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.

  • Opinion
  • Published:

Unravelling biology and shifting paradigms in cancer with single-cell sequencing

Abstract

The fundamental operative unit of a cancer is the genetically and epigenetically innovative single cell. Whether proliferating or quiescent, in the primary tumour mass or disseminated elsewhere, single cells govern the parameters that dictate all facets of the biology of cancer. Thus, single-cell analyses provide the ultimate level of resolution in our quest for a fundamental understanding of this disease. Historically, this quest has been hampered by technological shortcomings. In this Opinion article, we argue that the rapidly evolving field of single-cell sequencing has unshackled the cancer research community of these shortcomings. From furthering an elemental understanding of intra-tumoural genetic heterogeneity and cancer genome evolution to illuminating the governing principles of disease relapse and metastasis, we posit that single-cell sequencing promises to unravel the biology of all facets of this disease.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Single-cell sequencing for the decomposition of heterogeneous cellular populations and the analysis of rare cells in lung cancer.
Figure 2: Single-cell sequencing can be leveraged to study cancer genetics and biology at all stages of disease development.

Similar content being viewed by others

References

  1. Stratton, M. R., Campbell, P. J. & Futreal, P. A. The cancer genome. Nature 458, 719–724 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Vogelstein, B. et al. Cancer genome landscapes. Science 339, 1546–1558 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Heyer, J., Kwong, L. N., Lowe, S. W. & Chin, L. Non-germline genetically engineered mouse models for translational cancer research. Nat. Rev. Cancer 10, 470–480 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Sanchez-Rivera, F. J. & Jacks, T. Applications of the CRISPR-Cas9 system in cancer biology. Nat. Rev. Cancer 15, 387–395 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Shortt, J., Ott, C. J., Johnstone, R. W. & Bradner, J. E. A chemical probe toolbox for dissecting the cancer epigenome. Nat. Rev. Cancer 17, 268 (2017).

    Article  CAS  PubMed  Google Scholar 

  6. Schreiber, S. L. et al. Advancing biological understanding and therapeutics discovery with small-molecule probes. Cell 161, 1252–1265 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Mardis, E. R. & Wilson, R. K. Cancer genome sequencing: a review. Hum. Mol. Genet. 18, R163–R168 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Ostrem, J. M. & Shokat, K. M. Direct small-molecule inhibitors of KRAS: from structural insights to mechanism-based design. Nat. Rev. Drug Discov. 15, 771–785 (2016).

    Article  CAS  PubMed  Google Scholar 

  9. Ma, C. X., Reinert, T., Chmielewska, I. & Ellis, M. J. Mechanisms of aromatase inhibitor resistance. Nat. Rev. Cancer 15, 261–275 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Lito, P., Rosen, N. & Solit, D. B. Tumor adaptation and resistance to RAF inhibitors. Nat. Med. 19, 1401–1409 (2013).

    Article  CAS  PubMed  Google Scholar 

  11. Yates, L. R. & Campbell, P. J. Evolution of the cancer genome. Nat. Rev. Genet. 13, 795–806 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Nowell, P. C. The clonal evolution of tumor cell populations. Science 194, 23–28 (1976).

    Article  CAS  PubMed  Google Scholar 

  13. Tabassum, D. P. & Polyak, K. Tumorigenesis: it takes a village. Nat. Rev. Cancer 15, 473–483 (2015).

    Article  CAS  PubMed  Google Scholar 

  14. Gillies, R. J., Verduzco, D. & Gatenby, R. A. Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nat. Rev. Cancer 12, 487–493 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Korolev, K. S., Xavier, J. B. & Gore, J. Turning ecology and evolution against cancer. Nat. Rev. Cancer 14, 371–380 (2014).

    Article  CAS  PubMed  Google Scholar 

  16. Glickman, M. S. & Sawyers, C. L. Converting cancer therapies into cures: lessons from infectious diseases. Cell 148, 1089–1098 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Landau, D. A. et al. Mutations driving CLL and their evolution in progression and relapse. Nature 526, 525–530 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Nik-Zainal, S. et al. The life history of 21 breast cancers. Cell 149, 994–1007 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Shah, S. P. et al. The clonal and mutational evolution spectrum of primary triple-negative breast cancers. Nature 486, 395–399 (2012).

    Article  CAS  PubMed  Google Scholar 

  20. Helleday, T., Eshtad, S. & Nik-Zainal, S. Mechanisms underlying mutational signatures in human cancers. Nat. Rev. Genet. 15, 585–598 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Alexandrov, L. B. et al. Signatures of mutational processes in human cancer. Nature 500, 415–421 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fialkow, P. J. Single or multiple cell origin for tumors? N. Engl. J. Med. 285, 1198–1199 (1971).

    Article  CAS  PubMed  Google Scholar 

  23. Tang, F. et al. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6, 377–382 (2009).

    Article  CAS  PubMed  Google Scholar 

  24. Navin, N. et al. Tumour evolution inferred by single-cell sequencing. Nature 472, 90–94 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C. & Teichmann, S. A. The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610–620 (2015).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  27. Ramskold, D. et al. Full-length mRNA-seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Hashimshony, T., Wagner, F., Sher, N. & Yanai, I. CEL-Seq: single-cell RNA-seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  CAS  PubMed  Google Scholar 

  29. Islam, S. et al. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res. 21, 1160–1167 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Dean, F. B. et al. Comprehensive human genome amplification using multiple displacement amplification. Proc. Natl Acad. Sci. USA 99, 5261–5266 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mann, K. M. et al. Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq. Nat. Biotechnol. 34, 962–972 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Faridani, O. R. et al. Single-cell sequencing of the small-RNA transcriptome. Nat. Biotechnol. 34, 1264–1266 (2016).

    Article  CAS  PubMed  Google Scholar 

  35. Lovatt, D. et al. Transcriptome in vivo analysis (TIVA) of spatially defined single cells in live tissue. Nat. Methods 11, 190–196 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Eirew, P. et al. Dynamics of genomic clones in breast cancer patient xenografts at single-cell resolution. Nature 518, 422–426 (2015).

    Article  CAS  PubMed  Google Scholar 

  37. Porubsky, D. et al. Direct chromosome-length haplotyping by single-cell sequencing. Genome Res. 26, 1565–1574 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  40. Angermueller, C. et al. Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity. Nat. Methods 13, 229–232 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Adamson, B. et al. A multiplexed single-cell CRISPR screening platform enables systematic dissection of the unfolded protein response. Cell 167, 1867–1882.e21 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Jaitin, D. A. et al. Dissecting immune circuits by linking CRISPR-pooled screens with single-cell RNA-seq. Cell 167, 1883–1896 (2016).

    Article  CAS  PubMed  Google Scholar 

  44. Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Smallwood, S. A. et al. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods 11, 817–820 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Guo, H. et al. Single-cell methylome landscapes of mouse embryonic stem cells and early embryos analyzed using reduced representation bisulfite sequencing. Genome Res. 23, 2126–2135 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Rotem, A. et al. Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state. Nat. Biotechnol. 33, 1165–1172 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Cusanovich, D. A. et al. Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing. Science 348, 910–914 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Jin, W. et al. Genome-wide detection of DNase I hypersensitive sites in single cells and FFPE tissue samples. Nature 528, 142–146 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Corces, M. R. et al. Lineage-specific and single-cell chromatin accessibility charts human hematopoiesis and leukemia evolution. Nat. Genet. 48, 1193–1203 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Buenrostro, J. D. et al. Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Stevens, T. J. et al. 3D structures of individual mammalian genomes studied by single-cell Hi-C. Nature 544, 59–64 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Ramani, V. et al. Massively multiplex single-cell Hi-C. Nat. Methods 14, 263–266 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Nagano, T. et al. Single-cell Hi-C reveals cell-to-cell variability in chromosome structure. Nature 502, 59–64 (2013).

    Article  CAS  PubMed  Google Scholar 

  55. Flyamer, I. M. et al. Single-nucleus Hi-C reveals unique chromatin reorganization at oocyte-to-zygote transition. Nature 544, 110–114 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Schwartzman, O. & Tanay, A. Single-cell epigenomics: techniques and emerging applications. Nat. Rev. Genet. 16, 716–726 (2015).

    Article  CAS  PubMed  Google Scholar 

  57. Clark, S. J., Lee, H. J., Smallwood, S. A., Kelsey, G. & Reik, W. Single-cell epigenomics: powerful new methods for understanding gene regulation and cell identity. Genome Biol. 17, 72 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Marinov, G. K. et al. From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing. Genome Res. 24, 496–510 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    Article  CAS  PubMed  Google Scholar 

  60. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kharchenko, P. V., Silberstein, L. & Scadden, D. T. Bayesian approach to single-cell differential expression analysis. Nat. Methods 11, 740–742 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. Brennecke, P. et al. Accounting for technical noise in single-cell RNA-seq experiments. Nat. Methods 10, 1093–1095 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  65. Amir el, A. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–552 (2013).

    Article  CAS  Google Scholar 

  66. Roth, A. et al. Clonal genotype and population structure inference from single-cell tumor sequencing. Nat. Methods 13, 573–576 (2016).

    Article  CAS  PubMed  Google Scholar 

  67. Zafar, H., Wang, Y., Nakhleh, L., Navin, N. & Chen, K. Monovar: single-nucleotide variant detection in single cells. Nat. Methods 13, 505–507 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Muller, S. et al. Single-cell sequencing maps gene expression to mutational phylogenies in PDGF- and EGF-driven gliomas. Mol. Syst. Biol. 12, 889 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  69. Dueck, H. R. et al. Assessing characteristics of RNA amplification methods for single cell RNA sequencing. BMC Genomics 17, 966 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  70. Stegle, O., Teichmann, S. A. & Marioni, J. C. Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet. 16, 133–145 (2015).

    Article  CAS  PubMed  Google Scholar 

  71. Wang, Y. & Navin, N. E. Advances and applications of single-cell sequencing technologies. Mol. Cell 58, 598–609 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Gawad, C., Koh, W. & Quake, S. R. Single-cell genome sequencing: current state of the science. Nat. Rev. Genet. 17, 175–188 (2016).

    Article  CAS  PubMed  Google Scholar 

  73. Bacher, R. & Kendziorski, C. Design and computational analysis of single-cell RNA-sequencing experiments. Genome Biol. 17, 63 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  74. Wang, Y. et al. Clonal evolution in breast cancer revealed by single nucleus genome sequencing. Nature 512, 155–160 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Dong, X. et al. Accurate identification of single-nucleotide variants in whole-genome-amplified single cells. Nat. Methods 14, 491–493 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Chen, C. et al. Single-cell whole-genome analyses by Linear Amplification via Transposon Insertion (LIANTI). Science 356, 189–194 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Zahn, H. et al. Scalable whole-genome single-cell library preparation without preamplification. Nat. Methods 14, 167–173 (2017).

    Article  CAS  PubMed  Google Scholar 

  78. Vitak, S. A. et al. Sequencing thousands of single-cell genomes with combinatorial indexing. Nat. Methods 14, 302–308 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Prabhakaran, S. Azizi, E., Carr, A. & Pe'er, D. Dirichlet process mixture model for correcting technical variation in single-cell gene expression data. J. Machine Learn. Res. W&CP (ICML) 48, 1070–1079 (2016).

    Google Scholar 

  80. Vallejos, C. A., Richardson, S. & Marioni, J. C. Beyond comparisons of means: understanding changes in gene expression at the single-cell level. Genome Biol. 17, 70 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Hrvatin, S., Deng, F., O'Donnell, C. W., Gifford, D. K. & Melton, D. A. MARIS: method for analyzing RNA following intracellular sorting. PLoS ONE 9, e89459 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  82. Thomsen, E. R. et al. Fixed single-cell transcriptomic characterization of human radial glial diversity. Nat. Methods 13, 87–93 (2016).

    Article  CAS  PubMed  Google Scholar 

  83. Alles, J. et al. Cell fixation and preservation for droplet-based single-cell transcriptomics. BMC Biol. 15, 44 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Pollen, A. A. et al. Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol. 32, 1053–1058 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Baslan, T. et al. Optimizing sparse sequencing of single cells for highly multiplex copy number profiling. Genome Res. 25, 714–724 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Visvader, J. E. Cells of origin in cancer. Nature 469, 314–322 (2011).

    Article  CAS  PubMed  Google Scholar 

  88. Blanpain, C. Tracing the cellular origin of cancer. Nat. Cell Biol. 15, 126–134 (2013).

    Article  CAS  PubMed  Google Scholar 

  89. Krivtsov, A. V. et al. Cell of origin determines clinically relevant subtypes of MLL-rearranged AML. Leukemia 27, 852–860 (2013).

    Article  CAS  PubMed  Google Scholar 

  90. Barker, N. et al. Crypt stem cells as the cells-of-origin of intestinal cancer. Nature 457, 608–611 (2009).

    Article  CAS  PubMed  Google Scholar 

  91. Swerdlow, S. H. et al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood 127, 2375–2390 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Arber, D. A. et al. The 2016 revision to the World Health Organization classification of myeloid neoplasms and acute leukemia. Blood 127, 2391–2405 (2016).

    Article  CAS  PubMed  Google Scholar 

  93. Vermeulen, L. & Snippert, H. J. Stem cell dynamics in homeostasis and cancer of the intestine. Nat. Rev. Cancer 14, 468–480 (2014).

    Article  CAS  PubMed  Google Scholar 

  94. Bose, S. et al. Scalable microfluidics for single-cell RNA printing and sequencing. Genome Biol. 16, 120 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  95. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Rotem, A. et al. High-throughput single-cell labeling (Hi-SCL) for RNA-seq using drop-based microfluidics. PLoS ONE 10, e0116328 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  98. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  99. Bahar Halpern, K. et al. Single-cell spatial reconstruction reveals global division of labour in the mammalian liver. Nature 542, 352–356 (2017).

    Article  CAS  Google Scholar 

  100. Wang, Y. J. et al. Single-cell transcriptomics of the human endocrine pancreas. Diabetes 65, 3028–3038 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  101. Muraro, M. J. et al. A single-cell transcriptome atlas of the human pancreas. Cell Syst. 3, 385–394 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  102. Barriga, F. M. et al. Mex3a marks a slowly dividing subpopulation of Lgr5+ intestinal stem cells. Cell Stem Cell 20, 801–816.e7 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Villani, A. C. et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356, eaah4573 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  104. van de Wetering, M. et al. Prospective derivation of a living organoid biobank of colorectal cancer patients. Cell 161, 933–945 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Boj, S. F. et al. Organoid models of human and mouse ductal pancreatic cancer. Cell 160, 324–338 (2015).

    Article  CAS  PubMed  Google Scholar 

  106. Gao, D. et al. Organoid cultures derived from patients with advanced prostate cancer. Cell 159, 176–187 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Grun, D. et al. Single-cell messenger RNA sequencing reveals rare intestinal cell types. Nature 525, 251–255 (2015).

    Article  CAS  PubMed  Google Scholar 

  108. Hsu, P. D., Lander, E. S. & Zhang, F. Development and applications of CRISPR-Cas9 for genome engineering. Cell 157, 1262–1278 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Komor, A. C., Badran, A. H. & Liu, D. R. CRISPR-based technologies for the manipulation of eukaryotic genomes. Cell 168, 20–36 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  110. Mali, P., Esvelt, K. M. & Church, G. M. Cas9 as a versatile tool for engineering biology. Nat. Methods 10, 957–963 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Sadanandam, A. et al. A colorectal cancer classification system that associates cellular phenotype and responses to therapy. Nat. Med. 19, 619–625 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Johnson, K. J. & Gehlert, S. Return of results from genomic sequencing: a policy discussion of secondary findings for cancer predisposition. J. Cancer Policy 2, 75–80 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  113. Nishida, N. et al. Aberrant methylation of multiple tumor suppressor genes in aging liver, chronic hepatitis, and hepatocellular carcinoma. Hepatology 47, 908–918 (2008).

    Article  CAS  PubMed  Google Scholar 

  114. Ahnen, D. J., Bresalier, R. S., Levin, B. & Kaunitz, J. D. CRC screening, past, present, and future: a tribute to Emmet Keeffe. Dig. Dis. Sci. 60, 589–591 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  115. Reid, B. J., Li, X., Galipeau, P. C. & Vaughan, T. L. Barrett's oesophagus and oesophageal adenocarcinoma: time for a new synthesis. Nat. Rev. Cancer 10, 87–101 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  116. Stachler, M. D. et al. Paired exome analysis of Barrett's esophagus and adenocarcinoma. Nat. Genet. 47, 1047–1055 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  117. Ross-Innes, C. S. et al. Whole-genome sequencing provides new insights into the clonal architecture of Barrett's esophagus and esophageal adenocarcinoma. Nat. Genet. 47, 1038–1046 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Maley, C. C. et al. Genetic clonal diversity predicts progression to esophageal adenocarcinoma. Nat. Genet. 38, 468–473 (2006).

    Article  CAS  PubMed  Google Scholar 

  119. Kadri, S., Lao-Sirieix, P. & Fitzgerald, R. C. Developing a nonendoscopic screening test for Barrett's esophagus. Biomark Med. 5, 397–404 (2011).

    Article  PubMed  Google Scholar 

  120. Baselga, J. Bringing precision medicine to the clinic: from genomic profiling to the power of clinical observation. Ann. Oncol. 24, 1956–1957 (2013).

    Article  PubMed  Google Scholar 

  121. Chan, T. A., Wolchok, J. D. & Snyder, A. Genetic basis for clinical response to CTLA-4 blockade in melanoma. N. Engl. J. Med. 373, 1984 (2015).

    Article  CAS  PubMed  Google Scholar 

  122. Jain, R. K. Normalizing tumor microenvironment to treat cancer: bench to bedside to biomarkers. J. Clin. Oncol. 31, 2205–2218 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Collisson, E. A. et al. Subtypes of pancreatic ductal adenocarcinoma and their differing responses to therapy. Nat. Med. 17, 500–503 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Moffitt, R. A. et al. Virtual microdissection identifies distinct tumor- and stroma-specific subtypes of pancreatic ductal adenocarcinoma. Nat. Genet. 47, 1168–1178 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  125. Bailey, P. et al. Genomic analyses identify molecular subtypes of pancreatic cancer. Nature 531, 47–52 (2016).

    Article  CAS  PubMed  Google Scholar 

  126. Neesse, A., Algul, H., Tuveson, D. A. & Gress, T. M. Stromal biology and therapy in pancreatic cancer: a changing paradigm. Gut 64, 1476–1484 (2015).

    Article  CAS  PubMed  Google Scholar 

  127. Ozdemir, B. C. et al. Depletion of carcinoma-associated fibroblasts and fibrosis induces immunosuppression and accelerates pancreas cancer with reduced survival. Cancer Cell 25, 719–734 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Rhim, A. D. et al. Stromal elements act to restrain, rather than support, pancreatic ductal adenocarcinoma. Cancer Cell 25, 735–747 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Waddell, N. et al. Whole genomes redefine the mutational landscape of pancreatic cancer. Nature 518, 495–501 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  130. Turner, K. M. et al. Extrachromosomal oncogene amplification drives tumour evolution and genetic heterogeneity. Nature 543, 122–125 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  131. Snuderl, M. et al. Mosaic amplification of multiple receptor tyrosine kinase genes in glioblastoma. Cancer Cell 20, 810–817 (2011).

    Article  CAS  PubMed  Google Scholar 

  132. Mehra, R. et al. Characterization of bone metastases from rapid autopsies of prostate cancer patients. Clin. Cancer Res. 17, 3924–3932 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Naxerova, K. & Jain, R. K. Using tumour phylogenetics to identify the roots of metastasis in humans. Nat. Rev. Clin. Oncol. 12, 258–272 (2015).

    Article  CAS  PubMed  Google Scholar 

  134. Kim, M. Y. et al. Tumor self-seeding by circulating cancer cells. Cell 139, 1315–1326 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  135. Gundem, G. et al. The evolutionary history of lethal metastatic prostate cancer. Nature 520, 353–357 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Miething, C. et al. PTEN action in leukaemia dictated by the tissue microenvironment. Nature 510, 402–406 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  137. Logothetis, C. J. & Lin, S. H. Osteoblasts in prostate cancer metastasis to bone. Nat. Rev. Cancer 5, 21–28 (2005).

    Article  CAS  PubMed  Google Scholar 

  138. Wan, X. et al. Prostate cancer cell-stromal cell crosstalk via FGFR1 mediates antitumor activity of dovitinib in bone metastases. Sci. Transl. Med. 6, 252ra122 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  139. Alix-Panabieres, C. & Pantel, K. Challenges in circulating tumour cell research. Nat. Rev. Cancer 14, 623–631 (2014).

    Article  CAS  PubMed  Google Scholar 

  140. Yu, M. et al. Circulating breast tumor cells exhibit dynamic changes in epithelial and mesenchymal composition. Science 339, 580–584 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  141. Dago, A. E. et al. Rapid phenotypic and genomic change in response to therapeutic pressure in prostate cancer inferred by high content analysis of single circulating tumor cells. PLoS ONE 9, e101777 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  142. Sarioglu, A. F. et al. A microfluidic device for label-free, physical capture of circulating tumor cell clusters. Nat. Methods 12, 685–691 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  143. Aceto, N. et al. Circulating tumor cell clusters are oligoclonal precursors of breast cancer metastasis. Cell 158, 1110–1122 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  144. Sosa, M. S., Bragado, P. & Aguirre-Ghiso, J. A. Mechanisms of disseminated cancer cell dormancy: an awakening field. Nat. Rev. Cancer 14, 611–622 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  145. Pantel, K., Brakenhoff, R. H. & Brandt, B. Detection, clinical relevance and specific biological properties of disseminating tumour cells. Nat. Rev. Cancer 8, 329–340 (2008).

    Article  CAS  PubMed  Google Scholar 

  146. Naume, B. et al. Clinical outcome with correlation to disseminated tumor cell (DTC) status after DTC-guided secondary adjuvant treatment with docetaxel in early breast cancer. J. Clin. Oncol. 32, 3848–3857 (2014).

    Article  PubMed  Google Scholar 

  147. Weckermann, D. et al. Perioperative activation of disseminated tumor cells in bone marrow of patients with prostate cancer. J. Clin. Oncol. 27, 1549–1556 (2009).

    Article  PubMed  Google Scholar 

  148. Hyman, D. M., Taylor, B. S. & Baselga, J. Implementing genome-driven oncology. Cell 168, 584–599 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  149. Bowtell, D. D. et al. Rethinking ovarian cancer II: reducing mortality from high-grade serous ovarian cancer. Nat. Rev. Cancer 15, 668–679 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Russell, M. R. et al. Novel risk models for early detection and screening of ovarian cancer. Oncotarget 8, 785–797 (2016).

    Article  PubMed Central  Google Scholar 

  151. Burrell, R. A., McGranahan, N., Bartek, J. & Swanton, C. The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501, 338–345 (2013).

    Article  CAS  PubMed  Google Scholar 

  152. Cheng, D. T. et al. Memorial sloan kettering-integrated mutation profiling of actionable cancer targets (MSK-IMPACT): A hybridization capture-based next-generation sequencing clinical assay for solid tumor molecular oncology. J. Mol. Diagn. 17, 251–264 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  153. Martinez, P. et al. Parallel evolution of tumour subclones mimics diversity between tumours. J. Pathol. 230, 356–364 (2013).

    Article  CAS  PubMed  Google Scholar 

  154. Wang, M., He, X., Chang, Y., Sun, G. & Thabane, L. A sensitivity and specificity comparison of fine needle aspiration cytology and core needle biopsy in evaluation of suspicious breast lesions: A systematic review and meta-analysis. Breast 31, 157–166 (2017).

    Article  PubMed  Google Scholar 

  155. Knight, C. S. et al. Utility of endoscopic ultrasound-guided fine-needle aspiration in the diagnosis and staging of colorectal carcinoma. Diagn. Cytopathol. 41, 1031–1037 (2013).

    Article  PubMed  Google Scholar 

  156. Casadio, C. et al. Molecular testing for targeted therapy in advanced non-small cell lung cancer: suitability of endobronchial ultrasound transbronchial needle aspiration. Am. J. Clin. Pathol. 144, 629–634 (2015).

    Article  CAS  PubMed  Google Scholar 

  157. Mu, P. et al. SOX2 promotes lineage plasticity and antiandrogen resistance in TP53- and RB1-deficient prostate cancer. Science 355, 84–88 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  158. Ku, S. Y. et al. Rb1 and Trp53 cooperate to suppress prostate cancer lineage plasticity, metastasis, and antiandrogen resistance. Science 355, 78–83 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  159. Visakorpi, T. et al. In vivo amplification of the androgen receptor gene and progression of human prostate cancer. Nat. Genet. 9, 401–406 (1995).

    Article  CAS  PubMed  Google Scholar 

  160. Antonarakis, E. S. et al. AR-V7 and resistance to enzalutamide and abiraterone in prostate cancer. N. Engl. J. Med. 371, 1028–1038 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  161. Wagman, L. D. Importance of response to neoadjuvant therapy in patients with liver-limited mCRC when the intent is resection and/or ablation. Clin. Colorectal Cancer 12, 223–232 (2013).

    Article  PubMed  Google Scholar 

  162. Murtaza, M. et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 497, 108–112 (2013).

    Article  CAS  PubMed  Google Scholar 

  163. Newman, A. M. et al. An ultrasensitive method for quantitating circulating tumor DNA with broad patient coverage. Nat. Med. 20, 548–554 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  164. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  165. Abbosh, C. et al. Phylogenetic ctDNA analysis depicts early stage lung cancer evolution. Nature 545, 446–451 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  166. Scher, H. I. et al. Association of AR-V7 on circulating tumor cells as a treatment-specific biomarker with outcomes and survival in castration-resistant prostate cancer. JAMA Oncol. 2, 1441–1449 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  167. Martelotto, L. G. et al. Whole-genome single-cell copy number profiling from formalin-fixed paraffin-embedded samples. Nat. Med. 23, 376–385 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  168. Lee, J. H. et al. Highly multiplexed subcellular RNA sequencing in situ. Science 343, 1360–1363 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  169. Ke, R. et al. In situ sequencing for RNA analysis in preserved tissue and cells. Nat. Methods 10, 857–860 (2013).

    Article  CAS  PubMed  Google Scholar 

  170. Stahl, P. L. et al. Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353, 78–82 (2016).

    Article  CAS  PubMed  Google Scholar 

  171. Timp, W. & Feinberg, A. P. Cancer as a dysregulated epigenome allowing cellular growth advantage at the expense of the host. Nat. Rev. Cancer 13, 497–510 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  172. Mazor, T., Pankov, A., Song, J. S. & Costello, J. F. Intratumoral heterogeneity of the epigenome. Cancer Cell 29, 440–451 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  173. Plass, C. et al. Mutations in regulators of the epigenome and their connections to global chromatin patterns in cancer. Nat. Rev. Genet. 14, 765–780 (2013).

    Article  CAS  PubMed  Google Scholar 

  174. Spitzer, M. H. & Nolan, G. P. Mass cytometry: single cells, many features. Cell 165, 780–791 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  175. Miller, M. A. & Weissleder, R. Imaging of anticancer drug action in single cells. Nat. Rev. Cancer 17, 399–414 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  176. Michor, F. et al. Dynamics of chronic myeloid leukaemia. Nature 435, 1267–1270 (2005).

    Article  CAS  PubMed  Google Scholar 

  177. Anderson, A. R. & Quaranta, V. Integrative mathematical oncology. Nat. Rev. Cancer 8, 227–234 (2008).

    Article  CAS  PubMed  Google Scholar 

  178. Altrock, P. M., Liu, L. L. & Michor, F. The mathematics of cancer: integrating quantitative models. Nat. Rev. Cancer 15, 730–745 (2015).

    Article  CAS  PubMed  Google Scholar 

  179. Dai, L., Vorselen, D., Korolev, K. S. & Gore, J. Generic indicators for loss of resilience before a tipping point leading to population collapse. Science 336, 1175–1177 (2012).

    Article  CAS  PubMed  Google Scholar 

  180. Head, S. R. et al. Library construction for next-generation sequencing: overviews and challenges. Biotechniques 56, 61–77 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  181. Zack, T. I. et al. Pan-cancer patterns of somatic copy number alteration. Nat. Genet. 45, 1134–1140 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

  183. Venteicher, A. S. et al. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355, eaai8478 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  184. Bacher, R. et al. SCnorm: robust normalization of single-cell RNA-seq data. Nat. Methods 14, 584–586 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. McCurdy, S., Ntranos, V. & Pachter, L. Column subset selection for single-cell RNA-seq clustering. bioRxiv https://doi.org/10.1101/159079 (2017).

  186. Fan, H. C., Fu, G. K. & Fodor, S. P. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

    Article  PubMed  CAS  Google Scholar 

  187. Yuan, J. & Sims, P. A. An automated microwell platform for large-scale single cell RNA-seq. Sci. Rep. 6, 33883 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  188. Zheng, G. X. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  189. Wu, A. R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  191. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Leung, K. et al. Robust high-performance nanoliter-volume single-cell multiple displacement amplification on planar substrates. Proc. Natl Acad. Sci. USA 113, 8484–8489 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  193. Xu, L., Brito, I. L., Alm, E. J. & Blainey, P. C. Virtual microfluidics for digital quantification and single-cell sequencing. Nat. Methods 13, 759–762 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  194. Cristofanilli, M. et al. Circulating tumor cells, disease progression, and survival in metastatic breast cancer. N. Engl. J. Med. 351, 781–791 (2004).

    Article  CAS  PubMed  Google Scholar 

  195. Xu, L. et al. Optimization and evaluation of a novel size based circulating tumor cell isolation system. PLoS ONE 10, e0138032 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  196. Nieva, J. et al. High-definition imaging of circulating tumor cells and associated cellular events in non-small cell lung cancer patients: a longitudinal analysis. Phys. Biol. 9, 016004 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  197. Carlsson, A. et al. Paired high-content analysis of prostate cancer cells in bone marrow and blood characterizes increased androgen receptor expression in tumor cell clusters. Clin. Cancer Res. 23, 1722–1732 (2017).

    Article  CAS  PubMed  Google Scholar 

  198. Travis, W. D. Update on small cell carcinoma and its differentiation from squamous cell carcinoma and other non-small cell carcinomas. Mod. Pathol. 25, S18–S30 (2012).

    Article  CAS  PubMed  Google Scholar 

  199. Oser, M. G., Niederst, M. J., Sequist, L. V. & Engelman, J. A. Transformation from non-small-cell lung cancer to small-cell lung cancer: molecular drivers and cells of origin. Lancet Oncol. 16, e165–e172 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  200. Francis, J. M. et al. EGFR variant heterogeneity in glioblastoma resolved through single-nucleus sequencing. Cancer Discov. 4, 956–971 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  201. Carter, L. et al. Molecular analysis of circulating tumor cells identifies distinct copy-number profiles in patients with chemosensitive and chemorefractory small-cell lung cancer. Nat. Med. 23, 114–119 (2016).

    Article  PubMed  CAS  Google Scholar 

  202. Kloor, M. et al. Prevalence of mismatch repair-deficient crypt foci in Lynch syndrome: a pathological study. Lancet Oncol. 13, 598–606 (2012).

    Article  CAS  PubMed  Google Scholar 

  203. Misale, S. et al. Emergence of KRAS mutations and acquired resistance to anti-EGFR therapy in colorectal cancer. Nature 486, 532–536 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  204. Diaz, L. A. Jr. et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 486, 537–540 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  205. Tirosh, I. et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352, 189–196 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  206. Lavin, Y. et al. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169, 750–765 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  207. Singer, M. et al. A distinct gene module for dysfunction uncoupled from activation in tumor-infiltrating T cells. Cell 166, 1500–1511 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  208. Garvin, T. et al. Interactive analysis and assessment of single-cell copy-number variations. Nat. Methods 12, 1058–1060 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  210. Borgstrom, E., Paterlini, M., Mold, J. E., Frisen, J. & Lundeberg, J. Comparison of whole genome amplification techniques for human single cell exome sequencing. PLoS ONE 12, e0171566 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  211. Ning, L. et al. Quantitative assessment of single-cell whole genome amplification methods for detecting copy number variation using hippocampal neurons. Sci. Rep. 5, 11415 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  212. Svensson, V. et al. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14, 381–387 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  213. Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643 (2017).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors apologize to those whose valuable works were not cited due to space limitations. The authors thank two anonymous reviewers for their helpful comments and Jorge Nieva and Elham Azizi for insightful discussions. The authors would also like to thank Bartek Jacewicz, Allison Levy, Joseph Montecalvo, Natasha Rekhtman, Jinru Shia, Linas Mazutis, Jude Kendall and Peter Sims for providing images for the figures. T.B. is supported by the William C. and Joyce C. O'Neil Charitable Trust, Memorial Sloan Kettering Single Cell Sequencing Initiative. J.H. is supported by the Breast Cancer Research Foundation (BCRF) and the Susan G. Komen Foundation (IIR13265578).

Author information

Authors and Affiliations

Authors

Contributions

T.B. and J.H. contributed equally in conceptualizing the ideas put forth in the manuscript and contributed equally in writing the body of the work.

Corresponding authors

Correspondence to Timour Baslan or James Hicks.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Baslan, T., Hicks, J. Unravelling biology and shifting paradigms in cancer with single-cell sequencing. Nat Rev Cancer 17, 557–569 (2017). https://doi.org/10.1038/nrc.2017.58

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrc.2017.58

This article is cited by

Search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer