Skip to main content
SpringerLink
Log in

Clonal fate mapping quantifies the number of haematopoietic stem cells that arise during development

  • Article
  • Published:

From Nature Cell Biology

View current issue Submit your manuscript

A Corrigendum to this article was published on 31 January 2017

This article has been updated

Abstract

Haematopoietic stem cells (HSCs) arise in the developing aorta during embryogenesis. The number of HSC clones born has been estimated through transplantation, but experimental approaches to assess the absolute number of forming HSCs in a native setting have remained challenging. Here, we applied single-cell and clonal analysis of HSCs in zebrafish to quantify developing HSCs. Targeting creERT2 in developing cd41:eGFP+ HSCs enabled long-term assessment of their blood contribution. We also applied the Brainbow-based multicolour Zebrabow system with drl:creERT2 that is active in early haematopoiesis to induce heritable colour barcoding unique to each HSC and its progeny. Our findings reveal that approximately 21 HSC clones exist prior to HSC emergence and 30 clones are present during peak production from aortic endothelium. Our methods further reveal that stress haematopoiesis, including sublethal irradiation and transplantation, reduces clonal diversity. Our findings provide quantitative insights into the early clonal events that regulate haematopoietic development.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1: Contribution of embryonic cd41:eGFP+ HSCs to adult kidney marrow.
Figure 2: Zebrabow label induction with drl:creERT2 induces mosaic colour barcoding in blood and vasculature during embryogenesis.
Figure 3: Label induction in Zebrabow embryos leads to multilineage, polyclonal colour labelling in adult blood and marrow.
Figure 4: Quantification of colour barcodes in mature granulocytes from Zebrabow-labelled marrow.
Figure 5: Clonal analysis with fate mapping predicts the number of stem cells born during development.
Figure 6: Longitudinal dynamics of labelled erythrocytes.
Figure 7: Sublethal irradiation of Zebrabow-labelled marrow.
Figure 8: Transplant of Zebrabow-labelled marrow into lethally irradiated recipients.

Similar content being viewed by others

Change history

  • 20 December 2016

    In the version of this Article originally published, the colours of the flow cytometry gates in Fig. 1b were incorrect. The gates labelled 'Erythroid', 'Myeloid', 'Lymphoid' and 'Precursor' should have been coloured purple, red, green and blue, respectively. In addition, the first author of ref. 26 was incorrect and this has now been changed to 'Mosimann, C'. These errors have been corrected in the online versions of the Article.

References

  1. Bertrand, J. Y. et al. Haematopoietic stem cells derive directly from aortic endothelium during development. Nature 464, 108–111 (2010).

    Article  CAS  Google Scholar 

  2. Chen, M. J., Yokomizo, T., Zeigler, B. M., Dzierzak, E. & Speck, N. A. Runx1 is required for the endothelial to haematopoietic cell transition but not thereafter. Nature 457, 887–891 (2009).

    Article  CAS  Google Scholar 

  3. Zovein, A. C. et al. Fate tracing reveals the endothelial origin of hematopoietic stem cells. Cell Stem Cell 3, 625–636 (2008).

    Article  CAS  Google Scholar 

  4. Boisset, J.-C. et al. In vivo imaging of haematopoietic cells emerging from the mouse aortic endothelium. Nature 464, 116–120 (2010).

    Article  CAS  Google Scholar 

  5. Kissa, K. et al. Live imaging of emerging hematopoietic stem cells and early thymus colonization. Blood 111, 1147–1156 (2008).

    Article  CAS  Google Scholar 

  6. Tamplin, O. J. et al. Hematopoietic stem cell arrival triggers dynamic remodeling of the perivascular niche. Cell 160, 241–252 (2015).

    Article  CAS  Google Scholar 

  7. Kumaravelu, P. et al. Quantitative developmental anatomy of definitive haematopoietic stem cells/long-term repopulating units (HSC/RUs): role of the aortagonad–mesonephros (AGM) region and the yolk sac in colonisation of the mouse embryonic liver. Development 129, 4891–4899 (2002).

    CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  9. Kim, S. et al. Dynamics of HSPC repopulation in nonhuman primates revealed by a decade-long clonal-tracking study. Cell Stem Cell 14, 473–485 (2014).

    Article  CAS  Google Scholar 

  10. Jordan, C. T. & Lemischka, I. R. Clonal and systemic analysis of long-term hematopoiesis in the mouse. Genes Dev. 4, 220–232 (1990).

    Article  CAS  Google Scholar 

  11. Hess, I., Iwanami, N., Schorpp, M. & Boehm, T. Zebrafish model for allogeneic hematopoietic cell transplantation not requiring preconditioning. Proc. Natl Acad. Sci. USA 110, 4327–4332 (2013).

    Article  CAS  Google Scholar 

  12. Ma, D., Zhang, J., Lin, H., Italiano, J. & Handin, R. I. The identification and characterization of zebrafish hematopoietic stem cells. Blood 118, 289–297 (2011).

    Article  CAS  Google Scholar 

  13. Bertrand, J. Y., Kim, A. D., Teng, S. & Traver, D. CD41+ cmyb+ precursors colonize the zebrafish pronephros by a novel migration route to initiate adult hematopoiesis. Development 135, 1853–1862 (2008).

    Article  CAS  Google Scholar 

  14. Hans, S. et al. Generation of a non-leaky heat shock-inducible Cre line for conditional Cre/lox strategies in zebrafish. Dev. Dynam. 240, 108–115 (2011).

    Article  CAS  Google Scholar 

  15. Xu, J. et al. Temporal-spatial resolution fate mapping reveals distinct origins for embryonic and adult microglia in zebrafish. Dev. Cell 34, 632–641 (2015).

    Article  CAS  Google Scholar 

  16. Kissa, K. & Herbomel, P. Blood stem cells emerge from aortic endothelium by a novel type of cell transition. Nature 464, 112–115 (2010).

    Article  CAS  Google Scholar 

  17. Dixon, G., Elks, P. M., Loynes, C. A., Whyte, M. K. B. & Renshaw, S. A. A method for the in vivo measurement of zebrafish tissue neutrophil lifespan. ISRN Hematol. 2012, 1–6 (2012).

    Article  Google Scholar 

  18. Livet, J. et al. Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature 450, 56–62 (2007).

    Article  CAS  Google Scholar 

  19. Pan, Y. A. et al. Zebrabow: multispectral cell labeling for cell tracing and lineage analysis in zebrafish. Development 140, 2835–2846 (2013).

    Article  CAS  Google Scholar 

  20. Weissman, T. A. & Pan, Y. A. Brainbow: new resources and emerging biological applications for multicolor genetic labeling and analysis. Genetics 199, 293–306 (2015).

    Article  Google Scholar 

  21. Gupta, V. & Poss, K. D. Clonally dominant cardiomyocytes direct heart morphogenesis. Nature 484, 479–484 (2012).

    Article  CAS  Google Scholar 

  22. Loulier, K. et al. Multiplex cell and lineage tracking with combinatorial labels. Neuron 81, 505–520 (2014).

    Article  CAS  Google Scholar 

  23. Snippert, H. J. et al. Intestinal crypt homeostasis results from neutral competition between symmetrically dividing Lgr5 stem cells. Cell 143, 134–144 (2010).

    Article  CAS  Google Scholar 

  24. Martinez, R. J., Neeld, D. K. & Evavold, B. D. Identification of T cell clones without the need for sequencing. J. Immunol. Methods 424, 28–31 (2015).

    Article  CAS  Google Scholar 

  25. Mosimann, C. et al. Ubiquitous transgene expression and Cre-based recombination driven by the ubiquitin promoter in zebrafish. Development 138, 169–177 (2011).

    Article  CAS  Google Scholar 

  26. Mosimann, C. et al. Chamber identity programs drive early functional partitioning of the heart. Nat. Commun. 6, 8146 (2015).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  28. Catlin, S., Busque, L. & Gale, R. The replication rate of human hematopoietic stem cells in vivo. Blood 117, 4460–4466 (2011).

    Article  CAS  Google Scholar 

  29. Arora, N. et al. Effect of developmental stage of HSC and recipient on transplant outcomes. Dev. Cell 29, 621–628 (2014).

    Article  CAS  Google Scholar 

  30. Lu, R., Neff, N., Quake, S. & Weissman, I. Tracking single hematopoietic stem cells in vivo using high-throughput sequencing in conjunction with viral genetic barcoding. Nat. Biotechnol. 29, 928–933 (2011).

    Article  CAS  Google Scholar 

  31. Shlush, L. I. et al. Identification of pre-leukaemic haematopoietic stem cells in acute leukaemia. Nature 506, 328–333 (2014).

    Article  CAS  Google Scholar 

  32. Steensma, D. P. et al. Clonal hematopoiesis of indeterminate potential and its distinction from myelodysplastic syndromes. Blood 126, 9–16 (2015).

    Article  CAS  Google Scholar 

  33. Ablain, J., Durand, E. M., Yang, S., Zhou, Y. & Zon, L. I. A CRISPR/Cas9 vector system for tissue-specific gene disruption in zebrafish. Dev. Cell 32, 756–764 (2015).

    Article  CAS  Google Scholar 

  34. Westerfield, M. The Zebrafish Book. A Guide for the Laboratory Use of Zebrafish (Danio rerio) 5th edn (Univ. Oregon Press, 2007).

    Google Scholar 

  35. Lin, H.-F. et al. Analysis of thrombocyte development in CD41-GFP transgenic zebrafish. Blood 106, 3803–3810 (2005).

    Article  CAS  Google Scholar 

  36. Traver, D. et al. Transplantation and in vivo imaging of multilineage engraftment in zebrafish bloodless mutants. Nat. Immunol. 4, 1238–1246 (2003).

    Article  CAS  Google Scholar 

  37. Rodriguez, A. & Laio, A. Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014).

    Article  CAS  Google Scholar 

  38. Kaufman, L. & Rousseeuw, P. J. Finding Groups in Ordinal Data. An Introduction to Cluster Analysis (Wiley-Interscience, 2005).

    Google Scholar 

Download references

Acknowledgements

We thank the following for their support and contributions: M. Distel, E. Butko, J. Bertrand and D. Stachura for critical reading of the manuscript; K. Richter for laboratory management; J. Abkowitz for comments and suggestions; A. Schier and A. Pan for the Zebrabow-M line and guidance; L. Ding and D. Tom at the HMS Enhanced Neuroimaging Core for help with confocal microscopy. This work was supported by HHMI and NIH F31HL126338 (J.H.), NIH R01-DK074482 (D.T.), NIH F32DK752433 (B.S.), the Deutsche Forschungsgemeinschaft (SFB655), the TU Dresden and the European Union (Zf Health), NIH R01-HL04880, NIH P01-HL032262, NIH 5P30-DK49216, NIH 5R01-DK53298, NIH 5U01-HL10001, NIH R24-DK09276 and an MPN Research Foundation Grant. C.M. received funding from an EMBO long-term fellowship, an HSFP long-term fellowship, an SNF advanced fellowship and an SNF professorship.

Author information

Authors and Affiliations

  1. Stem Cell Program and Division of Hematology/Oncology, Boston Children’s Hospital and Dana Farber Cancer Institute, Howard Hughes Medical Institute, Harvard Stem Cell Institute, Harvard Medical School, Boston, Massachusetts 02115, USA

    Jonathan Henninger, Ellen Durand, Jessica Moore & Leonard Zon

  2. Biological and Biomedical Sciences, Harvard University, Cambridge, Massachusetts 02138, USA

    Jonathan Henninger, Ellen Durand & Leonard Zon

  3. Section of Cell and Developmental Biology, University of California at San Diego, La Jolla, California 92093, USA

    Buyung Santoso & David Traver

  4. Biotechnology Center and Center for Regenerative Therapies Dresden, Dresden University of Technology, Tatzberg 47-49, 01307 Dresden, Germany

    Stefan Hans & Michael Brand

  5. Institute of Molecular Life Sciences, University of Zürich, 8057 Zürich, Switzerland

    Christian Mosimann

  6. Department of Cellular and Molecular Medicine, University of California at San Diego, La Jolla, California 92093-0380, USA

    David Traver

Authors
  1. Jonathan Henninger
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Buyung Santoso
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Hans
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ellen Durand
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jessica Moore
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Christian Mosimann
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Michael Brand
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. David Traver
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Leonard Zon
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.H. and B.S. designed and carried out experiments, analysed the data, and wrote the manuscript. B.S. and S.H. designed and carried out laser fate mapping experiments. E.D. and J.M. carried out experiments. C.M., M.B., D.T. and L.Z. designed experiments, edited the manuscript and supervised the project.

Corresponding authors

Correspondence to David Traver or Leonard Zon.

Ethics declarations

Competing interests

L.Z. is a founder and stock holder of Fate, Inc., and Scholar Rock.

Integrated supplementary information

Supplementary Figure 1 Heat-shock and 4-OHT requirement for DsRed-Express labeling.

(a) Strategy to validate dual-inducible fate mapping system. (b) DsRed-Express and mCherry expression in double transgenic embryos Tg(bactin2:switch; hsp70l:mCherry-T2A-CreERT2) in the presence or absence of heat shock at 24 h post-fertilization (hpf), and in the presence or absence of 4-hydroxytamoxifen (4-OHT). Top, representative brightfield image of an embryo. Box, region of embryo in fluorescence images. Left column, DsRed-Express signals. Right column, mCherry signals. Dash, outline of embryo. Embryos were imaged at 5 days post-fertilization (dpf). Scale bar is 200 μm.

Supplementary Figure 2 Migration of labeled cd41:eGFP+ cells from VDA to thymus.

(a) Scheme of laser induction targeting 10 cd41:eGFP+ cells in VDA of triple transgenic Tg(cd41:eGFP; bactin2:switch; hsp70l:mCherry-T2A-CreERT2). (b) cd41:eGFP+ target cells in VDA. Top, transgene. Middle, transgenic embryo at 50 hpf (scale bar = 200 μm).; box, enlarged region (Scale bar is 50 μm). Bottom, close up of VDA. Arrows, cd41:eGFP+ cells in the VDA. Arrowheads, non-haematopoietic cd41:eGFP+ cells in pronephric duct. (c) Quantification of cd41:eGFP+DsRed-Express+ cells in one thymic lobe, with or without laser induction. All z-planes were analyzed. Error bars, s.e.m. ∗P = 0.036, two-tailed t-test (n = 8 for no heat shock, n = 8 for laser induction). (d) Representative confocal images (one z-plane shown) of thymi of 3-dpf embryos. Top row, whole embryo heat shock. Middle row, no heat shock. Bottom row, laser induction. Dash, outline of thymus. Left column, cd41:eGFP. Middle column, DsRed-Express. Right column, merged images (scale bar = 50 μm).

Supplementary Figure 3 Characterization of draculin reporter activity in HSPCs.

(a) Strategy to measure co-expression of drl:eGFP and Runx1 + 23:nls-mCherry, a stem cell reporter. (b) Time-lapse imaging of the VDA, showing a single drl:eGFP+;Runx1 + 23:nls-mCherry+ cell budding from the ventral wall of the dorsal aorta at 40 hpf and entering circulation four hours later (A = dorsal aorta, V = cardinal vein, arrows denote blood flow, scale bar = 10 μm). (c) Confocal image showing the co-expression of drl:eGFP + and Runx1 + 23:nls-mCherry in five stem cells that have engrafted in the CHT at 48 hpf (DA = dorsal aorta, arrows = blood flow, scale bar = 25 μm). (d) Representative flow cytometry plots showing expression and co-expression of drl:eGFP and Runx1 + 23:nls-mCherry in pooled whole embryos at 36 hpf. (e) Quantification of Runx1 + 23:nls-mCherry cells that are positive for drl:eGFP expression by flow cytometry at various developmental stages in pooled embryos. (f) Representative flow cytometry plot of FSC/SSC characteristics and back-gating of drl:eGFP+ and drl:eGFP+Runx1+23:mCherry+ cells from whole kidney marrow. (g) Transplant of 6 × 104 drl:eGFP+ubi:mCherry+ cells from the lymphoid/progenitor and precursor gates into lethally irradiated recipients. Recipient marrow was analyzed at 3 months post-fertilization. Here we show a representative FACS plot, demonstrating recapitulation of drl:eGFP expression and multilineage reconstitution demonstrated by the presence of ubi:mCherry in all populations. (h) Quantification of chimerism levels in recipient zebrafish. Each marker corresponds to an individual animal. (green cells = drl:eGFP+, purple cells = drl:eGFP+Runx1 + 23:mCherry+, red cells = ubi:mCherry+, and grey = total live cells, n = 15 engrafted recipients)

Supplementary Figure 4 Flow cytometry of the Zebrabow fluorophores and lineage analysis of labeled marrow.

(a) Gating strategy to analyze Zebrabow fluorophores in multiple lineages. Each lineage (myeloid, lymphoid, erythroid) expresses the Zebrabow transgenes at different levels, but the relative expression of each fluorophore between populations remains the same (gates drawn based on negative, non-fluorescent controls). (b) Erythrocytes contaminate the myeloid population after filtering live, single cells. The myeloid and erythroid populations can be easily distinguished by intensity level of the fluorophores. (c) Plotting the three Zebrabow colors in three dimensions demonstrates the similar color barcodes present in all three lineages. Because the erythrocytes are relatively dim, there is much more variation in the signal. (d) The myeloid and lymphoid populations were isolated from the marrow of adults that were treated at various developmental time points by the gating strategy in (a). Three-dimensional plots of the three Zebrabow fluorophores shows that the color distribution in the myeloid and lymphoid lineages are almost identical. These are representative examples of the adults used to quantify lineage output in Fig. 3e.

Supplementary Figure 5 Evaluation of clustering quality for small clones.

(a) Tg(drl:creERT2;ubi:Zebrabow-M) embryos were treated at 96 hpf, and their marrow was analyzed by flow cytometry at 5 months to measure small clonal contributions. (b) Silhouette plots from three representative marrows evaluate the quality of the clustering solutions. Small clones that contribute <1% of the granulocyte population were detected with high silhouette values, demonstrating the low threshold for detection in this system.

Supplementary Figure 6 Analysis of single color Zebrabow animals.

(a) Breeding and treatment strategy to carry the Zebrabow recombination through the germline. (b) Example image of single color Zebrabow fish from a single clutch. Individual channels (dTomato, CFP, YFP) were imaged and merged to generate the RGB colors (scale bar = 20 mm). (c) Example ternary plots of granulocytes from marrows of adult single color zebrafish. (d) Digital combination of 100,000 labeled granulocytes from 12 individual marrows from adult fish treated at 24 hpf with 4-OHT. The 3D scatter plot shows the normalized intensities of these combined cells. The right graph shows the alpha shape volume of these cells in color space (grey), with an example single color marrow (blue) mapped into the color space. (e) These ternary graphs show the same example from (d) in 2D space. (f) Boxplot that shows quantification of the predicted number of Zebrabow colors from mapping individual single colored marrows into 2D color space (red line is median and box represents inner quartile range, n = 13 fish).

Supplementary Figure 7 Example ternary plots from fate mapping at defined developmental time points.

This figure provides example ternary diagrams that depict color barcodes in the granulocyte lineage from adult fish treated during embryogenesis. Each ternary diagram depicts an individual marrow at 24 hpf (a), 48 hpf (b), or 72 hpf (c) with labeling efficiency (% recombination) below each plot. Bar graphs on the right show the quantification of clonal contribution for each ternary diagram. Brackets (∗) correspond to large clusters that are likely to be polyclonal populations, which were ignored for predicting clonal numbers.

Supplementary Figure 8 Repeat of Zebrabow fate mapping at defined developmental time points with brief 4-OHT treatment.

(a) Strategy to fate map HSCs at defined developmental time points. In this experiment, embryos were exposed to 4-OHT for only 4 h until they were switched to fresh water. (b) Color barcodes in granulocytes were quantified and extrapolated to predict the number of clones present during label induction that contribute to adult blood (dashed line marks the beginning of definitive haematopoiesis). Each marker represents one animal. Error bars show mean and s.e.m. (n = 8, 5, 5, and 9 fish for 24, 30, 51, and 74 hpf, respectively). (c) Table showing quantification of these predicted values with 95% confidence intervals. Cluster data is available in the Supplementary Table 1.

Supplementary information

Supplementary Information

Supplementary Information (PDF 4534 kb)

Supplementary Table 1

Supplementary Information (XLSX 35 kb)

Time lapse imaging of drl:eGFP embryos.

This movie demonstrates the activity of the drl regulatory elements during definitive blood development. drl:eGFP embryos were imaged by confocal microscopy beginning at 40 hpf. GFP + cells can be seen in the vasculature and haematopoietic cells in both the ventral dorsal aorta and the caudal haematopoietic tissue (CHT). (MP4 3073 kb)

Time lapse imaging of Tg(Zebrabow-M; drl:creERT2) embryos.

Zebrabow embryos were treated with 4-OHT at 24 hpf and imaged by confocal microscopy beginning at 48 hpf. This movie shows Zebrabow labeling in the CHT in both the vasculature and haematopoietic cells. A putative stem cell resides in the center of the frame, and it eventually divides to yield progeny with identical color. (MP4 7610 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Henninger, J., Santoso, B., Hans, S. et al. Clonal fate mapping quantifies the number of haematopoietic stem cells that arise during development. Nat Cell Biol 19, 17–27 (2017). https://doi.org/10.1038/ncb3444

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ncb3444

  • Springer Nature Limited

This article is cited by

Search

Navigation