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Phenotyping of tumor infiltrating immune cells using mass-cytometry (CyTOF)

Authors :
Aurélien Corneau
Pauline Maby
Jérôme Galon
Unité Mixte de Service Production et Analyse de données en Sciences de la vie et en Santé (PASS)
Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Cytométrie Pitié-Salpêtrière (PASS-CYPS)
Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU)-Institut National de la Santé et de la Recherche Médicale (INSERM)
Source :
Methods in Enzymology, Methods in Enzymology, pp.339-368, 2020, ⟨10.1016/bs.mie.2019.07.025⟩
Publication Year :
2020
Publisher :
Elsevier, 2020.

Abstract

The anti-tumor immune response plays a crucial role in cancer patient outcome as well as in response to the growing family of immunotherapeutic treatments. Improving patient prognostic and therapeutic management requires a better knowledge of the tumor microenvironment, for which a deep characterization of tumor-infiltrating immune populations is instrumental. Mass cytometry represents an excellent tool in tumor Immunology, as it allows the simultaneous analysis of >40 markers on single cells. In this chapter, we review challenging technical aspects of the mass cytometry phenotyping of tumor infiltrating immune cells, focusing on fresh human solid tumor samples. We first explain how to design mass cytometry experiments, then provide detailed protocols to isolate mononuclear immune cells from solid tissues and to stain them for an acquisition on a mass cytometer. We also discuss how to optimize the preparation of single immune cell samples, and how to ensure the reproducibility of data generated from distinct fresh human samples. Finally, we provide troubleshooting suggestions for difficult sample acquisitions on a mass cytometer.

Details

Database :
OpenAIRE
Journal :
Methods in Enzymology, Methods in Enzymology, pp.339-368, 2020, ⟨10.1016/bs.mie.2019.07.025⟩
Accession number :
edsair.doi.dedup.....f78d3bba42b36565eafdf0db31d0d1cd