TCellSI: A novel method for T cell state assessment and its applications in immune environment prediction.

Autor: Yang JM; Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.; Department of Thoracic Surgery West China Biomedical Big Data Center, West China Hospital, Sichuan University Chengdu China., Zhang N; Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China.; Department of Thoracic Surgery West China Biomedical Big Data Center, West China Hospital, Sichuan University Chengdu China., Luo T; BGI Education Center University of Chinese Academy of Sciences Shenzhen China., Yang M; Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China., Shen WK; Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China., Tan ZL; Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China., Xia Y; Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China., Zhang L; Hubei Bioinformatics & Molecular Imaging Key Laboratory, College of Life Science and Technology Huazhong University of Science and Technology Wuhan China., Zhou X; Center for Computational Systems Medicine, School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston Texas USA., Lei Q; Department of Thoracic Surgery West China Biomedical Big Data Center, West China Hospital, Sichuan University Chengdu China., Guo AY; Department of Thoracic Surgery West China Biomedical Big Data Center, West China Hospital, Sichuan University Chengdu China.
Jazyk: angličtina
Zdroj: IMeta [Imeta] 2024 Aug 26; Vol. 3 (5), pp. e231. Date of Electronic Publication: 2024 Aug 26 (Print Publication: 2024).
DOI: 10.1002/imt2.231
Abstrakt: 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.
Competing Interests: The authors declare no conflict of interest.
(© 2024 The Author(s). iMeta published by John Wiley & Sons Australia, Ltd on behalf of iMeta Science.)
Databáze: MEDLINE
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