Isolation of Extracellular Vesicles from Minimal Volume Ascites Fluid Using Strong Anion Exchange Magnetic Beads
Cooper, T. T.; Veliz, L.; Afzali, F.; Djoumessi, C.; Hovey, O. F. J.; Myette, R. L.; Johnston, T. P.; Wells, C.; Robertson, T.; Burger, D.; Abraham, S. A.; Shepherd, T. G.; Craig, A.; Lagugne-Labarthet, F.; Lajoie, G. A.; Postovit, L.-M.
Show abstract
Ovarian cancer (OC) remains a leading cause of gynecologic cancer mortality due to late-stage diagnosis and limited early detection strategies. Ascites fluid, a pathological hallmark of OC, is a rich source of tumor-derived extracellular vesicles (EVs) that reflect the tumor microenvironment and hold promise for biomarker discovery. However, isolating EVs from minimal ascites volumes (<100 {micro}L) poses technical challenges using conventional methods like ultracentrifugation or size-exclusion chromatography (SEC). This study explores the application of strong anion exchange (SAX) magnetic beads (Mag-Net) for efficient EV isolation from as little as 2 {micro}L of ascites fluid from both murine models and a human patient with mucinous borderline tumor. We demonstrate that SAX achieves robust EV capture at 10{micro}l of input volume, enabling comprehensive proteomic profiling and single-EV surface-enhanced Raman spectroscopy (SERS) with a >2-fold increase in proteomic depth compared to raw ascites. Notably, this study was able to identify 1000 proteins not previously annotated in Vesiclepedia for OC-derived EVs, alongside distinct SERS signatures, highlighting the potential for multiomic analysis. Comparative analysis with UC revealed enhanced proteomic depth obtained with SAX beads, albeit we also observed differential detection of canonical markers (e.g., CD9, CD81) between input volumes of ascites fluid. These findings establish SAX as a scalable, low-input platform for EV-based biomarker discovery, paving the way for improved early detection and molecular insights into OC progression.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.