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Workflow for high-dimensional flow cytometry analysis of T cells from tumor metastases

We describe a multi-step high-dimensional (HD) flow cytometry workflow for the deep phenotypic characterization of T cells infiltrating metastatic tumor lesions in the liver, particularly derived from colorectal cancer (CRC-LM). First, we applied a novel flow cytometer setting approach based on sing...

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Bibliographic Details
Published in:Life science alliance 2022-10, Vol.5 (10), p.e202101316
Main Authors: Faccani, Cristina, Rotta, Gianluca, Clemente, Francesca, Fedeli, Maya, Abbati, Danilo, Manfredi, Francesco, Potenza, Alessia, Anselmo, Achille, Pedica, Federica, Fiorentini, Guido, Villa, Chiara, Protti, Maria P, Doglioni, Claudio, Aldrighetti, Luca, Bonini, Chiara, Casorati, Giulia, Dellabona, Paolo, de Lalla, Claudia
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Language:English
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Summary:We describe a multi-step high-dimensional (HD) flow cytometry workflow for the deep phenotypic characterization of T cells infiltrating metastatic tumor lesions in the liver, particularly derived from colorectal cancer (CRC-LM). First, we applied a novel flow cytometer setting approach based on single positive cells rather than fluorescent beads, resulting in optimal sensitivity when compared with previously published protocols. Second, we set up a 26-color based antibody panel designed to assess the functional state of both conventional T-cell subsets and unconventional invariant natural killer T, mucosal associated invariant T, and gamma delta T (γδT)-cell populations, which are abundant in the liver. Third, the dissociation of the CRC-LM samples was accurately tuned to preserve both the viability and antigenic integrity of the stained cells. This combined procedure permitted the optimal capturing of the phenotypic complexity of T cells infiltrating CRC-LM. Hence, this study provides a robust tool for high-dimensional flow cytometry analysis of complex T-cell populations, which could be adapted to characterize other relevant pathological tissues.
ISSN:2575-1077
2575-1077
DOI:10.26508/lsa.202101316