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

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.

Keywords

T-cell receptor / autoimmune diseases (ADs) / systemic lupus erythematosus (SLE) / machine learning model / immune repertoire

Author Contributions

T.S.: Literature research, writing, and revision. M.H.: Literature research, writing, and revision. S.L.: supervision. All authors have read and agreed to the published version of the manuscript.

Acknowledgements and Funding

This work was supported by the National Natural Science Foundation of China (32270687).

Institutional Review Board Statement

The study did not involve humans or animals, and ethical review and approval were therefore not required for this research.

Informed Consent Statement

This study is a secondary analysis of publicly available anonymized datasets. The requirement for informed consent was therefore not applicable to this work, as the authors did not have any direct interaction with human subjects.

Data Availability Statement

All datasets analyzed in this study are publicly available from the immuneACCESS repository (Adaptive Biotechnologies). The datasets used are listed below:

• Mitchell et al.’s study [ 74 ]—https://clients.adaptivebiotech.com/pub/mitchell-2022-jcii

• Mustjoki et al.’s study [ 91 ]—https://clients.adaptivebiotech.com/pub/mustjoki-2017-natcomms

• Gold et al.’s study [ 92 ]—https://clients.adaptivebiotech.com/pub/gold-2019-cr

• Martinez et al.’s study [ 93 ]—https://clients.adaptivebiotech.com/pub/martinez-2025-s

These are previously published, publicly archived immunosequencing datasets; no new primary data were generated in this study. Processed analysis files and code used to produce the results are available from the corresponding author upon reasonable request.

Conflicts of Interest

The authors declare no conflict of interest.

Use of AI and AI-Assisted Technologies

During the preparation of this work, the authors used a large language model (ChatGPT, developed by OpenAI) and Grammarly to assist with language polishing and grammatical corrections. The authors are not native English speakers, and these tools were employed solely to improve the clarity and readability of the manuscript. After using these services, the authors thoroughly reviewed, revised, and take full responsibility for the final content of the publication.

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