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Please use this identifier to cite or link to this item: http://hdl.handle.net/10651/39997

Title: Point-of-care detection of extracellular vesicles: Sensitivity optimization and multiple-target detection
Author(s): Oliveira Rodríguez, Myriam
Serrano Pertierra, Esther
Costa García, Agustín
Martín, S. L.
Mo, M. Y.
Cernuda Morollón, Eva María
Blanco López, María del Carmen
Issue date: 2017
Publisher version: http://dx.doi.org/10.1016/j.bios.2016.08.001
Citation: Biosensors and Bioelectronics, 87, p. 38-45 (2017); doi:10.1016/j.bios.2016.08.001
Format extent: p. 38-45
Abstract: Extracellular vesicles (EVs) are membrane-bound nanovesicles delivered by different cellular lineages under physiological and pathological conditions. Although these vesicles have shown relevance as biomarkers for a number of diseases, their isolation and detection still has several technical drawbacks, mainly related with problems of sensitivity and time-consumed. Here, we reported a rapid and multiple-targeted lateral flow immunoassay (LFIA) system for the detection of EVs isolated from human plasma. A range of different labels (colloidal gold, carbon black and magnetic nanoparticles) was compared as detection probe in LFIA, being gold nanoparticles that showed better results. Using this platform, we demonstrated that improvements may be carried out by incorporating additional capture lines with different antibodies. The device exhibited a limit of detection (LOD) of 3.4×106 EVs/µL when anti-CD81 and anti-CD9 were selected as capture antibodies in a multiple-targeted format, and anti-CD63 labeled with gold nanoparticles was used as detection probe. This LFIA, coupled to EVs isolation kits, could become a rapid and useful tool for the point-of-care detection of EVs, with a total analysis time of two hours.
URI: http://hdl.handle.net/10651/39997
ISSN: 0956-5663
Sponsored: The authors would like to thank Dr. Sánchez-Madrid for the gift of antibodies. Funding from projects CTQ2013-47396-R (Spanish Ministry of Economy and Competitivity), FC15-GRUPIN14-022(Regional Government of Asturias) is acknowledged. M. Oliveira Rodríguez thanks FICYT for her pre-doctoral grant.
Project id.: MINECO/CTQ2013-47396-R
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