Dataset Information

Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization.

ABSTRACT: The accurate quantification of cellular and tissue mRNA and microRNA content is reliant upon the selection of stable endogenous control transcripts for normalizing quantitative real-time-PCR (qRT-PCR) data. Using the combination of unbiased and informed approaches and a wide range of human adipose tissues and cells, we sought to identify invariant control transcripts for mRNA and microRNA. A total of 26 mRNA transcript candidates were selected from the literature. MicroRNA candidates were selected from a microRNA-microarray (Agilent, n = 22 tissues), and together with candidates from the literature resulted in 14 different microRNAs. The variability of these mRNA and microRNA transcripts were then tested in a large (n = 180) collection of a variety of human adipose tissues and cell samples. Phosphoglycerate kinase-1 (PGK1) and peptidylprolyl isomerase A (PPIA) were identified as the most stable mRNAs across all tissues and panels. MiR-103 was overall the most stable microRNA transcript across all biological backgrounds. Several proposed and commonly used normalization transcripts were found to be highly variable. We then tested the effect on expression of two established adipocyte-related transcripts (fatty acid binding protein 4 (FABP4) and microRNA-145 (miR-145)), either normalized to the optimal or a commonly used controls transcript. This test clearly indicated that spurious results could arise from using less stable control transcripts for mRNA and microRNA qRT-PCR.

SUBMITTER: Neville MJ

PROVIDER: S-EPMC4623139 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization.

Neville Matt J MJ Collins Jenny M JM Gloyn Anna L AL McCarthy Mark I MI Karpe Fredrik F

Obesity (Silver Spring, Md.) 20101014 4

The accurate quantification of cellular and tissue mRNA and microRNA content is reliant upon the selection of stable endogenous control transcripts for normalizing quantitative real-time-PCR (qRT-PCR) data. Using the combination of unbiased and informed approaches and a wide range of human adipose tissues and cells, we sought to identify invariant control transcripts for mRNA and microRNA. A total of 26 mRNA transcript candidates were selected from the literature. MicroRNA candidates were select ...[more]

PMID: 20948521

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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

Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization.

Publications

Comprehensive human adipose tissue mRNA and microRNA endogenous control selection for quantitative real-time-PCR normalization.

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