1932

Abstract

Single-cell multiomics technologies typically measure multiple types of molecule from the same individual cell, enabling more profound biological insight than can be inferred by analyzing each molecular layer from separate cells. These single-cell multiomics technologies can reveal cellular heterogeneity at multiple molecular layers within a population of cells and reveal how this variation is coupled or uncoupled between the captured omic layers. The data sets generated by these techniques have the potential to enable a deeper understanding of the key biological processes and mechanisms driving cellular heterogeneity and how they are linked with normal development and aging as well as disease etiology. This review details both established and novel single-cell mono- and multiomics technologies and considers their limitations, applications, and likely future developments.

Loading

Article metrics loading...

/content/journals/10.1146/annurev-genom-091416-035324
2018-08-31
2024-06-21
Loading full text...

Full text loading...

/deliver/fulltext/genom/19/1/annurev-genom-091416-035324.html?itemId=/content/journals/10.1146/annurev-genom-091416-035324&mimeType=html&fmt=ahah

Literature Cited

  1. 1.  Abdelmoez MN, Iida K, Oguchi Y, Nishikii H, Yokokawa R et al. 2017. Correlation of gene expressions between nucleus and cytoplasm reflects single-cell physiology. bioRxiv 206672. https://doi.org/10.1101/206672
    [Crossref]
  2. 2.  Achim K, Pettit J-B, 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.  Alizadeh AA, Aranda V, Bardelli A, Blanpain C, Bock C et al. 2015. Toward understanding and exploiting tumor heterogeneity. Nat. Med. 21:846–53
    [Google Scholar]
  4. 4.  Angelo M, Bendall SC, Finck R, Hale MB, Hitzman C et al. 2014. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20:436–42
    [Google Scholar]
  5. 5.  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]
  6. 6.  Bagnoli JW, Ziegenhain C, Janjic A, Wange LE, Vieth B et al. 2017. mcSCRB-seq: sensitive and powerful single-cell RNA sequencing. bioRxiv 188367. https://doi.org/10.1101/188367
    [Crossref]
  7. 7.  Bakker B, Taudt A, Belderbos ME, Porubsky D, Spierings DCJ et al. 2016. Single-cell sequencing reveals karyotype heterogeneity in murine and human malignancies. Genome Biol 17:115
    [Google Scholar]
  8. 8.  Biesecker LG, Spinner NB 2013. A genomic view of mosaicism and human disease. Nat. Rev. Genet. 14:307–20
    [Google Scholar]
  9. 9.  Bjornson ZB, Nolan GP, Fantl WJ 2013. Single-cell mass cytometry for analysis of immune system functional states. Curr. Opin. Immunol. 25:484–94
    [Google Scholar]
  10. 10.  Bock C, Farlik M, Sheffield NC 2016. Multi-omics of single cells: strategies and applications. Trends Biotechnol 34:605–8
    [Google Scholar]
  11. 11.  Boyle AP, Davis S, Shulha HP, Meltzer P, Margulies EH et al. 2008. High-resolution mapping and characterization of open chromatin across the genome. Cell 132:311–22
    [Google Scholar]
  12. 12.  Budnik B, Levy E, Slavov N 2017. Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation. bioRxiv 102681. https://doi.org/10.1101/102681
    [Crossref]
  13. 13.  Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ 2013. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10:1213–18
    [Google Scholar]
  14. 14.  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]
  15. 15.  Bussard KM, Mutkus L, Stumpf K, Gomez-Manzano C, Marini FC 2016. Tumor-associated stromal cells as key contributors to the tumor microenvironment. Breast Cancer Res 18:84
    [Google Scholar]
  16. 16.  Cadwell CR, Palasantza A, Jiang X, Berens P, Deng Q et al. 2016. Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq. Nat. Biotechnol. 34:199–203
    [Google Scholar]
  17. 17.  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]
  18. 18.  Chen C, Xing D, Tan L, Li H, Zhou G et al. 2017. Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI). Science 356:189–94
    [Google Scholar]
  19. 19.  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]
  20. 20.  Cheow LF, Courtois ET, Tan Y, Viswanathan R, Xing Q et al. 2016. Single-cell multimodal profiling reveals cellular epigenetic heterogeneity. Nat. Methods 13:833–36
    [Google Scholar]
  21. 21.  Cheow LF, Quake SR, Burkholder WF, Messerschmidt DM 2015. Multiplexed locus-specific analysis of DNA methylation in single cells. Nat. Protoc. 10:619–31
    [Google Scholar]
  22. 22.  Chu WK, Edge P, Lee HS, Bansal V, Bafna V et al. 2017. Ultraaccurate genome sequencing and haplotyping of single human cells. PNAS 114:12512–17
    [Google Scholar]
  23. 23.  Clark SJ, Argelaguet R, Kapourani C-A, Stubbs TM, Lee HJ et al. 2018. scNMT-seq enables joint profiling of chromatin accessibility DNA methylation and transcription in single cells. Nat. Commun. 9:781
    [Google Scholar]
  24. 24.  Clark SJ, Smallwood SA, Lee HJ, Krueger F, Reik W, Kelsey G 2017. Genome-wide base-resolution mapping of DNA methylation in single cells using single-cell bisulfite sequencing (scBS-seq). Nat. Protoc. 12:534–47
    [Google Scholar]
  25. 25.  Cooper J, Ding Y, Song J, Zhao K 2017. Genome-wide mapping of DNase I hypersensitive sites in rare cell populations using single-cell DNase sequencing. Nat. Protoc. 12:2342–54
    [Google Scholar]
  26. 26.  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]
  27. 27.  Darmanis S, Gallant CJ, Marinescu VD, Niklasson M, Segerman A et al. 2016. Simultaneous multiplexed measurement of RNA and proteins in single cells. Cell Rep 14:380–89
    [Google Scholar]
  28. 28.  Davis A, Gao R, Navin N 2017. Tumor evolution: linear, branching, neutral or punctuated?. Biochim. Biophys. Acta 1867:151–61
    [Google Scholar]
  29. 29.  De S 2011. Somatic mosaicism in healthy human tissues. Trends Genet 27:217–23
    [Google Scholar]
  30. 30.  Deaton AM, Bird A 2011. CpG islands and the regulation of transcription. Genes Dev 25:1010–22
    [Google Scholar]
  31. 31.  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]
  32. 32.  Dong X, Zhang L, Milholland B, Lee M, Maslov AY et al. 2017. Accurate identification of single-nucleotide variants in whole-genome-amplified single cells. Nat. Methods 14:491–93
    [Google Scholar]
  33. 33.  Dumanski JP, Piotrowski A 2012. Structural genetic variation in the context of somatic mosaicism. Methods Mol. Biol. 838:249–72
    [Google Scholar]
  34. 34.  Eberwine J, Yeh H, Miyashiro K, Cao Y, Nair S et al. 1992. Analysis of gene expression in single live neurons. PNAS 89:3010–14
    [Google Scholar]
  35. 35.  Emara S, Amer S, Ali A, Abouleila Y, Oga A, Masujima T 2017. Single-cell metabolomics. Metabolomics: From Fundamentals to Clinical Applications A Sussulini 323–43 Cham, Switz: Springer
    [Google Scholar]
  36. 36.  Emmert-Buck MR, Bonner RF, Smith PD, Chuaqui RF, Zhuang Z et al. 1996. Laser capture microdissection. Science 274:998–1001
    [Google Scholar]
  37. 37.  Fallahi-Sichani M, Becker V, Izar B, Baker GJ, Lin J-R et al. 2017. Adaptive resistance of melanoma cells to RAF inhibition via reversible induction of a slowly dividing de-differentiated state. Mol. Syst. Biol. 13:905
    [Google Scholar]
  38. 38.  Faridani OR, Abdullayev I, Hagemann-Jensen M, Schell JP, Lanner F, Sandberg R 2016. Single-cell sequencing of the small-RNA transcriptome. Nat. Biotechnol. 34:1264–66
    [Google Scholar]
  39. 39.  Farlik M, Sheffield NC, Nuzzo A, Datlinger P, Schönegger A et al. 2015. Single-cell DNA methylome sequencing and bioinformatic inference of epigenomic cell-state dynamics. Cell Rep 10:1386–97
    [Google Scholar]
  40. 40.  Ficz G 2015. New insights into mechanisms that regulate DNA methylation patterning. J. Exp. Biol. 218:14–20
    [Google Scholar]
  41. 41.  Flyamer IM, Gassler J, Imakaev M, Brandão HB, Ulianov SV et al. 2017. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature 544:110–14
    [Google Scholar]
  42. 42.  Frei AP, Bava F-A, Zunder ER, Hsieh EWY, Chen S-Y et al. 2016. Highly multiplexed simultaneous detection of RNAs and proteins in single cells. Nat. Methods 13:269–75
    [Google Scholar]
  43. 43.  Garraway LA, Lander ES 2013. Lessons from the cancer genome. Cell 153:17–37
    [Google Scholar]
  44. 44.  Gawad C, Koh W, Quake SR 2016. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17:175–88
    [Google Scholar]
  45. 45.  Gay L, Baker A-M, Graham TA 2016. Tumour cell heterogeneity. F1000Research 5:238
    [Google Scholar]
  46. 46.  Genshaft AS, Li S, Gallant CJ, Darmanis S, Prakadan SM et al. 2016. Multiplexed, targeted profiling of single-cell proteomes and transcriptomes in a single reaction. Genome Biol 17:188
    [Google Scholar]
  47. 47.  Gierahn TM, Wadsworth MH II, Hughes TK, Bryson BD, Butler A et al. 2017. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14:395–98
    [Google Scholar]
  48. 48.  Giesen C, Wang HAO, Schapiro D, Zivanovic N, Jacobs A et al. 2014. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11:417–22
    [Google Scholar]
  49. 49.  Giresi PG, Kim J, McDaniell RM, Iyer VR, Lieb JD 2007. FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements) isolates active regulatory elements from human chromatin. Genome Res 17:877–85
    [Google Scholar]
  50. 50.  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]
  51. 51.  Guatelli JC, Whitfield KM, Kwoh DY, Barringer KJ, Richman DD et al. 1990. Isothermal, in vitro amplification of nucleic acids by a multienzyme reaction modeled after retroviral replication. PNAS 87:1874–78
    [Google Scholar]
  52. 52.  Guo F, Li L, Li J, Wu X, Hu B et al. 2017. Single-cell multi-omics sequencing of mouse early embryos and embryonic stem cells. Cell Res 27:967–88
    [Google Scholar]
  53. 53.  Guo H, Zhu P, Guo F, Li X, Wu X et al. 2015. Profiling DNA methylome landscapes of mammalian cells with single-cell reduced-representation bisulfite sequencing. Nat. Protoc. 10:645–59
    [Google Scholar]
  54. 54.  Guo H, Zhu P, Wu X, Li X, Wen L, Tang F 2013. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res 23:2126–35
    [Google Scholar]
  55. 55.  Guo H, Zhu P, Yan L, Li R, Hu B et al. 2014. The DNA methylation landscape of human early embryos. Nature 511:606–10
    [Google Scholar]
  56. 56.  Han H, Cortez CC, Yang X, Nichols PW, Jones PA, Liang G 2011. DNA methylation directly silences genes with non-CpG island promoters and establishes a nucleosome occupied promoter. Hum. Mol. Genet. 20:4299–310
    [Google Scholar]
  57. 57.  Han L, Zi X, Garmire LX, Wu Y, Weissman SM et al. 2014. Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform. Sci. Rep. 4:6485
    [Google Scholar]
  58. 58.  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]
  59. 59.  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]
  60. 60.  Hayatsu H 2008. The bisulfite genomic sequencing used in the analysis of epigenetic states, a technique in the emerging environmental genotoxicology research. Mutat. Res. 659:77–82
    [Google Scholar]
  61. 61.  Hon C-C, Shin JW, Carninci P, Stubbington MJT 2018. The Human Cell Atlas: technical approaches and challenges. Brief. Funct. Genom. 17:283–94
    [Google Scholar]
  62. 62.  Hou Y, Guo H, Cao C, Li X, Hu B et al. 2016. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res 26:304–19
    [Google Scholar]
  63. 63.  Hu P, Zhang W, Xin H, Deng G 2016. Single cell isolation and analysis. Front. Cell Dev. Biol. 4:116
    [Google Scholar]
  64. 64.  Hu Y, Huang K, An Q, Du G, Hu G et al. 2016. Simultaneous profiling of transcriptome and DNA methylome from a single cell. Genome Biol 17:88
    [Google Scholar]
  65. 65.  Huang Y, Pastor WA, Shen Y, Tahiliani M, Liu DR, Rao A 2010. The behaviour of 5-hydroxymethylcytosine in bisulfite sequencing. PLOS ONE 5:e8888
    [Google Scholar]
  66. 66.  Ito S, Shen L, Dai Q, Wu SC, Collins LB et al. 2011. Tet proteins can convert 5-methylcytosine to 5-formylcytosine and 5-carboxylcytosine. Science 333:1300–3
    [Google Scholar]
  67. 67.  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]
  68. 68.  Ji Z, Ji H 2016. TSCAN: pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis. Nucleic Acids Res 44:e117
    [Google Scholar]
  69. 69.  Jin S-G, Kadam S, Pfeifer GP 2010. Examination of the specificity of DNA methylation profiling techniques towards 5-methylcytosine and 5-hydroxymethylcytosine. Nucleic Acids Res 38:e125
    [Google Scholar]
  70. 70.  Jin W, Tang Q, Wan M, Cui K, Zhang Y et al. 2015. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528:142–46
    [Google Scholar]
  71. 71.  Jones PA 2012. Functions of DNA methylation: islands, start sites, gene bodies and beyond. Nat. Rev. Genet. 13:484–92
    [Google Scholar]
  72. 72.  Kawasaki ES 2004. Microarrays and the gene expression profile of a single cell. Ann. N.Y. Acad. Sci. 1020:92–100
    [Google Scholar]
  73. 73.  Ke R, Mignardi M, Hauling T, Nilsson M 2016. Fourth generation of next-generation sequencing technologies: promise and consequences. Hum. Mutat. 37:1363–67
    [Google Scholar]
  74. 74.  Kellinger MW, Song C-X, Chong J, Lu X-Y, He C, Wang D 2012. 5-Formylcytosine and 5-carboxylcytosine reduce the rate and substrate specificity of RNA polymerase II transcription. Nat. Struct. Mol. Biol. 19:831–33
    [Google Scholar]
  75. 75.  Kelly TK, Liu Y, Lay FD, Liang G, Berman BP, Jones PA 2012. Genome-wide mapping of nucleosome positioning and DNA methylation within individual DNA molecules. Genome Res 22:2497–506
    [Google Scholar]
  76. 76.  Kilgore JA, Hoose SA, Gustafson TL, Porter W, Kladde MP 2007. Single-molecule and population probing of chromatin structure using DNA methyltransferases. Methods 41:320–32
    [Google Scholar]
  77. 77.  Kim DH, Marinov GK, Pepke S, Singer ZS, He P et al. 2015. Single-cell transcriptome analysis reveals dynamic changes in lncRNA expression during reprogramming. Cell Stem Cell 16:88–101
    [Google Scholar]
  78. 78.  Kind J, Pagie L, de Vries SS, Nahidiazar L, Dey SS et al. 2015. Genome-wide maps of nuclear lamina interactions in single human cells. Cell 163:134–47
    [Google Scholar]
  79. 79.  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]
  80. 80.  Kloosterman WP, Cuppen E 2013. Chromothripsis in congenital disorders and cancer: similarities and differences. Curr. Opin. Cell Biol. 25:341–48
    [Google Scholar]
  81. 81.  Kloosterman WP, Guryev V, van Roosmalen M, Duran KJ, de Bruijn E et al. 2011. Chromothripsis as a mechanism driving complex de novo structural rearrangements in the germline. Hum. Mol. Genet. 20:1916–24
    [Google Scholar]
  82. 82.  Korbel JO, Campbell PJ 2013. Criteria for inference of chromothripsis in cancer genomes. Cell 152:1226–36
    [Google Scholar]
  83. 83.  Lee JH 2017. De novo gene expression reconstruction in space. Trends Mol. Med. 23:583–93
    [Google Scholar]
  84. 84.  Leontiou CA, Hadjidaniel MD, Mina P, Antoniou P, Ioannides M, Patsalis PC 2015. Bisulfite conversion of DNA: performance comparison of different kits and methylation quantitation of epigenetic biomarkers that have the potential to be used in non-invasive prenatal testing. PLOS ONE 10:e0135058
    [Google Scholar]
  85. 85.  Li G, Reinberg D 2011. Chromatin higher-order structures and gene regulation. Curr. Opin. Genet. Dev. 21:175–86
    [Google Scholar]
  86. 86.  Li W, Calder RB, Mar JC, Vijg J 2015. Single-cell transcriptogenomics reveals transcriptional exclusion of ENU-mutated alleles. Mutat. Res. 772:55–62
    [Google Scholar]
  87. 87.  Lister R, Pelizzola M, Dowen RH, Hawkins RD, Hon G et al. 2009. Human DNA methylomes at base resolution show widespread epigenomic differences. Nature 462:315–22
    [Google Scholar]
  88. 88.  Liu Z, Lou H, Xie K, Wang H, Chen N et al. 2017. Reconstructing cell cycle pseudo time-series via single-cell transcriptome data. Nat. Commun. 8:22
    [Google Scholar]
  89. 89.  Lorthongpanich C, Cheow LF, Balu S, Quake SR, Knowles BB, Burkholder WF 2013. Single-cell DNA-methylation analysis reveals epigenetic chimerism in preimplantation embryos. Science 341:1110–12
    [Google Scholar]
  90. 90.  Lu Y, Xue Q, Eisele MR, Sulistijo ES, Brower K et al. 2015. Highly multiplexed profiling of single-cell effector functions reveals deep functional heterogeneity in response to pathogenic ligands. PNAS 112:E607–15
    [Google Scholar]
  91. 91.  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]
  92. 92.  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]
  93. 93.  Macaulay IC, Ponting CP, Voet T 2017. Single-cell multiomics: multiple measurements from single cells. Trends Genet 33:155–68
    [Google Scholar]
  94. 94.  Macaulay IC, Teng MJ, Haerty W, Kumar P, Ponting CP, Voet T 2016. Separation and parallel sequencing of the genomes and transcriptomes of single cells using G&T-seq. Nat. Protoc. 11:2081–103
    [Google Scholar]
  95. 95.  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]
  96. 96.  Manno GL, Soldatov R, Hochgerner H, Zeisel A, Petukhov V et al. 2017. RNA velocity in single cells. bioRxiv 206052. https://doi.org/10.1101/206052
    [Crossref]
  97. 97.  McConnell MJ, Lindberg MR, Brennand KJ 2013. Mosaic copy number variation in human neurons. Science 342:632–37
    [Google Scholar]
  98. 98.  Meyer CA, Liu XS 2014. Identifying and mitigating bias in next-generation sequencing methods for chromatin biology. Nat. Rev. Genet. 15:709–21
    [Google Scholar]
  99. 99.  Miura F, Enomoto Y, Dairiki R, Ito T 2012. Amplification-free whole-genome bisulfite sequencing by post-bisulfite adaptor tagging. Nucleic Acids Res 40:e136
    [Google Scholar]
  100. 100.  Mooijman D, Dey SS, Boisset J-C, Crosetto N, van Oudenaarden A 2016. Single-cell 5hmC sequencing reveals chromosome-wide cell-to-cell variability and enables lineage reconstruction. Nat. Biotechnol. 34:852–56
    [Google Scholar]
  101. 101.  Moore DL, Jessberger S 2017. Creating age asymmetry: consequences of inheriting damaged goods in mammalian cells. Trends Cell Biol 27:82–92
    [Google Scholar]
  102. 102.  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]
  103. 103.  Nagano T, Lubling Y, Várnai C, Dudley C, Leung W et al. 2017. Cell-cycle dynamics of chromosomal organization at single-cell resolution. Nature 547:61–67
    [Google Scholar]
  104. 104.  Nagano T, Lubling Y, Yaffe E, Wingett SW, Dean W et al. 2015. Single-cell Hi-C for genome-wide detection of chromatin interactions that occur simultaneously in a single cell. Nat. Protoc. 10:1986–2003
    [Google Scholar]
  105. 105.  Neri F, Incarnato D, Krepelova A, Parlato C, Oliviero S 2016. Methylation-assisted bisulfite sequencing to simultaneously map 5fC and 5caC on a genome-wide scale for DNA demethylation analysis. Nat. Protoc. 11:1191–205
    [Google Scholar]
  106. 106.  Nussbaum RL, McInnes RR, Willard HF 2007. Thompson & Thompson Genetics in Medicine Philadelphia: Saunders, 7th ed..
    [Google Scholar]
  107. 107.  Ogrodnik M, Salmonowicz H, Brown R, Turkowska J, Średniawa W et al. 2014. Dynamic JUNQ inclusion bodies are asymmetrically inherited in mammalian cell lines through the asymmetric partitioning of vimentin. PNAS 111:8049–54
    [Google Scholar]
  108. 108.  Peng G, Suo S, Chen J, Chen W, Liu C et al. 2016. Spatial transcriptome for the molecular annotation of lineage fates and cell identity in mid-gastrula mouse embryo. Dev. Cell 36:681–97
    [Google Scholar]
  109. 109.  Perfetto SP, Chattopadhyay PK, Roederer M 2004. Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 4:648–55
    [Google Scholar]
  110. 110.  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]
  111. 111.  Picelli S 2017. Single-cell RNA-sequencing: the future of genome biology is now. RNA Biol 14:637–50
    [Google Scholar]
  112. 112.  Picelli S, Björklund ÅK, 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]
  113. 113.  Picelli S, Faridani OR, Björklund ÅK, Winberg G, Sagasser S, Sandberg R 2014. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9:171–81
    [Google Scholar]
  114. 114.  Poduri A, Evrony GD, Cai X, Walsh CA 2013. Somatic mutation, genomic variation, and neurological disease. Science 341:1237758
    [Google Scholar]
  115. 115.  Ponomarenko EA, Poverennaya EV, Ilgisonis EV, Pyatnitskiy MA, Kopylov AT et al. 2016. The size of the human proteome: the width and depth. Int. J. Anal. Chem. 2016:7436849
    [Google Scholar]
  116. 116.  Pott S 2017. Simultaneous measurement of chromatin accessibility, DNA methylation, and nucleosome phasing in single cells. eLife 6:e23203
    [Google Scholar]
  117. 117.  Prakadan SM, Shalek AK, Weitz DA 2017. Scaling by shrinking: empowering single-cell “omics” with microfluidic devices. Nat. Rev. Genet. 18:345–61
    [Google Scholar]
  118. 118.  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]
  119. 119.  Regev A, Teichmann S, Lander ES, Amit I, Benoist C et al. 2017. The Human Cell Atlas. eLife 6:e27041
    [Google Scholar]
  120. 120.  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]
  121. 121.  Rozenblatt-Rosen O, Stubbington MJT, Regev A, Teichmann SA 2017. The Human Cell Atlas: from vision to reality. Nature 550:451–53
    [Google Scholar]
  122. 122.  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]
  123. 123.  Schlesinger F, Smith AD, Gingeras TR, Hannon GJ, Hodges E 2013. De novo DNA demethylation and noncoding transcription define active intergenic regulatory elements. Genome Res 23:1601–14
    [Google Scholar]
  124. 124.  Scialdone A, Tanaka Y, Jawaid W, Moignard V, Wilson NK et al. 2016. Resolving early mesoderm diversification through single-cell expression profiling. Nature 535:289–93
    [Google Scholar]
  125. 125.  Scully S, Francescone R, Faibish M, Bentley B, Taylor SL et al. 2012. Transdifferentiation of glioblastoma stem-like cells into mural cells drives vasculogenic mimicry in glioblastomas. J. Neurosci. 32:12950–60
    [Google Scholar]
  126. 126.  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]
  127. 127.  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]
  128. 128.  Shah S, Lubeck E, Zhou W, Cai L 2017. seqFISH accurately detects transcripts in single cells and reveals robust spatial organization in the hippocampus. Neuron 94:752–58
    [Google Scholar]
  129. 129.  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]
  130. 130.  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]
  131. 131.  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]
  132. 132.  Snook RD, Harvey TJ, Correia Faria E, Gardner P 2009. Raman tweezers and their application to the study of singly trapped eukaryotic cells. Integr. Biol. 1:43–52
    [Google Scholar]
  133. 133.  Soumillon M, Cacchiarelli D, Semrau S, van Oudenaarden A, Mikkelsen TS 2014. Characterization of directed differentiation by high-throughput single-cell RNA-Seq. bioRxiv 003236. https://doi.org/10.1101/003236
    [Crossref]
  134. 134.  Spitzer MH, Nolan GP 2016. Mass cytometry: single cells, many features. Cell 165:780–91
    [Google Scholar]
  135. 135.  Stahl PL, Salmen F, Vickovic S, Lundmark A, Navarro JF et al. 2016. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353:78–82
    [Google Scholar]
  136. 136.  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]
  137. 137.  Strachan T, Read A 2010. Human Molecular Genetics New York: Garland Sci, 4th ed..
    [Google Scholar]
  138. 138.  Stratton MR, Campbell PJ, Andrew FP 2009. The cancer genome. Nature 458:719–24
    [Google Scholar]
  139. 139.  Subkhankulova T, Gilchrist MJ, Livesey FJ 2008. Modelling and measuring single cell RNA expression levels find considerable transcriptional differences among phenotypically identical cells. BMC Genom 9:268
    [Google Scholar]
  140. 140.  Svensson V, Natarajan KN, Ly L-H, Miragaia RJ, Labalette C et al. 2016. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14:381–87
    [Google Scholar]
  141. 141.  Svensson V, Vento-Tormo R, Teichmann SA 2017. Exponential scaling of single-cell RNA-seq in the last decade. arXiv:1704.01379 [q-bio.GN]
  142. 142.  Swanton C 2012. Intratumor heterogeneity: evolution through space and time. Cancer Res 72:4875–82
    [Google Scholar]
  143. 143.  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]
  144. 144.  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]
  145. 145.  Tsompana M, Buck MJ 2014. Chromatin accessibility: a window into the genome. Epigenet. Chromatin 7:33
    [Google Scholar]
  146. 146.  Ullal AV, Peterson V, Agasti SS, Tuang S, Juric D et al. 2014. Cancer cell profiling by barcoding allows multiplexed protein analysis in fine-needle aspirates. Sci. Transl. Med. 6:219ra9
    [Google Scholar]
  147. 147.  van Strijp D, Vulders RCM, Larsen NA, Schira J, Baerlocher L et al. 2017. Complete sequence-based pathway analysis by differential on-chip DNA and RNA extraction from a single cell. Sci. Rep. 7:11030
    [Google Scholar]
  148. 148.  Voet T, Vanneste E, Vermeesch JR 2011. The human cleavage stage embryo is a cradle of chromosomal rearrangements. Cytogenet. Genome Res. 133:160–68
    [Google Scholar]
  149. 149.  Wang R, Jin C, Hu X 2017. Evidence of drug-response heterogeneity rapidly generated from a single cancer cell. Oncotarget 8:41113–24
    [Google Scholar]
  150. 150.  Woodworth MB, Girskis KM, Walsh CA 2017. Building a lineage from single cells: genetic techniques for cell lineage tracking. Nat. Rev. Genet. 18:230–44
    [Google Scholar]
  151. 151.  Wu H, Wu X, Shen L, Zhang Y 2014. Single-base resolution analysis of active DNA demethylation using methylase-assisted bisulfite sequencing. Nat. Biotechnol. 32:1231–40
    [Google Scholar]
  152. 152.  Wu X, Inoue A, Suzuki T, Zhang Y 2017. Simultaneous mapping of active DNA demethylation and sister chromatid exchange in single cells. Genes Dev 31:511–23
    [Google Scholar]
  153. 153.  Yamane J, Mori T, Taniyama N, Kobayashi K, Fujibuchi W 2017. Development of enhanced reduced representation bisulfite sequencing method for single-cell methylome analysis. Genom. Comput. Biol. 3:49
    [Google Scholar]
  154. 154.  Yates LR, Campbell PJ 2012. Evolution of the cancer genome. Nat. Rev. Genet. 13:795–806
    [Google Scholar]
  155. 155.  Yu M, Hon GC, Szulwach KE, Song C-X, Zhang L et al. 2012. Base-resolution analysis of 5-hydroxymethylcytosine in the mammalian genome. Cell 149:1368–80
    [Google Scholar]
  156. 156.  Zahn H, Steif A, Laks E, Eirew P, VanInsberghe M et al. 2017. Scalable whole-genome single-cell library preparation without preamplification. Nat. Methods 14:167–73
    [Google Scholar]
  157. 157.  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]
  158. 158.  Zhu C, Gao Y, Guo H, Xia B, Song J et al. 2017. Single-cell 5-formylcytosine landscapes of mammalian early embryos and ESCs at single-base resolution. Cell Stem Cell 20:720–31
    [Google Scholar]
  159. 159.  Zhu YY, Machleder EM, Chenchik A, Li R, Siebert PD 2001. Reverse transcriptase template switching: a SMART approach for full-length cDNA library construction. Biotechniques 30:892–97
    [Google Scholar]
  160. 160.  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
    [Google Scholar]
  161. 161.  Zilionis R, Nainys J, Veres A, Savova V, Zemmour D et al. 2017. Single-cell barcoding and sequencing using droplet microfluidics. Nat. Protoc. 12:44–73
    [Google Scholar]
  162. 162.  Zong C, Lu S, Chapman AR, Xie XS 2012. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338:1622–26
    [Google Scholar]
/content/journals/10.1146/annurev-genom-091416-035324
Loading
/content/journals/10.1146/annurev-genom-091416-035324
Loading

Data & Media loading...

  • Article Type: Review Article
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