Meta-analysis refinement of plasma extracellular vesicle composition identifies proplatelet basic protein as a signaling messenger in type 1 diabetes
Vallejo, M. C.; Sarkar, S.; Elliott, E. C.; Henry, H. R.; Huang, F.; Payne, S. H.; Ramanadham, S.; Sims, E. K.; Metz, T. O.; Mirmira, R.; Nakayasu, E. S.
Show abstract
Extracellular vesicles (EVs) play important roles in cell-to-cell communication and are potential biomarkers as they carry markers of their derived tissues and disease signatures. However, obtaining pure EV preparations from biofluids is challenging due to contaminants with similar physicochemical properties. Here, we performed a meta-analysis of plasma EV proteomics data deposited in public repositories to refine the protein composition of EVs and investigate potential roles in type 1 diabetes development. With the concept that each purification method yields different proportions of distinct contaminants, we grouped proteins into clusters based on their abundance profiles. This allowed us to separate clusters with classical EV markers, such as CD9, CD40, C63 and CD81, from clusters of well-known contaminants, such as serum albumin, apolipoproteins and components of the complement and coagulation pathways. Two clusters containing a total of 1720 proteins combined were enriched with EV markers and depleted in common contaminants; therefore, they were considered to contain bona fide EV components. As possible origins of plasma EVs, these clusters had markers of tissues such as spleen, liver, brain, lungs, pancreas, and blood/immune cells. These clusters were also enriched in cell surface markers CD antigens, and proteins from cell-to-cell communication and signaling pathways, such as chemokine signaling and antigen presentation. We also show that the EV component and type 1 diabetes biomarker, platelet basic protein (PPBP/CXCL7) regulates apoptosis in both beta and macrophage cell lines. Overall, our meta-analysis refined the composition of plasma EVs, reinforcing a primary function as messengers for cell-to-cell communication and signaling. Furthermore, this analysis identifies optimal avenues to target EVs for development of disease biomarkers.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Proteomic Profiling of Mesenchymal Stem Cell-Derived Extracellular Vesicles: Impact of Isolation Methods on Protein Cargo 97%
- Comparative and Integrated Analysis of Plasma Extracellular Vesicles Isolations Methods in Healthy Volunteers and Patients Following Myocardial Infarction 96%
- Proteome encoded determinants of protein sorting into extracellular vesicles 95%
Similar papers in this journal
- Efficient enzyme-free isolation of brain-derived extracellular vesicles 95%
- Heat inactivation of foetal bovine serum causes protein contamination of extracellular vesicles 94%
- Physical association of low density lipoprotein particles and extracellular vesicles unveiled by single particle analysis 94%
Similar papers in this journal
- Analysis of small EV proteomes reveals unique functional protein networks regulated by VAP-A 94%
- Assessing extracellular vesicle proteins as predictive biomarkers for developing type 1 diabetes 94%
- A fast and sensitive size-exclusion chromatography method for plasma extracellular vesicle proteomic analysis 93%
Similar papers in this journal
- Enrichment of neurodegenerative microglia signature in brain-derived extracellular vesicles isolated from Alzheimer's disease mouse model 94%
- Messages From the Small Intestine Carried by Extracellular Vesicles in Prediabetes: a Proteomic Portrait 92%
- Multi-platforms approach for plasma proteomics: complementarity of Olink PEA technology to mass spectrometry-based protein profiling 92%
Similar papers in this journal
- Defining the Proteomic and Phosphoproteomic Landscape of Circulating Extracellular Vesicles in the Diabetes Spectrum 95%
- An optimized workflow for analyzing extracellular vesicles as biomarkers in liver diseases. 92%
- MixOmics Integration of Biological Datasets Identifies Highly Correlated Key Variables of COVID-19 severity. 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.