Li Ping, Wang Jie, Gao Mengqiu, Wang Jue, Ma Yi, Gu Yueqing
Department of Biomedical Engineering, School of Engineering, China Pharmaceutical University, Nanjing 211198, China.
The Affiliated Hospital of Qingdao University, Qingdao University, Qingdao 266071, China.
Anal Chem. 2021 Jul 20;93(28):9860-9868. doi: 10.1021/acs.analchem.1c01712. Epub 2021 Jul 12.
Extracellular vesicles (EVs) have recently emerged as a promising tumor biomarker, and EV phenotyping offers many benefits for cancer diagnosis. However, the practicality of EV assays remains a challenge due to macromolecule disturbances, biomarker heterogeneities, and EV abundance limitations. Here, we demonstrate a membrane-based biosensor for precise and sensitive EV identification. The sensor synergistically integrates EV capture and detection by virtue of EV membrane features (membrane protein and lipid bilayer), comprising antibody-conjugated magnetic beads (AbMBs) and duplex-specific nuclease (DSN)-mediated amplification cycles. Bivalent cholesterol (biChol)-modified RNA-DNA duplexes are designed to insert into the EV membrane, transforming EV signals into RNA signals and initiating the signal amplification. The membrane-based signal production pattern eliminates protein interference. By employing four antibodies specific to PCa-related membrane proteins, the AbMB-biChol platform enables the successful differentiation and monitoring of PCa-related EVs and distinguishes PCa patients from healthy donors with improved efficacy, exhibiting superior efficiency over the analyses based on clinically used biomarker CA19-9 and PCa-related proteins. As such, the developed system has great potential for clinical PCa diagnosis.
细胞外囊泡（EVs）最近已成为一种有前景的肿瘤生物标志物，并且EV表型分析为癌症诊断带来诸多益处。然而，由于大分子干扰、生物标志物异质性和EV丰度限制，EV检测的实用性仍然是一项挑战。在此，我们展示了一种基于膜的生物传感器，用于精确且灵敏地识别EVs。该传感器借助EV膜特征（膜蛋白和脂质双层）协同整合EV捕获和检测，包括抗体偶联磁珠（AbMBs）和双链特异性核酸酶（DSN）介导的扩增循环。二价胆固醇（biChol）修饰的RNA-DNA双链体被设计插入EV膜，将EV信号转化为RNA信号并启动信号放大。基于膜的信号产生模式消除了蛋白质干扰。通过使用四种针对前列腺癌（PCa）相关膜蛋白的抗体，AbMB-biChol平台能够成功区分和监测PCa相关的EVs，并以更高的效能将PCa患者与健康供体区分开来，与基于临床使用的生物标志物CA19-9和PCa相关蛋白的分析相比，表现出卓越的效率。因此，所开发的系统在临床PCa诊断方面具有巨大潜力。
