Since its introduction quantitative real-time polymerase chain reaction (qPCR) has become the standard method for quantification of gene expression. Its high sensitivity, large dynamic range, and accuracy led to the development of numerous applications with an increasing number of samples to be analyzed. Data analysis consists of a number of steps, which have to be carried out in several different applications. Currently, no single tool is available which incorporates storage, management, and multiple methods covering the complete analysis pipeline.
QPCR is a versatile web-based Java application that allows to store, manage, and analyze data from relative quantification qPCR experiments. It comprises a parser to import generated data from qPCR instruments and includes a variety of analysis methods to calculate cycle-threshold and amplification efficiency values. The analysis pipeline includes technical and biological replicate handling, incorporation of sample or gene specific efficiency, normalization using single or multiple reference genes, inter-run calibration, and fold change calculation. Moreover, the application supports assessment of error propagation throughout all analysis steps and allows conducting statistical tests on biological replicates. Results can be visualized in customizable charts and exported for further investigation.
We have developed a web-based system designed to enhance and facilitate the analysis of qPCR experiments. It covers the complete analysis workflow combining parsing, analysis, and generation of charts into one single application. The system is freely available at
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.
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 () standard curve quantification, which requires the time- and labor-intensive construction of a 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 standard curve quantification.
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 standard curve quantification for a variety of DNA targets and a wide range of concentrations.
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 sample throughput, where construction of a standard curve is impractical.
Reverse transcription quantitative real-time PCR (RT-qPCR) is a key method for measurement of relative gene expression. Analysis of RT-qPCR data requires many iterative computations for data normalization and analytical optimization. Currently no computer program for RT-qPCR data analysis is suitable for analytical optimization and user-controllable customization based on data quality, experimental design as well as specific research aims. Here I introduce an all-in-one computer program, SASqPCR, for robust and rapid analysis of RT-qPCR data in SAS. This program has multiple macros for assessment of PCR efficiencies, validation of reference genes, optimization of data normalizers, normalization of confounding variations across samples, and statistical comparison of target gene expression in parallel samples. Users can simply change the macro variables to test various analytical strategies, optimize results and customize the analytical processes. In addition, it is highly automatic and functionally extendable. Thus users are the actual decision-makers controlling RT-qPCR data analyses. SASqPCR and its tutorial are freely available at http://code.google.com/p/sasqpcr/downloads/list.
Real-time quantitative PCR (qPCR) is still the gold-standard technique for gene-expression quantification. Recent technological advances of this method allow for the high-throughput gene-expression analysis, without the limitations of sample space and reagent used. However, non-commercial and user-friendly software for the management and analysis of these data is not available.
The recently developed commercial microarrays allow for the drawing of standard curves of multiple assays using the same n-fold diluted samples. Data Analysis Gene (DAG) Expression software has been developed to perform high-throughput gene-expression data analysis using standard curves for relative quantification and one or multiple reference genes for sample normalization. We discuss the application of DAG Expression in the analysis of data from an experiment performed with Fluidigm technology, in which 48 genes and 115 samples were measured. Furthermore, the quality of our analysis was tested and compared with other available methods.
DAG Expression is a freely available software that permits the automated analysis and visualization of high-throughput qPCR. A detailed manual and a demo-experiment are provided within the DAG Expression software at http://www.dagexpression.com/dage.zip.
Real-time quantitative PCR (qPCR) is a widely used technique in microbial community analysis, allowing the quantification of the number of target genes in a community sample. Currently, the standard-curve (SC) method of absolute quantification is widely employed for these kinds of analysis. However, the SC method assumes that the amplification efficiency (E) is the same for both the standard and the sample target template. We analyzed 19 bacterial strains and nine environmental samples in qPCR assays, targeting the nifH and 16S rRNA genes. The E values of the qPCRs differed significantly, depending on the template. This has major implications for the quantification. If the sample and standard differ in their E values, quantification errors of up to orders of magnitude are possible. To address this problem, we propose and test the one-point calibration (OPC) method for absolute quantification. The OPC method corrects for differences in E and was derived from the ΔΔCT method with correction for E, which is commonly used for relative quantification in gene expression studies. The SC and OPC methods were compared by quantifying artificial template mixtures from Geobacter sulfurreducens (DSM 12127) and Nostoc commune (Culture Collection of Algae and Protozoa [CCAP] 1453/33), which differ in their E values. While the SC method deviated from the expected nifH gene copy number by 3- to 5-fold, the OPC method quantified the template mixtures with high accuracy. Moreover, analyzing environmental samples, we show that even small differences in E between the standard and the sample can cause significant differences between the copy numbers calculated by the SC and the OPC methods.
Quantitative PCR (qPCR) is more sensitive than microscopy for detecting Pneumocystis jirovecii in bronchoalveolar lavage (BAL) fluid. We therefore developed a qPCR assay and compared the results with those of a routine immunofluorescence assay (IFA) and clinical data. The assay included automated DNA extraction, amplification of the mitochondrial large-subunit rRNA gene and an internal control, and quantification of copy numbers with the help of a plasmid clone. We studied 353 consecutive BAL fluids obtained for investigation of unexplained fever and/or pneumonia in 287 immunocompromised patients. No qPCR inhibition was observed. Seventeen (5%) samples were both IFA and qPCR positive, 63 (18%) were IFA negative and qPCR positive, and 273 (77%) were both IFA and qPCR negative. The copy number was significantly higher for IFA-positive/qPCR-positive samples than for IFA-negative/qPCR-positive samples (4.2 ± 1.2 versus 1.1 ± 1.1 log10 copies/μl; P < 10−4). With IFA as the standard, the qPCR assay sensitivity was 100% for ≥2.6 log10 copies/μl and the specificity was 100% for ≥4 log10 copies/μl. Since qPCR results were not available at the time of decision-making, these findings did not trigger cotrimoxazole therapy. Patients with systemic inflammatory diseases and IFA-negative/qPCR-positive BAL fluid had a worse 1-year survival rate than those with IFA-negative/qPCR-negative results (P < 10−3), in contrast with solid-organ transplant recipients (P = 0.88) and patients with hematological malignancy (P = 0.26). Quantifying P. jirovecii DNA in BAL fluids independently of IFA positivity should be incorporated into the investigation of pneumonia in immunocompromised patients. The relevant threshold remains to be determined and may vary according to the underlying disease.
Quantitative real-time PCR (qPCR) has been the method of choice for the quantification of mRNA. Due to the various artifactual factors that may affect the accuracy of qPCR, internal reference genes are most often used to normalize qPCR data. Recently, many studies have employed computer programs such as GeNorm, BestKeeper and NormFinder in selecting reference genes, but very few statistically validate the outcomes of these programs. Thus, in this study, we selected reference genes for qPCR of liver and ovary samples of yellow (juvenile), migratory (silver) and 11-KT treated juveniles of New Zealand shortfinned eels (Anguilla australis) using the three computer programs and validate the selected genes statistically using REST 2009 software and the Mann-Whitney test. We also tested for the repeatability of use for the best reference genes by applying them to a data set obtained in a similar experiment conducted the previous year.
Out of six candidate genes, the combination of 18 s and eef1 was found to be the best statistically validated reference for liver, while in ovary it was l36. However, discrepancies in gene rankings were found between the different programs. Also, statistical validation procedures showed that several genes put forward as being the best by the programs were in fact, regulated, making them unsuitable as reference genes. Additionally, eef1 which was found to be a suitable - though not the top ranked - reference gene for liver tissues in one year, was regulated in another.
Our study highlights the need for external validations of reference gene selections made by computer programs. Researchers need to be vigilant in validating and reporting the rationale for the use of reference gene in published studies.
“Candidatus Accumulibacter” and total bacterial community dynamics were studied in two lab-scale enhanced biological phosphorus removal (EBPR) reactors by using a community fingerprint technique, automated ribosomal intergenic spacer analysis (ARISA). We first evaluated the quantitative capability of ARISA compared to quantitative real-time PCR (qPCR). ARISA and qPCR provided comparable relative quantification of the two dominant “Ca. Accumulibacter” clades (IA and IIA) detected in our reactors. The quantification of total “Ca. Accumulibacter” 16S rRNA genes relative to that from the total bacterial community was highly correlated, with ARISA systematically underestimating “Ca. Accumulibacter” abundance, probably due to the different normalization techniques applied. During 6 months of normal (undisturbed) operation, the distribution of the two clades within the total “Ca. Accumulibacter” population was quite stable in one reactor while comparatively dynamic in the other reactor. However, the variance in the clade distribution did not appear to affect reactor performance. Instead, good EBPR activity was positively associated with the abundance of total “Ca. Accumulibacter.” Therefore, we concluded that the different clades in the system provided functional redundancy. We disturbed the reactor operation by adding nitrate together with acetate feeding in the anaerobic phase to reach initial reactor concentrations of 10 mg/liter NO3-N for 35 days. The reactor performance deteriorated with a concomitant decrease in the total “Ca. Accumulibacter” population, suggesting that a population shift was the cause of performance upset after a long exposure to nitrate in the anaerobic phase.
The XML-based Real-Time PCR Data Markup Language (RDML) has been developed by the RDML consortium (http://www.rdml.org) to enable straightforward exchange of qPCR data and related information between qPCR instruments and third party data analysis software, between colleagues and collaborators and between experimenters and journals or public repositories. We here also propose data related guidelines as a subset of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) to guarantee inclusion of key data information when reporting experimental results.
Model-based analysis of data from quantitative reverse-transcription PCR (qRT-PCR) is potentially more powerful and versatile than traditional methods. Yet existing model-based approaches cannot properly deal with the higher sampling variances associated with low-abundant targets, nor do they provide a natural way to incorporate assumptions about the stability of control genes directly into the model-fitting process.
In our method, raw qPCR data are represented as molecule counts, and described using generalized linear mixed models under Poisson-lognormal error. A Markov Chain Monte Carlo (MCMC) algorithm is used to sample from the joint posterior distribution over all model parameters, thereby estimating the effects of all experimental factors on the expression of every gene. The Poisson-based model allows for the correct specification of the mean-variance relationship of the PCR amplification process, and can also glean information from instances of no amplification (zero counts). Our method is very flexible with respect to control genes: any prior knowledge about the expected degree of their stability can be directly incorporated into the model. Yet the method provides sensible answers without such assumptions, or even in the complete absence of control genes. We also present a natural Bayesian analogue of the “classic” analysis, which uses standard data pre-processing steps (logarithmic transformation and multi-gene normalization) but estimates all gene expression changes jointly within a single model. The new methods are considerably more flexible and powerful than the standard delta-delta Ct analysis based on pairwise t-tests.
Our methodology expands the applicability of the relative-quantification analysis protocol all the way to the lowest-abundance targets, and provides a novel opportunity to analyze qRT-PCR data without making any assumptions concerning target stability. These procedures have been implemented as the MCMC.qpcr package in R.
Numerous models for use in interpreting quantitative PCR (qPCR) data are present in recent literature. The most commonly used models assume the amplification in qPCR is exponential and fit an exponential model with a constant rate of increase to a select part of the curve. Kinetic theory may be used to model the annealing phase and does not assume constant efficiency of amplification. Mechanistic models describing the annealing phase with kinetic theory offer the most potential for accurate interpretation of qPCR data. Even so, they have not been thoroughly investigated and are rarely used for interpretation of qPCR data. New results for kinetic modeling of qPCR are presented.
Two models are presented in which the efficiency of amplification is based on equilibrium solutions for the annealing phase of the qPCR process. Model 1 assumes annealing of complementary targets strands and annealing of target and primers are both reversible reactions and reach a dynamic equilibrium. Model 2 assumes all annealing reactions are nonreversible and equilibrium is static. Both models include the effect of primer concentration during the annealing phase. Analytic formulae are given for the equilibrium values of all single and double stranded molecules at the end of the annealing step. The equilibrium values are then used in a stepwise method to describe the whole qPCR process. Rate constants of kinetic models are the same for solutions that are identical except for possibly having different initial target concentrations. Analysis of qPCR curves from such solutions are thus analyzed by simultaneous non-linear curve fitting with the same rate constant values applying to all curves and each curve having a unique value for initial target concentration. The models were fit to two data sets for which the true initial target concentrations are known. Both models give better fit to observed qPCR data than other kinetic models present in the literature. They also give better estimates of initial target concentration. Model 1 was found to be slightly more robust than model 2 giving better estimates of initial target concentration when estimation of parameters was done for qPCR curves with very different initial target concentration. Both models may be used to estimate the initial absolute concentration of target sequence when a standard curve is not available.
It is argued that the kinetic approach to modeling and interpreting quantitative PCR data has the potential to give more precise estimates of the true initial target concentrations than other methods currently used for analysis of qPCR data. The two models presented here give a unified model of the qPCR process in that they explain the shape of the qPCR curve for a wide variety of initial target concentrations.
Quantitative polymerase chain reaction; qPCR; Kinetic model
The direct quantification of multiple pathogens has been desired for diagnostic and public health purposes for a long time. In this study, we applied microfluidic quantitative PCR (qPCR) technology to the simultaneous detection and quantification of multiple food- and waterborne pathogens. In this system, multiple singleplex qPCR assays were run under identical detection conditions in nanoliter-volume chambers that are present in high densities on a chip. First, we developed 18 TaqMan qPCR assays that could be run in the same PCR conditions by using prevalidated TaqMan probes. Specific and sensitive quantification was achieved by using these qPCR assays. With the addition of two previously validated TaqMan qPCR assays, we used 20 qPCR assays targeting 10 enteric pathogens, a fecal indicator bacterium (general Escherichia coli), and a process control strain in the microfluidic qPCR system. We preamplified the template DNA to increase the sensitivity of the qPCR assays. Our results suggested that preamplification was effective for quantifying small amounts of the template DNA without any major impact on the sensitivity, efficiency, and quantitative performance of qPCR. This microfluidic qPCR system allowed us to detect and quantify multiple pathogens from fecal samples and environmental water samples spiked with pathogens at levels as low as 100 cells/liter. These results suggest that the routine monitoring of multiple pathogens in food and water samples is now technically feasible. This method may provide more reliable information for risk assessment than the current fecal contamination indicator approach.
Quantitative real-time polymerase chain reaction (qPCR) is a sensitive gene quantification method that has been extensively used in biological and biomedical fields. The currently used methods for PCR data analysis, including the threshold cycle method and linear and nonlinear model-fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence can hardly be accurate and therefore can distort results. We propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive PCR cycles, we subtract the fluorescence in the former cycle from that in the latter cycle, transforming the n cycle raw data into n−1 cycle data. Then, linear regression is applied to the natural logarithm of the transformed data. Finally, PCR amplification efficiencies and the initial DNA molecular numbers are calculated for each reaction. This taking-difference method avoids the error in subtracting an unknown background, and thus it is more accurate and reliable. This method is easy to perform, and this strategy can be extended to all current methods for PCR data analysis.
background subtraction; initial DNA amount; linear regression; polymerase chain reaction efficiency; quantitative real-time polymerase chain reaction
While many decisions rely on real time quantitative PCR (qPCR) analysis few attempts have hitherto been made to quantify bounds of precision accounting for the various sources of variation involved in the measurement process. Besides influences of more obvious factors such as camera noise and pipetting variation, changing efficiencies within and between reactions affect PCR results to a degree which is not fully recognized. Here, we develop a statistical framework that models measurement error and other sources of variation as they contribute to fluorescence observations during the amplification process and to derived parameter estimates. Evaluation of reproducibility is then based on simulations capable of generating realistic variation patterns. To this end, we start from a relatively simple statistical model for the evolution of efficiency in a single PCR reaction and introduce additional error components, one at a time, to arrive at stochastic data generation capable of simulating the variation patterns witnessed in repeated reactions (technical repeats). Most of the variation in values was adequately captured by the statistical model in terms of foreseen components. To recreate the dispersion of the repeats' plateau levels while keeping the other aspects of the PCR curves within realistic bounds, additional sources of reagent consumption (side reactions) enter into the model. Once an adequate data generating model is available, simulations can serve to evaluate various aspects of PCR under the assumptions of the model and beyond.
The purpose of this manuscript is to describe a reliable approach to quantitative real-time polymerase chain reaction (qPCR) assay development and project management, which is currently embodied in the Excel 2003-based software program named “PREXCEL-Q” (P-Q) (formerly known as “FocusField2-6Gallup-qPCRSet-upTool-001,” “FF2-6-001 qPCR set-up tool” or “Iowa State University Research Foundation [ISURF] project #03407”). Since its inception from 1997-2007, the program has been well-received and requested around the world and was recently unveiled by its inventor at the 2008 Cambridge Healthtech Institute’s Fourth Annual qPCR Conference in San Diego, CA. P-Q was subsequently mentioned in a review article by Stephen A. Bustin, an acknowledged leader in the qPCR field. Due to its success and growing popularity, and the fact that P-Q introduces a unique/defined approach to qPCR, a concise description of what the program is and what it does has become important. Sample-related inhibitory problems of the qPCR assay, sample concentration limitations, nuclease-treatment, reverse transcription (RT) and master mix formulations are all addressed by the program, enabling investigators to quickly, consistently and confidently design uninhibited, dynamically-sound, LOG-linear-amplification-capable, high-efficiency-of-amplification reactions for any type of qPCR. The current version of the program can handle an infinite number of samples.
PCR; qPCR; RT; gene expression; inhibition; RNA integrity; micro-array; real-time PCR; software
The purpose of this manuscript is to describe a reliable approach to quantitative real-time polymerase chain reaction (qPCR) assay development and project management, which is currently embodied in the Excel 2003-based software program named “PREXCEL-Q” (P-Q) (formerly known as “FocusField2-6Gallup-qPCRSet-upTool-001,” “FF2-6-001 qPCR set-up tool” or “Iowa State University Research Foundation [ISURF] project #03407”). Since its inception from 1997-2007, the program has been well-received and requested around the world and was recently unveiled by its inventor at the 2008 Cambridge Healthtech Institute's Fourth Annual qPCR Conference in San Diego, CA. P-Q was subsequently mentioned in a review article by Stephen A. Bustin, an acknowledged leader in the qPCR field. Due to its success and growing popularity, and the fact that P-Q introduces a unique/defined approach to qPCR, a concise description of what the program is and what it does has become important. Sample-related inhibitory problems of the qPCR assay, sample concentration limitations, nuclease-treatment, reverse transcription (RT) and master mix formulations are all addressed by the program, enabling investigators to quickly, consistently and confidently design uninhibited, dynamically-sound, LOG-linear-amplification-capable, high-efficiency-of-amplification reactions for any type of qPCR. The current version of the program can handle an infinite number of samples.
PCR; qPCR; RT; gene expression; inhibition; RNA integrity; micro-array; real-time PCR; software
One of the critical tasks in analytical testing is to monitor and assign the infectivity or potency of viral based vaccines from process development to production of final clinical lots. In this study, a high throughput RT-qPCR based approach was developed to evaluate the infectious titre in a replication-defective HSV-2 candidate vaccine, called HSV529. This assay is a combination of viral propagation and quantitative RT-PCR which measures the amount of RNA in infected cells after incubation with test samples.
The relative infectious titre of HSV529 candidate vaccine was determined by a RT-qPCR method targeting HSV-2 gD2 gene. The data were analyzed using the parallel-line analysis as described in the European Pharmacopoeia 8th edition. The stability of HSV529 test samples were also investigated in a concordance study between RT-qPCR infectivity assay and a classical plaque assays. A suitable correlation was determined between both assays using an identical sample set in both assays. The RT-qPCR infectivity assay was further characterized by evaluating the intermediate precision and accuracy. The coefficient of variation from the six independent assays was less than 10%. The accuracy of each of the assay was also evaluated in the range of 92.91% to 120.57%.
Our data demonstrate that the developed RT-qPCR infectivity assay is a rapid high throughput approach to quantify the infectious titer or potency of live attenuated or defective viral-based vaccines, an attribute which is associated with product quality.
Infectious titre; HSV type 2; RT-qPCR; Vaccine; Potency
RNA degradation can distort or prevent measurement of RNA transcripts. A mathematical model for degradation was constructed, based on random RNA damage and exponential polymerase chain reaction (PCR) amplification. Degradation, measured as the number of lesions/base, can be quantified by amplifying several sequences of a reference gene, calculating the regression of Ct on amplicon length and determining the slope. Reverse transcriptase–quantitative PCR (RT–qPCR) data can then be corrected for degradation using lesions/base, amplicon length(s) and the relevant equation obtained from the model. Several predictions of the model were confirmed experimentally; degradation in a sample quantified using the model correlated with degradation quantified using an additional control sample and the ΔΔCt method and application of the model corrected erroneous results for relative quantification resulting from degradation and differences in amplicon length. Compared with RIN, the method was quantitative, simpler, more sensitive and spanned a wider range of RNA damage. The method can use either random or specifically primed complementary DNA and it enables relative and absolute quantification of RNA to be corrected for degradation. The model and method should be applicable to many situations in which RNA is quantified, including quantification of RNA by methods other than nucleic acid amplification.
The use of malaria-specific quantitative real-time PCR (qPCR) is increasing due to its high sensitivity, speciation and quantification of malaria parasites. However, due to the lack of consensus or standardized methods in performing qPCR, it is difficult to evaluate and/or compare the quality of work reported by different authors for a cross-study and/or cross-platform assay analysis.
The performances of seven published qPCR assays that detect Plasmodium spp or Plasmodium falciparum were compared using standard DNA and samples from a clinical trial. Amplification and qPCR measurements were performed using the Applied Biosystems 7500 Fast Real-Time PCR System. All the analyses were automatically established using the default settings. For the TaqMan probe format, the assays were performed in the background of QuantiFast Probe Master Mix whereas in SYBR Green format, the assays were performed in the background of QuantiFast SYBR Green Master Mix and QuantiTect SYBR Green Master Mix background.
Assays with high PCR efficiencies outperformed those with low efficiencies in all categories including sensitivity, precision and consistency regardless of the assay format and background. With the exception of one assay, all assays evaluated showed lower sensitivity compared to what have been published. When samples from a malaria challenge study were analysed, the qPCR assay with the overall best performance detected parasites in subjects earliest and with most consistency.
The data demonstrate the need for increased consensus and guidelines that will encourage better experimental practices, allowing more consistent and unambiguous interpretation of qPCR results.
The quantitative polymerase chain reaction (qPCR) is widely utilized for gene expression analysis. However, the lack of robust strategies for cross laboratory data comparison hinders the ability to collaborate or perform large multicentre studies conducted at different sites. In this study we introduced and validated a workflow that employs universally applicable, quantifiable external oligonucleotide standards to address this question. Using the proposed standards and data-analysis procedure, we obtained a perfect concordance between expression values from eight different genes in 366 patient samples measured on three different qPCR instruments and matching software, reagents, plates and seals, demonstrating the power of this strategy to detect and correct inter-run variation and to enable exchange of data between different laboratories, even when not using the same qPCR platform.
Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the gold standard technique for mRNA quantification, but appropriate normalization is required to obtain reliable data. Normalization to accurately quantitated RNA has been proposed as the most reliable method for in vivo biopsies. However, this approach does not correct differences in RNA integrity.
In this study, we evaluated the effect of RNA degradation on the quantification of the relative expression of nine genes (18S, ACTB, ATUB, B2M, GAPDH, HPRT, POLR2L, PSMB6 and RPLP0) that cover a wide expression spectrum. Our results show that RNA degradation could introduce up to 100% error in gene expression measurements when RT-qPCR data were normalized to total RNA. To achieve greater resolution of small differences in transcript levels in degraded samples, we improved this normalization method by developing a corrective algorithm that compensates for the loss of RNA integrity. This approach allowed us to achieve higher accuracy, since the average error for quantitative measurements was reduced to 8%. Finally, we applied our normalization strategy to the quantification of EGFR, HER2 and HER3 in 104 rectal cancer biopsies. Taken together, our data show that normalization of gene expression measurements by taking into account also RNA degradation allows much more reliable sample comparison.
We developed a new normalization method of RT-qPCR data that compensates for loss of RNA integrity and therefore allows accurate gene expression quantification in human biopsies.
The quantitative real time polymerase chain reaction (qPCR) has become a key molecular enabling technology with an immense range of research, clinical, forensic as well as diagnostic applications. Its relatively moderate instrumentation and reagent requirements have led to its adoption by numerous laboratories, including those located in the Arabian world, where qPCR, which targets DNA, and reverse transcription qPCR (RT-qPCR), which targets RNA, are widely used for region-specific biotechnology, agricultural and human genetic studies. However, it has become increasingly apparent that there are significant problems with both the quality of qPCR-based data as well as the transparency of reporting. This realisation led to the publication of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines in 2009 and their more widespread adoption in the last couple of years. An analysis of the performance of biomedical research in the Arabian world between 2001–2005 suggests that the Arabian world is producing fewer biomedical publications of lower quality than other Middle Eastern countries. Hence we have analysed specifically the quality of RT-qPCR-based peer-reviewed papers published since 2009 from Arabian researchers using a bespoke iOS/Android app developed by one of the authors. Our results show that compliance with 15 essential MIQE criteria was low (median of 40%, range 0–93%) and few details on RNA quality controls (22% compliance), assays design (12%), RT strategies (32%), amplification efficiencies (30%) and the normalisation process (3%). These data indicate that one of the reasons for the poor performance of Arabian world biomedical research may be the low standard of any supporting qPCR experiments and identify which aspects of qPCR experiments require significant improvements.
Fitting four-parameter sigmoidal models is one of the methods established in the analysis of quantitative real-time PCR (qPCR) data. We had observed that these models are not optimal in the fitting outcome due to the inherent constraint of symmetry around the point of inflection. Thus, we found it necessary to employ a mathematical algorithm that circumvents this problem and which utilizes an additional parameter for accommodating asymmetrical structures in sigmoidal qPCR data.
The four-parameter models were compared to their five-parameter counterparts by means of nested F-tests based on the residual variance, thus acquiring a statistical measure for higher performance. For nearly all qPCR data we examined, five-parameter models resulted in a significantly better fit. Furthermore, accuracy and precision for the estimation of efficiencies and calculation of quantitative ratios were assessed with four independent dilution datasets and compared to the most commonly used quantification methods. It could be shown that the five-parameter model exhibits an accuracy and precision more similar to the non-sigmoidal quantification methods.
The five-parameter sigmoidal models outperform the established four-parameter model with high statistical significance. The estimation of essential PCR parameters such as PCR efficiency, threshold cycles and initial template fluorescence is more robust and has smaller variance. The model is implemented in the qpcR package for the freely available statistical R environment. The package can be downloaded from the author's homepage.