key: cord-0869837-ezuvedp6
authors: Ahmed, Warish; Bivins, Aaron; Smith, Suzanne Metcalfea Wendy J.M.; Verbyla, Matthew E.; Symonds, Erin M.; Simpson, Stuart L.
title: Evaluation of process limits of detection and quantification variation of SARS-CoV-2 RT-qPCR and RT-dPCR assays for wastewater surveillance
date: 2022-02-03
journal: Water Res
DOI: 10.1016/j.watres.2022.118132
sha: 282599e8ed5b475790d094a1eba739ff850105a8
doc_id: 869837
cord_uid: ezuvedp6

Effective wastewater surveillance of SARS-CoV-2 RNA requires the rigorous characterization of the limit of detection resulting from the entire sampling process - the process limit of detection (PLOD). Yet to date, no studies have gone beyond quantifying the assay limit of detection (ALOD) for RT-qPCR or RT-dPCR assays. While the ALOD is the lowest number of gene copies (GC) associated with a 95% probability of detection in a single PCR reaction, the PLOD represents the sensitivity of the method after considering the efficiency of all processing steps (e.g., sample handling, concentration, nucleic acid extraction, and PCR assays) to determine the number of GC in the wastewater sample matrix with a specific probability of detection. The primary objective of this study was to estimate the PLOD resulting from the combination of primary concentration and extraction with six SARS-CoV-2 assays: five RT-qPCR assays (US CDC N1 and N2, China CDC N and ORF1ab (CCDC N and CCDC ORF1ab), and E_Sarbeco RT-qPCR, and one RT-dPCR assay (US CDC N1 RT-dPCR) using two models (exponential survival and cumulative Gaussian). An adsorption extraction (AE) concentration method (i.e., virus adsorption on membrane and the RNA extraction from the membrane) was used to concentrate gamma-irradiated SARS-CoV-2 seeded into 36 wastewater samples. Overall, the US CDC N1 RT-dPCR and RT-qPCR assays had the lowest ALODs (< 10 GC/reaction) and PLODs (<3,954 GC/50 mL; 95% probability of detection) regardless of the seeding level and model used. Nevertheless, consistent amplification and detection rates decreased when seeding levels were < 2.32 × 10(3) GC/50 mL even for these assays. Consequently, when SARS-CoV-2 RNA concentrations are expected to be low, it may be necessary to improve the positive detection rates of wastewater surveillance by analyzing additional field and RT-PCR replicates. To the best of our knowledge, this is the first study to assess the SARS-CoV-2 PLOD for wastewater and provides important insights on the analytical limitations for trace detection of SARS-CoV-2 RNA in wastewater.

Wastewater surveillance is being utilized in many countries to monitor coronavirus disease 2019 via severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA presence and levels in community wastewater. Prior to surveillance sampling, SARS-CoV-2 RNA excreted by infected individuals is diluted by many orders of magnitude in wastewater. Therefore, to achieve trace detection and quantification of RNA, the wastewater samples require the application of optimized concentration methods (with varying primary and/or secondary concentration steps) before extraction of the RNA fragments and, finally, highly-sensitive molecular assays using reverse transcription PCR positive stool sample. Serially-diluted RNA sub-samples were shared with participating laboratories to determine sensitivity. Most RT-qPCR assays for SARS-CoV-2 examined in their study successfully detected ~5 GC/reaction, reflecting a high sensitivity. A reduced sensitivity was noted for the original RdRp assay from Charité Institute of Virology (Charité), which may have impacted the confirmation of some COVID-19 cases in the early weeks of the pandemic. A study by Vogels et al. (2020) compared the performance of nine primer-probe combinations targeting several genes (i.e., E, N, ORF1, RdRp) recommended by the World Health Organization (e.g., those developed by the China CDC, US CDC, Charité (Corman et al., 2020) , and Hong Kong University). This comparison was performed with standard reference materials and clinical samples (e.g., nasopharyngeal swabs, saliva, urine, and rectal swabs) seeded with the reference material. The authors demonstrated that at low viral concentrations (1 to 10 GC/µL), not all assays yielded positive results; thus, suggesting that some assays may be more prone to false-negative errors than others (Vogels et al., 2020) . Most notably, the RdRp reverse primer had mismatches with the reference material that were attributed to evolution of the virus, causing low analytical sensitivity.

While the ALOD is a useful assessment of the analytical sensitivity of SARS-CoV-2 RT-qPCR assays, the ALOD values of various SARS-CoV-2 assays during wastewater surveillance have ranged between 1 to 100 GC/reaction Randazzo et al., 2020; Chavarria-Miró et al., 2021) . For wastewater surveillance, the analytical sensitivity of methods must also account for the efficiency of the various processing steps, including primary concentration, loss through nucleic acid extraction, and inhibition of reverse transcription or PCR amplification. Together the RT-qPCR/dPCR ALOD and the process recovery efficiency (loss of target through all sample processing steps) against their plasmid-seeded samples (Zhang et al., 2022) . However, the generalizability of such findings is constrained by several limitations. The RT-qPCR cycling parameters were the same for all assays and deviations from the optimized assay-specific cycling parameters may have affected the sensitivity of one assay compared to another. Furthermore, plasmid control materials, rather than SARS-CoV-2 virions, were seeded by Zhang et al. (2022) into RNA extracted from wastewater to determine the spiked mocks limit of detection using seeded wastewater. The use of plasmid control materials (which are double-stranded DNA) for SARS-CoV-2 (single-stranded RNA virus) has well documented limitations, including heterogeneity in PCR efficiencies and non-linearity for RT-qPCR experiments (Bivins et al., 2021a) .

The primary objective of this study was to evaluate the PLOD of five RT-qPCR assays and one RT-dPCR assay for the detection of SARS-CoV-2 RNA in wastewater inclusive of the processing workflow efficiency. This was achieved by seeding a dilution series of known concentrations of gamma-irradiated SARS-CoV-2 virions into wastewater followed by primary concentration, nucleic acid extraction, and RT-qPCR/RT-dPCR analysis using each assay. In addition to determining the PLOD, the quantitative data from the seeding experiments were also used to assess the variation in SARS-CoV-2 RNA copy number along the dilution gradient. In conjunction with these experiments, the effect of variable seeding levels of SARS-CoV-2 in wastewater on recovery efficiency of each assay was also examined using 36 different wastewater samples. 

To determine the ability of each RT-qPCR and CDC N1 RT-dPCR to detect SARS-CoV-2 RNA in wastewater, known concentrations of gamma-irradiated SARS-CoV-2 were prepared by serial diluting stock suspension using DNase and RNase free water and seeding these serial dilutions into 50-mL wastewater samples. Final SARS-CoV-2 seeding levels ranged from 2.32 × 10 5 to 2.32 × 10 2 GC/50 mL along a serial dilution in 10-fold decrements to yield four unique titers of SARS-CoV-2 RNA.

Viruses were concentrated from the SARS-CoV-2 seeded wastewater samples using the adsorption extraction (AE) method. This method has been commonly used to concentrate SARS-CoV-2 RNA (Ahmed et al., 2020a) . Following filtration, using aseptic technique, the membrane was immediately removed, rolled, and inserted into a 5-mL-bead-beating tube (Qiagen, Valencia, CA) for nucleic acid extraction.

Immediately after virus concentration, nucleic acid was extracted directly from the HA membranes using the RNeasy PowerWater Kit (Cat. No. 14700-50-NF) (Qiagen, Valencia, CA). Prior to homogenization, 990 µL of buffer PM1 and 10 µL of β-Mercaptoethanol (Sigma-Aldrich; M6250-10 mL) were added into each bead-beating tube. The bead-beating tubes were then homogenized using a Precellys 24 tissue homogenizer (Bertin Technologies, Montigny-le-Bretonneux, FR) set for 3 × 15 s at 10,000 rpm at a 10 s interval. After homogenization, the tubes were centrifuged at 4,000 g for 5 min to pellet the filter debris and beads. Sample lysate supernatant ranging from 600-800 µL in volume was then used to extract nucleic acid following the manufacturer's specified protocol. Two 

After homogenization and before completing the rest of the nucleic acid extraction, known quantities (1.5 × 10 4 GC) of murine hepatitis virus (MHV) were seeded into each lysate and pellet as an inhibition process control. The same quantity of MHV suspension was also added to a distilled water extraction control (same volume of lysate) and subjected to extraction. The presence of PCR inhibition in RNA/DNA samples extracted from wastewater was assessed using an MHV RT-PCR assay (Besselen et al., 2002). The reference PCR quantification cycle (Cq) values obtained for MHV seeded into distilled water (for all methods) were compared with the Cq values of the MHV seeded into wastewater lysate to obtain information on potential RT-qPCR inhibition. If the Cq value resulting from the sample was greater than 2 different from the reference Cq value for the distilled water control, the sample was considered inhibited . In addition to the extraction control, all samples were analyzed alongside three PCR negative controls. Table ST1 ). For the MHV assay, positive control material in the form of gBlocks gene fragments was purchased from Integrated DNA Technologies (Integrated DNA Technology Coralville, IA, US).

Gamma-irradiated SARS-CoV-2, as previously described, was used as an RT-qPCR standard for the SARS-CoV-2 CDC N1, CDC N2, CCDC N, (CCDC ORF1ab) and E_Sarbeco assays. Prepared standard curve dilutions ranged from 6 × 10 5 to 0.6 GC/reaction. Primer and probe sequences, reaction concentrations, and thermal cycling conditions are listed in Table ST1 .

RT-qPCR analyses were performed in 20-µL reaction mixtures using TaqMan RNA. Two RT-dPCR replicates were analyzed for each sample. The 40-µL RT-dPCR reactions were prepared in a 96-well pre-plate and then transferred into the 26K 24-well Nanoplate. The Nanoplate was then loaded onto the QIAcuity dPCR 5-plex platform (Qiagen) and subjected to a workflow that included: (i) a priming and rolling step to generate and isolate the chamber partitions; (ii) an amplification step using the thermal cycling protocol; and (iii) a final imaging step in the FAM channel.

Each RT-dPCR experiment was performed using duplicate RT-dPCR no-template and positive (gamma-irradiated SARS-CoV-2 RNA) controls. Data were analyzed using the QIAcuity Suite Software V1.1.3 193 (Qiagen, Germany) and quantities exported as GC/µL of reaction. The RT-dPCR assays were performed using automatic settings for threshold and baseline. MIQE and dMIQE checklists are provided in Supplementary Tables ST4 and ST5, respectively.

To determine RT-qPCR and RT-dPCR assay limit of detection (ALOD), gamma-irradiated SARS-CoV-2 were diluted (6 × 10 5 to 0.6 GC/reaction) and analyzed using RT-qPCR and RT-dPCR. At each dilution, 15 replicates were analyzed. The 95% ALOD was defined by fitting an exponential survival model to the proportion of PCR replicates positive at each step along the gradient (Verbyla et al., 2016).

To minimize RT-qPCR and RT-dPCR contamination, nucleic acid extraction and RT-qPCR/dPCR set up were performed in separate laboratories. A sample negative control was included during the concentration process. An extraction negative control was also included during nucleic acid extraction to account for any contamination during extraction. All sample and extraction negative controls were negative for the analyzed targets.

For RT-qPCR and RT-dPCR, the ALOD is defined as the minimum GC number with a 95% probability of detection and determined as previously described (Verbyla et al., 2016) . For RT-qPCR, samples were considered positive (SARS-CoV-2 detected) if amplification was observed in at least one of the three replicates within 45 cycles. Samples were considered quantifiable if amplification was observed in all three replicates with concentrations above the ALOD. For RT-dPCR, samples were considered positive if there was at least one positive partition following the merging of nano wells from two replicate wells. Samples were considered quantifiable by RT-dPCR if the concentrations were above the ALOD, and the average number of partitions was >11,000 per sample well.

For RT-qPCR and RT-dPCR, the PLOD is defined as the minimum GC number with a 95% probability of detection, incorporating the loss of SARS-CoV-2 through sample concentration and RNA extraction and determined as previously described (Stokdyk et For the seeded levels that yielded a 100% detection rate, the variation in the estimated SARS-CoV-2 RNA GC number for each assay was assessed via the coefficient of variation (CV). The SARS-CoV-2 recovery efficiency for all RT-qPCR and RT-dPCR was calculated based on the GC quantified as follows:

Recovery Efficiency (%)= RNA GC recovered in concentrated wastewater RNA GC seeded × 100

At each concentration step with a 100% detection rate, differences in recovery efficiency between assays were assessed by the Kruskal-Wallis H test with Dunn's post hoc test (Kruskal and Wallis, 1952; Dunn, 1964) . Differences in recovery efficiency between concentration steps for each assay were assessed by the Mann-Whitney U test (Mann and Whitney, 1947) . Statistical significance was defined as p < 0.05. 

SARS-CoV-2 RNA was detected in all wastewater samples by all six assay replicates (RT-qPCR and RT-dPCR) when seeding was 2.32 × 10 5 (n = 9 samples) and 2.32 × 10 4 GC/50 mL (n = 9 samples) ( Table 2 ). At the lower seeding titer of 2.32 × 10 3 GC/50 mL (n = 9 samples), among the RT-qPCR assays, CCDC N1 provided detection in 9/9 (100%) wastewater samples, while US CDC N1 detected 8/9 (88.9%), CCDC ORF1ab 7/9 (77.8%), E_Sarbeco 6/9 (66.7%), and US CDC N2 1 of 9 samples (11.1%), respectively. At the lowest seeding level of 2.32 × 10 2 GC/50 mL (n = 9 samples), US CDC N1

was the most sensitive assay, providing detection in 6/9 (66.7%) samples, followed by CCDC N1 with 3/9 (33.3%), then both US CDC N2 and CCDC ORF1ab with detection in 1/9 (11.1%) samples, and the E_Sarbeco assay, which failed to produce any amplification (zero detection at lowest seed). Detection rates by both the US CDC N1 RT-qPCR and US CDC N1 RT-dPCR were the same for all serially diluted samples. Three RT-qPCR and two RT-dPCR replicates were analyzed for each wastewater samples.

At seeding levels 2.32 × 10 5 and 2.32 × 10 4 GC/50 mL, all RT-qPCR and RT-dPCR replicates yielded positive amplifications. However, inconsistent amplification occurred among RT-qPCR and RT-dPCR replicates at seeding levels 2.32 × 10 3 and 2.32 × 10 2 GC/50 mL. Among the five RT-qPCR assays, US CDC N1 replicates positivity was greater than other assays. Between the US CDC N1 RT-qPCR and RT-dPCR, RT-dPCR replicates positivity rates were slightly better than RT-qPCR.

Overall, the CDC N1 RT-qPCR and the CDC N1 RT-dPCR assays outperformed other assays.

CDC N2 and E_Sarbeco were the least sensitive, and this was most evident at the lower seeding dilution. For all six RT-qPCR and RT-dPCR assays, the exponential survival model effectively estimated the probabilities of SARS-CoV-2 detection considering the entire methodological process.

( We also determined the 50% and 95% probabilities of detection of SARS-CoV-2 RNA for all six RT-qPCR and RT-dPCR assays using the cumulative Gaussian model ( 

For all RT-qPCR and RT-dPCR assays, SARS-CoV-2 RNA was only quantifiable at seeding values ≥2.32 × 10 4 GC/50 mL wastewater as shown in Table 4 . The CDC N2 RT-qPCR assay demonstrated the largest variation in quantifiable samples with CVs of 89% and 147% at 2.32 × 10 5 GC/50 mL and 2.32 × 10 4 GC/50 mL, respectively. As shown in Fig. 1 , the CV of the RT-qPCR assays for CCDC N (50%, 52%), CCDC ORF1ab (51%, 40%), and E_Sarbeco (44%, 59%) were similar to that of the US CDC N1 RT-dPCR assay (52%, 54%) at each of the higher seeding levels. Besides the US CDC N2

RT-qPCR assay previously mentioned, the greatest increase in the CV between the two higher seeding levels was observed for the US CDC N1 RT-qPCR (47% to 76%). Interestingly for the CCDC ORF1ab RT-qPCR, the CV decreased from 51% to 40% suggesting improved quantitative precision as the seeded level decreased. For all others, the precision decreased with decreasing seeding level, as is expected. No assay yielded quantitative results when wastewater was seeded with 2.32 × 10 3 GC/50 or 2.32 × 10 2 GC/50 mL.

As summarized in Table 5 , the mean recoveries for RT-qPCR and RT-dPCR assays ranged from Mean recovery efficiency significantly increased with increasing seed level for the CDC N2 RT-qPCR assay (p = 0.011), CCDC ORF1ab (p = 0.008), and E_Sarbeco (p = 0.040). The CDC N1 RT-qPCR assay, CCDC N assay, and RT-dPCR N1 assay recovery efficiencies remained similar between the two seeding levels. Aside from the E_Sarbeco RT-qPCR assay, the mean recovery efficiencies were most often below 10% across all assays and seeding levels. Since the ALODs were measured over two days with minimal freeze-thawing, the degradation of RNA during this time frame is also expected to be minimal.

While the ALOD provides information on the lowest number of GC than can be reliably detected example, in this present study, we found that SARS-CoV-2 is detected using the US CDC N1 RT-qPCR assay with a probability of 50% when the concentration is 915 GC/50 mL of wastewater.

Therefore, if the concentration is 915 GC/50 mL, then processing 50 mL samples in triplicate would increase the probability of detection in at least one of the field triplicates to 87.5% (1-0.5 3 =0.875).

From a public health perspective, it is important to be able to detect the virus at concentrations below the 95% PLOD; however, laboratories may not be able to simply increase the volume of sample concentrated (e.g., due to filter clogging or increasing likelihood of inhibition).

Thus, another way to improve the positive predictive value of wastewater surveillance would be to analyze field replicates, which would increase the probability of amplification in at least one replicate. In the current study, we have characterized the variation through the entire sampling workflow by seeding SARS-CoV-2 RNA into wastewater from different WWTPs with replication of both filters and observed a significant difference in recovery of BCoV between the WWTPs with some WWTPs having more consistent recovery rates (0.21% to 3.0%) than others (0.89% to 28.0%).

However, relatively consistent recoveries were obtained for SARS-CoV-2 using US CDC N1 (both RT-qPCR and RT-dPCR), China CDC N and China CDC ORF1 assays. The variations in recovery efficiencies were greater for US CDC N2 and E_Sarbeco assays suggesting these assays alone may not be sensitive enough to detect trace concentrations of SARS-CoV-2 in wastewater and should be used in combination with other assays. This is most likely due to the lower ALOD of these two assays compared to the others. However, other factors such as wastewater characteristics, RT-qPCR efficiency, and Y-intercepts and standard curve materials also introduce variability between assays.

Based on the recovery data obtained in this study, we recommend recovery assessment via the most consistent assay if possible, using a dPCR platform. The recovery efficiency presented in this study should be interpreted with care because measuring the actual concentrations of SARS-CoV-2 in seeding stock is not straightforward (Kantor et al., 2021) . Importantly, the findings of the current study are based on seeding wastewater with gamma-irradiated SARS-CoV-2. The behavior of an exogenous control, such as the one used in the current study, compared to an endogenous SARS-CoV-2 shed into wastewater via infected individuals remains uncharacterized.

 Of the six assays evaluated, the US CDC N1 RT-dPCR, followed by its RT-qPCR assay, was most sensitive regardless of the statistical model or seeding concentrations used to determine the ALOD and PLOD associated with the AE concentration method.

 The US CDC N2 and E_Sarbeco assays were the least sensitive, especially with decreasing seeding concentrations, when evaluated alone (ALOD) and as a part of the AE concentration process (PLOD).

 Trends in SARS-CoV-2 RNA recovery efficiency mirrored the analytical sensitivities with recovery efficiencies being less variable for US CDC N1 RT-dPCR and RT-qPCR and most variable for US CDC N2 RT-qPCR and E_Sarbeco RT-qPCR.

 The greater the SARS-CoV-2 RNA concentration in a wastewater sample, the greater the recovery and downstream detection probability. When SARS-CoV-2 RNA seeding levels were < 2.32 × 10 3 GC/50 mL, inconsistent amplification was observed, and detection rates decreased for all assays.

 Thus, when SARS-CoV-2 RNA concentrations are expected to be low in wastewater, it may be necessary to improve the positive predictive value of wastewater surveillance by analyzing additional field and RT-PCR replicates.

 Comparing the behavior of endogenous SARS-CoV-2 RNA compared to various exogeneous controls, such as the seeded SARS-CoV-2 in the current study, remains a critical research need for wastewater surveillance. Proportion of samples and replicates positive for SARS-CoV-2 RNA in wastewater seeded at four concentrations using five RT-qPCR assays and one RT-dPCR assay. Table 3 The probability of detecting SARS-CoV-2 RNA as determined using wastewater samples seeded with SARS-CoV-2, the adsorption extraction concentration method, and assayed using five RT-qPCR assays and one RT-dPCR assay. The % probability of detection was estimated using two probability models: exponential survival and the cumulative Gaussian models. The process limit of detection (PLOD) was defined as the concentration associated with a 95% probability of detection. 

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Box and whisker plots of RT-qPCR a or RT-dPCR b -measured SARS-CoV-2 RNA recovery efficiency as observed for wastewater samples seeded with (A) 2.32 × 10 4 GC/50 mL and (B) 2.32 × 10 5 GC/50 mL. Upper and lower fences display the 10 th and 90 th percentile. Boxes display the interquartile range and median

We thank CSIRO Land and Water for strategic funding to complete this research project. We thank University of Queensland and Urban Utilities for providing untreated wastewater samples.Declaration of interests ☐ X The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.The above.☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: