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From single cells to deep phenotypes in cancer

Abstract

In recent years, major advances in single-cell measurement systems have included the introduction of high-throughput versions of traditional flow cytometry that are now capable of measuring intracellular network activity, the emergence of isotope labels that can enable the tracking of a greater variety of cell markers and the development of super-resolution microscopy techniques that allow measurement of RNA expression in single living cells. These technologies will facilitate our capacity to catalog and bring order to the inherent diversity present in cancer cell populations. Alongside these developments, new computational approaches that mine deep data sets are facilitating the visualization of the shape of the data and enabling the extraction of meaningful outputs. These applications have the potential to reveal new insights into cancer biology at the intersections of stem cell function, tumor-initiating cells and multilineage tumor development. In the clinic, they may also prove important not only in the development of new diagnostic modalities but also in understanding how the emergence of tumor cell clones harboring different sets of mutations predispose patients to relapse or disease progression.

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Figure 1: Signaling-responsiveness–dependent heterogeneity can correlate with signaling outcomes26.
Figure 2: Most antibodies, either against surface proteins delineating immune cells or intracellular targets, can be adapted for use with the CyTOF instrument.

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References

  1. Spencer, S.L. & Sorger, P.K. Measuring and modeling apoptosis in single cells. Cell 144, 926–939 (2011).

    Article  CAS  Google Scholar 

  2. Spencer, S.L. et al. Non-genetic origins of cell-to-cell variability in TRAIL-induced apoptosis. Nature 459, 428–432 (2009).

    Article  CAS  Google Scholar 

  3. Vincent, M. Cancer: a de-repression of a default survival program common to all cells? A life-history perspective on the nature of cancer. Bioessays 34, 72–82 (2012).

    Article  CAS  Google Scholar 

  4. Greaves, M. & Maley, C.C. Clonal evolution in cancer. Nature 481, 306–313 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  6. Medema, J.P. & Vermeulen, L. Microenvironmental regulation of stem cells in intestinal homeostasis and cancer. Nature 474, 318–326 (2011).

    Article  CAS  Google Scholar 

  7. Wang, X. et al. A luminal epithelial stem cell that is a cell of origin for prostate cancer. Nature 461, 495–500 (2009).

    Article  CAS  Google Scholar 

  8. Knoepfler, P. Journal club. A cell biologist looks at the risk and promise of a new insight into stem cells and cancer. Nature 457, 361 (2009).

    Article  CAS  Google Scholar 

  9. Yang, J. et al. Intratumoral heterogeneity determines discordant results of diagnostic tests for human epidermal growth factor receptor (HER) 2 in gastric cancer specimens. Cell Biochem. Biophys. 62, 221–228 (2012).

    Article  CAS  Google Scholar 

  10. Zhang, J., Yang, P.L. & Gray, N.S. Targeting cancer with small molecule kinase inhibitors. Nat. Rev. Cancer 9, 28–39 (2009).

    Article  Google Scholar 

  11. Taussig, D.C. et al. Leukemia-initiating cells from some acute myeloid leukemia patients with mutated nucleophosmin reside in the CD34(-) fraction. Blood 115, 1976–1984 (2010).

    Article  CAS  Google Scholar 

  12. Yap, T.A. et al. Intratumor heterogeneity: seeing the wood for the trees. Sci. Transl. Med. 4, 127ps10 (2012).

    Article  Google Scholar 

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

    Article  CAS  Google Scholar 

  14. Longo, D.L. Tumor heterogeneity and personalized medicine. N. Engl. J. Med. 366, 956–957 (2012).

    Article  CAS  Google Scholar 

  15. Cantor, H. et al. Characterization of subpopulations of T lymphocytes. I. Separation and functional studies of peripheral T-cells binding different amounts of fluorescent anti-Thy 1.2 (theta) antibody using a fluorescence-activated cell sorter (FACS). Cell. Immunol. 15, 180–196 (1975).

    Article  CAS  Google Scholar 

  16. Parks, D.R. et al. Antigen-specific identification and cloning of hybridomas with a fluorescence-activated cell sorter. Proc. Natl. Acad. Sci. USA 76, 1962–1966 (1979).

    Article  CAS  Google Scholar 

  17. Werner, M. et al. Microfluidic array cytometer based on refractive optical tweezers for parallel trapping, imaging and sorting of individual cells. Lab Chip 11, 2432–2439 (2011).

    Article  CAS  Google Scholar 

  18. Wlodkowic, D. & Darzynkiewicz, Z. Rise of the micromachines: microfluidics and the future of cytometry. Methods Cell Biol. 102, 105–125 (2011).

    Article  Google Scholar 

  19. Liu, A.Y., Roudier, M.P. & True, L.D. Heterogeneity in primary and metastatic prostate cancer as defined by cell surface CD profile. Am. J. Pathol. 165, 1543–1556 (2004).

    Article  Google Scholar 

  20. Bragado, P. et al. Analysis of marker-defined HNSCC subpopulations reveals a dynamic regulation of tumor initiating properties. PLoS ONE 7, e29974 (2012).

    Article  CAS  Google Scholar 

  21. Choijamts, B. et al. CD133+ cancer stem cell-like cells derived from uterine carcinosarcoma (malignant mixed Mullerian tumor). Stem Cells 29, 1485–1495 (2011).

    Article  CAS  Google Scholar 

  22. Bonnet, D. & Dick, J.E. Human acute myeloid leukemia is organized as a hierarchy that originates from a primitive hematopoietic cell. Nat. Med. 3, 730–737 (1997).

    Article  CAS  Google Scholar 

  23. Cho, R.W. & Clarke, M.F. Recent advances in cancer stem cells. Curr. Opin. Genet. Dev. 18, 48–53 (2008).

    Article  CAS  Google Scholar 

  24. Lobo, N.A. et al. The biology of cancer stem cells. Annu. Rev. Cell Dev. Biol. 23, 675–699 (2007).

    Article  CAS  Google Scholar 

  25. Nguyen, L.V. et al. Cancer stem cells: an evolving concept. Nat. Rev. Cancer 12, 133–143 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  27. Kotecha, N. et al. Single-cell profiling identifies aberrant STAT5 activation in myeloid malignancies with specific clinical and biologic correlates. Cancer Cell 14, 335–343 (2008).

    Article  CAS  Google Scholar 

  28. Irish, J.M. et al. B-cell signaling networks reveal a negative prognostic human lymphoma cell subset that emerges during tumor progression. Proc. Natl. Acad. Sci. USA 107, 12747–12754 (2010).

    Article  CAS  Google Scholar 

  29. Palazzo, A.L. et al. Association of reactive oxygen species-mediated signal transduction with in vitro apoptosis sensitivity in chronic lymphocytic leukemia B cells. PLoS ONE 6, e24592 (2011).

    Article  CAS  Google Scholar 

  30. Perez, O.D. & Nolan, G.P. Resistance is futile: assimilation of cellular machinery by HIV-1. Immunity 15, 687–690 (2001).

    Article  CAS  Google Scholar 

  31. Krutzik, P.O. & Nolan, G.P. Intracellular phospho-protein staining techniques for flow cytometry: monitoring single cell signaling events. Cytometry A 55, 61–70 (2003).

    Article  Google Scholar 

  32. Sachs, K. et al. Causal protein-signaling networks derived from multiparameter single-cell data. Science 308, 523–529 (2005).

    Article  CAS  Google Scholar 

  33. Rosen, D.B. et al. Distinct patterns of DNA damage response and apoptosis correlate with Jak/Stat and PI3kinase response profiles in human acute myelogenous leukemia. PLoS ONE 5, e12405 (2010).

    Article  Google Scholar 

  34. Krutzik, P.O. et al. High-content single-cell drug screening with phosphospecific flow cytometry. Nat. Chem. Biol. 4, 132–142 (2008).

    Article  CAS  Google Scholar 

  35. Sachs, K. et al. Characterization of patient specific signaling via augmentation of Bayesian networks with disease and patient state nodes. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, 6624–6627 (2009).

    PubMed  PubMed Central  Google Scholar 

  36. Sachs, K. et al. Learning signaling network structures with sparsely distributed data. J. Comput. Biol. 16, 201–212 (2009).

    Article  CAS  Google Scholar 

  37. Rosen, D.B. et al. Assessing signaling pathways associated with in vitro resistance to cytotoxic agents in AML. Leuk. Res. 900–904 (2012).

    Article  CAS  Google Scholar 

  38. Cesano, A. et al. Functional pathway analysis in acute myeloid leukemia using single cell network profiling assay: effect of specimen source (bone marrow or peripheral blood) on assay readouts. Cytometry B Clin. Cytom. 82, 158–172 (2012).

    Article  Google Scholar 

  39. Longo, D.M. et al. Single-cell network profiling of peripheral blood mononuclear cells from healthy donors reveals age- and race-associated differences in immune signaling pathway activation. J. Immunol. 188, 1717–1725 (2012).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  41. Bandura, D.R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813–6822 (2009).

    Article  CAS  Google Scholar 

  42. 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).

    Article  CAS  Google Scholar 

  43. Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011).

    Article  CAS  Google Scholar 

  44. Newell, E.W. et al. Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 36, 142–152 (2012).

    Article  CAS  Google Scholar 

  45. Behbehani, G. et al. Single cell mass cytometry adapted to measurements of the cell cycle. Cytometry A (in the press).

  46. Fienberg, H.G., Simonds. E.F. Fantl. W.J. Nolan. G.P. & Bodenmiller, B. A platinum-based covalent viability reagent for single-cell mass cytometry. Cytometry A 81, 467–475 (2012).

    Article  Google Scholar 

  47. Hoshida, Y. et al. Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res. 69, 7385–7392 (2009).

    Article  CAS  Google Scholar 

  48. Hoshida, Y. et al. Gene expression in fixed tissues and outcome in hepatocellular carcinoma. N. Engl. J. Med. 359, 1995–2004 (2008).

    Article  CAS  Google Scholar 

  49. Mullighan, C.G. et al. Genomic analysis of the clonal origins of relapsed acute lymphoblastic leukemia. Science 322, 1377–1380 (2008).

    Article  CAS  Google Scholar 

  50. Notta, F. et al. Evolution of human BCR-ABL1 lymphoblastic leukaemia-initiating cells. Nature 469, 362–367 (2011).

    Article  CAS  Google Scholar 

  51. Kapranov, P., Ozsolak, F. & Milos, P.M. Profiling of short RNAs using Helicos single-molecule sequencing. Methods Mol. Biol. 822, 219–232 (2012).

    Article  CAS  Google Scholar 

  52. Nagalakshmi, U. et al. The transcriptional landscape of the yeast genome defined by RNA sequencing. Science 320, 1344–1349 (2008).

    Article  CAS  Google Scholar 

  53. Ozsolak, F. & Milos, P.M. RNA sequencing: advances, challenges and opportunities. Nat. Rev. Genet. 12, 87–98 (2011).

    Article  CAS  Google Scholar 

  54. Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).

    Article  CAS  Google Scholar 

  55. Ley, T.J. et al. DNA sequencing of a cytogenetically normal acute myeloid leukaemia genome. Nature 456, 66–72 (2008).

    Article  CAS  Google Scholar 

  56. Mardis, E.R. et al. Recurring mutations found by sequencing an acute myeloid leukemia genome. N. Engl. J. Med. 361, 1058–1066 (2009).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  58. Walter, M.J. et al. Clonal architecture of secondary acute myeloid leukemia. N. Engl. J. Med. 366, 1090–1098 (2012).

    Article  CAS  Google Scholar 

  59. Anderson, K. et al. Genetic variegation of clonal architecture and propagating cells in leukaemia. Nature 469, 356–361 (2011).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  63. Dalerba, P. et al. Single-cell dissection of transcriptional heterogeneity in human colon tumors. Nat. Biotechnol. 29, 1120–1127 (2011).

    Article  CAS  Google Scholar 

  64. Gibbs, K.D. Jr. et al. Decoupling of tumor-initiating activity from stable immunophenotype in HoxA9-Meis1-driven AML. Cell Stem Cell 10, 210–217 (2012).

    Article  CAS  Google Scholar 

  65. Cornett, D.S. et al. MALDI imaging mass spectrometry: molecular snapshots of biochemical systems. Nat. Methods 4, 828–833 (2007).

    Article  CAS  Google Scholar 

  66. Ornatsky, O.I. et al. Study of cell antigens and intracellular DNA by identification of element-containing labels and metallointercalators using inductively coupled plasma mass spectrometry. Anal. Chem. 80, 2539–2547 (2008).

    Article  CAS  Google Scholar 

  67. Steinhauser, M.L. et al. Multi-isotope imaging mass spectrometry quantifies stem cell division and metabolism. Nature 481, 516–519 (2012).

    Article  CAS  Google Scholar 

  68. Engelhard, C. Inductively coupled plasma mass spectrometry: recent trends and developments. Anal. Bioanal. Chem. 399, 213–219 (2011).

    Article  CAS  Google Scholar 

  69. Nishiguchi, M. et al. Ion optical evaluation of a miniature double-focusing mass spectrograph. Eur. J. Mass Spectrom. (Chichester, Eng.) 14, 7–15 (2008).

    Article  CAS  Google Scholar 

  70. Schilling, G.D. et al. Continuous simultaneous detection in mass spectrometry. Anal. Chem. 79, 7662–7668 (2007).

    Article  CAS  Google Scholar 

  71. De Stefano, J.A. et al. Analysis of Pneumocystis carinii cyst wall. II. Sugar composition. J. Protozool. 37, 436–441 (1990).

    Article  CAS  Google Scholar 

  72. Barnes, J.H. 4th. et al. Characterization of a focal plane camera fitted to a Mattauch-Herzog geometry mass spectrograph. 2. Use with an inductively coupled plasma. Anal. Chem. 76, 2531–2536 (2004).

    Article  CAS  Google Scholar 

  73. Barnes, J.H. 4th. et al. Use of a novel array detector for the direct analysis of solid samples by laser ablation inductively coupled plasma sector-field mass spectrometry. J. Am. Soc. Mass Spectrom. 15, 769–776 (2004).

    Article  CAS  Google Scholar 

  74. Barnes, J.H. 4th. et al. Characterization of a focal plane camera fitted to a Mattauch-Herzog geometry mass spectrograph. 1. Use with a glow-discharge source. Anal. Chem. 74, 5327–5332 (2002).

    Article  CAS  Google Scholar 

  75. Zhang, D.S. et al. Multi-isotope imaging mass spectrometry reveals slow protein turnover in hair-cell stereocilia. Nature 481, 520–524 (2012).

    Article  CAS  Google Scholar 

  76. Lechene, C. et al. High-resolution quantitative imaging of mammalian and bacterial cells using stable isotope mass spectrometry. J. Biol. 5, 20 (2006).

    Article  Google Scholar 

  77. Irish, J.M., Kotecha, N. & Nolan, G.P. Mapping normal and cancer cell signalling networks: towards single-cell proteomics. Nat. Rev. Cancer 6, 146–155 (2006).

    Article  CAS  Google Scholar 

  78. Bendall, S.C. et al. A deep profiler's guide to cytometry. Trends Immunol. published online, doi:10.1016/j.it.2012.02.010 (2 April 2012).

  79. Ghosn, E.E. et al. Distinct B-cell lineage commitment distinguishes adult bone marrow hematopoietic stem cells. Proc. Natl. Acad. Sci. USA 109, 5394–5398 (2012).

    Article  CAS  Google Scholar 

  80. Tung, J.W. et al. Modern flow cytometry: a practical approach. Clin. Lab. Med. 27, 453–468 (2007).

    Article  Google Scholar 

  81. Giesen, C. et al. Multiplexed immunohistochemical detection of tumor markers in breast cancer tissue using laser ablation inductively coupled plasma mass spectrometry. Anal. Chem. 83, 8177–8183 (2011).

    Article  CAS  Google Scholar 

  82. Moreno-Gordaliza, E. et al. Elemental bioimaging in kidney by LA-ICP-MS as a tool to study nephrotoxicity and renal protective strategies in cisplatin therapies. Anal. Chem. 83, 7933–7940 (2011).

    Article  CAS  Google Scholar 

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Acknowledgements

G.P.N. is supported by the Rachford and Carlota A. Harris Endowed Professorship and grants from U19 AI057229, P01 CA034233, HHSN272200700038C, 1R01CA130826, CIRM DR1-01477 and RB2-01592, NCI RFA CA 09-011, NHLBI-HV-10-05(2), European Commission HEALTH.2010.1.2-1, and the Bill and Melinda Gates Foundation (GF12141-137101). S.C.B. is supported by the Damon Runyon Cancer Research Foundation Fellowship (DRG-2017-09). The authors would also like to thank M. Angelo for useful discussions pertaining to the information in Table 1.

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Correspondence to Garry P Nolan.

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G.P.N. is a paid consultant for, receives royalties from, or owns equity in the following companies whose products or services are directly or indirectly discussed in this article: Becton Dickinson, Nodality and DVS Sciences.

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Bendall, S., Nolan, G. From single cells to deep phenotypes in cancer. Nat Biotechnol 30, 639–647 (2012). https://doi.org/10.1038/nbt.2283

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