Extracellular vesicles (EVs) are membrane-bound nanoparticles containing protein and nucleic acids and are shed from cells into the surrounding environment. EVs are present in numerous circulating bodily fluids including blood, urine and saliva, and are thought to be a mechanism of cell-cell communication. Cancer cells appear to exploit this mechanism, constitutively releasing EVs to promote tumour growth and metastasis. As such, EVs are considered a promising minimally invasive biomarker source for cancer diagnosis and/or monitoring.
Proteomic analysis of EVs derived from complex biological fluids, such as serum or plasma, is challenging due to the co-isolation of highly abundant protein species such as serum albumin and apolipoproteins, with commonly used EV isolation methods.
In the current study, we aimed to optimise a method for size exclusion chromatography (SEC) purification of EVs from complex samples for downstream proteomics analysis. Whilst previous reports have demonstrated the potential of SEC for EV purification, the level of ‘contaminating’ plasma proteins that are co-isolated have not been evaluated. Using both a synthetic model system and real EV samples, we have demonstrated that both column size and resin composition have a substantial effect on the level of protein contamination, whilst the effect of mobile phase composition is negligible. These findings guided the development of an optimised workflow to purify EVs from healthy plasma and cell culture media samples and we were subsequently able to reduce the level of abundant plasma proteins detected by LC-MS/MS in purified EV samples. As proof-of-concept, a breast cancer cell line EV-specific protein signature could be detected when the EVs were spiked into healthy plasma, illustrating the utility of this method for preparing clinically relevant samples. We anticipate that this optimised method will accelerate future EV proteomics studies, enabling new insight into this promising source of protein biomarkers.