I conduct cell culture experiments where I collect and analyze RNA for gene transcription changes via RT-qPCR. How I can show that the changes I am documenting are biologically significant/relevant? Specifically, for the mRNA changes of gene transcripts, I am showing graphs of relative difference in fold change of treated versus untreated negative control, where I set the untreated control to 1. How do I assess whether these changes are indicative of biologically significant amounts? I'm not referring to statistical significance here, by the way.
Hello Nalini.
Differential transcriptome analysis is only a starting point for possible discovery of candidate genes involved in marked biological phenomena or states of the cells under investigation. Next thing to do depends on your favorite field of the biological phenomena. Are you interested in the cell differentiation showing different cell phenotypes or the toxicity of the agent(s) treated or gene regulation under two treatments or states of the cells?
Once you decide the direction of further pursuing, you will clone the candidate gene(s), 'gain of function' or 'loss of function' studies by transfection of the clone gene(s) into the cells you used or siRNA- or shRNA-based knowdown or deletion study via various tools available. By accumulating these early evidences, you could further investigate the details of the processes lying in the 'cause and effect' biological consequences.
Best wishes.
As stated above, for biological significance you need an assay. What will you be measuring? Assays might include measuring proliferation rates, cell motility or migration, rate of cell death, differentiation potential or transdifferentiation, ability to make tumors in mice, effects on the cell cycle, etc. In other words, in order to assign biological significance you need a biological assay for some functions.
I agree with both answers above. To summarize, I would say that the biological significance is not something to be calculated from numerical differences in expression. Biology is all too full of examples of huge statistical significance with little biological effects and vice-versa. However, if your data is good and the differences are statistically significant, you already have an important piece of knowledge, since the gene regulation itself is a biological response, even if you don't know what it does. If you have a clue about its function or regulation, then plan and carry on assays to test if that gene is really important to the effect of your treatment, as suggested above. Just keep in mind that you may just be investigating the wrong outcome.
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