深度学习模型在单细胞RNA测序分析中的应用揭示了双阴性T细胞的新标记。

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Tian Xu, Qin Xu, Ran Lu, David N Oakland, Song Li, Liwu Li, Christopher M Reilly, Xin M Luo
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引用次数: 0

摘要

双阴性T细胞(DNT)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细胞群的3-5%。它们在免疫系统中的新作用,特别是在自身免疫性疾病中,越来越得到认可。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)有能力捕获测序数据中的非线性基因表达模式。在这项研究中,我们采用深度学习方法,揭示了C57BL/6小鼠脾DNT细胞的新标记,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,分别是Ly6C表达分化的naïve DNT (nDNT)细胞和MHC-II表达分化的活化DNT (aDNT)细胞。先前的一项研究预测nDNT细胞中Tnfrsf9编码的CD137/4-1BB的表达升高;然而,我们的分析预测并验证了CD137是aDNT细胞而不是nDNT细胞的标记物。创新的是,我们的数据还鉴定了Tnfsf8编码的CD30和Tnfsf8编码的CD153/CD30L作为aDNT细胞的附加标记。此外,我们在nDNT细胞中划分了三个亚群,在aDNT细胞中划分了两个亚群。我们的scVI分析表明,流式细胞术分析证实,Slamf7编码的Ly49G2是nDNT0亚群的标记物。重要的是,我们证实MHC-II确实在人类DNT细胞的一个亚群中表达,这表明人类aDNT群体的存在。此外,与C57BL/6小鼠相比,我们发现MRL/lpr小鼠aDNT细胞上CD30、CD153和CD137的表达增加,这表明这些分子在自身免疫中的潜在致病作用。总之,我们的综合分析已经发现并验证了DNT细胞不同亚群的新标记,这些标记可用于这些相对罕见的健康和疾病细胞的表型和/或功能表征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells.

Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells.

Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells.

Application of deep learning models on single-cell RNA sequencing analysis uncovers novel markers of double negative T cells.

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.

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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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