I am quantifying expression of marker genes in both wild-type and knockout mutant Arabidopsis seedlings. I am having a discussion with my PI and another graduate student about whether or not you can calculate relative expression of the marker genes using the delta delta Ct method by relating the expression of a marker gene in a mutant plant at, for instance, treatment time point 60 back to the expression of the marker gene in a wild type plant at time point zero (no treatment). Ubiquitin (UBQ) is being used as the control, and delta Ct is calculated by subtracting the Ct of UBQ from the Ct of the marker gene.
I say that you cannot, as you would be comparing the results of two separate variables within the experiment. Rather, you can only compare the expression of the marker gene within the mutant samples across time points or compare the expression of the marker gene within a fixed time point across all genotypes (wild-type and mutants).
What do you all think?
Hi Cullen,
I am on your site ;). It is possible to do the calculation, but the results are biologically useless. You can only detect differences between two lines AND different time points. Therefore, it is not possible to distinguish between effects that are due to the time difference and line specific effects, respectively. However, you are probably interested in effects which are only related to the time course.
Good luck with the next discussion!
Hi Richard,
The expression of the marker gene in both lines could be different. Therefore, the expression at T0 would be different between the lines. If you assume that the expression is doubled after 60 minutes, the resulting values for both lines are also different due to the different start values. Moreover, the expression change could be different e.g. 2x in one line and 3x in the other line.
Fortunately, the marker gene expression in both lines will be much more similar than in my example above. Nevertheless, you should only compare gene expression between both lines at the same time point or within one line between time points.
Hi Richard,
I tried to match your problem to the Pfaffl method (http://www.gene-quantification.de/chapter-3-pfaffl.pdf) equation (3.4):
target = your marker gene
ref = UBQ
control = wild-type
sample = knock-out line
If I understood your problem correctly, the time point is missing in this equation. Please correct me if I am wrong.
However, there should be suitable models around for your data. For example DeSeq2 is offering time course analysis for RNA-seq data. It is probably not possible to use the same model for your data, but there should be something similar available. Unfortunately, I do not know an appropriate method at the moment.
Your problem with other variables like hormone abundance should be similar. There is a need to compare different variables (genotype, time point, gene) at the same time. The chi-squared test could be applicable in this case, but the power of this test is relatively low. I am sorry for not providing a great solution.
Best,
Boas
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