Heterogeneous Analysis of Extracellular Vesicles for Osteosarcoma Diagnosis

Abstract

Osteosarcoma (OS) is the most prevalent primary tumor of bones, often diagnosed late with a poor prognosis. Currently, few effective biomarkers or diagnostic methods have been developed for early OS detection with high confidence, especially for metastatic OS. Tumor-derived extracellular vesicles (EVs) are emerging as promising biomarkers for early cancer diagnosis through liquid biopsy. Here, we report a plasmonic imaging-based biosensing technique, termed subpopulation protein analysis by single EV counting (SPASEC), for size-dependent EV subpopulation analysis. In our SPASEC platform, EVs are accurately sized and counted on plasmonic sensor chips coated with OS-specific antibodies. Subsequently, EVs are categorized into distinct subpopulations based on their sizes, and the membrane proteins of each size-dependent subpopulation are profiled. We measured the heterogeneous expression levels of the EV markers (CD63, BMP2, GD2, and N-cadherin) in each of the EV subsets from both OS cell lines and clinical plasma samples. Using the linear discriminant analysis (LDA) model, the combination of four markers is applied to classify the healthy donors (n = 37), nonmetastatic OS patients (n = 13), and metastatic patients (n = 12) with an area under the curve of 0.95, 0.92, and 0.99, respectively. SPASEC provides accurate EV sensing technology for early OS diagnosis.