Hematopoietic stem cells retain functional potential and molecular identity in hibernation cultures.

Advances in the isolation and gene expression profiling of single hematopoietic stem cells (HSCs) have permitted in-depth resolution of their molecular program. However, long-term HSCs can only be isolated to near purity from adult mouse bone marrow, thereby precluding studies of their molecular program in different physiological states. Here, we describe a powerful 7-day HSC hibernation culture system that maintains HSCs as single cells in the absence of a physical niche. Single hibernating HSCs retain full functional potential compared with freshly isolated HSCs with respect to colony-forming capacity and transplantation into primary and secondary recipients. Comparison of hibernating HSC molecular profiles to their freshly isolated counterparts showed a striking degree of molecular similarity, further resolving the core molecular machinery of HSC self-renewal while also identifying key factors that are potentially dispensable for HSC function, including members of the AP1 complex (Jun, Fos, and Ncor2), Sult1a1 and Cish. Finally, we provide evidence that hibernating mouse HSCs can be transduced without compromising their self-renewal activity and demonstrate the applicability of hibernation cultures to human HSCs.

Single-cell molecular profiling provides a high-resolution map of basophil and mast cell development.

BACKGROUND: Basophils and mast cells contribute to the development of allergic reactions. Whereas these mature effector cells are extensively studied, the differentiation trajectories from hematopoietic progenitors to basophils and mast cells are largely uncharted at the single-cell level. METHODS: We performed multicolor flow cytometry, high-coverage single-cell RNA sequencing analyses, and cell fate assays to chart basophil and mast cell differentiation at single-cell resolution in mouse. RESULTS: Analysis of flow cytometry data reconstructed a detailed map of basophil and mast cell differentiation, including a bifurcation of progenitors into two specific trajectories. Molecular profiling and pseudotime ordering of the single cells revealed gene expression changes during differentiation. Cell fate assays showed that multicolor flow cytometry and transcriptional profiling successfully predict the bipotent phenotype of a previously uncharacterized population of peritoneal basophil-mast cell progenitors. CONCLUSIONS: A combination of molecular and functional profiling of bone marrow and peritoneal cells provided a detailed road map of basophil and mast cell development. An interactive web resource was created to enable the wider research community to explore the expression dynamics for any gene of interest.

Differentiation of transplanted haematopoietic stem cells tracked by single-cell transcriptomic analysis.

How transplanted haematopoietic stem cells (HSCs) behave soon after they reside in a preconditioned host has not been studied due to technical limitations. Here, using single-cell RNA sequencing, we first obtained the transcriptome-based classifications of 28 haematopoietic cell types. We then applied them in conjunction with functional assays to track the dynamic changes of immunophenotypically purified HSCs in irradiated recipients within the first week after transplantation. Based on our transcriptional classifications, most homed HSCs in bone marrow and spleen became multipotent progenitors and, occasionally, some HSCs gave rise to megakaryocytic-erythroid or myeloid precursors. Parallel in vitro and in vivo functional experiments supported the paradigm of robust differentiation without substantial HSC expansion during the first week. Therefore, this study uncovers the previously inaccessible kinetics and fate choices of transplanted HSCs in myeloablated recipients at early stage, with implications for clinical applications of HSCs and other stem cells.

Discrimination of Dormant and Active Hematopoietic Stem Cells by G0 Marker Reveals Dormancy Regulation by Cytoplasmic Calcium.

Quiescent hematopoietic stem cells (HSCs) are typically dormant, and only a few quiescent HSCs are active. The relationship between "dormant" and "active" HSCs remains unresolved. Here we generate a G0 marker (G0M) mouse line that visualizes quiescent cells and identify a small population of active HSCs (G0Mlow), which are distinct from dormant HSCs (G0Mhigh), within the conventional quiescent HSC fraction. Single-cell RNA-seq analyses show that the gene expression profiles of these populations are nearly identical but differ in their Cdk4/6 activity. Furthermore, high-throughput small-molecule screening reveals that high concentrations of cytoplasmic calcium ([Ca2+]c) are linked to dormancy of HSCs. These findings indicate that G0M separates dormant and active adult HSCs, which are regulated by Cdk4/6 and [Ca2+]c. This G0M mouse line represents a useful resource for investigating physiologically important stem cell subpopulations.

Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets.

Hematopoietic stem cells (HSCs) are an essential source and reservoir for normal hematopoiesis, and their function is compromised in many blood disorders. HSC research has benefitted from the recent development of single-cell molecular profiling technologies, where single-cell RNA sequencing (scRNA-seq) in particular has rapidly become an established method to profile HSCs and related hematopoietic populations. The classic definition of HSCs relies on transplantation assays, which have been used to validate HSC function for cell populations defined by flow cytometry. Flow cytometry information for single cells, however, is not available for many new high-throughput scRNA-seq methods, thus highlighting an urgent need for the establishment of alternative ways to pinpoint the likely HSCs within large scRNA-seq data sets. To address this, we tested a range of machine learning approaches and developed a tool, hscScore, to score single-cell transcriptomes from murine bone marrow based on their similarity to gene expression profiles of validated HSCs. We evaluated hscScore across scRNA-seq data from different laboratories, which allowed us to establish a robust method that functions across different technologies. To facilitate broad adoption of hscScore by the wider hematopoiesis community, we have made the trained model and example code freely available online. In summary, our method hscScore provides fast identification of mouse bone marrow HSCs from scRNA-seq measurements and represents a broadly useful tool for analysis of single-cell gene expression data.

Reconstructing Gene Regulatory Networks That Control Hematopoietic Commitment.

Hematopoietic stem cells (HSCs) reside at the apex of the hematopoietic hierarchy, possessing the ability to self-renew and differentiate toward all mature blood lineages. Along with more specialized progenitor cells, HSCs have an essential role in maintaining a healthy blood system. Incorrect regulation of cell fate decisions in stem/progenitor cells can lead to an imbalance of mature blood cell populations-a situation seen in diseases such as leukemia. Transcription factors, acting as part of complex regulatory networks, are known to play an important role in regulating hematopoietic cell fate decisions. Yet, discovering the interactions present in these networks remains a big challenge. Here, we discuss a computational method that uses single-cell gene expression data to reconstruct Boolean gene regulatory network models and show how this technique can be applied to enhance our understanding of transcriptional regulation in hematopoiesis.

PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells.

Single-cell RNA-seq quantifies biological heterogeneity across both discrete cell types and continuous cell transitions. Partition-based graph abstraction (PAGA) provides an interpretable graph-like map of the arising data manifold, based on estimating connectivity of manifold partitions ( https://github.com/theislab/paga ). PAGA maps preserve the global topology of data, allow analyzing data at different resolutions, and result in much higher computational efficiency of the typical exploratory data analysis workflow. We demonstrate the method by inferring structure-rich cell maps with consistent topology across four hematopoietic datasets, adult planaria and the zebrafish embryo and benchmark computational performance on one million neurons.

A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice.

Hematopoietic stem and progenitor cells (HSPCs) maintain the adult blood system, and their dysregulation causes a multitude of diseases. However, the differentiation journeys toward specific hematopoietic lineages remain ill defined, and system-wide disease interpretation remains challenging. Here, we have profiled 44 802 mouse bone marrow HSPCs using single-cell RNA sequencing to provide a comprehensive transcriptional landscape with entry points to 8 different blood lineages (lymphoid, megakaryocyte, erythroid, neutrophil, monocyte, eosinophil, mast cell, and basophil progenitors). We identified a common basophil/mast cell bone marrow progenitor and characterized its molecular profile at the single-cell level. Transcriptional profiling of 13 815 HSPCs from the c-Kit mutant (W41/W41) mouse model revealed the absence of a distinct mast cell lineage entry point, together with global shifts in cell type abundance. Proliferative defects were accompanied by reduced Myc expression. Potential compensatory processes included upregulation of the integrated stress response pathway and downregulation of proapoptotic gene expression in erythroid progenitors, thus providing a template of how large-scale single-cell transcriptomic studies can bridge between molecular phenotypes and quantitative population changes.

Mbd3/NuRD controls lymphoid cell fate and inhibits tumorigenesis by repressing a B cell transcriptional program.

Differentiation of lineage-committed cells from multipotent progenitors requires the establishment of accessible chromatin at lineage-specific transcriptional enhancers and promoters, which is mediated by pioneer transcription factors that recruit activating chromatin remodeling complexes. Here we show that the Mbd3/nucleosome remodeling and deacetylation (NuRD) chromatin remodeling complex opposes this transcriptional pioneering during B cell programming of multipotent lymphoid progenitors by restricting chromatin accessibility at B cell enhancers and promoters. Mbd3/NuRD-deficient lymphoid progenitors therefore prematurely activate a B cell transcriptional program and are biased toward overproduction of pro-B cells at the expense of T cell progenitors. The striking reduction in early thymic T cell progenitors results in compensatory hyperproliferation of immature thymocytes and development of T cell lymphoma. Our results reveal that Mbd3/NuRD can regulate multilineage differentiation by constraining the activation of dormant lineage-specific enhancers and promoters. In this way, Mbd3/NuRD protects the multipotency of lymphoid progenitors, preventing B cell-programming transcription factors from prematurely enacting lineage commitment. Mbd3/NuRD therefore controls the fate of lymphoid progenitors, ensuring appropriate production of lineage-committed progeny and suppressing tumor formation.

Reconstructing blood stem cell regulatory network models from single-cell molecular profiles.

Adult blood contains a mixture of mature cell types, each with specialized functions. Single hematopoietic stem cells (HSCs) have been functionally shown to generate all mature cell types for the lifetime of the organism. Differentiation of HSCs toward alternative lineages must be balanced at the population level by the fate decisions made by individual cells. Transcription factors play a key role in regulating these decisions and operate within organized regulatory programs that can be modeled as transcriptional regulatory networks. As dysregulation of single HSC fate decisions is linked to fatal malignancies such as leukemia, it is important to understand how these decisions are controlled on a cell-by-cell basis. Here we developed and applied a network inference method, exploiting the ability to infer dynamic information from single-cell snapshot expression data based on expression profiles of 48 genes in 2,167 blood stem and progenitor cells. This approach allowed us to infer transcriptional regulatory network models that recapitulated differentiation of HSCs into progenitor cell types, focusing on trajectories toward megakaryocyte-erythrocyte progenitors and lymphoid-primed multipotent progenitors. By comparing these two models, we identified and subsequently experimentally validated a difference in the regulation of nuclear factor, erythroid 2 (Nfe2) and core-binding factor, runt domain, alpha subunit 2, translocated to, 3 homolog (Cbfa2t3h) by the transcription factor Gata2. Our approach confirms known aspects of hematopoiesis, provides hypotheses about regulation of HSC differentiation, and is widely applicable to other hierarchical biological systems to uncover regulatory relationships.

Demystifying blood stem cell fates.

Determining the differentiation potential of stem and progenitor cells is essential for understanding their function, yet our ability to do so is limited by the restrictions of experimental assays. Based on single-cell functional and molecular profiling experiments, a new computational approach shows how lineage commitment may occur in human haematopoiesis.

A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation.

Maintenance of the blood system requires balanced cell fate decisions by hematopoietic stem and progenitor cells (HSPCs). Because cell fate choices are executed at the individual cell level, new single-cell profiling technologies offer exciting possibilities for mapping the dynamic molecular changes underlying HSPC differentiation. Here, we have used single-cell RNA sequencing to profile more than 1600 single HSPCs, and deep sequencing has enabled detection of an average of 6558 protein-coding genes per cell. Index sorting, in combination with broad sorting gates, allowed us to retrospectively assign cells to 12 commonly sorted HSPC phenotypes while also capturing intermediate cells typically excluded by conventional gating. We further show that independently generated single-cell data sets can be projected onto the single-cell resolution expression map to directly compare data from multiple groups and to build and refine new hypotheses. Reconstruction of differentiation trajectories reveals dynamic expression changes associated with early lymphoid, erythroid, and granulocyte-macrophage differentiation. The latter two trajectories were characterized by common upregulation of cell cycle and oxidative phosphorylation transcriptional programs. By using external spike-in controls, we estimate absolute messenger RNA (mRNA) levels per cell, showing for the first time that despite a general reduction in total mRNA, a subset of genes shows higher expression levels in immature stem cells consistent with active maintenance of the stem-cell state. Finally, we report the development of an intuitive Web interface as a new community resource to permit visualization of gene expression in HSPCs at single-cell resolution for any gene of choice.

Advancing haematopoietic stem and progenitor cell biology through single-cell profiling.

Haematopoietic stem and progenitor cells (HSPCs) sit at the top of the haematopoietic hierarchy, and their fate choices need to be carefully controlled to ensure balanced production of all mature blood cell types. As cell fate decisions are made at the level of the individual cells, recent technological advances in measuring gene and protein expression in increasingly large numbers of single cells have been rapidly adopted to study both normal and pathological HSPC function. In this review we emphasise the importance of combining the correct computational models with single-cell experimental techniques, and illustrate how such integrated approaches have been used to resolve heterogeneities in populations, reconstruct lineage differentiation, identify regulatory relationships and link molecular profiling to cellular function.