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Assessment of Reference Genes for Real-Time Quantitative PCR Gene Expression Normalization During C2C12 and H9c2 Skeletal Muscle Differentiation

Overview of attention for article published in Molecular Biotechnology, October 2013
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Title
Assessment of Reference Genes for Real-Time Quantitative PCR Gene Expression Normalization During C2C12 and H9c2 Skeletal Muscle Differentiation
Published in
Molecular Biotechnology, October 2013
DOI 10.1007/s12033-013-9712-2
Pubmed ID
Authors

Twinkle J. Masilamani, Julie J. Loiselle, Leslie C. Sutherland

Abstract

Skeletal muscle differentiation occurs during muscle development and regeneration. To initiate and maintain the differentiated state, a multitude of gene expression changes occur. Accurate assessment of these differentiation-related gene expression changes requires good quality template, but more specifically, appropriate internal controls for normalization. Two cell line-based models used for in vitro analyses of muscle differentiation incorporate mouse C2C12 and rat H9c2 cells. In this study, we set out to identify the most appropriate controls for mRNA expression normalization during C2C12 and H9c2 differentiation. We assessed the expression profiles of Actb, Gapdh, Hprt, Rps12 and Tbp during C2C12 differentiation and of Gapdh and Rps12 during H9c2 differentiation. Using NormFinder, we validated the stability of the genes individually and of the geometric mean generated from different gene combinations. We verified our results using Myogenin. Our study demonstrates that using the geometric mean of a combination of specific reference genes for normalization provides a platform for more precise test gene expression assessment during myoblast differentiation than using the absolute expression value of an individual gene and reinforces the necessity of reference gene validation.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Portugal 2 3%
Netherlands 1 2%
Austria 1 2%
Spain 1 2%
United States 1 2%
Unknown 56 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 18%
Researcher 11 18%
Student > Master 10 16%
Student > Bachelor 9 15%
Student > Postgraduate 3 5%
Other 6 10%
Unknown 12 19%
Readers by discipline Count As %
Agricultural and Biological Sciences 19 31%
Biochemistry, Genetics and Molecular Biology 15 24%
Engineering 3 5%
Chemistry 2 3%
Medicine and Dentistry 2 3%
Other 9 15%
Unknown 12 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 October 2013.
All research outputs
#15,283,138
of 22,727,570 outputs
Outputs from Molecular Biotechnology
#644
of 956 outputs
Outputs of similar age
#130,609
of 212,053 outputs
Outputs of similar age from Molecular Biotechnology
#5
of 5 outputs
Altmetric has tracked 22,727,570 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 956 research outputs from this source. They receive a mean Attention Score of 3.5. This one is in the 26th percentile – i.e., 26% of its peers scored the same or lower than it.
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