Optimization and assessment of an integrated workflow for the isolation and proteomic analysis of small urinary extracellular vesicles (suEVs)
Small urinary extracellular vesicles (suEVs) are 50–200 nm membrane-delimited vesicles secreted mainly by urothelial cells. suEVs have become a promising non-invasive source of biomarkers for urinary diseases. However, suEV proteomic studies are limited due to the low concentration of EVs in urine s...
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| Main Authors: | , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Extracellular Vesicle |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2773041725000125 |
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| Summary: | Small urinary extracellular vesicles (suEVs) are 50–200 nm membrane-delimited vesicles secreted mainly by urothelial cells. suEVs have become a promising non-invasive source of biomarkers for urinary diseases. However, suEV proteomic studies are limited due to the low concentration of EVs in urine samples and poor proteomic coverage caused by high abundant uromodulin. In this study, we compared four methods for suEV isolation, including ultracentrifugation (UC), ultracentrifugation with DTT treatment (DTT + UC), filtration and ultracentrifugation (F + UC), and filtration and ultrafiltration (F + UF). We evaluated their recovery, EV purity, and proteomic coverage using multiple techniques. The combination of filtration and ultracentrifugation (F + UC) showed the best performance with efficient removal of uromodulin fibers and successful in-depth proteome identification. Furthermore, we performed a deep-going proteomic analysis and characterized suEV subsets purified by the four methods. Lastly, we developed a statistical approach to evaluate universal suEV proteins, independent of the isolation techniques used, by calculating the correlation between protein abundance and sample purity. This study provided an integrated workflow for the isolation and proteomic analysis of suEVs, which could facilitate clinical biomarker discovery and diagnosis in urology disease. |
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| ISSN: | 2773-0417 |