De novo identification of CD4+ T cell epitopes
Year: 2024
Type: article
Abstract: ABSTRACT CD4+ T cells recognize peptide antigens presented on class II Major Histocompatibility Complex (MHC-II) molecules to carry out their function. The remarkable diversity of T cell receptor (TCR) sequences and lack of antigen discovery approaches for MHC-II make profiling the specificities of CD4+ T cells challenging. We have expanded our platform of Signaling and Antigen-presenting Bifunctional Receptors to encode MHC-II molecules presenting covalently linked peptides (SABR-IIs) for CD4+ cell antigen discovery. SABR-IIs can present epitopes to CD4+ T cells and induce signaling upon their recognition, allowing a readable output. Here, we demonstrate that SABR-IIs libraries presenting endogenous and post-translationally modified epitopes can be used for antigen discovery. Using SABR-II libraries in conjunction with single cell RNA sequencing, we de-convoluted multiple highly expanded TCRs from pancreatic islets of Non-Obese Diabetic (NOD) mice. We compounded antigen discovery by incorporating computational TCR similarity prediction metrics followed by experimental validation. Finally, we showed SABR-IIs presenting epitopes in class II HLA alleles can be used for antigen discovery for human CD4+ T cells. Taken together, we have developed a rapid, flexible, scalable, and versatile approach for the de novo identification of CD4+ T cell ligands from single cell RNA sequencing data using experimental and computational approaches.
Source: Nature Methods
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: hybrid
Estimated APC paid: $12,290
Funders: U.S. Department of Health and Human Services, National Institutes of Health, University of California, Los Angeles, University of California, San Francisco, Anschutz Medical Campus, University of Colorado, NIH Office of the Director, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases
OpenAlex ID: W4395074730
