Extracellular vesicles (EVs) are secreted from many cell types and play important roles in intercellular communication. EVs carry a range of biomolecules that reflect the identity and molecular state of their parental cell and are found in biological fluids. Omics studies have extensively focused on characterisation of the protein and nucleic acid cargo of EVs while lipids are less studied. EVs are increasingly being utilised in disease diagnosis as they are considered to carry valuable information about the disease state. Thus, novel disease biomarkers might be identified in EV lipidomes.
EVs were enriched from 1ml human plasma samples using ultracentrifugation, considered the gold standard approach for EV enrichment, and size exclusion chromatography(SEC) (Izon). Lipids extracted according to Matyash et al. (2008) were loaded on a C30 Acclaim column and analysed using targeted and untargeted lipidomics approaches using a Vanquish liquid chromatography (LC) system and Fusion orbitrap mass spectrometer (MS). LipidSearch software was used to annotate lipid species.
More than 250 lipid species were identified and quantified in the plasma EVs following both enrichment methods. The two methods generated highly similar lipid profiles, indicating that SEC may be a viable alternative to the cumbersome UC method. Interestingly, the SEC approach yielded less lysophosphatidylcholine lipids, which may be related to a more homogenous vesicle population captured by SEC. Various literature reviews refer to glycerolipids, likely originating from co-isolating vesicles such as low-density lipoproteins, as contaminants in the EV fractions. We detected these lipids and propose that if they are differentially expressed in states of disease, they can be used as biomarkers independent of their origin.
This study presents a workflow for comprehensive lipidomics of EVs using two isolation methods that are compatible with downstream state-of-the art LCMS, improving our ability to study the lipid components of EVs and identifying new disease biomarkers.