In this work, we report the design, the development and the characterization of the analytical performances of a colorimetric smartphone-based immunosensor for the detection of cancer antigen 125 (CA125). The immunosensor was based on a sandwich strategy in which the primary antibody was immobilized by spotting onto the 3D nitrocellulose membrane. The immunospots were subsequently incubated with CA125 solutions, followed by the affinity reaction with a secondary antibody labeled with gold nanoparticles (AuNPs). The antibody-AuNPs captured onto immunospots induced the silver deposition from a silver enhancer solution leading to the formation of gold-silver nanoparticles of different grey color spots depending on CA125 concentration. The 8 megapixels smartphone camera was integrated in a home-made dark box and used as transducer of color image acquisition and data handling. The pixel intensity of the captured images was determined by an image processing algorithm. The experimental parameters involved in each step of the immunosensor design were studied and optimized, obtaining a limit of detection of 30 U/mL CA125. The selectivity of the immunoassay was proven against different concentration solutions of Vascular Endothelial Growth Factor (VEGF) antigen as an unspecific protein when a blank signal was obtained for all tested solutions. Finally, preliminary experiments in human serum samples spiked with CA125 protein were also performed. Therefore, the proposed system could represent a powerful point-of-care tool for the next generation technology for detecting and monitoring cancer biomarkers at early stages by taking advantage of nowadays gadgets with enhanced features such as smartphones.

Smartphone-based immunosensor for CA125 detection / Hosu, Oana; Ravalli, Andrea; Lo Piccolo, Giuseppe Mattia; Cristea, Cecilia; Sandulescu, Robert; Marrazza, Giovanna. - In: TALANTA. - ISSN 0039-9140. - STAMPA. - 166:(2017), pp. 234-240. [10.1016/j.talanta.2017.01.073]

Smartphone-based immunosensor for CA125 detection

HOSU, OANA ALEXANDRA;Ravalli, Andrea;Lo Piccolo, Giuseppe Mattia;Marrazza, Giovanna
2017

Abstract

In this work, we report the design, the development and the characterization of the analytical performances of a colorimetric smartphone-based immunosensor for the detection of cancer antigen 125 (CA125). The immunosensor was based on a sandwich strategy in which the primary antibody was immobilized by spotting onto the 3D nitrocellulose membrane. The immunospots were subsequently incubated with CA125 solutions, followed by the affinity reaction with a secondary antibody labeled with gold nanoparticles (AuNPs). The antibody-AuNPs captured onto immunospots induced the silver deposition from a silver enhancer solution leading to the formation of gold-silver nanoparticles of different grey color spots depending on CA125 concentration. The 8 megapixels smartphone camera was integrated in a home-made dark box and used as transducer of color image acquisition and data handling. The pixel intensity of the captured images was determined by an image processing algorithm. The experimental parameters involved in each step of the immunosensor design were studied and optimized, obtaining a limit of detection of 30 U/mL CA125. The selectivity of the immunoassay was proven against different concentration solutions of Vascular Endothelial Growth Factor (VEGF) antigen as an unspecific protein when a blank signal was obtained for all tested solutions. Finally, preliminary experiments in human serum samples spiked with CA125 protein were also performed. Therefore, the proposed system could represent a powerful point-of-care tool for the next generation technology for detecting and monitoring cancer biomarkers at early stages by taking advantage of nowadays gadgets with enhanced features such as smartphones.
2017
166
234
240
Hosu, Oana; Ravalli, Andrea; Lo Piccolo, Giuseppe Mattia; Cristea, Cecilia; Sandulescu, Robert; Marrazza, Giovanna
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1080289
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