Amplification efficiency: linking baseline and bias in the analysis of quantitative PCR data

Despite the central role of quantitative PCR (qPCR) in the quantification of mRNA transcripts, most analyses of qPCR data are still delegated to the software that comes with the qPCR apparatus. This is especially true for the handling of the fluorescence baseline. This article shows that baseline estimation errors are directly reflected in the observed PCR efficiency values and are thus propagated exponentially in the estimated starting concentrations as well as ‘fold-difference’ results. Because of the unknown origin and kinetics of the baseline fluorescence, the fluorescence values monitored in the initial cycles of the PCR reaction cannot be used to estimate a useful baseline value. An algorithm that estimates the baseline by reconstructing the log-linear phase downward from the early plateau phase of the PCR reaction was developed and shown to lead to very reproducible PCR efficiency values. PCR efficiency values were determined per sample by fitting a regression line to a subset of data points in the log-linear phase. The variability, as well as the bias, in qPCR results was significantly reduced when the mean of these PCR efficiencies per amplicon was used in the calculation of an estimate of the starting concentration per sample

SPIDIA-RNA: First external quality assessment for the pre-analytical phase of blood samples used for RNA based analyses

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Model based analysis of real-time PCR data from DNA binding dye protocols

BACKGROUND: Reverse transcription followed by real-time PCR is widely used for quantification of specific mRNA, and with the use of double-stranded DNA binding dyes it is becoming a standard for microarray data validation. Despite the kinetic information generated by real-time PCR, most popular analysis methods assume constant amplification efficiency among samples, introducing strong biases when amplification efficiencies are not the same. RESULTS: We present here a new mathematical model based on the classic exponential description of the PCR, but modeling amplification efficiency as a sigmoidal function of the product yield. The model was validated with experimental results and used for the development of a new method for real-time PCR data analysis. This model based method for real-time PCR data analysis showed the best accuracy and precision compared with previous methods when used for quantification of in-silico generated and experimental real-time PCR results. Moreover, the method is suitable for the analyses of samples with similar or dissimilar amplification efficiency. CONCLUSION: The presented method showed the best accuracy and precision. Moreover, it does not depend on calibration curves, making it ideal for fully automated high-throughput applications

Addressing fluorogenic real-time qPCR inhibition using the novel custom Excel file system 'FocusField2-6GallupqPCRSet-upTool-001' to attain consistently high fidelity qPCR reactions

The purpose of this manuscript is to discuss fluorogenic real-time quantitative polymerase chain reaction (qPCR) inhibition and to introduce/define a novel Microsoft Excel-based file system which provides a way to detect and avoid inhibition, and enables investigators to consistently design dynamically-sound, truly LOG-linear qPCR reactions very quickly. The qPCR problems this invention solves are universal to all qPCR reactions, and it performs all necessary qPCR set-up calculations in about 52 seconds (using a pentium 4 processor) for up to seven qPCR targets and seventy-two samples at a time – calculations that commonly take capable investigators days to finish. We have named this custom Excel-based file system "FocusField2-6GallupqPCRSet-upTool-001" (FF2-6-001 qPCR set-up tool), and are in the process of transforming it into professional qPCR set-up software to be made available in 2007. The current prototype is already fully functional

A Mechanistic Model of PCR for Accurate Quantification of Quantitative PCR Data

Background: Quantitative PCR (qPCR) is a workhorse laboratory technique for measuring the concentration of a target DNA sequence with high accuracy over a wide dynamic range. The gold standard method for estimating DNA concentrations via qPCR is quantification cycle (Cq) standard curve quantification, which requires the time- and labor-intensive construction of a Cq standard curve. In theory, the shape of a qPCR data curve can be used to directly quantify DNA concentration by fitting a model to data; however, current empirical model-based quantification methods are not as reliable as Cq standard curve quantification. Principal Findings: We have developed a two-parameter mass action kinetic model of PCR (MAK2) that can be fitted to qPCR data in order to quantify target concentration from a single qPCR assay. To compare the accuracy of MAK2-fitting to other qPCR quantification methods, we have applied quantification methods to qPCR dilution series data generated in three independent laboratories using different target sequences. Quantification accuracy was assessed by analyzing the reliability of concentration predictions for targets at known concentrations. Our results indicate that quantification by MAK2-fitting is as reliable as Cq standard curve quantification for a variety of DNA targets and a wide range of concentrations. Significance: We anticipate that MAK2 quantification will have a profound effect on the way qPCR experiments are designed and analyzed. In particular, MAK2 enables accurate quantification of portable qPCR assays with limited sampl

A new real-time PCR method to overcome significant quantitative inaccuracy due to slight amplification inhibition

<p>Abstract</p> <p>Background</p> <p>Real-time PCR analysis is a sensitive DNA quantification technique that has recently gained considerable attention in biotechnology, microbiology and molecular diagnostics. Although, the cycle-threshold (<it>Ct</it>) method is the present "gold standard", it is far from being a standard assay. Uniform reaction efficiency among samples is the most important assumption of this method. Nevertheless, some authors have reported that it may not be correct and a slight PCR efficiency decrease of about 4% could result in an error of up to 400% using the <it>Ct </it>method. This reaction efficiency decrease may be caused by inhibiting agents used during nucleic acid extraction or copurified from the biological sample.</p> <p>We propose a new method (<it>Cy</it>_<it>0</it>) that does not require the assumption of equal reaction efficiency between unknowns and standard curve.</p> <p>Results</p> <p>The <it>Cy</it>_{<it>0 </it>}method is based on the fit of Richards' equation to real-time PCR data by nonlinear regression in order to obtain the best fit estimators of reaction parameters. Subsequently, these parameters were used to calculate the <it>Cy</it>_{<it>0 </it>}value that minimizes the dependence of its value on PCR kinetic.</p> <p>The <it>Ct</it>, second derivative (<it>Cp</it>), sigmoidal curve fitting method (<it>SCF</it>) and <it>Cy</it>_{<it>0 </it>}methods were compared using two criteria: precision and accuracy. Our results demonstrated that, in optimal amplification conditions, these four methods are equally precise and accurate. However, when PCR efficiency was slightly decreased, diluting amplification mix quantity or adding a biological inhibitor such as IgG, the <it>SCF</it>, <it>Ct </it>and <it>Cp </it>methods were markedly impaired while the <it>Cy</it>_{<it>0 </it>}method gave significantly more accurate and precise results.</p> <p>Conclusion</p> <p>Our results demonstrate that <it>Cy</it>_{<it>0 </it>}represents a significant improvement over the standard methods for obtaining a reliable and precise nucleic acid quantification even in sub-optimal amplification conditions overcoming the underestimation caused by the presence of some PCR inhibitors.</p

Altered expression of microRNAs in the myocardium of rats with acute myocardial infarction

<p>Abstract</p> <p>Background</p> <p>MicroRNAs(miRNAs) are important cellular components and their dysfunction is associated with various diseases. Acute myocardial infarction (AMI) is one of the most serious cardiovascular diseases. Although several miRNAs are reported to be associated with AMI, more novel miRNAs are needed to further investigate and improve certainty</p> <p>Methods</p> <p>We applied a well-established acute myocardial infarction rat model and performed miRNAs microarray experiments upon the myocardium tissue of rats with AMI and under sham control. We identified the differentially expressed miRNAs and analyzed the function of miRNA targets, transcription factors, and host genes based on bioinformatics.</p> <p>Results</p> <p>As a result, the levels of expression of seventeen miRNAs significantly deregulated, of which four miRNAs were further validated by qRT-PCR. In addition, we observed that the transcription factors, targets, and host genes of these deregulated miRNAs are enriched in cardiovascular-related functions.</p> <p>Conclusion</p> <p>We found that the miRNAs expression level altered in rats with AMI and differentially expressed miRNAs may be novel biomarkers of AMI.</p

Antimalarial Exposure Delays Plasmodium falciparum Intra-Erythrocytic Cycle and Drives Drug Transporter Genes Expression

BACKGROUND: Multi-drug resistant Plasmodium falciparum is a major obstacle to malaria control and is emerging as a complex phenomenon. Mechanisms of drug evasion based on the intracellular extrusion of the drug and/or modification of target proteins have been described. However, cellular mechanisms related with metabolic activity have also been seen in eukaryotic systems, e.g. cancer cells. Recent observations suggest that such mechanism may occur in P. falciparum. METHODOLOGY/PRINCIPAL FINDINGS: We therefore investigated the effect of mefloquine exposure on the cell cycle of three P. falciparum clones (3D7, FCB, W2) with different drug susceptibilities, while investigating in parallel the expression of four genes coding for confirmed and putative drug transporters (pfcrt, pfmdr1, pfmrp1 and pfmrp2). Mefloquine induced a previously not described dose and clone dependent delay in the intra-erythrocytic cycle of the parasite. Drug impact on cell cycle progression and gene expression was then merged using a non-linear regression model to determine specific drug driven expression. This revealed a mild, but significant, mefloquine driven gene induction up to 1.5 fold. CONCLUSIONS/SIGNIFICANCE: Both cell cycle delay and induced gene expression represent potentially important mechanisms for parasites to escape the effect of the antimalarial drug