Selection of suitable reference genes for quantitative real-time polymerase chain reaction in human meningiomas and arachnoidea
© Pfister et al; licensee BioMed Central Ltd. 2011
Received: 8 February 2011
Accepted: 2 August 2011
Published: 2 August 2011
At first 32 housekeeping genes were analyzed in six randomly chosen meningiomas, brain and dura mater using geNorm, NormFinder, Bestkeeper-1 software and the comparative ΔCt method. Reference genes were ranked according to an integration tool for analyzing reference genes expression based on those four algorithms. Eight highest ranked reference genes (CASC3, EIF2B1, IPO8, MRPL19, PGK1, POP4, PPIA, and RPL37A) plus GAPDH and ACTB were then analyzed in 35 meningiomas, arachnoidea, dura mater and normal brain. NormFinder and Bestkeeper-1 identified RPL37A as the most stable expressed gene in meningiomas and their normal control tissue. NormFinder also determined the best combination of genes: RPL37A and EIF2B1. Commonly used reference genes GAPDH and ACTB were considered least stable genes. The critical influence of reference genes on qPCR data analysis is shown for VEGFA transcription patterns.
In meningiomas quantitative real-time reverse transcription-polymerase chain reaction (qPCR) is most frequently used for accurate determination of gene expression using various reference genes. Although meningiomas are a heterogeneous group of tissue, no data have been reported to validate reference genes for meningiomas and their control tissues.
RPL37A is the optimal single reference gene for normalization of gene expression in meningiomas and their control tissues, although the use of the combination of RPL37A and EIF2B1 would provide more stable results.
Meningiomas are the most frequent intracranial tumours. They originate from the arachnoidal cap cells of the meningeal coverings of the spinal cord and brain, constituting for approximatively 13 to 26% of all intracranial pathologies [1, 2]. The conventional strategy for meningiomas is surgery [3, 4]. However, some meningiomas recur as resection might be sub totally due to their delicate location at skull-based structures. The definition of malignant potential is beset by the frequent discordance between histology and biology [5, 6]. Meningiomas are categorized in three WHO grades, in which there are several subtypes differentiated by their histological features.
Real-time quantitative reverse transcription polymerase chain reaction (RT-qPCR) is a sensitive and reliable method for quantifying gene expression. Most frequently the relative quantification method is used, which requires the use of an internal control gene for normalization. Reference genes are mostly genes, which are involved in basic metabolism and maintenance of the cell. An ideal reference gene should be expressed at a constant level in all examined tissues and cells, and should not be influenced by experimental conditions. However several studies have shown, that genes used as reference gene display significantly different gene expression levels [7–9].
Established housekeeping genes in meningioma RT-qPCR experiments are genes such as glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and β-Actin (ACTB) [10–18] as well as ribosomal RNA (18S rRNA) and TATA binding box protein (TBP) [19–21]. As the application of these various housekeeping genes shows, there are no reports that candidate reference genes in meningiomas were validated. Due to the heterogeneity of meningioma tissue and the necessity to compare meningiomas and their control tissue reliably, the selection of an appropriate reference gene with stable gene expression throughout the various tissues is essential for further use of RT-qPCR in meningioma research.
In this study, we investigate the gene expression levels of 32 commonly used housekeeping genes in meningiomas and their control tissues arachnoidea, dura and normal brain. The RT-qPCR results were analyzed with four different algorithms, to select eight suitable reference genes. Those genes plus GAPDH and ACTB were compared in an increased number of meningiomas and control tissues. These RT-qPCR results were further analyzed with two different algorithms: NormFinder and Bestkeeper-1.
Tumour Specimens and Cell Culture
Meningioma surgical specimens as well as arachnoidea and dura mater were obtained from the Neurosurgical Department in accordance to regulations of the Ethic Committee of the University of Tuebingen. Primary cultures were obtained from tumour tissue samples within 30 minutes of surgical removal. Samples were first washed in phosphate-buffered saline (PBS), reduced and mashed through a filter and placed in Dulbecco's modified Eagle's medium (DMEM) with fetal bovine serum (FBS), 2 mmol/L L-glutamine and 0,1% 10 mg/ml Gentamicin (Invitrogen, Grand Island, NY). Cells were plated in 25-mm2 tissue culture flasks and incubated at 37°C in a humidified atmosphere of 5% CO2. Medium was changed every 3 to 4 days and cultures were split using 600 μl Accutase (PAA, Pasching; Austria). Viable cells were stored in liquid nitrogen in 90% medium/10% dimethyl sulfoxide.
RNA isolation and reverse transcription
Meningioma total ribonucleic acid (RNA) was isolated directly from primary cell cultures before splitting and RNA of Arachnoidea and dura was isolated from fresh tissue using PARIS® kit (Ambion, Inc., Austin, TX), according to the manufacturer's protocol. RNA was treated with DNA-free™ (Ambion, Inc., Austin, TX) to remove residual genomic DNA. The concentration of the isolated RNA and the 260/280 absorbance ratio was measured in triplicates with Eppendorf Biophotometer (Eppendorf, Hamburg, Germany). The integrity of RNA samples was confirmed by electrophoresis on a 2% Sybr Green agarose gel (Invitrogen Inc., Carlsbad, CA). The criterion to include RNA samples was 260/280 ~ 2 (1.9 to 2.2) and 28S/18S ratio ≥ 1.7. The probes were stored at - 80°C until use. For normal brain FirstChoice® Human Brain Reference RNA (Ambion, Inc., Austin, TX) was used, which pools RNA from different donors and several brain regions. RNA samples were DNase treated using DNAfree kit (Ambion Inc., Austin, TX). Total RNA (1 μg) was reverse-transcribed to cDNA using des High Capacity RNA-to-cDNA Kits (Applied Biosystems, Foster City, CA) in a total volume of 20 μl, according to the manufacturer's protocol.
Candidate reference genes evaluated in this study
Genbank Acession No.
Eukaryotic 18S rRNA
v-abl Abelson murine leukemia oncogene homolog 1
NM_005157.3 + NM_007313.2
Actin, Beta, cytoplasmic
cancer susceptibility candidate 3
cyclin-dependent kinase inhibitor 1A (p21, Cip1)
NM_078467.1 + NM_000389.3
cyclin-dependent kinase inhibitor 1B (p27, Kip1)
eukaryotic translation initiation factor 2B, subunit 1 alpha, 26 kDa
E74-like factor 1/ets domain transcription factor)
NM_172373.3 + NM_001145353.1
growth arrest and DNA-damage-inducible, alpha
Hypoxanthine guanine phospho- ribosyl transferase 1
mitochondrial ribosomal protein L19
mitochondrially encoded ATP synthase 6
pescadillo homolog 1, containing BRCT domain (zebrafish)
Phosphoglycerate kinase 1
Polymerase (RNA) II (DNA directed) polypeptide A, 220 kDa
processing of precursor 4, ribonuclease P/MRP subunit
Peptidylprolyl Isomerase A
proteasome (prosome, macropain) 26S subunit, ATPase, 4
NM_153001.1 + NM_006503.2
pumilio homolog 1 (Drosophila)
NM_001020658.1 + NM_014676.2
ribosomal protein L30
ribosomal protein L37A
Ribosomal protein, large, P0
NM_053275.3 + NM_001002.3
ribosomal protein S17
TATA binding box protein
NM_001128148.1 + NM_003234.2
For further evaluation single TaqMan® Gene Expression Assays for ACTB, CASC3, EIF2B1, GAPDH, IPO8, MRPL19, PGK1, POP4, PPIA, RPL37A (Applied Biosystems, Foster City, CA) were used, which were identical with the assays used in TaqMan® Express Plate Human Endogenous Control Plates.
TaqMan® real-time PCR was run in triplicates in 48-well reaction plates with a StepOne™ (Applied Biosystems, Foster City, CA). Real-time PCR reaction was performed with 1 μl cDNA (5 ng/μl) in 20 μl reaction mix containing 10 μl TaqMan® Gene Expression Master Mix (Applied Biosystems, Foster City, CA) and 1 μl TaqMan® Gene Expression Assays (Applied Biosystems, Foster City, CA). The cycling conditions were as follows: initial holding period at 95°C for 10 min, followed by a two-step PCR program consisting of 95°C for 15 s and 60°C for 1 min for 40 cycles. Reverse transcriptase negative controls and "no template controls" (without cDNA in PCR) were included. Data were collected and quantitatively analyzed using StepOne™ Software v2.1. Relative quantitation analysis of gene expression data for VEGFA analysis was conducted according to the 2-ΔΔCt method .
Efficiency data for evaluated genes
To compare the stability of candidate reference genes, four validation software programs were used according to their original publication: geNorm http://medgen.ugent.be/~jvdesomp/genorm, NormFinder http://www.mdl.dk/publicationsnormfinder.htm, BestKeeper-1 http://www.gene-quantification.de/bestkeeper.html and the comparative delta Ct method . For geNorm and NormFinder the raw Ct values were transformed to quantities by using the delta Ct method . The highest relative quantities for each gene were set to 1. Bestkeeper-1 and the comparative delta Ct method use raw Ct values. To evaluate the results from the four algorithms an integration tool for analyzing reference genes expression was used http://www.leonxie.com/referencegene.php. First according to the reference genes ranking by every algorithm from the most stable gene to the least stable gene, a series of continuous integers starting from 1 as weight to each reference gene is assigned. The geomean of each gene weights across the four algorithms is calculated and then these reference genes are re-ranked. The gene with the less geomean is viewed as more stable reference gene. Input data is value data from Real-Time qRT-PCR. Statistical analysis was performed with GraphPad Prism V5.03 (GraphPad Software, La Jolla, USA). Normality was assessed according to D'Agostino-Pearson tests with alpha = 0.05. For evaluation of statistical equivalence a confidence-interval version of the Two One-Sided Tests (TOST) procedure of Schuirmann was used . The groups are considered equivalent at a 5% significance level if their difference has a 90% confidence interval that lies entirely inside the upper and lower equivalence limits. Therefore we considered ± δ = ± 1.5 to be reasonable limits of equivalence.
Expression levels of 32 reference genes in meningioma and normal tissue
Expression stability of 32 candidate reference genes in meningioma and brain
Expression stability of eight reference genes plus GAPDH and ACTB in meningiomas, arachnoidea, dura and normal brain
To validate the expression stability of CASC3, EIF2B1, IPO8, MRPL19, PGK1, POP4, PPIA, RPL37A plus GAPDH and ACTB, thirty-four randomly chosen primary cultured meningiomas, the meningioma cell line IOMM-Lee, two arachnoidea, six dura mater, one cerebral meninges and two pooled normal brain samples were screened for these reference genes. For analysis two different algorithms were chosen: NormFinder and Bestkeeper-1. NormFinder has a model-based approach whereas Bestkeeper-1 employs a pair-wise correlation analysis. NormFinder also estimates the variation between subgroups such as normal and cancer tissue.
Ranking of ten candidate reference genes in meningiomas and their control tissue based on average expression stability value as calculated by Bestkeeper-1 and NormFinder.
Ranking of ten candidate reference genes in normal control tissue based on average expression stability value as calculated by Bestkeeper-1 and NormFinder.
Ranking of ten candidate reference genes in meningiomas based on average expression stability value as calculated by Bestkeeper-1 and NormFinder.
TOST procedure showed statistical equivalence between normal tissue and meningiomas (± δ = ± 1.5) for three reference genes: CASC3 (+0.87), IPO8 (+0.57) and POP4 (+1.36). Those three genes were not normally distributed in meningiomas (CASC3 (P-value = 0.002), IPO8 (P-value < 0.0001) and POP4 (P-value = 0.0005). After inclusion of the normal tissue group IPO8 and POP4 remained not normally distributed.
Contribution of reference genes on expression levels of target genes
The requirement for distinct and reproducible results from quantitative gene expression analysis is accurate data normalization [23, 24, 29, 30]. The application of an inappropriate reference gene can lead to false experimental conclusions [31–33]. Therefore one or more reference need to be chosen dependent on used tissue and experimental conditions.
To our knowledge, this is the first systematic analysis of average expression stability of reference genes in meningiomas for data normalisation in qPCR experiments. To evaluate the average expression stability four analysis software programs (geNorm, NormFinder, Bestkeeper-1 and the comparative delta Ct method) based on different algorithms were used. So far various reference genes (GAPDH, ACTB, S18, TBP) were used in qPCR experiments in meningiomas [11–21, 34], although GAPDH was mainly used for normalizations. This study demonstrates that none of these reference genes were ranked under the ten most stable genes of 32 analyzed reference genes. However GAPDH and ACTB as the most used reference genes in meningioma qPCR experiments were further analyzed. After reducing the number of reference genes and increasing the number of samples both reference genes were considered one of the least stable genes. Bestkeeper-1 considered ACTB unsuitable as reference gene in meningiomas and their control tissues.
Because there is so few data available for gene expression of reference genes in meningiomas a large number of reference genes were screened. Using four randomly chosen meningiomas, the malignant meningioma cell line IOMM-Lee, pooled normal brain, cerebral meninges and dura mater was sufficient to determine expression levels of all reference genes as shown in Table 1. Because the four algorithms use different approaches for their rankings of the 32 reference genes, the ranking differed significantly making a selection of genes for further investigation difficult. Using the integration tool which weighs the ranking of each algorithm made the selection easier and more comprehensible. The six most stable reference genes according to the integration tool (PGK1, RPL37A, POP4, MRPL19, IPO8 and CASC3) were chosen for further analysis. Additionally PPIA and EIF2B1 were selected. PPIA was the highest ranked gene, which displayed high expression levels. EIF2B1 was the most stable gene with low expression levels. Because RPL30 is potentially co-regulated with RPL37A, it was not chosen, so the outcome of the result would not be affected.
For a more detailed analysis the remaining ten reference genes were analyzed using an increased number of samples (ntotal = 46 with nnormal = 11 and nmeningioma = 35) but a decreased number of software (NormFinder and Bestkeeper-1). NormFinder was chosen because of the model-based approach and the additional estimation of variation between normal and cancer tissue. In contrast Bestkeeper-1 employs a pair-wise correlation analysis and uses raw Ct values whereas NormFinder uses transformed quantities. Also Bestkeeper-1 directly includes qPCR efficiency.
Both algorithms considered RPL37A as the most suitable reference gene for normalization in qPCR in meningiomas and their control tissue. The following ranking differed significantly especially for CASC3, IPO8 and EIF2B1. Bestkeeper-1 considered CASC3 as the most stable genes in meningiomas, but ranked CASC3 only in ninth place for normal control tissue. This led to a second place in the combined ranking due to the higher number of tumour samples. In contrast NormFinder ranked EIF2B1 highest for normal control tissue and only in sixth place in meningiomas. Because NormFinder weighs the two subgroups, normal tissue versus meningiomas, the ranking of the control tissue has more influence on the combined ranking. This is also demonstrated with IPO8 and conversely with RPL37A. NormFinder ranks RPL37A in meningiomas only in ninth place and in normal control tissue in second place. But after including the variation between those subgroups NormFinder displays RPL37A as the most stable gene for both subgroups.
Considering the results of the normalization of VEGFA against every single reference genes with significantly altered results for CASC3 and IPO8, NormFinder displays a more accurate ranking for meningiomas and their control tissue.
Some researchers recommend the use of multiple reference genes for calculating a normalization factor . NormFinder also determines the best combination of two genes, when subgroups are included. For meningiomas and their normal control tissue the combination is RPL37A and EIF2B1.
In conclusion, the results from the current study demonstrate that RPL37A is the most appropriate single reference gene for the normalization process of gene profiling studies in meningiomas and their normal control tissue arachnoidea, dura mater and normal brain. If a combination of reference genes is applicable RPL37A and EIF2B1 are most suitable. Additionally results from the current study indicate that widely used GAPDH and ACTB are both inappropriate reference genes for meningiomas.
We are grateful to Anita Lal (UCSF, USA) for kindly providing the IOMM-Lee cell line.
- Kleihues P, Burger PC, Scheithauer B: The new WHO classification of brain tumors. Brain Pathol. 1993, 3: 255-268. 10.1111/j.1750-3639.1993.tb00752.x.PubMedView ArticleGoogle Scholar
- Riemenschneider MJ, Perry A, Reifenberger G: Histological classification and molecular genetics of meningiomas. Lancet Neurol. 2006, 5 (12): 1045-1054. 10.1016/S1474-4422(06)70625-1.PubMedView ArticleGoogle Scholar
- McMullen KP, Stieber VW: Meningioma: current treatment options and future directions. CurrTreatOptionsOncol. 2004, 5 (6): 499-509.Google Scholar
- Whittle IR, Smith C, Navoo P, Collie D: Meningiomas. Lancet. 2004, 363 (9420): 1535-1543. 10.1016/S0140-6736(04)16153-9.PubMedView ArticleGoogle Scholar
- Mahmood A, Caccamo DV, Tomecek FJ, Malik GM: Atypical and malignant meningiomas: a clinicopathological review. Neurosurgery. 1993, 33 (6): 955-963. 10.1227/00006123-199312000-00001.PubMedView ArticleGoogle Scholar
- Schittenhelm J, Mittelbronn M, Roser F, Tatagiba M, Mawrin C, Bornemann A: Patterns of SPARC expression and basement membrane intactness at the tumour-brain border of invasive meningiomas. Neuropathol Appl Neurobiol. 2006, 32 (5): 525-531. 10.1111/j.1365-2990.2006.00761.x.PubMedView ArticleGoogle Scholar
- Czechowski T, Stitt M, Altmann T, Udvardi MK, Scheible WR: Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. Plant Physiol. 2005, 139 (1): 5-17. 10.1104/pp.105.063743.PubMedPubMed CentralView ArticleGoogle Scholar
- Schmittgen TD, Zakrajsek BA: Effect of experimental treatment on housekeeping gene expression: validation by real-time, quantitative RT-PCR. J Biochem Biophys Methods. 2000, 46 (1-2): 69-81. 10.1016/S0165-022X(00)00129-9.PubMedView ArticleGoogle Scholar
- Suzuki T, Higgins PJ, Crawford DR: Control selection for RNA quantitation. Biotechniques. 2000, 29 (2): 332-337.PubMedGoogle Scholar
- Buccoliero AM, Castiglione F, Degl'Innocenti DR, Arganini L, Taddei A, Ammannati F, Mennonna P, Taddei GL: Cyclooxygenase-2 (COX-2) overexpression in meningiomas: real time PCR and immunohistochemistry. Appl Immunohistochem Mol Morphol. 2007, 15 (2): 187-192. 10.1097/01.pai.0000201807.58801.fc.PubMedView ArticleGoogle Scholar
- Buccoliero AM, Castiglione F, Degl'Innocenti DR, Gheri CF, Garbini F, Taddei A, Ammannati F, Mennonna P, Taddei GL: NF2 gene expression in sporadic meningiomas: relation to grades or histotypes real time-pCR study. Neuropathology. 2007, 27 (1): 36-42. 10.1111/j.1440-1789.2006.00737.x.PubMedView ArticleGoogle Scholar
- Huang H, Held-Feindt J, Buhl R, Mehdorn HM, Mentlein R: Expression of VEGF and its receptors in different brain tumors. Neurol Res. 2005, 27 (4): 371-377. 10.1179/016164105X39833.PubMedView ArticleGoogle Scholar
- Miracco C, Cosci E, Oliveri G, Luzi P, Pacenti L, Monciatti I, Mannucci S, De Nisi MC, Toscano M, Malagnino V, et al: Protein and mRNA expression of autophagy gene Beclin 1 in human brain tumours. Int J Oncol. 2007, 30 (2): 429-436.PubMedGoogle Scholar
- Paek SH, Kim DG, Park CK, Phi JH, Kim YY, Im SY, Kim JE, Park SH, Jung HW: The role of matrix metalloproteinases and tissue inhibitors of matrix metalloproteinase in microcystic meningiomas. Oncol Rep. 2006, 16 (1): 49-56.PubMedGoogle Scholar
- Puri S, Joshi BH, Sarkar C, Mahapatra AK, Hussain E, Sinha S: Expression and structure of interleukin 4 receptors in primary meningeal tumors. Cancer. 2005, 103 (10): 2132-2142. 10.1002/cncr.21008.PubMedView ArticleGoogle Scholar
- Rollison DE, Utaipat U, Ryschkewitsch C, Hou J, Goldthwaite P, Daniel R, Helzlsouer KJ, Burger PC, Shah KV, Major EO: Investigation of human brain tumors for the presence of polyomavirus genome sequences by two independent laboratories. Int J Cancer. 2005, 113 (5): 769-774. 10.1002/ijc.20641.PubMedView ArticleGoogle Scholar
- Uesaka T, Shono T, Suzuki SO, Nakamizo A, Niiro H, Mizoguchi M, Iwaki T, Sasaki T: Expression of VEGF and its receptor genes in intracranial schwannomas. J Neurooncol. 2007, 83 (3): 259-266. 10.1007/s11060-007-9336-0.PubMedView ArticleGoogle Scholar
- Yang Y, Shao N, Luo G, Li L, Nilsson-Ehle P, Xu N: Relationship between PTEN gene expression and differentiation of human glioma. Scand J Clin Lab Invest. 2006, 66 (6): 469-475. 10.1080/00365510600763285.PubMedView ArticleGoogle Scholar
- Andersson U, Guo D, Malmer B, Bergenheim AT, Brannstrom T, Hedman H, Henriksson R: Epidermal growth factor receptor family (EGFR, ErbB2-4) in gliomas and meningiomas. Acta Neuropathol. 2004, 108 (2): 135-142.PubMedView ArticleGoogle Scholar
- Denizot Y, De Armas R, Durand K, Robert S, Moreau JJ, Caire F, Weinbreck N, Labrousse F: Analysis of several PLA2 mRNA in human meningiomas. Mediators Inflamm. 2009, 2009: 689430-PubMedPubMed CentralView ArticleGoogle Scholar
- Laurendeau I, Ferrer M, Garrido D, D'Haene N, Ciavarelli P, Basso A, Vidaud M, Bieche I, Salmon I, Szijan I: Gene expression profiling of ErbB receptors and ligands in human meningiomas. Cancer Invest. 2009, 27 (6): 691-698. 10.1080/07357900802709175.PubMedView ArticleGoogle Scholar
- Erickson HS, Albert PS, Gillespie JW, Wallis BS, Rodriguez-Canales J, Linehan WM, Gonzalez S, Velasco A, Chuaqui RF, Emmert-Buck MR: Assessment of normalization strategies for quantitative RT-PCR using microdissected tissue samples. Lab Invest. 2007, 87 (9): 951-962. 10.1038/labinvest.3700659.PubMedView ArticleGoogle Scholar
- Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F: Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes. Genome Biol. 2002, 3 (7): RESEARCH0034-PubMedPubMed CentralView ArticleGoogle Scholar
- Andersen CL, Jensen JL, Orntoft TF: 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 (15): 5245-5250. 10.1158/0008-5472.CAN-04-0496.PubMedView ArticleGoogle Scholar
- Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP: Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper--Excel-based tool using pair-wise correlations. Biotechnol Lett. 2004, 26 (6): 509-515.PubMedView ArticleGoogle Scholar
- Nicholas Silver SB, Jiang Jie, Thein Swee Lay: Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR. BMC Molecular Biology. 2006, 7: 33-10.1186/1471-2199-7-33.PubMedPubMed CentralView ArticleGoogle Scholar
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262.PubMedView ArticleGoogle Scholar
- Schuirmann DJ: A comparison of the two one-sided tests procedure and the power approach for assessing the equivalence of average bioavailability. J Pharmacokinet Biopharm. 1987, 15 (6): 657-680. 10.1007/BF01068419.PubMedView ArticleGoogle Scholar
- Nolan T, Hands RE, Bustin SA: Quantification of mRNA using real-time RT-PCR. Nat Protoc. 2006, 1 (3): 1559-1582. 10.1038/nprot.2006.236.PubMedView ArticleGoogle Scholar
- Gutierrez L, Mauriat M, Pelloux J, Bellini C, Van Wuytswinkel O: Towards a systematic validation of references in real-time rt-PCR. Plant Cell. 2008, 20 (7): 1734-1735. 10.1105/tpc.108.059774.PubMedPubMed CentralView ArticleGoogle Scholar
- Bustin SA, Nolan T: Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. J Biomol Tech. 2004, 15 (3): 155-166.PubMedPubMed CentralGoogle Scholar
- Dheda K, Huggett JF, Chang JS, Kim LU, Bustin SA, Johnson MA, Rook GA, Zumla A: The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. Anal Biochem. 2005, 344 (1): 141-143. 10.1016/j.ab.2005.05.022.PubMedView ArticleGoogle Scholar
- Huggett J, Dheda K, Bustin S, Zumla A: Real-time RT-PCR normalisation; strategies and considerations. Genes Immun. 2005, 6 (4): 279-284. 10.1038/sj.gene.6364190.PubMedView ArticleGoogle Scholar
- Laurendeau I, Ferrer M, Garrido D, D'Haene N, Ciavarelli P, Basso A, Vidaud M, Bieche I, Salmon I, Szijan I: Gene expression profiling of the hedgehog signaling pathway in human meningiomas. Mol Med. 2010, 16 (7-8): 262-270.PubMedPubMed CentralView ArticleGoogle Scholar
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