Liu Yuan, Zhang Wei, Phan Thanh Huyen, Chrzanowski Wojciech, Rodger Alison, Wang Yuling
ARC Centre for Nanoscale BioPhotonics, Macquarie University, NSW 2109, Australia.
Anal Methods. 2020 Dec 23;12(48):5908-5915. doi: 10.1039/d0ay01770a.
Direct surface-enhanced Raman scattering (SERS) has contributed to characterizing extracellular vesicles (EVs) by providing molecular signatures. However, little work has been carried out to understand the heterogeneity of EVs created by different methods or from different biological sources. Herein, we pioneered the use of positively charged gold-silver nanostars to explore the SERS profiles of different EVs. The physical features of EVs from cancer cells including the size, concentration, morphology and surface potential have been characterized via nanoparticle tracking analysis, transmission electron microscopy and zeta potential analysis. The results show that negatively charged EVs are attracted to positively charged gold-silver nanostar surfaces via electrostatic forces resulting in SERS spectra showing characteristic vibrational modes of the different components of EVs (i.e. proteins, lipids and nucleic acids). SERS data were complemented by other spectroscopic techniques including atomic force microscopy-infrared spectroscopy, UV-visible absorbance spectroscopy and fluorescence spectroscopy providing a more complete molecular picture of EVs. SERS signatures of EVs from different origins, batches, and isolation approaches were compared and analyzed. A statistical method (principal component analysis-linear discriminant analysis) was utilized to differentiate EV subtypes. Consequently, a desirable discrimination outcome for blind samples was obtained. This study provides novel insights to deepen our understanding of EV heterogeneity.
直接表面增强拉曼散射（SERS）通过提供分子特征，有助于对细胞外囊泡（EVs）进行表征。然而，对于不同方法或不同生物来源产生的EVs的异质性，人们了解甚少。在此，我们率先使用带正电荷的金银纳米星来探索不同EVs的SERS谱。通过纳米颗粒跟踪分析、透射电子显微镜和zeta电位分析，对癌细胞来源的EVs的物理特征，包括大小、浓度、形态和表面电位进行了表征。结果表明，带负电荷的EVs通过静电力被吸引到带正电荷的金银纳米星表面，从而产生显示EVs不同成分（即蛋白质、脂质和核酸）特征振动模式的SERS光谱。SERS数据通过其他光谱技术得到补充，包括原子力显微镜 - 红外光谱、紫外 - 可见吸收光谱和荧光光谱，从而提供了更完整的EVs分子图景。对来自不同来源、批次和分离方法的EVs的SERS特征进行了比较和分析。利用一种统计方法（主成分分析 - 线性判别分析）来区分EV亚型。因此，对于盲样获得了理想的判别结果。本研究为加深我们对EV异质性的理解提供了新的见解。
