Letter | Published:

Whole-organism clone tracing using single-cell sequencing

Nature volume 556, pages 108112 (05 April 2018) | Download Citation


Embryonic development is a crucial period in the life of a multicellular organism, during which limited sets of embryonic progenitors produce all cells in the adult body. Determining which fate these progenitors acquire in adult tissues requires the simultaneous measurement of clonal history and cell identity at single-cell resolution, which has been a major challenge. Clonal history has traditionally been investigated by microscopically tracking cells during development1,2, monitoring the heritable expression of genetically encoded fluorescent proteins3 and, more recently, using next-generation sequencing technologies that exploit somatic mutations4, microsatellite instability5, transposon tagging6, viral barcoding7, CRISPR–Cas9 genome editing8,9,10,11,12,13 and Cre–loxP recombination14. Single-cell transcriptomics15 provides a powerful platform for unbiased cell-type classification. Here we present ScarTrace, a single-cell sequencing strategy that enables the simultaneous quantification of clonal history and cell type for thousands of cells obtained from different organs of the adult zebrafish. Using ScarTrace, we show that a small set of multipotent embryonic progenitors generate all haematopoietic cells in the kidney marrow, and that many progenitors produce specific cell types in the eyes and brain. In addition, we study when embryonic progenitors commit to the left or right eye. ScarTrace reveals that epidermal and mesenchymal cells in the caudal fin arise from the same progenitors, and that osteoblast-restricted precursors can produce mesenchymal cells during regeneration. Furthermore, we identify resident immune cells in the fin with a distinct clonal origin from other blood cell types. We envision that similar approaches will have major applications in other experimental systems, in which the matching of embryonic clonal origin to adult cell type will ultimately allow reconstruction of how the adult body is built from a single cell.

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Primary accessions

Gene Expression Omnibus


  1. 1.

    & Post-embryonic cell lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 56, 110–156 (1977)

  2. 2.

    , , & Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322, 1065–1069 (2008)

  3. 3.

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

  4. 4.

    et al. Genome sequencing of normal cells reveals developmental lineages and mutational processes. Nature 513, 422–425 (2014)

  5. 5.

    et al. Colon stem cell and crypt dynamics exposed by cell lineage reconstruction. PLoS Genet. 7, e1002192 (2011)

  6. 6.

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

  7. 7.

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

  8. 8.

    et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016)

  9. 9.

    et al. CRISPR-barcoding for intratumor genetic heterogeneity modeling and functional analysis of oncogenic driver mutations. Mol. Cell 63, 526–538 (2016)

  10. 10.

    , , , & Quantitative analysis of synthetic cell lineage tracing using nuclease barcoding. ACS Synth. Biol. 6, 936–942 (2017)

  11. 11.

    , & Rapidly evolving homing CRISPR barcodes. Nat. Methods 14, 195–200 (2017)

  12. 12.

    et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017)

  13. 13.

    et al. Massively parallel whole-organism lineage tracing using CRISPR/Cas9 induced genetic scars. Preprint at (2016)

  14. 14.

    et al. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548, 456–460 (2017)

  15. 15.

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

  16. 16.

    , & Efficient multiplex biallelic zebrafish genome editing using a CRISPR nuclease system. Proc. Natl Acad. Sci. USA 110, 13904–13909 (2013)

  17. 17.

    , & A zebrafish histone variant H2A.F/Z and a transgenic H2A.F/Z:GFP fusion protein for in vivo studies of embryonic development. Dev. Genes Evol. 211, 603–610 (2001)

  18. 18.

    et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394.e3 (2016)

  19. 19.

    et al. Clonal fate mapping quantifies the number of haematopoietic stem cells that arise during development. Nat. Cell Biol. 19, 17–27 (2017)

  20. 20.

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

  21. 21.

    & Zebrafish as a model for normal and malignant hematopoiesis. Dis. Model. Mech. 4, 433–438 (2011).

  22. 22.

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

  23. 23.

    , & Neurogenesis in zebrafish — from embryo to adult. Neural Dev. 8, 3 (2013)

  24. 24.

    , , & An exclusively mesodermal origin of fin mesenchyme demonstrates that zebrafish trunk neural crest does not generate ectomesenchyme. Development 140, 2923–2932 (2013)

  25. 25.

    & Fate restriction in the growing and regenerating zebrafish fin. Dev. Cell 20, 725–732 (2011)

  26. 26.

    et al. Bone regenerates via dedifferentiation of osteoblasts in the zebrafish fin. Dev. Cell 20, 713–724 (2011)

  27. 27.

    , & Regeneration of amputated zebrafish fin rays from de novo osteoblasts. Dev. Cell 22, 879–886 (2012)

  28. 28.

    et al. Live monitoring of blastemal cell contributions during appendage regeneration. Curr. Biol. 26, 2981–2991 (2016)

  29. 29.

    , , & Live fate-mapping of joint-associated fibroblasts visualizes expansion of cell contributions during zebrafish fin regeneration. Development 144, 2889–2895 (2017)

  30. 30.

    et al. Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat. Immunol. 13, 1118–1128 (2012)

  31. 31.

    et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. http://doi.org/10.1038/nbt.4103 (2018).

  32. 32.

    et al. Simultaneous lineage tracing and cell-type identification using CRISPR/Cas9-induced genetic scars. Nat. Biotechnol. (2018)

  33. 33.

    , , , & Single-cell ScarTrace. Protoc. Exch. (2018)

  34. 34.

    & Cellular dissection of zebrafish hematopoiesis. Methods Cell Biol. 101, 75–110 (2011)

  35. 35.

    , , , & Isolation and culture of adult zebrafish brain-derived neurospheres. J. Vis. Exp. 108, 53617 (2016).

  36. 36.

    et al. Roles for Fgf signaling during zebrafish fin regeneration. Dev. Biol. 222, 347–358 (2000)

  37. 37.

    , & Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014)

  38. 38.

    et al. Comparative gene expression analysis of zebrafish and mammals identifies common regulators in hematopoietic stem cells. Blood 115, e1–e9 (2010)

  39. 39.

    et al. Single-cell transcriptional analysis of normal, aberrant, and malignant hematopoiesis in zebrafish. J. Exp. Med. 213, 979–992 (2016)

  40. 40.

    et al. Single-cell RNA-sequencing reveals a continuous spectrum of differentiation in hematopoietic cells. Cell Reports 14, 966–977 (2016)

  41. 41.

    et al. Single-cell transcriptome analysis of fish immune cells provides insight into the evolution of vertebrate immune cell types. Genome Res. 27, 451–461 (2017)

  42. 42.

    et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008)

  43. 43.

    , , & The developmental sequence of gene expression within the rod photoreceptor lineage in embryonic zebrafish. Dev. Dyn. 237, 2903–2917 (2008)

  44. 44.

    , & Differential recruitment of brain networks following route and cartographic map learning of spatial environments. PLoS ONE 7, e44886 (2012)

  45. 45.

    et al. The microglial sensome revealed by direct RNA sequencing. Nat. Neurosci. 16, 1896–1905 (2013)

  46. 46.

    , , & Characterization of the calcium binding protein family in zebrafish. PLoS ONE 8, e53299 (2013)

  47. 47.

    et al. Allen Brain Atlas: an integrated spatio-temporal portal for exploring the central nervous system. Nucleic Acids Res. 41, D996–D1008 (2013)

  48. 48.

    . et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167, 566–580 (2016)

  49. 49.

    et al. Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system. Science 352, 1326–1329 (2016)

  50. 50.

    , , & Single-cell RNA-seq reveals hypothalamic cell diversity. Cell Reports 18, 3227–3241 (2017)

  51. 51.

    et al. Identification of a conserved and acute neurodegeneration-specific microglial transcriptome in the zebrafish. Glia 65, 138–149 (2017).

  52. 52.

    Transcription factors controlling osteoblastogenesis. Arch. Biochem. Biophys. 473, 98–105 (2008)

  53. 53.

    , & Spatial and temporal control of transgene expression in zebrafish. PLoS ONE 9, e92217 (2014)

  54. 54.

    et al. The extracellular matrix gene Frem1 is essential for the normal adhesion of the embryonic epidermis. Proc. Natl Acad. Sci. USA 101, 13560–13565 (2004)

  55. 55.

    On optimal and data-based histograms. Biometrika 66, 605–610 (1979)

  56. 56.

    & Order and coherence in the fate map of the zebrafish nervous system. Development 121, 2595–2609 (1995)

  57. 57.

    et al. Control-free calling of copy number alterations in deep-sequencing data using GC-content normalization. Bioinformatics 27, 268–269 (2011)

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This work was supported by a European Research Council Advanced grant (ERC-AdG 742225-IntScOmics), Nederlandse Organisatie voor Wetenschappelijk Onderzoek (NWO) TOP award (NWO-CW 714.016.001), and the Foundation for Fundamental Research on Matter, financially supported by NWO (FOM-14NOISE01). This work is part of the Oncode Institute which is partly financed by the Dutch Cancer Society. We thank M. Sen for help with sequencing, R. der Linden for cell sorting, and B. de Barbanson for help with programming, and all the other members of the A.v.O. laboratory for discussions and input. In addition, we thank B. Artegiani and J. Bakkers for discussions, P. Shang and N. Geijsen for sharing the Cas9–eScarlet fusion protein, the Hubrecht Sorting Facility, and the Utrecht Sequencing Facility, subsidized by the University Medical Center Utrecht, Hubrecht Institute and Utrecht University.

Author information

Author notes

    • Anna Alemany
    • , Maria Florescu
    • , Chloé S. Baron
    •  & Josi Peterson-Maduro

    These authors contributed equally to this work.


  1. Oncode Institute, Hubrecht Institute-KNAW (Royal Netherlands Academy of Arts and Sciences) and University Medical Center Utrecht, 3584 CT Utrecht, The Netherlands

    • Anna Alemany
    • , Maria Florescu
    • , Chloé S. Baron
    • , Josi Peterson-Maduro
    •  & Alexander van Oudenaarden


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A.v.O. conceived and designed the project. J.P.-M. developed the experimental protocol, with support from A.A., M.F. and C.S.B. C.S.B. performed WKM-related experiments; M.F. performed brain- and eye-related experiments; and C.S.B. and J.P.-M. performed fin-related experiments. A.A. developed the computational methods and modelling. A.A., C.S.B. and A.v.O. analysed WKM-related data; A.A. and M.F. analysed brain- and eye-related data; A.A., C.S.B. and J.P.-M. analysed fin-related data. All authors discussed and interpreted results, and wrote the manuscript. A.A., M.F., C.S.B. and J.P.-M. contributed equally to this work.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Alexander van Oudenaarden.

Reviewer Information Nature thanks L. Zon 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 Data 1

    This file summarizes the RaceID parameters used for transcriptome analysis.

  3. 3.

    Supplementary Data 3

    The reference manual for the scripts provided in Supplementary Data 2 to extract clones.

  4. 4.

    Supplementary Information

    This file contains Supplementary Sections 1-4.

Zip files

  1. 1.

    Supplementary Data 2

    This file contains a zipped file with scripts to extract extract scars and detect cones in single cells.

Excel files

  1. 1.

    Supplementary Table 1

    Cell specific barcodes used in transcriptome library preparation.

  2. 2.

    Supplementary Table 2

    Cell specific barcodes used in scar library preparation.

  3. 3.

    Supplementary Table 3

    Differently expressed genes detected in the different hematopoietic cell types.

  4. 4.

    Supplementary Table 4

    Differently expressed genes detected in the cell types detected in brain and eyes.

  5. 5.

    Supplementary Table 5

    Differently expressed genes detected in the cell types detected in the caudal fin.

  6. 6.

    Supplementary Table 6

    Differently expressed genes detected in subgroups of myeloid cells.

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