Heterogeneous Analysis of Extracellular Vesicles for Osteosarcoma Diagnosis

Area of Science:

• Biomedical Engineering
• Nanotechnology
• Oncology

Background:

• Osteosarcoma (OS) is a primary bone tumor with late diagnosis and poor prognosis.
• Effective early diagnostic biomarkers, especially for metastatic OS, are lacking.
• Tumor-derived extracellular vesicles (EVs) show promise as liquid biopsy biomarkers.

Purpose of the Study:

• To develop a novel biosensing technique for size-dependent extracellular vesicle (EV) subpopulation analysis.
• To utilize this technique for early detection and classification of osteosarcoma (OS) patients.

Main Methods:

• A plasmonic imaging-based biosensing technique, subpopulation protein analysis by single EV counting (SPASEC), was developed.
• SPASEC accurately sizes and counts EVs on antibody-coated sensor chips.
• EV subpopulations were profiled for membrane protein expression (CD63, BMP2, GD2, N-cadherin) using a linear discriminant analysis (LDA) model.

Main Results:

• SPASEC enabled size-dependent analysis and protein profiling of EV subpopulations.
• Heterogeneous expression of four key markers was measured in EVs from OS cell lines and patient plasma.
• The LDA model achieved high accuracy in classifying healthy donors (AUC=0.95), nonmetastatic OS (AUC=0.92), and metastatic OS (AUC=0.99).

Conclusions:

• SPASEC is an accurate EV sensing technology for early osteosarcoma diagnosis.
• This technique facilitates the classification of OS patients, including those with metastasis.
• EV subpopulation analysis holds significant potential for improving cancer diagnostics.