PO.CL01.21 · 临床研究
用于液体活检中高通量细胞外囊泡处理的集成盘式微流控平台
Integrated disc-fluidic platform for high-throughput extracellular vesicle processing in liquid biopsy
作者与单位 Authors & Affiliations
摘要 Abstract
中文摘要
细胞外囊泡(EVs)代表了一类有前景的癌症生物标志物,可实现对肿瘤来源分子信息的重复且微创的获取。然而,一个主要挑战是EVs嵌入于复杂多变的生物基质中,使得这些颗粒的纯化和标准化处理对于可靠的临床分析至关重要。现有的EV分离方法缓慢、劳动密集且易产生变异性,这些局限性阻碍了其在转化癌症研究中的广泛应用。在此,我们提出一个集成的离心盘式微流控平台,专为自动化、高通量EV处理而设计。该系统整合了若干创新特性,包括血浆分离、色谱EV纯化、向心液体转移,以及基于微珠的EV捕获和免疫标记。这些模块无缝协作,在单个一次性盘上执行完整的EV工作流程。使用全血输入,所开发的系统在8分钟内富集血浆EVs,同时去除>96%的脂蛋白污染物。它还实现了对16个EV蛋白靶标的多重免疫标记,在<75分钟内完成整个工作流程。在一项试点临床研究(n = 221份血浆样本)中,我们将该原型设备应用于多组合(30标志物)EV蛋白谱分析。所得特征稳健地区分了癌症与非癌症样本,并进一步对肿瘤类型进行分层。通过结合自动化、标准化和通量,该盘式微流控平台提供了一条通向可重复EV分析的可扩展途径,并在推动EV生物标志物进入临床肿瘤学应用方面具有强大潜力。
查看英文原文 English abstract
Extracellular vesicles (EVs) represent a promising class of cancer biomarkers, enabling repeated and minimally invasive access to tumor-derived molecular information. A major challenge, however, is that EVs are embedded in complex and variable biological matrices, making the purification and standardized processing of these particles essential for reliable clinical analysis. Existing EV isolation methods are slow, labor-intensive, and prone to variability; limitations that hinder widespread adoption in translational cancer research. Here, we present an integrated centrifugal disc-fluidics platform designed for automated, high-throughput EV processing. The system incorporates key innovative features, including plasma separation, chromatographic EV purification, centripetal liquid transfer, and bead-based EV capture and immunolabeling. These modules operate seamlessly to execute a complete EV workflow on a single disposable disc. Using whole blood inputs, the developed system enriched plasma EVs within 8 minutes while removing >96% of lipoprotein contaminants. It further enabled multiplexed immunolabeling for 16 EV protein targets, completing the entire workflow in <75 minutes. In a pilot clinical study ( n = 221 plasma samples), we applied the prototype device for multi-panel (30-marker) EV protein profiling. The resulting signatures robustly distinguished cancer from non-cancer samples and further stratified tumor types. By combining automation, standardization, and throughput, this disc-fluidic platform provides a scalable route toward reproducible EV analytics and has strong potential for advancing EV biomarkers into clinical oncology applications.
利益披露 Disclosure
H. Lee,
Ionis ).
H. Woo, None..
C. M. Castro, None..
J. Park, None..
Y. Choi, None.