Characterization of Extracellular Vesicles by Surface-Enhanced Raman Spectroscopy
Abstract
Extracellular vesicles (EVs), which are nanoscale vesicles secreted by cells into biofluids, are of research interest due to their roles in intercellular communication. EVs released from mesenchymal stromal cells (MSCs) have tremendous potential in cell-free regenerative medicine, while EVs released from diseased cells are being studied as biomarkers for minimally invasive and early disease detection. Presented in this thesis are gold nanohole arrays for the capture and sensitive detection of EVs by surface-enhanced Raman spectroscopy (SERS), a plasmonic technique capable of single molecule detection. Herein, we have characterized EVs released from MSCs and ovarian cancer cells, with a focus on cell lines that have been underexplored by SERS in literature. Using a hybrid principal component analysis-machine learning approach, we have demonstrated the platform’s potential in classifying EV groups with high (~ 99 %) accuracy, sensitivity, and specificity, which we hope will one day translate to point-of-care detection for disease diagnosis.