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

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Spectroscopy

Background:

• Extracellular vesicles (EVs) are vital for diagnostics and therapeutics but challenging to characterize at the nanoscale.
• Detecting drug-induced liver injury (hepatotoxicity) via EV molecular content is an emerging, unexplored area.

Purpose of the Study:

• To develop a rapid, label-free surface-enhanced Raman spectroscopy (SERS) platform for analyzing EV molecular content.
• To investigate the potential of this platform for detecting drug-induced hepatotoxicity using EV signatures.

Main Methods:

• A novel SERS spectroscopy approach was employed for EV analysis.
• The platform requires minimal sample volume (1.3 microliters) and provides results in under ten minutes.
• Hepatic cell cultures were used to model acetaminophen-induced hepatotoxicity.

Main Results:

• The platform successfully captured distinct and reproducible EV molecular changes in response to hepatotoxicity.
• High accuracy was achieved, with root mean squared error as low as 3.80%.
• Strong correlations were established between EV spectral data and conventional toxicity biomarkers, revealing drug-response signatures.

Conclusions:

• Extracellular vesicles serve as dynamic reporters of cellular drug responses.
• SERS-based EV detection offers a promising method for identifying and monitoring drug-induced hepatotoxicity.