Hi Maren,

I think your should try to test at least 5 or 6 different reference genes in your experiment to see which ones are more stable and then decide which ones to stick to. It does not matter if they have been successful in other experiments since stability changes depending on treatment conditions. Things like ETIFs, GADPH, UBI genes are commonly tested but you have to try them in your system. Also, you should try to put your results of amplification through geNorm and/or Bestkeeper, to see which genes are more stable, which ones are correlated and how many do you need. Sometimes these programs may inform you that you may not need more than 2 genes for normalizing and then you would have a statistical justification on why you use 2 instead of 3. Worse comes to worse if after testing many reference genes you only find that one is stable in your experiment and you can demonstrate that is the case with a statistical test, then you would have to stick with that one. Other things I would consider if stability is an issue, is to go back and make sure that all previous steps where done properly (RNA isolation, DNAse treatment, cDNA synthesis); high quality RNA and a standardized synthesis may also have an influence in the success of the experiment.

I am attaching the papers for the gene stability test. You can just google Bestkeeper to download the excel macro, but if you need geNorm and can not find it, I can sen you the macro.

good luck!!!

Hi Maren, I think you shouldn't believe in reference genes who other people say they will work fine. Test different reference genes! Cheers, Nadine

By browsing literature, I found these:

http://link.springer.com/content/pdf/10.1007%2Fs00299-005-0929-9.pdf

http://link.springer.com/article/10.1023/A:1025482432486

http://onlinelibrary.wiley.com/doi/10.1111/j.1365-294X.2006.02775.x/full

http://link.springer.com/article/10.1007/s00299-010-0944-3

https://dl.sciencesocieties.org/publications/cs/abstracts/43/2/678

http://onlinelibrary.wiley.com/doi/10.1111/j.1365-313X.2006.02865.x/full

http://www.sciencedirect.com/science/article/pii/S0176161704001324

http://onlinelibrary.wiley.com/doi/10.1111/j.1365-3059.2007.01575.x/full

http://www.sciencedirect.com/science/article/pii/S0981942803001992

http://link.springer.com/article/10.1007/s00122-004-1716-7

http://link.springer.com/article/10.1007/s00122-006-0419-7 .

They all use RT-PCR, so probably you'll find them a bit clarifying the situation in sunflower.

I think one problem is that qPCR is much more sensitive than semi qRT-PCR or Northern blot analysis used previously. Moreover, it is common now to use at least 3 different genes as endogenous controls. I already used actin and tubulin primers but it was not satisfying. Now I will try the 60S rRNA gene.

Thank you for support!

Hello Maren,

I would have a look on this Paper :

"Genome-Wide Identification and Testing of Superior

Reference Genes for Transcript Normalization

in Arabidopsis1" it´s writen by Czechowski et. al.

We adapted genes predicted in this paper for some other organisems and they did quite a good job.

Good luck

Hi, I advice you to use, at least, 2 well accepted ref genes. At present, I use 3 ref genes for qpcr. but, as you know, ref genes are related to tissue, developmental stage...it is almost impossible to said what is the universal ref gene. in ESC, most of data are done using gadph. People working with xenopus embryo uses ODC or Ef1a, as well as H4.

Hi Maren,

I think your should try to test at least 5 or 6 different reference genes in your experiment to see which ones are more stable and then decide which ones to stick to. It does not matter if they have been successful in other experiments since stability changes depending on treatment conditions. Things like ETIFs, GADPH, UBI genes are commonly tested but you have to try them in your system. Also, you should try to put your results of amplification through geNorm and/or Bestkeeper, to see which genes are more stable, which ones are correlated and how many do you need. Sometimes these programs may inform you that you may not need more than 2 genes for normalizing and then you would have a statistical justification on why you use 2 instead of 3. Worse comes to worse if after testing many reference genes you only find that one is stable in your experiment and you can demonstrate that is the case with a statistical test, then you would have to stick with that one. Other things I would consider if stability is an issue, is to go back and make sure that all previous steps where done properly (RNA isolation, DNAse treatment, cDNA synthesis); high quality RNA and a standardized synthesis may also have an influence in the success of the experiment.

I am attaching the papers for the gene stability test. You can just google Bestkeeper to download the excel macro, but if you need geNorm and can not find it, I can sen you the macro.

good luck!!!

It seems you can only add one attachment per comment...here is the other

Hi, I think that Leonardo gave to you a correct response, and If you want make a fast stadistical check, you can use a web site, that make a verification with genorm, bestkeeper and normfinder very fast. http://www.leonxie.com/referencegene.php

Hi Maren.. have you still remember me, we were in same Lab in Hannover before. Ok, about qPCR, we are doing quite many qPCR in our lab and so far TUB10, 18SrRNA, and EF-1alpha are the best for different set up experiments

Hi, You know reference gene selection varies with organisms, tissue and experimental conditions. If you want to systematically go, you have to try 5 or 6 reference genes in your experimental condition and then statistically be sure that a particular gene is best suited for your condition as internal control. We have genorm, bestkeeper and normfinder for that. Otherwise to make things fast and to start with, You can try tubulin as suggested by Silvina as that worked fine for sunflower.

Good luck

Hi Maren, failing to identify well workingreference genes another approach might be suitable. Look up Norma-Gene (published in BMC Bioinformatics). Good luck.

Hi Maren,

Based on my experience with qPCR, Gonzalez's comments will be the most useful although you have to invest some time to get the right reference genes for your purpose.