Interlaboratory performance and quantitative PCR data acceptance metrics for NIST SRM® 2917

• Published in: Water research (Oxford) 2022-10, Vol.225, p.119162-119162, Article 119162
• Main Authors: Sivaganesan, Mano, Willis, Jessica R., Karim, Mohammad, Babatola, Akin, Catoe, David, Boehm, Alexandria B., Wilder, Maxwell, Green, Hyatt, Lobos, Aldo, Harwood, Valerie J., Hertel, Stephanie, Klepikow, Regina, Howard, Mondraya F., Laksanalamai, Pongpan, Roundtree, Alexis, Mattioli, Mia, Eytcheson, Stephanie, Molina, Marirosa, Lane, Molly, Rediske, Richard, Ronan, Amanda, D'Souza, Nishita, Rose, Joan B., Shrestha, Abhilasha, Hoar, Catherine, Silverman, Andrea I., Faulkner, Wyatt, Wickman, Kathleen, Kralj, Jason G., Servetas, Stephanie L., Hunter, Monique E., Jackson, Scott A., Shanks, Orin C.
• Format: Article
• Language: English
• Subjects: Bayes Theorem; Benchmarking; DNA; Microbial source tracking; Multiple laboratory; qPCR; Rapid fecal indicator; Real-Time Polymerase Chain Reaction; Standard calibration material; Water Quality
• ISSN: 0043-1354; 1879-2448; 1879-2448
• DOI: 10.1016/j.watres.2022.119162

•SRM 2917 performance is evaluated across 16 labs with 12 qPCR assays.•Calibration model linearity (R2) was ≥ 0.992 regardless of lab or assay.•Acceptable amplification efficiencies observed in 99.5% of single run models.•Global model within-lab variability was ≤ between-lab for each assay.•Novel data acceptance metrics are proposed for SRM 2917. Surface water quality quantitative polymerase chain reaction (qPCR) technologies are expanding from a subject of research to routine environmental and public health laboratory testing. Readily available, reliable reference material is needed to interpret qPCR measurements, particularly across laboratories. Standard Reference Material® 2917 (NIST SRM® 2917) is a DNA plasmid construct that functions with multiple water quality qPCR assays allowing for estimation of total fecal pollution and identification of key fecal sources. This study investigates SRM 2917 interlaboratory performance based on repeated measures of 12 qPCR assays by 14 laboratories (n = 1008 instrument runs). Using a Bayesian approach, single-instrument run data are combined to generate assay-specific global calibration models allowing for characterization of within- and between-lab variability. Comparable data sets generated by two additional laboratories are used to assess new SRM 2917 data acceptance metrics. SRM 2917 allows for reproducible single-instrument run calibration models across laboratories, regardless of qPCR assay. In addition, global models offer multiple data acceptance metric options that future users can employ to minimize variability, improve comparability of data across laboratories, and increase confidence in qPCR measurements. [Display omitted]