UcTCRp: a TCRβ-based framework for identifying and profiling MAIT- and iNKT-associated repertoire signals.
6 affiliations
- 1Academy of Medical Engineering and Translational Medicine, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China.
- 2State Key Laboratory of Advanced Medical Materials and Devices, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China.
- 3Key Laboratory of Systems Bioengineering (Ministry of Education), Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China.
- 4Beijing Institute of Basic Medical Sciences, No. 27, Taiping Road, Haidian District, Beijing 100850, China.
- 5Beijing Institute of Radiation Medicine, No. 27, Taiping Road, Haidian District, Beijing 100850, China.
- 6Institute of Health Service and Transfusion Medicine, No. 27, Taiping Road, Haidian District, Beijing 100850, China.
Abstract
Mucosal-associated invariant T (MAIT) cells and invariant natural killer T (iNKT) cells are conventionally identified using invariant or semi-invariant TCRα chains, antigen-loaded tetramers, or transcriptomic phenotypes. These requirements limit their detection in public and clinical immune-repertoire datasets that contain only TCRβ sequences. Here we present UcTCRp, a TCRβ-only framework for detecting and profiling relative MAIT- and iNKT-associated signals in bulk immune repertoires. We first identified model-informative TCRβ features, including biased V-gene usage and reproducible CDR3β amino acid composition, in collected human and mouse MAIT and iNKT TCRβ sequences. UcTCRp subsequently integrates these features using species-specific transformer-based representation pretrained separately on large human and mouse TCRβ repertoires, followed by independent supervised modeling with curated MAIT, iNKT, and conventional T cell references for each species. The resulting species-specific models generalized robustly across independent human and mouse cohorts. In paired scRNA-seq/scTCR-seq datasets, UcTCRp recovered transcriptome-defined MAIT and iNKT cells and identified additional MAIT-like candidates supported by receptor evidence but missed by expression-only annotation. Bulk calibration against paired single-cell references and synthetic spike-in experiments established operating characteristics for repertoire-level abundance estimation. These results establish unpaired TCRβ repertoires as an actionable substrate for detecting relative unconventional T cell-associated immune signals, enabling archived repertoire resources to be repurposed for systems-level studies of tissue immunity, disease, and therapeutic response.
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BibTeX
@article{chen2026uctcrp,
title = {{UcTCRp: a TCRβ-based framework for identifying and profiling MAIT- and iNKT-associated repertoire signals.}},
author = {Linlin Chen and Yueyi Li and Shiwen Shan and Kaixuan Wang and Chao Feng and Yunsheng Dou and Qihang Xu and Linru Cai and Hao Wang and Haiyang Wang and Xiaochen Bo and Jian Zhang},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag553},
url = {https://europepmc.org/article/MED/42893562},
note = {CC BY-NC},
}
RIS
TY - JOUR
TI - UcTCRp: a TCRβ-based framework for identifying and profiling MAIT- and iNKT-associated repertoire signals.
AU - Linlin Chen
AU - Yueyi Li
AU - Shiwen Shan
AU - Kaixuan Wang
AU - Chao Feng
AU - Yunsheng Dou
AU - Qihang Xu
AU - Linru Cai
AU - Hao Wang
AU - Haiyang Wang
AU - Xiaochen Bo
AU - Jian Zhang
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag553
UR - https://europepmc.org/article/MED/42893562
AB - Mucosal-associated invariant T (MAIT) cells and invariant natural killer T (iNKT) cells are conventionally identified using invariant or semi-invariant TCRα chains, antigen-loaded tetramers, or transcriptomic phenotypes. These requirements limit their detection in public and clinical immune-repertoire datasets that contain only TCRβ sequences. Here we present UcTCRp, a TCRβ-only framework for detecting and profiling relative MAIT- and iNKT-associated signals in bulk immune repertoires. We first identified model-informative TCRβ features, including biased V-gene usage and reproducible CDR3β amino acid composition, in collected human and mouse MAIT and iNKT TCRβ sequences. UcTCRp subsequently integrates these features using species-specific transformer-based representation pretrained separately on large human and mouse TCRβ repertoires, followed by independent supervised modeling with curated MAIT, iNKT, and conventional T cell references for each species. The resulting species-specific models generalized robustly across independent human and mouse cohorts. In paired scRNA-seq/scTCR-seq datasets, UcTCRp recovered transcriptome-defined MAIT and iNKT cells and identified additional MAIT-like candidates supported by receptor evidence but missed by expression-only annotation. Bulk calibration against paired single-cell references and synthetic spike-in experiments established operating characteristics for repertoire-level abundance estimation. These results establish unpaired TCRβ repertoires as an actionable substrate for detecting relative unconventional T cell-associated immune signals, enabling archived repertoire resources to be repurposed for systems-level studies of tissue immunity, disease, and therapeutic response.
SN - 1467-5463
SN - 1477-4054
ER -