Certain molecules found on the surface or within the cargo of extracellular vesicles (EVs) are linked to osteoarthritis (OA) severity and progression. We aimed to identify plasma pathogenic EV subpopulations that can predict knee radiographic OA (rOA) progression. We analyzed the mass spectrometry-based proteomic data of plasma EVs and synovial fluid (SF) EVs from knee OA patients (n = 16, 50% female). The identified surface markers of interest were further evaluated in plasma EVs from an independent cohort of knee OA patients (n = 30, 47% female) using flow cytometry. A total of 199 peptides with significant correlation between plasma and SF EVs were identified. Of these, 41.7% were linked to immune system processes, 15.5% to inflammatory responses, and 16.7% to the complement system. Crucially, five previously identified knee rOA severity-indicating surface markers-FGA, FGB, FGG, TLN1, and AMBP-were confirmed on plasma EV subpopulations in an independent cohort. These markers' baseline frequencies on large plasma EVs predicted rOA progression with an AUC of 0.655-0.711. Notably, TLN1 was expressed in OA joint tissue, whereas FGA, FGB, FGG, and AMBP were predominantly liver derived. These surface markers define specific pathogenic EV subpopulations, offering potential OA prognostic biomarkers and novel therapeutic targets for disease modification.
Keywords: flow cytometry; plasma; predictor; progression; proteomics; surface marker.