Background:
Extracellular vesicles (EVs) play a crucial role in intercellular communication and are emerging as key biomarkers for disease diagnosis and monitoring. However, their metabolite composition remains largely unexplored, particularly in complex biological matrices such as plasma. Traditional GC-MS-based metabolomics often relies on solvent-intensive steps and liquid injection, which hinders automation and leads to contamination of the mass spectrometer’s analytical components, impacting performance and maintenance.
Methods:
To enhance metabolite detection, we applied sodium deoxycholate (SDC) in a preparatory step prior to PAL-Arrow-SPME GC-MS analysis. This detergent facilitates metabolite liberation from EVs, improving profiling sensitivity. To ensure compatibility with SPME extraction, SDC was subsequently precipitated out under acidic conditions, optimizing sample purity. EVs were isolated via ultracentrifugation and characterized through nanoparticle tracking analysis and ultrastructural examination.
Results:
This solvent-free workflow enabled broad-spectrum metabolite detection, with enhanced sensitivity and cleaner analytical performance compared to conventional approaches. Key lipid and amino acid profiles were identified, reinforcing their potential as biomarkers.
Discussion & Conclusion:
This automated, eco-friendly method improves EV metabolomics while ensuring greater analytical stability and clinical applicability. The ability to characterize EV metabolites from plasma offers promising biomarker discovery opportunities for precision medicine in neurodegenerative and systemic diseases.