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High-Dimensional Immunophenotyping with Fluorescence-Based Cytometry: A Practical Guidebook

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 2032))

Abstract

Recent technological advances have greatly diversified the platforms that are available for high-dimensional single-cell immunophenotyping, including mass cytometry, single-cell RNA sequencing, and fluorescent-based flow cytometry. The latter is currently the most commonly used approach, and modern instrumentation allows for the measurement of up to 30 parameters, revealing deep insights into the complexity of the immune system.

Here, we provide a practical guidebook for the successful design and execution of complex fluorescence-based immunophenotyping panels. We address common misconceptions and caveats, and also discuss challenges that are associated with the quality control and analysis of these data sets.

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References

  1. Fulwyler MJ (1965) Electronic separation of biological cells by volume. Science 150:910–911

    Article  CAS  PubMed  Google Scholar 

  2. Robinson JP, Roederer M (2015) History of science flow cytometry strikes gold. Science 350:739–740. https://doi.org/10.1126/science.aad6770

    Article  CAS  PubMed  Google Scholar 

  3. Bandura DR, Baranov VI, Ornatsky OI et al (2009) 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. https://doi.org/10.1021/ac901049w

    Article  CAS  PubMed  Google Scholar 

  4. Bendall SC, Simonds EF, Qiu P et al (2011) Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332:687–696. https://doi.org/10.1126/science.1198704

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Spitzer MH, Nolan GP (2016) Mass cytometry: single cells, many features. Cell 165:780–791. https://doi.org/10.1016/j.cell.2016.04.019

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Tang F, Barbacioru C, Wang Y et al (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6:377–382. https://doi.org/10.1038/nmeth.1315

    Article  CAS  PubMed  Google Scholar 

  7. Shalek AK, Satija R, Adiconis X et al (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498:236–240. https://doi.org/10.1038/nature12172

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Klein AM, Mazutis L, Akartuna I et al (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–1201. https://doi.org/10.1016/j.cell.2015.04.044

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Zheng GXY, Terry JM, Belgrader P et al (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049. https://doi.org/10.1038/ncomms14049

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Brodie TM, Tosevski V (2017) High-dimensional single-cell analysis with mass cytometry. Curr Protoc Immunol 118:5.11.1–5.11.25. https://doi.org/10.1002/cpim.31

    Article  Google Scholar 

  11. Papalexi E, Satija R (2017) Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol 510:363. https://doi.org/10.1038/nri.2017.76

    Article  CAS  Google Scholar 

  12. Chattopadhyay PK, Roederer M (2012) Cytometry: today’s technology and tomorrow’s horizons. Methods 57:251–258. https://doi.org/10.1016/j.ymeth.2012.02.009

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Mair F, Prlic M (2018) OMIP-044: 28-color immunophenotyping of the human dendritic cell compartment. Cytometry A 106:255. https://doi.org/10.1002/cyto.a.23331

    Article  Google Scholar 

  14. Grégori G, Patsekin V, Rajwa B et al (2012) Hyperspectral cytometry at the single-cell level using a 32-channel photodetector. Cytometry A 81:35–44. https://doi.org/10.1002/cyto.a.21120

    Article  PubMed  Google Scholar 

  15. Futamura K, Sekino M, Hata A et al (2015) Novel full-spectral flow cytometry with multiple spectrally-adjacent fluorescent proteins and fluorochromes and visualization of in vivo cellular movement. Cytometry A 87:830–842. https://doi.org/10.1002/cyto.a.22725

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Feher K, Volkmann von K, Kirsch J et al (2016) Multispectral flow cytometry: the consequences of increased light collection. Cytometry A 89:681–689. https://doi.org/10.1002/cyto.a.22888

    Article  PubMed  Google Scholar 

  17. Kvistborg P, Gouttefangeas C, Aghaeepour N et al (2015) Thinking outside the gate: single-cell assessments in multiple dimensions. Immunity 42:591–592. https://doi.org/10.1016/j.immuni.2015.04.006

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Saeys Y, Gassen SV, Lambrecht BN (2016) Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat Rev Immunol 16:449–462. https://doi.org/10.1038/nri.2016.56

    Article  CAS  PubMed  Google Scholar 

  19. Mair F, Hartmann FJ, Mrdjen D et al (2016) The end of gating? An introduction to automated analysis of high dimensional cytometry data. Eur J Immunol 46:34–43. https://doi.org/10.1002/eji.201545774

    Article  CAS  PubMed  Google Scholar 

  20. Chester C, Maecker HT (2015) Algorithmic tools for mining high-dimensional cytometry data. J Immunol 195:773–779. https://doi.org/10.4049/jimmunol.1500633

    Article  CAS  PubMed  Google Scholar 

  21. Aghaeepour N, Finak G, FlowCAP Consortium et al (2013) Critical assessment of automated flow cytometry data analysis techniques. Nat Methods 10:228–238. https://doi.org/10.1038/nmeth.2365

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Brinkman RR, Aghaeepour N, Finak G et al (2016) Automated analysis of flow cytometry data comes of age. Cytometry A 89:13–15. https://doi.org/10.1002/cyto.a.22810

    Article  PubMed  Google Scholar 

  23. Cossarizza A, Chang H-D, Radbruch A et al (2017) Guidelines for the use of flow cytometry and cell sorting in immunological studies. Eur J Immunol 47:1584–1797. https://doi.org/10.1002/eji.201646632

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Roederer M (2015) A proposal for unified flow cytometer parameter naming. Cytometry A. https://doi.org/10.1002/cyto.a.22670

    Article  Google Scholar 

  25. Perfetto SP, Chattopadhyay PK, Wood J et al (2014) Q and B values are critical measurements required for inter-instrument standardization and development of multicolor flow cytometry staining panels. Cytometry A 85:1037–1048. https://doi.org/10.1002/cyto.a.22579

    Article  CAS  PubMed  Google Scholar 

  26. Lawrence WG, Varadi G, Entine G et al (2008) Enhanced red and near infrared detection in flow cytometry using avalanche photodiodes. Cytometry A 73:767–776. https://doi.org/10.1002/cyto.a.20595

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Meinelt E, Reunanen M, Edinger M et al Standardizing application setup across multiple flow cytometers using BD FACSDiva™ Version 6 Software: technical bulletin

    Google Scholar 

  28. Perfetto SP, Ambrozak D, Nguyen R et al (2012) Quality assurance for polychromatic flow cytometry using a suite of calibration beads. Nat Protoc 7:2067–2079. https://doi.org/10.1038/nprot.2012.126

    Article  CAS  PubMed  Google Scholar 

  29. Bagwell CB, Adams EG (1993) Fluorescence spectral overlap compensation for any number of flow cytometry parameters. Ann N Y Acad Sci 677:167–184

    Article  CAS  PubMed  Google Scholar 

  30. Roederer M (2001) Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry 45:194–205

    Article  CAS  PubMed  Google Scholar 

  31. Ashhurst TM, Smith AL, King NJC (2017) High-dimensional fluorescence cytometry. Curr Protoc Immunol 10:5.8.1–5.8.38. https://doi.org/10.1002/cpim.37

    Article  Google Scholar 

  32. Nguyen R, Perfetto S, Mahnke YD et al (2013) Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design. Cytometry A 83:306–315. https://doi.org/10.1002/cyto.a.22251

    Article  PubMed  PubMed Central  Google Scholar 

  33. Roederer M, Tárnok A (2010) OMIPs—orchestrating multiplexity in polychromatic science. Cytometry A 77:811–812. https://doi.org/10.1002/cyto.a.20959

    Article  PubMed  Google Scholar 

  34. Mahnke Y, Chattopadhyay P, Roederer M (2010) Publication of optimized multicolor immunofluorescence panels. Cytometry A 77:814–818. https://doi.org/10.1002/cyto.a.20916

    Article  PubMed  Google Scholar 

  35. Liechti T, Günthard HF, Trkola A (2018) OMIP-047: high-dimensional phenotypic characterization of B cells. Cytometry A 103:2262–2596. https://doi.org/10.1002/cyto.a.23488

    Article  Google Scholar 

  36. Moncunill G, Han H, Dobaño C et al (2014) OMIP-024: pan-leukocyte immunophenotypic characterization of PBMC subsets in human samples. Cytometry A 85:995–998. https://doi.org/10.1002/cyto.a.22580

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Hulspas R (2010) Titration of fluorochrome-conjugated antibodies for labeling cell surface markers on live cells. Curr Protoc Cytom Chapter 6:Unit 6.29–6.29.9. https://doi.org/10.1002/0471142956.cy0629s54

  38. Perfetto SP, Chattopadhyay PK, Roederer M (2004) Seventeen-colour flow cytometry: unravelling the immune system. Nat Rev Immunol 4:648–655. https://doi.org/10.1038/nri1416

    Article  CAS  PubMed  Google Scholar 

  39. Mahnke YD, Roederer M (2007) Optimizing a multicolor immunophenotyping assay. Clin Lab Med 27:469–485. v. https://doi.org/10.1016/j.cll.2007.05.002

    Article  PubMed  PubMed Central  Google Scholar 

  40. Parks DR, Roederer M, Moore WA (2006) A new “Logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytometry A 69:541–551. https://doi.org/10.1002/cyto.a.20258

    Article  PubMed  Google Scholar 

  41. Trotter J (2007) Alternatives to log-scale data display. Curr Protoc Cytom Chapter 10:Unit 10.16–10.16.11. https://doi.org/10.1002/0471142956.cy1016s42

  42. Herzenberg LA, Tung J, Moore WA et al (2006) Interpreting flow cytometry data: a guide for the perplexed. Nat Immunol 7:681–685. https://doi.org/10.1038/ni0706-681

    Article  CAS  PubMed  Google Scholar 

  43. Roederer M (2016) Distributions of autofluorescence after compensation: be panglossian, fret not. Cytometry A 89:398–402. https://doi.org/10.1002/cyto.a.22820

    Article  CAS  PubMed  Google Scholar 

  44. Andersen MN, Al-Karradi SNH, Kragstrup TW, Hokland M (2016) Elimination of erroneous results in flow cytometry caused by antibody binding to Fc receptors on human monocytes and macrophages. Cytometry A 89:1001–1009. https://doi.org/10.1002/cyto.a.22995

    Article  CAS  PubMed  Google Scholar 

  45. Chattopadhyay PK, Gaylord B, Palmer A et al (2012) Brilliant violet fluorophores: a new class of ultrabright fluorescent compounds for immunofluorescence experiments. Cytometry A 81A:456–466. https://doi.org/10.1002/cyto.a.22043

    Article  CAS  Google Scholar 

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Acknowledgments

The authors thank all members of the Prlic lab and Dr. Sabine Spath for critical reading of the manuscript. F.M. and A.J.T. are supported by a Marylou scholarship from the International Society for Advancement of Cytometry (ISAC).

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Correspondence to Florian Mair .

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Mair, F., Tyznik, A.J. (2019). High-Dimensional Immunophenotyping with Fluorescence-Based Cytometry: A Practical Guidebook. In: McCoy, Jr, J. (eds) Immunophenotyping. Methods in Molecular Biology, vol 2032. Humana, New York, NY. https://doi.org/10.1007/978-1-4939-9650-6_1

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  • DOI: https://doi.org/10.1007/978-1-4939-9650-6_1

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  • Publisher Name: Humana, New York, NY

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  • Online ISBN: 978-1-4939-9650-6

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