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