Optimization and intra-assay validation of a multiparametric flow cytometric test for monitoring circulating TREGs.

Multiparametric flow cytometry (MFC) represents an essential tool for immune monitoring, and validation of MFC panels is a fundamental prerequisite in routine laboratory settings as well as for translational and clinical research purposes. Regulatory T cells (TREGs) constitute a subset of CD4+ effector T cells that modulate the immune response in numerous settings, including autoimmune disease, allergy, microbial infection, tumor immunity, transplantation, and more. These cells comprise a small fraction of total CD4+ T cells in human peripheral blood and mouse spleen. In oncology, TREG cells are highly relevant, as they are involved in the suppression of the anti-tumor response in many types of cancer, to the extent that the first immune checkpoint inhibitor approved for clinical use in humans was a monoclonal antibody directed against CTLA-4, a molecule functionally associated with TREGs. Due to all these factors, robust assays are mandatory to accurately determine TREG cell frequency and function. Here, we describe the validation of an 8-color flow-cytometry protocol for TREG detection and analysis in a real-world laboratory scenario. The entire process includes the workflow plan and the standard operating procedure resembling each phase, from the panel design to the staining, acquisition, and analysis steps. Validation is planned to be performed in replicates on fresh whole blood samples derived from multiple healthy subjects. The analytical validity of the TREG cell assay is ensured by testing the intra-assay accuracy. The detailed procedure for the entire process is accompanied by important troubleshooting suggestions and other useful tips.
Competing Interests: Competing interests Each author declares no conflict of interest that could significantly influence the objectivity or integrity of the results presented in this scientific paper.
(Copyright © 2024 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.)