Yang et al. BMC Research Notes 2012, 5:222 http://www.biomedcentral.com/1756-0500/5/222 RESEARCH ARTICLE Open Access Bone to pick: the importance of evaluating reference genes for RT-qPCR quantification of gene expression in craniosynostosis and bone-related tissues and cells Xianxian Yang1,2†, Jodie T Hatfield2†, Susan J Hinze2, Xiongzheng Mu1, Peter J Anderson2,3,4 and Barry C Powell2,4* Abstract Background: RT-qPCR is a common tool for quantification of gene expression, but its accuracy is dependent on the choice and stability (steady state expression levels) of the reference gene/s used for normalization. To date, in the bone field, there have been few studies to determine the most stable reference genes and, usually, RT-qPCR data is normalised to non-validated reference genes, most commonly GAPDH, ACTB and 18 S rRNA. Here we draw attention to the potential deleterious impact of using classical reference genes to normalise expression data for bone studies without prior validation of their stability. Results: Using the geNorm and Normfinder programs, panels of mouse and human genes were assessed for their stability under three different experimental conditions: 1) disease progression of Crouzon syndrome (craniosynostosis) in a mouse model, 2) proliferative culture of cranial suture cells isolated from craniosynostosis patients and 3) osteogenesis of a mouse bone marrow stromal cell line. We demonstrate that classical reference genes are not always the most ‘stable’ genes and that gene ‘stability’ is highly dependent on experimental conditions. Selected stable genes, individually or in combination, were then used to normalise osteocalcin and alkaline phosphatase gene expression data during cranial suture fusion in the craniosynostosis mouse model and strategies compared. Strikingly, the expression trends of alkaline phosphatase and osteocalcin varied significantly when normalised to the least stable, the most stable or the three most stable genes. Conclusion: To minimise errors in evaluating gene expression levels, analysis of a reference panel and subsequent normalization to several stable genes is strongly recommended over normalization to a single gene. In particular, we conclude that use of single, non-validated “housekeeping” genes such as GAPDH, ACTB and 18 S rRNA, currently a widespread practice by researchers in the bone field, is likely to produce data of questionable reliability when changes are 2 fold or less, and such data should be interpreted with due caution. Keywords: Osteocalcin, Alkaline phosphatase, 18 S RNA, Gapdh, β-actin, geNorm, Normfinder, Craniosynostosis, Bone, Mineralization * Correspondence: [email protected] †Equal contributors 2Women’s and Children’s Health Research Institute, Adelaide, Australia 4Discipline of Paediatrics, University of Adelaide, Adelaide, Australia Full list of author information is available at the end of the article © 2012 Yang et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Yang et al. BMC Research Notes 2012, 5:222 Page 2 of 9 http://www.biomedcentral.com/1756-0500/5/222 Background [4]. We compared the effect of normalizing data based To help elucidate the molecular mechanisms involved in on the least stable, most stable, and multiple stable skull development and bone growth and the dysregula- reference genes for two common markers of osteogen- tion that occurs during craniosynostosis, a medical con- esis, alkaline phosphatase (ALP) and osteocalcin (OC). dition that affects skull formation in 1 in 2500 live Our results indicate substantial variability in the com- births, the ability to accurately measure changes in gene monly used single housekeeping genes, GAPDH, ACTB expression levels is vital. The most popular method is and 18sRNA across three experimental bone models that of RT-qPCR where gene expression is measured and highlight the importance of validating and choosing against a reference gene. Alternatively, and less often, the most appropriate combination of reference genes for “absolute” quantification is used where gene expression each experimental dataset to avoid erroneous report- is compared to an external standard and expressed rela- ing of changes in gene expression levels in studies of tive to a biological unit, such as input RNA, cell number, bone biology. or even a reference gene [1]. No studies have been pub- lished on the selection of suitable reference genes for Results use in craniosynostosis and most gene expression profil- Selection of stable reference genes ing on bone-related conditions rely on using common Our panel of reference genes included members from dis- normalizers such as ACTB, GAPDH and 18 S rRNA, tinct cellular pathways (i.e. less likely to be co-regulated) with little evidence of validation of their stability. Fur- as well as classical housekeeping genes. RNA panels were thermore, most bone studies normalize data to only one selected to represent typical experiments in a bone lab: reference gene, typically GAPDH (see Additional file 1), 1) mouse cranial suture tissues from Fgfr2cC342Y/+ mice considered by many as a stable housekeeping gene des- harboring a Cys342Tyr replacement frequently observed pite substantial evidence to the contrary [1]. in human Crouzon and Pfeiffer-type craniosynostosis, There is an increasing call to assess reference genes 2) cultured primary human cranial suture cells from cra- for stability in sample sets for each new experiment to niosynostosis patients, and 3) a mouse osteoblastic cell avoid producing misleading data, particularly where the line induced to mineralize over 21 days in culture magnitudes of the gene expression changes are small. To (Table 1). Mineralisation was verified by the accumulation address these concerns, a set of guidelines have been of Alizarin red S in induced samples relative to uninduced proposed; MIQE, minimum information for publication samples (Additional file 2). RNAs representative of each of quantitative real-time PCR experiments [2]. These panel were chosen for geNorm and Normfinder analysis. guidelines invite a transparency in reporting so that the quality of data can be judged on several criteria such as RT-qPCR analysis experimental design, RNA quality control, normalization RT-qPCR data was analyzed using geNorm software to strategy and validation, data analysis and applied statis- obtain a stability value (M) for each reference gene and tics. Unfortunately, recent studies published in the bone the mean pairwise variation value (V) in a sample set. field indicate a widespread practice of reliance on nor- Genes with the lowest M values were considered the malising gene expression to non-validated, single gene most stable, while the V value indicated the optimal references (see Additional file 1) and are indicative that number of genes to use for normalization. The same there is insufficient awareness and appreciation of asso- data was then analysed with Normfinder and the two ciated pitfalls. approaches compared. Stabilities of reference genes in In this study we investigated three common biological our sample panels are shown in Additional files 3 and 4 tools for studying osteogenesis and craniosynostosis in and summarized in Table 2. the laboratory; primary human cranial suture cells from In our first test panel we determined the most stable affected patients, a mouse model of craniosynostosis and reference genes in craniosynostosis-related suture mater- a mouse cell line to model osteogenesis and mineralisa- ial from a commonly used mouse model for Crouzon tion in culture. Twelve candidate reference genes for syndrome. The geNorm rank order data analysis indi- human and mouse samples were chosen and assessed cated that Cyc1, Gapdh and Canx were the most stable for stability using geNorm and Normfinder software. combination of reference genes to use, while 18 S rRNA geNorm software uses geometric averaging of expression gene had the highest variability (Table 2; Additional of a defined number of genes in a given cDNA sample file 3). Normfinder also ranked 18 s rRNA as one of the set and determines the rank order of their relative sta- least stable genes and Canx as one of the most stable, bility [3] whereas Normfinder takes a “model-based with Cyc1 and Gapdh ranked towards the middle approach” where input data is organised into groups (e.g. (Table 2; Additional file 4). It proposes the use of Ubc wildtype vs. mutant) and gene expression stability esti- and Canx as the most stable normalisation factor, and mates take into account inter- and intragroup variations we note that Ubc is also considered an adequately stable Yang et al. BMC Research Notes 2012, 5:222 Page 3 of 9 http://www.biomedcentral.com/1756-0500/5/222 Table 1 RNA sample list Mouse suture tissues Time point Genotype Suture type E16.5 * Wildtype Coronal, Posterior-frontal, Lambdoid, Parietal bone D0 # Wildtype Coronal, Lambdoid D0 Fgfr2cC342Y/+ Posterior frontal, Sagittal D1 Fgfr2cC342Y/+ Coronal, Lambdoid D5 Wildtype Lambdoid, Parietal bone D5 Fgfr2cC342Y/+ Posterior frontal, Sagittal D10 Wildtype Coronal, Lambdoid D10 Fgfr2cC342Y/+ Posterior-frontal, Sagittal Human suture cells Patient code Phenotype Sex Age (months) Suture type Passage AC 125 Sagittal synostosis M 7 Unfused Coronal P5, P10 AC 124 Metopic synostosis M 7 Unfused Coronal P4, P8 AC 126 Metopic synostosis F 9 Unfused Coronal P4 AC 141 Sagittal synostosis M 6 Unfused Coronal P3 AC 125 Sagittal synostosis M 7 Unfused Lambdoid P4 AC 120 Crouzonoid syndrome F 96 Fused Sagittal P9 AC 113 Multi-suture synostosis F 10 Fused Coronal P5 AC 34 Multi-suture synostosis F 5 Fusing Sagittal P5 Kusa 4b10 - Osteogenesis assay+ Induced cells Uninduced cells -0 33 77 14 14 21 21 *E refers to embryonic day. #D refers to postnatal day. +days after induction or days after the equivalent time point for uninduced cells. gene by geNorm ranking (ranked below the M = 0.5 cut- commonly used reference gene, ACTB, was ranked off proposed by Vandesompele et al (2002).