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Mass Cytometry pp 147-158 | Cite as

Multiplex MHC Class I Tetramer Combined with Intranuclear Staining by Mass Cytometry

  • Yannick Simoni
  • Michael Fehlings
  • Evan W. NewellEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1989)

Abstract

Antigen-specific CD8+ T cells play a crucial role in the host protective immune response against viruses, tumors, and other diseases. Major histocompatibility complex (MHC) class I tetramers allow for a direct detection of such antigen-specific CD8+ T cells. Using mass cytometry together with multiplex MHC class I tetramer staining, we are able to screen more than 1000 different antigen candidates simultaneously across tissues in health and disease, while retaining the possibility to deliver an in-depth characterization of antigen-specific CD8+ T cells and associated phenotypes. Here we describe the method for a MHC class I tetramer multiplexing approach together with intracellular antibody staining for a parallel phenotypic cell characterization using mass cytometry in human specimens.

Key words

Mass cytometry MHC class I tetramer Antigen-specific T cell Multiplex tetramer staining 

Notes

Acknowledgments

The authors thank all members of Evan Newell lab.

Competing interests: E.W.N. is a board director and shareholder of immunoSCAPE Pte.Ltd. M.F. is Director, Scientific Affairs and shareholder of immunoSCAPE Pte. Ltd. All other authors declare no competing financial interests.

References

  1. 1.
    Wong P, Pamer EG (2003) CD8 T cell responses to infectious pathogens. Annu Rev Immunol 21:29–70.  https://doi.org/10.1146/annurev.immunol.21.120601.141114CrossRefPubMedGoogle Scholar
  2. 2.
    Zhang N, Bevan MJ (2011) CD8(+) T cells: foot soldiers of the immune system. Immunity 35(2):161–168.  https://doi.org/10.1016/j.immuni.2011.07.010CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Wherry EJ, Kurachi M (2015) Molecular and cellular insights into T cell exhaustion. Nat Rev Immunol 15(8):486–499.  https://doi.org/10.1038/nri3862CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Wherry EJ (2011) T cell exhaustion. Nat Immunol 12(6):492–499CrossRefGoogle Scholar
  5. 5.
    Newell EW, Becht E (2018) High-dimensional profiling of tumor-specific immune responses: asking T cells about what they “see” in cancer. Cancer Immunol Res 6(1):2–9.  https://doi.org/10.1158/2326-6066.CIR-17-0519CrossRefPubMedGoogle Scholar
  6. 6.
    Whiteside TL, Zhao Y, Tsukishiro T et al (2003) Enzyme-linked Immunospot, cytokine flow cytometry, and tetramers in the detection of T-cell responses to a dendritic cell-based multipeptide vaccine in patients with melanoma. Clin Cancer Res 9(2):641–649PubMedGoogle Scholar
  7. 7.
    Altman JD, Moss PA, Goulder PJ et al (1996) Phenotypic analysis of antigen-specific T lymphocytes. Science 274(5284):94–96CrossRefGoogle Scholar
  8. 8.
    Altman JD, Davis MM (2003) MHC-peptide tetramers to visualize antigen-specific T cells. Curr Protoc Immunol Chapter 17:Unit 17.3.  https://doi.org/10.1002/0471142735.im1703s53CrossRefPubMedGoogle Scholar
  9. 9.
    Simoni Y, Fehlings M, Kloverpris HN et al (2018) Human innate lymphoid cell subsets possess tissue-type based heterogeneity in phenotype and frequency. Immunity 48(5):1060.  https://doi.org/10.1016/j.immuni.2018.04.028CrossRefPubMedGoogle Scholar
  10. 10.
    Simoni Y, Fehlings M, Kloverpris HN et al (2017) Human innate lymphoid cell subsets possess tissue-type based heterogeneity in phenotype and frequency. Immunity 46(1):148–161.  https://doi.org/10.1016/j.immuni.2016.11.005CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Simoni Y, Newell EW (2017) Toward meaningful definitions of innate-lymphoid-cell subsets. Immunity 46(5):760–761.  https://doi.org/10.1016/j.immuni.2017.04.026CrossRefPubMedGoogle Scholar
  12. 12.
    Simoni Y, Newell EW (2018) Dissecting human ILC heterogeneity: more than just three subsets. Immunology 153(3):297–303.  https://doi.org/10.1111/imm.12862CrossRefPubMedGoogle Scholar
  13. 13.
    Wong MT, Chen J, Narayanan S et al (2015) Mapping the diversity of follicular helper T cells in human blood and tonsils using high-dimensional mass cytometry analysis. Cell Rep 11(11):1822–1833.  https://doi.org/10.1016/j.celrep.2015.05.022CrossRefPubMedGoogle Scholar
  14. 14.
    Wong MT, Ong DE, Lim FS et al (2016) A high-dimensional atlas of human T cell diversity reveals tissue-specific trafficking and cytokine signatures. Immunity 45(2):442–456.  https://doi.org/10.1016/j.immuni.2016.07.007CrossRefPubMedGoogle Scholar
  15. 15.
    Newell EW, Klein LO, Yu W et al (2009) Simultaneous detection of many T-cell specificities using combinatorial tetramer staining. Nat Methods 6(7):497–499.  https://doi.org/10.1038/nmeth.1344CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Newell EW, Sigal N, Bendall SC et al (2012) Cytometry by time-of-flight shows combinatorial cytokine expression and virus-specific cell niches within a continuum of CD8+ T cell phenotypes. Immunity 36(1):142–152.  https://doi.org/10.1016/j.immuni.2012.01.002CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Newell EW, Sigal N, Nair N et al (2013) Combinatorial tetramer staining and mass cytometry analysis facilitate T-cell epitope mapping and characterization. Nat Biotechnol 31(7):623–629.  https://doi.org/10.1038/nbt.2593CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Fehlings M, Chakarov S, Simoni Y et al (2018) Multiplex peptide-MHC tetramer staining using mass cytometry for deep analysis of the influenza-specific T-cell response in mice. J Immunol Methods 453:30–36.  https://doi.org/10.1016/j.jim.2017.09.010CrossRefPubMedGoogle Scholar
  19. 19.
    Fehlings M, Simoni Y, Penny HL et al (2017) Checkpoint blockade immunotherapy reshapes the high-dimensional phenotypic heterogeneity of murine intratumoural neoantigen-specific CD8(+) T cells. Nat Commun 8(1):562.  https://doi.org/10.1038/s41467-017-00627-zCrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Simoni Y, Becht E, Fehlings M et al (2018) Bystander CD8(+) T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557(7706):575–579.  https://doi.org/10.1038/s41586-018-0130-2CrossRefPubMedGoogle Scholar
  21. 21.
    Rodenko B, Toebes M, Hadrup SR et al (2006) Generation of peptide-MHC class I complexes through UV-mediated ligand exchange. Nat Protoc 1(3):1120–1132.  https://doi.org/10.1038/nprot.2006.121CrossRefPubMedGoogle Scholar
  22. 22.
    Bakker AH, Hoppes R, Linnemann C et al (2008) Conditional MHC class I ligands and peptide exchange technology for the human MHC gene products HLA-A1, -A3, -A11, and -B7. Proc Natl Acad Sci U S A 105(10):3825–3830.  https://doi.org/10.1073/pnas.0709717105CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Toebes M, Coccoris M, Bins A et al (2006) Design and use of conditional MHC class I ligands. Nat Med 12(2):246–251.  https://doi.org/10.1038/nm1360CrossRefPubMedGoogle Scholar
  24. 24.
    Leong ML, Newell EW (2015) Multiplexed peptide-MHC tetramer staining with mass cytometry. Methods Mol Biol 1346:115–131.  https://doi.org/10.1007/978-1-4939-2987-0_9CrossRefPubMedGoogle Scholar
  25. 25.
    Finck R, Simonds EF, Jager A et al (2013) Normalization of mass cytometry data with bead standards. Cytometry A 83(5):483–494.  https://doi.org/10.1002/cyto.a.22271CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Cheng Y, Wong MT, van der Maaten L et al (2016) Categorical analysis of human T cell heterogeneity with one-dimensional soli-expression by nonlinear stochastic embedding. J Immunol 196(2):924–932.  https://doi.org/10.4049/jimmunol.1501928CrossRefPubMedGoogle Scholar
  27. 27.
    Becht E, Dutertre C-A, Kwok IWH, et al (2018) Evaluation of UMAP as an alternative to t-SNE for single-cell data. bioRxiv.  https://doi.org/10.1101/298430
  28. 28.
    Qiu P, Simonds EF, Bendall SC et al (2011) Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 29(10):886–891.  https://doi.org/10.1038/nbt.1991CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Amir el AD, Davis KL, Tadmor MD et al (2013) viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 31(6):545–552.  https://doi.org/10.1038/nbt.2594CrossRefGoogle Scholar
  30. 30.
    van Unen V, Hollt T, Pezzotti N et al (2017) Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types. Nat Commun 8(1):1740.  https://doi.org/10.1038/s41467-017-01689-9CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Yannick Simoni
    • 1
    • 2
  • Michael Fehlings
    • 3
  • Evan W. Newell
    • 1
    • 2
    Email author
  1. 1.Agency for Science, Technology and Research (A*STAR), Singapore Immunology Network (SIgN)SingaporeSingapore
  2. 2.Fred Hutch Cancer Research Center, Vaccine and Infectious Disease DivisionSeattleUSA
  3. 3.ImmunoSCAPE Pte. Ltd.SingaporeSingapore

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