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

Abstract

AbstractJoint 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 MIST’s 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 athttps://github.com/aapupu/MIST.