Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Single-cell genome sequencing: current state of the science

Key Points

  • Single-cell genome sequencing aims to increase our understanding of complex microbial ecosystems and disease in multicellular organisms by isolating the contributions of distinct cellular populations.

  • Acquiring high-quality genotype data after starting from a single molecule of DNA from an individual cell has substantial technical challenges that are continuously being addressed.

  • The three main genome amplification methods have differences in their propensity to produce distinct types of artefacts, which should be carefully considered when designing experiments. The experimental design should also be informed by the questions of the study.

  • Single-cell microorganism sequencing has enabled genome assembly of new phyla and is beginning to provide new biological insights into microbial dark matter.

  • Genetic mosaicism is an area that is beginning to be studied at higher resolution using single-cell genome sequencing. Initial studies have begun to resolve intra-tumour heterogeneity, which have provided new biological insights into tumour formation.

  • Single-cell genome sequencing is rapidly evolving, and the use of these techniques is likely to expand as technologies improve and new discoveries are made.

Abstract

The field of single-cell genomics is advancing rapidly and is generating many new insights into complex biological systems, ranging from the diversity of microbial ecosystems to the genomics of human cancer. In this Review, we provide an overview of the current state of the field of single-cell genome sequencing. First, we focus on the technical challenges of making measurements that start from a single molecule of DNA, and then explore how some of these recent methodological advancements have enabled the discovery of unexpected new biology. Areas highlighted include the application of single-cell genomics to interrogate microbial dark matter and to evaluate the pathogenic roles of genetic mosaicism in multicellular organisms, with a focus on cancer. We then attempt to predict advances we expect to see in the next few years.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Opportunities enabled by single-cell sequencing strategies.
Figure 2: Overview of the three main whole-genome amplification methods.
Figure 3: Effects of various error types on specific single-cell sequencing applications.
Figure 4: Overcoming amplification artefacts when identifying SNVs in single-cell data.
Figure 5: Overview of methods used for determining the clonal structure of cancer samples despite missing data owing to false-negative variant detection.

References

  1. 1

    Turner, W. The cell theory, past and present. J. Anat. Physiol. 24, 253–287 (1890).

    CAS  PubMed  PubMed Central  Google Scholar 

  2. 2

    Avery, O. T., Macleod, C. M. & McCarty, M. Studies on the chemical nature of the substance inducing transformation of pneumococcal types: induction of transformation by a desoxyribonucleic acid fraction isolated from Pneumococcus type III. J. Exp. Med. 79, 137–158 (1944).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3

    Amberger, J., Bocchini, C. A., Scott, A. F. & Hamosh, A. McKusick's Online Mendelian Inheritance in Man (OMIM). Nucleic Acids Res. 37, D793–D796 (2009).

    CAS  Article  Google Scholar 

  4. 4

    Tringe, S. G. et al. Comparative metagenomics of microbial communities. Science 308, 554–557 (2005).

    CAS  Article  Google Scholar 

  5. 5

    Marcy, Y. et al. Dissecting biological “dark matter” with single-cell genetic analysis of rare and uncultivated TM7 microbes from the human mouth. Proc. Natl Acad. Sci. USA 104, 11889–11894 (2007). This study shows that we can identify uncultivated microorganisms using single-cell sequencing.

    CAS  Article  Google Scholar 

  6. 6

    McConnell, M. J. et al. Mosaic copy number variation in human neurons. Science 342, 632–637 (2013). This article provides the first evidence that mosaic CNV may be more common than previously appreciated.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7

    Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014). The study is an example of high-quality single-cell cancer sequencing data, which has enabled new insights into the pathogenesis of breast cancer.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8

    Emmert-Buck, M. R. et al. Laser capture microdissection. Science 274, 998–1001 (1996).

    CAS  Article  Google Scholar 

  9. 9

    Navin, N. E. Cancer genomics: one cell at a time. Genome Biol. 15, 452 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  10. 10

    Zhou, J., Bruns, M. A. & Tiedje, J. M. DNA recovery from soils of diverse composition. Appl. Environ. Microbiol. 62, 316–322 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Ham, R. G. Clonal growth of mammalian cells in a chemically defined, synthetic medium. Proc. Natl Acad. Sci. USA 53, 288–293 (1965).

    CAS  Article  Google Scholar 

  12. 12

    Zong, C., Lu, S., Chapman, A. R. & Xie, X. S. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338, 1622–1626 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13

    Gole, J. et al. Massively parallel polymerase cloning and genome sequencing of single cells using nanoliter microwells. Nat. Biotechnol. 31, 1126–1132 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  14. 14

    Landry, Z. C., Giovanonni, S. J., Quake, S. R. & Blainey, P. C. Optofluidic cell selection from complex microbial communities for single-genome analysis. Methods Enzymol. 531, 61–90 (2013).

    CAS  Article  Google Scholar 

  15. 15

    Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011). This study provides the first evidence that single-cell sequencing can be used to dissect intratumour heterogeneity.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16

    Leung, M. L., Wang, Y., Waters, J. & Navin, N. E. SNES: single nucleus exome sequencing. Genome Biol. 16, 55 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  17. 17

    Rinke, C. et al. Obtaining genomes from uncultivated environmental microorganisms using FACS-based single-cell genomics. Nat. Protoc. 9, 1038–1048 (2014).

    CAS  Article  Google Scholar 

  18. 18

    White, A. K. et al. High-throughput microfluidic single-cell RT-qPCR. Proc. Natl Acad. Sci. USA 108, 13999–14004 (2011).

    CAS  Article  Google Scholar 

  19. 19

    Leung, K. et al. A programmable droplet-based microfluidic device applied to multiparameter analysis of single microbes and microbial communities. Proc. Natl Acad. Sci. USA 109, 7665–7670 (2012).

    CAS  Article  Google Scholar 

  20. 20

    Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015). The study presents droplet-based microfluidics as a viable option for efficiently sequencing the transcriptomes of thousands of cells.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  21. 21

    Blainey, P. C. The future is now: single-cell genomics of bacteria and archaea. FEMS Microbiol. Rev. 37, 407–427 (2013).

    CAS  Article  Google Scholar 

  22. 22

    Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).

    CAS  Article  Google Scholar 

  23. 23

    Lichter, P., Ledbetter, S. A., Ledbetter, D. H. & Ward, D. C. Fluorescence in situ hybridization with Alu and L1 polymerase chain reaction probes for rapid characterization of human chromosomes in hybrid cell lines. Proc. Natl Acad. Sci. USA 87, 6634–6638 (1990).

    CAS  Article  Google Scholar 

  24. 24

    Troutt, A. B., McHeyzer-Williams, M. G., Pulendran, B. & Nossal, G. J. Ligation-anchored PCR: a simple amplification technique with single-sided specificity. Proc. Natl Acad. Sci. USA 89, 9823–9825 (1992).

    CAS  Article  Google Scholar 

  25. 25

    Telenius, H. et al. Degenerate oligonucleotide-primed PCR: general amplification of target DNA by a single degenerate primer. Genomics 13, 718–725 (1992).

    CAS  Article  Google Scholar 

  26. 26

    Zhang, L. et al. Whole genome amplification from a single cell: implications for genetic analysis. Proc. Natl Acad. Sci. USA 89, 5847–5851 (1992).

    CAS  Article  Google Scholar 

  27. 27

    Dean, F. B., Nelson, J. R., Giesler, T. L. & Lasken, R. S. Rapid amplification of plasmid and phage DNA using Phi29 DNA polymerase and multiply-primed rolling circle amplification. Genome Res. 11, 1095–1099 (2001). This paper provides the first evidence that isothermal amplification could be used to efficiently analyse whole genomes.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  28. 28

    Zhang, D. Y., Brandwein, M., Hsuih, T. & Li, H. B. Ramification amplification: a novel isothermal DNA amplification method. Mol. Diagn. 6, 141–150 (2001).

    CAS  Article  Google Scholar 

  29. 29

    de Bourcy, C. F. et al. A quantitative comparison of single-cell whole genome amplification methods. PLoS ONE 9, e105585 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  30. 30

    Lasken, R. S. & Stockwell, T. B. Mechanism of chimera formation during the multiple displacement amplification reaction. BMC Biotechnol. 7, 19 (2007).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  31. 31

    Marcy, Y. et al. Nanoliter reactors improve multiple displacement amplification of genomes from single cells. PLoS Genet. 3, 1702–1708 (2007).

    CAS  Article  Google Scholar 

  32. 32

    Zhang, K. et al. Sequencing genomes from single cells by polymerase cloning. Nat. Biotechnol. 24, 680–686 (2006).

    CAS  Article  Google Scholar 

  33. 33

    Langmore, J. P. Rubicon Genomics, Inc. Pharmacogenomics 3, 557–560 (2002).

    Article  Google Scholar 

  34. 34

    Hou, Y. et al. Comparison of variations detection between whole-genome amplification methods used in single-cell resequencing. Gigascience 4, 37 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. 35

    Huang, L., Ma, F., Chapman, A., Lu, S. & Xie, X. S. Single-cell whole-genome amplification and sequencing: methodology and applications. Annu. Rev. Genomics Hum. Genet. 16, 79–102 (2015).

    CAS  Article  Google Scholar 

  36. 36

    Blainey, P. C. & Quake, S. R. Digital MDA for enumeration of total nucleic acid contamination. Nucleic Acids Res. 39, e19 (2011).

    Article  CAS  Google Scholar 

  37. 37

    Yu, Z., Lu, S. & Huang, Y. A microfluidic whole genome amplification device for single cell sequencing. Anal. Chem. 86, 9386–9390 (2014).

    CAS  Article  Google Scholar 

  38. 38

    Nishikawa, Y. et al. Monodisperse picoliter droplets for low-bias and contamination-free reactions in single-cell whole genome amplification. PLoS ONE 10, e0138733 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  39. 39

    Fu, Y. et al. Uniform and accurate single-cell sequencing based on emulsion whole-genome amplification. Proc. Natl Acad. Sci. USA 112, 11923–11928 (2015).

    CAS  Article  Google Scholar 

  40. 40

    Gawad, C., Koh, W. & Quake, S. R. Dissecting the clonal origins of childhood acute lymphoblastic leukemia by single-cell genomics. Proc. Natl Acad. Sci. USA 111, 17947–17952 (2014). This paper uses microfluidics to efficiently resequence the genomes of almost 1,500 cells, allowing new insights into the development of leukaemia.

    CAS  Article  Google Scholar 

  41. 41

    Hughes, A. E. et al. Clonal architecture of secondary acute myeloid leukemia defined by single-cell sequencing. PLoS Genet. 10, e1004462 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  42. 42

    Zhang, C. Z. et al. Calibrating genomic and allelic coverage bias in single-cell sequencing. Nat. Commun. 6, 6822 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43

    Daley, T. & Smith, A. D. Modeling genome coverage in single-cell sequencing. Bioinformatics 30, 3159–3165 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44

    Clingenpeel, S., Clum, A., Schwientek, P., Rinke, C. & Woyke, T. Reconstructing each cell's genome within complex microbial communities-dream or reality? Front. Microbiol. 5, 771 (2014).

    Google Scholar 

  45. 45

    Nikolenko, S. I., Korobeynikov, A. I. & Alekseyev, M. A. BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics 14, S7 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  46. 46

    Baslan, T. et al. Genome-wide copy number analysis of single cells. Nat. Protoc. 7, 1024–1041 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47

    Zhang, C. et al. A single cell level based method for copy number variation analysis by low coverage massively parallel sequencing. PLoS ONE 8, e54236 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48

    Cheng, J. et al. Single-cell copy number variation detection. Genome Biol. 12, R80 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49

    Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012). This method overcomes some whole-genome amplification artefacts, resulting in more accurate single-cell genome assemblies.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50

    Peng, Y., Leung, H. C., Yiu, S. M. & Chin, F. Y. IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth. Bioinformatics 28, 1420–1428 (2012).

    CAS  Article  Google Scholar 

  51. 51

    Eisen, M. B., Spellman, P. T., Brown, P. O. & Botstein, D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 95, 14863–14868 (1998).

    CAS  Article  Google Scholar 

  52. 52

    Jaccard, P. Étude comparative de la distribution florale dans une portion des Alpes et des Jura. Bull. Société Vaudoise Sci. Naturelles37, 547–579 (in French) (1901).

  53. 53

    Fraley, C. & Raftery, A. E. Model-based clustering, discriminant analysis, and density estimation. J. Am. Statist. Associ. 97, 611–631 (2002).

    Article  Google Scholar 

  54. 54

    Dempster, A. P., Laird, N. M. & Rubin, D. B. Maximum likelihood from incomplete data via the EM algorithm. J. R. Statiscal Soc. 39, 1–38 (1977).

    Google Scholar 

  55. 55

    Fraley, C. & Raftery, A. E. How many clusters? Which clustering method? Answers via model-based cluster analysis. Computer J. 41, 578–588 (1998).

    Article  Google Scholar 

  56. 56

    Fraley, C. & Raftery, A. E. MCLUST: software for model-based cluster analysis. J. Classif. 16, 297–306 (2014).

    Article  Google Scholar 

  57. 57

    Bentley, D. R. et al. Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456, 53–59 (2008).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  58. 58

    Kim, K. I. & Simon, R. Using single cell sequencing data to model the evolutionary history of a tumor. BMC Bioinformatics 15, 27 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  59. 59

    Yang, Z. & Rannala, B. Molecular phylogenetics: principles and practice. Nat. Rev. Genet. 13, 303–314 (2012).

    CAS  Article  Google Scholar 

  60. 60

    Podar, M. et al. Targeted access to the genomes of low-abundance organisms in complex microbial communities. Appl. Environ. Microbiol. 73, 3205–3214 (2007).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  61. 61

    Youssef, N. H., Blainey, P. C., Quake, S. R. & Elshahed, M. S. Partial genome assembly for a candidate division OP11 single cell from an anoxic spring (Zodletone Spring, Oklahoma). Appl. Environ. Microbiol. 77, 7804–7814 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  62. 62

    Campbell, J. H. et al. UGA is an additional glycine codon in uncultured SR1 bacteria from the human microbiota. Proc. Natl Acad. Sci. USA 110, 5540–5545 (2013).

    CAS  Article  Google Scholar 

  63. 63

    McLean, J. S. et al. Candidate phylum TM6 genome recovered from a hospital sink biofilm provides genomic insights into this uncultivated phylum. Proc. Natl Acad. Sci. USA 110, E2390–E2399 (2013).

    CAS  Article  Google Scholar 

  64. 64

    Dodsworth, J. A. et al. Single-cell and metagenomic analyses indicate a fermentative and saccharolytic lifestyle for members of the OP9 lineage. Nat. Commun. 4, 1854 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  65. 65

    Rinke, C. et al. Insights into the phylogeny and coding potential of microbial dark matter. Nature 499, 431–437 (2013). This study identifies new phyla of microorganisms from diverse environments, enabling new insights into the biology of those ecosystems.

    CAS  Article  Google Scholar 

  66. 66

    Parks, D. H., Imelfort, M., Skennerton, C. T., Hugenholtz, P. & Tyson, G. W. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 25, 1043–1055 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  67. 67

    Tennessen, K. et al. ProDeGe: a computational protocol for fully automated decontamination of genomes. ISME J. 10, 269–272 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  68. 68

    Fitzsimons, M. S. et al. Nearly finished genomes produced using gel microdroplet culturing reveal substantial intraspecies genomic diversity within the human microbiome. Genome Res. 23, 878–888 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  69. 69

    Woyke, T. et al. One bacterial cell, one complete genome. PLoS ONE 5, e10314 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  70. 70

    Chitsaz, H. et al. Efficient de novo assembly of single-cell bacterial genomes from short-read data sets. Nat. Biotechnol. 29, 915–921 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  71. 71

    Mason, O. U. et al. Metagenome, metatranscriptome and single-cell sequencing reveal microbial response to Deepwater Horizon oil spill. ISME J. 6, 1715–1727 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  72. 72

    Lasken, R. S. & McLean, J. S. Recent advances in genomic DNA sequencing of microbial species from single cells. Nat. Rev. Genet. 15, 577–584 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  73. 73

    Tadmor, A. D., Ottesen, E. A., Leadbetter, J. R. & Phillips, R. Probing individual environmental bacteria for viruses by using microfluidic digital PCR. Science 333, 58–62 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  74. 74

    Roux, S. et al. Ecology and evolution of viruses infecting uncultivated SUP05 bacteria as revealed by single-cell- and meta-genomics. eLife 3, e03125 (2014).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  75. 75

    Roux, S., Hallam, S. J., Woyke, T. & Sullivan, M. B. Viral dark matter and virus–host interactions resolved from publicly available microbial genomes. eLife 4, e08490 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  76. 76

    Roux, S., Enault, F., Hurwitz, B. L. & Sullivan, M. B. VirSorter: mining viral signal from microbial genomic data. PeerJ 3, e985 (2015).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  77. 77

    Yoon, H. S. et al. Single-cell genomics reveals organismal interactions in uncultivated marine protists. Science 332, 714–717 (2011). This paper shows that single-cell sequencing can be used to study interactions of bacteria, protists and viruses at single-cell resolution.

    CAS  Article  Google Scholar 

  78. 78

    Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012).

  79. 79

    Martinez-Garcia, M. et al. Unveiling in situ interactions between marine protists and bacteria through single cell sequencing. ISME J. 6, 703–707 (2012).

    CAS  Article  Google Scholar 

  80. 80

    Hirschhorn, K., Decker, W. H. & Cooper, H. L. Human intersex with chromosome mosaicism of type XY/XO. Report of a case. N. Engl. J. Med. 263, 1044–1048 (1960).

    CAS  Article  Google Scholar 

  81. 81

    Happle, R. Mosaicism in human skin. Understanding the patterns and mechanisms. Arch. Dermatol. 129, 1460–1470 (1993).

    CAS  Article  Google Scholar 

  82. 82

    Weinstein, L. S. et al. Activating mutations of the stimulatory G protein in the McCune–Albright syndrome. N. Engl. J. Med. 325, 1688–1695 (1991).

    CAS  Article  Google Scholar 

  83. 83

    Groesser, L. et al. Postzygotic HRAS and KRAS mutations cause nevus sebaceous and Schimmelpenning syndrome. Nat. Genet. 44, 783–787 (2012).

    CAS  Article  Google Scholar 

  84. 84

    Lindhurst, M. J. et al. A mosaic activating mutation in AKT1 associated with the Proteus syndrome. N. Engl. J. Med. 365, 611–619 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  85. 85

    Lindhurst, M. J. et al. Mosaic overgrowth with fibroadipose hyperplasia is caused by somatic activating mutations in PIK3CA. Nat. Genet. 44, 928–933 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  86. 86

    Conlin, L. K. et al. Mechanisms of mosaicism, chimerism and uniparental disomy identified by single nucleotide polymorphism array analysis. Hum. Mol. Genet. 19, 1263–1275 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  87. 87

    Drake, J. W., Charlesworth, B., Charlesworth, D. & Crow, J. F. Rates of spontaneous mutation. Genetics 148, 1667–1686 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  88. 88

    Bianconi, E. et al. An estimation of the number of cells in the human body. Ann. Hum. Biol. 40, 463–471 (2013).

    Article  Google Scholar 

  89. 89

    Behjati, S. et al. Genome sequencing of normal cells reveals developmental lineages and mutational processes. Nature 513, 422–425 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  90. 90

    Piotrowski, A. et al. Somatic mosaicism for copy number variation in differentiated human tissues. Hum. Mutat. 29, 1118–1124 (2008).

    Article  Google Scholar 

  91. 91

    Wang, J., Fan, H. C., Behr, B. & Quake, S. R. Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm. Cell 150, 402–412 (2012). This study establishes the feasibility of using single-cell sequencing to identify genomic structural variants and SNVs genome-wide.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  92. 92

    Lu, S. et al. Probing meiotic recombination and aneuploidy of single sperm cells by whole-genome sequencing. Science 338, 1627–1630 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  93. 93

    Hou, Y. et al. Genome analyses of single human oocytes. Cell 155, 1492–1506 (2013).

    CAS  Article  Google Scholar 

  94. 94

    Cai, X. et al. Single-cell, genome-wide sequencing identifies clonal somatic copy-number variation in the human brain. Cell Rep. 8, 1280–1289 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  95. 95

    Knouse, K. A., Wu, J., Whittaker, C. A. & Amon, A. Single cell sequencing reveals low levels of aneuploidy across mammalian tissues. Proc. Natl Acad. Sci. USA 111, 13409–13414 (2014).

    CAS  Article  Google Scholar 

  96. 96

    Lodato, M. A. et al. Somatic mutation in single human neurons tracks developmental and transcriptional history. Science 350, 94–98 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  97. 97

    Handyside, A. H., Kontogianni, E. H., Hardy, K. & Winston, R. M. Pregnancies from biopsied human preimplantation embryos sexed by Y-specific DNA amplification. Nature 344, 768–770 (1990).

    CAS  Article  Google Scholar 

  98. 98

    Geraedts, J. et al. Polar body array CGH for prediction of the status of the corresponding oocyte. Part I: clinical results. Hum. Reprod. 26, 3173–3180 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  99. 99

    Alfarawati, S., Fragouli, E., Colls, P. & Wells, D. First births after preimplantation genetic diagnosis of structural chromosome abnormalities using comparative genomic hybridization and microarray analysis. Hum. Reprod. 26, 1560–1574 (2011).

    CAS  Article  Google Scholar 

  100. 100

    Cancer Genome Atlas Research Network et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 45, 1113–1120 (2013).

  101. 101

    Ding, L. et al. Clonal evolution in relapsed acute myeloid leukaemia revealed by whole-genome sequencing. Nature 481, 506–510 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  102. 102

    Gerlinger, M. et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N. Engl. J. Med. 366, 883–892 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  103. 103

    Hou, Y. et al. Single-cell exome sequencing and monoclonal evolution of a JAK2-negative myeloproliferative neoplasm. Cell 148, 873–885 (2012).

    CAS  Article  Google Scholar 

  104. 104

    Xu, X. et al. Single-cell exome sequencing reveals single-nucleotide mutation characteristics of a kidney tumor. Cell 148, 886–895 (2012).

    CAS  Article  Google Scholar 

  105. 105

    Li, Y. et al. Single-cell sequencing analysis characterizes common and cell-lineage-specific mutations in a muscle-invasive bladder cancer. Gigascience 1, 12 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  106. 106

    Yu, C. et al. Discovery of biclonal origin and a novel oncogene SLC12A5 in colon cancer by single-cell sequencing. Cell Res. 24, 701–712 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  107. 107

    Ni, X. et al. Reproducible copy number variation patterns among single circulating tumor cells of lung cancer patients. Proc. Natl Acad. Sci. USA 110, 21083–21088 (2013).

    CAS  Article  Google Scholar 

  108. 108

    Lohr, J. G. et al. Whole-exome sequencing of circulating tumor cells provides a window into metastatic prostate cancer. Nat. Biotechnol. 32, 479–484 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  109. 109

    Potter, N. E. et al. Single-cell mutational profiling and clonal phylogeny in cancer. Genome Res. 23, 2115–2125 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  110. 110

    Papaemmanuil, E. et al. RAG-mediated recombination is the predominant driver of oncogenic rearrangement in ETV6RUNX1 acute lymphoblastic leukemia. Nat. Genet. 46, 116–125 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  111. 111

    Jan, M. et al. Clonal evolution of preleukemic hematopoietic stem cells precedes human acute myeloid leukemia. Sci. Transl Med. 4, 149ra118 (2012).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  112. 112

    Shintaku, H., Nishikii, H., Marshall, L. A., Kotera, H. & Santiago, J. G. On-chip separation and analysis of RNA and DNA from single cells. Anal. Chem. 86, 1953–1957 (2014).

    CAS  Article  Google Scholar 

  113. 113

    Macaulay, I. C. et al. G&T-seq: parallel sequencing of single-cell genomes and transcriptomes. Nat. Methods 12, 519–522 (2015).

    CAS  Article  Google Scholar 

  114. 114

    Dey, S. S., Kester, L., Spanjaard, B., Bienko, M. & van Oudenaarden, A. Integrated genome and transcriptome sequencing of the same cell. Nat. Biotechnol. 33, 285–289 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  115. 115

    Stahlberg, A., Thomsen, C., Ruff, D. & Aman, P. Quantitative PCR analysis of DNA, RNAs, and proteins in the same single cell. Clin. Chem. 58, 1682–1691 (2012).

    Article  CAS  Google Scholar 

  116. 116

    Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014). This study presents a method for acquiring single-cell transcriptomic data while retaining intercellular and intracellular spatial information.

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  117. 117

    Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  118. 118

    Achim, K. et al. High-throughput spatial mapping of single-cell RNA-seq data to tissue of origin. Nat. Biotechnol. 33, 503–509 (2015).

    CAS  Article  Google Scholar 

  119. 119

    Yachida, S. & Iacobuzio-Donahue, C. A. Evolution and dynamics of pancreatic cancer progression. Oncogene 32, 5253–5260 (2013).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

C.G. is supported by funding from the Burroughs Wellcome Fund, American Lebanese Syrian Associated Charities, Hyundai Foundation for Pediatric Research, American Society of Haematology, and Leukaemia and Lymphoma Society. W.K. is supported by A*STAR (Agency of Science, Technology and Research; Singapore).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Stephen R. Quake.

Ethics declarations

Competing interests

S.R.Q. is a founder, consultant and equity holder of Fluidigm. W.K. and C.G. declare no competing interests.

PowerPoint slides

Glossary

Genetic mosaicism

Occurs when there are at least two genotypes in different cells of the same organism.

Whole-genome amplification

The use of biochemical methods to produce multiple copies of the entire genome.

Optical tweezers

Devices that use a laser to manipulate submicron particles, such as bacterial cells or cellular macromolecules.

Chimaeras

Amplification artefacts formed when two previously disconnected genome regions are combined on the same DNA molecule.

Gain

The extent to which a genome undergoes amplification.

Allelic dropout

Loss of one allele of a locus that can occur during whole genome amplification.

Structural variants

Variation in the genome that occurs as a result of the joining of two previously disconnected genomic locations. A subset of structural variation is copy number variation, which occurs when portions of the genome are amplified or deleted.

Somatic variants

Changes in the genome of an organism that are not present in germ cells and can thus not be passed on to offspring.

Molecular barcoding

Attaching a unique sequence to each molecule as a strategy to more accurately count nucleic acids by correcting for experimental artefacts. This approach is also used to decrease false-positive mutation call rates due to sequencing errors by creating a consensus genotype for each molecule.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gawad, C., Koh, W. & Quake, S. Single-cell genome sequencing: current state of the science. Nat Rev Genet 17, 175–188 (2016). https://doi.org/10.1038/nrg.2015.16

Download citation

Further reading

Search

Quick links