The first applications of PCR were rather unpractical due to the usage of thermolabile Klenow fragment for amplification, which needed to be added to the reaction after each denaturation step. The crucial innovation which enabled routine usage of PCR was utilization of thermostable polymerase from Thermus aquaticus Saiki et al. This improvement, together with the availability of PCR cyclers and chemical components, led to the worldwide recognition of PCR as the tool of choice for the specific enzymatic amplification of DNA in vitro.
It must be noted that the general concept of PCR, which includes primers, DNA polymerase, nucleotides, specific ions, and DNA template, and consisting of cycles that comprise steps of DNA denaturation, primer annealing, and extension, have not been changed since The invention of PCR has greatly boosted research in various areas of biology and this technology has significantly contributed to the current level of human knowledge in many spheres of research.
The most substantial milestone in PCR utilization was the introduction of the concept of monitoring DNA amplification in real time through monitoring of fluorescence Holland et al. In initial cycles the fluorescence is too low to be distinguishable from the background. However, the point at which the fluorescence intensity increases above the detectable level corresponds proportionally to the initial number of template DNA molecules in the sample.
This point is called the quantification cycle C q ; different manufactures of qPCR instruments use their own terminology, but since , the term C q is used exclusively and allows determination of the absolute quantity of target DNA in the sample according to a calibration curve constructed of serially diluted standard samples usually decimal dilutions with known concentrations or copy numbers Yang and Rothman, ; Kubista et al.
Moreover, qPCR can also provide semi-quantitative results without standards but with controls used as a reference material. It this case, the observed results can be expressed as higher or lower multiples with reference to control.
This application of qPCR has been extensively used for gene expressions studies Bustin et al. There are two strategies for the real time visualization of amplified DNA fragments—non-specific fluorescent DNA dyes and fluorescently labeled oligonucleotide probes. These two approaches were developed in parallel Holland et al. This is due to its higher specificity mediated by the additional oligonucleotide—the probe—and the lower susceptibility to visualize non-specific PCR products, e.
To fully understand the possibilities of qPCR in detecting and quantifying target DNA in samples it is essential to describe the mathematical principle of this method. More generally, the amplification reaction follows this equation:. If a calibration curve is run, usually fold serial dilutions are used.
The difference in C q values between two fold serial dilutions could be expressed as. When E should be determined the 1 is starting point and the equation is. The reliability of the calibration curve in enabling quantification is then determined by the spacing of the serial dilutions.
If the Log 10 of the concentration or copy number of each standard is plotted against its C q value Figure 1 , the E can be derived from the regression equation describing the linear function:. The intercept shows the C q value when one copy would be theoretically detected Kubista et al. The concentration or amount of target nucleic acid in unknown samples is then calculated according to the C q value through Equation 5.
Figure 1. Model calibration curve with the regression equation characterized by the slope and intercept and regression coefficient. From the definitions above it is evident that C q values are instrumental readings, and must be recalculated to values with specific units, e.
However, referral to C q values in scientific papers is widespread and interpretations based on C q values can lead to misleading conclusions. Concentrations in qPCR are expressed in the logarithmic scale Figure 1 and C q differences between fold serial dilutions are theoretically always 3. Therefore, although the numerical difference between C q 20 and 35 is rather negligible, the difference in real numbers copies, ng is almost five orders of magnitude Log This feature must be reflected in the subsequent calculations.
For example, the coefficient of variation CV, ratio between standard deviation and mean calculated from the C q values and real numbers results in profoundly different results. The same applies for any statistical tests where C q values are used, even for cases where the logarithm of C q values is used for the normalization of data before the statistical evaluation. The correct procedure should include initial recalculation to real numbers followed by logarithmic transformation.
With the increasing amount of sequencing data available, it is literally possible to design qPCR assays for every microorganism groups and subgroups of microorganisms, etc. The main advantages of qPCR are that it provides fast and high-throughput detection and quantification of target DNA sequences in different matrices.
The lower time of amplification is facilitated by the simultaneous amplification and visualization of newly formed DNA amplicons. Moreover, qPCR is safer in terms of avoiding cross contaminations because no further manipulation with samples is required after the amplification. Other advantages of qPCR include a wide dynamic range for quantification 7—8 Log 10 and the multiplexing of amplification of several targets into a single reaction Klein, The multiplexing option is essential for detection and quantification in diagnostic qPCR assays that rely on the inclusion of internal amplification controls Yang and Rothman, ; Kubista et al.
Therefore, although qPCR-based typing tests are faster, their results should be correlated with phenotypic and biochemical tests Levin, ; Osei Sekyere et al. As for the microbial diagnostics, there are different considerations in detecting and quantifying viral, bacterial, and parasitic agents.
This is because detection of important clinical and veterinary viruses using culture methods is time-consuming or impossible, while ELISA tests are not universally available and suffer from comparatively low sensitivity and specificity.
Moreover, determination of the viral load by RT -qPCR is used as an indicator of the response to antiviral therapies Watzinger et al. The situation is similar in the case of intestinal protozoan diagnostics Rijsman et al. The gold standard technique for the detection of protozoan agents, the microscopic examination of feces, is laborious, time-consuming, and requires specifically trained personnel. Therefore, qPCR is now emerging as a powerful tool in the routine detection, quantification, and typing of intestinal parasitic protozoa.
In contrast to viral and protozoan detection and quantification, many bacteria of clinical, veterinary, and food safety significance, can be cultured. For this reason, culture is considered as the gold standard in bacterial detection and quantification.
However, in cases when critical and timely intervention for infectious disease is required, the traditional, slow, and multistep culture techniques cannot provide results in a reasonable time. This limitation is compounded by the necessity of culturing fastidious pathogens and additional testing species determination, identification of virulence factors, and antimicrobial resistance.
In food safety, all international standards for food quality rely on the determination of pathogenic microorganisms using traditional culture methods. However, there are limitations with respect to the sensitivity of assays based on qPCR. As culture methods rely on the multiplication of bacteria during the pre-culture steps pre-enrichment , samples for DNA isolation usually initially contain very low numbers of target bacteria Rodriguez-Lazaro et al.
This limitation leads to the most important disadvantage of qPCR, which is its inherent incapability of distinguishing between live and dead cells. The usage of qPCR itself is therefore limited to the typing of bacterial strains, identification of antimicrobial resistance, detection, and possibly quantification in non-processed and raw food.
It is important to note that processed food can still contain amplifiable DNA even if all the potentially pathogenic bacteria in food are devitalized and the foodstuff is microbiologically safe for consumption Rodriguez-Lazaro et al.
To overcome this problem, a pre-enrichment of sample in culture media could be placed prior to the qPCR. This step may include non-selective enrichment in buffered peptone water or specific selective media for the respective bacterium. The extraction of the DNA from the culture media is easier than that from the food samples, which are much more heterogeneous in terms of composition Margot et al.
Although qPCR itself cannot distinguish among viable and dead cells attempts have been made to adapt qPCR for viability detection. It was shown that RNA has low stability and should be degraded in dead cells within minutes.
However, the correlation of cell viability with the persistence of nucleic acid species must be well characterized for a particular situation before an appropriate amplification-based analytical method can be adopted as a surrogate for more traditional culture techniques Birch et al. Moreover, difficulties connected with RNA isolation from samples like food, feces or environmental samples can provide false-negative results especially when low numbers of target cells are expected.
In these methods, the criterion for viability determination is membrane integrity. Metabolically active cells regardless of their cultivability with full membrane integrity keep the dyes outside the cells and are therefore considered as viable. However, if plasma membrane integrity is compromised, the dyes penetrate the cells, or react with the DNA outside of dead cells. The labeled DNA is then not available for the amplification by qPCR and the difference between treated and untreated cells provides information about the proportion of viable cells in the sample.
The limitation of this method is the necessity to have the cells in a light-transparent matrix, e. A PCR primer bank for quantitative gene expression analysis. Reynisson, E. Evaluation of probe chemistries and platforms to improve the detection limit of real-time PCR. Methods 66 , — Huang, Z. Hengen, P. Is RNase-free really RNase for free? Imbeaud, S. Towards standardization of RNA quality assessment using user-independent classifiers of microcapillary electrophoresis traces.
Sambrook, J. Huggett, J. Genes Immun. Goossens, K. Selection of reference genes for quantitative real-time PCR in bovine preimplantation embryos. BMC Dev. Tricarico, C. Quantitative real-time reverse transcription polymerase chain reaction: normalization to rRNA or single housekeeping genes is inappropriate for human tissue biopsies.
Dheda, K. The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Perez-Novo, C. Impact of RNA quality on reference gene expression stability. Biotechniques 39 52, 54, 56 Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes.
Genome Biol. Pfaffl, M. Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper--Excel-based tool using pair-wise correlations. Kanno, J. BMC Genomics 7 , 64 Bauer, P. Biotechniques 22 , — Nemeth, E. IL-6 mediates hypoferremia of inflammation by inducing the synthesis of the iron regulatory hormone hepcidin. Download references. We acknowledge numerous fruitful discussions with J.
Huggett, M. Kubista, R. Mueller, S. Mueller, M. Pfaffl, G. Shipley and J. We are grateful to N. Gerke Eppendorf AG and J. You can also search for this author in PubMed Google Scholar. Correspondence to Stephen A Bustin. Reprints and Permissions. Nat Protoc 1, — After extension of the Scorpion primer, the specific probe sequence is able to bind to its complement within the extended amplicon thus opening up the hairpin loop.
This prevents the fluorescence from being quenched and a signal is observed. Thus, as a PCR product accumulates, fluorescence increases. The disadvantage is that SYBR Green will bind to any double-stranded DNA in the reaction, including primer-dimers and other non-specific reaction products, which results in an overestimation of the target concentration. For single PCR product reactions with well designed primers, SYBR Green can work extremely well, with spurious non-specific background only showing up in very late cycles.
Since the dye binds to double-stranded DNA, there is no need to design a probe for any particular target being analyzed. Since the dye cannot distinguish between specific and non-specific product accumulated during PCR, follow up assays are needed to validate results. TaqMan probes, Molecular Beacons and Scorpions allow multiple DNA species to be measured in the same sample multiplex PCR , since fluorescent dyes with different emission spectra may be attached to the different probes.
Multiplex PCR allows internal controls to be co-amplified and permits allele discrimination in single-tube, homogeneous assays. These hybridization probes afford a level of discrimination impossible to obtain with SYBR Green, since they will only hybridize to true targets in a PCR and not to primer-dimers or other spurious products. Two strategies are commonly employed to quantify the results obtained by real-time RT-PCR; the standard curve method and the comparative threshold method.
These are discussed briefly below. In this method, a standard curve is first constructed from an RNA of known concentration. This curve is then used as a reference standard for extrapolating quantitative information for mRNA targets of unknown concentrations. Though RNA standards can be used, their stability can be a source of variability in the final analyses. In addition, using RNA standards would involve the construction of cDNA plasmids that have to be in vitro transcribed into the RNA standards and accurately quantitated, a time-consuming process.
However, the use of absolutely quantitated RNA standards will help generate absolute copy number data. Spectrophotometric measurements at nm can be used to assess the concentration of these DNAs, which can then be converted to a copy number value based on the molecular weight of the sample used.
However, since cDNA plasmids will not control for variations in the efficiency of the reverse transcription step, this method will only yield information on relative changes in mRNA expression.
This, and variation introduced due to variable RNA inputs, can be corrected by normalization to a housekeeping gene. Another quantitation approach is termed the comparative Ct method. This involves comparing the Ct values of the samples of interest with a control or calibrator such as a non-treated sample or RNA from normal tissue.
The Ct values of both the calibrator and the samples of interest are normalized to an appropriate endogenous housekeeping gene. For the [delta][delta]Ct calculation to be valid, the amplification efficiencies of the target and the endogenous reference must be approximately equal. This can be established by looking at how [delta]Ct varies with template dilution. If the plot of cDNA dilution versus delta Ct is close to zero, it implies that the efficiencies of the target and housekeeping genes are very similar.
If a housekeeping gene cannot be found whose amplification efficiency is similar to the target, then the standard curve method is preferred.
Real-time PCR requires an instrumentation platform that consists of a thermal cycler , a computer, optics for fluorescence excitation and emission collection, and data acquisition and analysis software.
These machines, available from several manufacturers, differ in sample capacity some are well standard format, others process fewer samples or require specialized glass capillary tubes , method of excitation some use lasers, others broad spectrum light sources with tunable filters , and overall sensitivity.
There are also platform-specific differences in how the software processes data. Confidence interval and statistical significance considerations are not explicit in many of the current data analysis approaches. Based on the standard curve method and other useful data analysis methods, we present and compare four statistical approaches and models for the analysis of real-time PCR data.
SAS programs were developed for all four models and data output for analysis of a sample set are presented. In addition, a data quality control model was developed and implemented using SAS. The analysis using the various models and programs yielded similar results.
Data quality control and analysis procedures presented here provide statistical elements for the estimation of the relative expression of genes using real-time PCR. Real-time PCR is one of the most sensitive and reliably quantitative methods for gene expression analysis.
It has been broadly applied to microarray verification, pathogen quantification, cancer quantification, transgenic copy number determination and drug therapy studies [ 1 — 4 ]. A PCR has three phases, exponential phase, linear phase and plateau phase as shown in Figure 1. The exponential phase is the earliest segment in the PCR, in which product increases exponentially since the reagents are not limited. The linear phase is characterized by a linear increase in product as PCR reagents become limited.
The PCR will eventually reach the plateau phase during later cycles and the amount of product will not change because some reagents become depleted. Real-time PCR exploits the fact that the quantity of PCR products in exponential phase is in proportion to the quantity of initial template under ideal conditions [ 5 , 6 ]. During the exponential phase PCR product will ideally double during each cycle if efficiency is perfect, i.
Real-time PCR. Three phases can be observed for PCRs: exponential phase, linear phase and plateau phase. The basis of real-time PCR is a direct positive association between a dye with the number of amplicons. As shown in Figure 1B and 1C , the plot of logarithm 2-based transformed fluorescence signal versus cycle number will yield a linear range at which logarithm of fluorescence signal correlates with the original template amount.
A baseline and a threshold can then be set for further analysis. The cycle number at the threshold level of log-based fluorescence is defined as Ct number, which is the observed value in most real-time PCR experiments, and therefore the primary statistical metric of interest.
Real-time PCR data are quantified absolutely and relatively. Absolute quantification employs an internal or external calibration curve to derive the input template copy number. Absolute quantification is important in case that the exact transcript copy number needs to be determined, however, relative quantification is sufficient for most physiological and pathological studies. Relative quantification relies on the comparison between expression of a target gene versus a reference gene and the expression of same gene in target sample versus reference samples [ 7 ].
Since relative quantification is the goal for most for real-time PCR experiments, several data analysis procedures have been developed. The experimental systems for both models are similar. The experiment will involve a control sample and a treatment sample. For each sample, a target gene and a reference gene for internal control are included for PCR amplification from serially diluted aliquots.
Typically several replicates are used for each diluted concentration to derive amplification efficiency. PCR amplification efficiency can be either defined as percentage from 0 to 1 or as time of PCR product increase per cycle from 1 to 2. Unless specified as percentage amplification efficiency PE , we refer the amplification efficiency E to PCR product increase 1 to 2 in this article.
Ct number is first plotted against cDNA input or logarithm cDNA input , and the slope of the plot is calculated to determine the amplification efficiency E. The software presented in this article is based on the efficiency-calibrated model and employed randomization tests to obtain the significance level. However, the article did not provide a detailed model for the effects of different experimental factors involved. Another statistical study of real-time PCR data used a simple linear regression model to estimate the ratio through Ct calculation [ 10 ].
However, the logarithm-based fluorescence was used as the dependent variable in the model, which we believe does not adequately reflect the nature of real-time PCR data. It follows that Ct should be the dependent variable for statistical analysis, because it is the outcome value directly influenced by treatment, concentration and sample effects. Both studies used the efficiency-calibrated models. Despite the publication of these two methods, many research articles published with real-time PCR data actually do not present P values and confidence intervals [ 11 — 13 ].
We believe that these statistics are desirable to facilitate robust interpretation of the data. Without a proper statistical modeling and analysis, the interpretation of real-time PCR data may lead the researcher to false positive conclusions, which is especially potentially troublesome in clinical applications. The statistical methodologies can be adapted to other mathematical models with modifications.
SAS programs implementing the methodologies and data control are presented with real-time PCR practitioners in mind for turnkey data analysis. Standard deviations, confidence levels and P values are presented directly from the SAS output. We also included analysis of the sample data set and SAS programs for the analysis in the online supplementary materials. From the two mathematical models for relative quantification of real-time PCR data, we observe disparities between data quality standards.
For efficiency-calibrated method, the author who described this procedure [ 7 ] assumed that the amplification efficiency for each gene target and reference is the same among different experimental samples treatment and control. In other words, the amount of product should double during each cycle [ 9 ]. However, this assumption neglects the effect of different cDNA samples. Data quality could be examined through a correlation model.
Even though examining the correlation between Ct number and concentration can provide an effective quality control, a better approach might be to examine the correlation between Ct and the logarithm base 2 transformed concentration of template, which should yield a significant simple linear relationship for each gene and sample combination.
For example, for a target gene in the control sample, the Ct number should correlate with the logarithm transformed concentration following the simple linear regression model in equation 3. The acceptable real-time PCR data should have two features from the regression analysis.
First, the slope should not be significantly different from Second, the slopes for all four combinations of genes and samples as shown in Table 1 should not be significantly different from one another. The input data is grouped as shown in Table 1 and additional file 2. Each combination of gene and sample was classified in one group named from 1 to 4.
The SAS procedure Proc Mixed was used for performing simple linear regression for each group based on the model described above.
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