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Volume - Related Inhibitors Standardization For Reverse Transcription Quantitative Polymerase Chain Reaction Experiments
Pascal Pugniere, Engineer, Biomedical Research Institute of the French Army
A large part of the reliability of reverse transcription quantitative polymerase chain reaction (RT-qPCR) data depends on technical variations. Such variations are mainly attributable to the reverse transcription step. Standardization is a key factor in decreasing the intersample variability. However, an ideal standardization is not always possible, and compromises must be found. Due to technical requirements, the current consensus is that a constant amount of total RNA should be used for the RT step (CA-RT). Because RNA isolation yields are variable, such a practice requires the use of variable volumes of nucleic acid extracts in RT reaction. We demonstrate that some RNA extracts contain both exogenous and endogenous inhibitors. These inhibitors induce a decreased RT efficiency that significantly impairs the reliability of RT-qPCR data. Conversely, these inhibitors have slight effects on the qPCR step. To overcome such drawbacks, we proposed to carry out the RT reaction with a constant volume of RNA extract by preserving a constant RNA amount through the supplementation of yeast transfer RNA (CV-RT). We show that CV-RT, compared with the usual CA-RT, allows us to decrease the RT-qPCR variability induced by intersample differences. Such a decrease is a prerequisite for the reliability of messenger RNA quantification.
Novel, Fully Automated Method Improves the Efficiency of qPCR Data Analysis and Interpretation without Sacrificing Sensitivity, Specificity or Accuracy of Quantification
Aron Cohen, CEO, Azure PCR
The qPCR technique is used as part of the quality control process in genetically modified seed manufacture. Analysis and interpretation of qPCR data is limited by sample variability, poor assay performance and arbitrarily set thresholds which can lead to ambiguous and subjective sample calling that is dependant upon the expertise and experience of the scientist interpreting the assay. Processing of raw data output from thermal cyclers using analysis software, to provide data more easily interpretable by the human eye, can lead to loss of data and error prone sample calling. In this study we retrospectively examined data from qPCR testing of a duplex assay performed at Pioneer, in order to assess if manual data analysis could be successfully replaced by an automated methodology based on the AzurePCR method, which analyses raw qPCR data directly and without the need for setting thresholds. 4,224 samples were interpreted by Pioneer and Azure PCR's respective methods. The AzurePCR method demonstrated similar sensitivity, specificity and accuracy of quantification when compared to manual data analysis. Implementation of this automated process would enable scientists unfamiliar with manual analysis methodology to successfully perform the assay.
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