{"title":"A pan-disease and population-level single-cell TCRαβ repertoire reference.","authors":"Ziwei Xue, Lize Wu, Bing Gao, Ruonan Tian, Yiru Chen, Yicheng Qi, Tianze Dong, Yadan Bai, Yu Zhao, Bing He, Lie Wang, Zuozhu Liu, Jianhua Yao, Linrong Lu, Wanlu Liu","doi":"10.1038/s41421-025-00836-7","DOIUrl":null,"url":null,"abstract":"<p><p>Recent advances in single-cell technology enable the simultaneous capture of T cell receptor (TCR) sequences and gene expression (GEX), providing an integrated view of T cell function. However, linking TCRαβ information and T cell phenotypes at the population level to elucidate their disease association remains an unaddressed gap. Here, by constructing a large-scale reference of paired single-cell RNA/TCR sequencing (scRNA/TCR-seq) comprising more than 2 million T cells from 70 studies, 1017 biological samples, 583 individuals, and 46 disease conditions, along with their single-cell transcriptome, full-length paired TCR, and human leukocyte antigen (HLA) genotypes, we revealed the intrinsic features of germline-encoded TCR-major histocompatibility complex (MHC) restriction in CD4<sup>+</sup>/CD8<sup>+</sup> lineages. We also observed widely existing public TCRαβs across the population, associated with higher clonal expansion levels and shared HLA alleles. The most publicly shared TCRs are likely to target epitopes from common viruses, such as Epstein-Barr virus (EBV), cytomegalovirus (CMV), and influenza A virus (IAV). Furthermore, we introduced TCR-DeepInsight, a computational framework to identify HLA-shared and disease-associated TCRαβ clusters that exhibit similar TCR sequence and GEX profiles, extensible for researchers to incorporate their data with our reference and characterize potentially functional TCRs. In summary, our work presents a panoramic scTCRαβ reference and computational methods for TCR study.</p>","PeriodicalId":9674,"journal":{"name":"Cell Discovery","volume":"11 1","pages":"82"},"PeriodicalIF":12.5000,"publicationDate":"2025-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12521495/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cell Discovery","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1038/s41421-025-00836-7","RegionNum":1,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Recent advances in single-cell technology enable the simultaneous capture of T cell receptor (TCR) sequences and gene expression (GEX), providing an integrated view of T cell function. However, linking TCRαβ information and T cell phenotypes at the population level to elucidate their disease association remains an unaddressed gap. Here, by constructing a large-scale reference of paired single-cell RNA/TCR sequencing (scRNA/TCR-seq) comprising more than 2 million T cells from 70 studies, 1017 biological samples, 583 individuals, and 46 disease conditions, along with their single-cell transcriptome, full-length paired TCR, and human leukocyte antigen (HLA) genotypes, we revealed the intrinsic features of germline-encoded TCR-major histocompatibility complex (MHC) restriction in CD4+/CD8+ lineages. We also observed widely existing public TCRαβs across the population, associated with higher clonal expansion levels and shared HLA alleles. The most publicly shared TCRs are likely to target epitopes from common viruses, such as Epstein-Barr virus (EBV), cytomegalovirus (CMV), and influenza A virus (IAV). Furthermore, we introduced TCR-DeepInsight, a computational framework to identify HLA-shared and disease-associated TCRαβ clusters that exhibit similar TCR sequence and GEX profiles, extensible for researchers to incorporate their data with our reference and characterize potentially functional TCRs. In summary, our work presents a panoramic scTCRαβ reference and computational methods for TCR study.
Cell DiscoveryBiochemistry, Genetics and Molecular Biology-Molecular Biology
CiteScore
24.20
自引率
0.60%
发文量
120
审稿时长
20 weeks
期刊介绍:
Cell Discovery is a cutting-edge, open access journal published by Springer Nature in collaboration with the Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences (CAS). Our aim is to provide a dynamic and accessible platform for scientists to showcase their exceptional original research.
Cell Discovery covers a wide range of topics within the fields of molecular and cell biology. We eagerly publish results of great significance and that are of broad interest to the scientific community. With an international authorship and a focus on basic life sciences, our journal is a valued member of Springer Nature's prestigious Molecular Cell Biology journals.
In summary, Cell Discovery offers a fresh approach to scholarly publishing, enabling scientists from around the world to share their exceptional findings in molecular and cell biology.