Presentations

Abstract

Introduction:

Extracellular vesicles (EVs) are small, membrane enclosed vessels that have recently gained interest as a mode of cell-cell communication in both pathologic and physiological conditions. Glycosylation of vesicle cargo proteins has been shown to play a role in the sorting of proteins into EVs and cell targeting and recognition. As aberrant protein glycosylation is a hallmark of cancer cells, and recent evidence has shown that cancer cells secrete significantly higher amounts of EVs compared to non-malignant cells, we set out to examine the glycosylation signatures across different breast cancer cell lines and their respective EVs.

Methods:

Breast cancer cell lines representing luminal type, HER2 enriched, and triple negative subtypes were cultured in appropriate cell media. Cell protein lysates were collected were collected and microvesicles (MVs) and small extracellular vesicles (sEVs) were isolated via the differential ultracentrifugation approach. The concentration and size of isolated EVs were determined utilizing the NanoSight NS300. The EV purity was assessed by Western blotting with a panel of positive and negative specific markers. Lectin blots were performed to examine and compare the glycosylation patterns of specific carbohydrate moieties (sialylation, fucosylation, and N-glycan branching) in the protein lysates from EVs and their parent cells.

Results:

Extracellular Vesicle size distributions for MVs and sEVs were within the expected ranges and samples were of ample purity based on the results of the western blot utilizing EV specific markers. Lectin blots revealed unique protein glycosylation patterns of breast cells representing different subtypes. Additionally, sialylation and N-glycan branching of sEVs secreted by the luminal type cells and aggressive cell types were distinct from their perspective parent cells, with regard to degree and pattern.

Conclusion:

These results confirm that there are subtype-specific glycosylation patterns existing at both cell and sEV levels. This exploratory work combined with our previous EV data obtained via metabolic glycoengineering provides a foundation to develop innovative EV glycosylation-based methods to improve breast cancer diagnosis and subtyping.