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
Affiliations
Jing‐Min Yang
Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China
Nan Zhang
Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China
Tao Luo
BGI Education Center University of Chinese Academy of Sciences Shenzhen China
Mei Yang
Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China
Wen‐Kang Shen
Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China
Zhen‐Lin Tan
Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China
Yun Xia
Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China
Libin Zhang
Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China
Xiaobo Zhou
Center for Computational Systems Medicine, School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston Texas USA
Qian Lei
Department of Thoracic Surgery West China Biomedical Big Data Center, West China Hospital, Sichuan University Chengdu China
An‐Yuan Guo
Department of Thoracic Surgery West China Biomedical Big Data Center, West China Hospital, Sichuan University Chengdu China
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.
