1932

Abstract

Thousands of transcripts and proteins confer function and discriminate cell types in the body. Using high-parameter technologies, we can now measure many of these markers at once, and multiple platforms are now capable of analysis on a cell-by-cell basis. Three high-parameter single-cell technologies have particular potential for discovering new biomarkers, revealing disease mechanisms, and increasing our fundamental understanding of cell biology. We review these three platforms (high-parameter flow cytometry, mass cytometry, and a new class of technologies called integrated molecular cytometry platforms) in this article. We describe the underlying hardware and instrumentation, the reagents involved, and the limitations and advantages of each platform. We also highlight the emerging field of high-parameter single-cell data analysis, providing an accessible overview of the data analysis process and choice of tools.

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2019-06-12
2024-04-22
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Literature Cited

  1. 1.
    De Rosa SC, Herzenberg LA, Herzenberg LA, Roederer M 2001. 11-Color, 13-parameter flow cytometry: identification of human naive T cells by phenotype, function and T-cell receptor diversity. Nat. Med. 7:2245–48
    [Google Scholar]
  2. 2.
    Mackay LK, Rahimpour A, Ma JZ, Collins N, Stock AT et al. 2013. The developmental pathway for CD103+CD8+ tissue-resident memory T cells of skin. Nat. Immunol. 14:121294–301
    [Google Scholar]
  3. 3.
    Bachmann MF, Waterhouse P, Speiser DE, McKall-Faienza K, Mak TW, Ohashi PS 1998. Normal responsiveness of CTLA-4-deficient anti-viral cytotoxic T cells. J. Immunol. 160:195–100
    [Google Scholar]
  4. 4.
    Chattopadhyay PK, Roederer M. 2010. Good cell, bad cell: flow cytometry reveals T-cell subsets important in HIV disease. Cytom. A 77:7614–22
    [Google Scholar]
  5. 5.
    Feugier P. 2015. A review of rituximab, the first anti-CD20 monoclonal antibody used in the treatment of B non-Hodgkin's lymphomas. Future Oncol 11:91327–42
    [Google Scholar]
  6. 6.
    Siddiqui MAA, Scott LJ. 2005. Infliximab: a review of its use in Crohn's disease and rheumatoid arthritis. Drugs 65:152179–208
    [Google Scholar]
  7. 7.
    Keating GM. 2016. Nivolumab: a review in advanced nonsquamous non-small cell lung cancer. Drugs 76:9969–78
    [Google Scholar]
  8. 8.
    Chattopadhyay PK, Roederer M. 2015. A mine is a terrible thing to waste: high content, single cell technologies for comprehensive immune analysis. Am. J. Transplant. 15:51155–61
    [Google Scholar]
  9. 9.
    Chattopadhyay PK, Gierahn TM, Roederer M, Love JC 2014. Single-cell technologies for monitoring immune systems. Nat. Immunol. 15:2128–35
    [Google Scholar]
  10. 10.
    Leelatian N, Doxie DB, Greenplate AR, Mobley BC, Lehman JM et al. 2017. Single cell analysis of human tissues and solid tumors with mass cytometry. Cytom. B Clin. Cytom. 92:168–78
    [Google Scholar]
  11. 11.
    Roy AL, Conroy R, Smith J, Yao Y, Beckel-Mitchener AC et al. 2018. Accelerating a paradigm shift: The Common Fund Single Cell Analysis Program. Sci. Adv. 4:8eaat8573
    [Google Scholar]
  12. 12.
    Pittet MJ, Garris CS, Arlauckas SP, Weissleder R 2018. Recording the wild lives of immune cells. Sci. Immunol. 3:27eaaq0491
    [Google Scholar]
  13. 13.
    Stavrakis S, Holzner G, Choo J, deMello A 2019. High-throughput microfluidic imaging flow cytometry. Curr. Opin. Biotechnol. 55:36–43
    [Google Scholar]
  14. 14.
    Estes JD, LeGrand R, Petrovas C 2018. Visualizing the immune system: providing key insights into HIV/SIV infections. Front. Immunol. 9:423
    [Google Scholar]
  15. 15.
    Giesen C, Wang HAO, Schapiro D, Zivanovic N, Jacobs A et al. 2014. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat. Methods 11:4417–22
    [Google Scholar]
  16. 16.
    Goltsev Y, Samusik N, Kennedy-Darling J, Bhate S, Hale M et al. 2018. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174:4968–81.e15
    [Google Scholar]
  17. 17.
    Robinson JP, Roederer M. 2015. Flow cytometry strikes gold. Science 350:6262739–40
    [Google Scholar]
  18. 18.
    Bray C, Spidlen J, Brinkman RR 2012. FCS 3.1 implementation guidance. Cytom. A 81A:6523–26
    [Google Scholar]
  19. 19.
    Ward MD, Kaduchak G. 2018. Fundamentals of acoustic cytometry. Curr. Protoc. Cytom. 84:1e36
    [Google Scholar]
  20. 20.
    Shrirao AB, Fritz Z, Novik EM, Yarmush GM, Schloss RS et al. 2018. Microfluidic flow cytometry: the role of microfabrication methodologies, performance and functional specification. Technology 6:11–23
    [Google Scholar]
  21. 21.
    Waggoner A. 1997. Optical filter sets for multiparameter flow cytometry. Curr. Protoc. Cytom. https://doi.org/10.1002/0471142956.cy0105s00
    [Crossref] [Google Scholar]
  22. 22.
    Wang L, Hoffman RA. 2017. Standardization, calibration, and control in flow cytometry. Curr. Protoc. Cytom. 79:1–27
    [Google Scholar]
  23. 23.
    Telford WG. 2018. Overview of lasers for flow cytometry. Methods Mol. Biol. 1678:447–79
    [Google Scholar]
  24. 24.
    Haugland RP. 1994. Spectra of fluorescent dyes used in flow cytometry. Methods Cell Biol 42:641–63
    [Google Scholar]
  25. 25.
    Leavesley SJ, Britain AL, Cichon LK, Nikolaev VO, Rich TC 2013. Assessing FRET using spectral techniques. Cytom. A 83:10898–912
    [Google Scholar]
  26. 26.
    Gratama JW, D'hautcourt J-L, Mandy F, Rothe G, Barnett D et al. 1998. Flow cytometric quantitation of immunofluorescence intensity: problems and perspectives. Cytometry 33:2166–78
    [Google Scholar]
  27. 27.
    Zarkowsky D, Lamoreaux L, Chattopadhyay P, Koup RA, Perfetto SP, Roederer M 2011. Heavy metal contaminants can eliminate quantum dot fluorescence. Cytom. A 79:184–89
    [Google Scholar]
  28. 28.
    Ganesan A, Chattopadhyay PK, Brodie TM, Qin J, Gu W et al. 2010. Immunologic and virologic events in early HIV infection predict subsequent rate of progression. J. Infect. Dis. 201:2272–84
    [Google Scholar]
  29. 29.
    Berlier JE, Rothe A, Buller G, Bradford J, Gray DR et al. 2003. Quantitative comparison of long-wavelength Alexa Fluor dyes to Cy dyes: fluorescence of the dyes and their bioconjugates. J. Histochem. Cytochem. 51:121699–1712
    [Google Scholar]
  30. 30.
    Bigos M, Baumgarth N, Jager GC, Herman OC, Nozaki T et al. 1999. Nine color eleven parameter immunophenotyping using three laser flow cytometry. Cytometry 36:136–45
    [Google Scholar]
  31. 31.
    Chattopadhyay PK, Price DA, Harper TF, Betts MR, Yu J et al. 2006. Quantum dot semiconductor nanocrystals for immunophenotyping by polychromatic flow cytometry. Nat. Med. 12:8972–77
    [Google Scholar]
  32. 32.
    Chattopadhyay PK, Perfetto SP, Yu J, Roederer M 2010. The use of quantum dot nanocrystals in multicolor flow cytometry. Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 2:4334–48
    [Google Scholar]
  33. 33.
    Lovrić J, Bazzi HS, Cuie Y, Fortin GRA, Winnik FM, Maysinger D 2005. Differences in subcellular distribution and toxicity of green and red emitting CdTe quantum dots. J. Mol. Med. 83:5377–85
    [Google Scholar]
  34. 34.
    Calattini S, Sereti I, Scheinberg P, Kimura H, Childs RW et al. 2010. Detection of EBV genomes in plasmablasts/plasma cells and non-B cells in the blood of most patients with EBV lymphoproliferative disorders by using Immuno-FISH. Blood 116:224546–59
    [Google Scholar]
  35. 35.
    Chattopadhyay PK, Gaylord B, Palmer A, Jiang N, Raven MA et al. 2012. Brilliant violet fluorophores: a new class of ultrabright fluorescent compounds for immunofluorescence experiments. Cytom. A 81A:6456–66
    [Google Scholar]
  36. 36.
    Nettey L, Giles A, Chattopadhyay PK 2018. A 28-color/30-parameter fluorescence flow cytometry panel to enumerate and characterize cells expressing a wide array of immune checkpoint molecules. Cytom. A 93:111094–96
    [Google Scholar]
  37. 37.
    Invitrogen 2010. Fluorophores and their amine-reactive derivatives. The Molecular Probes Handbook: A Guide to Fluorescent Probes and Labeling Technologies15–96 Waltham, MA: Thermo Fisher Sci.
    [Google Scholar]
  38. 38.
    Mahnkey Y, Chattopadhyay PK, Roederer M 2010. Publication of optimized multicolor immunofluorescence panels. Cytom. A 77:9814–18
    [Google Scholar]
  39. 39.
    Maciorowski Z, Chattopadhyay PK, Jain P 2017. Basic multicolor flow cytometry. Curr. Protoc. Cytom. 117:15.4.1–38
    [Google Scholar]
  40. 40.
    Cossarizza A, Chang H-D, Radbruch A, Akdis M, Andrä I et al. 2017. Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur. J. Immunol. 47:101584–1797
    [Google Scholar]
  41. 41.
    Perfetto SP, Ambrozak D, Nguyen R, Chattopadhyay PK, Roederer M 2012. Quality assurance for polychromatic flow cytometry using a suite of calibration beads. Nat. Protoc. 7:122067–79
    [Google Scholar]
  42. 42.
    Perfetto SP, Chattopadhyay PK, Wood J, Nguyen R, Ambrozak D et al. 2014. Q and B values are critical measurements required for inter-instrument standardization and development of multicolor flow cytometry staining panels. Cytom. A 85:121037–48
    [Google Scholar]
  43. 43.
    Saeys Y, Van Gassen S, Lambrecht BN 2016. Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat. Rev. Immunol. 16:7449–62
    [Google Scholar]
  44. 44.
    Kvistborg P, Gouttefangeas C, Aghaeepour N, Cazaly A, Chattopadhyay PK et al. 2015. Thinking outside the gate: single-cell assessments in multiple dimensions. Immunity 42:4591–92
    [Google Scholar]
  45. 45.
    Winters A, Alesandre J, Chattopadhyay PK 2018. ColorWheel: an automated tool for instrument setup and panel design in flow cytometry Paper presented at the 33rd Congress of the International Society for Advancement of Cytometry Prague
  46. 46.
    Fletez-Brant K, Špidlen J, Brinkman RR, Roederer M, Chattopadhyay PK 2016. flowClean: automated identification and removal of fluorescence anomalies in flow cytometry data. Cytom. A 89:5461–71
    [Google Scholar]
  47. 47.
    Bolton DL, McGinnis K, Finak G, Chattopadhyay P, Gottardo R, Roederer M 2017. Combined single-cell quantitation of host and SIV genes and proteins ex vivo reveals host-pathogen interactions in individual cells. PLOS Pathog 13:6e1006445
    [Google Scholar]
  48. 48.
    Dominguez MH, Chattopadhyay PK, Ma S, Lamoreaux L, McDavid A et al. 2013. Highly multiplexed quantitation of gene expression on single cells. J. Immunol. Methods 391:1–2133–45
    [Google Scholar]
  49. 49.
    Roederer M. 2016. Distributions of autofluorescence after compensation: be panglossian, fret not. Cytom. A 89:4398–402
    [Google Scholar]
  50. 50.
    Roederer M. 2002. Compensation in flow cytometry. Curr. Protoc. Cytom. 22:11.14.1–20
    [Google Scholar]
  51. 51.
    Nguyen R, Perfetto S, Mahnke YD, Chattopadhyay P, Roederer M 2013. Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design. Cytom. A 83:3306–15
    [Google Scholar]
  52. 52.
    Mahnke YD, Roederer M. 2007. Optimizing a multicolor immunophenotyping assay. Clin. Lab. Med. 27:3469–85
    [Google Scholar]
  53. 53.
    Sallusto F, Lenig D, Förster R, Lipp M, Lanzavecchia A 1999. Two subsets of memory T lymphocytes with distinct homing potentials and effector functions. Nature 401:6754708–12
    [Google Scholar]
  54. 54.
    Lugli E, Dominguez MH, Gattinoni L, Chattopadhyay PK, Bolton DL et al. 2013. Superior T memory stem cell persistence supports long-lived T cell memory. J. Clin. Investig. 123:2594–99
    [Google Scholar]
  55. 55.
    Picker LJ, Singh MK, Zdraveski Z, Treer JR, Waldrop SL et al. 1995. Direct demonstration of cytokine synthesis heterogeneity among human memory/effector T cells by flow cytometry. Blood 86:41408–19
    [Google Scholar]
  56. 56.
    Betts MR, Nason MC, West SM, De Rosa SC, Migueles SA et al. 2006. HIV nonprogressors preferentially maintain highly functional HIV-specific CD8+ T cells. Blood 107:124781–89
    [Google Scholar]
  57. 57.
    Pizzolla A, Nguyen THO, Sant S, Jaffar J, Loudovaris T et al. 2018. Influenza-specific lung-resident memory T cells are proliferative and polyfunctional and maintain diverse TCR profiles. J. Clin. Investig. 128:2721–33
    [Google Scholar]
  58. 58.
    Burel JG, Apte SH, Groves PL, McCarthy JS, Doolan DL 2017. Polyfunctional and IFN-γ monofunctional human CD4+ T cell populations are molecularly distinct. JCI Insight 2:3e87499
    [Google Scholar]
  59. 59.
    Lam JKP, Hui KF, Ning RJ, Xu XQ, Chan KH, Chiang AKS 2018. Emergence of CD4+ and CD8+ polyfunctional T cell responses against immunodominant lytic and latent EBV antigens in children with primary EBV infection. Front. Microbiol. 9:416
    [Google Scholar]
  60. 60.
    Doescher J, Jeske S, Weissinger SE, Brunner C, Laban S et al. 2018. Polyfunctionality of CD4+ T lymphocytes is increased after chemoradiotherapy of head and neck squamous cell carcinoma. Strahlenther. Onkol. 194:5392–402
    [Google Scholar]
  61. 61.
    Hassert M, Wolf KJ, Schwetye KE, DiPaolo RJ, Brien JD, Pinto AK 2018. CD4+ T cells mediate protection against Zika associated severe disease in a mouse model of infection. PLOS Pathog 14:9e1007237
    [Google Scholar]
  62. 62.
    Geldenhuys H, Mearns H, Miles DJC, Tameris M, Hokey D et al. 2015. The tuberculosis vaccine H4:IC31 is safe and induces a persistent polyfunctional CD4 T cell response in South African adults: a randomized controlled trial. Vaccine 33:303592–99
    [Google Scholar]
  63. 63.
    Brummelman J, Mazza EMC, Alvisi G, Colombo FS, Grilli A et al. 2018. High-dimensional single cell analysis identifies stem-like cytotoxic CD8 + T cells infiltrating human tumors. J. Exp. Med. 215:102520–35
    [Google Scholar]
  64. 64.
    Roederer M, Quaye L, Mangino M, Beddall MH, Mahnke Y et al. 2015. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell 161:2387–403
    [Google Scholar]
  65. 65.
    Bendall SC, Simonds EF, Qiu P, El-ad DA, Krutzik PO et al. 2011. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332:6030687–96
    [Google Scholar]
  66. 66.
    Bodenmiller B, Zunder ER, Finck R, Chen TJ, Savig ES et al. 2012. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat. Biotechnol. 30:9858–67
    [Google Scholar]
  67. 67.
    Bendall SC, Nolan GP, Roederer M, Chattopadhyay PK 2012. A deep profiler's guide to cytometry. Trends Immunol 33:7323–32
    [Google Scholar]
  68. 68.
    Chevrier S, Crowell HL, Zanotelli VRT, Engler S, Robinson MD, Bodenmiller B 2018. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst 6:5612–20.e5
    [Google Scholar]
  69. 69.
    Finck R, Simonds EF, Jager A, Krishnaswamy S, Sachs K et al. 2013. Normalization of mass cytometry data with bead standards. Cytom. A 83:5483–94
    [Google Scholar]
  70. 70.
    See P, Dutertre C-A, Chen J, Günther P, McGovern N et al. 2017. Mapping the human DC lineage through the integration of high-dimensional techniques. Science 356:6342eaag3009
    [Google Scholar]
  71. 71.
    van Unen V, Li N, Molendijk I, Temurhan M, Höllt T et al. 2016. Mass cytometry of the human mucosal immune system identifies tissue- and disease-associated immune subsets. Immunity 44:51227–39
    [Google Scholar]
  72. 72.
    Campbell TM, McSharry BP, Steain M, Ashhurst TM, Slobedman B, Abendroth A 2018. Varicella zoster virus productively infects human natural killer cells and manipulates phenotype. PLOS Pathog 14:4e1006999
    [Google Scholar]
  73. 73.
    Horowitz A, Guethlein LA, Nemat-Gorgani N, Norman PJ, Cooley S et al. 2015. Regulation of adaptive NK cells and CD8 T cells by HLA-C correlates with allogeneic hematopoietic cell transplantation and with cytomegalovirus reactivation. J. Immunol. 195:94524–36
    [Google Scholar]
  74. 74.
    Roy Chowdhury R, Vallania F, Yang Q, Lopez Angel CJ, Darboe F et al. 2018. A multi-cohort study of the immune factors associated with M. tuberculosis infection outcomes. Nature 560:7720644–48
    [Google Scholar]
  75. 75.
    Rao DA, Gurish MF, Marshall JL, Slowikowski K, Fonseka CY et al. 2017. Pathologically expanded peripheral T helper cell subset drives B cells in rheumatoid arthritis. Nature 542:7639110–14
    [Google Scholar]
  76. 76.
    Kaiser Y, Lakshmikanth T, Chen Y, Mikes J, Eklund A et al. 2017. Mass cytometry identifies distinct lung CD4+ T cell patterns in Löfgren's syndrome and non-Löfgren's syndrome sarcoidosis. Front. Immunol. 8:1130
    [Google Scholar]
  77. 77.
    Aghaeepour N, Ganio EA, Mcilwain D, Tsai AS, Tingle M et al. 2017. An immune clock of human pregnancy. Sci. Immunol. 2:15eaan2946
    [Google Scholar]
  78. 78.
    Krieg C, Nowicka M, Guglietta S, Schindler S, Hartmann FJ et al. 2018. High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat. Med. 24:2144–53
    [Google Scholar]
  79. 79.
    Chevrier S, Levine JH, Zanotelli VRT, Silina K, Schulz D et al. 2017. An immune atlas of clear cell renal cell carcinoma. Cell 169:4736–49.e18
    [Google Scholar]
  80. 80.
    Stubbington MJT, Rozenblatt-Rosen O, Regev A, Teichmann SA 2017. Single-cell transcriptomics to explore the immune system in health and disease. Science 358:635958–63
    [Google Scholar]
  81. 81.
    Peterson VM, Zhang KX, Kumar N, Wong J, Li L et al. 2017. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35:10936–39
    [Google Scholar]
  82. 82.
    Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK et al. 2017. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14:9865–68
    [Google Scholar]
  83. 83.
    Butler A, Hoffman P, Smibert P, Papalexi E, Satija R 2018. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36:5411–20
    [Google Scholar]
  84. 84.
    van der Maaten L, Hinton G 2008. Visualizing data using t-SNE. J. Mach. Learn. Res. 9:Nov.2579–605
    [Google Scholar]
  85. 85.
    Tenenbaum JB, de Silva V, Langford JC 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290:55002319–23
    [Google Scholar]
  86. 86.
    Simonds EF, Bendall SC, Gibbs KD, Bruggner RV, Linderman MD et al. 2011. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29:886–91
    [Google Scholar]
  87. 87.
    Van Gassen S, Callebaut B, Van Helden MJ, Lambrecht BN, Demeester P et al. 2015. FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytom. A 87:7636–45
    [Google Scholar]
  88. 88.
    Bruggner RV, Bodenmiller B, Dill DL, Tibshirani RJ, Nolan GP 2014. Automated identification of stratifying signatures in cellular subpopulations. PNAS 111:26E2770–77
    [Google Scholar]
  89. 89.
    Weber LM, Robinson MD. 2016. Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytom. A 89:121084–96
    [Google Scholar]
  90. 90.
    Aghaeepour N, Chattopadhyay P, Chikina M, Dhaene T, Van Gassen S et al. 2016. A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes. Cytom. A 89:116–21
    [Google Scholar]
  91. 91.
    Aghaeepour N, Finak G, Hoos H, Mosmann TR, Brinkman R et al. 2013. Critical assessment of automated flow cytometry data analysis techniques. Nat. Methods 10:3228–38
    [Google Scholar]
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