ArticleScience advances2024
Self-supervised learning of T cell receptor sequences exposes core properties for T cell membership.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning of Personal Repertoires From Public T Cell Receptors.Immunological reviews · 2026Review
- A lightweight TcrLM model predicts T cell receptor and epitope binding specificity.Cell reports methods · 2026Article
- TCR representation learning with protein language models: a comprehensive review.International immunology · 2026Review
- A pan-disease and population-level single-cell TCRαβ repertoire reference.Cell discovery · 2025Article
- Breast cancer is detectable from peripheral blood using machine learning over T cell receptor repertoires.NPJ systems biology and applications · 2025Article
- Enhancing sequence alignment of adaptive immune receptors through multi-task deep learning.Nucleic acids research · 2025Article
- MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.Science advances · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
The T cell receptor (TCR) repertoire is an extraordinarily diverse collection of TCRs essential for maintaining the body's homeostasis and response to threats. In this study, we compiled an extensive dataset of more than 4200 bulk TCR repertoire samples, encompassing 221,176,713 sequences, alongside 6,159,652 single-cell TCR sequences from over 400 samples. From this dataset, we then selected a representative subset of 5 million bulk sequences and 4.2 million single-cell sequences to train two specialized Transformer-based language models for bulk (CVC) and single-cell (scCVC) TCR repertoires, respectively. We show that these models successfully capture TCR core qualities, such as sharing, gene composition, and single-cell properties. These qualities are emergent in the encoded TCR latent space and enable classification into TCR-based qualities such as public sequences. These models demonstrate the potential of Transformer-based language models in TCR downstream applications.
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Registered trials
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