Department of Bio-convergence Engineering, Korea University, Seoul 02841, Korea.
School of Biomedical Engineering, Korea University, Seoul 02841, Korea.
Molecules. 2020 Nov 9;25(21):5209. doi: 10.3390/molecules25215209.
Extracellular vesicles (EVs) have been widely investigated as promising biomarkers for the liquid biopsy of diseases, owing to their countless roles in biological systems. Furthermore, with the notable progress of exosome research, the use of label-free surface-enhanced Raman spectroscopy (SERS) to identify and distinguish disease-related EVs has emerged. Even in the absence of specific markers for disease-related EVs, label-free SERS enables the identification of unique patterns of disease-related EVs through their molecular fingerprints. In this review, we describe label-free SERS approaches for disease-related EV pattern identification in terms of substrate design and signal analysis strategies. We first describe the general characteristics of EVs and their SERS signals. We then present recent works on applied plasmonic nanostructures to sensitively detect EVs and notable methods to interpret complex spectral data. This review also discusses current challenges and future prospects of label-free SERS-based disease-related EV pattern identification.
细胞外囊泡 (EVs) 因其在生物系统中的无数作用而被广泛研究，作为疾病液体活检的有前途的生物标志物。此外，随着外泌体研究的显著进展，使用无标记表面增强拉曼光谱 (SERS) 来识别和区分与疾病相关的 EV 已经出现。即使对于与疾病相关的 EV 没有特定的标记物，无标记 SERS 也可以通过它们的分子指纹识别出与疾病相关的 EV 的独特模式。在这篇综述中，我们根据基底设计和信号分析策略描述了用于疾病相关 EV 模式识别的无标记 SERS 方法。我们首先描述了 EVs 的一般特征及其 SERS 信号。然后，我们介绍了最近应用于灵敏检测 EVs 的等离子体纳米结构以及解释复杂光谱数据的显著方法。本综述还讨论了基于无标记 SERS 的疾病相关 EV 模式识别的当前挑战和未来前景。
