λ-DNA- and Aptamer-Mediated Sorting and Analysis of Extracellular Vesicles
λ-DNA- and Aptamer-Mediated Sorting and Analysis of Extracellular Vesicles
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DOI：
10.1021/jacs.9b00007
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发表时间：
2019-03-06
影响因子：
15
通讯作者：
Sun, Jiashu
中科院分区：
文献类型：
--
作者：
Liu, Chao;Zhao, Junxiang;Sun, Jiashu
细胞外小泡(EVS)与多种病理过程密切相关。由于它们体积小、生物发生明显、标记表达异质性，分离和检测单个EV亚群是困难的。在这里，我们开发了一种由lambda-DNA和适体介导的方法，允许同时对单个EV进行大小选择性分离和表面蛋白分析。使用机器学习算法根据其大小和标记表达来识别EV签名，我们证明了分离的微泡在区分HER2免疫组织化学表达不同的乳腺细胞系和II期乳腺癌患者方面比外切体和凋亡小体更有效。我们的方法为在单个EV水平上评估EV的异质性提供了一个重要工具，具有潜在的临床诊断价值。
Extracellular vesicles (EVs) are heavily implicated in diverse pathological processes. Due to their small size, distinct biogenesis, and heterogeneous marker expression, isolation and detection of single EV subpopulations are difficult. Here, we develop a lambda-DNA-and aptamer-mediated approach allowing for simultaneous size-selective separation and surface protein analysis of individual EVs. Using a machine learning algorithm to EV signature based on their size and marker expression, we demonstrate that the isolated microvesicles are more efficient than exosomes and apoptotic bodies in discriminating breast cell lines and Stage II breast cancer patients with varied immunohistochemical expression of HER2. Our method provides an important tool to assess the EV heterogeneity at the single EV level with potential value in clinical diagnostics.