We are currently working with murine bone marrow macrophages. We currently use the delta delta CT method comparing our treatments groups to our un-stimulated control cells and are getting very high fold changes (more then 40,000), due to the little to no expression of the genes in the control.
Is this the best way to compare fold changes between treatment groups?
Hello Hannah,
in my opinion delta delta CT method is OK in connection with the amplification efficiency correction, for example Pfaffl 2001(Article A new mathematical model for relative quantification in real...
)High fold change does not matter, the key indicator of result significance is p-value. Of course, you can manually replace all "zero" values by some negligible constant "1" - biologically such low expression has no effect and your data will look better.
may find this discussion useful:
https://www.researchgate.net/post/How_to_calculate_fold_change_expression_of_experiments_where_control_treatment_or_control_plants_do_not_have_any_Ct_value_of_the_target_gene
Best regards
Hi, Hannah
First, YES PFAFFL ^^^
So, how many treatment groups do you have? Is one of them a standard treatment of some sort? It seems to me that the more interesting question is how do the treatment groups compare among themselves, or how do the novel treatments compare with the "standard" one.
Pfaffl will get you reliable fold-changes per gene per animal, provided you know the relative reaction efficiencies (and you do, right? you did this yourself, right?). Anyway, once you have the expression of (say, IFNg) with treatment 1, 2 and 3, you'll have three datasets. Compare these using the Kruskal-Wallis test and then you'll know whether they are the same as one another, or not.
What is your hypothesis?
Good Luck!
G
Dr. Gallagher,
The relative reaction efficiencies for the genes that I have tested are between 90 and 100%.
We are comparing our BMMs stimulates with bacteria to the un-stimulated cells. The main concern that I have is that our un-stimulated cells which are serving as our control have Ct values of 39 to 40.
Thank you for the suggestions.
Hannah
Hi again
well... what is changing? The bacteria (eg testing 5 different bacteria on the same BMM) or the BMM (eg giving different treatments to the BMM to see how it affects their response to the same bacterium)? What's the hypothesis?
It's not necessarily unexpected that resting BMMs will not be making much immune mRNA since they're, well, resting. So, if you want to see whether they get excited when they see LPS then the answer is obviously "yes", as you've seen.
I'm still reading that you're interested in whether the cells behave differently depending on which bacteria they see. Is this incorrect? Can you share a snippet of data?
Good luck
G
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