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Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR–Cas9 barcodes by scGESTALT

Nature Protocolsvolume 13pages26852713 (2018) | Download Citation


Lineage relationships among the large number of heterogeneous cell types generated during development are difficult to reconstruct in a high-throughput manner. We recently established a method, scGESTALT, that combines cumulative editing of a lineage barcode array by CRISPR–Cas9 with large-scale transcriptional profiling using droplet-based single-cell RNA sequencing (scRNA-seq). The technique generates edits in the barcode array over multiple timepoints using Cas9 and pools of single-guide RNAs (sgRNAs) introduced during early and late zebrafish embryonic development, which distinguishes it from similar Cas9 lineage-tracing methods. The recorded lineages are captured, along with thousands of cellular transcriptomes, to build lineage trees with hundreds of branches representing relationships among profiled cell types. Here, we provide details for (i) generating transgenic zebrafish; (ii) performing multi-timepoint barcode editing; (iii) building scRNA-seq libraries from brain tissue; and (iv) concurrently amplifying lineage barcodes from captured single cells. Generating transgenic lines takes 6 months, and performing barcode editing and generating single-cell libraries involve 7 d of hands-on time. scGESTALT provides a scalable platform to map lineage relationships between cell types in any system that permits genome editing during development, regeneration, or disease.

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Figure 4 has associated raw data (Supplementary Data). There is no restriction on data availability. scGESTALT computational scripts and analysis pipeline are available at and are included as Supplementary Software 2 with this protocol.

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Key references using this protocol

Raj, B. et al. Nat. Biotechnol. 36, 442–450 (2018):

McKenna, A. et al. Science 353, aaf7907 (2016):


  1. 1.

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

  2. 2.

    Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016).

  3. 3.

    Kelsey, G., Stegle, O. & Reik, W. Single-cell epigenomics: recording the past and predicting the future. Science 358, 69–75 (2017).

  4. 4.

    Han, X. et al. Mapping the mouse cell atlas by Microwell-Seq. Cell 172, 1091–1107.e17 (2018).

  5. 5.

    Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).

  6. 6.

    Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

  7. 7.

    Rosenberg, A. B. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

  8. 8.

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

  9. 9.

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

  10. 10.

    Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

  11. 11.

    Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).

  12. 12.

    Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

  13. 13.

    Cusanovich, D. A. et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

  14. 14.

    Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017).

  15. 15.

    Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).

  16. 16.

    Preissl, S. et al. Single-nucleus analysis of accessible chromatin in developing mouse forebrain reveals cell-type-specific transcriptional regulation. Nat. Neurosci. 21, 432–439 (2018).

  17. 17.

    Mulqueen, R. M. et al. Highly scalable generation of DNA methylation profiles in single cells. Nat. Biotechnol. 36, 428–431 (2018).

  18. 18.

    Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

  19. 19.

    Haghverdi, L., Lun, A. T. L., Morgan, M. D. & Marioni, J. C. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36, 421–427 (2018).

  20. 20.

    Kiselev, V. Y., Yiu, A. & Hemberg, M. scmap: projection of single-cell RNA-seq data across data sets. Nat. Methods 15, 359–362 (2018).

  21. 21.

    Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

  22. 22.

    Shekhar, K. et al. Comprehensive classification of retinal bipolar neurons by single-cell transcriptomics. Cell 166, 1308–1323.e30 (2016).

  23. 23.

    Pandey, S., Shekhar, K., Regev, A. & Schier, A. F. Comprehensive identification and spatial mapping of Habenular neuronal types using single-cell RNA-seq. Curr. Biol. 28, 1052–1065.e7 (2018).

  24. 24.

    Cusanovich, D. A. et al. The cis-regulatory dynamics of embryonic development at single-cell resolution. Nature 555, 538–542 (2018).

  25. 25.

    Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290.e17 (2017).

  26. 26.

    Halpern, K. B. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).

  27. 27.

    La Manno, G. et al. Molecular diversity of midbrain development in mouse, human, and stem cells. Cell 167, 566–580.e19 (2016).

  28. 28.

    Da Mi et al. Early emergence of cortical interneuron diversity in the mouse embryo. Science 360, 81–85 (2018).

  29. 29.

    Tusi, B. K. et al. Population snapshots predict early haematopoietic and erythroid hierarchies. Nature 555, 54–60 (2018).

  30. 30.

    Hrvatin, S. et al. Single-cell analysis of experience-dependent transcriptomic states in the mouse visual cortex. Nat. Neurosci. 21, 120–129 (2018).

  31. 31.

    Baron, M. et al. A single-cell transcriptomic map of the human and mouse pancreas reveals inter- and intra-cell population structure. Cell Syst. 3, 346–360.e4 (2016).

  32. 32.

    Haber, A. L. et al. A single-cell survey of the small intestinal epithelium. Nature 551, 333–339 (2017).

  33. 33.

    Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018).

  34. 34.

    Hochgerner, H., Zeisel, A., Lönnerberg, P. & Linnarsson, S. Conserved properties of dentate gyrus neurogenesis across postnatal development revealed by single-cell RNA sequencing. Nat. Neurosci. 21, 290–299 (2018).

  35. 35.

    Nowakowski, T. J. et al. Spatiotemporal gene expression trajectories reveal developmental hierarchies of the human cortex. Science 358, 1318–1323 (2017).

  36. 36.

    Farrell, J. A. et al. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science 360, eaar3131 (2018).

  37. 37.

    Wagner, D. E. et al. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360, 981–987 (2018).

  38. 38.

    Briggs, J. A. et al. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360, eaar5780 (2018).

  39. 39.

    Spanjaard, B. & Junker, J. P. Methods for lineage tracing on the organism-wide level. Curr. Opin. Cell Biol. 49, 16–21 (2017).

  40. 40.

    Woodworth, M. B., Girskis, K. M. & Walsh, C. A. Building a lineage from single cells: genetic techniques for cell lineage tracking. Nat. Rev. Genet. 18, 230–244 (2017).

  41. 41.

    Ma, J., Shen, Z., Yu, Y.-C. & Shi, S.-H. Neural lineage tracing in the mammalian brain. Curr. Opin. Neurobiol. 50, 7–16 (2018).

  42. 42.

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

  43. 43.

    Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).

  44. 44.

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

  45. 45.

    Fuentealba, L. C. et al. Embryonic origin of postnatal neural stem cells. Cell 161, 1644–1655 (2015).

  46. 46.

    Harwell, C. C. et al. Wide dispersion and diversity of clonally related inhibitory interneurons. Neuron 87, 999–1007 (2015).

  47. 47.

    Mayer, C. et al. Clonally related forebrain interneurons disperse broadly across both functional areas and structural boundaries. Neuron 87, 989–998 (2015).

  48. 48.

    Rodriguez-Fraticelli, A. E. et al. Clonal analysis of lineage fate in native haematopoiesis. Nature 553, 212–216 (2018).

  49. 49.

    Gilbert, L. A. et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 154, 442–451 (2013).

  50. 50.

    Perez-Pinera, P. et al. RNA-guided gene activation by CRISPR-Cas9–based transcription factors. Nat. Methods 10, 973–976 (2013).

  51. 51.

    Maeder, M. L. et al. CRISPR RNA–guided activation of endogenous human genes. Nat. Methods 10, 977–979 (2013).

  52. 52.

    Cheng, A. W. et al. Multiplexed activation of endogenous genes by CRISPR-on, an RNA-guided transcriptional activator system. Cell Res. 23, 1163–1171 (2013).

  53. 53.

    Deng, W., Shi, X., Tjian, R., Lionnet, T. & Singer, R. H. CASFISH: CRISPR/Cas9-mediated in situ labeling of genomic loci in fixed cells. Proc. Natl. Acad. Sci. USA 112, 11870–11875 (2015).

  54. 54.

    Liu, X. et al. In situ capture of chromatin interactions by biotinylated dCas9. Cell 170, 1028–1043.e19 (2017).

  55. 55.

    Liao, H.-K. et al. In vivo target gene activation via CRISPR/Cas9-mediated trans-epigenetic modulation. Cell 171, 1495–1507.e15 (2017).

  56. 56.

    Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

  57. 57.

    Dixit, A. et al. Perturb-Seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866.e17 (2016).

  58. 58.

    Tang, W. & Liu, D. R. Rewritable multi-event analog recording in bacterial and mammalian cells. Science 360, eaap8992 (2018).

  59. 59.

    Komor, A. C., Kim, Y. B., Packer, M. S., Zuris, J. A. & Liu, D. R. Programmable editing of a target base in genomic DNA without double-stranded DNA cleavage. Nature 533, 420–424 (2016).

  60. 60.

    Kim, Y. B. et al. Increasing the genome-targeting scope and precision of base editing with engineered Cas9-cytidine deaminase fusions. Nat. Biotechnol. 35, 371–376 (2017).

  61. 61.

    Gaudelli, N. M. et al. Programmable base editing of A•T to G•C in genomic DNA without DNA cleavage. Nature 551, 464–471 (2017).

  62. 62.

    Nelles, D. A. et al. Programmable RNA tracking in live cells with CRISPR/Cas9. Cell 165, 488–496 (2016).

  63. 63.

    Mikuni, T., Nishiyama, J., Sun, Y., Kamasawa, N. & Yasuda, R. High-throughput, high-resolution mapping of protein localization in mammalian brain by in vivo genome editing. Cell 165, 1803–1817 (2016).

  64. 64.

    Perli, S. D., Cui, C. H. & Lu, T. K. Continuous genetic recording with self-targeting CRISPR-Cas in human cells. Science 353, aag0511 (2016).

  65. 65.

    Chow, R. D. et al. AAV-mediated direct in vivo CRISPR screen identifies functional suppressors in glioblastoma. Nat. Neurosci. 20, 1329–1341 (2017).

  66. 66.

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

  67. 67.

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

  68. 68.

    Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108 (2018).

  69. 69.

    Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018).

  70. 70.

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

  71. 71.

    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).

  72. 72.

    Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018).

  73. 73.

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

  74. 74.

    Lodato, M. A. et al. Aging and neurodegeneration are associated with increased mutations in single human neurons. Science 359, 555–559 (2018).

  75. 75.

    Bae, T. et al. Different mutational rates and mechanisms in human cells at pregastrulation and neurogenesis. Science 359, 550–555 (2018).

  76. 76.

    Kawakami, K. Tol2: a versatile gene transfer vector in vertebrates. Genome Biol. 8 (Suppl. 1), S7 (2007).

  77. 77.

    Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

  78. 78.

    Suster, M. L., Abe, G., Schouw, A. & Kawakami, K. Transposon-mediated BAC transgenesis in zebrafish. Nat. Protoc. 6, 1998–2021 (2011).

  79. 79.

    Fisher, S. et al. Evaluating the biological relevance of putative enhancers using Tol2 transposon-mediated transgenesis in zebrafish. Nat. Protoc. 1, 1297–1305 (2006).

  80. 80.

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

  81. 81.

    Yin, L. et al. Multiplex conditional mutagenesis using transgenic expression of Cas9 and sgRNAs. Genetics 200, 431–441 (2015).

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We thank D.E. Wagner, A. McKenna, and S. Pandey for discussion and advice. This work was supported by a postdoctoral fellowship from the Canadian Institutes of Health Research to B.R.; NIH grants U01MH109560, R01HD85905, and DP1 HD094764 to A.F.S.; and an Allen Discovery Center grant to A.F.S.

Author information


  1. Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA

    • Bushra Raj
    • , James A. Gagnon
    •  & Alexander F. Schier
  2. Allen Discovery Center for Cell Lineage Tracing, Seattle, WA, USA

    • Bushra Raj
    • , James A. Gagnon
    •  & Alexander F. Schier
  3. Department of Biology, University of Utah, Salt Lake City, UT, USA

    • James A. Gagnon
  4. Biozentrum, University of Basel, Basel, Switzerland

    • Alexander F. Schier
  5. Broad Institute of MIT and Harvard, Cambridge, MA, USA

    • Alexander F. Schier
  6. Harvard Stem Cell Institute, Harvard University, Cambridge, MA, USA

    • Alexander F. Schier
  7. Center for Brain Science, Harvard University, Cambridge, MA, USA

    • Alexander F. Schier


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B.R. and J.A.G. developed and optimized the scGESTALT protocols and analyzed the data. B.R. wrote the manuscript with edits by A.F.S. and J.A.G.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Bushra Raj.

Integrated supplementary information

  1. Supplementary Figure 1 Papain oxygenation setup.

    Left and middle panels, 95%O2: 5%CO2 gas tank fitted with a gas regulator, Tygon E-3603 tubing and a 5 ml serological pipette. Right panel, Oxygenation of papain/DNase mix in Neurobasal Medium (small vial to the right) is performed by bubbling 95%O2: 5%CO2 gas through tubing attached to a sterile 5 ml serological pipette for 2 min (Procedure Step 52). EBSS buffer (large vial to the left, used for resuspending ovumucoid, Procedure Step 53) and Neurobasal Medium (Procedure Step 50) are oxygenated in a similar manner.

  2. Supplementary Figure 2 Zebrafish brain dissection.

    Top panels, Anesthetized fish is transferred to a Sylgard dish covered with Neurobasal Medium and MESAB (left). The fish is pinned just posterior of the head, in the middle of the trunk and near the tail using 3 insect pins (right, asterisks mark pin positions). Bottom panels, The jaw, eyes, heart and gut tissues are removed. The skin on top of the head is pierced and peeled back to expose the brain (left, circle marks the exposed brain). Gently scoop the brain out taking care not to lose part of the hindbrain in the process (right, whole brain is encircled).

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