The need for transparency and good practices in the qPCR literature

Bustin, Stephen A.; Beneš, Vladimír; Garson, Jeremy A.; Hellemans, Jan; Huggett, Jim F.; Kubista, Mikael; Mueller, Reinhold; Nolan, Tania; Pfaffl, Michael W.; Shipley, Gregory L.; Wittwer, Carl T.; Schjerling, Peter; Day, Philip J.; Abreu, Marconi; Aguado, Begoña; Beaulieu, Jean‐François; Beckers, Anneleen; Bogaert, Sara; Browne, John A.; Carrasco-Ramiro, Fernando; Ceelen, Liesbeth; Ciborowski, Kate L.; Cornillie, Pieter; Coulon, S.; Cuypers, Ann; Brouwer, Sara De; Ceuninck, Leentje De; Craene, Jurgen De; Naeyer, Hélène De; Spiegelaere, Ward De; Deckers, Kato; Dheedene, Annelies; Durinck, Kaat; Ferreira-Teixeira, Margarida; Fieuw, Annelies; Gallup, Jack M.; Gonzalo-Flores, Sandra; Goossens, Karen; Heindryckx, Femke; Herring, Elizabeth; Hoenicka, Hans; Icardi, Laura; Jaggi, Rolf; Javad, Farzad; Karampelias, Michael; Kibenge, Frederick; Kibenge, Molly J. T.; Kumps, Candy; Lambertz, Irina; Lammens, Tim; Markey, Amelia; Messiaen, Peter; Mets, Evelien; Morais, Sofía; Mudarra-Rubio, Alberto; Nakiwala, Justine; Nelis, Hilde; Olsvik, Pål A.; Pérez-Novo, Claudina; Plusquin, Michelle; Remans, Tony; Rihani, Ali; Rodrigues-Santos, Paulo; Rondou, Pieter; Sanders, Rebecca; Schmidt‐Bleek, Katharina; Skovgaard, Kerstin; Smeets, Karen; Tabera, Laura; Toegel, Stefan; Acker, Tim Van; Broeck, Wim Van Den; Meulen, Joni Van der; Gele, Mireille Van; Peer, Gert Van; Poucke, Mario Van; Roy, Nadine Van; Vergult, Sarah; Wauman, Joris; Tshuikina-Wiklander, Marina; Willems, Erik; Zaccara, Sara; Zeka, Fjoralba; Vandesompele, Jo

doi:10.1038/nmeth.2697

Cited by 257 publications

(233 citation statements)

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“…3 Fluorescence-based qPCR is the most commonly used molecular technology for the quantification of gene expression. 20 To obtain consistent and biologically relevant gene expression data, the use of stable RGs is absolutely essential. Numerous candidate RGs have been reported for a wide variety of human cell lines and primary cells under different experimental conditions, but almost exclusively under standard 2D cultivation.…”

Section: Discussionmentioning

confidence: 99%

“…In this context, using single, nonvalidated RGs for qPCR analysis can lead to a systemic source of error, causing incorrect results and misinterpretation of gene expression data. 20 Ideally, RGs exhibit a stable gene expression under the experimental conditions given, and should be validated for each new experimental setup. Currently, various statistical algorithms exists to determine the most stable RG for qRT-PCR normalization, such as NormFinder, 21 Global Pattern Recognition, 22 Bestkeeper, ª23 and geNorm.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Stable Reference Genes for Gene Expression Analysis of Three-Dimensional Cultivated Human Bone Marrow-Derived Mesenchymal Stromal Cells for Bone Tissue Engineering

Rauh

Jacobi

Stiehler

2015

Tissue Engineering Part C: Methods

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The principles of tissue engineering (TE) are widely used for bone regeneration concepts. Three-dimensional (3D) cultivation of autologous human mesenchymal stromal cells (MSCs) on porous scaffolds is the basic prerequisite to generate newly formed bone tissue. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is a specific and sensitive analytical tool for the measurement of mRNA-levels in cells or tissues.For an accurate quantification of gene expression levels, stably expressed reference genes (RGs) are essential to obtain reliable results. Since the 3D environment can affect a cell's morphology, proliferation, and gene expression profile compared with two-dimensional (2D) cultivation, there is a need to identify robust RGs for the quantification of gene expression. So far, this issue has not been adequately investigated. The aim of this study was to identify the most stably expressed RGs for gene expression analysis of 3D-cultivated human bone marrow-derived MSCs (BM-MSCs). For this, we analyzed the gene expression levels of n = 31 RGs in 3D-cultivated human BM-MSCs from six different donors compared with conventional 2D cultivation using qRT-PCR. MSCs isolated from bone marrow aspirates were cultivated on human cancellous bone cube scaffolds for 14 days. Osteogenic differentiation was assessed by cell-specific alkaline phosphatase (ALP) activity and expression of osteogenic marker genes. Expression levels of potential reference and target genes were quantified using commercially available TaqMan Ò assays. mRNA expression stability of RGs was determined by calculating the coefficient of variation (CV) and using the algorithms of geNorm and NormFinder. Using both algorithms, we identified TATA box binding protein (TBP), transferrin receptor (p90, CD71) (TFRC), and hypoxanthine phosphoribosyltransferase 1 (HPRT1) as the most stably expressed RGs in 3D-cultivated BMMSCs. Notably, genes that are routinely used as RGs, for example, beta actin (ACTB) and ribosomal protein L37a (RPL37A), were among the least stable genes. We recommend the combined use of TBP, TFRC, and HPRT1 for the accurate and robust normalization of qRT-PCR data of 3D-cultivated human BM-MSCs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of Stable Reference Genes for Gene Expression Analysis of Three-Dimensional Cultivated Human Bone Marrow-Derived Mesenchymal Stromal Cells for Bone Tissue Engineering

Rauh

Jacobi

Stiehler

2015

Tissue Engineering Part C: Methods

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“…These are highlighted by the MIQE guidelines, which are increasingly seen as a valuable sentinel safeguarding the reliability and reproducibility of qPCR-based conclusions. The urgent need for their implementation is vividly demonstrated by a recent publication that reviewed over 2,000 publications for compliance with the MIQE guidelines and found that the vast majority report inadequate or inappropriate information [ 26 ]. MIQE aims to make nucleic acid analysis not just easy but reliable and allow qPCR to exploit its wide range of applications that today range from the quantifi cation of RNA to epigenetics and protein detection, using variations on essentially the same theme.…”

Section: Considerations For the Futurementioning

confidence: 99%

Minimum Information Necessary for Quantitative Real-Time PCR Experiments

Johnson

Nour

Nolan³

et al. 2014

Methods in Molecular Biology

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The MIQE (minimum information for the publication of quantitative real-time PCR) guidelines were published in 2009 with the twin aims of providing a blueprint for good real-time quantitative polymerase chain reaction (qPCR) assay design and encouraging the comprehensive reporting of qPCR protocols. It had become increasingly clear that variable pre-assay conditions, poor assay design, and incorrect data analysis were leading to the routine publication of data that were often inconsistent, inaccurate, and wrong. The problem was exacerbated by a lack of transparency of reporting, with the details of technical information inadequate for the purpose of assessing the validity of published qPCR data. This had, and continues to have serious implications for basic research, reducing the potential for translating fi ndings into valuable applications and potentially devastating consequences for clinical practice. Today, the rationale underlying the MIQE guidelines has become widely accepted, with more than 2,200 citations by March 2014 and editorials in Nature and related publications acknowledging the enormity of the problem. However, the problem we now face is rather serious: thousands of publications that report suspect data are populating and corrupting the peer-reviewed scientifi c literature. It will be some time before the many contradictions apparent in every area of the life sciences are corrected.

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“…Not only has it become possible to evaluate the gene-expression levels of many genes simultaneously by employing these transcriptomics techniques, but their results are also more quantitative than with the classical Northern blot and qPCR analysis (due to the variability of "housekeeping genes"). 1 One would expect that the introduction of such significantly improved detectors would have a major impact on how biologists design their new transcriptomics experiments. However, many biologists consider microarray technology merely a very highthroughput Northern blot and often still use "classical approaches" for their transcriptomics experiments.…”

Section: Lessons-learnedmentioning

confidence: 99%