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Published on: April 14, 2010
Validation of reference genes for normalization of real-time quantitative PCR studies of gene expression in brain
Maria Hersom1, Charlotte Goldeman1, Natasia Pretzer1
1Department of Pharmacy, Faculty of Health and Medical Sciences, University of Copenhagen, Universitetsparken 2, DK-2100 Copenhagen, Denmark.
Background:
The genes encoding β-actin and GAPDH are two of the most commonly used reference genes for normalization in in vitro blood-brain barrier studies. Studies have, however, shown that these reference genes might not always be the best choice. The aim of the present study was to evaluate 10 reference genes for use in mRNA profiling studies in primary cultures of brain endothelial cells of bovine origin.
Methods:
Gene evaluations were performed by qPCR in mono-culture and in co-cultures with astrocytes. The expression of reference genes was furthermore investigated during culture. Qbase+ software was used to analyze the stability of the tested genes and for determinations of the optimal number of reference genes.
Results:
The stability of the reference genes varied between the culture configurations, but for all culture configurations we found that the optimal number of reference genes were two. PMM-1, RPL13A and β-actin were the most stable genes in mono-cultures, non-contact co-culture and contact co-culture respectively. For studies comparing gene expression between different culture configurations the optimal number of reference genes was three and RPL13A was found to be most stable. During cell culture a number of four reference genes were found to be optimal and YWHAZ was found to be the most stable gene. β-actin and GAPDH were found to be the least stable genes during culture.
Conclusion:
Overall we found that the validation of reference genes was important in order to normalize target gene expression correctly, and suggest sets of reference genes to be used under different experimental conditions, in order to quantify mRNA transcript levels in blood-brain barrier cell models correctly.
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