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Is it possible that two genes, which come from two different cell cultures and which encodes the same protein, produces different quantity of mRNA? If yes, why?

My question comes from the fact that I am doing statistical analysis of a microarray dataset for the hyperthermophilic archaeon Pyrococcus furiosus exposed to gamma irradiation. Microarray experiments measure relative quantities of mRNA in a cell. My file has values, for each gene of the genome, that are positive or negative since the scientists who performed the experiment made the log10 ratio between the fluorescence intensity of the sample that was irradiated and the reference sample (not irradiated). Indeed, in the microarray experiment we have two channels that typically are one for the "controls" and the other for the cells which are stimulated by an event (in this case the event is gamma irradiation), in order to make a comparison.

In my dataset there are two columns (that are indicated as "reference") that refer to the case in which they should have put the cell cultures not irradiated in both the two channels. I would expect that the values in the file corresponding to these two columns are 0 (since they are the log10 ratio between the intensities of the signals of the genes which encode the same protein and that are in the same conditions). Instead they are different from 0. Therefore I wonder what is the reason for which there is different quantity of mRNA produced from two equal genes?

I made a graph of relative mRNA produced (calculated as I explained before) vs time, in which you can notice that the relative mRNA produced of the reference (I called them as "Ref" on the horizonal axis) is not 0. enter image description here

Is it only due to some noise or are there biological reasons ? (the picture refers to a gene which encodes Ferritin-like proteins but it is the same story also for other genes).

Even though I think that my interpretation of the data is correct, I tell you that the dataset I am talking about is here, if you want to have a look.

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  • $\begingroup$ Please edit your post to make it clear what you mean by "which are equal". More importantly how are these "equal" genes different — is the mRNA labeled differently? If so, does that give you any clues as to why you might see differences? In addition, I have edited your post to try to make your question clearer — please check to make sure I have not broken anything. Finally, please proofread your future posts and also use a spelling and grammar checker — this should help you improve your writing. Thanks! $\endgroup$ – tyersome Feb 15 at 19:26
  • $\begingroup$ @tyersome the fact is that it is not easy to explain but I will do the best say it better. Thank you anyway. $\endgroup$ – Manuela Feb 15 at 19:39
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    $\begingroup$ I agree I had a bit of trouble following this. Is it that there is a systematic difference, or that there is variability? In biological experiments measurement error and trial-to-trial variance are often relatively large compared to signals of interest. $\endgroup$ – Bryan Krause Feb 15 at 19:55
  • $\begingroup$ @tyersome I tried to make it clearer and your edits are ok. $\endgroup$ – Manuela Feb 15 at 21:03
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If I understand your question and graph correctly, your Y-axis is log(x/REF), where REF is some external standard. Your "Ref" on the x-axis you expect to be the same as REF, so that log(Ref/REF) "should be" zero, but you find it is not.

However, it looks like the mean(log(Ref/REF)) is still approximately zero. This is what I would expect: values that deviate a bit from perfectly 0, but having an average around 0 (and this average should approach 0 the more samples you have per the law of large numbers). You don't expect to have zero variance in data like this, which is why you repeat the experiment a number of times. You'll notice you have variance in all of your categories.

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  • $\begingroup$ Ok thank you, indeed now I notice that they are close to 0. But the variability is due to noise due to experimental setup or something like this for instance ? $\endgroup$ – Manuela Feb 15 at 21:09
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    $\begingroup$ @Manuela Unclear, but there are lots of places where "noise" is possible in this setup: in production and degradation of mRNA, which are going to be stochastic processes, in imaging of the microarrays themselves which are subject to noise in the camera and digitization, any optical aberrations, stochastic bleaching of fluorescent molecules to non-fluorescent compounds, etc. $\endgroup$ – Bryan Krause Feb 15 at 21:37
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    $\begingroup$ Another source of noise is hybridization of labeled cDNA to the microarray chip. It should happen under standard conditions and evenly across the chip - but in physical world nothing is realy ever exactly even. If you have the raw data with coordinates of the probes you should be able to display fluorescence intensities across 2D chip (There are R libraries for that). I once analyzed a chip with what looked like bubble in one corner. Even without bubbles one side could be warmer during hybridisation or other things. $\endgroup$ – BagiM Feb 16 at 8:12
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It is entirely possible that different cell-lines express the same genes at drastically different levels.

The proteinatlas provides data and analyses on differences between certain tissues or cell types / cell-lines.

If your cells are of the same line/tissue, then they might still differ dependent on the cell-cycle, age and external factors. There are chemicals that facilitate cell-cycle synchronization.

But I agree to the other answer, that your observations could be within margin of error.

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