Careful Selection of Reference Genes Is Required for Reliable Performance of RT-qPCR in Human Normal and Cancer Cell Lines

Reverse Transcription - quantitative Polymerase Chain Reaction (RT-qPCR) is a standard technique in most laboratories. The selection of reference genes is essential for data normalization and the selection of suitable reference genes remains critical. Our aim was to 1) review the literature since implementation of the MIQE guidelines in order to identify the degree of acceptance; 2) compare various algorithms in their expression stability; 3) identify a set of suitable and most reliable reference genes for a variety of human cancer cell lines. A PubMed database review was performed and publications since 2009 were selected. Twelve putative reference genes were profiled in normal and various cancer cell lines (n = 25) using 2-step RT-qPCR. Investigated reference genes were ranked according to their expression stability by five algorithms (geNorm, Normfinder, BestKeeper, comparative ΔCt, and RefFinder). Our review revealed 37 publications, with two thirds patient samples and one third cell lines. qPCR efficiency was given in 68.4% of all publications, but only 28.9% of all studies provided RNA/cDNA amount and standard curves. GeNorm and Normfinder algorithms were used in 60.5% in combination. In our selection of 25 cancer cell lines, we identified HSPCB, RRN18S, and RPS13 as the most stable expressed reference genes. In the subset of ovarian cancer cell lines, the reference genes were PPIA, RPS13 and SDHA, clearly demonstrating the necessity to select genes depending on the research focus. Moreover, a cohort of at least three suitable reference genes needs to be established in advance to the experiments, according to the guidelines. For establishing a set of reference genes for gene normalization we recommend the use of ideally three reference genes selected by at least three stability algorithms. The unfortunate lack of compliance to the MIQE guidelines reflects that these need to be further established in the research community.

[1]  V. Beneš,et al.  The MIQE guidelines: minimum information for publication of quantitative real-time PCR experiments. , 2009, Clinical chemistry.

[2]  Nigel J Walker,et al.  Tech.Sight. A technique whose time has come. , 2002, Science.

[3]  Samuel Arvidsson,et al.  QuantPrime – a flexible tool for reliable high-throughput primer design for quantitative PCR , 2008, BMC Bioinformatics.

[4]  M. Kubista,et al.  Properties of the reverse transcription reaction in mRNA quantification. , 2004, Clinical chemistry.

[5]  S A Bustin,et al.  Quantitative real-time RT-PCR--a perspective. , 2005, Journal of molecular endocrinology.

[6]  D. Ginzinger.  Gene quantification using real-time quantitative PCR: an emerging technology hits the mainstream. , 2002, Experimental hematology.

[7]  G. Gerken,et al.  Validation of putative reference genes for gene expression studies in human hepatocellular carcinoma using real-time quantitative RT-PCR , 2008, BMC Cancer.

[8]  R. Barber,et al.  GAPDH as a housekeeping gene: analysis of GAPDH mRNA expression in a panel of 72 human tissues. , 2005, Physiological genomics.

[9]  A. Hilton,et al.  RT-PCR for the pseudogene-free amplification of the glyceraldehyde-3-phosphate dehydrogenase gene (gapd). , 2003, Molecular and cellular probes.

[10]  E. Bockstaele,et al.  A critique of widely used normalization software tools and an alternative method to identify reliable reference genes in red clover (Trifolium pratense L.) , 2012, Planta.

[11]  Claus Lindbjerg Andersen,et al.  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 , 2004, Cancer Research.

[12]  S. Thein,et al.  Selection of housekeeping genes for gene expression studies in human reticulocytes using real-time PCR , 2006, BMC Molecular Biology.

[13]  M. Pfaffl,et al.  Validities of mRNA quantification using recombinant RNA and recombinant DNA external calibration curves in real-time RT-PCR , 2001, Biotechnology Letters.

[14]  Michael W Pfaffl,et al.  RNA integrity and the effect on the real-time qRT-PCR performance. , 2006, Molecular aspects of medicine.

[15]  Anders Ståhlberg,et al.  Comparison of reverse transcriptases in gene expression analysis. , 2004, Clinical chemistry.

[16]  Tom H. Pringle,et al.  The human genome browser at UCSC. , 2002, Genome research.

[17]  Yan-Li Li,et al.  Identification of suitable reference genes for gene expression studies of human serous ovarian cancer by real-time polymerase chain reaction. , 2009, Analytical biochemistry.

[18]  R. Coffey,et al.  Selective inhibition of growth-related gene expression in murine keratinocytes by transforming growth factor beta , 1988, Molecular and cellular biology.

[19]  Nigel J. Walker,et al.  A Technique Whose Time Has Come , 2002, Science.

[20]  Tania Nolan,et al.  Pitfalls of quantitative real-time reverse-transcription polymerase chain reaction. , 2004, Journal of biomolecular techniques : JBT.

[21]  L. Ercolani,et al.  An insulin response element in the glyceraldehyde-3-phosphate dehydrogenase gene binds a nuclear protein induced by insulin in cultured cells and by nutritional manipulations in vivo. , 1990, Proceedings of the National Academy of Sciences of the United States of America.

[22]  S. Walker,et al.  Quantitative RT-PCR : Pitfalls and Potential , 1999 .

[23]  F. Speleman,et al.  Accurate normalization of real-time quantitative RT-PCR data by geometric averaging of multiple internal control genes , 2002, Genome Biology.

[24]  M. Pfaffl,et al.  Determination of stable housekeeping genes, differentially regulated target genes and sample integrity: BestKeeper – Excel-based tool using pair-wise correlations , 2004, Biotechnology Letters.

[25]  W. Siegert,et al.  Guideline to reference gene selection for quantitative real-time PCR. , 2004, Biochemical and biophysical research communications.

[26]  Michael W Pfaffl,et al.  The ongoing evolution of qPCR. , 2010, Methods.

[27]  Karen Goossens,et al.  Selection of reference genes for quantitative real-time PCR in bovine preimplantation embryos , 2005, BMC Developmental Biology.

[28]  W. Yam,et al.  Coordinated induction of two unrelated glucose-regulated protein genes by a calcium ionophore: human BiP/GRP78 and GAPDH. , 1990, Biochemical and biophysical research communications.