Abstract
The reliability of reverse transcription-quantitative PCR (RT-qPCR) results in gene expression studies depends on the approaches used to account for non-biological variations. In order to find a proper normalization strategy for the study of genes related to growth hormone signaling in skeletal muscle of growing mice, nine unrelated genes were evaluated as internal controls. According to the most used algorithms–geNorm, the Comparative ΔCq method, NormFinder and BestKeeper–GSK3B, YWHAZ, RPL13A and RN18S were found as the most stable. However, the relative expression levels of eight of the potential reference genes assessed decreased with age in cDNA samples obtained from the same amount of total RNA. In a different approach to analyze this apparent discrepancy, experiments were performed with cDNA obtained from equal amounts of purified mRNA. Since the decline was still observed, the hypothesis of an age-related change in mRNA to total RNA ratio that could account for the systematic decrease was rejected. Differences among experimental groups could be due to a substantial increase with age in highly expressed mRNAs, which would bias the quantitation of the remaining genes. Consequently, those reference genes reflecting this dilution effect, which would have been discarded considering their variable relative expression levels, arose as suitable internal controls.
Highlights
Reverse transcription-quantitative PCR (RT-Quantitative PCR (qPCR)) is the most widely used technique to determine Messenger RNA (mRNA) levels in biological samples because of its sensitivity and large dynamic range
Initial reverse transcription-quantitative PCR (RT-qPCR) quality control was assessed by product analysis
No amplification product was detected in the no-reverse transcription and no-template controls included in each reaction plate
Summary
Reverse transcription-quantitative PCR (RT-qPCR) is the most widely used technique to determine mRNA levels in biological samples because of its sensitivity and large dynamic range. Like ensuring similar sample size prior to RNA extraction, submitting the same amount of RNA to reverse transcription or determining the expression levels of an internal control gene, are commonly used to reduce experimental error. Since there is no single procedure that controls for every error source, a combination is frequently adopted. The latter of these strategies is generally accepted to be the most suitable approach available since reference gene expression levels are measured in the same sample and subjected to almost the same sources of variability as the target genes under study [2]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
