Sensing of COVID-19 spike protein in nasopharyngeal samples using a portable surface plasmon resonance diagnostic system. Issue 5 (15th July 2022)

• Record Type: Journal Article
• Authors: Saada, Hiba; Pagneux, Quentin; Wei, James; Live, Ludovic; Roussel, Alain; Dogliani, Alexis; Die Morini, Lycia; Engelmann, Ilka; Alidjinou, Enagnon Kazali; Rolland, Anne Sophie; Faure, Emmanuel; Poissy, Julien; Labreuche, Julien; Lee, Gil; Li, Peng; Curran, Gerard; Jawhari, Anass; Yunda, Jhonny A.; Melinte, Sorin; Legay, Axel; Gala, Jean-Luc; Devos, David; Boukherroub, Rabah; Szunerits, Sabine
• Is Part Of: Sensors & diagnostics. Volume 1:Issue 5(2022)
• Page Start: 1021
• Page End: 1031
• Publication Date: 2022-07-15
• Subjects: Biosensors; Periodicals; Diagnosis; Chemical detectors; 616.0756
• DOI: 10.1039/d2sd00087c
• Languages: English
• ISSNs: 2635-0998

Abstract

Abstract : Rapid, yet sensitive and accurate testing concepts are critical in the control of spreading viral diseases. Abstract : Rapid, yet sensitive and accurate testing concepts are critical in the control of spreading diseases. With the COVID-19 pandemic still ongoing, the need for efficient, fast and accurate testing of the infection state of children and the elderly traveling as well as people with symptoms has not declined. Most current methods, which are highly sensitive, are rather slow and cannot be applied at the point of care. While rapid antigenic tests ascertain only high viral burden, here, we demonstrate an alternative, rapid point-of-care diagnostics with the ability to sense low viral loads. The goal of a portable and fast quantitative diagnostic device has been achieved via the use of VHH-72-Fc, a nanobody featuring high binding strength to the spike 1 glycoprotein of the SARS-CoV-2 viral envelope, a surface plasmon resonance sensing approach, and machine learning for predicting the cut-off value between positive and negative nasopharyngeal swab samples. The concept was validated on 119 nasopharyngeal samples, 50 positive and 69 negative, as identified by reverse transcription quantitative polymerase chain reaction (RT-qPCR) tests, showing a 88% positive percentage agreement and a 92% negative percentage agreement, as compared to RT-qPCR. Simple artificial neural network data processing revealed the influence in the sampling time to achieve unique performance in terms of speed, specificity and sensitivity. These sensing features combined with no sample preparation and portability of the diagnostic device suggest that this approach is well-adapted to be operated in hospital or laboratory located diagnostic centres.