U.S. Environmental Protection Agency, National Risk Management Research Laboratory, 26 W. M.L. King Dr, Cincinnati, OH, 45268, USA.
Department of Global Environmental Health Sciences, School of Public Health and Tropical Medicine, Tulane University, 1440 Canal Street, Suite 2100, New Orleans, LA, 70112, USA.
Water Res. 2019 Jun 1;156:456-464. doi: 10.1016/j.watres.2019.03.011. Epub 2019 Mar 15.
There is growing interest in the application of rapid quantitative polymerase chain reaction (qPCR) and other PCR-based methods for recreational water quality monitoring and management programs. This interest has strengthened given the publication of U.S. Environmental Protection Agency (EPA)-validated qPCR methods for enterococci fecal indicator bacteria (FIB) and has extended to similar methods for Escherichia coli (E. coli) FIB. Implementation of qPCR-based methods in monitoring programs can be facilitated by confidence in the quality of the data produced by these methods. Data quality can be determined through the establishment of a series of specifications that should reflect good laboratory practice. Ideally, these specifications will also account for the typical variability of data coming from multiple users of the method. This study developed proposed standardized data quality acceptance criteria that were established for important calibration model parameters and/or controls from a new qPCR method for E. coli (EPA Draft Method C) based upon data that was generated by 21 laboratories. Each laboratory followed a standardized protocol utilizing the same prescribed reagents and reference and control materials. After removal of outliers, statistical modeling based on a hierarchical Bayesian method was used to establish metrics for assay standard curve slope, intercept and lower limit of quantification that included between-laboratory, replicate testing within laboratory, and random error variability. A nested analysis of variance (ANOVA) was used to establish metrics for calibrator/positive control, negative control, and replicate sample analysis data. These data acceptance criteria should help those who may evaluate the technical quality of future findings from the method, as well as those who might use the method in the future. Furthermore, these benchmarks and the approaches described for determining them may be helpful to method users seeking to establish comparable laboratory-specific criteria if changes in the reference and/or control materials must be made.
人们对快速定量聚合酶链反应(qPCR)和其他基于 PCR 的方法在休闲水质量监测和管理计划中的应用越来越感兴趣。随着美国环境保护署(EPA)验证的粪肠球菌粪便指示菌(FIB)qPCR 方法和类似的大肠杆菌(E. coli)FIB 方法的发表,这种兴趣得到了加强。在监测计划中实施基于 qPCR 的方法可以通过对这些方法产生的数据质量的信心来实现。数据质量可以通过建立一系列规范来确定,这些规范应反映良好的实验室实践。理想情况下,这些规范还将考虑到来自该方法的多个用户的数据的典型可变性。本研究基于 21 个实验室生成的数据,为新的大肠杆菌 qPCR 方法(EPA 草案方法 C)的重要校准模型参数和/或对照物制定了拟议的标准化数据质量验收标准。每个实验室都遵循标准化协议,使用相同的规定试剂和参考及对照材料。去除离群值后,使用基于分层贝叶斯方法的统计建模来建立检测标准曲线斜率、截距和定量下限的指标,其中包括实验室间、实验室内部重复测试和随机误差变异性。嵌套方差分析(ANOVA)用于建立校准剂/阳性对照、阴性对照和重复样本分析数据的指标。这些数据验收标准应有助于那些可能评估该方法未来研究结果的技术质量的人,以及那些将来可能使用该方法的人。此外,如果必须更改参考材料和/或对照材料,这些基准和确定它们的方法可能对寻求建立可比实验室特定标准的方法用户有帮助。