For fibroblasts CLK2 is a good internal control. See this paper: http://www.ncbi.nlm.nih.gov/pubmed/16771605

I would try the following primer pairs as a start (all human sequences and validated efficiencies across high dilution range).Assumming you are working with human fibroblasts of course We use these for our leukemia work and have used in the past these in neuronal differentiation expts, breast cancer and melanoma work - where we also looked at fibroblasts . All work in the different cellular models, but best to test in your differentiation model to ensure their levels stay constant. The sequences are as follows:

1) TBP:

F:CGGCTGTTTAACTTCGCTTC

R:CACACGCCAAGAAACAGTGA

2) GusB

F:GCC AAT GAA ACC AGG TAT CCC

R:GCT CAA GTA AAC AGG CTG TTT TCC

3) HMBS

F: gagagtgattcgcgtgggta

R:cagggtacgaggctttcaat

4) HPRT

F: TGACACTGGCAAAACAATGCA

R:GGTCCTTTTCACCAGCAAGCT

You will have to check how far these sequences are from the polyA tail though to see if they are within 1kB or so. Is there any reason you chose oligodT rather than random hexamer primed cDNA?

Whether they change with Serum - I am not sure. you will have to test these out.

Also, please blast these so you are sure they meet your requirements in terms of Tm, Size of product, span intronic regions etc. I would plug them in here so you can check your self:

http://www.ncbi.nlm.nih.gov/tools/primer-blast/

Just paste in the Fwd and Rev sequences in the "use my own Forward..." and "use my own reverse primer..." Once both are pasted, hit blast. Hope this helps and good luck.

For fibroblasts CLK2 is a good internal control. See this paper: http://www.ncbi.nlm.nih.gov/pubmed/16771605

Hi Michael,

As suggested by Charles...you'll have to check the best available internal control for fibroblasts for your study. ....The best gene should be which is not significantly affected by the treatment.......if you are giving this treatment (experimental condition) for the first time it would be better if you screen the available genes to select the least affected one

bye

I entirely agree with Charles and Ajay. Go for multiple internal genes and select the best one. Based on our lab experience following are worth while to try±

HPRT1a

Sense 5′-TGACACTGGCAAAACAATGCA-3′

Antisense 5′-GGTCCTTTTCACCAGCAAGCT-3′

b-2 Microglobulin

Sense 5´GCTATCCAGAAAACCCCTCAA 3´

Antisense 5´CATGTCTCGATCCCAGTAGACGGT 3´

PPIA;

Sense 5´CATCTGCACTGCCAAGACTGAG 3´

Antisense 5´ TGCAATCCAGCTAGGCATG 3´

Goodluck

Thanks everyone for their responses!

See our blog post on this subject. In short: it requires validation on your end!

http://www.biogazelle.com/four-tips-rt-qpcr-data-normalization-using-reference-genes