Letter

Clonal analysis of lineage fate in native haematopoiesis

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Published online:

Abstract

Haematopoiesis, the process of mature blood and immune cell production, is functionally organized as a hierarchy, with self-renewing haematopoietic stem cells and multipotent progenitor cells sitting at the very top1,2. Multiple models have been proposed as to what the earliest lineage choices are in these primitive haematopoietic compartments, the cellular intermediates, and the resulting lineage trees that emerge from them3,4,5,6,7,8,9,10. Given that the bulk of studies addressing lineage outcomes have been performed in the context of haematopoietic transplantation, current models of lineage branching are more likely to represent roadmaps of lineage potential than native fate. Here we use transposon tagging to clonally trace the fates of progenitors and stem cells in unperturbed haematopoiesis. Our results describe a distinct clonal roadmap in which the megakaryocyte lineage arises largely independently of other haematopoietic fates. Our data, combined with single-cell RNA sequencing, identify a functional hierarchy of unilineage- and oligolineage-producing clones within the multipotent progenitor population. Finally, our results demonstrate that traditionally defined long-term haematopoietic stem cells are a significant source of megakaryocyte-restricted progenitors, suggesting that the megakaryocyte lineage is the predominant native fate of long-term haematopoietic stem cells. Our study provides evidence for a substantially revised roadmap for unperturbed haematopoiesis, and highlights unique properties of multipotent progenitors and haematopoietic stem cells in situ.

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References

  1. 1.

    , , , & Identification of a lineage of multipotent hematopoietic progenitors. Development 124, 1929–1939 (1997)

  2. 2.

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

  3. 3.

    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)

  4. 4.

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

  5. 5.

    , & Models of haematopoiesis: seeing the wood for the trees. Nat. Rev. Immunol. 9, 293–300 (2009)

  6. 6.

    , , , & New evidence supporting megakaryocyte–erythrocyte potential of flk2/flt3+ multipotent hematopoietic progenitors. Cell 126, 415–426 (2006)

  7. 7.

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

  8. 8.

    , , , & The branching point in erythro-myeloid differentiation. Cell 163, 1655–1662 (2015)

  9. 9.

    et al. Functionally distinct subsets of lineage-biased multipotent progenitors control blood production in normal and regenerative conditions. Cell Stem Cell 17, 35–46 (2015)

  10. 10.

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

  11. 11.

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

  12. 12.

    et al. Characterization of a bipotent erythro-megakaryocytic progenitor in human bone marrow. Blood 88, 1284–1296 (1996)

  13. 13.

    et al. Adult human megakaryocyte–erythroid progenitors are in the CD34+CD38mid fraction. Blood 128, 923–933 (2016)

  14. 14.

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

  15. 15.

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

  16. 16.

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

  17. 17.

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

  18. 18.

    et al. Lineage tracing of Pf4-Cre marks hematopoietic stem cells and their progeny. PLoS ONE 7, e51361 (2012)

  19. 19.

    & CD41 expression marks myeloid-biased adult hematopoietic stem cells and increases with age. Blood 121, 4463–4472 (2013)

  20. 20.

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

  21. 21.

    et al. Unipotent megakaryopoietic pathway bridging hematopoietic stem cells and mature megakaryocytes. Stem Cells 33, 2196–2207 (2015)

  22. 22.

    , & Single-cell analysis reveals cell division-independent emergence of megakaryocytes from phenotypic hematopoietic stem cells. Stem Cells 33, 3152–3157 (2015)

  23. 23.

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

  24. 24.

    et al. Identification and characterization of a bipotent (erythroid and megakaryocytic) cell precursor from the spleen of phenylhydrazine-treated mice. Blood 95, 2559–2568 (2000)

  25. 25.

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

  26. 26.

    et al. Hematopoietic stem cells are the major source of multilineage hematopoiesis in adult animals. Immunity 45, 597–609 (2016)

  27. 27.

    et al. Massively parallel clonal analysis using CRISPR/Cas9 induced genetic scars. Preprint at (2017)

  28. 28.

    . et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain by scGESTALT. Preprint at (2017)

  29. 29.

    et al. Analysis of histone 2B-GFP retention reveals slowly cycling hematopoietic stem cells. Nat. Biotechnol. 27, 84–90 (2009)

  30. 30.

    , & SLAM family markers resolve functionally distinct subpopulations of hematopoietic stem cells and multipotent progenitors. Cell Stem Cell 13, 102–116 (2013)

  31. 31.

    et al. Hematopoietic stem cells reversibly switch from dormancy to self-renewal during homeostasis and repair. Cell 135, 1118–1129 (2008)

  32. 32.

    et al. Multiarm high-throughput integration site detection: limitations of LAM-PCR technology and optimization for clonal analysis. Stem Cells Dev. 16, 381–392 (2007)

  33. 33.

    et al. DNA bar coding and pyrosequencing to analyze adverse events in therapeutic gene transfer. Nucleic Acids Res. 36, e49 (2008)

  34. 34.

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

  35. 35.

    et al. Classification of low quality cells from single-cell RNA-seq data. Genome Biol. 17, 29 (2016)

  36. 36.

    , & SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Preprint at (2016)

  37. 37.

    et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015)

  38. 38.

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

  39. 39.

    Cluster validation by measurement of clustering characteristics relevant to the user. Preprint at (2017)

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Acknowledgements

We are grateful to members of the Camargo and Klein laboratory for comments. A.R.F. is a Merck Fellow of the Life Sciences Research Foundation and a non-stipendiary European Molecular Biology Organization postdoctoral fellow. This work was supported by National Institutes of Health grants HL128850-01A1 and P01HL13147 to F.D.C. F.D.C. is a Leukemia and Lymphoma Society and a Howard Hughes Medical Institute Scholar. A.M.K. is supported by a Burroughs-Wellcome Fund CASI award, and by the Edward J. Mallinckrodt Fellowship.

Author information

Author notes

    • Jianlong Sun

    Present address: School of Life Science and Technology, ShanghaiTech University, Shanghai 201210, China.

Affiliations

  1. Stem Cell Program, Boston Children’s Hospital, Boston, Massachusetts 02115, USA

    • Alejo E. Rodriguez-Fraticelli
    • , Sachin H. Patel
    • , Maja Jankovic
    • , Jianlong Sun
    •  & Fernando D. Camargo
  2. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts 02138, USA

    • Alejo E. Rodriguez-Fraticelli
    • , Jianlong Sun
    •  & Fernando D. Camargo
  3. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, USA

    • Samuel L. Wolock
    • , Caleb S. Weinreb
    •  & Allon M. Klein
  4. Molecular Biotechnology Center, Department of Clinical and Biological Sciences, University of Torino, Torino 10126, Italy

    • Riccardo Panero
    •  & Raffaele A. Calogero

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Contributions

A.R.F. and F.D.C. designed the study, analysed the data, and wrote the manuscript. A.R.F. performed and analysed the experiments, assisted by M.J., S.P., and J.S. S.W., C.W., R.P., R.A.C., and A.M.K. designed and analysed inDrops experiments and transcriptome data. F.D.C. supervised the study.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Fernando D. Camargo.

Reviewer Information Nature thanks B. Gottgens 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.

    Supplementary Figures

    This file contains Supplementary Figures 1-3.

  2. 2.

    Life Sciences reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    Sorted cells, clones identified and % DsRed in each population analysed. This table compiles the information of numbers of barcodes and % of DsRed from each mouse analysed, including the sorting logics for each population in each experiment.

  2. 2.

    Supplementary Table 2

    Cluster differential expression analysis results. This set of tables contains the raw results of cluster differential expression analysis for each cluster under different tabs.

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