An alternative approach for bioanalytical assay optimization for wastewater-based epidemiology of SARS-CoV-2

Abstract

Wastewater-based epidemiology of SARS-CoV-2 could play a role in monitoring the spread of the virus in the population and controlling possible outbreaks. However, sensitive sample preparation and detection methods are necessary to detect trace levels of SARS-CoV-2 RNA in influent wastewater (IWW). Unlike predecessors, method optimization of a SARS-CoV-2 RNA concentration and detection procedure was performed with IWW samples with high viral SARS-CoV-2 RNA loads. This is of importance since the SARS-CoV-2 genome in IWW might have already been subject to in-sewer degradation into smaller genome fragments or might be present in a different form (e.g. cell debris, …). Centricon Plus-70 (100 kDa) centrifugal filter devices resulted in the lowest and most reproducible Ct-values for SARS-CoV-2 RNA. Lowering the molecular weight cut-off did not improve our limit of detection and quantification (approximately 10⁰ copies/μL for all genes). Quantitative polymerase chain reaction (qPCR) was employed for the amplification of the N1, N2, N3 and E-gene fragments. This is one of the first studies to apply digital polymerase chain reaction (dPCR) for the detection of SARS-CoV-2 RNA in IWW. dPCR showed high variability at low concentration levels (10⁰ copies/μL), indicating that variability in bioanalytical methods for wastewater-based epidemiology of SARS-CoV-2 might be substantial. dPCR results in IWW were in line with the results found with qPCR. On average, the N2-gene fragment showed high in-sample stability in IWW for 10 days of storage at 4 °C. Between-sample variability was substantial due to the low native concentrations in IWW. Additionally, the E-gene fragment proved to be less stable compared to the N2-gene fragment and showed higher variability. Freezing the IWW samples resulted in a 10-fold decay of loads of the N2- and E-gene fragment in IWW.

Keywords: Digital polymerase chain reaction; In-sample stability; SARS-CoV-2; Ultrafiltration; Wastewater-based epidemiology.

Graphical abstract

Fig. 1

Schematic overview of sewage surveillance for determining SARS-CoV-2 circulation in the general population.

Fig. 2

Schematic overview of the stability experiment.

Fig. 3

Optimization of MWCO and loading volumes with the different sample concentration methods in SAW using (A) the Viral RNA extraction kit and (B) the Maxwell PureFood GMO and Authentication kit. The colour of each cell represents the Ct-value, the y-axis the different sample concentration protocols and the x-axis the different PCR assays. Cells indicated with a red asterisk have higher Ct-values than the lowest point of the calibration curve and could therefore not be quantified. However, in these cells a positive signal was still detected. No signal was detected in cells with a black cross. Side-by-side cells for each location represent duplicate Ct-values. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Comparison of sample concentration methods in IWW from 8 Belgian WWTPs. The colour of each cell represents the Ct-value. Cells indicated with a red asterisk have higher Ct-values than the lowest point of the calibration curve and could therefore not be quantified. However, in these cells a positive signal was still detected. No signal was detected in cells with a black cross. Side-by-side cells for each location represent duplicate Ct-values. The Maxwell PureFood GMO and Authentication kit was used for RNA extraction. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 5

Evaluation of the precision of the calibration curve with dPCR as a means to validate qPCR results. CI = confidence interval; Ct = cycle threshold. The left y-axis represents the precision of the 95% confidence interval after running the calibration curve with dPCR. The right y-axis represents the Ct-values measured at the different concentration levels with qPCR.

Fig. 6

In-sample stability of the different SARS-CoV-2 gene fragments at (A) 4 °C and (B) −20 °C. The horizontal lines represent the mean residual percentage for the eight samples. Detection of the N1-gene fragment was generally low in the IWW samples (i.e. 10–1 copies/μL) with Ct-values below the LLOQ in most samples. Therefore, it was not possible to assess in-sample stability for the N1-gene fragment.