Comparison of extracellular vesicle isolation methods reveals method-dependent protein and miRNA profiles in saliva

Aim: Salivary extracellular vesicles (EVs) are promising non-invasive biomarkers for disease diagnosis and monitoring, reflecting both systemic and local physiological states. However, the diversity of EV isolation protocols and the possibility that each method enriches distinct EV subpopulations remain major barriers to reproducibility and data comparability. This study aimed to evaluate the impact of different EV isolation methods on the yield, purity, and molecular composition of salivary EVs from healthy volunteers, thereby clarifying how these methods affect the potential use of salivary EVs as a non-invasive biomarker source for disease diagnosis and monitoring. Methods: We conducted a comprehensive comparison of three EV isolation methods - ultracentrifugation (UC), polyethylene glycol (PEG)-based co-precipitation (Q), and immunoaffinity capture (M) - to evaluate their impact on EV yield, purity, and molecular composition. Salivary EVs from healthy volunteers were analyzed using proteomic and small RNA sequencing approaches. Results: Principal component analysis revealed clear isolation method-dependent clustering, where M-derived EVs displayed the most distinct profile. The UC and Q methods yielded broader proteomic repertoires with higher total protein content, while M-isolated EVs exhibited greater purity and enrichment of trafficking- and lysosome-associated proteins. Among the 731 microRNAs (miRNAs) analyzed, 28 were consistently altered across methods and 65 uniquely enriched in M isolates. Reverse transcription quantitative polymerase chain reaction (RT-qPCR) confirmed the key directional trends. These 93 method-dependent miRNAs were predicted to target genes associated with synaptic structure and neurodegenerative pathways. Conclusion: These findings show that EV isolation methodology profoundly influences the cargo composition of salivary EVs and suggest that M can isolate specific EV populations that may better meet diagnostic requirements.