qPCR is used routinely in our lab, however I'm only just starting to seriously use it and although I've worked out most of the basics from the internet, there's a few specifics with analysing my experiments that I can't seem to work out (I've given up asking my lab to explain).
The experiments involve taking lung samples over a timecourse in mice after treatment. Some experiments have just one treatment (virus), others have two (allergen and/or virus), giving either two groups (control, virus) or four for the more complex (control, virus, allergen, allergen and virus). There are usually 4 mice per group.
I take one lung lobe and store it in RNAlater, then process it using QIAgen kits. Finally I do the RT reaction on this RNA. I Nanodrop the RNA to check concentration and that it doesn't vary too much, but I'm not currently varying what I put in the RT reaction (it's not varying too much).
What I'm wondering is this: To analyse the data I either do absolute quantification off a standard curve (which is what I'm doing for the viral load in the lung), or relative quantification via either ddCt (if the assays are equally efficient) or the standard curve method (if they're not). For relative quantification we use 18S (we'll skip over whether this is best), and to get fold change I need to use a calibrator sample, which in these experiments the control group for each time point would be ideal. However I'm looking at cytokine mRNA expression, and it's undetectable in my controls, which is not unexpected. This leads to division by zero getting ddCt, and a headache. I've tried purer cell populations with no luck in getting a baseline expression.
So, how do I analyse this data? What I think might work is:
A - normalisation of RNA into RT reaction across whole experiment and read the Ct values against a standard curve (which I have), allowing comparison along the whole timecourse and between groups (though encouraging comparison between experiments and other groups which aren't neccesarily comparable if different amounts of RNA go in) - essentially copies of gene per RNA conc.
B - report just normalised dCt, i.e. Ct(target)-Ct(18S), which I'm guessing is OK to compare along the timecourse but I'm not sure and the numbers are a bit difficult to understand at a glance as they're arbitrary
C - use a calibrator from a high-expressing mouse to get fold changes, but this seems a bit irrelevent as their treatment would be something different
I'm not sure if any of the above make sense. Either way I attempt to purify and run the whole experiment in one go, but this isn't always possible.
My second query is how would you use a calibrator sample anyway, as the 4 baseline mice aren't paired with treatment mice? Would you just average their dCt and subtract from each mouse dCt in the treatment groups so you still got statistics?
Any suggestions gratefully received! Apologies for the long post.
Edited by tamlynpeel, 01 February 2012 - 11:41 AM.