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A funnel approach to enable analyses of epitope-specific human CD4 T cells specific for influenza and SARS-CoV-2.

Richards, K. A.; Mettelman, R. C.; Trombley, K.; Allen, E. K.; Scott, Z. B.; Joachimiak, A.; Schultz-Cherry, S.; Chaves, F.; Thomas, P. G.; Sant, A. J.

2025-10-17 immunology
10.1101/2025.10.16.682991 bioRxiv
Show abstract

Protection from pathogenic organisms relies heavily on the adaptive immune response, for which key regulators are CD4 T cells. CD4 T cells, notable in the complexity of their repertoire and functional potential, can most easily be dissected with the ability to identify, quantify, characterize and isolate epitope-specific cells. In the study reported here, we present a systematic and unbiassed strategy that has enabled identification of highly immunogenic peptide epitopes derived from influenza virus and SARS-CoV-2, presented by human HLA-DR proteins. Coupling the use of HLA-DR transgenic mice with infection and vaccination with highly sensitive epitope specific cytokine ELISpot assays, we have narrowed the potential epitopes from 450-600 peptides to 5-15 peptides, by an iterative process of elimination and selection which we have termed a funnel approach. These epitopes have been validated in HLA-DR typed human CD4 T cells directly ex vivo and enabled derivation and implementation of HLA-DR peptide tetramers. Tetramer staining of human PBMCs enriched CD4 T memory populations from healthy adult subjects highlighting this approach as a sensitive and specific method of identifying novel epitopes and subsequent CD4 T cell responses to human viral infections. ImportanceTracking single epitope-specific CD4 T cells enables sophisticated analyses of the human response to infectious pathogens, vaccines and probing the human CD4 T cell immune memory compartment. The studies presented here provide a unbiased strategy for accomplishing this goal and provide a verified compilation of candidate HLA-DR restricted CD4 T cell peptide epitopes for future studies by researchers in the field of human immunology.

Published in mBio (predicted rank #17) · training set

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