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Título: | High sensitivity detection of extracellular vesicles immune-captured from urine by conventional flow cytometry |
Autor: | Campos-Silva, Carmen CSIC ORCID; Suarez, Henar; Jara-Acevedo, Ricardo; Linares-Espinós, Estefanía; Martínez-Piñeiro, Luis; Yáñez-Mó, María; Valés-Gómez, Mar CSIC ORCID | Fecha de publicación: | 2019 | Editor: | Springer Nature | Citación: | Scientific Reports 9(1): 2042 (2019) | Resumen: | Extracellular vesicles (EVs) provide an invaluable tool to analyse physiological processes because they transport, in biological fluids, biomolecules secreted from diverse tissues of an individual. EV biomarker detection requires highly sensitive techniques able to identify individual molecules. However, the lack of widespread, affordable methodologies for high-throughput EV analyses means that studies on biomarkers have not been done in large patient cohorts. To develop tools for EV analysis in biological samples, we evaluated here the critical parameters to optimise an assay based on immunocapture of EVs followed by flow cytometry. We describe a straightforward method for EV detection using general EV markers like the tetraspanins CD9, CD63 and CD81, that allowed highly sensitive detection of urinary EVs without prior enrichment. In proof-of-concept experiments, an epithelial marker enriched in carcinoma cells, EpCAM, was identified in EVs from cell lines and directly in urine samples. However, whereas EVs isolated from 5–10 ml of urine were required for western blot detection of EpCAM, only 500 μl of urine were sufficient to visualise EpCAM expression by flow cytometry. This method has the potential to allow any laboratory with access to conventional flow cytometry to identify surface markers on EVs, even non-abundant proteins, using minimally processed biological samples. | Versión del editor: | http://dx.doi.org/10.1038/s41598-019-38516-8 | URI: | http://hdl.handle.net/10261/214556 | DOI: | 10.1038/s41598-019-38516-8 | Identificadores: | doi: 10.1038/s41598-019-38516-8 e-issn: 2045-2322 pmid: 30765839 |
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Mar Valés-Gómez - Sci. Rep. - 2019.pdf | 3,8 MB | Adobe PDF | Visualizar/Abrir |
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