Abstract
Cell growth and differentiation are often driven by subtle changes in gene expression. Many challenges still exist in detecting these changes, particularly in the context of a complex, developing tissue. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) allows relatively high-throughput evaluation of multiple genes and developmental time points. Proper quantification of gene expression levels by qRT-PCR requires normalization to one or more reference genes. Traditionally, these genes have been selected based on their presumed “housekeeping” function, with the implicit assumption that they are stably expressed over the entire experimental set. However, this is rarely tested empirically. Here we describe the identification of novel reference genes for the mouse mammary gland based on their stable expression in published microarray datasets. We compared eight novel candidate reference genes (Arpc3, Clock, Ctbp1, Phf7, Prdx1, Sugp2, Taf11 and Usp7) to eight traditional ones (18S, Actb, Gapdh, Hmbs, Hprt, Rpl13a, Sdha and Tbp) and analysed all genes for stable expression in the mouse mammary gland from pre-puberty to adulthood using four different algorithms (GeNorm, DeltaCt, BestKeeper and NormFinder). Prdx1, Phf7 and Ctbp1 were validated as novel and reliable, tissue-specific reference genes that outperform traditional reference genes in qRT-PCR studies of postnatal mammary gland development.
Highlights
Cell growth and differentiation are often driven by subtle changes in gene expression
The mammary gland is composed of multiple different cell types, including basal and luminal epithelial cells, stromal fibroblasts and adipocytes, as well as cells contributing to other structures in the gland such as nerves and blood vessels
No dedicated attempt has been made to determine whether suitable reference genes exist for use in Quantitative reverse transcription polymerase chain reaction (qRT-PCR) studies of whole mammary gland preparations across all stages of postnatal development
Summary
Cell growth and differentiation are often driven by subtle changes in gene expression. Proper quantification of gene expression levels by qRT-PCR requires normalization to one or more reference genes These genes have been selected based on their presumed “housekeeping” function, with the implicit assumption that they are stably expressed over the entire experimental set. Attempts have been made to identify universal reference genes, which could be applied to any sample of interest irrespective of its developmental origin, by comparing published datasets of multiple different human tissues and cell lines[14,15,16,17,18] This has failed to yield a consistent list of candidate genes, raising the question whether such universal references exist at all[19]. Finding the best performing reference genes requires a dedicated effort focussing on the specific tissue or organism of interest[5,7,20,21,22,23,24]
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