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

Felix Drost, Yang An, Irene Bonafonte-Pardàs, Lisa M. Dratva, Rik G. H. Lindeboom, Muzlifah Haniffa, Sarah A. Teichmann, Fabian Theis, Mohammad Lotfollahi () and Benjamin Schubert ()
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Felix Drost: Helmholtz Munich
Yang An: Helmholtz Munich
Irene Bonafonte-Pardàs: Helmholtz Munich
Lisa M. Dratva: Wellcome Genome Campus
Rik G. H. Lindeboom: The Netherlands Cancer Institute
Muzlifah Haniffa: Wellcome Genome Campus
Sarah A. Teichmann: Wellcome Genome Campus
Fabian Theis: Helmholtz Munich
Mohammad Lotfollahi: Helmholtz Munich
Benjamin Schubert: Helmholtz Munich

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49806-9

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