strategies in real-time RT-PCR
Quantification strategies in real-time RT-PCR
The quantification strategy is the principal marker in gene quantification. Generally two strategies can be performed in real-time RT-PCR. The levels of expressed genes may be measured by absolute or relative quantitative real-time RT-PCR. Absolute quantification relates the PCR signal to input copy number using a calibration curve, while relative quantification measures the relative change in mRNA expression levels. The reliability of an absolute real-time RT-PCR assay depends on the condition of ‘identical’ amplification efficiencies for both the native target and the calibration curve in RT reaction and in following kinetic PCR. Relative quantification is easier to perform than absolute quantification because a calibration curve is not necessary. It is based on the expression levels of a target gene versus a housekeeping gene (reference or control gene) and in theory is adequate for most purposes to investigate physiological changes in gene expression levels. The units used to express relative quantities are irrelevant, and the relative quantities can be compared across multiple real-time RT-PCR experiments.
link to speciefied absolute quantification sub-domain
Calibration curves are highly reproducible and allow the generation of highly specific, sensitive and reproducible data. However, the external calibration curve model has to be thoroughly validated as the accuracy of absolute quantification in real-time RT-PCR depends entirely on the accuracy of the standards. Standard design, production, determination of the exact standard concentration and stability over long storage time is not straightforward and can be problematic. The dynamic range of the performed calibration curve can be up to nine orders of magnitude from <101 to >1010 start molecules, depending on the applied standard material. The calibration curves used in absolute quantification can be based on known concentrations of DNA standard molecules, e.g. recombinant plasmid DNA (recDNA), genomic DNA, RT-PCR product, commercially synthesized big oligonucleotide. Sability and reproducibility in kinetic RT-PCR depends on the type of standard used and depends strongly on ‘good laboratory practice’. Cloned recDNA and genomic DNA are very stable and generate highly reproducible standard curves even after a long storage time, in comparison to freshly synthesized RNA. Furthermore, the longer templates derived from recDNA and genomic DNA mimic the average native mRNA length of about 2 kb better than shorter templates derived from RT-PCR product or oligonucleotides. They are more resistant against unspecific cleavage and proofreading activity of polymerase during reaction setup and in kinetic PCR (own unpublished results). One advantage of the shorter templates and commercially available templates is an accurate knowledge of its concentration and length. A second advantage is that their use avoids the very time consuming process of having to produce standard material: standard synthesis, purification, cloning, transformation, plasmid preparation, linearization, verification and exact determination of standard concentration.
A problem with DNA based calibration curves is that they are subject to the PCR step only, unlike the unknown mRNA samples that must first be reverse transcribed. This increases the potential for variability of the RT-PCR results and the amplification results may not be strictly comparable with the results from the unknown samples. However, the problem of the sensitivity of the RT-PCR to small variations in the reaction setup is always lurking in the background as a potential drawback to this simple procedure. Therefore, quantification with external standards requires careful optimization of its precision (replicates in the same kinetic PCR run – intra-assay variation) and reproducibility (replicates in separate kinetic PCR runs – inter-assay variation) in order to understand the limitations within the given application.
A recombinant RNA (recRNA) standard that was synthesized in vitro from a cloned RT-PCR fragment in plasmid DNA is one option. However, identical RT efficiency, as well as real-time PCR amplification efficiencies for calibration curve and target cDNA must be tested and confirmed if the recDNA is to provide a valid standard for mRNA quantification. This is because only the specific recRNA molecules are present during RT and the kinetics of cDNA synthesis are not like those in native RNA (the unknown sample) that also contain a high percentage of natural occurring sub-fractions, e.g. ribosomal RNA (rRNA, ~80%) and transfer RNA (tRNA, 10-15%). These missing RNA sub-fractions can influence the cDNA synthesis rate and in consequence RT efficiency rises and calibration curves are then overestimated in gene quantification. To compensate for background effects and mimic a natural RNA distribution like in native total RNA, total RNA isolated from bacterial or insect cell lines, can be used. Alternatively commercially available RNA sources can be used as RNA background, e.g. poly-A RNA or tRNA, but they do not represent a native RNA distribution over all RNA sub-species. Earlier results suggest, that a minimum of RNA background is generally needed and that it enhances RT synthesis efficiency rate. Low concentrations of recRNA used in calibration curves should always be buffered with background or carrier RNA, otherwise the low amounts can be degraded easily by RNAses. Very high background concentrations had a more significant suppression effect in RT synthesis rate and in later real-time PCR efficiency.
No matter how accurately the concentration of the standard material is known, the final result is always reported relatively compared to a defined unit of interest: e.g. copies per defined ng of total RNA, copies per genome (6.4 pg DNA), copies per cell, copies per gram of tissue, copies per ml blood, etc. If absolute changes in copy number are important then the denominator still must be shown to be absolute stable across the comparison. This accuracy may only be needed in screening experiments (amount of microorganism in food), to measure the percentage of GMO (genetic modified organism) in food, to measure the viral load or bacterial load in immunology and microbiology. The quality of your gene quantification data cannot be better than the quality of your denominator. Any variation in your denominator will obscure real changes, produce artificial changes and wrong quantification results. Careful use of controls is critical to demonstrate that your choice of denominator was a wise one. Under certain circumstances, absolute quantification models can also be normalized using suitable and unregulated references or housekeeping genes (see Normalization).
Relative quantification determines the changes in steady-state mRNA levels of a gene across multiple samples and expresses it relative to the levels of an internal control RNA. This reference gene is often a housekeeping gene and can be co-amplified in the same tube in a multiplex assay or can be amplified in a separate tube. Therefore, relative quantification does not require standards with known concentrations and the reference can be any transcript, as long as its sequence is known. Relative quantification is based on the expression levels of a target gene versus a reference gene and in many experiments is adequate for investigating physiological changes in gene expression levels. To calculate the expression of a target gene in relation to an adequate reference gene various mathematical models are established. Calculations are based on the comparison of the distinct cycle determined by various methods, e.g. crossing points (CP) and threshold values (Ct) at a constant level of fluorescence; or CP acquisition according to established mathematic algorithm. To date several calculation mathematical models that calculate the relative expression ratio have been developed. Relative quantification models without efficiency correction are available and published (equations 1-2)
and with kinetic PCR efficiency correction (equations 3-6). Further, the available models allow for the determination of single transcription difference between one control and one sample, assayed in triplicates (n =1/3), e.g. LightCycler Relative Quantification Software, or Q-Gene or for a groupwise comparison for more samples (up to 100), e.g. REST and REST-XL. The relative expression ratio of a target gene is computed, based on its real-time PCR efficiencies (E) or a static efficiency of 2, and the crossing point (CP) difference (D) of one unknown sample (treatment) versus one control (DCP control - treatment). Using REST and REST-XL the relative calculation procedure is based on the MEAN CP of the experimental groups (equation 4).
In these models the target gene expression is normalized by a non regulated reference gene expression, e.g. derived from classical and frequently described housekeeping genes. The crucial problem in this relative approach is that the most common reference gene transcripts from so-called housekeeping genes, whose mRNA expression can be regulated and whose levels vary significantly with treatment or between individuals. However, relative quantification can generate useful and biologically relevant information when used appropriately.
Advantages and disadvantages of external standards
External standard quantification is the method of choice for the nucleic acid quantification, independent of any hardware platform used. The specificity, sensitivity, linearity and reproducibility allows for the absolute and accurate quantification of molecules even in tissues with low mRNA abundance (<100 molecules) and a detection down to a few molecules (<10 molecules). The dynamic range of an optimal validated and optimized external standardized real-time RT-PCR assay can accurately detect target mRNA up to nine orders of magnitude or a billion-fold range with high assay linearity (Pearson correlation coefficient; r>0.99). In general a mean intra-assay variation of 10-20% and a mean inter-assay variation of 15-30% on molecule basis (maximal 2-4% variability on CP basis, respectively) is realistic over the wide dynamic range. At high (> 107) and low (< 103) template copy input levels the assay variability is higher than in the range between the two. At very low copy numbers, under 20 copies per tube, the random variation due to sampling error (Poisson´s error law) becomes significant.
A recDNA calibration curve model can quantify precisely only cDNA molecules derived from the RT step; it says nothing about the conversion to cDNA of the mRNA molecules present in the native total RNA sample. Variability in cDNA synthesis efficiency during reverse transcription must be always be kept in mind. Therefore, a recRNA calibration curve model has the advantage that both RNA templates undergo parallel RT and real-time PCR steps. However, a direct comparison suggests that the recDNA quantification model shows higher sensitivity, exhibits a larger quantification range, has a higher reproducibility, and is more stable than the recRNA model. Furthermore, recDNA external calibration curves exhibit lower variation (intra-assay variation < 0.7%; inter-assay variation < 2.6% on CP basis) than the recRNA model (< 2.7% and < 4.5%, respectively). Clearly, the RT step has a profound affect on the overall result obtained from an RT-PCR assay and more thorough consideration of RT efficiency is needed.
The main disadvantage of external standards is the lack of internal control for RT and PCR inhibitors. All quantitative PCR methods assume that the target and the sample amplify with similar efficiency. The risk with external standards is that some of the unknown samples may contain substances that significantly reduce the efficiency of the PCR reaction in the unknown samples. As discussed, sporadic RT and PCR inhibitors or different RNA/cDNA distributions can occur. A dilution series can be run on the unknown samples and the inhibitory factors can often be diluted out, causing a non-linear standard curve.
Real-time assays using SYBR Green I can easily reveal the presence of primer dimers, which are the product of nonspecific annealing and primer elongation events. These events take place as soon as PCR reagents are combined. During PCR, formation of primer dimers competes with formation of specific PCR product, leading to reduced amplification efficiency and a less successful specific RT-PCR product. To distinguish primer dimers from the specific amplicon a melting curve analysis can be performed in all available quantification software. The pure and homogeneous RT-PCR product produce a single, sharply defined melting curve with a narrow peak. In contrast, the primer dimers melt at relatively low temperatures and have broader peaks. To avoid primer dimer formation an intensive primer optimization is needed, by testing multiple primer pair by cross-wise combinations. Multiple optimization strategies have been developed and are published. The easiest and most affective way to get rid of any dimer structures, at least during the quantification procedure, is to add an additional 4th segment to the classical three segmented PCR procedure: 1st segment with denaturation at 95°C; 2nd segment with primer annealing at 55-65°C; 3rd segment with elongation at 72°C; 4th segment with fluorescence acquisition at elevated temperatures. The fluorescence acquisition in 4th segment is performed mainly in the range of 80-87°C, eliminates the non-specific fluorescence signals derived by primer dimers or unspecific minor products and ensures accurate quantification of the desired product. High temperature quantification keeps the background fluorescence and the ‘no template control’ fluorescence under 2-3% of maximal fluorescence at plateau.
“Do we need to run a calibration curve in each run ?” and “ Do we need a calibration curve at all ?” are frequently posed questions, together with “What about the reproducibility between the runs?”
Repeated runs of the same standard curve give minor variations of a
2-3% in the slope (real-time PCR efficiency) and about 10% in the
intercept of calibration curve. Since the variation in the standard
curve correlates with variation in the unknowns, a detection of a
difference over a wide range of target concentrations is possible. The
slope of the calibration curve is more reproducible than the intercept,
hence only a single standard point will be required to “re-register” a
performed calibration curve level for the new unknowns. The curve can
imported into any run, as done in the LightCycler software. Never
variations and 100% reproducibility are the big advantages of such a
curve import, but there are also disadvantages as variations of
primers and probe (sequence alterations and fluorescence intensity),
or sample-to-sample variations will not be covered in this ‘copy and
paste’ approach. Since these affect PCR efficiency, such an approach
can introduce significant errors into the quantification.
Absolute Quantification vs Relative Quantification
by Life Technologies
When calculating the results of your real-time PCR (qPCR) experiment, you can use either absolute or relative quantification.
Digital PCR works by partitioning a sample into many individual real-time PCR reactions; some portion of these reactions contain the target molecule (positive) while others do not (negative). Following PCR analysis, the fraction of negative answers is used to generate an absolute answer for the exact number of target molecules in the sample, without reference to standards or endogenous controls.
The standard curve method for absolute quantification is similar to the standard curve method for relative quantification, except the absolute quantities of the standards must first be known by some independent means.
The guidelines below are critical for proper use of the standard curve method for absolute quantification:
It is generally not possible to use DNA as a standard for absolute quantification of RNA because there is no control for the efficiency of the reverse transcription step.Standards
The absolute quantities of the standards must first be known by some independent means. Plasmid DNA and in vitro transcribed RNA are commonly used to prepare absolute standards. Concentration is measured by A260 and converted to the number of copies using the molecular weight of the DNA or RNA.
Relative quantification can be performed with data from all real-time PCR instruments. The calculation methods used for relative quantification are:
Real-time RT-PCR: Neue Ansätze zur exakten mRNA Quantifizierung
Die molekularen Technologien Genomics, Transcriptomics und Proteomics erobern immer mehr die klassischen Forschungsgebiete der Biowissenschaften. Die enorme Flut an gewonnenen Daten und Ergebnissen ist von überproportionalem Nutzen in der molekularen Diagnostik und Physiologie sowie die „Functional Genomics“. Immer neue ausgeklügelte Methoden und Anwendungen sind daher nötig um komplexe physiologische Vorgänge zu beschreiben. Da wir uns erst an Anfang dieser molekularen Ära befinden, ist es notwendig diese Techniken zu optimieren und komplett zu verstehen. Eine dieser technisch ausgefeilten Methoden zur zuverlässigen und exakten Quantifizierung spezifischer mRNA, stellt die real-time RT-PCR dar. Dieser Artikel beschreibt im Wesentlichen die effizienzkorrigierte
relative Quantifizierung, die Normalisierung der Expressionsergebnisse anhand eines nicht regulierten „Housekeeping Gens“, die Berechnung der real-time PCR Effizienz sowie die Verrechnung und statistische Auswertung der Expressionsergebnisse. Alle beschriebenen Themenkomplexe können im Detail auf der korrespondierenden Internetseite (http://www.gene-quantification.info) in internationalen publizierten Originalarbeiten nachgeschlagen werden.