Often you'll find no single gene is 100% stable, so you might have to use several. Given the behaviour of RNA it's usually better to use multiple genes anyway.

Take all of your candidate reference genes, and use them in a subset of your samples, being sure to take examples of all your controls and test conditions. The more samples you can test the better, and the more reference genes you investigate the better, but obviously it's a cost/benefit trade-off.

Then take the results of those qPCRs and run them through geNorm, Normfinder, or bestkeeper (or, ideally, all three). These will identify 'stable' genes according to slightly different criteria (geNorm looks at pairwise correlation, for instance, while Normfinder looks at inter- and intra-group stabilities), so anything ranked highly by all three is probably a good candidate. 

Pick two, or ideally three of these, and use these to normalise your data. You'll have to convert them into relative quantities, so instead of 23, 25, 24 you have 1, 0.25, 0.5 (etc) -assuming your reaction is 100% efficient, this is easily done by simply raising 2 to the power of (lowest Ct - sample Ct).

i.e. 23, 25, 24 -lowest Ct is 23, so

2(23-23) = 20 = 1

2(23-25) = 2-2 = 0.25

2(23-24) = 2-1 = 0.5

Then take these relative quantities for each of your reference gene and generate the geometric mean for each sample to get your normalisation factor: if we imagine the values for sample A for your three reference genes are 0.5, 0.45 and 0.4, the NF for sample A is the cube root of (0.5 x 0.45 x 0.4), or 0.448.

In a nutshell, normalising qPCR data well is not trivial, nor cheap. You may find you do more qPCRs of normalisation genes than you do test genes, but if you want to have high confidence in your data, it's worth it.

I have not tested reference genes in this conditions so what u can do is to run few samples with each of this genes and see which one is the best. You can find our publication about reference genes, there you can read more and find other reference genes.

Try this link:

http://scholar.google.pl/scholar?q=tomato+qPCR+%22reference+gene%22+%22heat+stress%22&btnG=&hl=pl&as_sdt=0%2C5

1604 papers must contain some information you seek...

Hi, you can try this... http://www.gene-quantification.de/bestkeeper.html

Often you'll find no single gene is 100% stable, so you might have to use several. Given the behaviour of RNA it's usually better to use multiple genes anyway.

Take all of your candidate reference genes, and use them in a subset of your samples, being sure to take examples of all your controls and test conditions. The more samples you can test the better, and the more reference genes you investigate the better, but obviously it's a cost/benefit trade-off.

Then take the results of those qPCRs and run them through geNorm, Normfinder, or bestkeeper (or, ideally, all three). These will identify 'stable' genes according to slightly different criteria (geNorm looks at pairwise correlation, for instance, while Normfinder looks at inter- and intra-group stabilities), so anything ranked highly by all three is probably a good candidate. 

Pick two, or ideally three of these, and use these to normalise your data. You'll have to convert them into relative quantities, so instead of 23, 25, 24 you have 1, 0.25, 0.5 (etc) -assuming your reaction is 100% efficient, this is easily done by simply raising 2 to the power of (lowest Ct - sample Ct).

i.e. 23, 25, 24 -lowest Ct is 23, so

2(23-23) = 20 = 1

2(23-25) = 2-2 = 0.25

2(23-24) = 2-1 = 0.5

Then take these relative quantities for each of your reference gene and generate the geometric mean for each sample to get your normalisation factor: if we imagine the values for sample A for your three reference genes are 0.5, 0.45 and 0.4, the NF for sample A is the cube root of (0.5 x 0.45 x 0.4), or 0.448.

In a nutshell, normalising qPCR data well is not trivial, nor cheap. You may find you do more qPCRs of normalisation genes than you do test genes, but if you want to have high confidence in your data, it's worth it.

Thank you john for your valuable suggestions..