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[Preprint]. 2020 Dec 7:2020.12.04.20241059. [Version 1] doi: 10.1101/2020.12.04.20241059

Sensitive detection and quantification of SARS-CoV-2 in saliva

Mustafa Fatih Abasiyanik, Blake Flood, Jing Lin, Sefika Ozcan, Sherin J Rouhani, Athalia Pyzer, Jonathan Trujillo, Chaojie Zhen, Ping Wu, Stephen Jumic, Andrew Wang, Thomas F Gajewski, Peng Wang, Madeline Hartley, Bekim Ameti, Rachael Niemiec, Marian Fernando, Bulent Aydogan, Cindy Bethel, Scott Matushek, Kathleen G Beavis, Nishant Agrawal, Jeremy Segal, Savaş Tay, Evgeny Izumchenko
PMCID: PMC7743089  PMID: 33330880

Abstract

Saliva has significant advantages as a test medium for detection of SARS-CoV-2 infection in patients, such as ease of collection, minimal requirement of supplies and trained personnel, and safety. Comprehensive validation in a large cohort of prospectively collected specimens with unknown SARS-CoV-2 status should be performed to evaluate the potential and limitations of saliva-based testing. We developed a saliva-based testing pipeline for detection of SARS-CoV-2 nucleic acids using real-time reverse transcription PCR (RT-PCR) and droplet digital PCR (ddPCR) readouts, and measured samples from 137 outpatients tested at a curbside testing facility and 29 inpatients hospitalized for COVID-19. These measurements were compared to the nasal swab results for each patient performed by a certified microbiology laboratory. We found that our saliva testing positively detects 100% (RT-PCR) and 93.75% (ddPCR) of curbside patients that were identified as SARS-CoV-2 positive by the Emergency Use Authorization (EUA) certified nasal swab testing assay. Quantification of viral loads by ddPCR revealed an extremely wide range, with 1 million-fold difference between individual patients. Our results demonstrate for both community screening and hospital settings that saliva testing reliability is on par with that of the nasal swabs in detecting infected cases, and has potential for higher sensitivity when combined with ddPCR in detecting low-abundance viral loads that evade traditional testing methods.

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