What question did this study set out to answer?

The aim is to explore how T-cell receptor repertoires are regulated in autoimmune diseases and to utilize machine learning for prediction.

March 14, 2026Open Access

T-Cell Receptor Repertoire in Autoimmune Diseases and Their Machine Learning-Based Prediction Analysis

Key Points

The aim is to explore how T-cell receptor repertoires are regulated in autoimmune diseases and to utilize machine learning for prediction.
Summarization of TCR repertoire dynamics in autoimmune disorders.
Comparison of V and J gene usage profiles across clinical labels.
Analysis of publicly available TCR datasets to inform machine learning feature engineering.
Development of diagnostic models based on TCR sequence features.
Identified key processes affecting TCR diversity, including tolerance failure and autoreactive T cell expansion.
Demonstrated that gene usage patterns can effectively classify autoimmune disease status.
Showcased improvements in diagnostic precision through machine learning approaches.

Abstract

The T-cell receptor (TCR) is a fundamental component of the adaptive immune system, playing a crucial role in the development and progression of autoimmune diseases through its remarkable diversity and antigen specificity. Advances in high-throughput sequencing technologies and multi-omics data integration have revolutionized the ability to characterize TCR repertoires at unprecedented resolution. Coupled with emerging machine learning methodologies, these advances have opened new avenues for unraveling the complex immunopathology underlying autoimmune disorders. This review comprehensively summarizes current knowledge on the dynamic regulation of TCR repertoires in autoimmune diseases, highlighting key processes such as central tolerance failure, clonal expansion of autoreactive T cells, and regulatory T cell dysfunction, as well as the influences of genetic predisposition and immunosenescence on shaping TCR diversity. This review also provides a 3 that demonstrates how to analyze publicly available TCR repertoire datasets. We compare V and J gene usage profiles and CDR3 summary features across clinical labels to characterize between-group variation and to inform feature engineering for downstream machine learning models. Furthermore, we detail various machine learning-based diagnostic models that utilize gene usage patterns and CDR3 sequence features to accurately classify autoimmune disease status, alongside recent breakthroughs in predicting TCR-epitope binding specificity. These computational approaches not only enhance diagnostic precision but also provide mechanistic insights into immune recognition and autoreactivity. By integrating immunological principles with data-driven techniques, this work aims to offer a robust theoretical framework and practical guidance for future research in immunology and precision medicine. Ultimately, the convergence of TCR repertoire profiling and machine learning promises to drive innovative strategies for early diagnosis, personalized therapy, and improved clinical management of autoimmune diseases, enabling the transition to antigen-specific tolerogenic therapies.

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