Comparison of electrochemical immunosensor platforms for indiscriminate SARS-CoV-2 variant detection

ABSTRACT

Since the first days of the pandemic, diagnosis of patients infected with the SARS-CoV-2 has been one of the most important parameters to control the virus. For this reason, many studies have been carried out to develop various methods for rapid and accurate diagnosis, but mutations and the occurrence of successive variants have made accurate diagnosis difficult. In this study, a screen-printed carbon electrode was used to develop magnetic nanoparticle (MNP)-based electrochemical biosensing systems that selectively detect SARS-CoV-2 virus and its variants (original, alpha, beta, and delta) in nasopharyngeal swabs. These electrodes were modified with MNPs conjugated to SARS-CoV-2 S1, S2 proteins and swab samples. Then, commercially available SARS-CoV-2-specific anti-S1 and anti-S2 antibodies and antibody cocktails purified from serum samples were applied to the surface and the performance of the platforms was compared. Analytical parameters and electrode surface characterizations were performed by electrochemical measurements after each modification step. After optimization studies of the developed biosensor platforms, the detection of limit for the antibody cocktail- based sensors were determined to be 0.53-0.75 ng/mL, while it was calculated to be 0.93-0.99 ng/mL for the anti-S1 and anti- S2-based sensors. The performance of the platforms in real nasopharyngeal swab samples (negative, original, alpha, beta, and delta variants) was evaluated and it was found that the polyclonal antibody cocktail outperformed the commercial anti-S1 and anti-S2 antibodies. As a result, polyclonal antibody cocktail, with an overall sensitivity, specificity, and accuracy of 100%, is a versatile electrochemical biosensor system for the detection of the different variants of SARS-CoV-2. We hope that the biosensor platform modified with polyclonal antibodies can be used as a potential diagnostic tool that can be applied to such epidemics in the future. Synthetic biology.