Using new technology to study antibody responses in SARS-CoV-2-infected individuals

Using a technology called VirScan to study coronavirus antibody responses in a large cohort of SARS-CoV-2-infected and control individuals, researchers identified epitopes recognized by a large fraction of COVID-19 patients, epitopes cross-reactive with antibodies developed in response to other human coronaviruses, and 10 epitopes likely recognized by neutralizing antibodies.

They used this VirScan data to design a tool for rapid SARS-CoV-2 antibody detection. The clinical course of COVID-19 is notable for its extreme variability. Understanding the factors influencing this spectrum of outcomes - including the variable human immune - is an area of focus.

Ellen Shrock and colleagues used a technology known as VirScan - a tool members of the same group developed previously - to explore the antibody response to SARS-CoV-2 and other human coronaviruses in more than 200 COVID-19 patients and nearly 200 pre-COVID-19 era controls.

Blood serum from COVID-19 patients exhibited much more SARS-CoV-2 reactivity compared to pre-COVID-19 era controls, though some cross-reactivity toward SARS-CoV-2 peptides was observed in the pre-COVID-19 era samples, say the authors; this was expected, they note, since nearly everyone has been exposed to human coronaviruses. However, only COVID-19 patients' antibodies primarily recognized peptides derived from the spike (S) and nucleoprotein (N) of the virus.

Those members of the SARS-CoV-2-infected group who had been hospitalized for their infection exhibited stronger and broader antibody responses to S and N peptides, the authors report. In the same (hospitalized) group, antibody responses to nearly all viruses, except SARS-CoV-2, were weaker; as well, males exhibited greater SARS-CoV-2 antibody responses than females. This latter finding, consistent with reported differences in disease outcomes for males and females, may indicate that males in this group are less able to control the virus soon after infection. A machine learning model trained on the VirScan results accurately classified patients infected with SARS-CoV-2.

Drawing insights from this model about peptides highly predictive of SARS-CoV-2 guided the design of simple, rapid diagnostic for COVID-19, which predicted SARS-CoV-2 exposure with 90% sensitivity and 95% specificity. "Altogether these data help explain why many serological assays for SARS-CoV-2 produce false positives and should be taken as a cautionary note for those trying to develop such assays," the authors write.