Single-cell analysis tools for drug discovery and development

Key Points

  • The recent significant advances in the development of single-cell analytical tools have enabled increasingly deep cellular analyses at the genomic, transcriptomic and proteomic levels.

  • Genomic and transcriptomic methods utilize next-generation sequencing technologies, which complement protocols for improved quantification and cost-effective analyses of statistically significant numbers of single cells.

  • Single-cell proteomics methods range from cytometry tools to microchip platforms. All these methods rely on antibodies, but different platforms yield different levels of quantification.

  • Single-cell analyses reveal biology that is masked when cell populations or tissues are analysed. Illustrative examples include tracing the lineage of diseased cells back to the healthy tissue of origin, or a deep analysis of how targeted inhibitors can alter the structure of signalling pathways.

  • Single-cell analysis tools are already playing important parts in drug discovery, particularly in the rapidly emerging field of cancer immunotherapy.

Abstract

The genetic, functional or compositional heterogeneity of healthy and diseased tissues presents major challenges in drug discovery and development. Such heterogeneity hinders the design of accurate disease models and can confound the interpretation of biomarker levels and of patient responses to specific therapies. The complex nature of virtually all tissues has motivated the development of tools for single-cell genomic, transcriptomic and multiplex proteomic analyses. Here, we review these tools and assess their advantages and limitations. Emerging applications of single cell analysis tools in drug discovery and development, particularly in the field of oncology, are discussed.

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Figure 1: Quantitative single-cell transcriptomic methods.
Figure 2: Emerging single-cell proteomics methods.
Figure 3: Single-cell analysis traces the lineage of a colon cancer.

References

  1. 1

    Sakmann, B. & Neher, E. Patch clamp techniques for studying ionic channels in excitable membranes. Annu. Rev. Physiol. 46, 455–472 (1984).

    CAS  PubMed  Google Scholar 

  2. 2

    Amann, R. & Fuch, B. M. Single-cell identification in microbial communities by improved fluorescence in situ hybridization techniques. Nat. Rev. Microbiol. 6, 339–348 (2008).

    CAS  PubMed  Google Scholar 

  3. 3

    Langer-Safer, P. R., Levine, M. & Ward, D. C. Immunological method for mapping genes on Drosophila polytene chromosomes. Proc. Natl Acad. Sci. USA 79, 4381–4385 (1982).

    CAS  PubMed  Google Scholar 

  4. 4

    Herzenberg, L. A. et al. The history of the fluorescence activated cell sorter and flow cytometry: a view from Stanford. Clin. Chem. 48, 1819–1827 (2002).

    CAS  PubMed  Google Scholar 

  5. 5

    Herzenberg, L. A., Julius, M. H. & Masuda, T. Demonstration that antigen-binding cells are precursors of antibody-producing cells after purification with a fluorescence-activated cell sorter. Proc. Natl Acad. Sci. USA 69, 1934–1938 (1972).

    PubMed  Google Scholar 

  6. 6

    Czerkinsky, C., Nilsson, L., Nygren, H., Ouchterlony, O. & Tarkowski, A. A solid-phase enzyme-linked immunospot (ELISPOT) assay for enumeration of specific antibody-secreting cells. Immunol. Methods 65, 109–121 (1983).

    CAS  Google Scholar 

  7. 7

    Perfetto, S. P., Chattopadhyay, P. K. & Roederer, M. Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 4, 648–655 (2004). An illustration of the state of the art of multiplex flow cytometry.

    CAS  PubMed  Google Scholar 

  8. 8

    Lu, Y. et al. Highly multiplexed profiling of single-cell effector functions reveals deep functional heterogeneity in response to pathogenic ligands. Proc. Natl Acad. Sci. USA 112, E607–E615 (2015).

    CAS  PubMed  Google Scholar 

  9. 9

    Bendall, S. C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Shi, Q. et al. Single cell proteomic chip for profiling intracellular signaling pathways in single tumor cells. Proc. Natl Acad. Sci. USA 109, 419–425 (2012). An illustration of quantitative and multiplex single-cell proteomics.

    CAS  PubMed  Google Scholar 

  11. 11

    Irish, J. M. et al. Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell 118, 217–228 (2004).

    CAS  PubMed  Google Scholar 

  12. 12

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

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13

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

    PubMed  PubMed Central  Google Scholar 

  14. 14

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Tay, S. et al. Single-cell NF-κB dynamics reveal digital activation and analogue information processing. Nature 466, 267–271 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16

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

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1400 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18

    Fan, H. C., Fu, G. K. & Fodor, S. P. A. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 6222 (2015). Describes the CytoSeq method.

    Google Scholar 

  19. 19

    Han, L. et al. Co-detection and sequencing of genes and transcripts from the same single cells facilitated by a microfluidics platform. Sci. Rep. 4, 6485 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    Mazumder, A., Tummler, K., Bathe, M. & Samson, L. D. Single-cell analysis of ribonucleotide reductase transcriptional and translational response to DNA damage. Mol. Cell. Biol. 33, 635–642 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Porichis, F. et al. High-throughput detection of miRNAs and gene-specific mRNA at the single-cell level by flow cytometry. Nat. Commun. 5, 5641 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22

    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).

    PubMed  Google Scholar 

  23. 23

    Xue, M. et al. Chemical methods for the simultaneous quantitation of metabolites and proteins from single cells. J. Am. Chem. Soc. 137, 4066–4069 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24

    Wang, J. et al. Quantitating cell–cell interaction functions, with applications to glioblastoma multiforme cancer cells. Nano Lett. 12, 6101–6106 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25

    Liadi, I. et al. Individual motile CD4+ T cells can participate in efficient multikilling through conjugation to multiple tumor cells. Cancer Immunol. Res. 3, 473–482 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    Elitas, M., Brower, K., Lu, Y., Chen, J. J. & Fan, R. A microchip platform for interrogating tumormacrophage paracrine signaling at the single-cell level. Lab Chip 14, 3582–3588 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Wei, W. et al. Hypoxia induces a phase transition within a kinase signaling network in cancer cells. Proc. Natl Acad. Sci. USA 110, e1352–e1360 (2013).

    CAS  PubMed  Google Scholar 

  28. 28

    Mehling, M., Frank, T., Albayrak, C. & Tay, S. Real-time tracking, retrieval and gene expression analysis of migrating human T cells. Lab Chip 15, 1276–1283 (2015).

    CAS  PubMed  Google Scholar 

  29. 29

    Brouzes, E. et al. Droplet microfluidic technology for single-cell high-throughput screening. Proc. Natl Acad. Sci. USA 106, 14195–14200 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. 30

    Sendra, V. G., Lie, A., Romain, G., Agarwal, S. K. & Varadarajan, N. Detection and isolation of auto-reactive human antibodies from primary B cells. Methods 64, 153–159 (2013). Describes the microengraving technique used to identify cells with desirable proteomic signatures and then separate those cells for further analysis.

    CAS  PubMed  Google Scholar 

  31. 31

    Love, J. C., Ronan, J. L., Grotenbreg, G. M., Van der Veen, A. G. & Ploegh, H. L. A microengraving method for rapid selection of single cells producing antigen specific antibodies. Nat. Biotechnol. 24, 703–707 (2006).

    CAS  PubMed  Google Scholar 

  32. 32

    Lubeck, E., Coskun, A. F., Zhiyentayev, T., Ahmad, M. & Long, C. Single-cell in situ RNA profiling by sequential hybridization. Nat. Methods 11, 360–361 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Yang, B. et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158, 945–958 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34

    Chen, K. H., Boettiger, A. N., Moffitt, J. R., Wang, S. & Zhuang, X. Spatially resolved, highly multiplexed RNA profiling in single cells. Science 348, 6233 (2015). Reports a single-cell transcriptomics method applied to cells in a bulk environment.

    Google Scholar 

  35. 35

    Angelo, M. et al. Multiplexed ion beam imaging of human breast tumors. Nat. Med. 20, 436–442 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Shin, Y. S. et al. Chemistries for patterning robust DNA microbarcodes enable multiplex assays of cytoplasm proteins from single cancer cells. ChemPhysChem 11, 3063 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37

    Amir, E. D. et al. viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol. 31, 545–548 (2013).

    CAS  Google Scholar 

  38. 38

    Shin, Y. S. et al. Protein signaling networks from single cell fluctuations and information theory profiling. Biophys. J. 100, 2378–2386 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Kravchenko-Balasha, N., Wang, J., Remacle, F., Levine, R. D. & Heath, J. R. Glioblastoma cellular architectures are predicted through the characterization of two-cell interactions. Proc. Natl Acad. Sci. USA 111, 6521–6526 (2014).

    CAS  PubMed  Google Scholar 

  41. 41

    Bruggner, R. V., Bodenmiller, B., Dill, D. L., Tibshirani, R. J. & Nolan, G. P. Automated identification of stratifying signatures in cellular subpopulations. Proc. Natl Acad. Sci. USA 111, E2770–E2777 (2014).

    CAS  PubMed  Google Scholar 

  42. 42

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

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43

    Blainey, P. C. & Quake, S. R. Dissecting genomic diversity, one cell at a time. Nat. Methods 11, 19–21 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44

    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  PubMed  Google Scholar 

  45. 45

    Acinas, S. G., Sarma-Rupavtarm, R., Klepac-Ceraj, V. & Polz, M. F. PCR-induced sequence artifacts and bias: insights from comparison of two 16S rRNA clone libraries constructed from the same sample. Appl. Environ. Microbiol. 71, 8966–8969 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. 46

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    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).

    PubMed  PubMed Central  Google Scholar 

  48. 48

    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). Reports the MALBAC technique.

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49

    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  PubMed  Google Scholar 

  50. 50

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

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51

    Dash, P. et al. Paired analysis of TCRα and TCRβ chains at the single-cell level in mice. J. Clin. Invest. 121, 288–295 (2011).

    CAS  Google Scholar 

  52. 52

    Kim, S.-M. et al. Analysis of the paired TCR α- and β-chains of single human T cells. PLoS ONE 7, e37338 (2013).

    Google Scholar 

  53. 53

    Han, A., Glanville, J., Hansmann, L. & Davis, M. M. Linking T-cell receptor sequence to functional phenotype at the single-cell level. Nat. Biotechnol. 32, 684–692 (2014). Illustrates an important method that is relevant to various classes of immunotherapies.

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54

    Ng, S. B. et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461, 272–276 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55

    Mamanova, L. et al. Target-enrichment strategies for next-generation sequencing. Nat. Methods 7, 111–118 (2010).

    CAS  Google Scholar 

  56. 56

    Hodges, E. et al. Genome-wide in situ exon capture for selective resequencing. Nat. Genet. 39, 1522–1527 (2007).

    CAS  PubMed  Google Scholar 

  57. 57

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

    CAS  PubMed  PubMed Central  Google Scholar 

  58. 58

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

    CAS  Google Scholar 

  59. 59

    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  Google Scholar 

  60. 60

    Eberwine, J. et al. Analysis of gene expression in single live neurons. Proc. Natl Acad. Sci. USA 89, 3010–3014 (1992).

    CAS  PubMed  Google Scholar 

  61. 61

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

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62

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

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

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

    CAS  PubMed  PubMed Central  Google Scholar 

  64. 64

    Shalek, A. K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. 65

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    Ting, D. T. et al. Single-cell RNA sequencing identifies extracellular matrix gene expression by pancreatic circulating tumor cells. Cell Rep. 8, 1905–1918 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. 67

    Luo, Y. et al. Single-cell transcriptome analyses reveal signals to activate dormant neural stem cells. Cell 161, 1175–1186 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  68. 68

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

    CAS  PubMed  PubMed Central  Google Scholar 

  69. 69

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

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70

    Kivioja, T. et al. Counting absolute numbers of molecules using unique molecular identifiers. Nat. Methods 9, 72–74 (2012). A description of molecular barcoding for quantitative biology.

    CAS  Google Scholar 

  71. 71

    Grun, D., Kester, L. & van Oudenaarden, A. Validation of noise models for single-cell transcriptomics. Nat. Methods 11, 637–640 (2014). A description of the limits and possibilities of absolute quantitation from single-cell transcriptome analysis.

    PubMed  Google Scholar 

  72. 72

    Polz, M. F. & Cavanaugh, C. M. Bias in template-to-product ratios in multitemplate PCR. Appl. Environ. Microbiol. 64, 3724–3730 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    Schloss, P. D., Gevers, D. & Westcott, S. L. Reducing the effects of PCR amplification and sequencing artifacts on 16S rRNA-based studies. PLoS ONE 6, e27310 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74

    Suzuki, M. T. & Giovannoni, S. J. Bias caused by template annealing in the amplification of mixtures of 16S rRNA genes by PCR. Appl. Environ. Microbiol. 62, 625–630 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. 75

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

    CAS  PubMed  PubMed Central  Google Scholar 

  76. 76

    Fu, G. K., Wilhelmy, J., Stern, D., Fan, H. C. & Fodor, S. P. A. Digital encoding of cellular mRNAs enabling precise and absolute gene expression measurement by single-molecule counting. Anal. Chem. 86, 2867–2870 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  77. 77

    Fu, G. K. et al. Molecular indexing enables quantitative targeted RNA sequencing and reveals poor efficiencies in standard library preparations. Proc. Natl Acad. Sci. USA 111, 1891–1896 (2014).

    CAS  PubMed  Google Scholar 

  78. 78

    Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  79. 79

    Yates, J. R., Ruse, C. I. & Nakorchevsky, A. Proteomics by mass spectrometry: approaches, advances, and applications. Annu. Rev. Biomed. Eng. 11, 49–79 (2009).

    CAS  PubMed  Google Scholar 

  80. 80

    Picotti, P. & Aebersold, R. Selected reaction monitoring-based proteomics: workflows, potential, pitfalls and future directions. Nat. Methods 9, 555–566 (2012).

    CAS  PubMed  Google Scholar 

  81. 81

    Torres, A. J., Contento, R. L., Gordo, S., Wucherpfennig, K. W. & Love, C. L. Functional single-cell analysis of T-cell activation by supported bilayer-tethered ligands on arrays of nanowells. Lab Chip 13, 90–99 (2013).

    CAS  PubMed  Google Scholar 

  82. 82

    Hughes, A. J. et al. Single-cell western blotting. Nat. Methods 11, 749–755 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  83. 83

    Guo, M. T., Rotem, A., Heyman, J. A. & Weitz, D. A. Droplet microfluidics for high-throughput biological assays. Lab Chip 12, 2146–2155 (2012).

    CAS  PubMed  Google Scholar 

  84. 84

    Huebner, A. et al. Static microdroplet arrays: a microfluidic device for droplet trapping, incubation and release for enzymatic and cell-based assays. Lab Chip 9, 692–698 (2009).

    CAS  PubMed  Google Scholar 

  85. 85

    Kintses, B., van Vliet, L. D., Devenish, S. R. A. & Hollfelder, F. Microfluidic droplets: new integrated workflows for biological experiments. Curr. Opin. Chem. Biol. 14, 548–555 (2010).

    CAS  PubMed  Google Scholar 

  86. 86

    Mazutis, L. et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8, 870–891 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87

    Yu, J. et al. Microfluidics-based single-cell functional proteomics for fundamental and applied biomedical applications. Annu. Rev. Anal. Chem. 7, 275–295 (2014). An overview of nanodroplet microfluidics methods for single-cell analysis.

    CAS  Google Scholar 

  88. 88

    McKelvey-Martin, V. J. et al. The single cell gel electrophoresis assay (comet assay): a European review. Mut. Res. 288, 47–63 (1993).

    CAS  Google Scholar 

  89. 89

    Weingeist, D. M. et al. Single-cell microarray enables high-throughput evaluation of DNA double-strand breaks and DNA repair inhibitors. Cell Cycle 12, 907–915 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  90. 90

    Han, Q. et al. Polyfunctional responses by human T cells result from sequential release of cytokines. Proc. Natl Acad. Sci. USA 109, 1607–1612 (2012).

    CAS  PubMed  Google Scholar 

  91. 91

    Lu, Y. et al. High-throughput secretomic analysis of single cells to assess functional cellular heterogeneity. Anal. Chem. 85, 2548–2556 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. 92

    Romain, G. et al. Antibody Fc-engineering improves frequency and promotes kinetic boosting of serial killing mediated by NK cells. Blood 124, 3241–3249 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93

    Marcon, E. et al. Assessment of a method to characterize antibody selectivity and specificity for use in immunoprecipitation. Nat. Methods 12, 725–731 (2015). Describes the limitations of antibodies for protein detection assays.

    CAS  PubMed  Google Scholar 

  94. 94

    Towbin, H., Staehelin, T. & Gordon, J. Electrophoretic transfer of proteins from polyacrylamide gels to nitrocellulose sheets: procedure and some applications. Proc. Natl Acad. Sci. USA 76, 4350–4354 (1979).

    CAS  PubMed  Google Scholar 

  95. 95

    Ma, C. et al. A clinical microchip for evaluation of single immune cells reveals high functional heterogeneity in phenotypically similar T cells. Nat. Med. 17, 738–744 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96

    Elowitz, M. B., Levine, A. J., Siggia, E. D. & Swain, P. S. Stochastic gene expression in a single cell. Science 297, 1183–1186 (2002). An excellent illustration of how single cells and bulk cell populations differ.

    CAS  Google Scholar 

  97. 97

    Kaern, M., Elston, T. C., Blake, W. J. & Collins, J. J. Stochasticity in gene expression: from theories to phenotypes. Nat. Rev. Genet. 6, 451–464 (2005).

    CAS  PubMed  Google Scholar 

  98. 98

    Bengtsson, M., Stahlberg, A., Rorsman, P. & Kubista, M. Gene expression profiling in single cells from the pancreatic islets of Langerhans reveals lognormal distribution of mRNA levels. Genome Res. 15, 1388–1392 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  99. 99

    Ma, C. et al. Multifunctional T-cell analyses to study response and progression in adoptive cell transfer immunotherapy. Cancer Discov. 3, 418–429 (2013). Illustrates the value of single-cell functional proteomics for cancer immunotherapy monitoring in patients.

    CAS  PubMed  PubMed Central  Google Scholar 

  100. 100

    Powell, A. A. et al. Single cell profiling of circulating tumor cells: transcriptional heterogeneity and diversity from breast cancer cell lines. PLoS ONE 7, e33788 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101

    Halo, T. L. et al. NanoFlares for the detection, isolation, and culture of live tumor cells from human blood. Proc. Natl Acad. Sci. USA 111, 17104–17109 (2014).

    CAS  PubMed  Google Scholar 

  102. 102

    Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011). An example of lineage tracing via single-cell transcriptomics.

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103

    Zhao, J. L. et al. Conversion of danger signals into cytokine signals by hematopoietic stem and progenitor cells for regulation of stress-induced hematopoiesis. Cell Stem Cell 14, 1–15 (2014).

    Google Scholar 

  104. 104

    Lin, L. et al. Human natural killer cells licensed by killer Ig receptor genes have an altered cytokine program that modifies CD4+ T cell function. J. Immunol. 193, 940–949 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. 105

    Van Buuren, M. M., Calis, J. J. & Schumacher, T. N. High sensitivity of cancer exome-based CD8 T cell neo-antigen identification. Oncoimmunology 3, e28836 (2014).

    PubMed  PubMed Central  Google Scholar 

  106. 106

    Kellogg, R. A., Gómez-Sjöberg, R., Leyrat, A. A. & Tay, S. High-throughput microfluidic single-cell analysis pipeline for studies of signaling dynamics. Nat. Protoc. 9, 1713–1726 (2014).

    CAS  Google Scholar 

  107. 107

    Kellogg, R. A. & Tay, S. Noise facilitates transcriptional control under dynamic inputs. Cell 160, 381–392 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108

    Yamanaka, Y. J. et al. Single-cell analysis of the dynamics and functional outcomes of interactions between human natural killer cells and target cells. Integr. Biol. 4, 1175–1184 (2012).

    CAS  Google Scholar 

  109. 109

    Gupta, P. B. et al. Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 146, 633–644 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  110. 110

    Nathanson, D. et al. Targeted therapy resistance mediated by dynamic regulation of extrachromosomal mutant EGFR DNA. Science 343, 72–76 (2014). A mechanism of adaptive resistance to a targeted inhibitor is revealed by single-cell analysis.

    CAS  PubMed  Google Scholar 

  111. 111

    Jaiswal, S. & Weissman, I. L. Hematopoietic stem and progenitor cells and the inflammatory response. Ann. NY Acad. Sci. 1174, 118–121 (2009).

    CAS  PubMed  Google Scholar 

  112. 112

    Baldridge, M. T., King, K. Y., Boles, N. C., Weksberg, D. C. & Goodell, M. A. Quiescent haematopoietic stem cells are activated by IFN-γ in response to chronic infection. Nature 465, 793–797 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  113. 113

    Baldridge, M. T., King, K. Y. & Goodell, M. A. Inflammatory signals regulate hematopoietic stem cells. Trends Immunol. 32, 57–65 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  114. 114

    King, K. Y. & Goodell, M. A. Inflammatory modulation of HSCs: viewing the HSC as a foundation for the immune response. Nat. Rev. Immunol. 11, 685–692 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  115. 115

    Nagai, Y. et al. Toll-like receptors on hematopoietic progenitor cells stimulate innate immune system replenishment. Immunity 24, 801–812 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. 116

    Bodenmiller, B. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30, 858–867 (2012). An illustration of CyTOF, combined with mass-label barcoding, for single-cell analysis and drug screening.

    CAS  PubMed  PubMed Central  Google Scholar 

  117. 117

    Couzin-Frankel, J. Cancer immunotherapy. Science 343, 1432 (2013).

    Google Scholar 

  118. 118

    Ledford, H. Cancer treatment: the killer within. Nature 508, 24–26 (2014).

    CAS  PubMed  Google Scholar 

  119. 119

    Palucka, K. & Banchereau, J. Cancer immunotherapy via dendritic cells. Nat. Rev. Cancer 12, 265–277 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120

    Rosenberg, S. A., Restifo, N. P., Yang, J. C., Morgan, R. A. & Dudley, M. E. Adoptive cell transfer: a clinical path to effective cancer immunotherapy. Nat. Rev. Cancer 8, 299–308 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121

    Leach, D. R., Krummel, M. F. & Allison, J. P. Enhancement of antitumor immunity by CTLA-4 blockade. Science 271, 1734–1736 (1996).

    CAS  Google Scholar 

  122. 122

    Ribas, A. & Tumeh, P. C. The future of cancer therapy: selecting patients who respond to PD-1/L1 blockade. Clin. Cancer Res. 20, 4982–4984 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. 123

    Iwai, Y. et al. Involvement of PD-L1 on tumor cells in the escape from host immune system and tumor immunotherapy by PD-L1 blockade. Proc. Natl Acad. Sci. USA 99, 12293–12297 (2002).

    CAS  Google Scholar 

  124. 124

    Crompton, J. G., Sukumar, M. & Restifo, N. P. Uncoupling T-cell expansion from effector differentiation in cell-based immunotherapy. Immunol. Rev. 257, 264–276 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. 125

    Segal, N. H. et al. Epitope landscape in breast and colorectal cancer. Cancer Res. 68, 889–892 (2008).

    CAS  PubMed  Google Scholar 

  126. 126

    Coulie, P. G., Van den Eynde, B. J., van der Bruggen, P. & Boon, T. Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy. Nat. Rev. Cancer 14, 135–146 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  127. 127

    DuPage, M., Mazumdar, C., Schmidt, L. M., Cheung, A. F. & Jacks, T. Expression of tumour-specific antigens underlies cancer immunoediting. Nature 482, 405–409 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  128. 128

    Matsushita, H. et al. Cancer exome analysis reveals a T-cell-dependent mechanism of cancer immunoediting. Nature 482, 400–404 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129

    Robbins, P. F. et al. Mining exomic sequencing data to identify mutated antigens recognized by adoptively transferred tumor-reactive T cells. Nat. Med. 19, 747–752 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  130. 130

    van Rooij, N. et al. Tumor exome analysis reveals neoantigen-specific T-cell reactivity in an ipilimumab-responsive melanoma. J. Clin. Oncol. 31, e439–e442 (2013).

    Google Scholar 

  131. 131

    De Rosa, S. C., Herzenberg, L. A., Herzeberg, L. A. & Roederer, M. 11-color, 13-parameter flow cytometry: identification of human naive T cells by phenotype, function, and T-cell receptor diversity. Nat. Med. 7, 245–248 (2001).

    CAS  PubMed  Google Scholar 

  132. 132

    Lee, P. P. et al. Characterization of circulating T cells specific for tumor-associated antigens in melanoma patients. Nat. Med. 5, 677–685 (1999).

    CAS  PubMed  Google Scholar 

  133. 133

    Altman, J. D. et al. Phenotypic analysis of antigen-specific T lymphocytes. Science 274, 94–96 (1996).

    CAS  Google Scholar 

  134. 134

    Ramachandiran, V. et al. A robust method for production of MHC tetramers with small molecule fluorophores. J. Immunol. Methods 319, 13–20 (2007).

    CAS  Google Scholar 

  135. 135

    Bakker, A. H. et al. Conditional MHC class I ligands and peptide exchange technology for the human MHC gene products HLA-A1, -A3, -A11, and -B7. Proc. Natl Acad. Sci. USA 105, 3825–3830 (2008).

    CAS  PubMed  Google Scholar 

  136. 136

    Celie, P. H. et al. UV-induced ligand exchange in MHC class I protein crystals. J. Am. Chem. Soc. 131, 12298 (2009).

    CAS  PubMed  Google Scholar 

  137. 137

    Kwong, G. A. et al. Modular nucleic acid assembled p/MHC microarrays for multiplexed sorting of antigen-specific lymphophocytes. J. Am. Chem. Soc. 131, 9695–9703 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  138. 138

    Rodenko, B. et al. Generation of peptide−MHC class I complexes through UV-mediated ligand exchange. Nat. Protoc. 1, 1120–1132 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  139. 139

    Bordon, Y. Immunotherapy: checkpoint parley. Nat. Rev. Cancer 15, 3 (2015).

    CAS  PubMed  Google Scholar 

  140. 140

    Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).

    CAS  Google Scholar 

  141. 141

    Rosenberg, S. A. & Restifo, N. P. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348, 62–68 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  142. 142

    Gattinoni, L., Klebanoff, C. A. & Restifo, N. P. Paths to stemness: building the ultimate antitumour T cell. Nat. Rev. Cancer 12, 671–684 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  143. 143

    Stroncek, D. F. et al. New directions in cellular therapy of cancer: a summary of the summit on cellular therapy for cancer. J. Transl. Med. 10, 48–52 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  144. 144

    Restifo, N. P. & Gattinoni, L. Lineage relationship of effector and memory T cells. Curr. Opin. Immunol. 25, 556–563 (2013).

    CAS  PubMed  Google Scholar 

  145. 145

    Polyak, K. Tumor heterogeneity confounds and illuminates: a case for Darwinian tumor evolution. Nat. Med. 20, 344–346 (2014).

    CAS  PubMed  Google Scholar 

  146. 146

    Furnari, F. B., Cloughesy, T. F., Cavenee, W. K. & Mischel, P. S. Heterogeneity of epidermal growth factor receptor signalling networks in glioblastoma. Nat. Rev. Cancer 15, 302–310 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  147. 147

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

    CAS  PubMed  Google Scholar 

  148. 148

    Aktipis, C. A., Boddy, A. M., Gatenby, R. A., Brown, J. S. & Maley, C. C. Life history trade-offs in cancer evolution. Nat. Rev. Cancer 13, 883–892 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  149. 149

    Almendro, V., Marusyk, A. & Polyak, K. Cellular heterogeneity and molecular evolution in cancer. Annu. Rev. Pathol. 8, 277–302 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  150. 150

    Lawrence, M. S. et al. Discovery and saturation analysis of cancer genes across 21 tumour types. Nature 505, 495–501 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  151. 151

    Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  152. 152

    Quail, D. F. & Joyce, J. A. Microenvironmental regulation of tumor progression and metastasis. Nat. Med. 19, 1423–1437 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  153. 153

    Semenza, G. L. Cancer–stromal cell interactions mediated by hypoxia-inducible factors promote angiogenesis, lymphangiogenesis, and metastasis. Oncogene 32, 4057–4063 (2013).

    CAS  PubMed  Google Scholar 

  154. 154

    Yaffe, M. B. The scientific drunk at the lamppost: massive sequencing efforts in cancer discovery and treatment. Sci. Signal. 6, e13 (2013).

    Google Scholar 

  155. 155

    Brennan, C. W. et al. The somatic genetic landscape of glioblastoma. Cell 155, 462–477 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  156. 156

    Chin, L. et al. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature 455, 1061–1068 (2008).

    Google Scholar 

  157. 157

    Inda, M.-d.-M. et al. Tumor heterogeneity is an active process maintained by a mutant EGFR-induced cytokine circuit in glioblastoma. Genes Dev. 24, 1731–1745 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  158. 158

    Zadeh, G., Bhat, K. P. L. & Aldape, K. EGFR and EGFRvIII in glioblastoma: partners in crime. Cancer Cell 24, 403–404 (2013).

    CAS  PubMed  Google Scholar 

  159. 159

    Bachoo, R. M. et al. Epidermal growth factor receptor and Ink4a/Arf: convergent mechanisms governing terminal differentiation and transformation along the neural stem cell to astrocyte axis. Cancer Cell 1, 269–277 (2002).

    CAS  PubMed  Google Scholar 

  160. 160

    Sottoriva, A. et al. Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc. Natl Acad. Sci. USA 110, 4009–4014 (2013).

    CAS  Google Scholar 

  161. 161

    Gill, B. J. et al. MRI-localized biopsies reveal subtype-specific differences in molecular and cellular composition at the margins of glioblastoma. Proc. Natl Acad. Sci. USA 111, 12550–12555 (2014).

    CAS  Google Scholar 

  162. 162

    Klages, R. et al. Nonequilibrium Statistical Physics of Small Systems: Fluctuation Relations and Beyond (Wiley, 2013).

    Google Scholar 

  163. 163

    Losick, R. & Desplan, C. Stochasticity and cell fate. Science 320, 65–68 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  164. 164

    Balaban, N. Q., Merrin, J., Chait, R., Kowalik, L. & Leibler, S. Bacterial persistence as a phenotypic switch. Science 305, 1622–1625 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  165. 165

    Aguirre-Ghiso, J. A. Models, mechanisms and clinical evidence for cancer dormancy. Nat. Rev. Cancer 7, 834–846 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  166. 166

    Kholodenko, B., Yaffe, M. B. & Kolch, W. Computational approaches for analyzing information flow in biological networks. Sci. Signal. 5, re1 (2012).

    PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors acknowledge the following funding agencies and grants for support of some of the work described in this Review: The National Cancer Institute (5R01CA170689 to J.R.H. as the principal investigator (PI) and A.R. as the co-PI, and 5U54 CA119347 to J.R.H. as the PI); Stand up to Cancer Foundation (to A.R. and J.R.H.); the Cancer Research Institute (to A.R. and J.R.H.); The Ben and Catherine Ivy Foundation (to P.S.M. and J.R.H.); and the Jean Perkins Foundation (to J.R.H. as the PI).

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Correspondence to James R. Heath.

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J.R.H. and A.R. are on the Scientific Advisory Board of Isoplexis, which is seeking to commercialize certain aspects of the single-cell barcode chip technology.

PowerPoint slides

Glossary

Whole-genome amplification

A method, first reported using PCR by Arnheim's group, for nonselectively amplifying all DNA sequences present in a given sample, including a single cell.

Multiple displacement amplification

A non-PCR based, room temperature DNA amplification technique reported by Lasken's group that is commonly used for whole-genome amplification.

Multiple annealing and looping-based amplification cycles

(MALBAC). A PCR-type approach reported by Xie's group in which the enzymatic amplification of cDNAs proceeds via a linear process.

Exome sequencing

Genome sequencing that is limited to only the small fraction (1%) of the genome that is protein encoding.

RNA-sequencing

(RNA-seq). Also called whole transcriptome shotgun sequencing, RNA-seq is a method for analysing the transcriptome of a sample using next-generation sequencing tools.

Molecular barcoding

An approach through which a DNA sequence or some other molecular identifier is used as an identifier of a specific cell or a specific transcript generated by that cell.

Cytoseq

A microchip-based single-cell transcriptomics method reported by Fodor's group at Cellular Research in 2015.

inDrop

A nanodrop-based single-cell transcriptomics method reported by Klein and others in 2015.

Unique molecular index

(UMI). A molecular barcode used to identify a specific transcript from a specific cell.

DropSeq

A nanodrop-based single-cell transcriptomics method reported by Macosko and others in 2015.

Nanodrops

Microfluidics methods in which individual assays are carried out in isolated nanolitre-size droplets of water, separated from one another by oil.

Mass cytometry

A single cell proteomics method based on traditional flow cytometry methods but uses mass labels and mass spectrometry for protein analysis.

Microengraving

A microfluidics single-cell proteomics method.

Single-cell barcode chips

(SCBCs). A single-cell proteomics method

Single-cell western blottings

(scWesterns). A microchip- based method for carrying out western blotting assays on single cells.

Neoantigens

Small peptide fragments that contain a genetic mutation. These fragments may be recognized by T cells during an antitumour immune response.

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Heath, J., Ribas, A. & Mischel, P. Single-cell analysis tools for drug discovery and development. Nat Rev Drug Discov 15, 204–216 (2016). https://doi.org/10.1038/nrd.2015.16

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