Unveiling Hematopoietic Stem Cell Dynamics: Identification and Isolation of Hematopoietic Stem Cells at Single-Cell Level INAUGURALDISSERTATION zur Erlangung des Doktorgrades der Naturwissenschaften Dr. rer. nat. Im Fachbereich Biologie und Chemie (FB 08) der Justus-Liebig-Universität in Giessen von Tessa Schmachtel aus Frankfurt Giessen Juni 2025 2. Korrigierte Auflage, März 2026 Vom Fachbereich Biologie und Chemie (FB 08) der Justus-Liebig -Universität Gießen als Dissertation angenommen. Dekan: Prof. Dr. Holger Zorn Gutachter: PD Dr. habil. Oliver Rossbach Prof. Dr. Michael A. Rieger Datum der Disputation: 27.11.2025 Erklärung Ich erkläre hiermit, dass ich mich bisher keiner Doktorprüfung im Mathematisch-Naturwissenschaftlichen Bereich unterzogen habe. Frankfurt am Main, den _________________ ___ ____ (Unterschrift Tessa Schmachtel) Teile dieser Arbeit sind bereits in internationalen Fachzeitschriften veröffentlicht worden: Komic, H.*, Schmachtel, T.*, Simoes, C.*, Kuelp, M.*, et al. Continuous map of early hematopoietic stem cell differentiation across human lifetime. Nat Commun 16, 2287 (2025). https://doi.org/10.1038/s41467-025-57096-y *equal contribution as first author Schmachtel, T., Bonig, H., & Rieger, M. A. (2025). FACS-Based Assessment of Human Hematopoietic Stem and Progenitor Cells. International journal of molecular sciences, 26(17), 8381. https://doi.org/10.3390/ijms26178381 Versicherung Ich erkläre: Ich habe die vorgelegte Dissertation selbstständig und ohne unerlaubte fremde Hilfe und nur mit den Hilfen angefertigt, die ich in der Dissertation angegeben habe. Alle Textstellen, die wörtlich oder sinngemäß aus veröffentlichten Schriften entnommen sind, und alle Angaben, die auf mündlichen Auskünften beruhen, sind als solche kenntlich gemacht. Ich stimme einer evtl. Überprüfung meiner Dissertation durch eine Antiplagiat-Software zu. Bei den von mir durchgeführten und in der Dissertation erwähnten Untersuchungen habe ich die Grundsätze guter wissenschaftlicher Praxis, wie sie in der „Satzung der Justus-Liebig-Universität Gießen zur Sicherung guter wissenschaftlicher Praxis“ niedergelegt sind, eingehalten. Frankfurt am Main, den _________________ ____ ___ (Unterschrift Tessa Schmachtel) Table of content I Table of content List of Figures .............................................................................................................................................. IV List of Tables ............................................................................................................................................... VI Summary ................................................................................................................................................... VIII Zusammenfassung ...................................................................................................................................... IX 1. Introduction ..................................................................................................................................... 1 1.1. The hematopoietic system – A brief journey through different developmental stages ..................... 2 1.1.1. Developmental hematopoiesis ....................................................................................................... 2 1.1.2. Adult hematopoiesis ....................................................................................................................... 4 1.2. Modules of hematopoiesis ................................................................................................................. 5 1.2.1. Classical module of hematopoiesis ................................................................................................. 5 1.2.1.1. Hierarchical tree model .............................................................................................................. 5 1.2.1.2. The clonal succession model ...................................................................................................... 6 1.2.3. The continuous differentiation model ................................................................................................... 6 1.3. A hierarchy roadmap through the hematopoietic compartment ....................................................... 8 1.4. The definition of hematopoietic stem cells ...................................................................................... 11 1.4.1. The terminological framework ..................................................................................................... 11 1.4.2. Heterogeneity of HSCs .................................................................................................................. 11 1.5. Emergency hematopoiesis ................................................................................................................ 13 1.6. Aging ................................................................................................................................................. 15 1.7. Objectives of the Present Study ........................................................................................................ 17 2. Materials and Methods .................................................................................................................. 18 2.1. Materials ........................................................................................................................................... 18 2.1.1. Chemicals and Reagents ............................................................................................................... 18 2.1.2. Enzymes ........................................................................................................................................ 18 2.1.3. Antibodies ..................................................................................................................................... 19 2.1.3.1. Fluorochrome conjugated antibodies used for flow cytometry analysis ......................................... 19 2.1.3.2. Unconjugated Primary antibodies used for protein analysis and neutralization ............................. 20 2.1.4. Cytokines and small molecules used for human cell culture ........................................................ 20 2.1.5. Cell culture medium ...................................................................................................................... 21 2.1.6. Kits ................................................................................................................................................ 21 2.1.7. Consumables ................................................................................................................................. 22 2.1.8. Laboratory equipment .................................................................................................................. 23 2.1.9. Buffers and Solutions .................................................................................................................... 24 2.1.10. BD™ AbSeq Antibody-oligonucleotide conjugates ................................................................... 25 2.1.11. Customized BDÔ Rhapspody gene panel ................................................................................. 29 2.1.12. Software and Algorisms ............................................................................................................ 37 Table of content II 2.2. Methods ............................................................................................................................................ 37 2.2.1. Cell culture methods ..................................................................................................................... 37 2.2.1.1. Human sample collection ................................................................................................................. 37 2.2.1.2. Isolation and purification of human hematopoietic subpopulations ............................................... 38 2.2.1.3. Isolation, activation and CellTraceÔ staining of human T-cells ....................................................... 39 2.2.1.4. Mixed lymphocyte reaction assay .................................................................................................... 40 2.2.1.5. T-cell proliferation index .................................................................................................................. 41 2.2.1.6. Ex vivo expansion and differentiation assay ..................................................................................... 41 2.2.1.7. Colony forming assay ....................................................................................................................... 41 2.2.1.8. Time-lapse imaging and subsequent single cell tracking .................................................................. 42 2.2.1.9. TNF-a culture of in vitro culture HSPCs ............................................................................................ 42 2.2.1.10. JC1 staining ..................................................................................................................................... 42 2.2.2. In vivo models ............................................................................................................................... 42 2.2.2.1. Xenotransplantation assay ............................................................................................................... 43 2.2.2.2. Isolation, Purification and Analysis of Human Hematopoietic Cells after Xenotransplantation from bone marrow and spleen .............................................................................................................................. 43 2.2.3. Molecular and Biochemical Methods ........................................................................................... 44 2.2.3.1. Protein analysis ................................................................................................................................ 44 2.2.3.2. RNA bulk sequencing ........................................................................................................................ 45 2.2.3.3. Cytokine profiling ............................................................................................................................. 45 2.2.3.4. Single cell sequencing ....................................................................................................................... 45 2.2.3.4.1. Single cell capture and cDNA synthesis ......................................................................................... 47 2.2.3.4.2. Library preparation and sequencing ............................................................................................. 48 2.2.4. Computational analysis ................................................................................................................. 48 2.2.4.1. Alignment and transcript quantification .......................................................................................... 48 2.2.4.2. Quality control, batch effect correction, filtering and normalization .............................................. 49 2.2.4.3. Dimensional reduction and clustering .............................................................................................. 52 2.2.4.4. Cell label transfer ............................................................................................................................. 52 2.2.4.5. Immature cell analysis ...................................................................................................................... 52 2.2.4.6. Differentiation expression analysis .................................................................................................. 53 2.2.5. Statistics ........................................................................................................................................ 53 3. Results ............................................................................................................................................ 54 3.1. Proteo-transcriptomic profiling of adult BM HSPCs .......................................................................... 54 3.2. Proteo-transcriptomic analysis of early HSPCs ................................................................................. 58 3.3. Continuous pseudotime expression of immature HSPCs .................................................................. 61 3.4. Differential gene and surface protein expression of HSCs ................................................................ 64 3.5. Surface marker analysis of HSC-1 revealed expression of CD273/PD-L2 on immature HSPCs ......... 66 3.6. CD273 is significantly upregulated in most immature HSPC compartments .................................... 68 Table of content III 3.7. In vitro characterization of CD273high and CD273low HSPCs .............................................................. 69 3.7.1. CD273high cells show delayed differentiation profile .................................................................... 70 3.7.2. CD273high and CD273low display similar colony forming potential ................................................ 71 3.7.3. CD273high showed lower mitochondrial potential ........................................................................ 72 3.7.4. CD273high presented delayed entry into cell cycle ........................................................................ 73 3.7.5. Transcriptomic analysis CD273high versus CD273low HSPCs ........................................................... 74 3.8. Xenotransplantation model showed no advantage of CD273high HSPCs in multilineage engraftment . ……………………………………………………………………………………………………………………………………………………..76 3.8.1. Engraftment chimerism in peripheral blood ................................................................................. 76 3.8.2. Endpoint analysis of BM and spleen ............................................................................................. 78 3.9. Upregulation of CD273 in pro-inflammatory culture ........................................................................ 79 3.10. The immunomodulatory role of CD273/PD-L2 on HSPCs ............................................................. 80 3.10.1. Neutralization of CD273/PD-L2 increases activation and proliferation of T-cells .................... 81 3.10.2. Neutralization of CD273/PD-L2 increases Tregs abundance and proinflammatory cytokine release ……………………………………………………………………………………………………………………………………………..83 3.10.3. Co-cultured HSPCs showed increased myeloid lineage differentiation and decreased stem-cell phenotype ……………………………………………………………………………………………………………………………………………..85 3.11. Proteo-transcriptomic analysis of MLR assay ............................................................................... 86 3.11.1. HSPCs and T-cells showed distinct clustering in dimensional reduction .................................. 86 3.11.2. Transcriptomic analysis confirmed delayed proliferation and activation of co-cultured T-cells …………………………………………………………………………………………………………………………………………….87 3.11.3. HSPCs co-cultured with unmatched T-cells showed distinct clustering, myeloid differentiation and upregulation of pro-inflammatory regulators ........................................................................................ 89 4. Discussion ...................................................................................................................................... 93 4.1. Lineage trajectory analysis over pseudotime confirmed a stepwise differentiation process ........... 93 4.2. Re-analysis of early HSPCs identified two HSC compartments with distinct transcriptional state ... 94 4.3. Targeted sequencing approach allowed better profiling of low-expressed genes ........................... 95 4.4. Age-group comparison displayed balanced lineage output .............................................................. 95 4.5. Surface proteome analysis of HSC-1 cells revealed exclusive upregulation of CD273/PD-L2 ........... 96 4.6. Functional analysis confirmed CD273/PD-L2 as marker on HSPCs with enhanced quiescence ........ 97 4.7. CD273 expressing HSPCs are immunomodulating cells suppressing T-cell activation and proliferation .................................................................................................................................................. 99 5. Conclusions .................................................................................................................................. 103 6. Future perspective ....................................................................................................................... 104 7. Appendix ...................................................................................................................................... 105 8. References .................................................................................................................................... 127 Acknowledgments .................................................................................................................................... 149 List of Figures IV List of Figures Figure 1. Developmental hematopoiesis in human and mouse embryos. .......................................................... 4 Figure 2. Models of HSC lineage commitment. ................................................................................................... 8 Figure 3. Differentiation hierarchy displaying the lineage determination in adult mouse and humans. .......... 10 Figure 4. Distinct sources contribute to heterogeneity within the HSC compartment. .................................... 13 Figure 5. Mechanism of emergency hematopoiesis induced by pathogen exposure. ...................................... 15 Figure 6. Age-related changes in lineage output. .............................................................................................. 16 Figure 7. Graphical representation of FACS-based assessment of human HSPCs. ............................................ 38 Figure 8. Experimental setting for T-cell activation. .......................................................................................... 40 Figure 9. Graphical representation displaying the workflow for MLR assays. ................................................... 41 Figure 10. Graphical representation of xenotransplantation assay in primary recipients. ............................... 43 Figure 11. CCA batch effect correction. ............................................................................................................. 49 Figure 12. Exclusion of low quality cells. ........................................................................................................... 51 Figure 13. Overview experimental workflow for BM analysis. .......................................................................... 54 Figure 14. Proteo-transcriptomic profiling of CD34+cells from human BM. ..................................................... 56 Figure 15. Association of surface marker expression with manual annotated cell clusters. ............................. 57 Figure 16. Distribution of CD34+ HSPCs from three age groups after batch effect correction. ........................ 58 Figure 17. Re-clustering of early immature HSPCs. ........................................................................................... 59 Figure 18. Annotation and proteo-transcriptomic analysis of immature HSPCs. .............................................. 60 Figure 19. Stem cell and lineage scores of early immature HSPCs. ................................................................... 61 Figure 20. Age and pseudotime comparison of early immature cells. .............................................................. 62 Figure 21. Differential gene expression of HSC-1 and HSC-2 pseudobulk according to age. ............................ 62 Figure 22. Upregulated genes in HSC-1 cells among all age groups and comparison of CHIP driver genes in immature cells. .......................................................................................................................................... 64 Figure 23. Transcriptomic profiling and differential gene expression of HSC-1 and HSC-2 specific cells. ......... 65 Figure 24. Surface protein expression on immature HSPCs. ............................................................................. 67 Figure 25. Expression of PD-L2/CD273 in different progenitor populations. .................................................... 68 Figure 26. CD273 surface expression on different HSPC population. ................................................................ 69 Figure 27. FACS sorting strategy for isolation of CD273high and CD273low HSPCs. ............................................. 70 Figure 28. In vitro differentiation assay of CD273high and CD273low HSPCs. ....................................................... 71 Figure 29. Colony forming analysis of CD273high and CD273low HSPCs. ............................................................. 72 Figure 30. Mitochondrial potential of CD273high and CD273low HSPCs. .............................................................. 73 Figure 31. In vitro cell expansion and proliferation analysis of CD273high and CD273low HSPCs. ....................... 74 Figure 32.Transcriptomic and protein analysis of CD273high and CD273low HSPCs. ........................................... 75 Figure 33. PB engraftment of CD273high, CD273low HSPCs and CD34+ cells treated with IgG or a-PD-L2 antibody. .................................................................................................................................................................. 77 List of Figures V Figure 34. Lineage reconstitution in PB of CD273high, CD273low HSPCs and CD34+ cells treated with IgG or a-PD- L2 antibody. ............................................................................................................................................... 77 Figure 35. Engraftment and reconstitution in the BM of CD273high, CD273low HSPCs and CD34+ cells treated with IgG or a-PD-L2 antibody. ........................................................................................................................... 78 Figure 36. Engraftment and lineage reconstitution in the spleen of CD273high, CD273low HSPCs and CD34+ cells treated with IgG or a-PD-L2 antibody. ...................................................................................................... 79 Figure 37. CD273 expression upon steady-state and pro-inflammatory in vitro culture. ................................. 80 Figure 38. STRING analysis of PDCD1LG2 (CD273/PD-L2). ................................................................................ 81 Figure 39. Increased activation of CD8+ T-cells upon CD273/PD-L2 neutralization. ......................................... 81 Figure 40. Increased proliferation of T-cells upon CD273/PD-L2 neutralization. .............................................. 82 Figure 41. Survival of T-cell in MLR assay. ......................................................................................................... 83 Figure 42. Proportion of different T-cell subsets in MLR assay. ........................................................................ 84 Figure 43. Cytokine secretion in MLR assay. ...................................................................................................... 85 Figure 44. HSPC differentiation in MLR assay. ................................................................................................... 86 Figure 45. UMAP projection of MLR assay. ....................................................................................................... 87 Figure 46. Analysis of T-cells in MLR assay. ....................................................................................................... 89 Figure 47. Analysis of HSPCs in MLR assay. ....................................................................................................... 89 Figure 48. Differential gene expression and GSEA of HSPCs in MLR assay. ....................................................... 91 Figure 49. Differential gene analysis of inflammatory HSPC regulators. ........................................................... 91 Figure 50. Interaction of mouse niche and human HSCs in Xenograft model. .................................................. 98 Figure 51. Graphical abstract displaying the immunomodulating function of CD273-expressing HSPCs. ...... 101 List of Tables VI List of Tables Table 1. Chemicals and reagents used in the presented thesis. ........................................................................ 18 Table 2. Enzymes used in the presented thesis. ................................................................................................ 18 Table 3. Fluorochrome conjugated antibodies used in the presented thesis. .................................................. 19 Table 4. Unconjugated primary antibodies used in the presented thesis. ........................................................ 20 Table 5. Cytokines and small molecules used in the presented thesis. ............................................................. 20 Table 6. Medias used in the presented thesis. .................................................................................................. 21 Table 7. Kits used in the presented thesis. ........................................................................................................ 21 Table 8. Instruments used in the presented thesis. .......................................................................................... 22 Table 9. Laboratory equipment used in the presented thesis. .......................................................................... 23 Table 10. Buffer and Solutions used in the presented thesis. ........................................................................... 24 Table 11. BD™ AbSeqs panel for adult BM analysis. .......................................................................................... 25 Table 12. Abseq panel for mixed lymphocyte reaction assay. .......................................................................... 27 Table 13. BD Rhapsody gene panel. .................................................................................................................. 29 Table 14. Software and algorism used in the presented thesis. ........................................................................ 37 Table 15. Characterization of HSPC subpopulations. ........................................................................................ 39 Table 16. Marker combination for multilineage analysis of xenograft endanalysis. ......................................... 44 Table 17. Marker combination for progenitor analysis of xenograft endanalysis. ............................................ 44 Table 18. Sample overview for scCITESeq analysis of adult BM samples. ......................................................... 46 Table 19. Sample overview from mPB samples used for scCITE Seq in MLR reaction assay. ............................ 47 Abbreviations VII Abbreviations % Percent AbSeqs Oligonucleotide-conjungated Antibodies AGM Aorta-Gonad-Mesonephros BM Bone Marrow c Centi CB Cord Blood CCA Canonical Correlation Analysis CD Cluster of Differentiation CFU-S Spleen Colony forming Units CH Clonal Hematopoiesis CHIP Clonal Hematopoiesis of Indeterminate Potential CLOUD-HSPCs Continuum of low-primed undifferentiated Hematopoietic Stem and Progenitor Cells CLPs Common Lymphoid Progenitors CMPs Common Myeloid Progenitors CS7 Carnegie Stages dHSCs Developmental Hematopoietic Stem Cells DMEM Dulbecco's Modified Eagle Medium DMSO Dimethyl Sulfoxide ECM Extracellular Matrix EDTA Ethylenediaminetetraacetic acid FACS Fluorescence-activated Cell Sorting, Fluorescence-Activated Cell Sorting FCS Fetal Calf Serum G-CSF Granulocyte-Colony Stimulating Factor, Granulocyte-Colony Stimulating Factor GSEA Gene Set Enrichment Analysis Gy Grey HS High Sensitivity, High Sensitivity HSCs Hematopoeitic Stem Cells HSCT Hematopoietic Stem Cell Transplantation HSPC Hematopoietic Stem and Progenitor Cell kNN K Nearest Neighbor L Liter Lin Lineage, Lineage LMPPs Lympho-Myeloid primed Progenitors LT-HSCs Long-Term Hematopoeitic Stem Cells LYP Lymphoid Progenitors m mili, Meter MDPs Monocyte Dendritic Cell Progenitors MEPs Megakaryocyte-Erythroid Progenitors MHC-II Major Histocompatibility Complex II MLR Mixed Lymphocyte Reaction MNC Mononuclear Cells MPPs Multipotent Progenitors NSG NOD/SCID IL2ycnull mouse, NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ PB Peripherial Blood PBS Phosphate Buffered Saline PCA Principle Component Analysis PDCD1 Programmed Cell Death Protein 1 PD-L2 Programmed Cell Death Ligand 2 SEM Standard Error of the Mean SFEM Serum Free Exansion Medium TAE TRIS-Acetat-EDTA TCM Central Memory T-cells TEM Effector Memory T-cells TLR Toll-like Receptors TN Naive T-cells Tregs Regulatory T-cells UV Ultraviolet w/o Without wpt Weeks post Transplantation WTA Whole Transcriptome Analysis Summary VIII Summary The human bone marrow constitutes the principal site of hematopoiesis, sustaining the continuous generation of platelets, erythrocytes and immune cells through the activity of hematopoietic stem and progenitor cells (HSPCs) residing within specialized microenvironmental niches. Within this compartment, a rare subset of hematopoietic stem cells (HSCs) maintains lifelong hematopoiesis by precisely regulating the balance between self-renewal and differentiation, predominantly persisting in a quiescent state under homeostatic conditions but capable of rapid activation in response to acute physiological stressors such as infection or hemorrhage. The equilibrium between steady-state and emergency hematopoiesis is fundamental to hematopoietic homeostasis; however, chronic inflammation and aging can disrupt this balance, leading to impaired blood cell production and increased risk of developing hematologic malignancies. Although substantial progress has been made in the characterization and prospective isolation of distinct human HSPC subsets using surface marker- based strategies, current strategies often yield in heterogeneous populations and several differentiation models have been proposed. To address this challenge and map differentiation trajectories, the presented research employed a single cell Transcriptomic/AbSeq approach, simultaneously quantifying the expression of 596 genes at mRNA level and 46 surface markers, on over 62,000 FACS-enriched HSPCs from 15 healthy donors across different age groups. Comprehensive computational analysis revealed four main lineage pathways, supporting a stepwise model of differentiation with an early branching point for megakaryocyte-erythroid progenitors. Notably, HSPCs from older donors exhibited a higher proportion of undifferentiated cells and diminished differentiation across all lineages. A key finding of this study is the identification of Programmed death ligand 2 (PD-L2/CD273) as a surface marker highly expressed on the most primitive HSPCs. CD273/PD- L2-positive HSPCs, isolated from mobilized PB samples, demonstrated a distinct molecular signature characterized by the enrichment of stemness genes such as Thy1, DLK1 and MPL, as well as delayed entry into the cell cycle, reduced mitochondrial activity and delayed in vitro differentiation indicating a deeper quiescent state. Functional assays, including in vitro colony forming assays and in vivo xenograft experiments, confirmed that CD273/PD-L2high HSPCs possess the capacity for multi-lineage reconstitution. Beyond their stem cell properties, CD273/PD-L2-expressing HSPCs exhibited notable immunomodulatory functions. In allogeneic lymphocyte reaction assays, these cells suppressed CD8+ T-cell proliferation and activation. Blocking PD-L2 promoted the secretion of pro-inflammatory cytokines (IFN-γ, TNF-α, IL-6), showed a reduced abundance of regulatory T-cells and a shift toward myeloid lineage bias in HSPCs, implying an increased inflammatory response reaction. These findings highlight a dual role for PD-L2: First as a marker of more quiescent, primitive HSPCs and second as a mediator of immune regulation within the hematopoietic compartment. In summary, this integrated study provides valuable insights into the organization of human hematopoiesis and the identification of PD-L2/CD273 as a defining marker of a particularly quiescent, immunomodulatory HSPC subset. These discoveries may have important implications for stem cell transplantation and the treatment of blood malignancies, as understanding and manipulating these pathways may improve therapeutic outcomes and help maintaining healthy hematopoiesis throughout life. Zusammenfassung IX Zusammenfassung Das menschliche Knochenmark ist der zentrale Ort der Hämatopoese, in der hämatopoetische Stamm- und Vorläuferzellen (engl. hematopoietic stem and progenitor cells; HSPCs) die kontinuierliche Bildung aller Blutzelllinien gewährleisten. Eine seltene Subpopulation, die hämatopoetischen Stammzellen (engl. hematopoietic stem cells; HSCs), hält dieses System durch ein fein balanciertes Gleichgewicht zwischen Selbsterneuerung und Differenzierung lebenslang aufrecht. Unter normalen Bedingungen sind HSCs überwiegend inaktiv, können jedoch bei Stress wie Infektionen oder Blutverlust rasch aktiviert werden. Störungen durch chronische Entzündungen oder Alterungsprozesse beeinträchtigen dieses Gleichgewicht und erhöhen das Risiko für Bluterkrankungen. Trotz erheblicher Fortschritte bei der funktionalen Charakterisierung von HSPC-Subpopulationen führt die Oberflächenmarker-basierte Isolierung häufig zu heterogenen Zellgemischen. Dies hat unter anderem zur Folge, dass insbesondere die unterschiedlichen Modelle zur Organisation der Differenzierung weiterhin Gegenstand intensiver Diskussionen sind. Um dieses Problem zu adressieren, wurde im Rahmen dieses Dissertationsprojekts mithilfe von Einzelzell- basierter Sequenzierung die Expression von 596 Genen und 46 Oberflächenmarkern in über 62.000 HSPCs aus dem Knochenmark von 15 gesunden Spendern unterschiedlichen Alters analysiert. Die bioinformatische Auswertung ergab vier Linienpfade und bestätigte ein stufenweises Differenzierungsmodell mit einem frühen Abzweigungspunkt für Megakaryozyten-Erythrozyten-Vorläufer. Auffällig war, dass HSPCs älterer Spender einen höheren Anteil undifferenzierter Zellen und eine verzögerte Differenzierung aufwiesen. Ein weiteres zentrales Ergebnis dieser Studie ist die Identifikation von CD273/PD-L2 (engl. Programmed death ligand 2; PD-L2) als Marker, welcher auf den unreifen HSPCs stark exprimiert wird. Diese CD273/PD-L2- positiven Zellen zeichneten sich durch eine ausgeprägte Stammzell-Signatur, verlangsamten Zellzyklus, reduzierte mitochondriale Aktivität und verzögerte Differenzierung aus. Zusammen weisen diese Merkmale auf eine erhöhte Quieszenz der Zellen hin. Darüber hinaus bestätigten Xenotransplantations-Modelle die Fähigkeit von CD273/PD-L2high HSPCs zur multilinearen Rekonstitution. Interessanterweise zeigte diese Zellpopulation nicht nur einen Stammzell-ähnlichen Phänotyp, sondern auch immunmodulatorische Eigenschaften. In einer Mischkultur aus unterschiedlichen Lymphozyten unterdrückte CD273/PD-L2 exprimierende HSPCs die Aktivierung und Proliferation von CD8+ T-Zellen. Im Gegensatz dazu führte die Blockade von CD273/PD-L2 zu einer verstärkten Immunreaktion, erkennbar an erhöhten Spiegeln proinflammatorischer Zytokine (IFN-γ, TNF-α, IL-6), einer verminderten Anzahl regulatorischer T-Zellen sowie einer vermehrten Differenzierung der HSPCs in myeloide Zelllinien. Diese Ergebnisse unterstreichen die doppelte Rolle von CD273/PD-L2: Einerseits als Oberflächenmarker primitiver HSPCs, andererseits als Regulator immunologischer Prozesse im hämatopoetischen Kompartiment. Zusammenfassend liefert unsere Studie neue Einblicke in die Organisation der menschlichen Hämatopoese und identifiziert PD-L2/CD273 als Marker einer unreifen, immunmodulatorischen HSPC-Subpopulation. Diese Erkenntnisse könnten wichtige Impulse für die Optimierung von Stammzelltransplantationen und die Behandlung hämatologischer Erkrankungen geben. Introduction Page | 1 1. Introduction The human bone marrow (BM), once described as a "waste product (excrementum ossium)" by the esteemed philosopher Aristotle, is now recognized as the primary source of the vital blood cells that are essential for the maintenance of our human body functions (Dechambre A, 1877). Over the centuries, theories about the role of the BM varied, with some suggesting it was simply a source of nutrients for the bones, others challenging these hypotheses by stating the formation of the marrow takes place after the bone is built (Cooper, 2011; Robin C., 1875). It was not until the late 18th century that Neumann's hypothesis that the bone marrow was "the seat of blood formation" emerged, a statement that remains the cornerstone of the contemporary hematopoietic research paradigm (Neumann, 1868). Besides its remarkable properties, the BM cannot function in isolation. Rather more, it needs to be considered as part of the hematopoietic system, a large mobile tissue that not only is the seat of the blood cell formation but connects all different organs in the human body. By that, blood cells encompass for supplying oxygen, mediating tissue repair and orchestrating our immune response in exposure to pathogens. To take care of all these functions, the hematopoietic system gives rise to producing around 350 billion platelets, 180 billion erythrocytes and 12 billion lymphocytes per day (Rieger and Schroeder, 2012). These cells varied in morphology and function but most of them arise from multipotent hematopoietic stem and progenitor cells (HSPCs) in an adult system (Weissman and Shizuru, 2008; Seita and Weissman, 2010). HSPCs reside in the BM niche, a microenvironment that functions as important gatekeeper in maintaining the blood cell system and exerts its regulatory activity on HSPCs and their progeny. Advancements in molecular, genetic and cellular technologies have unveiled the intricacies of the hematopoietic system with unprecedented clarity, revealing a complex landscape characterized by dynamic interactions between HSPCs and their microenvironment within the BM niche (Fröbel et al., 2021). The intricate crosstalk between these cellular elements orchestrates the finely tuned balance of hematopoiesis, ensuring the adaptive responsiveness of the system to environmental cues and physiological demands (Fröbel et al., 2021; Kode et al., 2014; Walkley et al., 2007). It is essential to recognize that the hematopoietic system encompasses various components, including HSPCs, a broad variety of functional effector cells and even non-hematopoietic cells. Alongside these cellular components, hematopoiesis is influenced by extracellular factors like the extracellular matrix (ECM) and chemical and physical factors (Zanetti & Krause, 2020). Understanding these complex interactions among these elements requires ongoing research efforts to elucidate the intricacies of HSPC biology and their interactions within the BM niche. Introduction Page | 2 1.1. The hematopoietic system – A brief journey through different developmental stages Hematopoiesis is the generation of blood cells throughout the lifespan and therefore plays a major role in health and disease. It is a highly regenerative process that produces over one trillion (1012) cells daily to maintain steady-state blood cell replacement and to adequately compensate for acute blood loss associated with emergency hematopoiesis (Rieger and Schroeder, 2012). While in the adult hematopoietic system all cells arise from hematopoietic stem cells (HSCs), embryonic hematopoiesis initiates through a distinct process where the first red blood cells emerge from an alternative source. To gain insight into adult hematopoiesis and gain a comprehensive understanding of the mechanisms guiding HSC identity and location, it is crucial to embark a brief exploration of its diverse developmental stages. 1.1.1. Developmental hematopoiesis The mammalian developmental hematopoiesis takes place in multiple anatomical locations and occurs in various successive stages, also determined as “hematopoietic waves”. It can be separated into 3 distinct waves: primitive, transient-definitive and definitive as illustrated in Figure 1. Primitive and transient- definitive waves are described as HSC-independent stage (Bertrand & Traver, 2009; Calvanese & Mikkola, 2023; McGrath et al., 2015; Palis, 2014; Tober et al., 2007). During these embryonic waves, the hematopoietic system must expand the blood cell compartment and produce progenitor cells while differentiating into different tissues (Canu & Ruhrberg, 2021; Dzierzak & Bigas, 2018; Vink et al., 2022). The primitive progenitor wave starts in humans by carnegie stage 7-8 (CS7-8) (2.5 weeks). These early stages are needed for the generation of primitive erythroid progenitors (oxygenation), embryonic macrophages (tissue remodeling and defense) and megakaryocytes precursors (vascular maintenance) (Bertrand & Traver, 2009; Gao et al., 2018; Kingsley et al., 2004; Palis, 2014). As these initial stages of hematopoiesis progress, a cohort of circulating red blood cells is formed, characterized by their large size and embryonic globin expression with higher oxygen carrying capacity (z/e globins) (Steiner and Vogel, 1973; Peschle et al., 1984; Wilkinson et al., 1987; Manning et al., 2020). These erythroblasts are the first blood-forming cells and derive from the yolk sac. During murine primitive hematopoiesis, they are formed within embryonic day 7-8 (E7-8) (Calvanese & Mikkola, 2023; Haar & Ackerman, 1971; Luckett, 1978). The transient-defined wave of hematopoiesis takes place within CS8-9 (3.25 weeks) in humans and E8-9 in mice (Calvanese & Mikkola, 2023). Following the development, the initialization of the heartbeat marks the phenotypical switch between primitive erythroblasts and the fetal erythroid lineage. After erythroblasts enter the circulation during E8-9 in mice and CS10-12 in humans, they are replaced by enucleated and globin a/g-synthesizing erythrocytes which are synthesized in the fetal liver (Fraser et al., 2007; Hikspoors et al., 2022; Tavian et al., 1999). Hematopoiesis in the fetal liver is capable of differentiating into myeloid, megakaryocytic and lymphoid cells (Houssaint, 1981; Moore & Metcalf, 1970). Introduction Page | 3 Following the HSC-independent hematopoiesis, the third and definitive developmental wave starts at stage E11.5/CS14-16 and initiates the synthesis of developmental HSCs (dHSCs). These dHSCs are capable of fully repopulating the hematopoietic system and have true multilineage potential. However, their number is very limited, with an estimated one dHSC per hematopoietic organ. They originate from the aorta-gonad- mesonephros (AGM) region and are reported to be a potent source of dHSC activity (Kumaravelu et al., 2002; Medvinsky & Dzierzak, 1996; Müller et al., 1994). At stage E12.5, these dHSC colonize the fetal liver, mediated by b1 integrin. Their subsequent proliferative expansion is transmitted by angiopoietin-like factors and the SOX17 transcription factor (Hirsch et al., 1996; I. Kim et al., 2007; Potocnik et al., 2000; C. C. Zhang et al., 2006). Despite previous publications, recent studies using lineage tracing strategies revealed only a two-fold increase in adult-blood-fated HSC precursors during this period. While fetal liver HSCs proliferate extensively, many exhibit differentiation bias over self-renewal, suggesting earlier developmental constraints limit adult-repopulating HSC expansion (Ganuza et al., 2022; Calvanese and Mikkola, 2023). In humans, dHSC colonization of the fetal liver is conveyed by the 6-gene signature (RUNX1, HOXA9, MLLT3, MECOM, HLF, SPINK2) in stage CS17 (Bian et al., 2020; Calvanese & Mikkola, 2023). The involvement of the fetal liver and the in-depth analysis of HLF+ HSCs shown evidence for a decreased expression of proliferative genes during the end of the embryonic period, implying a shift towards homoeostatic HSC in the first trimester and marking the start for the feta period (Calvanese et al., 2022). During the transition from embryonic to fetal period, human BM hematopoiesis begins with the myeloid cell colonization of the cavity of long bone cavities following vascularization around week 8 (Charbord et al., 1996). By week 12, long-term engraftable HSCs (LT-HSCs) are detectable and a fully active BM hematopoiesis is established by week 14 (Charbord et al., 1996; Z. Zheng et al., 2022). While transforming to fetal BM is associated with a shift towards a more quiescent state between week 17-22, further research is necessary to elucidate the changes in the niche supporting HSC maturation and lifelong maintenance between fetal liver and BM (Ranzoni et al., 2021). As illustrated in Figure 1 murine and human developmental hematopoiesis show tremendous differences when it comes to anatomy and pregnancy duration. Nevertheless, these initial sites of hematopoiesis are largely conserved among vertebrates and can be used to gain further understanding of early developments and their impacts on adult hematopoiesis (Medvinsky et al., 2011). Introduction Page | 4 Figure 1. Developmental hematopoiesis in human and mouse embryos. Hematopoiesis is conserved in mammals but differs in anatomical sites, timing, and progenitor output between humans and mice. In humans, hematopoiesis begins at 2.5 weeks in the yolk sac with primitive progenitors, producing nucleated erythroblasts and macrophages. By 3.25 weeks, a second wave of definitive progenitors emerges, contributing to tissue-resident macrophages and lymphoid cells. The third wave at 4–6 weeks originates hematopoietic stem cells (HSCs) in the AGM, which seed the liver, mature, and later migrate to the bone marrow by the second trimester. In mice, similar developmental waves occur but within a shorter timeframe, leading to overlaps. Mouse yolk sac progenitors favor erythromyeloid differentiation, while human progenitors exhibit myeloid skewing. Key developmental milestones, such as heartbeat onset and the embryonic-to-fetal transition, occur at different stages between species (taken from Calvanese and Mikkola, 2023). 1.1.2. Adult hematopoiesis After outlining the various developmental stages that illustrate HSC migration from the AGM to the fetal liver and ultimately into the BM, where they remain throughout adulthood, this thesis will focus on investigating adult hematopoiesis. As described above, adult hematopoiesis has an enormous quantity of blood cells produced every day. This highly regenerative organ is fully based on the proliferation and differentiation of HSCs. In contrast to the embryonic hematopoiesis, blood cell formation in adulthood cannot be HSC-independent. Studies indicate that the small population of HSCs, estimated to be less than 1.3 million, is responsible for generating mature peripheral blood cells throughout an individual's lifespan. However, this number can vary, ranging from a few hundred active HSCs to 50,000 - 600,000 HSCs in steady-state adult hematopoiesis (Abkowitz et al., 2002; Watson et al., 2020). Recent studies comparing different age cohorts have shown, that under the age of 65 approximately 20,000 – 200,000 HSC/MPPs are stably contributing to our blood production (Mitchell et al., 2022). Under homeostatic conditions most HSCs remain in quiescence (G0 phase), with infrequently proliferation occurring to ensure their long-term maintenance (Bigas & Waskow, 2016; Dzierzak & Bigas, 2018; Rossi et al., 2007). Nevertheless, the plasticity of the hematopoietic system is most evident during emergency situations such as induced stress, physical trauma, anemia or infections, when cell counts can Introduction Page | 5 rapidly increase (Rieger and Schroeder, 2012). These emergency situations require an adaptive regulation of the hematopoietic system to generate specific cell types in appropriate numbers and locations depending on various circumstances. Consequently, precise mechanisms must be orchestrated to ensure correct fate decisions. Over recent decades, numerous models have been proposed to elucidate the organization of our hematopoietic system and the initiation of differentiation. Advancements in technology have provided deeper insights into the factors influencing these cell fate decisions and response mechanisms. In the following chapter, we will examine these various models and their associated hypotheses. 1.2. Modules of hematopoiesis 1.2.1. Classical module of hematopoiesis 1.2.1.1. Hierarchical tree model The classical model of hematopoiesis places HSCs at the apex of the hematopoietic hierarchy. Alexander A. Maximow was the first to propose this theory, which elucidated the diverse cellular morphologies observed in cells across different blood lineages and developmental stages (Doulatov et al., 2012a; Maximow, 1909). According to that model HSCs restrict their self-renewal capacity to generate MPPs through stepwise differentiation into oligo-, bi- and unipotent progenitors (Kondo et al., 1997; Velten et al., 2017). This paradigm was proofed over the last century by endless studies exploring our understanding of HSC self- renewal and differentiation properties through transplantation assays (Olson et al., 2020b). The downstream located MPPs exhibit self-renewal capacity while maintaining multipotency (Busch et al., 2015). That construct of stepwise restriction of lineage potential at binary branching points results in a tree- like model (Haas et al., 2018) (Figure 2A). Subsequently, MPPs differentiate into oligopotent progenitors that have limited lineage differentiation capacity, forming common myloied progenitors (CMPs) or common lymphoid progenitors (CLPs) (Akashi et al., 2000; Cabezas-Wallscheid, 2014; Kondo et al., 1997; E. Pietras, 2015). Afterwards, these progenitors produce lineage-restricted precursors and mature cell types of the blood and immune systems (Haas et al., 2018; Seita & Weissman, 2010b). While the hierarchical organization of the hematopoietic system has been emphasized for the past century, recent publications exploring the clonal composition of the HSC compartment suggest that multiple HSCs contribute concurrently to peripheral blood cell production, resulting in stable hematopoiesis (Carrelha et al., 2018; Drize et al., 1996; Goyal & Zandstra, 2015). Technological advancements over the last two decades have led to the emergence of an alternative, more flexible model of hematopoiesis, challenging its strict hierarchical structure with a more heterogeneous HSC population summiting at the top. Consequently, hematopoiesis is inherently polyclonal with new clones of mature cells successively arising, due to the finite life span of the previously expanded primary pool. Introduction Page | 6 1.2.1.2. The clonal succession model The initial incorporation of genetic barcoding and next-generation sequencing techniques peaked in the so- called “clonal succession models”, offering insights into the dynamic nature of hematopoiesis. As stated before, these models suggest that numerous HSC clones participate in the process over time (Biasco et al., 2016; Gerrits et al., 2010; Glimm et al., 2011; S. Kim et al., 2014; Six et al., 2020). Following this hypothesis, the combination of the barcoding-based labeling with cell sorting revealed that a predominant fraction of transplanted clones consistently contributes to hematopoiesis over extended periods. However, the clonal composition of specific effector populations—such as granulocytes, T cells, and B cells—varies substantially over time. Individual clones show dynamic behavior, with many expanding, declining or changing their contribution to hematopoiesis throughout the observation period (Verovskaya et al., 2013). Importantly, clone sets may differ between varied compartments, such as PB and BM (Biasco et al., 2015, 2016). This perspective was further confirmed by another model based on transposon system established in transgenic mice, called “Sleeping Beauty”, which enabled in situ labelling and clonal tracking of hematopoietic cells. The successive measurement of progenitors through their common location of transposons could be used to detect their cellular origins, lineage relationships and dynamics of native blood production progenitors (R. Lu, 2014; Sun et al., 2014). Furthermore, this model enables their analysis in vivo over different time points without prior transplantation. The results shown strikingly different clones at different time points supported steady state hematopoiesis. Recent publications investigating the clonal composition in steady-state hematopoiesis using single-cell techniques were able to quantify the lineage output of distinct HSC clones. The tracing approach of mature hematopoietic cells demonstrated that 50 % of HSC clones gave rise to 60 % of progenitor and mature cells over different time points (Weng et al., 2024). These results of the observed clonal dynamic are consistent with the clonal succession model, stating that the putative clonal diversity is much higher than previously demonstrated and can differ to different time points and inquiries. In addition to providing detailed insights into clonal expansion, single-cell technologies have facilitated thorough examinations of phenotypically homogeneous populations. These analyses incorporate factors such as epigenetic, transcriptomic and metabolic states, challenging the concept that progenitor populations represent distinct cell types. Instead, they suggest that progenitors should be viewed as transitional states and displayed temporal dominance of different HSC clones in a demand-driven application. These revelations, among others, have revolutionized our comprehension of HSCs and their role in lineage commitment and build a new perspective on how HSCs contribute to blood production over time. 1.2.3. The continuous differentiation model Building on these insights, single-cell transplantations showed significant functional heterogeneity within the HSC compartment further contradicting the classical hierarchical model. Multiple studies indicate that Introduction Page | 7 true oligopotency is confined to a minority of HSCs, while the majority exhibit a stable predisposition—or lineage bias—toward the generation of specific blood cell lineages (Alejo E. Rodriguez-Fraticelli, 2018; Karamitros et al., 2018; Notta, 2016; Paul, 2015; Perié et al., 2015; Velten et al., 2017). These results underscore the influence of cell-intrinsic regulators on cell fate decisions, occurring at early stages of hematopoietic differentiation (CE Müller-Sieburg, 2002; Yu, 2016). Subsequent advances in large-scale single-cell gene expression profiling have enabled the reconstruction of developmental trajectories independently of traditional surface marker-based classification, displaying a gradual acquisition of lineage- committed transcriptomic states by HSCs, rather than an abrupt transitions between stable intermediate states (Karamitros et al., 2018; Nestorowa et al., 2016; Pina et al., 2012; Tusi et al., 2018; Velten et al., 2017). In addition to that, lineage tracing experiments have demonstrated the direct emergence of the megakaryocyte lineage from HSCs, a phenomenon challenging the traditional tree model (Alejo E. Rodriguez-Fraticelli, 2018). The identification of lineage-biased HSCs suggests that lineage separation begins early during hematopoietic development. At this stage, emerging barriers between lineages remain plastic but become increasingly restrictive over time, reflecting a gradual loss of multipotency and culminating in lineage commitment. Early transcriptional priming likely corresponds to multipotent cells with a bias toward specific lineages, whereas uni-lineage transcriptional signatures mark cells that are functionally committed (Velten et al., 2017). In such a model, progenitor cells are not defined as discrete entities but rather as transitional states along a continuous differentiation spectrum. This model accommodates the substantial heterogeneity observed within the HSC compartment, is consistent with the variable lineage output of single HSC clones following transplantation, and is supported by evidence of transcriptional priming, functional biases and early lineage divergence (Grover et al., 2016; Karamitros et al., 2018; Nestorowa et al., 2016; Pina et al., 2012; Tusi et al., 2018; Velten et al., 2017). Accordingly, hematopoietic differentiation is characterized by a gradual acquisition of lineage-biased gene expression without clear-cut boundaries between progenitor populations (Figure 2B). The continuous model of hematopoiesis proposes that differentiation trajectories are initiated early and, while still responsive to extrinsic signals, these early biases help explain the lineage output preferences observed throughout hematopoietic development (Laurenti & Göttgens, 2018). These findings underscore the complexity of hematopoietic regulation and suggest a hierarchical organization within the hematopoietic system, where various progenitor cells contribute to specific lineages of mature blood cells. Overall, a comprehensive understanding of the different models explaining adult hematopoiesis is essential for unraveling the intricacies of blood cell development and homeostasis. Introduction Page | 8 Figure 2. Models of HSC lineage commitment. (A) The classical tree-like model depicts hematopoietic stem cells (HSCs) differentiating into multipotent progenitors, which generate lineage-restricted progenitors and eventually all mature hematopoietic cells. This model assumes uniform developmental stages and uniform lineage decisions, driven either stochastically or by instructive cytokines. (B) The continuous differentiation model highlights gradual lineage specification and transcriptional bias emerging earlier than full lineage commitment. Progenitor states, defined by phenotypic markers, demonstrate the heterogeneity within these populations (taken from Olson, Kang and Passegué, 2020). 1.3. A hierarchy roadmap through the hematopoietic compartment To introduce the various cells within the hematopoietic compartment, we will refer to the traditional hematopoietic hierarchy to simplify the understanding (Figure 3). Independent of the model explaining the hematopoietic system, it is widely established that HSCs are the parental population of all other hematopoietic cells (Laurenti & Göttgens, 2018). The ability to isolate and characterize different stem cells or their subsequent progenitor cells opens avenues to understand how hematopoiesis is regulated. Selecting these populations for studies is based on a series of cell surface markers. These protein markers are mostly declared as cluster of differentiation (CD) and used for their prospective isolation using fluorescence-activated cell sorting (FACS). Despite the fact, that many hematopoietic studies have been performed in mice, there is a lack of congruence than it comes to the expression of their cell surface markers. In the following abstract we will predominantly focus on human CD markers and not discuss murine markers. In the clinical setting CD34+ cells isolated through direct aspiration or granulocyte-colony stimulating factor (G-CSF) directed mobilization are commonly used as stem cell source material. CD34 was first described around 1990 and since then is one of the cornerstones in immunophenotypic analysis for HSPCs (Civin et al., 1984; Krause et al., 1996). These cells compromise approximately 1 - 1.5 % of BM mononucleated cells and are capable of reconstituting hematopoiesis in patients undergoing autologous bone marrow refusion Introduction Page | 9 following myeloablation therapy, resulting in sustained durable donor-derived hematopoietic reconstitution. Therefore, CD34+ cells have been widely confirmed in clinical applications to define human stem and progenitor cells (Anjos-Afonso & Bonnet, 2023; Link et al., 1996). Although CD34 is considered the bona fide human HSC marker, it encounters diverse mixture of different progenitor populations. Because of that, researchers have to use a more refined isolation method to specifically identify and study the most primitive stem cells. CD90 (Thy1) was one of the first markers which in combination with CD34 described a small population containing multilineage capacity (Baum et al., 1992; Murray et al., 1995). Over the following decade further studies postulated the absence of CD45RA and CD38 expression leads to a more enriched HSC population. As a result, HSCs have been described as CD34+CD38-CD45RA-CD90+ cells until recent years (Bhatia et al., 1997; Conneally et al., 1997; Lansdorp et al., 1990). This isolation strategy could only be refined by work from Notta et al., who proposed the addition of the integrin a6 (CD49f). As a result, the predominantly applied marker combination to prospectively isolate human HSCs is defined as Lin-CD34+CD38-CD45RA- CD90+CD49f+ cells (Notta et al., 2011). According to the hierarchical structure of hematopoiesis, HSCs give rise to MPPs, which exhibit reduced self-renewal capacity and increased cell-cycle activity (Adolfsson, 2005; Forsberg et al., 2006). As outlined in the previous chapter, distinct transcriptional stages within a progenitor population drive lineage priming, resulting in the subdivision of MPPs based on their differentiation potential and lineage bias. This process has been extensively characterized in murine models. In human fetal samples, the research group of John Dick identified bipotent megakaryocytic-erythroid progenitors using CD34⁺CD38⁻ HSPCs. By incorporating the surface markers BAH1 and CD71, they distinguished two MPP subpopulations: BAH1⁻CD71⁺ (F2; ~1.23 %) and BAH1⁺CD71⁺ (F3; ~0.01 %). These findings suggest that, during developmental hematopoiesis, lineage-restricted progenitors can emerge directly from multipotent progenitors without transitioning through an oligopotent stage. In adult bone marrow samples, the frequencies of F2 and F3 MPPs are significantly lower (~0.01 %). Consequently, MPPs in adult samples are commonly isolated using the marker combination Lin⁻CD34⁺CD38⁻CD45RA⁻CD90⁻CD49f⁻ (Notta et al., 2011, 2016; R Majeti, 2007). Downstream of MPPs the megakaryocyte-erythrocyte differentiation branch is one of the earliest differentiation steps (Alejo E. Rodriguez-Fraticelli, 2018; Carrelha et al., 2018). Megakaryocyte-erythrocyte progenitors (MEPs) can be isolated using the marker combination Lin⁻CD34⁺CD38⁺CD123⁻CD45RA⁻CD135⁻CD10⁻ (Doulatov, 2010; Manz et al., 2002; R Majeti, 2007). In parallel, MPPs give rise to lympho-myeloid primed progenitors (LMPPs), characterized by the marker profile CD34⁺CD38⁻Thy1⁻/loCD45RA⁺, which represents approximately 1 % of CD34⁺ cells. Although these cells exhibit multilineage lymphoid potential (B, T, and NK cells), previous studies indicated an absence of in vivo repopulating activity, suggesting a more lineage-restricted progenitor state (R Majeti, 2007). The differentiation of LMPPs progresses towards the formation of CMPs, granulocyte-monocyte progenitors (GMPs), and CLPs. CMPs are defined by the marker profile Introduction Page | 10 Lin⁻CD34⁺CD38⁺CD123lowCD45RA⁻CD135⁺CD10⁻, while GMPs express Lin⁻CD34⁺CD38⁺CD123⁺CD45RA⁺CD135⁺CD10⁻. CLPs, committed to lymphoid differentiation, are characterized as Lin⁻CD34⁺CD38⁻CD45RA⁺CD7⁺CD10⁺CD135⁺. Notably, myeloid progenitors express CD123 and CD135, whereas erythroid progenitors lack these markers. The transition from CMPs to GMPs is marked by the acquisition of CD45RA. Furthermore, single CD135⁺CD45RA⁻ CMPs have been demonstrated to produce all myeloid, but not lymphoid, lineages both in vitro and following transplantation (Doulatov, 2010; Edvardsson et al., 2006; Manz et al., 2002). Regarding lymphoid commitment, CD7 and CD10 are recognized as early markers for T and B cell precursors, respectively. Hoa et al. reported that CD7⁺ cells within the CD34⁺CD38⁻ HSPC population possess the potential to differentiate into B and NK cells but lack myeloid and erythroid potential. Consequently, CLPs are restricted to the lymphoid lineage, giving rise to T, B, and NK cells, while CMPs are committed to the myeloid-erythroid pathway. This stepwise differentiation trajectory highlights how multipotent, lineage-biased progenitors progressively give rise to lineage-specific, committed progenitors, underscoring the complexity and regulation of hematopoietic differentiation (Olson et al., 2020b). The major stem and progenitor cell populations are defined by their cell surface phenotypes, as listed next to each population and in the red bars below the schematics. Terminally differentiated cells are shown on the bottom, with arrows indicating inferred lineage relationships. In mice (right), hematopoietic stem cells (HSCs) give rise to transiently engrafting multipotent progenitors (MPPs) and immature lymphoid-biased progenitors, such as lympho-myeloid primed progenitors (LMPPs), which undergo gradual lymphoid specification. Myeloid and erythroid differentiation proceeds through well-defined progenitor populations, including common myeloid progenitors (CMPs), granulocyte-macrophage progenitors (GMPs), and megakaryocyte-erythroid progenitors (MEPs). In humans (left), HSCs are identified by the expression of CD49f and other markers, with multipotent progenitors (MPPs) characterized by the loss of CD49f expression. Similar to mice, human hematopoiesis includes well-defined myelo-erythroid progenitor populations (CMPs, GMPs, and MEPs). Figure 3. Differentiation hierarchy displaying the lineage determination in adult mouse and humans. Introduction Page | 11 After introducing the different lineages that emerge from HSCs, we will shift our focus primarily to HSCs and their heterogeneity. As previously mentioned, numerous experiments have demonstrated that the HSC population is much more heterogeneous than initially thought. Thus, in the following chapter, we will explore the definitional framework to characterize HSCs and the various factors influencing HSC heterogeneity in greater depth. 1.4. The definition of hematopoietic stem cells 1.4.1. The terminological framework HSCs are defined by their ability to self-renew and replenish all the cell types presented in the hematopoietic system. Their multipotency was first defined in the middle of the 20th century by the first successful mouse transplantation of lethally irradiated mice (LO Jacobson, 1951). Following in vivo experiments using syngeneic BM cells transplanted into mice resulting in myeloid and erythroid colonies cells found in the spleen. These experiments gave more insight into the estimated stem cell numbers of these spleen colony forming units (CFU-S). It could be estimated that within 10,000 BM cells, 1 cell is capable of colonizing the spleen (AJ Becker, 1963; JE Till, 1961). In the 1990s, single-cell transplantation experiments were performed to truly show the capacity of a single blood-forming HSC to generate all hematopoietic lineages (Osawa et al., 1996). With these advances in technology, HSCs could be separated in LT-HSCs with a long-term reconstitution capacity over 3 - 4 months and ST-HSCs, multipotent cells which are only able to sustain hematopoiesis over a short period of time (approximately < 1 month), without durable and serial reconstitution ability. With these stepwise differentiation steps, progenitor populations limit their self- renewal potential (Yang et al., 2005). Until today, serial transplantation (secondary or tertiary recipient) displays the gold standard to interpret the self-renewal capacity of a hematopoietic subpopulation (Dykstra et al., 2007). To test human hematopoietic cell engraftment and multilineage reconstitution, the population of interest is injected into sublethal irradiated immune-deficient mice (NOD/SCID IL2gcnull mouse, referred to as NSG) as xenograft model (M. Ito et al., 2002). While HSC transplantation models offer significant advantages and valuable insights, it is important to acknowledge their limitations: these models create artificially induced conditions that compel the cells to expand, thereby failing to provide an uncompromised view of their natural fate in an unperturbed environment. As a result, they reveal the cells' "potential" fate rather than their actual behavior under physiological conditions (Haas et al., 2018). 1.4.2. Heterogeneity of HSCs As discussed in the previous chapter, HSCs are typically defined based on their immunophenotypic profiles, characterized by the expression of specific surface markers (Lin-CD34+CD38-CD45RA-CD90+CD49f+). While prospective isolation using monoclonal antibodies and FACS is a widely used and straightforward method, it often results in a relatively heterogeneous cell population, as it overlooks crucial factors contributing to HSC diversity, such as genetic and epigenetic variations, metabolic states, and cell cycle dynamics (Haas et Introduction Page | 12 al., 2018). To gain a deeper understanding of HSC heterogeneity, it is essential to explore and dissect these underlying factors in greater detail. As displayed in Figure 4A the BM microenvironment plays a pivotal role in shaping HSC heterogeneity. HSCs localize to distinct niches composed of various cell types (e.g., stromal, endothelial, and immune cells), each delivering unique biochemical and biophysical signals and influencing (Acar et al., 2015; Bruns et al., 2014; Ding & Morrison, 2013; Greenbaum et al., 2013; Sugimura et al., 2012; M. Zhao et al., 2014). Cytokines, extracellular matrix stiffness and niche-specific signals influence HSC function and lineage decisions (Anthony & Link, 2014; Asada et al., 2017; Çelebi et al., 2011; Choi et al., 2015; Ehninger & Trumpp, 2011; Lee-Thedieck et al., 2012; Mossadegh-Keller et al., 2013; Pinho & Frenette, 2019; Rieger et al., 2009; Uckelmann et al., 2016). HSC heterogeneity is also driven by chromatin accessibility, epigenetic alterations (e.g., DNA methylation and chromatin remodeling) and genetic mutation acquired over time (Beerman et al., 2013; Bock et al., 2012; Cabezas-Wallscheid, 2014; Cui et al., 2009; Farlik et al., 2016; Lara-Astiaso et al., 2014; Lipka et al., 2014; Pastore & Levine, 2016). These changes affect HSC maintenance, lineage commitment and response to stress or aging, contributing to variability in their function (Figure 4B+C). Variations in HSC cellular states (e.g., cell cycle phase, metabolic activity, and quiescence) further contribute to heterogeneity. Quiescent HSCs often rely on glycolysis to minimize reactive oxygen species (ROS) production, protecting DNA integrity, while active HSCs show increased biosynthesis and oxidative phosphorylation (Figure 4D) (K. Ito et al., 2012; K. Ito & Suda, 2014; Qian et al., 2016). Asymmetric segregation of cellular components, such as proteins and organelles, during cell division may lead to distinct fates in daughter cells (Beckmann et al., 2007). While mechanisms like Cdc42-mediated polarity have been observed, their precise role in HSC fate determination requires further exploration (Figure 4E) (Florian et al., 2012). The biochemical reactions and their stochastic nature taking place during cellular processes such as transcription or translation generate variability among HSCs, even in identical conditions (Figure 4F) (Elowitz et al., 2002; Raj & van Oudenaarden, 2008). To understand how lineage bias arises from HSC heterogeneity and how extrinsic signals affect the lineage output it is essential to understand the hematopoietic regulation during physiological stress, which will be further discussed in the following chapter. Introduction Page | 13 Figure 4. Distinct sources contribute to heterogeneity within the HSC compartment. (A) HSCs localize to distinct bone marrow niches, each characterized by unique biochemical and biophysical microenvironments that influence HSC fate and function. (B) Somatic mutations in HSCs and their progeny can give rise to genetic mosaics, leading to altered functional properties and contributing to clonal diversity. (C) Differential epigenetic modifications, including DNA methylation and histone modifications, across individual HSCs generate epigenetic heterogeneity that impacts lineage commitment and differentiation potential. (D) Variability in cellular states, such as differences in cell cycle phase, metabolic activity, or quiescence, contributes to biomolecular heterogeneity within the HSC population. (E) Asymmetric segregation of cellular components during HSC division can result in daughter cells with distinct functional capacities, further contributing to intra-compartmental diversity. (F) Intrinsic stochastic processes, including random fluctuations in gene expression and molecular interactions, drive cellular variability and influence HSC behavior and differentiation outcomes (taken from Haas, Trumpp and Milsom, 2018). 1.5. Emergency hematopoiesis Hematopoiesis must be very adaptable and responsive system to external stimuli like infection, chemotherapy or blood loss and tailor their lineage output and cell production to a specific demand. Pathogen driven infections and associated inflammations mobilize present innate immune effector cells as first line of defense (Cronkite & Strutt, 2018). These cells are rapidly used and need to be continuously replenished. This need shifts the steady state hematopoiesis into emergency hematopoiesis through activation of HSCs, increased proliferation and temporary expansion of the HSC pool to boost immune cell production (King & Goodell, 2011; E. M. Pietras, 2017; Takizawa et al., 2012). As stated in the previous chapter, HSCs are responsive to cytokines and chemokines through their extracellular and intracellular receptors. Especially inflammatory cytokines like Interferon I and II (IFN), Interleukin 6 (IL-6) or granulocyte colony stimulating factor (G-CSF) lead to HSC activation and emergency myelopoiesis and granulopoiesis to initiate the innate immune response (Baldridge et al., 2010a; Boettcher & Manz, 2017; Essers et al., 2009; Hirche et al., 2017; Schuettpelz & Link, 2013; Wilson, 2008). During this process, the lineage-biased HSCs play an important role to drive emergency production into certain cell lines and serve as an emergency backup for stress, capable of efficiently and specifically counter-balance the sudden loss of certain cell Introduction Page | 14 types. Haas et al. demonstrated this mechanism with megakaryocytic-restricted progenitors expressing von Willebrand factor (VWF) in a phenotypic HSC compartment which drive an emergency megakaryopoiesis in demand-driven response to infections or tissue damage (Haas, 2015; Haas et al., 2018). Despite target- driven manners, broader homeostatic perturbations can elicit similar effects on differentiation trajectories. The same HSC-like megakaryocyte progenitors activated by thrombopoietin-depended mechanism result in rapid expansion of the megakaryocyte lineage after platelet depletion (Olson et al., 2020b; Sanjuan-Pla et al., 2013). In addition to cytokine driven inflammatory reactions which can influence HSC biology indirectly, circulating HSPCs can directly sense infectious agents through pathogen-associated molecular patterns (PAMPs) by their expressed toll-like receptors (TLR) (J. M. Kim et al., 2005; Nagai et al., 2006; Sioud et al., 2006). The recognition by TLR can induce HSPC activation and differentiation into the myeloid lineages, stimulate cytokine release and boost the immune cell production (Figure 5) (Sezaki et al., 2020). These mechanisms collectively demonstrate the remarkable adaptability of the hematopoietic system, allowing for rapid and targeted responses to diverse physiological challenges, whether they be infectious threats or acute cellular depletions, ensuring the maintenance of hematopoietic homeostasis. Multiple protection mechanisms allow activated HSCs to restore their quiescence after induced inflammatory stress to ensure lifelong fitness of the hematopoietic system. For instance, in the lab of Emmanuelle Passegué It was shown that after acute stimulation, HSCs become desensitized to IFN-1 signaling and desensitize to further stimulation, terminating the response as a negative feedback loop (E. M. Pietras et al., 2014). On the contrary to acute inflammatory reactions, which can be balanced back to steady-state homeostasis, chronic inflammation or recurring infections can lead to decreased self-renewal and repopulating capacity of HSCs (Essers et al., 2009; Florez et al., 2020; E. M. Pietras, 2017). These effects are adding during the increased lifespan of a being. Therefore, aging is often associated with chronic low- grade inflammation and distinct lineage bias and molecular signatures which are further discussed in the following chapter (Vasto et al., 2007). Introduction Page | 15 Figure 5. Mechanism of emergency hematopoiesis induced by pathogen exposure. Steady-state hematopoiesis is characterized by the maintenance of lifelong blood cell production through the tightly regulated processes of self-renewal and differentiation of hematopoietic stem and progenitor cells (HSPCs). This homeostatic mechanism ensures the continuous replenishment of lymphoid and myeloid progenitors, sustaining the hematopoietic trajectory. This tightly regulated and balanced process can be perturbed by infections, entering the bone marrow via systemic blood circulation. Through various pattern recognition receptors (like toll-like receptors-TLR) HSPCs are activated, which induces proliferation and differentiation biases towards myeloid lineages. Pro-inflammatory cytokines (G-CSF/IL-6/IL-1/TNF) further stimulate granulopoiesis and myelopoiesis, prioritizing the generation of innate immune cells crucial for pathogen clearance (taken from Sezaki et al., 2020). 1.6. Aging Since the beginning of the 19th century, human life expectancy has doubled, rising from less than 30 years to over 72 years (Finch, 2009). This increase presents the unique challenges for HSCs to maintain their fitness during their significantly extended lifespan while exposure of various stressors accumulates over time (Mansell et al., 2023). In general, aging is associate with profound molecular and functional changes in both mature and immature hematopoietic cells, leading to a decline in the adaptive and immature immune response and HSC function (Kovtonyuk et al., 2016). These age-related alterations increase the susceptibility to infections and development of autoimmunity and hematologic malignancies (Dorshkind et al., 2009). Aged HSCs exhibit expanded pool sizes but diminished self-renewal capacity, accompanied by a bias toward myeloid differentiation (Chambers et al., 2007; Dykstra et al., 2011; Harrison & Astle, 1982; Morrison et al., 1996; Rossi et al., 2005; Sudo et al., 2000). In murine models, phenotypic HSCs upregulate the myeloid marker CD150, resulting in an increased prevalence of myeloid-biased HSCs (Figure 6). Transplantation studies further confirm that aged HSCs preferentially reconstitute myeloid lineages over serial transplantations (Beerman et al., 2010; Challen et al., 2010; Rossi et al., 2005). Similar tendencies Introduction Page | 16 were reported for the human hematopoietic system with aged immunophenotypic HSCs (Lin-CD34+CD38- CD90+CD45RA-) showing increased cell frequency, decreased quiescent and detectable myeloid differentiation bias (Pang et al., 2011). The increased myelopoiesis aligns with the hematopoietic alterations during inflammations discussed previously. Figure 6. Age-related changes in lineage output. A) Steady-state hematopoiesis: Diverse HSC subsets, including platelet-biased, lymphoid-biased, myeloid-biased and balanced HSCs, contribute equally to maintain equilibrium between myeloid and lymphoid lineages. B) Aged hematopoiesis: Aging induces an increased frequency of myeloid-biased HSCs, including platelet-biased HSCs. This shift results in enhanced production of myeloid progenitors and mature myeloid cells, leading to the characteristic myeloid bias observed in elderly individuals (taken from Kovtonyuk et al., 2016; Oakley C. Olson, Kang and Passegué, 2020). Besides lineage bias and increased HSC frequency, intrinsic aging mechanism impact HSC functionality. The accumulation of DNA damages, oxidative stress, telomer shortening as well as epigenetic and transcriptomic alterations highlight the complex interplay between cellular damage and transcriptional reprogramming driving hematopoietic dysfunction over time (Collado et al., 2007; K. Ito & Suda, 2014; Rossi et al., 2007). Especially somatic mutation in genes involved in epigenetic regulations such as DNMT3A, ASXL1 or TET2 show driving effects in the age-related clonal hematopoiesis (CH) (Busque et al., 2012; Dorsheimer et al., 2019; Jaiswal & Ebert, 2019; Pardali et al., 2020). These mutations are rare (<1 %) in younger individuals but become increasingly common with age. By the age of 70, >10 % of individuals harbor clones of appreciable size. Clinically, the presence of specific hematopoietic clones is described as clonal hematopoiesis of indeterminate potential (CHIP), characterized by an expanded somatic blood cell clone (>4 %) without other hematological abnormalities (Genovese et al., 2014; Jaiswal et al., 2014; Jaiswal & Ebert, 2019; Steensma et al., 2015). While CHIP has been reported to be associated with increased risk of developing blood malignancies, only 0.5 -1 % of individuals harboring CHIP develop these with various factors as clone size, mutation numbers and the specify of the mutated gene influencing the outcome (Genovese et al., 2014; Jaiswal & Ebert, 2019). Interestingly, recent publication showed a significant Introduction Page | 17 correlation of CHIP in TET2 and DNMT3A with poor prognosis in chronic heart failure patients underlying the complex interplay between the hematopoietic system with other organs (Dorsheimer et al., 2019). These observations highlight the molecular alterations that affect hematopoietic fitness during aging and emphasize the importance of understanding HSC biology in the context of extended human lifespans. 1.7. Objectives of the Present Study Over the past six decades, HSCs have been extensively studied and characterized for their pivotal role in hematopoiesis. Significant advancements have been made in the murine model, where the isolation of a relatively homogeneous immature subpopulation using phenotypic markers has enabled detailed functional characterization. However, the human HSC compartment remains less well-defined, with current isolation strategies yielding heterogeneous populations due to a lack of specific prospective markers. This study aims to identify and isolate highly purified human HSCs from healthy donors across different age cohorts, through an integrated multi-omics approach. By integrating FACS-based enrichment of human HSPC subpopulations (CD34+ and CD34+CD38-) with high-resolution single-cell sequencing technologies, the presented project aims to comprehensively profile both the transcriptome and surface proteome of these cells. This multi-omics approach seeks to uncover novel surface markers specific to undifferentiated HSC populations and elucidate their differentiation trajectories with unprecedented detail. Leveraging single-cell methodologies, we intend to dissect the heterogeneity within phenotypically similar HSCs and distinguish them from multipotent progenitors and potentially revealing distinct HSC subsets and their molecular signature. Newly identified markers will undergo functional validation through in vitro molecular characterization and in vivo transplantation assays to assess long-term repopulating potential. The findings of this study have the potential to refine HSC enrichment strategies, enhance our understanding of human hematopoiesis and contribute to improved therapeutic and clinical applications in the field of stem cell biology and regenerative medicine. Materials & Methods Page | 18 2. Materials and Methods The methods and materials outlined below are primarily standardized protocols from Prof. Michael Rieger's group (Department of Medicine, Hematology/Oncology, Goethe University Hospital Frankfurt/Main, Germany) and have been adapted from general work instructions. Some methods are described in already published articles and cited accordingly. Graphical illustrations were created using BioRender.com. 2.1. Materials 2.1.1. Chemicals and Reagents Table 1. Chemicals and reagents used in the presented thesis. Chemical Company Agencourt AMPure XP Beckman Coulter, Brea, California, USA Dimethylsulfoxide (DMSO) AppliChem, Darmstadt, Germany EDTA solution 0.5 M, pH 8.0 Invitrogen by Thermo Fisher Scientific, Waltham, Messachusetts USA Ethanol, absolute (≥99.8 %) Roth, Karlsruhe, Germany Hank's Balanced Salt Solution (HBSS) Sigma, St.Louis, Missouri, USA Isopropanol Sigma Aldrich, St.Louis, Missouri, USA PhiX Sequencing Control V3 Illumina, San Diego, California, USA Picric acid, saturated aqueous solution Sigma, St.Louis, Missouri, USA Sodium azide (NaN3) Sigma, St.Louis, Missouri, USA Trypan blue Sigma, St.Louis, Missouri, USA 2.1.2. Enzymes Table 2. Enzymes used in the presented thesis. Enzymes Company Deoxyribonuclease (DNase) I solution (1 mg/mL) Stem cell technologies, Vancouver, Canada NEBNext® High-Fidelity 2× PCR Master Mix New England Biolabs GmbH, Frankfurt/Main, Germany Q5® Hot Start High-Fidelity DNA Polymerase New England Biolabs GmbH, Frankfurt/Main, Germany Materials & Methods Page | 19 2.1.3. Antibodies 2.1.3.1. Fluorochrome conjugated antibodies used for flow cytometry analysis Table 3. Fluorochrome conjugated antibodies used in the presented thesis. Antigen Clone Conjugate Company CD2 RPA-2.10 Biotin eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA CD3 OKT3 Biotin BV510, PerCP-Cy5.5 eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA BD Bioscience, Frankling Lakes, New Jersey, USA CD4 SK3 FITC BD Bioscience, Frankling Lakes, New Jersey, USA CD10 HI10a PE-Cy7 BD Bioscience, Frankling Lakes, New Jersey, USA CD14 61D3 Biotin eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA CD16 CB16 Biotin eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA CD19 HIB19 Biotin, PE, PE-Cy7 eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA; BD Bioscience, Frankling Lakes, New Jersey, USA CD25 PE BD Bioscience, Frankling Lakes, New Jersey, USA CD33 P67.6 APC BD Bioscience, Frankling Lakes, New Jersey, USA CD34 8G12 APC, FITC BD Bioscience, Frankling Lakes, New Jersey, USA CD38 HB7 APC-H7, PE BD Bioscience, Frankling Lakes, New Jersey, USA; eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA CD49f GoH3 PE-Cy5 eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA CD45 2D1, HI30 FITC, V450-c, BV711 BD Bioscience, Frankling Lakes, New Jersey, USA CD45RA HI100 BV570 BioLegend, San Diego, California, USA CD45.1 A20 PerCP-Cy5.5, PE eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA /BioLegend CD56 CMSSB (NCAM) Biotin eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA CD69 FN50 R718 BD Bioscience, Frankling Lakes, New Jersey, USA CD71 OKT9 FITC eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA CD90/Thy1 5E10 PerCP-Cy5.5 BD Bioscience, Frankling Lakes, New Jersey, USA CD110/BAH1 BAH-1 PE BD Bioscience, Frankling Lakes, New Jersey, USA CD123 9F5 BV421 BD Bioscience, Frankling Lakes, New Jersey, USA Materials & Methods Page | 20 CD133 AC133 APC Miltenyi Biotec B.V. & Co. KG, Bergisch Gladbach, Germany CD197 (CCR7) 2-L1-A RB780 BD Bioscience, Frankling Lakes, New Jersey, USA CD235a HIR2 Biotin eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA CD273 (PD-L2) MIH18 BV711, BV650 BD Bioscience, Frankling Lakes, New Jersey, USA CD274 (PDL1) MIH1 PE-Cy7 BD Bioscience, Frankling Lakes, New Jersey, USA CD278 (ICOS) DX29 BV650 BD Bioscience, Frankling Lakes, New Jersey, USA CD279 (PD1) EH12.1 BV786 BD Bioscience, Frankling Lakes, New Jersey, USA FoxP3 236A/E7 R718 BD Bioscience, Frankling Lakes, New Jersey, USA KI67 FITC BD Bioscience, Frankling Lakes, New Jersey, USA TER119 TER-119 APC-eFlour®780 eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA Streptavidin PE-Dazzle 594 BioLegend, San Diego, California, USA Fixable viability dye eFluor®780, eFluor®506 eBioscience by Thermo Fisher Scientific, Waltham, Massachusetts, USA 2.1.3.2. Unconjugated Primary antibodies used for protein analysis and neutralization Table 4. Unconjugated primary antibodies used in the presented thesis. Antibody Company Monoclonal PD-L2 Cell Signaling Technology, Danvers, Massachusetts, USA Polyclonal HLF Thermo Fischer Scientific, Waltham, MA, USA Monoclonal ATP1B1 Cell Signaling Technology, Danvers, Massachusetts, USA Monoclonal a Tubulin Cell Signaling Technology, Danvers, Massachusetts, USA Human PD-L2/B7-DC Antibody R&D Systems, Minneapolis, USA Normal Goat IgG Control R&D Systems, Minneapolis, USA 2.1.4. Cytokines and small molecules used for human cell culture Table 5. Cytokines and small molecules used in the presented thesis. Cytokines Company Recombinant human SCF Peprotech by Thermo Fisher Scientific, Waltham, Massachusetts, USA Recombinant human TPO Peprotech by Thermo Fisher Scientific, Waltham, Massachusetts, USA Materials & Methods Page | 21 Recombinant human FLT-3 Peprotech by Thermo Fisher Scientific, Waltham, Massachusetts, USA Recombinant human IL-3 Peprotech by Thermo Fisher Scientific, Waltham, Massachusetts, USA Recombinant human IL-2 Peprotech by Thermo Fisher Scientific, Waltham, Massachusetts, USA TNF-a Peprotech by Thermo Fisher Scientific, Waltham, Massachusetts, USA UM171 STEMCELL Technologies, Vancouver, Canada Human LDL STEMCELL Technologies, Vancouver, Canada 2.1.5. Cell culture medium Table 6. Medias used in the presented thesis. Medium Components MLR assay media SFEM II, 100 ng/ml human SCF, 100 ng/ml human TPO, 100 ng/ml human FLT-3, 100 ng/ml human IL-3, 50 UE human IL-2, 38 nM UM171, 50ng/ml human LDL and 1 % Pen Strep HSPCs media SFEM II, 100 ng/ml human SCF, 100 ng/ml human TPO, 100 ng/ml human FLT-3, 100 ng/ml human IL-3, 38 nM UM171, 50ng/ml human LDL and 1 % Pen Strep TNF a media SFEM II, 100 ng/ml human SCF, 100 ng/ml human TPO, 100 ng/ml human FLT-3, 100 ng/ml human IL-3, 38 nM UM171, 50ng/ml human LDL, 1 µg/ml human TNF a and 1 % Pen Strep BSA control media SFEM II, 100 ng/ml human SCF, 100 ng/ml human TPO, 100 ng/ml human FLT-3, 100 ng/ml human IL-3, 38 nM UM171, 50ng/ml human LDL, 0.001 % BSA and 1 % Pen Strep Freezing medium I 50 % StemSpan SFEM II, 50 % FCS Freezing medium II 50 % StemSpan SFEM II, 30 % FCS, 20 % DMSO Thawing medium DMEM, 2 % FCS 2.1.6. Kits Table 7. Kits used in the presented thesis. Kit Company Anti-Rabbit Detection Module for Jess, Wes, Peggy Sue or Sally Sue Protein Simple, San Jose, USA BD Pharmingen™ FITC Mouse Anti-Ki-67 Set BD Bioscience, Franklin Lakes, New Jersey, USA BD Pharmingen™ Transcription Factor Buffer Set BD Bioscience, Franklin Lakes, New Jersey, USA BD Rhapsody™ Cartridge Kit BD Bioscience, Franklin Lakes, New Jersey, USA BD Rhapsody™ Cartridge Reagent Kit BD Bioscience, Franklin Lakes, New Jersey, USA Materials & Methods Page | 22 BD Rhapsody™ cDNA kit BD Bioscience, Franklin Lakes, New Jersey, USA BD Rhapsody™ WTA Amplification Kit BD Bioscience, Franklin Lakes, New Jersey, USA BD™ single-Cell Multiplexing Kit Human Immune Sample Tag BD Bioscience, Franklin Lakes, New Jersey, USA CD34 MicroBead Kit UltraPure, human Miltenyi Biotec, Bergisch Gladbach, Germany High Sensitivity DNA ScreenTape Analysis Agilent Technologies, Santa Clara, California, USA High Sensitivity RNA ScreenTape Analysis Agilent Technologies, Santa Clara, California, USA LEGENDplex™ Human CD8/NK Panel (13-plex) w/ VbP V02 BioLegend, San Diego, California, USA LEGENDplex™ Human HSC Myeloid Panel (7-plex) with V- bottom Plate BioLegend, San Diego, California, USA NextSeq 2000 P3 Reagents (200 Cycles) Illumina, San Diego, California, USA NextSeq 500/550 Mid Output Illumina, San Diego, California, USA miRNeasy Micro Kit Qiagen, Venlo, Netherlands Qubit dsDNA HS Assay Kit Thermo Fisher Scientific, Waltham, Massachusetts, USA Qubit RNA HS Assay Kit Thermo Fisher Scientific, Waltham, Massachusetts, USA SMART®-Seq HT Kit Talara, San Jose, USA T-cell Activation/Expansion Kit, human Miltenyi Biotec, Bergisch Gladbach, Germany 2.1.7. Consumables Table 8. Instruments used in the presented thesis. Instrument Company 2100 Bioanalyzer Agilent Technologies, Santa Clara, California, USA BD FACSAriaIII cell sorter BD Bioscience, Franklin Lakes, New Jersey, USA BD FACSCelesta BD Bioscience, Franklin Lakes, New Jersey, USA BD LSRFortessaII BD Bioscience, Franklin Lakes, New Jersey, USA CellObserver 430 optical microscope Carl Zeiss AG, Oberkochen, Germany Centrifuge Rotanta 460R Hettich, Tuttlingen, Germany Centrifuge, Heraeus Megafuge 1.0R Thermo Fisher Scientific, Waltham, Massachusetts, USA Centrifuge, tabletop (Rotanta 200, 220R) Hettich, Tuttlingen, Germany Clean bench, HERAsafe KSP Thermo Fisher Scientific, Waltham, Massachusetts, USA CO2 Incubator, HERAcell 150i Thermo Fisher Scientific, Waltham, Massachusetts, USA Materials & Methods Page | 23 DNA electrophoresis chamber BioRad, Hercules, California, USA Freezer - 20° C and refrigerators Liebherr, Bulle, Switzerland Freezer - 80° C, Heraeus Thermo Fisher Scientific, Waltham, Massachusetts, USA Incubator, Heraeus Thermo Fisher Scientific, Waltham, Massachusetts, USA Leica CM3050 S Kryostat Leica Mikrosysteme Vertrieb GmbH, Wetzlar, Germany MiSeq Illumina, San Diego, Carlifornia, USA Nano-Drop 1000 spectrometer Thermo Fischer Scientific, Waltham, MA, USA NextSeq 500 Illumina, San Diego, Carlifornia, USA Qubit 3 Fluorometer Invitrogen by Thermo Fischer Scientific, Waltham, MA, USA Qubit 4 Fluorometer Invitrogen by Thermo Fischer Scientific, Waltham, MA, USA T100 Thermal cycler BioRad, Hercules, Berkeley, CA, USA Thermomixer Biometra, Analytik Jena, Jena, Germany Ultracentrifuge Optima L-90K Beckman Coulter, Pasadena, CA, USA UV-Transilluminator GelDoc 2000 BioRad, Hercules, Berkeley, CA USA Vacuum pump Integra Biosciences, Fernwald, Germany Vortex Minishaker Roth, Karlsruhe, Germany 2.1.8. Laboratory equipment Table 9. Laboratory equipment used in the presented thesis. Laboratory equipment Company Aspiration pipettes (2 mL) BD Bioscience, Franklin Lakes, New Jersey, USA Cell culture flasks (25, 75 and 175 cm2) BD Bioscience, Franklin Lakes, New Jersey, USA Combitips plus (1, 5 and 10 mL) Hettich, Tuttlingen, Germany Conical polystyrene tubes Hettich, Tuttlingen, Germany Counting chamber C-Chip, Neubauer improved Thermo Fisher Scientific, Waltham, Massachusetts, USA Cryotubes (1 and 2 mL) Thermo Fisher Scientific, Waltham, Massachusetts, USA DNA LoBind Tubes Eppendorf, Hamburg, Germany Disposable base mold, 15×15 mm Thermo Fisher Scientific, Waltham, Massachusetts, USA Falcon sterile centrifuge tubes (5, 15 and 50 mL) Greiner Bio-One, Frickenhausen, Germany Freezing box neoLab, Heidelberg, Germany Materials & Methods Page | 24 Insulin syringe BD Bioscience, Franklin Lakes, New Jersey, USA Microvette Sarstedt, Nümbrecht, Germany Non-tissue culture plates (6 well) BD Bioscience, Franklin Lakes, New Jersey, USA Pasteur pipettes (glas) Roth, Karlsruhe, Germany Petri dishes, Greiner Greiner Bio-One, Frickenhausen, Germany Pipette tips (10, 100, 200 and 1000 μL) Gilson, Limburg-Offheim, Germany Polypropylene tube, sterile, round bottom, with cap, 5 mL Greiner Bio-One, Frickenhausen, Germany Protection gloves (Latex, Nitril) Meditrade, Kiefersfelden, Germany Reaction tubes (0.1, 0.2, 1.5 and 2 mL) Eppendorf, Hamburg, Germany Scalpels mediware Servoprax, Wesel, Germany Silicon stem cell inserts IBIDI, München, Germany Sterile cell strainer BD, Franklin Lakes, New Jersey, USA Sterile filters (0.22 and 0.50 μm) Merck Millipore, Billerica, Massachusetts, USA Sterile pipettes (2, 5, 10 and 25 mL) Costar, Corning, New York, USA Syringes (5,10 and 20 mL) Braun, Melsungen, Germany Thermo Scientific™ SuperFrost Plus™ Adhesion slides Thermo Fisher Scientific, Waltham, Massachusetts, USA Tissue culture dishes (3.5 and 10 cm) Greiner Bio-One, Frickenhausen, Germany Tissue culture plates (6, 12, 24, 96 well) Costar by Corning, Corning, New York, USA 2.1.9. Buffers and Solutions Table 10. Buffer and Solutions used in the presented thesis. Buffer/Solution Ingrediens/Source BD Pharm Lyse, Lysis Bu