PNNL

Abstract

Plasma extracellular vesicles (EVs) are considered excellent targets for biomarker development since they carry signatures of their cellular origin and disease process. In this paper, we evaluate the potential of plasma EV proteomics analysis for studying predictive biomarkers of developing type 1 diabetes (T1D). T1D results from islet autoimmunity that drives the death of the insulin producing ß cells. As plasma EV proteomics analysis is challenging due to copurifying contaminants, we built upon our recent concept of characterizing and quantifying EV proteins in separate measurements. This concept allows us to maximize EV recovery and protein quantification. Here, we used strong anion exchange beads (Mag-Net) to capture plasma EVs and analyze them by mass spectrometry. We analyzed plasma samples from 19 donors with islet autoimmunity (diagnosed by circulating autoantibodies against islet proteins – AAB+) vs. 17 control individuals. We identified and quantified 5,480 proteins, a 3.2-fold coverage increase compared to our previous T1D biomarker proteomics study using plasma depleted of the 14 most abundant proteins. The Mag-Net approach also detected 1,306 out of the 1,717 proteins (76%) that we previously validated as EV proteins. Statistical tests revealed 448 proteins to be quantitative or qualitatively differentially abundant in AAB+ vs control volunteers, including 69 previously validated EV proteins. A functional-enrichment analysis resulted in overrepresented 25 pathways among the differentially abundant proteins, including pathways related to autoimmune response and lipid metabolism. The capacity of this data to predict AAB+ was tested with a machine learning analysis using a random forest model, resulting in a receiver operating characteristic-area under the curve of 0.805. Overall, our study indicates that plasma EV proteomics analysis can be an exciting approach for studying biomarkers for developing T1D.