Although the concept of PCR is relatively simple, there are specific issues in qPCR that developers and users of this technology must bear in mind. These include the use of correct terminology and definitions, understanding of the principle of PCR, difficulties with interpretation and presentation of data, the limitations of qPCR in different areas of microbial diagnostics and parameters important for the description of qPCR performance.
It is not our intention in this review to describe every single aspect of qPCR design, optimization, and validation; however, it is our hope that this basic guide will help to orient beginners and users of qPCR in the use of this powerful technique. 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. Therefore, samples of insufficient light transparency do not permit the application of these dyes. Moreover, another topic we want to just to mention here is the generation and use of standards required for the calibration curves. In general, two are the most diffused approaches for the generation of calibration curves.
One employs dilutions of target genomic nucleic acid and the other plasmid standards. Both strategies can lead to a final quantification of the target, but plasmids containing specific target sequences offer the advantages of easy production, stability, and cheapness. On the other hand, in principle, PCR efficiency obtained by plasmid standards sometimes could differ compared to the efficiency obtained using genomic standard, which instead, for organisms fastidious to growth, could be isolated only starting from a given matrix, and thus susceptible to degradation and losses Chaouachi et al.
This parameter in qPCR refers to the specificity of primers for target of interest. Analytical specificity consists of two concepts: inclusivity describes the ability of the method to detect a wide range of targets with defined relatedness e. Another definition describes inclusivity as the strains or isolates of the target analyte s that the method can detect Anonymous, ISO and other standards recommend that inclusivity should be determined on 20—50 well-defined certified strains of the target organism Anonymous, , , , a ; Broeders et al.
On the other hand, exclusivity describes the ability of the method to distinguish the target from similar but genetically distinct non-targets. In other words, exclusivity can also be defined as the lack of interference from a relevant range of non-target strains, which are potentially cross-reactive Anonymous, , , , a.
The desirable number of positive samples in exclusivity testing is zero Johnson et al. Many official documents have discussed theories and procedures for the correct definition of the LOD for different methods. A general consensus was reached around the definition of the LOD as the lowest amount of analyte, which can be detected with more than a stated percentage of confidence, but, not necessarily quantified as an exact value Anonymous, , , In this regard, the confidence level obtained or requested for the definition of LOD can reflect the number of replicates both technical and experimental needed by the assay in order to reach the requested level of confidence e.
It is clear that the more replicates are tested, the narrower will be the interval of confidence. Another definition describes the LOD as the lowest concentration level that can be determined as statistically different from a blank at a specified level of confidence.
This value should be determined from the analysis of sample blanks and samples at levels near the expected LOD Anonymous, a. However, it should be noted that LOD definitions described above were reported for chemical methods, and are not perfectly suited for PCR methods Burns and Valdivia, This is because, for limited concentrations of analyte nucleic acids , the output of the reaction can be a success amplification , or a failure no amplification at all , without any blank, or critical level at which it is possible to set a cut-off value over which the sample can be considered as positive one.
Moreover, it should be remembered here that, by definition, a blank sample should never be positive in PCR. Since the definitions reported above are not practicable for PCRs, other approaches have been proposed.
In practice, multiple aliquots of a specific matrix are spiked with serial dilutions of the target organism and undergo the whole process of nucleic acid isolation and qPCR. For example, 10 replicates of milk samples were spiked with serial dilutions of Campylobacter jejuni in amounts of 10 5 —10 0 cells per 1 ml of milk. The experimentally determined LOD of the method for the detection of C.
In order to better define the most precise value, more dilutions can be tested before reaching a final LOD value as close as possible to the real one. The number of replicates tested should be at least six Slana et al. Figure 2. According to the Poisson distribution, it was concluded that the LOD for PCR cannot be lower than at least three copies of the nucleic acid targets Bustin et al.
Therefore, as stated above, the LOD must be related to the whole method that includes nucleic acid preparation and qPCR. Only under these conditions can it represent a valid parameter that describes the features of the respective qPCR method Anonymous, a. However, sometimes it is not possible to obtain large numbers of replicates, for both financial and technical reasons. Briefly, both mathematical functions are regressions used to analyse binomial response variables positive or negative and are able to transform the sigmoid dose-response curve, typical of a binomial variable, to a straight line that can then be analyzed by regression either through least squares or maximum likelihood methods.
The final end-point of the analysis is a concentration coupled with relative intervals of confidence , associated to a probability e. Moreover, Probit regression is exploitable only for normally distributed data, while Logit function can also be used for data not normally distributed; however, in this context, both functions have the same meaning. Finally, it must be noted that LOD is not a limiting value and therefore, that C q values below the LOD cannot automatically be considered as negative.
This feature is connected with the Poisson distribution when working with small numbers. The LOQ was defined as the smallest amount of analyte, which can be measured and quantified with defined precision and accuracy under the experimental conditions by the method under validation Armbruster and Pry, ; Anonymous, , An alternative definition is that the LOQ is the lowest amount or concentration of analyte that can be quantitatively determined with an acceptable level of uncertainty Anonymous, a.
In practice, the LOQ is determined as is the LOD, on replicates of spiked samples, but the assessment of results is quantitative. Numerically, the LOQ is defined as the lowest concentration of analyte, which gives a predefined variability, generally reported as the coefficient of variation CV.
Hoverer, this value was proposed based on the experience accrued in GMO detection laboratories Broeders et al.
A series of spiked samples with different concentrations of target DNA were analyzed and the J -values were calculated for each PCR cycle. Finally, an issue that should be addressed for the determination of the LOQ as well as LOD is the efficiency of recovery of target molecules during the nucleic acid extraction phases. Generally, nucleic acids are extracted from different complex matrices, like food, feces, or other samples using different procedures.
Due to the fact that these data are provided during the determination of the LOD and LOQ, it is not necessary to perform additional experiments. It is recommended that the median of mean DNA isolation values from different dilutions is used as the practical overall DNA isolation efficiency Kralik et al. Similarly to the LOD, quantity can also be assessed in samples with numbers of organisms or concentrations of DNA lower than the LOQ, but the confidence of such quantification will be lower than that declared by the definition of LOQ.
Moreover, there are possibilities of how to refer to such quantities in terms of semi-quantitative interpretation, e. This parameter was mentioned above in the section dedicated to the mathematical description of qPCR Equation 4. This is difficult to reach repeatedly over time.
This parameter can be estimated from the slope of the calibration curve. In connection to this issue, the lowest and highest concentrations of the standard included in the calibration curve, which can be truly quantified, should be determined according to the linear dynamic range of over at least 6 Log The dynamic range is defined by the MIQE guidelines as the range over which a reaction is linear Bustin et al. The determination of PCR efficiency by the standard curve actually provides two pieces of information.
If an inhibitor would be present in the most concentrated sample, there would be a visible increase in C q values in these and therefore a diminishment of the 3. However, this is not a frequent phenomenon, as standards are usually well-characterized and therefore, any inhibition is rather unlikely. If there would be a similar situation in lower concentration samples, this suggests a possible pipetting error rather than the presence of inhibitors.
An important function to assess this is the coefficient of determination R 2 value , that should be higher than 0. In reality, it is much more important to determine the PCR inhibition and subsequent diminishment of the PCR efficiency in analyzed samples. There are approaches based on the analysis of the fluorescent curve of each sample by specific software LinRegPCR , which can calculate the PCR efficiency of each sample without the series of dilutions.
However, this approach is not flawless as it does not take into account all possible variables that can affect the analysis Ruijter et al. The following parameters of qPCR deal with ways of how to compare novel qPCR methods with reference methods or materials.
Accuracy is defined as a measure of the degree of conformity of a value generated by a specific procedure to the assumed or accepted true value Anonymous, a. In other words, accuracy describes the level of agreement between reference and measured values. Animal Care. Personal Care. Drug Discovery. Predictive Toxicology. Stem Cells. Alphabetically [A-Z]. By Product Type.
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