Fachbereich Biologie, Technische Universität Darmstadt, 64287 Darmstadt, Germany.
Institute for Transfusion Medicine, University Hospital Essen, University of Duisburg-Essen, 45122 Essen, Germany.
Int J Mol Sci. 2022 Aug 1;23(15):8544. doi: 10.3390/ijms23158544.
Small extracellular vesicles (sEV) hold enormous potential as biomarkers, drug carriers, and therapeutic agents. However, due to previous limitations in the phenotypic characterization of sEV at the single vesicle level, knowledge of cell type-specific sEV signatures remains sparse. With the introduction of next-generation sEV analysis devices, such as the single-particle interferometric reflectance imaging sensor (SP-IRIS)-based ExoView R100 platform, single sEV analyses are now possible. While the tetraspanins CD9, CD63, and CD81 were generally considered pan-sEV markers, it became clear that sEV of different cell types contain several combinations and amounts of these proteins on their surfaces. To gain better insight into the complexity and heterogeneity of sEV, we used the ExoView R100 platform to analyze the CD9/CD63/CD81 phenotype of sEV released by different cell types at a single sEV level. We demonstrated that these surface markers are sufficient to distinguish cell-type-specific sEV phenotypes. Furthermore, we recognized that tetraspanin composition in some sEV populations does not follow a random pattern. Notably, the tetraspanin distribution of sEV derived from mesenchymal stem cells (MSCs) alters depending on cell culture conditions. Overall, our data provide an overview of the cell-specific characteristics of sEV populations, which will increase the understanding of sEV physiology and improve the development of new sEV-based therapeutic approaches.
小细胞外囊泡 (sEV) 作为生物标志物、药物载体和治疗剂具有巨大的潜力。然而,由于以前在单个囊泡水平上对 sEV 的表型特征进行分析存在局限性,因此对细胞类型特异性 sEV 特征的了解仍然很少。随着新一代 sEV 分析设备的引入,如基于单颗粒干涉反射成像传感器 (SP-IRIS) 的 ExoView R100 平台,现在可以对单个 sEV 进行分析。虽然四跨膜蛋白 CD9、CD63 和 CD81 通常被认为是泛 sEV 标志物,但很明显,不同细胞类型的 sEV 在其表面包含几种组合和数量的这些蛋白。为了更深入地了解 sEV 的复杂性和异质性,我们使用 ExoView R100 平台在单个 sEV 水平上分析了不同细胞类型释放的 sEV 的 CD9/CD63/CD81 表型。我们证明这些表面标志物足以区分细胞类型特异性 sEV 表型。此外,我们认识到,一些 sEV 群体中的四跨膜蛋白组成并不遵循随机模式。值得注意的是,间充质干细胞 (MSC) 衍生的 sEV 的四跨膜蛋白分布会根据细胞培养条件而改变。总的来说,我们的数据提供了 sEV 群体的细胞特异性特征概述,这将增加对 sEV 生理学的理解,并有助于开发新的基于 sEV 的治疗方法。