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

TL;DR

This study presents a rapid, label-free method combining surface-enhanced Raman spectroscopy and machine learning to accurately identify exosomes from different cell sources using minimal data, advancing diagnostics and biological research.

Contribution

The paper introduces a novel, efficient SERS-based approach with PCA-SVM analysis for exosome detection from various cell lines using limited spectral data.

Findings

Achieved 94.4% accuracy in classifying exosomes from six cell types.

Used small SERS datasets for rapid and simple exosome detection.

Demonstrated potential for timely cell communication and disease research.

Abstract

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.