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Exosomes are significant facilitators of inter-cellular communication thatcan unveil cell-cell interactions, signaling pathways, regulatory mechanismsand disease diagnostics. Nonetheless, current analysis required large amount ofdata for exosome identification that it hampers efficient and timely mechanismstudy and diagnostics. Here, we used a machine-learning assistedSurface-enhanced Raman spectroscopy (SERS) method to detect exosomes derivedfrom six distinct cell lines (HepG2, Hela, 143B, LO-2, BMSC, and H8) with smallamount of data. By employing sodium borohydride-reduced silver nanoparticlesand sodium borohydride solution as an aggregating agent, 100 SERS spectra ofthe each types of exosomes were collected and then subjected to multivariateand machine learning analysis. By integrating Principal Component Analysis withSupport Vector Machine (PCA-SVM) models, our analysis achieved a high accuracyrate of 94.4% in predicting exosomes originating from various cellular sources.In comparison to other machine learning analysis, our method used small amountof SERS data to allow a simple and rapid exosome detection, which enables atimely subsequent study of cell-cell interactions, communication mechanisms,and disease mechanisms in life sciences.

Label-free detection of exosomes from different cellular sources based on surface-enhanced Raman spectroscopy combined with machine learning models