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Guidelines For Analysis of Low-Frequency Antigen-Specific T Cell Results: Dye-Based Proliferation Assay vs 3H-Thymidine Incorporation

Daniela Di Blasi et.al., Journal of Immunological Methods (2020) - PMID: 33152332

Product(s) used in this publication: PepMix™ Peptide Pools

Abstract
 

It is generally recognized that dysregulation of the immune system plays a critical role in many diseases, including autoimmune diseases and cancer. T cells play a crucial role in maintaining self-tolerance, while loss of immune tolerance and T cell activation can lead to severe inflammation and tissue damage. T cell responses have a key role in the effectiveness of vaccination strategies and immunomodulating therapies. Immunomonitoring methods have the ability to elucidate immunological processes, monitor the development of disease and assess therapeutic effects. In this respect, it is of particular interest to evaluate antigen (Ag)-specific T cells by determining their frequency, type and functionality in cellular assays. Nevertheless, Ag-specific T cells are detected infrequently in most diseases using current techniques. Many efforts have been made to develop more sensitive, reproducible, and reliable methods for Ag-specific T cell detection. It has been found that analysis of cellular proliferation can be a useful tool to determine the presence and frequency of Ag-specific T cell and to provides insight into modulation of the T cell response by a specific antigen or therapy. However, the selection of a cut-off value for a positive response and therefore a more accurate interpretation of the data, continues to be a major concern. Here, we provide guidelines to select a proper cut-off for monitoring of Ag-specific CD4+ T cell responses. In vitro Ag-stimulation has been assessed with two methods; a dye-based proliferation assay and 3H-thymidine-based assay. Two cut-off approaches are compared; mean and variance of control wells, and the stimulation index. By evaluating the proliferative response to the in vitro Ag-stimulation using these two methods, we demonstrate the importance of taking into consideration the variability of the control wells to distinguish a positive from a false positive response.

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