School of Biomedical Engineering, Institute for Personalized Medicine, Shanghai Jiao Tong University, Shanghai, 200030, China.
Anal Chem. 2020 Apr 7;92(7):4884-4890. doi: 10.1021/acs.analchem.9b04622. Epub 2020 Mar 12.
Extracellular vesicles (EVs), including exosomes, are promising circulating biomarkers for disease diagnosis. Conventional EVs analysis requires multiple instrumentations to obtain their phenotypic features, which limits its wide applications. Here, we present a plasmonic biosensor technology for multifunctional analysis of EVs. The system is based on a functionalized surface plasmon resonance (SPR) biosensor and an advanced plasmonic microscopy to capture and image EVs at single-particle level. SPR images are processed with a home-developed deep learning algorithm to identify EVs and quantify image intensity automatically. By combining immunosensing and single particle analysis, this approach enables both physical and chemical characterization of EVs. As a proof-of-concept, we applied it to analyze EVs secreted from human lung cancer A549 cell lines. Results show the capabilities in the detection of size, concentration and affinity constant. Due to the single particle imaging and multifunctional analysis capability, we anticipate that this technology will find use in clinical and scientific applications.
细胞外囊泡（EVs），包括外泌体，是很有前途的疾病诊断用循环生物标志物。传统的 EVs 分析需要多种仪器来获得其表型特征，这限制了其广泛应用。在这里，我们提出了一种等离子体生物传感器技术，用于 EVs 的多功能分析。该系统基于功能化表面等离子体共振（SPR）生物传感器和先进的等离子体显微镜，可在单粒子水平上捕获和成像 EVs。SPR 图像由自主开发的深度学习算法进行处理，以自动识别 EVs 并定量图像强度。通过结合免疫传感和单颗粒分析，这种方法可以实现 EVs 的物理和化学特性分析。作为概念验证，我们将其应用于分析人类肺癌 A549 细胞系分泌的 EVs。结果表明，该方法能够检测 EVs 的大小、浓度和亲和常数。由于具有单颗粒成像和多功能分析的能力，我们预计这项技术将在临床和科学应用中得到应用。
