AAA Real-Time PCR Tech Guide – Volume 3

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Genome Technology Real-Time PCR Tech Guide vol. 3 3

Table of contents

Q1: How do you measure the relative levels oftwo genes within the same RNA sample? . . . 7

Q2: How do you predict assay qualityprior to experimental evaluation? . . . . . . . . . . 10

Q3: How do you measure and control for efficiency? . . . . . . . . . . . . . . . . . . . . . . 13

Q4: Which computational tools are bestfor data analysis? . . . . . . . . . . . . . . . . . . . . . . 17

Q5: What is the minimal assay informationyou should report? . . . . . . . . . . . . . . . . . . . . 18

List of Resources . . . . . . . . . . . . . . . . . . . . . . . . . 21

Letter from the Editor . . . . . . . . . . . . . . . . . . . . . . . 5

Index of Experts . . . . . . . . . . . . . . . . . . . . . . . . . . . 5


It was a little more than a year ago thatGenome Technology kicked off itstechnical reference guide series with ourfirst real-time PCR guide. I distinctlyremember being a little nervous aboutproducing two more volumes on thetopic in the space of a year. As it turns

out, my anxiety was completely unwarranted — andeven naïve. There actually seems to be enough PCR-related experimental issues to fill more tech guidesthan we could have produced in one year.

This is due to the technology’s wide applicability.It’s an indispensible tool for anyone toiling in biology’strenches — even the barest life science lab has a qPCRstation, and understanding the principles of PCR is a

first step in any life science education. Whether you’requantifying the expression of genes or microRNAs, real-time PCR is the gold standard for making rapid andspecific measurements.

For this latest installment on real-time PCR, we'veassembled another exemplary panel of experts. We putseveral questions to this impressive team ofresearchers, and they were each gracious enough toreply with thoughtful and detailed responses. Read onfor their advice on measuring relative expression,predicting assay quality, controlling for efficiency, andmore. Also, be sure not to miss the resource guide,which includes recommended reading and websitesmentioned by the experts below.

— Jennifer Crebs

Letter from the editor

Index of experts

Vladimir Benes

European Molecular BiologyLaboratory

Cristina Hartshorn

Department of Biology

Brandeis University

Michael Pfaffl

Technical University of Münich

Gregory Shipley

University of Texas Health ScienceCenter, Houston

Xiuling Zhang

Wadsworth Center

New York State Department of Health

Mikael Kubista

TATAA Biocenter

Genome Technology would like to thank the followingcontributors for taking the time to respond to thequestions in this tech guide.

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When there are two genes, or any number oftranscripts in the same RNA sample, we usereference genes for truly relative quantification. Weare using — for both defining them or for identifyingthem — TATAA's reference panel for human genes.They have just recently released a mouse panel.

In order to find suitable transcripts for this task,we are running geNorm, developed by JoVandesompele at the University of Ghent in Belgium. Lately, we have started applying an assaycalled EAR, which is anacronym standing forExpressed Alu Repeats, andwhich was also conceived byJo Vandesompele. CurrentlyJo's EAR is only available forhuman genes. In a nutshell,it's an almost universalreference, at least for humantranscripts, because there are about 1,500 expressedAlu elements in human transcripts. These Alu elementsare integrated in 3' UTR mostly, so they are part of theprocess right from the beginning. The slightdisadvantage of this assay is that in the humangenome there are more than 1 million of theseelements, so you would need to be really careful thatyou eliminate all genomic DNA. Otherwise, they simplycome up, so it's necessary to really care about theremoval of genomic DNA, which is, in this particularcase, contamination.

So, we start with panels, then we find that inthe most of cases we have been successful. If we arenot, we try to look into microarray data to findsuitable reference genes.

— Vladimir Benes

We routinely analyze RNA in very small samples, forinstance mouse embryos or even single cellsrecovered from embryos. Because we don't like tosubdivide such samples, our approach is tosimultaneously quantify both mRNAs of interest. Todo so, we perform RT followed by duplex LATE-PCR,an advanced form of asymmetric amplificationinvented in our laboratory (Sanchez et al., 2004;Pierce et al., 2005). One of the main advantages of this technique is that both the abundant

and the rare templates in a sample are amplified with equal efficiency, so that quantification isindependent of the relativeamounts of the twotemplates. This is not thecase for conventional,symmetric PCR that always

favors amplification of the most abundant templateand often suppresses signals from low copy numbertemplates. In order to calculate the copy number ofeach mRNA target we use standard scales built withserial dilutions of genomic DNA. We can do thisbecause all our template sequences are chosenwithin genes' exons and are thus identical ingenomic DNA and cDNA; this strategy also ensuresthat efficiency is the same for the standard curve andthe cDNA amplicons (Hartshorn et al., 2004).

I have been using this approach for RT-real-time-LATE-PCR assays and I employ two sequence-specificprobes labeled with different fluors to identify thetranscripts that I am interested in. The amplificationproducts of LATE-PCR are single-stranded, whichgives me great freedom for probe design and length

How do you measure the relative levels of two genes within the same RNA sample?

"Some folks don’t realize that theCT values derived from a real-timeqPCR experiment, in isolation, arereally meaningless as values."

— Greg Shipley

because the probe is free to bind to the targetamplicon without having to compete with acomplementary strand. Some of my colleagues arealso utilizing LATE-PCR for quantitative end-pointanalysis, which eliminates the need for real-timeanalysis. Because the amplification plots generatedby LATE-PCR are parallel lines that do not plateau,final fluorescence at a chosen end cycle isproportional to the amount of starting template.Using this strategy we have had some promisingpreliminary results with DNA templates and arethinking about RNA applications.

— Cristina Hartshorn

To accurately determine the relative levels of twogenes in a single sample is very complicated,requiring the determination of RT yields for bothRNAs, the PCR efficiencies for both assays (preferablyin matrix of the samples), and the relative sensitivityof the two assays (see eq. 5 in Kubista et al., 2006).Much better is to measure the relative expressionratio of two genes in two samples; for example, therelative expression level of the two genes in a testsample compared to the relative expression level in acontrol sample. In such comparisons most unknownscancel. For these kinds of measurements it is mostimportant that the samples are similar and that thesample matrices do not inhibit the two assaysdifferently.

— Mikael Kubista

Relative quantification determines the changes insteady-state mRNA levels of a gene across multiplesamples and expresses it relative to the levels of aninternal control RNA. This reference gene is often a

housekeeping gene and can be co-amplified in thesame tube in a multiplex assay or can be amplified in aseparate tube. Therefore, relative quantification doesnot require standards with known concentrations andthe reference can be any transcript, as long as itssequence is known. Relative quantification is based onthe expression levels of a target gene versus one ormore reference gene(s) and in many experiments it isadequate for investigating physiological changes ingene expression levels. To calculate the expression of atarget gene in relation to an adequate reference gene,various mathematical models are established.Calculations are based on the comparison of thedistinct cycle determined by various methods, e.g.crossing points (CP) and cycle threshold values (CT) ata constant level of fluorescence; or CP acquisitionaccording to established mathematic algorithm. Todate several mathematical calculation models havebeen developed calculating the relative expressionratio:

2 delta-delta CT approachThis is the most used application in quantitative

RT-PCR. Scientists want to measure the "relative"mRNA expression changes of a target gene on thebasis of a not regulated reference or housekeepinggene mRNA. To get a general overview about thephysiological expression changes the 2delta-delta CT

approach is applied. The "first delta" stands for thenormalization according to an internal control, thementioned reference gene expression. The "seconddelta" is the expression change compared to a non-treated control. What we get is the "delta-delta CT"level, showing the cycle threshold differences afternormalizing to an internal


(continued on page 19)

How do you measure the relative levels of two genes within the same RNA sample? (continued from page 7)

In gene courses we teach here, many people aresometimes rather impatient to go straight into qPCRand get started with the instrument. Before doingso, I recommend using software for assay design,such as Primer3, PrimerExpress by ABI, or some ofthe tools that Qiagen or Roche are offering throughtheir websites to design assays.

It also helps to Blast primers, but I think thatregardless of what the theory shows or predicts, theyshould run RT-PCR and look at it on the gel beforethey start burning their samples.

The other place that I send people to checkprimers is Jo Vandesompele's RTPrimerDB, which Ihave found to be a very useful and very thoughtfullyprepared database. But even so, I do say that primersare nowadays no longer prohibitively expensive.Order them, check them, and run them. I think thatpredictions are fine — they can sort out a lot of noiseand you can certainly disregard some primer pairs —but before you see it performing in vitro in your assay,you can't tell.

There are other considerations aboutpreparation, quality, the preparation of total RNA,priming, and consistencies throughout the wholeassay. We advocate using the SPUD assay (Nolan etal., 2006) to check the presence of inhibitors inpreparation or isolates of total RNA.

— Vladimir Benes

We do a number of controls to assess the quality ofour assays, some of them during assay developmentand some to test the finalized reaction. During assaydevelopment, our main concern is to eliminate mis-priming and dimerization of oligonucleotides. This canbe a real problem in multiplex reactions, so our

laboratory uses Primesafe (available [email protected]) to prevent thiskind of non-specific interaction. We titrate itsconcentration to obtain optimal amplification ofevery target sequence in the assay and analyze thePCR products on agarose gels.

In LATE-PCR, amplification is exponential in theinitial cycles and only switches to linear amplificationonce the limiting primer is deplete, just after the CTvalue is reached. Thus each double-strandedamplicon (specific or not) is sufficiently abundant tovisualize on a gel. At the end of LATE-PCR, however,the number of single-stranded molecules is typically10- to 20-fold higher than the correspondingdouble-stranded amplicons, so the single-strandedamplicons can be directly sequenced after a simpledilution step (Dilute-'N-Go Sequencing, Sanchez etal., in preparation; Salk et al., 2006). Sequencing isthe ultimate proof of product identity and purity andcan also be used to detect the presence of mutationsor polymorphism. We have proof that up to sixamplicons from a single multiplex LATE-PCR can besequenced by direct dilution (Rice et al., inpreparation).

Other parameters used to check the quality ofour assays include the slope and CT value of thecurves in real-time reactions across a wide range oftemplate copy numbers. Optimized assays shouldgenerate parallel curves, at the appropriate CTintervals, for all template concentrations tested.Because the "visible" portion of a real-time LATE-PCR assay is linear, any drop in efficiency is readilyobserved as a decrease in slope.


10 Real-Time PCR Tech Guide vol. 3 Genome Technology

How do you predict assay quality prior toexperimental evaluation?


Primers are validated as far as possible in silico usingmultiple primer design and evaluation softwares.Secondary structures of the primer binding sites andpossible complementarities between primers arestudied using for example mFold and NetPrimer.Specificity is tested using Blast.

The designed assay is then tested on a modeltemplate, which typically is a cloned target sequenceor representative cell line. Assay efficiency isdetermined as well as any complications arising fromprimer-dimers.

— Mikael Kubista

Before quantification, essential assay proofs have tobe done. Amplification history and melting curve ofthe assay will tell us a lot about the assayperformance. Melting curves should have one onlymajor peak, specific for the generated RT-PCRproduct, at least in intercalating dye assays. If aprimer-dimer peak appears, primer optimizationshould be performed until the peak disappears.

Amplification history curves should be stable(not variable and noisy), should have a steep increase(marker for good PCR efficiency), and should end ina stable and high plateau (standing for a highamount of the generated product and goodpolymerase performance).

Furthermore, the negative control should haveno amplification, or at least a very late amplification(>CT 45) in SYBR Green assays.

Very important is the reproducibility within onerun (intra-assay variation) and between day-to-dayrepeated measurements (inter-assay variability).Here, the variation should not exceed 10-15 percent

on a molecular basis, or speaking in CT levels, notmore than 0.1 or 0.2 CTs.

— Michael Pfaffl

This would only be an issue for folks that do not runstandard curves with their assays. There is nothingwrong with using the delta-delta CT method Imentioned in Q1. For some experimental data itmakes a lot of sense. However, it is critical that youknow the lowest limit of your assay and the PCRefficiency prior to applying this method of samplequantification. This is true regardless of whether theassay was designed by the user or comes from acommercial source.

The easiest way to get this information is to runa standard curve. If you have a commercial assay,take the first PCR products you make in a real-timeexperiment, pool those having the most signal andrun them through a PCR clean-up kit. Then, get arough idea of concentration from an A260 readingand make a 10-fold template dilution series over 6-7logs starting with roughly 1 pg of template at thehighest point. Then run the assay again, and put invalues for the standards. The values do not have tobe calculated, although that isn't that hard to do.Then, calculate the PCR efficiency from the slope(10(-1/slope)) - 1 x 100 = efficiency as a percentage.

Even more important is [asking], "What is thelowest dilution of standard that is still on the linearline?" You will want to analyze this run the same asyou will analyze all the subsequent runs in terms ofthreshold and baseline settings. Then you will havethe lowest CT value that is valid for the assay inquestion. Actually, you can add one more cycle tothat value. Empirically, (continued on page 19)

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I say to everyone I know that the relative expressionsoftware tool (REST, Pfaffl et al. 2002) can deal withreduced efficiencies, but the only way to determineefficiency of your assay is to run a standard curve.Before running it, one can't tell anything. Anycomparisons based upon assays without determiningefficiency, in my eyes, are not fully valid. This isbecause I still believe — although it may beimmaterial — that when you put determiningefficiency to some software or algorithm, it can do itand may even be correct, but it is not sufficient. Ifyou know you can compare your assays, then youcan find out from your standard curves, and youdon't need any algorithms to adjust forshortcomings of the assays or some limitations on atechnical level.

I think that the qPCR component is really veryrobust and reproducible, and there is no need to runmany technical replicates on this quantitative bit.People should concentrate on running reversetranscription replicates and then compare those.

— Vladimir Benes

The slope of real-time LATE-PCR curves give me thefirst indication of the efficiency of the reaction, evenwhen testing unknown samples where the CT values(determined by the RNA content) cannot bepredicted. So, when assaying sets of single cells,samples whose slopes are clearly outside the groupcan be eliminated based on inefficient amplification.After having used symmetric PCR for years, I wasglad to realize that this feature of LATE-PCR is anextremely sensitive indicator of changes in efficiencycaused by a number of factors (e.g. mispriming orthe addition of different reagents to the PCR mix).

In addition, I utilize biological samples (embryosand embryonic blastomeres) as controls. Takingadvantage of the fact that my primers land withinexons, I can run "No RT" controls that allow me todetect my genomic DNA sequence(s) in the gene(s) ofinterest but not in cDNA. I do these tests on singlecells, hence my assay has to be sensitive enough toamplify one or two copies of each target, dependingif the target gene is on a sex chromosome or on anautosome.

Also, I recently developed a duplex assay for agene expressed only in female embryos and a geneexpressed in both sexes (Hartshorn et al., inpreparation). This assay required several optimizationsteps, until quantification of the mRNA present in allembryo was completely unaffected by the presenceor absence of the second, sex-dependent mRNAspecies, as shown both by average measurements inmale and female samples and by the constant slopeof the real-time curves. Controls of this kind can bedevised according to the characteristics andrequirements of different experimental systems.


We usually talk about PCR efficiency withoutdefining what we mean by efficiency. In essence,there is assay efficiency, which is the PCR efficiencymeasured on a purified template in absence of anyinhibitors. The template is usually ratheruncomplicated. For a well designed assay theefficiency should be 0.9 or higher. But this efficiencycan be difficult to reach in biological samples with acomplex matrix. Also, the biological template maybe more complicated, for example, being heavilysupercoiled, which reduces priming efficiency. The


How do you measure and control for efficiency?

PCR efficiency in the biological sample can beestimated by, for example, in situ calibration(Ståhlberg et al., 2003). But this requires performinga dilution series on each sample, which is costly andtime consuming.

A practical approach is to perform this detailedanalysis on some representative samples and, if thevariation among them is not substantial, determinean average efficiency that is assumed to berepresentative for the particular samples.

Another possibility is to inspect the fluorescenceresponse curve and identify anomalous samples bykinetic outlier detection (Bar et al., 2003). Internet-based software solution for this kind of qualityassurance will soon be available through LabonNet.

— Mikael Kubista

This is a very sensitive topic and we can discuss thisfor hours. Most applied is, of course, the calibration-or dilution-curve method. From the slope of thecurve the efficiency can be calculated. In my eyes avery robust method, but often too optimisticoverestimating the real PCR efficiency. We often endin efficiencies higher than 2.0 — up to 2.2 or higher.

How can this be? What is wrong with themethod? I do not know up to now, but as we knowfrom nature, no biological reaction is always 100percent or even more than that. Therefore myworkgroup looked deeper in the problem and wecame up with single run efficiency estimating modelson the LightCycler.

The problem lies in various factors: Whathappens in the PCR tube and how can we measureit correctly? Therefore the reporter dye, the tube

itself, the optical unit, and the cycler fluorescencemeasurement influence the algorithms. Each cyclerplatform has its own characteristic fluorescencehistory and amplification efficiency performance. Inthe near future we have to adapt for each cyclerplatform individual algorithms.

More details and available algorithms can befound at http://efficiency.gene-quantification.info/

— Michael Pfaffl

For measuring amplification efficiency, I usually makeserial dilutions (e.g., 1:10, 1:100, and 1:1000) of anRT sample, a cloned cDNA, or a purified RT-PCRproduct, and use the series as arbitrary standards. Astandard curve is generated using this set of samplesby plotting CT values versus abundance of thetemplate in arbitrary units.

To improve efficiency, I usually optimize thereactions by sequentially varying the followingparameters: Mg++ concentration, annealingtemperature and time, and concentration of otherreagents (primers, dNTPs, and polymerase).Increasing Mg++ concentration may increaseamplification efficiency, but at a risk of losingamplification specificity. Too high annealingtemperature or too short annealing time will result inlower efficiency. If the efficiency is still low after allthe above parameters have been optimized, I wouldthen try new primers.

— Xiuling Zhang


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The Relative Expression software tool (REST, Pfaffl etal. 2002), I believe, is very good and very robust. Forranking reference transcripts, we use geNorm and inmost of the cases we use ddCT, which is more or lessthe standard setup.

— Vladimir Benes

We use GenEx from MultiD Analyses. It's Windows-based, and has a user-friendly, spreadsheet-basedpreprocessing module that starts with CT data. It isvery easy to test the effect of, for example,differential inhibition or assay variability. It has bothgeNorm and Normfinder, which is nice, because thetwo approaches to identify optimum reference genesare complementary and suited for differentsituations. The professional version of GenEx has alsovery powerful methods for expression profiling andsample classification, including principal componentanalysis, hieratical clustering, self organizing mapsand much more. These methods are very useful forthe kind of profiling studies we mainly do today.

— Mikael Kubista

Today the relative gene expression approach isincreasingly used in gene expression studies, wherethe expression of a target gene is standardized by anon-regulated reference-gene or by an indexcontaining more reference-genes (at least three).Several mathematical algorithms have beendeveloped to compute the expression ratio, based onreal-time PCR efficiency and the crossing point (CT orCP) deviation (=> delta CP) of an unknown sampleversus a control. But all published equations andavailable models for the calculation of relativeexpression ratio allow only for the determination of a

single transcription difference between one controland one sample.

After developing the efficiency correctionalgorithm, we set up the Relative Expression softwaretool (Pfaffl et al., 2002).

New software tools were established, whichcompare two or more treatments groups orconditions (in REST-MCS), with up to 100 data pointsin sample or control group (in REST-XL), for multiplereference genes and up to 15 target genes (in REST-384). The mathematical model used is based on thecorrection for exact PCR efficiencies and the meancrossing point deviation between sample and controlgroup(s). Subsequently the expression ratio results ofthe investigated transcripts are tested forsignificance by a Pair Wise Fixed ReallocationRandomization Test and plotted using standard errorestimation via a complex Taylor algorithm.

Several updates for lots of applications areavailable. REST software applications are freelyavailable online.

— Michael Pfaffl

There are two levels of post-run data analysis. Thefirst is to establish the baseline and threshold settingsfor the assay and run. Most folks these days let theinstrument do that function. That is not a good ideafor two reasons. One, in an as-yet-unpublished studydone by the Nucleic Acids Research Group within theABRF [Association of Biomolecular ResourceFacilities], we compared the results from the samestandard curve, reagents, chemistry, and person,analyzed by the software that comes with the ninedifferent real-time instruments used for thecomparison. What we

Which computational toolsare best for data analysis?


I think there should be sequences of the primers, andthere should be efficiencies of the primers or of theassay. The information about the primer is not onlysequence, but it is also the target sequence of theamplicon. Then it can be made clear — let's say byaccession number — which sequence was used todesign the assay, not only primers.

I think if you provide efficiency information, it ismuch more telling in the article because thisinformation is more complete than if you provide justprimer pairs. It would then perhaps be easier to takethese primers and use themright away. However I doadvise caution — validate[primers] before going andusing them at their facevalue. But if I see thatefficiency is at 99 percent, Ithink it's good value.

In short, I think that theminimal assay informationshould include: sequence, accession number of thetarget sequence on which the primer or assay wasdesigned, and efficiency of the assay.

— Vladimir Benes

I think that all details, including the thermal profileused, should be reported in scientific papers, althoughcommercially available products can be cited.

The thermal profile is not always included inpapers but it is fundamental to reproduce results. Ifthe assays are part of a commercial kit, it is notnecessary to reveal the content but I personallyprefer to purchase kits (for instance for RT) thatinclude the buffer composition, etc. This knowledge

is very helpful if one needs to introduce modificationto the suggested protocols, which is almost alwaysthe case in a research lab.


This question is not as simple as it may sound,because all the information that one would like tohave about an assay is not always available, and wecannot expect companies to make it available. But, inessence, enough information should be providedthat makes it possible to reproduce the experiment.

This includes samplingdetails, such as how thesample was taken, in whatmedium it was collected,and after what time it wasplaced there; other storageinformation, including anychanges in temperature etc.during transportation;detailed extraction protocol,

and detailed protocol for reverse transcription. Thelatter should include the enzyme and primer strategyused, since it has a profound effect on the yield(Ståhlberg et al., 2004). The qPCR primer and probesequences should be presented, if available, or thecatalogue number for commercial assays,experimental conditions including primer, probe/dye,dNTP, Mg2+ concentrations, and for home madeassays and whenever possible buffer conditions.

For SYBR and BOXTO assays, concentration ofstock solution should be provided. If spikes were used,their sequences should be provided, with informationon how and when the samples were spiked. Assayefficiency should be


What is the minimal assayinformation you shouldreport?


"In essence, enough informationshould be provided that makes it possible to reproduce theexperiment." — Mikael Kubista


control and to a non-treated control. To calculated theexpression difference we have to assume a doubling ofthe amplicon during PCR in each cycle, therefore wecan set 2 as the basis in the following equation =>2delta-delta CT

This method was developed and published byLivak and Schmittgen (2001) and is the state-of-the-art relative quantification method.

Efficiency-corrected Pfaffl MethodDuring the last years we have seen that

amplification efficiency is not always constant and isnot always 2. Therefore a correction for efficiencychanges between target, reference gene, andmultiple gene-to-gene comparisons should beapplied (Pfaffl, 2001). The so-called efficiency-corrected relative quantification model [equationbelow] revolutionized the mRNA quantification andis increasingly used in academic research anddiagnostics.

R= (Etarget)ACPtarget (control — sample)

(Eref)ACPref (control — sample)

— Michael Pfaffl

I have always used the standard curve method ofquantification. I like this method for the followingreasons:

1) Standard curves allow you a way to monitorhow well the assay has run every time inevery plate by comparing the slope, y-intercept, and r2 values. If these are notconsistent from plate to plate, the resultsfrom the unknowns are questionable. If thereis a problem, what has to be determined iswhether there was a problem with makingthe standard curve itself or if a bad standardcurve is reflecting a problem with the assay.Since we have the luxury of robotics to set upour plates, we see few problems with thestandard curves themselves.

2) You can derive the number of molecules foreach unknown sample in the assay byinterpolation from the standard curve.

Q1: How do you measure therelative levels of two geneswithin the same RNAsample? (continued from p.9)

Therefore, each sample has a numerical valueindependent of any other sample.

3) Having a distinct value for each unknownallows you to apply any number of statisticalanalyses, which is not so simple with only afold-difference, and allows you to compareany sample or group to any other sample orgroup readily. This is particularly useful whenlooking at data from multiple plates over along time span.

What some folks don't realize is that the CTvalues derived from a real-time qPCR experiment, inisolation, are really meaningless as values. The mostused alternative method to convert CT values into afold-difference is the ddCT method. This method isonly valid if the PCR efficiencies of the assays usedare very similar. Most folks performing this methodhave no idea what the PCR efficiency of their assaysis or their lowest limit of detection. These are twocritical pieces of information for publication. What Ifear is that folks are reporting values for the ddCT inthe literature that are not valid for their assay.

— Gregory Shipley

another two-fold dilution always works for anyassay. Once you know that CT value, you will notneed the standard curve again. You will also knowwhether the assay you plan to use to normalize yourdata has a similar slope to your assay(s) of interest.

— Gregory Shipley

First, I look at the melting curves produced for thePCR products. A melting curve showing only asingle, sharp peak suggests that the amplification isspecific. Second, I look at the amplification curves,which should move upward smoothly in the logphase, and should not have secondary peaks in theplateau phase. Third, I look at the standard curve. Ifall the standards are on one straight line, which hasa slope close to -3.33, the quality and efficiency ofthe amplification reaction are good. Finally, I checkthe difference in CT values between duplicatesamples, which should be smaller than one-halfcycle for the assay to be reliable.

— Xiuling Zhang

Q2: How do you predict assayquality prior to experimentalevaluation? (continued from p.11)


Q4: Which computational toolsare best for data analysis?(continued from p.17)

given, as well as typical efficiency for the samplematrix. If data are normalized with reference genes, thebasis for choosing those reference genes should beprovided. This means that either the author shouldhave validated the reference genes or a reference isgiven to a study that validated the reference genes forthe particular samples. There are today convenientpanels available for validation (see for example: TATAA’sgene panels), and the time has passed when studiesgave shaky data because of the usage of improperreference genes.

— Mikael Kubista

Q5: What is the minimal assayinformation you shouldreport? (continued from p.18)

found was that none of the software packages gavean optimal analysis alone regardless of the settingsused. Only when we analyzed them manually did weget the best standard curves and the mostcomparable data from instrument to instrument.This brings up another point — and that is that youshould determine during assay QC what the baselineand threshold settings for an assay will be and thenstick to those settings throughout the use of thatassay. The baseline can be moved slightly toaccommodate differences in sample or standardamounts but the threshold should stay constant. Ifyou let the software determine threshold andbaseline, they will change slightly from run to runand your data will not be as comparable, especiallyif you are using the ddCT method.

The second software group includes those usedto analyze the data once you have done a good jobwith the initial post-run analysis described above. Forthat I like GenEx from MultiD, and before thatgeNorm and BestFit. I'm sure everyone uses Excel inone way or another, and I like Prism for statistics andmaking graphs.

— Gregory Shipley

If you made the assay yourself or had it madecommercially, you should know everything about thatassay. In that case you should present:

1) the NCBI name of the transcript (or gene)and any synonyms that may be morecommonly recognized

2) the accession number of the sequence usedfor assay design or a reference to thesequence used if it isn't in the NCBIdatabase

3) the sequence of the primers and/or probeused in the assay including numbersreferring to the position in the sequence ofthe 5' base in the sequences

4) the length of the amplicon5) the PCR efficiency from your empirical data6) the lowest limit of detection for the assay

from your empirical data

If you purchased a commercial assay, you shouldpresent:

1) the NCBI name of the transcript (or gene) andany synonyms that may be more commonlyrecognized (from the datasheet)

2) the accession number of the sequence usedfor assay design or a reference to thesequence used if it isn't in the NCBI database(from the datasheet)

3) the catalogue number of the purchased assay4) the PCR efficiency from your empirical data5) the lowest limit of detection for the assay

from your empirical dataAll of this information can be easily presented in

a table. It will also put the editors at ease so they canconcentrate on your data and not on the assays fromwhich the data was derived.

— Gregory Shipley

1) RNA isolation method2) Reverse transcription method 3) Genomic DNA removal method and controls

used4) Primer sequence5) Components of amplification reaction and

temperature program6) Quantification standards information7) Real-time PCR system and kit used8) Calculation method

— Xiuling Zhang


Pattyn F, Robbrecht P, De Paepe A, Speleman F, andVandesompele J. (2006)RTPrimerDB: the real-time PCR primer andprobe database, major update 2006.Nucleic Acids Res, 34(Database issue): D684-8.

Pfaffl MW, Horgan GW, Dempfle L. (2002)Relative expression software tool (REST) forgroup-wise comparison and statistical analysisof relative expression results in real-time PCR.Nucleic Acids Res. May 1;30(9):e36

Pierce KE, Sanchez JA, Rice JE, and Wangh LJ.(2005)Linear-After-The-Exponential (LATE)-PCR:primer design criteria for high yields of specificsingle-stranded DNA and improved real-timedetection. Proc Natl Acad Sci USA, 102: 8609-8614.

Salk JJ, Sanchez JA, Pierce KE, Rice JE, Soares KC,Wangh LJ. (2006)Direct amplification of single-stranded DNA forpyrosequencing using linear-after-the-exponential (LATE)-PCR. Anal Biochem 2006, 353:124-132.

Sanchez JA, Pierce KE, Rice JE, and Wangh LJ.(2004)Linear-after-the-exponential (LATE)-PCR: anadvanced method of asymmetric PCR and itsuses in quantitative real-time analysis. Proc Natl Acad Sci USA, 101: 1933-1938.

Ståhlberg A, Åman P, Ridell B, Mostad P, andKubista M. (2003)Quantitative Real-Time PCR Method forDetection of B-Lymphocyte Monoclonality byComparison of {kappa} and {lambda}Immunoglobulin Light Chain ExpressionClin Chem, 49: 51-59.

Ståhlberg A, Håkansson J, Xian X, Semb H, andKubista M. (2004)Properties of the Reverse TranscriptionReaction in mRNA Quantification.Clin Chem, 50: 509-515.

Hartshorn C, Rice JE, and Wangh LJ. (2004)Optimized real-time RT-PCR for quantitativemeasurements of DNA and RNA in singleembryos and blastomeres. In: Bustin SA, ed. A-Z of Quantitative PCR. International University Line: La Jolla; pp.675-702.

Kubista M, Andrade JM, Bengtsson M, Forootan A,Jonak J, Lind K, Sindelka R, Sjoback R, Sjogreen B,Strombom L, Stahlberg A, Zoric N. (2006)The real-time polymerase chain reaction.Mol Aspects Med. Apr-Jun;27(2-3):95-125.

Nolan T, Hands RE, Ogunkolade W, Bustin SA. (2006)SPUD: a quantitative PCR assay for thedetection of inhibitors in nucleic acidpreparations.Anal Biochem. Apr 15; 351(2):308-10.

Publications

A-Z of Quantitative PCR.Stephen A. Bustin, ed. International University Line (July 2004)

PCR (The Basics)McPherson MJ, Moller SG. Taylor & Francis; 2nd edition (March 30, 2006)

Prins And in Situ PCR Protocols (Methods in Molecular Biology)Franck Pellestor, ed.Humana Press; 2nd edition (April 21, 2006)

PCR Troubleshooting: The Essential GuideMichael AltshulerCaister Academic Pr (June 30, 2006)

Books

List of resourcesOur RT-PCR experts referred to a number ofpublications and Web tools, which we’ve compiled inthe following list.


Index of Names

Adams, Scottie, I: 5, 7, 11, 13, 15

Andersen, Claus Lindbjerg, I: 5, 7, 11

Benes, Vladimir, I: 5, 7, 9, 11, 13, 15, 16; III: 5, 7,10, 13, 17, 18

Bustin, Stephen, I: 5, 9, 11, 15, 16, 17

Ding, Xinxin, II: 19; III: 22

Hartshorn, Cristina, II: 5, 7, 12, 15, 17; III: 5, 7, 9,10, 13, 18

Huggett, Jim, II: 5, 7, 11, 12, 15, 17

Hunter, Tim, II: 5, 7-8, 11, 12, 15, 17

Kubista, Mikael, I: 5, 9, 11, 15-17; II: 5, 8, 11, 13,15, 17, 21-22; III: 5, 9, 11, 13-14, 17, 18, 20

Levy, Shawn, I: 5, 9, 15, 16, 17

Pfaffl, Michael, III: 5, 9, 11, 14, 17, 19

Shipley, Gregory, III: 5, 7, 11, 17, 19, 20

Vandesompele, Jo, I: 5, 9, 15, 17; II: 5, 11, 13, 21,22; III: 10

Wong, Marisa, II: 5, 13, 21, 22

Zhang,Xiuling, II: 5, 13, 21; III: 5, 14, 19, 20

Zianni, Michael, I: 5, 9, 15, 17

WebsitesEndogenous Control Gene Panels (TATAA Biocenter)http://www.tataa.com/referencepanels.htm

Gene Quantification web site (edited by Michael Pfaffl)http://www.gene-quantification.info

GenEx softwarehttp://www.multid.se/GenEx/genex.htm

GeNormhttp://medgen.ugent.be/genorm

LabonNet, Ltd.http://www.labonnet.com

NormFinderhttp://www.mdl.dk/publicationsnormfinder.htm

Primer3http://biotools.umassmed.edu/bioapps/primer3_www.cgi

PrimerExpresshttp://www.appliedbiosystems.com

Qbasehttp://medgen.ugent.be/qbase/

REST applicationshttp://rest.gene-quantification.info

RTPrimerDBhttp://medgen.ugent.be/rtprimerdb

Many thanks again to Xinxin Ding of theWadsworth Center for advising on the answerssubmitted by Xiuling Zhang.

Locate expert advice across all three volumes of theqPCR reference guide series using the index below.

Acknowledgments

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©2006 Biosearch Technologies, Inc.Products and technologies appearing in this ad may have trademark or patent restrictions associated with them. Please visit www.biosearchtech.com for full legal disclosure.

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Documents

AAA Real-Time PCR Tech Guide – Volume 3