This protocol outlines a comprehensive, label-free platform that integrates surface-enhanced Raman spectroscopy (SERS) with machine learning (ML) to detect and molecularly profile individual small extracellular vesicles (sEVs) for diagnostic and therapeutic applications. The method begins with sEV isolation using either size exclusion chromatography or ultracentrifugation. Isolated vesicles are then analyzed on engineered plasmonic gold nanopyramid 2D array substrates capable of single-vesicle sensitivity. By leveraging intrinsic Raman biochemical fingerprints, the protocol enables high-specificity detection without external labels. Following spectral acquisition, data undergo preprocessing and are analyzed using trained machine learning algorithms (e.g., LDA, SVC) to classify disease states, successfully distinguishing gastric cancer from healthy controls using sEVs from tissue, plasma, and saliva with respective classification accuracies of 90.1%, 70.9%, and 60.7%. Additionally, its therapeutic application is shown by quantifying doxorubicin loading in single sEVs, a measurement enhanced by using graphene-coated substrates as an internal standard. This approach allows for high-throughput analysis that captures population heterogeneity essential for early disease detection and understanding drug loading efficiency at the single-vesicle level.
Liu et al. (Fri,) studied this question.