Reproductive Sciences, Toronto Zoo, Scarborough, ON, M1B 5K7, Canada.
Department of Biomedical Sciences, Ontario Veterinary College, University of Guelph, Guelph, ON, N1G 2W1, Canada.
Sci Rep. 2021 May 13;11(1):10253. doi: 10.1038/s41598-021-89657-8.
Proper normalization of RT-qPCR data is pivotal to the interpretation of results and accuracy of scientific conclusions. Though different approaches may be taken, normalization against multiple reference genes is now standard practice. Genes traditionally used and deemed constitutively expressed have demonstrated variability in expression under different experimental conditions, necessitating the proper validation of reference genes prior to utilization. Considering the wide use of fibroblasts in research and scientific applications, it is imperative that suitable reference genes for fibroblasts of different animal origins and conditions be elucidated. Previous studies on bovine fibroblasts have tested limited genes and/or samples. Herein, we present an extensive study investigating the expression stability of 16 candidate reference genes across 7 untreated bovine fibroblast cell lines subjected to controlled conditions. Data were analysed using various statistical tools and algorithms, including geNorm, NormFinder, BestKeeper, and RefFinder. A combined use of GUSB and RPL13A was determined to be the best approach for data normalization in untreated bovine fibroblasts.
正确的 RT-qPCR 数据归一化对于解释结果和科学结论的准确性至关重要。尽管可以采用不同的方法，但现在通常的做法是针对多个参考基因进行归一化。传统上用于表示组成型表达的基因在不同的实验条件下表现出表达的可变性，因此在使用之前需要对参考基因进行适当的验证。鉴于成纤维细胞在研究和科学应用中的广泛使用，阐明不同动物来源和条件下成纤维细胞的合适参考基因是当务之急。以前关于牛成纤维细胞的研究仅测试了有限的基因和/或样本。在此，我们进行了一项广泛的研究，调查了 16 个候选参考基因在 7 种经处理的牛成纤维细胞系中的表达稳定性，这些细胞系处于受控条件下。使用各种统计工具和算法（包括 geNorm、NormFinder、BestKeeper 和 RefFinder）对数据进行了分析。确定联合使用 GUSB 和 RPL13A 是未经处理的牛成纤维细胞数据归一化的最佳方法。
