The integration of robust single-pot, solid-phase-enhanced sample preparation with powerful liquid chromatography-tandem mass spectrometry (LC-MS/MS) is routinely used to define the extracellular vesicle (EV) proteome landscape and underlying biology. However, EV proteome studies are often limited by sample availability, requiring upscaling cell cultures or larger volumes of biofluids to generate sufficient materials.
Researchers at the Baker Heart and Diabetes Institute, Melbourne have refined data independent acquisition (DIA)-based MS analysis of EV proteome by optimizing both protein enzymatic digestion and chromatography gradient length (ranging from 15 to 44 min). Their short 15 min gradient length can reproducibly quantify 1168 (from as little as 500 pg of EV peptides) to 3882 proteins groups (from 50 ng peptides), including robust quantification of 22 core EV marker proteins. Compared to data-dependent acquisition, DIA achieved significantly greater EV proteome coverage and quantification of low abundant protein species. Moreover, the researchers have achieved optimal magnetic bead-based sample preparation tailored to low quantities of EVs (0.5 to 1 µg protein) to obtain sufficient peptides for MS quantification of 1908-2340 protein groups. They demonstrate the power and robustness of their pipeline in obtaining sufficient EV proteomes granularity of different cell sources to ascertain known EV biology. This underscores the capacity of their optimised workflow to capture precise and comprehensive proteome of EVs, especially from ultra-low sample quantities (sub-nanogram), an important challenge in the field where obtaining in-depth proteome information is essential.
Ultra-low proteome analyses reveal granularity in small extracellular vesicle biology
from different donor cells
Using their developed EV proteome analysis pipeline, the researchers performed ultrasensitive proteome sample preparation workflow from sub microgram starting quantities of sEVs from different donor cells. (A) Based on initial starting quantity of sEV (0.5 µg and 1 µg), pearson correlation matrix reveals distinct correlation in sEVs (normalised, 50 ng peptide amount injected for each sample) from SW480 and SW620 cells. (B) Comparative analysis of sEV proteome using EV core marker proteins and MISEV2018 recommended EV proteins. (C) Fluorescent nanoparticle tracking images of SW480 (left) and SW620 (right) sEV positive for CD63 (cyan), CD81 (magenta), and CD9 (yellow) (labelled). (D) Scatter plot of relative protein abundance reported in our pipeline between SW620 sEVs versus SW480 sEVs. (E) Heatmap of differentially abundant proteins (p < 0.05, lf c > 1.2). (F) Bar plot of relative protein abundance (centered) of proteins known to regulate sEVs function. (G) Gene Ontologies (Biological Processes) enriched in differentially abundant proteins (p < 0.05, lfc > 1.2).