Detection of a SARS-CoV-2 Sequence with Genosensors Using Data Analysis Based on Information Visualization and Machine Learning Techniques
| Autor: | Luiz H. C. Mattoso, Paulo A. Raymundo-Pereira, Daniel S. Correa, Matias Eliseo Melendez, Lorenzo A. Buscaglia, de Carvalho, Acplf, Carrilho, E., Laís Canniatti Brazaca, Leonardo F. S. Scabini, M. C. de Oliveira, L. D. C. de Castro, Lucas Correia Ribas, Andrey Soares, Osvaldo N. Oliveira, Odemir Martinez Bruno, José L. Bott-Neto, Josélia Costa Soares, Valquiria da Cruz Rodrigues, P. R. A. Oiticica |
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| Rok vydání: | 2020 |
| Předmět: |
Materials science
02 engineering and technology 010402 general chemistry Machine learning computer.software_genre 01 natural sciences Matrix (chemical analysis) localized surface plasmon resonance Materials Chemistry General Materials Science Surface plasmon resonance Electrical impedance Detection limit impedance spectroscopy business.industry SARS-CoV-2 Viral nucleocapsid COVID-19 021001 nanoscience & nanotechnology 0104 chemical sciences Dielectric spectroscopy image processing machine learning Complementary sequences Electrode Artificial intelligence 0210 nano-technology business APRENDIZADO COMPUTACIONAL computer genosensor |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP |
| DOI: | 10.26434/chemrxiv.13366379 |
| Popis: | We report on genosensors to detect an ssDNA sequence from the SARS-CoV-2 genome, which mimics the GU280 gp10 gene (coding the viral nucleocapsid phosphoprotein), using four distinct principles of detection and treating the data with information visualization and machine learning techniques. Genosensors were fabricated on either gold (Au) interdigitated electrodes for electrical and electrochemical measurements or on Au nanoparticles on a glass slide for optical measurements. They contained a matrix of 11-mercaptoundecanoic acid (11-MUA) self-assembled monolayer (SAM) onto which a layer of capture probe (cpDNA) sequence was immobilized. Detection was performed using electrical and electrochemical impedance spectroscopies and localized surface plasmon resonance (LSPR). The highest sensitivity was reached with impedance spectroscopy, including using a low-cost (US$ 100) homemade impedance analyzer. Complementary ssDNA sequences were detected with a detection limit of 0.5 aM (0.3 copy per µL). This performance may be attributed to the high sensitivity of the electrical impedance technique combined with an appropriate arrangement of the sequences on the electrodes and hybridization between the complementary sequences, as inferred from polarization-modulated infrared reflection absorption spectroscopy (PM-IRRAS). The selectivity of the genosensor was confirmed by plotting the impedance spectroscopy data with a multidimensional projection technique (interactive document mapping, IDMAP), where a clear separation was observed among the samples of the complementary DNA sequence at various concentrations and from buffer samples containing a non-complementary sequence and other DNA biomarkers. The diagnosis of SARS-CoV-2 mimicking sequences was also achieved with machine learning techniques applied to scanning electron microscope images taken from genosensors exposed to distinct concentrations of the complementary ssDNA sequences. In summary, the genosensors proposed here are promising for detecting SARS-CoV-2 genetic material (RNA) in biological fluids in point-of-care settings. The authors are thankful to CAPES (88887.510657/2020-00 and 88887.364257/2019-00), São Paulo Research Foundation (FAPESP) (2013/14262, 2016/01919-6, 2018/18953-8, 2018/19750-3, 2018/22214-6, 2019/00101-8, 2019/13514-9, 2016/23763-8, 2019/07811-0, 2020/02938-0), INEO, INCTBio grants (FAPESP 2014/50867-3) and CNPq (423952/2018-8, 465389/2014-7, and 401256/2020-0) for the financial support. The authors are grateful to Angelo Luiz Gobbi and Maria Helena de Oliveira Piazzetta (LMF/LNNano/CNPEM) for producing the Au electrodes. |
| Databáze: | OpenAIRE |
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