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April 24, 2026Applied and Environmental Microbiology1 citationsOpen Access

Bio-Rad and QIAGEN digital PCR platforms provide equivalent quantification for wastewater-based SARS-CoV-2 surveillance

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TCThomas ClerkinSSSteph SmithKZKevin Zhu

Key Points

  • This research aims to compare the performance of two digital PCR platforms for SARS-CoV-2 quantification in wastewater.
  • Conducted a blinded comparison of Bio-Rad QX200 and QIAGEN QIAcuity dPCR platforms using 93 archived wastewater samples.
  • Samples were stratified by concentration and analyzed for N1 and N2 gene targets and a bovine coronavirus control.
  • Measured precision and detection limits across three concentration bins.
  • Both dPCR platforms showed statistically equivalent quantification with mean differences ≤0.12 log copies L<sup>-1</sup>.
  • Bio-Rad QX200 was 32% faster in processing time compared to QIAGEN QIAcuity.
  • Process limits of detection for Bio-Rad ranged from 2,160 to 2,680 copies L<sup>-1</sup> versus 5,650 to 9,700 copies L<sup>-1</sup> for QIAGEN.

Abstract

Digital PCR (dPCR) is increasingly used for wastewater surveillance due to its precision, absolute quantification, and reduced sensitivity to inhibition compared to quantitative PCR (qPCR). Although the Bio-Rad QX200 and QIAGEN QIAcuity dPCR platforms are widely adopted, their performance has not been directly compared for wastewater applications. We conducted a blinded comparison of these platforms using 93 archived wastewater influent samples from North Carolina collected in 2021-2022, spanning three orders of magnitude in SARS-CoV-2 concentration (1 × 103-5 × 105 copies L-1). Samples were stratified into low, medium, and high concentration bins and analyzed in triplicate for N1 and N2 gene targets and a bovine coronavirus processing control. Both platforms demonstrated statistically equivalent quantification across all targets, with mean differences ≤0. 12 log copies L-1 (R2 > 0. 93). Coefficients of variation were similar (3. 96%-7. 61%), with no significant differences across concentration bins except for N2 in the low bin (difference: 0. 87 percentage points). Measurement variability correlated strongly with wastewater treatment plant site (R2 = 0. 89) rather than platform, indicating that sample matrix characteristics drive precision more than the analytical platform. Process limits of detection ranged from 2, 160 to 2, 680 copies L-1 for Bio-Rad QX200 and 5, 650-9, 700 copies L-1 for QIAGEN QIAcuity for N1 and N2, respectively. The Bio-Rad QX200 platform processed samples 32% faster (305 vs 435 minutes per 96 wells), while QIAGEN QIAcuity offered 29% lower consumables cost (4. 68 vs 6. 11 per well). These findings support the interchangeable use of both platforms for wastewater surveillance, with platform selection based on laboratory-specific operational needs. IMPORTANCEAs wastewater-based epidemiology transitions from emergency response to sustained public health infrastructure, standardized molecular methods are essential for reliable data integration across surveillance networks. This study provides the first blinded comparison of two dPCR platforms widely deployed for wastewater pathogen surveillance in the United States. We demonstrate quantitative equivalence between Bio-Rad QX200 and QIAGEN QIAcuity platforms across three orders of magnitude in viral concentration, establishing that data from both platforms can be interpreted interchangeably for public health decision-making. This platform equivalence is critical as national surveillance systems aggregate data from diverse laboratories and as monitoring expands beyond SARS-CoV-2 to encompass additional respiratory viruses, antimicrobial resistance genes, and emerging pathogens. Our findings provide a methodological foundation for multi-platform surveillance networks and demonstrate that measurement variability is driven primarily by sample matrix characteristics rather than analytical platform choice.

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Cite This Study

Clerkin et al. (2026) studied this question.

synapsesocial.com/papers/69eb0b8d553a5433e34b533bhttps://doi.org/10.1128/aem.00083-26
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