Yang Jing-Min, Zhang Nan, Luo Tao, Yang Mei, Shen Wen-Kang, Tan Zhen-Lin, Xia Yun, Zhang Libin, Zhou Xiaobo, Lei Qian, Guo An-Yuan
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.
Imeta. 2024 Aug 26;3(5):e231. doi: 10.1002/imt2.231. eCollection 2024 Oct.
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 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.
T细胞是免疫系统不可或缺的组成部分，其多方面的功能由不同的T细胞类型及其各种状态所塑造。尽管存在多种用于预测不同T细胞类型丰度的计算模型，但缺乏用于评估其状态以表征其静止、激活和抑制程度的工具。为了填补这一空白，建立了一种强大且细致入微的评分工具，称为T细胞状态标识符（TCellSI），它利用了曼-惠特尼统计量。TCellSI方法能够从转录组数据评估八种不同的T细胞状态——静止、调节、增殖、辅助、细胞毒性、祖细胞耗竭、终末耗竭和衰老——通过特定的标记基因集和编译的参考谱为样本提供T细胞状态评分（TCSS）。通过对一系列T细胞类型的大量伪批量和实际批量RNA测序数据进行验证，TCellSI不仅能准确表征T细胞状态，而且在反映T细胞本质方面超越了现有的已充分发现的特征。值得注意的是，该工具在免疫环境中显示出预测价值，将T细胞状态与患者预后和免疫治疗反应相关联。为了更好地利用，可通过用户友好的R包和网络服务器（https://guolab.wchscu.cn/TCellSI/）轻松访问TCellSI。通过提供对个性化癌症治疗的见解，TCellSI有潜力改善治疗结果和疗效。
