Today I will briefly present to you the quantitative real time polymerase chain reaction (qRT-PCR) that I used for my master thesis to verify the genic expression of some genes of interest (such as c-FOS, a marker gene for neuronal activation, or CGRP, have a look here for a brief explanation of why I elected this gene) in the areas that are involved in generating the migraine pain in our animal model of behavior already presented in last week post (have a look here if you missed it).
The qRT-PCR is a molecular biology technique used to amplify and quantify a gene of interest. This method employs the concomitant amplification and quantification of our target gene(s), thanks to the use of fluorescent probes that allow to correlate the concentration of the PCR product(s) (that is the quantified target gene) with the fluorescence intensity, that increases proportionally with the PCR product(s) accumulation.
The qRT-PCR can be divided in four stages (see picture below for an example graph):
- linear stage: PCR is just starting, fluorescent signal has not risen above background. This stage is used to calculate the background signal;
- early exponential phase: is where fluorescent signal just rise significantly above background. The cycle at which occurs is called cycle threshold (Ct). The more the number of copies of our target gene, the least the time needed to reach the Ct;
- linear exponential phase: PCR is in its optimal amplification stage with doubling PCR products in every cycle. The number of copies of our target gene produced in the PCR reaction is proportional to the number of copies that were present in the sample;
- plateau phase: it is the last phase, when the substrates are exhausted and the enzyme used in this reaction is in its end of life, determining that the fluorescent signal will no long increase.
The quantification of our target gene(s) can be: absolute, where you quantitate unknowns based on a known quantity. However, at first you create a standard curve and then you compare unknowns to the standard curve and extrapolate a value. This is used for example to correlate viral copy number with a disease state; relative, where you analyze changes in gene(s) expression in a given sample relative to another reference sample (such as an untreated control sample or a housekeeping gene to normalize the expression of the gene(s) of interest).
To end up this short (but I hope informative) post, I would like to describe what an housekeeping gene and/or protein is: typically housekeeping genes/proteins are ubiquitous genes/proteins, constitutively expressed and required for the maintenance of basic cellular function and are expressed in all cells of an organism under normal and patho-physiological conditions. Moreover, to be the perfect reference for an experiment, the housekeeping gene/protein selected should not be affected by the treatment/manipulation object of the study and its expression should be constant across different cell types. Good example of housekeeping genes/proteins that are commonly used as references are the Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), beta-actin or ribosomal subunits.