Clonal analysis of lineage fate in native haematopoiesis

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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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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.


  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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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.

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    Supplementary Figures

    This file contains Supplementary Figures 1-3.

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    Life Sciences reporting Summary

Excel files

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    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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