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Rapid homogeneous assay for detecting antibodies against SARS-CoV-2 (preprint)
medrxiv; 2020.
Preprint in English | medRxiv | ID: ppzbmed-10.1101.2020.11.01.20224113
ABSTRACT
Accurate and rapid diagnostic tools are needed for management of the ongoing coronavirus disease 2019 (COVID-19) pandemic. Antibody tests enable detection of individuals past the initial phase of infection and will help to examine possible vaccine responses. The major targets of human antibody response in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) are the spike glycoprotein (S) and nucleocapsid protein (N). We have developed a rapid homogenous approach for antibody detection termed LFRET (protein L-based time-resolved Forster resonance energy transfer immunoassay). In LFRET, fluorophore-labeled protein L and antigen are brought to close proximity by antigen-specific patient immunoglobulins of any isotype, resulting in TR-FRET signal generation. We set up LFRET assays for antibodies against S and N and evaluated their diagnostic performance using a panel of 77 serum/plasma samples from 44 individuals with COVID-19 and 52 negative controls. Moreover, using a previously described S construct and a novel N construct, we set up enzyme linked immunosorbent assays (ELISAs) for antibodies against SARS-CoV-2 S and N. We then compared the LFRET assays with these enzyme immunoassays and with a SARS-CoV-2 microneutralization test (MNT). We found the LFRET assays to parallel ELISAs in sensitivity (90-95% vs. 90-100%) and specificity (100% vs. 94-100%). In identifying individuals with or without a detectable neutralizing antibody response, LFRET outperformed ELISA in specificity (91-96% vs. 82-87%), while demonstrating an equal sensitivity (98%). In conclusion, this study demonstrates the applicability of LFRET, a 10-minute 'mix and read' assay, to detection of SARS-CoV-2 antibodies.

Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2020 Document Type: Preprint

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Full text: Available Collection: Preprints Database: medRxiv Language: English Year: 2020 Document Type: Preprint