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Importance of suitable reference gene selection for quantitative RT-PCR during ATDC5 cells chondrocyte differentiation.

ABSTRACT: Real-time quantitative reverse transcription-polymerase chain reaction (qPCR) is an efficient and accurate method to detect and compare patterns of gene expression. The reliability of qPCR is highly dependent on the selection of appropriate reference genes used for normalization. By analyzing 16 potential candidates of reference genes (GAPDH, Actb, 18 s, PGK1, Hprt, Tbp, Rpl5, B2M, Gusb, Ppia, UBC, Sdha, Eef1a1, H2afz, Tkt and Ldha) through geNorm, we identified Ppia, Tbp, Hprt and Eef1a1 as the most stable reference genes while UBC, B2M, Gusb as the least stable ones during the chondrocyte differentiation of ATDC5 cells. Considering the low expression of Eef1a1 and Tbp would cause divergent results for they failed to provide accurate normalization for RNA extraction and reverse transcription efficiency, we recommended the use of Ppia and Hprt as the most suitable genes to normalize qPCR. In addition, although GAPDH, Actb and 18 s were usually adopted in most of studies using ATDC5 cells, they were found unstable and then were not ideal reference genes for qPCR assay in ATDC5 cells chondrocyte differentiation. Also, we further confirmed that the Ppia and Hprt worked well during chondrocyte differentiation of mouse mesenchymal cells.

SUBMITTER: Zhai Z

PROVIDER: S-EPMC3660368 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Importance of suitable reference gene selection for quantitative RT-PCR during ATDC5 cells chondrocyte differentiation.

Zhai Zhichen Z Yao Yongchang Y Wang Yingjun Y

PloS one 20130521 5

Real-time quantitative reverse transcription-polymerase chain reaction (qPCR) is an efficient and accurate method to detect and compare patterns of gene expression. The reliability of qPCR is highly dependent on the selection of appropriate reference genes used for normalization. By analyzing 16 potential candidates of reference genes (GAPDH, Actb, 18 s, PGK1, Hprt, Tbp, Rpl5, B2M, Gusb, Ppia, UBC, Sdha, Eef1a1, H2afz, Tkt and Ldha) through geNorm, we identified Ppia, Tbp, Hprt and Eef1a1 as the ...[more]

PMID: 23705012

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Project description:BackgroundQuantitative real-time PCR (qPCR) is a widely used technique for gene expression analysis. Its reliability is highly dependent upon selection of the appropriate reference genes for accurate gene expression normalization. In this study, we investigated the expression stability of 10 commonly used reference genes in a mouse myocardial infarction model.Methods & resultsThe expression stability of the 10 reference genes (Actb, B2m, Eef1a1, Gapdh, Hprt, Polr2a, Ppia, Rpl13a, Tbp, Tpt1) was analyzed using the geNorm software. Overall, the combination of Hprt, Rpl13a and Tpt1 was the most stable reference gene set in our experiments. Gapdh, Polr2a and Actb consistently showed the highest gene expression variability and the expression levels of Gapdh, Polr2a, Actb, B2m and Eef1a1 were found to be selectively up- or downregulated after myocardial infarction. We normalized the expression of Nppb and Vcam1, using different reference gene strategies and demonstrated that their induction after myocardial infarction was most clearly revealed with the optimal reference gene combination. However, the use of suboptimal reference gene combinations resulted in detrimental effects on gene expression levels and variability with a gradual loss of the expression differences and a significant reduction in statistical power.ConclusionsHprt, Rpl13a and Tpt1 are a set of stably expressed reference genes for accurate gene expression normalization in myocardial infarction studies in mice. We found that Gapdh, Polr2a and Actb display high expression variability in mouse myocardial infarction tissues and that loss of statistical power and increase in sample size are the evident consequences of choosing suboptimal combinations of reference genes. We furthermore caution against the use of Gapdh, Polr2a, Actb, B2m and Eef1a1 for gene expression normalization in myocardial infarction studies because of selective up- or downregulation after myocardial infarction, which could potentially lead to biased study outcomes.

Project description:Selection of reference genes has become an integral step in any real time quantitative PCR (RT-qPCR) based expression studies. The importance of this study stems from the fact that riverine buffaloes are major dairy species of Indian sub-continent and the information generated here will be of great interest to the investigators engaged in functional genomic studies of this important livestock species. In this study, an effort was made to evaluate a panel of 10 candidate reference genes (glyceraldehyde 3-phosphate dehydrogenase (GAPDH), beta- actin (ACTB), ubiquitously expressed transcript (UXT), ribosomal protein S15 (RPS15), ribosomal protein L-4 (RPL4), ribosomal protein S9 (RPS9), ribosomal protein S23 (RPS23), hydroxymethylbilane synthase (HMBS), ?2 Microglobulin (?2M) and eukaryotic translation elongation factor 1 alpha 1 (EEF1A1) across 12 tissues (mammary gland, kidney, spleen, liver, heart, intestine, ovary, lung, muscle, brain, subcutaneous fat and testis) of riverine buffaloes. In addition to overall analysis, tissue wise evaluation of expression stability of individual RG was also performed. Three different algorithms provided in geNorm, NormFinder and BestKeeper softwares were used to evaluate the stability of 10 potential reference genes from different functional classes. The M-value given by geNorm ranged from 0.9797 (RPS9 and UXT) to 1.7362 (RPS15). From the most stable to the least stable, genes were ranked as: UXT/RPS9> RPL4> RPS23> EEF1A1> ACTB> HMBS> GAPDH> B2M> RPS15. While NormFinder analysis ranked the genes as: UXT> RPS23> RPL4> RPS9> EEF1A1> HMBS> ACTB> ?2M> GAPDH> RPS15. Based on the crossing point SD value and range of fold change expression, BestKeeper analysis ranked the genes as: RPS9> RPS23/UXT> RPL4> GAPDH> EEF1A1> ACTB> HMBS> ?2M> RPS15. Overall the study has identified RPS23, RPS9, RPL4 and UXT genes to be the most stable and appropriate RGs that could be utilized for normalization of transcriptional data in various tissues of buffaloes. This manuscript thus provide useful information on panel of reference genes that could be helpful for researchers conducting functional genomic studies in riverine buffaloes.

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

Importance of suitable reference gene selection for quantitative RT-PCR during ATDC5 cells chondrocyte differentiation.

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Importance of suitable reference gene selection for quantitative RT-PCR during ATDC5 cells chondrocyte differentiation.

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