Gene Quantification & real time PCR quantification strategy

Quantification strategies in real-time RT-PCR

Absolute Quantification

Relative Quantification

Absolute Quantification vs. Relative Quantification

Absolute Quantification using the Digital PCR Method

Absolute Quantification using the Standard Curve Method

Relative Quantification

Real-time RT-PCR: Neue Ansätze zur exakten mRNA Quantifizierung (in German)

Relative Quantification - Slide show

		A-Z of quantitative PCR ( Editor: S.A. Bustin ) International University Line (IUL), La Jolla, CA, USA Flyer for the book => PDF Chapter 3: Quantification strategies in real-time PCR by Michael W. Pfaffl Download => Chapter 3 PDF This chapter analyzes the quantification strategies in real-time RT-PCR and all corresponding markers of a successful real-time RT-PCR. The following aspects are describes in detail: RNA extraction, reverse transcription (RT), and general quantification strategies—absolute vs. relative quantification, real-time PCR efficiency calculation, data evaluation, automation of quantification, data normalization, and statistical comparison. The discussion turns into practical considerations with focus on specificity and sensitivity. The book chapter has been frequently cited by other researchers: http://Scholar.Google.com/

		Quantification strategies in real-time RT-PCR (RT-qPCR) Download => "Quantitative Real-time PCR in Applied Microbiology" Edited by: Martin Filion Published: 2012 ISBN: 978-1-908230-01-0 The present chapter explains the applied quantification strategies using real-time RT-PCR and which elements they essential to fulfil the MIQE guidelines. The necessity of an initial proper data adjustment and background correction is discussed to allow later a reliable quantification. In the following the advantages and disadvantages of the absolute and relative quantification approaches are described in detail. In conjunction with the relative quantification, the importance of an amplification efficiency correction is shown, and which software tools are available to calculate the relative expression changes.

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.

Absolute quantification
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
link to speciefied relative quantification sub-domain

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)

equation 1
equation 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).

equation 3
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.

equation 5
equation 6

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?”

(http://www.idahotec.com/lightcycler_u/lectures/quantification_on_lc.htm). 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 2-fold 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 previously performed calibration curve level for the new unknowns. The curve can be imported into any run, as done in the LightCycler software. Never changing variations and 100% reproducibility are the big advantages of such a calibration curve import, but there are also disadvantages as variations of reagents, primers and probe (sequence alterations and fluorescence intensity), day-to-day 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.

	Absolute vs Relative Quantification at a Glance

	Absolute Quantification (Digital PCR Method)	Absolute Quantification (Standard Curve Method)	Relative Quantification
Overview	In absolute quantification using Digital PCR, no known standards are needed. The target of interest can be directly quantified with precision determined by number of digital PCR replicates.	In absolute quantification using the Standard Curve Method, you quantitate unknowns based on a known quantity. First you create a standard curve; then you compare unknowns to the standard curve and extrapolate a value.	In relative quantification, you analyze changes in gene expression in a given sample relative to another reference sample (such as an untreated control sample).
Example	Quantify copies of rare allele present in heterogenous mixtures. Count the number of cell equivalents in sample by targeting genomic DNA. Determine absolute number of viral copies present in a given sample without reference to a standard.	Correlating viral copy number with a disease state.	Measuring gene expression in response to a drug. In this example, you would compare the level of gene expression of a particular gene of interest in a chemically treated sample relative to the level of gene expression in an untreated sample.

Absolute Quantification Using the Digital PCR Method

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.

Figure 1: Digital PCR uses the ratio of positive (White) to negative (Black) PCR reactions
to count the number of target molecules.

Absolute Quantification Using the Standard Curve Method

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.

link to speciefied absolute quantification sub-domain

Figure 2: Amplification Plot and Standard Curve for Absolute Quantification

Critical Guidelines
The guidelines below are critical for proper use of the standard curve method for absolute quantification:

It is important that the DNA or RNA be a single, pure species. For example, plasmid DNA prepared from E. coli often is contaminated with RNA, which increases the A260 measurement and inflates the copy number determined for the plasmid.
Accurate pipetting is required because the standards must be diluted over several orders of magnitude. Plasmid DNA or in vitro transcribed RNA must be concentrated in order to measure an accurate A260 value. This concentrated DNA or RNA must then be diluted 106–1012 -fold to be at a concentration similar to the target in biological samples.
The stability of the diluted standards must be considered, especially for RNA. Divide diluted standards into small aliquots, store at –80 °C, and thaw only once before use.

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

Calculation Methods for Relative Quantification
Relative quantification can be performed with data from all real-time PCR instruments. The calculation methods used for relative quantification are:

Standard curve method
Comparative CT method
link to speciefied relative quantification sub-domain

Figure 3: Relative Quantification

	Which Method Should I Use?

	Digital PCR Method	Standard Curve Method	Comparative CT Method
Overview	Nucleic acid quantification utilizes theory of limiting sample dilution that is spread across many technical replicate PCR reactions. Absolute quantification is determined by ratio of number of negative versus total reactions. This type of analysis is different from Ct and delta Ct comparisons and instead allows each assayed target to be quantified independently without the need for reference standards.	It is easy to prepare standard curves for relative quantification because quantity is expressed relative to some basis sample (called a calibrator), such as an untreated control. For all experimental samples, you determine target quantity from the standard curve and divide by the target quantity of the calibrator. Thus, the calibrator becomes the 1× sample, and all other quantities are expressed as an n-fold difference relative to the calibrator.	This method compares the Ct value of one target gene to another (using the formula: 2^ΔΔCT)—for example, an internal control or reference gene (e.g., housekeeping gene)—in a single sample.
Advantages	No need to rely on references or standards Desired precision can be achieved by increasing total number of PCR replicates Highly tolerant to inhibitors Capable of analyzing complex mixtures Unlike traditional qPCR, digital PCR provides a linear response to the number of copies present to allow for small fold change differences to be detected	Running the target and endogenous control amplifications in separate tubes and using the standard curve method of analysis requires the least amount of optimization and validation.	You don't need a standard curve, which can increase throughput because wells no longer need to be used for the standard curve samples. This also eliminates dilution errors made in creating the standard curve samples. You can amplify the target and endogenous control in the same tube, increasing throughput and reducing pipetting errors. When RNA is the template, performing amplification in the same tube provides some normalization against variables such as RNA integrity and reverse transcription efficiencies.
Experimental Validation	Validated digital PCR results with side by side comparison to well characterized sample with known copy number.	See Advantages above.	You have to run a validation experiment to show that the efficiencies of the target and endogenous control amplifications are approximately equal. To amplify the target and endogenous control in the same tube, limiting primer concentrations must be identified and shown not to affect CT values.
Critical Guidelines	It is important to use low binding plastics as much as possible throughout experimental set-up. Being that digital PCR emphasizes assaying limiting dilution, any sample that sticks to intermediate set-up equipment will be lost and skew results. We recommend using low binding 2.0mL tubes for dilutions and low-retention pipette tips. It is important to know the digital area of the desired sample to be tested. If unknown, consult user guide for help in determining copy number of known species (gDNA) or perform a preliminary screening experiment with multiple dilutions of sample/assay combination to determine optimal digital concentration to ensure meaningful data attained. Sample should not be kept at low concentration for extended periods of time, nor exposed to excessive freeze-thawing. Carriers have not been shown to be as important to reproducibility as much as using non-stick plastics for experimental set-up. Careful planning of dilutions is desired to minimize variability due to dilution scheme.	It is important that stock RNA or DNA be accurately diluted, but the units used to express this dilution are irrelevant. If two-fold dilutions of a total RNA preparation from a control cell line are used to construct a standard curve, the units could be the dilution values 1, 0.5, 0.25, 0.125, and so on. By using the same stock RNA or DNA to prepare standard curves for multiple plates, the relative quantities determined can be compared across the plates. You can use a DNA standard curve for relative quantification of RNA. Doing this requires the assumption that the reverse transcription efficiency of the target is the same in all samples, but the exact value of this efficiency need not be known. For quantification normalized to an endogenous control, standard curves are prepared for both the target and the endogenous reference. For each experimental sample, the amount of target and endogenous reference is determined from the appropriate standard curve. Then, the target amount is divided by the endogenous reference amount to obtain a normalized target value. Again, one of the experimental samples is the calibrator, or 1× sample. Each of the normalized target values is divided by the calibrator normalized target value to generate the relative expression levels.	For the comparative CT method to be valid, the efficiency of the target amplification (your gene of interest) and the efficiency of the reference amplification (your endogenous control) must be approximately equal.
Endogenous Control	Digital PCR does not rely on the presence of endogenous controls for reference measurements.	Amplification of an endogenous control may be performed to standardize the amount of sample RNA or DNA added to a reaction. For the quantification of gene expression, researchers have used ß-actin, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ribosomal RNA (rRNA), or other RNAs as an endogenous control.
Standards	Because Digital PCR uses the fraction of negative to total replicates to determine an absolute count of molecules, no standards are required.	Because the sample quantity is divided by the calibrator quantity, the unit from the standard curve drops out. Thus, all that is required of the standards is that their relative dilutions be known. For relative quantification, this means any stock RNA or DNA containing the appropriate target can be used to prepare standards.

Real-time RT-PCR: Neue Ansätze zur exakten mRNA Quantifizierung
BioSpektrum 1/2004

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.

Relative Quantification Slide show