I am looking for an effective way to normalise miRNA qPCR when using samples treated with PKC, PI3K, MAPK inhibitors, etc. I have tried using RNU62 and SNORD72 however, both have altered expressions when the sample has been treated with inhibitors. Has anyone come across this before? Or have any suggestions?
The best way would be to do first an unbiased analysis of miRNA expression in your system comparing treated and un-treated cells. You can do this for example by using Taqman or Exiqon miRNA panels for qPCR. The unbiased screen will highlight 1) which miRNAs are the most stable in your model system so that you can use these ones for subsequent experiments when you will run only a limited number of miRNA assays (i.e. your normalizers and your biologically interesting miRNAs) and 2) miRNAs that are changing due to your treatment.
Thanks for the quick reply...I originally used the miScript PCR array with 6 housekeeping miRNAs on treated and untreated samples and ranked the most stable miRNAs with Normfinder. SNORD72 was the most stable of the panel followed by RNU62. Then I treated the samples with an additional inhibitor and thats when I started seeing the housekeeping gene expression changes as well as the target miRNA. Should I still try the panels? I have got a lot of inhibitors to run and things could get expensive.
If budget is limiting, then I would suggest: 1) choosing ~10 miRNAs that are reported in the literature to be supposedly not altered by your treatments, or miRNAs that are widely used as normalizers in your cell system; 2) check all of these candidates across all the treatments you intend to perform; 3) run Normfinder analysis on the complete dataset. In this way you will be able to identify one or (better) two RNA species that are the most stable for all your experimental conditions. Take into consideration that SNORs and RNUs are not miRNAs, while they are used for miRNA normalization purposes (like U6) it would be nicer to have a couple of actual miRNAs as normalizers.
An advantage of the panels is that you can also already see which miRNAs will be actually altered by your treatments. Perhaps you can design a pilot experiment with the panels, identify promising candidates and further validate the analysis using single assays on a higher number of experimental replicates.
I have identified a few non SNOR and RNUs that are unchanged in my arrays, I will identify those which hopefully are not altered by the inhibitors and see how it goes. Thanks again for your help I will keep you updated.
I ended up using C.ele_39 exogenous spike in control which seems to be stable enough to give some significant results, but take a bit of optimising. Thanks again for all the suggestions
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