A proteomic meta-analysis refinement of plasma extracellular vesicles
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 separa...
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Published in | Scientific data Vol. 10; no. 1; p. 837 |
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Main Authors | , , , , , , , , , , , , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
28.11.2023
Nature Publishing Group Nature Portfolio |
Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 USDOE AC05-76RL01830; U01 DK127786; R01 DK060581; R01 DK133881; R01 DK121929; R01 DK126444 National Institutes of Health (NIH) PNNL-SA-178237 |
ISSN: | 2052-4463 2052-4463 |
DOI: | 10.1038/s41597-023-02748-1 |