Single Extracellular Vesicles and Omics

IF 4.1 4区 生物学 Q2 BIOCHEMICAL RESEARCH METHODS
Proteomics Pub Date : 2026-05-28 Epub Date: 2026-03-20 DOI:10.1002/pmic.70119
David W. Greening, Yanling Cai, Ying Zhang
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引用次数: 0

Abstract

Extracellular vesicles (EVs) are fundamental intercellular signaling organelles; these lipid-bilayer-enclosed particles are secreted by all cell types and carry a cargo of proteins, lipids, and nucleic acids to facilitate such cell communication and transfer. Advances in the field of single EV analyses, including profiling techniques, capture and detection strategies, digital technologies, multiplexing capability, machine learning, and resolution of molecular analysis have led new insights into EV biology, crucial for detecting subtle variations and rare EV populations, determine the origin and molecular features of EVs, and the absolute quantification of biomarker expression levels. This issue focuses on how single EV analysis through omics and imaging technology is advancing the characterization and refinement of EVs. These studies highlight how single-EV approaches reveal multimodal distributions, challenging assumptions of discrete EV classes toward often shared physical and molecular properties (including markers across EV types) and extending this toward other EV types, refining technology pipelines, and integrated analytical strategies.

单个细胞外囊泡和组学。
细胞外囊泡(EVs)是基本的细胞间信号传导细胞器;所有类型的细胞都分泌这些脂质双分子层封闭的颗粒,并携带蛋白质、脂质和核酸,以促进细胞之间的交流和转移。单EV分析领域的进步,包括分析技术、捕获和检测策略、数字技术、多路复用能力、机器学习和分子分析分辨率,为EV生物学带来了新的见解,这对于检测微小变异和罕见EV群体、确定EV的起源和分子特征以及生物标志物表达水平的绝对定量至关重要。本期重点介绍了通过组学和成像技术对单个EV进行分析如何促进EV的表征和细化。这些研究强调了单电动汽车方法如何揭示多模态分布,挑战了离散电动汽车类别的假设,这些假设通常具有共享的物理和分子特性(包括跨电动汽车类型的标记),并将其扩展到其他电动汽车类型,完善技术管道和集成分析策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Proteomics
Proteomics 生物-生化研究方法
CiteScore
6.30
自引率
5.90%
发文量
193
审稿时长
3 months
期刊介绍: PROTEOMICS is the premier international source for information on all aspects of applications and technologies, including software, in proteomics and other "omics". The journal includes but is not limited to proteomics, genomics, transcriptomics, metabolomics and lipidomics, and systems biology approaches. Papers describing novel applications of proteomics and integration of multi-omics data and approaches are especially welcome.
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