Multi-modal generative modeling for joint analysis of single-cell T cell receptor and gene expression data.
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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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Acknowledgements: This work was supported by the BMBF grant DeepTCR (#031L0290A), by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (Projektnummer 490846870—TRR355/1 TPZ02), by the Helmholtz Association’s Initiative and Networking Fund on the HAICORE@FZJ partition, and Helmholtz International Lab “Causal Cell Dynamics” awarded to B.S. M.L. appreciates F.J.T for enabling and supporting him to conduct this research. Y.A., F.D., and I.B.P. are supported by the Helmholtz Association under the joint research school “Munich School for Data Science - MUDS”. M.L. and F.D. acknowledge financial support from the Joachim Herz Stiftung. L.M.D is supported by European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 955321. Figure 1 and Supplementary Fig. 1 were partially created with BioRender.com.
Funder: Joachim Herz Stiftung (Joachim Herz Foundation); doi: https://doi.org/10.13039/100008662
Funder: This work was supported by the BMBF grant DeepTCR (#031L0290A), by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (Projektnummer 490846870 - TRR355/1 TPZ02), by the Helmholtz Association’s Initiative and Networking Fund on the HAICORE@FZJ partition, and Helmholtz International Lab “Causal Cell Dynamics”. Y.A., F.D., and I.B.P. are supported by the Helmholtz Association under the joint research school “Munich School for Data Science - MUDS”. M.L. and F.D. acknowledge financial support from the Joachim Herz Stiftung. L.M.D is supported by European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 955321.
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2041-1723
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Deutsche Forschungsgemeinschaft (German Research Foundation) (490846870)