TCellSI: A novel method for T cell state assessment and its applications in immune environment prediction
Jing‐Min Yang, Nan Zhang, Tao Luo, Mei Yang, Wen‐Kang Shen, Zhen‐Lin Tan, Yun Xia, Libin Zhang, Xiaobo Zhou, Qian Lei, An‐Yuan Guo
Abstract
Abstract T cell is an indispensable component of the immune system and its multifaceted functions are shaped by the distinct T cell types and their various states. Although multiple computational models exist for predicting the abundance of diverse T cell types, tools for assessing their states to characterize their degree of resting, activation, and suppression are lacking. To address this gap, a robust and nuanced scoring tool called T cell state identifier (TCellSI) leveraging Mann–Whitney U statistics is established. The TCellSI methodology enables the evaluation of eight distinct T cell states—Quiescence, Regulating, Proliferation, Helper, Cytotoxicity, Progenitor exhaustion, Terminal exhaustion, and Senescence—from transcriptome data, providing T cell state scores (TCSS) for samples through specific marker gene sets and a compiled reference spectrum. Validated against sizeable pseudo‐bulk and actual bulk RNA‐seq data across a range of T cell types, TCellSI not only accurately characterizes T cell states but also surpasses existing well‐discovered signatures in reflecting the nature of T cells. Significantly, the tool demonstrates predictive value in the immune environment, correlating T cell states with patient prognosis and responses to immunotherapy. For better utilization, the TCellSI is readily accessible through user‐friendly R package and web server ( https://guolab.wchscu.cn/TCellSI/ ). By offering insights into personalized cancer therapies, TCellSI has the potential to improve treatment outcomes and efficacy.