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Drost, F. ; An, Y. ; Bonafonte Pardás, I. ; Dratva, L.M.* ; Lindeboom, R.G.H.* ; Haniffa, M.* ; Teichmann, S.A.* ; Theis, F.J. ; Lotfollahi, M. ; Schubert, B.

Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data.

Nat. Commun. 15:5577 (2024)

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	Open Access Gold

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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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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Memory; Effector; Naive; Activation; Prediction; Landscape; Profiles; Reveals

ISSN (print) / ISBN 2041-1723

e-ISSN 2041-1723

Journal Nature Communications

Quellenangaben Volume: 15, Issue: 1, Article Number: 5577

Publisher Nature Publishing Group

Publishing Place London

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Computational Biology (ICB)

Grants European Union
Joachim Herz Stiftung
Helmholtz Association
Helmholtz International Lab "Causal Cell Dynamics"
Helmholtz Association's Initiative and Networking Fund on the HAICORE@FZJ partition
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)
BMBF grant DeepTCR
Projekt DEAL