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Robotic RNA extraction for SARS-CoV-2 surveillance using saliva samples

Authors :
Jennifer R. Hamilton
Elizabeth C. Stahl
Connor A. Tsuchida
Enrique Lin-Shiao
C. Kimberly Tsui
Kathleen Pestal
Holly K. Gildea
Lea B. Witkowsky
Erica A. Moehle
Shana L. McDevitt
Matthew McElroy
Amanda Keller
Iman Sylvain
Ariana Hirsh
Alison Ciling
Alexander J. Ehrenberg
Bradley R. Ringeisen
Garth Huberty
Fyodor D. Urnov
Petros Giannikopoulos
Jennifer A. Doudna
IGI SARS-CoV-2 Consortium
Source :
PLoS ONE, Vol 16, Iss 8 (2021)
Publication Year :
2021
Publisher :
Public Library of Science (PLoS), 2021.

Abstract

Saliva is an attractive specimen type for asymptomatic surveillance of COVID-19 in large populations due to its ease of collection and its demonstrated utility for detecting RNA from SARS-CoV-2. Multiple saliva-based viral detection protocols use a direct-to-RT-qPCR approach that eliminates nucleic acid extraction but can reduce viral RNA detection sensitivity. To improve test sensitivity while maintaining speed, we developed a robotic nucleic acid extraction method for detecting SARS-CoV-2 RNA in saliva samples with high throughput. Using this assay, the Free Asymptomatic Saliva Testing (IGI FAST) research study on the UC Berkeley campus conducted 11,971 tests on supervised self-collected saliva samples and identified rare positive specimens containing SARS-CoV-2 RNA during a time of low infection prevalence. In an attempt to increase testing capacity, we further adapted our robotic extraction assay to process pooled saliva samples. We also benchmarked our assay against nasopharyngeal swab specimens and found saliva methods require further optimization to match this gold standard. Finally, we designed and validated a RT-qPCR test suitable for saliva self-collection. These results establish a robotic extraction-based procedure for rapid PCR-based saliva testing that is suitable for samples from both symptomatic and asymptomatic individuals.

Subjects

Subjects :
Medicine
Science

Details

Language :
English
ISSN :
19326203
Volume :
16
Issue :
8
Database :
Directory of Open Access Journals
Journal :
PLoS ONE
Publication Type :
Academic Journal
Accession number :
edsdoj.250fe7c14c81427f8666bd5d2e84261b
Document Type :
article