1932

Abstract

Despite being a relatively recent technological development, single-cell transcriptional analysis through high-throughput sequencing has already been used in hundreds of fruitful studies to make exciting new biological discoveries that would otherwise be challenging or even impossible. Consequently, this has fueled a virtuous cycle of even greater interest in the field and compelled development of further improved technical methodologies and approaches. Thanks to the combined efforts of the research community, including the fields of biochemistry and molecular biology, technology and instrumentation, data science, computational biology, and bioinformatics, the single-cell RNA-sequencing field is advancing at a pace that is both astounding and unprecedented. In this review, we provide a broad introduction to this revolutionary technology by presenting the state-of-the-art in sample preparation methodologies, technology platforms, and computational analysis methods, while highlighting the key considerations for designing, executing, and interpreting a study using single-cell RNA sequencing.

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2017-06-12
2024-03-28
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Literature Cited

  1. Fritzsch FSO, Dusny C, Frick O, Schmid A. 1.  2012. Single-cell analysis in biotechnology, systems biology, and biocatalysis. Annu. Rev. Chem. Biomol. Eng. 3:129–55 [Google Scholar]
  2. Shapiro E, Biezuner T, Linnarsson S. 2.  2013. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14:9618–30 [Google Scholar]
  3. Sandberg R. 3.  2014. Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods 11:122–24 [Google Scholar]
  4. Junker JP, van Oudenaarden A. 4.  2014. Every cell is special: genome-wide studies add a new dimension to single-cell biology. Cell 157:18–11 [Google Scholar]
  5. Stegle O, Teichmann SA, Marioni JC. 5.  2015. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16:3133–45 [Google Scholar]
  6. Kolodziejczyk AA, Kim JK, Svensson V, Marioni JC, Teichmann SA. 6.  2015. The technology and biology of single-cell RNA sequencing. Mol. Cell 58:4610–20 [Google Scholar]
  7. Wang Y, Navin NE. 7.  2015. Advances and applications of single-cell sequencing technologies. Mol. Cell 58:4598–609 [Google Scholar]
  8. Leng N, Chu L-F, Barry C, Li Y, Choi J. 8.  et al. 2015. Oscope identifies oscillatory genes in unsynchronized single-cell RNA-seq experiments. Nat. Methods 12:10947–50 [Google Scholar]
  9. Treutlein B, Brownfield DG, Wu AR, Neff NF, Mantalas GL. 9.  et al. 2014. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509:7500371–75 [Google Scholar]
  10. Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C. 10.  et al. 2015. A survey of human brain transcriptome diversity at the single cell level. PNAS 112:237285–90 [Google Scholar]
  11. Grün D, van Oudenaarden A. 11.  2015. Design and analysis of single-cell sequencing experiments. Cell 163:4799–810 [Google Scholar]
  12. Trapnell C, Cacchiarelli D, Grimsby J, Pokharel P, Li S. 12.  et al. 2014. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32:4381–86 [Google Scholar]
  13. Treutlein B, Lee QY, Camp JG, Mall M, Koh W. 13.  et al. 2016. Dissecting direct reprogramming from fibroblast to neuron using single-cell RNA-seq. Nature 534:7607391–95 [Google Scholar]
  14. Ramsköld D, Luo S, Wang Y-C, Li R, Deng Q. 14.  et al. 2012. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30:8777–78 [Google Scholar]
  15. Miyamoto DT, Zheng Y, Wittner BS, Lee RJ, Zhu H. 15.  et al. 2015. RNA-seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science 349:62541351–56 [Google Scholar]
  16. Tang F, Barbacioru C, Wang Y, Nordman E, Lee C. 16.  et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6:5377–82 [Google Scholar]
  17. Blainey PC, Quake SR. 17.  2013. Dissecting genomic diversity, one cell at a time. Nat. Methods 11:119–21 [Google Scholar]
  18. Tang F, Barbacioru C, Bao S, Lee C, Nordman E. 18.  et al. 2010. Tracing the derivation of embryonic stem cells from the inner cell mass by single-cell RNA-Seq analysis. Cell Stem Cell 6:5468–78 [Google Scholar]
  19. Yan L, Yang M, Guo H, Yang L, Wu J. 19.  et al. 2013. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat. Struct. Mol. Biol. 20:91131–39 [Google Scholar]
  20. Durruthy-Durruthy J, Wossidlo M, Pai S, Takahashi Y, Kang G. 20.  et al. 2016. Spatiotemporal reconstruction of the human blastocyst by single-cell gene-expression analysis informs induction of naive pluripotency. Dev. Cell 38:1100–15 [Google Scholar]
  21. Petropoulos S, Edsgärd D, Reinius B, Deng Q, Panula SP. 21.  et al. 2016. Single-cell RNA-seq reveals lineage and X chromosome dynamics in human preimplantation embryos. Cell 165:41012–26 [Google Scholar]
  22. Shalek AK, Satija R, Shuga J, Trombetta JJ, Gennert D. 22.  et al. 2014. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510:7505363–69 [Google Scholar]
  23. Tan L, Li Q, Xie XS. 23.  2015. Olfactory sensory neurons transiently express multiple olfactory receptors during development. Mol. Syst. Biol. 11:12844–44 [Google Scholar]
  24. Liu SJ, Nowakowski TJ, Pollen AA, Lui JH, Horlbeck MA. 24.  et al. 2016. Single-cell analysis of long non-coding RNAs in the developing human neocortex. Genome Biol 17:167 [Google Scholar]
  25. Adams MD, Kelley JM, Gocayne JD, Dubnick M, Polymeropoulos MH. 25.  et al. 1991. Complementary DNA sequencing: expressed sequence tags and human genome project. Science 252:50131651–56 [Google Scholar]
  26. Schuler GD, Boguski MS, Stewart EA, Stein LD, Gyapay G. 26.  et al. 1996. A gene map of the human genome. Science 274:5287540–46 [Google Scholar]
  27. Venter JC, Adams MD, Myers EW, Li PW, Mural RJ. 27.  et al. 2001. The sequence of the human genome. Science 291:55071304–51 [Google Scholar]
  28. Lander ES, Linton LM, Birren B, Nusbaum C, Zody MC. 28.  et al. 2001. Initial sequencing and analysis of the human genome. Nature 409:6822860–921 [Google Scholar]
  29. Modrek B, Lee C. 29.  2002. A genomic view of alternative splicing. Nat. Genet. 30:113–19 [Google Scholar]
  30. Mortazavi A, Williams BA, McCue K, Schaeffer L, Wold B. 30.  2008. Mapping and quantifying mammalian transcriptomes by RNA-Seq. Nat. Methods 5:7621–28 [Google Scholar]
  31. Wang Z, Gerstein M, Snyder M. 31.  2009. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10:157–63 [Google Scholar]
  32. Trapnell C, Pachter L, Salzberg SL. 32.  2009. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics 25:91105–11 [Google Scholar]
  33. Ashwal-Fluss R, Meyer M, Pamudurti NR, Ivanov A, Bartok O. 33.  et al. 2014. CircRNA biogenesis competes with pre-mRNA splicing. Mol. Cell 56:155–66 [Google Scholar]
  34. Zhang X-O, Wang H-B, Zhang Y, Lu X, Chen L-L, Yang L. 34.  2014. Complementary sequence-mediated exon circularization. Cell 159:1134–47 [Google Scholar]
  35. Fan X, Zhang X, Wu X, Guo H, Hu Y. 35.  et al. 2015. Single-cell RNA-seq transcriptome analysis of linear and circular RNAs in mouse preimplantation embryos. Genome Biol 16:148 [Google Scholar]
  36. Dang Y, Yan L, Hu B, Fan X, Ren Y. 36.  et al. 2016. Tracing the expression of circular RNAs in human pre-implantation embryos. Genome Biol 17:11–15 [Google Scholar]
  37. Jaitin DA, Kenigsberg E, Keren-Shaul H, Elefant N, Paul F. 37.  et al. 2014. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343:6172776–79 [Google Scholar]
  38. Zeisel A, Muñoz-Manchado AB, Codeluppi S, Lönnerberg P, La Manno G. 38.  et al. 2015. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347:62261138–42 [Google Scholar]
  39. Nelson AC, Mould AW, Bikoff EK, Robertson EJ. 39.  2016. Single-cell RNA-seq reveals cell type-specific transcriptional signatures at the maternal-foetal interface during pregnancy. Nat. Commun. 7:11414 [Google Scholar]
  40. Björklund ÅK, Forkel M, Picelli S, Konya V, Theorell J. 40.  et al. 2016. The heterogeneity of human CD127+ innate lymphoid cells revealed by single-cell RNA sequencing. Nat. Immunol. 17:4451–60 [Google Scholar]
  41. Patel AP, Tirosh I, Trombetta JJ, Shalek AK, Gillespie SM. 41.  et al. 2014. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344:61901396–401 [Google Scholar]
  42. Zhou F, Li X, Wang W, Zhu P, Zhou J. 42.  et al. 2016. Tracing haematopoietic stem cell formation at single-cell resolution. Nature 533:7604487–92 [Google Scholar]
  43. Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K. 43.  et al. 2015. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:51202–14 [Google Scholar]
  44. Klein AM, Mazutis L, Akartuna I, Tallapragada N, Veres A. 44.  et al. 2015. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:51187–201 [Google Scholar]
  45. Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW. 45.  et al. 2017. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 814049
  46. Yuan J, Sims PA. 46.  2016. An automated microwell platform for large-scale single cell RNA-Seq.. Sci. Rep. 633883
  47. Treutlein B, Gokce O, Quake SR, Südhof TC. 47.  2014. Cartography of neurexin alternative splicing mapped by single-molecule long-read mRNA sequencing. PNAS 111:13E1291–99 [Google Scholar]
  48. Macaulay IC, Svensson V, Labalette C, Ferreira L, Hamey F. 48.  et al. 2016. Single-cell RNA-sequencing reveals a continuous spectrum of differentiation in hematopoietic cells. Cell Rep 14:4966–77 [Google Scholar]
  49. Streets AM, Huang Y. 49.  2014. How deep is enough in single-cell RNA-seq?. Nat. Biotechnol. 32:101005–6 [Google Scholar]
  50. Drissen R, Buza-Vidas N, Woll P, Thongjuea S, Gambardella A. 50.  et al. 2016. Distinct myeloid progenitor-differentiation pathways identified through single-cell RNA sequencing. Nat. Immunol. 17:6666–76 [Google Scholar]
  51. Liu Y, Zhou J, White KP. 51.  2014. RNA-seq differential expression studies: more sequence or more replication?. Bioinformatics 30:3301–4 [Google Scholar]
  52. Sims D, Sudbery I, Ilott NE, Heger A, Ponting CP. 52.  2014. Sequencing depth and coverage: key considerations in genomic analyses. Nat. Rev. Genet. 15:2121–32 [Google Scholar]
  53. Grün D, Kester L, van Oudenaarden A. 53.  2014. Validation of noise models for single-cell transcriptomics. Nat. Methods 11:6637–40 [Google Scholar]
  54. Munro SA, Lund SP, Pine PS, Binder H, Clevert D-A. 54.  et al. 2014. Assessing technical performance in differential gene expression experiments with external spike-in RNA control ratio mixtures. Nat. Commun. 5:5125 [Google Scholar]
  55. Lee H, Pine PS, McDaniel J, Salit M, Oliver B. 55.  2016. External RNA controls consortium beta version update. J. Genom. 4:19–22 [Google Scholar]
  56. Jiang L, Schlesinger F, Davis CA, Zhang Y, Li R. 56.  et al. 2011. Synthetic spike-in standards for RNA-seq experiments. Genome Res 21:91543–51 [Google Scholar]
  57. Schurch NJ, Schofield P, Gierliński M, Cole C, Sherstnev A. 57.  et al. 2016. How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?. RNA 22:6839–51 [Google Scholar]
  58. Peixoto L, Risso D, Poplawski SG, Wimmer ME, Speed TP. 58.  et al. 2015. How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets. Nucleic Acids Res 43:167664–74 [Google Scholar]
  59. Gubler U. 59.  1987. Second-strand cDNA synthesis: mRNA fragments as primers. Methods Enzymol 152:330–35 [Google Scholar]
  60. Sasagawa Y, Nikaido I, Hayashi T, Danno H, Uno KD. 60.  et al. 2013. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol 14:4R31 [Google Scholar]
  61. Islam S, Kjällquist U, Moliner A, Zajac P, Fan JB. 61.  et al. 2011. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 21:71160–67 [Google Scholar]
  62. Picelli S, Björklund ÅK, Faridani OR, Sagasser S, Winberg GOS, Sandberg R. 62.  2013. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10:1096–98 [Google Scholar]
  63. Shiroguchi K, Jia TZ, Sims PA, Xie XS. 63.  2012. Digital RNA sequencing minimizes sequence-dependent bias and amplification noise with optimized single-molecule barcodes. PNAS 109:41347–52 [Google Scholar]
  64. Hashimshony T, Wagner F, Sher N, Yanai I. 64.  2012. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep 2:3666–73 [Google Scholar]
  65. Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y. 65.  et al. 2016. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol 17:177 [Google Scholar]
  66. Pan X, Durrett RE, Zhu H, Tanaka Y, Li Y. 66.  et al. 2012. Two methods for full-length RNA sequencing for low quantities of cells and single cells. PNAS 110:2594–99 [Google Scholar]
  67. Fu Y, Li C, Lu S, Zhou W, Tang F. 67.  et al. 2015. Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification. PNAS 112:3811923–28 [Google Scholar]
  68. Fu Y, Chen H, Liu L, Huang Y. 68.  2016. Single cell total RNA sequencing through isothermal amplification in picoliter-droplet emulsion. Anal. Chem. 88:2210795–99 [Google Scholar]
  69. Picelli S, Björklund ÅK, Reinius B, Sagasser S, Winberg G, Sandberg R. 69.  2014. Tn5 transposase and tagmentation procedures for massively scaled sequencing projects. Genome Res 24:122033–40 [Google Scholar]
  70. Tang F, Lao K, Surani MA. 70.  2011. Development and applications of single-cell transcriptome analysis. Nat. Methods 8:4S6–11 [Google Scholar]
  71. Wu AR, Neff NF, Kalisky T, Dalerba P, Treutlein B. 71.  et al. 2014. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11:141–46 [Google Scholar]
  72. Fan HC, Fu GK, Fodor SPA. 72.  2015. Combinatorial labeling of single cells for gene expression cytometry. Science 347:62221258367 [Google Scholar]
  73. Svensson V, Natarajan KN, Ly L-H, Miragaia RJ, Labalette C. 73.  et al. 2017. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14381–87
  74. Streets AM, Zhang X, Cao C, Pang Y, Wu X. 74.  et al. 2014. Microfluidic single-cell whole-transcriptome sequencing. PNAS 111:197048–53 [Google Scholar]
  75. Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G. 75.  et al. 2010. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28:5511–15 [Google Scholar]
  76. Wagner GP, Kin K, Lynch VJ. 76.  2012. Measurement of mRNA abundance using RNA-seq data: RPKM measure is inconsistent among samples. Theory Biosci 131:4281–85 [Google Scholar]
  77. Fu GK, Xu W, Wilhelmy J, Mindrinos MN, Davis RW. 77.  et al. 2014. Molecular indexing enables quantitative targeted RNA sequencing and reveals poor efficiencies in standard library preparations. PNAS 111:51891–96 [Google Scholar]
  78. Islam S, Zeisel A, Joost S, La Manno G, Zajac P. 78.  et al. 2013. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11:163–66 [Google Scholar]
  79. Hicks SC, Teng M, Irizarry RA. 79.  2015. On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data. bioRxiv 025528. http://dx.doi.org/10.1101/025528 [Crossref]
  80. Tung P-Y, Blischak JD, Hsiao C, Knowles DA, Burnett JE. 80.  et al. 2017. Batch effects and the effective design of single-cell gene expression studies. Sci. Rep. 839921
  81. Risso D, Ngai J, Speed TP, Dudoit S. 81.  2014. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32:9896–902 [Google Scholar]
  82. Buettner F, Natarajan KN, Casale FP, Proserpio V, Scialdone A. 82.  et al. 2015. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33:2155–60 [Google Scholar]
  83. Vallejos CA, Marioni JC, Richardson S. 83.  2015. BASiCS: Bayesian analysis of single-cell sequencing data. PLOS Comput. Biol. 11:6e1004333 [Google Scholar]
  84. Lun ATL, Bach K, Marioni JC. 84.  2016. Pooling across cells to normalize single-cell RNA sequencing data with many zero counts. Genome Biol 17:175 [Google Scholar]
  85. Brennecke P, Anders S, Kim JK, Kołodziejczyk AA, Zhang X. 85.  et al. 2013. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10:111093–95 [Google Scholar]
  86. Fan J, Salathia N, Liu R, Kaeser GE, Yung YC. 86.  et al. 2016. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13:3241–44 [Google Scholar]
  87. Deng Q, Ramsköld D, Reinius B, Sandberg R. 87.  2014. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343:6167193–96 [Google Scholar]
  88. Guo M, Wang H, Potter SS, Whitsett JA, Xu Y. 88.  2015. SINCERA: a pipeline for single-cell RNA-Seq profiling analysis. PLOS Comput. Biol. 11:11e1004575 [Google Scholar]
  89. Juliá M, Telenti A, Rausell A. 89.  2015. Sincell: An R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq. Bioinformatics 31:203380–82 [Google Scholar]
  90. Shin J, Berg DA, Zhu Y, Shin JY, Song J. 90.  et al. 2015. Single-cell RNA-Seq with waterfall reveals molecular cascades underlying adult neurogenesis. Cell 17:3360–72 [Google Scholar]
  91. Žurauskienė J, Yau C. 91.  2016. pcaReduce: hierarchical clustering of single cell transcriptional profiles. BMC Bioinform 17:1140 [Google Scholar]
  92. Setty M, Tadmor MD, Reich-Zeliger S, Angel O, Salame TM. 92.  et al. 2016. Wishbone identifies bifurcating developmental trajectories from single-cell data. Nat. Biotechnol. 34:6637–45 [Google Scholar]
  93. Pierson E, Yau C. 93.  2015. ZIFA: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol 16:1241 [Google Scholar]
  94. van der Maaten L, Hinton G. 94.  2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:2579–605 [Google Scholar]
  95. Amir ED, Davis KL, Tadmor MD, Simonds EF, Levine JH. 95.  et al. 2013. ViSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31:6545–52 [Google Scholar]
  96. Grün D, Lyubimova A, Kester L, Wiebrands K, Basak O. 96.  et al. 2015. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525:7568251–55 [Google Scholar]
  97. Bendall SC, Davis KL, Amir ED, Tadmor MD, Simonds EF. 97.  et al. 2014. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157:3714–25 [Google Scholar]
  98. Marco E, Karp RL, Guo G, Robson P, Hart AH. 98.  et al. 2014. Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. PNAS 111:52E5643–50 [Google Scholar]
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