Asked 29th Jul, 2015
Deleted profile

How can I calculate the expression of gene using 2-ddct method?

I have data containing diseased plant (treated) and control (untreated) and I would be like to study the expression of gene of my interest in comparison to housekeeping gene actin. I have 3 biological replicates which are further divided into 3 technical replicates. I am following 2-ddct method in such a way that 
for each biological replicate
dct - Average Ct of gene - average Ct of Actin (for both control and disease)
ddct - Disease Ct - control Ct
2-ddt  and then log values. 
However, the results are completely reverse what I expected. Can anyone correct me if I am doing it correctly and is there is need to add any other parameters. 

Most recent answer

20th Nov, 2017
Ajit Ghosh
Shahjalal University of Science and Technology
Dear Khushwant Singh,
Did you identified your problem? I am also facing the same problem with my real time data. I have followed the same protocol for calculation that you have mentioned.
Kindly inform me about your trouble shooting.

Popular Answers (1)

30th Jul, 2015
Ilya Korotetskiy
Republican State Enterprise "Scientific Center for anti-infectious drugs"

All Answers (7)

29th Jul, 2015
Marcus Vinicius P.S. Nascimento
Federal University of Santa Catarina
Khushwant, first of all you have to analyse if your PCR is ok. For example, your housekeeping gene (Actin) need to have a stable Ct, that means that the Ct values must no vary to the untreated and the treated samples (I'm telling you this cause my first PCR analysis I had some problems with this). Setting correct baselines and thresholds will also interfere in your results.
The calculus idea is this:
  1. DCt: Target Ct - Housekeeping Ct
  2. DDCt: Sample DCt - Calibrator DCt (Calibrator is your group of comparison)
  3. Fold calculus: 2^-DDCt
If you're comparing your results with literature and nothing is working even after reanalyse your calculus maybe there's something wrong with the technique itself since that PCR is a very delicate technique.
Hope it helps,
2 Recommendations
30th Jul, 2015
Nathaniel Wade Mcgregor
Stellenbosch University
Hi Khushwant
If I may - which machine are you using for your real-time expression? If it's say the Roche LightCycler or Corbett they usually allow for this in experimental run.  Be sure to run standard curves, housekeeping genes, controls and 'patients/ disease samples' in triplicate on the same plate (or in the same run) and set housekeeping as such, controls as such, disease samples as such and calibrators (if you have them).
You will then get relative expression for the samples of interest graphed, and in numerical value in your output file.
Remember to follow the MIQE guidelines for publication as well.
1 Recommendation
30th Jul, 2015
Ilya Korotetskiy
Republican State Enterprise "Scientific Center for anti-infectious drugs"
Deleted profile
Thank you all for providing useful information. 
1 Recommendation
28th Jul, 2016
Supratim Ghatak
I had same problem like Mr. Khushwant singh stated. In my siRNA transfection sample, the target gene was found to be 'upregulated' compared to control sample (using the same 2^-DDCt method. What went wrong?
1 Recommendation
19th Jan, 2017
Johana Madronero
El Bosque University
likely there is an error in the livack method.... I observed the same thing and look at these response from @Jochen Wilhelm
Jochen Wilhelm · 283.40 · Justus-Liebig-Universität Gießen
Note that log2(2^-ddCt) == -ddCt.
As you say you take 2^-ddCt as a fold change it seems that you follow the steps of Livak and Schmittgen. They made a mistake in the calculation of the dCt values. They wrote: dCt = Ct[goi] - Ct[ref], but is should have been dCt = (-Ct[goi]) - (-Ct[ref]) = Ct[ref] - Ct[goi]. As a consequence of this mistake the ddCt they calculated as the log of the resicprocal fold-change (FC). They corrected their mistake at the end when they gave the FC by switching the sign of the ddCt (instead of FC = 2^ddCt they wrote FC = 2^-ddCt).
You don't deed to repeat this mistake, and so you would not need to correct/switch the sign of the ddCt:
If you calculate dCt as Ct[ref] - Ct[goi], the dCt values will have the correct sign, i.e., samples with a higher goi expression (normalized to ref) will have higher dCt values.
If you then calculate ddCt as dCt[treat]-dCt[control], the sign of the ddCt value is also correct, i.e., positive ddCt indicate induction (up-regulation) and negative ddCt indicate repression (down-regulation).
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