The Cq is used to calculate the amount of cDNA - either relatively, by normalizing to a “housekeeper” or “reference” sample or absolutely, by comparing to samples of known concentration and size. The point at which cDNA amplification produce a detectable fluorescent signal is termed the cycle threshold (Ct) or quantification cycle (Cq). Several methods exist for the detection of amplified reverse transcriptase (RT) cDNA the simplest of these involves the incorporation of a fluorescent dye (such as SYBR green) that binds to double stranded DNA, emitting a signal which can be measured in real-time at the end of each RT-qPCR cycle. The underlying principal of this technique is to enzymatically amplify short sequences using oligonucleotide or probes in a polymerase driven reaction. Quantitative real-time PCR (qPCR) is a sensitive fluorescence based technique used to quantify gene transcription and consequently give insight into gene expression and function. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. KGRQ is supported by an Early Career Fellowship from the NHMRC of Australia (0511981). XFZ is supported by a Dora Lush Biomedical Scholarship from the National Health and Medical Research Council of Australia (1038991). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: This project was funded in part by a grant from the National Health and Medical Research Council of Australia (1002033). Received: OctoAccepted: JanuPublished: February 11, 2014Ĭopyright: © 2014 Thomas et al. PLoS ONE 9(2):Įditor: Robert Dettman, Northwestern University, United States of America (2014) Evidence Based Selection of Commonly Used RT-qPCR Reference Genes for the Analysis of Mouse Skeletal Muscle. In the analysis of mouse skeletal muscle, strain and intervention played an important role in selecting the most stable reference genes.Ĭitation: Thomas KC, Zheng XF, Garces Suarez F, Raftery JM, Quinlan KGR, Yang N, et al. For the most accurate normalization, it is important to test several genes and use the geometric mean of at least three of the most stably expressed genes. Our data demonstrate that reference genes need to be validated prior to use. Furthermore we demonstrate that a poor reference gene results in increased variability in the normalized expression of a gene of interest, and can result in loss of significance. Analysis of our experimental variant ( Actn3 KO) also resulted in an altered ranking of reference gene suitability.

Interestingly the commonly used reference genes Gapdh, Rn18s, Hprt1 and Actb were not the most stable. We have developed a ranked profile of the top performing reference genes in skeletal muscle across these common mouse strains. Comparing WT mice across three genetic backgrounds, we found that different genes were more tightly regulated in each strain. Using the MIQE guidelines we compared wild-type (WT) mice across three genetic backgrounds (R129, C57BL/6j and C57BL/10) as well as analyzing the α-actinin-3 knockout ( Actn3 KO) mouse, which is a model of the common null polymorphism (R577X) in human ACTN3. Here we present an analysis of 10 reference genes in mouse skeletal muscle ( Actb, Aldoa, Gapdh, Hprt1, Ppia, Rer1, Rn18s, Rpl27, Rpl41 and Rpl7L1), which were identified as stable either by microarray or in the literature. However, commonly used reference genes are often poorly validated and may change as a result of genetic background, environment and experimental intervention. The use of ‘housekeeping’ or ‘reference’ genes is the most common technique used to normalize RT-qPCR data. The ability to obtain accurate and reproducible data using quantitative real-time Polymerase Chain Reaction (RT-qPCR) is limited by the process of data normalization.

Rotor gene 6000 software