High-dimensional single-cell proteomics analysis reveals the landscape of immune cells and stem-like cells in renal tumors
High-dimensional single-cell proteomics analysis reveals the landscape of immune cells and stem-like cells in renal tumors
复制标题
高维单细胞蛋白质组学分析揭示肾肿瘤中免疫细胞和干细胞样细胞的概况
DOI：
10.1002/jcla.23155
复制
发表时间：
2019-12-19
影响因子：
2.7
通讯作者：
Li, Tianyu
中科院分区：
文献类型：
--
作者：
Li, Zhijian;Hu, Jiaxin;Li, Tianyu
背景肾脏肿瘤具有高度的异质性，识别肿瘤的异质性是临床有效治疗的迫切需要。为了更准确地观察和分类细胞群体中的表型，可以通过飞行时间(CyTOF)细胞术对异质样本进行高维单细胞蛋白质组学分析。本研究旨在开发一种高维MC方法来检测和分析肾脏肿瘤的异质性。材料与方法收集8例不同类型肾肿瘤患者的肾组织标本。制备单细胞悬液，用28种免疫细胞中心抗体和21种干细胞样细胞中心抗体进行染色。对染色后的细胞进行细胞免疫荧光检测。结果肾肿瘤分为25个免疫细胞亚群(4个CD4+T细胞、7个CD8+T细胞、1个B细胞、8个巨噬细胞、1个树突状细胞、2个自然杀伤细胞、1个粒细胞和1个其他细胞亚群)和7个干细胞亚群(根据波形蛋白、CD326、CD34、CD90、CD13、CD44和CD47的表达)。不同类型的肾脏肿瘤具有不同的细胞亚群，具有明显不同的特征。结论MC高维单细胞蛋白质组学分析有助于发现和分析肾脏肿瘤的异质性。此外，它还可以用来准确地对免疫细胞群进行分类，并分析干细胞相关标志物在肾脏肿瘤中的表达。我们的发现为破译肿瘤的异质性提供了有价值的资源，并可能改善肾肿瘤患者的临床治疗。
Background Renal tumors are highly heterogeneous, and identification of tumor heterogeneity is an urgent clinical need for effective treatment. Mass cytometry (MC) can be used to perform high-dimensional single-cell proteomics analysis of heterogeneous samples via cytometry by time-of-flight (CyTOF), in order to achieve more accurate observation and classification of phenotypes within a cell population. This study aimed to develop a high-dimensional MC method for the detection and analysis of heterogeneity in renal tumors. Materials and Methods We collected tissue samples from 8 patients with different types of renal tumors. Single-cell suspensions were prepared and stained using a panel of 28 immune cell-centric antibodies and a panel of 21 stem-like cell-centric antibodies. The stained cells were detected using CyTOF. Result Renal tumors were divided into 25 immune cell subsets (4 CD4+ T cells, 7 CD8+ T cells, 1 B cells, 8 macrophages, 1 dendritic cells, 2 natural killer (NK) cells, 1 granulocyte, and 1 other subset) and 7 stem-like cells subsets (based on positivity of vimentin, CD326, CD34, CD90, CD13, CD44, and CD47). Different types of renal tumors have different cell subsets with significantly different characteristics. Conclusion High-dimensional single-cell proteomics analysis using MC aids in the discovery and analysis of renal tumors heterogeneity. Additionally, it can be used to accurately classify the immune cell population and analyze the expression of stem cell-related markers in renal tumors. Our findings provide a valuable resource for deciphering tumor heterogeneity and might improve the clinical management of patients with renal tumors.