Aims
The heterogeneity of EVs still poses a challenge to accurately identify specific disease-related EV subpopulations. To address this important issue, a multi-factor authentication (MFA) approach for EV verification can provide high resolution data on specific EV subpopulations. This MFA approach leverages multiple structural and molecular features to deliver a more accurate and reliable identification of EVs, allowing us to focus on EV subpopulations that are particularly relevant to therapeutic response (Zhou et al., Biosensors and Bioelectronics, 2024) and disease diagnosis (Zhou et al., Advanced Science, 2024).
Methods
This MFA approach is achieved by a nanotechnology platform integrating surface-enhanced Raman scattering to simultaneously detect multiple proteins and glycans in disease-associated EV subpopulations, and alternating current-induced nanomixing to enhance sensitivity. EVs are isolated by size-exclusion chromatography and characterized by TEM, NTA, and tetraspanin expression following MISEV guidelines.
Results
(1) This MFA approach successfully tracked the epithelial-to-mesenchymal transition-like phenotype switching in EVs from both melanoma cell lines (n=3) and longitudinally monitored melanoma patients (n=8) receiving targeted therapy, which potentially can be leveraged to predict the development of resistance. (2) This MFA approach was applied to the multiplex detection of EV proteins and glycans in a prospective cancer screening study (n=40), which distinguished between benign lung changes and early-stage lung cancer with an AUC of 0.89 (95% CI=0.77-1.00).
Conclusions
This nanotechnology-enabled MFA approach shows potential to address the heterogeneity issue and deliver precise, high-resolution insights into disease-specific EV subpopulations that play crucial roles in intercellular communication, disease progression, and therapeutic response.