Advertisement

Guidelines for Gating Flow Cytometry Data for Immunological Assays

  • Janet Staats
  • Anagha Divekar
  • J. Philip McCoyJr.
  • Holden T. MaeckerEmail author
Protocol
  • 1.7k Downloads
Part of the Methods in Molecular Biology book series (MIMB, volume 2032)

Abstract

“Gating” refers to the selection of successive subpopulations of cells for analysis in flow cytometry. It is usually performed manually, based on expert knowledge of cell characteristics. However, there can be considerable disagreement in how gates should be applied, even between individuals experienced in the field. While clinical software often automates gating, and some guidelines do exist (especially for clinical assays), there are no comprehensive guidelines across the various types of immunological assays performed using flow cytometry. Here we attempt to provide such guidelines, focused on the most general and pervasive types of gates, why they are important, and what recommendations can be made regarding their use. We do so through the display of example data, collected by academic, government, and industry representatives. These guidelines should be of value to both novice and experienced flow cytometrists analyzing a wide variety of immunological assays.

Key words

Flow cytometry Gating Analysis 

Notes

Acknowledgments

The authors thank the EQAPOL consortium and Jennifer Enzor for providing data and analysis examples, and the FOCIS Immunophenotyping Course participants for helpful suggestions and vetting of these guidelines.

References

  1. 1.
    Burel JG, Qian Y, Lindestam Arlehamn C et al (2017) An integrated workflow to assess technical and biological variability of cell population frequencies in human peripheral blood by flow cytometry. J Immunol 198:1748–1758CrossRefGoogle Scholar
  2. 2.
    Disis ML, dela Rosa C, Goodell V et al (2006) Maximizing the retention of antigen specific lymphocyte function after cryopreservation. J Immunol Methods 308:13–18CrossRefGoogle Scholar
  3. 3.
    Posevitz-Fejfár A, Posevitz V, Gross CC et al (2014) Effects of blood transportation on human peripheral mononuclear cell yield, phenotype and function: implications for immune cell biobanking. PLoS One 9:e115920CrossRefGoogle Scholar
  4. 4.
    Perfetto SP, Chattopadhyay PK, Lamoreaux L et al (2006) Amine reactive dyes: an effective tool to discriminate live and dead cells in polychromatic flow cytometry. J Immunol Methods 313:199–208CrossRefGoogle Scholar
  5. 5.
    Shankey TV, Rabinovitch PS, Bagwell B et al (1993) Guidelines for implementation of clinical DNA cytometry. International Society for Analytical Cytology. Cytometry 14:472–477CrossRefGoogle Scholar
  6. 6.
    Perfetto SP, Roederer M (2007) Increased immunofluorescence sensitivity using 532 nm laser excitation. Cytometry A 71:73–79CrossRefGoogle Scholar
  7. 7.
    Maecker HT, Trotter J (2006) Flow cytometry controls, instrument setup, and the determination of positivity. Cytometry A 69:1037–1042CrossRefGoogle Scholar
  8. 8.
    Watson JV (1987) Time, a quality-control parameter in flow cytometry. Cytometry 8:646–649CrossRefGoogle Scholar
  9. 9.
    Fletez-Brant K, Špidlen J, Brinkman RR et al (2016) flowClean: automated identification and removal of fluorescence anomalies in flow cytometry data. Cytometry A 89:461–471CrossRefGoogle Scholar
  10. 10.
    Maecker HT, McCoy JP, Nussenblatt R (2012) Standardizing immunophenotyping for the Human Immunology Project. Nat Rev Immunol 12:191–200CrossRefGoogle Scholar
  11. 11.
    Lamoreaux L, Roederer M, Koup R (2006) Intracellular cytokine optimization and standard operating procedure. Nat Protoc 1:1507–1516CrossRefGoogle Scholar
  12. 12.
    Maecker HT, Frey T, Nomura LE et al (2004) Selecting fluorochrome conjugates for maximum sensitivity. Cytometry A 62:169–173CrossRefGoogle Scholar
  13. 13.
    Roederer M (2001) Spectral compensation for flow cytometry: visualization artifacts, limitations, and caveats. Cytometry A 45:194–205CrossRefGoogle Scholar
  14. 14.
    Chevrier S, Crowell HL, Zanotelli VRT et al (2018) Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst 6:612–620.e5CrossRefGoogle Scholar
  15. 15.
    Finak G, Perez J-M, Weng A et al (2010) Optimizing transformations for automated, high throughput analysis of flow cytometry data. BMC Bioinformatics 11:546CrossRefGoogle Scholar
  16. 16.
    Novo D, Wood J (2008) Flow cytometry histograms: transformations, resolution, and display. Cytometry A 73:685–692CrossRefGoogle Scholar
  17. 17.
    (1992) Guidelines for the performance of CD4+ T-cell determinations in persons with human immunodeficiency virus infection. MMWR Recomm Rep 41:1–17Google Scholar
  18. 18.
    (1994) 1994 revised guidelines for the performance of CD4+ T-cell determinations in persons with human immunodeficiency virus (HIV) infections. Centers for Disease Control and Prevention. MMWR Recomm Rep 43:1–21Google Scholar
  19. 19.
    (1997) 1997 revised guidelines for performing CD4+ T-cell determinations in persons infected with human immunodeficiency virus (HIV). Centers for Disease Control and Prevention. MMWR Recomm Rep 46:1–29Google Scholar
  20. 20.
    Mandy FF, Nicholson JKA, McDougal JS et al (2003) Guidelines for performing single-platform absolute CD4+ T-cell determinations with CD45 gating for persons infected with human immunodeficiency virus. Centers for Disease Control and Prevention. MMWR Recomm Rep 52:1–13PubMedGoogle Scholar
  21. 21.
    O.W. Health (2007) Laboratory guidelines for enumerating CD4 T lymphocytes in the context of HIV/AIDS. World Health Organization Regional Office for South-East Asia, New DelhiGoogle Scholar
  22. 22.
    Sutherland DR, Anderson L, Keeney M et al (1996) The ISHAGE guidelines for CD34+ cell determination by flow cytometry. International Society of Hematotherapy and Graft Engineering. J Hematother 5:213–226CrossRefGoogle Scholar
  23. 23.
    Illingworth A, Marinov I, Sutherland DR et al (2018) ICCS/ESCCA consensus guidelines to detect GPI-deficient cells in paroxysmal nocturnal hemoglobinuria (PNH) and related disorders part 3—data analysis, reporting and case studies. Cytometry B Clin Cytom 94:49–66CrossRefGoogle Scholar
  24. 24.
    McNeil LK, Price L, Britten CM et al (2013) A harmonized approach to intracellular cytokine staining gating: results from an international multiconsortia proficiency panel conducted by the Cancer Immunotherapy Consortium (CIC/CRI). Cytometry A 83:728–738CrossRefGoogle Scholar
  25. 25.
    Britten CM, Janetzki S, Ben-Porat L et al (2009) Harmonization guidelines for HLA-peptide multimer assays derived from results of a large scale international proficiency panel of the Cancer Vaccine Consortium. Cancer Immunol Immunother 58:1701–1713CrossRefGoogle Scholar
  26. 26.
    Nomura L, Maino VC, Maecker HT (2008) Standardization and optimization of multiparameter intracellular cytokine staining. Cytometry A 73:984–991CrossRefGoogle Scholar
  27. 27.
    Finak G, Langweiler M, Jaimes M et al (2016) Standardizing flow cytometry immunophenotyping analysis from the human immunophenotyping consortium. Sci Rep 6:20686CrossRefGoogle Scholar
  28. 28.
    Verschoor CP, Lelic A, Bramson JL et al (2015) An introduction to automated flow cytometry gating tools and their implementation. Front Immunol 6:380CrossRefGoogle Scholar
  29. 29.
    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 46:34–43CrossRefGoogle Scholar
  30. 30.
    Martin JC, Swartzendruber DE (1980) Time: a new parameter for kinetic measurements in flow cytometry. Science 207:199–201CrossRefGoogle Scholar
  31. 31.
    Hoffman RA (2009) Pulse width for particle sizing. Curr Protoc Cytom Chapter 1:Unit 1.23Google Scholar
  32. 32.
    Wersto RP, Chrest FJ, Leary JF et al (2001) Doublet discrimination in DNA cell-cycle analysis. Cytometry 46:296–306CrossRefGoogle Scholar
  33. 33.
    Furman MI, Barnard MR, Krueger LA et al (2001) Circulating monocyte-platelet aggregates are an early marker of acute myocardial infarction. J Am Coll Cardiol 38:1002–1006CrossRefGoogle Scholar
  34. 34.
    Nomura LE, Walker JM, Maecker HT (2000) Optimization of whole blood antigen-specific cytokine assays for CD4(+) T cells. Cytometry 40:60–68CrossRefGoogle Scholar
  35. 35.
    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 69:541–551CrossRefGoogle Scholar
  36. 36.
    Bagwell CB, Hill BL, Herbert DJ et al (2016) Sometimes simpler is better: VLog, a general but easy-to-implement log-like transform for cytometry. Cytometry A 89:1097–1105CrossRefGoogle Scholar
  37. 37.
    Andersen MN, Al-Karradi SNH, Kragstrup TW et al (2016) Elimination of erroneous results in flow cytometry caused by antibody binding to Fc receptors on human monocytes and macrophages. Cytometry A 89:1001–1009CrossRefGoogle Scholar
  38. 38.
    Richards AJ, Staats J, Enzor J et al (2014) Setting objective thresholds for rare event detection in flow cytometry. J Immunol Methods 409:54–61CrossRefGoogle Scholar
  39. 39.
    Hultin LE, Chow M, Jamieson BD et al (2010) Comparison of interlaboratory variation in absolute T-cell counts by single-platform and optimized dual-platform methods. Cytometry B Clin Cytom 78:194–200PubMedPubMedCentralGoogle Scholar
  40. 40.
    Vogt RF, Cross GD, Henderson LO et al (1989) Model system evaluating fluorescein-labeled microbeads as internal standards to calibrate fluorescence intensity on flow cytometers. Cytometry 10:294–302CrossRefGoogle Scholar
  41. 41.
    Maecker HT, Rinfret A, D’Souza P et al (2005) Standardization of cytokine flow cytometry assays. BMC Immunol 6:13CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Janet Staats
    • 1
  • Anagha Divekar
    • 2
  • J. Philip McCoyJr.
    • 3
  • Holden T. Maecker
    • 4
    Email author
  1. 1.Duke Immune Profiling CoreDuke University Medical CenterDurhamUSA
  2. 2.Department for Cellular AnalysisBiolegendSan DiegoUSA
  3. 3.FrederickUSA
  4. 4.Institute for Immunity, Transplantation, and InfectionStanford University School of MedicineStanfordUSA

Personalised recommendations