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口腔液中大型细胞外囊泡的鉴别:小力离心与沉降模式分析联合方案

Differentiation of large extracellular vesicles in oral fluid: Combined protocol of small force centrifugation and sedimentation pattern analysis.

作者信息

Kawano Takamasa, Okamura Kohji, Shinchi Hiroki, Ueda Koji, Nomura Takeshi, Shiba Kiyotaka

机构信息

Division of Protein Engineering, Cancer Institute Japanese Foundation for Cancer Research Koto-ku Tokyo Japan.

Department of Oral Oncology Oral and Maxillofacial Surgery, Tokyo Dental College Ichikawa Chiba Japan.

出版信息

J Extracell Biol. 2024 Feb 14;3(2):e143. doi: 10.1002/jex2.143. eCollection 2024 Feb.

Abstract

Extracellular vesicles (EVs) in biofluids are highly heterogeneous entities in terms of their origins and physicochemical properties. Considering the application of EVs in diagnostic and therapeutic fields, it is of extreme importance to establish differentiating methods by which focused EV subclasses are operationally defined. Several differentiation protocols have been proposed; however, they have mainly focused on smaller types of EVs, and the heterogeneous nature of large EVs has not yet been fully explored. In this report, to classify large EVs into subgroups based on their physicochemical properties, we have developed a protocol, named EV differentiation by sedimentation patterns (ESP), in which entities in the crude large EV fraction are first moved through a density gradient of iodixanol with small centrifugation forces, and then the migration patterns of molecules through the gradients are analysed using a non-hierarchical data clustering algorithm. Based on this method, proteins in the large EV fractions of oral fluids clustered into three groups: proteins shared with small EV cargos and enriched in immuno-related proteins (Group 1), proteins involved in energy metabolism and protein synthesis (Group 2), and proteins required for vesicle trafficking (Group 3). These observations indicate that the physiochemical properties of EVs, which are defined through low-speed gradient centrifugation, are well associated with their functions within cells. This protocol enables the detailed subclassification of EV populations that are difficult to differentiate using conventional separation methods.

摘要

生物流体中的细胞外囊泡(EVs)在起源和物理化学性质方面是高度异质的实体。考虑到EVs在诊断和治疗领域的应用,建立能够操作性地定义特定EV亚类的区分方法极为重要。已经提出了几种区分方案;然而,它们主要集中在较小类型的EVs上,大型EVs的异质性尚未得到充分探索。在本报告中,为了根据其物理化学性质将大型EVs分类为亚组,我们开发了一种名为沉降模式EV区分(ESP)的方案,其中首先通过小离心力使粗大型EV部分中的实体通过碘克沙醇密度梯度,然后使用非层次数据聚类算法分析分子通过梯度的迁移模式。基于此方法,口腔液大型EV部分中的蛋白质聚为三组:与小型EV货物共有的且富含免疫相关蛋白质的蛋白质(第1组)、参与能量代谢和蛋白质合成的蛋白质(第2组)以及囊泡运输所需的蛋白质(第3组)。这些观察结果表明,通过低速梯度离心定义的EVs的物理化学性质与其在细胞内的功能密切相关。该方案能够对使用传统分离方法难以区分的EV群体进行详细的亚分类。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bb92/11080912/90f2d90bbec4/JEX2-3-e143-g002.jpg

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