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Characterization of murine hematopoietic stem cells with high self-renewal activity Kent, David Geoffrey 2009

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CHARACTERIZATION OF MURINE HEMATOPOIETIC STEM CELLS WITH HIGH SELF-RENEWAL ACTIVITY by DAVID GEOFFREY KENT B.Sc. (Hons), University of Western Ontario, 2003  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Genetics)  THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver)  April 2, 2009 © David Geoffrey Kent, 2009  Abstract  Hematopoietic stem cells (HSCs) produce all blood cell types required throughout life. They are identified by their ability to sustain the production of at least 1% of the mature white blood cells for 4 or more months, as demonstrated in limiting dilution or single-cell transplants. Recently, methods for obtaining suspensions of highly purified HSCs from mouse bone marrow have been developed. This has made possible the design of experiments to address: (i) the nature and extent of their biological heterogeneity, (ii) whether maintenance of durable in vivo reconstituting activity is regulated separately from HSC survival and proliferative activity, and (iii) whether differences in gene expression distinguish HSCs with durable as compared to finite in vivo self-renewal potential. Assessment of the different types of donor-derived blood cells produced in ~100 mice transplanted with a single HSC (or a 4-day in vitro-derived clone) allowed identification of 4 subtypes, only 2 of which could propagate continuing blood formation in secondary and even tertiary recipients. To facilitate the investigation of HSC subtypes with durable self-renewal potential (as compared to the other two subtypes with large, but finite, self-renewal potential), I devised a strategy that achieves their simultaneous, but separate, isolation from each other prior to transplantation. From time course experiments that compared the effects of altered extrinsic cytokine stimulation on different HSC activities, I showed that low Steel factor concentrations can rapidly (within 16 hours) extinguish their in vivo regenerative ability without affecting their immediate subsequent survival or mitogenesis in vitro. Finally, from comparative gene expression analyses I identified 3 genes (Vwf, Rhob, and Pld3) that are consistently expressed at higher levels in HSCs with durable self-renewal potential than in several closely related cell types with less extensive or already extinct self-renewal potential. Together, these findings provide strong support for a model of HSC regulation that includes a degree of separation in the mechanisms that control HSC self-renewal from those ii  influencing their survival, mitogenesis, and lineage commitment probabilities. Further investigations of this model using the tools and molecules herein identified should facilitate improvements in ex vivo HSC expansion and in understanding leukemogenesis.  iii  Table of Contents Abstract ........................................................................................................................................ ii Table of Contents ....................................................................................................................... iv List of Tables ............................................................................................................................. vii List of Figures ........................................................................................................................... viii List of Abbreviations .................................................................................................................. x Acknowledgements ................................................................................................................... xii Dedication ................................................................................................................................. xiii Co-Authorship Statement ....................................................................................................... xiv Chapter 1 – Introduction............................................................................................................ 1 1.1 – Concept of tissue stem cells and their regulation ............................................. 1 1.2 – Overview of the blood forming system and its hierarchical organization ..... 2 1.3 – Developmental origin of the blood forming system ......................................... 5 1.4 – Heterogeneity in the differentiation patterns of individual HSCs.................. 6 1.5 – Regulation of HSC Numbers.............................................................................. 7 1.5.1 – General principles ................................................................................... 7 1.5.2 – Cell cycle .................................................................................................. 8 1.5.3– Apoptosis................................................................................................... 9 1.6 – Extrinsic regulation of HSCs ........................................................................... 10 1.6.1 – HSC niches ............................................................................................. 10 1.6.2 – Molecular mediators ............................................................................. 11 1.7 – Intrinsic regulators of HSC self-renewal ........................................................ 18 1.7.1 – The JAK/STAT pathway ...................................................................... 19 1.7.2 – Polycomb transcription factors ........................................................... 19 1.7.3 – Cell cycle regulators .............................................................................. 20 1.8 – Methodologies for analyzing HSC transcriptomes ........................................ 21 1.9 – Thesis objectives ................................................................................................ 24 Chapter 2 – Materials and Methods........................................................................................ 34 2.1 – Mice .................................................................................................................... 34 2.2 – Isolation of subsets of FL and adult BM cells................................................. 34 2.3 – Analysis of transplanted mice .......................................................................... 37 2.4 – Cell cultures ....................................................................................................... 38 2.5 – Statistical analysis ............................................................................................. 39 2.6 – Quantitative real-time PCR analysis............................................................... 39 2.7 – Construction and sequencing of LongSAGE libraries .................................. 40 Chapter 3 – Heterogeneity of HSC programs and their Resolution by Improved Purification methods ...................................................................................... 44 3.1 – Introduction ....................................................................................................... 44 iv  3.2 – Results ................................................................................................................ 46 3.2.1 – Identification of subtypes of murine HSCs with differing selfrenewal programs ............................................................................................. 46 3.2.2 – Isolation of different HSC subtypes using EPCR as a substitute phenotypic marker for Ho efflux ability ......................................................... 48 3.2.3 – Staining for EPCR in combination with CD48 and CD150 allows high and low self-renewal HSCs to be separately purified ........................... 49 3.2.4 – The E-SLAM phenotype is useful as a strategy for isolating highly purified suspensions of HSCs from reconstituted animals ................ 51 3.3 – Discussion .......................................................................................................... 52 Chapter 4 – Steel Factor Blocks Rapid Hematopoietic Stem Cell Differentiation and Leads to Predominantly Asymmetric Divisions .......................................... 66 4.1 – Introduction ....................................................................................................... 66 4.2 – Results ................................................................................................................ 67 4.2.1 – Different SF concentrations alter maintenance of HSC activity ...... 67 4.2.2 – SF concentration can rapidly alter the defining properties of HSCs without affecting their viability or cell cycle progression................... 68 4.2.3 – Most HSC self-renewal divisions stimulated by SF and IL-11 in vitro are asymmetric ......................................................................................... 69 4.2.4 – Potential intrinsic molecular mediators of the ability of SF to sustain the stem cell state of HSCs in vitro ..................................................... 70 4.3 – Discussion .......................................................................................................... 70 Chapter 5 – Comparative transcriptome analysis of purified HSCs from Highly Purified Murine Fetal Liver and Adult Bone Marrow HSCs .................... 79 5.1 – Introduction ....................................................................................................... 79 5.2 – Results ................................................................................................................ 80 5.2.1 – Construction and characteristics of LongSAGE libraries prepared from highly purified FL and adult BM HSCs ............................... 80 5.2.2 – A gene expression signature for FL and adult BM HSCs ................. 81 5.2.3 – Identification of a subset of genes whose lower expression is associated with “nearest neighbours” of high self-renewal HSCs ................ 82 5.3 – Discussion .......................................................................................................... 83 Chapter 6 – Discussion and Future Directions..................................................................... 113 6.1 – Major contributions ........................................................................................ 113 6.2 – Implications and future directions ................................................................ 116 6.2.1 – Opportunities for direct and high-resolution analyses of HSC heterogeneity afforded by simpler and more selective isolation strategies .......................................................................................................... 116 6.2.2 – Exogenous regulation of HSC self-renewal ...................................... 120 6.2.3 – Molecular mechanisms regulating different HSC self-renewal states ..................................................................................................... 122 6.2.4 – New mediators of HSC regulation ..................................................... 123 6.3 – Concluding comments .................................................................................... 124 v  References ................................................................................................................................ 127 Appendix .................................................................................................................................. 147  vi  List of Tables Table 1.1 – Regulators of HSCs in the Stem Cell Niche ........................................................... 26 Table 1.2 – Genes Shown to Regulate HSC Self-Renewal ........................................................ 27 Table 2.1 – List of primers used in Chapters 4 and 5................................................................. 42 Table 4.1 – HSC yields are reduced in cultures containing a low concentration of SF ............. 74 Table 5.1 – Tags over-represented in the adult BM HSC library vs. the FL HSC library ......... 87 Table 5.2 – Tags over-represented in the FL HSC library vs. the adult BM HSC library ....... 104  vii  List of Figures Figure 1.1 – In vivo transplantation assay to detect HSCs ......................................................... 28 Figure 1.2 – Hierarchical model of hematopoiesis .................................................................... 29 Figure 1.3 – SF signalling and its role in HSCs ......................................................................... 30 Figure 1.4 – Key regulators of HSCs ......................................................................................... 31 Figure 1.5 – Polycomb complexes induce epigenetic silencing ................................................ 32 Figure 1.6 – Possible outcomes following the division of an HSC ........................................... 33 Figure 2.1 – cDNA amplification protocol for PCR-serial analysis of gene expression (PCRSAGE) library generation ................................................................................... 43 Figure 3.1 – WBC outputs in recipients of single HSCs or their clonal progeny generated in vitro .............................................................................................................. 54 Figure 3.2 – Identification of HSC subtypes in ternary plots of their lineage-specific contributions at 16 weeks post-transplant ........................................................ 56 Figure 3.3 – Rapid alteration of HSC distributions in vitro ....................................................... 57 Figure 3.4 – Clonal propagation of repopulation patterns in secondary and tertiary recipients ............................................................................................................. 58 Figure 3.5 – CD45midRho-EPCR+ cells are almost exclusively SP ............................................ 60 Figure 3.6 – Approximately half of all CD45midRho-EPCR+ cells are HSCs and these segregate into the same distribution of subtypes as CD45midlin-Rho-SP HSCs .. 61 Figure 3.7 – All 4 patterns of HSC differentiation are observed in the recipients of single CD45midRho-EPCR+ cells ................................................................................... 62 Figure 3.8 – CD45+EPCR+CD48-CD150+ (E-SLAM) cells from E14.5 FL cells and adult BM are highly enriched for HSCs ...................................................................... 63 Figure 3.9 – CD150 expression divides the CD45+EPCR+CD48- adult BM cell population into fractions differentially enriched in HSCs with high and low self-renewal properties............................................................................................................. 64 Figure 3.10 – CD45+EPCR+CD48-CD150+ (E-SLAM) is a surrogate marker for HSCs with high self-renewal and isolates HSCs from reconstituted animals at high purity 65 Figure 4.1 – Kinetics of division of CD45midlin-Rho-SP cells cultured in different SF concentrations ..................................................................................................... 75 viii  Figure 4.2 – Time course of changes in HSC activity under different concentrations of SF .... 76 Figure 4.3 – Asymmetry of HSC expansion and maintenance divisions stimulated by 20 ng/mL IL-11 plus either 10 ng/mL or 300 ng/mL SF ......................................... 77 Figure 4.4 – Bmi1, Ezh2 and Lnk transcripts are downregulated with loss of HSC activity ..... 78 Figure 5.1 – LongSAGE libraries prepared from HSC-enriched fractions of E14.5 FL and adult BM cells ................................................................................................... 107 Figure 5.2 – Prnp, Gata3, and Bmi1 transcripts are differentially expressed by E-SLAM and CD45+EPCR+CD48-CD150- adult BM cells .............................................. 108 Figure 5.3 – Elevated Vwf, Rhob, and Pld3 expression is consistently associated with high self-renewal activity in HSCs ........................................................................... 109 Figure 5.4 – Q-RT-PCR analyses of transcript levels of selected genes in various FL and adult BM cell populations as indicated ............................................................. 110 Figure 5.5 – Q-RT-PCR analyses of transcript levels of selected genes in various adult BM cell populations as indicated ...................................................................... 111 Figure 5.6 – Q-RT-PCR analyses of transcript levels of selected genes in cytokine stimulated adult BM HSCs ............................................................................... 112 Figure 6.1 – A model detailing the relationship between the different HSC subtypes ............ 126  ix  List of Abbreviations AGM  Aorta gonad mesonephros  APC  Allophycocyanine  ATRA  All-trans retinoic acid  BM  Bone marrow  CFC  Colony forming cell  CFU-C  Colony forming unit-cell  CFU-S  Colony forming unit-spleen  CLP  Common lymphoid progenitor  CMP  Common myeloid progenitor  CRU  Competitive repopulating unit  E  Embryonic day  FBS  Fetal bovine serum  FITC  Fluorescein isothiocyanate  FL  Fetal liver  Flt3L  Fms-like tyrosine kinase 3 ligand  G-CSF  Granulocyte-colony stimulating factor  GFP  Green fluorescence protein  GM  Granulocytes/monocytes  GM-CSF  Granulocyte/macrophage-colony stimulating factor  HF  HBSS plus 2% fetal bovine serum  Ho  Hoechst 33342  HSC  Hematopoietic stem cell  IGF  Insulin-like growth factor  IL  Interleukin  KSL  cKit+Sca1+Lin-  LIF  Leukemia inhibitory factor  Lin  Lineage markers  M-CSF  Macrophage-colony stimulating factor  MHC  Major histocompatibility complex  PBS  Phosphate buffered saline  PE  Phycoerythrin x  PI  Propidium iodide  Q-RT-PCR  Quantitative reverse transcription PCR  RAR  Retinoic acid receptor  RB  Retinoblastoma  RBC  Red blood cell  Rho  Rhodamine 123  SA  Streptavidin  SAGE  Serial analysis of gene expression  SF  Steel factor  SFM  Serum free medium  SP  Side population  STRC  Short-term reconstituting cell  TGF-β  Transforming growth factor beta  TNF  Tumour necrosis factor  TPO  Thrombopoietin  W41  C57Bl/6JW41/W41  WBC  White blood cell  xi  Acknowledgements To Connie, I could not have asked for a more supportive or challenging supervisor. You provided the perfect balance of motivation and freedom and had the uncanny ability to always know when I needed boosting up or knocking down. You taught me much more than science (though the encylclopedic knowledge was useful!) and I truly value your dedication to training. To Jamie, Kelly, and Marco for all of your guidance, criticism, and inspiring discussion. To Keith Humphries and Allen Eaves, for being wonderful mentors, even without official roles, your academic contributions and encouragement have been extremely valuable. To Brad, for teaching an annoying newbie all the tricks of the trade at ridiculous hours of the morning and night and for being a scientific and personal role model of enormous proportion. To the others with whom I worked closely on research projects, Kai, Mike C., Claudia, Stefan, Michelle, Sanja, Florian, and Maura for keeping the work environment positive and being willing and able to buckle down and get some great work done during busy times. To Mike O, Peter, Yun, John, Afshin, Maisam, and Mel for your special brands of thinking and your contributions to the many other things that go on outside of lab work. To the highly motivated and talented group of co-op and research students – Lindsay, Shannon, Jay, Elaine, and Heidi – who I was fortunate to supervise in some capacity, the work and energy that you gave to the lab group was amazing. I hope that you all took great memories and experience from here and go on to enormous success in your futures. To the many friends and colleagues that have made the Terry Fox Lab and the BCCA a thoroughly enjoyable place to work: specifically the Martini’s, OJ’s, Bean So Good, and “outdoorsy” folks. You have all given the lab a wonderful sense of camaraderie; I can only hope to work with such a great group in the future. To past and present GrasPods executive members, this place is pretty special and it is because people like you are willing to donate your time to making it so. Keep it up. To Erika and Beth and the huge number of volunteers and collaborators involved in the UBC Let’s Talk Science Partnership Program, for making my time at UBC about far more than lab research. To Janet, Chad, and Brian for putting your faith in us to build something big, I know that you will continue to give such support to the next generation of students. To UWO professors Burr Atkinson and Tony Percival-Smith for teaching me that Genetics (and Biology for that matter) was much more about problem solving than swallowing text books. To my brother Eddy and sister-in-law Terri, for your choice of cities over the years, for your support, company, good judgment, and for bringing nephew number one, Felix, into our lives. To my mom, Eileen, for your version of pre-Kindergarten and your unwavering support for me throughout life, even though I have not always deserved it. To my dad, Ed, for just the right amount of pressure to perform at school and for distracting me during stressful times with talk of how well hockey players with (sometimes questionable) ties to Newfoundland were doing. To Lindsay, without whom I would be a complete train wreck, for teaching me more about myself than I could ever learn on my own, for supporting me over the PhD years (especially this last one) and for getting me out into the wilderness of BC much more frequently than I would have otherwise. xii  To Lindsay, For being you, and loving me for being me  xiii  Co-Authorship Statement Chapter 3: This article draws on two major bodies of work – the first of which I worked jointly with Brad Dykstra (a PhD Candidate in the Eaves lab) and the second of which was primarily conducted by me. The work in the first section (HSC subtype identification, Figures 3.1 - 3.4) was initiated by Brad Dykstra in 2002 and was completed in 2006. The second section (Improved purification and prospective isolation of HSCs with high self-renewal activity, Figures 3.5-3.10) began in 2005 and was completed in 2008. HSC subtype identification - The work presented was designed by Brad Dykstra with intellectual input from Connie Eaves and me. Transplants of single cells and in vitro clones were performed by Brad Dykstra and me. Assistance with peripheral blood sampling, staining, and FACS analysis, was provided by Melisa Hamilton, Lindsay McCaffrey, and Kristin Lyons. Brad Dykstra performed the in-depth data analysis and generated the figures. The resulting manuscript was prepared by Brad Dykstra, Connie Eaves, and me. Improved purification and prospective isolation of HSCs with high self-renewal activity – The work presented was designed primarily by me with intellectual input from Brad Dykstra, Claudia Benz, Maura Gasparetto, and Connie Eaves. Transplants of bone marrow HSCs were performed by me and transplants of fetal liver HSCs were performed by Claudia Benz. Assistance with peripheral blood sampling, staining, and FACS analysis, was provided by Shannon Russell, Jay Cheyne, Elaine Ma, and Heidi Mader. The E-SLAM staining strategy was jointly discovered by Maura Gasparetto and myself. I generated the figures and tables that appeared in the resulting manuscript which were prepared by me with input from Brad Dykstra and Connie Eaves. Chapter 4: The work presented in this chapter took place between 2003 and 2007. I designed the experiments with significant input from Brad Dykstra and Connie Eaves. Transplants of single cells and in vitro clones were performed by Brad Dykstra and me. Together with Brad Dykstra, I designed the cell doublet splitting technique. I optimized the RNA isolation strategy designed for small cell numbers. Assistance with peripheral blood sampling, staining, FACS analysis, RNA isolation, and QPCR was provided by Jay Cheyne and Elaine Ma.  xiv  I generated the figures and tables and prepared an initial draft of the manuscript. Brad Dykstra, Connie Eaves and I were involved in all other aspects of manuscript preparation. Chapter 5: The work presented in this chapter was the result of the simultaneous development of multiple different technologies and discoveries that took place from 2003 through to 2008. Yun Zhao and Afshin Raouf in our lab, together with the help of the Michael Smith Genome Sciences Centre, were able to devise and validate a strategy that modifies the Clontech SMART amplification system to make Serial Analysis of Gene Expression libraries from limiting amounts of input RNA (10 ng). I was involved in the bioinformatic analyses to validate this strategy’s accuracy when compared to non-amplified material. Using this strategy, in collaboration with Brad Dykstra and Michelle Bowie, I collected HSCs from adult bone marrow and fetal liver. I amplified the collected material with assistance from Yun Zhao and the two libraries were constructed and analysed at the Genome Sciences Centre by Yongjun Zhao, Allen Delaney, and Martin Hirst. The candidate gene selection, isolation and culture of HSCs and progenitor cells, and follow-up QPCR assays were designed and performed jointly by Michael Copley and myself. Assistance with RNA Isolation and QPCR was provided by Jay Cheyne and Elaine Ma. I performed the data analysis and generated the figures. The resulting manuscript was primarily prepared by me, with significant input from Connie Eaves.  xv  1.  Introduction  1.1  Concept of tissue stem cells and their regulation  The fundamental definition of a stem cell is a cell that has the competence to produce the specialized cells of a particular tissue but also possesses a mechanism to maintain this competence for an indefinite period. In order for stem cells to sustain their numbers and hence provide for the variable lifetime needs for specialized cell production, there must be mechanisms for regulating whether stem cells retain their differentiation potential or whether they begin to activate its expression. The establishment of competence for particular differentiation programs is presumably mediated by a combination of genetic and epigenetic mechanisms. The regulation of whether and how that competence is sustained (or not) is often referred to as the “selfrenewal” process that is viewed as the cardinal feature of stem cells. This process and its regulation underlie the ability of stem cells to remain as the lifetime source of the specialized cells of the tissue throughout development as well as under conditions of variable demand. In model systems, stem cells have been studied at purity and in great detail. This has allowed many molecules and pathways to be described as necessary or sufficient for stem cell maintenance or differentiation. For example, studies in Drosophila melanogaster have been particularly useful in generating concrete evidence of a localized “niche” that provides positive support for germ stem cells (see Section 1.3) and plays a role in the asymmetric partitioning of molecules within these cells that dictate their subsequent fates. The first formal indication for the existence of a stem cell in the hematopoietic system was provided by the seminal experiments of Till and McCulloch1. They showed that small collections of cells appeared on the spleens of transplanted myeloablated mice upon injection of 1  small numbers of bone marrow (BM) cells and that the number of these collections of cells was proportional to the number of BM cells originally transplanted. They also introduced the important principle of assigning functional names to cells that could only be detected retrospectively by functional read-outs (hence the term colony-forming unit-spleen, abbreviated as CFU-S and later the term, colony-forming unit-culture, abbreviated as CFU-C). This group also established that the discrete collections of cells visible on the spleen surface were indeed clones and that, at later time points, they contained multiple lineages of myeloid cells as well as cells able to give rise to secondary spleen colonies (i.e.: the original CFU-S were multipotent and had self-renewal ability)2. Subsequent efforts to purify CFU-S culminated in a landmark paper from Spangrude et al.3 who demonstrated that this could be achieved by selecting for a subset of adult mouse BM cells that express both Thy1 and Sca1 after removal of all cells expressing markers for each of the major types of mature blood cells (so-called lineage or “lin” markers). This advance ushered in a new era of HSC biology that began to focus on interrogating directly the properties of very primitive subsets of multipotent cells, more akin to the experiments that had previously been limited to simpler model organisms. Interestingly, as detailed below, these eventually showed that most CFU-S are downstream of a more primitive hematopoietic cell type that has much more extensive self-renewal potential4-7, although the original concept of stem cell self-renewal has remained largely unchanged.  1.2  Overview of the blood-forming system and its hierarchical organization  The hematopoietic system is responsible for producing all of the various types of specialized blood cells in numbers required throughout life. These specialized blood cells 2  comprise those of the lymphoid lineages, which include B-cells, T-cells, and natural killer cells, as well as a diversity of myeloid cell types, including erythrocytes, monocytes, dendritic cells, neutrophilic, eosinophilic and basophilic granulocytes, mast cells, and megakaryocytes that generate platelets. Most of the mature forms of these cells cannot proliferate and also have short lifespans (a few days or weeks). Thus, in order for the required numbers of mature blood cells to be maintained, new ones must be continuously generated. The first indication of a common origin of different blood cell types was provided by morphological8 and cytogenetic9 studies of what are now recognized as clonal myeloproliferative diseases in humans. This concept was later experimentally validated by the characterization of CFU-S as a population of cells with multi-lineage differentiation potential and their origin from a cell not detectable as CFU-S but that had lymphoid as well as myeloid reconstituting activity2. Later, genetic tracking analyses confirmed the ability of single hematopoietic cells from normal murine and human donors to establish chimerism in the blood-forming system of transplanted recipients for periods of months to years10-13. In some of these latter studies, the ability of the original cell to produce progeny with extensive multi-lineage regenerative potential was also documented. Together, these observations established the existence of HSCs in mice and humans. Subsequent experiments focused on the development of a limiting dilution approach for quantifying HSCs14,15. This led to the adoption of a second operational term, competitive repopulating units (CRUs) to define murine HSCs as those cells that are able to regenerate at least 1% of the white blood cells, including both lymphoid and myeloid lineages for a minimum of 4 months (Figure 1.1)16-21. Much evidence indicates that the differentiation of HSCs is achieved throughout adult life by a complex multi-step differentiation process - one that originates in a rare population of cells individually capable of generating all (or most) of the differentiated blood cell types (shown schematically for the murine system in Figure 1.2). In order for this population of cells to be 3  sustained in adequate numbers, a process must exist which ensures that their numbers are maintained throughout life. This means that, on average, when HSCs divide, at least one daughter cell must retain the same latent developmental properties to offset losses due to death or differentiation (reviewed in Bryder et al.22). Equally important for the regulation of blood production is the differentiation process which allows the step-wise and regulated progression of cells from HSCs to multipotent progenitors, to oligopotent progenitors, to unipotent progenitors leading finally to the execution of terminal differentiation programs that generate the different types of specialized mature blood cells. Experiments that have given strength to this hierarchical model have provided evidence of distinct and separable cell types that are derivative of HSCs and display the range of potentialities characteristic of the generally accepted sequential branching model shown in Figure 1.2. Importantly, this includes evidence of a hierarchy within rare cells that are not fully committed to a single lineage and even extends into the compartment of cells that display no lineage restriction at all in their differentiation potential. Examples of these primitive subsets include the Lin-IL7R+Thy1-Sca1-Kitlo common lymphoid progenitors (CLPs) that both in vitro and in vivo display an ability to give rise to large numbers of exclusively lymphoid cells but only for a short period of time23, the Lin-Kit+Sca1-(KSL)IL7Rα-FcγRloCD34+ common myeloid progenitors (CMPs) that display the complementary repertoire (i.e., transient output of only myeloid cells)24, and KSLFlt3+ multipotent cells capable of making both lymphoid and myeloid lineages but only for 8 weeks following transplantation (hence their designation as short term (ST)-HSCs)7,25.  4  1.3  Developmental origin of the blood forming system  During development, two independent waves of hematopoiesis are readily distinguished. The first is called primitive and the second definitive. Current evidence suggests that both derive from mesodermal cells that differentiate into cells with endothelial features and/or potential, although this remains controversial. Primitive hematopoiesis creates the immediately necessary red blood cells (RBCs) and a few macrophages first seen in mice around embryonic (E) day 7.5 in the yolk sac blood islands26. Embryonic RBCs are unique in that they remain nucleated and express embryonic hemoglobin. A few days later, definitive hematopoiesis is initiated independently in multiple sites that include the aorta-gonad-mesonephros (AGM) region27,28 and the placenta29,30. This process is associated with the first appearance of transplantable HSCs. Circulation is established around E10 which is thought to allow the cells from the sites of origin of HSCs to enter the developing liver and spleen. Just before birth, hematopoietic cells also migrate to the BM. The BM then becomes the primary site of blood cell production after birth (reviewed in Orkin et al.26). Importantly, a number of key properties of HSCs have been documented to change markedly between fetal life and adulthood18. Fetal HSCs are actively cycling as compared to their primarily quiescent adult counterparts and fetal HSCs also display faster regenerative kinetics and a greater average output of granulocytes and macrophages after their transplantation into myeloablated hosts. Fetal and adult HSCs also have distinct gene expression signatures and distinct processes of lineage restriction and differentiation kinetics.  5  1.4  Heterogeneity in the differentiation patterns of individual HSCs  Studies of the clonal outputs of individual HSCs with longterm in vivo repopulating activity have revealed wide variability in the absolute and relative numbers of the different types of mature cells they generate. This heterogeneous behavior has brought into focus the uncertainties of describing HSCs and their properties by the regenerative activity they display in bulk assays. Such retrospective approaches are necessarily limited by the fact that they cannot discriminate behavioral differences caused by variations in the types or sequence of extrinsic cues received by the original cells (or their progeny) or the role of co-transplanted cells of other types versus pre-existing intrinsic heterogeneity and/or the contribution of intrinsic stochastic events. Resolution of these issues requires experiments that can track all the clonal progeny produced by many HSCs over extensive periods and preferably in the absence of other cell types or even other HSCs. The HSC compartment of fetal and young adult mice is one example where a compartment of cells that meet the same defining criteria (as CRUs) display different differentiation properties as well as different self-renewal activities when transplanted into irradiated recipients. Unfortunately, the combination of phenotypic markers historically used to obtain these cells at very high purities cannot yet be assumed to measure, or even detect, cells with the same properties in uncharacterized cell suspensions. This is because many of the markers in question show variable expression on HSCs according to the activation status of the HSCs and may also be variably expressed on non-HSCs31-33. The identification of molecular markers that stably associate with self-renewing HSCs independent of their cycling status or differentiation program should help to elucidate the mechanism that allows for long-term maintenance of HSC activity.  6  Tracking the clonal progeny of single adult BM HSCs (or their in vitro progeny) through 2 or 3 serial transplants has revealed their possession of specific lineage preferences that can be propagated over many self-renewal divisions in vivo. However, these differentiation programs can also change rapidly, both in vivo (e.g., at 3 weeks after birth in mice) and under certain culture conditions18,21,34. Taken together, these findings support a new model of HSC development in which the mechanisms that control their lineage preferences and selfmaintenance have distinct, although possibly overlapping, elements with the previously unrecognized possibility that lineage preferences may be established before, rather than after, self-renewal potential is lost.  1.5  Regulation of HSC numbers  1.5.1  General principles  The number of HSCs maintained in an organism is thought to be regulated by a number of factors either intrinsic or extrinsic to the stem cell35-38. This can occur in a number of ways, including changes mediated by both external and internal factors that control their cell cycle, apoptotic pathways, or their self-renewal ability. Molecules intrinsic to the cell that govern HSC fates are discussed in Section 1.7. Many model systems have documented extrinsic mechanisms of stem cell regulation that are both necessary and sufficient for controlling their key properties. Perhaps the best studied are those operative in the neuroblast and germ stem cell niches of Drosophila 36,39,40. Demonstration of the importance of a single factor is exemplified by studies of decapentaplegic (dpp) in Drosophila germ stem cells. In these niche cells, genetic loss of dpp function has been 7  shown to cause a depletion of germline stem cells. Conversely, Flp-induced mitotic recombination experiments indicate that the heat shock protein-induced production of germline tumours (in which many cells resemble germline stem cells) is mediated by receptors/transducers of a dpp signal (i.e.: Mothers against dpp [Mad] and punt)40,41. One of the major reasons that model organisms like Drosophila have been so powerful for delineating mechanism(s) of stem cell self-renewal control is their ability to enable the measurement of responses of single cells to pure populations of supporting cells. It is for this reason that the prospective isolation of highly purified HSCs has been so hotly pursued as it gives great power to gene expression and protein localization studies, and also allows for determination of direct effects of various molecules (cytokines, drugs, etc) on the cells of interest.  1.5.2 Cell cycle  One of the simplest ways to alter the rate of expansion/contraction of a cell population is to alter the proportion of the cells that are proliferating. Measurements of HSC numbers during development have shown that they expand, consistent with the expectation that this would be required to accommodate the needs of the growing organism. A number of groups have demonstrated that the HSCs found in the fetal liver (FL) at E14.5 are actively in cycle42,43 whereas the vast majority (>90%) of those in the adult BM are generally in the G0 (quiescent) phase of the cell cycle44. During this more stable phase, where mature blood cells are being destroyed and renewed at an impressively large yet relatively constant pace, questions surrounding the activation of cells within the HSC compartment have generated much debate. Do HSCs sit dormant in G0 until the body requires activation of one or many HSCs and the 8  remaining HSCs refill the compartment with self-renewal expansion divisions (a clonal succession model)45? or do HSCs continually enter and exit the cell cycle as suggested by the BrDU labelling experiments of Bradford et al. 46 and Cheshier et al.44 which indicated that >99% of the KSLThy1+ BM cells entered the cell cycle at least once every 2 months? Two recent papers47,48 have utilized a histone 2B (H2B)-green fluorescent protein (GFP) fusion protein for the tracking of cell divisions in populations of long term HSCs. These newer studies now suggested that the most primitive HSCs divide, on average, only once every 145 days (i.e.: only a few times over the course of the entire lifespan of a mouse)47. Elucidation of the mechanism(s) that regulate the proliferative activity of HSCs in vivo is critical to understanding the pressures for control at later stages and has attracted the attention of mathematical modelers and stem cell biologists alike. These models and questions can only be addressed with the recent advances that allow highly purified HSCs to be studied independent of their immediate downstream progenitor cell populations. Some cell cycle regulators with specific roles in HSC self-renewal are described below in Section 1.7.3.  1.5.3  Apoptosis  Apoptosis is a form of programmed cell death that results in loss of membrane integrity, cell shrinkage, and DNA fragmentation and it is an essential process during development and homeostasis49. A large body of evidence supports apoptosis as a major player in the regulation of mature blood cell numbers50,51, especially in the regulation of T-cell production during which a few thymocytes differentiate but most die. Specifically, thymocytes that bind with low affinity to Major Histocompatibility Complex (MHC) class I or MHC class II differentiate into cytotoxic T cells or helper T cells, respectively, while those that bind with high affinity to self-MHC 9  ligands undergo apoptosis52. Investigations of the role of apoptosis in regulating HSC numbers has been initiated but is less well developed. Most information has come from studies of HSCs generated in a Bcl-2 transgenic mouse that overexpresses Bcl-2 under the control of the H2Kb promoter53,54. These mice show increased HSC activity both in vitro and in vivo relative to control mice.  1.6  Extrinsic regulation of HSCs  1.6.1  HSC niches  The influence of external cues on the fate of stem cells is thought to be mediated largely by localized interactions that reflect the existence of specialized molecular microenvironments referred to as stem cell niches. The concept of localized as opposed to humoral control of HSC activity dates back to historical findings that intravenously injected cell suspensions of normal BM cannot cure the anemia of Sl/Sld mice, whereas grafts of tissue fragments lacking hematopoietic cells can55. Subsequent observations by Lord et al.56 showed that the majority of CFU-S within the BM are concentrated in the peripheral areas as opposed to the central marrow cavity. In 1978, Schofield57 introduced the term “niche” to highlight the idea of specialized sites of HSC (CFU-S) support and regulation. Although this idea has since gained much interest, the anatomical location and cellular make-up of HSC niches remain subjects of debate. Nevertheless, there appears to be a consensus that in the BM the vast majority of HSCs reside close to the bone like the closely related CFU-S population. Using a Y chromosome-specific probe to identify male HSCs (cells that excluded Hoechst 33342 [Ho] and Rhodamine 123 [Rho]), Nilsson et al. showed that these cells migrate to 10  the endosteal surface of the bone following intravenous transplantation58. Similarly, TIE2positive KSL cells were found to be located in close contact with osteoblasts at the endosteal surface of the BM of transplanted mice59. However, recent studies using a combination of markers that are more selective for HSCs (i.e.: a Lin-CD150+CD48-CD41+ phenotype) indicate that HSCs are more commonly found at the surface of the sinusoidal endothelium19 which has recently also been supported by live animal in vivo imaging studies using two-photon microscopy60.  1.6.2 Molecular mediators  The niche concept has also stimulated much research into the cells and molecules that promote HSC self-renewal. Feeder cells from many different sources have been found to have such activity and these include stromal cells derived from the AGM61, urogenital ridge62, FL62, brain endothelium63, BM64, and osteoblasts59,65-67. From these and other studies, many molecules that can influence HSC activity have been identified as summarized in Table 1.1 (adapted from Kiel et al.68). In order to determine accurately the effect that various molecules have on HSC selfrenewal, it is most powerful to ask these questions on single cells and in a defined medium where the factors can be assessed on an individual basis for their direct impact on HSC maintenance (self-renewal). Use of defined media will also be essential for future ex vivo expansion of HSCs for clinical applications. Many growth factors have been identified as having roles in regulating specific stages of hematopoiesis. Most of these studies identified effects on populations where the precise cellular mechanisms were not discriminated. Others have identified effects on cell survival and/or 11  proliferation. Only in a few instances have effects on lineage selection been conclusively demonstrated, as in the cases of Granulocyte-Colony Stimulating Factor (G-CSF), Macrophage (M)-CSF, GM-CSF, Interleukin (IL)-5, and Leukemia Inhibitory Factor (LIF)69. Importantly, none of these also include effects on the self-renewal mechanism operative in HSCs with long term in vivo regenerative activity31,70. Elucidating the role of soluble extrinsic factors on HSC self-renewal requires the use of sufficiently pure HSC populations that the effects seen can be definitively attributed to effects on HSCs. Counteracting the supportive and stimulating effects of positive influences are numerous studies that point to the existence of molecular interactions that can have a direct negative impact on HSC functions (e.g.: Transforming Growth Factor-β [TGF-β]71, Tumour Necrosis Factor [TNF]72, and inappropriately high concentrations of IL-1 or IL-373, or IL-6 or IL-1174). In addition, there is an extensive literature indicating that HSCs express cell surface components like CD3475, CD4476, VLA477 and VLA578 that enable them to interact with “fixed” molecules in the extracellular matrix or adjacent cells (reviewed in Chan et al.79). The following section summarizes some selected examples of studies that have been undertaken to address the issue of how extrinsic factors can modulate expansion of HSC populations in vitro and illustrates how little definitive information there is as yet on this subject.  Steel Factor (SF) - SF (also known as Stem Cell Factor, Mast Cell Growth Factor, and/or KIT-ligand) is a transmembrane growth factor encoded by the Sl gene. SF binds to and activates a type III transmembrane receptor tyrosine kinase called KIT (also referred to as CD117, see Figure 1.3). KIT contains a split intracellular kinase domain and is encoded by a transcriptional unit found at the W locus. Both SF and its receptor can be expressed as different isoforms with different activities and can be cleaved proteolytically to yield soluble forms with similar binding affinity80,81. 12  Even before the products encoded by the W and Sl loci were known to represent a receptor-ligand pair, studies of the defects caused by mutations at both loci had pointed to their involvement in HSC regulation. For example, both fetal and adult hematopoietic tissues from mice carrying mutations within the kinase domain of Kit (e.g., see Figure 1.3) show reduced CFU-S/HSC activity82. Mice with a W41/W41 genotype are of particular interest because they are viable and fertile (in contrast to those with more severe W-mutations)83, but still have significantly reduced HSCs numbers (10 to 20-fold). As a result, sublethally irradiated adult W41/W41 mice can be used as hosts to detect transplanted (wild-type) HSCs with the same sensitivity as lethally irradiated wild-type hosts given a minimal radioprotective transplant42,84. In contrast, Sl-mutant mice, which have deletions in the SF genomic sequence85, have a defect in the microenvironmental niche that supports the regenerative activity of CFU-S/HSCs55. HSCs from all stages of development express the same levels of the KIT receptor on the cell surface regardless of their cycling status or position in the cell cycle43,86-88. In vitro, the ability of different concentrations of soluble SF to modulate HSC self-renewal divisions directly has been demonstrated using highly purified starting populations and subsequent in vivo readouts of retained or lost HSC activity17,89,90. Moreover, these effects on HSC self-renewal can be elicited even in the absence of changes in HSC viability or cell cycle progression. These experiments have further shown that the self-renewal responses of fetal and adult murine HSCs to soluble SF in serum-free suspension cultures are both steeply SF concentration-dependent above and below an optimum level but their specific sensitivities to SF are markedly different. Fetal HSCs are 6-fold more sensitive to SF than their adult counterparts with maximum maintenance of fetal HSC activity in medium containing 50 ng/ml of SF (only) as compared to the 300 ng/ml of SF (+ 20 ng/ml IL-11) required to achieve a similar result with adult HSCs87. The different SF sensitivity displayed by fetal and adult HSCs is likely due to differences in how KIT-activated signals are transmitted to downstream intracellular targets in these cells since both 13  express similar levels of KIT and, under their respective optimal conditions of SF stimulation, they show no differences in apoptosis and divide with the same cell cycle times18. Figure 1.3 summarizes the downstream signaling pathways likely to influence HSC selfrenewal responses. SF binding induces receptor homodimerization and auto-crossphosphorylation of tyrosine residues in the cytoplasmic domain which then serve as docking sites for various SH2 domain-containing signalling intermediates91. Activation of KIT also leads to the recruitment and activation of adjacent kinases including JAK292, TEC93, and MATK94, the tyrosine phosphatases SHP-1 and SHP-295, phospholipase C, and the p85 subunit of phosphatidylinositol 3-kinase (PI3-K)96. The activated KIT receptor complex is then recruited transiently to cell surface lipid rafts where the p110 catalytic subunit of PI3-K is located97. This allows a functional PI3-K holoenzyme to assemble leading to the subsequent activation of cytosolic PDK1 and AKT/PKB. This is accompanied by a reduction in PTEN levels (a negative regulator of the PI3-K pathway) in the rafts thus reinforcing the activation of the PI3-K pathway and multiple downstream events including inactivation of the forkhead transcription factor (FOXO3)98 and activation of the MAPK pathway99. SF-mediated activation of the JAK2 leads, in turn, to the activation of STAT 1, 3, and 5 which then form dimers and translocate to the nucleus to alter the transcription of specific target genes100-102. Some of these signaling elements and their ultimate transcriptional targets appear to participate in the regulation of HSC amplification although, in many cases, it has not yet been possible to discriminate between effects on HSC viability or mitogenesis as compared to independent effects on their self-renewal control.  Gp130 - Unpaired is the Drosophila homolog for gp130 and is critical in the male germ stem cell niche as a stimulator of the JAK/STAT pathway and it is made by the cap cells which surround the germ stem cells, a location central to maintaining their stem cell function103. In 14  mice, lack of Gp130 causes major hematological disorders104 and multiple lines of evidence indicate that cytokines that signal through gp130 can affect HSC self-renewal activity, particularly in combination with Steel factor (SF) and FMS-like tyrosine kinase-3 ligand (Flt3L)105-107. For example, a number of different investigators have cultured adult mouse BM cells in a feeder-free medium containing SF, Flt3L, IL-6, and IL-11 in varying combinations and concentrations for 7-21 days and have found that either maintenance or moderate expansion of the HSC compartment could be achieved over this period, which was inhibited by the further addition of IL-1, IL-3, or TNF70,72,107-110. Audet et al. then showed that while SF and Flt3L together were substantially mitogenic for HSCs, stimulation of the gp130 pathway was further required to optimize HSC selfrenewal74. Additionally, they showed that this response was limited to a narrow window of effective concentrations of gp130 stimulation74. In a subsequent study, a multi-factorial design was utilized to investigate the combined effects of multiple cytokines on HSC self-renewal and proliferation90. The modelling component of this study produced from the collected biological data was subsequently found to be predictive of cytokine combinations that are optimally stimulatory for expanding HSC and progenitor numbers in 10-14 day cultures90. The model also predicted the observed ability of SF and Flt3L to substitute to some extent for one another and the inhibitory effects of excessive exposure to both in combination. In addition, these studies showed that SF and IL-11 on their own could sustain input HSCs numbers for a period of 10 days90. Subsequent validation of this prediction was provided by the experiments of Uchida et al. who cultured individual HSCs in 300 ng/mL SF and 20 ng/mL IL-11 with or without 1 ng/mL Flt3L and showed maintenance of transplantable HSC activity in the first division progeny17.  Thrombopoietin (TPO) – The receptor for TPO is MPL and in studies of mice where one gene is deleted111,112, each was found to be necessary for the generation of normally-sized HSC 15  compartments in mice. A positive action of TPO on HSC expansion has also been demonstrated in 7-day serum-free, feeder-free cultures113. However, TPO alone sustains HSC numbers without inducing their proliferation114, suggesting that this factor may be unique in promoting HSC survival and maintenance of the stem cell state without affecting HSC mitogenesis.  All Trans-Retinoic Acid (ATRA) - ATRA is a Vitamin A derivative that acts as an agonist on the RA receptor (RAR). ATRA was first noted to have an ability to stimulate the generation of progenitors with in vitro colony forming cell (CFC) activity as well as CFU-S in vivo115. Subsequent studies showed that when added to SF, FLT3L, IL-6, and IL-11 in 7-day to 14-day serum-containing cultures of KSL cells, HSC maintenance was slightly improved relative to control cultures without added ATRA and this effect was blocked in the presence of AGN 193109 (an RAR antagonist)115. In a later study, it was shown that ATRA-treated HSCs from RARγ−null mice were unable to repopulate animals in contrast to ATRA-treated HSCs from RARα−null mice which behaved like wild type HSCs116.  Wingless (WNT) – The Wnt family of genes are homologs of Drosophila wingless (wg) which is a well-characterized segment polarity gene. Additionally, wg has been implicated in the maintenance of somatic stem cells in the fly117. In 2003, 2 groups obtained evidence that WNT proteins may have a role in stimulating HSC population expansion. One group reported that 100 ng/mL of purified WNT3A was potently mitogenic and promoted HSC self-renewal in 6-day cultures containing serum and limiting concentrations of SF118. However, the transplantation assays used to quantify changes in HSC numbers were only taken out to 6 weeks, which is insufficient to ascribe effects on cells with long term reconstituting ability. The second group extended this finding using HSCs transduced with a constitutively active β-catenin construct and an 11-week in vivo assay to provide evidence of expansion of HSC numbers in prior 7-day 16  cultures119. However, even interpretation of these experiments was compromised by the fact that they were performed using Bcl-2 transgenic mice, and it has recently been reported that these findings may not be generally applicable120.  Notch Ligands – Notch is another evolutionarily conserved pathway that has been implicated as having a role in HSC biology. Overexpression of Notch ligands in vitro has been shown to expand HSC and progenitor numbers and activity121,122. Additionally, overexpression of the downstream target Hes1 had a similar effect123. Recently however, it has been shown that the Notch pathway is not essential for HSC generation or self-renewal as normal HSC numbers and potency were shown to be retained when this pathway was inhibited, either by forced expression of a dominant-negative form of Mastermind-like1 (a potent inhibitor of Notchmediated transcriptional activation) or genetic inactivation of CSL/RBPJ, a DNA-binding factor required for canonical Notch signaling124.  Insulin-related factors – Insulin signalling is essential to the viability and growth of many cells and is an essential component of most defined serum substitutes125. Insulin-like growth factor2 (IGF2) has its own specific, but non-signalling, receptor and can bind to the insulin receptor INSR126. IGF2 was identified as a candidate stimulator of fetal HSCs from a global gene expression comparison of supportive and non-supportive feeder cells127. Subsequent experiments indicated that when combined with serum and SF, IL-6, and Flt3L, a 2-fold net expansion of fetal HSC number could be obtained127. More recently, the addition of IGF Binding Factor (BF) 2 has also been attributed such activity128. However, Bowie et al., using a similar approach but a defined insulin-containing serum substitute instead of serum, was unable to confirm these findings87. Rather, the results they obtained revealed fetal HSCs to be much  17  more sensitive to SF activation than adult HSCs. Thus the precise role of IGF2 signalling in HSC self-renewal remains poorly defined. In summary, reproducibly significant (>10-fold) expansion in vitro of rigorously defined long term repopulating HSCs has not been achieved in the absence of genetic manipulation of the responding HSCs themselves. Furthermore, the dynamics of change in HSC competence mediated by altered actions of a single growth factor have not been described. Thus, it was not clear whether the extent to which an HSC is stimulated by a single growth factor in vitro could, on its own, dictate the maintenance or loss of self-renewal activity.  1.7  Intrinsic regulators of HSC self-renewal  The molecular regulation of HSC self-renewal is a complex and dynamic process that has been shown through a large number of studies to involve impressive networks of transcription factors, signaling pathways, and regulators of chromatin structure (see summary in Table 1.2). Although the precise role of any of these molecules and the manner with which they interact with each other remains unresolved, some exciting trends have emerged highlighting the roles of canonical signaling pathways, polycomb genes, and cell cycle regulators (reviewed in Zon129, Figure 1.4). Some examples particularly relevant to the body of work presented later in this thesis are highlighted below.  1.7.1  The JAK/STAT pathway  Not surprisingly, many pathways activated by growth factors have been identified as having a role in HSC self-renewal. Noted above are those related to mediating effects of SF, 18  ligands that activate Gp130, TPO, and IGF2. All of these activate STAT3 and/or 5. In the mouse, this activation of both STAT3 and STAT5A appears to be important as shown by studies of HSCs transduced to express a dominant-negative version of STAT3130 and cells from Stat5a-/mice131 or human CD34+ cells with RNAi-suppressed STAT5132,133. Similarly, transduction of primitive hematopoietic cells with constitutively active forms of STAT3134 or STAT5A134,135 enhanced HSC self-renewal divisions under certain conditions and, in the case of STAT5A, led to a myeloproliferative syndrome in vivo. However, levels of STAT3 appear to be non-limiting in HSCs since overexpression of the native form did not alter the amplification of HSC numbers in vivo130 and levels of STAT3 mRNA are found to be significantly higher (~2-fold) in proliferating adult HSCs than in their fetal counterparts87.  1.7.2  Polycomb transcription factors  Polycomb family members are extremely well conserved and form 2 major complexes (PRC1 and PRC2) that have been well-studied as regulators of gene transcription by controlling the levels of open chromatin in specific areas of the genome136,137. PRC2 elements (encoded by Suz12, Ezh2, and Eed) initiate this process by methylating the lysine 27 residue and this is followed by PRC1 elements (variably comprised of the products of the Bmi1, Phc1, Pcgf2, Ring1 genes) where it further compacts chromatin or interferes with transcription (see Figure 1.5)137. In the mouse model, Bmi1, Phc1 (Rae28), Ezh2, and Pcgf2 (Mel18) have all been implicated in regulating HSC self-renewal activity either positively (Bmi1138,139, Rae28 140, and Ezh2141 or negatively (Mel18)142. Mel18 is of additional interest here because it can inhibit the activity of cyclin D2 by direct physical interaction in the nucleus143.  19  Polycomb family members have also been shown to be upstream regulators of homeobox genes. This is of significant interest to understanding HSC self-renewal as forced overexpression of HOXB4 was one of the first strategies found to enable large (~40 fold) net expansions of HSCs numbers to be achieved in 14-day cultures without inducing leukemia144. Remarkably, subsequent studies with several novel HOX-fusion proteins have shown such non-leukemogenic expansions of HSC populations can be further increased in the same time frame another several hundred-fold145. Interestingly, both Mel18 and Ezh2 transcripts are expressed at much higher levels (~10-fold) in quiescent adult as compared to proliferating fetal HSCs whereas Bmi-1 transcript levels are similar in both18.  1.7.3 Cell cycle regulators  Expression of the D-type cyclins is induced by mitogenic cytokines and their expression allows the formation of complexes with partner cyclin-dependent kinases leading to entry into Sphase146. In mice, loss of all 3 D-type cyclins severely impairs the expansion of HSC numbers in the FL and post-transplant in adults although both of these effects are likely due to an inability of the cells to transmit a mitogenic signal rather than a direct effect on the HSC self-renewal mechanism itself147. p21, p16, and p18 are 3 of several cyclin-dependent kinase inhibitors with well-established roles in regulating the progression of cells through specific phases of the cell cycle146. All 3 of these have also been implicated as regulators of HSC self-renewal either directly or indirectly. Cells from adult p21-/- mice have reduced numbers of myeloid progenitors148. They also have markedly reduced numbers of CFU-S and HSC activity and this appears to be caused by their failure to respond to signals that normally induce HSC quiescence149, which occurs when mice reach 4 weeks of age43. In contrast, p16150 and p18151 20  deficiencies each endow HSCs with improved longterm repopulating and self-renewal activity and the latter can partially offset the negative consequence of a p21 deficiency in HSCs152. Interestingly, p18 mRNA is present at significantly lower (~10-fold) levels in fetal as compared to proliferating adult HSCs even after the cells have been stimulated to proliferate by SF in vitro87, a finding consistent with a role of p18 in mediating the observed differences between fetal and adult self-renewal activities in vivo. Other key components of the cell cycle machinery are the retinoblastoma (RB) family of proteins (RB, p107, and p130) and the E2F transcription factors, which RB represses (reviewed in153). Inactivation of RB results in profound myeloproliferation and mobilization of HSCs to extramedullary sites154. Most recently, it has been shown that conditional knockout of all 3 RB family members also leads to a myeloproliferation and increased apoptosis of lymphoid progenitors. The BM HSCs are then no longer quiescent and they are also rendered less competitive in transplantation compared to wild type HSCs. These findings suggest that loss of RB may lead to a premature exhaustion of the HSC compartment in vivo.  1.8  Methodologies for analyzing HSC transcriptomes  Many platforms are currently used for gene expression analysis to detect changes in the transcriptome of cells undergoing particular developmental changes. Three of the most powerful techniques are: Serial Analysis of Gene Expression (SAGE); oligonucleotide arrays; and quantitative real-time reverse transcription-PCR (Q-RT-PCR). SAGE provides a snapshot of the transcriptome via direct sequencing of short 14 or 21-mer (LongSAGE) RNA segments. Three principles are inherent to SAGE methodology: 1) the tags generated can uniquely identify a useful proportion of transcripts, with LongSAGE giving greater specificity and frequency of 21  unique tag-to-gene assignments. 2) Tags can be linked in tandem for cloning and sequencing. 3) The number of times a tag is represented in the library is linearly related to the level of expression of the corresponding transcript in the cells being analyzed. Because SAGE does not depend on a pre-defined set of transcripts, it can measure changes in expression of transcripts that have not been previously identified and can also serve as a platform for gene discovery155,156. One major limitation, however, is that the original SAGE method required ~10 µg of sample RNA making amplification of cDNA a necessity for small cell numbers. Oligonucleotide arrays (e.g.: Affymetrix) are designed to make a comparison of differential transcript levels in 2 samples. The expression of thousands of genes are concurrently monitored and compared between samples with a read-out of up- or down-regulated transcripts in relation to a reference. Oligonucleotide arrays have a greater number of probe sets and have been suggested to be more accurate than 2-colour spotted cDNA arrays due to internal controls and the recent release of calibration data by Affymetrix157. Specifically, Affymetrix chips contain 11 to 25 pairs of oligonucleotide probes for each target RNA in which one of the pair has a mutated base in the middle to control for stray signals. Using the differences between the intensities of the pairs, software judges the reliability of each set and calculates a quantitative and qualitative measurement158. A limitation of these arrays, however, is that they are not useful for gene discovery and they require at least 1-2 µg of sample RNA which, again, requires amplification when only small numbers of cells are available. Whole genome oligonucleotide arrays are also available and have been suggested as an unbiased way of assessing gene expression because transcripts are not pre-selected to be on the chip. Q-RT-PCR has been considered to be the most accurate method for measuring the particular transcript levels in cells. Based on PCR technology and quantification using a DNA binding fluorescent dye (typically SYBR Green), this method is a very powerful tool for comparing the level of expression of transcripts in different samples159,160. Typically, a Q-RT22  PCR experiment relies on a relative measure of abundance by comparing the test signal to a single or a set of housekeeping genes (like Actb or Gapdh). As with oligonucleotide arrays, QRT-PCR is not useful for gene discovery and also suffers from not being as high-throughput as the other methods described above. The major issue that faces biologists trying to perform high throughput gene expression analyses of HSCs is the low amount of sample RNA available. These cells are very rare and RNA from pure populations in a reasonably sized experiment would fall far short of the necessary amount for most conventional SAGE and microarray protocols. Efforts to overcome this barrier are summarized below: •  A global RT-PCR procedure has been developed that amplifies RNA as much as 3 x 1011, making it useful for the amplification of picogram amounts of RNA. This method is exponential (thereby quick and efficient) and is purported to preserve abundance relationships between replicates161.  •  SAGE-Lite is a modification of conventional SAGE that allows the user to reduce the amount of input RNA to as little as 50 ng. Because the amplification is PCR based, biases can be introduced if the number of cycles is not optimal162.  •  In vitro transcription has been employed for the amplification of DNA for a number of years. Groups have begun to apply the technique to RNA and one group has demonstrated a Pearson correlation value of 0.91 between 2 samples amplified from 200 pg of starting material163. When applied to RNA, it has been demonstrated that the T7 based linear amplification introduces no systematic bias between amplified samples. However, when an amplified sample was compared to a non-amplified sample, differences in expression of some genes were observed164.  Currently, no published data exists for SAGE libraries built from highly purified (>10%) HSCs. (SAGE libraries do exist for CD34+CD38-Lin- human hematopoietic cells from cord 23  blood, adult bone marrow and mobilized peripheral blood165 and fetal liver166; however, the actual HSC content of these populations is estimated at <1%). Microarray data, on the other hand, has emerged from the profiling of more highly HSC-enriched populations of murine HSCs19,167-174.  1.9  Thesis Objectives  As summarized above, much evidence indicates that the competence of HSCs can either be activated or sustained in a latent state, which is controlled by a mechanism that involves a complex integration of extracellular cues and intracellular components. However, whether and how this “self-renewal machinery” is connected to the pathways that control the proliferative (or viable) state of HSC remains poorly understood. Historically, it has been assumed that they are tightly interwoven and a particular set of molecules determines whether a given HSC will execute a self-renewal expansion division vs. a symmetric maintenance division vs. a symmetric depletion division (Figure 1.6). On the other hand, it is also possible that stem cell competency is maintained, at least in part by mechanisms that are independent of the process that actually makes the cells divide. Exploration of this question requires access to populations of HSCs that are of such homogeneity and purity that definitive conclusions about their self-renewal responses and gene expression profiles can be generated from bulk populations. Investigations of these questions served as the starting point for this thesis. Chapter 3 begins with a presentation of work performed jointly by myself and another PhD student, Brad Dykstra, that examined the heterogeneity present within the HSC compartment defined historically by the CRU assay both in terms of their individual differentiation and/or self-renewal activities revealed from analyses of a large number of 24  transplants of single cells or their in vitro-derived clones. Evidence of heterogeneity in differentiation potential had been previously suggested175-178, but the extent to which it reflected intrinsic differences179 in the HSCs themselves versus their exposure to different factors180,181 versus stochastic events that alter their potential182-184 was not clear. Our work provided strong evidence of functionally distinct subsets of HSCs with extensive (durable) vs. no further selfrenewal activity (rather than a continuum). These findings led me to seek new ways of separating these cells from each other so that their properties could then be studied in more depth and using more direct approaches. Based on the studies of Audet et al.74,90, I hypothesized that modulation of SF concentrations alone could alter the stem cell status of HSCs and hence influence the frequency of their executing a self-renewal division. This required designing an experiment that would allow the rate of entry into division and the rate of change in HSC status to be monitored independently at the single cell level in vitro. The design and execution of such studies are presented in Chapter 4. I then hypothesized that there might be additional, as yet unidentified intracellular components that are essential to maintaining a durable self-renewal state in HSCs. To investigate this possibility, I exploited a variety of approaches that included the methods and findings described in Chapters 3 and 4 to compare their gene expression profiles with multiple closely related cell types. These studies are presented in Chapter 5.  25  Table 1.1 - Regulators of HSCs in the Stem Cell Niche (adapted from Kiel et al.68) Factor  Genetic Evidence for Regulation of HSCs  Angiopoietin  Combined loss of Tie2 (the receptor for angiopoietin) as well as Tie1, leads to defects in postnatal HSCs185; angiopoietin appears to promote the maintenance of quiescent HSCs59  Ca2+ ions  Deletion of the Ca2+-sensing receptor leads to reduced bone-marrow cellularity and HSC content with increased progenitor-cell mobilization into the circulation and spleen186  CXCL12  SHH  Osteopontin  SCF  Mice deficient in the chemokine CXCL12 or its receptor CXCR4 show disrupted colonization of the bone marrow, whereas conditional deletion of CXCR4 in adult mice leads to reductions in HSC numbers in the bone marrow and reduced HSC activity upon transplantation187,188,188,189 Patched (the SHH receptor) heterozygous HSCs have reduced long-term reconstituting activity, suggesting that a member of the SHH family of ligands can negatively regulate HSC self-renewal, at least when the pathway is over-activated190 The matrix glycoprotein osteopontin is expressed at the endosteum by bonelining cells and negatively regulates HSC numbers; osteopontin-deficient mice have moderately increased HSC numbers in the marrow191,192 Mice with mutations in SCF (Sl/Sld, Steel dickie mutants) or in its receptor KIT (W/Wv, dominant spotting mutants) have fewer HSCs and exhibit less HSC function193,194  Thrombopoietin is synthesized in the liver, kidney, bone-marrow stroma and Thrombopoietin by osteoblasts and may be transported into the bone marrow through the blood195-197 mice deficient in thrombopoietin or the thrombopoietin receptor c-Mpl have profound reductions in HSC numbers195,198-200  26  Table 1.2 - Genes Shown to Regulate HSC Self-Renewal Factor Apc Bmi1 Cdkn1a (p21) Cdkn2c (p18) Cdkn2a (p16) Gfi1 Hoxb4 Kit Mef Mel18 Notch1 Phc1 (Rae28) Rarg Stat3 Stat5a Tel/Etv Tie1 and Tie2 Wnt3a  Effect on HSCs or Hematopoiesis Conditional inactivation increases apoptosis, decreases entry into cell cycle201 Deletion enhances HSC symmetric divisions138,139 Absence of p21 increases HSC cycling and causes stem cell exhaustion149 Overexpression increases self-renewal divisions152 Overexpression increases self-renewal divisions150 Deletion results in a reduced HSC competitive repopulating ability202 Overexpression causes expansion of HSCs144 Deletion results in a cell intrinsic defect in the CFU-S compartment203 Deletion maintains quiescence of HSCs204 Deletion negatively regulates the proliferation of HSCs142 Activation causes expansion of HSC205 Overexpression enhances HSC self-renewal140 Knockout animals show reduced numbers of HSCs116 Dominant negative Stat3 suppresses HSCs, overexpression increases HSCs130,134 Overexpression promotes HSC self-renewal in vitro135, knockout reduces selfrenewal131 Deletion results in a complete depletion of bone marrow, HSC specific effect206 Chimeric mice show that these receptors are critical for adult HSC maintenance185 Overexpression results in proliferation and expansion of HSCs119,207  27  Figurre 1.1 – In vivo v transplaantation asssay to detecct HSCs (froom Kent et al. a 208) Test cells c are asseessed for HS SC function in a retrospeective transpplantation asssay where ceells are transpplanted into a geneticallyy distinguishhable (in thiss case by thee CD45 surfa face marker) irradiiated recipient (to comprromise the host h blood ceells) and assaayed for theiir ability to produce p the many m types of white bloood cells at vaarious periodds post transpplantation. HSCs H are typpically determ mined to hav ve been pressent in the orriginal test cell c populatioon if they caan produce thhe multiiple lineagess of the hemaatopoietic syystem out to 4 months poost-transplannt.  28  abilitty to producee the many tyypes of whitte blood cellls at various periods postt  Figurre 1.2 – Hieerarchical model m of hem matopoiesis (adapted froom Bryder ett al.209) HSCss are placed atop the hierarchy of heematopoiesiss and have thhe ability to give g rise to all a of the downnstream lineaages of the hematopoieti h ic system wiith both myeeloid and lym mphoid elem ments. The HSC H is the only o cell in thhe hierarchyy with longteerm self-reneewal activityy indicated byy the arrow w at the top.  29  Figure 1.3 – SF signalling and its role in HSCs (from Kent et al.37) Schematic representation of key signalling events activated in primitive hematopoietic cells exposed to SF (here shown as two membrane-bound molecules bound to a dimerized receptor complex). Blue arrows, pathway-promoting activities; red stop lines, inhibiting activities.  30  Figurre 1.4 – Key y regulatorss of HSCs (aadapted from m Zon129) A schhematic depiicting some key k regulatoors of HSCs and how theey are thoughht to interactt with each other. Arrow ws indicate positive p inteeractions andd lines with bars b indicatee repressive interaactions. Exttrinsic regulaators appear on the outside of the celll and intrinssic regulatorrs are in the innside of the cell. c  31  ycomb complexes indu uce epigenetic silencing (adapted froom Sparmannn et Figurre 1.5 – Poly al.210) The nucleosome n w recruitts a PRC1 coomplex and is first bounnd by a PRC22 complex which methyylates lysinee 27 on histoone H3. Thee PRC1 compplex plays a major role in i inhibiting transccription and ubiquitylatiion of lysine 119 of histoone H2A, whhile the PRC C2 complex induces i chrom matin compaaction via reccruitment off DNA methyyltransferasees (DNMTs in the schem matic).  32  Figurre 1.6 Possib ble outcomees followingg the division of an HSC C Theree are 3 possiible outcomees for an HSC – to not diivide, to die,, or to underrgo a divisionn with one of o the 3 depiccted divisionn outcomes: 1) a self-rennewal expannsion divisioon where onee HSC givess rise to 2 HS SCs 2) a maiintenance divvision wheree one HSC gives g rise to one HSC annd anothher non-HSC C and 3) a deepletion divission where one o HSC givves rise to 2 non-HSCs. n  33  2.  Materials and Methods  2.1  Mice  BM donors were 8 to 12 week-old C57Bl/6J-CD45.1 or CD45.2 mice. FLs were obtained from E14.5 fetuses derived from timed matings of the same strains. All transplant recipients were Cd45-congenic C57Bl/6J-W41/W41 (W41/W41) mice previously given a sublethal dose of irradiation (360 cGy X-rays at 350 cGy per minute). Mice were bred and maintained in microisolator cages and provided with sterile food, water and bedding. Animal procedures were carried out in accordance with the Canadian Council on Animal Care with specific project and protocol approval from the University of British Columbia Animal Care Committee.  2.2  Isolation of subsets of FL and adult BM cells  Suspensions of BM cells were flushed from the femurs and tibias of adult (8-12 weekold) B6 donors into 1 mL of 2% (v/v) fetal bovine serum (FBS) supplemented-Hank’s Balanced Salt Solution (collectively called HF) using a 21 gauge needle and the cells then depleted of RBCs by a 10-minute incubation on ice in the presence of NH4Cl. To isolate the CD45midlin-Rho-side population (SP) fraction, the BM cells were first suspended at 1-2.5x106 cells/mL in serum-free medium (SFM = Iscove's modified Dulbecco's medium supplemented with 10 mg/mL bovine serum albumin, 10 µg/mL insulin, and 200 µg/mL transferrin, 100 units/mL penicillin, 100 µg/mL streptomycin, all from STEMCELL Technologies, and 10−4 M β-mercaptoethanol [Sigma Chemicals, St. Louis, MO, USA]). The 34  cell suspension was warmed for 15 minutes at 37°C, after which 0.1 µg/mL Rho (Molecular Probes Inc., Eugene, OR, USA) was added to the cells in SFM and the suspension incubated for an additional 30 minutes at 37°C, followed by washing in cold HF and centrifugation. Cells were resuspended at 1-2.5x106 cells/mL in SFM. The cells were then incubated with 0.1 µg/ml of Ho (Molecular Probes) for 90 minutes at 37°C. Cells were then washed, resuspended at 107 cells/ml in ice-cold HF plus 5% rat serum (Sigma) and 3 µg/ml of a Fc receptor blocking antibody (2.4G2)211 (blocking reagent) for 10 minutes followed by monoclonal antibody staining for 30 minutes on ice. Antibodies used for sorting were biotinylated anti-Mac1, anti-Gr1, antiB220, anti-Ly1, and anti-Ter119 (all from STEMCELL Technologies), and allophycocyanin (APC)-conjugated anti-CD45 (from Becton Dickenson (BD), San Jose, CA, USA)). Cells were then washed, resuspended at 107 cells/ml in ice-cold HF and incubated for 15 minutes on ice with Streptavidin-phycoerythrin (SA-PE, BD), then washed once with HF and resuspended in HF plus 2 µl/ml propidium iodide (PI, Sigma) for sorting. To isolate the CD45midRho-EPCR+ fraction, the cells were prepared as above with the following two changes: 1) The Ho stain was not performed and the cells were directly resuspended in HF following the Rho stain, 2) The lineage marker SA-PE stains were not performed and were replaced with a PE-conjugated anti-EPCR antibody (STEMCELL Technologies). To isolate the CD45+EPCR+CD48-CD150+ (E-SLAM) and/or CD45+EPCR+CD48CD150- fractions, the freshly obtained BM cells were first stained for 30 minutes with fluorescein-isothiocyanate (FITC)-conjugated anti-CD45, PE-conjugated anti-EPCR (STEMCELL Technologies), biotin-conjugated or PE-Cyanin7-conjugated anti-CD150 (BioLegend, San Diego, CA, USA), and APC-conjugated anti-CD48 (BioLegend). When biotin conjugated anti-CD150 was used, cells were secondarily stained with SA-PE-Texas Red (BD).  35  FLs were suspended in HF by forcing the tissue through a sieve and then through a 40 µm filter. Erythroid precursors were depleted by immunomagnetic removal of biotin-conjugated anti-Ter119-labelled cells using EasySep reagents (STEMCELL Technologies) as recommended by the manufacturer. The unlabeled fraction of cells was then centrifuged and suspended at 107108 cells/mL of Hank’s containing blocking reagent described above. E-SLAM cells were then isolated using the same antibody stain described above for BM cells. Lin-CD43+Mac1+Sca1+ E14.5 FL cells were prepared by staining Ter119-depleted cells with biotinylated anti-Gr1, anti-B220, and anti-Ly1 (all of which were prepared in the Terry Fox Laboratory), and Ter119 (from BD), and PE-labelled anti-Sca-1 (BD), FITC-conjugated antiCD43 and APC-conjugated anti-Mac1 (all from BD). Cells were then washed with HF, centrifuged, resuspended at 107-108 cells/mL of HF and incubated for an additional 15 minutes on ice with SA-PE-TexasRed. Cells were washed again in HF and then resuspended in HF plus 2 µg/ml PI (Sigma). To isolate lin- populations from BM or FL cells, the cells were stained for 30 minutes with biotin-conjugated anti-Ly1, anti-Gr1, anti-CD5, and anti-B220 (STEMCELL Technologies), washed, resuspended in HF, stained for 15 minutes with SA-PE (BD), washed and suspended in HF containing DAPI to mark dead cells (Sigma). All cell sorting was performed using either a FACSVantage (BD), FACSDiVa (BD), FACSAria (BD) or Influx Cell Sorter (Cytopeia, Seattle, WA, USA). For CD45+EPCR+CD48CD150+ (E-SLAM) and/or CD45+EPCR+CD48-CD150- FL or adult BM sorts, cells were first sorted at a high rate (10,000-15,000 cells per second) using an EPCR+CD48- gate that captured approximately 0.5% of all the viable cells and were then re-sorted at a slower rate (1-200 cells/second) to improve the purity. When single cells were required, the single cell deposition unit of the sorter was used to place these individually into the wells of round-bottom 96-well  36  plates, each well having been preloaded with 100-200 µL SFM. Each well was then visually inspected to identify those that contained only one viable cell.  2.3  Analysis of transplanted mice  All transplants were injected intravenously into irradiated mice via the tail vein. For single cell injections, each cell was sorted into 100-200 µL SFM in an individual well of a 96well plate and the plate was centrifuged at ~180 g for 5 minutes to bring the cells to the bottom of each well without damaging them. Each well was examined using a standard inverted microscope and the wells were confirmed to have one and only one cell present. For each well, a single use 28 G1/2 insulin syringe filled with ~300 µL phosphate buffered saline (PBS) and no air bubbles was used to gently push approximately 50 µL of the 300 µL into the well to disturb the cell from the bottom of the well. The syringe was then used to remove almost all of the liquid from the well which was gently dispensed back into the well. Finally, all of the liquid was aspirated into the syringe and the entire volume was injected into the tail vein of an irradiated mouse that had been exposed to an infra-red heat lamp for ~ 2-3 minutes to dilate the vein prior to injection. For secondary or tertiary transplantations, BM was harvested from selected repopulated primary recipients of single HSCs, and the cell content equivalent of one femur (~10% of the total BM) was injected into each of 2 secondary sublethally irradiated W41/W41 recipients. Peripheral blood samples were collected from the tail vein of mice 8, 16 and 24 weeks post-transplant. RBCs were lysed with NH4CL for 10 minutes on ice and the WBCs stained with antibodies for both donor and recipient CD45 allotypes (using anti-CD45.1-APC and antiCD45.2-FITC antibodies) as well as anti-Ly6g-PE/anti-Mac1-PE for myeloid (GM) cells, anti37  B220-PE for B-cells, and anti-CD5-PE for T-cells (CD45.2-FITC purified and conjugated in the Terry Fox Laboratory; CD45.1-APC from eBiosciences, San Diego CA; all other antibodies from BD). All double negative or double positive CD45.1 and CD45.2 events were excluded from further analysis. To calculate repopulation levels, the contributions of the donor CD45 allotype to the populations of circulating GM, B, T and total WBCs were calculated. Recipients with >1% donor-derived white blood cells (WBCs) at 16 and/or 24 weeks post-transplant were considered to be repopulated with HSCs. α (myeloid-biased), β (balanced lympho-myeloid), γ (lymphoid-biased and still multi-lineage at 4 months), and δ (lymphoid-biased but no longer multi-lineage at 4 months) subtypes of HSCs were discriminated by calculating the relative ratios of the donor contributions to the GM versus B plus T lineages measured at 4 months post transplant as follows: The α and β clusters were defined, respectively, by donor-derived GM:(B + T) ratios of ≥2 and 0.25 to 2. Mice that had donor-derived GM:(B + T) ratios of <0.25 were further subdivided into 2 clusters according to whether there was a continuing ≥1% donor contribution to the myeloid lineage as well as to both lineages of lymphoid cells at 16 weeks (γtype), or whether they contributed exclusively to the lymphoid lineages at this time (δ-type)21.  2.4  Cell cultures  Sixteen-hour, 4-day, and 10-day cultures of E-SLAM or CD45midlin-Rho-SP cells were incubated in SFM plus 20 ng/mL IL-11 (Genetics Institute, Cambridge, MA, USA) and either 1, 10, or 300 ng/mL SF (STEMCELL Technologies).  38  2.5  Statistical Analysis  L-Calc software (STEMCELL Technologies) was used to quantify the number of HSCs present by limiting dilution analysis of pooled 10-day clones. This calculation is based on Poisson statistics which predicts that one HSC per animal was present on average when 37% of the animals read out negatively. In Table 4.1, the range defined by ± SEM is shown in brackets underneath the calculated frequency of HSCs. Fisher’s Exact test (available in the open source statistics package R [www.r-project.org]) was used to determine whether or not the frequency of HSCs calculated in individual 8-hour, 16-hour and 1- to 2-day doublet cells, and 4-day clones was significantly different from the input frequency of HSCs. DiscoverySpace software version 4.01212, which utilizes Audic Claverie statistics213, was used to identify tags that were over- or under-represented in a comparison of the LongSAGE libraries. Q-RT-PCR data was collected from 3-6 biological replicates and each sample was assigned a δ-ct value with respect to the levels of Gapdh transcripts present in the same sample. The SEM of the resulting data was then calculated and plotted and significant differences (p<0.05) were established using the Students t-test.  2.6  Quantitative real-time PCR analysis  A PicoPure kit (Arcturus Bioscience, Mountain View, CA) was used to extract RNA from cells harvested from cultures initiated 16 hours previously with 70 to 200 CD45midRho-EPCR+ adult mouse BM cells placed in 200 µL of SFM containing 20 ng/mL IL-11 and either 1, 10 or 300 ng/mL SF. The extracted RNA was reverse transcribed into cDNA using the SuperScript III First-Strand Synthesis System for RT-PCR (Invitrogen, Burlington, ON, Canada) and then Q-RT39  PCR analyses were performed using Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA). The primers used in this thesis are listed in Table 2.1.  2.7  Construction and sequencing of LongSAGE libraries  Five ng RNA was collected for cDNA synthesis and then amplified by PCR to generate 200 ng214 and then LongSAGE libraries constructed. In order to enable useful LongSAGE libraries to be made from small amounts of RNA (<10 ng), I first helped to evaluate the applicability of the SMART technology developed by Clontech (catalog number 635000; Clontech, Mountain View, CA, http://www.clontech.com). This technology is used in the SAGELite protocol214, but for the libraries to be made here, 2 further modifications were introduced. The first was to change the cDNA amplification primer to contain a biotin molecule and the second was to change the template switching primer by introducing an AscI restriction endonuclease site into it. These steps allow the isolation of a final product in which the cDNAs are biotinylated only on the 5’ end which is a requirement for generating SAGE libraries (Figure 2.1). The ability of this method to be applied to very small (10 ng) of RNA and allow the generation of representative libraries was first established using aliquots of a large pool of human embryonic stem cell RNA and then on extracts of HSC/progenitor-enriched CD34+ cells isolated from normal human cord blood215. In brief, the test RNA used here was first reverse transcribed and the first strand cDNA was then purified with a NucleoSpin column and amplified using an Advantage II PCR Kit (BD) in combination with an AscI template switching primer and a modified PCR primer that contained a biotin molecule at its 5’ end. The biotinylated 5’ ends of the amplified cDNAs were then removed by digestion of the initial amplified product with AscI (New England BioLabs, Beverly, MA, USA). The cDNA was purified on a Chroma-Spin 200 40  Column (BD) and its concentration determined using a spectrophotometer (GeneQuant Pro, Biochrom, Cambridge, UK). The amplified cDNA was then processed according to the standard LongSAGE protocol modified in BCCA Genome Sciences Centre using the I-SAGE Long kit (Invitrogen, Carlsbad, CA, USA) and described in detail in Khattra et al.215. Following analysis of data quality from a first 384-well sequencing plate, each library was sequenced to a sampling depth of 44,506 (adult BM) and 200,319 (FL) raw tags (GEO Series accession number GSE13243, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13243).  41  Table 2.1 – List of primers used in Chapters 4 and 5 Gene  GenBank #  Forward Primer  Reverse Primer  Gapdh Stat3 MEF ATM Gata2 Lyl1 G3bp1 Eif1a Tubb5 Hmga2 Psap Prnp Pld3 Rhob Vwf Car3 Plp1 Hdac3 Chd4 Cul4a Trim27 Fus Cebpa Mlx Smarcc1 Smarcc2 Cdkn1a 1200009F10Rik Ezh2 Gata3 Bmi1 Sh2b3 Rae28 Cdkn2c Gfi1 HoxA9 Meis1 Runx1  NM_008084 NM_213659 NM_019680 NM_007499 NM_008090 NM_008535.2 NM_013716.2 NM_010120.3 NM_011655.3 NM_010441.1 NM_011179.2 NM_011170.1 NM_0111161.1 NM_007483.2 NM_011708.3 NM_007606.3 NM_011123.2 NM_010411 NM_145979 NM_146207 NM_009054 NM_139149 NM_007678 NM_011550 NM_009211 NM_198160 NM_007669 NM_026166 NM_007971.1 NM_008091.2 NM_007552.3 NM_008507.3 NM_001042623 NM_007671 NM_010278.2 NM_010456.2 NM_010789.2 NM_009821  AACTTTGGCATTGTGGAAGG GGCACCTTGGATTGAGAGTC TCTGTGGATGAGGAGGTTCC GCAGAGTGTCTGAGGGTTTGT TGACTATGGCAGCAGTCTCTTC GACCCTTCAGCATCTTCCCTAACA GGGAAGCCAGCAGATGCAGT CCGCTGCGTTTTGGTCACTA CAGCTGGACCGAATCTCTGTGT GGTGCCACAGAAGCGAGGAC ACTGTGGGGCCGTGAAGC TCCAATTTAGGAGAGCCAAGCA GCTGAGGAACCGGAAGCTGT GGGGCACGCAGAGTGGTT GGCGAGGATGGAGTTCGACA TGGCTAAGGAGTGGGGCTAC TCAGTCTATTGCCTTCCCTAGCAA TCAACGTGGGTGATGACTGC GCTGCCAGAGATCCCAAACG GGAGAACATTTGACAGCAATTCTACA GAAGAGACGGCGGGCACA CACTGGTTGCATTCATTTCTCCA CCAGTCAGACCAGAAAGCTGA TGTGTCTTCAGCTGGATTGAGGA TGGGAGAGCCCGGACACG TCTTCAGCCGAAGCCTCCAC GTACTTCCTCTGCCCTGCTG GAAAAACAATACCGGTTACTGCAAA CGCTCTTCTGTCGACGATGTTTT GGTATCCTCCGACCCACCAC AAACCAGACCACTCCTGAACA CAACACACACAAGGCTGTCA CGCACATCATTGAAGGCTTTGTT GAACTGCGCTGCAGGTTAT CGAGATGTGCGGCAAGACC GTTCCAGCGTCTGGTGTTTT GCACAGGTGACGATGATGAC AAGACCCTGCCCATCGCTTT  ATGCAGGGATGATGTTCTGG ACTCTTGCAGGAATCGGCTA GGGTGCTGGAGAAGAACTCA AACTTCCAGCAACCTTCACC ACACACTCCCGGCCTTCT AGCCACCTTCTGGGGTTGGT ATCCACGTGGCGGATCTTG TCGGTTCTGGCCTGGTTCTC GGACCTGAGCGAACGGAGTC GGGCTCACAGGTTGGCTCTT GTCGCAAGGAAGGGATTTCG GCCGACATCAGTCCACATAGTCA GGGAAAGGGGTGGTCCTGAG GCAACAGTAGTGGCTTGCTGGTT TGACAGGGCTGATGGTCTGG GTCCCCTTTGGCAATTGGAT GCATTCCATGGGAGAACACCA GCAGAGATGCGCCTGTGTA TTGCCCTTAAGAGCTGGACAA GTGAGGTCGGGCACCCTGT GCTGCTCAAACTCCCAGACAA CCAGTGGAGGTGGTGGAGGT CCACAAAGCCCAGAAACCTA GGACACCGATCACAATCTCTCG TTGGTAGGAGCATCTGCATGAAC CCCTTCTCAGGGAAGTTCAGCA TCTGCGCTTGGAGTGATAGA CCACGAGAGCTTCACATTCCTG GTTGGGTGTTGCATGGAAGG CCAGCCAGGGCAGAGATCC TCTTCTTCTCTTCATCTCATTTTTGA CCTGTGCACAAGAACTACATCTG TTCTTTCAGGAACTGAGAACATCC TCAAATTGGGATTAGCACCTC ACAGTCAAAGCTGCGTTCCT ACAATGCCGAGAATGAGA AGGGTGTGTTAGATGCTGGAA TGCCATGACGGTGACCAGAG  42  Figure 2.1 - cDNA amplification protocol for PCR-serial analysis of gene expression (PCRSAGE) library generation Incorporation of a template switching primer containing an AscI sequence allows an end-to-end amplification of the first-strand cDNA using a single biotinylated oligonucleotide primer and then subsequent removal of the 3' biotin via AscI digestion. The 5' end of the double-stranded cDNA is then available for capture on streptavidin-coated beads for SAGE library construction.  43  3.  Heterogeneity of HSC Programs and Their Resolution by Improved  Purification Methods  3.1  Introduction  As reviewed in Chapter 1, much evidence indicates that throughout adult life, the bone marrow maintains a rare population of HSCs extensive with self-renewal ability. The most specific and reliable methods for identifying HSCs have historically made use of retrospective functional assays that detect the ability of HSCs to produce mature blood cells for sufficient periods of time in vivo to discriminate them from cells able to produce the same spectrum of mature cells but for only relatively short periods. Over the years, a variety of strategies have been utilized to distinguish donor-derived cells in the recipient mice. These include DNA-based detection of sex-mismatched transplants by Southern blot216, PCR or FISH analysis217, electrophoretic detection of different glucose isomerase isoforms218 or hemoglobin allotypes219, and more recently, flow cytometric detection of a cell surface alloantigen (CD45)3 or the product of a reporter transgene like GFP220. A major advantage of the latter strategies is the ease with which they can be combined with flow cytometric identification of different mature blood cell types, thus allowing the differentiation potential of the injected cells to be tracked with considerable precision and sensitivity. Nevertheless, there is still considerable controversy as to the probability, durability, and molecular basis of the observed heterogeneity in self-renewal behaviour of individually tracked HSCs and hence their specific and fundamental definition. Empirically, an association of different phenotypes with the subsequent durability of the regenerative behaviour they display in 44  irradiated recipients has led to the identification of a 4 to 6 month period of sustained repopulation as demarcating the difference between the repopulating activity of the most primitive HSCs and various types of derivative cells with transient, albeit extensive, repopulating activity (see Figure 1.2 in Chapter 1). During the last decade, various methods have been developed for isolating populations of cells from suspensions of adult mouse bone marrow that are at least 20% pure HSCs16,17,19,25,168,221-225 as defined by a repopulating activity that lasts for at least 4 to 6 months. However, many of these methods rely on the use of markers that change according to the activation/cycling status of HSCs226-230. Therefore, most purification strategies are applicable only for steady-state BM in which the majority of adult HSCs are quiescent44,46,231. Consequently, these methods are often not useful for the direct enumeration of HSCs that have been biologically or genetically manipulated. Therefore, enumeration of HSCs has to still rely on functional endpoints of their activity in vivo. The demonstration that the highly discriminating CD150+CD48- phenotype of HSCs found in adult BM19 (like the absence of most lineage markers and the expression of Sca1) extends to HSCs in cytokine-mobilized, regenerating and fetal populations of hematopoietic cells20 was thus a welcome advance. Although the specificity of this phenotype for HSCs manipulated in vitro remains to be examined, these studies suggest that more direct readouts for these cells will eventually be found. Our group has been investigating the extent of HSC self-renewal heterogeneity by analyzing their clonal patterns of differentiation and self-renewal. In a series of ~100 mice repopulated by single CD45midlin-Rho-SP cells (~30% pure HSCs) or their 4-day in vitro clones, we were able to identify 4 distinct subtypes of HSCs based on their relative lineage contributions. To determine whether these programs were random expressions of multiple optional states, or more stably perpetuated in a clonal fashion, we then performed secondary and tertiary transplants of the cells regenerated in the BM of the primary hosts 6-8 months after their 45  initial injection. The results showed a remarkable and unexpected stability of the differentiation patterns seen in the primary recipients and, more relevant to the studies in this thesis, a demarcation of retained or lost self-renewal ability amongst the 4 HSC subtypes. Two of the 4 subtypes were able to regenerate sufficient HSCs so that, after 6 months post-transplant, these progeny HSCs could be detected in a standard CRU assay in secondary recipients (in which the total cellular contents of 2 femurs from the primary hosts were assessed). Moreover, most of the primary clones produced in vivo from either of these 2 subtypes displayed extensive high selfrenewal activity in these assays. In contrast, none of the primary hosts of the other 2 original HSC subtypes had any detectable HSCs in such assays. To facilitate further studies of the “highself-renewal HSCs”, separate from those with more limited self-renewal activity, I undertook a series of experiments to determine if and how these subtypes of HSC might be differentially purified.  3.2  Results  3.2.1  Identification of subtypes of murine HSCs with differing self-renewal programs  To examine the WBC outputs over time of a large number of single HSCs, 352 mice were injected with a single CD45midlin-Rho-SP cell or a 4-day clone derived in vitro from such cells in the presence of 100 ng/mL SF and 20 ng/mL IL-11. Figure 3.1 summarizes the results obtained from the 93 mice in this group in which the contribution to the total WBC count at 16 weeks post-transplant was at least 1%. Although extensive heterogeneity was seen in the absolute outputs of both lymphoid and myeloid elements over time, analysis of the ratio of the relative contributions of the donor-derived lymphoid (B + T) and myeloid (GM) cells to the total 46  compartments of each of these populations revealed 3 statistically distinct contributions to the total output of mature circulating WBCs (Figure 3.2 and for additional details see Dykstra et al.21). We arbitrarily named the HSCs that contributed relatively poorly to the lymphoid WBCs seen 16 weeks post-transplant α HSCs. Those that gave a relatively balanced contribution to both lymphoid and myeloid WBCs at 16 weeks post-transplant we named β HSCs. The remaining group in which the relative contribution to the myeloid WBCs after 16 weeks was reduced, we split further into those that were still producing a detectable (>1%) contribution of myeloid WBCs, which we called γ HSCs, and those that were not, which we called δ HCSs. Interestingly, when the initial cells to be transplanted were first cultured as single cells for 4 or 10 days prior to transplant (in 300 ng/mL SF and 20 ng/mL IL-11), the proportion of α and β subtypes decreased and the proportion of γ and δ subtypes detected increased (Figure 3.3). However, a different and unexpected result was obtained when we assessed the HSC activity of the cells that were produced in the BM of the primary mice over a period of 6-8 months. Analysis of the outputs of the WBCs produced over the following 4-6 months in secondary mice transplanted with these cells showed that only the initial α and β subtypes of HSCs produced progeny HSCs. Remarkably, the WBC outputs obtained from these regenerated HSCs demonstrated a remarkable fidelity to the lineage pattern characteristic of the original HSC injected into the primary mouse. Similar results were obtained when BM cells from the secondary mice were assayed 6-8 months later for evidence of continuing donor-derived hematopoietic activity in tertiary hosts (Figure 3.4). In addition, the frequency of original α and β HSCs that were capable of producing progeny HSCs through this serial transplant protocol was remarkably high (91% after one cycle and 70% after the second cycle), in contrast to the absence of serial transplantability of the 6-8-month progeny of the other 2 HSC subtypes. These 47  observations, suggest that both the α and β HSC subtypes share a common mechanism that allows them to display a durable self-renewal activity when they are stimulated to divide in vivo. However, there also exist “HSC”-like cells (the γ and δ subtypes) that can produce multi-lineage clones of mature WBCs for up to 4 months in vivo and hence can display an extensive operational self-renewal ability, but one that appears qualitatively different in its finite longevity and association with different differentiation patterns. Accordingly, I have chosen to refer to the first type of HSCs as “high-self-renewal HSCs” and the latter as “low self-renewal” HSCs.  3.2.2 Isolation of different HSC subtypes using EPCR as a substitute phenotypic marker for Ho efflux ability  The Ho staining protocol used to obtain the CD45midlin-Rho-SP cells is cumbersome and associated with some toxicity232 and studies by others suggested it might be replaced by selecting for cells expressing very high levels (top 0.1% of total mouse BM cells) of EPCR223. To evaluate this possibility, I co-stained the CD45midlin-Rho-SP fraction of C57Bl/6 BM cells with EPCR to determine their phenotypic overlap and found that >75% of the CD45midRho-EPCR+ cells were contained within the SP fraction (Figure 3.5). To determine the HSC frequency in the CD45midRho-EPCR+ population, I then transplanted 27 sub-lethally irradiated W41/W41 mice each with a single CD45-congenic cell of this phenotype in a total of 3 experiments and then measured their contribution to the B, T, and GM WBCs present in the blood from 8 to 24 weeks later (Figure 3.6A-B). The clones derived from 18 (66%) of these single-cell transplants contributed at least 1% of the total circulating WBCs at one or more time points during the 6 months of follow-up. Of these 18 clones, 13 (48% of the original 27) were clearly multi-lineage at some time point and reached an overall level of >1% of all the WBCs at 4 months post48  transplant Figure 3.6C-D. The 4 types of repopulation patterns observed previously (i.e., α = myeloid-biased, β = balanced lympho-myeloid, γ = lymphoid-biased and still multi-lineage at 4 months and δ = lymphoid-biased but no longer multi-lineage at 4 months, Figure 3.1) were also seen in the mice injected with single CD45midRho-EPCR+ cells. These patterns are illustrated in Figure 3.7 where the data are shown both as clonal contributions to all 3 lineages combined (Figure 3.7A-D) and also as proportional contributions to each of the 3 lineages monitored (Figure 3.7E-H). Importantly, the frequency of each of the 4 subtypes within the CD45midRhoEPCR+ fraction was similar to that previously described for single CD45midlin-Rho-SP cells (Figure 3.6E-F).  3.2.3  Staining for EPCR in combination with CD48 and CD150 allows high and low self-  renewal HSCs to be separately purified  Current strategies for obtaining murine HSCs at high purities from both FL and adult BM exploit a combination of features (e.g., lin-Kit+Sca1+3,233 and CD150+CD48−19) that distinguish these cells from their more mature derivatives. However, to obtain high HSC purities, additional elements are required and these typically differ to accommodate the differing phenotypic characteristics of fetal and adult HSCs18,43,230 caused by their different cycling status18,43. In preliminary studies, I found that a high expression of EPCR, a powerful discriminator of adult BM HSCs223,234 was stable on HSCs that had been proliferating in vitro for 5 to 7 days (data not shown). This finding suggested that selection of EPCR positivity in combination with a CD48CD150+ phenotype might allow fetal and adult HSCs to be purified using a simple identical protocol. Representative FACS profiles of the CD45+EPCR+CD48-CD150+ (hereafter referred to  49  as E-SLAM) cells thus obtained from both E14.5 FL and adult BM are shown in Figures 3.8 and 3.9. The repopulation results obtained when mice were transplanted with a single E-SLAM cell from E14.5 FL (n = 49, 3 independent experiments) or adult BM (n = 62, 4 independent experiments) are also shown in Figure 3.9. In the experiments with E-SLAM FL cells, a HSC purity of 24% (50% high self-renewal HSCs, β only, data not shown) was measured (Figure 3.8A) with no additional cells identified as having any repopulating activity for 8 weeks or more. Since all FL HSCs are proliferating and only those in the G1 phase of the cell cycle are detectable by conventional transplantation strategies44,235, a demonstrated FL HSC purity of 24% (12% high self-renewal HSCs) can be estimated to represent an actual FL HSC purity of ~48% (24% high self-renewal HSCs), assuming G1 occupies ~50% of the total cell cycle. The adult BM E-SLAM population was found to be 56% pure HSCs (Figure 3.8B) of which >75% are high self-renewing HSCs (α and β). Thus the absolute purity of high selfrenewal HSCs in the E-SLAM fraction of adult BM is 43% (Figure 3.9B). This value is modestly increased from previously reported for other powerful HSC isolation strategies (e.g.: CD45midlin-Rho-SP cells17,21). I found another 2% of the adult BM E-SLAM population to be STRCs, defined here as cells that generated clones containing >1% of the circulating WBC population between 4 and 8 weeks post-transplant but not later. The remainder did not have repopulating activity detectable at or beyond 8 weeks post-transplant. Thus, isolation of ESLAM cells offers a simple method for obtaining high-self renewal HSCs at high purities from both fetal and adult sources. Because of the apparent depletion of low self-renewal HSCs from the CD150+ fraction of CD45+EPCR+CD48- adult BM cells, I examined the matching CD150- subset for HSC activity. Analysis of 28 mice (2 independent experiments) transplanted with single cells of this CD150subset (see Figure 3.9A) showed that 39% of these cells also had HSC repopulating activity but 50  these were predominantly of the low self-renewal subtypes (γ and δ, Figure 3.9Β). Thus, within the total CD45+EPCR+CD48- population, ~90% of the high self-renewal HSCs were in the CD150+ subset at a purity of 43%, and ~50% of the low self-renewal HSCs were in the corresponding CD150- subset at a purity of 32%. These results indicate that increasing expression of CD150 distinguishes adult BM HSCs with low and high self-renewal activity and this parameter can be exploited for the differential isolation of these 2 biologically distinct cell types at suitable purities for direct characterization studies (as described in Chapter 5).  3.2.4 The E-SLAM phenotype is useful as a strategy for isolating highly purified suspensions of HSCs from reconstituted animals  Next, I examined the HSC content of the E-SLAM population produced in reconstituted primary mice to determine if a similar purity of HSCs would be isolated and hence allow the ESLAM phenotype to serve as a surrogate measure of the regenerated HSCs. Analysis of the donor-type E-SLAM content of the BM cells from primary mice reconstituted 32 weeks previously with either a single α, β, or γ HSC revealed that the levels of BM chimerism were highest in the α− and β−reconstituted mice (50% and 60%, respectively) compared to the γ−reconstituted mice (<5%) (Figures 3.10A-B) and this was inversely correlated to the level of chimerism in the blood, which was highest in the β− and γ−reconstituted mice (70% and 50%, respectively) compared to the α−reconstituted mice (20%). Furthermore, when I transplanted single donor-derived E-SLAM cells from the α− or β−reconstituted mice into secondary recipients, I found the frequency of HSCs in the regenerated E-SLAM population to be 26%  51  (n=33) (Figure 3.10B-C), i.e., only slightly lower than in the original E-SLAM population present in the young adult BM.  3.3  Discussion  HSCs remain one of the best models for studying stem cell self-renewal in the adult mouse and their isolation at purity is critical to the types of studies that can address questions about the mechanism behind self-renewal. Using one such purification method for isolating HSCs at a frequency of ~1/3, we document significant heterogeneity in the cell population that gives rise to 1% of total white blood cells with multi-lineage reconstitution 4-6 months following transplantation. While heterogeneity in the biological behaviour of individual HSC has been well-documented in numerous other studies177,178,236,237, this current study allowed us to prospectively isolate HSCs with heterogeneous potentials. Based on the patterns of WBC output obtained, 4 distinct subtypes of HSC could be identified, only 2 of which could regenerate robust and durable hematopoiesis upon transplantation into secondary and tertiary mice. Detailed characterization of the clonal differentiation patterns displayed by the CD45midRho-EPCR+ cells in irradiated recipients revealed the same 4 subtypes of HSCs (α, β, γ, and δ) previously identified in similar experiments in which the progeny of individually transplanted CD45midlin-Rho-SP cells were analyzed. Moreover, these 4 HSC subtypes were present in the same proportions within the CD45midlin-Rho-SP or CD45midRho-EPCR+ populations of cells. These results indicate that the CD45midlin-Rho-SP and CD45midRho-EPCR+ fractions of normal adult mouse BM are highly overlapping populations. They also provide further evidence that the quiescent HSCs present in normal adult mouse BM are heterogeneous with respect to the differentiation programs they exhibit on transplantation into irradiated hosts. 52  The differentiation programs displayed by HSCs also appear to be an intrinsic component, as the E-SLAM strategy described in these experiments prospectively isolate HSCs with high selfrenewal activity from those with low self-renewal activity. When HSCs are stimulated to exit quiescence, they rapidly lose the ability to efflux Rho228, a phenotypic lability that extends to many markers used to purify quiescent adult HSCs (e.g., CD34, MAC1, AA4.1, CD38, and the ability to efflux Ho)227,228,238-241. Interestingly, in these experiments, we have found that the EPCR+ phenotype exhibited by HSCs is stable regardless of their activation/cycling status in 5 and 7 day cultures. Thus, high EPCR expression on HSCs may more closely resemble the recently reported stable expression of the SLAM family antigen (CD150) on HSCs20. Additionally, the E-SLAM sorting strategy appears to not only discriminate the relative contribution to the HSC compartment from repopulated mice of different HSC subtypes, but it also isolates HSCs at high purity (>20% as measured by singlecell transplantation). A stable phenotype for HSCs is highly desirable as it could potentially be used as a surrogate marker in functional screens for molecular and cellular manipulations of HSCs. More work is clearly required to validate the stability of this strategy for HSC isolation in additional settings such as HSC cell culture in vitro and during HSC mobilization to peripheral blood in vivo. These caveats aside, the E-SLAM strategy offers an attractive method for comparing the differences in molecular program of HSCs and studying the process of self renewal with a level of detail not previously possible. .  53  Figure 3.1 - WBC outputs in recipients of single HSCs or their clonal progeny generated in vitro A) Donor contributions to the total circulating WBCs for each of the 93 reconstituted mice shown individually. B) Variations in the donor contributions to the total circulating WBCs at 16 weeks posttransplant of all 4 HSC subtypes. Each point represents an individual mouse. Horizontal bars indicate mean values. 54  C) Distinct patterns of WBC reconstitution by each of the 4 HSC subtypes. Values are the mean ±SEM of all mice in each HSC group as defined in the text. D) and E) Examples of individual mice repopulated with freshly isolated HSCs (D), or HSCs from 4-day clones generated in vitro (E). Top panels: Colored areas represent donor WBC of GM (red), B-cell (blue) and T-cell (yellow) lineages as a percentage of all WBCs over time posttransplant. Data are stacked such that the sum of the individual contributions to each lineage by donor cells represents the total donor contribution to all WBCs. Bottom panels: For each time point, the separate donor contributions to the GM (red), B-cell (blue) and T-cell (yellow) lineages are shown as bars, and the percentage donor contribution to the total WBCs is shown as a grey area. F) Distinct patterns of donor contributions to the GM (red), B-cell (blue) and T-cell (black/yellow) lineages over time are shown. Values are the mean ±SEM of data from all mice in each HSC group (n=93).  55  Figure 3.2 - Identification of HSC subtypes in ternary plots of their lineage-specific contributions at 16 weeks post-transplant A) Comparison of the ratio of donor clone contributions to the GM (up), B-lineage (lower left) and T-lineage (lower right) WBCs. The graph can be divided into 3 sections based on specific myeloid to lymphoid contribution ratios: High GM:(B+T) = ratio ≥ 2:1; low GM:(B+T) = ratio ≤ 1:4; balanced GM:(B+T) = ratio between 1:4 and 2:1. B) Subdivision of LTRCs according to the ratio of their contributions to the myeloid and lymphoid lineages at 16 weeks post-transplant. α-LTRCs display a high GM:(B+T) contribution (blue squares), β-LTRCs a balanced GM:(B+T) contribution (pink circles), γ-LTRCs a low GM:(B+T) contribution (green triangles), δ-LTRCs contribute B-cells and/or T-cells but no (<1%) GM cells at 16 weeks (yellow diamonds).  56  Figure 3.3 - Rapid alteration of HSC distributions in vitro A) The distribution of α-, β-, γ-, or δ-HSCs identified in freshly isolated CD45midlin-Rho-SP BM cells. B) The distribution of α-, β-, γ-, or δ-HSCs identified in 4-day clones generated in vitro from freshly isolated CD45midlin-Rho-SP BM cells. C) The distribution of α-, β-, γ-, or δ-HSCs identified in subdivided 10-day clones generated in vitro from freshly isolated CD45midlin-Rho-SP BM cells. Eight clones were subdivided into halves or thirds and those fractions were injected into a total of 21 recipients.  57  Figure 3.4 - Clonal propagation of repopulation patterns in secondary and tertiary recipients A) Summary of the repopulation patterns seen in pairs of secondary and tertiary recipients transplanted with the progeny of each of the 4 HSC subtypes. B) Five examples of serial transplants originating from each of the 4 HSC types are shown. Bars represent the percent donor contribution to the GM (red), B-cell (blue) and T-cell (yellow) 58  lineages at 16 weeks post-transplant in primary, secondary and tertiary recipients (as indicated). Negative recipients (<1% donor WBCs at 16 weeks post-transplant) are indicated with an asterisk. nd = not done. † indicates the recipient mouse died before 16 weeks post-transplant.  59  A  C  B  D  E  Figure 3.5 – CD45midRho-EPCR+ cells are almost exclusively SP A) The upper 0.1% of the EPCR+ cells are CD45mid. B) EPCR+CD45+ cells plotted against Rho. C), D), and E) Subdivision into 3 clusters (Rho-, Rhomid and, Rhohigh). When further examined by dual wavelength analysis of their Ho content, the degree of Rho staining appears to correlate with the level of Ho staining, as can be seen by the proportion of each group that falls within the indicated SP fraction.  60  D  Experiment  Total  Frequency of HSC  Figure 3.6 - Approximately half of all CD45midRho-EPCR+ cells are HSCs and these segregate into the same distribution of subtypes as CD45midlin-Rho-SP HSCs A) Rho- cells appear in the left gate and were defined by cells unstained with Rho. B) Rho- cells plotted on a CD45 vs. EPCR plot. The majority of the remaining EPCR+ cells are CD45mid and comprise a distinct population that represents only 0.11% of the Rho- fraction for a total of 0.005% of the total adult mouse bone marrow cells. C) When 3 independent transplantation experiments were performed and the results were pooled, the frequency of HSCs was approximately 50%. D) Representative FACS plots for a repopulated mouse transplanted 16 weeks earlier with a single CD45midRho-EPCR+ cell showing donor derived contribution to GM, B, and T cells in the peripheral blood. E) and F) By comparing the ratio of donor-derived B, T, and GM cells in the peripheral blood at 16 weeks, mice were determined to have been repopulated by one of the 4 previously defined HSC subtypes.21 The pie charts in show the percentage of each subtype that is represented in each HSC population.  61  Figure 3.7 - All 4 patterns of HSC differentiation are observed in the recipients of single CD45midRho-EPCR+ cells A), B), C), and D) Examples of individual mice repopulated with freshly isolated cells where coloured areas represent donor WBCs of GM (red), B-cell (blue) and T-cell (yellow) lineages as a percentage of all WBCs over time post-transplant. Data are stacked such that the sum of the individual contributions to each lineage by donor cells represents the total donor contribution to all WBCs. E), F), G), and H) Separate donor contributions to the individual GM (red), B-cell (blue) and Tcell (yellow) lineages and are shown as bars. The percentage donor contribution to the total WBCs is shown as a grey area behind the bars.  62  Figure 3.8 - CD45+EPCR+CD48-CD150+ (E-SLAM) cells from E14.5 FL cells and adult BM are highly enriched for HSCs Representative profiles of viable cells after being stained with antibodies to CD45-FITC, EPCRPE, CD48-APC, and one of CD150-biotin/SA-PE-TexasRed or CD150-PECy7 from E14.5 FL (A) and adult mouse BM (B). The E-SLAM fraction of cells represents ~0.02% of Ter119depleted FL cells and ~0.004% of the adult BM population. The HSC purities in these suspensions were determined from monitoring mice injected with single E-SLAM cells. Plots were generated using FlowJo software from TreeStar.  63  Figure 3.9 - CD150 expression divides the CD45+EPCR+CD48- adult BM cell population into fractions differentially enriched in HSCs with high and low self-renewal properties A) Gates used to subdivide CD45+EPCR+CD48- adult BM cells into CD150+ and CD150fractions as generated by FlowJo with single cell transplantation frequencies of each population immediately below that population. B) Distribution of different types of repopulating cells according to their self-renewal capacity (pie chart on the left) or differentiation (diff’n) programs displayed (bar chart on the right) from analyses of 62 mice transplanted with single E-SLAM cells (top) and 28 mice transplanted with single CD45+EPCR+CD48-CD150- cells (bottom).  64  B  A  Figure 3.10 – CD45+EPCR+CD48-CD150+ (E-SLAM) is a surrogate marker for HSCs with high self-renewal and isolates HSCs from reconstituted animals at high purity A) Peripheral blood and bone marrow chimerism for primary animals transplanted with a single α, β, or γ HSC. B) Frequency and relative donor contribution of E-SLAM cells from α, β, or γ HSC repopulated mice. C) HSC frequency of the E-SLAM population isolated from α− or β−reconstituted mice as detected by 10 cell and single cell transplantation experiments into recipient mice.  65  4.  Exposure to High Concentrations of Steel Factor in Combination with  IL-11 Can Block the Otherwise Rapid Differentiation of Adult HSCs In Vitro  4.1  Introduction  The development of methods to isolate highly purified HSC populations16,17,19,221 has permitted extensive analyses of their individual in vivo differentiation and self-renewal activities in serial transplantation experiments initiated with single HSCs17,21,242-244. These experiments have revealed that some HSCs have self-renewal ability in vivo that is strongly and consistently associated with the display of either a balanced lympho-myeloid differentiation ability or a highly skewed generation of myeloid progeny, at least in the HSCs present in the BM of young adult mice21. Intense interest in understanding the mechanisms that control HSC self-renewal has led to the identification of multiple genes that intrinsically regulate HSC properties. As summarized in Chapter 1, these include regulators of the cell cycle, such as Cdkn1a (p21)149 and Cdkn2c (p18)151, and members of the transcriptional control machinery, such as HoxA9,245 HoxB4,144 Phc1 (Rae28) 140 , Bmi1138, Ezh2141, Gfi1246, Gata2167, and Gata3247, Etv6206, Pcgf2 (Mel18) 142 , Mcl1248, Meis1245, and Runx1167. In addition, growth factor receptors like GP13074 and KIT87 and their downstream signaling intermediates, including STAT3134, STAT5135, PTEN249, and LNK244, have been shown to mediate the ability of HSCs to execute self-renewal divisions. These findings reinforce data suggesting that multiple external cues, including SF250, TPO113,198, TGF-β251, Flt3-L252 and factors like IL-6 and IL-1190,253 that signal through GP130 can modulate HSC self-renewal, proliferation, and viability independently. Nevertheless, only a few 66  studies17,114 have provided definitive evidence of the directed extrinsic alteration of HSC selfrenewal since this requires clonal analysis of highly purified HSCs stimulated under conditions where differences in cell death cannot account for changes in HSC recoveries. Thus, both the intrinsic heterogeneity of the HSC compartment itself and the mechanisms that regulate the behaviour individual HSCs may display appear to be more complex than initially anticipated. To investigate how these 2 issues may be interrelated, I examined the timing and magnitude of changes in HSC activity that occur over a 4-day period in cultures initiated with a single cell maintained under different conditions of SF stimulation plus a constant dose of IL-11 (20 ng/mL). The highest SF concentration chosen (300 ng/mL) was one predicted to optimize HSCs self-renewal under these conditions and the others (10 and 1 ng/mL) were predicted to not affect either the viability or the initial mitogenic response of the cells37.  4.2  Results  4.2.1  Different SF concentrations alter maintenance of HSC activity  In a first series of experiments, a total of 100 single cells were cultured for 10 days in either 10 ng/mL or 300 ng/mL SF (plus 20 ng/mL IL-11). At this time, there was no obvious difference in the frequency or distribution of clone sizes generated under the 2 different conditions. However, when the cells from each set of cultures were pooled and assayed using a limiting dilution (CRU) transplant protocol to determine the overall number of HSCs produced during the 10 day culture period, the cultures containing 300 ng/mL SF showed maintenance of the input number of HSCs, whereas the cultures containing 10 ng/mL SF showed that the yield of HSCs numbers was 13-fold lower (Table 4.1). 67  4.2.2 SF concentration can rapidly alter the defining properties of HSCs without affecting their viability or cell cycle progression  To determine how quickly exposure to different concentrations of SF might alter the ability of HSCs to sustain their stem cell properties and the relationship of such effects to their viability and mitogenic responses, single CD45midlin-Rho-SP or CD45midRho-EPCR+ cells were cultured individually for defined periods in SFM supplemented with 20 ng/mL IL-11 and 1, 10 or 300 ng/mL of SF and then visually examined every 4 to 6 hours to determine if and when any cells died and the timing of any divisions that took place based on the first appearance of 2 and then 3 or 4 cells. A cumulative plot of the data obtained from 130 such single-cell cultures (3 independent experiments) is shown in Figure 4.1. In the cultures containing 300 ng/mL SF, no divisions occurred prior to 22 hours of incubation, but >90% of the original cells completed a first mitosis within the following 20 hours (i.e., between 22 and 42 hours after the cells were placed at 37oC). The division kinetics of the cells incubated in the 10 ng/mL SF condition were indistinguishable from those displayed by the cells incubated in the 300 ng/mL SF condition and no cell death was observed in any of the 114 wells containing cells maintained under either of these 2 conditions. Cultures containing 1 ng/mL SF showed a slightly delayed entry into the first and second division. Dead cells first became evident in these latter cultures after the second division and 35% to 40% of the clones were no longer viable after 4 days of culture. We then compared the frequency of clones that retain HSC activity during the first 4 days in culture when maintained in 300 ng/mL vs. 10 ng/mL SF (plus 20 ng/mL IL-11). For these experiments, another 160 single-cell cultures were initiated. Then 8, 16, or 96 hours later, each single cell, or the clone it had generated, was harvested individually and transplanted into an irradiated mouse to determine whether one or more HSCs were present. As shown in Figure 4.2, the proportion of cells still detectable as HSCs remained unchanged for the first 16 hours in the 68  cultures containing 300 ng/mL SF and, even after 4 days, the frequency of clones containing at least one HSC was still 50% of the frequency of HSCs in the original suspension. In the cultures containing 10 ng/mL SF, the number of HSCs also did not change during the first 8 hours of incubation (Fishers Exact Test, n=17, p=1.0) but, within the next 8 hours, their numbers decreased to one-third of the input value (Fishers Exact Test, n=24, p=0.10) and remained significantly reduced thereafter (Fishers Exact Test, n=43, p=0.009). These findings raise the intriguing possibility that changes in SF stimulation can directly and very rapidly regulate the functional integrity of HSCs in vitro while they are still in a quiescent state, without affecting their immediate viability or the timing of their subsequent proliferative response.  4.2.3  Most HSC self-renewal divisions stimulated by SF and IL-11 in vitro are  asymmetric  We then investigated the type of repopulating cells generated from the first division of 21 CD45midlin-Rho-SP cells maintained in 20 ng/mL IL-11 plus either 300 ng/mL SF (n=15) or 10 ng/mL SF (n=6) SF. Single-cell cultures were visually monitored to determine the timing of the first division and then, between 4 and 8 hours later, the 2 daughter cells were harvested, separated, and each transplanted into a separate irradiated recipient. Assessment of the repopulation patterns obtained showed that 5 of the 15 progeny pairs generated under the high SF condition contained daughter HSCs (i.e.: maintenance of the starting frequency of 1 in 3), whereas none of the 6 pairs generated under the 10 ng/mL SF condition showed HSC activity, again indicative of a significant (Fishers Exact Test, p=.048) rapid reduction in HSCs under this condition (Figure 4.3A). Interestingly, even in the presence of 300 ng/mL SF, both progeny were HSCs in only 2 cases and were then not of the same differentiation subtype (i.e.: they did 69  not produce a similar ration of lymphoid:myeloid progeny in transplantation assays). In the other 3 positive pairs, only one of the 2 progeny was an HSC (Figure 4.3B). An example of the 4-month repopulation data obtained from one of the 2 pairs where both progeny were HSCs is shown in Figure 4.3C. Taken together, these results demonstrate the highly preferential execution of asymmetric divisions by HSCs in vitro under the conditions examined.  4.2.4  Potential intrinsic molecular mediators of the ability of SF to sustain the stem cell  state of HSCs in vitro  To determine whether this loss of HSC functional activity might be correlated with differences in the expression of critical genes associated with HSC self-renewal control, we set up parallel bulk cultures of CD45midRho-EPCR+ cells using the same conditions of 20 ng/mL IL11 plus 1, 10, or 300 ng/mL SF and then analyzed the RNA extracted from them 16 hours later by Q-RT-PCR. No significant differences were seen in the levels of transcripts for Gata2, Gata3, Gfi1, Hoxa9, Meis1 or Runx1 in cells incubated in any of the different concentrations of SF tested. However, transcript levels for both Bmi-1 and Ezh2, 2 members of the group of repressive Polycomb transcription factor genes were significantly higher in the cultures containing 300 ng/mL SF (Figure 4.4). A similar finding was also noted for Lnk, a gene encoding a negative regulator of receptor tyrosine kinase receptor signaling.  4.3  Discussion  Recent experiments utilizing single-cell transplants have provided evidence of heterogeneity within the rare subset of quiescent adult mouse BM cells that produce mature 70  blood cells for extended periods of time (>4 months) in irradiated recipients. This heterogeneity is manifested as a marked skewing (or not) in favour of either granulopoietic or lymphoid differentiation in vivo that is faithfully transmitted to derivative repopulating cells21. This finding raises the important notion that in HSCs, self-renewal, cell cycle entry and progression, and lineage restriction are independent processes that are regulated by mechanisms with at least some distinct features. To further investigate the prevalence of heterogeneity among the HSCs present in adult mouse bone marrow, we performed a series of single cell transplants using a population that was isolated via a simpler, but similarly powerful, procedure (using only anti-CD45 and anti-EPCR antibodies in combination with Rho staining). We were thus able to use the CD45midlin-Rho-SP and CD45midRho-EPCR+ purification strategies interchangeably to determine the proportion of HSCs that would execute at least 2 self-renewal divisions when cultured under “optimal” cytokine conditions in vitro (300 ng/mL SF plus 20 ng/mL IL-11)17,90. Visual monitoring of clone development in vitro showed that every input cell completed at least 2 divisions within 4 days. Functional assessment of 4–day clones showed that the frequency of clones containing at least one HSC was about half the frequency of HSCs in the original population. In fact, this would likely be an underestimate because any HSCs in S/G2/M would not have been detected.43,235 Thus, continuous exposure to a high concentration of SF in combination with IL11 appears sufficient to allow many input HSCs to execute multiple self-renewal divisions over a 4–day period. To address this question more directly, I analyzed the individual first division G1 progeny of 15 input cells cultured under the same conditions. The results showed that one or both of the members of 5 of these progeny pairs (30%) was an HSC - again, a similar proportion to the frequency of HSCs in the starting cells. However, characterization of the types and levels of WBCs produced in vivo by each of the 2 cells in these 5 progeny pairs showed that they all 71  appeared to be the product of asymmetric divisions; i.e., 3 pairs contained only one HSC and the other 2 pairs contained 2 HSCs but each member of the pair was a different subtype. These findings are consistent with a more rapid loss in vitro of the HSC subtypes that retain extensive self-renewal activity in vivo21 and a stochastic distribution of self-renewal events amongst individual multi-potent cells proliferating in vitro254. At the same time they appear inconsistent with the reported symmetry of the first 2 divisions of CD34-KSL cells in vitro255 and of the clonally amplified HSCs seen after 10 days in vitro21. Clearly, additional experiments will be required to determine more precisely when and how symmetry is established when HSCs are stimulated under different conditions in vitro. Our experiments also provide new information about the effects of exposing HSCs and/or their clonal derivatives in vitro to different SF concentrations. For this comparison, single cells from the same HSC-enriched populations were incubated in IL-11 plus a level of SF (1 or 10 ng/mL) that was predicted to give a markedly decreased output of HSCs, as suggested by previous results using 10-day cultures of less purified bone marrow cells.90 Assessment of the cells cultured in the 10 ng/mL SF condition confirmed the predicted loss of HSC activity within 10 days. More detailed time course studies further revealed that the loss of HSC activity induced by inadequate SF stimulation did not become manifest until after 8 hours but was evident within the ensuing 8 hours. From the subsequent kinetics of division seen, it can be readily inferred that most of the cells would not have exited G0 until after the first 16 hours of incubation. Thus, loss of HSC function likely occurred in most affected cells before they had entered the cell cycle. Interestingly, these effects also occurred in the absence of any evidence of effects on their immediate viability or mitogenic responses. Previous evidence of a similarly rapid induction of differentiation in the absence of proliferation by pluripotent hematopoietic cells was noted many years ago in studies of the FDCP-mix(bcl-2) cell line model256. The findings presented here now show that this is also a property of normal adult HSCs. 72  The ability of HSCs to undergo such abrupt changes in their function raises intriguing questions about the nature of the molecular events involved and provides a novel experimental system for the further analysis of this process. Here we performed an initial survey of a small number of transcripts associated with HSC integrity. Strikingly, even this early data set revealed a significant rapid and specific reduction in transcripts for Bmi1 and Ezh2 that accompanied the biological changes in HSC function apparent within 16 hours of incubation under low SF conditions. Both Bmi1 and Ezh2 are of interest because they have been strongly implicated as regulators of the adult HSC self-renewal program138,139,141. Our findings also extend those demonstrating that HSCs are detectable amongst one or both of the first division progeny of Lnk-/cells incubated in cultures containing 50 ng/mL SF244,257. Since LNK is a negative regulator of SF-activated kit signalling257, deletion of Lnk might be expected to enhance HSC output in cultures containing a concentration of SF that is suboptimal for wild-type HSCs. Curiously, we found that Lnk transcripts were lower in cells maintained in the lower SF conditions. This result suggests that our current picture of how HSC self-renewal is controlled by known regulators may be incomplete, including the predicted consequence of reduced Lnk transcripts. Accordingly, it will be important in future studies to evaluate multiple elements of the self-renewal machinery, as well as measuring changes at the level of the encoded proteins and their activity. In summary, this study provides further support for a model of HSC control in which there are elements regulating self-renewal activity that are different from those controlling lineage restriction, quiescence, or survival. The rapidity with which HSCs can be induced to differentiate under defined signalling conditions now provides a novel framework for delineating in molecular terms precisely how these elements achieve the biological readouts used to define HSC function.  73  Table 4.1  HSC yields are reduced in cultures containing a low concentration of SF  Cytokine condition  Dose of cells injecteda  300 ng/mL SF + 20 ng/mL IL-11  7 2 0.7  5/5 3/8 0/6  27 (19 to 39)  10 ng/mL SF + 20 ng/mL IL-11  7 2 0.7  1/5 0/5 0/3  2 (0.8 to 6)  No. of positive mice/ No. of HSCs detected No. of mice tested after 10 days (±SEM)b  a. Each dose of transplanted cells was sampled from a large pool of 10-day clones generated in either 300 ng/mL or 10 ng/mL SF in combination with 20 ng/mL IL-11. The cell doses are expressed as “starting equivalents” (day zero = ~30 HSCs). Since each experiment was initiated with 100 starting cells, a cell dose of 7 represents 7% of all the cells from the pooled clones. b. L-Calc software (STEMCELL Technologies) was used to calculate the mean frequency of HSCs in the pooled 10-day clones from limiting dilution transplant results. The starting HSC frequency (3/10 = 30%) was determined from single cell transplants of freshly isolated mid – – CD45 lin Rho SP cells.  74  Figurre 4.1 - Kinetics of diviision of CD445midlin-Rhoo-SP cells cu ultured in diifferent SF conceentrations 130 cells c (n = 56 for 300 ng/m mL, n = 58 for f 10ng/mL L and n = 16 for 1 ng/mL L) were depoosited indivvidually into 96-well plattes, visually confirmed to be single cells c and theen observed every e 4 to 6 hours h to deteermine their kinetics of division. d A cell c was scorred as havinng undergonee a first divisiion when a second s cell could c be observed in the well and a second s divission when a third t cell couldd be seen. There was <1% cell deathh when the cells c were cuultured in eithher 10 or 300 ng/mL SF. A Lowess sp pline curve was w generateed in GraphP Pad Prism (V Version 4.03)) using 248 values v estim mated based on o the markeed values in the time couurse and is shhown for eacch of the firsst and seconnd divisions under each condition. c  75  Figurre 4.2 - Tim me course off changes in HSC activiity under different conccentrations of SF Singlle cells or their clonal prrogeny were injected folllowing 8, 166 or 96 hourss of culture in i 20 ng/m mL IL-11 pluss either 300 ng/mL (bluee) or 10 ng/m mL (red) SF.. Each data point represents the perceentage of possitive transpllants (numbeer of positivve mice as a proportion p o the total annalyzed) of for eaach condition n and time point p assesseed. The straiight line acrooss the graphh represents the startinng HSC con ntent as deterrmined by siingle cell traansplants of freshly f isolaated CD45middlin-RhoSP orr CD45midRh ho-EPCR+ ceells. The astterisk indicattes values thhat are signifficantly diffeerent (p<0..05) from thee input HSC C frequency.  76  Figurre 4.3 - Asymmetry of HSC H expan nsion and maaintenance divisions stiimulated byy 20 ng/m mL IL-11 plu us either 10 ng/mL or 300 3 ng/mL SF S A) Siingle cell culltures of CD D45midlin-Rhoo-SP cells were monitoreed every 4 hours to deterrmine whenn a first divission took plaace. Between 4 and 8 hoours followinng the first mitosis, m doubblets were separated an nd each of thhe 2 cells injjected into a separate reccipient with the t resultingg divisiion type sho own. SR = seelf-renewal, SF = Steel Factor. F B) Thhe distributio on of HSC subtypes is detailed d in the cases wherre at least onne of the 2 daughter cells repopulated d an irradiated recipient. ACS plots fo or a pair of repopulated mice m transplanted 16 weeeks earlier with w individuual cells C) FA from one separateed daughter pair.  77  Figurre 4.4 – Bmi1, Ezh2 and d Lnk transscripts are downregulat d ted with losss of HSC acctivity Q-RT T-PCR analy yses of transccript levels in i extracts off cells harveested from 166-hour cultures initiaated with CD D45midRho-EP PCR+ cells and a maintainned in 20 ng//mL IL-11 plus p 300 ng/m mL (blue bars), 10 ng/mL (red ( bars), orr 1 ng/mL SF (brown baars). All valuues normalizzed to Gapdh dh; ND = not detected. d Values shown are a the meann ± SEM of values v obtained in 2-3 inndependent experriments, with h each measuurement derrived from trriplicate assaays. Data wiithout error bars b are from a single exp periment.  78  5.  New Candidate Regulators of Hematopoietic Stem Cells with High Self-  renewal Activity  5.1 Introduction  An output of at least 1% of all the WBCs for a period of at least 4 months has thus been generally adopted as the minimal criteria for the specific and sensitive enumeration of HSCs transplanted into congenic irradiated mice258. This definition has also been widely assumed to capture a unique and homogeneous population of HSCs with similar self-renewal potential. However, a precise and widely accepted definition of what this means molecularly, or even biologically, at the single cell level is less well defined and remains a subject of intense interest and investigation38. This situation is due, in part, to the heterogeneity in differentiation behaviour and clone maintenance that is encountered when the numbers and types of cells produced by individual hematopoietic repopulating cells are monitored over time176,254,259,260. It is now clear that some of this heterogeneity is predetermined - as demonstrated by a consistent association of shorter term WBC outputs with a distinguishing phenotype of the cells initially transplanted (e.g.: CD49b+ and CD34+) when these are taken from the BM of adult mice261,262. More recently, we and others have provided evidence that even amongst cells with longer term repopulating activity, there is predetermined heterogeneity in HSC differentiation and selfrenewal potential21,236,243,263. For example, from analyses of a large number of single-cell transplants, we have resolved 2 subtypes of adult BM HSCs that can be discriminated by their lymphopoietic ability but share a relatively high myelopoietic ability and a pervasive high selfrenewal capability that persists through 3 cycles of clonal expansion in vivo21. In contrast, 2 79  other subtypes that share a robust lymphopoietic ability but more transient myelopoietic activity failed to generate detectable HSCs after 6 months of clonal expansion in primary recipients, suggesting their possession of a self-renewal mechanism that was destined to be extinguished after a finite, albeit large number of divisions. Importantly, we found that these high and low self-renewal HSC subtypes were both EPCR+CD48- but could be separated to some extent on the basis of their high and low expression of CD150, respectively. Similar evidence of HSCs with different regenerative activity and in vivo turnover rates has recently been reported by others47,48. To gain further insights into regulators that may be of major importance to the maintenance of an unlimited self-renewal state, I applied a comparative gene expression approach to multiple populations that are variably enriched in high HSCs. The cells analyzed were purified from both FL and adult BM and extracts of these were analyzed for differences in transcript levels using a combination of a global strategy (LongSAGE) and a more specific strategy (Q-RT-PCR analysis) of a group of genes identified from the LongSAGE data and from the literature.  5.2 Results  5.2.1 Construction and characteristics of LongSAGE libraries prepared from highly purified FL and adult BM HSCs  Ten thousand lin-CD43+Mac1+Sca1+ E14.5 FL cells and 3,700 CD45midlin-Rho-SP adult BM cells were isolated and used to prepare LongSAGE libraries that were then sequenced to a depth of ~160,000 and ~36,000 tags, respectively. In previous studies carried out by our lab at the same time as these samples were isolated, HSC purities of 20% (assuming half of the HSCs 80  are undetectable because they are in the S/G2/M phases of the cell cycle43and 30% in these populations, respectively, were measured. Further details of the library characteristics are summarized in Figure 5.1A and given on line (GEO Series accession number GSE13243, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13243). Tag frequencies for many genes previously reported as having a role in fetal and/or adult HSCs were present at expected relative levels (Figure 5.1B). The tag profile for the FL library showed a large degree of overlap with published lists generated from other proliferating (or activated) HSC-enriched populations. For example, I found tags for 36 of the 50 most upregulated transcripts in the activated HSC population identified by Venezia et al. 264 to be present in the E14.5 FL HSC library. Figure 5.1C shows a quantitative display of the overall pattern of significantly differentially expressed genes between the 2 libraries. All tags found to be significantly overrepresented in the adult BM library are listed in Table 5.1 and those significantly overrepresented in the FL library are listed in Table 5.2. When these lists of differentially represented tags were surveyed for their Gene Ontology assignments (www.geneontology.org), the “mitotic cell cycle”, “protein kinase activity” and “ATP-binding” categories were overrepresented in the FL library and the “chemotaxis” and “cell surface and receptor-linked signal transduction” categories were over-represented in the adult BM library (Figure 5.1D). These libraries thus provide a new resource for global transcriptome analyses and comparisons of fetal and adult HSC programs.  81  5.2.2 A gene expression signature for FL and adult BM HSCs  I used the PCR SAGE libraries and the literature as a starting point to identify 27 candidate genes whose expression was anticipated might be specifically up-regulated in high self-renewal HSCs. The 27 genes selected were: Rhob, Hmga2, Trim27, Mef, Pld3, Gata2, Cdkn2c, Vwf, Atm, Cdkn1a, Lyl1, Car3, Plp1, Smarcc1, Cul4a, Stat3, G3bp1, Chd4, Eif1a, Cebpa, Smarcc2, Fus, Psap, Tubb5, Mlx, Hdac3, and Rik1200009F10. As a preliminary test of this hypothesis, the transcript levels of these 27 genes were compared in E14.5 FL and adult BM E-SLAM cells with their corresponding lin- fractions (in which high self-renewal HSCs would be expected to constitute <1% of the population. The results showed that 9 of the 27 genes were consistently expressed at higher levels in the E-SLAM cell fractions (Chd4, Pld3, Lyl1, Psap, Stat3, Vwf, Rhob, Smarcc2 and Hmga2, Figure 5.2A-B).  5.2.3 Identification of a subset of genes whose lower expression is associated with “nearest neighbours” of high self-renewal HSCs  I then asked whether the biological differences in self-renewal activity between E-SLAM and the more closely related CD45-EPCR+CD48-CD150- adult BM cells (described in Chapter 3) are associated with differences in expression of genes previously implicated in the regulation of HSC self-renewal, i.e., Prnp265, Bmi1138,139, Gata3247, Rae28140, Lnk257,266, and Ezh2141. Figure 5.3 shows the results of a comparison of the transcript levels measured in these 2 populations of adult BM cells by Q-RT-PCR. Transcripts for all 6 genes were consistently detected in the ESLAM subset (selectively enriched in high self-renewal HSCs), albeit at low levels relative to Gapdh, as expected. Moreover, transcript levels for 3 of these 6 genes (Prnp, Gata3, and Bmi1) 82  were significantly higher in the CD150+ subset (p<0.05), suggesting a possible functional role for these molecules in maintaining a high self-renewal state in HSCs. When I assayed these populations for their expression of the 9 candidate genes, there was no difference in Lyl1, Stat3, Hmga2, Psap, and Chd4 transcript levels, but the CD150+ fraction (i.e., the HSCs with high self-renewal activity) contained significantly higher levels of Smarcc2, Vwf, Rhob, and Pld3 transcripts (Figure 5.2C). To examine the potential importance of these genes I then examined their stability in another system where HSCs rapidly lose their high selfrenewal activity. For this, I took advantage of my earlier finding (see Chapter 4) that freshly isolated adult BM HSCs remain viable and clonogenic in vitro, but show a differential retention (100% vs. 20%) of their longterm in vivo repopulating ability when exposed for 16 hours in vitro to varying concentrations of SF (from 300 ng/mL down to 1 ng/mL) in combination with 20 ng/mL IL-11. Previous measurements showed that Bmi-1 and Ezh-2 transcript levels in such cultured HSC populations are markedly lower in the cells cultured in limiting concentrations of SF (Chapter 4). These findings were confirmed in the new experiments described here. In addition, the present set of experiments identified transcripts for Vwf, Rhob and Pld3 to be present at reduced levels in the cells cultured in concentrations of SF that selectively and rapidly eliminate their repopulating activity (Figure 5.2D). Additional Q-RT-PCR data from these experiments is displayed for all comparisons in Figures 5.4, 5.5, and 5.6.  5.3  Discussion  Understanding HSC self-renewal control remains a priority in medicine as this knowledge is likely to provide insights into methods for expanding HSC populations and producing blood products in vitro in addition to identifying key pathways that are perturbed or 83  hijacked by leukemic stem cells. Furthermore, it is possible that parts of this machinery are used by other stem cell populations to maintain their undifferentiated status. Already, a surprisingly large number of genes have been identified from overexpression or knock-out experiments as able to influence HSC function to varying degrees in assays designed to test the ability of these cells to amplify their numbers either in vitro or in vivo. These include genes like Bmi1139 and Cdkn1a (p21)149 whose expression appears to be required for optimal HSC self-renewal, as well as Lnk244 and Cdkn2c (p18)151, whose expression appears to suppress HSC self-renewal. However, whether these genes constitute the complete self-renewal circuitry of HSCs is unclear. It also remains a mystery as to how cells are molecularly configured to have very large but discrete differences in self-renewal capabilities that determine whether their ability to produce mature progeny will be sustained for 2-3 months or 4-8 months vs. longer periods that can exceed the lifetime of the mouse. Indeed, evidence of a clear distinction between the latter 2 types of HSCs has only recently become available from studies that have assessed the in vivo self-renewal properties of a large number of individual HSCs at a clonal level21 and have subsequently revealed associated differences in the expression of CD150 on these cells and its association with different compartment turnover rates (Chapter 3 and47,48) Thus, it has not been possible to implicate any of the genes thus far identified as to whether they might be particularly important in keeping HSCs in a state where the probability of executing a self-renewal division when stimulated in vivo remains high. We found that Prnp, Bmi1, and Gata3 were selectively up-regulated in the E-SLAM (high self-renewal HSC) population, consistent with their suggested roles in the maintenance of this state. We now present evidence from pairwise comparisons of several other genes and show that 3 genes (Rhob, Pld3 and Vwf) may have similar roles given their selective but consistently elevated expression in populations that are most enriched in high-self-renewal HSCs.  84  Prion protein (Prnp) transcripts, which appeared in the LongSAGE screen as being highly upregulated in adult BM, and doubles as a positive control for our screen, has a very interesting role in HSCs. In 2005, Zhang et al.265 showed that PRNP was expressed on adult BM HSCs both by phenotypic overlap studies and by affinity purification followed by competitive repopulation experiments. The primary transplants of Prnp-/- BM cells were found to compete equally with wild-type cells, but the secondary and tertiary transplants of Prnp-/- cells showed a 2 to 3-fold reduction in repopulating activity when compared to wild-type HSCs. This is in strong agreement with Prnp having a functional role in maintaining a durable self-renewal capability in HSCs265. Rhob encodes a small GTPase which has been described to have a role in the inhibition of malignant cell growth and is the putative target of farnesyltransferase inhibitors267. It has been shown to interact with mDia1268, which in both heterozygous and homozygous knockout animals leads to age-dependent myeloproliferative defects that include splenomegaly, a hypercellular bone marrow, and extramedullary hematopoiesis269. Phospholipase D3, on the other hand, is a gene about which relatively little is known. Pld3, also known as Sam-9 is strongly expressed in neural tissue270 and transcripts have also been identified in T-cell lines271, T-regulatory cells and Foxp3-transduced CD25-CD4+ T-cells272. Pld3 has 30-40% homology to Pld1 and Pld2 whose products are stimulated by PIP2, G proteins, ARF and RHOA. PLD1 is directly regulated by PKC in most mammalian cells273. Most interesting is the strong connection between Phospholipases and Rho family members as 2 of the 3 new candidate regulators identified here are RHOB and PLD3. Absence of VWF is primarily associated with defective hemostasis and thrombosis. Vwf knockout mice are normal at birth, but have a highly prolonged bleeding time and reduced levels of Factor VIII274. However, a role of Vwf in the control of HSC self-renewal has not been previously indicated. 85  Interestingly, Lyl1 (an Scl family member) is a gene that remains high in the low selfrenewal fraction of HSCs, which show poor maintenance of myelopoietic potential. Recently, Capron et al.275 showed that Lyl1-/- BM cells are 1.6- to 5.7-fold less competitive than wild-type BM cells with the vast majority of the long-term impairment being attributable to poor B- and Tcell production. Furthermore, overexpression of Lyl1 has been clearly implicated in B and T-cell lymphoma with 30% of Lyl1 transgenic mice developing malignant lymphoma276. In summary, we have now shown that HSCs with longterm multi-lineage potential in vivo, but markedly different self-renewal properties, can be prospectively isolated as separate, highly purified cell types and found to display unique gene expression signatures. These findings strongly support the hypothesis that HSCs self-renewal activity is not gradually lost with successive divisions but is maintained in a state where differentiation is indefinitely postponed until an initiation event occurs, which may bring on a termination of self-renewal divisions more or less rapidly according to the signals the cell receives even when it is quiescent. The generation of whole transcriptome LongSAGE data should provide a useful resource for many future studies and the identification of 3 genes not previously associated with HSC selfrenewal activity offers an exciting approach to further deciphering the molecular machinery of HSC self-renewal.  86  Table 5.1 Tags over-represented in the adult BM HSC library vs. the FL HSC library Sequence data  FL  FL HSC  BM  BM HSC  Fold  HSC  /100K  HSC  /100K  Diff.  Accession  Gene Name  GCTCTGGGAGTTATAAG  0  0  88  243.32  N/A  NM_008293  Hsd3b1  TGCTTTATAGGCCTCAA  0  0  74  204.61  N/A  NM_009062  Rgs4  AATTAAAAACATTACAA  0  0  66  182.49  N/A  NM_019946  Mgst1  TATATTAAATCCCTCCT  0  0  55  152.075  N/A  NM_011428  Snap25  GAGGAGTGGGATGTGCC  0  0  46  127.19  N/A  NM_022422  Gng13  CCACAGAGAGGTGGTGG  0  0  41  113.365  N/A  NM_016805  Hnrnpu  CACTCTTCTATCACTGA  0  0  41  113.365  N/A  NM_027885  Smug1  TAACTGGTGCACATTTA  0  0  41  113.365  N/A  NM_027920  Mar8  GACCAGAACAGAGACGG  0  0  40  110.6  N/A  NM_010861  Myl2  CAGTCTTGAGTGCCCTA  0  0  40  110.6  N/A  NM_029787  Cyb5r3  GCATAAGCAATGATTCT  0  0  39  107.835  N/A  NM_007471  App  ATTAATTATGTATTAGT  0  0  39  107.835  N/A  NM_009441  Ttc3  ACACCCGAAGAATGGAC  0  0  37  102.305  N/A  NM_029934  Mboat7  TATGTATGTATGTATGC  0  0  37  102.305  N/A  NM_175534  Mrgpre  AACTGAGGGGTCTCTGT  0  0  35  96.775  N/A  NM_011179  Psap  GGCCCTTGCTCCAGCTG  0  0  35  96.775  N/A  NM_026065  D10Ertd322e  TTCCTTGTTAACATTTT  0  0  35  96.775  N/A  NM_028262  Setd3  TTACAGAAATGCTGAAA  0  0  34  94.01  N/A  NM_001042650  Dcun1d2  TTAGCAACATATTTATC  0  0  34  94.01  N/A  NM_008098  Mtpn  TACTATGTGGATGTGCT  0  0  34  94.01  N/A  NM_021431  Nudt11  AACAACCAAGTGAAAAG  0  0  34  94.01  N/A  NM_023290  Mkrn2  ATTCACACTGAAAAAAG  0  0  34  94.01  N/A  NM_134255  Elovl5  TCTATAGAGTGTGCCCT  0  0  34  94.01  N/A  NM_184053  Calu  GGGACTGCATTGAGAGC  0  0  33  91.245  N/A  NM_008219  Hbb-bh1  TAATGATTCTTTCTCAC  0  0  33  91.245  N/A  NM_008409  Itm2a  CTCAGTATCCCTTCCAG  0  0  33  91.245  N/A  NM_008512  Lrp1  ACTGCAAACTAAAACCC  0  0  33  91.245  N/A  NM_008683  Nedd8  AACTGTACAAATTTACA  0  0  32  88.48  N/A  NM_007569  Btg1  GATGTGGTTTTTGTTTT  0  0  32  88.48  N/A  NM_009930  Col3a1  CATAGGTATCAGGCTTG  0  0  32  88.48  N/A  NM_019413  Robo1  CCCTTAGCGCAGGCTAC  0  0  32  88.48  N/A  NM_021473  Akr1a4  GTAAATTGTTAAGCTGT  0  0  31  85.715  N/A  NM_010917  Nid1  CATAGGCTGCTATGTGA  0  0  31  85.715  N/A  NM_011135  Cnot7  TCCTTTTTGACTGTGTG  0  0  31  85.715  N/A  NM_019635  Stk3  GCTGCCGCCTTCGCGCG  0  0  31  85.715  N/A  NM_138748  Ppp2r4  GGTCGGCATCTTCTTAA  0  0  30  82.95  N/A  NM_009269  Sptlc1  CTGGACCCGGGTCTGGA  0  0  30  82.95  N/A  NM_011680  Usf2  ATGTGATAAATGTGAAA  0  0  30  82.95  N/A  NM_020025  B3galt2  GGCTGAAGGTATGATAG  0  0  30  82.95  N/A  NM_025837  Mpi  TTTAAGATACAACATCT  0  0  30  82.95  N/A  NM_146207  Cul4a  GAACATTCCAGATCTGA  0  0  29  80.185  N/A  NM_007431  Alpl  87  CCGCCGTGGGAGCGTGA  0  0  29  80.185  N/A  NM_008635  Mtap7  GAACAGTCGACACACTT  0  0  29  80.185  N/A  NM_009605  Adipoq  TTTCCTACTGCCTTCCC  0  0  29  80.185  N/A  NM_011550  Mlx  TGCATAAGTCACAGAGA  0  0  29  80.185  N/A  NM_016762  Matn2  ATAATTGAAAGCCAGCC  0  0  29  80.185  N/A  NM_145962  Pank3  AGAAAGTAGATTTTCAG  0  0  28  77.42  N/A  NM_001029934  Usp32  TGAAAAATGACAAATGT  0  0  28  77.42  N/A  NM_001040399  Larp2  TCTGCAATTTAAATTCT  0  0  28  77.42  N/A  NM_007937  Epha5  GGACTAACTACGGACTC  0  0  28  77.42  N/A  NM_010064  Dync1i2  GCCTCATCCCTGCACCA  0  0  28  77.42  N/A  NM_010929  Notch4  GAGTTGGGAATTAAAGA  0  0  28  77.42  N/A  NM_013865  Ndrg3  CCACATCCAGTACTCAG  0  0  28  77.42  N/A  NM_022721  Fzd5  CTCACAAATGTGAGAGA  0  0  28  77.42  N/A  NM_025278  Gng12  TAACAGAACCACTTGAG  0  0  28  77.42  N/A  NM_028343  Tmem135  TCCTGTGTCAACAACCC  0  0  27  74.655  N/A  NM_007765  Crmp1  AAAACCATTGCAGGAAA  0  0  27  74.655  N/A  NM_009127  Scd1  GACAGTGGAGATGAGGC  0  0  27  74.655  N/A  NM_010773  Mbd2  TTTATTAAATGAATGAG  0  0  27  74.655  N/A  NM_019986  Habp4  GTTTAAAATAAATCAGA  0  0  27  74.655  N/A  NM_021414  Ahcyl2  CTGGTTGGAGGAAGGCA  0  0  27  74.655  N/A  NM_027400  Lman1  ACAAATACGGCAATATG  0  0  27  74.655  N/A  NM_030052  Cox7b2  TGCATCTGCGTTGTTCT  0  0  27  74.655  N/A  NM_144875  Rab7l1  ACTATAAATACCGGGTG  0  0  27  74.655  N/A  NM_199195  Bckdhb  GTTGCTGTTTTAGGCCC  0  0  26  71.89  N/A  NM_001114096  Smarcc2  GAATTGATGATGCTCAC  0  0  26  71.89  N/A  NM_009129  Scg2  ATCCCTGAGCTGGTGCC  0  0  26  71.89  N/A  NM_009995  Cyp21a1  AAGGTGAAGGAGACAAA  0  0  26  71.89  N/A  NM_020494  Ddx24  ATTTAATAAAATAACTT  0  0  26  71.89  N/A  NM_032008  Slmap  AGAATATGTAAGAGTAA  0  0  26  71.89  N/A  NM_172616  C330027C09Rik  AGGTGCCTCCAAGGACA  0  0  26  71.89  N/A  NM_172687  Coq3  AGGCATCTTCTGATATC  0  0  26  71.89  N/A  NM_199197  1110032A13Rik  TGGGGCCTCTCCTCTGA  0  0  25  69.125  N/A  NM_009778  C3  CCAACGCTTTACTACTG  0  0  25  69.125  N/A  NM_010233  Fn1  AAATTATTGGGAATTCC  0  0  25  69.125  N/A  NM_011123  Plp1  TATTTTGTTTTGTCATA  0  0  25  69.125  N/A  NM_011170  Prnp  TGTATTCTGTCAGGAAT  0  0  25  69.125  N/A  NM_026200  Kcnv1  CCAACACCACTATGGAG  0  0  25  69.125  N/A  NM_146207  Cul4a  GCTTGGTACACCTGGGG  0  0  25  69.125  N/A  NM_178920  Mal2  AATTCCTTTGCTCATCT  0  0  25  69.125  N/A  NR_003368  Pvt1  TGCTCAGATGAATGCTC  0  0  24  66.36  N/A  NM_001107952  Zfyve9  GGGGGAGTGGAGGGCGT  0  0  24  66.36  N/A  NM_010923  Nnat  GGGACCAGGTATGCAAG  0  0  24  66.36  N/A  NM_011485  Star  CCACTCTCAGTCAACAG  0  0  24  66.36  N/A  NM_019791  Maged1  GCTTGCTTACTTCGGTG  0  0  24  66.36  N/A  NM_029734  2410187C16Rik  TTCTATAGAAACTTTTC  0  0  24  66.36  N/A  NM_153175  Gimap6  88  TTGAGTTACTACTTTGC  0  0  24  66.36  N/A  NM_178362  Sorbs1  CTGCATAGCTCTTTAAA  0  0  23  63.595  N/A  NM_007643  Cd36  CTTGGGTGCAATTGGTC  0  0  23  63.595  N/A  NM_011164  Prl  GTGAGAGGAAGCAAGCA  0  0  23  63.595  N/A  NM_015784  Postn  GTGAGGACACCAGCTTT  0  0  23  63.595  N/A  NM_029688  Srxn1  GTGCCAACAGATTAGAA  0  0  23  63.595  N/A  NM_177608  3110001I20Rik  GTGCTGTGAGATTGTGG  0  0  22  60.83  N/A  NM_009995  Cyp21a1  TATAATTATACGTATCA  0  0  22  60.83  N/A  NM_025703  Tceal8  GACTAGCATCCCCAGTC  0  0  22  60.83  N/A  NM_028292  Ppme1  AGTTGTGAAGTTTCACT  0  0  22  60.83  N/A  NM_053079  Slc15a1  CCTTTAATCCTCCCACT  0  0  22  60.83  N/A  NM_178619  1810026J23Rik  TATATTAAAGATTTTTT  0  0  22  60.83  N/A  NM_198620  Rundc3b  CCCAGGCTTGGGGGAGG  0  0  21  58.065  N/A  NM_009271  Src  TGTGCCGGCCTCGAGAC  0  0  21  58.065  N/A  NM_011485  Star  GCCAGTTTTCTTCCCTC  0  0  21  58.065  N/A  NM_019535  Sh3gl2  AGGGGATCTGATGCCCT  0  0  21  58.065  N/A  NM_027415  Tmem70  GAACAGTACAGGACCAG  0  0  21  58.065  N/A  NM_138681  Bcas3  CTGAAGTGGGATTAAAG  0  0  21  58.065  N/A  NM_139063  Muted  TACTGAAAGAGCATTTT  0  0  21  58.065  N/A  NM_207653  Cflar  AGCCCTTTTACAGTGAC  0  0  20  55.3  N/A  NM_007711  Clcn3  AGGAGCAAAAACCCGTT  0  0  20  55.3  N/A  NM_009266  Sephs2  TTTTACATATAAATTGC  0  0  20  55.3  N/A  NM_011806  Dmtf1  CTGGGTTCGGGCCAGAC  0  0  20  55.3  N/A  NM_013805  Cldn5  CTTTAAGACCTTGTTCA  0  0  20  55.3  N/A  NM_020000  Med8  TTGTAAAATCCTACTGT  0  0  20  55.3  N/A  NM_025565  Spc25  TTGCCTGTATGAAATTG  0  0  20  55.3  N/A  NM_026456  Tceb1  TCTTCTATGCATCTACA  0  0  19  52.535  N/A  NM_007743  Col1a2  GTTCAAGTGACAATAAA  0  0  19  52.535  N/A  NM_010545  Cd74  CTACTTCTATCACAAGG  0  0  19  52.535  N/A  NM_011739  Ywhaq  CAAAATACATTTATTTG  0  0  19  52.535  N/A  NM_019791  Maged1  CACTATTTATATAGACT  0  0  19  52.535  N/A  NM_152816  Dnm1l  GTGCTGGAGGGGCTCGG  0  0  19  52.535  N/A  NM_178873  Adck2  TGACTAGCGTGACTTGT  0  0  18  49.77  N/A  NM_007694  Chgb  TTGTCAGGTAGTTGTAA  0  0  18  49.77  N/A  NM_008615  Me1  TGAAATGGTCAAAGAAT  0  0  18  49.77  N/A  NM_009138  Ccl25  ATGACAAAGAAAAAGAC  0  0  18  49.77  N/A  NM_009946  Cplx2  TCCCCCTTCCCTATGGG  0  0  18  49.77  N/A  NM_010131  Emx1  ATCCTGGTAATGCACTT  0  0  18  49.77  N/A  NM_013494  Cpe  GCAATCAGACGTCATTT  0  0  18  49.77  N/A  NM_013900  Mfi2  GAAGTGAATGTGGACGT  0  0  18  49.77  N/A  NM_025772  Dtnbp1  TTCACGAATGTGGCTCT  0  0  18  49.77  N/A  NM_026045  Prpf18  TTTAAGGATACAAGACA  0  0  18  49.77  N/A  NM_026218  Fgfr1op2  AGTATGCGCCAACACAT  0  0  18  49.77  N/A  NM_026609  Leprotl1  TTTGGTTTAATAGTGTA  0  0  18  49.77  N/A  NM_028059  Zfp654  GAGACACAGTTTAGTCC  0  0  18  49.77  N/A  NM_133809  Kmo  89  ATAGATTTTTTATTATT  0  0  18  49.77  N/A  NM_177782  BC067047  AACGAACACATACTGGA  0  0  17  47.005  N/A  NM_001013379  D10627  TTATGGATCTTACCTTT  0  0  17  47.005  N/A  NM_001038990  Ids  CTATCCTCTCACTCTGT  0  0  17  47.005  N/A  NM_001083929  Gpx3  CAAATACCTTTGATACT  0  0  17  47.005  N/A  NM_001111292  Caprin1  TCTCTCTATTCATTCTA  0  0  17  47.005  N/A  NM_007784  Csn1s1  CACACACAAACACACAC  0  0  17  47.005  N/A  NM_008121  Gja5  CTAGAAACTTTATGACA  0  0  17  47.005  N/A  NM_009242  Sparc  TTTGCACCTTTCTAGTT  0  0  17  47.005  N/A  NM_010217  Ctgf  GTTATGTGCCAACACCC  0  0  17  47.005  N/A  NM_011485  Star  GGGGGAAACACCTCAAG  0  0  17  47.005  N/A  NM_018747  Akap7  GTCTCTGTTGAACATTT  0  0  17  47.005  N/A  NM_023565  Cse1l  GAGTTGAATGGAATGCC  0  0  17  47.005  N/A  NM_025976  Bfar  ATATTAAACTCTTGAGA  0  0  17  47.005  N/A  NM_026936  Oxa1l  CAAAGTTCCCACATCTC  0  0  17  47.005  N/A  NM_029017  Mrpl47  GGAACCAACATCGGGGT  0  0  17  47.005  N/A  NM_030087  1500032D16Rik  CACGCCTTGTTTCCCCA  0  0  17  47.005  N/A  NM_175028  Adnp2  TGTGTTTGTACGTATTT  0  0  17  47.005  N/A  NM_201369  N4bp2l2  GCTCAAGGAATTGTGTG  0  0  16  44.24  N/A  NM_001013391  Cpsf6  CCTATTAAAAACAAAAA  0  0  16  44.24  N/A  NM_007606  Car3  GGGGACTGGAGATTTTT  0  0  16  44.24  N/A  NM_008560  Mc2r  GATAGAGAGTTTTGGAG  0  0  16  44.24  N/A  NM_008721  Npdc1  GTGTCTGATAATGAGCC  0  0  16  44.24  N/A  NM_009931  Col4a1  AAGCTGGAGGAGGGCAA  0  0  16  44.24  N/A  NM_018749  Eif3d  ACAGTGCCCCTGCGAAG  0  0  16  44.24  N/A  NM_025548  Tbcb  GCTGACTCTTGCCTGCT  0  0  16  44.24  N/A  NM_026481  Tppp3  AATGACCTCCACTTCAG  0  0  16  44.24  N/A  NM_172624  Dpp9  GCGGGCCTTTTACTGGG  0  0  16  44.24  N/A  NM_197996  Tspan15  GATGACATTCTAGTGAA  0  0  15  41.475  N/A  NM_001040611  Peg10  CTATCGAGATCTGAAGG  0  0  15  41.475  N/A  NM_019750  Nat6  GAGTGAAATAGATGATT  0  0  15  41.475  N/A  NM_023873  Cep70  GTCACATATAGGTCACA  0  0  15  41.475  N/A  NM_026697  Rab14  TAAAAGTTGTTTGTTCT  0  0  15  41.475  N/A  NM_134071  Ankrd32  TTCTGTGAATCTGCCAT  0  0  15  41.475  N/A  NM_145575  Cald1  CCGAAGTGTCACAACTG  0  0  15  41.475  N/A  NM_146090  Zadh2  CACTGAGGTGCCTCAGC  0  0  15  41.475  N/A  NM_175143  Qrich1  ATCTTGCAATGTTGGGT  0  0  14  38.71  N/A  NM_001081394  0610007L01Rik  GTTTTGAGAAAAGCAAA  0  0  14  38.71  N/A  NM_001111314  Ngef  AAGTGTCGCCGCTTTGT  0  0  14  38.71  N/A  NM_008117  Gh  GTGTGCCAGCACCCTGA  0  0  14  38.71  N/A  NM_010840  Mthfr  GGGCATTTGAGGGTGGT  0  0  14  38.71  N/A  NM_019779  Cyp11a1  ATGTAATGAAAACACAC  0  0  14  38.71  N/A  NM_027379  Far1  TCTTTTTGGTGCTGTGC  0  0  14  38.71  N/A  NM_027995  Paqr7  CCCCGGGCCTGCCCCCC  0  0  14  38.71  N/A  NM_134002  Csnk1g2  TTTGAGAGGTTTTACCA  0  0  14  38.71  N/A  NM_134063  BC016423  90  ACCCAGGAGATTCCCAG  0  0  14  38.71  N/A  NM_138651  Cds2  AAAATAAAAATGTTACC  0  0  14  38.71  N/A  NM_172775  Plxnb1  GATGGCAAGATCAAGCT  0  0  13  35.945  N/A  NM_001013366  Wdr91  GGAGCTCACGCCCCTGT  0  0  13  35.945  N/A  NM_001039089  Sel1l  GCTATGGCCAGTCTCTC  0  0  13  35.945  N/A  NM_007752  Cp  GTTTGTCAGCCCACACT  0  0  13  35.945  N/A  NM_008010  Fgfr3  GCCAGTTCTTTAAAGAA  0  0  13  35.945  N/A  NM_008775  Pafah1b2  GCTTCGTCCAGACAGCG  0  0  13  35.945  N/A  NM_010777  Mbp  TGACGTAGCCCACGGAG  0  0  13  35.945  N/A  NM_019661  Ykt6  AGGAAGAATTAGGTAGT  0  0  13  35.945  N/A  NM_025866  Cdca7  TGAATCCATTCACAGGA  0  0  13  35.945  N/A  NM_025882  Pole4  GTTCAGCTGTGGACCTG  0  0  13  35.945  N/A  NM_027269  1110034A24Rik  AAGAGCTGGAGTATTTA  0  0  13  35.945  N/A  NM_028177  Ndufab1  TAATTCTAATTTTAAAT  0  0  13  35.945  N/A  NM_028705  Herc3  GCTCTGACTGTTGTGGA  0  0  13  35.945  N/A  NM_146142  Tdrd7  GAAATACCTTTTCTAGA  0  0  13  35.945  N/A  NM_172424  Med13l  CACGGCTACTTCCCAGG  0  0  12  33.18  N/A  NM_001081214  Pprc1  GAGACTAACAGAACCGC  0  0  12  33.18  N/A  NM_007496  Zfhx3  GATACAGTCGAGCTGCT  0  0  12  33.18  N/A  NM_007520  Bach1  GTGAGATCATTATGGAC  0  0  12  33.18  N/A  NM_007652  Cd59a  GCTTAAGTGTTGAAAAT  0  0  12  33.18  N/A  NM_007874  Reep5  GACAGTAATATATTTTG  0  0  12  33.18  N/A  NM_008822  Pex7  TACTAGCCTTAATACAC  0  0  12  33.18  N/A  NM_009874  Cdk7  ATTTAATGAAACTGGGT  0  0  12  33.18  N/A  NM_011499  Strap  AATTCGCGGATGTGGTT  0  0  12  33.18  N/A  NM_013697  Ttr  GCTGGGTTAGCAGGCCC  0  0  12  33.18  N/A  NM_026472  Mki67ip  CCATTACATTAAAATTA  0  0  12  33.18  N/A  NM_026487  Atad1  AACACTGGGGCTTGGAT  0  0  12  33.18  N/A  NM_172961  Abat  GCTTAATGTATGGACGG  0  0  12  33.18  N/A  NM_175752  Chn1  AAGCAGGAGAAGCAGTG  0  0  12  33.18  N/A  NM_178874  Tmcc2  TGCAGTGCCTGTGGCCT  0  0  11  30.415  N/A  NM_001030025  Upp1  ACAAACTCCTTTACATA  0  0  11  30.415  N/A  NM_001081430  Nat12  GATCAATGGGAGAAGGG  0  0  11  30.415  N/A  NM_001113325  Gria1  ACTGCTCTCTCTCCTTC  0  0  11  30.415  N/A  NM_007590  Calm3  GACCTCATTCCTCTCTG  0  0  11  30.415  N/A  NM_007933  Eno3  TTTCAAGAGTGCTAAGA  0  0  11  30.415  N/A  NM_007998  Fech  TCACTTTTAATATTACC  0  0  11  30.415  N/A  NM_009163  Sgpl1  CATACTTCAAAAGTATT  0  0  11  30.415  N/A  NM_009537  Yy1  TACACAATAATTTTTTT  0  0  11  30.415  N/A  NM_010258  Gata6  GTCTGGGGGGAGGCAGG  0  0  11  30.415  N/A  NM_011844  Mgll  ATGTCGAAGACGAGCTT  0  0  11  30.415  N/A  NM_013758  Add3  TGATTTTTGTTTCTCAA  0  0  11  30.415  N/A  NM_023118  Dab2  CGGTCTAGTCCAGTTTT  0  0  11  30.415  N/A  NM_025451  Camk2n1  TACAGCACAGATCCTGA  0  0  11  30.415  N/A  NM_025569  Mgst3  TCTTAAAAACTCAGATG  0  0  11  30.415  N/A  NM_027412  Ttc9c  91  ACTGCTTGCCCTAAGCA  0  0  11  30.415  N/A  NM_145569  Mat2a  TGATTTGTAAATGTACC  0  0  11  30.415  N/A  NM_175194  Slc25a16  TGTGGGTCTGGGAATGA  0  0  11  30.415  N/A  NM_178620  Mfsd11  ATGCGGTACAGGGCATT  0  0  10  27.65  N/A  NM_001001326  St5  AAAGGATGTTAACCTTT  0  0  10  27.65  N/A  NM_001004190  Zfp560  GACCACAGCCTTCTAAC  0  0  10  27.65  N/A  NM_001102650  Nt5c3l  ATGTTGCTGGCCAGATG  0  0  10  27.65  N/A  NM_007408  Adfp  CCAGCTGTCAACATTCA  0  0  10  27.65  N/A  NM_008640  Laptm4a  GGCGCTCGTACACCCCT  0  0  10  27.65  N/A  NM_011520  Sdc3  GAATATGGAACTTTGTA  0  0  10  27.65  N/A  NM_145546  Gtf2b  CTTATATTTAAGGCTTA  0  0  9  24.885  N/A  NM_001007580  Gm784  GGGTTCTTCCACTGAAA  0  0  9  24.885  N/A  NM_008212  Hadh  GTTAATAAACGTCGATG  0  0  9  24.885  N/A  NM_009670  Ank3  TGATTTCGAGGTCCTTG  0  0  9  24.885  N/A  NM_010276  Gem  GGGGGCTGTGGTGTTCC  0  0  9  24.885  N/A  NM_010890  Nedd4  GGTGAGAAAACAGGCCA  0  0  9  24.885  N/A  NM_023305  Ubap1  TTATTTAAAGAGGAAGA  0  0  9  24.885  N/A  NM_028108  Nat13  TCTGGACTTGTCCTGAG  0  0  9  24.885  N/A  NM_029929  Vps33a  GGACCCGGGCCAACTTC  0  0  9  24.885  N/A  NM_144942  Csad  TCTACATCTCCCTCCTA  0  0  9  24.885  N/A  NM_148950  Pknox2  CAGACCTCAACCCCTTG  0  0  8  22.12  N/A  NM_001039089  Sel1l  CTGTGTGTCAATCCTCC  0  0  8  22.12  N/A  NM_001110499  Canx  ATGCAGTGGCGGGATGT  0  0  8  22.12  N/A  NM_009258  Spink3  CAGCGGGAGCTGGGGCC  0  0  8  22.12  N/A  NM_016801  Stx1a  GCACTTGAAAGTGTATT  0  0  8  22.12  N/A  NM_019759  Dpt  CTCACTTAGTGTAAGCT  0  0  8  22.12  N/A  NM_023865  9530002B09Rik  TATACTGTGATATAAAA  0  0  8  22.12  N/A  NM_030750  Sgpp1  ATCCCCCTCTGGGAGAG  0  0  8  22.12  N/A  NM_133934  Isy1  GATTCTACAGTGGAGGA  0  0  7  19.355  N/A  NM_001029895  Ate1  CCTCCTCCAGGAATGAG  0  0  7  19.355  N/A  NM_001082961  Snrpn  CCTGAACTTTAAGAAAT  0  0  7  19.355  N/A  NM_008083  Gap43  TAGAAGCCTAGCCTTTT  0  0  7  19.355  N/A  NM_008630  Mt2  GCCTATCCAAATCTCGT  0  0  7  19.355  N/A  NM_008784  Igbp1  TGTTCATCTTGTTTTAA  0  0  7  19.355  N/A  NM_009930  Col3a1  GGTGACCACACCCCAAC  0  0  7  19.355  N/A  NM_013459  Cfd  TGAAGTAAGGAGCCTGT  0  0  7  19.355  N/A  NM_013677  Surf1  TCCCATCAGTTGGAATT  0  0  7  19.355  N/A  NM_020586  Herpud2  CGGAGCGACCACCTGAC  0  0  7  19.355  N/A  NM_021366  Klf13  GGTAAGAATAGTCCATT  0  0  7  19.355  N/A  NM_025586  Rpl15  GTGGACTCACAAGGGTG  0  0  7  19.355  N/A  NM_029645  Gatc  AGGCCAAGAAGTATGCT  0  0  7  19.355  N/A  NM_030559  Vps16  GCACAGTATCACATCTG  0  0  7  19.355  N/A  NM_080855  Zcchc14  TAATGTGAAACTCCTCC  0  0  7  19.355  N/A  NM_133945  Vrk3  TAAGAGGTGCCAGTCCC  0  0  7  19.355  N/A  NM_152915  Dner  AACAAGAGGAAATTTTT  0  0  7  19.355  N/A  NM_172664  Tlk1  92  CTCCTGTCTTGATGATA  0  0  7  19.355  N/A  NM_176842  Tprkb  TGTAGAAACCATTTTGG  0  0  7  19.355  N/A  NM_177798  Frs2  GGTCTTCAGAGTTATAG  0  0  6  16.59  N/A  NM_007984  Fscn1  TCAGAGACCCGGCCACT  0  0  6  16.59  N/A  NM_008091  Gata3  TATCCTCCTGATTATTG  0  0  6  16.59  N/A  NM_009418  Tpp2  GCATAGTAACTGGAAAT  0  0  6  16.59  N/A  NM_009706  Arhgap5  CCTCCCCCTCCCCCAGG  0  0  6  16.59  N/A  NM_013651  Sf3a2  TCCCAATTAGGGAAGAA  0  0  6  16.59  N/A  NM_133818  AI597479  GAACCCAACAAGCAGAA  0  0  6  16.59  N/A  NM_133953  Sf3b3  CCTCTTGAGCAGCTTTC  0  0  5  13.825  N/A  NM_001081213  Ermp1  AATGACCTGGTGTCTGA  0  0  5  13.825  N/A  NM_009450  Tubb2a  TCTTCTCCACTTGGTAC  0  0  5  13.825  N/A  NM_013842  Xbp1  TATTCTTCATAATGGAG  0  0  5  13.825  N/A  NM_013918  Usp25  GAGACGGTGCTGGTCCT  0  0  5  13.825  N/A  NM_022004  Fxyd6  TGACTGAGGTCAGTCAG  0  0  5  13.825  N/A  NM_025291  Sra1  ACAGCAGGAGACTTTCA  0  0  5  13.825  N/A  NM_026120  2410127L17Rik  TTGGGAGGCAAGCATCT  0  0  5  13.825  N/A  NM_026176  Pdcl  TTCTCAGAACTTCTTAG  0  0  5  13.825  N/A  NM_026386  Snx2  TAGGCCAGGCTGGCCTT  0  0  5  13.825  N/A  NM_027222  2010001M09Rik  GGAAACAGCTGAGTACT  0  0  5  13.825  N/A  NM_029094  Pik3cb  ACATTTACAGAGCACGG  0  0  5  13.825  N/A  NM_145529  Cstf3  TTACTGCAATGTCAGTG  0  0  5  13.825  N/A  NM_173369  Cyld  CACAGGCCTGCAATCTG  0  0  5  13.825  N/A  NM_177151  Vps13b  GGCTCAGGACAAGCCCT  0  0  4  11.06  N/A  NM_001001565  Chpf  CTGGGCATCTTAGTTAA  0  0  4  11.06  N/A  NM_001039201  Hdhd2  CAGCAACCTTGATTTAA  0  0  4  11.06  N/A  NM_001081049  Mll1  TAAATACTATATCTCAG  0  0  4  11.06  N/A  NM_008341  Igfbp1  CTCTTATTTTCCTTAAC  0  0  4  11.06  N/A  NM_008783  Pbx1  GTGACCTGGCCTCAGGA  0  0  4  11.06  N/A  NM_008963  Ptgds  GATGTGTGGGGAATCCA  0  0  4  11.06  N/A  NM_009533  Xrcc5  GTGGAAGAAGGTGAGGA  0  0  4  11.06  N/A  NM_009702  Aqr  AAAAAGTGAATATCATC  0  0  4  11.06  N/A  NM_009859  Sept7  ACAGAAAAAAACAAAAC  0  0  4  11.06  N/A  NM_010136  Eomes  CACTAGGGGTAGCTTAG  0  0  4  11.06  N/A  NM_010193  Fem1b  GAAGAACCAAGTTTTTC  0  0  4  11.06  N/A  NM_011767  Zfr  CTCTTTCCTCCTTCCCC  0  0  4  11.06  N/A  NM_021395  Hyou1  TGAAGAGGAAATAGCTG  0  0  4  11.06  N/A  NM_025441  Sdccag1  CTGCCTGAAGACTCTTC  0  0  4  11.06  N/A  NM_028262  Setd3  TGTAACAAATGCCAGCC  0  0  4  11.06  N/A  NM_130450  Elovl6  CTGCTGCAGCCTTTTAA  0  0  4  11.06  N/A  NM_145625  Eif4b  TTTTAATTTTATACAAT  0  0  4  11.06  N/A  NM_148926  Zfand3  AAAATTGAATAGGAAAT  0  0  4  11.06  N/A  NM_173739  Galntl4  GCTTCATCTATTTGGAT  0  0  4  11.06  N/A  NM_175313  A130022J15Rik  TCCCAGGCCAAGGAGCT  0  0  4  11.06  N/A  NM_178622  Suds3  GGCACAAGACTTGTAGT  1  0.622  54  149.31  240.08  NM_001077529  Nme2  93  GGCTTCCGCGAGGGTAC  1  0.622  54  149.31  240.08  NM_010784  Mdk  GTTTTGTATATTTATTC  1  0.622  50  138.25  222.29  NM_145943  BC031781  CCACACAAGCGTTTATT  1  0.622  48  132.72  213.4  NM_021540  Rnf130  GAGATAATTTCTGGCAT  1  0.622  47  129.955  208.96  NM_152816  Dnm1l  TACTGGGAGCTGTAATG  1  0.622  46  127.19  204.51  NM_007801  Ctsh  ACTGGCTGGGCCTGCTG  1  0.622  46  127.19  204.51  NM_008910  Ppm1a  AAGAATACAAGATAAGG  1  0.622  36  99.54  160.05  NM_001099637  Cep170  CTACCAAAACTGACTTA  1  0.622  36  99.54  160.05  NM_023248  Sbds  GCATCCTGTTAAAAAAA  1  0.622  35  96.775  155.61  NM_009433  Tspyl1  TGTGGGAACCACAGCGC  1  0.622  35  96.775  155.61  NM_133716  Smap2  CAGAATGCTGTTATTCT  1  0.622  35  96.775  155.61  NM_133718  Tmem30a  TCTGGAACAAGCAGTAT  1  0.622  33  91.245  146.71  NM_011956  Nubp2  TAACACCAACCCCAGTT  1  0.622  33  91.245  146.71  NM_028152  Mms19  TAAGAAACCACTAGAAG  1  0.622  32  88.48  142.27  NM_008231  Hdgf  GGTGTGGCAGTGGGCAG  1  0.622  32  88.48  142.27  NM_145409  Chtf18  TCTTTTTTAGGTTGACC  1  0.622  31  85.715  137.82  NM_007516  Hnrnpd  CAGCTCTGCCTCCGCAG  1  0.622  31  85.715  137.82  NM_022029  Nrgn  CCCAGCTTTCTTTTCAG  1  0.622  30  82.95  133.38  NM_053102  Sep15  GAGGGGGAGCCGCTGCC  1  0.622  30  82.95  133.38  NM_139229  Cog8  CCAAGTCAGGCCTGTAG  1  0.622  30  82.95  133.38  NM_182990  Ssrp1  GTCTCTACTGCATTTTC  1  0.622  29  80.185  128.93  NM_009054  Trim27  TAAATATAAGCCTTACT  1  0.622  29  80.185  128.93  NM_009270  Sqle  TGTATAAAAATAAAAAA  1  0.622  29  80.185  128.93  NM_011631  Hsp90b1  ACTGGAATGACAAAGAG  1  0.622  29  80.185  128.93  NM_025942  Ola1  CCCTACTTCATCCTTTG  2  1.244  55  152.075  122.26  NR_001592  H19  GTAGCAGTTTGGGCCTT  1  0.622  27  74.655  120.04  NM_025292  Synj2bp  TGATGACCGAACCCCCA  1  0.622  27  74.655  120.04  NM_026307  Cuta  GGCTTAAGTAGGAAAGT  1  0.622  26  71.89  115.59  NM_010730  Anxa1  CTATGACCTGGCTATCC  1  0.622  26  71.89  115.59  NM_011708  Vwf  GCCCAGACTGGCCCAGA  1  0.622  26  71.89  115.59  NM_011743  Zfp106  CAGCTGCACCACAGCAA  1  0.622  26  71.89  115.59  NM_013671  Sod2  GCTCCTCCCACTGTGTC  1  0.622  26  71.89  115.59  NM_021493  4933428G20Rik  GTTGGGAGCTGTGAGGT  1  0.622  25  69.125  111.15  NM_009305  Syp  GGAGGCTTTTCCCTAAG  1  0.622  25  69.125  111.15  NM_015735  Ddb1  ATTAGTTACCCTACACT  1  0.622  25  69.125  111.15  NM_015762  Txnrd1  ACCACCAGATGCCCACC  1  0.622  25  69.125  111.15  NM_027196  Pold4  GCACTAGCTGGGTGAGT  1  0.622  24  66.36  106.7  NM_016783  Pgrmc1  TGTAGCCTCATCCCCCT  1  0.622  23  63.595  102.26  NM_007503  Fxyd2  AGACAAAGTTTAGGAAA  1  0.622  23  63.595  102.26  NM_008300  Hspa4  ATAAGGGATTGGGTTCC  1  0.622  23  63.595  102.26  NM_016752  Slc35b1  ACGCTATGGATTCCTAC  1  0.622  23  63.595  102.26  NM_178112  Ints8  GCCTCCCCTGGTTAGCA  1  0.622  22  60.83  97.81  NM_025400  Nat9  AGTATCTGATGTTTTCA  1  0.622  22  60.83  97.81  NM_025901  Polr3k  GAGAACACACCTGTTAT  1  0.622  22  60.83  97.81  NM_027453  Btf3l4  CCTGATGGTAGACATCG  1  0.622  20  55.3  88.92  NM_013898  Timm8a1  94  TGACCGCCCAGTTTGGA  1  0.622  20  55.3  88.92  NM_027189  Gemin7  AATACACAAGTGTTGCC  1  0.622  20  55.3  88.92  NM_028148  Sfrs2ip  CCAATCTTTGAATGTAA  1  0.622  19  52.535  84.47  NM_001110251  Hmbs  AAATATACAGTGTTTGG  1  0.622  19  52.535  84.47  NM_027374  Ppil3  ACTGTAGATGAGGATAG  4  2.488  75  207.375  83.36  NM_019411  Ppp2ca  AACCCCTTTGAGTCTTG  2  1.244  37  102.305  82.25  NM_010192  Fem1a  ACAGCCCACCCCCCAGC  1  0.622  18  49.77  80.03  NM_009721  Atp1b1  TGCCGTTTTGAGGCTTT  1  0.622  18  49.77  80.03  NM_010360  Gstm5  CTGGGCCCAGACCTTTG  1  0.622  18  49.77  80.03  NM_011116  Pld3  GTAGTGGAGCCCTTAAA  1  0.622  18  49.77  80.03  NM_022417  Itm2c  TGCACCCTGCCCTGGAG  1  0.622  18  49.77  80.03  NM_025397  Med11  CTGCGCTCTTTGTCTAC  1  0.622  18  49.77  80.03  NM_145135  Rnh1  AGTTATTTTGATTGAGA  1  0.622  17  47.005  75.58  NM_001039522  Leo1  TCAGGAAGAGCAACCGG  1  0.622  17  47.005  75.58  NM_001042408  Txnl4a  TTTTTTGTGTGTCAATT  1  0.622  17  47.005  75.58  NM_016891  Ppp2r1a  AAGGTTTATCATCTTGA  1  0.622  17  47.005  75.58  NM_025547  Mterfd1  ATTTGATTAGCCCCAAA  1  0.622  17  47.005  75.58  NM_025596  Prelid1  GGTCCTTGCTGATCCCT  1  0.622  17  47.005  75.58  NM_027353  Cd2bp2  TAGCTGTAACGGGGGGC  1  0.622  17  47.005  75.58  NM_144900  Atp1a1  GTGATGCGGAGCCACTG  1  0.622  17  47.005  75.58  NM_153791  Flywch1  ACTTTAATCCCAGCACT  2  1.244  33  91.245  73.36  NM_001012200  Ttll1  CATTTTCTGGCAAAATC  1  0.622  16  44.24  71.13  NM_001122737  Igf2  CCTTACCGCTGTAATGG  1  0.622  16  44.24  71.13  NM_175692  A930034L06Rik  CACCAGGCTGGCCACTA  2  1.244  31  85.715  68.91  NM_144826  Utp6  GATTTCTTTGACAAAAA  1  0.622  15  41.475  66.69  NM_016697  Gpc3  TGTAACTGGTCACAAGG  1  0.622  15  41.475  66.69  NM_024457  Rap1b  CACATTAACTCTTTACT  1  0.622  15  41.475  66.69  NM_028136  Dhx36  TTGGTTATTTTACCCCT  2  1.244  29  80.185  64.47  NM_010690  Lats1  GAATGTCAAGGTAGAGG  2  1.244  29  80.185  64.47  NM_018753  Ywhab  AATGTTTGTAACACTGA  2  1.244  28  77.42  62.24  NM_001014995  1110013L07Rik  CATTTAAAATGAGATGG  1  0.622  14  38.71  62.24  NM_028234  Prr8  AAGAAGTCATCAGTCAC  1  0.622  14  38.71  62.24  NM_133232  Pfkfb3  TTGTTAGTGCTGCAATC  4  2.488  53  146.545  58.91  NM_008618  Mdh1  ACATTCTGCAATATTTT  2  1.244  26  71.89  57.8  NM_026386  Snx2  TTTCCTTCCTCCTGTCA  1  0.622  13  35.945  57.8  NM_009516  Wee1  GAGGAGCTGAGCAGCCA  1  0.622  13  35.945  57.8  NM_026382  6530403A03Rik  GTCAATTCAGAAAAGTC  3  1.866  36  99.54  53.35  NM_022309  Cbfb  AATTTCTCCTGCAGGAG  2  1.244  24  66.36  53.35  NM_177298  Pisd  GCGCTGGCCGCCCGTCT  1  0.622  12  33.18  53.35  NM_001081181  9430016H08Rik  CACCAGTGAGCTGGCTA  1  0.622  12  33.18  53.35  NM_001107959  Atpaf1  CCTTTGGCAATTGCTCA  3  1.866  35  96.775  51.87  NM_011187  Psmb7  ACCTTTACTGTGTTAGC  2  1.244  23  63.595  51.13  NM_011638  Tfrc  CTTCGGATGTCTTGGAG  3  1.866  34  94.01  50.39  NM_025287  Spop  GTTTGCTGTGTGACCGA  4  2.488  45  124.425  50.02  NM_007798  Ctsb  GAGGTCCTAGCCTTTCC  3  1.866  33  91.245  48.9  NM_009058  Ralgds  95  ATAGCTGGGCTTCAGTC  2  1.244  22  60.83  48.9  NM_008927  Map2k1  CTCCCCCTAGCCCAGGC  2  1.244  22  60.83  48.9  NM_145417  Rnpep  TGTTTGTACATTTTTGT  1  0.622  11  30.415  48.9  NM_019835  B4galt5  TCTGCGTCCTAGCATAA  1  0.622  11  30.415  48.9  NM_021535  Smu1  ACACTGTGTGCGGTTCC  1  0.622  11  30.415  48.9  NM_023232  Diablo  AAGCCACAGAACCAAGC  1  0.622  11  30.415  48.9  NM_145925  Pttg1ip  CAAGTGCACTCAGCTTG  8  4.975  84  232.26  46.68  NM_026160  Map1lc3b  CTACTGAACACAGGAAC  2  1.244  21  58.065  46.68  NM_177680  Ythdc1  GTGACAAAAAACCAGGG  3  1.866  31  85.715  45.94  NM_019996  Rnuxa  AAAGCTCTATGTAAAGG  3  1.866  31  85.715  45.94  NM_183308  Pon2  ACCTTGCCCTCCATAGG  5  3.11  51  141.015  45.35  NM_025448  Ssr2  TGACCGAGGACATAAGG  3  1.866  30  82.95  44.46  NM_028250  Acbd6  CTATTAAATAGGTTTTT  3  1.866  30  82.95  44.46  NM_145422  D10Wsu52e  TGATGTATATTAAACTT  2  1.244  20  55.3  44.46  NM_008321  Id3  GATTTTGTATGTTCCTC  2  1.244  20  55.3  44.46  NM_016755  Atp5j  ACTTCAGACTCCTTATT  2  1.244  20  55.3  44.46  NM_178142  Lcorl  AGCCACGGGGGCTGGCT  1  0.622  10  27.65  44.46  NM_011565  Tead2  GGAAACAGTGGTTGTTT  1  0.622  10  27.65  44.46  NM_013915  Zfp238  CCATAAAAGACCGATCC  1  0.622  10  27.65  44.46  NM_019806  Vapb  TGAATAAACTACTTGAT  1  0.622  10  27.65  44.46  NM_019975  Hacl1  CAGGGTGATGGTGAAGA  3  1.866  29  80.185  42.98  NM_009536  Ywhae  GGGGCTCACAACCATCT  5  3.11  48  132.72  42.68  NR_001463  Xist  ATCCGGGCTTTACGAAT  2  1.244  19  52.535  42.24  NM_008845  Pip4k2a  GGAGAGGGTATCCGGTT  2  1.244  19  52.535  42.24  NM_011739  Ywhaq  ATTTTAATAACACTTTT  4  2.488  37  102.305  41.12  NM_027494  Zcchc8  TAACTTGTAGCTATAAA  4  2.488  36  99.54  40.01  NM_009761  Bnip3l  TTATTAAAATAGTGATG  3  1.866  27  74.655  40.01  NM_011692  Vbp1  AGGCCCAGGGTGCTGAC  2  1.244  18  49.77  40.01  NM_013895  Timm13  TGAATGGCCTAGAGAAC  2  1.244  18  49.77  40.01  NM_027117  Klhdc2  GTGGCACACGCCTTTAG  1  0.622  9  24.885  40.01  NM_006989  RASA4  TGCAGAGAAAAGCCTCC  1  0.622  9  24.885  40.01  NM_007483  Rhob  GTGTATGACATCACCAG  1  0.622  9  24.885  40.01  NM_026697  Rab14  CACAAGGCTACCAAGCT  1  0.622  9  24.885  40.01  NM_026752  Zfyve21  TAGCAGTATGCTCATTT  1  0.622  9  24.885  40.01  NM_146075  Lemd2  GAGACTAGCAAAATAGT  6  3.732  52  143.78  38.53  NM_019793  Tspan3  ATCACAGGTGATTCACA  3  1.866  26  71.89  38.53  NM_001108393  Zmiz1  GGAGGGGGGAACAAGTC  6  3.732  51  141.015  37.79  NM_022325  Ctsz  TGCAATATTTCTTACTG  2  1.244  17  47.005  37.79  NM_001110131  Cic  GCATAGAACATAGCATT  2  1.244  17  47.005  37.79  NM_153762  Rnf26  CTAACAGAATCCAGGCC  3  1.866  25  69.125  37.05  NM_153792  Traf7  AAGAAAAAGTGATGACA  3  1.866  24  66.36  35.57  NM_023735  Actr3  ATTGAGCGGCCAGAGCC  2  1.244  16  44.24  35.57  NM_019745  Pdcd10  CTCCTGTACTCCTCGGA  2  1.244  16  44.24  35.57  NM_133949  Ptov1  AAAATGTATCACGTGTT  2  1.244  16  44.24  35.57  NM_144545  Eif3j  ATCTAGAGTTTGTTTTC  2  1.244  16  44.24  35.57  NM_144833  Zfp410  96  GATTAGAAGAACAAGGA  1  0.622  8  22.12  35.57  NM_001127346  Ndufaf2  AGCAGGTTTTCTTTAAG  1  0.622  8  22.12  35.57  NM_007589  Calm2  GAACCCATCAAAGTTCT  1  0.622  8  22.12  35.57  NM_019734  Asah1  TGCTGGCTGCAGGCTTC  1  0.622  8  22.12  35.57  NM_134011  Tbrg4  AGAAGGACCTCGGAGGC  4  2.488  31  85.715  34.46  NM_011400  Slc2a1  GATATCATCAACCTCTT  4  2.488  31  85.715  34.46  NM_026617  Tmbim4  TGCAGGAGCTGGCGGGC  5  3.11  38  105.07  33.79  NM_145979  Chd4  CCGTGCAGCAGAAGCCC  2  1.244  15  41.475  33.34  NM_011512  Surf4  ATAGCACAGTGGACTTT  2  1.244  15  41.475  33.34  NM_025391  Nip7  GCCTATCCAAGAGGAAA  3  1.866  22  60.83  32.6  NM_026388  Tprgl  CCAGACTGATCACCGGG  3  1.866  22  60.83  32.6  NM_175287  A430005L14Rik  AATGTGAGTCATTATGT  11  6.841  78  215.67  31.53  NM_018871  Ywhag  CAGTTATAAAAATCAGC  4  2.488  28  77.42  31.12  NM_008410  Itm2b  CCGAATATTCCAGCAGC  4  2.488  28  77.42  31.12  NM_011370  Cyfip1  GAAACGCAGAGACCAAG  3  1.866  21  58.065  31.12  NM_001037878  Tcf25  GCCCAGCCTTGAGTGGA  3  1.866  21  58.065  31.12  NM_001115132  Ncaph2  GGCAATAATGGGAGCTG  3  1.866  21  58.065  31.12  NM_010497  Idh1  TCTGTAGCCCAGTGCCC  3  1.866  21  58.065  31.12  NM_031134  Thra  GTGGCTCACAAACATCT  3  1.866  21  58.065  31.12  NM_153159  Zc3h12a  TGAACTGGTGTGACCAA  2  1.244  14  38.71  31.12  NM_001033379  Dcp1b  CTGTATATTCAGATGGA  2  1.244  14  38.71  31.12  NM_001033634  Zyg11b  ATTGCCTACGATGCCCT  1  0.622  7  19.355  31.12  NM_007414  Adprh  TATACGGTTCAGAGTGA  1  0.622  7  19.355  31.12  NM_010615  Kif11  AAACAGAAGTCTGAGAT  1  0.622  7  19.355  31.12  NM_027604  Usp15  GGGGGCCCAGGTGTCCT  5  3.11  35  96.775  31.12  NM_145135  Rnh1  AGCATTGGCGGTTCGTG  4  2.488  27  74.655  30.01  NM_011650  Tsn  GGGAGCGGGCAGAGTTG  7  4.353  47  129.955  29.85  NM_134101  Psmd2  CCCGGGTACAAGTGCAG  4  2.488  26  71.89  28.9  NM_011876  Twf2  TGGACTGTTTTCTGAGC  2  1.244  13  35.945  28.9  NM_008014  Ppm1g  CCCTATGTTTGTGCCTT  2  1.244  13  35.945  28.9  NM_016686  Vezf1  CCCCCCGTCAGTCATCT  5  3.11  30  82.95  26.68  NM_029565  Tmem59  GTGTTTTAAAGCTAAAT  3  1.866  18  49.77  26.68  NM_009775  Tspo  GTTAGAGACTAACTGGA  2  1.244  12  33.18  26.68  NM_011650  Tsn  TGAGTTCCCTTTAAATA  2  1.244  12  33.18  26.68  NM_027769  Cpne3  GGTGACTGTTCTCTGTC  2  1.244  12  33.18  26.68  NM_172424  Med13l  AAGAAAAAAAAAAAAAA  1  0.622  6  16.59  26.68  NM_006206  PDGFRA  GCTTGACATAGAGGGTT  1  0.622  6  16.59  26.68  NM_153194  Zfp740  AGAGCAGAGAAGCAGCT  1  0.622  6  16.59  26.68  NM_172661  5830434P21Rik  TGAACTTATTATGAAGG  1  0.622  6  16.59  26.68  NM_172920  Dpy19l1  TTCAAGTCTTCCAGTTA  6  3.732  34  94.01  25.19  NM_178218  Hist3h2a  TATGCCCATTTTTTCAA  3  1.866  17  47.005  25.19  NM_013771  Yme1l1  AAGAGAAGGTGGCTGCA  3  1.866  17  47.005  25.19  NM_026819  Dhrs1  TGTGAATGATTTAAAAA  5  3.11  28  77.42  24.9  NM_029409  Mff  CTGGGGTGCGCGCGACC  6  3.732  33  91.245  24.45  NM_174987  1810063B05Rik  ATGCAACTACTAATAAA  9  5.597  49  135.485  24.21  NM_025628  Cox6b1  97  GCCCAGGTTTATTAAAG  5  3.11  27  74.655  24.01  NM_011341  Sdf4  GAGCGCAGTGAGCTGGC  5  3.11  27  74.655  24.01  NM_053193  Cpsf1  TTGATGCCATTTCCCTT  3  1.866  16  44.24  23.71  NM_010064  Dync1i2  AAATAGACATTTCCCCC  3  1.866  16  44.24  23.71  NM_020003  0610031J06Rik  TCTGTCCCGCCAGGCCC  5  3.11  26  71.89  23.12  NM_016852  Wbp2  TGCCCAGGACTTACGCG  7  4.353  35  96.775  22.23  NM_019566  Rhog  AATGAAATAAAGATTTG  6  3.732  30  82.95  22.23  NM_133726  St13  ATTCTTTATAAGTGTTA  4  2.488  20  55.3  22.23  NM_009861  Cdc42  GCGACAGAGGTGGCAGA  4  2.488  20  55.3  22.23  NM_194068  D12Ertd647e  TTGAACACTTTATGATG  4  2.488  20  55.3  22.23  NR_001592  H19  ATGCAGACTTCAGGATG  3  1.866  15  41.475  22.23  NM_026444  Cs  GATATCTGAAGCCTTGT  2  1.244  10  27.65  22.23  NM_011981  Zfp260  AAATTGGACACACCAGC  2  1.244  10  27.65  22.23  NM_016698  Rnf10  AGTTCTTTTTGGGAAAG  2  1.244  10  27.65  22.23  NM_024179  0610009O20Rik  CAAGGTGAACCTGAGGG  1  0.622  5  13.825  22.23  NM_010411  Hdac3  TTGACGACTCCTCCAGA  1  0.622  5  13.825  22.23  NM_010724  Psmb8  ATATTGAAGAAAAGTGC  1  0.622  5  13.825  22.23  NM_011899  Srp54a  ACCTGCTCACAGTGCTG  1  0.622  5  13.825  22.23  NM_026373  Cdk2ap2  AATAAACTCAGCATATT  1  0.622  5  13.825  22.23  NM_029271  Mrpl32  TGGAAAAAAAAAAAAAA  1  0.622  5  13.825  22.23  NM_152894  Pop1  CCCCCACCCCAACCCTT  1  0.622  5  13.825  22.23  NM_198862  Nlgn2  GCCGGAGGTTCCTGGCT  5  3.11  25  69.125  22.23  NM_001082532  Pigyl  CCATTGATCAAGGGTCG  10  6.219  49  135.485  21.78  NM_025947  Dynlrb1  AAGAGAAGAGTGGGGGA  6  3.732  29  80.185  21.49  NM_001130150  Arhgef1  TAAGGAACAAATGACTA  4  2.488  19  52.535  21.12  NM_024206  Sec13  GGGCAGCTGGCACACTC  3  1.866  14  38.71  20.75  NM_133918  Emilin1  GTTAAATAAAGGGTTCT  7  4.353  32  88.48  20.32  NM_024174  Mrps23  CAGGAGCGGACTTTGCT  8  4.975  36  99.54  20.01  NM_007917  Eif4e  ACAAACTTAGGAGAAAA  8  4.975  36  99.54  20.01  NM_009790  Calm1  AGGAAGTCCCAAGGTTC  4  2.488  18  49.77  20.01  NM_022994  Dap3  GGGATTTCTAGTGATGG  4  2.488  18  49.77  20.01  NM_133954  AA960436  TGTACCCAGGGGTGGCT  2  1.244  9  24.885  20.01  NM_008060  Ganab  TGTGCCTGCATCTGCCC  2  1.244  9  24.885  20.01  NM_025300  Mrpl15  GAGTAATACAAACTGAA  5  3.11  22  60.83  19.56  NM_177470  Acaa2  TTGGTGAAGGAAAAAGC  23  14.304  100  276.5  19.33  NM_021278  Tmsb4x  CGGGTCATATATTGGAG  6  3.732  26  71.89  19.27  NM_011185  Psmb1  TTAATGAGGTGGGTCTG  3  1.866  13  35.945  19.27  NM_009037  Rcn1  CAGCTGAAAACCCTGGG  3  1.866  13  35.945  19.27  NM_016805  Hnrnpu  CATACGCTCACAAAAGA  3  1.866  13  35.945  19.27  NM_133933  Rpn1  GGGTGTCCCAGTGTATA  4  2.488  17  47.005  18.9  NM_016772  Ech1  AACCTCGCTGGGTGTTC  9  5.597  36  99.54  17.78  NM_153526  Insig1  ATCTGACTCCCTCTTCT  8  4.975  32  88.48  17.78  NM_026701  Pbld  ATCGTGGCTGCTATCCA  6  3.732  24  66.36  17.78  NM_008197  H1f0  ACTTCAGCCAGATTAGC  5  3.11  20  55.3  17.78  NM_009342  Dynlt1  ATCTATTCTTGCAGTTT  5  3.11  20  55.3  17.78  NM_198304  Nup188  98  TACTCTCCTTTCACTGT  4  2.488  16  44.24  17.78  NM_009773  Bub1b  ATCCGCACCCTTGCTTC  3  1.866  12  33.18  17.78  NM_007590  Calm3  GGCACACAGCTCATAGG  3  1.866  12  33.18  17.78  NM_024174  Mrps23  CGATCCCCTTCCTACCT  3  1.866  12  33.18  17.78  NM_024256  B3gat3  CCTCAGGGATCCTTGGC  3  1.866  12  33.18  17.78  NM_025895  Med28  GGTGGGACACATTAAAA  3  1.866  12  33.18  17.78  NM_026775  Tmed10  GTGGCTCACATCCATCT  2  1.244  8  22.12  17.78  NM_023045  Xpo7  GATGCTTACGGTTTACC  2  1.244  8  22.12  17.78  NM_025849  3110001D03Rik  GCTACAGGTAGAACTGG  2  1.244  8  22.12  17.78  NM_028862  Rnf145  CCCTGCACAGATCTCAC  7  4.353  27  74.655  17.15  NM_019953  Cnpy2  ATCTCAAACCTAAAGGG  6  3.732  23  63.595  17.04  NM_026851  Mrpl52  TGGTTTTGGGTGTTTTT  5  3.11  19  52.535  16.89  NM_011942  Lypla2  TAATAGTAACTTGGACT  7  4.353  26  71.89  16.51  NM_007747  Cox5a  CAGCGCGCCCTTCAGGA  3  1.866  11  30.415  16.3  NM_001033458  Gm1673  CTCCTGCAGCTGAATCG  3  1.866  11  30.415  16.3  NM_016903  Esd  GCAAGGTCAGTGTGGAG  9  5.597  33  91.245  16.3  NM_013536  Emg1  ACCCAGGCTGGAGTAGA  10  6.219  36  99.54  16.01  NM_024219  Hsbp1  GACTGGAGACCTGGCAG  6  3.732  21  58.065  15.56  NM_001033136  1200015F23Rik  TCCCTCCTTAGCCTAAG  4  2.488  14  38.71  15.56  NM_013477  Atp6v0d1  TGGACATTTGTAATATC  2  1.244  7  19.355  15.56  NM_001080926  Lrp8  ACAATATGTATCGAAAG  2  1.244  7  19.355  15.56  NM_010926  Cox4nb  GACTGCTGGCCTGCTCT  2  1.244  7  19.355  15.56  NM_021356  Gab1  AATGTTAACAAGTACAG  2  1.244  7  19.355  15.56  NM_178647  Cggbp1  GCTGCTGCCAGAACTGG  2  1.244  7  19.355  15.56  NM_201364  BC055324  CTGAGCTGTGGCCAAGG  7  4.353  24  66.36  15.24  NM_009045  Rela  CTCATTTCCTAAAAGAT  5  3.11  17  47.005  15.12  NM_001113564  Serbp1  CTCAACAGCAACATCAA  11  6.841  37  102.305  14.95  NM_025344  Eif3f  TTTTCTGCTGGGTGGGA  7  4.353  23  63.595  14.61  NM_146036  Ahsa1  CGGATAACCAGTGGTCC  11  6.841  35  96.775  14.15  NM_011119  Pa2g4  GGGAGCGAAAACGTTAA  7  4.353  22  60.83  13.97  NM_010496  Id2  GCCTTCTCAGGCCTGGC  9  5.597  28  77.42  13.83  NM_025567  Cyc1  GCCTTTATGAGAAGAAA  29  18.036  87  240.555  13.34  NM_207634  Rps24  TTAATATTTAACTAGAG  13  8.085  39  107.835  13.34  NM_025987  Ndufa6  CTGCTGGTGGCTCTGGC  10  6.219  30  82.95  13.34  NM_010261  Rabac1  CGCCTGCAGGGTGTAGC  6  3.732  18  49.77  13.34  NM_009422  Traf2  CGCCTGTACTATGTTTG  4  2.488  12  33.18  13.34  NM_027259  Polr2i  TGCTTTTTTCTGTAAAT  3  1.866  9  24.885  13.34  NM_008048  Igfbp7  GCGACAGCCACTCTCAG  3  1.866  9  24.885  13.34  NM_011293  Polr2j  ATCCTCGCTGAACGGCA  3  1.866  9  24.885  13.34  NM_023260  Mrps34  GGCTATGCCAAAATTAA  3  1.866  9  24.885  13.34  NM_026313  3300001P08Rik  AAGGTCTTTAATTTTTT  2  1.244  6  16.59  13.34  NM_007788  Csnk2a1  ATGGGAAACTCAGAATC  2  1.244  6  16.59  13.34  NM_025662  Pigk  AGCAAAATATGGCGAAA  2  1.244  6  16.59  13.34  NM_145380  Eif3m  TCTTCTTTGGTTCTGGG  10  6.219  29  80.185  12.89  NM_010227  Flna  AATCCTGTGGAGCATCC  36  22.389  103  284.795  12.72  NM_012053  Rpl8  99  GAGGACTGCCACCCCTC  16  9.951  45  124.425  12.5  CAATGAATTGCTAAACC  5  3.11  14  38.71  12.45  NM_008529  Ly6e  NM_001130169  Zfp207  TGCTTTTACAATAAATG  5  3.11  14  38.71  12.45  NM_019870  Ard1a  TAGACCAGACCAGCTCT  9  5.597  25  69.125  12.35  NM_019802  Ggcx  GCCTAGAACTATGTAGC  9  5.597  25  69.125  12.35  NM_026774  Hbxip  AAGCACTGGTTTAAGCC  9  5.597  25  69.125  12.35  NM_134093  Letmd1  CAGCCAGTGTATCAGCC  13  8.085  36  99.54  12.31  NM_026155  Ssr3  GTTCTGATCCTCCTCCT  11  6.841  30  82.95  12.13  NM_001082548  Spint2  GACTGAATCTGCTTGTT  10  6.219  27  74.655  12  NM_133668  Slc25a3  GCTGCCAGGGGCAACTA  6  3.732  16  44.24  11.86  NM_009736  Bag1  ATGCTAAAAAAAAAAAA  3  1.866  8  22.12  11.86  NM_016776  Mybbp1a  GGGCTTTAGATGGCGGC  3  1.866  8  22.12  11.86  NM_026374  Ilf2  TGTAATTTGTGTTCTAC  3  1.866  8  22.12  11.86  NM_211355  Smek1  CCCTTCTTCTCTCCCTT  18  11.195  47  129.955  11.61  NM_001083955  Hba-a2  TGGGTTGTCTAAAAATA  35  21.767  91  251.615  11.56  NM_009429  Tpt1  TTGGCCAGAGTGGAATC  10  6.219  26  71.89  11.56  NM_001039474  Tcerg1  AGCAAGAATTCCTAAGC  5  3.11  13  35.945  11.56  NM_007996  Fdx1  GCCGATCCTCGCGTGAG  5  3.11  13  35.945  11.56  NM_009321  Tbca  GGCTGCATTCTGCTGTT  7  4.353  18  49.77  11.43  NM_178600  Vkorc1  GAGTGGATTCTCCAGGT  8  4.975  20  55.3  11.11  NM_007653  Cd63  GATACCATTAATAAAAA  4  2.488  10  27.65  11.11  NM_011327  Scp2  TGAGAGAACAAGGGATG  4  2.488  10  27.65  11.11  NM_011682  Utrn  GACGTGAATGGCAAGTA  11  6.841  27  74.655  10.91  NM_021789  Trappc4  CTACCAGAGAAATGGGA  7  4.353  17  47.005  10.8  NM_026873  Ptcd2  ATGTCATCAAATGGGTG  5  3.11  12  33.18  10.67  NM_009679  Ap2m1  GCTGGAATGACTCACAC  5  3.11  12  33.18  10.67  NM_011779  Coro1c  TTGTGAAATCTTTAGAG  5  3.11  12  33.18  10.67  NM_024459  Ppp3r1  CCGTGTGTTCTAATGGG  5  3.11  12  33.18  10.67  NM_027350  Nars  CAATATCATTTAAGAGA  6  3.732  14  38.71  10.37  NM_001013028  AI597468  ATGTACTCGGGGGAGCT  6  3.732  14  38.71  10.37  NM_011830  Impdh2  CATATACTCCCAAGGAG  6  3.732  14  38.71  10.37  NM_172301  Ccnb1  TATGTAGGACTCACAGA  3  1.866  7  19.355  10.37  NM_025548  Tbcb  GCAAAACCAGCTGGTGG  16  9.951  37  102.305  10.28  NM_009840  Cct8  GCCACACCCCGCACACG  11  6.841  25  69.125  10.1  NM_012053  Rpl8  GGACTTAACAGGTGAGG  9  5.597  20  55.3  9.88  NM_023211  Usmg5  AATTTTACAAGTGACCT  6  3.732  13  35.945  9.63  NM_011874  Psmc4  CCCCACCCCACTCCCAC  6  3.732  13  35.945  9.63  NM_175403  2410014A08Rik  TTGAAAATAAACAAAAA  6  3.732  13  35.945  9.63  NM_201358  Lyrm4  CAGGACTCCGTTTCCTT  13  8.085  28  77.42  9.58  NM_009128  Scd2  AATTTTTGTTAAGCAGT  7  4.353  15  41.475  9.53  NM_019989  Sh3bgrl  TGAAAAAAAAAAAAAAA  10  6.219  21  58.065  9.34  NM_183024  Raver2  CAGAGGCCGCCCATCCT  15  9.329  31  85.715  9.19  NM_178576  Cpsf4  GGCAATGTGGTGGAGGC  24  14.926  48  132.72  8.89  NM_026318  2310003F16Rik  CACAGACTGTGGTTCTT  16  9.951  32  88.48  8.89  NM_026467  Rps27l  TGCTGGGATGGGGTTTG  9  5.597  18  49.77  8.89  NM_011885  Mrps12  100  TTCCATTAAATGTAGTT  7  4.353  14  38.71  8.89  NM_011879  Ik  CTGAATATGGAAGGCAG  7  4.353  14  38.71  8.89  NM_013543  H2-Ke6  TGACAATTTTATAAAAG  6  3.732  12  33.18  8.89  NM_012922  Casp3  GGTCACACTATGACGCC  6  3.732  12  33.18  8.89  NM_025710  Uqcrfs1  TGTTGTGTTCTTTCTTT  6  3.732  12  33.18  8.89  NM_134054  1110002B05Rik  GCTTGTGACGAATGAAT  5  3.11  10  27.65  8.89  NM_011528  Taldo1  GTTTGCAGAGGCGGGTG  5  3.11  10  27.65  8.89  NM_172860  Cbfa2t2  GCGAGGAGTGACCAGTC  4  2.488  8  22.12  8.89  NM_019403  Rnf5  ATCCATATTGCAGACAC  4  2.488  8  22.12  8.89  NM_145139  Eif3eip  TGATGTTTGACTGTACC  18  11.195  35  96.775  8.64  NM_011873  Dazap2  CACAGAACCAGCAGTCT  11  6.841  21  58.065  8.49  NM_009836  Cct3  TATTGTTTACCTTCAAA  9  5.597  17  47.005  8.4  NM_010817  Psmd7  CTGCACTCCTGAACTGT  7  4.353  13  35.945  8.26  NM_013908  Fbxw5  GAAGATGGAGAAATTGA  20  12.438  37  102.305  8.22  NM_010880  Ncl  GCACCGAACACTGTAGA  6  3.732  11  30.415  8.15  NM_019771  Dstn  TAACATACACATTTGTA  6  3.732  11  30.415  8.15  NM_028151  Skiv2l2  TCAGCCTTGTCGGTGGC  6  3.732  11  30.415  8.15  NM_153152  2410015M20Rik  TATGTGGTTTGGAGGCA  16  9.951  29  80.185  8.06  NM_009121  Sat1  GATGTGGTACGGAAGGT  15  9.329  27  74.655  8  NM_011149  Ppib  GTGGATTTCCACGTCAG  9  5.597  16  44.24  7.9  NM_146047  Clptm1l  GATAGATAATTCAAAGC  17  10.573  30  82.95  7.85  NM_007505  Atp5a1  TGTATGTCTGGTTTTAT  17  10.573  30  82.95  7.85  NM_023431  Mum1  GATGAAGACTGCCTGTT  8  4.975  14  38.71  7.78  NM_009186  Sfrs10  AAGGCCTTCCAGAAGAT  8  4.975  14  38.71  7.78  NM_025840  Bzw2  GTACCAGGACACCACAT  8  4.975  14  38.71  7.78  NM_030083  Lsmd1  CAGAAGAAAAGCTCAGG  4  2.488  7  19.355  7.78  NM_001040435  Tacc3  TAGATTTGGGGGTTTCT  4  2.488  7  19.355  7.78  NM_009703  Araf  ATTACGGTGGATGGGAA  4  2.488  7  19.355  7.78  NM_021473  Akr1a4  TATTAATAAGAGGTCAG  4  2.488  7  19.355  7.78  NM_026554  Ncbp2  CGGCTGTCGCAACTGAA  4  2.488  7  19.355  7.78  NM_026964  Ccdc124  TGGATCCTGAGAACTTC  73  45.4  127  351.155  7.73  NM_016956  Hbb-b2  CCCTTTGTCACAGAGGA  15  9.329  26  71.89  7.71  NM_011690  Vars  TCTTAATGAAGTTTGAA  10  6.219  17  47.005  7.56  NM_001123037  Eif4a2  TTTGCAGATCATCTGCA  42  26.121  70  193.55  7.41  NM_023230  Ube2v1  CTCCTTGTATGGCTCTG  6  3.732  10  27.65  7.41  NM_026998  Snx6  GGGAGGCCTCAGCCACC  6  3.732  10  27.65  7.41  NM_134002  Csnk1g2  TCTGTGTGAAGGCTTTC  8  4.975  13  35.945  7.22  NM_021498  Pole3  TTTCAAGGGAGGAGGCT  8  4.975  13  35.945  7.22  NM_023168  Grina  GTTCTGGGCCAACTGTG  15  9.329  24  66.36  7.11  NM_145410  Fam173a  TGGGCAAAGCCGTCAAT  40  24.877  63  174.195  7  NM_026007  Eef1g  AGAAGGAGGTGCTCCAG  14  8.707  22  60.83  6.99  NM_019641  Stmn1  AACTCCACCCCTGTGAG  7  4.353  11  30.415  6.99  NM_001134646  1110019J04Rik  AGGCGGAGGCAGCAGGT  16  9.951  25  69.125  6.95  NM_016905  Galk1  CTGGACACTGGATCTCC  14  8.707  21  58.065  6.67  NM_029097  Atp13a2  GGTATCAGTCTGTTTTG  10  6.219  15  41.475  6.67  NM_011278  Rnf4  101  TCCCTGGCCAGCCCGGA  6  3.732  9  24.885  6.67  NM_008956  Ptbp1  CAGTTACATCTCTTTCT  6  3.732  9  24.885  6.67  NM_009761  Bnip3l  GGCTTCGGTCTTTTTGA  137  85.203  196  541.94  6.36  NM_018853  Rplp1  CTAGTCTTTGTACACAA  14  8.707  20  55.3  6.35  NM_009093  Rps29  GGGAGAGATAAAGCAAC  20  12.438  28  77.42  6.22  NM_145382  BC021381  CTAGTTCGGAAGATCCA  10  6.219  14  38.71  6.22  NM_146200  Eif3c  AACAATTTGGGCTCTTT  61  37.937  84  232.26  6.12  NM_011292  Rpl9  CTCGAGTTTCTTCCTGT  27  16.792  37  102.305  6.09  NM_011354  Serf2  GATTGTGAAGAATGAAC  11  6.841  15  41.475  6.06  NM_001033268  BC010304  GCCTCCTCCCAGTAAGC  17  10.573  23  63.595  6.02  NM_028659  Eif3k  ACATACAATTGAGCCCC  6  3.732  8  22.12  5.93  NM_011188  Psmc2  CTAGTTTCTCCACTGGG  9  5.597  12  33.18  5.93  NM_009304  Syngr2  CAGGCCACACAAGAGCC  57  35.45  75  207.375  5.85  NM_016774  Atp5b  GTGGCTCATAACCATCC  38  23.633  50  138.25  5.85  NR_003632  EG627782  CTGCACTTTTTTATTCT  11  6.841  14  38.71  5.66  NM_008568  Mcm7  TGCTGTCAGCTGGAGAC  8  4.975  10  27.65  5.56  NM_021884  Tsg101  TTTGTCAGATTTTTTCA  8  4.975  10  27.65  5.56  NM_138593  Larp7  TCAATAAAGGAATGACC  9  5.597  11  30.415  5.43  NM_025356  Ube2d3  ATGTAGTAGTGTCTTAC  14  8.707  17  47.005  5.4  NM_007516  Hnrnpd  CAGCCAAACCGCAGAGC  19  11.817  23  63.595  5.38  NM_016876  Eif3g  TTTTATGTTTAAATAAA  30  18.658  36  99.54  5.34  NM_172086  Rpl32  ATTGGCTTAAAGTGAAG  15  9.329  18  49.77  5.34  NM_008831  Phb  TAGCAGACAGGGAGAGG  10  6.219  12  33.18  5.34  NM_026846  Zfand2b  ACAACTTCCTGAGCCTC  12  7.463  14  38.71  5.19  NM_010299  Gm2a  TTTGGGCCCAGATTGTC  13  8.085  15  41.475  5.13  NM_019642  Rpn2  TGGGCATCCACCCCAGC  72  44.778  83  229.495  5.13  NM_009080  Rpl26  TATTGGCTCTGCTTGGT  56  34.828  64  176.96  5.08  NM_007750  Cox8a  GAACGCGACGGCTCCAG  21  13.06  24  66.36  5.08  NM_008946  Psmb6  AGTACAATGACATCAAG  14  8.707  16  44.24  5.08  NM_175190  2900010J23Rik  GCTATGGTGCCTTTCTG  8  4.975  9  24.885  5  NM_011970  Psmb2  GCATCTGCCCTGCCTTG  8  4.975  9  24.885  5  NM_134138  Psmg2  GTGGCTCACAACCATTC  10  6.219  11  30.415  4.89  NM_001101478  D3Ertd254e  GCACAACTTGCCTCAAA  11  6.841  12  33.18  4.85  NM_007589  Calm2  TTTCAGCAGTGTTGGCT  11  6.841  12  33.18  4.85  NM_013556  Hprt1  TCGTGGGAAAGGCTGCA  11  6.841  12  33.18  4.85  NM_181392  Gtf2h5  GATTGAGAATGCTTAGA  15  9.329  16  44.24  4.74  NM_010380  H2-D1  TGTGTGAGGACCTGCTG  17  10.573  18  49.77  4.71  NM_029663  Eef1d  CAGCGCGGGAAAAGGAG  18  11.195  19  52.535  4.69  NM_001081379  Ankrd11  ATGTATGAAAGGAGCAG  18  11.195  19  52.535  4.69  NM_025509  2310008M10Rik  AGATCTATACAGTCGGG  77  47.888  80  221.2  4.62  NM_011291  Rpl7  GAAGGACTAAATGGTCA  17  10.573  17  47.005  4.45  NM_025875  Rbm8a  GACTGATGTCTCAGGTT  10  6.219  10  27.65  4.45  NM_013715  Cops5  CATCGCCAGTGGGCAAA  22  13.682  21  58.065  4.24  NM_009696  Apoe  AAGAAACCAGAATCCTT  21  13.06  20  55.3  4.23  NM_001077529  Nme2  GAATAATAAAACTATTT  61  37.937  58  160.37  4.23  NM_031165  Hspa8  102  CAGTTGTAAATAAAAGT  16  9.951  15  41.475  4.17  NM_016792  Txnl1  AATACTTTTGTATTGCT  31  19.28  29  80.185  4.16  NM_001113564  Serbp1  TAGGGTACAGATGAGGG  13  8.085  12  33.18  4.1  NM_016843  Atxn10  AAGTTCAGAACACATTT  24  14.926  22  60.83  4.08  NR_002702  Npm3-ps1  CCAAATAAAACCTTGAA  54  33.584  49  135.485  4.03  NM_010699  Ldha  TGGCTCGGTCACTTGGG  79  49.132  71  196.315  4  NM_009609  Actg1  TCGCAAGCAAACGTATC  58  36.071  51  141.015  3.91  NM_001113199  Naca  GTTCTCAATTAATTTGG  54  33.584  47  129.955  3.87  NM_023871  Set  ATGAACACACGGCGGAC  15  9.329  13  35.945  3.85  NM_008302  Hsp90ab1  CCTTTGAGATCATCCAC  42  26.121  36  99.54  3.81  NM_009095  Rps5  CTGTCATTTGTAATATG  22  13.682  18  49.77  3.64  NM_013663  Sfrs3  TTCTCCTCAGACCATCC  20  12.438  16  44.24  3.56  NM_019880  Mtch1  CGCTGGTTCCAGCAGAA  48  29.852  38  105.07  3.52  NM_025919  Rpl11  GTGAGCCCATTGGCCGG  45  27.986  35  96.775  3.46  NM_008302  Hsp90ab1  AATATGTGTGGGCTAAG  43  26.743  32  88.48  3.31  NM_053071  Cox6c  GAAAGCAATGTCAAGTC  25  15.548  18  49.77  3.2  NM_001110233  Ngfrap1  GGCTGCAGCCAATCAGG  45  27.986  32  88.48  3.16  NM_020261  Psg23  GATTCCGTGAGGGAACA  55  34.206  38  105.07  3.07  NM_026069  Rpl37  ATGGCTCACAACCATCT  44  27.365  30  82.95  3.03  NM_001005508  Arhgap30  CCTCCATCCTTTATACT  26  16.17  17  47.005  2.91  NM_009536  Ywhae  AAGACCTATGTGGAAAA  41  25.499  25  69.125  2.71  NM_001037999  Dbi  ATGTGGTGTGATTCCAG  53  32.962  32  88.48  2.68  NM_011034  Prdx1  GTGGCTCATAACCATCT  45  27.986  27  74.655  2.67  NM_172773  Slc17a5  AAAACAGTGGCCGGTGG  124  77.118  69  190.785  2.47  NM_009084  Rpl37a  AGAAACAAGATGACTTG  170  105.727  91  251.615  2.38  NM_011434  Sod1  GTGTAATAAGACATAAC  60  37.315  32  88.48  2.37  NM_016806  Hnrnpa2b1  TCTTTGGAACCACTTGA  45  27.986  24  66.36  2.37  NM_008617  Mdh2  CCGACGGGCGCTGACCC  53  32.962  28  77.42  2.35  NR_003278  Rn18s  GGTGAGCCTGAAGCTTG  65  40.425  33  91.245  2.26  NM_011563  Prdx2  GTCTGCTGATGGCCAGA  235  146.152  111  306.915  2.1  NM_008143  Gnb2l1  TAAGTAGCAAACAGGGC  176  109.458  83  229.495  2.1  NM_008410  Itm2b  GAGCGTTTTGGGTCCAG  272  169.163  124  342.86  2.03  NM_008907  Ppia  TGTGCCAAGTGTGTCCG  125  77.74  50  138.25  1.78  NM_001005859  Rpl34  TGTAGTGTAATAAAGGT  207  128.738  80  221.2  1.72  NM_009098  Rps8  GGATTTGGCTTGTTTGA  389  241.927  147  406.455  1.68  NM_026020  Rplp2  GAAGCAGGACCAGTAAG  397  246.903  126  348.39  1.41  NM_007687  Cfl1  103  Table 5.2 Tags over-represented in the FL HSC library vs. the adult BM HSC library FL Sequence data  HSC  FL HSC  BM  BM HSC  /100K  HSC  /100K  Fold  Accession  Gene Name  Diff.  TTCCTGGCATTATTTTG  103  64.05791  0  0  N/A  NM_001001806  Zfp36l2  AATATGCCTTGTTCAAT  63  39.18105  0  0  N/A  NM_001001806  Zfp36l2  TTCTCTTACCCTGAAAC  48  29.85223  0  0  N/A  NM_001003963  Dnmt3b  AGAATTTGGTGTTTGGA  75  46.64411  0  0  N/A  NM_001005507  Smg7  CAGATTTAGGTGCTTTC  95  59.08254  0  0  N/A  NM_001005859  Rpl34  TATCTGTCTACTTTCTC  72  44.77835  0  0  N/A  NM_001008551  BC085271  GTGGTTCACAACCATCT  61  37.93721  0  0  N/A  NM_001039158  Ube2j2  ATCTCTTTTCCCTTCCT  79  49.1318  0  0  N/A  NM_001105843  Lsm12  TTCAATGGTGTCAAAAC  46  28.60839  0  0  N/A  NM_001108324  Ranbp1  TGGAATGCAGATGGGTG  45  27.98647  0  0  N/A  NM_001110499  Canx  GACGCGACCATCCTCCT  175  108.8363  0  0  N/A  NM_007393  Actb  TTGATCATCACAAACTC  40  24.87686  0  0  N/A  NM_007486  Arhgdib  TACAACACTCTGTAAAT  142  88.31285  0  0  N/A  NM_007624  Cbx3  GGATGGGGAGGGATACG  93  57.8387  0  0  N/A  NM_007687  Cfl1  CTCACAAGGGGTTCCCA  52  32.33992  0  0  N/A  NM_007968  Ewsr1  GTTGTGATTTTTTTTTT  43  26.74262  0  0  N/A  NM_007968  Ewsr1  ATTTGAAATAATAAAAT  37  23.0111  0  0  N/A  NM_008138  Gnai2  CCAAAGAGGTACACTGG  48  29.85223  0  0  N/A  NM_008143  Gnb2l1  AGAGCTGCACAGGTGCA  50  31.09607  0  0  N/A  NM_008376  Gimap1  TTTGCTACAAATTTTGG  43  26.74262  0  0  N/A  NM_008388  Eif3e  TAGCAATCAAATTATCC  64  39.80298  0  0  N/A  NM_008391  Irf2  GAGGTACATTTCAGTTG  79  49.1318  0  0  N/A  NM_008505  Lmo2  ATCCTGTTAACCGCGAC  59  36.69337  0  0  N/A  NM_008535  Lyl1  TGAAATAAACTCAGTAT  157  97.64167  0  0  N/A  NM_008722  Npm1  CTCGAGTCTCCAGAGTC  53  32.96184  0  0  N/A  NM_008774  Pabpc1  GAGAGTAACAGGCCTGA  39  24.25494  0  0  N/A  NM_009030  Rbbp4  CTTTTCAGCAACACTTC  280  174.138  0  0  N/A  NM_009536  Ywhae  GAATTAACATTAAACTT  130  80.84979  0  0  N/A  NM_009536  Ywhae  GATAATGTGCTTTGGAA  50  31.09607  0  0  N/A  NM_009593  Abcg1  AGAAAGGAAGTCTGGGA  48  29.85223  0  0  N/A  NM_009593  Abcg1  TTGCTGTGTGATTATCT  39  24.25494  0  0  N/A  NM_009600  Macf1  CAAATCAGGCTGACCTA  45  27.98647  0  0  N/A  NM_009829  Ccnd2  GCAATTGACAGTCCTAA  64  39.80298  0  0  N/A  NM_010193  Fem1b  ACTGTTAACAATGCATT  47  29.23031  0  0  N/A  NM_010441  Hmga2  TCGGTTGCATCCCAGAG  38  23.63302  0  0  N/A  NM_010485  Elavl1  CAGAATGTGCTAAGCGG  55  34.20568  0  0  N/A  NM_010885  Ndufa2  TTTTTGAGTTAAGGTGC  43  26.74262  0  0  N/A  NM_010891  Sept2  TTGACTGATTCTTAGAA  42  26.1207  0  0  N/A  NM_010891  Sept2  GGGAAGGGGCTTCTCTG  42  26.1207  0  0  N/A  NM_011117  Plec1  104  GAGGACCTGGGACTACG  99  61.57023  0  0  N/A  NM_011218  Ptprs  TGGGAAAGATGCACACA  38  23.63302  0  0  N/A  NM_011218  Ptprs  CTTCCCCGGGAGACGCG  47  29.23031  0  0  N/A  NM_011434  Sod1  TACTACTTTGACTTTTG  63  39.18105  0  0  N/A  NM_011480  Srebf1  TGAAGGAACAAGCTGGT  37  23.0111  0  0  N/A  NM_011508  Eif1  TAAAAATTAATTTAACC  54  33.58376  0  0  N/A  NM_011543  Skp1a  GGGCAGAGGTGGTGACA  323  200.8806  0  0  N/A  NM_011655  Tubb5  CCACCTCTCAGGCGAAG  44  27.36455  0  0  N/A  NM_011664  Ubb  ATCTTTCTGGCTCCACT  150  93.28822  0  0  N/A  NM_011740  Ywhaz  GTATGTGCCCGAGGGGT  43  26.74262  0  0  N/A  NM_011969  Psma7  GACCTGAGTACCACATT  37  23.0111  0  0  N/A  NM_012002  Cops6  TCAACATCTAGGCTCCA  37  23.0111  0  0  N/A  NM_012023  Ppp2r5c  GGCCTGCTTCTGGAAGC  59  36.69337  0  0  N/A  NM_013535  Grcc10  TTTTATTTGTATTTGTA  52  32.33992  0  0  N/A  NM_013716  G3bp1  GCGGCTCACCTGTCCTT  46  28.60839  0  0  N/A  NM_015816  Lsm4  TGCACTTTGGGATTTTG  64  39.80298  0  0  N/A  NM_016756  Cdk2  GCGAAGGCTGGAACGGG  131  81.47172  0  0  N/A  NM_018796  Eef1b2  GAATCCAACTACTTCGA  120  74.63058  0  0  N/A  NM_019435  Ndufb11  AAAGACTTTGTGTTAGT  75  46.64411  0  0  N/A  NM_019776  Snd1  ATTTGACTGGTTTTCCT  70  43.5345  0  0  N/A  NM_022410  Myh9  AAGGATGTGCCCAACTG  50  31.09607  0  0  N/A  NM_023312  Ndufa13  CAGACTTGGTAAAACCC  295  183.4668  0  0  N/A  NM_023595  Dut  CTCACAGTGCAGACTTA  49  30.47415  0  0  N/A  NM_023595  Dut  GGCTGCTCTGCTCGGAG  94  58.46062  0  0  N/A  NM_025573  Sfrs9  GCGTCGAGCCCAGCCCT  68  42.29066  0  0  N/A  NM_025577  2810428I15Rik  GCCATTGGAATGGGCAT  99  61.57023  0  0  N/A  NM_025946  2010100O12Rik  TGGAAGAAACTGCAGGA  130  80.84979  0  0  N/A  NM_026063  2900010M23Rik  TCTGAATCTGCGGTAGA  48  29.85223  0  0  N/A  NM_026069  Rpl37  CAAAGACAACTCTGTGT  46  28.60839  0  0  N/A  NM_026521  Zfp706  GGAGCAGAGGAGCCCGA  41  25.49878  0  0  N/A  NM_026552  Arpc4  CTCAATCCCACTCTGTC  42  26.1207  0  0  N/A  NM_026636  5430437P03Rik  TCGCGTCGCTCACTTCA  84  52.24141  0  0  N/A  NM_028291  Pan3  TAAGATTTCAATAAAAC  52  32.33992  0  0  N/A  NM_028871  Hnrnpr  CAGGAGTTCAAAGAAGG  39  24.25494  0  0  N/A  NM_029711  Arpc2  TTGACCTCGCTAGGCCG  47  29.23031  0  0  N/A  NM_029767  Rps9  GATCTCAAAGAGTAAGT  37  23.0111  0  0  N/A  NM_033618  Supt16h  GCAGAGACATCTTGACC  57  35.44952  0  0  N/A  NM_133362  Erdr1  TAGCCTCTGGTCCTGTC  38  23.63302  0  0  N/A  NM_138586  Exosc5  CGGGAGATGCTCTGAGA  91  56.59486  0  0  N/A  NM_138597  Atp5o  GCACAAACTACTCCTTG  60  37.31529  0  0  N/A  NM_144901  Csde1  CACACAGACTTCTCCTA  97  60.32638  0  0  N/A  NM_146243  Actr2  ACTGTTAAAATGATGTA  97  60.32638  0  0  N/A  NM_146243  Actr2  TAACTGGAGGATGTGCT  37  23.0111  0  0  N/A  NM_172736  Leng8  105  TGCAAGCATCCTCTCCA  45  27.98647  0  0  N/A  NM_172746  Hirip3  TATCTTGGCACATTTCA  96  59.70446  0  0  N/A  NM_177301  Hnrnpl  GCCTTGGTGAAACTCCC  56  34.8276  0  0  N/A  NM_181582  Eif5a  ATCTCTTTGTGTAGTTC  39  24.25494  0  0  N/A  NM_198609  BC003885  CTCCAGGGCAGCTGGTA  62  38.55913  0  0  N/A  NM_198652  6430706D22Rik  GGAATAACGCCGCCGCA  2474  1538.634  0  0  N/A  NR_003278  Rn18s  106  Figurre 5.1 - Lon ngSAGE librraries prepaared from HSC-enrich H hed fractionss of E14.5 FL F and adultt BM cells A) Suummary of the t size and content c of LongSAGE L liibraries preppared from HSC-enriche H ed fractiions of E14.5 5 FL and aduult BM. B) Noormalized nu umber of taggs per 105 total tags pressent in each library l for geenes expecteed to be preseent. N.D. = not n detected. C) Sccatter plot off individual tags t and theiir respectivee counts in thhe 2 LongSA AGE libraries. Confi fidence interv vals of 95%,, 99%, and 99.9% 9 were determined d u using Audic Claverie statistics213. gy analysis of o significanttly over-reprresented tagss in the adultt BM libraryy (white) D) Gene Ontolog as compared to th he FL libraryy and vice veersa (black).  107  np, Gata3, an nd Bmi1 traanscripts are differentiaally expresssed by E-SL LAM Figurre 5.2 – Prnp + and CD45 C EPCR R+CD48-CD D150- adult BM cells Q-RT T-PCR analy yses of transccript levels in i extracts off 400 E-SLA AM cells (daark bars) or CD455+EPCR+CD D48-CD150- cells (light bars). b All vaalues normallized to Gappdh. Values shown are thhe mean ± SE EM of valuees obtained in 3-6 indepeendent experriments, withh each measuurement beingg derived fro om triplicate assays. Ressults for Prnp np, Gata3, annd Bmi1 in thhe 2 fractionns are signifficantly diffeerent (p<0.05).  108  Figurre 5.3 – Elev vated Vwf, Rhob, R and Pld3 P expresssion is consiistently assoociated with h high self-rrenewal actiivity in HSC Cs A) Q-RT-PCR an nalyses of traanscript leveels in extractts of 300-4000 E-SLAM cells c (dark bars) or 104 liin- (white baars) from FL. B) Q--RT-PCR an nalyses of traanscript leveels in extractts of 300-4000 E-SLAM cells c (dark bars) or 4 10 liin (white baars) from aduult BM. C) & D) Q-RT-PCR analysess of transcrippt levels from m adult BM E-SLAM ceells (dark barrs) + + 45 EPCR CD D48 CD1500 cells (C, ligght bars) andd E-SLAM cells c culturedd in 20 comppared to CD4 ng/m ml of IL-11 su upplementedd with 10 ng//ml SF (D, grey g bars) orr 1 ng/mL SF F (D, light baars). All values normaalized to Gappdh. Values shown are the t mean ± SEM S of valuues obtained in 3-6 indeppendent expeeriments, witth each measurement beeing derived from triplicaate assays. N.D. N meanns not done.  109  RT-PCR analyses of traanscript levvels of selectted genes in various FL L and Figurre 5.4 – Q-R adultt BM cell po opulations as a indicated d Transscript levels of all genes assayed in the t original screen s for geenes commoonly upregulated in HSCss vs. lin- adu ult BM cells (black bars = HSCs, whhite bars = linn-) and FL ceells (grey baars = HSCss, white hatcched bars = lin l -). All vallues were noormalized to Gapdh. Vaalues shown are the meann ± SEM of values v obtainned in 2-6 inndependent experiments, e , with each measuremen m t being derivved from trip plicate assayss.  110  Figurre 5.5 – Q-R RT-PCR analyses of traanscript levvels of selectted genes in various adu ult BM cell populations p as indicated d Transscript levels in the adult BM E-SLAM cells (darrk bars) and CD45+EPCR R+CD48-CD D150cells (light bars). All values were normalized to Gappdh. Values shown are the t mean ± SEM S of valuees obtained in n 2-6 indepeendent experriments, withh each measuurement beinng derived from fr tripliccate assays.  111  RT-PCR analyses of traanscript levvels of selectted genes in cytokine Figurre 5.6 – Q-R stimu ulated adultt BM HSCs Transscripts levelss in E-SLAM M cells cultuured in 20 ngg/ml of IL-111 supplemennted with eithher 300 ng/m mL SF (dark bars), b 10 ng//mL SF factoor (light barss) or 1 ng/mL L SF (white bars). All values v were normalized to Gapdh. Values V show wn are the meean ± SEM of o values obbtained in 2-66 indeppendent expeeriments, witth each measurement beeing derived from triplicaate assays.  112  6.  Discussion and Future Directions  6.1  Major contributions  The work presented in this thesis is focussed on the regulation of mouse HSC selfrenewal. It has long been postulated that HSCs are unique in their possession of a self-renewal mechanism that can sustain hematopoiesis throughout life. However, investigations of the cellular and molecular basis of this mechanism have been hindered, mostly by an inability to isolate self-renewing cells at sufficient purities to allow the discrimination of pre-determined and stochastically decided events. Consequently, the identification and characterization of HSCs has relied historically on the detection of empirically defined levels of multi-lineage differentiation and sustained hematopoietic activity in myeloablated recipients of transplanted test cell populations. This approach has been accompanied by considerable inconsistency in the exact definition of what is or is not an HSC with self-renewal and multilineage potential. In this thesis, I describe a series of studies that now allow discrete subsets of selfrenewing cells to be isolated from the BM of adult mice. Importantly, the features shown to distinguish these subsets suggest that a sharp demarcation exists between the ability of a primitive hematopoietic cell to self-renew indefinitely versus its variable exhaustion within a few days to 6 months (Figure 6.1). Using the new purification methods described for isolating different subsets of primitive quiescent adult BM HSCs, I then further defined the different in vitro growth factor dependences of their viability, proliferation, and maintenance (or loss) of stem cell potential. Finally, I exploited both of these developments to evaluate the gene expression patterns of HSCs and determine which transcripts might be differentially associated  113  with a high self-renewal state independent of the lineage preferences of the particular HSCs in which they were found. Chapter 3 builds on initial experiments that allowed the heterogeneous differentiation and self-renewal behavior displayed by ~100 individually tracked HSCs to be segregated into 4 subtypes in terms of their relative contributions to the lymphoid and myeloid WBCs for at least 16 weeks. Two of these subtypes of HSCs were consistently found to possess lifelong selfrenewal potential as determined from secondary and tertiary transplant assays and showed frequent stable propagation of the same differentiation program characteristic of the original cell. In contrast, when the same spectrum of input HSCs were placed in vitro under the best known cytokine conditions for their maintenance, there was a rapid shift toward the HSC subtypes that do not generate progeny HSCs able to repopulate secondary mice out to 6 months. Using EPCR223 and CD15019 as positive markers and CD4819 as a negative marker, I was able to further increase the purity of HSCs obtainable (56%) with a selective enrichment of with high self-renewal HSCs. At the same time, I identified a similarly rare sister population of cells which differed only in their lack of expression of CD150 and contained the low self-renewal HSCs subtypes at an equivalent purity (39%). Thus, at least in quiescent adult mouse BM populations, differences in CD150 expression appear to distinguish HSCs with high (durable) self-renewal potential from those whose self-renewal potential has been destined to decline. Functional characterization of the regenerative properties of CD45+EPCR+CD48-CD150+ (ESLAM) cells present in E14.5 FLs (where the HSCs are actively cycling) or in reconstituted mice indicated that this phenotype was also selective for these disparate, and previously difficult to purify, sources of transplantable HSCs. In Chapter 4, I showed that exposure of 130 individually monitored HSCs to different concentrations of a single growth factor (SF) could directly alter the frequency with which they would execute one or more self-renewal divisions in culture without affecting their viability or 114  their kinetics of cell division. While 300 ng/mL of SF could maintain HSC numbers in IL-11 supplemented cultures, 10 ng/mL SF resulted in a net 13-fold decrease in HSC numbers. Remarkably, more detailed assessment of the timing of these differential responses showed that a reduction in HSC numbers could be obtained within the first 16 (but not 8) hours. This shows that such changes can manifest themselves before HSCs complete a first mitosis and likely before even entering the cell cycle. Furthermore, I identified Bmi1, Ezh2, and Lnk as 3 transcripts that might play a role in or at least be co-regulated by the events precipitating these rapid differential responses. Assessment of the post-first division progeny confirmed the marked loss of transplantable HSC activity with continued maintenance of the cells under low SF conditions (>95%). Interestingly, when separate daughter cells were cultured in a high concentration of SF, ~1/3 of the HSCs could give rise to at least 1 HSC and in 40% of these divisions, 2 daughter HSCs were created. However, 2 daughter HSCs of the high self-renewal type were not seen in any instance suggesting that this condition promotes asymmetric HSC divisions. Thus careful examination of the response kinetics of HSCs to variable concentrations of SF indicates that the intensity of signaling obtained from this growth factor alone can regulate the speed with which the durable self-renewal state can be lost within a few hours even though it may occur over a period of many months in vivo. Chapter 5 describes a comparative gene expression approach to identify potential regulators of the high self-renewal HSC state. Using results from LongSAGE libraries prepared from highly purified E14.5 FL HSCs and adult BM HSCs and a survey of published data, 27 initial candidates were identified for more detailed quantitative analysis in a variety of populations using Q-RT-PCR to compare transcript levels. The expression of 9 of these were found to be consistently higher in HSC-enriched fractions compared to a variety of related fractions in which these cells were present at much lower levels. In addition, 3 of the 9 genes  115  (Vwf, Pld3, and Rhob) were consistently associated with the most purified source of high selfrenewal HSCs. Together this work identifies and prospectively isolates HSCs with durable self-renewal from a similar subset with extensive but finite self-renewal and identifies candidate genes that strongly associate with these states. Furthermore, these studies identify extrinsic regulatory cytokine conditions where depending on the concentration of a single growth factor (SF), HSCs self-renewal is either promoted to preserve overall HSC numbers or dramatically reduced without any impact on the viability or divisional kinetics of the cells. Importantly, though, this work also describes that these changes can be manifest even before the cell physically divides, suggesting that self-renewal can be influenced in a cell division-independent fashion.  6.2  Implications and Future Directions  6.2.1 Opportunities for direct and high-resolution analyses of HSC heterogeneity afforded by simpler and more selective isolation strategies  Since the first description of a highly purified HSC population in 1996, when Osawa et al.221 showed that 1 in 4 Lin-Sca1+Kit+CD34- adult mouse BM cells was capable of repopulating an irradiated recipient for 3 months, many refinements and variations of mouse HSC purification have been described17,19,221,277. Some of these have involved adding more markers to the Osawa protocol (e.g.: removal of Flt3+ cells222) to enhance the purity of HSCs with longterm repopulating activity. Others have exploited the high Ho278 and/or Rho279,280 dye exclusion activity and/or the more recently discovered EPCR+223 and SLAM (CD150+CD48-) phenotypes19 that are also selective for HSCs from adult mouse BM. One of the most significant contributions 116  of these advances has been to enable both direct and clonal functional analyses of large numbers of individual mouse HSCs. This has led to an appreciation of a greater heterogeneity of HSC phenotypes that are coupled to distinct differentiation and self-renewal programs than was historically anticipated. Thus, even these purification schemes have become increasing complex and hence difficult to standardize. Moreover, the vast majority of reports from other groups still do not yet include studies of serially analyzed single cell transplants, and hence it has not been possible to deduce their content of specific HSC subsets. The E-SLAM subset that we describe in Chapter 3 replicates the power of simple SPbased strategies but avoids the toxicity of Ho232 and also shortens the length of time that the HSCs spend outside of the host before being isolated. More importantly, it provides the greatest selectivity for high self-renewal HSCs thus far achieved. A second issue that has challenged the generalization of HSC purification strategies or the direct measurement of phenotypes to replace functional endpoints for HSC quantification has been the fact that the expression of many markers useful for purifying unmanipulated (quiescent) HSCs from normal adult mouse BM (e.g., CD34226, Mac1230, CD38227, PrP229, Tie2229, Ho228, MPL229, and CD105229) is not stable. Yilmaz et al. provided the first evidence that the SLAM phenotype remains selective for HSCs independent of their activation20. The experiments in Chapter 3 confirm the data for the SLAM phenotype and identify EPCR also as a stable marker. Thus the E-SLAM combination allows HSCs from multiple activated settings (fetal liver, cultured cells, and reconstituted animals) to be obtained at purities of >20%. In addition, the experiments in Chapter 3 identify a very closely related phenotype (the CD150- subset of the CD45+EPCR+CD48-) that is equally rare and highly enriched in mainly low-self-renewal HSCs (constituting 39% of the population). Moving forward, these prospectively isolated populations should prove extremely useful for studying the properties of HSCs with subtle, but potentially important molecular differences that determine whether their subsequent self-renewal potential has already been limited or not. Indeed the work 117  presented in Chapter 4 and discussed below provides preliminary support for this prediction. One must use caution, however, as these sorting strategies may be mouse strain-specific and genes identified in one set of analyses clearly require additional validation. Excitingly, though, my experiments demonstrate that it is now technically feasible to investigate primary mouse HSCs with the same power as has previously been restricted to model systems in lower organisms or cell lines. Reasonable numbers of HSCs can be studied at the single-cell level and novel insights can be made with respect to their underlying biology through the adaptation of other analytical tools for single-cell analyses. These include transcriptome profiling of single cells161,165,264,281,282, immunostaining of subcellular elements of manipulated HSCs283,284, time-lapse video imaging of living single cells in vitro285,286, and in vivo60, and studies of cytokine effects on HSC self-renewal, lineage fate selection and differentiation in single-cell cultures17,87,257,287. Thus, addressing many previously inaccessible questions concerning the process of murine HSC self-renewal is now within reach. One of these is the capability of determining the properties of the daughter cells generated from the mitosis of individual HSCs in vitro. Manipulations of HSCs (genetic, epigenetic, extrinsic, etc) prior to division should permit identification of the key players that maintain the original stem cell state versus a state that allows some properties to be lost. One group has recently shown by transplantation of each first division progeny pair of purified HSCs that Lnk-/- HSCs are more likely to undergo HSC expansion type divisions in vitro when compared to wild type257. The rapid advancement of high-throughput gene expression analysis technology (e.g.: microarray19,169,172,173, cDNA arrays264, single-cell cDNA amplification161, and SAGE (see Chapter 5)) is also revolutionizing our ability to analyze this aspect of HSC phenotypes. Furthermore, transcriptional networks are now being developed that show how these datasets may be interconnected. A web-based tool has recently been developed called BloodExpress 118  (http://hscl.cimr.cam.ac.uk/bloodexpress/index.html). This database incorporates over 250 microarray experiments from 15 different studies and allows cross-comparisons to be made to look for commonly expressed genes across the hematopoietic tree288. For example, this approach has also led to the identification of a triad of the SCL/TAL1, GATA2, and FLI1 transcription factors with important roles in regulating HSCs through a self-reinforcing mechanism289. The application of real-time video imaging and analysis and highly sensitive gene and protein labeling/marking strategies to highly purified HSC populations likewise can now enable experiments to interrogate when and under what conditions asymmetric partitioning of molecules290 accompanies the retention or loss of HSC-specific traits285. For example, Wu et al.290 have reported asymmetric partitioning of Notch into each daughter cell of putative HSCs. While not yet validated with follow-up in vivo readouts, this sets the stage for exciting experiments surrounding asymmetric partitioning of key self-renewal regulators. Striking proofof-principle experiments illustrating the power of single cell analyses have also recently been reported by Yamazaki et al.291. These studies showed with impressive clarity that inhibition of lipid raft clustering suppressed the PI3K–Akt–FOXO pathway and induced hibernation in HSCs ex vivo, the first description involving lipid raft clustering in HSC divisional kinetics292. The results presented in this thesis suggest the following model describing the HSC state of adult mouse BM. These cells have an unlimited potential for sustaining a high probability of self-renewal coupled to a latent pervasive multi-lineage myeloid differentiation potential but variable propensities for lymphoid differentiation. Once this state changes, the cell is irreversibly destined to generate a self-exhausting clone. However, its multilineage potential may still be retained for many more cell divisions resulting in the generation of clones that may contribute a substantial fraction of the blood produced and do so for up a substantial fraction (10%-15%) of the animal’s ~2-year lifetime. The evidence presented here to support this model is 3-fold. First, singly transplanted HSCs exhibiting α and β differentiation programs were 119  shown to consistently possess a durable self-renewal capacity that was perpetuated through 2 serial regenerative cycles. In contrast, HSCs exhibiting γ and δ differentiation programs showed a complete inability to regenerate detectable HSCs after 6 months. Second, these features were shown to segregate with CD150 expression within a population of CD45+EPCR+CD48- cells, which allows their prospective isolation as distinct subsets. Third, HSCs with durable and finite self-renewal phenotypes have distinct genetic programs. This model is similar features to one proposed by Muller-Sieburg et al.34 who have also obtained evidence of intrinsic, but agedependent heterogeneity in HSCs differentiation programs, but have not linked these to separate self-renewal programs. Underlying both of these models, however, is a series of assumptions that exclude the possibility of reversibility of HSC properties in steady-state BM and hence, depending on the time/situation might be variably equipped with properties essential for their detection in vivo. On the other hand, the finding that HSCs with finite self-renewal potential can generate robust but only transient multilineage clones when assayed in irradiated hosts poses a strong argument against the reversibility hypothesis.  6.2.2  Exogenous regulation of HSC self-renewal  Many growth factors have been identified as having a role in HSC self-renewal but the highest maintenance of HSC numbers has been speculated to be through the use of very high concentrations of SF and a factor like IL-11 that can act on HSCs to generate signalling through Gp130 (see Section 1.3.3 in Chapter 1)17,74,90. Here I provide direct confirmation of that prediction in single cell cultures and further demonstrate that the concentration of SF alone can dramatically alter the probability of a given HSC undertaking a next self-renewal division or a symmetric differentiation division. Furthermore, I discovered that such changes in HSC 120  potential are surprisingly rapid, i.e., they occur even before the cells enter mitosis, but not before an initial period of several hours, for an initially quiescent population. These findings strongly suggest that changes to the molecular mechanisms that preserve HSC self-renewal activity can be activated well before cells physically partition their contents. Furthermore, they indicate that this activation process can be triggered by changes in signalling cues from the environment that may then require several hours to become effective. Interestingly, the data presented in Chapter 4 indicate that the so-called self-renewal divisions that HSCs undergo, even under the high SF concentration conditions, are overstated in light of the proposed model. Most give rise to only one daughter cell high selfrenewal/reconstitution pattern and even this event is rare. This is also evidenced in the data presented in Chapter 3, where few of the HSCs detected after 4 days displayed differentiation programs associated with a high self-renewal status (i.e.: most were lymphoid-biased) and no HSCs detected after 10 days of culture showed a robust contribution to the output of myeloid cells in recipient animals. Together, these data suggest that the best feeder free conditions are poor at sustaining durable self-renewal ability in HSCs. Given the known ability of, as yet undefined, factors produced by stromal cells to achieve this support62,64, revisiting their mechanisms of action on defined HSC subtypes would now be warranted. One potential hypothesis is that immobilization of growth factors is required in order to establish polarity in the HSC prior to division. As an alternative strategy to investigate mechanisms that regulate HSC self-renewal, genetic techniques have been applied to assess the consequences of overexpression or deletion of specific molecules. Perhaps the most dramatic results of this approach have been the large expansion of HSCs that results from retroviral over-expression of Hoxb4 (40-fold)144,145 and, more recently, the nearly maximal expansion of HSCs numbers (10,000-fold) obtained using a novel fusion gene consisting of Nucleoporin98 and the homeodomain of HoxA10144,145. 121  The availability of a strategy that can prospectively isolate HSCs with durable and finite self-renewal activity as separate populations should facilitate more precise examination of the following types of questions: Do HSCs with durable self-renewal activity divide more slowly or more quickly when stimulated with high SF and IL-11? Does this change when supportive stromal cells are also present and, if so, how? Is the loss of self-renewal seen within 16 hours of exposure of HSCs to a suboptimal concentration of SF irreversible?  6.2.3 Molecular mechanisms regulating different HSC self-renewal states  As discussed above, multiple lines of evidence now suggest that HSCs have predetermined programs of self-renewal and/or differentiation potential whose regulation is likely to have distinct although linked components. In Chapter 5, I show that HSCs with durable selfrenewal activity express higher levels of several transcripts previously associated with a HSC molecular signature; i.e.: Bmi1, Prnp, and Gata3. Bmi1 is a member of the PRC1 group of Polycomb genes and has been previously shown to play an important and non-redundant role in supporting the generation of adult populations of mouse HSCs consistent with an involvement in maintaining their self-renewal ability138,139,293. Interestingly, BMI1 has also been found to be important in other stem cell populations, including mammary294 and neural295 tissue. This suggests that some common mechanisms may be used to stably repress the activation of preestablished, but latent differentiation programs. The fact that higher Prnp expression is a feature of high-self-renewal HSCs corroborates the previously reported finding that lack of PRNP (prion protein) has no effect on the generation of HSC in normal mice or their ability to regenerate WBC production in primary irradiated hosts, but can severely compromise secondary and tertiary reconstitution ability265. While no direct role has yet been described for Gata3 in HSCs, 122  its absence has critical effects on fetal hematopoiesis247 and its overexpression has been shown to preferentially bias HSCs toward myeloid (specifically megakaryoctytic and erythroid lineages) cell outputs296.  6.2.4 New mediators of HSC regulation  Recently, much hype has centred on epigenetic regulation of stem cell properties and the role of small non-coding RNAs (called microRNAs) as regulators of transcript availability. The relative amount of DNA compaction around particular coding regions determines whether or not the transcriptional machinery can access and transcribe RNA molecules and altered expression of many epigenetic regulators, particularly those of the Polycomb group have been shown to affect HSC biology (Chapter 1). Recently, in mouse and human ES cells, it has been shown that methylation of lysine 3 and lysine 27 on histone H3 play a major role in whether or not developmental genes drive an ES cell to differentiate or not, thereby playing a major role in their differentiation297. It will be interesting to see if a similar mechanism exists for HSCs and their lineage commitment. Although such studies are likely to require further technical advances given the small numbers of purified HSCs obtainable, initial developments along these lines appear to be underway298. MicroRNAs have already been implicated as major players in regulating stem cell divisions (reviewed in Croce et al.299), with evidence showing that mutations in dicer-1 render a stem cell unable to bypass the G1/S checkpoint of the cell cycle300. More recently, mouse hematopoietic cells have been shown to require specific microRNAs in order to differentiate into particular lineages (reviewed in Baltimore et al.301). MicroRNAs have also been implicated in leukemic transformation (e.g.: mir223302) and the onset of myeloproliferative disorder (e.g.: 123  mir155303) and HSCs, themselves, are currently being profiled by a number of groups to determine their expression signature. As classic chromatin regulatory complexes and microRNAs can simultaneously regulate large numbers of genes, it is attractive to posit their involvement with major developmental changes and shifts in cell states. Interestingly, mir101 has been shown to play a role in prostate cancer by suppressing the activity of Ezh2304, another member of the Polycomb gene group. Another microRNA family that has garnered particular interest is the let-7 family of microRNAs which regulate large numbers of genes whose transcripts are up and down-regulated during development. Specifically, let-7 has been shown to regulate Hmga2, a gene implicated in aging in the context of stem cells305,306. Interestingly, Hmga2 is also significantly over-expressed in FL HSCs vs. BM HSCs and appears to be stem cell specific in both the FL and BM (Chapter 5).  6.3  Concluding Comments  The results and ideas presented in this thesis support the existence of a molecularly regulated mechanism that defines a state of unlimited HSC self-renewal independent of the differentiation programs expressed when the cells are released from that state or whether they are quiescent or being activated to progress through the cell cycle. One function of such a mechanism could be to stabilize a repressed state of various chromatin configurations that would otherwise allow multiple hematopoietic differentiation programs, particularly those associated with myelopoiesis to be rapidly activated. The recent observations that transcription factors regulating HSCs also playing a role in regulating myelopoiesis (Gata2307) and vice versa (Pu.1308,309) is an interesting linkage between myelopoiesis and HSC self-renewal.  124  A greater understanding of exactly how the self-renewal, proliferation, and lineage commitment machinery work in relation to each other will have major implications for clinical hematology. For example, HSCs with high self-renewal and distinct lineage differentiation programs could be isolated (or manipulated) to create the cell types needed on a patient by patient basis. Donated cells could be expanded in a fashion that would vastly decrease the strain on many current clinical transplantation programs. 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