4.6 Article

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

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LIFE SCIENCE ALLIANCE
卷 5, 期 10, 页码 -

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LIFE SCIENCE ALLIANCE LLC
DOI: 10.26508/lsa.202101316

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  1. Italian Ministry of University and Research [PRIN 2017WC8499]
  2. Italian Healthy Ministry project on CART [RCR-2019-23669115]
  3. Italian Association for Cancer Research (AIRC) [IG 18458]
  4. AIRC [22737]

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This study describes a workflow for high-dimensional flow cytometry analysis of T cells infiltrating liver metastatic tumor lesions. By optimizing the flow cytometer settings, establishing a multi-color antibody panel, and accurately tuning the dissociation of samples, the phenotypic complexity of T cells infiltrating the tumor lesions was effectively captured. This study provides a robust tool for analyzing complex T cell populations and can be adapted to characterize other relevant pathological tissues.
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 (gamma delta T)-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.

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