ArticleNature communications2024
Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
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Who cites it
22 citing papers in PubMed.
- T cell immunomonitoring: a comparative analysis of traditional and novel methods to quantify and characterize human antigen-specific T cells.Nature protocols · 2026Review
- TransTCR: Integrating TCRs and Transcriptomes Through Optimal Transport for Antigen Specificity Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- TSTScope Unifies Single-Cell Multi-Omics to Identify Functional T Cell States Predictive of Immunotherapy Response.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- scDAU: a disentangled representation learning method for cross-modal translation in single-cell multi-omics data.Bioinformatics (Oxford, England) · 2026Article
- The Role of the T Cell Receptor Sequence in Shaping T Cell Functional Fate.Immunological reviews · 2026Review
- What Do Generative Models Learn About Adaptive Immune Receptor Repertoires? A Benchmark Study.bioRxiv : the preprint server for biology · 2026Article
- The challenge and promise of studying human antigen-specific T cells.Nature reviews. Immunology · 2026Review
- Article
- The T Cell Receptor: Molecular Sensor, Therapeutic Mediator and Probabilistic Driver of Adaptive Immunity.Immunological reviews · 2026Review
- T Cell Receptor Repertoires Across the Continuum of Vascular, Myocardial, and Age-Related Diseases.Immunological reviews · 2026Review
- Geometry-aware graph attention networks to explain single-cell chromatin states and gene expression with SEAGALL.Genome biology · 2026Article
- Multimodal framework for the joint analysis of single-cell RNA and T cell receptor sequencing data predicts T cell response to cancer immunotherapy.Nature communications · 2026Article
- A pan-disease and population-level single-cell TCRαβ repertoire reference.Cell discovery · 2025Article
- Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.Research square · 2025Article
- Deciphering Cell Fate and Clonal Dynamics via Integrative Single-Cell Lineage Modeling.bioRxiv : the preprint server for biology · 2025Article
- T cell transcriptional landscapes are shaped by TCR sequence similarity.bioRxiv : the preprint server for biology · 2025Article
- MIST: An interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis.Science advances · 2025Article
- Article
- Foundation models in bioinformatics.National science review · 2025Review
- Integrative mapping of human CD8Nature methods · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Recent advances in single-cell immune profiling have enabled the simultaneous measurement of transcriptome and T cell receptor (TCR) sequences, offering great potential for studying immune responses at the cellular level. However, integrating these diverse modalities across datasets is challenging due to their unique data characteristics and technical variations. Here, to address this, we develop the multimodal generative model mvTCR to fuse modality-specific information across transcriptome and TCR into a shared representation. Our analysis demonstrates the added value of multimodal over unimodal approaches to capture antigen specificity. Notably, we use mvTCR to distinguish T cell subpopulations binding to SARS-CoV-2 antigens from bystander cells. Furthermore, when combined with reference mapping approaches, mvTCR can map newly generated datasets to extensive T cell references, facilitating knowledge transfer. In summary, we envision mvTCR to enable a scalable analysis of multimodal immune profiling data and advance our understanding of immune responses.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.