Disease associated human TCR characterization by deep-learning framework TCR-DeepInsight

Xue, Z.; Wu, L.; Tian, R.; Liu, Z.; Bai, Y.; Sun, D.; Guo, Y.; Chen, P.; Zhao, Y.; He, B.; Wang, L.; Yao, J.; Lu, L.; Liu, W.

doi:10.1101/2023.05.22.541406

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bioRxiv · 10.1101/2023.05.22.541406

Disease associated human TCR characterization by deep-learning framework TCR-DeepInsight

Xue, Z.·Wu, L.·Tian, R.·Liu, Z.·Bai, Y.·Sun, D.·Guo, Y.·Chen, P.·Zhao, Y.·He, B.·Wang, L.·Yao, J.·Lu, L.·Liu, W.

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Abstract

T cell function is defined by both T cell receptors (TCR) and T cell gene expression (GEX). Although single-cell technology enables the simultaneous capture of TCR and GEX information, the lack of a reference atlas and computational tools hinders our ability to uncover the fundamental TCR usage rules and to efficiently characterize disease-associated TCRs (dTCR). Here, through the collection of million-scale single-cell GEX-TCR reference atlas comprising 20 diverse disease conditions, we revealed the intrinsic features of TCR-MHC (Major Histocompatibility Complex) restriction in CD4/CD8 lineages. We observed the higher coherence for TCR/{beta} chains in memory T cells, and detected widely-existing public TCR/{beta} pairs across individuals. Building upon the reference atlas, we introduced TCR- DeepInsight, a deep-learning framework featuring a disease specificity scoring system that enables the characterization of dTCR clusters with similar GEX-TCR. Our study provides a valuable tool for researchers to analyze single-cell GEX-TCR data and identify dTCRs comprehensively and robustly.

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Xue, Z., Wu, L., Tian, R., Liu, Z., Bai, Y., Sun, D., Guo, Y., Chen, P., Zhao, Y., He, B., Wang, L., Yao, J., Lu, L., Liu, W.. 2023-05-24. Disease associated human TCR characterization by deep-learning framework TCR-DeepInsight. https://doi.org/10.1101/2023.05.22.541406

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