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Systems-Level Immune Monitoring by Mass Cytometry

  • Tadepally Lakshmikanth
  • Petter BrodinEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1913)

Abstract

As therapies involving the modulation, stimulation, and deliberate excitation of the immune system are becoming routine, better methods for monitoring immune responses in human patients are needed. Mass cytometry allows for detailed profiling of all immune cell populations and their functional responses using a simple blood sample. When combined with appropriate computational analyses, the resolution for distinguishing desired responses from unproductive or even adverse reactions to immunotherapeutic interventions increases. Here we describe a core experimental and computational framework for global, systems-level immune monitoring by mass cytometry.

Key words

Mass cytometry Systems immunology CyTOF Human immunology Tumor immunology Immune monitoring 

References

  1. 1.
    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 81:6813–6822CrossRefGoogle Scholar
  2. 2.
    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 332:687–696CrossRefGoogle Scholar
  3. 3.
    Brodin P, Jojic V, Gao T et al (2015) Variation in the human immune system is largely driven by non-heritable influences. Cell 160:37–47CrossRefGoogle Scholar
  4. 4.
    Kaczorowski KJ, Shekhar K, Nkulikiyimfura D et al (2017) Continuous immunotypes describe human immune variation and predict diverse responses. Proc Natl Acad Sci U S A 114:E6097–E6106CrossRefGoogle Scholar
  5. 5.
    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 45:442–456CrossRefGoogle Scholar
  6. 6.
    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 31:623–629CrossRefGoogle Scholar
  7. 7.
    Chevrier S, Levine JH, VRT Z et al (2017) An immune atlas of clear cell renal cell carcinoma. Cell 169:736–749 e18CrossRefGoogle Scholar
  8. 8.
    Lavin Y, Kobayashi S, Leader A et al (2017) Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169:750–765 e17CrossRefGoogle Scholar
  9. 9.
    Lakshmikanth T, Olin A, Chen Y et al (2017) Mass cytometry and topological data analysis reveal immune parameters associated with complications after allogeneic stem cell transplantation. Cell Rep 20:2238–2250CrossRefGoogle Scholar
  10. 10.
    Wei SC, Levine JH, Cogdill AP et al (2017) Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 170:1120–1133 e17CrossRefGoogle Scholar
  11. 11.
    Phelan MC and Lawler G (2001) Cell counting. Current Protocols in Cytometry 00:A.3A.1–A.3A.4Google Scholar
  12. 12.
    Shekhar K, Brodin P, Davis MM et al (2014) Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). Proc Natl Acad Sci U S A 111:202–207CrossRefGoogle Scholar
  13. 13.
    Weber LM, Robinson MD (2016) Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A 89:1084–1096CrossRefGoogle Scholar
  14. 14.
    Brodin P, Davis MM (2016) Human immune system variation. Nat Rev Immunol 17:21–29CrossRefGoogle Scholar
  15. 15.
    Lou X, Zhang G, Herrera I et al (2007) Polymer-based elemental tags for sensitive bioassays. Angew Chem Int Ed 46:6111–6114CrossRefGoogle Scholar
  16. 16.
    Sumatoh HR, Teng KW, Cheng Y et al (2017) Optimization of mass cytometry sample cryopreservation after staining. Cytometry A 91:48–61CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Science for Life Laboratory, Division of Clinical Paediatrics, Department of Women’s and Children’s HealthKarolinska InstitutetStockholmSweden
  2. 2.Department of Newborn MedicineKarolinska University HospitalStockholmSweden

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