Analysis of extracellular vesicles as emerging theranostic nanoplatforms
Analysis of extracellular vesicles as emerging theranostic nanoplatforms
复制标题
细胞外囊泡作为新兴治疗诊断纳米平台的分析
DOI：
10.1016/j.ccr.2020.213506
复制
发表时间：
2020-12
影响因子：
20.6
通讯作者：
Fabiao Yu
中科院分区：
文献类型：
--
作者：
Yanlong Xing;Ziyi Cheng;Rui Wang;Chuanzhu Lv;Tony D. James;Fabiao Yu
细胞外囊泡（Extracellular vesicles，EVs）是由原核生物和真核生物细胞分泌的纳米级脂质膜结合囊泡，携带来源细胞的蛋白质、核酸和脂质等生物活性物质。鉴于其在运输生物活性成分方面的突出能力，EV被认为是用于疾病诊断和新兴治疗纳米颗粒的有前途的生物标志物。然而，为了在临床应用中发挥其作用，需要从复杂的生物流体中有效分离和灵敏分析EV。EV相关研究的最新进展为开发使用EV的新兴治疗性纳米平台提供了可行的方法。通过这篇综述，我们的目标是提供一个全面和深入的总结，包括荧光，拉曼/表面增强拉曼光谱（Sers）分析和其他方法，以及它们在构建基于EV的治疗诊断纳米平台对各种疾病的临床潜力的各种检测方法的最新进展。特别是，微流体辅助分析系统，单一EV检测和利用EV用于治疗目的的主要方法被强调。我们预计这篇综述将对相关领域的研究人员有所启发，并将为具有各种研究背景的科学家提供一个一般性的介绍。
Extracellular vesicles (EVs) are nanoscale lipid membrane–bound vesicles that are secreted by cells of both prokaryotes and eukaryotes and carry bioactive cargos including proteins, nucleic acid and lipids from source cells. Given their prominent ability in transporting bioactive components, EVs are regarded as promising biomarkers for disease diagnosis and emerging therapeutic nanoparticles. However, to exert their effect in clinical applications, effective isolation and sensitive analysis of EVs from complex biofluids is required. Recent advances in EV-related research has provided feasible approaches for developing emerging therapeutic nanoplatforms using EVs. With this review, we aim to provide a comprehensive and in-depth summary of recent advances in diverse assay methods for EVs including fluorescence, Raman/Surface-enhanced Raman Spectroscopy (SERS) analysis and other methods, as well as their clinical potential in constructing EV-based theranostic nanoplatforms towards various diseases. In particular, microfluidic-assisted analysis sytems, single EV detection and the main approaches of utilizing EVs for therapeutic purposes are highlighted. We anticipate this review will be inspirational for researchers in related fields and will provide a general introduction to scientists with various research backgrounds.
登录
查看更多内容
影响因子：
16
作者：
Varga Z;Yuana Y;Grootemaat AE;van der Pol E;Gollwitzer C;Krumrey M;Nieuwland R
通讯作者：
Nieuwland R
影响因子：
7.4
作者：
Yaokun Xia;Liangliang Wang;Juan Li;Xiangqi Chen;Jianming Lan;An Yan;Yun Lei;Sheng Yang;
通讯作者：
Yaokun Xia;Liangliang Wang;Juan Li;Xiangqi Chen;Jianming Lan;An Yan;Yun Lei;Sheng Yang;
影响因子：
14
作者：
Yuan D;Zhao Y;Banks WA;Bullock KM;Haney M;Batrakova E;Kabanov AV
通讯作者：
Kabanov AV
影响因子：
29.4
作者：
Tran, Phuong H. L.;Xiang, Dongxi;Duan, Wei
通讯作者：
Duan, Wei
影响因子：
4
作者：
Chivet M;Hemming F;Pernet-Gallay K;Fraboulet S;Sadoul R
通讯作者：
Sadoul R