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. 2021 Jan 20:7:504-520.
doi: 10.1039/d0ew00946f.

Reproducibility and sensitivity of 36 methods to quantify the SARS-CoV-2 genetic signal in raw wastewater: findings from an interlaboratory methods evaluation in the U.S

Brian M Pecson #  1 , Emily Darby #  1 , Charles N Haas  2 , Yamrot M Amha  3 , Mitchel Bartolo  4 , Richard Danielson  5 , Yeggie Dearborn  5 , George Di Giovanni  6 , Christobel Ferguson  7 , Stephanie Fevig  7 , Erica Gaddis  8 , Donald Gray  9 , George Lukasik  10 , Bonnie Mull  10 , Liana Olivas  3 , Adam Olivieri  11 , Yan Qu  3 ; SARS-CoV-2 Interlaboratory Consortium
Affiliations

Reproducibility and sensitivity of 36 methods to quantify the SARS-CoV-2 genetic signal in raw wastewater: findings from an interlaboratory methods evaluation in the U.S

Brian M Pecson et al. Environ Sci (Camb). .

Abstract

In response to COVID-19, the international water community rapidly developed methods to quantify the SARS-CoV-2 genetic signal in untreated wastewater. Wastewater surveillance using such methods has the potential to complement clinical testing in assessing community health. This interlaboratory assessment evaluated the reproducibility and sensitivity of 36 standard operating procedures (SOPs), divided into eight method groups based on sample concentration approach and whether solids were removed. Two raw wastewater samples were collected in August 2020, amended with a matrix spike (betacoronavirus OC43), and distributed to 32 laboratories across the U.S. Replicate samples analyzed in accordance with the project's quality assurance plan showed high reproducibility across the 36 SOPs: 80% of the recovery-corrected results fell within a band of ±1.15 log10 genome copies per L with higher reproducibility observed within a single SOP (standard deviation of 0.13 log10). The inclusion of a solids removal step and the selection of a concentration method did not show a clear, systematic impact on the recovery-corrected results. Other methodological variations (e.g., pasteurization, primer set selection, and use of RT-qPCR or RT-dPCR platforms) generally resulted in small differences compared to other sources of variability. These findings suggest that a variety of methods are capable of producing reproducible results, though the same SOP or laboratory should be selected to track SARS-CoV-2 trends at a given facility. The methods showed a 7 log10 range of recovery efficiency and limit of detection highlighting the importance of recovery correction and the need to consider method sensitivity when selecting methods for wastewater surveillance.

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Conflict of interest statement

Conflicts of interest There are no conflicts of interest to declare.

Figures

Fig. 1
Fig. 1
Recovery-corrected SARS-CoV-2 concentrations (N1 and N2 targets) at Plant 1 measured by each SOP. NDs and data excluded based on the quality control criteria are not plotted. The dashed lines show 10th and 90th percentiles across all N1 and N2 results.
Fig. 2
Fig. 2
Log-transformed OC43 recovery efficiency at Plant 1 (Hyperion) and Plant 2 (JWPCP), measured by each SOP. The SARS-CoV-2 results from the SOPs highlighted are not represented in Fig. 1 due to the fact that the results were all non-detect (gray), the recovery was below the quality control cut-off of 0.01% (blue), or both (orange).
Fig. 3
Fig. 3
Comparison of the log-transformed SARS-CoV-2 (N1) concentrations at Plant 1 measured by each of the eight method groups (grouped by concentration step and solids removal). The number of SOPs and total sample replicates included in each method group are shown at the top of the box plot.
Fig. 4
Fig. 4
Log-transformed theoretical limits of detection for each SOP at Plant 1 (Hyperion) and Plant 2 (JWPCP). The dashed lines show 10th and 90th percentiles across both Plant 1 and Plant 2. The total number of non-detects (ND) (combined for SARS-CoV-2 N1 and N2 targets) out of total number of sample replicates processed by each SOP is shown in the table below the box plot (a blank cell indicates no NDs). An “X” indicates the sample was not processed by that SOP.
Fig. 5
Fig. 5
Fraction of sample replicates that were non-detect at Plant 1 as a function of the theoretical LOD. The outlier shown in gray (SOP 3S.1) processed the sample using a different PCR platform to enumerate OC43 and SARS-CoV-2.
Fig. 6
Fig. 6
Comparison of the log-transformed theoretical limits of detection (combined for Plant 1 and Plant 2) for each of the eight method groups (grouped by concentration step and solids removal).
Fig. 7
Fig. 7
Impact of heat pasteurization on the log-transformed SARS-CoV-2 (N1 target) concentrations (corrected for recovery efficiency) at Plant 1. Five sample replicates for each SOP, with and without heat pasteurization, were performed.
Fig. 8
Fig. 8
Impact of the PCR platform (digital or quantitative) on the log-transformed SARS-CoV-2 (N1 target) concentrations (corrected for recovery efficiency) at Plant 1. The data are from 22 SOPs (93 replicates) that used quantitative PCR and 8 SOPS (39 replicates) that used digital PCR.
Fig. 9.
Fig. 9.
Impact of the surrogate used for the matrix spike on the log-transformed recovery efficiency at Plant 1. Five sample replicates for each SOP were processed and analyzed for both OC43 and the second matrix spike surrogate.
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