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Internal control genes for quantitative RT-PCR expression analysis in mouse osteoblasts, osteoclasts and macrophages.

ABSTRACT: BACKGROUND: Real-time quantitative RT-PCR (qPCR) is a powerful technique capable of accurately quantitating mRNA expression levels over a large dynamic range. This makes qPCR the most widely used method for studying quantitative gene expression. An important aspect of qPCR is selecting appropriate controls or normalization factors to account for any differences in starting cDNA quantities between samples during expression studies. Here, we report on the selection of a concise set of housekeeper genes for the accurate normalization of quantitative gene expression data in differentiating osteoblasts, osteoclasts and macrophages. We implemented the use of geNorm, an algorithm that determines the suitability of genes to function as housekeepers by assessing expression stabilities. We evaluated the expression stabilities of 18S, ACTB, B2M, GAPDH, HMBS and HPRT1 genes. FINDINGS: Our analyses revealed that 18S and GAPDH were regulated during osteoblast differentiation and are not suitable for use as reference genes. The most stably expressed genes in osteoblasts were ACTB, HMBS and HPRT1 and their geometric average constitutes a suitable normalization factor upon which gene expression data can be normalized. In macrophages, 18S and GAPDH were the most variable genes while HMBS and B2M were the most stably expressed genes. The geometric average of HMBS and B2M expression levels forms a suitable normalization factor to account for potential differences in starting cDNA quantities during gene expression analysis in macrophages. The expression stabilities of the six candidate reference genes in osteoclasts were, on average, more variable than that observed in macrophages but slightly less variable than those seen in osteoblasts. The two most stably expressed genes in osteoclasts were HMBS and B2M and the genes displaying the greatest levels of variability were 18S and GAPDH. Notably, 18S and GAPDH were the two most variably expressed control genes in all three cell types. The geometric average of HMBS, B2M and ACTB creates an appropriate normalization factor for gene expression studies in osteoclasts. CONCLUSION: We have identified concise sets of genes suitable to use as normalization factors for quantitative real-time RT-PCR gene expression studies in osteoblasts, osteoclasts and macrophages.

SUBMITTER: Stephens AS

PROVIDER: S-EPMC3204251 | biostudies-literature | 2011

REPOSITORIES: biostudies-literature

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Internal control genes for quantitative RT-PCR expression analysis in mouse osteoblasts, osteoclasts and macrophages.

Stephens Alexandre S AS Stephens Sebastien R SR Morrison Nigel A NA

BMC research notes 20111014

<h4>Background</h4>Real-time quantitative RT-PCR (qPCR) is a powerful technique capable of accurately quantitating mRNA expression levels over a large dynamic range. This makes qPCR the most widely used method for studying quantitative gene expression. An important aspect of qPCR is selecting appropriate controls or normalization factors to account for any differences in starting cDNA quantities between samples during expression studies. Here, we report on the selection of a concise set of house ...[more]

PMID: 21996334

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Project description:BackgroundThe elucidation of gene expression patterns leads to a better understanding of biological processes. Real-time quantitative RT-PCR has become the standard method for in-depth studies of gene expression. A biologically meaningful reporting of target mRNA quantities requires accurate and reliable normalization in order to identify real gene-specific variation. The purpose of normalization is to control several variables such as different amounts and quality of starting material, variable enzymatic efficiencies of retrotranscription from RNA to cDNA, or differences between tissues or cells in overall transcriptional activity. The validity of a housekeeping gene as endogenous control relies on the stability of its expression level across the sample panel being analysed. In the present report we describe the first systematic evaluation of potential internal controls during tomato development process to identify which are the most reliable for transcript quantification by real-time RT-PCR.ResultsIn this study, we assess the expression stability of 7 traditional and 4 novel housekeeping genes in a set of 27 samples representing different tissues and organs of tomato plants at different developmental stages. First, we designed, tested and optimized amplification primers for real-time RT-PCR. Then, expression data from each candidate gene were evaluated with three complementary approaches based on different statistical procedures. Our analysis suggests that SGN-U314153 (CAC), SGN-U321250 (TIP41), SGN-U346908 ("Expressed") and SGN-U316474 (SAND) genes provide superior transcript normalization in tomato development studies. We recommend different combinations of these exceptionally stable housekeeping genes for suited normalization of different developmental series, including the complete tomato development process.ConclusionThis work constitutes the first effort for the selection of optimal endogenous controls for quantitative real-time RT-PCR studies of gene expression during tomato development process. From our study a tool-kit of control genes emerges that outperform the traditional genes in terms of expression stability.

Project description:BackgroundReference genes are commonly used as the endogenous normalisation measure for the relative quantification of target genes. The appropriate application of quantitative real-time PCR (RT-qPCR), however, requires the use of reference genes whose level of expression is not affected by the test, by general physiological conditions or by inter-individual variability. For this purpose, seven reference genes were investigated in tissues of the most important cereals (wheat, barley and oats). Titre of Barley yellow dwarf virus (BYDV) was determined in oats using relative quantification with different reference genes and absolute quantification, and the results were compared.ResultsThe expression of seven potential reference genes was evaluated in tissues of 180 healthy, physiologically stressed and virus-infected cereal plants. These genes were tested by RT-qPCR and ranked according to the stability of their expression using three different methods (two-way ANOVA, GeNorm and NormFinder tools). In most cases, the expression of all genes did not depend on abiotic stress conditions or virus infections. All the genes showed significant differences in expression among plant species. Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta-tubulin (TUBB) and 18S ribosomal RNA (18S rRNA) always ranked as the three most stable genes. On the other hand, elongation factor-1 alpha (EF1A), eukaryotic initiation factor 4a (EIF4A), and 28S ribosomal RNA (28S rRNA) for barley and oat samples; and alpha-tubulin (TUBA) for wheat samples were consistently ranked as the less reliable controls.The BYDV titre was determined in two oat varieties by RT-qPCR using three different quantification approaches. There were no significant differences between the absolute and relative quantifications, or between quantification using GAPDH + TUBB + TUBA +18S rRNA and EF1A + EIF4A + 28S rRNA. However, there were discrepancies between the results of individual assays.ConclusionsThe geometric average of GAPDH, 18S rRNA and TUBB is suitable for normalisation of BYDV quantification in barley tissues. For wheat and oat samples, a combination of four genes is necessary: GAPDH, 18S rRNA, TUBB and EIF4A for wheat; and GAPDH, 18S rRNA, TUBB and TUBA for oat is recommended.

Project description:Studies on the transcriptional control of gene expression are crucial to understand changes in organism's physiological or cellular conditions. To obtain reliable data on mRNA amounts and the estimation of gene expression levels, it is crucial to normalize the target gene with one or more internal reference gene(s). However, the use of constitutive genes as reference genes is controversial, as their expression patterns are sometimes more complex than previously thought. In various arthropod vectors, including ticks, several constitutive genes have been identified by studying gene expression in different tissues and life stages. The cattle tick Rhipicephalus microplus is a major vector for several pathogens and is widely distributed in tropical and subtropical regions globally. Tick developmental physiology is an essential aspect of research, particularly embryogenesis, where many important developmental events occur, thus the identification of stable reference genes is essential for the interpretation of reliable gene expression data. This study aimed to identify and select R. microplus housekeeping genes and evaluate their stability during embryogenesis. Reference genes used as internal control in molecular assays were selected based on previous studies. These genes were screened by quantitative PCR (qPCR) and tested for gene expression stability during embryogenesis. Results demonstrated that the relative stability of reference genes varied at different time points during the embryogenesis. The GeNorm tool showed that elongation factor 1α (Elf1a) and ribosomal protein L4 (Rpl4) were the most stable genes, while H3 histone family 3A (Hist3A) and ribosomal protein S18 (RpS18) were the least stable. The NormFinder tool showed that Rpl4 was the most stable gene, while the ranking of Elf1a was intermediate in all tested conditions. The BestKeeper tool showed that Rpl4 and cyclophilin A (CycA) were the more and less stable genes, respectively. These data collectively demonstrate that Rpl4, Elf1a, and GAPDH are suitable internal controls for normalizing qPCR during R. microplus embryogenesis. These genes were consistently identified as the most stable in various analysis methods employed in this study. Thus, findings presented in this study offer valuable information for the study of gene expression during embryogenesis in R. microplus.

Project description:BackgroundNormalization in real-time qRT-PCR is necessary to compensate for experimental variation. A popular normalization strategy employs reference gene(s), which may introduce additional variability into normalized expression levels due to innate variation (between tissues, individuals, etc). To minimize this innate variability, multiple reference genes are used. Current methods of selecting reference genes make an assumption of independence in their innate variation. This assumption is not always justified, which may lead to selecting a suboptimal set of reference genes.ResultsWe propose a robust approach for selecting optimal subset(s) of reference genes with the smallest variance of the corresponding normalizing factors. The normalizing factor variance estimates are based on the estimated unstructured covariance matrix of all available candidate reference genes, adjusting for all possible correlations. Robustness is achieved through bootstrapping all candidate reference gene data and obtaining the bootstrap upper confidence limits for the variances of the log-transformed normalizing factors. The selection of the reference gene subset is optimized with respect to one of the following criteria: (A) to minimize the variability of the normalizing factor; (B) to minimize the number of reference genes with acceptable upper limit on variability of the normalizing factor, (C) to minimize the average rank of the variance of the normalizing factor. The proposed approach evaluates all gene subsets of various sizes rather than ranking individual reference genes by their stability, as in the previous work. In two publicly available data sets and one new data set, our approach identified subset(s) of reference genes with smaller empirical variance of the normalizing factor than in subsets identified using previously published methods. A small simulation study indicated an advantage of the proposed approach in terms of sensitivity to identify the true optimal reference subset in the presence of even modest, especially negative correlation among the candidate reference genes.ConclusionsThe proposed approach performs comprehensive and robust evaluation of the variability of normalizing factors based on all possible subsets of candidate reference genes. The results of this evaluation provide flexibility to choose from important criteria for selecting the optimal subset(s) of reference genes, unless one subset meets all the criteria. This approach identifies gene subset(s) with smaller variability of normalizing factors than current standard approaches, particularly if there is some nontrivial innate correlation among the candidate genes.

Dataset Information

Internal control genes for quantitative RT-PCR expression analysis in mouse osteoblasts, osteoclasts and macrophages.

Publications

Internal control genes for quantitative RT-PCR expression analysis in mouse osteoblasts, osteoclasts and macrophages.

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