BIOSENSOR DETECTION ASSAY FOR ANTI-SARS-COV-2 ANTIBODIES
Researchers at UCSF and the Chan Zuckerberg Biohub have developed a serological detection assay for anti-SARS-CoV-2 antibodies.
COVID-19, caused by the SARS-CoV-2 virus, has spread throughout the world. Early detection of disease using viral detection assays is critical for containing the spread of this virus. The most widely used tests are PCR-based, which detect viral RNA in patient samples. However, these methods are limited in throughput and take hours or days to produce results.
Stage of Research
The inventors have developed a sensitive and rapid solution-based protein biosensor serology assay for anti-SARS-CoV-2 antibodies. The protein biosensor comprises a pair of fusion proteins that are used together to detect antibodies against various SAR-CoV-2 antigens. Each fusion protein of the pair contains a viral protein domain and a detection moiety domain, and the detection moieties are complementary portions of a split reporter. The inventors demonstrate the effectiveness of two anti-SARS-CoV-2 biosensors, capable of detecting patient antibodies against viral Spike (S) or nucleocapsid (N) proteins.
Applications
- Clinical or point-of-care detection of antibodies (IgG and IgM) against viral antigens in patient samples
- Epitope mapping or assaying neutralization potency of patient sera
Advantages
- Sensitive, rapid and modular solution-based detection of patient anti-SARS-CoV-2 antibodies
- S and N biosensors remain functional after lyophilization and can be used in combination
- May be adapted for detecting antibodies against a broad range of other viral or bacterial antigens
Stage of Development
Research – in vitro
Publications
Elledge SK, Zhou X, Byrnes JR, et al. Engineering luminescent biosensors for point-of-care SARS-CoV-2 antibody detection. medRxiv. 2020. Doi: 10.1101/2020.08.17.20176925
Related Web Links
https://pharm.ucsf.edu/wells
Keywords
Antibody, Biosensor system, Diagnosis, IgG, Epitope Shield
Technology Reference
Chan Zuckerberg CZB-175F, UCSF SF2021-002
