High-Throughput Extracellular Vesicle Isolation Using Plate-Based Size Exclusion Chromatography and Automation

化学 大小排阻色谱法 分离(微生物学) 胞外囊泡 吞吐量 自动化 色谱法 细胞外 细胞外小泡 小泡 生物化学 微泡 细胞生物学 微生物学 基因 小RNA 机械工程 电信 计算机科学 无线 生物 工程类
作者
Tal Gilboa,Dmitry Ter‐Ovanesyan,Clarissa May Babila,Sara Whiteman,Shad Morton,David Kalish,Julie Johnston,David Tesin,Matthew Davies,Jenny M. Tam,George M. Church,David R. Walt
出处
期刊:Journal of the American Chemical Society [American Chemical Society]
标识
DOI:10.1021/jacs.4c17948
摘要

Extracellular vesicles (EVs) are natural, cell-derived nanoparticles released into biofluids, such as plasma, and hold great potential as a new class of biomarkers. However, the utility of analyzing EVs in clinical samples has been hampered by a lack of suitable EV isolation methods that can be performed reproducibly in a scalable manner. The current method of choice for isolating EVs, size exclusion chromatography (SEC), is performed manually one column at a time, and thus does not have the throughput for isolating EVs from clinical samples. In this work, we adapt SEC to a plate-based format to increase its throughput. We show that SEC can be performed using plates containing frits packed with resin, where each well of a 24-well plate can be used for a different sample. By measuring EV markers CD63 and CD81, as well as Albumin as a representative free protein, we optimize the separation of EVs from free proteins in the 24-well format. We also demonstrate that performing SEC in these plates can be automated using liquid handling platforms with the use of custom adapters. We quantify the high reproducibility of this automated platform and then apply the platform to analyze the tetraspanins CD63 and CD81 across individuals. Our work represents a solution to the long-standing challenge in the EV biomarker field of reproducible high-throughput EV isolation from plasma and other biofluids. We envision that the automated methods we have developed will scale SEC to hundreds of samples per day, enabling the use of EVs for biomarker discovery and diagnostics.
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