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Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis

Nature Cell Biology volume 20, pages 836–846 (2018)Cite this article

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Abstract

The dynamics of haematopoietic stem cell differentiation and the hierarchy of oligopotent stem cells in the bone marrow remain controversial. Here we dissect haematopoietic progenitor populations at single cell resolution, deriving an unbiased reference model of transcriptional states in normal and perturbed murine bone marrow. We define the signature of the naive haematopoietic stem cell and find a continuum of core progenitor states. Core cell populations mix transcription of pre-myeloid and pre-lymphoid programs, but do not mix erythroid or megakaryocyte programs with other fates. CRISP-seq perturbation analysis confirms our models and reveals that Cebpa regulates entry into all myeloid fates, while Irf8 and PU.1 deficiency block later differentiation towards monocyte or granulocyte fates. Our transcriptional map defines a reference network model for blood progenitors and their differentiation trajectories during normal and perturbed haematopoiesis.

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Fig. 1: Multi-tier single-cell sequencing of haematopoietic progenitors.
Fig. 2: Characterization of lineage-primed haematopoietic progenitors.
Fig. 3: Identification of a transcription program associated with dormant stem cells.
Fig. 4: Exit from the HSC state is characterized by a myeloid–erythrocyte bifurcation.
Fig. 5: Stimulation by different cytokines activates a convergent exit from the HSC state.
Fig. 6: Initiation of neutrophil and monocyte transcriptional programs.
Fig. 7: Hierarchy of myeloid regulators revealed by CRISP-seq.
Fig. 8: Dynamic role of PU.1 in neutrophil and monocyte differentiation.

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  • 11 July 2018

    In the version of this Resource originally published, Supplementary Note 1 was missing from the attached Supplementary Information files. This has now been amended.

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Acknowledgements

The authors thank members of the Tanay and Amit laboratories for critical discussions. Research by I.A. and A.Ta. is supported by the Chan Zuckerberg Initiative. I.A. is supported by a Howard Hughes Medical Institute International Scholar Award, the European Research Council (309788), the Israel Science Foundation, the Ernest and Bonnie Beutler Research Program of Excellence in Genomic Medicine, the Helen and Martin Kimmel award for innovative investigation, a Minerva Stiftung research grant, the Israeli Ministry of Science, Technology and Space, the David and Fela Shapell Family Foundation and the Abramson Family Center for Young Scientists. I.A. is the incumbent of the Alan and Laraine Fischer Career Development Chair. Research in the A.Ta. laboratory is supported by the European Research Council, FAMRI, the I-CORE for chromatin and RNA regulation, and a grant from the Israel Science Foundation. A.Ta. is a Kimmel investigator. A.G. is a recipient of the Clore fellowship. F.P. is a fellow of the German–Israeli Helmholtz Research School in Cancer Biology. This work was supported by the Deutsche Forschungsgemeinschaft (SFB873), the José Carreras Leukämie-Stiftung and the Dietmar Hopp Stiftung (all to A.Tr.).

Author information

Author notes

  1. These authors contributed equally: Amir Giladi, Franziska Paul.

  2. These authors jointly supervised: Amos Tanay, Ido Amit.

Authors and Affiliations

  1. Department of Immunology, Weizmann Institute of Science, Rehovot, Israel

    Amir Giladi, Franziska Paul, Assaf Weiner, Ido Yofe, Diego Jaitin & Ido Amit

  2. Department of Computer Science and Applied Mathematics, Department of Biological Regulation, Weizmann Institute of Science, Rehovot, Israel

    Yoni Herzog, Yaniv Lubling & Amos Tanay

  3. Division of Stem Cells and Cancer, Deutsches Krebsforschungszentrum (DKFZ) and DKFZ-ZMBH Alliance, Heidelberg, Germany

    Nina Cabezas-Wallscheid & Andreas Trumpp

  4. Heidelberg Institute for Stem Cell Technology and Experimental Medicine (HI-STEM GmbH), Heidelberg, Germany

    Nina Cabezas-Wallscheid

  5. Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany

    Nina Cabezas-Wallscheid

  6. Singapore Immunology Network (SIgN), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore

    Regine Dress & Florent Ginhoux

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Contributions

A.G., F.P., A.Ta. and I.A. conceived the project and designed the experiments. F.P. performed the experiments. A.G. analysed the data. A.G., Y.H. and Y.L. developed computational algorithms. F.P., A.W., I.Y. and D.J. implemented the CRISP-Seq pipeline. N.C-W. and A.Tr. contributed the label retention assay. R.D. and F.G. supplied evidence of myeloid fate choice. A.G., F.P., A.Ta. and I.A. wrote the paper. A.Ta. and I.A. supervised the project.

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Correspondence to Amos Tanay or Ido Amit.

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Integrated supplementary information

Supplementary Figure 1 Multi-tiered collection and assessment of single cells.

a, A graphical representation of the seven sorting schemes (tiers) used to deplete and enrich different fractions of the bone marrow for MARS-seq. Each circle represents a tier; circle size is proportional to cell frequency in the bone marrow. Right panel zooms in on tier 3 sub-sorting. b, Summarized metadata for the multi-tiered sorting strategy. c, Summary of all cells analyzed in this work, divided into experimental procedures. #mice – number of biological replicates. #batches – number of technical replicates. #cells – number of analyzed cells (after filtering for cell quality). d-e, Number of Illumina reads (d), and detected molecules (unique molecular identifier, UMI) (e) per single cell. Cells are colored by experimental procedure as in c. f-g, Fraction of analyzed cells (f) and estimation of technical noise (g) for each amplification batch (Supplementary Table 1, Methods). Technical noise is assessed by genomic reads in empty wells as previously described20 (Methods). h-i, Color coded tracks summarizing single cells analyzed in this work. Colors represent mouse specimen (h, 47 mice total) and sequencing batches (i, 27 sequencing batches total).

Supplementary Figure 2 Unbiased reconstruction of bone marrow progenitors using a multi-tiered approach.

a, Sorting schemes for tiers 1,2 and 3. b, MetaCell analysis of tier 1 single cells (whole bone marrow), 28 meta-cells total. Lower bar indicates enrichment of clusters in tier 2. c, MetaCell analysis of tier 2 single cells (Lin-), 26 meta-cells total. Lower bar indicates enrichment of clusters in tier 3. d, MetaCell analysis of tier 3 single cells (Lin- c-Kit+), 228 meta-cells total.

Supplementary Figure 3 MetaCell analysis of tier 3 cells.

a-c, Selection of 376 marker genes for tier 3 clustering (labeled in red, Methods). Plots depict gene specifity (the fraction of its total expression that is concentrated in 20% of the cells) against its total expression (a), gene correlation to the total UMI count of cells against total gene expression (c), and gene variance to mean ratio against its mean value (calculated on a UMI table down-sampled to 750 UMI/cell). d, Co-clustering of tier 3 (Lin- c-Kit + ) cells. Values indicate how likely two cells are to belong to the same meta-cell in each bootstrap iteration. Color bars depict lineage annotation as in Fig. 2a–b. e, Meta-cell distribution across 105 tier 3 amplification batches; meta-cells are annotated as in Fig. 2a. Color bar represents individual mice and FACS sorting sessions. f, Expression of 15 lineage specific marker genes used for meta-cell annotation across 138 fine grained meta-cells of tier 3 cells (Fig. 2a–b). g, Digital expression of lineage markers across tier 3 cells. h, Expression of key lymphoid and B cell genes in tier 3 meta-cells. Axes represent geometric mean divided by the median value across all meta-cells. Meta-cells are annotated by lineage marker as in Fig. 2a–b. i, In silico gating schemes for seven classical hematopoietic progenitor populations. Single cells were assigned to gates after sorting by their recorded FACS indices. j, Localization of conventional hematopoietic progenitor FACS-based populations when projected onto all tier 3 connected components.

Supplementary Figure 4 Assembly of the hematopoietic core dataset.

a, In silico lineage depletion of hematopoietic progenitors derived from tiers 3, 5, 6 and 7 to generate the hematopoietic core dataset. Each lineage dictates a score based on expression of its enriched genes. Red lines represent score cutoffs determined for depletion (Methods). b, MetaCell analysis of the core dataset. Meta-cell annotation as in Fig. 3b. c, Co-clustering of the core dataset. Values indicate how likely two cells are to belong to the same meta-cell in bootstrap analysis. d, Distribution of tier 7 cells on the core meta-cells. Columns represent meta-cells of the core dataset. Top bars indicate the fraction of each core meta-cell in tier 7. Heat-map shows fold change of mean expression of HSC related genes across tier 7 meta-cells. Apoe and Gata2 mark erythroid/megakaryocyte priming. * marks the stringent HSC definition. e, Differential gene expression (log2 of pooled size-normalized expression) between cells from stringent HSC and the rest of the hematopoietic core dataset. Dashed lines indicate a 2-fold threshold. f, Correlation between stem-score (x axis) and proliferation signature (y axis) in single cells. Dashed lines represent stratification by top stem-score percentiles. g, Dormancy quantification by a label retaining assay, where GFP expression of H2B-GFP mice was inhibited by doxycycline administration for 150 days. h, Expression of genes defining the stem-score in label retaining cells (LRC), non-LR HSC and MPP1. n = 3 independent experiments, horizontal lines indicate mean (Supplementary Table 5). i, Expression patterns of prominent marker genes and transcription factors on the core model. j, Pairwise Pearson correlations across core meta-cells of erythrocyte and lymphoid/myeloid transcription factors with themselves (left) and non-transcription factors genes (right). n = 50 meta-cells over 9,307 single cells.

Supplementary Figure 5 Single cell characterization of hematopoietic progenitors stimulated with cytokines.

a, Experimental procedure of cytokine stimulation. Mice were injected with erythropoietin (Epo) or granulocyte-colony stimulating factor (G-CSF) for two consecutive days. Mice were sacrificed on third day (48 hours) and tiers 3 & 7 single cells were collected and processed for MARS-seq. b-c, Fold change in bone marrow composition (b) and Tier 3 (c-Kit+, c) fraction in the bone marrow of cytokine treated mice as determined by FACS. (Supplementary Table 5). d, Frequencies of transcriptionally annotated lineages (as in Fig. 2) in Epo or G-CSF treated mice vs. PBS treated control. Horizontal lines indicate mean. e, Distribution of the stem-score in tier 7 cells (CD150+ LT-HSC) collected from Epo treated, G-CSF treated and untreated mice. The central mark in box plot is median, with 5/95 percentiles at the whiskers and 25/75 percentiles at the box. Two sided Kolmogorov-Smirnov test. ***P « 10−5. For b-e, n = 4 (Epo), n = 3 (G-CSF), n = 5 (control) independent animals.

Supplementary Figure 6 Assembly of a myeloid progenitor map.

a, Sorting scheme for tier 4 cells. b, In silico Filtering of non-myeloid primed cells from tiers 3, 4 and 5 (like in Supplementary Fig. 4a, see Methods) to generate the myeloid progenitor data set. c, In silico gating schemes for progenitor subpopulations implicated in myeloid and DC development. d, Projections of tier 4 FACS-based myeloid progenitor populations onto the hematopoietic model. e, Detailed gene expression map of 36 meta-cells from the myeloid dataset (Fig. 6a). Each heat-map depicts single cell expression patterns for a subset of functionally related meta-cells. f, Correlation between the neutrophil (y axis) and the monocyte scores (x axis). Each data point represents a meta-cell from the myeloid dataset. g, Pearson correlation of transcription factors with the monocyte and neutrophil scores across the myeloid meta-cells. n = 36 meta-cells over 8,395 single cells.

Supplementary Figure 7 CRISP-seq screen for myeloid transcription factors.

a, Gene expression profiles of 15,049 CRISP-seq c-Kit+ (tier 3) single cells. Lower panel shows detection of gRNA (Supplementary Table 3). b, As in a but for 8,805 single cells featuring gRNA below the detection limit or with insignificant impact that were excluded from further analysis (Methods). c, Differential gene expression analysis comparing pooled lineage annotated cells from uninfected homeostatic mice (-) and infected cells from the CRISP-seq transplantation experiment ( + ). d, Erythroid share of gRNA-clones, as determined by the fraction of erythroid cells (annotated by Car1, Pf4, Mt2 and Hba-a2) per gRNA-clone plotted against gRNA-clone size. e, FDR corrected p-values (two sided Mann-Whitney) testing changes in lineage output for different gRNA compared to control gRNA. Bars indicate total number of cells for each gRNA. *p < 0.05.

Supplementary Figure 8 PU.1 is not essential for neutrophil priming.

a, Distribution of the neutrophil signature (Fig. 6c–h) in different CRISP-seq infected mice. Mice are colored by whether PU.1 was included in the virus mix. The central mark in box plot is median, with 5/95 percentiles at the whiskers and 25/75 percentiles at the box. n = 30 independent animals. b, FACS plots of CRISP-seq output 11 days after transplantation with a mix of LSK cells infected separately with mCherry PU.1 gRNA and BFP control gRNA (mix 3 and mix 4 in Supplementary Table 3). PU.1 KO Ly6G+ lack CD11b expression. These results were repeated in four independent animals. c, Gene expression profiles of 6,529 cells pooled from four mice transplanted with a mix of mCherry PU.1 gRNA and BFP control gRNA and grouped by MetaCell analysis. Lower panel indicate enrichments across different samples. d, Defining the mature neutrophil transcriptional program. Differential expression of genes seen solely in a subset of control cells and not found in PU.1 infected cells, compared to the rest of control and PU.1 infected cells (Supplementary Table 2). e, MetaCell analysis of 1,501 LSK cells infected with mCherry PU.1 gRNA and BFP control gRNA (mix 3 and mix 4 in Supplementary Table 3) after stimulation in vitro with G-CSF or GM-CSF. Lower panel indicates enrichments in different samples. f, FACS analyzed cell counts from the in vitro assay after 9 and 14 days. g, Fraction of monocytes in control and PU.1 gRNA infected cells grown with GM-CSF. h, Pooled expression of genes defining the monocyte score (Fig. 6d–j) in cells infected with PU.1 gRNA (y axis) and control gRNA (x axis). i, Representative May-Grünewald Giemsa stain of day 11 in vivo PU.1 KO Ly6G+ donor cells highlighting morphological segmentation. Image is representative of at least 2 independent experiments.

Supplementary information

Supplementary Information

Supplementary Figures 1–8, Supplementary Table, Supplementary Note legends.

Reporting Summary

Supplementary Table 1

Summary of all amplification batches used for analysis.

Supplementary Table 2

Gene modules used in this work.

Supplementary Table 3

Summary of CRISPR guide-RNAs.

Supplementary Table 4

Antibodies used for single-cell sorting.

Supplementary Table 5

Source data for Fig. 8 and Supplementary Figs 4, 5 and 8.

Supplementary Note

MetaCell — correcting and clustering single-cell RNA-seq data using k-nn graph covering.

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Giladi, A., Paul, F., Herzog, Y. et al. Single-cell characterization of haematopoietic progenitors and their trajectories in homeostasis and perturbed haematopoiesis. Nat Cell Biol 20, 836–846 (2018). https://doi.org/10.1038/s41556-018-0121-4

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