Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

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

This article has been updated

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.

Your institute does not have access to this article

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

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.

Change history

  • 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.

References

  1. Orkin, S. H. & Zon, L. I. Hematopoiesis: an evolving paradigm for stem cell biology. Cell 132, 631–644 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. Till, J. E. & Mc, C. E. A direct measurement of the radiation sensitivity of normal mouse bone marrow cells. Radiat. Res. 14, 213–222 (1961).

    CAS  PubMed  Google Scholar 

  3. Spangrude, G. J., Heimfeld, S. & Weissman, I. L. Purification and characterization of mouse hematopoietic stem cells. Science 241, 58–62 (1988).

    CAS  PubMed  Google Scholar 

  4. Meuwissen, H. J., Gatti, R. A., Terasaki, P. I., Hong, R. & Good, R. A. Treatment of lymphopenic hypogammaglobulinemia and bone-marrow aplasia by transplantation of allogeneic marrow. Crucial role of histocompatibility matching. N. Engl. J. Med. 281, 691–697 (1969).

    CAS  PubMed  Google Scholar 

  5. Tho, E. D., Lochte, H. L., W, C. L. U. & Ferrebee, J. W. Intravenous infusion of bone marrow in patients receiving radiation and chemotherapy. N. Engl. J. Med. 257, 491–496 (1957).

    Google Scholar 

  6. Arinobu, Y. et al. Reciprocal activation of GATA-1 and PU.1 marks initial specification of hematopoietic stem cells into myeloerythroid and myelolymphoid lineages. Cell Stem Cell 1, 416–427 (2007).

    CAS  PubMed  Google Scholar 

  7. Pronk, C. J. et al. Elucidation of the phenotypic, functional, and molecular topography of a myeloerythroid progenitor cell hierarchy. Cell Stem Cell 1, 428–442 (2007).

    CAS  PubMed  Google Scholar 

  8. Schroeder, T. Hematopoietic stem cell heterogeneity: subtypes, not unpredictable behavior. Cell Stem Cell 6, 203–207 (2010).

    CAS  PubMed  Google Scholar 

  9. Yamamoto, R. et al. Clonal analysis unveils self-renewing lineage-restricted progenitors generated directly from hematopoietic stem cells. Cell 154, 1112–1126 (2013).

    CAS  PubMed  Google Scholar 

  10. Lara-Astiaso, D. et al. Immunogenetics. Chromatin state dynamics during blood formation. Science 345, 943–949 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. Cabezas-Wallscheid, N. et al. Identification of regulatory networks in HSCs and their immediate progeny via integrated proteome, transcriptome, and DNA methylome analysis. Cell Stem Cell 15, 507–522 (2014).

    CAS  PubMed  Google Scholar 

  12. Sawyers, C. L., Denny, C. T. & Witte, O. N. Leukemia and the disruption of normal hematopoiesis. Cell 64, 337–350 (1991).

    CAS  PubMed  Google Scholar 

  13. Sykes, M. & Nikolic, B. Treatment of severe autoimmune disease by stem-cell transplantation. Nature 435, 620–627 (2005).

    CAS  PubMed  Google Scholar 

  14. Spitzer, M. H. et al. An interactive reference framework for modeling a dynamic immune system. Science 349, 1259425 (2015).

    PubMed  PubMed Central  Google Scholar 

  15. Guo, G. et al. Mapping cellular hierarchy byÿsingle-cellÿanalysis of the cell surface repertoire. Cell Stem Cell 13, 492–505 (2013).

    CAS  PubMed  Google Scholar 

  16. Perie, L., Duffy, K. R., Kok, L., de Boer, R. J. & Schumacher, T. N. The branching point in erythro–myeloid differentiation. Cell 163, 1655–1662 (2015).

    CAS  PubMed  Google Scholar 

  17. Adolfsson, J. et al. Identification of Flt3+ lympho–myeloid stem cells lacking erythro-megakaryocytic potential a revised road map for adult blood lineage commitment. Cell 121, 295–306 (2005).

    CAS  PubMed  Google Scholar 

  18. Kondo, M. Lymphoid and myeloid lineage commitment in multipotent hematopoietic progenitors. Immunol. Rev. 238, 37–46 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Gorgens, A. et al. Revision of the human hematopoietic tree: granulocyte subtypes derive from distinct hematopoietic lineages. Cell Rep. 3, 1539–1552 (2013).

    PubMed  Google Scholar 

  20. Paul, F. et al. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163, 1663–1677 (2015).

    CAS  PubMed  Google Scholar 

  21. Naik, S. H. et al. Diverse and heritable lineage imprinting of early haematopoietic progenitors. Nature 496, 229–232 (2013).

    CAS  PubMed  Google Scholar 

  22. Notta, F. et al. Distinct routes of lineage development reshape the human blood hierarchy across ontogeny. Science 351, aab2116 (2016).

    PubMed  Google Scholar 

  23. Sanjuan-Pla, A. et al. Platelet-biased stem cells reside at the apex of the haematopoietic stem-cell hierarchy. Nature 502, 232–236 (2013).

    CAS  PubMed  Google Scholar 

  24. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Grun, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).

    PubMed  Google Scholar 

  26. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    CAS  PubMed  Google Scholar 

  27. Habib, N. et al. Div-Seq: single-nucleus RNA-seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  29. Scialdone, A. et al. Resolving early mesoderm diversification through single-cell expression profiling. Nature 535, 289–293 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Han, X. et al. Mapping the mouse cell atlas by microwell-Seq. Cell 172, 1091–1107 (2018).

    CAS  PubMed  Google Scholar 

  31. Tanay, A. & Regev, A. Scaling single-cell genomics from phenomenology to mechanism. Nature 541, 331–338 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Nestorowa, S. et al. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128, e20–e31 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Drissen, R. et al. Distinct myeloid progenitor-differentiation pathways identified through single-cell RNA sequencing. Nat. Immunol. 17, 666–676 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Schlitzer, A. et al. Identification of cDC1- and cDC2-committed DC progenitors reveals early lineage priming at the common DC progenitor stage in the bone marrow. Nat. Immunol. 16, 718–728 (2015).

    CAS  PubMed  Google Scholar 

  35. Olsson, A. et al. Single-cell analysis of mixed-lineage states leading to a binary cell fate choice. Nature 537, 698–702 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. See, P. et al. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356, eaag3009 (2017).

    PubMed  PubMed Central  Google Scholar 

  37. Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Tusi, B. K. et al. Population snapshots predict early haematopoietic and erythroid hierarchies. Nature 555, 54–60 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Zheng, S., Papalexi, E., Butler, A., Stephenson, W. & Satija, R. Molecular transitions in early progenitors during human cord blood hematopoiesis. Mol. Syst. Biol. 14, e8041 (2018).

    PubMed  PubMed Central  Google Scholar 

  40. Sun, J. et al. Clonal dynamics of native haematopoiesis. Nature 514, 322–327 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896 (2016).

    CAS  PubMed  Google Scholar 

  42. Ogawa, M. et al. Expression and function of c-kit in hemopoietic progenitor cells. J. Exp. Med. 174, 63–71(1991).

    CAS  PubMed  Google Scholar 

  43. Morrison, S. J. & Weissman, I. L. The long-term repopulating subset of hematopoietic stem cells is deterministic and isolatable by phenotype. Immunity 1, 661–673 (1994).

    CAS  PubMed  Google Scholar 

  44. Yang, L. et al. Identification of Lin Sca1+ kit+ CD34+ Flt3 short-term hematopoietic stem cells capable of rapidly reconstituting and rescuing myeloablated transplant recipients. Blood 105, 2717–2723 (2005).

    CAS  PubMed  Google Scholar 

  45. Kiel, M. J. et al. SLAM family receptors distinguish hematopoietic stem and progenitor cells and reveal endothelial niches for stem cells. Cell 121, 1109–1121 (2005).

    CAS  PubMed  Google Scholar 

  46. Shojaei, F. et al. Hierarchical and ontogenic positions serve to define the molecular basis of human hematopoietic stem cell behavior. Dev. Cell 8, 651–663 (2005).

    CAS  PubMed  Google Scholar 

  47. Riddell, J. et al. Reprogramming committed murine blood cells to induced hematopoietic stem cells with defined factors. Cell 157, 549–564 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Cabezas-Wallscheid, N. et al. Vitamin A–retinoic acid signaling regulates hematopoietic stem cell dormancy. Cell 169, 807–823 (2017).

    CAS  PubMed  Google Scholar 

  49. Laslo, P. et al. Multilineage transcriptional priming and determination of alternate hematopoietic cell fates. Cell 126, 755–766 (2006).

    CAS  PubMed  Google Scholar 

  50. Huang, S., Guo, Y. P., May, G. & Enver, T. Bifurcation dynamics in lineage-commitment in bipotent progenitor cells. Dev. Biol. 305, 695–713 (2007).

    CAS  PubMed  Google Scholar 

  51. Rodriguez-Fraticelli, A. E. et al. Clonal analysis of lineage fate in native haematopoiesis. Nature 553, 212–216 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Metcalf, D. The granulocyte-macrophage colony-stimulating factors. Science 229, 16–22 (1985).

    CAS  PubMed  Google Scholar 

  53. Cohen, A. M. et al. In vivo stimulation of granulopoiesis by recombinant human granulocyte colony-stimulating factor. Proc. Natl Acad. Sci. USA 84, 2484–2488 (1987).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Zhang, J. et al. Characterization of Siglec-H as a novel endocytic receptor expressed on murine plasmacytoid dendritic cell precursors. Blood 107, 3600–3608 (2006).

    CAS  PubMed  Google Scholar 

  55. Fogg, D. K. et al. A clonogenic bone marrow progenitor specific for macrophages and dendritic cells. Science 311, 83–87 (2006).

    CAS  PubMed  Google Scholar 

  56. Onai, N. et al. Identification of clonogenic common Flt3+ M-CSFR+ plasmacytoid and conventional dendritic cell progenitors in mouse bone marrow. Nat. Immunol. 8, 1207–1216 (2007).

    CAS  PubMed  Google Scholar 

  57. Waskow, C. et al. The receptor tyrosine kinase Flt3 is required for dendritic cell development in peripheral lymphoid tissues. Nat. Immunol. 9, 676–683 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Scott, E. W., Simon, M. C., Anastasi, J. & Singh, H. Requirement of transcription factor PU.1 in the development of multiple hematopoietic lineages. Science 265, 1573–1577 (1994).

    CAS  PubMed  Google Scholar 

  59. Anderson, K. L., Smith, K. A., Pio, F., Torbett, B. E. & Maki, R. A. Neutrophils deficient in PU.1 do not terminally differentiate or become functionally competent. Blood 92, 1576–1585 (1998).

    CAS  PubMed  Google Scholar 

  60. McKercher, S. R. et al. Targeted disruption of the PU.1 gene results in multiple hematopoietic abnormalities. EMBO J. 15, 5647–5658 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Lominadze, G. et al. Proteomic analysis of human neutrophil granules. Mol. Cell Proteom. 4, 1503–1521 (2005).

    CAS  Google Scholar 

  62. Theilgaard-Monch, K. et al. The transcriptional program of terminal granulocytic differentiation. Blood 105, 1785–1796 (2005).

    PubMed  Google Scholar 

  63. Zhang, J. et al. Identification of the haematopoietic stem cell niche and control of the niche size. Nature 425, 836–841 (2003).

    CAS  PubMed  Google Scholar 

  64. Mendez-Ferrer, S. et al. Mesenchymal and haematopoietic stem cells form a unique bone marrow niche. Nature 466, 829–834 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Cordeiro Gomes, A. et al. Hematopoietic stem cell niches produce lineage-instructive signals to control multipotent progenitor differentiation. Immunity 45, 1219–1231 (2016).

    CAS  PubMed  Google Scholar 

  66. Haas, S. et al. Inflammation-induced emergency megakaryopoiesis driven by hematopoietic stem cell-like megakaryocyte progenitors. Cell Stem Cell 17, 422–434 (2015).

    CAS  PubMed  Google Scholar 

  67. Essers, M. A. et al. IFNα activates dormant haematopoietic stem cells in vivo. Nature 458, 904–908 (2009).

    CAS  PubMed  Google Scholar 

  68. Rieger, M. A., Hoppe, P. S., Smejkal, B. M., Eitelhuber, A. C. & Schroeder, T. Hematopoietic cytokines can instruct lineage choice. Science 325, 217–218 (2009).

    CAS  PubMed  Google Scholar 

  69. Giladi, A. & Amit, I. Single-cell genomics: a stepping stone for future immunology discoveries. Cell 172, 14–21 (2018).

    CAS  PubMed  Google Scholar 

  70. Vallejos, C. A., Marioni, J. C. & Richardson, S. BASiCS: Bayesian analysis of single-cell sequencing data. PLoS Comput. Biol. 11, e1004333 (2015).

    PubMed  PubMed Central  Google Scholar 

Download references

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

Authors and Affiliations

Authors

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.

Corresponding authors

Correspondence to Amos Tanay or Ido Amit.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41556-018-0121-4

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing