Article | Published:

Population snapshots predict early haematopoietic and erythroid hierarchies

Nature volume 555, pages 5460 (01 March 2018) | Download Citation

Abstract

The formation of red blood cells begins with the differentiation of multipotent haematopoietic progenitors. Reconstructing the steps of this differentiation represents a general challenge in stem-cell biology. Here we used single-cell transcriptomics, fate assays and a theory that allows the prediction of cell fates from population snapshots to demonstrate that mouse haematopoietic progenitors differentiate through a continuous, hierarchical structure into seven blood lineages. We uncovered coupling between the erythroid and the basophil or mast cell fates, a global haematopoietic response to erythroid stress and novel growth factor receptors that regulate erythropoiesis. We defined a flow cytometry sorting strategy to purify early stages of erythroid differentiation, completely isolating classically defined burst-forming and colony-forming progenitors. We also found that the cell cycle is progressively remodelled during erythroid development and during a sharp transcriptional switch that ends the colony-forming progenitor stage and activates terminal differentiation. Our work showcases the utility of linking transcriptomic data to predictive fate models, and provides insights into lineage development in vivo.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Accessions

Primary accessions

Gene Expression Omnibus

References

  1. 1.

    , , , & Arrested development of embryonic red cell precursors in mouse embryos lacking transcription factor GATA-1. Proc. Natl Acad. Sci. USA 93, 12355–12358 (1996)

  2. 2.

    et al. Suppression of Fas–FasL coexpression by erythropoietin mediates erythroblast expansion during the erythropoietic stress response in vivo. Blood 108, 123–133 (2006)

  3. 3.

    et al. Resolving the distinct stages in erythroid differentiation based on dynamic changes in membrane protein expression during erythropoiesis. Proc. Natl Acad. Sci. USA 106, 17413–17418 (2009)

  4. 4.

    & Erythropoietic precursors in mice under erythropoietic stimulation and suppression. Exp. Hematol. 5, 141–148 (1977)

  5. 5.

    , & The cellular basis for the defect in haemopoiesis in flexed-tailed mice. III. Restriction of the defect to erythropoietic progenitors capable of transient colony formation in vivo. Br. J. Haematol. 30, 401–410 (1975)

  6. 6.

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

  7. 7.

    , , , & HIF1α synergizes with glucocorticoids to promote BFU-E progenitor self-renewal. Blood 117, 3435–3444 (2011)

  8. 8.

    et al. Isolation and transcriptome analyses of human erythroid progenitors: BFU-E and CFU-E. Blood 124, 3636–3645 (2014)

  9. 9.

    , , , & Prospective isolation of human erythroid lineage-committed progenitors. Proc. Natl Acad. Sci. USA 112, 9638–9643 (2015)

  10. 10.

    et al. Mapping cellular hierarchy by single-cell analysis of the cell surface repertoire. Cell Stem Cell 13, 492–505 (2013)

  11. 11.

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

  12. 12.

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

  13. 13.

    et al. Fundamental properties of unperturbed haematopoiesis from stem cells in vivo. Nature 518, 542–546 (2015)

  14. 14.

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

  15. 15.

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

  16. 16.

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

  17. 17.

    & Not all created equal: lineage hard-wiring in the production of blood. Cell 163, 1568–1570 (2015)

  18. 18.

    , & Identification of clonogenic common lymphoid progenitors in mouse bone marrow. Cell 91, 661–672 (1997)

  19. 19.

    , , & A clonogenic common myeloid progenitor that gives rise to all myeloid lineages. Nature 404, 193–197 (2000)

  20. 20.

    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)

  21. 21.

    , , , & Time-variant clustering model for understanding cell fate decisions. Proc. Natl Acad. Sci. USA 111, E4797–E4806 (2014)

  22. 22.

    et al. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl Acad. Sci. USA 111, E5643–E5650 (2014)

  23. 23.

    et al. Single-cell RNA-seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360–372 (2015)

  24. 24.

    & TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res. 44, e117 (2016)

  25. 25.

    , & SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol. 17, 106 (2016)

  26. 26.

    , , , & Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016)

  27. 27.

    et al. Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol. 33, 269–276 (2015)

  28. 28.

    & High throughput single-cell fate potential assay of murine hematopoietic progenitors in vitro. Ex. Hematol. (2018)

  29. 29.

    et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015)

  30. 30.

    & The long-term repopulating subset of hematopoietic stem cells is deterministic and isolatable by phenotype. Immunity 1, 661–673 (1994)

  31. 31.

    , , & Isolation of c-kit receptor-expressing cells from bone marrow, peripheral blood, and fetal liver: functional properties and composite antigenic profile. Blood 78, 1403–1412 (1991)

  32. 32.

    ., & SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics (2017)

  33. 33.

    ., ., ., & Fundamental limits on dynamic inference from single cell snapshots. Proc. Natl Acad. Sci. USA. (2018)

  34. 34.

    et al. Granulocyte-monocyte progenitors and monocyte-dendritic cell progenitors independently produce functionally distinct monocytes. Immunity 47, 890–902.e4 (2017)

  35. 35.

    , & Reconstructing the temporal ordering of biological samples using microarray data. Bioinformatics 19, 842–850 (2003)

  36. 36.

    et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014)

  37. 37.

    et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014)

  38. 38.

    , , , & GATA switches as developmental drivers. J. Biol. Chem. 285, 31087–31093 (2010)

  39. 39.

    et al. Regulation of bone marrow hematopoietic stem cell is involved in high-altitude erythrocytosis. Exp. Hematol. 39, 37–46 (2011)

  40. 40.

    et al. Erythropoietin guides multipotent hematopoietic progenitor cells toward an erythroid fate. J. Exp. Med. 211, 181–188 (2014)

  41. 41.

    et al. FOG-1 and GATA-1 act sequentially to specify definitive megakaryocytic and erythroid progenitors. EMBO J. 31, 351–365 (2012)

  42. 42.

    ., ., & in A Systems Biology Approach to Blood, Vol. 844 (eds et al.) Ch. 3, 37–58 (Springer New York, 2014)

  43. 43.

    , , , & A KIT juxtamembrane PY567-directed pathway provides nonredundant signals for erythroid progenitor cell development and stress erythropoiesis. Exp. Hematol. 37, 159–171 (2009)

  44. 44.

    & Erythropoietin retards DNA breakdown and prevents programmed death in erythroid progenitor cells. Science 248, 378–381 (1990)

  45. 45.

    , , & Isolation of a novel receptor tyrosine kinase cDNA expressed by developing erythroid progenitors. Blood 82, 1335–1343 (1993)

  46. 46.

    et al. Tyrosine kinase receptor RON functions downstream of the erythropoietin receptor to induce expansion of erythroid progenitors. Blood 103, 4457–4465 (2004)

  47. 47.

    et al. A key commitment step in erythropoiesis is synchronized with the cell cycle clock through mutual inhibition between PU.1 and S-phase progression. PLoS Biol. 8, e1000484 (2010)

  48. 48.

    et al. Global increase in replication fork speed during a p57KIP2-regulated erythroid cell fate switch. Sci. Adv. 3, e1700298 (2017)

  49. 49.

    et al. Global DNA demethylation during mouse erythropoiesis in vivo. Science 334, 799–802 (2011)

  50. 50.

    & Anemia of inflammation. Hematol. Oncol. Clin. North Am. 28, 671–681 (2014)

  51. 51.

    et al. A systems approach identifies essential FOXO3 functions at key steps of terminal erythropoiesis. PLoS Genet. 11, e1005526 (2015)

  52. 52.

    et al. Identification of genes periodically expressed in the human cell cycle and their expression in tumors. Mol. Biol. Cell 13, 1977–2000 (2002)

  53. 53.

    et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protocols 12, 44–73 (2017)

  54. 54.

    ., ., & A density-based algorithm for discovering clusters in large spatial databases with noise. In Proc. 2nd International Conference on Knowledge Discovery and Data Mining (Eds . et al.) 226–231 (AAAI, 1996)

  55. 55.

    , & Looking for natural patterns in data: Part 1. Density-based approach. Chemomtr. Intell. Lab. Syst. 56, 83–92 (2001)

  56. 56.

    Accelerating t-SNE using tree-based algorithms. J. Mach. Learn. Res. 15, 3221–3245 (2014)

  57. 57.

    et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015)

  58. 58.

    , & SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics (2017)

  59. 59.

    , & Algorithms for detecting significantly mutated pathways in cancer. J. Comput. Biol. 18, 507–522 (2011)

  60. 60.

    et al. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008)

  61. 61.

    et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005)

  62. 62.

    et al. ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments. Bioinformatics 26, 2438–2444 (2010)

  63. 63.

    et al. Computational assignment of cell-cycle stage from single-cell transcriptome data. Methods 85, 54–61 (2015)

  64. 64.

    , & Cyclebase 3.0: a multi-organism database on cell-cycle regulation and phenotypes. Nucleic Acids Res. 43, D1140–D1144 (2015)

  65. 65.

    et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323.e1330 (2016)

  66. 66.

    & Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B Stat. Methodol. 57, 289–300 (1995)

  67. 67.

    & Novel FACS strategy for identification of early hematopoietic progenitors including BFU-e, CFU-e and erythroid-biased MPPs Protoc. Exch. (2018)

  68. 68.

    et al. An alternative pathway of imiquimod-induced psoriasis-like skin inflammation in the absence of interleukin-17 receptor a signaling. J. Invest. Dermatol. 133, 441–451 (2013)

  69. 69.

    , , , & Stat5 signaling specifies basal versus stress erythropoietic responses through distinct binary and graded dynamic modalities. PLoS Biol. 10, e1001383 (2012)

Download references

Acknowledgements

This work was funded by a Leukemia and Lymphoma Society Scholar award (1728-13) and R01DK100915 and R01099281 (M.S.). A.M.K. is supported by a BW Fund CASI award and an Edward J Mallinckrodt Foundation Grant. S.L.W. and C.W. are supported by National Institutes of Health (NIH) training grant 5T32GM080177-07.

Author information

Author notes

    • Betsabeh Khoramian Tusi
    • , Samuel L. Wolock
    •  & Caleb Weinreb

    These authors contributed equally to this work.

Affiliations

  1. Department of Molecular, Cell and Cancer Biology, University of Massachusetts Medical School, Worcester, Massachusetts, USA

    • Betsabeh Khoramian Tusi
    • , Yung Hwang
    • , Daniel Hidalgo
    •  & Merav Socolovsky
  2. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA

    • Samuel L. Wolock
    • , Caleb Weinreb
    • , Rapolas Zilionis
    •  & Allon M. Klein
  3. Institute for Molecular Medicine, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany

    • Ari Waisman
  4. Division of Immunology, Department of Microbiology and Immunobiology and Evergrande Center for Immunological Diseases, Harvard Medical School and Brigham and Women’s Hospital, Boston, Massachusetts, USA

    • Jun R. Huh
  5. Department of Pediatrics, Hematology/Oncology Division, University of Massachusetts Medical School, Worcester, Massachusetts, USA

    • Merav Socolovsky

Authors

  1. Search for Betsabeh Khoramian Tusi in:

  2. Search for Samuel L. Wolock in:

  3. Search for Caleb Weinreb in:

  4. Search for Yung Hwang in:

  5. Search for Daniel Hidalgo in:

  6. Search for Rapolas Zilionis in:

  7. Search for Ari Waisman in:

  8. Search for Jun R. Huh in:

  9. Search for Allon M. Klein in:

  10. Search for Merav Socolovsky in:

Contributions

M.S. and A.M.K. designed the experiments and supervised the project. B.K.T., S.L.W., Y.H., D.H. and R.Z. performed experiments including inDrops (B.K.T., R.Z., S.L.W.), FACS and antibody screening (B.K.T., D.H.), single-cell fate assays and cell cycle analysis (B.K.T.), western blotting (Y.H.), qRT–PCR (B.T.K.), pSTAT3/5 (Y.H., D.H.) and colony assays for novel growth factors (Y.H.). S.L.W. and C.W. performed single-cell data analysis, informatics and PBA modelling. A.W. and J.R.H. provided Il17ra−/− mice. B.K.T., S.L.W., C.W., Y.H., D.H., A.M.K. and M.S. prepared figures and wrote the manuscript.

Competing interests

A.M.K. is a co-founder of 1Cell-Bio.

Corresponding authors

Correspondence to Allon M. Klein or Merav Socolovsky.

Reviewer Information Nature thanks B. Göttgens, F. Hamey and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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

Extended data

Supplementary information

PDF files

  1. 1.

    Life Sciences Reporting Summary

  2. 2.

    Supplementary Information

    This file contains full legends for Supplementary Tables 1-7, gel source data and Supplementary Tables 1, 2 and 7.

Excel files

  1. 1.

    Supplementary Table 3

    This table contains a list of significantly varying genes in the basal BM dataset - see Supplementary Information document for the full description.

  2. 2.

    Supplementary Table 4

    This table shows gene set enrichment analysis (GSEA) of dynamic gene clusters - see Supplementary Information document for the full description.

  3. 3.

    Supplementary Table 5

    This table shows genes differentially expressed in stress - see Supplementary Information document for the full description.

  4. 4.

    Supplementary Table 6

    This table shows genes correlated with progression through the CEP stage - see Supplementary Information document for the full description.

Zip files

  1. 1.

    Supplementary Data

    This zipped file contains the input data files and code for running Population Balance Analysis on the Bone Marrow and Fetal Liver data sets.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nature25741

Further reading

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.