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Lineage tracing meets single-cell omics: opportunities and challenges

Nature Reviews Genetics volume 21, pages 410–427 (2020)Cite this article

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Abstract

A fundamental goal of developmental and stem cell biology is to map the developmental history (ontogeny) of differentiated cell types. Recent advances in high-throughput single-cell sequencing technologies have enabled the construction of comprehensive transcriptional atlases of adult tissues and of developing embryos from measurements of up to millions of individual cells. Parallel advances in sequencing-based lineage-tracing methods now facilitate the mapping of clonal relationships onto these landscapes and enable detailed comparisons between molecular and mitotic histories. Here we review recent progress and challenges, as well as the opportunities that emerge when these two complementary representations of cellular history are synthesized into integrated models of cell differentiation.

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Fig. 1: Inferring cell histories from state manifolds.
Fig. 2: Limitations of cell state manifolds.
Fig. 3: Methods and logic for lineage barcoding experiments.
Fig. 4: Reading and writing transgenic DNA barcodes.
Fig. 5: Applications and pitfalls of lineage tracing on state manifolds.
Fig. 6: Developmental paradigms that shape state–lineage relationships.

References

  1. Whitman, C. O. Memoirs: the embryology of clepsine. J. Cell Sci. s2-18, 215–315 (1878).

    Google Scholar 

  2. Waddington, C. H. The strategy of the genes. A discussion of some aspects of theoretical biology. (George Allen & Unwin, Ltd., London, 1957).

    Google Scholar 

  3. Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Tritschler, S. et al. Concepts and limitations for learning developmental trajectories from single cell genomics. Development 146, dev170506 (2019).

    Article  PubMed  Google Scholar 

  5. McKenna, A. & Gagnon, J. A. Recording development with single cell dynamic lineage tracing. Development 146, dev169730 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Kester, L. & van Oudenaarden, A. Single-cell transcriptomics meets lineage tracing. Cell Stem Cell 23, 166–179 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Kotliar, D. et al. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq. eLife 8, e43803 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Mezger, A. et al. High-throughput chromatin accessibility profiling at single-cell resolution. Nat. Commun. 9, 3647 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  15. Karemaker, I. D. & Vermeulen, M. Single-cell DNA methylation profiling: technologies and biological applications. Trends Biotechnol. 36, 952–965 (2018).

    Article  CAS  PubMed  Google Scholar 

  16. Budnik, B., Levy, E., Harmange, G. & Slavov, N. SCoPE-MS: mass spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. Genome Biol. 19, 161 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  17. Duncan, K. D., Fyrestam, J. & Lanekoff, I. Advances in mass spectrometry based single-cell metabolomics. Analyst 144, 782–793 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Mimitou, E. P. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16, 409–412 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Han, K. Y. et al. SIDR: simultaneous isolation and parallel sequencing of genomic DNA and total RNA from single cells. Genome Res. 28, 75–87 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Cai, L. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rodriques, S. G. et al. Slide-seq: a scalable technology for measuring genome-wide expression at high spatial resolution. Science 363, 1463–1467 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, aaa6090 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  27. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Tikhonova, A. N. et al. The bone marrow microenvironment at single-cell resolution. Nature 569, 222–228 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Montoro, D. T. et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560, 319–324 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Plasschaert, L. W. et al. A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature 560, 377–381 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Young, M. D. et al. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361, 594–599 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. de Soysa, T. Y. et al. Single-cell analysis of cardiogenesis reveals basis for organ-level developmental defects. Nature 572, 120–124 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Nowotschin, S. et al. The emergent landscape of the mouse gut endoderm at single-cell resolution. Nature 569, 361–367 (2019). The authors analyse >100,000 single-cell transcriptomes from developing mouse endoderm and describe the convergence of visceral and definitive lineages into spatially defined transcriptional states.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Diaz-Cuadros, M. et al. In vitro characterization of the human segmentation clock. Nature https://doi.org/10.1038/s41586-019-1885-9 (2020).

  37. Zeisel, A. et al. Molecular architecture of the mouse nervous system. Cell 174, 999–1014.e1022 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Soldatov, R. et al. Spatiotemporal structure of cell fate decisions in murine neural crest. Science 364, eaas9536 (2019).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Packer, J. S. et al. A lineage-resolved molecular atlas of C. elegans embryogenesis at single-cell resolution. Science 365, eaax1971 (2019). The authors interrogate the temporal dynamics of lineage–state relationships, as well as transcriptional convergence and divergence, in the invariant C. elegans embryonic lineage.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Sebé-Pedrós, A. et al. Cnidarian cell type diversity and regulation revealed by whole-organism single-cell RNA-seq. Cell 173, 1520–1534.e1520 (2018).

    Article  PubMed  CAS  Google Scholar 

  42. Siebert, S. et al. Stem cell differentiation trajectories in Hydra resolved at single-cell resolution. Science 365, eaav9314 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Achim, K. et al. Whole-body single-cell sequencing reveals transcriptional domains in the annelid larval body. Mol. Biol. Evol. 35, 1047–1062 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Zeng, A. et al. Prospectively isolated tetraspanin(+) neoblasts are adult pluripotent stem cells underlying planaria regeneration. Cell 173, 1593–1608.e1520 (2018).

    Article  CAS  PubMed  Google Scholar 

  45. Fincher, C. T., Wurtzel, O., de Hoog, T., Kravarik, K. M. & Reddien, P. W. Cell type transcriptome atlas for the planarian Schmidtea mediterranea. Science 360, eaaq1736 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Plass, M. et al. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science 360, eaaq1723 (2018).

    Article  PubMed  CAS  Google Scholar 

  47. Wagner, D. E. et al. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360, 981–987 (2018). The authors describe non-tree-like cell state trajectories using combined lineage barcoding and single-cell transcriptomics in zebrafish embryos.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019). This study presents the largest single-cell transcriptome atlas for mouse embryogenesis to date, spanning >2 million cells and 56 cell state trajectories.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Pijuan-Sala, B. et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 490–495 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017).

    Article  CAS  PubMed  Google Scholar 

  53. Cao, C. et al. Comprehensive single-cell transcriptome lineages of a proto-vertebrate. Nature 571, 349–354 (2019). This study performs comprehensive single-cell profiling of ascidian embryos from the early gastrula to larval stages and maps the transcriptomic signatures onto a virtual map of the determinate embryonic lineage tree.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. https://doi.org/10.1038/nbt.4314 (2018).

    Article  PubMed  Google Scholar 

  55. Weinreb, C., Wolock, S. & Klein, A. M. SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics 34, 1246–1248 (2018).

    Article  CAS  PubMed  Google Scholar 

  56. Jacomy, M., Venturini, T., Heymann, S. & Bastian, M. ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software. PLoS One 9, e98679 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  57. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Wolf, F. A. et al. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol. 20, 59 (2019). This study presents ‘PAGA’, a graph-based computational approach for mapping non-tree-like topologies in single-cell state landscapes.

    Article  PubMed  PubMed Central  Google Scholar 

  60. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Shin, J. et al. Single-Cell RNA-Seq with Waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360–372 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016).

    Article  CAS  PubMed  Google Scholar 

  63. Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M. & Klein, A. M. Fundamental limits on dynamic inference from single-cell snapshots. Proc. Natl Acad. Sci. USA 115, E2467–E2476 (2018). This is one of several studies to provide a framework for predicting fate trajectories from single-cell state manifolds.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Herman, J. S., Sagar & Grün, D. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat. Methods 15, 379–386 (2018).

    Article  CAS  PubMed  Google Scholar 

  66. Setty, M. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37, 451–460 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 1517 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Furchtgott, L. A., Melton, S., Menon, V. & Ramanathan, S. Discovering sparse transcription factor codes for cell states and state transitions during development. eLife 6, e20488 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  69. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Hendriks, G.-J. et al. NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10, 3138 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Erhard, F. et al. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571, 419–423 (2019).

    Article  CAS  PubMed  Google Scholar 

  72. Gorin, G., Svensson, V. & Pachter, L. Protein velocity and acceleration from single-cell multiomics experiments. Genome Biol. 21, 39 (2019).

    Article  CAS  Google Scholar 

  73. Qiu, X. et al. Mapping vector field of single cells. Preprint at https://doi.org/10.1101/696724 (2019).

  74. Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015).

    Article  CAS  PubMed  Google Scholar 

  75. Coifman, R. R. et al. Geometric diffusions as a tool for harmonic analysis and structure definition of data: diffusion maps. Proc. Natl Acad. Sci. USA 102, 7426–7431 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Chen, H. et al. Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat. Commun. 10, 1903 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  77. Traag, V. A., Waltman, L. & van Eck, N. J. From Louvain to Leiden: guaranteeing well-connected communities. Sci. Rep. 9, 5233 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Blondel, V. D., Guillaume, J.-L., Lambiotte, R. & Lefebvre, E. Fast unfolding of communities in large networks. J. Stat. Mech. https://doi.org/10.1088/1742-5468/2008/10/P10008 (2008).

    Article  Google Scholar 

  79. Kimmel, C. B., Warga, R. M. & Schilling, T. F. Origin and organization of the zebrafish fate map. Development 108, 581–594 (1990).

    CAS  PubMed  Google Scholar 

  80. Kretzschmar, K. & Watt, F. M. Lineage tracing. Cell 148, 33–45 (2012).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Wagers, A. J. & Weissman, I. L. Plasticity of adult stem cells. Cell 116, 639–648 (2004).

    Article  CAS  PubMed  Google Scholar 

  83. Wagers, A. J., Sherwood, R. I., Christensen, J. L. & Weissman, I. L. Little evidence for developmental plasticity of adult hematopoietic stem cells. Science 297, 2256–2259 (2002).

    Article  CAS  PubMed  Google Scholar 

  84. Weinreb, C., Rodriguez-Fraticelli, A., Camargo, F. D. & Klein, A. M. Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science 367, eaaw3381 (2020). The authors implement the ‘LARRY’ clonal resampling approach to map single-cell transcriptomes and lineage relationships in differentiating cells in the mouse haematopoietic system.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Chan, M. M. et al. Molecular recording of mammalian embryogenesis. Nature 570, 77–82 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Alemany, A., Florescu, M., Baron, C. S., Peterson-Maduro, J. & van Oudenaarden, A. Whole-organism clone tracing using single-cell sequencing. Nature 556, 108–112 (2018). This study describes the Cas9-editing-based ‘ScarTrace’ method for simultaneous measurement of single-cell transcriptomes and lineage relationships in the zebrafish embryo and the regenerating fin of its adult form.

    Article  CAS  PubMed  Google Scholar 

  87. Conklin, E. G. The organization and cell lineage of the ascidian egg. J. Acad. Nat. Sci. Phila. 13, 1–119 (1905).

    Google Scholar 

  88. Sulston, J. E., Schierenberg, E., White, J. G. & Thomson, J. N. The embryonic cell lineage of the nematode Caenorhabditis elegans. Dev. Biol. 100, 64–119 (1983).

    Article  CAS  PubMed  Google Scholar 

  89. 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).

    Article  CAS  PubMed  Google Scholar 

  90. McDole, K. et al. In toto imaging and reconstruction of post-implantation mouse development at the single-cell level. Cell 175, 859–876.e833 (2018).

    Article  CAS  PubMed  Google Scholar 

  91. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  93. Keller, G., Paige, C., Gilboa, E. & Wagner, E. F. Expression of a foreign gene in myeloid and lymphoid cells derived from multipotent haematopoietic precursors. Nature 318, 149–154 (1985).

    Article  CAS  PubMed  Google Scholar 

  94. Lemischka, I. R., Raulet, D. H. & Mulligan, R. C. Developmental potential and dynamic behavior of hematopoietic stem cells. Cell 45, 917–927 (1986).

    Article  CAS  PubMed  Google Scholar 

  95. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e1322 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Xu, J. et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. eLife 8, e45105 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  100. Kalhor, R. et al. Developmental barcoding of whole mouse via homing CRISPR. Science 361, eaat9804 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  101. Raj, B. et al. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36, 442–450 (2018). This study combines the previously established Cas9-editing GESTALT approach for lineage barcoding with inDrops-based single-cell transcriptome analysis to reconstruct developmental trajectories in the zebrafish brain.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Spanjaard, B. et al. Simultaneous lineage tracing and cell-type identification using CRISPR–Cas9-induced genetic scars. Nat. Biotechnol. 36, 469–473 (2018). This study introduces ‘LINNAEUS’ and a network algorithm for reconstructing Cas9-editing-based lineage phylogenies between cell states of the 5-day-old zebrafish embryo.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Ihry, R. J. et al. p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells. Nat. Med. 24, 939–946 (2018).

    Article  CAS  PubMed  Google Scholar 

  104. Haapaniemi, E., Botla, S., Persson, J., Schmierer, B. & Taipale, J. CRISPR–Cas9 genome editing induces a p53-mediated DNA damage response. Nat. Med. 24, 927–930 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Pei, W. et al. Using Cre-recombinase-driven Polylox barcoding for in vivo fate mapping in mice. Nat. Protoc. 14, 1820–1840 (2019).

    Article  CAS  PubMed  Google Scholar 

  107. Klompe, S. E., Vo, P. L. H., Halpin-Healy, T. S. & Sternberg, S. H. Transposon-encoded CRISPR-Cas systems direct RNA-guided DNA integration. Nature 571, 219–225 (2019).

    Article  CAS  PubMed  Google Scholar 

  108. Strecker, J. et al. RNA-guided DNA insertion with CRISPR-associated transposases. Science 365, 48–53 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Hwang, B. et al. Lineage tracing using a Cas9-deaminase barcoding system targeting endogenous L1 elements. Nat. Commun. 10, 1234 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Hess, G. T. et al. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells. Nat. Methods 13, 1036–1042 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Grunewald, J. et al. Transcriptome-wide off-target RNA editing induced by CRISPR-guided DNA base editors. Nature 569, 433–437 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Jin, S. et al. Cytosine, but not adenine, base editors induce genome-wide off-target mutations in rice. Science 364, 292–295 (2019).

    CAS  PubMed  Google Scholar 

  113. Zuo, E. et al. Cytosine base editor generates substantial off-target single-nucleotide variants in mouse embryos. Science 364, 289–292 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Biddy, B. A. et al. Single-cell mapping of lineage and identity in direct reprogramming. Nature 564, 219–224 (2018). This study introduces the ‘CellTag’ clonal resampling method for retroviral barcoding of cell lineages with a combined single-cell transcriptomic readout.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Loveless, T. B. et al. Ordered insertional mutagenesis at a single genomic site enables lineage tracing and analog recording in mammalian cells. Preprint at https://doi.org/10.1101/639120 (2019).

  116. Guo, C. et al. CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics. Genome Biol. 20, 90 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Kwon, G. S., Viotti, M. & Hadjantonakis, A.-K. The endoderm of the mouse embryo arises by dynamic widespread intercalation of embryonic and extraembryonic lineages. Dev. Cell 15, 509–520 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Tian, L. et al. SIS-seq, a molecular ‘time machine’, connects single cell fate with gene programs. Preprint at https://doi.org/10.1101/403113 (2018).

  119. Raj, B., Gagnon, J. A. & Schier, A. F. Large-scale reconstruction of cell lineages using single-cell readout of transcriptomes and CRISPR–Cas9 barcodes by scGESTALT. Nat. Protoc. 13, 2685–2713 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  120. Jones, M. G. et al. Inference of single-cell phylogenies from lineage tracing data. Preprint at https://doi.org/10.1101/800078 (2019).

  121. Feng, J. et al. Estimation of cell lineage trees by maximum-likelihood phylogenetics. Preprint at https://doi.org/10.1101/595215 (2019).

  122. Zafar, H., Lin, C. & Bar-Joseph, Z. Single-cell lineage tracing by integrating CRISPR–Cas9 mutations with transcriptomic data. Preprint at https://doi.org/10.1101/630814 (2019).

  123. Salvador-Martínez, I., Grillo, M., Averof, M. & Telford, M. J. Is it possible to reconstruct an accurate cell lineage using CRISPR recorders? eLife 8, e40292 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  124. Synapse. Allen Institute Cell Lineage Reconstruction DREAM Challenge. Sage Bionetworks https://www.synapse.org/#!Synapse:syn20692755/wiki/595096 (2019).

  125. Klein, A. M. & Simons, B. D. Universal patterns of stem cell fate in cycling adult tissues. Development 138, 3103–3111 (2011).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank R. Ward and S. Mekhoubad for critical reading of the manuscript. D.E.W. is supported by grant R00GM121852.

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  1. Daniel E. Wagner

    Present address: Department of Obstetrics, Gynecology and Reproductive Science, Center for Reproductive Sciences, Eli and Edythe Broad Center for Regeneration Medicine and Stem Cell Research, University of California San Francisco, San Francisco, CA, USA

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  1. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Daniel E. Wagner & Allon M. Klein

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  1. Daniel E. Wagner
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The authors contributed equally to all aspects of the article.

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Correspondence to Daniel E. Wagner or Allon M. Klein.

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A.M.K. is a founder of 1CellBio, Inc. D.E.W. declares no competing interests.

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Nature Reviews Genetics thanks J. P. Junker and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Glossary

Cell differentiation

The process by which uncommitted progenitor cells are specified and transform into functional (and typically postmitotic) cells that carry out the specialized tasks of a particular tissue or organ.

Landscape

An informal term for a state manifold, typically used in developmental biology to represent the ensemble of cell states during their differentiation.

State manifolds

Approximate representations of high-dimensional cell states (for example, the whole-animal embryonic cell state atlas Tabula Muris) as lower-dimensional shapes.

State trajectories

The paths taken by individual cells or clones of cells through a state manifold.

Prospective lineage tracing

A lineage-tracing experiment that introduces a label for marking cells in a specified state.

Barcodes

Units of DNA with a large number of sequence possibilities, such as those used to uniquely label cells and their progeny.

Cell lineage

A representation of a series of mitotic events that trace back to a single founder cell.

Cell state

A designation of cell identity (defined with respect to a particular measurement) that can be used to classify or quantify physical or molecular differences between cells (for example, ‘basophilic’, ‘KRT4+’, ‘columnar’, ‘RNA-Seq cluster 4’).

RNA velocity

The rate of change in mRNA transcript abundance — more specifically, a set of computational techniques for calculating these rates across all genes from measurements of spliced and unspliced transcript abundances.

Clonal analysis

A lineage-tracing experiment that involves marking an individual cell, followed by state analysis of that founder cell’s clonal descendants.

Retrospective lineage tracing

A lineage-tracing experiment based on phylogenetic reconstruction of endogenous genetic polymorphisms (that is, no experimental intervention).

Hidden variables

Molecular or environmental properties of a cell that correlate with — or could be used to predict — a cell fate decision, which are obscured from a state manifold.

Direct observations

Lineage-tracing experiments that rely on in vivo live imaging of cells as they divide.

Determinate

In the context of developmental processes, when the relationship between lineage and molecular state is tightly controlled at each cell division event and is invariant between individuals.

Indeterminate

In the context of developmental processes, when the relationship between lineage and molecular state can vary greatly between individuals and between cell clones.

Lineage phylogenies

Trees of lineage relationships constructed from end point measurements.

Drop-outs

Type II errors that are common in single-cell omics experiments in which transcripts, lineage barcodes or other features present in cells fail to be detected.

Barcode homoplasy

A type I error in which identical DNA sequence barcodes are randomly recovered from cells with no close lineage relationship.

Cell ontogeny

The developmental history of a cell.

State convergence

A differentiation scenario in which cells with distinct origins converge onto the same end point on a state manifold.

Mitotic coupling

A class of developmental fate regulation mechanisms that specify states to the daughter cells of a mitotic division, either symmetrically or asymmetrically.

Population coupling

A class of developmental fate regulation mechanisms in which the cell state specification is uncoupled from cell division but the proportion of cells specified to each state is controlled.

State divergence

A scenario in which the asymmetric partitioning of cellular components between two daughters of a single cell division differentiates them rapidly or instantaneously into distinct states.

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Wagner, D.E., Klein, A.M. Lineage tracing meets single-cell omics: opportunities and challenges. Nat Rev Genet 21, 410–427 (2020). https://doi.org/10.1038/s41576-020-0223-2

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