship. A.Y.C. is
supported by the California Institute for Regenerative Medicine Training Grant and the Hearst
Fellowship Award.
Author Contributions
L.N. and R.L. designed and performed the experiments. Z.W. and A.Y.C. assisted with RNA
experiments. E.C., J.E. and D. J wrote custom Python codes for data analysis. L.N. and R.L.
wrote the manuscript. All authors edited the manuscript.
Conflict of Interest
The authors declare that they have no conflict of interest.

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Figure 2.1: Wildtype (WT) HSCs compensate for the lymphopoietic deficiencies of co-
transplanted mutant HSCs in blood production.
A We co-transplanted barcoded WT HSCs with competitor WT, B cell deficient (uMT
-/-
),
or B and T cell deficient (NSG or Rag2
-/-
γc
-/-
) HSCs into irradiated recipient mice. Peripheral
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blood was harvested from recipient mice at 6 to 8 months after transplantation and sorted into
granulocytes (Gr), B, CD4 T, and CD8 T cells for population and clonal level analyses.
B-C Competitor donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood. In the
control group, competitor donor cells are WT HSCs. In the deficient group, competitor donor
cells are uMT
-/-
or NSG HSCs.
D-E WT donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood.
F-G Total production of Gr, B, CD4 T, and CD8 T cells in the peripheral blood (derived from
WT HSCs, competitor HSCs, helper whole bone marrow cells, and residual host cells) shown as
percentages of the total number of white blood cells (WBCs).
Data information: Data are shown as percentages of the total number of white blood cells
(WBCs). Data were collected at month 7 post transplantation for the uMT
-/-
group and month 8
post-transplantation for the NSG group, and presented as mean ± SEM. n=7 mice for the control
and n=7 for the treatment groups in the uMT
-/-
experiment and n=6 for the control and n=6 for
the treatment groups in the NSG experiment. *p<0.05, **p<0.01, ***p<0.001 (two-tailed and
two-sample equal variance Student’s t-test).

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Figure 2.2: Highly expanded HSC clones increase their differentiation in response to the
lymphopoietic deficiencies of other HSCs.
A-B Total numbers of barcoded WT clones that give rise to granulocytes (Gr), B cells, CD4 T
cells, and CD8 T cells.
C-J Number of HSC clones that produce different amounts of Gr, B, CD4 T, and CD8 T cells.
We combined the sequencing data with the flow cytometry data to calculate clonal abundance for
each clone as follows: Clonal abundance =100%*(Each cell population (Gr, B, CD4T or CD8T
cells) % WBCs) * (Donor % Each cell population) * (GFP

% Donor cells) * (number of reads for
each barcode) / (total reads of all barcodes)
K-L Production of Gr, B, CD4 T, and CD8 T cells by expanding WT clones that produce
more than 0.1% of WBCs in each lineage.
Data information: Data were collected at month 7 post-transplantation for the uMT
-/-
group and
month 8 post-transplantation for the NSG group, and presented as mean ± SEM. n=7 mice for the
control and n=7 for the treatment groups in the uMT
-/-
experiment and n=6 for the control and
n=6 for the treatment groups in the NSG experiment. *p<0.05 (two-tailed and two-sample equal
variance Student’s t-test).
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Figure 2.3: Persistence of lymphopoietic compensation in secondary transplantation.
A We co-transplanted barcoded wildtype (WT) HSCs and B cell deficient (uMT
-/-
)
competitor HSCs into irradiated recipient mice. WT HSCs were used as competitor HSCs in the
control group. 7 months after the primary transplantation, we harvested peripheral blood cells
and purified both WT HSCs and competitor HSCs from the bone marrow. HSCs from one
primary recipient mouse were transplanted into one secondary recipient mouse. 4 months after
the secondary transplantation, peripheral blood cells were sorted into granulocytes (Gr), B, CD4
T, and CD8 T cells for population and clonal level analyses.
B WT donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood.
C Competitor donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood. In the
control group, competitor donor cells are WT HSCs. In the deficient group, competitor donor
cells are uMT
-/-
or NSG HSCs.
D Total production of Gr, B, CD4 T, and CD8 T cells in the peripheral blood (derived from
WT HSCs, competitor HSCs, helper whole bone marrow cells, and residual host cells) shown as
percentages of the total number of white blood cells (WBCs).
E Total numbers of barcoded WT clones that give rise to Gr, B, CD4 T, and CD8 T cells.
F-I Number of HSC clones that produce different amounts of Gr, B, CD4 T, and CD8 T
cells. We combined the sequencing data with the flow cytometry data to calculate clonal
abundance for each clone as follows: Clonal abundance =100%*(Each cell population (Gr, B,
CD4T or CD8T cells) % WBCs) * (Donor % Each cell population) * (GFP

% Donor cells) *
(number of reads for each barcode) / (total reads of all barcodes)
J Production of Gr, B, CD4 T, and CD8 T cells by expanding clones that produce more
than 0.1% of WBCs in each lineage.
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K Percentage of expanding clones in the B cell lineage from the primary recipients that
continued to expand in the B cell lineage of the secondary recipients. Expanding clones are
defined as those producing more than 0.1% of WBCs.
Data information: Data were collected at month 4 after secondary transplantation and presented
as mean ± SEM. n=7 mice for the primary control (WT+WT) and deficient co-transplantation
(WT + uMT
-/-
) groups. n=4 mice for the secondary transplantation control group (WT + WT) and
n=6 for the secondary deficient co-transplantation group (WT + uMT
-/-
). *p<0.05 and **p< 0.01
(two-tailed and two-sample equal variance Student’s t-test).

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Figure 2.4: Compensation for lymphopoietic deficiency is manifested as an increase in cell
numbers at the progenitor level.
A-B Total amount of progenitors as a percentage of ckit

and Il7rα enriched bone marrow (BM)
cells. Shown are hematopoietic stem cells (HSCs), Flk2- and Flk2+ multipotent progenitors
(MPP), common lymphoid progenitors (CLP), common myeloid progenitors (CMP),
megakaryocyte-erythroid progenitors (MEP), and granulocyte-monocyte progenitors (GMP).
C-D WT donor derived progenitors as a percentage of ckit

and Il7rα enriched BM cells.
Data information: Data were collected at month 7 post transplantation for the uMT
-/-
group and
month 8 post-transplantation for the NSG group, and presented as mean ± SEM. n=7 mice for
control and n=7 for the treatment groups in the uMT
-/-
experiment and n=6 for the control and
n=6 for the treatment groups NSG experiment. *p<0.05 and **p< 0.01 (two-tailed and two-
sample equal variance Student’s t-test).
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Figure 2.5: Differential gene expression of HSCs during lymphopoietic compensation.
A Number of genes that are differentially expressed in WT HSCs co-transplanted with NSG
or uMT
-/-
HSCs as compared to WT HSCs co-transplanted with WT HSCs in the control group.
Gene lists are generated by Partek Flow. Thresholds are defined as p<0.05 and fold change <-2
or >2.
B Biological functions that are expected to increase or decrease based on the observed gene
expression changes generated by the Ingenuity Pathway Analysis (IPA). Representative genes
involved in each biological function are shown on the left. HSPC is an abbreviation for
hematopoietic stem and progenitor cells. NK is an abbreviation for natural killer cells. Shown are
the functions with Fisher’s exact test p-value<0.05.
C Top genes whose expressions are both up regulated or both down regulated in the NSG
and uMT
-/-
co-transplantation groups. Shown are the genes whose expression passes the
threshold p<0.05 and fold change <-2 or >2.
D Predicted upstream regulators based on the observed gene expression changes and the
known gene regulatory relationships in the IPA data base.
E Regulatory genes of key hematopoiesis steps that are significantly changed in the NSG
co-transplantation group. Common myeloid progenitor (CMP), megakaryocyte/erythrocyte
(MEP), granulocyte/macrophage progenitor (GMP), Erythrocytes (Ery), granulocytes (Gr),
megakaryocytes (Meg), monocytes (Mon), and macrophages (Mac), and common lymphoid
progenitors (CLP).
Data information: Ingenuity Pathway Analysis (IPA) was used to perform gene ontology analysis
on differentially expressed genes. Data were collected at month 7 post transplantation for the
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uMT
-/-
group and month 8 post-transplantation for the NSG group. n = 3 mice for each group,
except for the NSG control group where n=2 mice.

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Expanded View Figure 2.1: Wildtype (WT) HSCs compensate for the lymphopoietic deficiencies
of co-transplanted mutant HSCs in blood production (Supplemental data for Figure 1).
A uMT
-/-
mice do not produce B cells, but produce normal levels of Gr, CD4 T and CD8 T
cells.
B-C Competitor donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood. In the
control group, competitor donor cells are WT HSCs. In the deficient group, competitor donor
cells are uMT
-/-
, NSG or Rag2
-/-
γc
-/-
HSCs.
D-E WT donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood.
F-G Total production of Gr, B, CD4 T, and CD8 T cells in the peripheral blood (derived from
WT HSCs, competitor HSCs, helper whole bone marrow cells, and residual host cells) shown as
percentages of the total number of white blood cells (WBCs).
Data information: All panels are shown as percentages of the total number of white blood cells
(WBCs). Data from experiments in Figure 1 and Appendix Figure S4 were combined and
presented as mean ± SEM. n=15 mice for the control and n=15 for the treatment groups for the
uMT
-/-
experiment and n=14 for the control and n=14 for the treatment groups for the NSG and
Rag2
-/-
γc
-/-
experiments. *p< 0.05, **p<0.01, and ***p<0.001 (two-tailed and two-sample equal
variance Student’s t-test).
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Expanded View Figure 2.2: Highly expanded HSC clones increase their differentiation in
response to the lymphopoietic deficiencies of other HSCs (replicate experiment for Figure 2).
A-B Total numbers of barcoded WT clones that give rise to Gr, B, CD4 T, and CD8 T cells.
C-J Number of HSC clones that produce different amounts of Gr, B, CD4 T, and CD8 T cells.
We combined sequencing data with flow cytometry data to calculate clonal abundance for each
clone as follows: Clonal abundance =100%*(Each cell population (Gr, B, CD4T or CD8T cells)
% WBCs) * (Donor % Each cell population) * (GFP

% Donor cells) * (number of reads for each
barcode) / (total reads of all barcodes)
K-L Production of Gr, B, CD4 T, and CD8 T cells by expanding clones that produced more
than 0.1% of WBCs in each lineage.
Data information: Data were collected at month 6 post transplantation and presented as mean ±
SEM. n=8 mice for the control and n=8 for the treatment groups for the uMT
-/-
and Rag2
-/-
γc
-/-
experiments. *p<0.05 (two-tailed and two-sample equal variance Student’s t-test).
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Expanded View Figure 2.3: A comprehensive list of diseases and biological functions that are
activated in WT HSCs co-transplanted with lineage deficient HSCs as compared to WT HSCs
co-transplanted with WT HSCs in the control group.
A Diseases and biological functions identified from the WT and NSG HSC co-
transplantation group.
B Diseases and biological functions identified from the WT and uMT
-/-
HSC co-
transplantation group.
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Data information: Data were collected at month 7 post transplantation for the uMT
-/-
group and
month 8 post-transplantation for the NSG group. n = 3 mice for each group, except for the NSG
control group where n=2 mice. Functions with fewer than 10 differentially expressed genes are
excluded from the list. The threshold is -log (p-value) = 1.6.
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Expanded View Table 2.1: Gene expression signature of WT HSCs co-transplanted with NSG
HSCs as compared to WT HSCs co-transplanted with WT HSCs in the control group.
A-B Top genes that are differentially expressed.
C Top molecular and cellular functions that are activated.
D Top physiological systems, diseases and functions that are significantly affected.
E Top networks that are significantly affected. The scores account for the number of focus
genes and the size of the network to approximate the relevance of the network to the original list
of genes.
F Top diseases and disorders that are significantly affected.
Data information: Ingenuity Pathway Analysis (IPA) was used to perform gene ontology analysis
on differentially expressed genes. Data were collected 8 months post transplantation. n=3 mice
for the NSG and n=2 mice for the control groups.

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Expanded View Table 2.2: Gene expression signature of WT HSCs co-transplanted with uMT
-/-

HSCs as compared to WT HSCs co-transplanted with WT HSCs in the control group.
A-B Top genes that are differentially expressed.
C Top molecular and cellular functions that are activated.
D Top physiological systems, diseases and functions that are significantly affected.
E Top networks that are significantly affected. The scores consider the number of focus
genes and the size of the network to approximate the relevance of the network to the original list
of genes.
F Top diseases and disorders that are significantly affected.
Data information: Ingenuity Pathway Analysis (IPA) was used to perform gene ontology analysis
on differentially expressed genes. Data were collected 7 months post transplantation for the
uMT
-/-
group and 8 months post transplantation for the NSG group. n = 3 mice for the control
and treatment groups.

Appendix Figure S2.1: FACS plots and gating for hematopoietic stem cells (HSCs) (lineage
(CD3, CD4, B220, Gr1, Mac1, Ter119)-/ckit+/Sca1+/Flk2-/CD34-/CD150+) from ckit and Il7r
enriched bone marrow cells.
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Appendix Figure S2.2: HSCs from NSG mice engraft similarly as those from WT mice. Shown
are the amounts of granulocytes in the peripheral blood that are derived from WT, NSG, and
helper cells.
A FACS analysis of granulocytes that are derived from WT HSCs, NSG HSCs, and helper
cells.
B WT donor chimerism in granulocyte cell population.
C NSG donor chimerism in granulocyte cell population.
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Appendix Figure S2.3: FACS plots and gating for peripheral blood cells. The four harvested
blood cell populations are highlighted in blue. Red blood cells were lysed before sorting (details
described in Methods). A list of the cell surface markers for each harvested cell population is
included in Methods.
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Appendix Figure S2.4: HSCs compensate for the lymphopoietic deficiencies of co-transplanted
HSCs in blood production (replicate experiment of Figure 1).
A-B Competitor donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood. In the
control group, competitor donor cells are WT HSCs. In the deficient group, competitor donor
cells are uMT
-/-
or Rag2
-/-
γc
-/-
HSCs.
C-D WT donor derived granulocytes (Gr), B cells, CD4 T cells, and CD8 T cells in the
peripheral blood.
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E-F Total production of Gr, B, CD4 T, and CD8 T cells in the peripheral blood (derived from
WT HSCs, competitor HSCs, helper whole bone marrow cells, and residual host cells) shown as
percentages of the total number of white blood cells (WBCs).
Data information: Data are shown as percentages of the total number of white blood cells
(WBCs). Data were collected at month 6 post transplantation and presented as mean ± SEM. n=8
mice for each group. *p<0.05, **p<0.01, and ***p<0.001 (two-tailed and two-sample equal
variance Student’s t-test).

Appendix Figure S2.5: Schematic depicting genetic barcoding technology. A DNA barcode
consists of a common 6-bp library ID at the 5´ end, followed by a random 27-bp cellular
barcode. Different colors illustrate different barcode sequences. A lentiviral vector delivers
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barcodes from a large library into a small number of cells at a titer low enough such that most
cells receive a single unique barcode. After transplantation, barcodes replicate with host cells in
recipient mice. The progeny cells of donor cells are harvested, and barcodes are recovered from
their genomic DNA by PCR. Barcodes are identified and quantified using high-throughput
sequencing (Illumina GA II). The 6-bp library ID aids the separation of the barcodes from the
sequencing results. Identical 33-bp barcodes are combined for further analysis.

Appendix Figure S2.6: FACS plots and gating for hematopoietic progenitor populations in the
bone marrow. HSCs and six additional progenitor cells are highlighted in blue. Bone marrow
cells are enriched by ckit and Il7rα (details described in Methods).
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Appendix Figure S2.7: Biological replicate samples are clustered together based on gene
expression.
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A-B Unsupervised clustering of individual samples using average linkage for cluster and
Pearson correlation for point distance metrics.
C-D Principal component analysis of individual samples.
Data information: Data were collected at month 7 post transplantation for the uMT
-/-
group and
month 8 post-transplantation for the NSG group. n=3 mice for each group, except for the NSG
control group where n=2 mice.

Appendix Figure S2.8: Supplemental RNA sequencing graphs for Il10ra.
A Changes of Il10ra expression in WT HSCs from the (WT + NSG) and (WT + uMT
-/-
) co-
transplantation groups as compared to WT HSCs co-transplanted with WT HSCs in the control
group.
B Genes downstream of Il10ra that are differentially expressed in the co-transplantation
groups. The activation z score corresponds to predicted activation or inactivation based on the
comparison between expected and observed changes in gene expression.
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Data information: Data were collected at month 7 post transplantation for uMT
-/-
group and
month 8 post-transplantation for NSG group. n=3 mice for each group, except for the NSG
control group where n=2 mice.
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Appendix Table S2.1: Tables detailing p-values and fold changes of genes in Figure 5.
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A Table detailing p-values and fold changes for genes whose expressions are both up
regulated or both down regulated in the NSG and uMT-/- co-transplantation groups shown in
Figure 5C.
B Regulatory factors involved in lymphopoiesis shown in Figure 5D.
C Regulatory factors involved in myelopoiesis shown in Figure 5D.

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Appendix Table S2.2: Antibodies used for flow cytometry sorting and analysis.
A Hematopoietic stem cells
B Peripheral blood cells
C Progenitor cells in the bone marrow

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Chapter III: Dynamics of Clonal compensation for Induced Lineage Deficiencies
Abstract
Many hematopoietic disorders are characterized by an unbalanced blood system whereby certain
cell types are either abnormally abundant or deficient. Few studies have addressed the dynamics
of the intercellular coordination between stem cells, which play a critical role in maintaining a
balanced blood system. Currently, the primary treatment available for these diseases involves
bone marrow transplantation from healthy donors. In this treatment, donor HSCs adapt their
differentiation programs to the presence or absence of other HSCs. This chapter will discuss how
WT HSCs dynamically respond to induced hematopoietic deficiencies. In particular, HSCs with
specific differentiation characteristics (i.e., highly abundant) possess a greater capacity to
respond to changes to other HSCs. We investigated the dynamics of HSC coordination by
precisely perturbing a specific cell type in the blood system and assaying the temporal responses
of individual HSCs using our single cell tracking technology that was previously developed in
our lab. These experiments identified HSCs that responded quickly and/or persistently to
perturbations to the HSC population, two characteristics that are highly desirable for therapeutic
purposes. We addressed how HSCs dynamically adapt their differentiation programs in response
to deficiencies in their partner HSCs.
Our recent study suggested that HSCs and their progeny heterogeneously compensate for
differentiation deficiencies of other HSCs by increasing their clonal expansion specifically in the
blood lineages that are undersupplied (Nguyen et al., 2018). It is unclear how the diverse
differentiation programs are coordinated during the compensation, and whether the increased
clonal expansion arose from clones that have already exhibited clonal expansion prior to the
perturbations in the blood system. We hypothesized that individual HSCs heterogeneously alter
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their differentiation programs in response to changes to the HSC population. To study the
dynamics of compensation, we performed co-transplantations of barcoded WT HSCs with HSCs
containing genetic modifications that allow the induced ablation of defined lineages. After
establishment of stable engraftment, we ablated select populations and evaluated the ability of
the unperturbed HSCs to regenerate these undersupplied cell types by quantifying the clonal
origin of mature lineages. Two types of ablation were performed: 1) ablation of HSCs and 2)
sequential ablation of monocytes.
Dynamics of Compensation for Induced Loss of Competitor HSCs
To understand how WT HSCs responded to changes in the HSC population, we
performed a co-transplantation experiment using WT and competitor HSCs that allowed for
inducible ablation (Figure 3.1). We hypothesized that individual WT HSCs heterogeneously
adjusted their differentiation behavior with varying temporal patterns in response to the induced
ablation of competitor HSCs. We used the Cre/loxP recombination system to conditionally
express diphtheria toxin (DT) to remove select HSCs. In the offspring, intraperitoneal (IP) TAM
injection triggered recombination of the floxed-STOP cassette and induced the expression of DT,
ablating all cells including HSCs and their progeny.
Four months after transplantation when blood regeneration returned to a steady state, we
collected and sorted the peripheral blood into granulocytes, B cells, CD4 T cells and CD8 T cells
using FACS. We analyzed these four types of blood cells because they were the most abundant
white blood cells which constituted 70-80% of all white blood cells. We found the levels of
engraftment of the WT, competitor, and helper donor cells to be approximately 25, 50 and 30
percent, respectively. We conditionally removed competitor HSCs in the treatment group over
the course of 1 month by administering TAM intraperitoneal injections to the recipient mice 9
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times at a daily dose of 4 mg per mouse. We harvested the peripheral blood before the induction
and after the induction monthly to track how the WT HSCs adjusted their differentiation patterns
(Figure 3.2 and 3.3).
We measured the production by the WT and competitor donors separately and combined
(Figures 3.2 and 3.3). We confirmed that after TAM injections, the inducible competitor donor
derived granulocytes, B, CD4 T, and CD8 T cells were significantly reduced (Figures 3.2 A-D
and 3.3 A-D). Following ablation of competitor HSCs (Figures 3.2 A-D and 3.3 A-D), we found
that the unperturbed WT HSCs responded to ablation of competitor HSCs by increasing their
contribution to each lineage (Figures 3.2 E-H and 3.3 E-H). The WT HSCs increased their
granulocyte and CD8 T production immediately after competitor HSCs were ablated at 1 month
(Figures 3.2 E and H and Figures 3.3 E and H). The WT HSCs continue to oversupply the
granulocyte population until 7 months post-induction, resulting in higher total granulocyte
production between 1 to 4 months post-induction (Figures 3.2 E and I). At 4 months post-
induction, we observed WT HSCs compensating for B cells and CD4 T cells until 7 months post-
induction (Figures 3.2 F-G and 3.3 F-G). While the oversupply of the granulocyte and CD8 T
populations by the WT donor significantly increased their total production, the total production
of B cells remained below normal levels despite compensation by the WT donor, suggesting
differences in mechanism of compensation (Figure 3.2 J and L and 3.3 J and L). As
compensation for CD4 T cells began later at 4 months, we observed an initial decrease in total
CD4 T production at 2 months followed by an increase at 5 months that is consistent with the
WT donor contribution (Figure 3.2 K). Our data indicated that upon ablation of competitor
HSCs, compensation for each lineage occurs with varying temporal patterns.
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Methods. We crossed two strains of mice: (1) ROSA-DTA mice (Jackson Laboratory,
stock#009669) with a floxed-STOP cassette safeguarding the expression of DT; and (2)
ROSA
CreER
mice expressing Cre-ER
T2
that allows the Cre recombinase to be activated by TAM
administration and thereby trigger the recombination of genomic loxP sites (Jackson Laboratory,
stock#008463). HSCs derived from the offspring mice described above were used as competitor
HSCs for the co-transplantation. We co-transplanted 1000 WT barcoded HSCs, 2000 competitor
HSCs from mice with inducible cell ablation, and 250,000 whole bone marrow helper cells into
lethally irradiated mice (Figure 3.1). WT donor mice used in the co-transplantation experiments
were C57BL/6J (CD45.1). The recipient mice were off-springs of C57BL/6J and B6.SJL-Ptprca
Pepcb/BoyJ (CD45.1/ CD45.2). Mice of both sexes were used without discrimination. Irradiation
was performed on all recipient mice before transplantation at 950 cGy. We examined 7 mice for
each experimental group, and performed biological replicates. Mice were bred and maintained at
the Research Animal Facility of the University of Southern California. Animal procedures were
approved by the Institutional Animal Care and Use Committee. We injected into recipient mice 4
mg of TAM dissolved in 100 ul corn oil a total of 9 times to induce ablation.
Dynamics of Compensation for Induced Monocyte Deficiency
To understand how WT HSCs dynamically alter their differentiation programs in
response to specific lineage deficiencies, we co-transplanted WT and competitor HSCs and
conditionally ablated competitor derived monocytes into lethally irradiated recipient mice. We
hypothesized that individual HSCs heterogeneously adapt their differentiation programs to help
restore the monocyte population. We established two transgenic mouse models that allowed for
inducible short-term and long-term monocyte ablation (Figures 3.4 and 3.5). We harvested and
sorted the peripheral blood before and after each induction.
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In the short-term monocyte ablation model, we observed ablation of competitor donor
derived monocytes within 24 hours of the DT injections (Figure 3.6 A). In the long-term
monocyte ablation model, we observed ablation of competitor-derived monocytes within 9 TAM
injections (Figure 3.7 A). In both experimental models, the WT donor increased their
contribution towards the monocyte lineage (Figure 3.6 B and 3.7 B). The competitor and WT
monocyte population returned to normal levels within 7 and 30 days for the short- and long-term
ablation models, respectively (Figure 3.6 and 3.7). Contribution to the monocyte population by
the WT donor increased during periods of ablation across all inductions for both short- and long-
term models. For the short-term monocyte ablation model, we purified the WT and competitor
HSCs from the recipient mice at the end of the third induction and performed secondary
transplantations. In the secondary recipients, we performed three additional inductions of
monocyte deficiency and observed consistent compensation by the WT donor after the secondary
transplantation.
To understand how WT HSC clones responded to the induced monocyte deficiency, we
extracted the barcodes from sorted blood populations and performed high-throughput
sequencing. We used custom Python algorithms to identify clones that increased their lineage
bias and/or clonal expansion in the monocyte population. We tracked individual HSC clones to
determine their differentiation behaviors by analyzing the abundance of barcodes in samples
collected before and after monocyte ablation. We performed cluster analysis on the clonal blood
contribution data using the Short Time-series Expression Miner (STEM). STEM identified
significant temporal differentiation profiles. The clustering algorithm selected a set of distinct
and representative temporal expression profiles. Each clone passed the filtering criteria and was
assigned to a profile as determined by the correlation coefficient. Using STEM, we identified
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clusters of HSC clones with distinct compensation behaviors across multiple inductions (Figure
3.8 A).
In addition to the STEM clustering method, we employed unsupervised k-means
hierarchical clustering and self-organizing maps (SOM) to analyze individual time points using
Partek Genomic Suite (Figure 3.8 B-C). We found that a subset of WT clones that previously
produced low levels of monocytes would increase their monocyte contribution during some
inductions when competitor monocytes were ablated (Figure 3.8 C). A distinct population of
clones continually supplied the monocyte lineage, and would also increase their monocyte
contribution upon each induction. Altogether, our data indicated the presence of clones that can
consistently compensate for induced monocyte deficiency.
To determine the level of differentiation at which expansion was occurring, we analyzed
the hematopoietic progenitor contribution by the WT donor (Figure 3.9). We observed
significantly higher production of GMP by the WT donor. GMP is the progenitor stage that
directly precedes monocyte, indicating that an increase in production of monocytes is due to
expansion at the intermediate oligopotent progenitor level. We found no significant changes in
other progenitor populations.
We identified a subset of HSCs that responded only during some inductions of lineage
deficiency, suggesting they may be compensating in a stochastic manner. We also observed a
subset of HSC clones that consistently compensated for the lineage deficiency long-term. These
HSCs may be used to improve bone marrow transplantation for patients with permanent blood
deficiencies. Finally, we found that expansion during compensation for monocytes occurred at
the oligopotent progenitor stage and did not affect self-renewal or lymphoid differentiation.
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Methods. To establish a short-term monocyte ablation inducible mouse model, we obtained a
mouse model that express simian DT receptor under the control of the CD11b promoter
administration (Jackson Laboratory, stock #006000) (Figure 3.4). We also established a long-
term monocyte ablation model by crossing ROSA-DTR mice (Jackson Laboratory, stock
#009669) with Cx3cr1
CreER
mice whose Cre-ER fusion protein is regulated by the Cx3cr1
promoter that is only active in monocytes among blood cells (Jackson Laboratory, stock
#0021160) (Figure 3.5). Monocytes (CD4-/CD8-/B220-/CD19-/Mac1+/Gr1+/CD115+) derived
from both mouse lines can be ablated upon TAM or DT (Figures 3.6 B and 3.7 B). We co-
transplanted 1000 normal barcoded HSCs, 2000 competitor HSCs from mice with inducible cell
ablation, and 250,000 whole bone marrow helper cells into lethally irradiated mice (Figures 3.4
and 3.5). WT donor mice used in the co-transplantation experiments were C57BL/6J (CD45.1).
The recipient mice were off-springs of C57BL/6J and B6.SJL-Ptprca Pepcb/BoyJ (CD45.1/
CD45.2). We examined 5-6 mice for each experimental group. We induced short-term monocyte
ablation with DT (20 ng/g) injections and long-term monocyte ablation with 9 injections of 4 mg
TAM dissolved in corn oil within the recipient mice. Control mice were injected with the carrier,
PBS or corn oil.

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Figure 3.1: Schematic depicting experimental set-up for understanding how individual HSCs
respond to loss of co-transplanted HSCs. We co-transplanted barcoded WT HSCs with inducible
competitor WT, or competitor inducible HSCs into irradiated recipient mice. Peripheral blood
was harvested from recipient mice before and after 9 TAM or corn oil injections in the span of 1
month.
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Figure 3.2: WT HSCs compensate for loss of competitor HSCs.
A-D Competitor donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood. In the
control group, competitor donor cells are WT HSCs. In the deficient group, competitor donor
cells are ROSA-Cre-ER
T2
.
E-H WT donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood.
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I-L Total production of Gr, B, CD4 T, and CD8 T cells in the peripheral blood (derived from
WT HSCs, competitor HSCs, helper whole bone marrow cells, and residual host cells) shown as
percentages of the total number of white blood cells (WBCs).
Data information: Data are shown as percentages of the total number of white blood cells
(WBCs). Data were collected before induction and every month following induction. Data are
presented as mean ± SEM. n=7 mice for the control and n=7 for the TAM treatment group.
*p<0.05, **p<0.01 (two-tailed and two-sample equal variance Student’s t-test).

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Figure 3.3: WT HSCs compensate for loss of competitor HSCs (replicate experiment of Figure
3.2).
A-D Competitor donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood. In the
control group, competitor donor cells are WT HSCs. In the deficient group, competitor donor
cells are ROSA-Cre-ER
T2
.
E-H WT donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood.
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I-L Total production of Gr, B, CD4 T, and CD8 T cells in the peripheral blood (derived from
WT HSCs, competitor HSCs, helper whole bone marrow cells, and residual host cells) shown as
percentages of the total number of white blood cells (WBCs).
Data information: Data are shown as percentages of the total number of white blood cells
(WBCs). Data were collected before induction and every month following induction. Data are
presented as mean ± SEM. n=7 mice for the control and n=7 for the TAM treatment group.
*p<0.05, **p<0.01 (two-tailed and two-sample equal variance Student’s t-test).

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Figure 3.4: Schematic depicting experimental set-up for understanding how individual HSCs
respond to short-term induced monocyte deficiency. We co-transplanted barcoded WT HSCs
with inducible competitor WT, or competitor inducible HSCs into irradiated recipient mice.
Peripheral blood was harvested from recipient mice before and 24 hours after DT or PBS
injections.

Figure: 3.5 Schematic depicting experimental set-up for understanding how individual HSCs
respond to long-term induced monocyte deficiency. We co-transplanted barcoded WT HSCs
with inducible competitor WT, or competitor inducible HSCs into irradiated recipient mice.
Peripheral blood was harvested from recipient mice before and after 9 TAM or corn oil
injections.

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Figure 3.6: WT donor compensates for induced short-term monocyte deficiency. Monocyte
production of competitor and WT HSCs as a percentage of total WBCs. * p <0.05 (two-tailed
and two-sample equal variance Student’s t-test).
A Competitor donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood. In the
control group, competitor donor cells are WT HSCs. In the deficient group, competitor donor
cells are ROSA-Cx3cr1
CreER
.
B WT donor derived monocyte production
Data information: Data were collected before, during, and 7 days after ablation of monocytes. n
= 5 for control and n = 5 for treatment groups.

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Figure 3.7: WT donor compensates for induced long-term monocyte deficiency. Monocyte
production of competitor and WT HSCs as a percentage of total WBCs.
A Competitor donor derived Gr, B, CD4 T, and CD8 T cells in the peripheral blood. In the
control group, competitor donor cells are WT HSCs. In the deficient group, competitor donor
cells are CD11b-DTR.
B WT donor derived monocyte production
Data information: Data were collected before, during, and 7 days after ablation of monocytes. n
= 6 for control and n = 6 for treatment groups. * p <0.05 (two-tailed and two-sample equal
variance Student’s t-test).

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Figure 3.8: Subset of WT HSC clones consistently compensate for induced monocyte deficiency.
A Cluster analysis of WT clones based on their monocyte contribution using STEM. Each
line represents a clone in the DT treated group.
B k-means hierarchical clustering of clones in the DT treated group based on their
monocyte contribution as represented by color intensity.
C SOM to visualize the clustering of clones in the control and DT treated groups based on
their monocyte contribution. Each window depicts all the clones at a time point and the color
intensity represents the monocyte contribution of the clone.
Data information: Data were collected during the third monocyte ablation, and presented as mean
± SEM. n = 5 for the treatment group. * p < 0.05 (two-tailed and two-sample equal variance
Student’s t-test).

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Figure 3.9: Compensation for lymphopoietic deficiency is manifested as an increase in cell
numbers at the progenitor level.
WT donor derived progenitors as a percentage of ckit

and Il7rα enriched BM cells.
Data information: Data were collected during the third monocyte ablation, and presented as mean
± SEM. n = 5 mice for control and n = 5 for the treatment groups. * p < 0.05 (two-tailed and two-
sample equal variance Student’s t-test).

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Chapter IV: Phenotypic In Vivo Compound Screening Assay
Identifies Compounds that Alter Compensation
Abstract
The principle goal of bone marrow transplantation is to infuse patients with exogenous donor
HSCs to replenish the patient’s blood and immune system (Buckley et al., 1986; Gatti et al.,
1968; O’Reilly et al., 1977; Parkman et al., 1975; Reisner et al., 1983). HSC based therapy is the
best and sometimes the sole option available to treat several blood and immune disorders. HSCs
are also transplanted to facilitate blood recovery after cancer treatment, to deliver gene therapy,
and to reset the immune system after organ transplantation (Cartier et al., 2009; Gatti et al., 1968;
Maguire et al., 2008; Thomas et al., 1957). In these therapeutic treatments, the efficacy of the
treatment is measured by the amount of blood production generated by the transplanted donor
HSCs. While HSCs can generate all types of blood and immune cells, many patients only require
the supply or elimination of specific cell types (Demirci, Uchida, & Tisdale, n.d.; Lipof, Loh,
O’Dwyer, & Liesveld, 2018; Shallis Rory M., Ahmad Rami, & Zeidan Amer M., 2018).
Currently, we are unable to tailor the blood production of donor HSCs to patient specific
requirements. All patients are infused with the same standard dosage of donor HSCs. This
generic approach results in sub-optimal therapeutic efficacy. For example, in some patients,
solving an overabundance of red blood cells with transplantation therapy triggers a deficiency of
white blood cells.
Recent data from our lab suggested that the HSC transplantation regimen can be
optimized to achieve specific results (Brewer et al., 2016). HSCs that immediately supply blood
production after transplantation preferentially produce more lymphoid cells over time. This
preference appears to last over the lifetime of the recipient mice. Additionally, our lab has shown
that a subset of expanded HSCs are more capable of compensating for lineage deficiencies
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(Nguyen et al., 2018). If our goal is to promote lymphoid cell production in patients with
immune deficiency, it would be more effective to enrich and transplant these specific expanded
and lymphoid-biased HSCs. These studies suggested that it is possible to manipulate HSCs into
specific differentiation programs that match the specific requirement of the patient. And thus, we
hypothesized that a short stimulation of HSCs before transplantation can substantially alter their
differentiation program after transplantation. Here, we identified small molecule regulators that
altered the HSC engraftment and differentiation program. The results may initiate further
translational research to tailor HSC transplantation treatments to fit patient specific requirements.
Our discoveries can be translated into clinical applications as an augmentation to the standard
bone marrow transplantation procedure by simply adding a short ex vivo treatment procedure to
donor HSCs before transplantation.
Identifying Candidate Compounds that Alter HSC Differentiation
To identify HSC regulators that trigger changes in HSC engraftment and differentiation
programs, we developed a novel screening approach that combined our genetic barcoding
technology with high throughput compound screening. Specifically, we used our cellular
barcoding technology to track HSCs that have been treated with different compounds. We treated
barcoded HSCs with US Food and Drug Administration (FDA) approved compounds from
commercial libraries (EMD Millipore Stem Select Small Molecule Regulators Library, EMD
Millipore Kinase Collection and Cayman Chemical Epigenetics Collection) (Figure 4.1).
Compounds in these libraries have been shown to play roles in regulating hematopoiesis in other
experimental systems. These libraries contained 303, 327 and 90 small molecules respectively.
The three small molecule libraries were provided by the Choi Family Therapeutic Screening
Facility. To identify compounds that alter lymphoid differentiation, WT HSCs labeled with
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different genetic barcode libraries were treated with individual compounds and combined into a
single transplantation into the same recipient mouse (Figure 4.1). Each compound was
represented by a unique barcode library. We combined 24 different genetic barcode libraries with
the treatment of 24 different small molecule regulators and screened them simultaneously in
vivo. Together, we transplanted 30 mice in the initial screening.
To monitor the blood production of barcoded and small molecule treated HSCs, we
harvested peripheral blood from the recipient mice 4 months post-transplantation and sorted into
granulocytes, B, CD4 T, and CD8 T cells. The effect of each small molecule on HSC
differentiation was clearly identified by its impact on the composition of the retrieved barcodes.
As the presence of the genetic barcodes in blood reflects the differentiation of the barcoded
HSCs, we observed three main patterns: 1) compounds that enhanced engraftment or survival of
donor HSCs, 2) compounds that increased differentiation toward a lineage, and 3) compounds
that suppressed differentiation into a lineage. We used custom Python algorithms to identify
library IDs that had higher reads in certain cell populations. We compared the number of total
reads of barcodes in the B, CD4 T, and CD8 T cell populations to assess which compounds
improved compensation (Figure 4.2 A-E). Additionally, we compared the number of unique
barcodes for each library to determine whether any compound enhanced HSC engraftment or
clonal expansion (Figure 4.2 F-G).
Our in vivo screen identified 6 compounds that altered the standard HSC differentiation
program to either promote or suppress the production of specific blood cells (Figure 4.2). We
found that AG112 and Staurosporine promoted differentiation of HSCs into the CD8 T and CD4
T lineages, respectively (Figure 4.2 A and D). This suggested that these compounds biased
differentiation towards the lymphoid lineage. We observed a corresponding higher number of
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unique reads in the CD4 T lineage associated with HSCs treated with Staurosporine, indicating
that the increase in production of CD4 T was likely caused by altered HSC differentiation
preference (Figure 4.2 F). No differences in number of unique reads in the CD4 T lineage were
observed for AG112, suggesting that the increase in CD4 T production is likely due to selective
clonal expansion. GTP-14564 treatment resulted in library IDs with higher unique reads in the
CD8 T population but no difference in percentage of total reads was observed (Figure 4.2 G).
Conversely, some compounds suppressed lymphoid differentiation. PKR and Lck
inhibitors reduced the percentage of total reads in the CD8 T lineage (Figures 4.2 B-C).
PD174265 reduced total reads in the B cell lineage (Figures 4.2 E). No differences in numbers of
unique reads were observed in these cell types, suggesting that the compound treatment reduced
the clonal expansion of HSCs differentiating into these cell types. These compounds that
suppress differentiation into certain blood lineages can be used in treatment for diseases
characterized by proliferation of certain blood cell types.
To identify compounds that may improve compensation for B cell deficiency in the
blood, we performed a co-transplantation of treated WT and untreated B deficient (uMT
-/-
) HSCs
into WT mice (Figure 4.3 schematic). Barcoded WT HSCs were treated for 6 hours with each of
the 6 candidate compounds. We discovered that Staurosporine, PD 174265 and GTP-14564
increased the total reads percentage in the B cell population specifically in the uMT
-/-
co-
transplantation setting (Figure 4.4). We observed similar numbers of unique barcodes in the B
cell lineage for these compounds, suggesting that the increase in blood production of B cells was
likely caused by selective clonal expansion (Figure 4.5). This indicated that these compounds
may bias differentiation away from the granulocyte lineage and towards the B cell lineage. Such
small molecules can be used to treat patients whose blood lack specific cell types such as SCID.
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Conversely, PKR and LCK inhibitor treatments resulted in higher total and unique reads in the
granulocyte population in both WT + WT and WT + uMT
-/-
co-transplantation settings,
suggesting that these compounds enhanced engraftment or survival of HSCs and promoted
granulocyte production.
Finally, we identified PD174265, GTP-14564, and AG112 as having a suppressive effect
on granulocyte differentiation (Figures 4.4 and 4.5). We found similar numbers of unique
barcodes, indicating that the treatment reduced the clonal expansion of HSCs differentiating into
the granulocyte population. These treatments have great potential to help patient that suffer from
an oversupply of myeloid cells such as myeloproliferative disorder.
Methods. Mice were purchased from Jackson Laboratories(“Mouse Mutant Resource Website,
The Jackson Laboratory, Bar Harbor, Maine,” 2018). WT donor mice used in the co-
transplantation experiments were C57BL/6J. The lymphoid deficient recipient mice were
B6.129S2(B6)-Ighm
tm1Cgn
/J (uMT
-/-
) and NOD-scid IL2Rgamma
null
(NSG). We barcoded and
treated WT HSCs with each compound in a 96-well plate. For the initial screen, each well
contained 500 WT HSCs labeled with a different genetic barcode library for 15 hours. For both
experiments, we treated the barcoded HSCs with 3 uM of a compound diluted in X-Vivo
hematopoietic media for an additional 6 hours. WT HSCs were washed and combined into a
single transplantation of 12,000 HSCs into the same lymphoid deficient recipient mouse. Control
recipient mice were transplanted with combined barcoded WT HSCs treated with the compounds
individually. HSCs treated with DMSO served as the internal control. For the co-transplantation
experiment, we treated 1,000 WT HSCs with individual compounds and transplanted with 2,000
uMT
-/-
HSCs into WT mice. WT donor mice used in the co-transplantation experiments were
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C57BL/6J (CD45.1). The recipient mice were off-springs of C57BL/6J and B6.SJL-Ptprca
Pepcb/BoyJ (CD45.1/ CD45.2).
Verification of Candidate Compounds in a Non-barcoded System
The differentiation patterns we observed can potentially be due to differences in the
barcode efficiency of some libraries. To verify the effects of these compounds in a non-barcoded
system, we performed a competitive transplantation in which half of the transplanted WT HSCs
were treated with individual compounds and the other half were untreated (Figure 4.6). The
granulocyte, B, CD4 T, and CD8 T production by each donor was examined 4 months post-
transplantation. WT HSCs stimulated with Staurosporine and PD174265 increased their
production of B cells (Figure 4.7). We found no significant differences in blood production by
other compounds. Our non-barcoded experimental system verified the lymphoid stimulatory
effects of Saurosporine and PD 174265.
In this study, we developed a phenotypic small molecule screen to identify small
molecule compounds that affect HSC programs. Using this assay, we identified several
compounds that alter differentiation and engraftment. These compounds can be used as short
pre-treatments to augment the standard bone marrow transplantation procedure. Small molecule
compounds that are shown to increase lymphopoiesis can be particularly useful for compensating
disorders characterized by lymphoid deficiency.
Methods. 3,000 WT HSCs from B6.SJL-Ptprca Pepcb/BoyJ mice with the cell surface marker
CD45.1 were treated with 3 uM of a single compound diluted in X-Vivo hematopoietic media for
6 hours, and co-transplanted with 3,000 untreated WT HSCs from C57BL/6J mice marked by
CD45.2 into recipient mice marked by CD45.1/ CD45.2. The recipient mice were off-springs of
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C57BL/6J and B6.SJL-Ptprca Pepcb/BoyJ (CD45.1/ CD45.2). We examined 7 mice for each
experimental group.
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Figure 4.1 Schematic depicting experimental set-up for in vivo compound screen. HSCs were
isolated from donor mice, barcoded, treated with compounds, and transplanted into NSG
recipient mice. Wells containing HSCs barcoded with unique library IDs that are combined into a
single transplantation are highlighted.
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Figure 4.2 Identifying compounds that alter lymphoid differentiation.
A-E Comparison of percentage of total reads in two lymphoid lineages.
F-G Comparison of number of unique reads in two lymphoid lineages.
Each numbered arrow indicates a unique barcode library ID associated with a compound. The
red circle highlights the library ID that represents a specific candidate compound that increased
or decreased the production of a lymphoid cell type.
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Figure 4.3 Schematic depicting experimental set-up for in vivo compound screen. WT HSCs
were isolated from donor mice, barcoded, treated with compounds, and co-transplanted with
untreated uMT
-/-
HSCs into NSG recipient mice.
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Figure 4.4 Percentage of total reads of HSCs treated with with candidate compounds skew
differentiation.
A Percentage of total reads of library IDs in granulocyte (Gr) and B cell populations. p <
0.05
A
B
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B Table summarizing the significant differences in differentiation.

Figure 4.5 Number of unique reads of HSCs treated with with candidate compounds skew
differentiation.
A
B
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A Number of unique reads of library IDs in granulocyte (Gr) and B cell populations. p <
0.05
B Table summarizing the significant differences in differentiation.

Figure 4.6 Schematic depicting experimental set-up for verification of candidate compounds in
non-barcoded system. HSCs were isolated from WT and uMT
-/-
donor mice. WT HSCs were
barcoded and treated with individual candidate compounds, and transplanted into lethally
irradiated recipient mice.
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Figure 4.7 Verifying the effects of candidate compounds in non-barcoded experimental system.
WT donor production of granulocytes (Gr), B, CD4 T, and CD8 T cells are shown are
percentages of all white blood cells (WBC). p < 0.05

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Discussion
Elucidation of the heterogeneity of HSCs has been grounded on functional assays that can assess
the potential of individual cells to repopulate. Since the initial discovery of CFU-S, significant
progress has been made over the last several decades with respect to understanding of
heterogeneity in the murine hematopoietic system. Our knowledge of HSC heterogeneity has
been guided by clonal assays ranging from single cell transplantations that defined an HSC as
being capable of long-term blood reconstitution (Osawa et al., 1996), to genetic barcoding and
tracking of individual HSCs in mice (Lu et al., 2011b; Sun et al., 2014).
At single cell resolution, the studies presented in this thesis provide a unique perspective
on stem cell interaction during tissue regeneration that was previous unobtainable using
conventional approaches. Our work offered the first direct evidence that the differentiation
activities of this widely dispersed stem cell population are coordinated to ensure that blood
production is balanced and functional. For instance, individual HSC clones have different
capacities to compensate for blood lineage deficiencies with the same mouse. Transcription
profiling of HSCs during compensation gave us an unprecedented insight into the priming that
takes place at the stem cell level and allowed us to identify specific gene regulatory networks
that are involved. Major regulators such as Pax5 and other notable lymphoid transcription factors
comprise this network.
During the early stage of blood diseases in our model, a patient’s healthy HSCs are to
compensate for the diseased lineage deficient cells and maintain a balanced blood system.
Eventually, the healthy HSCs are evicted or overwhelmed by the diseased cells and the disease
symptoms manifest. Our work highlighted the heterogenous ability of HSCs to adjust their
differentiation and respond to lineage deficiencies. Specifically, we characterized the dynamics
COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 109

and mechanisms of how some HSCs are resilient to the presence of lineage deficient cells.
Finally, we developed a novel assay with the potential to identify compounds that can modulate
this compensatory capacity.

COMPENSATION BETWEEN HEMATOPOIETIC STEM CELLS 110

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Abstract (if available)

Abstract In most organ systems, regeneration is a coordinated effort that involves many stem cells, but little is known about whether and how individual stem cells compensate for the differentiation deficiencies of other stem cells. Functional compensation is critically important during disease progression and treatment. Here, we show how individual hematopoietic stem cell (HSC) clones heterogeneously compensate for the lymphopoietic deficiencies of other HSCs in a mouse. This compensation rescues the overall blood supply and influences blood cell types outside of the deficient lineages in distinct patterns. We find that highly differentiating HSC clones expand their cell numbers at specific differentiation stages to compensate for the deficiencies of other HSCs. Some of these clones continue to expand after transplantation into secondary recipients. In addition, lymphopoietic compensation involves gene expression changes in HSCs that are characterized by increased lymphoid priming, decreased myeloid priming and HSC self-renewal. Our in vivo small molecule screen identified small molecules that can alter the capacity of HSCs to compensate for lineage deficiency. Our data illustrate how HSC clones coordinate to maintain the overall blood supply. Exploiting the innate compensation capacity of stem cell networks may improve the prognosis and treatment of many diseases.

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Asset Metadata

Creator Nguyen, Lisa (author)

Core Title Functional compensation between hematopoietic stem cell clones in vivo

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Development, Stem Cells and Regenerative Medicine

Publication Date 10/18/2018

Defense Date 10/18/2018

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

Tag clonal tracking,genetic barcoding,hematopoietic stem cells,OAI-PMH Harvest,small molecule screening

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Crump, Gage (committee chair), Cannon, Paula (committee member), Ichida, Justin (committee member), Lu, Rong (committee member), Merchant, Akil (committee member)

Creator Email lisanguyen@panoramamedicine.com,lisanguyen5288@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-85639

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Legacy Identifier etd-NguyenLisa-6888.pdf

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Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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