Xiaoming Lyu | Searcharxiv

arXiv2026

AI-Driven SERS for Non-invasive and Label-Free Extracellular Vesicle Detection Across Cellular Origins in Tears and Sweat

Wearable sensing technology capable of point-of-care, continuous and non-invasive analysis of exosomes in biofluid such as tears and sweat is an essential part for future personalized medicine. Major detection and identification methods of cell secreted Extracellular Vesicles (EVs) often require labeling and are time-consuming, resulting in low efficiency in EV mechanism research and disease diagnosis. While the label-free Surface-enhanced Raman spectroscopy (SERS) has been combined with deep learning model for EV identification in blood, their application to non-invasive detection of EVs in tears and sweat are missing. Here, we filled this gap by developing an artificial intelligence (AI)-assisted Surface-enhanced Raman spectroscopy (SERS) method based on salt-induced nanoparticle aggregation for fast EV identification in tears and sweat with high accuracy. Significantly, our label-free detection and AI differentiation of EVs from 6 cell lines (HepG2, Hela, 143B, LO-2, BMSC, H8) achieved the identification of EVs in tear fluids from 7 different disease sources with accuracies >92%. Our results showed that this platform can not only distinguish EVs from multiple cell sources but also generate highly reproducible and selective EV signals in tear fluids without a need for chemical labeling or separation steps. Molecular dynamics simulations revealed that silver atoms (Ag) form electrostatic interactions with oxygen atoms of multiple amino acid residues in proteins, suggesting a high affinity. This strategy realizes ultra-sensitive and anti-interference detection of EVs, providing a new idea for the rapid diagnosis of clinical diseases.

cond-mat.mes-hall↗

arXiv2024

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

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.

q-bio.BM↗