Improving SERS biosensors for the analysis of ovarian cancer-derived small extracellular vesicles.

Abstract

Small extracellular vesicles (sEVs) are lipid bilayer vesicles that carry key molecules (e.g., proteins, DNAs, RNAs, and lipids) for cell-to-cell communication, being regarded as promising biomarkers for cancer diagnosis. However, the detection of sEVs is still challenging due to their unique characteristics such as size and phenotype heterogeneity. The surface-enhanced Raman scattering (SERS) assay is a promising tool for sEV analysis as it shows the advantages of robustness, high sensitivity, and specificity. Previous studies proposed different "sandwich" immunocomplex assembling strategies and various capturing probes for sEV detection by the SERS assay. However, no studies have reported the effect of immunocomplex assembling strategies and capturing probes on the analysis of sEVs using this assay. Hence, to achieve the highest performance of the SERS assay for analysing ovarian cancer-derived sEVs, we first assessed the presence of ovarian cancer markers such as EpCAM on cancer cells and sEVs by using flow cytometry and immunoblotting. We found that cancer cells and their derived sEVs present EpCAM and therefore EpCAM was used to functionalise SERS nanotags for the comparison study of "sandwich" immunocomplex assembling strategies. Then, we compared three types of capturing probes (magnetic beads conjugated with anti-CD9, CD63, or CD81 antibodies) for sEV detection. Our study showed the strategy of pre-mixing of sEVs with SERS nanotags and the anti-CD9 capturing probe would achieve the best performance with the minimum detection of sEVs down to 1.5 × 105 particles per μL and with high specificity in distinguishing sEVs from different ovarian cancer cell lines. We further profiled the surface protein biomarkers (EpCAM, CA125, and CD24) on ovarian cancer-derived sEVs in both PBS and plasma (sEVs spiked in healthy plasma) using the improved SERS assay, showing high sensitivity and specificity. As such, we anticipate that our improved SERS assay has the potential to be used clinically as one of the effective detection methods of ovarian cancer.