A pan-disease and population-level single-cell TCRαβ repertoire reference

Area of Science:

• Immunology
• Genomics
• Computational Biology

Background:

• Single-cell technologies now capture T cell receptor (TCR) sequences and gene expression (GEX) simultaneously.
• Linking TCR repertoire to T cell phenotypes for disease association at a population level is a significant gap.

Purpose of the Study:

• To construct a large-scale reference of paired single-cell RNA/TCR sequencing (scRNA/TCR-seq) data.
• To reveal intrinsic features of TCR-major histocompatibility complex (MHC) restriction and identify public TCRs.
• To develop a computational framework for identifying disease-associated TCR clusters.

Main Methods:

• Assembled a reference dataset of >2 million T cells from 70 studies, including scRNA/TCR-seq, full-length paired TCR, and HLA genotypes.
• Analyzed TCR-MHC restriction, public TCR prevalence, clonal expansion, and association with viral epitopes (EBV, CMV, IAV).
• Developed TCR-DeepInsight, a computational framework for clustering TCRs based on sequence, GEX, and HLA sharing.

Main Results:

• Revealed germline-encoded TCR-MHC restriction in CD4+/CD8+ T cells.
• Observed widespread public TCRs associated with high clonal expansion and shared HLA alleles, likely targeting common viral epitopes.
• Demonstrated TCR-DeepInsight's capability to identify HLA-shared and disease-associated TCR clusters.

Conclusions:

• The study presents a comprehensive scTCRαβ reference and novel computational methods for TCR analysis.
• Identified public TCRs associated with viral infections and potential disease links.
• TCR-DeepInsight facilitates the characterization of functional TCRs and their disease associations.