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Lineage tracing meets single-cell omics: opportunities and challenges

Wagner, Daniel E., Klein, Allon M.·2020·Nature Reviews Genetics
DOI 10.1038/s41576-020-0223-2PMC PMC7307462
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  1. Whitman, C. O. Memoirs: the embryology of clepsine. J. Cell Sci. s2-18, 215–315 (1878). · DOI
  2. Waddington, C. H. The strategy of the genes. A discussion of some aspects of theoretical biology. (George Allen & Unwin, Ltd., London, 1957).
  3. Tritschler, S. et al. Concepts and limitations for learning developmental trajectories from single cell genomics. Development 146, dev170506 (2019). · DOI
  4. McKenna, A. & Gagnon, J. A. Recording development with single cell dynamic lineage tracing. Development 146, dev169730 (2019). · DOI
  5. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015). · DOI · Paper page
  6. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). · DOI · Paper page
  7. Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017). · DOI
  8. Wagner, A., Regev, A. & Yosef, N. Revealing the vectors of cellular identity with single-cell genomics. Nat. Biotechnol. 34, 1145–1160 (2016). · DOI · Paper page
  9. Kotliar, D. et al. Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq. eLife 8, e43803 (2019). · DOI · Paper page
  10. Lareau, C. A. et al. Droplet-based combinatorial indexing for massive-scale single-cell chromatin accessibility. Nat. Biotechnol. 37, 916–924 (2019). · DOI · Paper page
  11. Mezger, A. et al. High-throughput chromatin accessibility profiling at single-cell resolution. Nat. Commun. 9, 3647 (2018). · DOI
  12. 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). · DOI · Paper page
  13. Duncan, K. D., Fyrestam, J. & Lanekoff, I. Advances in mass spectrometry based single-cell metabolomics. Analyst 144, 782–793 (2019). · DOI
  14. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017). · DOI · Paper page
  15. Mimitou, E. P. et al. Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells. Nat. Methods 16, 409–412 (2019). · DOI · Paper page
  16. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017). · DOI · Paper page
  17. 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). · DOI · Paper page
  18. Eng, C. L. et al. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568, 235–239 (2019). · DOI · Paper page
  19. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014). · DOI · Paper page
  20. Montoro, D. T. et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560, 319–324 (2018). · DOI · Paper page
  21. Plasschaert, L. W. et al. A single-cell atlas of the airway epithelium reveals the CFTR-rich pulmonary ionocyte. Nature 560, 377–381 (2018). · DOI · Paper page
  22. Park, J. et al. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360, 758–763 (2018). · DOI · Paper page
  23. de Soysa, T. Y. et al. Single-cell analysis of cardiogenesis reveals basis for organ-level developmental defects. Nature 572, 120–124 (2019). · DOI
  24. Diaz-Cuadros, M. et al. In vitro characterization of the human segmentation clock. Nature https://doi.org/10.1038/s41586-019-1885-9 (2020). · DOI
  25. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017). · DOI · Paper page
  26. 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. · DOI
  27. Siebert, S. et al. Stem cell differentiation trajectories in Hydra resolved at single-cell resolution. Science 365, eaav9314 (2019). · DOI
  28. Achim, K. et al. Whole-body single-cell sequencing reveals transcriptional domains in the annelid larval body. Mol. Biol. Evol. 35, 1047–1062 (2018). · DOI
  29. Zeng, A. et al. Prospectively isolated tetraspanin(+) neoblasts are adult pluripotent stem cells underlying planaria regeneration. Cell 173, 1593–1608.e1520 (2018). · DOI
  30. 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. · DOI · Paper page
  31. Briggs, J. A. et al. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360, eaar5780 (2018). · DOI
  32. Pijuan-Sala, B. et al. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566, 490–495 (2019). · DOI · Paper page
  33. Karaiskos, N. et al. The Drosophila embryo at single-cell transcriptome resolution. Science 358, 194–199 (2017). · DOI · Paper page
  34. 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. · DOI
  35. Becht, E. et al. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. https://doi.org/10.1038/nbt.4314 (2018). · DOI · Paper page
  36. Weinreb, C., Wolock, S. & Klein, A. M. SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics 34, 1246–1248 (2018). · DOI · Paper page
  37. 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). · DOI · Paper page
  38. Qiu, X. et al. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14, 979–982 (2017). · DOI · Paper page
  39. Setty, M. et al. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34, 637–645 (2016). · DOI · Paper page
  40. Shin, J. et al. Single-Cell RNA-Seq with Waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17, 360–372 (2015). · DOI · Paper page
  41. Haghverdi, L., Buttner, M., Wolf, F. A., Buettner, F. & Theis, F. J. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13, 845–848 (2016). · DOI · Paper page
  42. Bendall, S. C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014). · DOI · Paper page
  43. 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. · DOI · Paper page
  44. Schiebinger, G. et al. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176, 1517 (2019). · DOI · Paper page
  45. 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). · DOI
  46. La Manno, G. et al. RNA velocity of single cells. Nature 560, 494–498 (2018). · DOI · Paper page
  47. Hendriks, G.-J. et al. NASC-seq monitors RNA synthesis in single cells. Nat. Commun. 10, 3138 (2019). · DOI
  48. Gorin, G., Svensson, V. & Pachter, L. Protein velocity and acceleration from single-cell multiomics experiments. Genome Biol. 21, 39 (2019). · DOI
  49. Qiu, X. et al. Mapping vector field of single cells. Preprint at https://doi.org/10.1101/696724 (2019). · DOI
  50. Haghverdi, L., Buettner, F. & Theis, F. J. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31, 2989–2998 (2015). · DOI · Paper page
  51. 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). · DOI · Paper page
  52. Chen, H. et al. Single-cell trajectories reconstruction, exploration and mapping of omics data with STREAM. Nat. Commun. 10, 1903 (2019). · DOI
  53. 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). · DOI · Paper page
  54. Kimmel, C. B., Warga, R. M. & Schilling, T. F. Origin and organization of the zebrafish fate map. Development 108, 581–594 (1990). · DOI
  55. Kretzschmar, K. & Watt, F. M. Lineage tracing. Cell 148, 33–45 (2012). · DOI
  56. Wagers, A. J. & Weissman, I. L. Plasticity of adult stem cells. Cell 116, 639–648 (2004). · DOI
  57. 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). · DOI
  58. 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. · DOI
  59. Conklin, E. G. The organization and cell lineage of the ascidian egg. J. Acad. Nat. Sci. Phila. 13, 1–119 (1905).
  60. 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). · DOI
  61. Frieda, K. L. et al. Synthetic recording and in situ readout of lineage information in single cells. Nature 541, 107–111 (2017). · DOI · Paper page
  62. 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). · DOI
  63. Lemischka, I. R., Raulet, D. H. & Mulligan, R. C. Developmental potential and dynamic behavior of hematopoietic stem cells. Cell 45, 917–927 (1986). · DOI
  64. Ludwig, L. S. et al. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176, 1325–1339.e1322 (2019). · DOI · Paper page
  65. Xu, J. et al. Single-cell lineage tracing by endogenous mutations enriched in transposase accessible mitochondrial DNA. eLife 8, e45105 (2019). · DOI
  66. McKenna, A. et al. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353, aaf7907 (2016). · DOI · Paper page
  67. Kalhor, R., Mali, P. & Church, G. M. Rapidly evolving homing CRISPR barcodes. Nat. Methods 14, 195–200 (2017). · DOI
  68. 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. · DOI
  69. 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. · DOI · Paper page
  70. Ihry, R. J. et al. p53 inhibits CRISPR-Cas9 engineering in human pluripotent stem cells. Nat. Med. 24, 939–946 (2018). · DOI · Paper page
  71. 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). · DOI · Paper page
  72. Pei, W. et al. Using Cre-recombinase-driven Polylox barcoding for in vivo fate mapping in mice. Nat. Protoc. 14, 1820–1840 (2019). · DOI
  73. 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). · DOI
  74. Strecker, J. et al. RNA-guided DNA insertion with CRISPR-associated transposases. Science 365, 48–53 (2019). · DOI
  75. Hess, G. T. et al. Directed evolution using dCas9-targeted somatic hypermutation in mammalian cells. Nat. Methods 13, 1036–1042 (2016). · DOI
  76. 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. · DOI
  77. 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). · DOI
  78. Guo, C. et al. CellTag Indexing: genetic barcode-based sample multiplexing for single-cell genomics. Genome Biol. 20, 90 (2019). · DOI
  79. 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). · DOI
  80. Jones, M. G. et al. Inference of single-cell phylogenies from lineage tracing data. Preprint at https://doi.org/10.1101/800078 (2019). · DOI
  81. Feng, J. et al. Estimation of cell lineage trees by maximum-likelihood phylogenetics. Preprint at https://doi.org/10.1101/595215 (2019). · DOI
  82. 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). · DOI
  83. 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). · DOI
  84. Klein, A. M. & Simons, B. D. Universal patterns of stem cell fate in cycling adult tissues. Development 138, 3103–3111 (2011). · DOI

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Metadata

Authors
Wagner, D. E., & Klein, A. M.
Venue
Nature Reviews Genetics
Publisher
Springer Science and Business Media LLC
Published
2020-03-31
Volume
21
Issue
7
Pages
410-427
ISSN
  • 1471-0056
  • 1471-0064
DOI
10.1038/s41576-020-0223-2
PMC
PMC7307462
Cited by
646
References
40
Language
en
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Wagner, D. E., & Klein, A. M. (2020). Lineage tracing meets single-cell omics: opportunities and challenges. Nature Reviews Genetics. https://doi.org/10.1038/s41576-020-0223-2