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Annual Review of Genetics

Volume 52, 2018

Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues

Abstract

The growing scale and declining cost of single-cell RNA-sequencing (RNA-seq) now permit a repetition of cell sampling that increases the power to detect rare cell states, reconstruct developmental trajectories, and measure phenotype in new terms such as cellular variance. The characterization of anatomy and developmental dynamics has not had an equivalent breakthrough since groundbreaking advances in live fluorescent microscopy. The new resolution obtained by single-cell RNA-seq is a boon to genetics because the novel description of phenotype offers the opportunity to refine gene function and dissect pleiotropy. In addition, the recent pairing of high-throughput genetic perturbation with single-cell RNA-seq has made practical a scale of genetic screening not previously possible.

Keyword(s): pooled CRISPR, rare cell types, single-cell power analysis, single-cell RNA-seq, trajectory analysis
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2018-11-23
2024-04-24
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Literature Cited

  1. 1.  Aarts M, Georgilis A, Beniazza M, Beolchi P, Banito A et al. 2017. Coupling shRNA screens with single-cell RNA-seq identifies a dual role for mTOR in reprogramming-induced senescence. Genes Dev 31:2085–98
     [Google Scholar]
  2. 2.  Achim K, Pettit JB, Saraiva LR, Gavriouchkina D, Larsson T et al. 2015. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33:503–9
     [Google Scholar]
  3. 3.  Adamson B, Norman TM, Jost M, Cho MY, Nunez JK et al. 2016. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167:1867–82.e21
     [Google Scholar]
  4. 4.  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]
  5. 5.  Bagnoli JW, Ziegenhain C, Janjic A, Wange LE, Vieth B et al. 2018. Sensitive and powerful single-cell RNA sequencing using mcSCRB-seq. Nat. Commun. 9:2937.
  6. 6.  Baslan T, Hicks J 2017. Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat. Rev. Cancer 17:557–69
     [Google Scholar]
  7. 7.  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]
  8. 8.  Brennecke P, Anders S, Kim JK, Kolodziejczyk AA, Zhang X et al. 2013. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10:1093–95
     [Google Scholar]
  9. 9.  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]
  10. 10.  Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A et al. 2015. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33:155–60
     [Google Scholar]
  11. 11.  Butler A, Satija R 2017. Integrated analysis of single cell transcriptomic data across conditions, technologies, and species. bioRxiv 164889. https://doi.org/10.1101/164889
    [Crossref]
  12. 12.  Cannoodt R, Saelens W, Saeys Y 2016. Computational methods for trajectory inference from single-cell transcriptomics. Eur. J. Immunol. 46:2496–506
     [Google Scholar]
  13. 13.  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]
  14. 14.  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]
  15. 15.  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]
  16. 16.  Datlinger P, Rendeiro AF, Schmidl C, Krausgruber T, Traxler P et al. 2017. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14:297–301
     [Google Scholar]
  17. 17.  Dixit A, Parnas O, Li B, Chen J, Fulco CP et al. 2016. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167:1853–66.e17
     [Google Scholar]
  18. 18.  Efroni I, Ip PL, Nawy T, Mello A, Birnbaum KD 2015. Quantification of cell identity from single-cell gene expression profiles. Genome Biol 16:9
     [Google Scholar]
  19. 19.  Efroni I, Mello A, Nawy T, Ip PL, Rahni R et al. 2016. Root regeneration triggers an embryo-like sequence guided by hormonal interactions. Cell 165:1721–33
     [Google Scholar]
  20. 20.  Enge M, Arda HE, Mignardi M, Beausang J, Bottino R et al. 2017. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171:321–30.e14
     [Google Scholar]
  21. 21.  Enver T, Pera M, Peterson C, Andrews PW 2009. Stem cell states, fates, and the rules of attraction. Cell Stem Cell 4:387–97
     [Google Scholar]
  22. 22.  Farlik M, Sheffield NC, Nuzzo A, Datlinger P, Schonegger A et al. 2015. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep 10:1386–97
     [Google Scholar]
  23. 23.  Gravina S, Dong X, Yu B, Vijg J 2016. Single-cell genome-wide bisulfite sequencing uncovers extensive heterogeneity in the mouse liver methylome. Genome Biol 17:150
     [Google Scholar]
  24. 24.  Gravina S, Ganapathi S, Vijg J 2015. Single-cell, locus-specific bisulfite sequencing (SLBS) for direct detection of epimutations in DNA methylation patterns. Nucleic Acids Res 43:e93
     [Google Scholar]
  25. 25.  Grun D, Kester L, van Oudenaarden A 2014. Validation of noise models for single-cell transcriptomics. Nat. Methods 11:637–40
     [Google Scholar]
  26. 26.  Guo J, Grow EJ, Yi C, Mlcochova H, Maher GJ et al. 2017. Chromatin and single-cell RNA-seq profiling reveal dynamic signaling and metabolic transitions during human spermatogonial stem cell development. Cell Stem Cell 21:533–46.e6
     [Google Scholar]
  27. 27.  Habib N, Avraham-Davidi I, Basu A, Burks T, Shekhar K et al. 2017. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14:955–58
     [Google Scholar]
  28. 28.  Habib N, Li Y, Heidenreich M, Swiech L, Avraham-Davidi I et al. 2016. Div-seq: single-nucleus RNA-seq reveals dynamics of rare adult newborn neurons. Science 353:925–28
     [Google Scholar]
  29. 29.  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]
  30. 30.  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]
  31. 31.  Hashimshony T, Wagner F, Sher N, Yanai I 2012. CEL-seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep 2:666–73
     [Google Scholar]
  32. 32.  Herring CA, Banerjee A, McKinley ET, Simmons AJ, Ping J et al. 2017. 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]
  33. 33.  Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F et al. 2014. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343:776–79
     [Google Scholar]
  34. 34.  Jaitin DA, Weiner A, Yofe I, Lara-Astiaso D, Keren-Shaul H et al. 2016. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167:1883–96.e15
     [Google Scholar]
  35. 35.  Kalhor R, Mali P, Church GM 2017. Rapidly evolving homing CRISPR barcodes. Nat. Methods 14:195–200
     [Google Scholar]
  36. 36.  Kang HM, Subramaniam M, Targ S, Nguyen M, Maliskova L et al. 2018. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36:89–94
     [Google Scholar]
  37. 37.  Karaiskos N, Wahle P, Alles J, Boltengagen A, Ayoub S et al. 2017. The Drosophila embryo at single-cell transcriptome resolution. Science 358:194–99
     [Google Scholar]
  38. 38.  Kim JK, Kolodziejczyk AA, Ilicic T, Teichmann SA, Marioni JC 2015. Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression. Nat. Commun. 6:8687
     [Google Scholar]
  39. 39.  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]
  40. 40.  Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC et al. 2016. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352:1586–90
     [Google Scholar]
  41. 41.  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]
  42. 42.  Lee JH 2017. Quantitative approaches for investigating the spatial context of gene expression. Wiley Interdiscip. Rev. Syst. Biol. Med. 9:e1369
     [Google Scholar]
  43. 43.  Li J, Klughammer J, Farlik M, Penz T, Spittler A et al. 2016. Single-cell transcriptomes reveal characteristic features of human pancreatic islet cell types. EMBO Rep 17:178–87
     [Google Scholar]
  44. 44.  Liu Z, Wang L, Welch JD, Ma H, Zhou Y et al. 2017. Single-cell transcriptomics reconstructs fate conversion from fibroblast to cardiomyocyte. Nature 551:100–4
     [Google Scholar]
  45. 45.  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]
  46. 46.  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]
  47. 47.  Martinez-Jimenez CP, Eling N, Chen HC, Vallejos CA, Kolodziejczyk AA et al. 2017. Aging increases cell-to-cell transcriptional variability upon immune stimulation. Science 355:1433–36
     [Google Scholar]
  48. 48.  McKenna A, Findlay GM, Gagnon JA, Horwitz MS, Schier AF, Shendure J 2016. Whole-organism lineage tracing by combinatorial and cumulative genome editing. Science 353:aaf7907
     [Google Scholar]
  49. 49.  Michlits G, Hubmann M, Wu S-H, Vainorius G, Budusan E et al. 2017. CRISPR-UMI: single-cell lineage tracing of pooled CRISPR–Cas9 screens. Nat. Methods 14:1191–97
     [Google Scholar]
  50. 50.  Miles LA, Garippa RJ, Poirier JT 2016. Design, execution, and analysis of pooled in vitro CRISPR/Cas9 screens. FEBS J 283:3170–80
     [Google Scholar]
  51. 51.  Moffitt JR, Hao J, Wang G, Chen KH, Babcock HP, Zhuang X 2016. High-throughput single-cell gene-expression profiling with multiplexed error-robust fluorescence in situ hybridization. PNAS 113:11046–51
     [Google Scholar]
  52. 52.  Moffitt JR, Zhuang X 2016. RNA imaging with multiplexed error-robust fluorescence in situ hybridization (MERFISH). Methods Enzymol 572:1–49
     [Google Scholar]
  53. 53.  Muraro MJ, Dharmadhikari G, Grun D, Groen N, Dielen T et al. 2016. A single-cell transcriptome atlas of the human pancreas. Cell Syst 3:385–94.e3
     [Google Scholar]
  54. 54.  Ofengeim D, Giagtzoglou N, Huh D, Zou C, Yuan J 2017. Single-cell RNA sequencing: unraveling the brain one cell at a time. Trends Mol. Med. 23:563–76
     [Google Scholar]
  55. 55.  Papalexi E, Satija R 2018. Single-cell RNA sequencing to explore immune cell heterogeneity. Nat. Rev. Immunol. 18:35–45
     [Google Scholar]
  56. 56.  Paul F, Arkin Y, Giladi A, Jaitin DA, Kenigsberg E et al. 2015. Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell 163:1663–77
     [Google Scholar]
  57. 57.  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]
  58. 58.  Peterson VM, Zhang KX, Kumar N, Wong J, Li L et al. 2017. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35:936–39
     [Google Scholar]
  59. 59.  Picelli S 2017. Single-cell RNA-sequencing: The future of genome biology is now. RNA Biol 14:637–50
     [Google Scholar]
  60. 60.  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]
  61. 61.  Poran A, Notzel C, Aly O, Mencia-Trinchant N, Harris CT et al. 2017. Single-cell RNA sequencing reveals a signature of sexual commitment in malaria parasites. Nature 551:95–99
     [Google Scholar]
  62. 62.  Pott S 2017. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6:e23203
     [Google Scholar]
  63. 63.  Rizvi AH, Camara PG, Kandror EK, Roberts TJ, Schieren I et al. 2017. Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development. Nat. Biotechnol. 35:551–60
     [Google Scholar]
  64. 64.  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]
  65. 65.  Satija R, Farrell JA, Gennert D, Schier AF, Regev A 2015. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33:495–502
     [Google Scholar]
  66. 66.  Segerstolpe Å, Palasantza A, Eliasson P, Andersson E-M, Andréasson A-C et al. 2016. Single-cell transcriptome profiling of human pancreatic islets in health and type 2 diabetes. Cell Metab 24:593–607
     [Google Scholar]
  67. 67.  Semrau S, Goldmann JE, Soumillon M, Mikkelsen TS, Jaenisch R, van Oudenaarden A 2017. Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells. Nat. Commun. 8:1096
     [Google Scholar]
  68. 68.  Shahi P, Kim SC, Haliburton JR, Gartner ZJ, Abate AR 2017. Abseq: ultrahigh-throughput single cell protein profiling with droplet microfluidic barcoding. Sci. Rep. 7:44447
     [Google Scholar]
  69. 69.  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]
  70. 70.  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]
  71. 71.  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]
  72. 72.  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]
  73. 73.  Stoeckius M, Zheng S, Houck-Loomis B, Hao S, Yeung B et al. 2017. Cell “hashing” with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. bioRxiv 237693. https://doi.org/10.1101/237693
    [Crossref]
  74. 74.  Svensson V, Natarajan KN, Ly L-H, Miragaia RJ, Labalette C et al. 2017. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14:381–87
     [Google Scholar]
  75. 75.  Tang F, Barbacioru C, Nordman E, Li B, Xu N et al. 2010. RNA-seq analysis to capture the transcriptome landscape of a single cell. Nat. Protoc. 5:516–35
     [Google Scholar]
  76. 76.  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]
  77. 77.  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]
  78. 78.  Usoskin D, Furlan A, Islam S, Abdo H, Lonnerberg P et al. 2015. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 18:145–53
     [Google Scholar]
  79. 79.  van der Maaten L, Hinton G 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:2579–605
     [Google Scholar]
  80. 80.  Villani A-C, Satija R, Reynolds G, Sarkizova S, Shekhar K et al. 2017. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356:eaah4573
     [Google Scholar]
  81. 81.  Wang YJ, Schug J, Won KJ, Liu C, Naji A et al. 2016. Single-cell transcriptomics of the human endocrine pancreas. Diabetes 65:3028–38
     [Google Scholar]
  82. 82.  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]
  83. 83.  Wurtzel O, Cote LE, Poirier A, Satija R, Regev A, Reddien PW 2015. A generic and cell-type-specific wound response precedes regeneration in planarians. Dev. Cell 35:632–45
     [Google Scholar]
  84. 84.  Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW et al. 2017. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8:14049
     [Google Scholar]
  85. 85.  Ziegenhain C, Vieth B, Parekh S, Reinius B, Guillaumet-Adkins A et al. 2017. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65:631–43.e4
     [Google Scholar]
/content/journals/10.1146/annurev-genet-120417-031247
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Power in Numbers: Single-Cell RNA-Seq Strategies to Dissect Complex Tissues
Annual Review of Genetics 52, 203 (2018); https://doi.org/10.1146/annurev-genet-120417-031247
/content/journals/10.1146/annurev-genet-120417-031247
/content/journals/10.1146/annurev-genet-120417-031247
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