Based upon defining a common reference point, current real-time quantitative PCR technologies compare relative differences in amplification profile position. As such, absolute quantification requires construction of target-specific standard curves that are highly resource intensive and prone to introducing quantitative errors. Sigmoidal modeling using nonlinear regression has previously demonstrated that absolute quantification can be accomplished without standard curves; however, quantitative errors caused by distortions within the plateau phase have impeded effective implementation of this alternative approach.
Recognition that amplification rate is linearly correlated to amplicon quantity led to the derivation of two sigmoid functions that allow target quantification via linear regression analysis. In addition to circumventing quantitative errors produced by plateau distortions, this approach allows the amplification efficiency within individual amplification reactions to be determined. Absolute quantification is accomplished by first converting individual fluorescence readings into target quantity expressed in fluorescence units, followed by conversion into the number of target molecules via optical calibration. Founded upon expressing reaction fluorescence in relation to amplicon DNA mass, a seminal element of this study was to implement optical calibration using lambda gDNA as a universal quantitative standard. Not only does this eliminate the need to prepare target-specific quantitative standards, it relegates establishment of quantitative scale to a single, highly defined entity. The quantitative competency of this approach was assessed by exploiting "limiting dilution assay" for absolute quantification, which provided an independent gold standard from which to verify quantitative accuracy. This yielded substantive corroborating evidence that absolute accuracies of ± 25% can be routinely achieved. Comparison with the LinReg and Miner automated qPCR data processing packages further demonstrated the superior performance of this kinetic-based methodology.
Called "linear regression of efficiency" or LRE, this novel kinetic approach confers the ability to conduct high-capacity absolute quantification with unprecedented quality control capabilities. The computational simplicity and recursive nature of LRE quantification also makes it amenable to software implementation, as demonstrated by a prototypic Java program that automates data analysis. This in turn introduces the prospect of conducting absolute quantification with little additional effort beyond that required for the preparation of the amplification reactions.
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
Linear regression of efficiency (LRE) introduced a new paradigm for real-time qPCR that enables large-scale absolute quantification by eliminating the need for standard curves. Developed through the application of sigmoidal mathematics to SYBR Green I-based assays, target quantity is derived directly from fluorescence readings within the central region of an amplification profile. However, a major challenge of implementing LRE quantification is the labor intensive nature of the analysis.
Utilizing the extensive resources that are available for developing Java-based software, the LRE Analyzer was written using the NetBeans IDE, and is built on top of the modular architecture and windowing system provided by the NetBeans Platform. This fully featured desktop application determines the number of target molecules within a sample with little or no intervention by the user, in addition to providing extensive database capabilities. MS Excel is used to import data, allowing LRE quantification to be conducted with any real-time PCR instrument that provides access to the raw fluorescence readings. An extensive help set also provides an in-depth introduction to LRE, in addition to guidelines on how to implement LRE quantification.
The LRE Analyzer provides the automated analysis and data storage capabilities required by large-scale qPCR projects wanting to exploit the many advantages of absolute quantification. Foremost is the universal perspective afforded by absolute quantification, which among other attributes, provides the ability to directly compare quantitative data produced by different assays and/or instruments. Furthermore, absolute quantification has important implications for gene expression profiling in that it provides the foundation for comparing transcript quantities produced by any gene with any other gene, within and between samples.
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.
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.
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 genetic analysis of faecal material represents a relatively non-invasive way to study animal diet and has been widely adopted in ecological research. Due to the heterogeneous nature of faecal material the primary obstacle, common to all genetic approaches, is a means to dissect the constituent DNA sequences. Traditionally, bacterial cloning of PCR amplified products was employed; less common has been the use of species-specific quantitative PCR (qPCR) assays. Currently, with the advent of High-Throughput Sequencing (HTS) technologies and indexed primers it has become possible to conduct genetic audits of faecal material to a much greater depth than previously possible. To date, no studies have systematically compared the estimates obtained by HTS with that of qPCR. What are the relative strengths and weaknesses of each technique and how quantitative are deep-sequencing approaches that employ universal primers? Using the locally threatened Little Penguin (Eudyptula minor) as a model organism, it is shown here that both qPCR and HTS techniques are highly correlated and produce strikingly similar quantitative estimates of fish DNA in faecal material, with no statistical difference. By designing four species-specific fish qPCR assays and comparing the data to the same four fish in the HTS data it was possible to directly compare the strengths and weaknesses of both techniques. To obtain reproducible quantitative data one of the key, and often overlooked, steps common to both approaches is ensuring that efficient DNA isolation methods are employed and that extracts are free of inhibitors. Taken together, the methodology chosen for long-term faecal monitoring programs is largely dependent on the complexity of the prey species present and the level of accuracy that is desired. Importantly, these methods should not be thought of as mutually exclusive, as the use of both HTS and qPCR in tandem will generate datasets with the highest fidelity.
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.
Circulating cell-free microRNAs (miRNAs) are potential biomarkers of cancer. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is widely used in miRNA expression studies. The aim of this study was to identify suitable reference genes for RT-qPCR analyses of miRNA expression levels in pleural effusion. The expression levels of candidate reference miRNAs were investigated in 10 benign pleural effusion (BPE) and 10 lung adenocarcinoma-associated malignant pleural effusion (LA-MPE) samples using miRNA microarrays. The expression levels of candidate reference miRNAs, together with those of U6 small nuclear RNA (snRNA), RNU6B, RNU44 and RNU48 small RNAs, in 46 BPE and 45 LA-MPE samples were validated by RT-qPCR, and were analyzed using the NormFinder and BestKeeper algorithms. The impact of different normalization approaches on the detection of differential expression levels of miR-198 in BPE and LA-MPE samples was also assessed. As determined by the miRNA microarray data, five candidate reference miRNAs were identified. Following RT-qPCR validation, U6 snRNA, miR-192, miR-20a, miR-221, miR-222 and miR-16 were evaluated using the NormFinder and BestKeeper software programs. U6 snRNA and miR-192 were identified as single reference genes and the combination of these genes was preferred for the relative quantification of miRNA expression levels in pleural effusion. Normalization of miR-98 expression levels to those of U6 snRNA, miR-192 or a combination of these genes enabled the detection of a significant difference between BPE and LA-MPE samples. Therefore, U6 snRNA and miR-192 are recommended as reference genes for the relative quantification of miRNA expression levels in pleural effusion.
microRNAs; pleural effusion; reference
Medium- to large-scale expression profiling using quantitative polymerase chain reaction (qPCR) assays are becoming increasingly important in genomics research. A major bottleneck in experiment preparation is the design of specific primer pairs, where researchers have to make several informed choices, often outside their area of expertise. Using currently available primer design tools, several interactive decisions have to be made, resulting in lengthy design processes with varying qualities of the assays.
Here we present QuantPrime, an intuitive and user-friendly, fully automated tool for primer pair design in small- to large-scale qPCR analyses. QuantPrime can be used online through the internet or on a local computer after download; it offers design and specificity checking with highly customizable parameters and is ready to use with many publicly available transcriptomes of important higher eukaryotic model organisms and plant crops (currently 295 species in total), while benefiting from exon-intron border and alternative splice variant information in available genome annotations. Experimental results with the model plant Arabidopsis thaliana, the crop Hordeum vulgare and the model green alga Chlamydomonas reinhardtii show success rates of designed primer pairs exceeding 96%.
QuantPrime constitutes a flexible, fully automated web application for reliable primer design for use in larger qPCR experiments, as proven by experimental data. The flexible framework is also open for simple use in other quantification applications, such as hydrolyzation probe design for qPCR and oligonucleotide probe design for quantitative in situ hybridization. Future suggestions made by users can be easily implemented, thus allowing QuantPrime to be developed into a broad-range platform for the design of RNA expression assays.
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.
DNA or RNA amplification methods for detection of Leishmania parasites have advantages regarding sensitivity and potential quantitative characteristics in comparison with conventional diagnostic methods but are often still not routinely applied. However, the use and application of molecular assays are increasing, but comparative studies on the performance of these different assays are lacking. The aim of this study was to compare three molecular assays for detection and quantification of Leishmania parasites in serial dilutions of parasites and in skin biopsies collected from cutaneous leishmaniasis (CL) patients in Manaus, Brazil. A serial dilution of promastigotes spiked in blood was tested in triplicate in three different runs by quantitative nucleic acid sequence-based amplification (QT-NASBA), quantitative real-time reverse transcriptase PCR (qRT-PCR), and quantitative real-time PCR (qPCR). In addition, the costs, durations, and numbers of handling steps were compared, and 84 skin biopsies from patients with suspected CL were tested. Both QT-NASBA and qRT-PCR had a detection limit of 100 parasites/ml of blood, while qPCR detected 1,000 parasites/ml. QT-NASBA had the lowest range of intra-assay variation (coefficients of variation [CV], 0.5% to 3.3%), while qPCR had the lowest range of interassay variation (CV, 0.4% to 5.3%). Furthermore, qRT-PCR had higher r2 values and amplification efficiencies than qPCR, and qPCR and qRT-PCR had faster procedures than QT-NASBA. All assays performed equally well with patient samples, with significant correlations between parasite counts. Overall, qRT-PCR is preferred over QT-NASBA and qPCR as the most optimal diagnostic assay for quantification of Leishmania parasites, since it was highly sensitive and reproducible and the procedure was relatively fast.
Several computer programs are available for detecting copy number variants (CNVs) using genome-wide SNP arrays. We evaluated the performance of four CNV detection software suites—Birdsuite, Partek, HelixTree, and PennCNV-Affy—in the identification of both rare and common CNVs. Each program's performance was assessed in two ways. The first was its recovery rate, i.e., its ability to call 893 CNVs previously identified in eight HapMap samples by paired-end sequencing of whole-genome fosmid clones, and 51,440 CNVs identified by array Comparative Genome Hybridization (aCGH) followed by validation procedures, in 90 HapMap CEU samples. The second evaluation was program performance calling rare and common CNVs in the Bipolar Genome Study (BiGS) data set (1001 bipolar cases and 1033 controls, all of European ancestry) as measured by the Affymetrix SNP 6.0 array. Accuracy in calling rare CNVs was assessed by positive predictive value, based on the proportion of rare CNVs validated by quantitative real-time PCR (qPCR), while accuracy in calling common CNVs was assessed by false positive/false negative rates based on qPCR validation results from a subset of common CNVs. Birdsuite recovered the highest percentages of known HapMap CNVs containing >20 markers in two reference CNV datasets. The recovery rate increased with decreased CNV frequency. In the tested rare CNV data, Birdsuite and Partek had higher positive predictive values than the other software suites. In a test of three common CNVs in the BiGS dataset, Birdsuite's call was 98.8% consistent with qPCR quantification in one CNV region, but the other two regions showed an unacceptable degree of accuracy. We found relatively poor consistency between the two “gold standards,” the sequence data of Kidd et al., and aCGH data of Conrad et al. Algorithms for calling CNVs especially common ones need substantial improvement, and a “gold standard” for detection of CNVs remains to be established.
Reference genes (RG) as sample internal controls for gene transcript level analyses by quantitative RT-PCR (RT-qPCR) must be stably expressed within the experimental range. A variety of in vitro cell culture settings with primary human hepatocytes, and Huh-7 and HepG2 cell lines, were used to determine candidate RG expression stability in RT-qPCR analyses. Employing GeNorm, BestKeeper and Normfinder algorithms, this study identifies PSMB6, MDH1 and some more RG as sufficiently unregulated, thus expressed at stable levels, in hepatocyte-like cells in vitro. Inclusion of multiple RG, quenching occasional regulations of single RG, greatly stabilises gene expression level calculations from RT-qPCR data. To further enhance validity and reproducibility of relative RT-qPCR quantifications, the ΔCT calculation can be extended (e-ΔCT) by replacing the CT of a single RG in ΔCT with an averaged CT-value from multiple RG. The use of two or three RG - here identified suited for human hepatocyte-like cells - for normalisation with the straightforward e-ΔCT calculation, should improve reproducibility and robustness of comparative RT-qPCR-based gene expression analyses.
The fourth component of human complement (C4), an essential factor of the innate immunity, is represented as two isoforms (C4A and C4B) in the genome. Although these genes differ only in 5 nucleotides, the encoded C4A and C4B proteins are functionally different. Based on phenotypic determination, unbalanced production of C4A and C4B is associated with several diseases, such as systemic lupus erythematosus, type 1 diabetes, several autoimmune diseases, moreover with higher morbidity and mortality of myocardial infarction and increased susceptibility for bacterial infections. Despite of this major clinical relevance, only low throughput, time and labor intensive methods have been used so far for the quantification of C4A and C4B genes.
A novel quantitative real-time PCR (qPCR) technique was developed for rapid and accurate quantification of the C4A and C4B genes applying a duplex, TaqMan based methodology. The reliable, single-step analysis provides the determination of the copy number of the C4A and C4B genes applying a wide range of DNA template concentration (0.3–300 ng genomic DNA). The developed qPCR was applied to determine C4A and C4B gene dosages in a healthy Hungarian population (N = 118). The obtained data were compared to the results of an earlier study of the same population. Moreover a set of 33 samples were analyzed by two independent methods. No significant difference was observed between the gene dosages determined by the employed techniques demonstrating the reliability of the novel qPCR methodology. A Microsoft Excel worksheet and a DOS executable are also provided for simple and automated evaluation of the measured data.
This report describes a novel real-time PCR method for single-step quantification of C4A and C4B genes. The developed technique could facilitate studies investigating disease association of different C4 isotypes.
Downstream applications in metabolomics, as well as mathematical modelling, require data in a quantitative format, which may also necessitate the automated and simultaneous quantification of numerous metabolites. Although numerous applications have been previously developed for metabolomics data handling, automated calibration and calculation of the concentrations in terms of μmol have not been carried out. Moreover, most of the metabolomics applications are designed for GC-MS, and would not be suitable for LC-MS, since in LC, the deviation in the retention time is not linear, which is not taken into account in these applications. Moreover, only a few are web-based applications, which could improve stand-alone software in terms of compatibility, sharing capabilities and hardware requirements, even though a strong bandwidth is required. Furthermore, none of these incorporate asynchronous communication to allow real-time interaction with pre-processed results.
Here, we present EasyLCMS (http://www.easylcms.es/), a new application for automated quantification which was validated using more than 1000 concentration comparisons in real samples with manual operation. The results showed that only 1% of the quantifications presented a relative error higher than 15%. Using clustering analysis, the metabolites with the highest relative error distributions were identified and studied to solve recurrent mistakes.
EasyLCMS is a new web application designed to quantify numerous metabolites, simultaneously integrating LC distortions and asynchronous web technology to present a visual interface with dynamic interaction which allows checking and correction of LC-MS raw data pre-processing results. Moreover, quantified data obtained with EasyLCMS are fully compatible with numerous downstream applications, as well as for mathematical modelling in the systems biology field.
Metabolomics; LC-MS; Data handling; Asynchronous; Quantification; Quantitation; Automated calibration; Web application; LC distortion
The somatic embryogenesis tissue culture process has been utilized to propagate high yielding oil palm. Due to the low callogenesis and embryogenesis rates, molecular studies were initiated to identify genes regulating the process, and their expression levels are usually quantified using reverse transcription quantitative real-time PCR (RT-qPCR). With the recent release of oil palm genome sequences, it is crucial to establish a proper strategy for gene analysis using RT-qPCR. Selection of the most suitable reference genes should be performed for accurate quantification of gene expression levels.
In this study, eight candidate reference genes selected from cDNA microarray study and literature review were evaluated comprehensively across 26 tissue culture samples using RT-qPCR. These samples were collected from two tissue culture lines and media treatments, which consisted of leaf explants cultures, callus and embryoids from consecutive developmental stages. Three statistical algorithms (geNorm, NormFinder and BestKeeper) confirmed that the expression stability of novel reference genes (pOP-EA01332, PD00380 and PD00569) outperformed classical housekeeping genes (GAPDH, NAD5, TUBULIN, UBIQUITIN and ACTIN). PD00380 and PD00569 were identified as the most stably expressed genes in total samples, MA2 and MA8 tissue culture lines. Their applicability to validate the expression profiles of a putative ethylene-responsive transcription factor 3-like gene demonstrated the importance of using the geometric mean of two genes for normalization.
Systematic selection of the most stably expressed reference genes for RT-qPCR was established in oil palm tissue culture samples. PD00380 and PD00569 were selected for accurate and reliable normalization of gene expression data from RT-qPCR. These data will be valuable to the research associated with the tissue culture process. Also, the method described here will facilitate the selection of appropriate reference genes in other oil palm tissues and in the expression profiling of genes relating to yield, biotic and abiotic stresses.
Quantitative polymerase chain reactions (qPCR) are used to monitor relative changes in very small amounts of DNA. One drawback to qPCR is reproducibility: measuring the same sample multiple times can yield data that is so noisy that important differences can be dismissed. Numerous analytical methods have been employed that can extract the relative template abundance between samples. However, each method is sensitive to baseline assignment and to the unique shape profiles of individual reactions, which gives rise to increased variance stemming from the analytical procedure itself.
We developed a simple mathematical model that accurately describes the entire PCR reaction profile using only two reaction variables that depict the maximum capacity of the reaction and feedback inhibition. This model allows quantification that is more accurate than existing methods and takes advantage of the brighter fluorescence signals from later cycles. Because the model describes the entire reaction, the influences of baseline adjustment errors, reaction efficiencies, template abundance, and signal loss per cycle could be formalized. We determined that the common cycle-threshold method of data analysis introduces unnecessary variance because of inappropriate baseline adjustments, a dynamic reaction efficiency, and also a reliance on data with a low signal-to-noise ratio.
Using our model, fits to raw data can be used to determine template abundance with high precision, even when the data contains baseline and signal loss defects. This improvement reduces the time and cost associated with qPCR and should be applicable in a variety of academic, clinical, and biotechnological settings.
Harmful algal blooms (HABs) are a global problem that affects both human and ecosystem health. One of the most serious and widespread HAB poisoning syndromes is paralytic shellfish poisoning, commonly caused by Alexandrium spp. dinoflagellates. Like many toxic dinoflagellates, Alexandrium produces resistant resting cysts as part of its life cycle. These cysts play a key role in bloom initiation and decline, as well as dispersal and colonization of new areas. Information on cyst numbers and identity is essential for understanding and predicting blooms, yet comprehensive cyst surveys are extremely time- and labor-intensive. Here we describe the development and validation of a quantitative real-time PCR (qPCR) technique for the enumeration of cysts of A. tamarense of the toxic North American/Group I ribotype. The method uses a cloned fragment of the large subunit ribosomal RNA gene as a standard for cyst quantification, with an experimentally determined conversion factor of 28,402±6152 LSU ribosomal gene copies per cyst. Tests of DNA extraction and PCR efficiency show that mechanical breakage is required for adequate cyst lysis, and that it was necessary to dilute our DNA extracts 50-fold in order to abolish PCR inhibition from compounds co-extracted from the sediment. The resulting assay shows a linear response over 6 orders of magnitude and can reliably quantify ≥10cysts/cc sediment.
For method validation, 129 natural sediment samples were split and analyzed in parallel, using both the qPCR and primulin-staining techniques. Overall, there is a significant correlation (p<0.001) between the cyst abundances determined by the two methods, although the qPCR counts tend to be lower than the primulin values. This underestimation is less pronounced in those samples collected from the top 1 cm of sediment, and more pronounced in those derived from the next 1–3 cm of the core. These differences may be due to the condition of the cysts in the different layers, as the top 1cm contains more recent cysts while those in the next 1–3cm may have been in the sediments for many years. Comparison of the cyst densities obtained by both methods shows that a majority (56.6%) of the values are within a two-fold range of each other and almost all of the samples (96.9%) are within an order of magnitude. Thus, the qPCR method described here represents a promising alternative to primulin-staining for the identification and enumeration of cysts. The qPCR method has a higher throughput, enabling the extraction and assay of 24 samples in the time required to process and count 8–10 samples by primulin staining. Both methods require prior expertise, either in taxonomy or molecular biology. Fewer person-hours per sample are required for qPCR, but primulin staining has lower reagent costs. The qPCR method might be more desirable for large-scale cyst mapping, where large numbers of samples are generated and a higher sample analysis rate is necessary. While the qPCR and primulin-staining methods generate similar data, the choice of counting method may be most influenced by the practical issue of the different relative costs of labor and materials between the two methods.
quantitative PCR; ribosomal; toxic dinoflagellate; saxitoxins; algal blooms; red tides
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.
A cytochrome b (cytb) gene quantitative PCR (qPCR) assay was developed to diagnose malaria in travelers. First, manual and automated DNA extractions were compared and automated DNA extraction of 400 μl of blood was found to be more efficient. Sensitivity was estimated using the WHO international standard for Plasmodium falciparum DNA and compared to that of a previously published qPCR targeting the 18S rRNA coding gene (18S qPCR). The limit of detection of the cytb qPCR assay was 20 DNA copies (i.e., 1 parasite equivalent) per 400 μl of extracted whole blood and was comparable for the two qPCR assays. Both qPCR assays were used on blood samples from 265 consecutive patients seen for suspicion of malaria. There were no microscopy-positive and qPCR-negative samples. Positive cytb qPCR results were observed for 51 samples, and all but 1 were also 18S qPCR positive. Eight (16%) of these 51 samples were negative by microscopic examination. The 8 cytb qPCR-positive and microscopy-negative samples were from African patients, 3 of whom had received antimalarial drugs. Three non-P. falciparum infections were correctly identified using an additional qPCR assay. The absence of PCR inhibitors was tested for by the use of an internal control of mouse DNA to allow reliable quantification of circulating DNA. The high analytical sensitivity of both qPCR assays combined with automated DNA extraction supports its use as a laboratory tool for diagnosis and parasitemia determination in emergencies. Whether to treat qPCR-positive and microscopy-negative patients remains to be determined.
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.
Reverse transcription quantitative real-time PCR (RT-qPCR) is widely used in microRNA (miRNA) expression studies on cancer. To compensate for the analytical variability produced by the multiple steps of the method, relative quantification of the measured miRNAs is required, which is based on normalization to endogenous reference genes. No study has been performed so far on reference miRNAs for normalization of miRNA expression in urothelial carcinoma. The aim of this study was to identify suitable reference miRNAs for miRNA expression studies by RT-qPCR in urothelial carcinoma.
Candidate reference miRNAs were selected from 24 urothelial carcinoma and normal bladder tissue samples by miRNA microarrays. The usefulness of these candidate reference miRNAs together with the commonly for normalization purposes used small nuclear RNAs RNU6B, RNU48, and Z30 were thereafter validated by RT-qPCR in 58 tissue samples and analyzed by the algorithms geNorm, NormFinder, and BestKeeper.
Based on the miRNA microarray data, a total of 16 miRNAs were identified as putative reference genes. After validation by RT-qPCR, miR-101, miR-125a-5p, miR-148b, miR-151-5p, miR-181a, miR-181b, miR-29c, miR-324-3p, miR-424, miR-874, RNU6B, RNU48, and Z30 were used for geNorm, NormFinder, and BestKeeper analyses that gave different combinations of recommended reference genes for normalization.
The present study provided the first systematic analysis for identifying suitable reference miRNAs for miRNA expression studies of urothelial carcinoma by RT-qPCR. Different combinations of reference genes resulted in reliable expression data for both strongly and less strongly altered miRNAs. Notably, RNU6B, which is the most frequently used reference gene for miRNA studies, gave inaccurate normalization. The combination of four (miR-101, miR-125a-5p, miR-148b, and miR-151-5p) or three (miR-148b, miR-181b, and miR-874,) reference miRNAs is recommended for normalization.
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