MAIT Cells pp 71-82 | Cite as

Methods for High-Dimensional Flow Cytometry Analysis of Human MAIT Cells in Tissues and Peripheral Blood

  • Benedikt Strunz
  • Christine L. Zimmer
  • Jonna Bister
  • Martin A. Ivarsson
  • Niklas K. BjörkströmEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2098)


Mucosal-associated invariant T (MAIT) cells can be found throughout the human body, in peripheral blood, at mucosal sites, and, among other organs, in the liver. As unconventional T cells, MAIT cells have the capacity to readily respond to bacterial infections and are also engaged during anti-viral responses. To thoroughly investigate the MAIT cell phenotype and function in such conditions, multi-color flow cytometry is an appropriate and powerful tool. Yet, the recent rapid technological development within this methodology, with generation of highly complex data, has increased the need for downstream dimensionality reducing methods to fully interpret obtained results. Among such methods, stochastic neighbor embedding (SNE) analysis stands out as it provides intuitive low-dimensional representations of complex data. Here, we describe techniques and workflow for high-dimensional state-of-the-art investigation and analysis of human MAIT cells from blood and peripheral tissues.

Key words

Human MAIT cells Immunophenotyping Multi-color flow cytometry SNE 


  1. 1.
    Amir E-AD, Davis KL, Tadmor MD, Simonds EF, Levine JH, Bendall SC, Shenfeld DK, Krishnaswamy S, Nolan GP, Pe'er D (2013) visNe enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 31:545–552CrossRefGoogle Scholar
  2. 2.
    Saeys Y, Gassen SV, Lambrecht BN (2016) Computational flow cytometry: helping to make sense of high-dimensional immunology data. Nat Rev Immunol 16:449–462CrossRefGoogle Scholar
  3. 3.
    Kjer-Nielsen L, Patel O, Corbett AJ et al (2012) MR1 presents microbial vitamin B metabolites to MAIT cells. Nature 491:717–723CrossRefGoogle Scholar
  4. 4.
    Godfrey DI, Uldrich AP, McCluskey J, Rossjohn J, Moody DB (2015) The burgeoning family of unconventional T cells. Nat Immunol 16:1114–1123CrossRefGoogle Scholar
  5. 5.
    Koay H-F, Gherardin NA, Enders A et al (2016) A three-stage intrathymic development pathway for the mucosal-associated invariant T cell lineage. Nat Immunol 17:1300–1311CrossRefGoogle Scholar
  6. 6.
    Leeansyah E, Loh L, Nixon DF, Sandberg JK (2014) Acquisition of innate-like microbial reactivity in mucosal tissues during human fetal MAIT-cell development. Nat Commun 5:3143CrossRefGoogle Scholar
  7. 7.
    Rahimpour A, Koay H-F, Enders A et al (2015) Identification of phenotypically and functionally heterogeneous mouse mucosal-associated invariant T cells using MR1 tetramers. J Exp Med 212:1095–1108CrossRefGoogle Scholar
  8. 8.
    Dias J, Leeansyah E, Sandberg JK (2017) Multiple layers of heterogeneity and subset diversity in human MAIT cell responses to distinct microorganisms and to innate cytokines. Proc Natl Acad Sci U S A 114:E5434–E5443CrossRefGoogle Scholar
  9. 9.
    Hengst J, Strunz B, Deterding K, Ljunggren H-G, Leeansyah E, Manns MP, Cornberg M, Sandberg JK, Wedemeyer H, Björkström NK (2016) Nonreversible MAIT cell-dysfunction in chronic hepatitis C virus infection despite successful interferon-free therapy. Eur J Immunol 46:2204–2210CrossRefGoogle Scholar
  10. 10.
    Leeansyah E, Ganesh A, Quigley MF et al (2013) Activation, exhaustion, and persistent decline of the antimicrobial MR1-restricted MAIT-cell population in chronic HIV-1 infection. Blood 121:1124–1135CrossRefGoogle Scholar
  11. 11.
    Kwon Y-S, Cho Y-N, Kim M-J et al (2015) Mucosal-associated invariant T cells are numerically and functionally deficient in patients with mycobacterial infection and reflect disease activity. Tuberculosis (Edinb) 95:267–274CrossRefGoogle Scholar
  12. 12.
    Seth v E, Zimmer CL, Reuterwall-Hansson M, Barakat A, Arnelo U, Bergquist A, Ivarsson MA, Björkström NK (2018) Primary sclerosing cholangitis leads to dysfunction and loss of MAIT cells. Eur J Immunol 48:1997–2004CrossRefGoogle Scholar
  13. 13.
    Böttcher K, Rombouts K, Saffioti F, Roccarina D, Rosselli M, Hall A, Luong T, Tsochatzis EA, Thorburn D, Pinzani M (2018) MAIT cells are chronically activated in patients with autoimmune liver disease and promote profibrogenic hepatic stellate cell activation. Hepatology 68:172–186CrossRefGoogle Scholar
  14. 14.
    Dias J, Boulouis C, Gorin J-B et al (2018) The CD4−CD8− MAIT cell subpopulation is a functionally distinct subset developmentally related to the main CD8+ MAIT cell pool. Proc Natl Acad Sci U S A 115:E11513–E11522CrossRefGoogle Scholar
  15. 15.
    Hengst J, Theorell J, Deterding K, Potthoff A, Dettmer A, Ljunggren H-G, Wedemeyer H, Björkström NK (2015) High-resolution determination of human immune cell signatures from fine-needle liver aspirates. Eur J Immunol 45:2154–2157CrossRefGoogle Scholar
  16. 16.
    Han G, Spitzer MH, Bendall SC, Fantl WJ, Nolan GP (2018) Metal-isotope-tagged monoclonal antibodies for high-dimensional mass cytometry. Nat Protoc 13:2121–2148CrossRefGoogle Scholar
  17. 17.
    Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK, Swerdlow H, Satija R, Smibert P (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 9:2579Google Scholar
  18. 18.
    Becht E, McInnes L, Healy J, Dutertre C-A, Kwok IWH, Ng LG, Ginhoux F, Newell EW (2018) Dimensionality reduction for visualizing single-cell data using UMAP. Nat Biotechnol. Scholar
  19. 19.
    Sobkowiak MJ, Davanian H, Heymann R et al (2019) Tissue-resident MAIT cell populations in human oral mucosa exhibit an activated profile and produce IL-17. Eur J Immunol 49:133–143CrossRefGoogle Scholar
  20. 20.
    Gibbs A, Leeansyah E, Introini A, Paquin-Proulx D, Hasselrot K, Andersson E, Broliden K, Sandberg JK, Tjernlund A (2016) MAIT cells reside in the female genital mucosa and are biased towards IL-17 and IL-22 production in response to bacterial stimulation. Mucosal Immunol. Scholar
  21. 21.
    Berhanu D, Mortari F, De Rosa SC, Roederer M (2003) Optimized lymphocyte isolation methods for analysis of chemokine receptor expression. J Immunol Methods 279:199–207CrossRefGoogle Scholar
  22. 22.
    Reantragoon R, Corbett AJ, Sakala IG et al (2013) Antigen-loaded MR1 tetramers define T cell receptor heterogeneity in mucosal-associated invariant T cells. J Exp Med 210:2305–2320CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Benedikt Strunz
    • 1
  • Christine L. Zimmer
    • 1
  • Jonna Bister
    • 1
  • Martin A. Ivarsson
    • 1
  • Niklas K. Björkström
    • 1
    Email author
  1. 1.Center for Infectious Medicine, Department of Medicine HuddingeKarolinska Institutet, Karolinska University Hospital HuddingeStockholmSweden

Personalised recommendations