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.MRC, Wellcome Trust, Bloodwise, CRU

Sorting apples from oranges in single-cell expression comparisons.

Two methods for comparing single-cell expression datasets help address the challenge of integrating data across conditions and experiments

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.Work in the author’s laboratory is supported by grants from Bloodwise, Cancer Research UK, Biotechnology and Biological Sciences Research Council, Leukemia Lymphoma Society, the National Institute for Health Research Cambridge Biomedical Research Centre and core support grants by the Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust-MRC Cambridge Stem Cell Institute. S.N. and F.K.H. are recipients of Medical Research Council PhD Studentships. D.G.K. is supported by a Bloodwise Bennett Fellowship (15008) and a European Hematology Association Non-Clinical Advanced Research Fellowship

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.Wellcome Trust, MRC, CRUK, Bloodwise, Swedish Research Council, Helmholtz Association, German Center for Cardiovascular Research, German Research Foundatio

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

Funder: Karolinska InstitutetFunder: Magnus Bergvall FoundationFunder: Lars Hierta Memorial FoundationFunder: Swedish Cancer SocietyFunder: Åke Wiberg FoundationAbstract: 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

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.This work was supported by grants from Bloodwise, Cancer Research UK, Biotechnology and Biological Sciences Research Council, Leukemia Lymphoma Society, the National Institute for Health Research Cambridge Biomedical Research Centre, and core support grants by Wellcome Trust to the Cambridge Institute for Medical Research and Wellcome Trust-Medical Research Council Cambridge Stem Cell Institute. S.N. and F.K.H. are recipients of Medical Research Council PhD studentships. D.G.K. is the recipient of a Bennett Fellowship from Bloodwise, and E.L. is the recipient of a Sir Henry Dale Fellowship from the Wellcome Trust.This is the author accepted manuscript. The final version is available from the American Society of Hematology via http://dx.doi.org/10.1182/blood-2016-05-71648

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

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