Published November 28, 2023 | Version v1
Journal article Open

A proteomic meta-analysis refinement of plasma extracellular vesicles

Description

Extracellular vesicles play major roles in cell-to-cell communication and are excellent biomarker candidates. However, studying plasma extracellular vesicles is challenging due to contaminants. Here, we performed a proteomics meta-analysis of public data to refine the plasma EV composition by separating EV proteins and contaminants into different clusters. We obtained two clusters with a total of 1717 proteins that were depleted of known contaminants and enriched in EV markers with independently validated 71% true-positive. These clusters had 133 clusters of differentiation (CD) antigens and were enriched with proteins from cell-to-cell communication and signaling. We compared our data with the proteins deposited in PeptideAtlas, making our refined EV protein list a resource for mechanistic and biomarker studies. As a use case example for this resource, we validated the type 1 diabetes biomarker proplatelet basic protein in EVs and showed that it regulates apoptosis of β cells and macrophages, two key players in the disease development. Our approach provides a refinement of the EV composition and a resource for the scientific community.

Data availability

Additional data are available in Open Science Framework (https://doi.org/10.17605/OSF.IO/2UQPK). This includes MaxQuant results and parameter files for each dataset under the folder "MaxQuant_results_and_parameters", which are named based on their Pride accession numbers; an excel file containing the complete abundance data matrix under the folder "Processed_data_matrix"; and the results from the DAVID functional enrichment analysis under the folder "Enrichment analyses".

The R and Python scripts written to generate Fig. 5 and Figure S2 are available in GitHub (https://doi.org/10.5281/zenodo.10079817).

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Additional details

Identifiers

DOI
10.1038/s41597-023-02748-1
Other
oai:uchicago.tind.io:10033

Funding

National Institute of Diabetes and Digestive and Kidney Diseases
U01 DK127786
National Institute of Diabetes and Digestive and Kidney Diseases
R01 DK060581
National Institute of Diabetes and Digestive and Kidney Diseases
R01 DK133881
National Institute of Diabetes and Digestive and Kidney Diseases
R01 DK121929
National Institute of Diabetes and Digestive and Kidney Diseases
R01 DK126444
UAB
DRC P&F
UAB
Comprehensive Diabetes Center

UChicago Information

Division(s)
Biological Sciences Division
Department(s)
Medicine