Synovial exosomal type II collagen as a biomarker for osteoarthritis Progression: From molecular evaluation to AI-powered SERS-based diagnosis

Abstract

Osteoarthritis (OA) is a degenerative joint disease which lacks reliable biomarkers for monitoring disease progression. Current assessment methods rely primarily on radiographic Kellgren-Lawrence (K-L) grading and symptom-based scores, which often show poor concordance with underlying molecular changes. Here, we present a label-free diagnostic strategy for classifying OA severity based on type II collagen in synovial exosomes, using surface-enhanced Raman spectroscopy (SERS). Exosomal surface type II collagen (ExoCOL2A1), quantified from synovial fluid of OA patients, exhibited a significant inverse correlation with radiographic severity and demonstrated superior diagnostic capability compared to other protein markers. To enable non-destructive detection, a plasmonic gold nanoparticle substrate was used to acquire the SERS spectra of whole synovial exosomes, which were subsequently analyzed using a deep neural network (DNN). The DNN model accurately classified OA severity based on spectral characteristics, achieving a prediction accuracy of 95.3 %, and outperforming traditional machine learning models such as LDA, SVM, and kNN. Principal component analysis further identified ExoCOL2A1-associated spectral peaks as key contributing factors for the exosomal SERS spectrum. These findings establish the clinical potential of exosomal type II collagen as OA severity-associated markers and highlight the utility of AI-integrated SERS as a non-invasive diagnostic platform.