Decoding Human T Cell Immunity with Artificial Intelligence and Single-Cell Genomics

T cells are key mediators of adaptive immunity, yet their highly dynamic response states and immense T cell receptor (TCR) diversity pose challenges in deciphering their functional roles in human diseases. Recent advances in single-cell genomics and TCR sequencing provide unprecedented opportunities to resolve T cell heterogeneity, decode clonal response dynamics, and facilitate high-throughput antigen specificity mapping. In this review, we summarize major technological innovations that have transformed T cell research, from experimental tools for antigen-specific T cell profiling to machine learning frameworks for predicting interactions between the TCR and peptide–MHC (pMHC) and structural modeling powered by deep learning. We discuss current bottlenecks, including data limitations and model generalizability, and explore emerging strategies to guide the next generation of T cell discoveries. We argue that artificial intelligence and single-cell genomics will collectively pave the way for dissecting T cell heterogeneity, mapping TCR sequence to pMHC specificity, and interpreting these features in the context of clinical outcomes.