SHINE: SERS-based Hepatotoxicity detection using Inference from Nanoscale Extracellular vesicle content

Abstract

Extracellular vesicles (EV) are becoming crucial tools in liquid biopsy, diagnostics, and therapeutic applications, yet their nanoscale characterization remains challenging. In this context, the detection of drug-induced liver injury, i.e., hepatotoxicity, through extracellular vesicle molecular content remains an unexplored frontier. To this end, we present a label-free surface-enhanced Raman (SERS) spectroscopy approach, which provides rapid EV content analysis under ten minutes and requires only 1.3 microliters of sample. Using hepatic cultures as a model, our platform captures distinct and reproducible EV molecular changes in response to acetaminophen-induced hepatoxicity. Our platform achieves exceptional accuracy with root mean squared error values as low as 3.80%, establishing strong correlations between EV spectra and conventional toxicity biomarkers. Unlike previous EV-SERS studies limited to vesicle identification and disease markers, this approach reveals EV drug-response signatures strongly correlated with conventional toxicity markers. These findings establish EVs as dynamic reporters of cellular drug responses and demonstrate use of SERS-based EV detection of hepatotoxicity.