- Research article
- Open Access
Validation of endogenous reference genes for qRT-PCR analysis of human visceral adipose samples
© Mehta et al; licensee BioMed Central Ltd. 2010
- Received: 7 April 2010
- Accepted: 21 May 2010
- Published: 21 May 2010
Given the epidemic proportions of obesity worldwide and the concurrent prevalence of metabolic syndrome, there is an urgent need for better understanding the underlying mechanisms of metabolic syndrome, in particular, the gene expression differences which may participate in obesity, insulin resistance and the associated series of chronic liver conditions. Real-time PCR (qRT-PCR) is the standard method for studying changes in relative gene expression in different tissues and experimental conditions. However, variations in amount of starting material, enzymatic efficiency and presence of inhibitors can lead to quantification errors. Hence the need for accurate data normalization is vital. Among several known strategies for data normalization, the use of reference genes as an internal control is the most common approach. Recent studies have shown that both obesity and presence of insulin resistance influence an expression of commonly used reference genes in omental fat. In this study we validated candidate reference genes suitable for qRT-PCR profiling experiments using visceral adipose samples from obese and lean individuals.
Cross-validation of expression stability of eight selected reference genes using three popular algorithms, GeNorm, NormFinder and BestKeeper found ACTB and RPII as most stable reference genes.
We recommend ACTB and RPII as stable reference genes most suitable for gene expression studies of human visceral adipose tissue. The use of these genes as a reference pair may further enhance the robustness of qRT-PCR in this model system.
- Reference Gene
- Visceral Adipose Tissue
- Candidate Reference Gene
- Pairwise Variation
The increasing prevalence of obesity worldwide has drawn research on adipose tissue into the spotlight. Adipose tissue is a complex and highly active tissue with important metabolic and endocrine functions. It not only plays a central role in energy balance but also functions as an endocrine organ secreting various adipokines and cytokines [1, 2]. On the basis of its distribution, adipose tissue is divided into three main regions: subcutaneous, intramuscular and visceral fat [3, 1].
Accumulation of excessive visceral fat (visceral obesity) is associated with an array of metabolic perturbations including type 2 diabetes, insulin resistance, non-alcoholic fatty liver disease (NAFLD), non-alcoholic steatohepatitis (NASH), cardiovascular disease, hypertension and hyperlipidemia together referred to as "metabolic syndrome" [4, 5]. However, the role of visceral obesity in metabolic syndrome is yet to be fully elucidated . Furthermore, a causal relationship between insulin resistance and metabolic syndrome has not been shown conclusively; Obesity seemingly causes insulin resistance, on the other hand insulin resistance appears to aggravate and propagate the adverse effects of obesity . This somewhat co-dependent and circular relationship is difficult to untangle and has generated a multitude of clinical and research publications.
Another area of disagreement involves NAFLD, a common condition affecting about 70% of obese and overweight individuals and increasingly being recognized as a major cause of liver-related morbidity and mortality . The pathological picture of NAFLD encompasses a spectrum of liver injury ranging from simple hepatic steatosis to more severe manifestations, including NASH, which can progress to fibrosis, cirrhosis, and liver failure [9, 10]. Studies have reported frequent association of metabolic syndrome and diabetes in patients with NASH, which can progress to NAFLD [9, 11, 12]. It has even been suggested that hepatic steatosis itself may be the primary cause of insulin resistance and metabolic syndrome in obesity . However, it is still unclear whether NAFLD is a cause or a consequence of insulin resistance  and if metabolic syndrome precedes NAFLD or is a result of NAFLD . Many NAFLD centered studies involve the profiling of adipose samples for the production of various soluble mediators of inflammation produced by components of the visceral fat and released in circulation.
Real-time PCR (qRT-PCR) is the standard method for studying changes in relative gene expression in different tissues and experimental conditions. The popularity of this technique is attributed to its high sensitivity and specificity . However, variations in amount of starting material, enzymatic efficiency and presence of inhibitors can lead to quantification errors. Hence the need for accurate data normalization is vital . Among several known strategies for data normalization , the use of reference genes as an internal control is the most common approach .
An ideal reference gene is one which is consistently expressed at the same level in all samples under investigation regardless of tissue type, disease state, medication or experimental conditions, and exhibits expression levels comparable to that of the target gene . 18S, β-Actin (ACTB), Glyceraldehyde-3-phosphate dehydrogenase (GAPDH), Beta-2-microglobulin (B2M), RNA polymerase II (RPII or POLR2A), Tyrosine-3 monooxygenase/Tryptophan-5 monooxygenase activation protein, zeta polypeptide (YWHAZ), Ubiquitin C (UBC) and Hypoxanthine phosphoribosyl transferase 1 (HPRT1) are some of the most commonly used reference genes in RT-PCR studies [2, 19, 15]. However, numerous studies have shown that expression of these common reference genes vary with tissue type as well as physiological state [20, 19]. This variation can potentially explain the often encountered divergence between studies and more seriously, may ultimately result in misinterpretation of data . The suitability of a particular reference gene thus depends on the system being investigated and the inherent experimental conditions [21, 19, 18].
Recent studies have shown differences in reference gene expression in omental fat tissue between lean and obese patients. In addition there is strong evidence to suggest that obesity and type 2 diabetes mellitus (T2D) exert a detectable influence on reference gene expression in subcutaneous and visceral fat depot [1, 2]. In light of these findings, it is crucial for studies involving visceral adipose tissues to validate the stability of the reference genes being used.
Increasing concerns about normalization using ideal reference genes have led to the development of several mathematical algorithms aimed at determining the stability of reference genes . In 2002, Vandesompele et al. have developed the software GeNorm that addresses the critical issues of reference gene validation and ranks candidate reference genes according to their expression stability using raw, non-normalized expression levels. Pfaffl et al. have developed similar software, BestKeeper that takes into account Ct values of candidate reference genes instead of relative quantities. This software employs a statistical algorithm wherein the Pearson correlation coefficient for each candidate reference gene pair is calculated along with the probability of correlation significance of the pair. Andersen et al. used a model-based evaluation strategy which ranks candidate genes with minimal inter and intra-group variation and developed the software NormFinder. In all three softwares, the top ranked genes are recommended for further use in the similar experimental systems as endogenous controls.
In this study we used GeNorm. NormFinder and BestKeeper to validate candidate reference genes suitable for qRT-PCR profiling experiments using visceral adipose samples from obese and lean individuals with and without diabetes.
To determine the expression stability of eight selected reference genes, RNA expression levels were measured in 19 visceral adipose tissue samples (10 obese visceral adipose tissues and 9 lean visceral adipose tissue samples) and cross-validated using three popular algorithms GeNorm v3.4 , NormFinder and BestKeeper. Genes encoding for 18S, beta-actin (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta-2-microglobulin (B2M), hypoxanthine guanine phosphoribosyl transferase1 (HPRT1), tyrosine 3-monooxygensae/tryptophane 5- monooxygenase activation protein, zeta polypeptide (YWHAZ), ubiquitin C (UBC) and RNA polymerase II (RPII, or POLR2A) were selected according to previously published studies that relied on these genes as reference controls [2, 19, 11]. For each tissue sample, expression stability of each gene was calculated using the mean Ct values. The input data for BestKeeper algorithm was raw Ct values, while the analysis using GeNorm and NormFinder converted raw Ct values to relative quantities using the comparative Ct method .
Comparison of highly ranked genes by all three software (n = 9).
of Correlation (r)
Comparison of highly ranked genes by all three software (n = 19).
of Correlation (r)
Unlike GeNorm and NormFinder, input data for analysis by BestKeeper was raw Ct values of each gene. Initial analysis of the data with 9 samples calculated variations (SD (± Ct) and CV (%Ct)) for all the candidate reference genes in the samples, and showed the overall stability in gene expression. None of the candidate reference genes under study showed a SD value higher than 1 indicating that all of the genes under study were suitable to be considered for selection as reference genes.
However, further data processing using pairwise correlation and regression analysis assessed the inter-gene relations and eliminated 18S, as the gene with the highest variation (CV = 4.36) and least correlation (r = 0.434). The lowest variation was seen for the gene YWHAZ (CV = 0.96). However, YWHAZ demonstrated only a weak correlation to BestKeeper index compared to other candidates (r = 0.653). Therefore, both 18S and YWHAZ were excluded from further analysis. Subsequent elimination singled out UBC and B2M as genes with low correlation with the BestKeeper index. The analysis of the remaining four genes (HPRT1, ACTB, GAPDH and RPII) showed a strong and significant correlation (0.914 < r < 0.960) between their expression levels and the BestKeeper index (p < 0.001).
BestKeeper correlation analysis (n = 9).
Coefficient of Correlation (r)
BestKeeper correlation analysis (n = 19).
Coefficient of Correlation (r)
Ranking the four tightly correlated genes on the basis of variation from the most stable to the least stable was as follows: YWHAZ → GAPDH → ACTB → RPII, and yielded the best genes for defining a robust standardizing index. Amongst these four genes ACTB and RPII were the most highly correlated (0.975 < r < 0.981) to the BestKeeper index (Table 2).
To eliminate non-biological variation, gene expression analysis involving qRT-PCR requires stringent normalization strategies. Among the several approaches proposed, use of reference genes is currently the preferred way of normalization . However, the use of improper reference genes is known to lead to erroneous results . Importantly, the studies of the expression levels for the reference gene themselves, particularly, for GADPH and ACTB, showed considerable variation in different tissues and experimental conditions. Specifically in omental and subcutaneous fat depots, a variation in expression of these reference genes was found to be dependent on the presence of obesity and type 2 diabetes mellitus (T2D) . These findings necessitate the need to validate reference genes for studies of human visceral adipose samples.
Scientifically, the validation of reference genes presents a circular problem: assessing stability of expression of a given gene cannot be achieved without using another gene as a reference. Several algorithms have been proposed to address this conundrum . GeNorm software  is one of the most popular algorithms for validating candidate reference genes with low variability. It utilizes two parameters to quantify the reference gene expression stability: M (average expression stability) and V (pairwise variation). A low M value is indicative of a more stable expression, hence, increasing the suitability of a particular gene as a reference gene. Another feature of GeNorm is that it does not require a normal distribution of data. However, co-regulation of candidate genes does seem to influence the efficiency of this algorithm due to the use of pair-wise comparisons. To minimize this risk, the eight candidate reference genes selected for this analysis were chosen on the basis of the difference in their physiological functions-cytoskeleton (ACTB), carbohydrate metabolism (GAPDH), signaling pathways (YWHAZ), transcription (RPII or POLR2A), metabolic salvaging of nucleotides (HPRT1), protein synthesis (18S) and protein degradation (UBC).
The GeNorm algorithm determines expression stability (M) via a pair-wise comparison of one candidate reference gene and all other candidate genes independent of the level of gene expression for each sample. An identical expression ratio of two reference genes in all samples is used as an indicator of expression stability. Thus GeNorm analysis is independent of variation in amount of starting material between samples. According to this analysis, ACTB and RPII represented the best combination of reference genes for visceral adipose tissue among lean and obese patients (Figure 1a), while HPRT1 and GAPDH were ranked third and fourth, respectively. After completion of this step, a pairwise variation (V) was calculated between two sequential normalization factors (NFn and NFn+1) for all genes. A large variation indicates that the added gene has a significant effect and should be included for calculation for a reliable normalization factor. Figure 2a, b show that further inclusion of additional reference genes did not significantly increase the pair-wise variation and that the use of two reference genes is sufficient for accurate normalization. An advantage to the GeNorm algorithm is that it is minimally affected by expression intensity of the candidate genes. In addition, since the approach is based on multiple pair-wise comparisons, the need for large sample size is mitigated. This was reinforced by the observation that increase in sample size did not dramatically alter the final results. On increasing sample size from 9 to 19, ACTB and RPII were again found to be the most stable genes with GAPDH and HPRT1 being ranked the next two best genes (Figure 1b).
Housekeeping genes, in addition to their basic functions, exert pleiotropic effects on other cellular systems, decreasing the value of the function-based predictions of co-regulation. To overcome this problem Anderson et al., proposed a model based approach incorporated into the software NormFinder. This algorithm ranks candidate reference genes according to the least estimated intra and inter group variation, which serves as an effective method to overcome the influence of co-regulation. Although NormFinder takes into account the heterogeneity in the tested samples, and attempts to distinguish between stability and bias, this model-based approach is self-restricted by the importance it places on overall expression intensity of each candidate gene. A close inspection of the analysis of the results produced by NormFinder showed that it biased towards candidate reference genes that have overall similar expression values (in terms of Ct). Consequently, the robustness of this method is linked to the sample size.
The ultimate objective of NormFinder is to identify candidate reference genes(s) with an inter group variation as close to zero as possible, while at the same time having small intra-group variation. When the genes were ranked solely by their stability values, GAPDH and HPRT1 appeared to be the best combination of endogenous controls (Table 1). Further examination of the results reveals that although UBC was ranked third, its intra-group variation was large (Figure 3a), therefore, UBC gene was eliminated from further consideration leaving the next most stable reference genes: ACTB, B2M and RPII.
The same genes, GAPDH, HPRT1, ACTB and RPII, were ranked as the most stable both by GeNorm and NormFinder softwares. However, the best combinations of two genes proposed by these two algorithms were different. This variation was expected based on the vastly different approaches used by each of the analysis softwares and dependence of robustness of NormFinder on sample size. In GeNorm, gene expression stability (M) based on the expression ratio of the two genes (pairwise comparison) is the most important criteria for evaluating a reference gene, while NormFinder focuses on genes with the least intra and inter group variations. Thus, in cases when two genes show high expression variation while their ratio (M) remains unchanged, there will be discordance in ranking by the two algorithms.
Further, NormFinder gains in robustness as the number of samples is increased. This was confirmed by increasing sample size to 19. Ranking of genes from most stable to least stable revealed - ACTB and RPII as the best combination of genes and this was in accordance with with GeNorm results (Figure 3b).
In contrast to the previous study by Catalan et al., in visceral adipose tissue samples both the algorithms highlighted 18S as one of the least stable gene. This was not unexpected, as several arguments against the use of rRNA as reference genes have been previously put forth. The strongest argument against its use in real time RT-PCR data analysis is its high abundance compared to other target mRNA which hinders accurate subtraction from the baseline value .
In order to compare the GeNorm and NormFinder results with an independent ranking method, the data was also analyzed with the BestKeeper tool . In this approach, ideal reference genes are expected to have stable expression, indicated by low variation in the tissue under consideration . With BestKeeper, stability (SD) and relationship to the BestKeeper index (r and p values) are the two most important criteria for evaluating the stability of reference genes. This algorithm uses a pair-wise correlation analysis for all pairs of candidate genes based on the raw Ct values and calculates the geometric mean of the best suited ones. Based on low CV and high coefficient of correlation (r) to the BestKeeper index, ACTB and RPII followed by GAPDH and HPRT1 were ranked as the top four genes (Table 3). High correlation coefficient is an indicator of stable expression of the reference genes in visceral adipose tissue. Again, 18S was ranked as the least stable and excluded from further analysis. Robustness of the algorithm was assessed by increasing sample size (n = 19). Sample size was found to have minimal effect on the results. The same two genes ACTB and RPII were identified as the most stable followed by YWHAZ and GAPDH (Table 4).
Overall, the BestKeeper results were in line with the NormFinder data, and with minor differences, the GeNorm data, indicating the reliability of the validation for reference genes in the present study (Table 2). Regardless of the algorithm used, all three software ranked the same set of genes as the most stable.
In conclusion, we recommend ACTB and RPII as stable reference genes most suitable for gene expression studies of human visceral adipose tissue. The use of these genes as a reference pair may further enhance the robustness of qRT-PCR in this model system.
Visceral adipose tissue samples were obtained from 10 patients diagnosed with morbid obesity and one of the NAFLD spectrum diseases (n = 10) and nine lean patients with normal liver biopsies (n = 9). Samples were collected at the time of bariatric or other intra abdominal surgery. Samples were flash frozen in liquid nitrogen and stored in -80°C. The samples were de-identified in compliance with HIPAA regulations and this study was approved by Inova IRB.
Selection of reference genes
Candidate reference genes previously reported as housekeeping genes in adipose tissue were selected as follows: 18S, beta-actin (ACTB), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), beta-2-microglobulin (B2M), hypoxanthine guanine phosphoribosyl transferase1 (HPRT1), tyrosine 3-monooxygensae/tryptophane 5-monooxygenase activation protein, zeta polypeptide (YWHAZ), ubiquitin C (UBC) and RNA polymerase II (RPII, or POLR2A).
Primer sequences of eight reference genes used in the validation study.
RNA extraction and reverse transcription
Total RNA was extracted from visceral adipose tissues (n = 19) using mirVana RNA extraction kit (Ambion, USA) according to manufacturers protocol. Purity of total RNA was determined as 260 nm/280 nm absorbance ratio with expected values between 1.8 - 2.00 by the GeneQuant1300 spectrophotometer (GE Healthcare, USA). RNA integrity was confirmed by gel electrophoresis using 1% agarose with ethidium bromide [Additional file 1]. 112 ng of extracted total RNA was reverse transcribed using RT2 first strand kit (SABiosciences, USA). According to manufacturer's protocol, total RNA was treated to eliminate genomic DNA and random hexamers and oligo-dT primers were used to prime reverse transcription.
Quantitative real-time analysis
Quantitative real-time PCR was performed in a 96 well format in the Bio-rad CFX96 Real Time System (BioRad Laboratories, USA). The real-time PCR mixtures consisted of 5 μL cDNA corresponding to ~600 ng total RNA, 0.1 uM of Real-Time primers or 0.2 nM of Invitrogen primer and 1× Sso Fast Evagreen Supermix ( BioRad, USA ) in a final volume of 15 μL. The assay included no template and RT minus controls to detect reagent contamination and presence of genomic DNA. The thermal profile of the RT-PCR procedure repeated for 50 cycles was: 1) 95°C for 10 min; 2)10 s denaturation at 95°C, 40 s annealing at 55°C for Real Time primers and 60°C for Invitrogen primers (amplification data collected at the end of each amplification step); 3) dissociation curve consisting of 10 s incubation at 95°C, 5 s incubation at 65°C, a ramp up to 95°C. (Bio-rad CFX96 Real Time System, USA). Melting curves were used to validate product specificity. All samples were amplified in triplicates from the same total RNA preparation and the mean value was used for further analysis.
Determination of reference gene expression stability
To assess stability of expression of candidate reference genes across all samples three different statistical algorithms - GeNorm, v3.4, NormFinder v0.953 and BestKeeper v1, were used according to developer's recommendations.
qRT-PCR GeNorm Analysis
The GeNorm tool was used to calculate candidate reference gene stability values (M) using raw non-normalized expression values. For each pair of genes, GeNorm calculates a pairwise variation in terms of the standard deviation (Vjk) of each gene' logarithmically transformed expression ratios (aij) for each tissue sample (m), for any combination of two internal control genes (j or k):
In iterative steps of exclusion, genes with the lowest expression stability (i.e. the highest Mj value) are removed. This procedure is repeated until only the genes with the lowest Mj values and most stable expression remain. The minimum number of genes for which the pair-wise variation Vjk/Vjk+1, is smaller than 0.15, is used to define the optimal number of reference genes. The normalization factor is calculated based on the geometric mean of the final optimal set of reference genes.
qRT-PCR NormFinder Analysis
This expression effectively combines multiple sources of variation, and indicates the overall systemic error per gene. Therefore the top ranked gene (which has the smallest stability value, hence the smallest combined variation) is the candidate reference gene most stably expressed in the sample set being investigated. However, since the systemic error value (ρig) is calculated with the null assumption that expression levels of each gene will be group independent, further manual inspection of inter and intra-group variability was performed. As can be derived from the mathematical expression of the model, this approach gains in robustness as the number of samples is increased.
qRT-PCR BestKeeper Analysis
Samples with efficiency corrected intrinsic variation within 3 fold over or under expression are considered acceptable. Hence the BestKeeper software seeks to eliminate outliers and thereby increases the reliability and consistency of the BestKeeper index.
This work has been performed at the Translational Research Institute, as part of the collaborative effort between George Mason University and Inova Fairfax Hospital. We would like to thank both our Inova and George Mason teams for their invaluable help and assistance.
- Gomez-Ambrosi J, Catalan V, Diez-Caballero A, et al.: Gene expression profile of omental adipose tissue in human obesity. FASEB J 2003. 03-0591fjeGoogle Scholar
- Catalán V, Gómez-Ambrosi J, Rotellar F, et al.: Validation of Endogenous Control Genes in Human Adipose Tissue: Relevance to Obesity and Obesity-associated Type 2 Diabetes Mellitus. Horm Metab Res 2007, 39: 495-500. 10.1055/s-2007-982502 10.1055/s-2007-982502View ArticlePubMedGoogle Scholar
- Bosello O, Zamboni M: Visceral obesity and metabolic syndrome. Obesity Reviews 2000, 1: 47-56. 10.1046/j.1467-789x.2000.00008.x 10.1046/j.1467-789x.2000.00008.xView ArticlePubMedGoogle Scholar
- Linder K, Arner P, Flores-Morales A, Tollet-Egnell P, Norstedt G: Differentially expressed genes in visceral or subcutaneous adipose tissue of obese men and women. J Lipid Res 2004, 45: 154. 148Google Scholar
- Shoelson S, Herrero L, Naaz A: Obesity, inflammation, and insulin resistance. Gastroenterology 2007, 132: 2180. 2169 10.1053/j.gastro.2007.03.059 10.1053/j.gastro.2007.03.059View ArticleGoogle Scholar
- Furukawa S, Fujita T, Shimabukuro M, et al.: Increased oxidative stress in obesity and its impact on metabolic syndrome. J Clin Invest 2004, 114: 1752-1761.View ArticlePubMedPubMed CentralGoogle Scholar
- Grundy SM: Obesity, Metabolic Syndrome, and Cardiovascular Disease. J Clin Endocrinol Metab 2004, 89: 2595-2600. 10.1210/jc.2004-0372 10.1210/jc.2004-0372View ArticlePubMedGoogle Scholar
- Hamaguchi M, Kojima T, Takeda N, et al.: The Metabolic Syndrome as a Predictor of Nonalcoholic Fatty Liver Disease. Annals of Internal Medicine 2005, 143: 722-728.View ArticlePubMedGoogle Scholar
- Hanley AJG, Williams K, Festa A, et al.: Liver Markers and Development of the Metabolic Syndrome. Diabetes 2005, 54: 3140-3147. 10.2337/diabetes.54.11.3140 10.2337/diabetes.54.11.3140View ArticlePubMedGoogle Scholar
- Rafiq N, Younossi ZM: Interaction of metabolic syndrome, nonalcoholic fatty liver disease and chronic hepatitis C. Expert Review of Gastroenterology & Hepatology 2008, 2: 207-215.View ArticleGoogle Scholar
- Kim S, Kim T: Benchmarks - Selection of optimal internal controls for gene expression profiling of liver disease. Biotechniques 2003, 35: 456.PubMedGoogle Scholar
- Younossi ZM: Review article: current management of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis. Alimentary Pharmacology & Therapeutics 2008, 28: 2-12.View ArticleGoogle Scholar
- Garg A, Misra A: Hepatic Steatosis, Insulin Resistance, and Adipose Tissue Disorders. J Clin Endocrinol Metab 2002, 87: 3019-3022. 10.1210/jc.87.7.3019 10.1210/jc.87.7.3019View ArticlePubMedGoogle Scholar
- Tilg H, Hotamisligil GS: Nonalcoholic Fatty Liver Disease: Cytokine-Adipokine Interplay and Regulation of Insulin Resistance. Gastroenterology 2006, 131: 934-945. 10.1053/j.gastro.2006.05.054 10.1053/j.gastro.2006.05.054View ArticlePubMedGoogle Scholar
- de Jonge HJM, Fehrmann RSN, de Bont ESJM, et al.: Evidence based selection of housekeeping genes. PloS one 2007,2(9):e898. 10.1371/journal.pone.0000898 10.1371/journal.pone.0000898View ArticlePubMedPubMed CentralGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, et al.: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biology 2002, 3: research0034.1-research0034.11. 10.1186/gb-2002-3-7-research0034 10.1186/gb-2002-3-7-research0034View ArticleGoogle Scholar
- Huggett J, Dheda K, Bustin S, Zumla A: Real-time RT-PCR normalisation; strategies and considerations. Genes and Immunity 2005, 6: 279-284. 10.1038/sj.gene.6364190 10.1038/sj.gene.6364190View ArticlePubMedGoogle Scholar
- Suzuki T, Higgins PJ, Crawford DR: Reviews - Control Selection for RNA Quantitation. Biotechniques 2000, 29: 332.PubMedGoogle Scholar
- Thellin O, Zorzi W, Lakaye B, et al.: Housekeeping genes as internal standards: use and limits. Journal of Biotechnology 1999, 75: 291-295. 10.1016/S0168-1656(99)00163-7 10.1016/S0168-1656(99)00163-7View ArticlePubMedGoogle Scholar
- Bustin S: Absolute quantification of mRNA using real-time reverse transcription polymerase chain reaction assays. Journal of molecular endocrinology 2000, 25: 193. 169 10.1677/jme.0.0250169 10.1677/jme.0.0250169View ArticleGoogle Scholar
- Schmittgen TD, Zakrajsek BA: Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. Journal of Biochemical and Biophysical Methods 2000, 46: 69-81. 10.1016/S0165-022X(00)00129-9 10.1016/S0165-022X(00)00129-9View ArticlePubMedGoogle Scholar
- Vandesompele J, Kubista M, Pfaffl MW: Reference Gene Validation Software for Improved Normalization. In Real-time PCR: current technology and applications. Volume 47. Edited by: Logan J, Edwards K, Saunders N. Norfolk, UK: Caister Academic Press; 2009:64.Google Scholar
- Andersen C, Jensen J, Ørntoft T: Normalization of real-time quantitative reverse transcription-PCR data: a model-based variance estimation approach to identify genes suited for normalization, applied to bladder and colon cancer data sets. Cancer Res 2004, 64: 5250. 5245Google Scholar
- Pfaffl M, Tichopad A, Prgomet C, Neuvians T: Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper--Excel-based tool using pair-wise correlations. Biotechnology letters 2004, 26: 509-515. 10.1023/B:BILE.0000019559.84305.47 10.1023/B:BILE.0000019559.84305.47View ArticlePubMedGoogle Scholar
- Dheda K, Huggett J, Chang J, et al.: The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Analytical Biochemistry 2005, 344: 141-143. 10.1016/j.ab.2005.05.022 10.1016/j.ab.2005.05.022View ArticlePubMedGoogle Scholar
- Baranova A, Hammarsund M, Ivanov D, et al.: Distinct organization of the candidate tumor suppressor gene RFP2 in human and mouse: multiple mRNA isoforms in both species- and human-specific antisense transcript RFP2OS. Gene 2003, 321: 103-112. 10.1016/j.gene.2003.08.007 10.1016/j.gene.2003.08.007View ArticlePubMedGoogle Scholar
- Rasmussen R: Quantification on the LightCycler instrument. In Rapid Cycle Real-Time PCR: Methods and Applications. Edited by: Meuer S, Wittwer C, Nakagawara K. Heidelberg: Springer-Verlag Press; 2001:21-34.View ArticleGoogle Scholar
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