Surface enhanced Raman scattering of extracellular vesicles for cancer diagnostics despite isolation dependent lipoprotein contamination. Issue 35 (2nd September 2021)

• Record Type: Journal Article
• Authors: Koster, Hanna J.; Rojalin, Tatu; Powell, Alyssa; Pham, Dina; Mizenko, Rachel R.; Birkeland, Andrew C.; Carney, Randy P.
• Is Part Of: Nanoscale. Volume 13:Issue 35(2021)
• Page Start: 14760
• Page End: 14776
• Publication Date: 2021-09-02
• Subjects: Nanoscience -- Periodicals; Nanotechnology -- Periodicals; 620.505
• DOI: 10.1039/d1nr03334d
• Languages: English
• ISSNs: 2040-3364

Abstract

Abstract : SERS can "see through" varying lipoprotein contamination present in extracellular vesicles isolated from cancer patient samples for accurate diagnostic classification. Abstract : Given the emerging diagnostic utility of extracellular vesicles (EVs), it is important to account for non-EV contaminants. Lipoprotein present in EV-enriched isolates may inflate particle counts and decrease sensitivity to biomarkers of interest, skewing chemical analyses and perpetuating downstream issues in labeling or functional analysis. Using label free surface enhanced Raman scattering (SERS), we confirm that three common EV isolation methods (differential ultracentrifugation, density gradient ultracentrifugation, and size exclusion chromatography) yield variable lipoprotein content. We demonstrate that a dual-isolation method is necessary to isolate EVs from the major classes of lipoprotein. However, combining SERS analysis with machine learning assisted classification, we show that the disease state is the main driver of distinction between EV samples, and largely unaffected by choice of isolation. Ultimately, this study describes a convenient SERS assay to retain accurate diagnostic information from clinical samples by overcoming differences in lipoprotein contamination according to isolation method.