Optimizing Size Exclusion Chromatography for Extracellular Vesicle Enrichment and Proteomic Analysis from Clinically Relevant Samples
Optimizing Size Exclusion Chromatography for Extracellular Vesicle Enrichment and Proteomic Analysis from Clinically Relevant Samples
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DOI：
10.1002/pmic.201800156
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发表时间：
2019-04-01
期刊：
影响因子：
3.4
通讯作者：
Hill, Michelle M.
中科院分区：
文献类型：
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作者：
Lane, Rebecca E.;Korbie, Darren;Hill, Michelle M.
近年来，细胞外囊泡（EV）的研究领域迅速发展，人们对其作为循环生物标志物的潜力特别感兴趣。由于EV蛋白相对于白蛋白和载脂蛋白等高丰度的循环蛋白丰度较低，临床样品中EV蛋白的蛋白质组学分析变得复杂。为了克服这个问题，粒径排除色谱（SEC）被提出作为一种富集ev的方法，同时消耗蛋白质污染物；然而，EV蛋白质组学的最佳SEC参数尚未得到充分的研究。本文报道了SEC用于分离ev和污染蛋白的定量评价和优化。使用合成模型系统，然后使用细胞系衍生的ev，发现PBS中10 mL Sepharose 4B柱可以从背景蛋白中获得最佳的ev分辨率。通过将癌细胞衍生的EV注入健康血浆，研究表明，当来自癌细胞系的EV仅占血浆EV总数的1%时，纳米lc -MS/MS可以检测到一些癌症EV相关蛋白。这些结果表明，优化的SEC和nanoLC-MS/MS工作流程对于从患者来源的临床样本中发现疾病EV蛋白生物标志物可能足够敏感。
The field of extracellular vesicle (EV) research has rapidly expanded in recent years, with particular interest in their potential as circulating biomarkers. Proteomic analysis of EVs from clinical samples is complicated by the low abundance of EV proteins relative to highly abundant circulating proteins such as albumin and apolipoproteins. To overcome this, size exclusion chromatography (SEC) has been proposed as a method to enrich EVs whilst depleting protein contaminants; however, the optimal SEC parameters for EV proteomics have not been thoroughly investigated. Here, quantitative evaluation and optimization of SEC are reported for separating EVs from contaminating proteins. Using a synthetic model system followed by cell line-derived EVs, it is found that a 10 mL Sepharose 4B column in PBS produces optimal resolution of EVs from background protein. By spiking-in cancer cell-derived EVs to healthy plasma, it is shown that some cancer EV-associated proteins are detectable by nano-LC-MS/MS when as little as 1% of the total plasma EV number are derived from a cancer cell line. These results suggest that an optimized SEC and nanoLC-MS/MS workflow may be sufficiently sensitive for disease EV protein biomarker discovery from patient-derived clinical samples.