Xu Tian, Xu Qin, Lu Ran, Oakland David N, Li Song, Li Liwu, Reilly Christopher M, Luo Xin M
Department of Biomedical Sciences and Pathobiology, Virginia Tech, Blacksburg, VA, USA.
Department of Mathematics, The University of Arizona, Tucson, AZ, USA.
Sci Rep. 2024 Dec 28;14(1):31158. doi: 10.1038/s41598-024-82406-7.
Double negative T (DNT) cells are a unique subset of CD3 + TCRαβ + T lymphocytes that lack CD4, CD8, or NK1.1 expression and constitute 3-5% of the total T cell population in C57BL/6 mice. They have increasingly gained recognition for their novel roles in the immune system, especially under autoimmune conditions. Conventional machine learning approaches such as principal component analysis have been employed in single-cell RNA sequencing (scRNA-seq) analysis to characterize DNT cells. However, advanced deep learning models such as Single Cell Variational Inference (scVI) have the capability to capture nonlinear gene expression patterns in the sequencing data. In this study, employing the deep learning methodology, we have revealed novel markers for splenic DNT cells in C57BL/6 mice which were validated with flow cytometry analysis. We classified DNT cells into two subgroups, naïve DNT (nDNT) cells differentiated by the expression of Ly6C and activated DNT (aDNT) cells differentiated by the expression of MHC-II. A prior study had predicted elevated expression of CD137/4-1BB encoded by Tnfrsf9 in nDNT cells; however, our analysis predicted and validated that CD137 was a marker for aDNT cells instead of nDNT cells. Innovatively, our data also identified CD30 encoded by Tnfrsf8 and CD153/CD30L encoded by Tnfsf8 as additional markers for aDNT cells. In addition, we classified three subgroups in nDNT cells and two subgroups in aDNT cells. Our scVI analysis suggested, and flow cytometry analysis confirmed, that Ly49G2 encoded by Slamf7 was a marker for the nDNT0 subgroup. Importantly, we validated that MHC-II was indeed expressed by a subset of human DNT cells suggesting the presence of a human aDNT population. Furthermore, we found increased expression of CD30, CD153, and CD137 on aDNT cells in MRL/lpr mice compared to those in C57BL/6 mice suggesting potential pathogenic roles of these molecules in autoimmunity. Together, our comprehensive analysis has uncovered and validated novel markers for different subpopulations of DNT cells that can be used in the phenotypic and/or functional characterization of these relatively rare cells in health and disease.
双阴性T（DNT）细胞是CD3 + TCRαβ + T淋巴细胞的一个独特亚群，缺乏CD4、CD8或NK1.1表达，占C57BL/6小鼠总T细胞群体的3-5%。它们因其在免疫系统中的新作用而越来越受到认可，尤其是在自身免疫条件下。传统的机器学习方法，如主成分分析，已被用于单细胞RNA测序（scRNA-seq）分析以表征DNT细胞。然而，先进的深度学习模型，如单细胞变分推理（scVI），有能力捕捉测序数据中的非线性基因表达模式。在本研究中，我们采用深度学习方法，揭示了C57BL/6小鼠脾脏DNT细胞的新标志物，并通过流式细胞术分析进行了验证。我们将DNT细胞分为两个亚组，即通过Ly6C表达分化的初始DNT（nDNT）细胞和通过MHC-II表达分化的活化DNT（aDNT）细胞。先前的一项研究预测Tnfrsf9编码的CD137/4-1BB在nDNT细胞中表达升高；然而，我们的分析预测并验证CD137是aDNT细胞而非nDNT细胞的标志物。创新性地，我们的数据还确定Tnfrsf8编码的CD30和Tnfsf8编码的CD153/CD30L是aDNT细胞的额外标志物。此外，我们在nDNT细胞中分类出三个亚组，在aDNT细胞中分类出两个亚组。我们的scVI分析表明并经流式细胞术分析证实，Slamf7编码的Ly49G2是nDNT0亚组的标志物。重要的是，我们验证了人类DNT细胞的一个亚群确实表达MHC-II，提示存在人类aDNT群体。此外，我们发现与C57BL/6小鼠相比，MRL/lpr小鼠的aDNT细胞上CD30、CD153和CD137的表达增加，提示这些分子在自身免疫中可能具有致病作用。总之，我们的综合分析发现并验证了DNT细胞不同亚群的新标志物，这些标志物可用于这些相对罕见细胞在健康和疾病状态下的表型和/或功能特征分析。
