Improved strategies and optimization of calibration models for real-time PCR absolute quantification
Improved strategies and optimization of calibration models for real-time PCR absolute quantification
复制标题
DOI：
10.1016/j.watres.2010.07.066
复制
发表时间：
2010-09-01
期刊：
影响因子：
12.8
通讯作者：
Shanks, Orin C.
中科院分区：
文献类型：
--
作者：
Sivaganesan, Mano;Haugland, Richard A.;Shanks, Orin C.
实时聚合酶链反应绝对定量应用越来越普遍，在娱乐和饮用水水质行业。许多方法依赖于使用标准曲线来估计未知样品中的DNA靶浓度。传统的绝对定量方法要求每次实验都必须有一个标准曲线。然而，为每个qPCR实验设置生成标准曲线可能是昂贵和耗时的，特别是对于具有大量未知样本的研究。因此，许多研究人员采用了一种主校准策略，其中一条曲线是从多个仪器运行产生的DNA标准测量中得出的。然而，主曲线可能会增加与截距和斜率参数相关的不确定性，并降低未知样品DNA目标浓度估计的准确性。在这里，我们报告了从绝对标准曲线生成校准方程的两种替代策略，称为“汇集”和“混合”，这有助于减少实验室测试的成本和时间，以及校准模型参数估计的不确定性。在本研究中，通过蒙特卡罗马尔可夫链方法对两种不同的qPCR检测进行一系列重复实验，比较了四种不同的校准模型生成策略。分层贝叶斯方法可以比较模型截距和斜率参数的不确定性，优化实验设计。数据表明，与传统的单曲线方法相比，“池化”模型可以减少斜率和截距参数估计的不确定性。此外，“混合”模型获得了与“单一”模型相似的不确定性估计，同时增加了每次仪器运行可用反应井的数量。Elsevier Ltd.出版。
Real-time PCR absolute quantification applications are becoming more common in the recreational and drinking water quality industries. Many methods rely on the use of standard curves to make estimates of DNA target concentrations in unknown samples. Traditional absolute quantification approaches dictate that a standard curve must accompany each experimental run. However, the generation of a standard curve for each qPCR experiment set-up can be expensive and time consuming, especially for studies with large numbers of unknown samples. As a result, many researchers have adopted a master calibration strategy where a single curve is derived from DNA standard measurements generated from multiple instrument runs. However, a master curve can inflate uncertainty associated with intercept and slope parameters and decrease the accuracy of unknown sample DNA target concentration estimates. Here we report two alternative strategies termed 'pooled' and 'mixed' for the generation of calibration equations from absolute standard curves which can help reduce the cost and time of laboratory testing, as well as the uncertainty in calibration model parameter estimates. In this study, four different strategies for generating calibration models were compared based on a series of repeated experiments for two different qPCR assays using a Monte Carlo Markov Chain method. The hierarchical Bayesian approach allowed for the comparison of uncertainty in intercept and slope model parameters and the optimization of experiment design. Data suggests that the 'pooled' model can reduce uncertainty in both slope and intercept parameter estimates compared to the traditional single curve approach. In addition, the 'mixed' model achieved uncertainty estimates similar to the 'single' model while increasing the number of available reaction wells per instrument run. Published by Elsevier Ltd.