Label-Free SERS Analysis of Biological and Physical Information Heterogeneity of Nanoscale Extracellular Vesicle by Matching Specific Sizes of Enhanced Particles.

The heterogeneity of extracellular vesicles (EVs) surface information represents different functions, which is neglected in previous studies. In this study, a label-free SERS analysis approach is demonstrated to study fundamental EV biological and physical information heterogeneity by matching specific sizes of nano-enhanced particles. This strategy reveals informative, comprehensive, and high-quality SERS spectra of the overall exosome surface, and effectively circumvents the key information loss caused by the spatial resistance of NPs binding to the 293 exosomes' concave structure. The classification of normal and cancerous cell-derived exosomes by PCA method, the accuracy is improved from 91.2% to 95.1% by optimizing sizes of nano-enhanced particles. In addition, stem cell-derived EVs of diverse sizes and morphologies similarly show acuity of spectrum variation to NPs size, which is conductive to qualitative studies. This new strategy will offer a widened in-depth understanding of the surface information, size, and morphology of EVs, which can be applied to the study of biological functions.