1932
0
  1. Home
  2. A-Z Publications
  3. Annual Review of Biomedical Data Science
  4. Volume 3, 2020
  5. Article

Annual Review of Biomedical Data Science

Volume 3, 2020

Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis

Abstract

Cellular differentiation is a common underlying feature of all multicellular organisms through which naïve cells progressively become fate restricted and develop into mature cells with specialized functions. A comprehensive understanding of the regulatory mechanisms of cell fate choices during development, regeneration, homeostasis, and disease is a central goal of modern biology. Ongoing rapid advances in single-cell biology are enabling the exploration of cell fate specification at unprecedented resolution. Here, we review single-cell RNA sequencing and sequencing of other modalities as methods to elucidate the molecular underpinnings of lineage specification. We specifically discuss how the computational tools available to reconstruct lineage trajectories, quantify cell fate bias, and perform dimensionality reduction for data visualization are providing new mechanistic insights into the process of cell fate decision. Studying cellular differentiation using single-cell genomic tools is paving the way for a detailed understanding of cellular behavior in health and disease.

Keyword(s): cell fate, differentiation trajectory, dimensionality reduction, lineage specification, lineage tracing, lineage tree, single-cell RNA-seq
Loading

Article metrics loading...

/content/journals/10.1146/annurev-biodatasci-111419-091750
2020-07-20
2024-04-25
Loading full text...

Full text loading...

/deliver/fulltext/biodatasci/3/1/annurev-biodatasci-111419-091750.html?itemId=/content/journals/10.1146/annurev-biodatasci-111419-091750&mimeType=html&fmt=ahah

Literature Cited

  1. 1. 
    Kopp JL, Grompe M, Sander M 2016. Stem cells versus plasticity in liver and pancreas regeneration. Nat. Cell Biol. 18:238–45
     [Google Scholar]
  2. 2. 
    Brockes JP, Kumar A. 2005. Appendage regeneration in adult vertebrates and implications for regenerative medicine. Science 310:1919–23
     [Google Scholar]
  3. 3. 
    Raj A, van Oudenaarden A 2008. Nature, nurture, or chance: stochastic gene expression and its consequences. Cell 135:216–26
     [Google Scholar]
  4. 4. 
    Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y et al. 2016. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol 17:77
     [Google Scholar]
  5. 5. 
    Picelli S, Bjorklund AK, Faridani OR, Sagasser S, Winberg G, Sandberg R 2013. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10:1096–98
     [Google Scholar]
  6. 6. 
    Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML et al. 2015. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523:486–90
     [Google Scholar]
  7. 7. 
    Pijuan-Sala B, Griffiths JA, Guibentif C, Hiscock TW, Jawaid W et al. 2019. A single-cell molecular map of mouse gastrulation and early organogenesis. Nature 566:490–95
     [Google Scholar]
  8. 8. 
    Farrell JA, Wang Y, Riesenfeld SJ, Shekhar K, Regev A, Schier AF 2018. Single-cell reconstruction of developmental trajectories during zebrafish embryogenesis. Science 360:eaar3131
     [Google Scholar]
  9. 9. 
    Wagner DE, Weinreb C, Collins ZM, Briggs JA, Megason SG, Klein AM 2018. Single-cell mapping of gene expression landscapes and lineage in the zebrafish embryo. Science 360:981–87
     [Google Scholar]
  10. 10. 
    Briggs JA, Weinreb C, Wagner DE, Megason S, Peshkin L et al. 2018. The dynamics of gene expression in vertebrate embryogenesis at single-cell resolution. Science 360:eaar5780
     [Google Scholar]
  11. 11. 
    Cao J, Spielmann M, Qiu X, Huang X, Ibrahim DM et al. 2019. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566:496–502
     [Google Scholar]
  12. 12. 
    Haber AL, Biton M, Rogel N, Herbst RH, Shekhar K et al. 2017. A single-cell survey of the small intestinal epithelium. Nature 551:333–39
     [Google Scholar]
  13. 13. 
    Tusi BK, Wolock SL, Weinreb C, Hwang Y, Hidalgo D et al. 2018. Population snapshots predict early haematopoietic and erythroid hierarchies. Nature 555:54–60
     [Google Scholar]
  14. 14. 
    Dahlin JS, Hamey FK, Pijuan-Sala B, Shepherd M, Lau WWY et al. 2018. A single-cell hematopoietic landscape resolves 8 lineage trajectories and defects in Kit mutant mice. Blood 131:e1–11
     [Google Scholar]
  15. 15. 
    Le Douarin NM. 1980. The ontogeny of the neural crest in avian embryo chimaeras. Nature 286:663–69
     [Google Scholar]
  16. 16. 
    Serbedzija GN, Bronner-Fraser M, Fraser SE 1989. A vital dye analysis of the timing and pathways of avian trunk neural crest cell migration. Development 106:809–16
     [Google Scholar]
  17. 17. 
    Selleck MA, Stern CD. 1991. Fate mapping and cell lineage analysis of Hensen's node in the chick embryo. Development 112:615–26
     [Google Scholar]
  18. 18. 
    Kretzschmar K, Watt FM. 2012. Lineage tracing. Cell 148:33–45
     [Google Scholar]
  19. 19. 
    Kester L, van Oudenaarden A 2018. Single-cell transcriptomics meets lineage tracing. Cell Stem Cell 23:166–79
     [Google Scholar]
  20. 20. 
    Etzrodt M, Endele M, Schroeder T 2014. Quantitative single-cell approaches to stem cell research. Cell Stem Cell 15:546–58
     [Google Scholar]
  21. 21. 
    McKenna A, Gagnon JA. 2019. Recording development with single cell dynamic lineage tracing. Development 146:dev169730
     [Google Scholar]
  22. 22. 
    Papalexi E, Satija R. 2018. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18:35–45
     [Google Scholar]
  23. 23. 
    Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A et al. 2015. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–201
     [Google Scholar]
  24. 24. 
    Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K et al. 2015. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–14
     [Google Scholar]
  25. 25. 
    Tritschler S, Buttner M, Fischer DS, Lange M, Bergen V et al. 2019. Concepts and limitations for learning developmental trajectories from single cell genomics. Development 146:dev170506
     [Google Scholar]
  26. 26. 
    Saelens W, Cannoodt R, Todorov H, Saeys Y 2019. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37:547–54
     [Google Scholar]
  27. 27. 
    Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S et al. 2014. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32:381–86
     [Google Scholar]
  28. 28. 
    Qiu X, Mao Q, Tang Y, Wang L, Chawla R et al. 2017. Reversed graph embedding resolves complex single-cell trajectories. Nat. Methods 14:979–82
     [Google Scholar]
  29. 29. 
    Park J, Shrestha R, Qiu C, Kondo A, Huang S et al. 2018. Single-cell transcriptomics of the mouse kidney reveals potential cellular targets of kidney disease. Science 360:758–63
     [Google Scholar]
  30. 30. 
    Zhong S, Zhang S, Fan X, Wu Q, Yan L et al. 2018. A single-cell RNA-seq survey of the developmental landscape of the human prefrontal cortex. Nature 555:524–28
     [Google Scholar]
  31. 31. 
    Marco E, Karp RL, Guo G, Robson P, Hart AH et al. 2014. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. PNAS 111:E5643–50
     [Google Scholar]
  32. 32. 
    Guo M, Bao EL, Wagner M, Whitsett JA, Xu Y 2017. SLICE: determining cell differentiation and lineage based on single cell entropy. Nucleic Acids Res 45:e54
     [Google Scholar]
  33. 33. 
    Ji Z, Ji H. 2016. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res 44:e117
     [Google Scholar]
  34. 34. 
    Shin J, Berg DA, Zhu Y, Shin JY, Song J et al. 2015. Single-cell RNA-seq with Waterfall reveals molecular cascades underlying adult neurogenesis. Cell Stem Cell 17:360–72
     [Google Scholar]
  35. 35. 
    Bendall SC, Davis KL, Amir ED, Tadmor MD, Simonds EF et al. 2014. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157:714–25
     [Google Scholar]
  36. 36. 
    Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM et al. 2016. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34:637–45
     [Google Scholar]
  37. 37. 
    Wolf FA, Hamey FK, Plass M, Solana J, Dahlin JS et al. 2019. PAGA: graph abstraction reconciles clustering with trajectory inference through a topology preserving map of single cells. Genome Biol 20:59
     [Google Scholar]
  38. 38. 
    Plass M, Solana J, Wolf FA, Ayoub S, Misios A et al. 2018. Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics. Science 360:eaaq1723
     [Google Scholar]
  39. 39. 
    Welch JD, Hartemink AJ, Prins JF 2016. SLICER: inferring branched, nonlinear cellular trajectories from single cell RNA-seq data. Genome Biol 17:106
     [Google Scholar]
  40. 40. 
    Herring CA, Banerjee A, McKinley ET, Simmons AJ, Ping J et al. 2018. Unsupervised trajectory analysis of single-cell RNA-seq and imaging data reveals alternative tuft cell origins in the gut. Cell Syst 6:37–51.e9
     [Google Scholar]
  41. 41. 
    Grün D, Muraro MJ, Boisset JC, Wiebrands K, Lyubimova A et al. 2016. De novo prediction of stem cell identity using single-cell transcriptome data. Cell Stem Cell 19:266–77
     [Google Scholar]
  42. 42. 
    Herman JS, Sagar, Grün D 2018. FateID infers cell fate bias in multipotent progenitors from single-cell RNA-seq data. Nat. Methods 15:379–86
     [Google Scholar]
  43. 43. 
    Street K, Risso D, Fletcher RB, Das D, Ngai J et al. 2018. Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics. BMC Genom 19:477
     [Google Scholar]
  44. 44. 
    Haghverdi L, Buttner M, Wolf FA, Buettner F, Theis FJ 2016. Diffusion pseudotime robustly reconstructs lineage branching. Nat. Methods 13:845–48
     [Google Scholar]
  45. 45. 
    Setty M, Kiseliovas V, Levine J, Gayoso A, Mazutis L, Pe'er D 2019. Characterization of cell fate probabilities in single-cell data with Palantir. Nat. Biotechnol. 37:451–60
     [Google Scholar]
  46. 46. 
    Teschendorff AE, Enver T. 2017. Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome. Nat. Commun. 8:15599
     [Google Scholar]
  47. 47. 
    La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H et al. 2018. RNA velocity of single cells. Nature 560:494–98
     [Google Scholar]
  48. 48. 
    Soldatov R, Kaucka M, Kastriti ME, Petersen J, Chontorotzea T et al. 2019. Spatiotemporal structure of cell fate decisions in murine neural crest. Science 364:eaas9536
     [Google Scholar]
  49. 49. 
    Schiebinger G, Shu J, Tabaka M, Cleary B, Subramanian V et al. 2019. Optimal-transport analysis of single-cell gene expression identifies developmental trajectories in reprogramming. Cell 176:928–43.e22
     [Google Scholar]
  50. 50. 
    Fischer DS, Fiedler AK, Kernfeld EM, Genga RMJ, Bastidas-Ponce A et al. 2019. Inferring population dynamics from single-cell RNA-sequencing time series data. Nat. Biotechnol. 37:461–68
     [Google Scholar]
  51. 51. 
    Erhard F, Baptista MAP, Krammer T, Hennig T, Lange M et al. 2019. scSLAM-seq reveals core features of transcription dynamics in single cells. Nature 571:419–23
     [Google Scholar]
  52. 52. 
    Cao J, Zhou W, Steemers F, Trapnell C, Shendure J 2019. Characterizing the temporal dynamics of gene expression in single cells with sci-fate. bioRxiv 666081. https://doi.org/10.1101/666081
    [Crossref]
  53. 53. 
    Velten L, Haas SF, Raffel S, Blaszkiewicz S, Islam S et al. 2017. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19:271–81
     [Google Scholar]
  54. 54. 
    Nestorowa S, Hamey FK, Pijuan Sala B, Diamanti E, Shepherd M et al. 2016. A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation. Blood 128:e20–31
     [Google Scholar]
  55. 55. 
    Macaulay IC, Svensson V, Labalette C, Ferreira L, Hamey F et al. 2016. Single-Cell RNA-sequencing reveals a continuous spectrum of differentiation in hematopoietic cells. Cell Rep 14:966–77
     [Google Scholar]
  56. 56. 
    Buenrostro JD, Corces MR, Lareau CA, Wu B, Schep AN et al. 2018. Integrated single-cell analysis maps the continuous regulatory landscape of human hematopoietic differentiation. Cell 173:1535–48.e16
     [Google Scholar]
  57. 57. 
    Lonnberg T, Svensson V, James KR, Fernandez-Ruiz D, Sebina I et al. 2017. Single-cell RNA-seq and computational analysis using temporal mixture modelling resolves TH1/TFH fate bifurcation in malaria. Sci. Immunol. 2:eaal2192
     [Google Scholar]
  58. 58. 
    Rodrigues PF, Alberti-Servera L, Eremin A, Grajales-Reyes GE, Ivanek R, Tussiwand R 2018. Distinct progenitor lineages contribute to the heterogeneity of plasmacytoid dendritic cells. Nat. Immunol. 19:711–22
     [Google Scholar]
  59. 59. 
    Dress RJ, Dutertre CA, Giladi A, Schlitzer A, Low I et al. 2019. Plasmacytoid dendritic cells develop from Ly6D+ lymphoid progenitors distinct from the myeloid lineage. Nat. Immunol. 20:852–64
     [Google Scholar]
  60. 60. 
    Weinreb C, Wolock S, Tusi BK, Socolovsky M, Klein AM 2018. Fundamental limits on dynamic inference from single-cell snapshots. PNAS 115:E2467–76
     [Google Scholar]
  61. 61. 
    Tabula Muris Consort 2018. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. . Nature 562:367–72
     [Google Scholar]
  62. 62. 
    van der Maaten L, Hinton G 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:2579–605
     [Google Scholar]
  63. 63. 
    Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH et al. 2018. Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol. 37:38–44
     [Google Scholar]
  64. 64. 
    Haghverdi L, Buettner F, Theis FJ 2015. Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31:2989–98
     [Google Scholar]
  65. 65. 
    Ding J, Condon A, Shah SP 2018. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat. Commun. 9:2002
     [Google Scholar]
  66. 66. 
    Fruchterman TMJ, Reingold EM. 1991. Graph drawing by force-directed placement. Softw. Pract. Exp. 21:1129–64
     [Google Scholar]
  67. 67. 
    Weinreb C, Wolock S, Klein AM 2018. SPRING: a kinetic interface for visualizing high dimensional single-cell expression data. Bioinformatics 34:1246–68
     [Google Scholar]
  68. 68. 
    Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C et al. 2017. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:661–67
     [Google Scholar]
  69. 69. 
    Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P et al. 2018. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360:176–82
     [Google Scholar]
  70. 70. 
    Harmer SL, Panda S, Kay SA 2001. Molecular bases of circadian rhythms. Annu. Rev. Cell Dev. Biol. 17:215–53
     [Google Scholar]
  71. 71. 
    Oates AC, Morelli LG, Ares S 2012. Patterning embryos with oscillations: structure, function and dynamics of the vertebrate segmentation clock. Development 139:625–39
     [Google Scholar]
  72. 72. 
    Morrison SJ, Kimble J. 2006. Asymmetric and symmetric stem-cell divisions in development and cancer. Nature 441:1068–74
     [Google Scholar]
  73. 73. 
    Haghverdi L, Lun ATL, Morgan MD, Marioni JC 2018. Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors. Nat. Biotechnol. 36:421–27
     [Google Scholar]
  74. 74. 
    Butler A, Hoffman P, Smibert P, Papalexi E, Satija R 2018. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36:411–20
     [Google Scholar]
  75. 75. 
    Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E et al. 2019. Comprehensive integration of single-cell data. Cell 177:1888–902.e21
     [Google Scholar]
  76. 76. 
    Hie B, Bryson B, Berger B 2019. Efficient integration of heterogeneous single-cell transcriptomes using Scanorama. Nat. Biotechnol. 37:685–91
     [Google Scholar]
  77. 77. 
    Rotem A, Ram O, Shoresh N, Sperling RA, Goren A et al. 2015. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33:1165–72
     [Google Scholar]
  78. 78. 
    Luo C, Keown CL, Kurihara L, Zhou J, He Y et al. 2017. Single-cell methylomes identify neuronal subtypes and regulatory elements in mammalian cortex. Science 357:600–4
     [Google Scholar]
  79. 79. 
    Smallwood SA, Lee HJ, Angermueller C, Krueger F, Saadeh H et al. 2014. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11:817–20
     [Google Scholar]
  80. 80. 
    Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK et al. 2017. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14:865–68
     [Google Scholar]
  81. 81. 
    Hainer SJ, Boskovic A, McCannell KN, Rando OJ, Fazzio TG 2019. Profiling of pluripotency factors in single cells and early embryos. Cell 177:1319–29.e11
     [Google Scholar]
  82. 82. 
    Ai S, Xiong H, Li CC, Luo Y, Shi Q et al. 2019. Profiling chromatin states using single-cell itChIP-seq. Nat. Cell Biol. 21:1164–72
     [Google Scholar]
  83. 83. 
    Kaya-Okur HS, Wu SJ, Codomo CA, Pledger ES, Bryson TD et al. 2019. CUT&Tag for efficient epigenomic profiling of small samples and single cells. Nat. Commun. 10:1930
     [Google Scholar]
  84. 84. 
    Nagano T, Lubling Y, Stevens TJ, Schoenfelder S, Yaffe E et al. 2013. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502:59–64
     [Google Scholar]
  85. 85. 
    Ramani V, Deng X, Qiu R, Gunderson KL, Steemers FJ et al. 2017. Massively multiplex single-cell Hi-C. Nat. Methods 14:263–66
     [Google Scholar]
  86. 86. 
    Cao J, Cusanovich DA, Ramani V, Aghamirzaie D, Pliner HA et al. 2018. Joint profiling of chromatin accessibility and gene expression in thousands of single cells. Science 361:1380–85
     [Google Scholar]
  87. 87. 
    Angermueller C, Clark SJ, Lee HJ, Macaulay IC, Teng MJ et al. 2016. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13:229–32
     [Google Scholar]
  88. 88. 
    Macaulay IC, Haerty W, Kumar P, Li YI, Hu TX et al. 2015. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12:519–22
     [Google Scholar]
  89. 89. 
    Dey SS, Kester L, Spanjaard B, Bienko M, van Oudenaarden A 2015. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33:285–89
     [Google Scholar]
  90. 90. 
    Shah S, Lubeck E, Zhou W, Cai L 2016. In situ transcription profiling of single cells reveals spatial organization of cells in the mouse hippocampus. Neuron 92:342–57
     [Google Scholar]
  91. 91. 
    Eng CL, Lawson M, Zhu Q, Dries R, Koulena N et al. 2019. Transcriptome-scale super-resolved imaging in tissues by RNA seqFISH. Nature 568:235–39
     [Google Scholar]
  92. 92. 
    Chen KH, Boettiger AN, Moffitt JR, Wang S, Zhuang X 2015. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348:aaa6090
     [Google Scholar]
  93. 93. 
    Wang X, Allen WE, Wright MA, Sylwestrak EL, Samusik N et al. 2018. Three-dimensional intact-tissue sequencing of single-cell transcriptional states. Science 361:eaat5691
     [Google Scholar]
  94. 94. 
    Welch JD, Kozareva V, Ferreira A, Vanderburg C, Martin C, Macosko EZ 2019. Single-cell multi-omic integration compares and contrasts features of brain cell identity. Cell 177:1873–87.e17
     [Google Scholar]
  95. 95. 
    Kebschull JM, Zador AM. 2018. Cellular barcoding: lineage tracing, screening and beyond. Nat. Methods 15:871–79
     [Google Scholar]
  96. 96. 
    Walsh C, Cepko CL. 1992. Widespread dispersion of neuronal clones across functional regions of the cerebral cortex. Science 255:434–40
     [Google Scholar]
  97. 97. 
    Lu R, Neff NF, Quake SR, Weissman IL 2011. Tracking single hematopoietic stem cells in vivo using high-throughput sequencing in conjunction with viral genetic barcoding. Nat. Biotechnol. 29:928–33
     [Google Scholar]
  98. 98. 
    Naik SH, Perie L, Swart E, Gerlach C, van Rooij N et al. 2013. Diverse and heritable lineage imprinting of early haematopoietic progenitors. Nature 496:229–32
     [Google Scholar]
  99. 99. 
    Rodriguez-Fraticelli AE, Wolock SL, Weinreb CS, Panero R, Patel SH et al. 2018. Clonal analysis of lineage fate in native haematopoiesis. Nature 553:212–16
     [Google Scholar]
  100. 100. 
    Gerlach C, Rohr JC, Perie L, van Rooij N, van Heijst JW et al. 2013. Heterogeneous differentiation patterns of individual CD8+ T cells. Science 340:635–39
     [Google Scholar]
  101. 101. 
    van Heijst JW, Gerlach C, Swart E, Sie D, Nunes-Alves C et al. 2009. Recruitment of antigen-specific CD8+ T cells in response to infection is markedly efficient. Science 325:1265–69
     [Google Scholar]
  102. 102. 
    Peikon ID, Gizatullina DI, Zador AM 2014. In vivo generation of DNA sequence diversity for cellular barcoding. Nucleic Acids Res 42:e127
     [Google Scholar]
  103. 103. 
    Pei W, Feyerabend TB, Rossler J, Wang X, Postrach D et al. 2017. Polylox barcoding reveals haematopoietic stem cell fates realized in vivo. Nature 548:456–60
     [Google Scholar]
  104. 104. 
    Kalhor R, Mali P, Church GM 2017. Rapidly evolving homing CRISPR barcodes. Nat. Methods 14:195–200
     [Google Scholar]
  105. 105. 
    Perli SD, Cui CH, Lu TK 2016. Continuous genetic recording with self-targeting CRISPR-Cas in human cells. Science 353:aag0511
     [Google Scholar]
  106. 106. 
    Kalhor R, Kalhor K, Mejia L, Leeper K, Graveline A et al. 2018. Developmental barcoding of whole mouse via homing CRISPR. Science 361:eaat9804
     [Google Scholar]
  107. 107. 
    Alemany A, Florescu M, Baron CS, Peterson-Maduro J, van Oudenaarden A 2018. Whole-organism clone tracing using single-cell sequencing. Nature 556:108–12
     [Google Scholar]
  108. 108. 
    Spanjaard B, Hu B, Mitic N, Olivares-Chauvet P, Janjuha S et al. 2018. Simultaneous lineage tracing and cell-type identification using CRISPR-Cas9-induced genetic scars. Nat. Biotechnol. 36:469–73
     [Google Scholar]
  109. 109. 
    Raj B, Wagner DE, McKenna A, Pandey S, Klein AM et al. 2018. Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain. Nat. Biotechnol. 36:442–50
     [Google Scholar]
  110. 110. 
    Weinreb C, Rodriguez-Fraticelli A, Camargo FD, Klein AM 2020. Lineage tracing on transcriptional landscapes links state to fate during differentiation. Science 367:eaaw3381
     [Google Scholar]
  111. 111. 
    Chan MM, Smith ZD, Grosswendt S, Kretzmer H, Norman TM et al. 2019. Molecular recording of mammalian embryogenesis. Nature 570:77–82
     [Google Scholar]
  112. 112. 
    Frieda KL, Linton JM, Hormoz S, Choi J, Chow KHK et al. 2017. Synthetic recording and in situ readout of lineage information in single cells. Nature 541:107–11
     [Google Scholar]
  113. 113. 
    Laurenti E, Gottgens B. 2018. From haematopoietic stem cells to complex differentiation landscapes. Nature 553:418–26
     [Google Scholar]
  114. 114. 
    Cusanovich DA, Daza R, Adey A, Pliner HA, Christiansen L et al. 2015. Multiplex single cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348:910–14
     [Google Scholar]
  115. 115. 
    Stuart T, Satija R. 2019. Integrative single-cell analysis. Nat. Rev. Genet. 20:257–72
     [Google Scholar]
  116. 116. 
    Tang F, Barbacioru C, Wang Y, Nordman E, Lee C et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6:377–82
     [Google Scholar]
  117. 117. 
    Regev A, Teichmann SA, Lander ES, Amt I, Benoist C et al. 2017. The Human Cell Atlas. eLife 6:e27041
     [Google Scholar]
  118. 118. 
    Eraslan G, Avsec Z, Gagneur J, Theis FJ 2019. Deep learning: new computational modelling techniques for genomics. Nat. Rev. Genet. 20:389–403
     [Google Scholar]
/content/journals/10.1146/annurev-biodatasci-111419-091750
Loading
Deciphering Cell Fate Decision by Integrated Single-Cell Sequencing Analysis
Annual Review of Biomedical Data Science 3, 1 (2020); https://doi.org/10.1146/annurev-biodatasci-111419-091750
/content/journals/10.1146/annurev-biodatasci-111419-091750
/content/journals/10.1146/annurev-biodatasci-111419-091750
Loading

Data & Media loading...

  • Article Type: Review Article

Most Cited Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error