Cellular and Molecular Architecture of the
Human Hematopoietic Hierarchy
by
Sergei Doulatov
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy
Graduate Department of Molecular and Medical Genetics
University of Toronto
© Copyright by Sergei Doulatov 2010 ii
Cellular and Molecular Architecture of the Human Hematopoietic Hierarchy
For the degree of Doctor of Philosophy, 2010
By Sergei Doulatov
Graduate Department of Molecular Genetics
University of Toronto
ABSTRACT
The blood system is organized as a developmental hierarchy in which rare hematopoietic stem cells (HSCs) generate large numbers of immature progenitors and differentiated mature blood cells. In this process, at least ten distict lineages are specified from multipotent stem cells, however the cellular and molecular organization of the hematopoietic hierarchy is a topic of intense investigation. While much has been learned from mouse models, there is also an appreciation for species-specific differences and the need for human studies. Blood lineages have been traditionally grouped into myeloid and lymphoid branches, and the long- standing dogma has been that the separation between these branches is the earliest event in fate specification. However, recent murine studies indicate that the progeny of initial specification retain the more ancestral myeloid potential. By contrast, much less is known about the progenitor hierarchy in human hematopoiesis. To dissect human hematopoiesis, we developed a novel sorting scheme to isolate human stem and progenitor cells from neonatal cord blood and adult bone marrow. As few as one in five single sorted HSCs efficiently repopulated immunodeficient mice enabling interrogation of homogeneous human stem cells.
By analyzing the developmental potential of sorted progenitors at a single-cell level we
iii showed that earliest human lymphoid progenitors (termed LMPs) possess myelo-monocytic potential. In addition to B-, T-, and natural killer cells, LMPs gave rise to dendritic cells and macrophages indicating that these closely related myeloid lineages also remain entangled in lymphoid development. These studies provide systematic insight into the organization of the human hematopoietic hierarchy, which provides the basis for detailed genetic analysis of molecular regulation in defined cell populations. In a pilot study, we investigated the role of a zinc finger transcription factor (ZNF145), PLZF, in myeloid development. We found that
PLZF restrained proliferation and differentiation of myeloid progenitors and maintained the progenitor pool. Induction of ERK1/2 by myeloid cytokines, reflective of a stress response, leads to nuclear export and inactivation of PLZF, which augments mature cell production.
Thus, negative regulators of differentiation can serve to maintain developmental systems in a primed state, so that their inactivation by extrinsic signals can induce proliferation and differentiation to rapidly satisfy increased demand for mature cells. Taken together, these studies advance our understanding of the cellular and molecular architecture of human hematopoiesis.
iv
Acknowledgements
There are a number of people who have made this thesis possible.
First of all, I want to thank my mentor, John. It’s not easy to be at the helm of a large successful lab, and for that matter, many stem-cell-related groups and consortia in Toronto and all over the country. During PhD, one defines themselves as a scientist, and that’s exactly what John did for all of his students – he let us define ourselves. Instead of imposing his own ideas of how science should be done, as do many others, he guided us to formulate our own ways of thinking and approaching problems. Never once has he told me that I couldn’t or shouldn’t do a particular experiment or pursue some flight of fancy idea, I suspect knowing full well that many of them were doomed to failure causing much pain and mental anguish.
But he let us explore these wasteful directions anyways, so that we could learn from them, work through the anguish, and in the end become successful, productive, and independent scientists. I am indebted to him for guiding me through these difficult and exciting times.
Science should never be done alone, if only because sometimes the sheer pain or joy that comes with this work has to be shared with someone. In this, I’ve been fortunate to have found a great partner, Faiyaz Notta, with whom we’ve split the scientific ‘bread’ every day. I know no one else who has such intelligence, tenacity and integrity, to which I credit so many of our successes. I also want to thank Jennifer Warner, for helping me get on my feet when I was starting in the lab. It’s been such a long time, but I will always be grateful to her for supporting me during that vulnerable time. I’ve also been lucky to have so many good friends from among colleagues in the lab, especially Antonija and Peter.
v
Luck plays a big part in our lives, on and off the bench. I’ve had the greatest luck of my life during my PhD to have met my fiancee, Nastasia, and after one amazing year know that I want to spend all of them with her. The least likely of chances have brought us together and I remember as clearly as it was yesterday the day I went to meet a total stranger, only to have met my sweetheart. There hasn’t been a day since that I have reminded myself of how incredible life’s ways can be. My sister, Masha, who will be starting on her own graduate path in philosophy soon, and whom I love and am so proud of. Lastly, my parents, to whom I owe everything that I am. No words that I have can express the gratitude and debt that I have to them.
vi
Table of Contents
ABSTRACT...... ii
Acknowledgements...... iv
Table of Contents ...... vi
LIST OF TABLES ...... xii
LIST OF FIGURES ...... xiii
LIST OF ABBREVIATIONS ...... xv
1. Introduction...... 1
1.1. Models of hematopoiesis...... 1 1.1.1 The structure of hematopoietic hierarchies: basic principles...... 2
1.1.2 Hematopoietic stem cells ...... 3
1.1.3 The classical model of hematopoiesis...... 6
1.1.4 Towards a revised model of hematopoiesis ...... 9
1.1.4.1 The diversity of lymphoid progenitors ...... 10
1.1.4.2 Developmental flexibility of lymphoid progenitors...... 13
1.1.5 Xenotransplantation of human cells...... 15
1.1.6 A model for human hematopoiesis ...... 16
1.1.7 Progenitor origins of dendritic cells ...... 18
vii
1.1.8 Conclusions 1 ...... 20
1.2 Transcription factor networks that control self-renewal and lineage choice...... 21 1.2.1 Lineage commitment: basic principles ...... 21
1.2.2 Stages of lineage commitment...... 22
1.2.2.1 Lineage priming ...... 22
1.2.2.2 Lineage reinforcement ...... 23
1.2.2.3 Lineage commitment...... 25
1.2.3 Mechanisms of lymphoid lineage commitment...... 26
1.2.4 Mechanisms of myeloid differentiation...... 27
1.2.5 Mechanisms of progenitor homeostasis...... 31
1.2.6 Conclusions II...... 33
2. Isolation of single human HSCs...... 37
2.1 Abstract...... 38
2.2 Matherials and Methods ...... 38
2.3 Results and Discussion...... 41
2.4 Tables and Figures ...... 49
3. Macrophage and dendritic cell lineages remain entangled in early human lymphoid development: a revised map of the human progenitor hierarchy...... 57
3.1 Abstract...... 58
viii
3.2 Introduction...... 59
3.3 Materials and Methods ...... 60 3.3.1 Cell isolation and sorting...... 60
3.3.2 Clonal assays...... 61
3.3.3 Quantitative PCR...... 62
3.3.4 Microarray analysis...... 62
3.3.5 Mouse transplantation ...... 62
3.3.6 Dendritic cell cultures ...... 63
3.3.7 Statistics ...... 64
3.4 Results ...... 64 3.4.1 Isolation and clonal assays of human progenitors...... 64
3.4.2 Human myeloid progenitor series ...... 65
3.4.3 Identification of human multi-lymphoid progenitors...... 66
3.4.4 Analysis of progenitors generated de novo from HSCs...... 68
3.4.5 Characterization of the developmental potential of MLPs...... 68
3.4.6 Gene expression profile of human progenitors...... 71
3.4.7 Expansion and differentiation of MLPs into DCs ...... 72
3.5 Tables and Figures ...... 74
3.6 Discussion...... 92
4. PLZF IS A REGULATOR OF HOMEOSTATIC AND CYTOKINE-INDUCE
MYELOID DEVELOPMENT ...... 94
ix
4.1 Abstract...... 95
4.2 Introduction...... 95
4.3 Materials and Methods ...... 98 4.3.1 Sample Collection and Purification ...... 98
4.3.2 Viral Constructs...... 98
4.3.3. Viral Transduction...... 99
4.3.4 Cell Sorting ...... 99
4.3.5 Flow Cytometry ...... 99
4.3.6 Liquid Cultures...... 100
4.3.7 Colony Assays...... 100
4.3.8 BrdU and Annexin Assays...... 100
4.3.9 NOD/SCID Transplantation...... 100
4.3.10 Lineage-Depletion ...... 101
4.3.11 Cytokine Stimulation...... 101
4.3.12 Immunofluorescence Microscopy...... 102
4.3.13 Quantitative RT-PCR...... 102
4.3.14 Chromatin Immunoprecipitation...... 102
4.3.15 Statistical Analysis...... 103
4.4 Results ...... 103 4.4.1 PLZF is expressed in human HSCs and progenitors...... 103
4.4.2 PLZF restricts myeloid proliferation and differentiation in vitro...... 103
x
4.4.3 PLZF restricts human myelopoiesis in vivo...... 105
4.4.4 PLZF regulates the balance between progenitor and mature compartments...... 107
4.4.5 Cytokines modulate PLZF repression of myeloid development...... 108
4.4.6 PLZF regulates expression of myeloid transcription factors...... 111
4.5 Tables and Figures ...... 113
4.6 Discussion...... 119 4.6.1 Modulation of PLZF by stress-induced myeloid cytokines...... 119
4.6.2 Molecular mechanisms of differentiation downstream of PLZF...... 121
5. Discussion & Future Directions...... 123
5.1. The architecture of the human hematopoietic hierarchy...... 123 5.1.1 Hematopoiesis of man and model organisms...... 124
5.1.2 Improved engraftment of hHSCs in female NSG mice...... 126
5.1.3 Isolation of human hematopoietic stem cells ...... 127
5.1.4 The structure of the human progenitor compartment...... 129
5.1.5 Manufacturing of DCs for immune therapy applications...... 132
5.1.6 Self-renewal of hematopoietic stem cells...... 133
5.2 Identification of higher-order networks in self-renewal and lineage choice...... 135 5.2.1 Using next-generation sequence census methods...... 135
5.2.2 Networks involved in lineage outcome ...... 137
5.3 Concluding remarks ...... 138
xi
6. REFERENCES ...... 140
xii
LIST OF TABLES
Table 3-1. The legend of candidate progenitor fractions...... 80
Table 3-2. Limiting dilution analysis of candidate human MLPs...... 81
Supplementary Table 1. Limiting dilution analysis of myeloid progenitors...... 86
Supplementary Table 4. Expression of lymphoid transcripts by MLPs...... 89
xiii
LIST OF FIGURES
Chapter 1
Figure 1-1. The classical model of the hematopoietic hierarchy...... 35
Figure 1-2. The revised model of the hematopoietic hierarchy...... 36
Figure 2-1. Lin-CD34+CD38-CD90+CD45RA- cells are highly enriched for human HSCs...49
Figure 2-2. Female mice more efficiently support human HSCs than males...... 51
Figure 2-3. Human HSCs can be further enriched with Rhodamine or CD49f...... 52
Figure 2-4. Single CD90+RholoCD49f+ human HSCs can repopulate NSG recipients...... 53
Supplementary Figure 2-1. Accurate detection of low level of human engraftment...... 54
Supplementary Figure 2-2. Cell cycle analysis of various human progenitors...... 54
Supplementary Figure 2-3. Integrin expression profiling of CD90+ and CD90- cells...... 55
Supplementary Figure 2-4. Analysis of engraftment after transplantation of human CMPs..55
Supplementary Figure 2-5. Transplantation of single human HSCs...... 56
Figure 3-1. The comprehensive sorting scheme for CB and BM progenitors...... 74
Figure 3-2. Clonal analysis of candidate CB and BM progenitor fractions...... 76
Figure 3-3. Clonal analysis of human multi-lymphoid progenitors...... 78
Figure 3-4. Progenitor origins of human dendritic cells...... 79
Supplementary Figure 1. Single cell flow sorting analysis...... 82
Supplementary Figure 2. Limiting dilution analysis of CB progenitors...... 83
Supplementary Figure 3. Secondary colony-forming potential...... 83
Supplementary Figure 4. Lineage-specific gene expression of MLPs...... 84
xiv
Supplementary Figure 5. Cytokine secretion by DCs...... 85
Supplementary Figure 6. Proposed model of the human hematopoietic hierarchy...... 85
Figure 4-1. PLZF expression and viral vector design...... 113
Figure 4-2. PLZF restricts myeloid proliferation and differentiation of human progenitors in
vitro...... 114
Figure 4-3. PLZF regulates myeloid development in vivo...... 115
Figure 4-4. PLZF expands the human progenitor compartment in vivo...... 116
Figure 4-5. Cytokines modulate the effects of PLZF on growth and differentiation...... 117
Figure 4-6. Transcriptional regulation of myeloid target genes by PLZF...... 118
xv
LIST OF ABBREVIATIONS
ALL Acute lymphoid leukemia
AML Acute myeloid leukemia
ATRA All-trans retinoic acid
B/T B-cell and T-cell
BM Bone marrow (adult)
BMC Bone marrow cell
BMP Bone morphogenic protein
CB Umbilical cord blood (neonatal)
CD Cluster of differentiation
CD49f Integrin alpha 6 cDC Conventional dendritic cell
CDP Common dendritic cell progenitor
C/EBP CCAAT-enhancer binding protein
CFU Colony-forming unit
CFU-C Colony-forming unit in culture
CFU-G/M Granulocyte or macrophage colony-forming unit
CFU-S Spleen colony-forming unit
ChIP Chromatin immunoprecipitation
ChIPseq Sequence-based chromatin immunoprecipitation
CLP Common lymphoid progenitor
xvi
CMP Common myeloid progenitor
CTL Cytotoxic T lymphocyte cPPT Central polypurine tract
DC Dendritic cell
DL1 Delta-like 1
DL4 Delta-like 4
DN Double-negative thymocyte
EBF Early B-cell factor
E-MK Erythoid and megakaryocyte
EGFP Enhanced green fluorescent protein
ERK1/2 Extracellular signal-regulated kinases 1 and 2
ETP Earliest thymic progenitor
EYFP Enhanced yellow fluorescent protein
FACS Fluorescence-activated cell sorting
FCS Fetal calf serum
FLT3 Deformylase-like tyrosine kinase receptor-3
G-CSF Granulocyte-colony-stimulating factor
GFI1 Growth-fractor independent 1
GFP Green fluorescent protein
GM-CSF Granulocyte-macrophage colony-stimulating factor
GMP Granulocyte-macrophage progenitor h Human
xvii
HOX Homeobox
HSC Hematopoietic stem cell
IF Intrafemoral
ID2 Inhibitor of differentiation 2
IL Interleukin
IL-2 Interleukin-2
IL-3 Interleukin-3
IL-7 Interleukin-7
IL-12 Interleukin-12
IMDM Iscove’s modified eagle’s medium
JAK Janus kinase
KD Knock-down
KLS Kit+ Lin- Sca1+ bone marrow fraction
KTLS Kit+ Thy1+ Lin- Sca1+ bone marrow fraction
LDA Limiting dilution analysis
Lin- Lineage depleted
LMP Lymphoid progenitor with myeloid potential
LMPP Lymphoid-primed multipotent progenitor
LT-HSC Long-term hematopoietic stem cell
MAPK Mitogen-activated protein kinase
MC Methylcellulose
MDP Macrophage-dendritic cell progenitor
xviii
MEK MAPK/ERK kinase
Mφ Macrophage
MK Megakaryocyte
MEP Megakaryocyte-erythroid progenitor moDC Monocyte-derived dendritic cell mPB Mobilized peripheral blood
MPP Multi-potent progenitor
MPG MSCV-PGK-GFP vector
MPS Mononuclear phagocyte system
MSCV Murine stem cell virus
NKT Natural killer-like T-cell
NOD Nonobese diabetic
NOD/SCID NOD/LtSz-scid/scid
NSG NOD/LtSz-scid/scid γNull
OX Over-expression
PB Peripheral blood
PBM Peripheral blood monocytes
PBS Phosphate buffered saline
PCR Polymerase chain reaction pDC Plasmacytoid dendritic cell
PGK Phosphoglycerate kinase promoter
xix
PI-3K Phosphoinositide 3-kinase
PLZF Promyelocytic leukemia zinc finger qPCR Quantitative PCR
Rag Recombination-activating gene
RARα Retinoic acid receptor alpha
RF Injected right femur
RFP Red fluorescent protein
Rho Rhodamine-123
RNAseq Sequence-based RNA profiling
Sca-1 Stem cell antigen-1
SCF Stem cell factor
SCID Severe combined immunodeficient
SD Standard deviation
SEM Standard error of the mean
SFM Serum-free medium shRNA Short hairpin RNA
SRC SCID-repopulating cells
SSC Side scatter
STAT Signal transducers and activators of transcription
ST-HSC Short-term hematopoietic stem cell
T/NK T-cell and natural killer cell
TLR Toll-like receptor
xx
TNFα Tumor necrosis factor alpha
VCAM1 Vascular cell adhesion molecule 1
VSV-G Vesicular stomatitis virus-G
1
1. Introduction
1.1. Models of hematopoiesis
The formative studies in hematopoiesis established the basic principles of stem cell biology. Hematopoietic stem cells (HSCs) are defined based on the capacity to replentish themselves (self-renewal) and give rise to all mature hematopoietic cell types (mutli- lineage differentiation) over the lifetime of an animal. Blood and other highly regenerative tissues are organized as hierarchies derived from multipotent stem cells. As
HSCs differentiate, they give rise to progenitors, which lack self-renewal capacity, but undergo lineage commitment to one of ten distinct blood lineages. Blood cells have been traditionally categorized into two basic branches: lymphoid and myeloid. The lymphoid lineage consists of T-, B-, and nautral killer (NK) cells, which carry out adaptive and innate immune responses. The myeloid lineage includes a number of morphologically and functionally distinct cell types, which include granulocytes (neutrophils, eosinophils, mast cells, and basophils), monocytes, erythrocytes and megakaryocytes. Stem cells and their downstream progeny are connected by a series of complex ontogenic relationships.
A model to describe hematopoiesis must circumscribe all of these relationships, and represents a major goal of biological research in this field. The elucidation of transcriptional networks that regulate these transitions (fate decisions) represents another major goal in this field. This thesis will address both of these problems.
2
1.1.1 The structure of hematopoietic hierarchies: basic principles
The blood system is the most highly regenerative tissue in the body with a daily output of
~1012 cells. This regenerative potential of the blood system also makes it highly susceptible to the effects of cytotoxic and genotoxic stress, with hematopoietic failure being the most common cause of death following exposure to ionizing irradiation. One of the initial observations that initiated experimental hematopoiesis was that the effects of irradiation can be rescued following injection of spleen or marrow cells from an unirradiated donor (Lorenz et al., 1951). Although it was evident from these early experiments that cellular, and not humoral, factors were responsible for this rescue, it remained unknown which cell populations contribute to the reconstitution of the hematopoietic system after transplantation (Ford et al., 1956). We now know that this protection is endowed by donor hematopoietic stem cells (HSCs), which following transplant home to supportive ‘niches’ in the spleen and bone marrow and reconstitute life-long blood production providing the basis for bone marrow transplantation (Nilsson and Simmons, 2004; Schofield, 1978). In uncompromised animals, niches are occupied by resident stem cells which preclude the engraftment of donor HSCs. However, exposure to lethal irradiation liberates these niches and creates a supportive environment for homing and expansion of donor HSCs. Thus, HSC activity can be assayed by a functional repopulation of a lethally irradiated host.
This principle was first directly linked to a single cell by Till and McCulloch, who showed that donor marrow cells formed macroscopic colonies composed of erythroid and myeloid cells in the spleens of lethally irradiated syngenic mice (Till and McCulloch,
1961). Importantly, studies with cells bearing radiation-induced chromosomal markers
3 showed that all cells within a colony were clonally derived from a single progenitor cell, termed the CFU-S (Becker et al., 1963). When these colonies were dissociated and serially passaged some formed secondary colonies indicating that a fraction of CFU-S could self-renew, or generate daughter CFU-S (Siminovitch et al., 1963). These formative studies defined the notion of a stem cell as a cell capable of multi-lineage differentiaton and self-renewal, and also outlined a simple hierarchy composed of rare
CFU-S and their mature progeny that lack stem cell properties.
The CFU-S was the first operationally defined hematopoietic population. The advent of in vitro clonal assays expanded the notion of the hierarchy to include a CFU-C, or a cell capable of giving rise to colonies in semi-solid agar (Bradley and Metcalf, 1966;
Pluznik and Sachs, 1965). Like CFU-S, some CFU-C produced both erythroid and myeloid cells and could be serially replated indicating self-renewal potential, however their exact ontogenic relationship remained elusive (Wu et al., 1968a). Thus, the early view of the hematopoietic hierarchy involved CFU-S- and/or CFU-C-generating HSCs, and mature cells. A pervasive complication to this model was the inability to conclusively establish the clonal origin of lymphocytes from CFUs (Wu et al., 1968b).
1.1.2 Hematopoietic stem cells
The most important principle of a model of hematopoiesis that emerged from these early studies is the clonal origin of all hematopoietic cells from an HSC. In the early model of the hierarchy, mature hematopoietic cells were clonally derived from a CFU-S (Becker et al., 1963). However, it later became evident that the CFU-S content did not correlate with long-term bone marrow reconstitution. For instance, bone marrow from mice treated with
4
5-flourouracil, which depletes cycling cells, contained few CFU-S, but provided robust marrow reconstitution (Hodgson and Bradley, 1979; Phillips, 1985). We now know that
CFU-S are derived from a restricted progenitor of myeloid and erythroid lineages, and are distinct from HSCs. Although CFU-S arise from a progenitor cell, and not an HSC, the principle of clonal origin was later reaffirmed using retroviral clonal tracking (Dick et al.,
1985). The definitive proof of this principle was that single HSCs, prospectively isolated using a combination of surface antigens, gave rise to long-term reconstitution of all blood lineages in an irradiated host (Benveniste et al., 2003; Wagers et al., 2002).
HSCs are uniquely defined by their capacity to self-renew, or generate daughter stem cells, and differentiate into all hematopoietic cell types. In other words, cell division of stem cells is linked with either maintenance or loss of the stem cell state. By contrast, cell division of differentiated cells is inevitably linked with the loss of their developmental state and further differentiation. The only exception to this rule are the memory B- and T-cells that proliferate clonally in response to specific antigens.
Interestingly, self-renewal of HSCs, B- and T-cells is governed by a similar molecular program (Luckey et al., 2006). The ability to balance the competing self-renewal and differentiation demands is termed the ‘stemness’ property of HSCs. Stemness is governed by a complex network of signaling pathways and transcription factors (Chapter 5.1.3, and also (Burns and Zon, 2002). Elucidation of these networks is critically dependent on the isolation of a homogeneous population of HSCs.
The early attempts to purify stem cell populations employed gradient centifugation and were based on physical parameters, such as cell size and density
(Worton et al., 1969). The development of flow cytometric sorting techniques enabled
5 accurate cell fractionation based on the expression of multiple surface antigens which uniquely define a population. All HSC activity in the mouse bone marrow is contained within the lineage-negative (Lin-), c-Kit+, Sca-1+ (KLS) subset (Ikuta and Weissman,
1992; Spangrude et al., 1988). Within this subset, CD34- Thy1.1lo (KTLS) cells possess the unique capacity to give rise to long-term multilineage reconstitution and self-renewal in irradiated mice (Wagers et al., 2002). These cells have been termed long-term HSCs
(LT-HSCs) to discriminate them from stem cells providing only transient reconstitution, such as short-term HSCs (ST-HSCs) or multipotent progenitors (MPPs) (Morrison et al.,
1997a; Osawa et al., 1996). HSCs are extremely rare - a frequency of 1 in 105 and 1 in
108, murine and human bone marrow cells (BMC), respectively (Abkowitz et al., 2000).
The KTLS subset of murine bone marrow overlaps with CD150+CD48- cells, which was recently reported as an alternative method of isolating mouse HSCs (Kiel et al., 2005).
KTLS and CD150+CD48- populations both contain approximately 1 in 2 HSC activity
(Kiel et al., 2005). The residual heterogeneity in these fractions is likely a reflection of the stochastic nature of reconstitution by single HSCs (Benveniste et al., 2003). Thus, these fractions can be considered essentially homogeneous. The ability to isolate a pure
HSC population has led to the detailed analysis of their transcriptional and epigenetic status, which in turn enabled the identification of the components of the transcription factor network in stem cells (Ivanova et al., 2002; Ramalho-Santos et al., 2002). This knowledge can then be applied to manipulate HSCs for clinical benefit, which represents a critical endpoint of stem cell research.
At present, efforts to understand the molecular circuitry of hHSCs by gene expression profiling or targeted genetic approaches have used a heterogeneous population
6 of cells, most of which are not stem cells. The factors that have complicated the analysis of hHSCs include: shortage of the primary sources of human hematopoietic cells – neonatal cord blood (CB), adult bone marrow (BM) and adult mobilized peripheral blood
(PB); reliance on functional xenotransplant assays to read out stem cell activity; and almost complete incongruence of the markers of murine HSCs with their human counterparts. For instance, c-Kit and Sca-1 do not enrich for hHSCs, since most immature human cells express these antigens. Also, unlike murine stem cells, hHSCs are CD34+ and Flt3+ (Sitnicka et al., 2003), and do not express CD150 (Doulatov et al, unpublished observations). It is known, that hHSCs reside in the CD34+, CD38-, Thy1+ compartment, but remain heterogeneous (Baum et al., 1992a; Majeti et al., 2007). Curiously, it is not known exactly how heterogeneous the exisitng human HSCs populations are, since no study to date has supplied a rigorous limiting dilution analysis. Although human HSC transplantation is widely employed clinically, the inability to isolate highly purified HSC has stymied efforts to develop more advanced HSC-based therapeutics, such as gene therapy, HSC expansion, and HSC purging for cancer autotransplantation. Thus, isolation of a homogeneous population of hHSCs is presently a major goal of stem cell biology
(Chapter 2).
1.1.3 The classical model of hematopoiesis
Hematopoietic stem cells give rise to a plethora of lineages and progenitor cell types which form a complex lineage tree. While stem cell properties were begun to be established by the formative studies, until recently there has been little understanding of the ontogenic relationships that connect HSCs with their downstream progeny. The CFU-
7
S and CFU-C were the first operationally defined progenitor populations. In other words, these cell types were defined by their activity in a single assay, such as the spleen colony assay. As a result, it was extremely difficult to relate these populations to an assay- independent model of the hematopoietic hierarchy. Even the relationship between between CFU-S and CFU-C in the elementary model has remained unclear until recently, with the developmental primacy of either population advanced by different investigators
(ironically, both CFU-S and CFU-C containing erythroid and myeloid cells are likely produced by common myeloid progenitors, or CMPs, which have a different ‘read-out’ in these assays) (Nakahata and Ogawa, 1982; Phillips, 1985; Wu et al., 1968a). The development of flow-sorting allowed for prospective isolation of progenitor populations based on the expression of surface antigens. Thus, cell types could be defined based on shared molecular parameters and characterized by multiple independent assays, paving the way for assay-independent models of hematopoiesis.
Reports of the isolation of committed progenitors of the myeloid and lymphoid lineages using flow cytometric methods led to the formulation of the first comprehensive model of hematopoiesis (Akashi et al., 2000b; Kondo et al., 1997), referred to herein as the ‘classical’ model. Committed lymphoid progenitors (CLPs) isolated by flow sorting as Lin-, c-Kitlo, Sca-1lo, IL-7Ra+ cells from mouse bone marrow, gave rise to all the lymphoid cell types, but not myeloid cells, in culture and following intravenous (IV) transplantation in the bone marrow, spleen and thymus (Kondo et al., 1997). Similarly, committed myeloid progenitors (CMPs) isolated as CD34+, c-Kit+, FcgRII/IIIlo (CD16) cells exclusively gave rise to all the myeloid cell types (Akashi et al., 2000b). Other reports detailed the isolation of more committed bi-potent progenitors downstream of
8
CMPs and CLPs, including the CD34+ CD16hi granulocyte-monocyte progenitors
(GMPs), CD34- CD16lo erythroid-MK progenitors (MEPs), and Lin-, CD25-, c-Kithi early thymic T-NK (ETPs) progenitors (Akashi et al., 2000b; Gounari et al., 2002; Moore and
Zlotnik, 1995).
Based on these findings, Weissman and colleagues elaborated the outline of the classical model in a series of influential reviews (see Figure 1-1, and also (Akashi et al.,
2000a; Kondo et al., 2003; Reya et al., 2001a). The first key postulate of this model is that loss of self-renewal capacity during differentiation from HSCs precedes lineage commitment. This was inferred from the existence of MPPs, a progenitor type phenotypically defined as LSK, CD34+, Flt3+ that remains multipotent, but posesses only a transient repopulation capacity (Adolfsson et al., 2001; Morrison et al., 1997a). Hence, in theory, MPPs have lost self-renewal capacity, but have not yet undergone any fate decisions. Another crucial postulate of the classical model is that the earliest commitment decision (downstream of MPPs) segregates lymphoid and myeloid lineages, which is evident from the existence of CLPs and CMPs. Consequently, the progeny of common progenitors should also retain their myeloid or lymphoid lineage identity. Lastly, the classical model predicts that lineage decisions occur as irreversible and stepwise bifurcations. The earliest myelo-lymphoid split gives rise to CMPs and CLPs, and each common progenitor undergoes further commitment steps. CMPs give rise to GMPs, which become committed to the granulocyte-monocyte fate, and MEPs, which only produce erythroid and MK cells. On the lymphoid side, CLPs give rise to pre-pro-B cells which only produce B-cells, and ETPs in the thymus, which are committed to the T- or
NK-cell lineages.
9
It is now evident how operationally defined progenitor populations, such as the
CFU-C, are related to prospectively isolated ones. For instance, GMPs produce CFU-
GM, CFU-G or CFU-M, whereas CFU-GM can be produced by GMPs, CMPs, and any other progenitor with myeloid lineage potential. Progenitor classes can be defined using a set of molecular markers and characterized by multiple independent functional and molecular criteria. Because of this, the classical model quickly became popular owing to its simplicity in representing a complex developmental process such as hematopoiesis.
1.1.4 Towards a revised model of hematopoiesis
A number of recent findings have challenged the universality of the classical model by questioning its three main postulates. Murine LT-HSCs lack expression of CD34 or Flt3
(Christensen and Weissman, 2001), whereas ST-HSCs are CD34+, but remain Flt3- (Yang et al., 2005). As they differentiate, HSCs lose self-renewal capacity, which is concurrent with acquisition of Flt3 expression. In line with the classical model, LSK CD34+Flt3+ cells were first defined as MPPs (Adolfsson et al., 2001). However, in subsequent clonal experiments, Jacobsen and colleagues demonstrated that LSK CD34+Flt3+ cells lacked appreciable erythroid-MK (E-MK) lineage potential, and were strongly biased towards lymphoid development. They were termed lymphoid-primed MPPs (LMPPs) (Adolfsson et al., 2005; Lai and Kondo, 2006; Mansson et al., 2007). This finding is notable for two reasons: it implies that lymphoid-myeloid commitment is not the earliest fate decision.
Instead, E-MK is the first lineage option lost upon HSC differentiation. Moreover, rather than following a simple stepwise pattern of commitment, lineage specification can be biased from the earliest stage.
10
In response, the proponents of the classical model retorted that LMPPs had the potential to generate E-MK progeny in vivo, but with different kinetics, which accounted for the failure to detect these progeny in the Adolfsson et al. study (Forsberg et al., 2005).
However, since ~2 – 3% of single LSK CD34+Flt3+ cells do give rise to E-MK colonies
(Adolfsson et al., 2005), the low-level E-MK potential observed by Forsberg et al. likely represents residual heterogeneity in the sorted LMPP fraction, rather than a bona fide E-
MK lineage potential. While the nature of the earliest fate decisions downstream of HSCs remains contraversial, current concensus favors the non-classical interpretation, which discards the CMP-CLP bifurcation. The new or ‘revised’ model requires that the E-MK program be segregated from the myelo-lymphoid program. This can occur in one of two ways. HSC divisions can generate an E-MK-committed cell (i.e. an MEP) and an LMPP.
Alternatively, a myelo-erythroid progenitor (i.e. a CMP) can inherit the E-MK program, and segregate the E-MK and myeloid programs upon subsequent divisions. In this case, both CMPs and LMPPs represent independent sources of myeloid precursors (i.e. GMPs).
The present evidence and the understanding of transcription factor networks that control lineage outcomes favors this latter interpretation (see Chapter 1.2, also (Lai and Kondo,
2006). Thus, in the revised paradigm for hematopoiesis, the first fate decision separates
E-MK and lymphoid lineages, but retains myeloid potential.
1.1.4.1 The diversity of lymphoid progenitors
In addition to the identification of the LMPPs, recent studies have revealed an unexpected diversity in the lymphoid progenitor compartment. Lymphoid commitment is a complex process that is initiated by the specification of early lymphoid progenitors, e.g. LMPPs,
11 from HSCs. According to the classical model, the CLP is the sole source of all lymphoid cells. However, recent studies have revealed that lymphopoiesis involves a number of progenitors distinct in ontogeny and lineage bias both upstream and parallel to the CLP
(Martin et al., 2003; Welner et al., 2008). This improved understanding of lymphoid commitment have forced additional revisions to the classical model.
Activation of Rag1 recombinase marks one of the earliest steps of definitive lymphopoiesis. Rag1-GFP is expressed by a small fraction of primitive LSK IL-7Ra- cells, termed early lymphoid progenitors (ELPs), which are developmentally upstream of the IL-7Ra+ CLPs (Igarashi et al., 2002). Since ELPs are defined based on reporter gene expression and not a set of surface markers, it is difficult to correlate this subset with the other progenitor classes. A more recent analysis demonstrated that Rag1+ ELPs reside within the LSK CD34+Flt3+ LMPP fraction (Arinobu et al., 2007). However, since ELPs are more advanced in lymphoid commitment than most LMPPs, it is clear that the LSK
CD34+Flt3+ fraction remains functionally heterogeneous and contains progenitor populations at different stages of lymphoid commitment (Arinobu et al., 2007). To further sub-fractionate this population, one study took advantage of the differential expression of vascular cell adhesion molecule 1 (VCAM1). Flt3+VCAM1+ LMPPs have a much higher myeloid potential compared with VCAM1- progenitors, which are more lymphoid-committed (Lai and Kondo, 2006). Thus, even the earliest stages of lymphoid commitment are complex and the hierarchical relationships between different progenitor subsets remain to be fully mapped.
The next milestone in lymphoid specification is the segregation of B- and T/NK lineages. Early B-cell development takes place in the bone marrow, whereas T-cells are
12 specified in the thymus. Since self-renewing cells are not present in the thymus, thymus seeding is a continuous process that begins at birth and continues into adolescence. It is believed that immature lymphoid progenitors enter the circulation and seed the thymus
(Foss et al., 2001; Petrie, 2003). However, the identity these progenitor(s) remains contraversial. In the classical model, CLPs are predicted to repopulate the thymus. Early studies indicated that CLPs robustly initiated T-cell development in vitro or when delivered directly to the thymus (Kondo et al., 1997). However, it was later shown that
CLPs do not enter into circulation, and do not efficiently home to the thymus when introduced by IV (Allman et al., 2003; Schwarz and Bhandoola, 2004). Furthermore, mice lacking an early lymphoid transcription factor Ikaros do not generate detectable numbers of CLPs (Georgopoulos, 2002). While their B-cell development is severely compromised, Ikaros-null mice still generate a normal T-cell repertoire (Allman et al.,
2003). Thus, the bulk of thymopoiesis occurs through progenitor intermediates other than
CLPs.
To identify early intermediates in T-cell development, several studies assessed the expression of surface adhesion molecules previously implicated in homing and retention in the thymus, such as CCR7 and CCR9, on marrow progenitors. Interestingly, a fraction of LMPPs express CCR9 and possess an efficient capacity to home to the thymus (Lai and Kondo, 2007; Martin et al., 2003). However, it is not clear if this also represents the thymus-seeding fraction under physiological conditions. Detailed analysis of circulating progenitors revealed that only primitive LSK cells are found in peripheral blood under steady-state (non-mobilized) conditions (Allman et al., 2003; Bhandoola et al., 2003;
Schwarz and Bhandoola, 2004). These results suggest that only HSCs or multipotent
13 progenitors, such as LMPPs, have the capacity to enter circulation under physiological conditions. By contrast, CLPs, which do not reside in the LSK fraction, were not detected in circulation, substantiating the argument that CLPs do not appreciably contribute to T- cell development, and most likely represent a B-lymphoid-biased progenitor population
(Schwarz and Bhandoola, 2006).
1.1.4.2 Developmental flexibility of lymphoid progenitors
The earliest progenitor with lymphoid lineage potential, the LMPP, also expresses the myeloid developmental program. According to the classical model, lineage decisions occur as irreversible bifurcations. So, at what point do these programs become segregated and lymphoid commitment, in the true sense of the term, takes place? Clonal studies of the various classes of lymphoid progenitors reveal an extensive degree of developmental flexibility, with most progenitors developmentally upstream of the B/T-cell commitment displaying some propensity for myeloid differentiation. For instance, initial identification of CLPs found them lacking detectable myeloid potential (Kondo et al., 1997). However, colony assays in semi-solid (methylcellulose) media used in these early studies may not be a reliable indicator of myeloid potential. Instead, using stromal-supported cultures, a residual (<1 in 10 frequency) myeloid potential was detected from CLPs (Balciunaite et al., 2005). However, results such as these have to be approached with caution due to their dubious physiological relevance. It is well-known that certain cytokines commonly used in cell culture, such as GM-CSF and IL-2, can have a cell-fate instructive effect on lymphoid development (Kondo et al., 2000). It is not surprising that culturing of lymphoid cells in media containing high concentrations of ‘instructive’ cytokines can
14 lead to re-activation of myeloid gene expression. Thus, a definitive demonstration of relevant developmental options in cell culture should involve multi-lineage outputs (e.g. myeloid and B/T-cells) from single cells, as well as a demonstration of their lineage potential in vivo.
An interesting example of developmental flexibility among lymphoid progenitors are the early thymocyte populations. T-cell development in the thymus follows a well- defined sequence of developmental steps initiated by the Lin- CD25-Kithi earliest thymic progenitors (ETPs; also known as DN1 cells) followed by the CD4-CD8- double-negative
DN2-4 subsets, double positive, and lastly CD4/8-single positive T-cells (Shortman and
Wu, 1996). This defined developmental hierarchy allows a precise analysis of their developmental flexibility. Recent reports indicate that ETPs display a bi-potent T- myeloid lineage output and give rise to a small population of Gr1+Mac1+ thymic myeloid cells (Bell and Bhandoola, 2008; Wada et al., 2008). Interestingly, this myeloid potential is largely lost upon differentiation into the DN2 subset. However, ETPs do not retain B- cell potential, since Notch signaling in the thymus strongly antagonizes B-cell development (Bell and Bhandoola, 2008). The observation that B-cell potential is lost before myeloid potential implies that thymopoiesis also does not follow the hierarchical specification steps predicted by the classical model, which predicts that B- and T-cell programs remain coupled after the segregation of myelo-erythroid fates.
Taken together, these recent findings strongly support the idea that lymphoid specification is not a single lineage bifurcation from HSCs, but a gradual and possibly parallel process with several intermediate progenitor stages each with a characteristic lineage bias. While the classical model predicts that lineage choices are executed as
15 binary fate decisions, the revised model incorporates the crucial concepts of lineage bias and lineage priming, which allow for a gradual specification of various lineages. In this scheme, lymphoid development is initiated by lineage priming in LMPPs, which give rise to more committed ELPs, CLPs, and other progenitors. The CLP merely represents one of many progenitor types, with a bias for B-lymphopoiesis. The potential to give rise to certain myeloid cell types persists during early lymphopoiesis, likely reflecting a degree of plasticity in the underlying transcription factor interactions (Chapter 1.2.2). The revised model of hematopoiesis (Figure 1-2) is the synthesis of these findings, and in its present state represents a work in progress.
1.1.5 Xenotransplantation of human cells
The study of human hematopoiesis has lagged behind that of the mouse, owing to the scarcity of sources of primary human cells and the critical lack of adequate assay systems. Transplantation into irradiated syngeneic hosts is the principal method of measuring stem cell activity. Transplantation of human cells requires the development of xenograft models which must fulfill two essential criteria: immunodeficiency, or failure to induce an innate or adaptive immune response against human cells, and cross- compatibility, or the capacity to support the development of human hematopoiesis.
The first practical xenotransplantation model utilized was the SCID mice, which lack B- and T-cells, and thus are unable to mount an adaptive immune response. Prior to intravenous transplantation, mice were conditioned with a sublethal dose of irradiation, and human-specific cytokines were administered to support human cell reconstitution
(Kamel-Reid and Dick, 1988; Lapidot et al., 1992). These chimeras supported multi-
16 lineage human grafts composed of myeloid and B-cells. While the SCID mouse was retained as the basic immunodeficiency platform, further improvements to the model over the next decade eliminated the adaptive and innate immune functions and improved the degree of ‘humanization’ (Manz and Di Santo, 2009). The current ‘gold standard’ in the field, employed our study, is the NOD/SCID γNull strain that enables sensitive long-term detection of most blood lineages from a single transplanted hHSC (Shultz et al., 2000).
1.1.6 A model for human hematopoiesis
Most of the studies aiming to eulcidate the structure of the hematopoietic hierarchy, including all the studies presented in the preceding sections, were performed using murine models. A number of previous reports used flow sorting to isolate and assay primitive cells from neonatal CB or adult BM – the two primary sources of immature human hematopoietic cells. However, definitive evidence as to which model best applies to human hematopoiesis is still lacking.
The prevailing assumption in the field has been that the structure of the human hierarchy would be consistent with the classical model. In line with this assumption, several groups reported the isolation of progenitor populations matching the expected developmental potential of hCMPs and hCLPs. In contrast to murine cells, all immature human progenitors (as well as HSCs) express the CD34 antigen. Human CMPs, GMPs, and MEPs were isolated from adult BM based on the expression of IL-3 receptor
(CD123) and CD45RA (Manz et al., 2002). Myeloid, but not erythroid, progenitors expressed CD123, and the CMP to GMP transition was marked by the acquisition of
CD45RA (Manz et al., 2002). In this study, myeloid lineage output was assayed using
17 methylcellulose colony assays, which do not support lymphoid lineages. Thus, it remains possible that these populations also contain lymphoid progenitors. Also, the expression of
CD123 on CB progenitors is less defined than in BM (Doulatov et al, unpublished data).
The identity of human CLPs, and lymphoid progenitors in general, is more contraversial, due to the lack of reliable assay systems. In adult BM, CD34+ CD10+ cells were shown to give rise to B-, T- and NK cells, but not myeloid or erythroid progeny, thus representing CLPs (Galy et al., 1995). Early lymphoid markers, including CD7 and
CD10, are also expressed by more mature B- and T-cells. Thus, the CD34+ CD10+ fraction studied by Galy et al. was highly heterogeneous containing abundant more mature CD10+ pro-B cells (Rossi et al., 2003). More recently, another report showed that
CD34+ CD10+ cells depleted of CD24+ pro-B cells had CLP potential in both CB and BM
(Six et al., 2007). However, other reports suggested that the hCLP in neonatal CB resided in the CD7+ rather than CD10+ fraction (Haddad et al., 2004; Hao et al., 2001; Hoebeke et al., 2007). Thus, the identity of hCLPs remains disputed. Moreover, these findings also raise the possibility that CB and BM may contain phenotypically distinct progenitor populations.
The discordant findings of these studies of human hematopoiesis reveal a number of critical flaws in their experimental design. First, most experiments were not performed at a clonal level opening the possibility that the detected output could be due to rare cells present in tested fractions, not reflecting their predominant developmental output. This is particularly critical in the identification of multi-lineage progenitors, such as CLPs and
CMPs, since some of the output could be due to a dominant cell population, and some by rare contaminating populations. In cases when clonal experiments were performed, only a
18 single cell type was assayed at a time opening the possibility that different uni-lineage progenitors were present in each fraction, rather than a single multi-lineage cell. No study has employed a unified assay system that has the potential to read out any lineage potential if it exists within the test cell type subjected to the assay; without such an assay it is impossible to conclude that any cell would have a restricted lineage potential. Lastly, while the revised model of hematopoiesis is now firmly established, its relevance to human hematopoiesis remains unclear. Thus, understanding the global ‘architecture’ of the human hematopoietic hierarchy represents a major goal in developmental biology
(Chapter 3).
1.1.7 Progenitor origins of dendritic cells
Every cell type in the hematopoietic hierarchy uniquely arises from the lymphoid or myeloid branch, with the exception of dendritic cells (DCs). DCs are a rare heterogeneous population of professional antigen-presenting cells that are present in most tissues. Immature DCs arise from bone marrow progenitors and traffic to peripheral lymphoid organs, where they can mature upon exposure to various stimuli, such as the
Toll-like receptor (TLR) ligands (Shortman and Naik, 2007; Wu and Liu, 2007). DCs are subdivided into conventional DCs (cDC), specialized for antigen presentation, and plasmacytoid DCs (pDC), which secrete type I interferon. Interestingly, both cDCs and pDCs are derived from the Flt3+ subset of either lymphoid (e.g. CLP) or myeloid (e.g.
CMP) progenitors (D'Amico and Wu, 2003; Karsunky et al., 2003). A clonal cDC-pDC precursor (CDP) in the mouse was recently identified as Flt3+M-CSFR+, however it is not clear if this is the sole intermediate shared by both lymphoid and myeloid pathways (Onai
19 et al., 2007). Since CMPs and CLPs have divergent transcription programs, it is not presently understood how these gene expression patterns are radically realigned to execute the program for DC development.
The myeloid branch carries out an additional pathway that gives rise to dendritic cells through a common macrophage-DC progenitor (MDP), which is not shared with
CLPs. Macrophages and DCs are closely related monocytic cell types that are part of the mononuclear phagocyte system (MPS), which plays a major role in immune surveillance
(Hume et al., 2002). It has been long hypothesized that cells of the MPS are derived from a common progenitor (van Furth and Cohn, 1968). In support of this proposal, circulating monocytes, which are the immediate precursors to tissue macrophages, can give rise to
DCs (moDCs) in response to certain signals. MoDCs are not present in lymphoid organs during steady-state conditions, but appear in cases of infection and inflammation, and play a central role in mounting an immune response against certain pathogens (Leon et al., 2007; Shortman and Naik, 2007). It was recently demonstrated that MDPs, isolated as
+ + CX3CR1 M-CSFR bone marrow cells, give rise to blood monocytes, macrophages, and steady-state spleen DC subsets (Fogg et al., 2006). MDPs are phenotypically identical to
GMPs, except for the expression of CX3CR1. Thus, the authors proposed that GMPs are
+ - composed of CX3CR1 MDPs and CX3CR1 myeloblast precursors of granulocytes, both derived from myeloid progenitors, such as CMPs or LMPPs. A major discovery in this thesis is that macrophages and DCs can also be derived from early lymphoid progenitors in human hematopoiesis. Thus, it appears that the molecular programs for these two MDS cell types remain ‘entangled’ in lymphoid development, and thus cannot be classified as either myeloid- or lymphoid-specific (Chapter 3).
20
From a therapeutic standpoint, moDCs represent a potent stimulatory DC subset, and can be readily derived from CD34+ progenitors or peripheral blood monocytes
(PBMs). For these reasons, moDCs have been extensively studied for use in DC-based anti-tumor immunotherapy. This approach relies on transplanting DCs loaded with tumor-specific MHC class I or II peptides leading to antigen presentation and expansion of specific cytotoxic T cell (CTL) responses (Melief, 2008). However, so far, despite the induction of robust tumor-specific T cell responses in many patients, the clinical success of these therapies has been limited, likely reflecting a combination of the suppressive immune environment created by most tumors and the limitations of existing DC vaccination strategies (Gabrilovich, 2004; Melief, 2008; Palucka et al., 2007).
1.1.8 Conclusions 1
A model of hematopoiesis is most analogous to a world map that pinpoints the exact location and relevant properties of the numerous cell types that populate that hierarchy.
Just as our map of the world has changed over the centuries of discovery and exploration, so has our map of hematopoiesis been reshaped since the first report of hematopoietic stem cells. Today, we can hardly imagine going on a trip without a map or a GPS locator.
In the same way, any experiment involving blood development is unthinkable without precisely defined populations of stem or progenitor cells. Thus, like a map, the model is first and foremost a tool. The next chapter will discuss the primary application of this tool
- to decipher the complex networks of transcription factors underlying fate decisions that specify ten distinct blood cell lineages from a single HSC.
21
1.2 Transcription factor networks that control self-renewal and lineage choice
A comprehensive model of hematopoiesis describing lineage potentials of all hematopoietic progenitor and their ontogenic relationships represents a major advancement in the field, and the explicit aim of the project presented in Chapter 3.
However, in itself, it contains little biological insight. Rather, it represents a stepping stone towards understanding of the molecular mechanisms that govern these complex developmental transitions. It enables one to prospectively isolate the respective HSC and progenitor subsets and interrogate their transcriptome, epigenome and proteome status, as well as carry out targeted genetic experiments using gain- and loss-of-function retroviral vectors. Once again, murine models have been extensively used to pursue these aims leading to the identification of many master transcription factors involved in self-renewal and lineage choice. However, substantial differences exist between murine and human hematopoiesis, highlighted by comparing our model of the human hematopoietic hierarchy (Chapter 3), with that of the revised model based on mouse studies. As such, there is a need to study molecular mechanisms in the context of primary human hematopoietic cells, which is precipitated by the prescience of developing therapeutic interventions.
1.2.1 Lineage commitment: basic principles
Developmental outcomes are controlled by gene-regulatory networks, which display recurrent patterns of organization across different organisms and tissues, and minimally
22 consist of transcription factors, lineage-specific genes, and signaling components.
Lineage determination results in stable global changes of gene expression. As such, it also requires reshaping of the epigenetic landscape, which involves interaction of transcription factors with chromatin modifying and remodeling factors. Transcription factors that belong a gene regulatory network are organized into functional units, according to the lineage that they specify. Transcription factors within a ‘lineage unit’ typically display positive interactions, such as positive feedback, cooperative binding to regulatory elements and cross-activation, while transcription factors across different units display antagonistic interactions, such as promoter competition, negative feedback, and cross-inhibition. Different transcription factor units compete for activation of their cognate lineage-specific genes, while repressing competing units and genes of other lineages. A collection of transcription factor units that yield particular lineage outcomes is called a network.
1.2.2 Stages of lineage commitment
1.2.2.1 Lineage priming
How do transcription factor networks specify lineage outcomes? It is currently known that lineage specification occurs in several phases. An uncommitted cell has low-level expression of a medley of master transcription factors from various units. This phase is termed lineage priming. The molecular basis of lineage priming is not well understood, but it is likely due to the basal rate of transcription of genes that encode transcription factors, in the absence of specific transcription activation (Huang et al., 2007; Miyamoto and Akashi, 2005). It is also facilitated by the open chromatin configuration at the
23 promoter regions in uncomitted cells, such as stem cells. Since undifferentiated cells express transcription factors belonging to all lineage units, they are also predicted to have low-level expression of many lineage-specific genes. Indeed, analysis of single HSCs revealed low-level expression of myeloid (e.g. Mpo), erythroid (e.g. EpoR), and select lymphoid (e.g. Gata-3) genes (Miyamoto et al., 2002). This was rigorously confirmed by lineage tracing, for instance, using the LysM-Cre/ROSA-EYFP knockin reporter mice, in which cells activating lysM (encoding myeloid lysozyme, an enzyme found only in mature neutrophils) transcription are stably marked with EYFP. A fraction of HSCs were
EYFP+ and these cells were capable of long-term reconstitution (Ye et al., 2003), demonstrating that the observed low level expression of mature genes is not due to low- level contamination of sorted HSC fractions with more mature cells. As expected, CLPs and CMPs prime only lymphoid or myeloid genes, respectively (Miyamoto et al., 2002).
Interestingly, single LMPPs also show low level expression of myeloid and lymphoid, but not erythroid or MK genes, providing molecular evidence for the loss of those lineage potentials at the multipotent progenitor stage (Mansson et al., 2007).
1.2.2.2 Lineage reinforcement
Lineage priming continues until one of the lineage programs becomes reinforced due to intrinsic or extrinsic signal(s), which promotes a transcriptional program for that lineage by autoregulatory and/or positive feedback mechanisms, while repressing other priming lineages. Extrinsic reinforcement occurs via lineage-specific signal, which is spatially or temporally segregated. While this mode of fate specification is prevalent in embryonic development, it is less common in adult developmental processes. A classical example of
24 extrinsic reinforcing is T-cell specification. Immature lymphoid progenitors enter circulation and home to the thymus, where they are exposed to high levels of Notch
Delta-like ligand. Delta-like activates Notch signal transduction inducing expression of
Notch target genes (e.g. Hes1, Gata3), which repress priming of B-cell master transcription factor, Ebf (Smith et al., 2005). By contrast, in the bone marrow, where
Delta-like levels are low, EBF primes progenitors to B-cell development (Medina et al.,
2004).
The basis for intrinsic reinforcement is the stochastic fluctuation in the levels of transcription factors undergoing lineage priming. In a population of progenitors, a fraction of cells will express higher levels of one transcription factor compared to others.
At a certain rate, these cells attain a threshold level of fluctuation that will reinforce a lineage choice by positive feedback (Chang et al., 2008). A classical example of intrinsic reinforcing is myeloid versus erythroid lineage choice by myeloid and erythroid master transcription factors, PU.1 and GATA-1 (Graf, 2002). This circuit was discovered by the early studies of avian progenitors transformed with the E26 virus with the Myb-Ets oncogene. E26-transformed cells were multipotent and differentiated into either erythroblasts, or myeloblasts by Ras activation (Graf et al., 1992), a process which could be directed by enforced expression of GATA-1 or PU.1, respectively (Kulessa et al.,
1995; Nerlov and Graf, 1998). Subsequent mechanistic studies demonstrated that the N- terminus of PU.1 binds to the C-terminal zinc fingers of GATA-1 inhibiting its DNA binding capacity (Nerlov et al., 2000). At the same time, GATA-1 interacts with the Ets domain of PU.1 preventing its recruitment of co-activator c-Jun (Zhang et al., 1999).
Cells in which PU.1 level is high, induce expression of Cebpa encoding myeloid
25 transcription factor CEBPα, which reinforces inhibition of GATA-1. By contrast, cells in which GATA-1 level is high, turn on transcription factor Fog, which reinforces inhibition of PU.1 (Tsai and Orkin, 1997).
1.2.2.3 Lineage commitment
The final phase of fate specification is commitment. Cells undergoing lineage priming are open to all permissible developmental outputs. Reinforcing increases the probability of commitment to a particular lineage, but cells still retain some developmental plasticity, and can assume other fates given a permissive environement. However, lineage fate is irreversible upon commitment barring reprogramming by ectopic transcription factors
(see below). In some cases, commitment rapidly follows initial reinforcement, such that no discernible phenotypic intermediates can be isolated. For instance, in the erythroid- myeloid specification, CMPs represent a bi-potent state, whereas MEPs and GMPs are fully committed downstream progenitors. However, in other cases, distinct intermediate states at various pre-commitment stages can be readily isolated. In fact, delayed lineage commitment occurs in lymphoid development, and accounts for the diversity of early lymphoid progenitors displaying various degrees of lymphoid lineage bias, providing the backing for the revised model of hematopoiesis.
In conclusion, lineage detemination in hematopoietic progenitors, as well as other developmental systems, occurs in at least three distinct stages: priming, reinforcement, and commitment. Each stage is characterized by a different state of interactions between groups of transcription factors, from an entirely unbiased state to complete dominance of a single transcriptional program and suppression of all others. Moreover, the molecular
26 data goes a long way towards explaining the hematopoietic architecture predicted by the revised model of hematopoiesis. For instance, it nicely accounts for lineage bias and the diversity of some progenitors such as LMPPs, by suggesting that lymphoid transcription factor networks in these progenitors are underogoing active reinforcement, whereas the residual myeloid potential persists even in more mature lymphoid progenitors, such as the
ETPs. The ability to isolate defined populations of human progenitors (Chapter 3) will lead to similar insights into the mechanisms that govern lineage choice in human cells.
1.2.3 Mechanisms of lymphoid lineage commitment
The previous section explored the general principles pertaining to lineage determination.
Much of the recent work has focused on identifying the members of the lymphoid and myeloid transcription factor networks. Lymphoid lineage priming is initiated in HSCs by
Ikaros, which also restricts genetic programs that are compatible with self-renewal (Ng et al., 2007). High levels of Tcfe2a (E2A) in LMPPs then biases them towards lymphoid fate, at the expense of myeloid lineages (Dias et al., 2008). E2A activates early B-cell factor (EBF), which reinforces the lymphoid choice, and induces B-cell lineage priming
(Zandi et al., 2008). Consequently, LMPPs expressing high levels of EBF are B-lineage biased, but not restricted (Pongubala et al., 2008). While EBF reinforcing of E2A priming induces a B-cell lineage bias in CLPs and downstream pre-pro-B cells, B-cell commitment does not occur until the induction of Pax5 at the pro-B stage, which represses Notch1 and Cebpa required for T-cell and myeloid fates, respectively (Nutt et al., 1999; Souabni et al., 2002). Notably, conditional loss of Pax5 at the pro-B stage reverses B-cell commitment and restores the multipotent state, indicating that at least in
27
B-cell development, commitment is dependent on active maintenance of repression of competing lineage units (Mikkola et al., 2002). Immigration of LMPPs to the thymus, exposes them to Notch signaling. Priming by E2A is also essential at this stage, evidenced by the dose-dependent loss of ETPs in Tcfe2a-null mice (Dias et al., 2008).
Notch antagonizes EBF, abolishing the B-cell potential of ETPs. However, ETPs do retain a limited myeloid potential, despite the inhibiton of myeloid transcription factors
PU.1 and C/EBPa by Notch (Bell and Bhandoola, 2008; Laiosa et al., 2006b). Thus,
Notch signaling provides extrinsic reinforcing of E2A priming, but in itself does not commit progenitors to T-cell fate. Furthermore, as for Pax5 in B-cell commitment, continued exposure to Notch ligands is required to actively maintain the T-cell- committed state.
1.2.4 Mechanisms of myeloid differentiation
The intricacies of lymphoid lineage specification have only recently began to be appreciated. By contrast, there is a perception that the basic mechanisms that control myeloid commitment and differentiation have long been deciphered. Recent advances have clarified the details, and elaborated on the specification of minor myeloid lineages: basophils, eosinophils and mast cells, that arise from the GMP (Iwasaki et al., 2006). In part, this is owing to the ease in manipulating and culturing myeloid cells, with the CFU-
S and CFU-C being some of the first assays used to study hematopoietic cells (Metcalf et al., 1969; Pluznik and Sachs, 1965; Till and McCulloch, 1961). However, it is also apparent that the molecular mechanisms of myeloid specification are not nearly as complex as those involved in lymphopoiesis, even when considering the number of
28 relevant transcription factors. Commitment immediately follows lineage priming in myeloid determination, without extended reinforcement by multiple transcription factors required for lymphoid commitment (Chapter 1.2.3). As a result, the myeloid progenitor hierarchy is simpler, consisting of CMPs, GMPs and MEPs, in contrast to an extended and growing list of lymphoid progenitors. This conclusion is consistent with the more ancient evolutionary origin of myeloid cells, including erythrocytes, megakaryocytes, and cells of the innate immune system. By contrast, lymphoid cells which form the adaptive immune system, are a much more recent evolutionary innovation (Boehm and Bleul,
2007).
Another interesting observation that supports this evolutionary perspective is that at the genome-wide level, HSCs tend to display priming of myeloid (including E-MK), but not lymphoid genes (e.g. Mpo, Csf1r, EpoR) (Akashi et al., 2003; Mansson et al.,
2007). While some lymphoid transcription factors are also expressed, these likely have independent roles in HSC function (e.g. Gata-3), or are uniquely involved in lymphoid priming (e.g. Ikaros). Thus, it appears that HSCs display a bias towards myelopoiesis, and require other cues to override the intrinsic myeloid programming (Iwasaki and
Akashi, 2007). Consistent with this idea, fate options downstream of HSCs according to the revised model, include LMPPs and CMPs, both of which possess myeloid potential.
Taken together, these findings suggest that mammalian HSCs retain an ancestral CMP- like transcriptional state (with self-renewal potential) and incorporate the more recent acquired lymphoid programs into a downstream progenitor (i.e. the LMPP).
The basic mechanisms of myelopoiesis are thought to be well-established.
Myeloid development begins with lineage priming in HSCs by PU.1 and GATA-1. The
29 antagonism between PU.1 and GATA-1 is a classical example of cell intrinsic reinforcing
(Chapter 1.2.2). In the revised model of hematopoiesis, E-MK lineages branch off in early development. Consistent with this model, recent results suggest that GATA-1 is primed with PU.1 as early as the multipotent progenitor stage, as opposed to the CMP stage, as predicted by classical model. PU.1-expressing MPPs possess myelo-lymphoid progenitor activity, whereas GATA-1-expressing MPPs are restricted to the myeloid and erythroid lineages (Arinobu et al., 2007). These results suggest that PU.1-high cells adopt an LMPP fate, whereas GATA-1-high cells become CMPs. The role of PU.1 is more complex, however, since it is also involved in the myeloid versus lymphoid lineage outcome (presumably in the context of LMPPs). High levels of PU.1 expression biases towards myeloid, while lower levels (combined with expression of E2A and other lymphoid factors) prime lymphoid, development (Dakic et al., 2007). Notably, low levels of PU.1 are important for B-cell development, and conditional disruption of PU.1 in adult bone marrow results in loss of both myeloid and lymphoid lineages, and high incidence of myeloid leukemias, whereas erythroid differentiation is unaffected (Iwasaki et al.,
2005).
High levels of PU.1 in multipotent cells prime a myeloid lineage outcome.
However, expression of PU.1 itself does not commit cells to the myeloid lineage.
Expression of many genes critical to myeloid development, including cytokine receptors
(e.g. Csf1r, Csf3r), is dependent on the cooperative regulation by PU.1 and another master myeloid transcription factor, C/EBPα (Smith et al., 1996). C/EBPα is a member of the CCAAT family of transcription factors, which play diverse roles in differentiation and metabolism. Mice deficient for C/EBPα lack neutrophils and eosinophils (cells of
30 granulocytic lineage), but have normal numbers of lymphoid and erythroid cells (Zhang et al., 1997; Zhang et al., 2004). While C/EBPα is not required for the initial stages of myeloid specification, it cooperates with PU.1 in the CMP to GMP transition, as well as the monocyte versus granulocyte lineage choice (Zhang et al., 2004). Consistent with its role in commitment, rather than initial priming, ectopic expression of C/EBPα in committed cells of other lineages reprograms them into myeloid cells. For instance, committed B- and T-cells that express C/EBPα from a constitutive retroviral promoter are efficiently converted into macrophages (Laiosa et al., 2006b; Xie et al., 2004). In this process, C/EBPα antagonizes Pax5- and Notch-directed lymphoid programs and instructs myeloid gene expression, in cooperation with endogenous PU.1 which is expressed at low levels in lymphocytes.
A surprisingly diverse array of myeloid cell types are derived from GMPs, including macrophages, neutrophils, myeloid DCs, basophils, eosinophils, and mast cells, each developing from a committed precursor downstream of GMPs (Iwasaki and Akashi,
2007). Of these, monocytes and granulocytes are the two most abundant cell types in PB, with other lineages making up <1% of mononuclear cells. The loss of granulocytes in
Cebpa-null mice and the more severe effect of PU.1 deletion on monocytic development, initially led to the idea that, by analogy with PU.1 and GATA-1, antagonism between
PU.1 and C/EBPα determined the outcome between competing granulocytic and monocytic programs. However, PU.1 and C/EBPα are highly expressed in both lineages, and surprisingly, cooperatively activate transcription of granulocytic and monocytic genes (Smith et al., 1996). To reconcile these observations, a model was put forward which proposed that myeloid lineage determination involves two sets of lineage
31 determinants, in contrast to the previous two-factor models. In this model, a pair of primary determinants PU.1 and C/EBPα, induce their respective secondary determinants,
EGR1/2 and GFI1, in a concentration-dependent manner (Laslo et al., 2006). While primary determinants effectively co-occupy the promoters of both sets of lineage-specific genes, EGR1/2 exclusively activate monocyte-specific and repress granulocytic genes, while GFI1 activates granulocyte-specific and represses monocytic gene expression. It seems that the utility of this two-tier transcription factor scheme is apparent for cell types that share an extensive number of commonly expressed genes, such as the cytokine receptors Csf1r, Csf3r, which are expressed in both monocytes and granulocytes, and are thus coordinately regulated by the primary determinants.
1.2.5 Mechanisms of progenitor homeostasis
The number of stem cells, progenitors and mature cells of any given lineage in the bone marrow and blood of a healthy normal animal remains remarkably constant. Thus, a key question is how homeostasis is maintained in such a complex system. This poses a somewhat different problem than the basic mechanisms of lineage specification, which are concerned with lineage outcome, but are blind to the temporal component of this process, in other words, how many cells of a given lineage are produced per unit time.
In this respect, the HSC compartment is unique, since stem cells can replentish themselves by self-renewal. To maintain homeostasis, self-renewal must be balanced with differentiation. The mechanisms that govern this process are now beginning to be elucidated (Chapter 5.1.4). The situation is entirely different with progenitors. Since they have little or no self-renewal capacity, homeostasis is maintained by balancing the
32 rate of stem cell differentiation (entry of cells into the progenitor compartment) with the rate at which progenitors enter terminal differentiation (when they acquire characteristics of mature blood cells), thereby exiting the compartment. Progenitor homeostasis plays a particularly significant role in myeloid development, since mature myeloid cells are completely devoid of self-renewal capacity. As such, the rate of mature cell output is critically dependent on the regulation of proliferation and differentiation at the progenitor level. By contrast, a latent self-renewal program is retained by mature B- and T-cells, which can become re-activated by extrinsic signals, for instance upon TCR binding in T- cells, and trigger clonal expansion. While the mechanisms that control myeloid lineage choice and differentiation have been extensively studied and modeled (Chapter 1.2.4), it is largely unknown how the size of the myeloid progenitor compartment is regulated.
This is the main question addressed in Chapter 4 of this thesis.
The size of the progenitor compartment must be closely regulated not only to maintain homeostasis under steady-state conditions, but also to augment the supply of mature cells in response to various stress stimuli. Myeloid development can be perturbed by a number of stress conditions, inclusing infection, cytotoxic or genotoxic insults, transplantation and so on. This is sometimes referred to as ‘emergency’ myelopoiesis to differentiate it from the homeostatic mode. During infection, activated lymphocytes secrete an array of cytokines which act on myeloid cells to activate the innate immune response (Cannistra and Griffin, 1988). Of these, G-CSF, GM-CSF and IL-3, stimulate myeloid progenitors to proliferate and differentiate augmenting the production of mature myeloid cells (Donahue et al., 1988). After binding to their cognate receptors, cytokines activate parallel JAK/STAT, MAPK and PI-3K signal transduction pathways, which
33 modify expression and activation of downstream effectors (Barreda et al., 2004). Thus for instance, STAT3, a component of the cytokine signaling pathway, is required only during emergency myelopoiesis (Panopoulos et al., 2006). Cytokines positively regulate the rate of progenitor cycling, which is irrevocably linked with entry into terminal differentiation: since progenitors do not self-renew, they only undergo a fixed number of cell divisions, after which they irreversibly exit the cell cycle and differentiate. Thus, by regulating the cycling of myeloid progenitors, cytokines control their rate of entry into terminal differentiation.
The network of myeloid transcription factors is altered during stress. For instance,
C/EBPα is required for the production of neutrophils during steady-state, while C/EBPβ is dispensable (Zhang et al., 1997). The exact opposite is observed during stress, with
C/EBPβ assuming a critical role (Hirai et al., 2006). ERK signaling induced by myeloid cytokines can activate C/EBPβ by direct phosphorylation (Nakajima et al., 1993). Thus, myeloid development can be viewed as a bi-modal system, with a steady-state program fine-tuned to replenish the pool of mature cells and an emergency program that responds to increased demand during stress. Still, it is largely unknown how cytokine signals interact with the network of myeloid transcription factors to fulfill these requirements.
This is the second question addressed in Chapter 4 of this thesis.
1.2.6 Conclusions II
All mature blood cells are specified from a multipotent hematopoietic stem cell. The model of hematopoiesis provides a guide to the intermediate steps in this process allowing a detailed interrogation of the underlying molecular mechanisms. Lineage
34 determination involves activation and repression of lineage-specific gene sets, which is controlled by networks of transcription factors displaying both positive and antagonistic modes of interaction. The activity of these networks defines the three stages of lineage determination: priming, which displays low (or sub-threshold) activity of one or more different lineage programs; reinforcement, which features an unequal activation of one program over others; lastly, commitment, which represents a complete and sometimes irreversible dominance of one program and repression of all others. According to the revised model of hematopoiesis, the earliest lineage segregation separates lymphoid and
E-MK programs. On the molecular level, this involves the classical antagonism between
PU.1 and GATA-1 transcription factors, and activation of lymphoid master regulators,
Ikaros and E2A. GATA-1 segregates with the E-MK program to CMPs, while PU.1 and
Ikaros/E2A segregate to LMPPs. Lineage specification downstream of LMPPs involves a gradual reinforcement of the lymphoid program by B- or T-cell specific EBF- or Notch- centric networks, and the exclusion of myeloid programs driven by C/EBPα. By contrast, myelo-erythroid specification does not involve an extended reinforcement stage, as the myeloid program is quickly suppressed by activation of GATA-1 in MEPs, whereas the
E-MK program is repressed by PU.1 in GMPs. The discrimination between monocytic and granulocytic lineages in GMPs is controlled by a two-tier transcriptional network, in which primary determinants, PU.1 and C/EBPα, cooperatively regulate a set of shared genes, while secondary determinants EGR1/2 and GFI1, control lineage choice. Lastly, in addition to lineage determination, progenitor homestasis is also highly regulated to adjust the supply of mature cells of various lineages in response to emergent events.
35
Figure 1-1. The classical model of the hematopoietic hierarchy. The first lineage bifurcation separates lymphoid and myeloid lineages into their respective common progenitors,
CLP/CMP. Adopted from (Reya et al., 2001b).
36
Figure 1-2. The revised model of the hematopoietic hierarchy. The earliest lineage bifurcation downstream of HSCs separates the E-MK and the lympho-myeloid (LMPP) lineages. Since there is no final concensus on the state of the revised model, each publication features a slightly different version. In this report, the E-MK lineages split off from MPPs without the CMP intermediate. Also, the gradual nature of lymphoid lineage specification is not illustrated here. Adopted from (Murre, 2009).
37
2. Isolation of single human HSCs
The work presented here is currently in submission for publication:
Notta, FN.*, Doulatov, S.* & Dick, J.E. Identification of single human hematopoietic stem cells capable of long-term multilineage engraftment and self-renewal
* These authors contributed equally to this work
Author Contributions / Acknowledgements:
We thank K. Moore and the obstetrics unit of Trillium Hospital (Mississauga, Ontario) for providing cord blood samples and co-op students for processing cord blood samples; S.
Zhao, P.A. Penttilä, L. Jamieson at the UHN/SickKids Flow Cytometry Facility for sorting; and the Dr. J.E. Dick lab and Dr. N. Iscove for critical review of the manuscript. This work was supported by funds from Canadian Institutes for Health Research (CIHR) studentships
(FN, SD), The Stem Cell Network of Canadian National Centres of Excellence; the Canadian
Cancer Society and the Terry Fox Foundation; Genome Canada through the Ontario
Genomics Institute; Ontario Institute for Cancer Research with funds from the province of
Ontario; the Leukemia and Lymphoma Society; the Canadian Institutes for Health Research; and a Canada Research Chair.
38
2.1 Abstract
A fundamental tenet that has guided our insight into the biology of hematopoietic stem cells
(HSCs) over the past 50 years is the principle that an HSC can only be assayed by functional repopulation of an irradiated host(McCulloch and Till, 2005). In its strictest definition, only a
HSC can provide long-term reconstitution of all the major lineages following single cell transplantation. However, the existing strategies for human HSC isolation lack quantitation and do not submit to this rigorous standard, thus precluding further biological analysis. Here, we report the prospective and quantitative analysis of human cord blood (CB) HSCs transplanted into female NOD/SCID/IL-2Rgcnull mice. We identify integrin a6 (CD49f) as a novel marker of cord blood (CB) HSCs and report that single Lin-CD34+CD38-
CD90+CD45RA-RholoCD49fhi cells can reconstitute myeloid, B-, and T-cell lineages for 18 weeks. 5 of 29 mice transplanted with single cells gave rise to human cells indicating that approximately 20% of cells in this fraction are HSCs. This advance finally enables utilization of near-homogeneous populations of human HSCs to gain insight into their biology and to harness them for stem cell-based therapeutics.
2.2 Matherials and Methods
Human Cord blood. Samples of human cord blood were obtained from Trillium Hospital
(Mississauga, Ontario, Canada) and processed in accordance to guidelines approved by
University Health Network. Various cord blood samples were pooled and an equal volume of phosphate buffered saline was added prior to layering on Ficoll/Paque gradient (Pharmacia) in 50mL conical tubes. Tubes were subjected to 25min centrifugation at 400xg followed by
39 careful removal of mononuclear layer and washed with Iscove’s modified Dulbecco’s medium (IMDM, GIBCO/BRL). Lineage negative cells were enriched by magnetic negative cell depletion by using human hematopoietic progenitor enrichment cocktail (Stem cell technologies, Vancouver, BC, Canada) according to manufacturer protocol. Lin- cells were stored at -150oC.
Cell preparation for cell sorting. Lin- cells were thawed via the dropwise addition of
IMDM+DNase (200ug/mL final concentration) and resuspended at 106cells/mL in PBS/2.5%
FBS (Sigma, St. Louis, MO, USA). Cells were subsequently stained with CD45RA Fitc or pe, CD90Pe or biotin, CD49f Pe-Cy5, CD34Apc or CD34Apc-Cy7 and CD38 Pe-Cy7
(Becton Dickinson) and incubated for 30min at 4oC. Cells were subsequently washed with
PBS/2.5% FBS and secondary staining with streptavidin-bound quantum dot 605 (Molecular
Probes) was performed (30min, 4oC) when CD90biotin conjugated antibody was used. Cells were washed again with PBS/2.5% FBS and resuspended at 106-107/mL in PBS/0.5% FBS prior to sort. Cells were sorted on FACS Aria (488nm Blue [100mW], 633nm Red [30mW],
Becton Dickinson) and collected in 1.5mL microfuge tubes. Cells were spun down, counted via trypan blue exclusion, and resuspended in appropriate volume of PBS/0.1% FBS or
IMDM for transplant. A fraction of the final volume was recounted to ensure the cell dose being transplanted was accurate. In experiments were Rhodamine 123 (Eastman Kodak,
Rochester, NY, USA) was used, the protocol was adjusted as previously described. Briefly, freshly thawed lin- cells were incubated at 37oC with 0.1ug/mL Rho, washed and destained at 37oC for an additional 30 mins. Cells were subsequently subjected to staining with appropriate antibodies as mentioned above.
40
Single Cell transplant. Single Lin-CD34+CD38-CD90+CD45RA-RholoCD49f+ cells were sorted into Nunc MiniTrays (163118) in 10uL of IMDM/1% FBS or 96well plates using the
FACS Aria. Cells were allowed to settle for 1h at 4oC or centrifuged at 600xg for 5min.
Single cells were visualized using a microscope and transferred into a 28.5g insulin syringe.
Wells were revisualized to ensure the cell was absent after transferring into the needle. Post- sort cell viability was assessed independently using a second Minitray in which single cells were sorted. Trypan blue was added to the well and 60/60 wells analyzed had single viable cells.
Xenotransplant Assay. NOD/LtSz-scidIL2Rgnull (NSG) (Jackson Laboratory) were bred and housed at the Toronto Medical Discovery Tower/University Health Network animal care facility. Animal experiments were performed in accordance to institutional guidelines approved by UHN Animal care committee. The intrafemoral transplant has been previously described. Briefly, 10-12wk old mice were irradiated (200-250cGy) 24h before transplant.
Prior to transplantation, mice were temporarily sedated with isoflurane. A 27g needle was used to drill the right femur (injected femur – IF), and subsequently, cells were transplanted in 25uL volume using a 28.5g insulin needle. For serial transplantation, IF and BM were combined and transplanted into the right femur of secondary recipients.
Assessment of human cell engraftment. All NSG mice were sacrifice >16wk post- transplant. The right and left femur and tibiae, spleen and thymus were removed cells were extracted using standard flushing or cell dissociation techniques. Cell were then stained in
PBS/2% FBS and analyzed by multiparameter flow cytometry (LSRII, Becton Dickinson) using automated compensation of anti-mouse Ig,k and negative control compensation particles (Ca. 552843, Becton Dickinson). The marrow (IF and BM) were analyzed with 2
41 non-competing CD45 clones (H130 PC7 – Becton Dickinson, and J.33 PE or PC5 –
Beckman coulter). Other lineage markers used were CD3, CD4 (Beckman coulter), CD5,
CD7, CD8, CD11b, CD19, CD33 (Beckman coulter), CD56, GlyA (Beckman coulter), IgM
(all Becton Dickinson unless otherwise indicated).
Statistics. Data is represented as mean±s.e.m. The significance of the differences between groups was determined by using Mann-Whitney test. Limiting dilution analysis was performed using online software provided by WEHI bioinformatics
(http://bioinf.wehi.edu.au/software/elda/index.html Hu, Y. and Smyth, G. (2009). ELDA:
Limiting Dilution Analysis for comparing depleted and enriched populations, Walter and
Eliza Hall Institute of Medical Research, Australia)
2.3 Results and Discussion
Enormous insight into stem cell biology garnered from mouse studies has crucially depended on the identification and isolation of a near-homogeneous population of HSCs. These studies are heavily dependent on functional and clonal assays that accurately assess the specific activity of HSCs. The spleen colony forming unit (CFU-S) assay was the first quantitative assay to reveal the proliferation and differentiation capacity of single hematopoietic cells with repopulation potential(Till and Mc, 1961). With recognition that CFU-S lack sufficient self-renewal capacity to enable long-term repopulation, significant progress has been made over the last 40 years to refine repopulation assays and to identify cell surface markers that permit prospective isolation of HSCs with discrete functional capacity(Harrison, 1980;
Szilvassy et al., 1990; Till and Mc, 1961). Current mouse HSC purification studies have
42 reveal that 1 in 2.1 Lin-CD150+CD48-Sca+c-Kit+ cells have the ability to generate long-term engraftment (>16 wks) in competitive repopulation assays(Kiel et al., 2005). By contrast, due to the short-term survival of xenograft models, such as NOD/SCID and
NOD/SCID/beta2mnull, long-term engraftment of human HSCs has been limited to 8 - 12wk post transplant(Dick, 2008). Additionally, these were also plagued by residual innate immunity that decreases the sensitivity of human cell detection. However, the recent development of NOD/SCID/IL-2Rgcnull (NSG) mice(Ito et al., 2002; Shultz et al., 2005) reconcile these issues enabling precise quantitation of candidate human HSC fractions according to criteria established in murine studies.
Prior studies based on xenotransplant assays reveal that human HSCs reside in Lin-
CD34+CD38-(Bhatia et al., 1997) or Lin-CD34+CD90+(Baum et al., 1992b) fractions of umbilical cord blood (CB) or adult bone marrow. Since the vast majority of cells within these fractions are not stem cells, limited insight into the molecular circuitry of human HSCs can be obtained from analysis of such fractions. Recently, Weissman and colleagues have further enriched human HSCs within the Lin-CD34+CD38-CD90+CD45RA- (CD90+) subfraction and candidate human multipotent progenitors (MPPs) within Lin-CD34+CD38-CD90-CD45RA-
(CD90-) fractions(Majeti et al., 2007). Although this represents a significant advance from previous standards, the degree of functional heterogeneity within these fractions remains to be quantified. Thus, we first isolated CD90+ (cell dose: 25 – 1000) and CD90- (cell dose: 25
– 3000) cells by FACS purification and transplanted them intrafemorally into NSG mice (Fig
1a). Transplantation of human HSCs into the femur (injected femur - IF) improves engraftment efficiency(Mazurier et al., 2003; McKenzie et al., 2005) and allows the monitoring of human cell migration to the rest of the bone marrow (BM), and secondary
43 hematopoietic sites such as the spleen (SP) and thymus (TH). Human multi-lineage engraftment (Fig. 1b) was examined by flow cytometry in IF, BM, SP and TH, and the detection limit was set a priori to 0.1% (Fig. S1). The engraftment potential of CD90+ and
CD90- cells after 16-24wk post-transplant was not significantly different (IF: CD90+-20.3%,
CD90--34.4%, p=0.15, n=43,29 respectively) (Fig. 1b,c). To account for the various cell numbers transplanted, engraftment levels were normalized to the transplanted cell dose.
Again, this revealed that both CD90+ and CD90- cells have similar engraftment potential (IF:
CD90+- 0.172, CD90--0.089, p=0.66) (Fig. 1d). Cell cycle analysis confirmed that CD90+ and CD90- cells have similar level of quiescence (Fig. S2). With a more sensitive HSC assay our results point to a similar long-term multi-lineage reconstitution capacity of CD90+ and
CD90- fractions.
HSCs are distinct from MPPs in their capacity for long-term reconstitution and self- renewal upon serial transplantation(Morrison et al., 1997b). Secondary (2°) transplants were performed to determine if CD90+ or CD90- cells represented bona fide HSCs or candidate human MPPs(Majeti et al., 2007). Interestingly, 7/8 and 8/12 mice transplanted with marrow from primary CD90+ and CD90- recipients were engrafted, respectively. However, mean engraftment levels of CD90+ cells were much higher compared to CD90-, approaching statistical significance (IF: CD90+- 28.0%, CD90- - 7.3%, p=0.057) (Fig 1e). Thus, we hypothesized that both fractions contain HSCs but that the CD90+ fraction of human CB contains higher numbers of HSCs. To test this, transplantation of limiting doses of CD90+ and CD90- cells into primary recipients demonstrated that 1 in 36.6 (n=43) and 1 in 221.1
(n=23) cells within CD90+ and CD90- fractions, respectively, is an HSC capable of long-term multi-lineage reconstitution (Fig. 1f). Therefore, the addition of CD90 to the purification
44 scheme allows for 6-fold enrichment of HSCs. Since the CD90- fraction retains capacity for long-term engraftment and serial transplant, albeit at a reduced capacity, we cannot conclude that this fraction represents human MPPs. Future in depth studies of the dynamics of human hematopoietic cell engraftment in NSG mice will be crucial in defining these subpopulations.
During the course of our analyses of CD90+ and CD90- cells in NSG mice, we recognized that human engraftment could be stratified according to the gender of the recipient. When multiple HSCs (non-limiting dose) were transplanted in female and male
NSG mice, female mice displayed a modest but significantly higher level of human chimerism (female vs. male: IF – 49.7±5.8 vs. 26.5±7.7, p=0.03; BM – 40.6±4.4 vs.
12.1±4.8, p=0.0009; SP – 38.1±4.5 vs. 15.1±5.7, p=0.01; TH – 42.9±8.0 vs. 29.3±12.7, p=0.18; n=28 females[F], 13 males[M]) (Fig 2a-c). However, transplantation of doses equivalent to a single HSC (limiting dose) unveiled striking differences between male and female recipients (female vs. male: IF – 8.1±2.7 vs. 0.7±0.7, p=0.0001; BM – 4.8±1.7 vs.
0.1±0.04, p<0.0001; SP – 3.2±0.8 vs. 0.1±0.1, p<0.0001; TH –2.1±1.2 vs. 0, p=0.04; n=29F,
20M) (Fig. 2d). Female mice had 11- and 76-fold higher mean IF and BM engraftment, respectively, compared to age-matched, syngeneic males (Fig. 2e,f). Next, we performed parallel secondary transplants into male and female recipients from single primary females.
In every case, higher levels of human engraftment were observed in female secondary recipients (Fig. 2g) providing direct evidence that human HSCs are more efficiently detected in female NSG mice. Retrospective assessment of the initial HSC frequency in female recipients revealed that in 1 in 20 CD90+ (n=24f) and 1 in 112 CD90- (n=19f) cells is an HSC
(Fig. 2h,i). Further experiments are required to dissect the molecular determinants of sex- dependent difference in engraftment of human HSCs. Overall, refinement of the frequency
45 analyses and optimization of the NSG model to detect limiting doses of HSCs sets the foundation for further purification.
Cellular processes such as quiescence and energy state are closely associated with stem cell function(Bertoncello et al., 1985; McKenzie et al., 2007; Spangrude and Johnson,
1990). Since mitochondria are regulators of these processes, we sought to determine if the differential efflux of the mitochondrial dye, Rhodamine-123 (Rho, Figure 3a,c,e,f), could be used to functionally enrich for human HSCs in combination with CD90+. We sorted CD90+ fraction into Rholo and Rhohi cells (Fig. 3a) and transplanted them into female NSG mice.
After 18 wks, CD90hiRholo mice had 40-fold higher IF engraftment compared to CD90hiRhohi mice (Fig. 3d,g). Five of 8 mice transplanted with 10 CD90+Rholo cells versus 3 of 4
CD90+Rhohi mice at the 25-cell dose were engrafted (Fig. 3e). Therefore, using Poisson statistics, we approximate that 1 in 10.2 cells within the CD90+Rholo fraction is an HSC. This illustrates that the addition of Rho can enrich for HSC activity in the CD90+ fraction by ~2 fold.
Integrins play a critical role in retaining stem cells within their niche (reviewed in
(Scadden, 2006)). We evaluated the expression of various integrins (a2, a4, a5, a6) and other molecules involved in migration, e.g. CD44 and CXCR4 (Fig. S3). Integrin alpha-6 (a6 or
CD49f) was the only molecule that displayed >2-fold differential expression between CD90+, enriched for human HSCs, and CD90- cells (Fig. 3b). Since all CD90+ cells expressed
CD49f, we isolated CD49flo, CD49fint and CD49fhi subfractions (Fig. 3d) and tested their engraftment potential in NSG mice. After 22 wks, 8 of 8 mice injected with 25 CD49fint and
CD49fhi cells were engrafted, but only 1 of 8 CD49flo mice had detectable human cells.
Furthermore, CD49f levels correlated with the proliferative capacity of HSCs (IF:
46
CD90+CD49flo – 0.1%, CD90+CD49fint – 15.1%, CD90+CD49fhi – 21.1%) (Fig 3g). Since all mice transplanted with CD49fhi/int cells were engrafted, the frequency of HSCs is much lower than 1 in 25 cells. Based on these results, we conclude that high levels of CD49f expression can identify human HSCs.
Long-term and multilineage repopulation following transplantation of single cells remains that most stringent assay with which to define a stem cell since the single cell must self renew to enable long term repopulation(Benveniste et al., 2003; Osawa et al., 1996); downstream progenitors are not able to sustain a graft long term. Since sorting experiments using both Rho and CD49f independently enriched for HSCs in combination with CD90+, we investigated the engraftment potential of single CD90+RholoCD49fhi cells (Fig. 4a). Due to the stringency of the assay, detection of human cells was considered to be an indication of the presence of an HSC 18 wks post-transplant. Five of 29 mice injected with single
CD90+RholoCD49fhi cells had human engraftment (Fig. 4b), indicating the approximate frequency of human HSCs within this fraction is 1 in 5.3 or 18%. Interestingly, 4 of 5 mice had a lympho-myeloid graft in the bone marrow, and 2 of these mice also contained T-cells in the thymus (Fig. 4b,c). One mouse had only myeloid cells in the bone marrow (Fig. 4b, m27), however we suspect that this graft is an indication that an HSC was present since progenitors do not persist for 18 wks (see below and accompanying manuscript, Doulatov et al.). These results indicate that that single CD90+RholoCD49fhi cells are multipotent and are highly enriched for HSC activity.
Self-renewal is an obligate characteristic of HSCs. Although long-term repopulation is a good measure of self-renewal, we also conducted serial transplantation from primary recipients that received single cells as a second measure of self-renewal. Three of 17 mice
47 transplanted with a dose equivalent of a single HSC, from CD90+ or CD90- cells, engrafted secondary recipients. These data indicate that these cells can self-renew, but our ability to efficiently detect rare stem cell divisions is limited by the proportion of total bone marrow that was retransplanted (<20%) presenting a unique challenge to assessing clonal self- renewal events. Single HSCs injected into the femur must undergo self-renewal divisions to migrate to distant sites. In contrast, progenitors lack self-renewal capacity and are predicted to remain confined to the IF. As a proof of principle, we injected sorted progenitors (early lymphoid precursor (ELP), common myeloid precursor (CMP) and granulocyte-macrophage precursor (GMP), see Doulatov et al. accompanying paper), and in each case human engraftment was observed in the IF, but not BM, SP or TH (Fig. S4 and data not shown).
Therefore, in conjunction with long-term engraftment, the presence of a multilineage graft at non-injected sites is a sensitive surrogate test for self-renewal and migration of HSCs from the IF. In 4 of 5 mice engrafted from single CD90+RholoCD49fhi cells, human cells could be detected in the non-injected bones (Fig. S5) indicating that these cells give rise to long-term multilineage engraftment, self-renew and migrate, and thus represent bona fide human HSCs.
To date, this study represents a dramatic advance in human HSC purification schemes. Previous HSC fractions used for molecular analyses have been far too heterogeneous to ensure that a particular gene expression signature or gene function derives from HSCs and not the bulk of contaminating cells. Thus, the isolation of a near- homogeneous population of human HSCs is a prerequisite for their genetic and genomic analysis. Here, we present a novel strategy for isolating highly enriched human HSCs based on the expression of CD34, CD38, CD45RA, CD90, CD49f and Rho efflux. Our ability to assay single human HSCs is dependent on several improvements to xenograft models, such
48 as the use of female NSG mice, which greatly augment the detection of single human HSCs.
Our measurement that 1 in 5.3 of CD90+RholoCD49fhi is an HSC could imply that additional markers are needed to purify to homogeneity. Alternatively, it is highly likely that this is an underestimate, since there remain substantial barriers to human engraftment. Thus, further
‘humanization’ of mouse models using accessory cells or human cytokines may revise our current estimate to levels approaching homogeneity. The highly purified HSC reported here combined with genetic approaches(Barabe et al., 2007) that are now extending to primary human cells provide powerful tools to map the molecular networks involved in maintaining the stem cell state, self-renewal and differentiation, which in turn, will be instrumental in manipulating these cells for use in clinical application.
49
2.4 Tables and Figures
Figure 2-1. Lin- CD34+CD38- CD90+CD45RA- cells are highly enriched for human HSCs. a. Lineage depleted cord blood was flow sorted into CD34+CD38- CD90+CD45RA- (CD90+) and CD34+CD38- CD90-CD45RA- (CD90-) cells and transplanted intrafemorally into sublethally irradiated null NOD/SCID/IL-2Rgc (NSG) mice. b. Representative flow cytometric analyses of human hematopoietic cells from an engrafted mouse 18wk post- transplant. Donor chimerism was detected with a pan- hematopoietic, human specific, CD45. CD19 (B-lymphoid), CD33 (myeloid), CD56 (natural killer), CD4 (T-cell) and CD8 (T-cell) markers were used to interrogate lineage specificity in the bone marrow, spleen and thymus. c. d. Human engraftment levels (% hCD45+, c.) for the injected femur (IF), non-injected bones (BM), spleen (SP) and thymus (TH) 16 – 22wk post transplant of various doses of CD90+ and CD90- cells. Normalization to cell dose transplanted is shown in (d.). e. Mean human engraftment levels of secondary recipients transplanted with whole bone marrow from CD90+ and CD90- primary recipients. f. Limiting dilution analyses of various doses of CD90+ (n=43) and CD90- (n=23) cells indicates HSC frequency is 1/36.6 and 221.1, respectively.
50
51
Figure 2-2. Female mice more efficiently support human HSCs than males. a. Representative flow cytometric analysis of human hematopoietic cells from the injected femur of male and female recipients transplanted with the identical cell dose of sorted lineage depleted cord blood (CB). b.,c. Donor human chimerism (b.) and fold difference in engraftment (c.) for male and female NSG recipients transplanted with non-limiting HSC doses (>1 HSC). d.,e. Donor human chimerism (d.) and fold difference in engraftment (e.) for male and female NSG recipients transplanted with a dose equivalent of a single HSC according to LDA analyses in Fig. 1f. f. Fold difference in engraftment between male and female recipients between limiting and non-limiting HSC doses for the IF, BM, SP and TH. g. Representative flow cytometric analysis of a simultaneous secondary transplant into a single male and female donor from a female that was transplanted with sorted Lin-CB. h. Reanalysis of HSC frequency for CD90+ (left, n=24f, 19m) and CD90- (right, n= 19f, 4m) fractions from Fig. 1f according to sex of the recipient. i. Summary of HSC frequency for CD90+ and CD90- fractions according to sex of the recipient. (bars represent mean. *P < 0.05, ***P < 0.001).
52
Figure 2-3. Human HSCs within CD90+ compartment can be further enriched with Rhodamine or CD49f. CD90+ cells were further subfractionated into Rholo (cell dose =10, n=8) and Rhohi (cell dose=25, n=4) fractions and transplanted intrafemorally into sublethally irradiated female NSG mice (a.). Mean engraftment levels in IF, BM, SP and TH (c.) and fold difference in engraftment between Rholo and Rhohi mice (e.). 5 of 8 Rholo and 3 of 4 Rhohi mice transplanted were engrafted at the doses indicated. Limiting dilution analyses indicates that 1 in 10.2 in –CD90+Rholo cells represents an HSC (f.). (b,d,g) Normalized mean fluorescence intensity (MFI) for indicated candidate HSC markers between –CD90+ and –CD90- cells (b. left panel). Integrin a6 or CD49f levels are 2- fold higher in the –CD90+ fraction (b., right and left panel). Sorting scheme for fractionation of –CD90+ cells into CD49flo, CD49fint, and CD49fhi cells (d.). Highly purified sorted fractions were transplanted at a 25 cell dose into sublethally irradiated female recipients. Mean human engraftment levels between CD49flo, CD49fint, and CD49fhi fractions (g.).
53
Figure 2-4. Single CD90+RholoCD4 9f+ human HSCs can functionally repopulate NSG recipients. a. Single – CD90+RholoCD49f+ were flow sorted and transplanted according to the scheme shown. b. Mice were sacrificed 18wk post transplant and analyzed for human cells by using 2 non-competing human CD45 clones, CD19 (B-cell), and CD33 (myeloid). 5 of 29 mice (m5,11,12,24,27) generated a detectable human graft (row 1,2). 1 of 5 and 4 of 5 mice generated a myeloid only and lympho- myeloid graft (row 3), respectively. Appropriate controls are shown in column 1 and 2. c. Representative flow cytometric analysis of human hematopoietic cells in the spleen and thymus (CD4, CD8 – T cell) of mice transplanted with single –CD90+RholoCD49flo/int cells.
54
Supplementary Figure 2-1. Accurate detection of low level of human engraftment in NSG mice. Bone marrow from the injected femur or non-injected bones was stained with two separate human CD45 clones (clone J.33 – Coulter, clone H130 – BD) and analyzed by flow cytometry. Costaining with human specific CD19 and/or CD33 verified the human lineage being detected.
Supplementary Figure 2-2. Cell cycle analysis of various human HSC and progenitor fractions.
55
+ - lo/- + + + a. –CD90 , –CD90 , –CD90 CD45RA and CD34 CD38 cells were sorted and processed for cell cycle (Go,
G1 and G2SM) analysis using Ki-67 and 7-AAD.
Supplementary Figure 2-3. Integrin expression profiling of –CD90+ and –CD90- cells. Mean fluorescence intensity (MFI) of –CD90+ and –CD90- cells for various markers was assessed by flow cytometry.
Supplementary Figure 2-4. Analysis of engraftment after transplantation of human CMPs. Human CMPs (Lin-CD34+CD38+CD135+CD45RA-CD7-CD10-) were sorted and transplanted intrafemorally into immune-deficient recipients. Mice were sacrificed 2-4wk post transplanted and analyzed for human cells in the IF, BM, SP and TH. Representative flow cytometric analysis of an engrafted mice is shown above.
56
Supplementary Figure 2-5. Transplantation of single human HSCs. Analysis of human engraftment in the injected femur (IF) and non-injected bones (BM) of single – CD90+RholoCD49fint/hi cells. Flow cytometric analysis of all engrafted mice is shown. Mice were sacrificed 18wk post transplant and analyzed for human cells by using 2 non-competing human CD45 clones, CD19 (B- cell), and CD33 (myeloid).
3. Macrophage and dendritic cell lineages remain entangled in early human lymphoid development: a revised map of the human progenitor hierarchy
The work presented here is currently in submission for publication:
Doulatov, S.*, Notta, FN.*, Nguyen, L., Ohashi, PS., & Dick, J.E. Macrophage and dendritic cell lineages remain entangled in early human lymphoid development: a revised map of the human progenitor hierarchy. Science
* These authors contributed equally to this work
Author Contributions / Acknowledgements:
We thank K. Moore and the obstetrics unit of Trillium Hospital (Mississauga, Ontario) for providing cord blood samples and co-op students for processing cord blood samples; S.
Zhao, P.A. Penttilä, L. Jamieson at the UHN/SickKids Flow Cytometry Facility for sorting; and the Dr. J.E. Dick lab and Dr. N. Iscove for critical review of the manuscript. This work was supported by funds from Canadian Institutes for Health Research (CIHR) studentships
(FN, SD), The Stem Cell Network of Canadian National Centres of Excellence; the Canadian
Cancer Society and the Terry Fox Foundation; Genome Canada through the Ontario
Genomics Institute; Ontario Institute for Cancer Research with funds from the province of
Ontario; the Leukemia and Lymphoma Society; the Canadian Institutes for Health Research; and a Canada Research Chair.
58
3.1 Abstract
The classical model of hematopoiesis posits the segregation of lymphoid and myeloid lineages as the earliest fate decision. Although the validity of this model in the mouse has recently been questioned, its status in human hematopoiesis is unclear, since little is known concerning lineage potential of human progenitors at the clonal level. We isolated and clonally mapped the developmental potential of seven distinct progenitor classes from neonatal cord blood and adult bone marrow based on a panel of 7 markers providing a comprehensive analysis of the human hematopoietic hierarchy. Human multi-lymphoid progenitors (MLPs) were identified as a distinct population of Thy1-CD45RA+ cells within the CD34+CD38- stem cell compartment. Whereas human myeloid development followed the classical pattern of lineage restriction, MLPs gave rise to all lymphoid cell types, as well as macrophages - a myeloid cell type, and dendritic cells (DCs), indicating that these lineages remain entangled in lymphoid lineage specification. Thus, as in the mouse, human lymphoid development does not follow a rigid model of myeloid-lymphoid segregation. We further demonstrate that MLPs represent a source of autologous T-cells and DCs. The prospective isolation and elucidation of clonal lineage potential of human progenitors provides the basis for the development of novel cellular therapeutics and a powerful means to uncover the cellular and molecular regulators that govern lineage commitment.
59
3.2 Introduction
The popular ‘classical model’ of hematopoiesis postulates that the earliest fate decision involves the segregation of lymphoid and myeloid lineages into their common progenitors
(CLPs and CMPs, respectively) (1, 2). However, the recent identification of lymphoid- primed multipotent progenitors (LMPPs) suggests that erythroid and megakaryocytic (E-
MK) lineages branch off prior to the lymphoid-myeloid split (3-5). Moreover, a number of multi-lymphoid progenitors (MLPs; a cell minimally capable of giving rise to B-, T-, and natural killer (NK) cells, without complete lymphoid restriction) have been isolated in mice.
These include the LMPP, early lymphoid progenitor (ELP), CLP, CLP-2, and earliest thymic progenitor (ETP), which differ in the extent of their lymphoid restriction and retention of myeloid lineage potential (6-9). There is increasing consensus for revision of the classical model to account for this evidence (4, 10).
Definitive evidence for the model that best fits human hematopoiesis is still lacking.
Reports identifying human CMP and CLP analogs in neonatal cord blood (CB) and adult bone marrow (BM) support the assumption that the classical model is the most accurate (11-
14). However, the identity of human MLPs remains controversial. In BM, CD34+ CD10+ cells gave rise to B- and NK cells, but not myeloid or erythroid progeny (11). The CD34+
CD10+ fraction is highly heterogeneous containing abundant CD19+ pro-B cells. More recently, another report showed that CD34+ CD10+ cells depleted of CD24+ B-cells had CLP potential in CB and BM (14). However, other reports suggested that CB MLPs resided in the
CD38- CD7+, rather than the CD10+, fraction (12, 15, 16). Progress to understand the
60 organization of the human progenitor hierarchy has been impaired by the absence of assays that efficiently detect multi-lineage output from single cells, discordant sorting strategies, and the limited number of markers used to distinguish pure populations in previous studies. To characterize the fate outcomes of the cell types that comprise the earliest steps of human hematopoietic development, we isolated seven progenitor classes from CB and BM and interrogated their developmental potential using clonal analysis under conditions that supported multiple lineage fates. By assembling such a comprehensive ‘roadmap’, we identified human MLPs as a distinct Thy1–CD45RA+ population within the CD34+CD38– stem cell compartment and demonstrated that MLPs generate macrophages and dendritic cells through a common downstream progenitor.
3.3 Materials and Methods
3.3.1 Cell isolation and sorting
CB samples were obtained according to the procedures approved by the institutional review boards of the University Health Network and Trillium Hospital. Lineage-depleted (Lin-) CB cells were purified by negative selection using the StemSep Human Progenitor Cell
Enrichment Kit according to the manufacturer’s protocol (StemCell Technologies). CD34+- selected BM and mPB cells were obtained from Lonza. Lin– cells were thawed and stained at
1 x 106 cells/100µl with CD45RA FITC (4 µl), CD135 PE (8 µl), CD7 PE-Cy5 (Coulter; 2
µl), CD10 APC (4 µl), CD38 PE-Cy7 (3 µl), CD34 APC-Cy7 (4 µl), and CD90 Biotin (4 µl)
(+Qdot 605 2°; 2 µl). Cells were flow sorted (1-8 cells/well, in single cell or limiting dilution format) directly into 96-well plates pre-seeded with stroma by a single cell deposition unit
61 coupled to BD FACSAria sorter, providing the indicated number of cells in 88% of wells, as assessed by counting the number of cells deposited into empty wells after single cell sorting
(fig S1). The purity of single cell sorting was routinely assessed by recovering sorted cells and found to be >99%. All antibodies from BD, unless stated.
3.3.2 Clonal assays
MS-5 stroma was seeded in 96-well plates (Nunc) coated with 0.2% gelatin at 5 x 103 cells/well in H5100 media (StemCell Technologies) plus cytokines (in ng/ml): SCF (100),
IL-7 (20), TPO (50), IL-2 (10), and in some experiments: GM-CSF (20), G-CSF (20), and M-
CSF (10). All cytokines from R&D. After 24 – 48 hrs, single sorted progenitor cells were sorted onto stromal monolayers. For co-culture experiments, MS-5 and MS-5/DL4 were mixed at 4:1 ratio and cultured with SCF, IL-7, TPO, FLT3 (10), and GM-CSF. MS-5 cultures were maintained for 4 wks with weekly ½ media changes. Wells were resuspended by physical dissociation, filtered through Nytex membrane, stained with: CD45, CD19,
CD14, CD15, CD33, CD56, CD33, and analyzed by high-throughput flow cytometry. DL4 co-cultures were analyzed with CD5, CD7, CD33, CD11b and CD19. OP9 stroma was seeded in 96-well plates (Nunc) at 5 x 103 cells/well in αmem (Gibco) with 20% FBS. Sorted progenitors were expanded for 9 days with SCF (100), TPO (50), IL-7 (10), FLT3 (10), then differentiated into DCs with GM-CSF (50) and IL-4 (20), or macrophages with M-CSF (20) and IL-6 (20), or a combination of these cytokines, for 7 days. OP9-DL1 stroma was seeded in 96-well plates at 5 x 103 cells/well in αmem (Gibco), 20% FBS (previously tested for T- cell support), plus FLT-3 (5) and IL-7 (5). Cells were transferred onto fresh stroma 2x a week, or as needed and analyzed for T-cell proliferation after 7-8 wks with CD45, CD3,
CD5, CD7, CD4, CD8. Clones were required to have >20 CD45+ gated events (of indicated
62 cell-surface phenotypes) to be scored as positive. MC cultures were prepared as described
(13).
3.3.3 Quantitative PCR
RNA was extracted from ~2 x 104 sorted progenitors using Trizol reagent (Invitrogen),
DNAse I-treated, and reverse transcribed with SuperScript II (Invitrogen). Real-time PCR reactions were prepared using the SYBR Green PCR Master Mix (Applied Biosystems), 200 nM primers (Qiagen), and >20 ng cDNA. Reactions were performed in triplicate on Applied
Biosystems 7900HT. Gene expression was quantified using the SDS software (Applied
Biosystems) based on the standard curve method.
3.3.4 Microarray analysis
Total RNA extracted from 5-10 x 103 cells from HSC, MLP, CMP, GMP and MEP populations (Table 1) using Trizol (Invitrogen) was amplified, hybridized to Illumina HT-12 microarrays, and analyzed using GeneSpring GX 10.0.2 software (Agilent Technologies) after quantile normalization. Differentially expressed probes were determined using ANOVA analysis followed by Benjamini Hochberg FDR correction (0.05). MLP-specific gene expression signature was generated from probes showing MLP > MEP expression pattern, after an initial filter for probes differentially expressed at least 2-fold between any two populations, except between HSC and MPP. Cluster analysis was performed with MeV.
3.3.5 Mouse transplantation
NOD/LtSz-scidIL2Rgnull (NSG) (Jackson Laboratory) were bred and housed at the
TMDT/UHN animal care facility. Animal experiments were performed in accordance to institutional guidelines approved by UHN Animal care committee. Mice were sublethally
63 irradiated (200 - 250cGy) 24h before transplant. Cells were transplanted intrafemorally into anesthetized mice, as previously described. Briefly, a 27g needle was used to drill the right femur, and cells were transplanted in a 25µL volume using an 28.5g insulin needle. Mice were sacrificed after 2 and 4 wks for progenitor, or 10 wks for HSC, analysis. Marrow was isolated by flushing bone cavities with 2 mL IMDM, and 100 µL stained for surface markers:
CD45, CD19, CD33, CD14, CD15, CD56. For analysis of HSC-derived hierarchy, human progenitors were isolated from pooled bone marrow using the Mouse/Human Chimera Enrichment Kit (StemCell Technologies) according to the manufacturer’s protocol, with the addition of 100 µL/mL StemSep Human Hematopoietic Progenitor Enrichment Cocktail
(StemCell Technologies) and the anti-biotin antibody.
3.3.6 Dendritic cell cultures
OP9 stroma was seeded in 6-well plates at 1 x 106 cells/well in αmem, 20% FBS, plus SCF
(100), FLT-3 (100), TPO (50), and IL-7 (20). Human progenitors were sorted from CB, BM or mPB and seeded on OP9 stroma at 100 - 1,000 cells/well. Cultures were carried for 2 wks, with bi-weekly ½ media change. Wells were resuspended by physical dissociation, Nytex- filtered, and CD45+ cells sorted into suspension cultures with αmem, 20% FBS, plus GM-
CSF (50) and IL-4 (20). Cultures were carried for 5 d with 1x media change. Cells were harvested and 2 x 105 cells/well matured in RPMI, 2% human serum, L-glutamine, plus TLR ligands for a total of 24 hrs. IFN/LPS: IFNγ (1000 U) 4 h, LPS (10) 20 h; LPS (10);
TNF/IL1b: TNFa (10), IL-1b (10), IL-6 (1000 IU), PGE2 (10 µM); poly I:C (10,000); CpG
(10 µM); Imiquimod (1,000); LTA (1,000); IFN/LTA: IFNγ (1000 U) 4 h, LTA (1,000) 20 h.
Cells were stained with CD14, CD80, CD86, CD83, CD40 or CD14, HLA-DR, CD11c,
64
CD1a, CD11b and analyzed by FACS; all antibodies from BD. Cytokine secretion was measured by ELISA as described.
3.3.7 Statistics
Limiting dilution data is represented as the estimated limiting dilution frequency ± lower and upper limits of the 95% confidence interval. Limiting dilution analysis was performed using the software by WEHI bioinformatics (http://bioinf.wehi.edu.au/software/elda/index.html,
Hu Y. and Smyth G. (2009), ELDA: Limiting dilution analysis for comparing depleted and enriched populations, Walter and Eliza Hall Institute of Medical Research, Australia).
3.4 Results
3.4.1 Isolation and clonal assays of human progenitors
To investigate the ‘architecture’ of the human progenitor hierarchy, we began by isolating progenitor (CD34+) fractions based on the expression of CD45RA (RA), CD135 (FLT3),
CD7, CD10, CD38 and CD90 (Thy1). Our studies established that this combination provided a meaningful separation of human progenitors into functionally distinct subsets. Staining of lineage-depleted (Lin-) or CD34+-selected neonatal CB and adult BM samples with this marker panel revealed 7 distinct populations (labeled Fractions A-G) (Fig. 1; Table 1).
The shortcomings of previous approaches in part were due to the lack of assay systems to efficiently detect lymphoid and myeloid lineages from single human cells.
Murine MS-5 stromal cells supported the development of human myeloid, B-cell, natural killer (NK), and mixed colonies, in the presence of SCF, TPO, IL-7 and IL-2 (Fig. 2A) (17).
We also employed the OP9-DL1 stromal assay to detect T-cell potential (18), and
65 conventional colony assays (MC) for myeloid and E-MK lineages. Evidence of lineage fate potential of any purified population is only definitive when assessment occurs at the level of single cells. Thus, we used limiting dilution analysis or deposition of single cells (which resulted in comparable estimates of clonogenic potential, see Fig. 2B and S2) to establish clonal read-out of lineage potential (see fig. S1 for assessment of single cell sorting efficiency).
3.4.2 Human myeloid progenitor series
In our analysis of lineage potential on MS-5 stroma, progenitor fractions D and E (Table 1) gave rise exclusively to myeloid, but not B or NK colonies (Fig. 2, B and C, and table S1; cloning efficiency = 29% (fraction D, CB), 54% (D, BM), 44% (E, CB), 29% (E, BM).
These fractions also had no T-cell potential with the exception of fraction E from CB (Fig.
2D). Both D and E fractions gave rise to myeloid colonies in MC, and D also generated erythroid and myelo-erythroid colonies consistent with a common progenitor of myeloid lineages (CMP; Fig. 2E). By contrast, erythroid colonies were never observed from fraction
E cells consistent with a more restricted progenitor of granulocyte and monocyte lineages
(GMP; Fig. 2E). It remains unclear why GMPs in CB retained significant T-cell potential, however a similar finding has been reported recently in the mouse (19). CMPs from CB, but not BM, possessed serial replating potential, albeit with less capacity than multipotent cells
(fig. S3). In contrast to the Flt3+ fractions, fraction F cells produced no colonies in the MS-5 or OP9-DL1 assays (Fig. 2, B to D), but gave rise to erythroid colonies, with no detectable myeloid potential, consistent with a restricted E-MK progenitor (MEP; Fig. 2E). These results establish the identity of key myeloid progenitor types and indicate that myeloid
66 commitment in human hematopoiesis proceeds along a developmental path consistent with the classical model.
3.4.3 Identification of human multi-lymphoid progenitors
Previous reports of human multi-lymphoid progenitors (MLPs) placed them in the
CD34+CD10+CD24– or the CD34+CD38–CD7+ fractions (11, 12). To refine this analysis, we assayed the developmental potential of all fractions expressing lymphoid markers CD7 or
CD10. CD10 was expressed by two populations – a subset of CD34+CD38+ cells (fraction G) and a distinct population of Thy1–RA+ cells within the CD34+CD38– HSC compartment (Fig.
1; Table 1). Fraction G cells gave rise to B or NK, but not mixed B-NK, colonies on MS-5 stroma, and lacked appreciable myeloid potential (Fig. 2, B, C and E; table S2; cloning efficiency = 22% CB, 11% BM). This fraction also had no detectable T-cell potential (Fig.
2D) indicating that these cells were precursors of B- and NK cells, but not MLPs.
We next tested the lymphoid potential of Thy1–RA+ cells within the CD34+CD38– compartment. In CB, these cells expressed CD10 and could be sub-divided into CD7–
(fraction B) and CD7+ (fraction C) populations; by contrast, BM cells were CD7– (Fig. 1).
These cells were relatively rare comprising ~1-2% of Lin- CB, and their frequency was unchanged in BM. In limiting dilution and single cell plating on MS-5 stroma, every colony generated by fraction B cells from CB contained lymphoid (B, NK, B-NK) cells, and 57% of colonies also contained CD33+CD11b+ myeloid cells (Fig. 2B; Table 2; cloning efficiency =
19% CB, 27% BM). However, these cells never produced myeloid colonies without lymphoid progeny. Similar results were obtained with fraction B cells from BM (Fig. 2C;
Table 2). Notably, fraction B cells isolated from CB or BM displayed robust T-cell potential, with all of T-lineage potential in the BM residing in this fraction (Fig. 2D). Thus, these cells
67 could be identified as MLPs that were not restricted to the lymphoid lineages, and hence they could not be defined as CLPs, which are expected to be lymphoid-restricted.
To assess the myeloid potential of human MLPs we used colony assays. CB and BM
MLPs gave rise to macrophage (Mφ) CFU-M, independently established on the basis of their
CD14+CD11b+ phenotype and cell morphology (Fig. 2E). No granulocytic CFU-G colonies arose from MLPs. Since GMPs always gave rise to a mixture of CFU-G and CFU-M under the same conditions (Fig. 2E), we can conclude that MLPs retain only macrophage potential.
While only 10% of freshly sorted CB MLPs formed colonies, CFU efficiency could be dramatically increased by pre-culturing them on OP9 stroma. After 4 d of OP9 pre-culture,
50% of MLPs generated CFU-M colonies, comparable to Thy1+ HSCs (Fig. 2E, right panel).
MLP-derived colonies could not be replated indicating that MLPs do not possess self- renewal capacity (fig. S3). Thus, single MLPs gave rise to B-, T-, NK cells and Mφ, but lacked granulocytic or erythroid lineage potential.
We next tested the developmental potential CD7+ cells within the CD34+CD38–
Thy1–RA+ compartment (fraction C) that were previously proposed to be hCLPs in CB (12).
Surprisingly, their lineage output was identical to the CD7– MLPs, albeit at a lower cloning efficiency, with a similar proportion of lymphoid and lympho-myeloid colonies (Fig. 2B;
Table 2; cloning efficiency = 13%). Fraction C MLPs did not give rise to MC colonies (Fig.
2E) indicating that the standard colony assays may underestimate myeloid potential and providing an explanation as to why it was not detected in prior reports (12). Thus, Thy1–RA+ cells within the CD34+CD38– compartment can be identified as MLPs irrespective of their
CD7 expression.
68
3.4.4 Analysis of progenitors generated de novo from HSCs
To determine if the progenitor classes we identified were generated de novo from HSCs, we analyzed the composition of the progenitor compartment in NOD/SCIDγcNull (NSG) mice stably repopulated by CB HSCs. Each of the 7 progenitor fractions identified in CB and BM including the CD34+CD38–Thy1–RA+ MLPs were faithfully reconstituted by transplanted
HSCs (Fig. 2F). Moreover, the developmental potential of each fraction isolated from NSG mice was identical to those in CB or BM, as determined by clonal analysis on MS-5 stroma.
In particular, as for CB MLPs (compare to Fig. 2C), every colony generated by CD7+ and
CD7– MLPs contained lymphoid progeny, and 70% of colonies also contained myeloid cells
(45% and 34% cloning efficiencies, respectively; Fig. 2F and table S3). These results indicate that MLPs and other progenitors isolated from steady-state CB and BM are intrinsic components of the human hematopoietic tree derived from HSCs.
3.4.5 Characterization of the developmental potential of MLPs
Taken together, these results suggest that MLPs possess lymphoid and myelo-monocytic potential. However due to the inability to read-out T-cell potential in the same assay as the other lineages we could not rule out the possibility that T-cells are produced from a different precursor in the MLP fraction. To address this possibility, we developed a co-culture system in which MS-5 transduced with the Delta-like 4 gene were mixed with untransduced MS-5 cells enabling T-cell and myeloid development in a single well. Single MLPs gave rise to
CD5+CD7+ T-cell and mixed, but not CD33+CD11b+ myeloid, colonies (Fig. 3A). By contrast, CMPs from CB or BM generated only myeloid colonies under the same conditions
(Fig. 3A). These data confirm that MLPs can give rise both T-cell and myeloid lineages.
69
The discovery of myeloid potential in human MLPs was unexpected, and so we undertook a more rigorous analysis of their lineage potential to confirm this finding. We considered whether the fact that only 57% of MLP colonies exhibited bi-potent myelo- lymphoid potential could be due to inadequate myeloid support in our standard MS-5 assays.
Consistent with this idea, we observed small numbers of myeloid cells, below our detection cut-off of 20 cells, in most MLP wells categorized as ‘lymphoid’ (data not shown). To determine whether we could improve the detection of myeloid maturation, we cultured single
MLPs on MS-5 in the presence of myeloid cytokines, G-CSF and GM-CSF. Clonal efficiency was dramatically improved under these conditions, with 29% of CD7– and 21% of
CD7+ CB MLPs giving rise to colonies (Fig 3B). Inclusion of a monocyte-specific cytokine,
M-CSF, augmented cloning efficiency to 44% indicating that we could detect output from nearly half of sorted cells (Fig. 3B). However, taking into account the 77% maximum efficiency of detection for our single cell sorting protocol (fig. S1), the above data suggests that we are actually detecting lineage output from over half of successfully seeded wells.
Lymphoid cells were found in virtually all positive wells indicating that myeloid cytokines did not exert instructive effects on lineage commitment. Notably, 85% of positive wells also contained CD14+CD11b+ Mφ (Fig. 3B). Since a high proportion of human MLPs possess lympho-monocytic potential, these results suggest that their myeloid potential is not attributable to developmental plasticity, by contrast with murine CLPs which retain a residual myeloid potential (20). Importantly, none of the fractions we characterized had lineage potential consistent with a CLP; rather all progenitors with multi-lymphoid output also retained macrophage lineage potential.
70
Under certain conditions, the precursors of macrophages can differentiate into potent antigen-presenting dendritic cells (DCs) (21-23). We tested whether MLPs can differentiate into macrophages and DCs via a common downstream intermediate (such as a macrophage-
DC progenitor, or MDP (23)). To investigate this possibility, we seeded single CB MLPs on
OP9 stroma which strongly promotes myeloid, but not B- or T-cell, lineages at a clonal level.
Single cells were first expanded into colonies with ‘primitive-acting’ cytokines and then matured into macrophages with M-CSF + IL-6 and DCs using GM-CSF + IL-4 (see
Methods). As expected, M-CSF cultures were largely composed of CD14+CD11c+CD1a–
Mφ, whereas the IL-4 cultures contained CD14–CD11c+CD1a+ immature DCs (Fig. 3C, right panel). Over 45% of single CD7- CB MLPs cultured in M-CSF, GM-CSF, IL-6 and IL-4 gave rise to colonies, consistent with the myeloid potential from MPPs and CMP/GMPs (Fig.
3C, left panel). Of these, 78% contained both Mφ and DC progeny (Fig. 3C). Taken together, these data suggest that CD34+CD38–Thy1–RA+ cells are MLPs with a combined Mφ and DC potential.
To determine the lineage potential of MLPs in vivo, we injected a near-limiting dose of 1,000 CB MLPs or CMPs directly into the femur of NSG mice and analyzed the composition of the graft after 2 and 4 wks. CMPs gave rise to CD33+CD19– myeloid grafts at
2 wks in all recipients tested (Fig 3D). However, by 4 wks the remaining myeloid cells were at or below the limit of detection (0.01%; data not shown). These data indicated that the myeloid output of progenitors in NSG mice peaks at 2 wks and declines thereafter.
Transplanted CB MLPs (n = 4) gave rise to grafts in the injected femur containing both
CD19+ B-cells and CD33+ myeloid cells at 2 wks (Fig. 3D). The myeloid graft was substantially reduced at 4 wks, consistent with the kinetics of myeloid output (data not
71 shown). No T-cells were detected, since MLPs only generated a transient graft in the injected femur, and T-cell development requires long-term engraftment (Notta et al. manuscript in preparation). Notably, of the MLP-derived myeloid cells, we detected CD14+ monocytes, but not CD15+ granulocytes (data not shown). These data indicate that MLPs possess a bi-potent lympho-monocytic potential in vivo.
3.4.6 Gene expression profile of human progenitors
To investigate the transcriptional program that underlies human progenitor development, we performed quantitative PCR (qPCR) for lineage-specific markers (Fig. 3E); expression detection in uncommitted progenitors would be indicative of their lineage potential as a result of lineage promiscuity (24). SPI1 and CEBPA, which encode early myeloid transcription factors PU.1 and C/EBPa, were expressed in myeloid progenitors and also in MLPs. By contrast, the enzyme myeloperoxidase (MPO) produced by mature myeloid cells was only detected in GMPs. GATA-1, an erythroid master regulator, was selectively expressed in
MEPs. Lastly, the key lymphoid transcription factors, PAX5 and GATA-3, were selectively expressed in MLPs (Fig. 3E). Thus, the expression of lineage markers in progenitors correlated with their functional potential providing an independent line of evidence to support the hierarchical organization we propose. This conclusion was further supported by gene expression profiling. MLPs differentially expressed a distinct set of annotated lymphoid genes as compared to multipotent (HSC/MPP, p = 3.2 x 10-5), myeloid (CMP; p = 3.9 x 10-7), and erythroid (MEP; p = 5.8 x 10-11) progenitors (table S4). This gene signature included
LY96, SYK, LTB, MIST, MHC class I and II and Ig loci. To obtain a signature of lineage- specific gene expression in MLPs, we used MEPs as a reference population for MLP- enriched gene set, excluding stem cell-specific transcripts. The resulting set of 392 genes
72 displayed two distinct expression patterns (fig. S4). A set of MLP-specific genes included
LY96, SYK, LTB, MIST, LST1, MHC loci, transcription factors BCL6, BCL11A, NOTCH3 associated with lymphoid development, and monocyte transcription factor MAF. A distinct cluster expressed by MLPs, GMPs, and CMPs, but not MPPs or MEPs, indicated a shared expression pattern between myeloid progenitors and MLPs. This set included myeloid transcription factors CEBPA, SPI1 (shown in Fig. 3E), and genes associated with innate immunity: IFITM1, LILRA2, INFGR1, CLEC4A, ITGB2, CCL3, and transcription factors
IRF7 and IRF8 critical for development of DCs and monocytes (fig. S4). These results suggest that MLPs initiate priming of lymphoid transcripts, but maintain a shared expression signature with myeloid progenitors.
3.4.7 Expansion and differentiation of MLPs into DCs
There is a great need to develop ex vivo culture systems to expand DCs, since the inability to obtain high enough numbers of DCs limits the effectiveness of this potentially powerful vaccination system. To investigate the potential of MLPs to mature into DCs, we developed a method to generate large quantities of DCs from human progenitors. MLPs or GMPs sorted from CB or BM were first expanded on OP9 stroma, differentiated into immature DCs with
GM-CSF + IL-4, and matured by exposure to Toll-like receptor (TLR) ligands (22) (see
Methods). These DCs were compared to ‘standard’ DCs derived from CD14+ peripheral blood monocytes (PBMs). Mature CD14– DCs that upregulated MHC class II, CD40, maturation marker CD83 and co-stimulatory molecules CD80 and CD86, were readily produced from MLPs in a TLR-dependent manner (Fig. 4A). PBMs and MLPs generally yielded the highest proportion of mature DCs when the TLR4 ligand LPS was used with
IFNγ yielding cultures composed of >70% mature DCs (Fig. 4B). Using our OP9 protocol, a
73 single BM MLP generated ~0.5 x 104 DCs, and a CB MLP yielded >104 DCs (Fig. 4C). By contrast, previous approaches that use bulk CD34+ cells generate comparable total cell numbers, but the resultant culture is heterogeneous with a small proportion of mature DCs
(25). DCs from all fractions secreted IL-12, important for activation of cytotoxic T-cells
(CTL) (26), as well as IL-6 and TNFα, but not IL-10 which impairs CTL response (27) (Fig.
4D; fig. S5). DCs could also be produced from patient mobilized peripheral blood (mPB)
MLPs (data not shown). Thus, MLPs are a source of mature DCs and may be suitable for large-scale immune therapy applications.
74
3.5 Tables and Figures
Figure 3-1. The comprehensive sorting scheme for CB and BM progenitors. Human bone marrow mononuclear cells were lineage-depleted and stained with antibodies against CD34, CD38, Thy1, RA, CD7, CD10 and Flt3. Each fraction is denominated A-J, corresponding to the nomenclature used in Table 1. The CD34+CD38– population was separated using Thy1 and RA, revealing Thy1+RA- HSCs, Thy1–RA– (Fraction A), and Thy1–RA+ fractions. The latter was subgated on CD7 and CD10 (Fractions B-E). The CD34+CD38+ population was depleted of CD7+ T-cells (Fraction J), separated on Flt3 and RA, revealing Flt3+RA– (Fraction F), Flt3–RA– (Fraction H), and Flt3+RA+ fractions, and the latter was subgated on CD10 (Fractions I-G).
75
76
Figure 3-2. Clonal analysis of candidate CB and BM progenitor fractions. (A) Colony types formed by single CD34+CD38–Thy1–RA– MPPs (fraction A) cultured for 4 wks on MS-5 stroma with SCF, TPO, IL-7, and IL-2. The contents of single wells were stained with the antibodies for lineage markers and analyzed by flow cytometry; CD45 (pan), CD33 and CD11b (myeloid), CD19 (B-cell) and CD56
(NK cell); only CD45+-gated cells are shown. (B) Cloning efficiency of myeloid (left bar graph) and lymphoid
(right bar graph) lineages of single CB progenitors (labeled as fractions A-G; see Table 1) deposited by flow sorting onto MS-5 stroma. Myeloid colonies were identified as CD33+CD11b+ wells negative for CD19 and
CD56. Lymphoid colonies were identified as either CD19+ (B-cell) or CD56+ (NK-cell) wells also negative for myeloid markers. Cell morphology was used to validate lineage assignment (right panels, fraction B). The height of each bar indicates total cloning efficiency of which the proportion of myeloid or lymphoid colonies is filled in black. Data are shown as mean ± SEM of 3 independent cord bloods, with >12 wells for each fraction per experiment. (C) Same as (C) for single progenitors isolated from adult BM samples. (D) T-cell potential of progenitors sorted from CB (left) or BM (right) seeded at limiting dilution on OP9-DL1 stroma. Positive wells were identified after 6 wks by counting the number of wells with CD4+CD8+ T-cells. Representative FACS plots are shown on the right. Data are shown as limiting dilution frequency ± lower and upper limits of the 95% confidence interval. (E) Cloning efficiency of myeloid and erythroid lineages of single CB and BM progenitors deposited by flow sorting into wells with methylcellulose. The height of each bar indicates total colony-forming efficiency of which the proportion of each colony type is filled in color: granulocytic (CFU-G), macrophage
(CFU-M), mixed myeloid (CFU-GM), erythroid (BFU-E), and myelo-erythroid (CFU-GEMM). Middle panel:
Colonies formed by MLPs and GMPs were cytospun and stained with Giemsa stain to reveal cell morphology.
Right panel: colony-forming (CFU-M) efficiency of CB MLPs and Thy1+ HSCs cultured for 4 d on OP9 stroma. (F) Cloning efficiency of myeloid (left bar graph) and lymphoid (right bar graph) lineages of single human progenitors isolated from NSG mice transplanted with CB HSCs. After 10 wks mice were sacrificed,
Lin- cells isolated by column purification, and single cells from indicated populations (flow plots), deposited by flow sorting onto MS-5 stroma, as in (B) and (C).
77
78
Figure 3-3. Clonal analysis of human multi-lymphoid progenitors. (A) Cloning efficiency of T-cell and myeloid lineages in the MS-5 Delta-like 4 co-culture system. Single CB and BM CD34+CD38-RA+CD10+ MLPs or CMPs were deposited by flow sorting onto MS-5 DL4 stroma.
Myeloid colonies were identified as CD33+CD11b+ wells negative for CD5 or CD7. T-cell colonies were identified as CD5+CD7+ negative for myeloid markers, with representative profiles of T-cell and myeloid colonies shown. Data is pooled from at 2 independent replicates, with >12 wells each. (B) Cloning efficiency of myeloid (left panel) and lymphoid (right panel) potential of single CB progenitors deposited by flow sorting onto MS-5 stroma and cultured for 4 wks with the addition of G-CSF and GM-CSF, with or without M-CSF.
The height of each bar indicates total cloning efficiency of which the proportion of myeloid or lymphoid colonies is shown filled in black. Right panel: 3 representative MLP colonies analyzed using lineage markers
CD19 (B-cell), CD56 (NK cell), CD14 (monocyte), and CD15 (granulocyte). (C) Cloning efficiency of monocyte and DC lineage potential of single CB progenitors deposited by flow sorting onto OP9 stroma and cultured for 2 wks. The height of each bar indicates total cloning efficiency of which the proportion of colonies containing both monocytes and DCs is shaded in black. Myeloid colonies were identified as CD11b+CD11c+, which were further discriminated using CD14 (monocytes) and CD1a (DCs). Marker profiles of 4 representative MLP colonies and cell morphology of sorted Giemsa-stained CD14+ and CD1a+ cells in shown.
(D) Engraftment of NSG mice 2 wks after intra-femoral transplantation of 1,000 CB MLPs (n = 4) or CMPs (n
= 4). Graft composition was analyzed using CD45 (pan-human), CD19 (B-cell), and CD33 (pan-myeloid). (E) qPCR analysis of the expression of lineage-specific factors in human progenitors. Data is pooled from two independent experiments and plotted on a linear scale. Unless otherwise stated, all data are shown as mean ±
SEM of 3 independent cord bloods, with >12 wells for each fraction per experiment.
79
Figure 3-4. Progenitor origins of human dendritic cells. Ex vivo expansion of DCs from human MLPs. (A) Phenotypic and morphological characterization of progenitor-derived DCs. Differentiated CB MLPs and GMPs and PBMs isolated by leukopheresis were matured with IFNγ and LPS or without TLR ligands (‘No stim’). The expression of DC maturation markers CD80 and
CD83 was assessed by flow staining and cell morphology was evaluated using Giemsa staining. (B) Proportion of mature CD80+, CD83+, CD86+, CD40+ DCs in cultures of CB MLPs, GMPs, and PBMs, matured in the presence of various cytokines and TLR ligands (see Methods for reagents list). (C) Total expansion of CB- and
BM-derived MLPs and GMPs cultured using DC conditions. (D) Secretion of IL-12 (left panel) and IL-6 (right panel) by DCs produced from MLPs, GMPs, or PBMs analyzed by ELISA.
80
Table 3-1. The legend of candidate progenitor fractions.
# Phenotype Name Freq (%) Lineage output
- CD34+CD38-Thy1+CD45RA-Flt3+CD7-CD10- HSC 0.04 All*
A CD34+CD38-Thy1-CD45RA-Flt3+CD7-CD10- MPP 0.04 All* B CD34+CD38-Thy1-CD45RA+Flt3+CD7-CD10+ MLP7- 0.01 B, T, NK, MDC C CD34+CD38-Thy1-CD45RA+Flt3+CD7+CD10+ MLP7+ 0.01 B, T, NK, MDC + + - - + - - D CD34 CD38 Thy1 CD45RA Flt3 CD7 CD10 CMP 0.15 EMK, G, MDC E CD34+CD38+Thy1-CD45RA+Flt3+CD7-CD10- GMP 0.05 G, MDC
F CD34+CD38+Thy1-CD45RA-Flt3-CD7-CD10- MEP 0.30 EMK + + - + + - + G CD34 CD38 Thy1 CD45RA Flt3 CD7 CD10 B/NK 0.05 B or NK H CD34+CD38+Thy1-CD45RA+Flt3+CD7+CD10- none 0.20 Nd
The list of candidate progenitor fractions sorted from CB and BM based on the 7-color flow cytometric analysis using the indicated combinations of cell surface markers. The flow cytometric representation of these populations is shown in Fig. 1. For each fraction, the fraction # (A-H), full phenotype, functional designation, frequency (% of CB mononuclear cells), and lineage output are indicated. Legend: B, B-cell; T, T-cell; NK, natural killer cell; MDC, macrophage and dendritic cell; G, granulocyte; EMK, erythroid and megakaryocyte; nd, not detected. *Multipotency of HSC and MPP fractions has been demonstrated in vivo (Notta et al. manuscript in preparation).
81
Table 3-2. Limiting dilution analysis of candidate human MLPs.
Cells/well # wells positive wells Phenotype of cells in wells Myeloid Lymphoid Mixed Fraction B (CD34+CD38-Thy1-CD45RA+CD10+CD7-) Cord Blood 4 12 9 (75%) 1 4 4 2 36 14 (39%) 0 7 7 1 96 18 (19%) 0 6 12 Total 144 41 1 17 (43%) 23 (57%) Fraction C (CD34+CD38-Thy1-CD45RA+CD10+CD7+) Cord Blood 5 24 10 (42%) 0 4 6 2 24 4 (17%) 0 4 0 1 48 4 (8%) 0 1 3 Total 96 18 0 9 (50%) 9 (50%)
+ - - + + - Fraction B (CD34 CD38 Thy1 CD45RA CD10 CD7 ) Bone Marrow 4 12 8 (50%) 1 4 3 1 48 13 (27%) 1 6 6 Total 48 21 2 10 (48%) 9 (43%)
Limiting dilution analysis of candidate human MLP fractions on MS-5 stroma. The indicated number of cells from fractions B and C isolated from CB and BM (fraction C is not found in BM) were deposited by flow sorting into individual wells with MS-5 stroma and cultured for 4 wks with SCF, TPO, IL-7, and IL-2. Myeloid (granulocyte or monocyte), lymphoid (B- or NK cell), or mixed lympho-myeloid colonies were identified using a panel of lineage markers, as described in the text and Methods. Colony counts were pooled from 3 or more independent experiments, with 12 or more wells per fraction each. Colony types representing >80% of total output for each fraction are shaded to indicate the most likely developmental output. Cells/well, number of cells deposited into each well; # wells, total number of wells seeded; positive wells, number of wells containing human cell colonies; phenotype of cells in wells, number of wells containing >20 cells of indicated lineage. 7 3
82
Supplementary Figure 1. Single cell flow sorting analysis.
Experiment 1 Experiment 2
1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 A 1 1 1 1 1 1 1 1 1 A 1 1 1 1 1 1 1 1 B 1 1 1 1 1 1 1 1 1 B 1 1 1 1 1 1 1 1 C 1 1 1 1 1 1 1 1 1 C 1 1 1 1 1 1 1 1 1 1 D 1 1 1 1 1 1 1 1 D 1 1 1 1 1 1 1 1 1 E 1 1 1 1 1 1 1 1 E 1 1 1 1 1 1 1 1 1 F 1 1 1 1 1 1 1 1 1 F 1 1 1 1 1 1 1 1 1 1
single cell wells: 52/60 (87%) single cell wells: 54/60 (90%) Total sorting efficiency: 106/120 (88%)
Experiment 3
24 hours 1 2 3 4 5 6 7 8 9 10 11 12 A 1 1 1 1 1 1 1 2 1 1 1 B 1 1 1 1 1 1 1 1 1 1 C 1 1 1 1 1 1 1 1 1
single cell wells: 30/36
56 hours 1 2 3 4 5 6 7 8 9 10 11 12 A 0 1 1 1 1 2 2 4 2 1 1 B 2 3 2 0 1 2 0 2 2 0 C 2 2 1 1 2 1 1 2 2
0 cell wells: 4/30 (13%); 1 cell wells: 11/30 (37%); ≥2 cell wells: 15/30 (50%) Figure S1. To determine sorting efficiency in single cell sort mode, single Lin- CB cells were sorted into Terasaki plates and the presence of individual cells validated by visual examination (Experiments 1-2). Single cells were identified in 88% of wells, with the rest of wells containing no cells, and no wells containing >1 cell. This experiment demonstrates that some wells do not receive a cell limiting the theoretical detection efficiency to ≤88%. In Experiment 3, MLPs were labeled with PKH-26 tracking dye, and single MLPs were sorted on OP9 stroma in a 96-well plate and cell division tracked over time. After 24 hrs of culture 30/36 wells contained cells consistent with the efficiency of single cell sorting above. After 56 hrs, 87% of positive wells contained 1 (no cell division), or 2 or more (1+ cell division) cells. However, 13% of wells that received a cell contained no cells at 56 hrs indicating that some cells die shortly after sorting. This, in addition to sorting efficiency, further limits the maximum detection efficiency to about 77% of positive wells. Thus, the maximum clonogenic efficiency for any fraction sorted at single cell level to about 77%.
83
Supplementary Figure 2. Limiting dilution analysis of CB progenitors.
Figure S2. Limiting numbers of progenitors were deposited by single cell sorting on MS-5 stroma and cultured for 4 wks with SCF, TPO, IL-7, IL-2. Positive wells were identified as containing >20 CD45+ human cells. Data are shown as limiting dilution frequency ± lower and upper limits of the 95% confidence interval. The complete dataset is shown in Table 2 and S1-S3. There is a high degree of concordance between limiting dilution and single cell analyses (compare to Fig. 2B) for each fraction indicating that lineage potentials are a linear function of cell number.
Supplementary Figure 3. Secondary colony-forming potential.
Figure S3. Secondary colony-forming efficiency of CB and BM progenitors. Single progenitor cells from the indicated fractions were deposited by flow sorting into 96-well methylcellulose colony assays. Individual colonies that arose after 2 wks were replated into secondary methycellulose cultures and the proportion of primary colonies that retained clonogenic potential scored
84
Supplementary Figure 4. Lineage-specific gene expression of MLPs.
-2.5 2.5
Myeloid cluster (MLPs, GMPs, CMPs): Myeloid cluster (MLPs and GMPs): Gene Gene name Description Description name SPI1 PU.1 transcription factor CEBPA C/EBPα transcription factor IRF8 Interferon response factor 8 IRF7 Interferon response factor 7 INFGR1 IFNγ receptor alpha chain CSF2RA GM-CSF receptor alpha MAP3K1 MEK kinase 1 LY86 Lymphocyte antigen 86 PIK3R1 PI3-kinase p85 subunit CLEC4A C-type lectin family 4A RUNX2 AML2 transcription factor CCL3 MIP 1-alpha chemokine LILRA2 Ig-like receptor A2 PRAM1 PML-RARα adaptor IFI6 IFNα-inducible 6 HCK Tyrosine kinase HCK ITGAL Integrin alpha-L, CD11a ITGB2 Complement receptor C3 KBTBD11 Kelch and BTB domain 11 GAS7 Growth arrest-specific 7 CD99 CD99 antigen TNFRSF1B p80 TNFα receptor LAT2 Linker for activation of T-cell ADA Adenosine deaminase
MLP-specific cluster (MLPs only): (excluding MHC and Ig components) Gene name Description Gene name Description
ETS1 ETS oncogene LIME1 LCK intertacting adaptor EPHB6 Ephrin type B receptor CXCL16 CXCL16 chemokine LY96 Lymphocyte antigen 96 BCL6 B-cell lymphoma 6 LTB Lymphotoxin beta SYK Tyrosine kinase SYK MAF c-MAF transcription factor ZAP70 SYK-related kinase NOTCH3 Notch 3 receptor LST1 Leukocyte-specific transcript 1 BCL11A B-cell lymphoma 11A CD79A B-cell receptor associated CDKN2D P19-INK4d MIST Cytokine-dependent cell linker
Figure S4. Lineage-specific gene expression in MLPs. Since MLPs do not display erythroid potential, MEPs were chosen as a reference population to subtract hematopoietic and E-MK lineage programs. A set of 668 probes more highly expressed in MLPs (FDR < 0.05) than MEPs was generated. Stem cell genes (more highly expressed in HSCs and MPPs compared to MLPs) were removed to generate an MLP-centric lineage expression signature comprised of 451 probes (392 genes) and clustered using Spearmann rank correlation (shown in figure). This signature is composed of MLPs-specific (bottom cluster) and myeloid-shared (top cluster) transcripts. Top ranked genes from both clusters are listed on the right.
85
Supplementary Figure 5. Cytokine secretion by DCs.
Figure S5. Secretion of IL-10 (left panel) and TNFα (right panel) by DCs derived from CB and BM MLPs, CB GMPs, and PBMs on OP9 stroma (BM GMPs did not expand sufficiently to test for cytokine production; see Fig. 4C). DCs were matured in the presence of indicated TLR ligands for 24 h and cytokine secretion measured by ELISA.
Supplementary Figure 6. Proposed model of the human hematopoietic hierarchy.
Figure S6. The proposed model of human hematopoietic hierarchy. HSCs differentiate into CMPs and MLPs. CMPs are myeloid-committed and further differentiate into GMPs and MEPs. GMPs give rise to MDPs which generate monocytes, macrophages and dendritic cells, as well as myeloblasts, which produce granulocytic cells. MLPs give rise to the precursors of all the canonical lymphoid lineages: B-, T-, and NK cells, but also differentiate into monocytes, macrophages and dendritic cells, likely through a committed downstream progenitor, such as the MDP.
86
Supplementary Table 1. Limiting dilution analysis of myeloid progenitors.
cells/well # wells positive wells Phenotype of cells in wells Myeloid Lymphoid Mixed Fraction D (CD34+CD38+Flt3+CD45RA-CD10-CD7-) Cord Blood 2 36 13 (36%) 13 0 0 1 36 9 (25%) 9 0 0 Total 72 22 22 (100%) 0 0
Fraction E (CD34+CD38+Flt3+CD45RA+CD7-CD10-) Cord Blood 2 12 8 (67%) 7 1 0 1 84 36 (43%) 36 0 0 Total 96 44 43 (100%) 1 0 Fraction D (CD34+CD38+Flt3+CD45RA-CD10-CD7-) Bone Marrow 2 12 10 (83%) 10 0 0 1 36 19 (53%) 19 0 0 Total 48 29 29 (100%) 0 0 Fraction E (CD34+CD38+Flt3+CD45RA+CD7-CD10-) Bone Marrow 5 12 9 (75%) 7 0 2 1 36 10 (28%) 10 0 0 Total 48 19 17 (89%) 0 2
Limiting dilution analysis of myeloid progenitor fractions on MS-5 stroma. The indicated number of cells from fractions D and E isolated from CB and BM were deposited by flow sorting into individual wells and cultured for 4 wks with SCF, TPO, IL-7, and IL-2. Myeloid (granulocyte or monocyte), lymphoid (B-, or NK), or mixed lympho-myeloid colonies were identified using a panel of lineage markers, as described in the text and Methods. Colony counts were pooled from 3 or more independent experiments, with 12 or more wells per fraction each. Colony types representing >80% of total output for each fraction are shaded to indicate the most likely developmental output. Cells/well, number of cells deposited into each well; # wells, total number of wells seeded; positive wells, number of wells containing human cell colonies; phenotype of cells in wells, number of wells containing >20 cells of indicated lineage.
87
Supplementary Table 2. Limiting dilution analysis of progenitor fractions.
cells/well # wells positive wells Phenotype of cells in wells Myeloid Lymphoid Mixed Fraction G (CD34+CD38+Flt3+CD45RA+CD10+CD7-) Cord Blood 1 48 8 (17%) 0 7 1 Total 48 8 0 7 (87%) 1 Fraction H (CD34+CD38+Flt3+CD45RA+CD7+CD10-) Cord Blood 2 36 7 (19%) 3 3 1 Fraction G (CD34+CD38+Flt3+CD45RA+CD10+CD7-) Bone Marrow 10 12 9 (75%) 0 8 1 5 12 5 (42%) 0 5 0 1 48 1 (2%) 0 1 0 Total 72 15 0 14 (93%) 1 Fraction H (CD34+CD38+Flt3+CD45RA+CD7+CD10-) Bone Marrow 2 36 0 0 0 0
Limiting dilution analysis of progenitor fractions on MS-5 stroma. The indicated number of cells from fractions G and H isolated from CB and BM were deposited by flow sorting into individual wells and cultured for 4 wks with SCF, TPO, IL-7, and IL-2. Myeloid (granulocyte or monocyte), lymphoid (B-, or NK), or mixed lympho- myeloid colonies were identified using a panel of lineage markers, as described in the text and Methods. Colony counts were pooled from 3 or more independent experiments, with 12 or more wells per fraction each. Colony types representing >80% of total output for each fraction are shaded to indicate the most likely developmental output. Cells/well, number of cells deposited into each well; # wells, total number of wells seeded; positive wells, number of wells containing human cell colonies; phenotype of cells in wells, number of wells containing >20 cells of indicated lineage.
88
Supplementary Table 3. Limiting dilution analysis of progenitors from NSG mice.
cells/well # wells positive wells Phenotype of cells in wells Myeloid Lymphoid Mixed MLP 7- (CD34+CD38-Thy1-CD45RA+CD10-CD7-) NSG mice 2 24 17 (71%) 2 1 14 1 48 20 (42%) 1 6 13 Total 72 37 3 7 (19%) 27 (73%) MLP 7+ (CD34+CD38+Thy1-CD45RA+CD10-CD7+) NSG mice 2 24 10 (42%) 0 4 6 1 48 16 (33%) 3 4 9 Total 72 26 3 8 (31%) 15 (58%)
Human HSCs generate MLPs in vivo. NSG mice were transplanted with 100,000 Lin- CB cells and HSCs were allowed to engraft for 10 wks. Lin- human cells were extracted using column selection and progenitor fractions sorted using sorting scheme shown in Fig.1. The indicated number of cells from MLP fractions isolated from NSG mice were deposited by flow sorting into individual wells and cultured on MS-5 stroma for 4 wks with SCF, TPO, IL-7, and IL-2. Myeloid (granulocyte or monocyte), lymphoid (B- or NK cell), or mixed lympho- myeloid colonies were identified using a panel of lineage markers, as described in the text and Methods. Colony counts were pooled from 2 independent experiments. Colony types representing >80% of total output for each fraction are shaded to indicate the most likely developmental output. Cells/well, number of cells deposited into each well; # wells, total number of wells seeded; positive wells, number of wells containing human colonies; phenotype of cells in wells, number of wells containing >20 cells of indicated lineage.
89
Supplementary Table 4. Expression of lymphoid transcripts by MLPs.
Genes enriched in MLP compared with HSC/MPP (FDR < 0.05), 92 total
Gene Ontology (category 1) Match Total P-value Fold Bonferroni Benjamini FDR GO:0002376 immune system process 18 826 3.22x10-5 3.19 6.76x10-4 6.76x10-4 0.02 GO:0050896 response to stimulus 24 2580 0.089 1.36 0.861 0.482 51.6
GO:0002376 immune system process genes, 18 total:
Gene ID Gene Name 100 ADENOSINE DEAMINASE 23643 LYMPHOCYTE ANTIGEN 96 3512 IMMUNOGLOBULIN J POLYPEPTIDE 3394 INTERFERON REGULATORY FACTOR 8 3683 INTEGRIN, ALPHA L (ANTIGEN CD11A (P180) 7462 LINKER FOR ACTIVATION OF T CELLS FAMILY, MEMBER 2 9214 FAS APOPTOTIC INHIBITORY MOLECULE 3 10288 LEUKOCYTE IMMUNOGLOBULIN-LIKE RECEPTOR, SUBFAMILY B 4050 LYMPHOTOXIN BETA (TNF SUPERFAMILY, MEMBER 3) 2204 FC FRAGMENT OF IGA, RECEPTOR FOR 84106 PML-RARA REGULATED ADAPTOR MOLECULE 1 6850 SPLEEN TYROSINE KINASE 7940 LEUKOCYTE-SPECIFIC TRANSCRIPT 1 7441 PRE-B LYMPHOCYTE GENE 1 116449 MAST CELL IMMUNORECEPTOR SIGNAL TRANSDUCER 3556 INTERLEUKIN 1 RECEPTOR ACCESSORY PROTEIN 55824 PHOSPHOPROTEIN ASSOCIATED WITH GLYCOSPHINGOLIPID MICRODOMAINS 1 11027 LEUKOCYTE IMMUNOGLOBULIN-LIKE RECEPTOR, SUBFAMILY A, MEMBER 2
Genes enriched in MLP compared with CMP (FDR < 0.05), 116 total
Gene Ontology (category 1) Match Total P-value Fold Bonferroni Benjamini FDR GO:0002376 immune system process 24 826 3.90x10-7 3.38 8.19x10-6 8.19x10-6 3.01x10-4 GO:0050896 response to stimulus 33 2580 0.014 1.49 0.266 0.143 10.8
GO:0002376 immune system process genes, 24 total:
Gene ID Gene name 947 CD34 ANTIGEN 100 ADENOSINE DEAMINASE 3512 IMMUNOGLOBULIN J POLYPEPTIDE 23643 LYMPHOCYTE ANTIGEN 96 3394 INTERFERON REGULATORY FACTOR 8 3683 INTEGRIN, ALPHA L (ANTIGEN CD11A (P180) 9214 FAS APOPTOTIC INHIBITORY MOLECULE 3
90
10288 LEUKOCYTE IMMUNOGLOBULIN-LIKE RECEPTOR, SUBFAMILY B, MEMBER 2 4050 LYMPHOTOXIN BETA (TNF SUPERFAMILY, MEMBER 3) 9450 LYMPHOCYTE ANTIGEN 86 3117 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS II, DQ ALPHA 1 2204 FC FRAGMENT OF IGA, RECEPTOR FOR 84106 PML-RARA REGULATED ADAPTOR MOLECULE 1 6850 SPLEEN TYROSINE KINASE 414062 CHEMOKINE (C-C MOTIF) LIGAND 3-LIKE 1 3109 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS II, DM BETA 7940 LEUKOCYTE-SPECIFIC TRANSCRIPT 1 7441 PRE-B LYMPHOCYTE GENE 1 604 B-CELL CLL/LYMPHOMA 6 (ZINC FINGER PROTEIN 51) 116449 MAST CELL IMMUNORECEPTOR SIGNAL TRANSDUCER 2534 FYN ONCOGENE RELATED TO SRC, FGR, YES 55824 PHOSPHOPROTEIN ASSOCIATED WITH GLYCOSPHINGOLIPID MICRODOMAINS 1 11027 LEUKOCYTE IMMUNOGLOBULIN-LIKE RECEPTOR, SUBFAMILY A, MEMBER 2 6688 SPLEEN FOCUS FORMING VIRUS (SFFV) PROVIRAL INTEGRATION ONCOGENE SPI1
Genes enriched in MLP compared with MEP (FDR < 0.05), 280 total
Gene Ontology (category 1) Match Total P-value Fold Bonferroni Benjamini FDR GO:0002376 immune system process 49 826 5.75x10-11 2.85 1.21x10-9 1.21x10-9 4.44x10-8 GO:0050896 response to stimulus 82 2580 3.59x10-5 1.53 7.53x10-4 3.77x10-4 0.027 GO:0022610 biological adhesion 28 730 0.002 1.84 0.05 0.017 1.910 GO:0032502 developmental process 83 3169 0.012 1.26 0.23 0.053 9.556 GO:0051704 multi-organism process 13 303 0.024 2.06 0.40 0.081 17.19
GO:0002376 immune system process genes, 49 total:
Gene ID Gene name 1050 CCAAT/ENHANCER BINDING PROTEIN (C/EBP), ALPHA 3126 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS II, DR BETA 1 3683 INTEGRIN, ALPHA L (ANTIGEN CD11A (P180) 3105 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS I, A 84106 PML-RARA REGULATED ADAPTOR MOLECULE 1 8519 INTERFERON INDUCED TRANSMEMBRANE PROTEIN 1 (9-27) 3108 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS II, DM ALPHA 3135 HLA-G HISTOCOMPATIBILITY ANTIGEN, CLASS I, G 3109 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS II, DM BETA 7441 PRE-B LYMPHOCYTE GENE 1 3600 INTERLEUKIN 15 115361 GUANYLATE BINDING PROTEIN 4 2534 FYN ONCOGENE RELATED TO SRC, FGR, YES 3902 LYMPHOCYTE-ACTIVATION GENE 3 3512 IMMUNOGLOBULIN J POLYPEPTIDE 7462 LINKER FOR ACTIVATION OF T CELLS FAMILY, MEMBER 2 10288 LEUKOCYTE IMMUNOGLOBULIN-LIKE RECEPTOR, SUBFAMILY B, MEMBER 2 4050 LYMPHOTOXIN BETA (TNF SUPERFAMILY, MEMBER 3)
91
3133 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS I, E 3106 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS I, B 6850 SPLEEN TYROSINE KINASE 116449 MAST CELL IMMUNORECEPTOR SIGNAL TRANSDUCER 11027 LEUKOCYTE IMMUNOGLOBULIN-LIKE RECEPTOR, SUBFAMILY A, MEMBER 2 947 CD34 ANTIGEN 100 ADENOSINE DEAMINASE 868 CAS-BR-M (MURINE) ECOTROPIC RETROVIRAL TRANSFORMING SEQUENCE B 2634 GUANYLATE BINDING PROTEIN 2, INTERFERON-INDUCIBLE 3122 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS II, DR ALPHA 3117 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS II, DQ ALPHA 1 10346 TRIPARTITE MOTIF-CONTAINING 22 9450 LYMPHOCYTE ANTIGEN 86 2204 FC FRAGMENT OF IGA, RECEPTOR FOR 3136 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS I, H (PSEUDOGENE) 7940 LEUKOCYTE-SPECIFIC TRANSCRIPT 1 604 B-CELL CLL/LYMPHOMA 6 (ZINC FINGER PROTEIN 51) 10410 INTERFERON INDUCED TRANSMEMBRANE PROTEIN 3 (1-8U) 3111 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS II, DO ALPHA 4939 2'-5'-OLIGOADENYLATE SYNTHETASE 2, 69/71KDA 23643 LYMPHOCYTE ANTIGEN 96 3394 INTERFERON REGULATORY FACTOR 8 9214 FAS APOPTOTIC INHIBITORY MOLECULE 3 5871 MITOGEN-ACTIVATED PROTEIN KINASE KINASE KINASE KINASE 2 7292 TUMOR NECROSIS FACTOR (LIGAND) SUPERFAMILY, MEMBER 4 3113 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS II, DP ALPHA 1 414062 CHEMOKINE (C-C MOTIF) LIGAND 3-LIKE 1 55824 PHOSPHOPROTEIN ASSOCIATED WITH GLYCOSPHINGOLIPID MICRODOMAINS 1 7852 CHEMOKINE (C-X-C MOTIF) RECEPTOR 4 3115 MAJOR HISTOCOMPATIBILITY COMPLEX, CLASS II, DP BETA 1 6688 SPLEEN FOCUS FORMING VIRUS (SFFV) PROVIRAL INTEGRATION ONCOGENE SPI1
Differential expression of early lymphoid transcripts by MLPs. Transcripts with significantly higher (FDR < 0.05) expression in MLPs compared with HSC/MPPs, CMPs, or MEPs were subjected to Gene Ontology (GO) annotation using DAVID annotation software. GO (biological process) functional categories enriched in the MLP dataset (match, number of genes matching GO category in the dataset; total, number of genes in the GO category) are listed showing the most significant enrichment (fold, fold enrichment of GO genes in the dataset) of lymphocyte genes (GO:0002376). An associated P-value with the Bonferroni, Benjamini corrections and FDR are shown, followed by a list of MLP-enriched lymphoid genes of the ‘immune system process’ GO category.
92
3.6 Discussion
Our findings reveal the first comprehensive picture of early fate determination in human hematopoiesis (fig. S6). We found that myeloid commitment followed the classical model, with the loss of lymphoid potential at the CMP stage. By contrast, human multi-lymphoid progenitors are not lymphoid-restricted, but give rise to dendritic cells and macrophages, in sharp opposition to the classical model. MLPs can be uniquely identified as Thy1–RA+ cells within the immature CD34+CD38– compartment that also harbors Thy1+ HSCs and Thy1–
RA– proposed MPPs (28). Single CD7+ or CD7– cells within this population gave rise to all the lymphoid and monocytic, but not erythroid or granulocytic, lineages. Thus, early lymphoid commitment involves a previously unknown lineage choice between the canonical lymphoid B-, T-, and NK fates, and monocyte/DC lineages traditionally viewed as myeloid- restricted.
Monocytes, macrophages, and DCs belong to a network of immune cells termed the mononuclear phagocyte system, and share a common progenitor, the MDP (23, 29).
Macrophages specialize in phagocytosis and innate immunity, while DCs specialize in antigen-presentation to shape adaptive immune responses (30). DCs have been known to arise from both myeloid and lymphoid progenitors, while monocytes and macrophages were thought to arise uniquely from myeloid progenitors (31). Our findings indicate that DC and macrophage lineages remain entangled in the context of human lymphopoiesis, which supports a version of the ‘myeloid-based’ model of hematopoiesis (32). From an evolutionary standpoint, since macrophage-like phagocytes represent the earliest immune cell
93 type, it is tempting to envision it as an ancestral lineage program that was retained within the adaptive immune system that arose upon subsequent diversification of the hematopoietic tree.
DCs have a potent capacity to present antigens and stimulate T-cells making them useful tools for immune therapy applications (33, 34). Since MLPs can be readily isolated from patient CB, mPB, or BM biopsies, expanded and differentiated to obtain large quantities of autologous T-cells and DCs, they provide an attractive platform for tailoring immunotherapies for research purposes and for ongoing immune therapy trials.
94
4. PLZF IS A REGULATOR OF HOMEOSTATIC AND
CYTOKINE-INDUCE MYELOID DEVELOPMENT
The work presented in this section has been previously published:
Doulatov, S., Notta, F.N., Rice, K.L., Howell, L., Zelent, A., Licht, J.D. & Dick,J.E. PLZF is a regulator of homeostatic and cytokine-induced myeloid development. Genes Dev 23, 2076-
87 (2009).
Author Contributions / Acknowledgements:
The authors would like to thank J.E.D. lab members for critical reading and discussion of the manuscript, UHN/SickKids Flow Cytometry Facility staff (P. Pentilla and S. Zhao) for flow cytometry assistance and Jason Moffat (University of Toronto) for providing lentiviral shRNA vectors. This work was supported by a Canadian Institute of Health Research
(CIHR)-University of Toronto Collaborative Graduate Training Program in Molecular
Medicine studentship (to S.D.), CIHR Doctoral Research award (to F.N.), and grants from the CIHR, the Ontario Institute for Cancer Research and a Summit Award both with funds from the Province of Ontario, Genome Canada through the Ontario Genomics Institute, a
Canada Research Chair, the Leukemia and Lymphoma Society, the Canadian Cancer Society and the Terry Fox Foundation (to J.E.D.). J.D.L and A.Z. were sponsored by the NIH grant
CA 59936-JDL.
95
4.1 Abstract
A major question in hematopoiesis is how the system maintains long-term homeostasis whereby the generation of large numbers of differentiated cells is balanced with the requirement for maintenance of progenitor pools, while remaining sufficiently flexible to respond to periods of perturbed cellular output during infection or stress. We have focused on the development of the myeloid lineage and present evidence that PLZF provides a novel function that is critical for both normal and stress-induced myelopoiesis. During homeostasis,
PLZF restricts proliferation and differentiation of human cord blood-derived myeloid progenitors to maintain a balance between the progenitor and mature cell compartments.
Analysis of PLZF promoter binding sites revealed that it represses transcription factors involved in normal myeloid differentiation, including GFI-1, C/EBPα, and LEF-1, and induces negative regulators, DUSP6 and ID2. Loss of ID2 relieves PLZF-mediated repression of differentiation identifying it as a functional target of PLZF in myelopoiesis.
Furthermore, induction of ERK1/2 by myeloid cytokines, reflective of a stress response, leads to nuclear export and inactivation of PLZF, which augments mature cell production.
Thus, negative regulators of differentiation can serve to maintain developmental systems in a primed state, so that their inactivation by extrinsic signals can induce proliferation and differentiation to rapidly satisfy increased demand for mature cells.
4.2 Introduction
Myeloid cells, including macrophages and neutrophils, are an intrinsic component of the innate immune system. Mature myeloid cells in the periphery rapidly turn over and are
96 replenished by differentiation from myeloid progenitors in the bone marrow. This process is orchestrated by a complex interplay of transcription factors and cytokine signals that govern an ordered activation and repression of lineage-specific genes. Networks of transcription factors that regulate myeloid development have been extensively studied and modeled
(Laiosa et al., 2006a; Laslo et al., 2006). Macrophage and neutrophil specification require master regulators PU.1 and C/EBPα, which control more ‘specialized’ downstream transcription factors, including GFI1 and EGR2 (Laslo, 2006). Since blood production is enormous (~1012 cells per day in human) and continues for a lifetime, homeostatic mechanisms must be in place to permit ongoing differentiation without depletion of progenitor pools. Although the molecular mechanisms that govern lineage determination are well understood, how myeloid transcription factors maintain the balance between the progenitor and mature cell compartments is less clear. Proper regulation ensures an adequate supply of mature cells, while leukemogenesis and other myeloid malignancies are often driven by inappropriate activity of these transcription factors (Rosenbauer and Tenen, 2007).
Thus, identification and characterization of novel regulators of myelopoiesis is of interest both from the standpoint of both normal and malignant hematopoiesis.
Besides maintaining homeostasis, developmental systems must remain flexible to respond to emergent events. During immune activation, high levels of secreted cytokines stimulate myeloid-mediated innate immunity (Cannistra and Griffin, 1988; Oster et al.,
1988). Of these, G-CSF, GM-CSF and IL-3, were initially isolated as colony-stimulating factors owing to their potential to stimulate proliferation, survival and differentiation of hematopoietic progenitors (Donahue et al., 1988; Lieschke et al., 1994). Following binding to their cognate receptors, cytokines activate parallel JAK/STAT, MAPK and PI3K signal
97 transduction pathways, which modify expression and activation of downstream transcription factor effectors (Barreda et al., 2004). For instance, STAT3 and C/EBPβ are dispensable for normal, but required for cytokine-induced, myelopoiesis (Hiraiu, 2006; Panoupolos, 2006).
Still, the mechanisms by which stress-induced cytokines interact with the network of myeloid transcription factors remain largely unexplored.
Kruppel-like transcription factor promyelocytic leukemia zinc finger (PLZF) is expressed in CD34+ hematopoietic progenitors, but not mature cells, suggesting it may play a role in lineage determination (Reid et al., 1995). PLZF is a negative regulator of cell division in embryogenesis and controls segment patterning through repression of HOX and BMP expression (Barna et al., 2000). In the adult, PLZF augments self-renewal of spermatogonial stem cells and its deletion results in sterility (Buaas et al., 2004; Costoya et al., 2004). In hematopoiesis, PLZF has been implicated in the development of megakaryocytic (Labbaye et al., 2008) and NKT cell lineages (Kovalovsky et al., 2008; Savage et al., 2008). PLZF was identified as a chromosomal fusion partner with RARα in promyelocytic leukemia, a disease marked by an accumulation of undifferentiated myeloid blasts (Chen et al., 1993). Consistent with a role in myelopoiesis, enforced expression of PLZF in myeloid cell lines resulted in inhibition of proliferation and differentiation (McConnell et al., 2003; Shaknovich et al.,
1998; Ward et al., 2001). In these distinct systems, PLZF is a sequence-specific transcriptional repressor, which recruits nuclear co-repressors to establish silenced chromatin structure at target promoters (Barna et al., 2002; Hong et al., 1997). Thus, PLZF can maintain long-term epigenetic repression of target genes, which is an integral aspect of cellular
‘memory’ in fate determination and differentiation (Fisher, 2002).
98
Most studies of mammalian hematopoiesis have used the mouse as a model organism.
The improvement of gene transfer methods and ‘humanized’ xenograft mouse models now provide the possibility to carry out comparable studies with primary human cells, which may be a more relevant model for many aspects of normal human biology and disease. Using this approach, we identify a previously unrecognized mechanism of homeostatic regulation, whereby the balance between maintenance and commitment myeloid progenitors is controlled by cytokine-mediated inhibition of a differentiation-suppressing transcription factor, PLZF.
4.3 Materials and Methods
4.3.1 Sample Collection and Purification
Cord blood samples were obtained according to procedures approved by the institutional review boards of the University Health Network and Trillium Hospital and were collected, processed, and stored as described (Mazurier et al., 2004). Briefly, Lin- CB cells were purified by negative selection using the StemSep Human Progenitor Cell Enrichment Kit according to manufacturer’s protocol (StemCell Technologies).
4.3.2 Viral Constructs
Full-length PLZF cDNA was cloned into a two-promoter MSCV-PGK-EGFP (MPG) retroviral vector and a cppt-PGK-EGFP-WPRE-derived lentiviral vector (Mazurier et al.,
2004) with a truncated E1Fα promoter. shRNAs against PLZF were designed using the
Dharmacon algorithm (Dharmacon Inc.; see Supplemental Materials for sequences), synthesized as complimentary 5’-P oligonucleotides, annealed and cloned into the modified cppt-PGK-EGFP-WPRE vector containing the H1 promoter and an ires-PAC selection
99 cassette. Viral particles pseudotyped with VSV-G were generated by transient transfection as described (Mazurier et al., 2004).
4.3.3. Viral Transduction
Lin- CB cells were prestimulated and transduced in X-VIVO 10 (BioWhittaker) medium with
1% BSA, 2 mM L-glutamine, 100 U/ml penicillin-streptomycin, plus SCF (100 ng/ml),
FLT3L (100 ng/ml), TPO (50 ng/ml) and IL-6 (20 ng/ml) (all Amgen) for 4 h (lenti) or 24 h
(retro). For lentiviral infections, cells were transduced at MOI 50-100 for 24 h. For retroviral infections, ~1 x 106 cells were transduced with 4x changes of 1-5 x 107 viral particles each for 48 h; wells were pre-coated with CH-296 fibronectin (4µg/cm2, Retronectin, Takara Bio
Inc.).
4.3.4 Cell Sorting
Retrovirus-transduced cells were cultured in transduction medium for 24 h post gene- transfer. Cells were stained with antibodies against CD34 and CD71 (BD Pharmingen). GFP+
CD34+ (CD71-) progenitors were sorted on BD FACSAria cytometer operating in the
“Purity” mode, consistently yielding >98% purity.
4.3.5 Flow Cytometry
Routine flow cytometry was performed using BD FACSCalibur or BD LSRII cytometers on fresh cells using monoclonal mouse anti-human antibodies (see Supplemental Materials).
Data were analyzed with FlowJo software (Tree Star Inc.).
100
4.3.6 Liquid Cultures
2 - 5 x 104 sorted cells were seeded into 1 ml IMDM + 15% FCS plus SCF (20 ng/ml) and
IL-3 (2 ng/ml) or IMDM + 20% BIT (StemCell Technologies), 2 mM L-glutamine, 0.1 mM
β-mercaptoethanol, plus SCF (100 ng/ml), FLT3L (100 ng/ml), IL-6 (20 ng/ml), G-CSF (20 ng/ml) and GM-CSF (20 ng/ml) (all cytokines - Amgen). Cultures were maintained at a density of ~1 x 106 cells/ml and 1 x 105 cells used for analysis of lineage markers with antibodies against CD11b, CD14 and CD15 (Beckman Coulter) and CD33 (BD Pharmingen).
4.3.7 Colony Assays
1 x 103 sorted progenitors were seeded into 3 ml MethoCult medium (StemCell
Technologies) with 20% FCS, 5% human plasma, 5% CB plasma, 0.1 mM β- mercaptoethanol, plus “basic” cytokines SCF (10 ng/ml), FLT3L (10 ng/ml), TPO (50 ng/ml), IL-6 (10 ng/ml), GM-CSF (50 ng/ml) and EPO (4 U/ml). Duplicate 1 ml aliquots were plated into 35 mm dishes (Nunc) and hematopoietic colonies of erythroid, myeloid or mixed lineage were scored after 14 d.
4.3.8 BrdU and Annexin Assays
BrdU was added to day 7 liquid cultures at 10 µM final concentration. After 3 h incubation, 1 x 105 cells were removed, stained with anti-CD11b-PE (Beckman Coulter) and assayed with
BrdU Flow Kit and Annexin V-PE Apoptosis Detection Kit (both BD Pharmingen) according to manufacturer’s protocols.
4.3.9 NOD/SCID Transplantation
NOD_LtSz-scid_scid (NOD/SCID) mice were sublethally irradiated (300 cGy) and injected i.p. with 200 µg anti-CD122 monoclonal antibody 24 h prior to transplantation (McKenzie et
101 al., 2005). Transduced cells were washed, resuspended at 7.5 x 104 cells in 20 µl PBS + 1%
FCS and injected intrafemorally into anesthetized mice. Mice were sacrificed after 8 wks,
BMCs isolated by flushing with 2 ml IMDM and 50 µl stained for surface markers (see
Supplemental Materials for antibodies). Remaining cells were pooled from all mice within a group (n = 4 - 8), red blood cell lysed, and used for surface antigen staining and lineage- depletion (see below).
4.3.10 Lineage-Depletion
Human progenitors were isolated from pooled bone marrow using the Mouse/Human
Chimera Enrichment Kit (StemCell Technologies) according to manufacturer’s protocol, with the addition of 100 µl/ml StemSep Human Hematopoietic Progenitor Enrichment
Cocktail (StemCell Technologies) and the anti-Biotin antibody. Fresh column purified cells were used for flow cytometry and seeded in colony assays at 1 x 103 cells/plate and GFP+ colonies scored using an inverted fluorescent microscope.
4.3.11 Cytokine Stimulation
Sorted GFP+ CD34+ CD71- progenitors were either directly seeded into methylcellulose with basic cytokines, without EPO, ± 50 ng/ml IL-3; or 1 x 104 cells were stimulated in IMDM +
20% BIT + 2 mM L-glutamine ± 20 ng/ml IL-3, G-CSF, GM-CSF, IL-6 or 10-7M all-trans- retinoic acid (Sigma Aldrich) for 24 h, after which 1.5 x 103 cells seeded into methylcellulose with basic cytokines. For inhibitor studies, cells were stimulated as above with the addition of 10 µM SB203580, 50 µM SB202190, 10 µM PD98059, 10 µM U0126, 25 µM SP600125,
25 µM AG490, 50 µM LY294002 or DMSO vehicle (all Tocris Bioscience). All inhibitors
102 were used at previously determined concentrations (see Supplemental Materials for references).
4.3.12 Immunofluorescence Microscopy
Untransduced CD34+ CD71- progenitors were cultured in IMDM + 20% BIT + 2 mM L- glutamine ± 50 ng/ml IL-3, 10-7M all-trans-retinoic acid or 10 µM PD98059 for 24 h. Cells were fixed in 2% paraformaldehyde and 5 x 104 cells cytospun onto microscope slides. See
Supplemental Materials for a complete staining protocol.
4.3.13 Quantitative RT-PCR
> 5 x 104 sorted GFP+ CD34+ CD71- progenitors were cultured in IMDM + 20% BIT + 2 mM
L-glutamine ± 20 ng/ml IL-3 for 24 h. RNA was extracted with the Trizol reagent
(Invitrogen), DNAse I-treated (Qiagen) and reverse transcribed using SuperScript II
(Invitrogen). Real-time PCR reactions were prepared using SYBR Green PCR Master Mix
(Applied Biosystems), with 200 nM of each primer and >20 ng cDNA/reaction (see
Supplemental Data for the primer list; all 60ºC annealing T). Reactions were performed in triplicate on Applied Biosystems 7900HT instruments. Absolute gene expression was quantified using SDS software (Applied Biosystems) based on the standard curve method and presented as: transcript expression (pg) /GAPDH (pg) x 100.
4.3.14 Chromatin Immunoprecipitation see Supplementary Materials for complete protocol.
103
4.3.15 Statistical Analysis
Unless otherwise stated, significance of differences among groups was determined by two- tailed unpaired Student’s t test. Standard errors of the products or norms of two independent data sets were calculated using the MSTAT software (McArdle Laboratory for Cancer
Research).
4.4 Results
4.4.1 PLZF is expressed in human HSCs and progenitors
PLZF is expressed in human CD34+ hematopoietic progenitors and its expression declines in differentiated cells (Reid et al., 1995). To refine this analysis, PLZF transcript expression was assessed by quantitative real-time PCR (qPCR) in stem cell (HSC)-enriched (CD34+
CD38-), myeloid progenitor (CD34+ CD38+ CD71-) and erythroid progenitor (CD34+ CD38+
CD71+) fractions, isolated from lineage-depleted (Lin-) umbilical cord blood (CB).
Comparable levels of PLZF expression were detected in all three fractions, however its expression rapidly declined in progenitors isolated from culture under conditions that promote differentiation (Fig. 1A). This pattern of expression suggests that PLZF may play a role in differentiation of hematopoietic progenitors.
4.4.2 PLZF restricts myeloid proliferation and differentiation in vitro
To characterize the requirement for PLZF in human hematopoiesis, we designed lentiviral and retroviral vectors to enforce or silence expression (Fig. 1B). Transduction of 293T cells with retroviral MPG–PLZF or lentiviral TAP-tagged CEP–PLZF vectors yielded protein of expected size (Fig. 1D). Transcript abundance in PLZF-transduced (termed PLZFOX) CD34+
104 cells was 2.8 ± 0.3 and 1.9 ± 0.3-fold compared with Lin- CB myeloid progenitors or HSCs, respectively (Fig. 1C). Lastly, infection of PLZFOX 293T cells with short-hairpin PLZF knockdown viruses (sh-PLZF) effectively abolished protein expression (Fig. 1D; also see
Supplemental Fig. S1).
Since PLZF expression is rapidly downregulated during differentiation, we tested the effect of enforcing its constitutive expression in cultured primary human progenitors. Lin-
CB cells were infected with control or MPG-PLZF vectors and GFP+ CD34+ progenitors
(which contain myeloid and erythroid progenitors, but negligible HSCs) were assayed in liquid cultures and colony assays. After 3 wks, PLZFOX progenitors displayed 4.0- and 5.2- fold lower proliferative capacity in serum-free and serum-supplemented cultures, respectively (Fig. 2A). Annexin V staining revealed no significant differences in the proportion of apoptotic cells in these cultures (Fig. 2B). Notably, BrdU incorporation assays demonstrated that the decrease in proliferation was directly attributable to reduced entry into
S-phase (Fig. 2C). These observations are consistent with the proposed tumor suppressor function of PLZF (McConnell et al., 2003).
To assess the effect of constitutive expression of PLZF on differentiation, we analyzed the frequency of cells that had acquired CD14, CD15 and CD11b mature myeloid lineage markers. As control Lin- cells differentiated, 92.1 ± 2.0% of them acquired these markers after 3 wks in culture. In contrast, 29 ± 9.6% of PLZFOX cells did not express the markers of differentiation at this time point (Fig. 2D). Thus, combined with the 4.0-fold reduction of proliferation, PLZF decreased total output of mature myeloid cells by 12.1-fold over 3 wks (Fig. 2E).
105
Myeloid development can also be functionally tested using colony assays, which measure the frequency and growth potential of clonogenic progenitors. Freshly sorted (day 0)
CD34+ PLZFOX progenitors formed an equal proportion of erythroid colonies (Fig. 2F,
‘BFU-E’) as controls, but gave rise to 2.7 ± 0.2-fold fewer granulocyte or macrophage colonies (Fig. 2F, ‘CFU-G/M’). Since an equal number of transduced progenitors were seeded, these results represent the effect of PLZF on the clonal growth potential of myeloid progenitors. This conclusion is also supported by the equivalent reduction of the total cell output from PLZFOX progenitors in colony assays (Fig. 2F, ‘CFU cells’). Collectively, these findings indicate that PLZF inhibits myeloid proliferation and differentiation in vitro.
Expression of PLZF is quickly lost in culture, as primitive cells differentiate (see Fig.
1B), so further reduction of PLZF levels by knockdown is expected to have no phenotypic consequences. Indeed, 3 independent sh-PLZF vectors had no effect on the growth of myeloid progenitors when PLZF was not expressed (Supplemental Fig. S1, “control”) indicating that these hairpins do not have off-target effects that alter cell growth and differentiation. However, all 3 shRNA vectors partially rescue (72 – 74%) colony growth in the context of ectopic PLZF expression providing a functional measure of knockdown efficiency in primary progenitors (Supplemental Fig. S1, “PLZFOX”). These data demonstrate that sh-PLZF lentiviruses specifically target PLZF and provide a functional rescue of its effects in myeloid progenitors.
4.4.3 PLZF restricts human myelopoiesis in vivo
During culture, progenitors are not maintained due to rapid differentiation making it impossible to study the process of human hematopoiesis over time. By contrast, in vivo xenotransplantion of human cells into immunodeficient mice does allow for maintenance of
106 progenitors, so we used this system to more fully investigate the role of PLZF in human hematopoiesis. Lin- CB cells were transduced with PLZF overexpression and knockdown vectors containing a GFP reporter. The level of gene transfer into CD34+ CD133+ HSCs ranged from 5 - 10% (Fig. 3A, indicated by broken lines), and NOD/SCID mice were transplanted with a mixture of transduced and untransduced (GFP-) cells. Silencing of PLZF
(termed PLZFKD), using 2 independent sh-PLZF vectors, enhanced the capacity of GFP- marked hematopoietic cells to contribute to the repopulation of the NOD/SCID bone marrow with human bone marrow cells (termed BMCs). There was a 2.5 ± 0.4-fold increased proportion of GFP+ PLZFKD BMCs in the human CD45+ graft at 8 wks post-transplant compared with the proportion of GFP+ cells prior to transplantation (3 experiments, p < 0.02 for all) (Fig. 3A, ‘Knockdown’, compare solid and broken lines). This data suggests that
PLZFKD cells have a modest competitive advantage over their untransduced counterparts. As expected, the proportion of GFP+ cells after transplantation of empty vector- or irrelevant red fluorescent protein (shRFP) hairpin-transduced cells was unchanged compared to input transduction levels indicating that these cells had no proliferative advantage over GFP- cells
(Figure 3A). By contrast, the proportion of GFP+ PLZFOX BMCs was decreased in the CD45+ graft by 3.9 ± 0.1-fold (3 experiments, p < 0.002) (Fig. 3A, ‘Overexpression’).
To determine if the increased mature cell production was specific to a particular lineage, we assessed lineage distribution within the GFP+ graft. PLZFKD BMCs were composed of 77.5 ± 1.6% (control 85.9 ± 1.6%) SSClo CD33- B-lymphocytes, 12.3 ± 1.3%
(control 5.2 ± 0.3%) SSChi CD33lo granulocytes and 9.2 ± 1.0% (control 7.8 ± 1.5%) SSClo
CD33hi monocytes (5 experiments; granulocytes, p < 0.001) (Fig. 3B). There were not enough cells of other lineages for flow analysis. Taking into account the overall 2.5-fold
107 expansion of PLZFKD BMCs, knockdown of PLZF increased the absolute number of granulocytes by 6.0 ± 1.7-fold, compared to controls (Fig. 3C). Further, increased granulocyte numbers were largely due to augmented production of neutrophils expressing maturation markers CD11b and CD16 (Fig. 3D). These observations suggest that PLZF decreases proliferation and/or differentiation of myeloid cells in vivo.
4.4.4 PLZF regulates the balance between progenitor and mature compartments
To determine if the increased contribution of PLZFKD cells to the myeloid graft reflects an expansion of immature progenitors, marrow from mice transduced with control or PLZF- bearing viruses was pooled and depleted of murine and mature human cells to enable more precise flow cytometric analysis of the immature engrafted populations (see Methods). As expected for empty vector and shRFP controls, the proportion of GFP+ cells in unfractionated
CD45+ bone marrow and Lin- CD34+ fractions was equivalent (Fig. 4A, ‘Control’).
Surprisingly, knockdown of PLZF decreased the contribution of GFP+ cells to the progenitor compartment by 2.6 ± 0.1-fold, compared to total BMCs (Fig. 4A, ‘Knockdown’; Fig. 4B).
Conversely, the proportion of GFP+ PLZFOX cells in the primitive Lin- CD34+ fraction was increased by 5.4 ± 0.5-fold (Fig. 4A, ‘Overexpression’; Fig. 4B). Thus, while negatively regulating the mature myeloid compartment, PLZF also expanded the pool of undifferentiated progenitors.
The number of progenitors in vivo was independently determined using colony assays. PLZFOX CFU-G/M progenitors were overrepresented by 15.7-fold, whereas PLZFKD progenitors were depleted by 4.6-fold (Fig. 4B), providing an independent correlation with the phenotypic data. In contrast to our in vitro experiments, the number of seeded progenitors was not the same between control and PLZF mice. However, since PLZF expanded Lin-
108
CD34+ cells, we infer that the increase in CFU-G/M in vivo is due to an increased number of progenitor cells, rather than their clonogenic potential.
Taken together, these results demonstrate that rather than conferring a general proliferative advantage to human cells in vivo, PLZF specifically regulates the balance between the progenitor and mature compartments. Higher levels of PLZF maintain undifferentiated progenitors and curb their mature output, while silencing causes depletion of progenitors due to an increased rate of proliferation and differentiation.
4.4.5 Cytokines modulate PLZF repression of myeloid development
Since our data suggests that PLZF normally functions to negatively regulate entry of progenitors into terminal differentiation, we sought to determine the involvement of PLZF in myelopoiesis stimulated by stress-induced cytokines. Overexpression of PLZF in the absence of cytokine exposure reduced the growth potential of clonogenic progenitors (see Fig. 2D).
However, when PLZFOX progenitors were seeded in the presence of IL-3, myeloid colony- forming potential was restored to nearly normal levels suggesting that IL-3 signaling can alleviate the defect in growth and differentiation (Fig. 5A). As expected, IL-3 also improved the clonogenic potential of control cells, but to a much lesser extent (Fig. 5A). As colony assays are performed in the presence of several cytokines (see Methods), we sought to examine the effect of individual cytokines. CD34+ CD71- myeloid progenitors were stimulated with a select cytokine for 24 hrs in serum-free medium supplemented with BSA, insulin and transferrin (SFM + BIT) to maintain cellular viability and seeded in methylcellulose. The potential of PLZF to repress colony-forming capacity was significantly alleviated by stimulation with IL-3, GM-CSF, or G-CSF (all p < 0.01), all of which have been implicated as key mediators of stress-induced myelopoiesis (Fig. 5B) (Hirai et al.,
109
2006). However, IL-6 or all-trans retinoic acid (ATRA), which have distinct biological roles, did not promote myelopoiesis in the presence of PLZF (Fig. 5B). These results suggest that stress-induced cytokines can act by interfering with the capacity of PLZF to inhibit proliferation and differentiation.
To probe the interaction of PLZF and cytokines on the molecular level, we profiled the expression of key indicators of myeloid differentiation in culture by qPCR. Both c-MYC
(MYC) and GATA-2 transcription factors are normally expressed in myeloid progenitors and expression declines with differentiation (Johansen et al., 2001; Kobayashi-Osaki et al., 2005).
Consistent with their impaired differentiation, PLZFOX myeloid progenitors had higher levels of MYC and GATA-2 transcripts, however normal levels were restored if the transduced cells were cultured with IL-3 (Fig. 5C). The C/EBP-family of transcription factors (C/EBPs) are expressed in an ordered fashion during normal neutrophil differentiation (Bjerregaard et al.,
2003). Transcript expression of C/EBPs in CB cells was blocked by PLZF (all p < 0.05, except CEBPA), however normal levels were restored in the presence of IL-3 (Fig. 5D).
Notably, the degree of repression by PLZF correlated with their normal order of expression during differentiation (C/EBPα < ε < δ < β), such that C/EBPs expressed in more mature cells were more strongly repressed (Bjerregaard et al., 2003). The ordered repression of these transcription factors suggests that PLZF establishes a molecular state which impairs differentiation, while the addition of cytokines restores a normal pattern of myeloid development.
To investigate the pathway(s) responsible for cytokine-mediated inhibition of PLZF, sorted CD34+ CD71- myeloid progenitors were cultured in SFM + BIT, treated with IL-3 in the presence of specific protein kinase inhibitors for 24 hrs and seeded in methylcellulose.
110
Control and PLZFOX progenitors treated with p38 MAPK (SB203580 and SB202190), JNK
(SP600125), or PI-3K (LY294002) inhibitors showed a similar response to IL-3 stimulation
(Fig. 5E). Treatment with JAK inhibitor AG490 drastically reduced colony formation of both control and PLZFOX cells, but there was no difference in their response to IL-3. Only specific
MEK inhibitors PD98059 and U0126, which impair downstream ERK1/2 (ERK) activation, restored PLZF-mediated repression of myeloid colony formation in the presence of IL-3
(both p < 0.01) (Fig. 5E). Thus, IL-3 interferes with PLZF repression by activating ERK signal transduction.
In CD34+ progenitors and myeloid cell lines, PLZF is localized to distinct nuclear domains, while its loss-of-function mutants exhibit aberrant localization (Guidez et al., 2005;
Reid et al., 1995). To examine the effect of cytokines on native cellular localization of PLZF, untransduced CD34+ CD71- myeloid progenitors were cultured in SFM + BIT with or without IL-3 for 24 hrs and distribution of PLZF was visualized by immunofluorescence microscopy. In the absence of cytokines, most cells expressed PLZF protein, which was found in the nucleus and the cytoplasm (Fig. 5F, ‘-IL-3’). Stimulation with IL-3 resulted in a redistribution of nuclear PLZF to the cytoplasm (Fig. 5F, ‘+IL-3’), and this effect was enhanced by co-treatment with other differentiation-inducing agents, such as ATRA (Fig. 5F,
‘+IL-3 +ATRA’). However, nuclear localization was completely restored upon co-treatment with the specific ERK inhibitor PD98059 (Fig. 5F, ‘+IL-3 +iMEK’). These results suggest that cytokines mediate inactivation of PLZF by triggering its export from the nucleus in an
ERK-dependent manner.
111
4.4.6 PLZF regulates expression of myeloid transcription factors
To gain insight into the mechanisms that underlie the differentiation-suppressing function of
PLZF, a genome-wide analysis of PLZF promoter binding was performed using chromatin immunoprecipitation microarray (ChIP-ChIP) in KG1a myeloid cells (K. Rice, manuscript in preparation). The top candidates were then validated using conventional ChIP (Fig. 6A), and the transcriptional regulation of candidate targets was verified by qPCR in Lin- CB. PLZF bound to promoters of transcription factors that control myeloid differentiation, including
LEF1 (TCF1A) and GFI1 and repressed transcription indicating that these are direct targets of PLZF (Fig. 6A,B). LEF1 is a nuclear co-factor of β-catenin critical for lymphoid and myeloid development (Skokowa et al., 2006). GFI1 is a proto-oncogene that functions as a neutrophil lineage determinant downstream of C/EBPα (Laslo et al., 2006). Notably, PLZF also repressed CEBPA transcription, but did not bind to its promoter indicating indirect regulation (Fig. 6A,B).
Classically, PLZF has been regarded as a transcriptional repressor, which recruits nuclear co-repressor complexes to silence promoter activation (Hong et al., 1997). However,
PLZF also bound to promoter sites of negative regulators of signaling and differentiation, including MYC, DUSP6 and ID2, and activated their transcription (Fig. 6A,C). MYC is a proto-oncogene whose downregulation is critical for myeloid differentiation (Johansen et al.,
2001). DUSP6 is an ERK-specific phosphatase that acts as part of a negative feedback loop in FGF signaling (Li et al., 2007). ID2 is a member of the bHLH transcription factor family and a negative regulator of differentiation (Ji et al., 2008). These data suggest that PLZF can recruit either co-repressors or co-activators to target promoters to regulate gene expression.
112
Whereas MYC is ubiquitously expressed in hematopoiesis, expression of DUSP6 and ID2 is highest in HSCs and declines with differentiation (Fig. 6D and K. Takenaka, manuscript in preparation). This observation suggests that DUSP6 and ID2 may act as cooperating factors downstream of PLZF. To test this hypothesis, CD34+ CD71- myeloid progenitors were first transduced with the control (MPG) or PLZFOX vectors to induce ectopic PLZF expression.
Cells were then co-transduced with shRNA vectors for PLZF target genes (MYC, ID2,
DUSP6) and plated in colony assays. Since each shRNA was introduced side-by-side into control and PLZF overexpressing cells, any putative off-target effects would be equal in the presence or absence of ectopic PLZF. By normalizing the number of colonies formed by
PLZFOX + shRNA transduced progenitors to those transduced with MPG + shRNA, we selectively measured the effects of gene silencing that are specific to PLZF overexpressing cells. PLZFOX myeloid progenitors transduced with shGFP or shMYC gave rise to fewer colonies compared to shGFP or shMYC alone (0.41 ± 0.03 and 0.46 ± 0.06 fold, respectively) indicating that PLZF retained its inhibitory effect on myeloid growth (Fig. 6E). However, shDUSP6 or shID2 restored colony-forming potential of PLZFOX myeloid progenitors to 0.84
± 0.18 and 0.78 ± 0.03 fold of the knockdown-alone, although only the shID2 effect was significant (p < 0.005) (Fig. 6E). These data identify ID2 as a necessary downstream effector of the differentiation-suppressing function of PLZF.
113
4.5 Tables and Figures
Figure 4-1. PLZF expression and viral vector design. (A) Expression of PLZF mRNA in human hematopoietic subsets, before and after culture, was quantified by qPCR. Hematopoietic subsets were isolated by FACS-sorting from fresh Lin- CB (day 0) or after 5 d in serum- free culture (day 5) based on the following phenotypes: HSCs, CD34+ CD38-, erythroid progenitors (E), CD34+ CD38+ CD71+, myeloid progenitors (M), CD34+ CD38+ CD71-. (B) Schematic representation of viral vectors used to overexpress (MPG, retroviral; CEP, lentiviral) or silence human PLZF (shPLZF). (C) Fold overexpression of PLZF in myeloid progenitors transduced with MPG-PLZF and cultured for 5 d, compared with same day control cells (M, d5), or freshly sorted myeloid progenitors (M, d0) or HSCs (HSC, d0) from the same CB. (D) Western blot analysis of total protein extracts from 293T cells transduced with control- or MPG- PLZF viruses (left panel). Silencing vectors (shPLZF1-3) were transduced into 293T cells stably expressing PLZF (right panel). Equal total protein was loaded in all lanes and the blots probed with an anti-PLZF antibody.
114
Figure 4-2. PLZF restricts myeloid proliferation and differentiation of human progenitors in vitro. (A) Effect of PLZF on progenitor proliferation. Growth of CD34+ progenitors transduced with MPG (control) or MPG-PLZF (PLZFOX) vectors in serum-free and serum-supplemented cultures quantified by total cell counting. (B) Effect of PLZF on apoptosis. Proportion of annexin V+ (7AAD-) apoptotic cells after 7 d in serum- supplemented cultures initiated with control or PLZFOX CD34+ cells; the difference between groups is not significant. (C) Effect of PLZF on cell cycle kinetics. BrdU incorporation of control and PLZFOX CD34+ cells cultured for 7 d in serum-free conditions; representative flow cytometric profiles (left panel) and the proportion of cells in
G0/G1, S and G2/M phases of the cell cycle (right panel) are shown. (D) Effect of PLZF on myeloid differentiation. Proportion of non-myeloid (CD15- CD14- CD11b-) cells in serum-free cultures initiated with control or PLZFOX CD34+ cells. (E) Production of mature lineage-positive myeloid cells in serum-free cultures initiated with sorted control or PLZFOX CD34+ cells. (F) Effect of PLZF on clonogenic progenitors. Colony-forming capacity (colonies counted as proportion of input; % CFU) of freshly sorted control or PLZFOX CD34+ cells. BFU-E, erythroid blast-forming units; CFU-G/M, granulocyte or monocyte colony-forming units; CFU cells, total myeloid cells x 105 in a CFU-G/M assay. All data expressed as mean ± SEM of 3 independent experiments (CB samples), except (F) which has 4 experiments (* p < 0.05, ** p < 0.005).
115
Figure 4-3. PLZF regulates myeloid development in vivo. (A) Effect of PLZF on CD45+ human cell engraftment. Proportion of GFP+ BMCs in individual mice each denoted with a symbol at 8 wks post- transplant (mean marked as solid lines) compared with mean GFP- positivity of input HSCs prior to transplantation (broken lines). Lin- CB cells were transduced with control (clear triangles), PLZF knockdown (PLZFKD) or CEP-PLZF (PLZFOX) vectors (black triangles) and transplanted into NOD/SCID mice. The level of transduction in 3 independent experiments ranged from 5 to 12%; as a result, mice were transplanted with a mixture of transduced and non-transduced cells. Thus, equivalent means in the values of the pre- and post-transplant % GFP in each experiment indicates a lack of competitive advantage of GFP+ cells relative to untransduced cells, while increased or decreased % GFP+ indicates competitive advantage or disadvantage, respectively, in vivo. (B) Frequency of human myeloid BMCs in mice transplanted with PLZFKD cells (right) and a representative flow analysis (left). The frequency of granulocytes was calculated as the proportion of SSChi CD33lo, and monocytes as SSClo CD33hi, cells within the GFP+ CD45+ graft; the remaining cells were SSClo CD33- lymphocytes. To obtain enough cells for accurate flow cytometric analysis, bone marrow was pooled from 4 – 8 mice. Data expressed as mean ± SEM of 5 independent experiments (** p < 0.01). (C) Expansion of the absolute number of human granulocytes, monocytes, and lymphoid cells in mice transplanted with PLZFKD cells normalized to controls. The absolute number was calculated by multiplying the frequency of each cell type by the number of GFP+ CD45+ cells in both femurs, tibiae and pelvis. Data expressed as mean ± SEM of 5 independent experiments (* p < 0.02). (D) Representative analysis of the proportion of CD11b+ CD16+ human neutrophils in the GFP+ fraction of mice engrafted with Lin- CB cells transduced with control, PLZFKD or PLZFOX viruses. To obtain enough cells for accurate flow cytometric analysis, bone marrow was pooled from 4 – 8 mice.
116
Figure 4-4. PLZF expands the human progenitor compartment in vivo. (A) Proportion of GFP+ cells within the total human CD45+ graft in the bone marrow (left) compared with Lin- CD34+ progenitor fraction (right) in a representative experiment 8 wks post-transplant. Bone marrow was pooled from 4 – 8 mice, depleted of murine and mature human cells and analyzed for GFP and CD34 expression by flow cytometry before and after lineage depletion. Equal % GFP indicates equal contribution of transduced cells to mature and progenitor compartments, while increased or decreased proportion reflects expansion or depletion of progenitors relative to total cells. (B) Proportion of Lin- CD34+ progenitors and myeloid colony-forming units (CFU-G/M) within the total CD45+ graft in mice transplanted with PLZFKD or PLZFOX cells. Data expressed as mean ± SEM of 2 independent experiments, each with Lin- cells isolated from 4 – 8 engrafted mice.
117
Figure 4-5. Cytokines modulate the effects of PLZF on growth and differentiation. (A) Myeloid colony- forming capacity of CD34+ CD71- myeloid progenitors transduced with MPG (control) or MPG-PLZF (PLZFOX) viruses and seeded in methylcellulose ± IL-3. (B) Same as (A), except progenitors were stimulated with the indicated cytokines for 24 h in SFM + BIT and seeded in methylcellulose. Data normalized to BSA-treated samples (dashed line). (C) Expression of MYC and GATA-2 in control or PLZFOX CD34+ CD71- progenitors cultured for 4 d in serum-free media ± IL-3. Expression is normalized to cells infected with a control virus (dashed line). (D) Same as (C) for C/EBP transcription factors. Expression is normalized to cells infected with a control virus (dashed line) (* p < 0.05). (E) Same as (A), except progenitors were stimulated with IL-3 plus an indicated protein kinase inhibitor or DMSO vehicle for 24 h in SFM + BIT and seeded in methylcellulose. SB203580, inhibits p38-1; SB202190, p38-2; PD98059, MEK-1; U0126, MEK-2; SP600125, JNK; AG490, JAK; LY294002, PI-3K. Data normalized to DMSO-treated samples (dashed line). (F) Immunofluorescence staining of PLZF localization (green) in DAPI-stained nuclei or cytoplasm of untransduced Lin- CD34+ CD71- progenitors cultured in SFM + BIT ± IL- 3, ATRA or PD98059 (iMEK) for 24 h; 100X magnification. All data expressed as mean ± SEM of 3 independent experiments (* p < 0.02, ** p < 0.006).
118
Figure 4-6. Transcriptional regulation of myeloid target genes by PLZF. (A) ChIP analysis of target promoter occupancy by PLZF in KG1a myeloid cells. ChIP was performed using an anti-PLZF antibody (PLZF) or control IgG (IgG). Binding is represented as fold enrichment over input determined by qPCR for promoter sequence containing consensus PLZF-binding sites, and an internal control sequence 6 kB upstream of transcriptional start site (Upstream). Binding to an intronic sequence of the BCL6 gene was used as a negative control. (B) Expression levels of GFI1, CEBPA and IL-3R in control or PLZFOX CD34+ CD71- myeloid progenitors 24 h after transduction. (C) Expression levels of ID2, DUSP6 and MYC in control or PLZFOX CD34+ CD71- myeloid progenitors 24 h after transduction. (D) Expression levels of ID2 in human CD34+ CD38- HSCs, CD34+ CD38+ CD71+ erythroid (E), and CD34+ CD38+ CD71- myeloid (M), progenitors sorted from fresh Lin- CB (day 0) or after 5 d culture in serum-free media (day 5). (E) Myeloid colony-forming capacity of CD34+ CD71- progenitors co-transduced with control or PLZFOX vectors and knockdown viruses for GFP, ID2, DUSP6 and MYC. All data expressed as mean ± SEM of 3 independent experiments, except (E) which includes 4 experiments (* p < 0.005).
119
4.6 Discussion
In this report, we present evidence that transcription factor networks that control hematopoietic specification also incorporate factors that inhibit normal differentiation, thereby playing an essential role in maintaining the steady-state output of mature cells and priming the system to be able to respond to conditions of immune activation. Although several transcription factors that suppress normal differentiation have been described, their biological role has remained unclear (Gery et al., 2004; Khanna-Gupta et al., 2001). In our model, the significance of this regulatory mechanism is readily apparent in PLZF-deficient cells, which generate increased numbers of mature progeny in a competitive transplant setting, but suffer from a depletion of the progenitor pool. Thus, the lack of negative regulation destines progenitors to eventual exhaustion as a result of excessive differentiation.
At the other extreme, overexpression of PLZF is equally disruptive - shutting off the production of mature cells and causing a build-up of undifferentiated progenitors in vitro and in vivo. Thus, negative control of differentiation is essential for setting the balance between proliferation and differentiation, such that the loss of myeloid progenitors by differentiation is offset by their regeneration from primitive precursors including HSCs.
4.6.1 Modulation of PLZF by stress-induced myeloid cytokines
In addition to its role during homeostasis, negative control of proliferation and differentiation by PLZF is involved in priming the system for increased production of mature cells in response to cytokines. Cytokine stimulation induces progenitor cycling and causes a rapid differentiative response which depletes the progenitor pool (Hirai et al., 2006). Accordingly,
120 sufficient numbers of progenitors must be made available in the event of emergencies; our data indicate that this is dependent on PLZF function. When the production of mature cells must be rapidly ramped up, this restriction on proliferation and differentiation is lifted via cytokine-induced signal transduction pathways, most notably p38 and ERK MAPKs. These pathways directly augment differentiation by activating positive regulators of differentiation, such as C/EBPβ and C/EBPε (Hu et al., 2001; Williamson et al., 2005). We show that activation of ERK by stress-induced cytokines, including IL-3, GM-CSF and G-CSF, also interferes with PLZF-mediated repression of growth and differentiation. Thus, our data reveal greater complexity in the cytokine response, whereby the inactivation of anti- differentiation factors can function as a switch that shifts the balance in favor of differentiation.
Several protein modifications are required for full transcriptional activity of PLZF, notably its acetylation by p300 (Guidez et al., 2005). The acetylated form localizes to discrete nuclear subdomains, while the deacetylated protein has a diffuse nuclear staining
(Guidez et al., 2005). In another example, the proteolytic fragment of the heparin-binding epidermal growth factor inactivates PLZF by interacting with it in the nucleus and targeting it for export (Nanba et al., 2003). In both cases, its activation state is linked with altered cellular localization in the cytoplasm. Consistent with the previous reports (Reid et al., 1995), our data show that in CB-derived progenitors, PLZF is primarily localized in the nucleus.
Following stimulation with IL-3, PLZF is redistributed to the cytoplasm, a localization which can be reversed by MEK inhibitors. This observation illustrates a potential mechanism by which cytokines interfere with PLZF function to quickly ramp up proliferation and differentiation. It remains to be shown whether active ERK inhibits PLZF directly targeting
121 it for nuclear export, or if inhibition involves other factors. It was previously shown that activating FLT3 mutations antagonize PLZF by impairing its interaction with corepressors
(Takahashi et al., 2004). A similar mechanism may be involved in PLZF inactivation by
ERK, with reduced corepressor interaction targeting it for nuclear export.
4.6.2 Molecular mechanisms of differentiation downstream of PLZF
Our evidence indicates that PLZF shifts the balance against differentiation by concertedly repressing expression of myeloid transcription factors GFI1, LEF1 and C/EBPα, and activating MYC and ID2. Normal granulocytic differentiation is orchestrated by the C/EBPα
– GFI1 regulatory circuit (Hock et al., 2003; Laslo et al., 2006). Consistent with this, our results showed that granulocyte numbers were most significantly increased in mice transplanted with PLZF-deficient cells, compared to other lineages. Interestingly, mutations in GFI1 and the loss of LEF1 expression are associated with severe congenital neutropenia, a disorder marked by susceptibility to opportunistic infections due to low neutrophil counts
(Person et al., 2003; Skokowa et al., 2006). Our study therefore provides the rationale to investigate the role of PLZF in this disorder.
ID2 may establish a molecular state refractory to differentiation by repressing tissue- specific transcription factors, such as E2A and SCL (Lasorella et al., 2001). In hematopoiesis, ID2 functions as a negative regulator of B-lymphoid and myeloid commitment owing to its interaction with E2A and PU.1 (Ji et al., 2008). We demonstrated that ID2 mRNA is expressed at the highest levels in HSCs and declines in progenitors.
Furthermore, our results establish that PLZF upregulates ID2 expression in myeloid progenitors and that ID2 is required to maintain the repressive effect of PLZF on
122 differentiation. Thus, a major mechanism by which PLZF regulates myeloid development is by inducing ectopic ID2 expression, which antagonizes other lineage-specific transcription factors.
In conclusion, our findings point to a central role of differentiation-suppressing mechanisms in establishing developmental homeostasis by regulating the balance between progenitor pool and the production of mature cells. More broadly, our study demonstrates the practicality of taking a genetic approach to unraveling the developmental program in primary human hematopoietic cells. Such approach is important, as surrogate models may not entirely recapitulate the intrinsic complexity of the human developmental processes.
123
5. Discussion & Future Directions
5.1. The architecture of the human hematopoietic hierarchy
The current revised model of murine hematopoiesis, which incorporates many of the findings reported over the last several years, is shown in Figure 1-2. Our findings in Chapter 2 and
Chapter 3 enable us to outline, for the first time, a corresponding model that describes human hematopoiesis (Supplementary Figure 3-4). While this work is certainly encouraging considering the limitations of the previous studies in the human system
(Chapter 1.1.6), it would be prudent to note here that the depth of our analysis does not yet approach that in the murine system. For instance, our transplantation of single purified hHSCs estimates a frequency of 1 in 5 cells, compared to approximately 1 in 2 for murine
HSCs (Kiel et al., 2005). While this may be an underestimate reflecting the inherent limitations of human xenotransplantation (see below), it can also be acknowledged that the use of additional markers and/or novel sorting approaches is necessary to achieve the same level of purity. Similarly, in our analysis of the progenitor compartment we achieved cloning efficiencies of ~50% for the LMP fraction, compared with 60-95% commonplace for murine progenitors (Mansson et al., 2007). This means that, at best, half of the sorted LMPs are
‘read-out’ in our culture conditions, which has to be considered when interpreting these results. Future work will improve on these results, and no doubt revise our account of the hematopoietic tree.
124
5.1.1 Hematopoiesis of man and model organisms
The vast majority of studies that have contributed to our understanding of blood development are based on the genetics of mouse models, or biochemical analyses in cell lines. However, we must ask how relevant this body of work is to human biology, in particular clinically relevant biology, including drug targets, prognostic criteria, mechanisms of leukemogenesis
(including the questions of the cell of origin and the role of cancer stem cells), and so on. A popular catch phrase of molecular medicine has been ‘patient-tailored’ medicine. Ironically, the extensive genetic diversity in human populations which contributes to differential disease outcomes and drug responses, cannot in principle be modeled in inbred mouse strains.
Our work is one of the few to carry out detailed genetic investigation in primary human cells. Admittedly, this approach is more challenging, since human primary cells are genetically heterogeneous, more difficult to transduce or culture, and display lower transgene expression, all of which result in reduced ability to assess biological effects. However, we believe that this system provides an invaluable insight into the underlying biology that is not captured by studies of model organisms. For instance, molecular comparison of murine and human HSCs revealed that of the 822 mHSC-specific genes with human homologs, 322 were also hHSC enriched (Ivanova et al., 2002). The fact that 61% were not may reflect technical problems with such a comparison, but likely also reflects underlying molecular differences.
Our analysis revealed global similarities between human and murine progenitor hierachies.
However, there are also substantial differences, in marker expression and lineage potential of the different progenitors (Chapter 5.1.4).
Perhaps the best illustration of the pervasive species-specific differences in stem cell biology and hematopoiesis is the NSG xenograft model itself. The fact that hHSCs engraft
125 and proliferate in NSG mice reproducing many characteristics of human hematopoiesis can be considered a testament to the extent of complimentarity between hematopoiesis in man and mouse. By the same token, the failure to replicate many aspects of normal hematopoiesis and stem cell function by xenografted cells can be considered a testament to the species- specific differences. The list of the latter features is extensive; one only needs to consider the dominance of B-lymphopoiesis, poor support for myeloid lineages, and the lack of sustained erythropoiesis. In addition, most hHSCs engraft mice transiently and do not robustly self- renew; only 25% of clones established in primary mice re-establish a graft in secondary recipients (McKenzie et al., 2006). This is consistent with our results showing that about
25% of mice engrafted with a dose of 1 – 2 HSCs can serially transplant (Chapter 2).
These results suggest that while hHSCs can self-renew in mice, they do not self- renew efficiently. One explanation for these findings is that human and murine HSCs have different requirements for diffusible factors from the stem cell niche. Most of these ligands, including signals of metabolic state, converge on the PI-3K pathway. PI-3K phosphorylates
Akt protein kinase, which in turn positively regulates the mTOR pathway and negatively regulates GSK, a negative regulator of Wnt signal transduction (Engelman, 2009). Akt phosphorylation is subject to negative regulation by the PTEN phosphatase. Deletion of Pten causes hyper-activation of mTOR, which transduces a permissive metabolic signal to cells.
Accordingly, PTEN is a tumor suppressor gene frequently deleted in human cancers (Di
Cristofano and Pandolfi, 2000). Pten-null HSCs cycle at a higher rate, but their proliferation is not linked to self-renewal, resulting in gradual depletion of the stem cell pool (Yilmaz et al., 2006). This effect is reversible upon treatment with the mTOR inhibitor rapamycin. Thus, long-term maintenance of HSCs is critically dependent on the precise flux through the PI-3K
126 pathway. It is possible that the level of PI-3K agonists, as well as other ligands, in the mouse marrow is not titrated for hHSCs resulting in their gradual loss. This idea is further supported by our preliminary evidence showing that xenografted hHSC fail to return to quiescence.
Thus, proliferation and self-renewal of hHSCs in the mouse marrow may be uncoupled. This hypothesis will be tested by treating NSG engrafted with hHSCs with rapamycin followed by secondary transplantation to assess their self-renewal potential. In addition, we will compare the gene expression profiles of xenografted and non-xenografted HSCs and perform pathway analysis to identify other relevant pathways that may limit self-renewal capacity of hHSCs in the murine microenvironement.
Not surprisingly, there are vast species-specific differences between humans and mice reflecting the intervening 80 million years of evolution. There is also substantial genetic heterogeneity within human populations. To draw straight lines between model organism and human biology is pure folly, which often leads to ‘wild goose chases’, such as the purported
CD34- human HSCs (Zanjani et al., 1999). If substantial progress is to be made in translating basic science to the clinic, investigations in primary human cells must become mainstream.
We have laid some of the groundwork for these efforts by elucidating the structure of the human hematopoietic hierarchy.
5.1.2 Improved engraftment of hHSCs in female NSG mice
As part of our effort to characterize the engraftment potential of purified hHSCs we found a striking previously unappreciated sex-specific dichotomy in the support for human HSCs in xenografted NOD/SCID/IL-2Rγcnull (NSG) mice. When multiple HSCs were transplanted, there was a modest, but statistically significant improvement in engraftment of female mice.
127
However, when dose equivalents of single HSCs were transplanted, there was a dramatic difference between the groups. Notably, the HSCs were derived from a pool of both male and female cord bloods and transplanted in parallel into male and female mice, controlling for the effects of donor sex.
Previous studies have shown the effect of estrogen on the proliferative capacity of various hematopoietic cells, notably B-cells (Medina and Kincade, 1994). However, little has been reported on the interaction of sex hormones with HSCs. While xenotransplantation has been routinely carried out now for over a decade, this difference has gone unnoticed. This owes to the fact that the difference in HSC engraftment is alleviated when high doses of
HSCs are transplanted and only become dramatic at limiting dose. This is also potentially why no differences has been found in the course of human bone marrow transplants. While further experiments are required to identify sex-specific mechansims of HSC engraftment, our study has revealed their key role and supports that the sex of the recipients has to be rigorously controlled in the context of stem cell-transplantation studies.
5.1.3 Isolation of human hematopoietic stem cells
Using limiting dilution analysis in NSG mice, we showed that Lin-CD34+CD38-CD90+
CD45RA- (CD90+) fraction of CB is dramatically enriched for hHSCs. Even in female recipients, where improved engraftment was observed, only 1 in 20 CD90+ cells was a functional HSC indicating that the existing stem cell fraction, as reported by Majeti et al. was still highly heterogeneous (Majeti et al., 2007).
To improve the purity, we used two independent methods, one based on Rhodamine exclusion (Rholo), and the other using a new marker, integrin α6 (CD49f). The frequency of
128
HSCs in the CD90+Rholo fraction was 1 in 10 when using female mice, and similar purity was obtained with CD90+CD49fhi cells. Single CD90+RholoCD49fhi cells engrafted 5 of 29 recipients at 18 weeks, corresponding to an HSC frequency of 1 in 5 - 6 cells. Most mice engrafted with single HSCs had both myeloid and B-cells in the bone marrow and spleen, and T-cells in the thymus. Since HSCs are defined by the capacity to sustain all blood lineages long-term from a single cell, these results at long last provide direct experimental proof for the existence of hHSCs.
However, this frequency may be an underestimate for a number of reasons. Since human cells have to be delivered intrafemorally to maximize their engraftment capacity, the delivery volume must be >40 µl. We determined that as many as 40% of single cells are not expelled from the syringe during the injection. Our HSC frequency estimate thus has to be corrected upwards to account for this technical limitation. Following their delivery into the mouse bone marrow, hHSCs encounter a number of xenogeneic barriers which reduce their engraftment capacity. For instance, many cytokines secreted by murine stromal cells do not cross-react with their human receptor homologs. As a result, transplanted human cells do not have the same growth factor support as murine HSCs, which effectively reduces their ability to read out in this assay. With these considerations in mind, our sorted HSC fraction is likely already nearly homogeneous and the read-out frequency of 1 in 5 represents a combination of the above constraints. Studies designed to dissect the molecular program of hHSCs using the next generation sequencing methods are now underway (Chapter 5.2).
A key question raised by our work is the functional status of the Lin-CD34+CD38-
CD90+CD45RA- (CD90-) cells. In a recent paper, Weissman and colleagues attest that these cells are hMPPs due to their lack of self-renewal capacity in the NSG model (Majeti et al.,
129
2007). Our results do not support this conclusion. CD90- cells do reconstitute long-term multilineage hematopoiesis, albeit with a reduced frequency compared to CD90+ cells (1 in
100 compared to 1 in 20 cells). CD90- cells can also engraft secondary recipients, albeit with reduced efficiency. Using established criteria, MPPs should only enable transient repopulation (8-12 wks) and should not give rise to secondary grafts. Thus, using the established criteria, CD90- cells are not MPPs, but may be ST-HSCs. The Majeti et al. study reported than CD90+ and CD90- cells are hierarchically related i.e. CD90+ cells give rise to
CD90-, but not vice versa. Our unpublished results indicate that each fraction can give rise to the other, when HSCs are cultured on stromal support or in vivo. We are currently evaluating if the progeny produced by this phenotypic conversion are also functional HSCs. If that is the case, the conclusions of the Majeti et al. study will be called into question.
5.1.4 The structure of the human progenitor compartment
To determine the composition of the human progenitor compartment, we isolated the major progenitor classes from CB and BM using a novel 7-color sorting scheme, and measured the developmental potential of each fraction side by side in a series of clonal assays. By assembling such a comprehensive ‘roadmap’, we discovered that the classical model, presently thought to describe human hematopoiesis, must be revised. To summarize our findings, myeloid development followed the steps prescribed by the classical model, which include CMPs, GMPs and MEPs. By contrast, lymphoid development proceeded through
LMPs, a novel cell type identified as Lin-CD34+CD38-CD90-CD45RA+ (CD90-RA+), which gave rise to all the lymphoid lineages (B-, T-, and NK cells), but also had the robust capacity to give rise to macrophages, in sharp opposition to the classical model. LMPs from CB and
130
BM showed differential expression of lymphoid markers of CD7 and CD10, previously used to isolate proposed hCLPs (Chapter 1.1.6). However regardless of their CD7 or CD10 expression, LMPs possessed equivalent activity in clonal assays. This finding resolves the long-standing question of whether human multi-lymphoid progenitors are CD10+ or CD7+, by determining that both of these sub-fractions of the CD90-RA+ cells had equivalent lineage potential (Haddad et al., 2004; Hao et al., 2001).
The retention of myeloid potential is a conserved theme in murine lymphopoiesis.
However, the robust myelo-monocytic potential of hLMPs distinguishes them from murine lymphoid progenitors, such as CLPs or ELPs, which retain only residual myeloid potential.
Thus, the myeloid output of early human lymphoid progenitors appears highly regulated cannot be ascribed to developmental plasticity (Welner et al., 2008).
While macrophages were thought to arise uniquely from myeloid cells, DCs have been known to arise from both myeloid and lymphoid progenitors. DCs and macrophages belong to a network of immune cells termed the mononuclear phagocyte system and share a common progenitor, the MDP (Auffray et al., 2009a; Auffray et al., 2009b). The MDP can give rise to common dendritic cell progenitors, which in turn, generate cDCs and pDCs in the periphery, as well as monocytes, which are the precursors of macrophages (Auffray et al.,
2009a; Auffray et al., 2009b). Murine monocytes and MDPs are phenotypically similar to
GMPs and thought to arise in the context of the myeloid lineage. Our findings indicate that
DC and macrophage lineages also remain entangled in the context of human lymphopoiesis.
During lymphoid development, HSCs or early lymphoid progenitors leave the bone marrow and migrate to the thymus where T-cell development takes place. Human ETPs were previously identified as CD34+CD1a- (Haddad et al., 2006). We noted that CD34+CD1a- cells
131 in neonatal thymus were also CD90-CD45RA+CD7+CD10+, and a fraction also expressed C- kit, which remarkably matched the phenotype of CD7+ LMPs. Thus, the current hypothesis is that LMPs migrate from the bone marrow to the thymus. We noted that human ETPs had a combined T/NK potential, but did not have any myeloid potential. Thus, hETPs do not retain myeloid potential of LMPs. B-cell development takes place in the marrow. We showed that
Lin-CD34+CD38+CD10+ cells in the bone marrow were committed B-cell and NK cell precursors. These cells also had no detectable myeloid potential. Since ETPs and pre-B/NK populations are developmentally downstream of LMPs, the myeloid potential of LMPs must be lost at, or prior to, the split between B- and T-cell lineages.
Interestingly, hLMPs display some similarities to murine LMPPs. Both progenitors: i) lack erythroid and megakaryocyte potential, ii) are lymphoid-biased, but possess a robust myeloid output, iii) express a chimeric lympho-myeloid transcriptional program, and iv) lead to transient (4-8 wks), primarily lymphoid, reconstitution in vivo. The notable difference is that LMPPs can give rise to all myeloid lineages, including granulocytic cells, presumably through a GMP intermediate, whereas LMPs only give rise to monocytes and DCs. However, in both murine and human hematopoiesis, the myeloid program is retained at the initial fate decision. The global structure of the murine hematopoietic hierarchy predicted by the revised model (Figure 1-2) is similar to the human hierarchy outlined here. CD90+ and CD90- cells comprise the HSC compartment, although their functional inter-relationship has to be better defined. Either fraction can differentiate into CMPs, which generate GMPs and MEPs; and
LMPs, which give rise to: i) B/NK-cell precursors in the marrow, ii) T/NK-committed ETPs in the thymus, and iii) a macrophage-DC progenitor, or MDP, which can further differentiate
132 into macrophages or myeloid DCs. Our revised model for human hematopoiesis is illustrated in Supplementary Figure 3-4.
5.1.5 Manufacturing of DCs for immune therapy applications
Current efforts in improving the efficacy of DC vaccination are based on development of adjuvants and inhibitors of suppressor cells. However, since DCs cannot proliferate, large numbers of DCs must be injected to prime an effective immune response. For instance, in mouse models of diabetes, >106 DCs must be injected to induce anti-islet autoimmunity
(Ohashi PS, unpublished observations), translating into a dose of 2 – 3 x 109 DCs per patient.
By contrast, current immune therapy protocols utilize >1 x 108 DCs per patient (Czerniecki et al., 2007; Palucka et al., 2006) reflecting the limitations of existing methods for harvesting patient-specific DCs. Presently, two main sources of DCs are used: CD34+ progenitors and peripheral blood monocytes (PBMs). However, both methods have obvious shortcomings that impact the quality and amount of collected mature DCs. In particular, CD34+ progenitors can be expanded, but contain numerous contaminating cell types with no DC potential, which may impair differentiation or maturation of DCs. PBMs are abundant in peripheral blood, but cannot be expanded, placing an upward theoretical yield of 2 x 108 DCs per leukopheresis procedure. Understanding of the progenitor origins of human DCs would allow isolation of pure DC progenitors and tailoring of expansion, differentiation and maturation regimens to maximize their output.
Since LMPs possess a robust DC potential, we propose that these cells be recovered from patient mPB using a tailored antibody column or a kit, for instance a positive selection column for CD34 and CD45RA, which will enrich for GMPs and LMPs, both of which have
133
DC potential. Following isolation, cells will be processed to manufacture DCs as described in
Chapter 3. Using this protocol, we estimate a yield of >2 x 109 DCs from a single leukopheresis procedure with (G-CSF) mobilized PB. This would allow an increased vaccine dose to be administered; whether it will correlate with increased efficacy remains to be seen.
5.1.6 Self-renewal of hematopoietic stem cells
The long-standing aim of the project described in Chapter 2 is to isolate a homogeneous population of human HSCs. Given this resource, how do we begin elucidating transcription factor networks and signaling pathways that contribute to stem cell function?
HSCs fundamentally differ from their progeny in their capacity to undergo long-term self-renewal. As such, fate decisions executed by HSCs are dramatically different from progenitors. The classical model predicts that HSCs do not undergo lineage determination, however this notion is unlikely to be upheld, since HSCs undergo active lineage priming, and already display an intrinsic myeloid lineage bias. However, it is clear that HSCs do face a unique decision process that involves balancing self-renewal and differentiation. Self- renewal refers to the capacity of stem cells to regenerate multipotent daughter cells upon cell division. HSCs also continuously generate differentiated progeny to maintain hematopoiesis.
Thus, to maintain steady supply of blood cells, stem cells must maintain a balance between self-renewal and differentiation.
It is clear that self-renewal is a compound property impacted by many distinct aspects of general cellular function, such as metabolism, cell cycle, survival, and so on. In order to self-renew, a stem cell has to survive, grow and progress through the cell cycle, as does any other cell. As such, a conditional knockout of Gapdh would probably result in loss of stem
134 cells. However, Gapdh is not a stem cell-specific gene, whereas self-renewal is a unique property of stem cells. Thus, we must distill the requirement for self-renewal to stem cell- specific genes. In the mouse, previous studies have provided an extensive profiling of the
HSC transcriptiome (Ivanova et al., 2002), however even these will have to be revised with the advent of the next generation sequencing methods (Chapter 5.2.1). In the human, the previous expression profiling studies are of limited use, since the populations that they profiled were highly heterogeneous (Georgantas et al., 2004). Thus, identifying HSC-specific genes is the first step towards decoding the molecular networks that control self-renewal.
Self-renewal can be assessed by measuring the capacity of transplanted donor HSCs to sustain long-term hematopoiesis, or even more rigorously by serial transplantation. Using these approaches, recent studies have identified an extensive ‘laundry list’ of genes with roles in HSC self-renewal. These include transcription factors (e.g. MYB, GATA-2), Hox genes
(e.g. HoxB4), chromatin modifiers (e.g. BMI1), growth factors (e.g. KIT ligand), signaling molecules (e.g. TCF), and cell cycle regulators (e.g. p21, p53) (Zon, 2008). However, the meta-level logic of how these components fit together into a fate determination network remains elusive hampering our ability to design rational strategies for expansion and manipulation of HSCs for clinical benefit.
A single remarkable example of self-renewal regulation is HoxB4. Overexpression of
HoxB4 causes a dramatic 1,000-fold expansion of mouse HSCs (Antonchuk et al., 2002).
Furthermore, knockdown of a HOX cofactor PBX1 results in a further 20-fold enhancement of self-renewal activity i.e. >104-fold expansion of the stem cell compartment (Krosl et al.,
2003). Importantly, expansion of HSCs occurs without enhanced proliferation, loss of multi- lineage differentiation capacity, or increased incidence of leukemogenesis. These findings
135 indicate that HoxB4 selectively alters the fate of HSCs to undergo symmetric (i.e. expansion) self-renewal divisions, without sacrificating long-term capacity to undergo differentiation divisions. HoxB4 was also found to expand hHSCs, albeit to a lesser extent (Sorrentino,
2004). However, despite the profound magnitude of this effect, virtually nothing is known about its molecular basis. This status quo represents the general state of the field. Thus, grasping the larger-scale levels of organization of these molecular components represents the next crucial goal of research in stem cell biology.
5.2 Identification of higher-order networks in self-renewal and lineage choice
5.2.1 Using next-generation sequence census methods
The first step towards assembling regulatory networks involved in self-renewal and lineage choice is to identify a set of stem cell- and progenitor-specific transcription factors. Since
HSCs are very rare, they are not easily susceptible to genomic profiling methods. Moreover, traditional microarray profiling methods constrain the outcome to available probe sets. This is problem is likely greater than previously anticipated, due to the complexity of the human transcriptome, only a small portion whereof is captured on existing array platforms (Birney et al., 2007). Thus, there is increased appreciation for the fact that the transcriptome landscape has to be surveyed using unbiased deep sequencing approaches i.e. RNA-seq (Mortazavi et al., 2008). Current methods increase sensitivity enabling reads from a relatively small cell numbers (103-104), and feature improved analytic tools to extract information from the vast number of sequencing runs. It is well known that a large fraction of transcripts, especially of
136 lower abundance (a category that includes most transcription factors), are not translated.
Meanwhile, reliable proteomic methods are not available for analysis of low cell numbers.
One way of overcoming this problem could be to isolate mRNAs that are associated with polyribosomes, and thus likely undergoing translation (del Prete et al., 2007). Thus, the first step to elucidation of stem cell and lineage commitment networks will be to perform RNA- seq analysis on purified human HSCs and critical progenitor populations, such as LMPs.
Another incentive for employing sequence census methods is to obtain a measure of functional variation. It is well-known that human populations display a high degree of sequence variation, however it has yet to be appreciated how this pool of genetic variation impacts functional gene products. Since stem and progenitor cell populations can be isolated from individual CB, BM or mPB samples, patient-specific profiles can be constructed. This data would yield invaluable information on how stem cell-specific genes are expressed across the population. It would also allow data to be stratified by various characteristics, such as the age of the donor. HSC gene expression, including factors involved in genome survelliance, such as INK4a/ARF, p53 and Hmga2, changes during aging limiting self-renewal (and hence also regenerative) potential of ‘old’ HSCs (Nishino et al., 2008). On the other hand, increased level of these molecules is thought to protect HSCs from accumulation of cancerous lesions that cause leukemias. It would be of tremendous interest to obtain a more complete genomic view of this process in human samples.
Going from transcriptome-based RNA-seq studies to network analysis is particularly challenging. Significant computational resources have been developed to obtain pathway information from transcriptome data, for instance the connectivity map (Lamb et al., 2006).
However, these represent theoretical networks, and do not include gene network information,
137 which are most relevant in developmental fate decisions. The next level of analysis will be to construct gene regulatory networks centered on a subset of functionally validated HSC and progenitor-specific transcription factors using global chromatin immunoprecipitation (ChIP).
As with transcriptome profiling, traditional ChIP-ChIP uses microarray technology to obtain a set of genomic promoter regions bound by each transcription factor. This approach biases binding site discovery to the represented probeset, which typically contains known promoter- proximal (<500 bp) sequences. By contrast, the new ChIP-seq approach permits an unbiased sampling of the binding site distribution across the genome using the same deep sequencing and data analysis platforms as RNA-seq (Johnson et al., 2007). Using ChIPseq, we can begin to construct first-order gene regulatory networks that include a handful of stem cell-specific transcription factors and their immediate target genes. This process is recursive; as new gene targets are identified and functionally validated, we can develop new antibodies and perform additional ChIP-seq experiments. However, the main objective is to construct a ‘core’ self- renewal network, similar to the partially completed embryonic stem cell (ESC) pluripotency network (Kim et al., 2008).
5.2.2 Networks involved in lineage outcome
While networks that control lineage outcomes have been extensively studied and modeled
(Chapter 1.2), the majority of studies were carried out using murine models. The relevance of these studies for human hematopoiesis is generally difficult to gauge, unless the relevant genes are also involved in leukemogenesis, which represents a distortion of the normal developmental hierarchy. In such cases, the in vivo manifestation of the disease in human patients can be compared directly to the murine models. Leukemogenesis commonly
138 involves alterations in the transcription factor networks that govern normal development, either directly via mutations in the relevant transcription factors, or the components of the broader gene regulatory network, such as signaling molecules. For instance, acute myeloid leukemias (AMLs) are often associated with loss of function mutations of PU.1 and C/EBPα
(Rosenbauer and Tenen, 2007), while acute lymphoblastic leukemias are associated with the loss of function mutations or deletions of Ikaros and Pax5 (Mullighan et al., 2009). Not surprisingly, there are many species-specific differences in the effects and mechanisms of various oncogenes (Kennedy and Barabe, 2008). These differences must to large extent reflect the underlying differences in the normal developmental networks.
Our identification of the progenitor classes in human hematopoiesis persents another opportunity to obtain transcript census data at the critical steps of lineage specification. These include the LMP, ETP and pre-pro-B cells on the lymphoid arm; CMP, GMP, MEP on the myeloid side. Transcription factors that display a pattern of expression characteristic of a role in lineage specification will be subjected to ChIPseq analysis to build higher-order networks, similarly to the proposal for HSCs. It is clear that identification of broader transcriptional networks that regulate differentiation and lineage outcome in primary human progenitors will be insightful in both normal and leukemic hematopoiesis.
5.3 Concluding remarks
The investigation of self-renewal and lineage determination in blood stem cells has thus far followed a reductionist paradigm. Since stem and progenitor cells are exquisitely rare and not amenable to robust ex vivo expansion, most studies have relied on the power of mouse model
139 genetics to dissect the contributions of various genes. The same principles can be applied to the study of human cells using retroviral gene transfer to gain detailed insight into the role of a single gene, PLZF, in myeloid development (Chapter 4). However, our ability to integrate these individual studies into a coherent ‘omic’ portrait of a stem cell is still rudimentary. The lack of this integrated picture precludes an understanding of how various pertubrations, such as signals or drugs, alter the global state of the network. If we knew how the components of the self-renewal network e.g. HoxB4, Bmi1, and so on, were connected to each other and to basic cellular processes, such as metabolism, cell cycle, apoptosis and so on, we could model and design small molecules to target specific nodes within the network in order to achieve desired outcomes, such as stem cell expansion, increased regenerative capacity in aging, and so on. The same applies for individual cell types that make up the hematopoietic hierarchy.
While we know individual molecules that play a role in myeloid or lymphoid specification, we have little conception of how these are built into networks. To achieve this daunting task, we must work with defined homogeneous cell populations, and have the ability to interrogate them at an unprecedented depth. Recently, the ENCODE consortium revealed a new level of transcriptome analysis based on deep sequencing technologies. Advances in high-throughput sequencing and analysis platforms has reduced the cost and the number of cells needed for such analysis. Based on my graduate work, and the work of many others, we can now isolate defined populations of human progenitors and stem cells. Both human and murine cells must now be interrogated using a combination of sequence census methods and whole-genome
RNAi-based screening methodologies, to create network maps of core transcription factors in self-renewal and lineage outcomes.
140
6. REFERENCES
Abkowitz, J.L., Golinelli, D., Harrison, D.E., and Guttorp, P. (2000). In vivo kinetics of murine hemopoietic stem cells. Blood 96, 3399-3405.
Adolfsson, J., Borge, O.J., Bryder, D., Theilgaard-Monch, K., Astrand-Grundstrom, I., Sitnicka, E., Sasaki, Y., and Jacobsen, S.E. (2001). Upregulation of Flt3 expression within the bone marrow Lin(- )Sca1(+)c-kit(+) stem cell compartment is accompanied by loss of self-renewal capacity. Immunity 15, 659-669.
Adolfsson, J., Mansson, R., Buza-Vidas, N., Hultquist, A., Liuba, K., Jensen, C.T., Bryder, D., Yang, L., Borge, O.J., Thoren, L.A., et al. (2005). Identification of Flt3+ lympho-myeloid stem cells lacking erythro- megakaryocytic potential a revised road map for adult blood lineage commitment. Cell 121, 295-306.
Akashi, K., He, X., Chen, J., Iwasaki, H., Niu, C., Steenhard, B., Zhang, J., Haug, J., and Li, L. (2003). Transcriptional accessibility for genes of multiple tissues and hematopoietic lineages is hierarchically controlled during early hematopoiesis. Blood 101, 383-389.
Akashi, K., Reya, T., Dalma-Weiszhausz, D., and Weissman, I.L. (2000a). Lymphoid precursors. Curr Opin Immunol 12, 144-150.
Akashi, K., Traver, D., Miyamoto, T., and Weissman, I.L. (2000b). A clonogenic common myeloid progenitor that gives rise to all myeloid lineages. Nature 404, 193-197.
Allman, D., Sambandam, A., Kim, S., Miller, J.P., Pagan, A., Well, D., Meraz, A., and Bhandoola, A. (2003). Thymopoiesis independent of common lymphoid progenitors. Nat Immunol 4, 168-174.
Antonchuk, J., Sauvageau, G., and Humphries, R.K. (2002). HOXB4-induced expansion of adult hematopoietic stem cells ex vivo. Cell 109, 39-45.
Arinobu, Y., Mizuno, S., Chong, Y., Shigematsu, H., Iino, T., Iwasaki, H., Graf, T., Mayfield, R., Chan, S., Kastner, P., et al. (2007). Reciprocal activation of GATA-1 and PU.1 marks initial specification of hematopoietic stem cells into myeloerythroid and myelolymphoid lineages. Cell Stem Cell 1, 416-427.
Arrighi, J.F., Hauser, C., Chapuis, B., Zubler, R.H., and Kindler, V. (1999). Long-term culture of human CD34(+) progenitors with FLT3-ligand, thrombopoietin, and stem cell factor induces extensive amplification of a CD34(-)CD14(-) and a CD34(-)CD14(+) dendritic cell precursor. Blood 93, 2244-2252.
Auffray, C., Fogg, D.K., Narni-Mancinelli, E., Senechal, B., Trouillet, C., Saederup, N., Leemput, J., Bigot, K., Campisi, L., Abitbol, M., et al. (2009a). CX3CR1+ CD115+ CD135+ common macrophage/DC precursors and the role of CX3CR1 in their response to inflammation. J Exp Med 206, 595-606.
Auffray, C., Sieweke, M.H., and Geissmann, F. (2009b). Blood monocytes: development, heterogeneity, and relationship with dendritic cells. Annu Rev Immunol 27, 669-692.
Balciunaite, G., Ceredig, R., Massa, S., and Rolink, A.G. (2005). A B220+ CD117+ CD19- hematopoietic progenitor with potent lymphoid and myeloid developmental potential. Eur J Immunol 35, 2019-2030.
141
Barabe, F., Kennedy, J.A., Hope, K.J., and Dick, J.E. (2007). Modeling the initiation and progression of human acute leukemia in mice. Science 316, 600-604.
Barna, M., Hawe, N., Niswander, L., and Pandolfi, P.P. (2000). Plzf regulates limb and axial skeletal patterning. Nat Genet 25, 166-172.
Barna, M., Merghoub, T., Costoya, J.A., Ruggero, D., Branford, M., Bergia, A., Samori, B., and Pandolfi, P.P. (2002). Plzf mediates transcriptional repression of HoxD gene expression through chromatin remodeling. Dev Cell 3, 499-510.
Barreda, D.R., Hanington, P.C., and Belosevic, M. (2004). Regulation of myeloid development and function by colony stimulating factors. Dev Comp Immunol 28, 509-554.
Baum, C.M., Weissman, I.L., Tsukamoto, A.S., Buckle, A.-S., and Peault, B. (1992a). Isolation of a candidate human hematopoietic stem-cell population. Proc Natl Acad Sci USA 89, 2804-2808.
Baum, C.M., Weissman, I.L., Tsukamoto, A.S., Buckle, A.M., and Peault, B. (1992b). Isolation of a candidate human hematopoietic stem-cell population. Proc Natl Acad Sci U S A 89, 2804-2808.
Becker, A.J., McCulloch, E.A., and Till, J.E. (1963). Cytological Demonstration of the Clonal Nature of Spleen Colonies Derived from Transplanted Mouse Marrow Cells. Nature 197, 452-454.
Bell, J.J., and Bhandoola, A. (2008). The earliest thymic progenitors for T cells possess myeloid lineage potential. Nature 452, 764-767.
Benveniste, P., Cantin, C., Hyam, D., and Iscove, N.N. (2003). Hematopoietic stem cells engraft in mice with absolute efficiency. Nat Immunol 4, 708-713.
Bertoncello, I., Hodgson, G.S., and Bradley, T.R. (1985). Multiparameter analysis of transplantable hemopoietic stem cells: I. The separation and enrichment of stem cells homing to marrow and spleen on the basis of rhodamine-123 fluorescence. Exp Hematol 13, 999-1006.
Bhandoola, A., Sambandam, A., Allman, D., Meraz, A., and Schwarz, B. (2003). Early T lineage progenitors: new insights, but old questions remain. J Immunol 171, 5653-5658.
Bhatia, M., Wang, J.C., Kapp, U., Bonnet, D., and Dick, J.E. (1997). Purification of primitive human hematopoietic cells capable of repopulating immune-deficient mice. Proc Natl Acad Sci U S A 94, 5320- 5325.
Birney, E., Stamatoyannopoulos, J.A., Dutta, A., Guigo, R., Gingeras, T.R., Margulies, E.H., Weng, Z., Snyder, M., Dermitzakis, E.T., Thurman, R.E., et al. (2007). Identification and analysis of functional elements in 1% of the human genome by the ENCODE pilot project. Nature 447, 799-816.
Bjerregaard, M.D., Jurlander, J., Klausen, P., Borregaard, N., and Cowland, J.B. (2003). The in vivo profile of transcription factors during neutrophil differentiation in human bone marrow. Blood 101, 4322-4332.
Boehm, T., and Bleul, C.C. (2007). The evolutionary history of lymphoid organs. Nat Immunol 8, 131- 135.
Bradley, T.R., and Metcalf, D. (1966). The growth of mouse bone marrow cells in vitro. Aust J Exp Biol Med Sci 44, 287-299.
142
Buaas, F.W., Kirsh, A.L., Sharma, M., McLean, D.J., Morris, J.L., Griswold, M.D., de Rooij, D.G., and Braun, R.E. (2004). Plzf is required in adult male germ cells for stem cell self-renewal. Nat Genet 36, 647- 652.
Burns, C.E., and Zon, L.I. (2002). Portrait of a stem cell. Dev Cell 3, 612-613.
Cannistra, S.A., and Griffin, J.D. (1988). Regulation of the production and function of granulocytes and monocytes. Semin Hematol 25, 173-188.
Chang, H.H., Hemberg, M., Barahona, M., Ingber, D.E., and Huang, S. (2008). Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature 453, 544-547.
Chen, Z., Brand, N.J., Chen, A., Chen, S.J., Tong, J.H., Wang, Z.Y., Waxman, S., and Zelent, A. (1993). Fusion between a novel Kruppel-like zinc finger gene and the retinoic acid receptor-alpha locus due to a variant t(11;17) translocation associated with acute promyelocytic leukaemia. Embo J 12, 1161-1167.
Chomarat, P., Dantin, C., Bennett, L., Banchereau, J., and Palucka, A.K. (2003). TNF skews monocyte differentiation from macrophages to dendritic cells. J Immunol 171, 2262-2269.
Christensen, J.L., and Weissman, I.L. (2001). Flk-2 is a marker in hematopoietic stem cell differentiation: a simple method to isolate long-term stem cells. Proc Natl Acad Sci U S A 98, 14541-14546.
Costoya, J.A., Hobbs, R.M., Barna, M., Cattoretti, G., Manova, K., Sukhwani, M., Orwig, K.E., Wolgemuth, D.J., and Pandolfi, P.P. (2004). Essential role of Plzf in maintenance of spermatogonial stem cells. Nat Genet 36, 653-659.
Czerniecki, B.J., Koski, G.K., Koldovsky, U., Xu, S., Cohen, P.A., Mick, R., Nisenbaum, H., Pasha, T., Xu, M., Fox, K.R., et al. (2007). Targeting HER-2/neu in early breast cancer development using dendritic cells with staged interleukin-12 burst secretion. Cancer Res 67, 1842-1852.
D'Amico, A., and Wu, L. (2003). The early progenitors of mouse dendritic cells and plasmacytoid predendritic cells are within the bone marrow hemopoietic precursors expressing Flt3. J Exp Med 198, 293-303.
Dakic, A., Wu, L., and Nutt, S.L. (2007). Is PU.1 a dosage-sensitive regulator of haemopoietic lineage commitment and leukaemogenesis? Trends Immunol 28, 108-114. del Prete, M.J., Vernal, R., Dolznig, H., Mullner, E.W., and Garcia-Sanz, J.A. (2007). Isolation of polysome-bound mRNA from solid tissues amenable for RT-PCR and profiling experiments. RNA 13, 414-421.
Di Cristofano, A., and Pandolfi, P.P. (2000). The multiple roles of PTEN in tumor suppression. Cell 100, 387-390.
Dias, S., Mansson, R., Gurbuxani, S., Sigvardsson, M., and Kee, B.L. (2008). E2A proteins promote development of lymphoid-primed multipotent progenitors. Immunity 29, 217-227.
Dick, J.E. (2008). Stem cell concepts renew cancer research. Blood 112, 4793-4807.
Dick, J.E., Magli, M.C., Huszar, D., Phillips, R.A., and Bernstein, A. (1985). Introduction of a selectable gene into primitive stem cells capable of long-term reconstitution of the hemopoietic system of W/Wv mice. Cell 42, 71-79.
143
Donahue, R.E., Seehra, J., Metzger, M., Lefebvre, D., Rock, B., Carbone, S., Nathan, D.G., Garnick, M., Sehgal, P.K., Laston, D., et al. (1988). Human IL-3 and GM-CSF act synergistically in stimulating hematopoiesis in primates. Science 241, 1820-1823.
Engelman, J.A. (2009). Targeting PI3K signalling in cancer: opportunities, challenges and limitations. Nat Rev Cancer 9, 550-562.
Fisher, A.G. (2002). Cellular identity and lineage choice. Nat Rev Immunol 2, 977-982.
Fogg, D.K., Sibon, C., Miled, C., Jung, S., Aucouturier, P., Littman, D.R., Cumano, A., and Geissmann, F. (2006). A clonogenic bone marrow progenitor specific for macrophages and dendritic cells. Science 311, 83-87.
Ford, C., Hamerton, J., Barns, W., and Loutet, J. (1956). Cytological identification of radiation chimeras. Nature 177, 452-454.
Forsberg, E.C., Prohaska, S.S., Katzman, S., Heffner, G.C., Stuart, J.M., and Weissman, I.L. (2005). Differential expression of novel potential regulators in hematopoietic stem cells. PLoS Genet 1, e28.
Foss, D.L., Donskoy, E., and Goldschneider, I. (2001). The importation of hematogenous precursors by the thymus is a gated phenomenon in normal adult mice. J Exp Med 193, 365-374.
Gabrilovich, D. (2004). Mechanisms and functional significance of tumour-induced dendritic-cell defects. Nat Rev Immunol 4, 941-952.
Galy, A., Travis, M., Cen, D., and Chen, B. (1995). Human T, B, natural killer, and dendritic cells arise from a common bone marrow progenitor cell subset. Immunity 3, 459-473.
Georgantas, R.W., 3rd, Tanadve, V., Malehorn, M., Heimfeld, S., Chen, C., Carr, L., Martinez-Murillo, F., Riggins, G., Kowalski, J., and Civin, C.I. (2004). Microarray and serial analysis of gene expression analyses identify known and novel transcripts overexpressed in hematopoietic stem cells. Cancer Res 64, 4434- 4441.
Georgopoulos, K. (2002). Haematopoietic cell-fate decisions, chromatin regulation and ikaros. Nat Rev Immunol 2, 162-174.
Gery, S., Park, D.J., Vuong, P.T., Chih, D.Y., Lemp, N., and Koeffler, H.P. (2004). Retinoic acid regulates C/EBP homologous protein expression (CHOP), which negatively regulates myeloid target genes. Blood 104, 3911-3917.
Gounari, F., Aifantis, I., Martin, C., Fehling, H.J., Hoeflinger, S., Leder, P., von Boehmer, H., and Reizis, B. (2002). Tracing lymphopoiesis with the aid of a pTalpha-controlled reporter gene. Nat Immunol 3, 489-496.
Graf, T. (2002). Differentiation plasticity of hematopoietic cells. Blood 99, 3089-3101.
Graf, T., McNagny, K., Brady, G., and Frampton, J. (1992). Chicken "erythroid" cells transformed by the Gag-Myb-Ets-encoding E26 leukemia virus are multipotent. Cell 70, 201-213.
Guidez, F., Howell, L., Isalan, M., Cebrat, M., Alani, R.M., Ivins, S., Hormaeche, I., McConnell, M.J., Pierce, S., Cole, P.A., et al. (2005). Histone acetyltransferase activity of p300 is required for transcriptional repression by the promyelocytic leukemia zinc finger protein. Mol Cell Biol 25, 5552-5566.
144
Haddad, R., Guardiola, P., Izac, B., Thibault, C., Radich, J., Delezoide, A.L., Baillou, C., Lemoine, F.M., Gluckman, J.C., Pflumio, F., et al. (2004). Molecular characterization of early human T/NK and B- lymphoid progenitor cells in umbilical cord blood. Blood 104, 3918-3926.
Haddad, R., Guimiot, F., Six, E., Jourquin, F., Setterblad, N., Kahn, E., Yagello, M., Schiffer, C., Andre- Schmutz, I., Cavazzana-Calvo, M., et al. (2006). Dynamics of thymus-colonizing cells during human development. Immunity 24, 217-230.
Hao, Q.L., Zhu, J., Price, M.A., Payne, K.J., Barsky, L.W., and Crooks, G.M. (2001). Identification of a novel, human multilymphoid progenitor in cord blood. Blood 97, 3683-3690.
Harrison, D.E. (1980). Competitive repopulation: a new assay for long-term stem cell functional capacity. Blood 55, 77-81.
Hirai, H., Zhang, P., Dayaram, T., Hetherington, C.J., Mizuno, S., Imanishi, J., Akashi, K., and Tenen, D.G. (2006). C/EBPbeta is required for 'emergency' granulopoiesis. Nat Immunol 7, 732-739.
Hock, H., Hamblen, M.J., Rooke, H.M., Traver, D., Bronson, R.T., Cameron, S., and Orkin, S.H. (2003). Intrinsic requirement for zinc finger transcription factor Gfi-1 in neutrophil differentiation. Immunity 18, 109-120.
Hodgson, G.S., and Bradley, T.R. (1979). Properties of hematopoietic stem cells surviving 5-fluorouracil treatment: Evidence for a pre-CFU-S cell? Nature 281, 381-382.
Hoebeke, I., De Smedt, M., Stolz, F., Pike-Overzet, K., Staal, F.J., Plum, J., and Leclercq, G. (2007). T-, B- and NK-lymphoid, but not myeloid cells arise from human CD34(+)CD38(-)CD7(+) common lymphoid progenitors expressing lymphoid-specific genes. Leukemia 21, 311-319.
Hong, S.H., David, G., Wong, C.W., Dejean, A., and Privalsky, M.L. (1997). SMRT corepressor interacts with PLZF and with the PML-retinoic acid receptor alpha (RARalpha) and PLZF-RARalpha oncoproteins associated with acute promyelocytic leukemia. Proc Natl Acad Sci U S A 94, 9028-9033.
Hu, J., Roy, S.K., Shapiro, P.S., Rodig, S.R., Reddy, S.P., Platanias, L.C., Schreiber, R.D., and Kalvakolanu, D.V. (2001). ERK1 and ERK2 activate CCAAAT/enhancer-binding protein-beta- dependent gene transcription in response to interferon-gamma. J Biol Chem 276, 287-297.
Huang, S., Guo, Y.P., May, G., and Enver, T. (2007). Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev Biol 305, 695-713.
Hume, D.A., Ross, I.L., Himes, S.R., Sasmono, R.T., Wells, C.A., and Ravasi, T. (2002). The mononuclear phagocyte system revisited. J Leukoc Biol 72, 621-627.
Igarashi, H., Gregory, S.C., Yokota, T., Sakaguchi, N., and Kincade, P.W. (2002). Transcription from the RAG1 locus marks the earliest lymphocyte progenitors in bone marrow. Immunity 17, 117-130.
Ikuta, K., and Weissman, I.L. (1992). Evidence that hematopoietic stem cells express mouse c-kit but do not depend on steel factor for their generation. Proc Natl Acad Sci U S A 89, 1502-1506.
Ito, M., Hiramatsu, H., Kobayashi, K., Suzue, K., Kawahata, M., Hioki, K., Ueyama, Y., Koyanagi, Y., Sugamura, K., Tsuji, K., et al. (2002). NOD/SCID/gamma(c)(null) mouse: an excellent recipient mouse model for engraftment of human cells. Blood 100, 3175-3182.
145
Ivanova, N.B., Dimos, J.T., Schaniel, C., Hackney, J.A., Moore, K.A., and Lemischka, I.R. (2002). A stem cell molecular signature. Science 298, 601-604.
Iwasaki, H., and Akashi, K. (2007). Myeloid lineage commitment from the hematopoietic stem cell. Immunity 26, 726-740.
Iwasaki, H., Mizuno, S., Arinobu, Y., Ozawa, H., Mori, Y., Shigematsu, H., Takatsu, K., Tenen, D.G., and Akashi, K. (2006). The order of expression of transcription factors directs hierarchical specification of hematopoietic lineages. Genes Dev 20, 3010-3021.
Iwasaki, H., Somoza, C., Shigematsu, H., Duprez, E.A., Iwasaki-Arai, J., Mizuno, S., Arinobu, Y., Geary, K., Zhang, P., Dayaram, T., et al. (2005). Distinctive and indispensable roles of PU.1 in maintenance of hematopoietic stem cells and their differentiation. Blood 106, 1590-1600.
Ji, M., Li, H., Suh, H.C., Klarmann, K.D., Yokota, Y., and Keller, J.R. (2008). Id2 intrinsically regulates lymphoid and erythroid development via interaction with different target proteins. Blood 112, 1068-1077.
Johansen, L.M., Iwama, A., Lodie, T.A., Sasaki, K., Felsher, D.W., Golub, T.R., and Tenen, D.G. (2001). c-Myc is a critical target for c/EBPalpha in granulopoiesis. Mol Cell Biol 21, 3789-3806.
Johnson, D.S., Mortazavi, A., Myers, R.M., and Wold, B. (2007). Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497-1502.
Kamel-Reid, S., and Dick, J.E. (1988). Engraftment of immune-deficient mice with human hematopoietic stem cells. Science 242, 1706-1709.
Karsunky, H., Merad, M., Cozzio, A., Weissman, I.L., and Manz, M.G. (2003). Flt3 ligand regulates dendritic cell development from Flt3+ lymphoid and myeloid-committed progenitors to Flt3+ dendritic cells in vivo. J Exp Med 198, 305-313.
Kennedy, J.A., and Barabe, F. (2008). Investigating human leukemogenesis: from cell lines to in vivo models of human leukemia. Leukemia 22, 2029-2040.
Khanna-Gupta, A., Zibello, T., Sun, H., Lekstrom-Himes, J., and Berliner, N. (2001). C/EBP epsilon mediates myeloid differentiation and is regulated by the CCAAT displacement protein (CDP/cut). Proc Natl Acad Sci U S A 98, 8000-8005.
Kiel, M.J., Yilmaz, O.H., Iwashita, T., Terhorst, C., and Morrison, S.J. (2005). SLAM family receptors distinguish hematopoietic stem and progenitor cells and reveal endothelial niches for stem cells. Cell 121, 1109-1121.
Kim, J., Chu, J., Shen, X., Wang, J., and Orkin, S.H. (2008). An extended transcriptional network for pluripotency of embryonic stem cells. Cell 132, 1049-1061.
Kobayashi-Osaki, M., Ohneda, O., Suzuki, N., Minegishi, N., Yokomizo, T., Takahashi, S., Lim, K.C., Engel, J.D., and Yamamoto, M. (2005). GATA motifs regulate early hematopoietic lineage-specific expression of the Gata2 gene. Mol Cell Biol 25, 7005-7020.
Kondo, M., Scherer, D.C., Miyamoto, T., King, A.G., Akashi, K., Sugamura, K., and Weissman, I.L. (2000). Cell-fate conversion of lymphoid-committed progenitors by instructive actions of cytokines. Nature 407, 383-386.
146
Kondo, M., Wagers, A., Manz, M., Prohaska, S., Scherer, D., Beilhack, G., Shizuru, J., and Weissman, I.L. (2003). Biology of hematopoietic stem cells and progenitors:Implications for clinical application. Annu Rev Immunol 21, 759-806.
Kondo, M., Weissman, I.L., and Akashi, K. (1997). Identification of clonogenic common lymphoid progenitors in mouse bone marrow. Cell 91, 661-672.
Kovalovsky, D., Uche, O.U., Eladad, S., Hobbs, R.M., Yi, W., Alonzo, E., Chua, K., Eidson, M., Kim, H.J., Im, J.S., et al. (2008). The BTB-zinc finger transcriptional regulator PLZF controls the development of invariant natural killer T cell effector functions. Nat Immunol 9, 1055-1064.
Krosl, J., Beslu, N., Mayotte, N., Humphries, R.K., and Sauvageau, G. (2003). The competitive nature of HOXB4-transduced HSC is limited by PBX1: the generation of ultra-competitive stem cells retaining full differentiation potential. Immunity 18, 561-571.
Krutzik, S.R., Tan, B., Li, H., Ochoa, M.T., Liu, P.T., Sharfstein, S.E., Graeber, T.G., Sieling, P.A., Liu, Y.J., Rea, T.H., et al. (2005). TLR activation triggers the rapid differentiation of monocytes into macrophages and dendritic cells. Nat Med 11, 653-660.
Kulessa, H., Frampton, J., and Graf, T. (1995). GATA-1 reprograms avian myelomonocytic cell lines into eosinophils, thromboblasts, and erythroblasts. Genes Dev 9, 1250-1262.
La Motte-Mohs, R.N., Herer, E., and Zuniga-Pflucker, J.C. (2004). Induction of T cell development from human cord blood hematopoietic stem cells by Delta-like 1 in vitro. Blood.
Labbaye, C., Spinello, I., Quaranta, M.T., Pelosi, E., Pasquini, L., Petrucci, E., Biffoni, M., Nuzzolo, E.R., Billi, M., Foa, R., et al. (2008). A three-step pathway comprising PLZF/miR-146a/CXCR4 controls megakaryopoiesis. Nat Cell Biol 10, 788-801.
Lai, A.Y., and Kondo, M. (2006). Asymmetrical lymphoid and myeloid lineage commitment in multipotent hematopoietic progenitors. J Exp Med 203, 1867-1873.
Lai, A.Y., and Kondo, M. (2007). Identification of a bone marrow precursor of the earliest thymocytes in adult mouse. Proc Natl Acad Sci U S A 104, 6311-6316.
Laiosa, C.V., Stadtfeld, M., and Graf, T. (2006a). Determinants of lymphoid-myeloid lineage diversification. Annu Rev Immunol 24, 705-738.
Laiosa, C.V., Stadtfeld, M., Xie, H., de Andres-Aguayo, L., and Graf, T. (2006b). Reprogramming of committed T cell progenitors to macrophages and dendritic cells by C/EBP alpha and PU.1 transcription factors. Immunity 25, 731-744.
Lamb, J., Crawford, E.D., Peck, D., Modell, J.W., Blat, I.C., Wrobel, M.J., Lerner, J., Brunet, J.P., Subramanian, A., Ross, K.N., et al. (2006). The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929-1935.
Lapidot, T., Pflumio, F., Doedens, M., Murdoch, B., Williams, D.E., and Dick, J.E. (1992). Cytokine stimulation of multilineage hematopoiesis from immature human cells engrafted in scid mice. Science 255, 1137-1141.
147
Laslo, P., Spooner, C.J., Warmflash, A., Lancki, D.W., Lee, H.J., Sciammas, R., Gantner, B.N., Dinner, A.R., and Singh, H. (2006). Multilineage transcriptional priming and determination of alternate hematopoietic cell fates. Cell 126, 755-766.
Lasorella, A., Uo, T., and Iavarone, A. (2001). Id proteins at the cross-road of development and cancer. Oncogene 20, 8326-8333.
Leon, B., Lopez-Bravo, M., and Ardavin, C. (2007). Monocyte-derived dendritic cells formed at the infection site control the induction of protective T helper 1 responses against Leishmania. Immunity 26, 519-531.
Li, C., Scott, D.A., Hatch, E., Tian, X., and Mansour, S.L. (2007). Dusp6 (Mkp3) is a negative feedback regulator of FGF-stimulated ERK signaling during mouse development. Development 134, 167-176.
Lieschke, G.J., Grail, D., Hodgson, G., Metcalf, D., Stanley, E., Cheers, C., Fowler, K.J., Basu, S., Zhan, Y.F., and Dunn, A.R. (1994). Mice lacking granulocyte colony-stimulating factor have chronic neutropenia, granulocyte and macrophage progenitor cell deficiency, and impaired neutrophil mobilization. Blood 84, 1737-1746.
Lorenz, E., Uphoff, D., Reid, T.R., and Shelton, E. (1951). Modification of irradiation injury in mice and guinea pigs by bone marrow injections. J Natl Cancer Inst 12, 197-201.
Luckey, C.J., Bhattacharya, D., Goldrath, A.W., Weissman, I.L., Benoist, C., and Mathis, D. (2006). Memory T and memory B cells share a transcriptional program of self-renewal with long-term hematopoietic stem cells. Proc Natl Acad Sci U S A 103, 3304-3309.
Majeti, R., Park, C.Y., and Weissman, I.L. (2007). Identification of a hierarchy of multipotent hematopoietic progenitors in human cord blood. Cell Stem Cell 1, 635-645.
Mansson, R., Hultquist, A., Luc, S., Yang, L., Anderson, K., Kharazi, S., Al-Hashmi, S., Liuba, K., Thoren, L., Adolfsson, J., et al. (2007). Molecular evidence for hierarchical transcriptional lineage priming in fetal and adult stem cells and multipotent progenitors. Immunity 26, 407-419.
Manz, M.G., and Di Santo, J.P. (2009). Renaissance for mouse models of human hematopoiesis and immunobiology. Nat Immunol 10, 1039-1042.
Manz, M.G., Miyamoto, T., Akashi, K., and Weissman, I.L. (2002). Prospective isolation of human clonogenic common myeloid progenitors. Proc Natl Acad Sci U S A 99, 11872-11877.
Martin, C.H., Aifantis, I., Scimone, M.L., von Andrian, U.H., Reizis, B., von Boehmer, H., and Gounari, F. (2003). Efficient thymic immigration of B220+ lymphoid-restricted bone marrow cells with T precursor potential. Nat Immunol 4, 866-873.
Mazurier, F., Doedens, M., Gan, O.I., and Dick, J.E. (2003). Rapid myeloerythroid repopulation after intrafemoral transplantation of NOD-SCID mice reveals a new class of human stem cells. Nat Med 9, 959-963.
Mazurier, F., Gan, O., McKenzie, J., Doedens, M., and Dick, J. (2004). Lentivector-mediated clonal tracking reveals intrinsic heterogeneity in the human hematopoietic stem cell compartment and culture- induced stem cell impairment. Blood 103, 545-552.
148
McConnell, M.J., Chevallier, N., Berkofsky-Fessler, W., Giltnane, J.M., Malani, R.B., Staudt, L.M., and Licht, J.D. (2003). Growth suppression by acute promyelocytic leukemia-associated protein PLZF is mediated by repression of c-myc expression. Mol Cell Biol 23, 9375-9388.
McCulloch, E.A., and Till, J.E. (2005). Perspectives on the properties of stem cells. Nat Med 11, 1026- 1028.
McKenzie, J.L., Gan, O.I., Doedens, M., and Dick, J.E. (2005). Human short-term repopulating stem cells are efficiently detected following intrafemoral transplantation into NOD/SCID recipients depleted of CD122+ cells. Blood 106, 1259-1261.
McKenzie, J.L., Gan, O.I., Doedens, M., Wang, J.C., and Dick, J.E. (2006). Individual stem cells with highly variable proliferation and self-renewal properties comprise the human hematopoietic stem cell compartment. Nat Immunol 7, 1225-1233.
McKenzie, J.L., Takenaka, K., Gan, O.I., Doedens, M., and Dick, J.E. (2007). Low rhodamine 123 retention identifies long-term human hematopoietic stem cells within the Lin-CD34+CD38- population. Blood 109, 543-545.
Medina, K.L., and Kincade, P.W. (1994). Pregnancy-related steroids are potential negative regulators of B lymphopoiesis. Proc Natl Acad Sci U S A 91, 5382-5386.
Medina, K.L., Pongubala, J.M., Reddy, K.L., Lancki, D.W., Dekoter, R., Kieslinger, M., Grosschedl, R., and Singh, H. (2004). Assembling a gene regulatory network for specification of the B cell fate. Dev Cell 7, 607-617.
Melief, C.J. (2008). Cancer immunotherapy by dendritic cells. Immunity 29, 372-383.
Metcalf, D., Moore, M.A., and Warner, N.L. (1969). Colony formation in vitro by myelomonocytic leukemic cells. J Natl Cancer Inst 43, 983-1001.
Mikkola, I., Heavey, B., Horcher, M., and Busslinger, M. (2002). Reversion of B cell commitment upon loss of Pax5 expression. Science 297, 110-113.
Miyamoto, T., and Akashi, K. (2005). Lineage promiscuous expression of transcription factors in normal hematopoiesis. Int J Hematol 81, 361-367.
Miyamoto, T., Iwasaki, H., Reizis, B., Ye, M., Graf, T., Weissman, I.L., and Akashi, K. (2002). Myeloid or lymphoid promiscuity as a critical step in hematopoietic lineage commitment. Dev Cell 3, 137-147.
Moore, T.A., and Zlotnik, A. (1995). T-cell lineage commitment and cytokine responses of thymic progenitors. Blood 86, 1850-1860.
Morrison, S.J., Wandycz, A.M., Hemmati, H.D., Wright, D.E., and Weissman, I.L. (1997a). Identification of a lineage of multipotent hematopoietic progenitors. Development 124, 1929-1939.
Morrison, S.J., Wandycz, A.M., Hemmati, H.D., Wright, D.E., and Weissman, I.L. (1997b). Identification of a lineage of multipotent hematopoietic progenitors. Development 124, 1929-1939.
Mortazavi, A., Williams, B.A., McCue, K., Schaeffer, L., and Wold, B. (2008). Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat Methods 5, 621-628.
149
Mullighan, C.G., Su, X., Zhang, J., Radtke, I., Phillips, L.A., Miller, C.B., Ma, J., Liu, W., Cheng, C., Schulman, B.A., et al. (2009). Deletion of IKZF1 and prognosis in acute lymphoblastic leukemia. N Engl J Med 360, 470-480.
Murre, C. (2009). Developmental trajectories in early hematopoiesis. Genes Dev 23, 2366-2370.
Nakahata, T., and Ogawa, M. (1982). Identification in culture of a class of hemopoietic colony-forming units with extensive capability to self-renew and generate multipotential hemopoietic colonies. Proc Natl Acad Sci U S A 79, 3843-3847.
Nakajima, T., Kinoshita, S., Sasagawa, T., Sasaki, K., Naruto, M., Kishimoto, T., and Akira, S. (1993). Phosphorylation at threonine-235 by a ras-dependent mitogen-activated protein kinase cascade is essential for transcription factor NF-IL6. Proc Natl Acad Sci U S A 90, 2207-2211.
Nanba, D., Mammoto, A., Hashimoto, K., and Higashiyama, S. (2003). Proteolytic release of the carboxy- terminal fragment of proHB-EGF causes nuclear export of PLZF. J Cell Biol 163, 489-502.
Nerlov, C., and Graf, T. (1998). PU.1 induces myeloid lineage commitment in multipotent hematopoietic progenitors. Genes Dev 12, 2403-2412.
Nerlov, C., Querfurth, E., Kulessa, H., and Graf, T. (2000). GATA-1 interacts with the myeloid PU.1 transcription factor and represses PU.1-dependent transcription. Blood 95, 2543-2551.
Ng, S.Y., Yoshida, T., and Georgopoulos, K. (2007). Ikaros and chromatin regulation in early hematopoiesis. Curr Opin Immunol 19, 116-122.
Nilsson, S.K., and Simmons, P.J. (2004). Transplantable stem cells: home to specific niches. Curr Opin Hematol 11, 102-106.
Nishino, J., Kim, I., Chada, K., and Morrison, S.J. (2008). Hmga2 promotes neural stem cell self-renewal in young but not old mice by reducing p16Ink4a and p19Arf Expression. Cell 135, 227-239.
Nutt, S.L., Heavey, B., Rolink, A.G., and Busslinger, M. (1999). Commitment to the B-lymphoid lineage depends on the transcription factor Pax5. Nature 401, 556-562.
Onai, N., Obata-Onai, A., Schmid, M.A., Ohteki, T., Jarrossay, D., and Manz, M.G. (2007). Identification of clonogenic common Flt3+M-CSFR+ plasmacytoid and conventional dendritic cell progenitors in mouse bone marrow. Nat Immunol 8, 1207-1216.
Osawa, M., Hanada, K., Hamada, H., and Nakauchi, H. (1996). Long-term lymphohematopoietic reconstitution by a single CD34-low/negative hematopoietic stem cell. Science 273, 242-245.
Oster, W., Lindemann, A., Mertelsmann, R., and Herrmann, F. (1988). Regulation of gene expression of M-, G-, GM-, and multi-CSF in normal and malignant hematopoietic cells. Blood Cells 14, 443-462.
Palucka, A.K., Ueno, H., Connolly, J., Kerneis-Norvell, F., Blanck, J.P., Johnston, D.A., Fay, J., and Banchereau, J. (2006). Dendritic cells loaded with killed allogeneic melanoma cells can induce objective clinical responses and MART-1 specific CD8+ T-cell immunity. J Immunother 29, 545-557.
Palucka, A.K., Ueno, H., Fay, J.W., and Banchereau, J. (2007). Taming cancer by inducing immunity via dendritic cells. Immunol Rev 220, 129-150.
150
Panopoulos, A.D., Zhang, L., Snow, J.W., Jones, D.M., Smith, A.M., El Kasmi, K.C., Liu, F., Goldsmith, M.A., Link, D.C., Murray, P.J., et al. (2006). STAT3 governs distinct pathways in emergency granulopoiesis and mature neutrophils. Blood 108, 3682-3690.
Payne, K.J., and Crooks, G.M. (2002). Human hematopoietic lineage commitment. Immunol Rev 187, 48-64.
Payne, K.J., and Crooks, G.M. (2007). Immune-cell lineage commitment: translation from mice to humans. Immunity 26, 674-677.
Person, R.E., Li, F.Q., Duan, Z., Benson, K.F., Wechsler, J., Papadaki, H.A., Eliopoulos, G., Kaufman, C., Bertolone, S.J., Nakamoto, B., et al. (2003). Mutations in proto-oncogene GFI1 cause human neutropenia and target ELA2. Nat Genet 34, 308-312.
Petrie, H.T. (2003). Cell migration and the control of post-natal T-cell lymphopoiesis in the thymus. Nat Rev Immunol 3, 859-866.
Phillips, R.A. (1985). Comparison of different assays for multipotent hematopoietic stem cells (New York, Raven Press).
Pluznik, D.H., and Sachs, L. (1965). The cloning of normal "mast" cells in tissue culture. J Cell Physiol 66, 319-324.
Pongubala, J.M., Northrup, D.L., Lancki, D.W., Medina, K.L., Treiber, T., Bertolino, E., Thomas, M., Grosschedl, R., Allman, D., and Singh, H. (2008). Transcription factor EBF restricts alternative lineage options and promotes B cell fate commitment independently of Pax5. Nat Immunol 9, 203-215.
Ramalho-Santos, M., Yoon, S., Matsuzaki, Y., Mulligan, R.C., and Melton, D.A. (2002). "Stemness": transcriptional profiling of embryonic and adult stem cells. Science 298, 597-600.
Reid, A., Gould, A., Brand, N., Cook, M., Strutt, P., Li, J., Licht, J., Waxman, S., Krumlauf, R., and Zelent, A. (1995). Leukemia translocation gene, PLZF, is expressed with a speckled nuclear pattern in early hematopoietic progenitors. Blood 86, 4544-4552.
Reya, T., Morrison, S.J., Clarke, M.F., and Weissman, I.L. (2001a). Stem cells, cancer, and cancer stem cells. Nature 414, 105-111.
Reya, T., Morrison, S.J., Clarke, M.F., and Weissman, I.L. (2001b). Stem cells, cancer, and cancer stem cells. Nature 414, 105-111.
Rosenbauer, F., and Tenen, D.G. (2007). Transcription factors in myeloid development: balancing differentiation with transformation. Nat Rev Immunol 7, 105-117.
Rossi, D.J., Bryder, D., Zahn, J.M., Ahlenius, H., Sonu, R., Wagers, A.J., and Weissman, I.L. (2005). Cell intrinsic alterations underlie hematopoietic stem cell aging. Proc Natl Acad Sci U S A 102, 9194-9199.
Rossi, M.I., Yokota, T., Medina, K.L., Garrett, K.P., Comp, P.C., Schipul, A.H., Jr., and Kincade, P.W. (2003). B lymphopoiesis is active throughout human life, but there are developmental age-related changes. Blood 101, 576-584.
151
Savage, A.K., Constantinides, M.G., Han, J., Picard, D., Martin, E., Li, B., Lantz, O., and Bendelac, A. (2008). The transcription factor PLZF directs the effector program of the NKT cell lineage. Immunity 29, 391-403.
Scadden, D.T. (2006). The stem-cell niche as an entity of action. Nature 441, 1075-1079.
Schofield, R. (1978). The relationship between the spleen colony-forming cell and the haemopoietic stem cell. Blood Cells 4, 7-25.
Schwarz, B.A., and Bhandoola, A. (2004). Circulating hematopoietic progenitors with T lineage potential. Nat Immunol 5, 953-960.
Schwarz, B.A., and Bhandoola, A. (2006). Trafficking from the bone marrow to the thymus: a prerequisite for thymopoiesis. Immunol Rev 209, 47-57.
Shaknovich, R., Yeyati, P.L., Ivins, S., Melnick, A., Lempert, C., Waxman, S., Zelent, A., and Licht, J.D. (1998). The promyelocytic leukemia zinc finger protein affects myeloid cell growth, differentiation, and apoptosis. Mol Cell Biol 18, 5533-5545.
Sharma, S., Stolina, M., Lin, Y., Gardner, B., Miller, P.W., Kronenberg, M., and Dubinett, S.M. (1999). T cell-derived IL-10 promotes lung cancer growth by suppressing both T cell and APC function. J Immunol 163, 5020-5028.
Shortman, K., and Naik, S.H. (2007). Steady-state and inflammatory dendritic-cell development. Nat Rev Immunol 7, 19-30.
Shortman, K., and Wu, L. (1996). Early T lymphocyte progenitors. Annu Rev Immunol 14, 29-47.
Shultz, L.D., Lang, P.A., Christianson, S.W., Gott, B., Lyons, B., Umeda, S., Leiter, E., Hesselton, R., Wagar, E.J., Leif, J.H., et al. (2000). NOD/LtSz-Rag1null mice: an immunodeficient and radioresistant model for engraftment of human hematolymphoid cells, HIV infection, and adoptive transfer of NOD mouse diabetogenic T cells. J Immunol 164, 2496-2507.
Shultz, L.D., Lyons, B.L., Burzenski, L.M., Gott, B., Chen, X., Chaleff, S., Kotb, M., Gillies, S.D., King, M., Mangada, J., et al. (2005). Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2R gamma null mice engrafted with mobilized human hemopoietic stem cells. J Immunol 174, 6477-6489.
Siminovitch, L., McCulloch, E.A., and Till, J.E. (1963). The distribution of colony-forming cells among spleen colonies. J Cell Comp Physiol 62, 327-336.
Sitnicka, E., Buza-Vidas, N., Larsson, S., Nygren, J.M., Liuba, K., and Jacobsen, S.E. (2003). Human CD34+ hematopoietic stem cells capable of multilineage engrafting NOD/SCID mice express flt3: distinct flt3 and c-kit expression and response patterns on mouse and candidate human hematopoietic stem cells. Blood 102, 881-886.
Six, E.M., Bonhomme, D., Monteiro, M., Beldjord, K., Jurkowska, M., Cordier-Garcia, C., Garrigue, A., Dal Cortivo, L., Rocha, B., Fischer, A., et al. (2007). A human postnatal lymphoid progenitor capable of circulating and seeding the thymus. J Exp Med 204, 3085-3093.
Skokowa, J., Cario, G., Uenalan, M., Schambach, A., Germeshausen, M., Battmer, K., Zeidler, C., Lehmann, U., Eder, M., Baum, C., et al. (2006). LEF-1 is crucial for neutrophil granulocytopoiesis and its expression is severely reduced in congenital neutropenia. Nat Med 12, 1191-1197.
152
Smith, E.M., Akerblad, P., Kadesch, T., Axelson, H., and Sigvardsson, M. (2005). Inhibition of EBF function by active Notch signaling reveals a novel regulatory pathway in early B-cell development. Blood 106, 1995-2001.
Smith, L.T., Hohaus, S., Gonzalez, D.A., Dziennis, S.E., and Tenen, D.G. (1996). PU.1 (Spi-1) and C/EBP alpha regulate the granulocyte colony-stimulating factor receptor promoter in myeloid cells. Blood 88, 1234-1247.
Sorrentino, B.P. (2004). Clinical strategies for expansion of haematopoietic stem cells. Nat Rev Immunol 4, 878-888.
Souabni, A., Cobaleda, C., Schebesta, M., and Busslinger, M. (2002). Pax5 promotes B lymphopoiesis and blocks T cell development by repressing Notch1. Immunity 17, 781-793.
Spangrude, G.J., Heimfeld, S., and Weissman, I.L. (1988). Purification and characterization of mouse hematopoietic stem cells. Science 241, 58-62.
Spangrude, G.J., and Johnson, G.R. (1990). Resting and activated subsets of mouse multipotent hematopoietic stem cells. Proc Natl Acad Sci U S A 87, 7433-7437.
Szilvassy, S.J., Humphries, R.K., Lansdorp, P.M., Eaves, A.C., and Eaves, C.J. (1990). Quantitative assay for totipotent reconstituting hematopoietic stem cells by a competitive repopulation strategy. Proc Natl Acad Sci U S A 87, 8736-8740.
Tacken, P.J., de Vries, I.J., Torensma, R., and Figdor, C.G. (2007). Dendritic-cell immunotherapy: from ex vivo loading to in vivo targeting. Nat Rev Immunol 7, 790-802.
Takahashi, S., McConnell, M.J., Harigae, H., Kaku, M., Sasaki, T., Melnick, A.M., and Licht, J.D. (2004). The Flt3 internal tandem duplication mutant inhibits the function of transcriptional repressors by blocking interactions with SMRT. Blood 103, 4650-4658.
Till, J.E., and Mc, C.E. (1961). A direct measurement of the radiation sensitivity of normal mouse bone marrow cells. Radiat Res 14, 213-222.
Till, J.E., and McCulloch, E.A. (1961). A direct measurement of the radiation sensitivity of normal mouse bone marrow cells. Radiat Res 14, 213-222.
Trinchieri, G. (2003). Interleukin-12 and the regulation of innate resistance and adaptive immunity. Nat Rev Immunol 3, 133-146.
Tsai, F.Y., and Orkin, S.H. (1997). Transcription factor GATA-2 is required for proliferation/survival of early hematopoietic cells and mast cell formation, but not for erythroid and myeloid terminal differentiation. Blood 89, 3636-3643. van Furth, R., and Cohn, Z.A. (1968). The origin and kinetics of mononuclear phagocytes. J Exp Med 128, 415-435.
Wada, H., Masuda, K., Satoh, R., Kakugawa, K., Ikawa, T., Katsura, Y., and Kawamoto, H. (2008). Adult T-cell progenitors retain myeloid potential. Nature 452, 768-772.
Wagers, A.J., Sherwood, R.I., Christensen, J.L., and Weissman, I.L. (2002). Little evidence for developmental plasticity of adult hematopoietic stem cells. Science 297, 2256-2259.
153
Ward, J.O., McConnell, M.J., Carlile, G.W., Pandolfi, P.P., Licht, J.D., and Freedman, L.P. (2001). The acute promyelocytic leukemia-associated protein, promyelocytic leukemia zinc finger, regulates 1,25- dihydroxyvitamin D(3)-induced monocytic differentiation of U937 cells through a physical interaction with vitamin D(3) receptor. Blood 98, 3290-3300.
Welner, R.S., Pelayo, R., and Kincade, P.W. (2008). Evolving views on the genealogy of B cells. Nat Rev Immunol 8, 95-106.
Williamson, E.A., Williamson, I.K., Chumakov, A.M., Friedman, A.D., and Koeffler, H.P. (2005). CCAAT/enhancer binding protein epsilon: changes in function upon phosphorylation by p38 MAP kinase. Blood 105, 3841-3847.
Worton, R.G., McCulloch, E.A., and Till, J.E. (1969). Physical separation of hemopoietic stem cells differing in their capacity for self-renewal. J Exp Med 130, 91-103.
Wu, A.M., Siminovitch, L., Till, J.E., and McCulloch, E.A. (1968a). Evidence for a relationship between mouse hemopoietic stem cells and cells forming colonies in culture. Proc Natl Acad Sci USA 59, 1209- 1215.
Wu, A.M., Till, J.E., Siminovitch, L., and McCulloch, E.A. (1968b). Cytological evidence for a relationship between normal hemopoietic colony-forming cells and cells of the lymphoid system. J Exp Med 127, 455-463.
Wu, L., and Liu, Y.J. (2007). Development of dendritic-cell lineages. Immunity 26, 741-750.
Xie, H., Ye, M., Feng, R., and Graf, T. (2004). Stepwise reprogramming of B cells into macrophages. Cell 117, 663-676.
Yang, L., Bryder, D., Adolfsson, J., Nygren, J., Mansson, R., Sigvardsson, M., and Jacobsen, S.E. (2005). Identification of Lin(-)Sca1(+)kit(+)CD34(+)Flt3- short-term hematopoietic stem cells capable of rapidly reconstituting and rescuing myeloablated transplant recipients. Blood 105, 2717-2723.
Ye, M., Iwasaki, H., Laiosa, C.V., Stadtfeld, M., Xie, H., Heck, S., Clausen, B., Akashi, K., and Graf, T. (2003). Hematopoietic stem cells expressing the myeloid lysozyme gene retain long-term, multilineage repopulation potential. Immunity 19, 689-699.
Yilmaz, O.H., Valdez, R., Theisen, B.K., Guo, W., Ferguson, D.O., Wu, H., and Morrison, S.J. (2006). Pten dependence distinguishes haematopoietic stem cells from leukaemia-initiating cells. Nature 441, 475- 482.
Yoshikawa, Y., Ikebuchi, K., Ohkawara, J., Hirayama, F., Yamaguchi, M., Sato, N., Mori, K.J., Kasai, M., and Sekiguchi, S. (1999). A clonal culture assay for human cord blood lymphohematopoietic progenitors. Hum Immunol 60, 75-82.
Zandi, S., Mansson, R., Tsapogas, P., Zetterblad, J., Bryder, D., and Sigvardsson, M. (2008). EBF1 is essential for B-lineage priming and establishment of a transcription factor network in common lymphoid progenitors. J Immunol 181, 3364-3372.
Zanjani, E.D., Almeida-Porada, G., Livingston, A.G., Porada, C.D., and Ogawa, M. (1999). Engraftment and multilineage expression of human bone marrow CD34- cells in vivo. Ann N Y Acad Sci 872, 220- 231; discussion 231-222.
154
Zhang, D.E., Zhang, P., Wang, N.D., Hetherington, C.J., Darlington, G.J., and Tenen, D.G. (1997). Absence of granulocyte colony-stimulating factor signaling and neutrophil development in CCAAT enhancer binding protein alpha-deficient mice. Proc Natl Acad Sci U S A 94, 569-574.
Zhang, P., Behre, G., Pan, J., Iwama, A., Wara-Aswapati, N., Radomska, H.S., Auron, P.E., Tenen, D.G., and Sun, Z. (1999). Negative cross-talk between hematopoietic regulators: GATA proteins repress PU.1. Proc Natl Acad Sci U S A 96, 8705-8710.
Zhang, P., Iwasaki-Arai, J., Iwasaki, H., Fenyus, M.L., Dayaram, T., Owens, B.M., Shigematsu, H., Levantini, E., Huettner, C.S., Lekstrom-Himes, J.A., et al. (2004). Enhancement of hematopoietic stem cell repopulating capacity and self-renewal in the absence of the transcription factor C/EBP alpha. Immunity 21, 853-863.
Zon, L.I. (2008). Intrinsic and extrinsic control of haematopoietic stem-cell self-renewal. Nature 453, 306-313.