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bioRxiv · 10.1101/2024.07.05.602192

MIST: an interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis

Lai, W.·Li, Y.·Luo, O. J.
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Abstract

Joint analysis of transcriptomic and T cell receptor (TCR) features at single-cell resolution provides a powerful approach for in-depth T cell immune function research. Here, we introduce a deep learning framework for single-T cell transcriptome and receptor analysis, MIST (Multi-Insight for T cell). MIST features three latent spaces: gene expression, TCR, and a joint latent space. Through analyses of antigen- specific T cells and T cells related to lung cancer immunotherapy, we demonstrate MISTs interpretability and flexibility. MIST easily and accurately resolves cell function and antigen-specificity by vectorizing and integrating transcriptome and TCR data of T cells. In addition, using MIST, we identified the heterogeneity of CXCL13+ subsets in lung cancer infiltrating CD8+ T cells and their association with immunotherapy, providing additional insights into the functional transition of CXCL13+ T cells related to anti-PD-1 therapy that were not reported in the original study. MIST is available at https://github.com/aapupu/MIST.

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Lai, W., Li, Y., Luo, O. J.. 2024-07-07. MIST: an interpretable and flexible deep learning framework for single-T cell transcriptome and receptor analysis. https://doi.org/10.1101/2024.07.05.602192

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