Whole-organism clone tracing using single-cell sequencing

Abstract

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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Figure 1: Single-cell clonal tracing in zebrafish.
Figure 2: Few clones produce haematopoietic cells.
Figure 3: Clonality in the brain and eyes.
Figure 4: Clonality during caudal fin regeneration.

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References

  1. 1

    Sulston, J. E. & Horvitz, H. R. Post-embryonic cell lineages of the nematode, Caenorhabditis elegans. Dev. Biol. 56, 110–156 (1977)

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Keller, P. J., Schmidt, A. D., Wittbrodt, J. & Stelzer, E. H. Reconstruction of zebrafish early embryonic development by scanned light sheet microscopy. Science 322, 1065–1069 (2008)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  3. 3

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  4. 4

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  5. 5

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

    CAS  PubMed  PubMed Central  Google Scholar 

  6. 6

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  8. 8

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

    PubMed  PubMed Central  Google Scholar 

  9. 9

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

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Schmidt, S. T., Zimmerman, S. M., Wang, J., Kim, S. K. & Quake, S. R. Quantitative analysis of synthetic cell lineage tracing using nuclease barcoding. ACS Synth. Biol. 6, 936–942 (2017)

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Kalhor, R., Mali, P. & Church, G. M. Rapidly evolving homing CRISPR barcodes. Nat. Methods 14, 195–200 (2017)

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

    Junker, J. P. et al. Massively parallel whole-organism lineage tracing using CRISPR/Cas9 induced genetic scars. Preprint at https://www.biorxiv.org/content/early/2016/06/01/056499 (2016)

  14. 14

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

    Jao, L. E., Wente, S. R. & Chen, W. Efficient multiplex biallelic zebrafish genome editing using a CRISPR nuclease system. Proc. Natl Acad. Sci. USA 110, 13904–13909 (2013)

    ADS  CAS  Google Scholar 

  17. 17

    Pauls, S., Geldmacher-Voss, B. & Campos-Ortega, J. A. 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)

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

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

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

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

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

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

    ADS  Google Scholar 

  21. 21

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

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    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)

    CAS  PubMed  Google Scholar 

  23. 23

    Schmidt, R., Strähle, U. & Scholpp, S. Neurogenesis in zebrafish — from embryo to adult. Neural Dev. 8, 3 (2013)

    PubMed  PubMed Central  Google Scholar 

  24. 24

    Lee, R. T., Knapik, E. W., Thiery, J. P. & Carney, T. J. An exclusively mesodermal origin of fin mesenchyme demonstrates that zebrafish trunk neural crest does not generate ectomesenchyme. Development 140, 2923–2932 (2013)

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

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

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

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

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Singh, S. P., Holdway, J. E. & Poss, K. D. Regeneration of amputated zebrafish fin rays from de novo osteoblasts. Dev. Cell 22, 879–886 (2012)

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

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

    CAS  PubMed  PubMed Central  Google Scholar 

  29. 29

    Tornini, V. A., Thompson, J. D., Allen, R. L. & Poss, K. D. Live fate-mapping of joint-associated fibroblasts visualizes expansion of cell contributions during zebrafish fin regeneration. Development 144, 2889–2895 (2017)

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Gautier, E. L. 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)

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31

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

  32. 32

    Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR/Cas9-induced genetic scars. Nat. Biotechnol. https://doi.org/10.1038/nbt/4124 (2018)

  33. 33

    Peterson-Maduro, J., Florescu, M., Baron, C. S., Alemany, A. & van Oudenaarden, A. Single-cell ScarTrace. Protoc. Exch. https://doi.org/10.1038/protex.2018.017 (2018)

  34. 34

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

    PubMed  PubMed Central  Google Scholar 

  35. 35

    Lopez-Ramirez, M. A., Calvo, C. F., Ristori, E., Thomas, J. L. & Nicoli, S. Isolation and culture of adult zebrafish brain-derived neurospheres. J. Vis. Exp. 108, 53617 (2016).

    Google Scholar 

  36. 36

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

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Grün, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014)

    PubMed  PubMed Central  Google Scholar 

  38. 38

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

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

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

    PubMed  PubMed Central  Google Scholar 

  40. 40

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

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    Carmona, S. J. 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)

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Cahoy, J. D. 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)

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Nelson, S. M., Frey, R. A., Wardwell, S. L. & Stenkamp, D. L. The developmental sequence of gene expression within the rod photoreceptor lineage in embryonic zebrafish. Dev. Dyn. 237, 2903–2917 (2008)

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    Zhang, H., Copara, M. & Ekstrom, A. D. Differential recruitment of brain networks following route and cartographic map learning of spatial environments. PLoS ONE 7, e44886 (2012)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

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

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    Di Donato, V., Auer, T. O., Duroure, K. & Del Bene, F. Characterization of the calcium binding protein family in zebrafish. PLoS ONE 8, e53299 (2013)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

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

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48

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

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

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

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  50. 50

    Chen, R., Wu, X., Jiang, L. & Zhang, Y. Single-cell RNA-seq reveals hypothalamic cell diversity. Cell Reports 18, 3227–3241 (2017)

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

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

    PubMed  PubMed Central  Google Scholar 

  52. 52

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

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Akerberg, A. A., Stewart, S. & Stankunas, K. Spatial and temporal control of transgene expression in zebrafish. PLoS ONE 9, e92217 (2014)

    ADS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Smyth, I. 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)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

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

    MathSciNet  MATH  Google Scholar 

  56. 56

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

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57

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

    CAS  Google Scholar 

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Acknowledgements

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.

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Authors

Contributions

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.

Corresponding author

Correspondence to Alexander van Oudenaarden.

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Reviewer Information Nature thanks L. Zon and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 gDNA versus gDNA–mRNA detection of scars.

a, Scar percentage per cell (top bar indicates clones), and pie chart of fraction of cells per clone (colours matching histograms’ top bar) detected via gDNA (ScarTrace without step 1) and gDNA–mRNA detection (full protocol). b, Number of detected cells per clone in gDNA versus gDNA–mRNA detection and Pearson’s correlation coefficient computed using the 20 different clones identified. Dot sizes are proportional to the total number of cells found taking together the two detection strategies. c, Fraction of cells detected per clone in gDNA (green) and gDNA–mRNA (purple) detection, in which clone ‘0’ represents unscarred cells. d, Top, one-sided Fisher’s exact test on a contingency table made of the number of cells detected for the given scar clone x for each detection strategy (a and b, respectively), and the number of cells taking together all other clones (c and d, respectively) found in gDNA detection (n = 147 cells in total) and gDNA–mRNA detection (n = 128 cells in total). Bottom, heat map shows the one-sided P value of each scar clone to be enriched in the gDNA or mRNA/gDNA detection protocol. No enrichment is found with P <0.05, therefore results found for the two detection protocols are compatible. e, Normalized histograms and corresponding fit noise model function (grey line; Supplementary Information section 4) for the scar percentage detected for 1 clone found in fish P1. Scar detection efficiency is defined as the area above 5% scar content (vertical red line). Efficiency of detection of unscarred molecules is assumed to be the same as for scarred molecules. f, Normalized histogram of the scar detection efficiencies found after pooling all clones from all organs for all fish (in total, n = 371 detected clones; Extended Data Table 1). The vertical black line and the grey area indicate the mean scar detection efficiencies and s.e.m., respectively.

Extended Data Figure 2 Transcriptome analysis of the zebrafish WKM.

a, b, Scar percentage per cell for fish P2 (a) and R2 (b). The bar above each panel indicates clones and corresponding P values. c, d, Lineage trees for clones detected in P2 (c) and R2 (d) obtained as described in Fig. 2. e, t-SNE map of cells from fish R1 and R2. Colours and numbers indicate RaceID clusters. f, Heat map of the Pearson correlation between cells sorted according to RaceID clusters. Cluster numbers are indicated on the x and y axes. g, t-SNE map for the WKM of R1 and R2 coloured according to fish of origin (R1 in pink and R2 in green). All cell types intermingle well, except erythrocytes. Even though erythrocytes appear separately on the t-SNE space, they belong to the same RaceID cluster. h, t-SNE maps for R1 and R2, coloured according to the number of unique transcripts per single cell for each marker38,39,40,41. A full list of marker genes for each cell type is available in Supplementary Table 3. i, t-SNE map for fish R2 with cells coloured according to clone. j, Clonal cell fraction per cell type for fish R2.

Extended Data Figure 3 Cell types and batch effects in the brain and eyes for fish R2 and R3.

a, t-SNE map obtained with RaceID of pooled cells with a minimum of 100 total transcripts from fish R2 and R3 (isolated from the right and left eyes, right and left midbrain, forebrain and hindbrain). Different symbols indicate cells with different minimal total transcript counts. Cells are coloured according to the assigned cell type using the lowest cut off (that is, taking into account cells with at least 100 transcripts). We do not lose any cell type cluster when applying higher transcript cut offs, nor do we generate new clusters of low transcript cells when applying lower cut offs. The fraction of cells that would be termed a different cell type with a higher cut off is very low (<1%). Low transcript cells cluster mainly around the clusters formed by high transcript cells. b, c, t-SNE maps as in a, in which cells are coloured according to organ (b) and fish (c) of origin. d, t-SNE map as in a, but showing only cells from fish R3, with corresponding cell types indicated. e, t-SNE maps for fish R2 and R3 coloured according to the number of unique transcripts per single cell42,43,44,45,46,47,48,49,50,51. A full list of marker genes for each cell type is available in Supplementary Table 4.

Extended Data Figure 4 ScarTrace in the zebrafish brain and eyes.

a, Heat map of the fraction of cells per clones for cell types in fish R3 (COP, OPC and MFOL clones merged as oligodendrocytes), and two-sided Fisher’s exact test for enriched (magenta upwards triangle) and depleted (blue downwards triangle) clones per cell type with P < 0.05. The bars at the top depict organ and corresponding total number of cells. All clones have P values < 10−5. b, c, t-SNE map of fish R2 and R3 cells showing different colours for enriched clones detected in glia cells, neurons or retinal interneurons. Other cells are shown in grey. d, e, Scar percentage per cell for clones found in the WKM, forebrain, hindbrain, left and right eyes, and left and right midbrain for R2 and R3. In all panels, each colour represents the same scar (for example, the yellow scar is the same for R2 and R3), and unscarred GFP is shown in green. f, g, Heat maps of the fraction of cells per clones for each organ for R2 (f) and R3 (g). Enriched (magenta upwards triangles) and depleted (blue downwards triangles) scar clones per organ are determined by a two-sided Fisher’s exact test with P <0.05. The bar above each panel depicts the number of cells and P value for each clone. h, Histograms of the relative clone frequency in the left (blue) and right (red) midbrain and eye for R3. i, Image of dome-stage embryo injected with Cas9–eScarlet in one cell at the two-cell stage (n > 10 embryos showed similar patterns). BF, bright-field. j, Scarring efficiency shown as the percentage of unscarred GFP in S1, P1 and P2 for the left and right eyes, and the WKM. k, t-SNE map of cells isolated from the left and right eyes of S1, in which cells are coloured according to their cell type. l, Heat map of the fraction of cells per clones for each cell type in S1. Enriched (magenta upwards triangle) and depleted (blue downwards triangle) scar clones per cell type are determined from a two-sided Fisher’s exact test with P < 0.05. The bars at the top depict the total number of cells and the fraction of cells found in the right eye for each clone. All P values are below 10−5.

Extended Data Figure 5 Clones for fish P1 and P2 and lineage tree of the eyes and midbrain.

a, b, Scar percentage per cell for clones found in the WKM, forebrain, hindbrain, left and right eyes, and left and right midbrain for P1 (a) and P2 (b). Each colour represents a different scar, in which unscarred GFP is always shown in green. Colour legend per scars is different between panels (yellow scar in a is not yellow scar in b). c, d, Heat maps of the fraction of cells per clones for each organ for P1 (c) and P2 (d). Enriched (magenta upwards triangles) and depleted (blue downwards triangles) scar clones per organ are determined from a two-sided Fisher’s exact test with P <0.05. The bar above each panel depicts the number of cells and P value for each clone. eh, Lineage trees obtained assuming the principle of maximum parsimony as described in Supplementary Information section 5 for clones detected in the right and the left eyes (e, g) and right and left midbrain (f, h) of fish P1 (e, f) and R2 (g, h). The root of the trees is set as an unscarred clone, with eight copies of the GFP transgene. In the tips there are the detected clones. The statistical confidence of each branch is computed as the proportion of each branch among 10,000 tree replicates constructed by bootstrapping scars present in all clones. To assess statistically whether clones from the left or the right side co-evolve together, we randomized the clones at the tips of the tree and checked how many times, randomly, clones from the right or the left were found to be sisters with other clones from the right or the left. This allowed us to build a distribution of co-evolution (histograms in each tree) of clones for the null hypothesis and check whether the number of times we saw clones from one side together was statistically significant or not. The vertical dashed line in each histogram indicates the number of times we see clones from one side together as sisters in the reference tree. When such line is found at the right-hand (left-hand) side of the maximum, we assume that the coevolution of the clones is enhanced (depleted). In the heat maps, we indicate the degree of co-evolution of clones in the right or the left eye or midbrain, computed as the fraction of the area of the histogram at the right- or the left-hand side (that is, enhanced or depleted co-evolution, respectively) of the vertical line divided by the corresponding area of the histogram at the right- or left-hand side of maximum of the distribution.

Extended Data Figure 6 Transcriptome analysis of the zebrafish caudal fin.

a, t-SNE maps obtained by pooling together cells from primary and regenerated fins from fish R4, R5 and R6. In each panel, single cells are coloured according to the number of unique transcripts observed for a given gene. The corresponding cell type is indicated in parenthesis52,53,54. A complete list of marker genes used for each cell type is available in Supplementary Table 5. b, t-SNE maps for the caudal fin of R4, R5 and R6 coloured based on fish (left) and fin version (right) of origin. All cells are present in all fins and fin version. No batch effects are observed.

Extended Data Figure 7 Clonal analysis in the caudal fin.

a, Scar percentage per cell in clones detected in fish R4 (left), R5 (middle) and R6 (right). The corresponding organ is indicated above each barplot (WKM or fin version). Spatial information (dorsal or ventral) is indicated when available. The bars at the top indicate clones. Each colour represent a scar, the same colour scheme is used for all panels. b, c, Heat maps of the fraction of cells per clones for each cell type and fin in fish R5 (b) and R6 (c). Enriched (magenta upwards triangle) and depleted (blue downwards triangle) scar clones per cell type per primary, secondary and tertiary fin obtained from two-sided Fisher’s exact test with P < 0.05. Top bars depict clones found in the WKM of the same fish, the corresponding number of cells, and the P value for each clone. d, t-SNE map of R6, in which cells with clone 24 (as a representative example of clones shared between mesenchymal and epidermal cells) are highlighted in red. e, t-SNE map of all cells detected in the caudal fin, in which cells from clone 19 (as a representative example of clones shared between mesenchymal and epidermal cells) in R4 are highlighted in red. f, t-SNE map of R6 primary (left) and regenerated (right) caudal fin cells (grey circles), in which cells from osteoblast clones are highlighted in red. Dashed lines represent mesenchymal cells (Fig. 4b, Extended Data Fig. 6). The percentages indicate the fraction of mesenchymal cells that share clones with osteoblasts. g, Magnified view of the t-SNE maps of R4, R5 and R6 for immune cells (dashed line on Fig. 4b). Cells are coloured based on fish (left) and fin version (right) of origin. Subpopulations of lymphoid (dashed circles) and myeloid (solid circles) are found in all fish and fin versions. h, Scar percentage for cells detected in the WKM (top) and RICs (bottom) for R4 (left), R5 (middle) and R6 (right) in the primary fins reveals the absence of common scars between the two. The bar above each panel indicates the different clones.

Extended Data Figure 8 The histone-GFP transgene and scar characterization.

a, Scheme of one copy of the h2afva:GFP transgene as previously described18. b, Copy number of the transgene. Top, average number of reads in bin sizes of 1 kb and sliding window of 200 bp obtained in whole-genome sequencing data. Bottom, copy number extracted using FREEC-11.0 with default parameters (Methods). c, Number of clones detected with a given number of scars, obtained by pooling all data from all fish used in this study. d, Scar percentage per cell in a clone in which seven different scars are detected. e, Probability density function (normalized histogram) of the fraction of scars detected in the clone depicted in d (colour code as in d). f, Average fraction of a scar per embryo computed over ten independently injected embryos (for times larger than 6 h) and over three pools of ten embryos (for times lower or equal to 6 h) detected from gDNA as a function of time for Cas9 RNA injections, for the six most observed scars (described with CIGAR strings). Error bars denote s.e.m. Solid green lines are the fit to equation (5) in Supplementary Information section 2. g, Top 100 observed scars, sorted according to their probability, and corresponding position of deletions (red) and insertions (blue) along the GFP coordinate. h, Scar probabilities as a function of sorted scars. Error bars denote s.e.m. from the fit (in f). i, Probabilities of measuring the percentages xi for three scars with copy numbers 2 (top), 1 (middle) and 1 (bottom), in which the expected percentages are fi = 50%, 25% and 25%, respectively, present in the same cell with four surviving integrations of GFP. The probability has been obtained by independently simulating 1,000 times the ScarTrace protocol for ε = 0.50 (left) and ε = 0.85 (right). Solid green lines are the fit to equation (7) in Supplementary Information section 3. j, Scar percentage for cells from the same clone made of three scars, in which each scar is represented with a different colour. k, Corresponding probability density functions (normalized histogram) for the fraction of each scar per cell (colour code as in j). Black lines denote the best fit for each scar to equation (7) (see Supplementary Information). l, Violin plot showing the distribution of measured P values obtained using a Gaussian kernel density with bandwidth determined using the Scott method55, for all clones with a given estimated NGFP. Labels indicate the number of clones observed for NGFP.

Extended Data Table 1 Clone number in different organs
Extended Data Table 2 Overview of the scarred fish and organs dissected for each

Supplementary information

Life Sciences Reporting Summary (PDF 77 kb)

Supplementary Data 1

This file summarizes the RaceID parameters used for transcriptome analysis. (PDF 37 kb)

Supplementary Data 2

This file contains a zipped file with scripts to extract extract scars and detect cones in single cells. (ZIP 74 kb)

Supplementary Data 3

The reference manual for the scripts provided in Supplementary Data 2 to extract clones. (PDF 1978 kb)

Supplementary Information

This file contains Supplementary Sections 1-4. (PDF 392 kb)

Supplementary Table 1

Cell specific barcodes used in transcriptome library preparation. (XLSX 25 kb)

Supplementary Table 2

Cell specific barcodes used in scar library preparation. (XLSX 18 kb)

Supplementary Table 3

Differently expressed genes detected in the different hematopoietic cell types. (XLSX 149 kb)

Supplementary Table 4

Differently expressed genes detected in the cell types detected in brain and eyes. (XLSX 15794 kb)

Supplementary Table 5

Differently expressed genes detected in the cell types detected in the caudal fin. (XLSX 527 kb)

Supplementary Table 6

Differently expressed genes detected in subgroups of myeloid cells. (XLSX 191 kb)

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Alemany, A., Florescu, M., Baron, C. et al. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018). https://doi.org/10.1038/nature25969

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