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Get Free AccessAbstract SARS-CoV-2 is the seventh coronavirus known to infect humans and has caused an emerging and rapidly evolving global pandemic (COVID-19) with significant morbidity and mortality. To meet the urgent and massive demand for the screening and diagnosis of infected individuals, many in vitro diagnostic assays using nucleic acid tests (NATs) have been urgently authorized by regulators worldwide. The limit of detection (LoD) is a crucial feature for a diagnostic assay to detect SARS-CoV-2 in clinical samples, and a reference standard with a well-characterized concentration or titer is of the utmost importance for LoD studies. Although several reference standards of plasmids or synthetic RNA carrying specific genomic regions of SARS-CoV-2 have already been announced, a reference standard for inactivated virus particles with accurate concentration is still needed to evaluate the complete procedure including nucleic acid extraction and to accommodate customized primer-probe sets targeting different genome sequences. Here, we performed a collaborative study to estimate the NAT-detectable units as viral genomic equivalent quantity (GEQ) of an inactivated whole-virus SARS-CoV-2 reference standard candidate using digital PCR (dPCR) on multiple commercialized platforms. The median of the quantification results (4.6×10 5 ± 6.5×10 4 GEQ/mL) was treated as the consensus true value of GEQ of virus particles in the reference standard. This reference standard was then used to challenge the LoDs of six officially approved diagnostic assays. Our study demonstrates that an inactivated whole virus quantified by dPCR can serve as a reference standard and provides a unified solution for assay development, quality control, and regulatory surveillance.
Haiwei Zhou, Donglai Liu, Liang Ma, Tingting Ma, Tingying Xu, Lili Ren, Liang Li, Sihong Xu (2020). A SARS-CoV-2 Reference Standard Quantified by Multi-digital PCR Platforms for Quality Assessment of Molecular Tests. , DOI: https://doi.org/10.1101/2020.09.14.20193904.
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Type
Preprint
Year
2020
Authors
8
Datasets
0
Total Files
0
Language
en
DOI
https://doi.org/10.1101/2020.09.14.20193904
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