Hi,

Using the ddCT method for relative quantification might work, but only if you can confirm, that the efficiencies of your target gene and your reference genes are absolutely identical (for ddCT they actually have to be 2, what is very unlikely). If they are not you will suffer from huge calculation mistakes! Some people say ddCT is only an approximation method. But there are other methods for relative quantification. To check out, I suggest Čikoš and Koppel, 2009. Actually, to determine your efficiencies is not such a complicated thing to do. You don't even need to run standard curves for that (which can also be error prone). Efficiencies can easily and better be calculated for each individueal sample by the kinetics of the fluorescene curve: Read Ramakers et al. 2003 and further Ruijter et al. 2009 (LinRegPCR software).

That ist the way I go for, I think it is the most reliable, if you make an effort and understand the background of the calculations. It might save you also a lot of money.

Good luck!

Your referee gave you a perfect hint. Don't use one reference (I don't call it HK gene) gene, as your referee mentioned and as you already did, use 3 (or more).

Cheers, Nadine

If you want my advise, I would recommend you to switch to absolute quantification instead of relative deltadeltaCt. I think there is no possibility to find any HK gene which is expressed uniformly. If you use more reference HK genes you will hit into serious interpretation troubles. You can also use reference of the HK reference, thats really something to get headache.

You should use the arithmetic mean of the Cq values. If your input is relative quantities, only then you should use the geometric average. We recommend the latter approach, as it allows you to do efficiency corrections. All formulas are published in Hellemans et al., Genome Biology, 2007. Biogazelle's qbase+ software is based on these formulas and includes the geNorm algorithm for finding stably expressed reference genes.

http://www.qbaseplus.com

I do agree with Alexander, as you have to test different genes for finding the suitable -reference or HK - gene . The refernce gene is the one that is stable in your control aswell as in your experiments, that is the funtion of the HK gene. Depending the experiments you may have to change the HK gene as there is no standard ref gene available. I always use the delta Ct method for analysis, and was well being accepted.

dear Daniel, adopting more than one reference genes it's not so useful if you have not checked first their expression under your experimental conditions. there's sufficient literature that you can consult about variations of housekeepiung genes. anyway, for a general guide for using the delta delta Ct method, you can read:

Nat Protoc. 2008;3(6):1101-8.

Analyzing real-time PCR data by the comparative C(T) method.

Schmittgen TD, Livak KJ.

I understood many things after studying it.

Using 3-4 HK genes is helpful if you're not sure which of them are stable. However, in theory you only need one, as long as you know that your experimental setup does not affect that gene's expression. This might however be a pain to establish unless you did a pilot experiment where you tested the stability of the HK gene against absolute quantification (and probably did the experiment more than just once). Therefore, in reality people tend to do 3-4 HK genes and drop the one that "stands out" (if any one does). If you do 4 genes you don't expect to change between control and treated/manipulated samples and 3 show the same comparative expression and one doesn't, then it is highly likely that the 3 are representative of the initial RNA amount in the samples, whereas the 4th one was affected by the treatment/manipulation. If you did one or two, you have no way of seeing if the gene was affected or of deciding which gene was affected by the manipulation. It is in the end also an imperfect method, but better than running only 1 gene (unless you know exactly how it behaves).

I'm really thrilled by the amount and quality of replies my question generated. This was actually my first question post here in RG, and I'm stunned by the results.

I use this method mainly for analyzing different transcription levels of several targets of interest in the diverse mouse models that I work with (most of my work is with host-microorganism interaction, particularly in the context of inflammation and innate immunity).

I'm going to adapt my analysis method and I just got the papers you pointed me to. I'm a bit embarrassed to admit that I was unfamiliar with the protocol article by Schmittgen & Livak. My reading was almost entirely confined in the material that Applied supplied during the training we received when we got our instruments. Most of the references in the material points to some papers in BMC molecular biology, and CSH Protocols searches helped me a lot too.

Absolute quantitation is good, but extremely taxing, as it would require the cloning of the target cDNA sequences in plasmid vectors, then expansion, then careful measurements and trials and we simply don't have the resources for it.

It's unfortunate though that I'm unable to perform detailed pilots before each set of RT and subsequent qPCR runs, the amount of enzymes and mixes spent already weights considerably in our budget, so I try to be as meticulous and stringent as possible with all the steps.

I'd like to sincerely thanks all present and future replies, I'll be watching closely.

just an example suitable for you, Daniel: if you are interested in checking transcript variations after inflammatory stimuli on intestinal mucosa... as a consequence, actin should be considered "the devil" among housekeeping genes, due to actual and drastic rearrangements of the enterocyte's cytoskeleton (and not only of enterocytes).

Comparative ΔCT method

Data is presented as individual data points

Fold change = 2^-ΔCT

ΔCT = (CT gene of interest – CT ctrl)

Comparative ΔΔCT method

Expression of gene of interest relative to internal control in treated sample compared with the untreated control

Fold change = 2^-ΔΔCT

ΔΔCT = [(CT gene of interest – CT ctrl)A – ((CT gene of interest – CT ctrl)B]

http://www.nature.com/nprot/journal/v3/n6/full/nprot.2008.73.html

You may find details in: Kenneth J. Livak and Thomas D. Schmittgen (2001). Analysis of Relative Gene Expression Data Using Real-Time Quantitative PCR and the 22DDCT Method. METHODS 25, 402–408. doi:10.1006/meth.2001.1262

better to start with, make serial dilution for the DNa and cDNA sample and amplify your Hk gene to prepare standard curve.it will give u a good idea about what range of concentration you would use for your experiment ..better than absolute quantification then use those for amplifying your gene of interest..

Try NormFinder to analyze the stability of your reference genes. It's similar to GeNorm, but more simple to use.

Regarding the method, I agree with previous comments... delta delta CT method for relative expression assays. But before you have to select your reference/s gene/s with NormFinder, and calculate the efficiencies of each gene (targets and references). Then you could correct the equation of the method with the Efficiency of each gene. A classic paper of Pffafl describe the formula.

If you have enough target genes (at least five) and may search for a way to save money, you can also try the method described by Heckmann and coworkers (NORMA-Gene; 2011). For this you do not need housekeeping genes but compare the results of different target genes per sample. Doing so will generate a normalization factor which is low for samples that always have higher values than the mean and high for samples that always have lower values than the mean. The more target genes and replicates you have, the more reliable are your data.

Dear Daniel,

ddCT is quick and easy, but not so precise method.

The main disadvantage of ddCT method is, that it considers that every PCR product doubles in each cycle in the log-phase of your PCR (PCR Efficiency =1). This is not completely true for most of the primers (Efficiencies typically vary from 0,7 to 1). If your housekeeping genes and genes of interest have different PCR efficiences, using ddCT can result to significant deviations in your calculation. More laborous, but more precise alternative is pfaffl method, which accounts for the differences in efficiences of your primers : http://www.ncbi.nlm.nih.gov/pubmed/11328886. But…you have to make a dilution curve two-three times ( ideally in parallel with your PCR).

Hi,

Using the ddCT method for relative quantification might work, but only if you can confirm, that the efficiencies of your target gene and your reference genes are absolutely identical (for ddCT they actually have to be 2, what is very unlikely). If they are not you will suffer from huge calculation mistakes! Some people say ddCT is only an approximation method. But there are other methods for relative quantification. To check out, I suggest Čikoš and Koppel, 2009. Actually, to determine your efficiencies is not such a complicated thing to do. You don't even need to run standard curves for that (which can also be error prone). Efficiencies can easily and better be calculated for each individueal sample by the kinetics of the fluorescene curve: Read Ramakers et al. 2003 and further Ruijter et al. 2009 (LinRegPCR software).

That ist the way I go for, I think it is the most reliable, if you make an effort and understand the background of the calculations. It might save you also a lot of money.

Good luck!

I would definitely agree with Juliane that any work you do to characterize your assays can be well worth it in the long run. If you use poor assays to do a lot of measurements you wind up with worthless data and a lot of wasted money. One other point is that you don't necessarily need purified template to prepare a reference standard. (In fact, preparing such a highly concentrated template can represent a serious lab contamination hazard.) An alternative is just to make a reference RNA with any mixture of RNA samples that you have on hand which express your gene of interest. You just have to make a big enough pool of this reference to last you through all of the experiments that you plan to do. Whenever you run a new batch of samples, you just include this standard reference as well. This can also serve as a control for all steps of the reverse transcription and qPCR set-up.

Well, you can use any methods, but at the same time you can also compare all the results from diff methods so that you will be happy at the end.

Keep atleast one positive control gene where you are sure of high expression or low expression of that gene., if possible.

Now coming to your question, ddCt method can work better just because you know how calculations are being done by you rather than just entering the Ct values in a software.

How to calculate??

Well, when you have triplicate Ct values per sample of control , A ( n=3 or 4 or 5 etc ) and treated group, B (n=3 or 4 or 5 etc) then take gene of interest(X) along with atleast one house keeping gene (Y) and subtract all the corresponding Ct values of X with Y, which is dCt. i.e., dCt = X-Y.

Now, take the mean of only A group dCt values.

Subtract all dCt values ( both A and B) with mean of A group.

This gives you ddCt.

Finally,

Take the mean of each sample. ie., if n=3 or 4 samples then you will have 9 or 12 ddCt values but when you take the mean of each sample, you will have again 3 or 4 values for A and B group as well. You can now use the formula for fold change.

Fold change = 2^-ΔΔCT

Typically, fold change for A will be 1 or should be near to 1.

Once you get value for fold change, use simple t-test either in MS excel or in graph pad prism software to get the significance of difference.

I tried to make it as simple as possible for all kind of readers. :)

Suggestions are welcome.

All the best,

Dp

Dear Daniel, please follow the Pfaffl method.

A new mathematical model for relative quantification in real-time RT–PCR.

Pfaffl, MW (2001) Nucleic Acids Research. Vol. 29, No. 9 00

hi Dinesh, do you enter just a mean of triplicate values in REST program (Pfaffl method) or just directly all the Ct values???? thanks

Directly all Ct value. Good Luck,.

Hi Daniel,

The ddCt method is never going to be accurate but is good enough for estimating approximate expression levels. For ddCt it is best to include at least two or three internal control genes and all the primers used must have same efficiency. For that you can design primers with expected amplicon size around 70-90 bp, if this is not possible try to keep it below 120bp (and in rare cases you might not able to find those amplicon, sizes, in that case 150-180 is OK but may need extensive optimization). The lower amplicon size primers are easy to optimize.

For each new RNA sample preparation or new RT (cDNA) reaction you always need to perform new standard curve and get the newer efficiency. I know Pfaffl method is among the best, however could be laborious and costlier as you need to include std curve for each new plate, etc but you can modify it depending on your needs and accuracy required. Unless, you are working for diagnostic lab, approximate relative quantification should suffice the purpose. And it all depends personal choices.

Dear Ulhas,

why is it easier to optimize primers for an amplicon size of 70-90 bp than for a size of 150-180 bp?

Hi,

I'm using 2-ΔΔCT method. In my opinion it is a good method and I have a chance to do calculation easy, even manual as Prakash explaned :). Reasults are good and as many of colleagues said, using of good/stable houskeeping gene is essential!

There are many very good suggestion, it was usefull for me too.

Good question Daniel!

Luck to everybody!

Hello, I read all of these useful comments for relative quantification. I'm trying to use qRT-PCR to determine the copy number per chromosome of a problem DNA in a sample. I amplified my problem DNA and got a Ct of 20, in parallel I amplified rpoD gene that is in 1 copy per chromosome (so rpoD is a measure of number of chromosomes in the sample) and got a Ct of 10. So, to calculate the copy number of my problem DNA per chromosome, I first determined the amplification efficiency for both my problem DNA and rpoD and got 99%. Considering the very similar efficiencies, can I just calculate the quocient 2^Ct(problem DNA)/2^Ct(rpoD), to obtain the copy number of problem DNA per number of chromosomes?

Thanks for your help!