Selection of reference genes for quantitative real-time polymerase chain reaction studies in rat osteoblasts.
Rodrigo P F AbunaFabiola S OliveiraJaqueline I R RamosHelena B LopesGileade P FreitasAlann T P SouzaMarcio M BelotiAdalberto Luiz RosaPublished in: Journal of cellular physiology (2018)
Quantitative real-time polymerase chain reaction (qRT-PCR) is a powerful tool to evaluate gene expression, but its accuracy depends on the choice and stability of the reference genes used for normalization. In this study, we aimed to identify reference genes for studies on osteoblasts derived from rat bone marrow mesenchymal stem cells (bone marrow osteoblasts), osteoblasts derived from newborn rat calvarial (calvarial osteoblasts), and rat osteosarcoma cell line UMR-106. The osteoblast phenotype was characterized by ALP activity and extracellular matrix mineralization. Thirty-one candidates for reference genes from a Taqman® array were assessed by qRT-PCR, and their expressions were analyzed by five different approaches. The data showed that several of the most traditional reference genes, such as Actb and Gapdh, were inadequate for normalization and that the experimental conditions may affect gene stability. Eif2b1 was frequently identified among the best reference genes in bone marrow osteoblasts, calvarial osteoblasts, and UMR-106 osteoblasts. Selected stable and unstable reference genes were used to normalize the gene expression of Runx2, Alp, and Oc. The data showed statistically significant differences in the expression of these genes depending on the stability of the reference gene used for normalization, creating a bias that may induce incorrect assumptions in terms of osteoblast characterization of these cells. In conclusion, our study indicates that a rigorous selection of reference genes is a key step in qRT-PCR studies in osteoblasts to generate precise and reliable data.
Keyphrases
- genome wide
- genome wide identification
- gene expression
- bone marrow
- bioinformatics analysis
- dna methylation
- oxidative stress
- extracellular matrix
- genome wide analysis
- high resolution
- transcription factor
- mesenchymal stem cells
- electronic health record
- copy number
- poor prognosis
- cell death
- deep learning
- cell cycle arrest
- long non coding rna
- single cell
- data analysis