Normalization of qRT-PCR data: the necessity of adopting a systematic, experimental conditions-specific, validation of references.

Quantitative RT-PCR (reverse transcription polymerase chain reaction, also known as qRT-PCR or real-time RT-PCR) has been used in large proportions of transcriptome analyses published to date. The accuracy of the results obtained by this method strongly depends on accurate transcript normalization using stably expressed genes, known as references. Statistical algorithms have been developed recently to help validate reference genes but, surprisingly, this robust approach is under-utilized in plants. Instead, putative 'housekeeping' genes tend to be used as references without any proper validation. The concept of normalization in transcript quantification is introduced here and the factors affecting its reliability in qRT-PCR are discussed in an attempt to convince molecular biologists, and non-specialists, that systematic validation of reference genes is essential for producing accurate, reliable data in qRT-PCR analyses, and thus should be an integral component of them.

[1]  M. Holland,et al.  Transcript Abundance in Yeast Varies over Six Orders of Magnitude* , 2002, The Journal of Biological Chemistry.

[2]  T. Moritz,et al.  The lack of a systematic validation of reference genes: a serious pitfall undervalued in reverse transcription-polymerase chain reaction (RT-PCR) analysis in plants. , 2008, Plant biotechnology journal.

[3]  D. A. Lee,et al.  Quantification of mRNA using real-time PCR and Western blot analysis of MAPK events in chondrocyte/agarose constructs. , 2011, Methods in molecular biology.

[4]  R. Volkov,et al.  Heat-stress-dependency and developmental modulation of gene expression: the potential of house-keeping genes as internal standards in mRNA expression profiling using real-time RT-PCR. , 2003, Journal of experimental botany.

[5]  J. Warner,et al.  The economics of ribosome biosynthesis in yeast. , 1999, Trends in biochemical sciences.

[6]  Mark Stitt,et al.  Real-time RT-PCR profiling of over 1400 Arabidopsis transcription factors: unprecedented sensitivity reveals novel root- and shoot-specific genes. , 2004, The Plant journal : for cell and molecular biology.

[7]  J. Vangronsveld,et al.  Normalisation of real-time RT-PCR gene expression measurements in Arabidopsis thaliana exposed to increased metal concentrations , 2008, Planta.

[8]  W. Scheible,et al.  Eleven Golden Rules of Quantitative RT-PCR , 2008, The Plant Cell Online.

[9]  J. Ecker,et al.  DELLA Proteins and Gibberellin-Regulated Seed Germination and Floral Development in Arabidopsis1[w] , 2004, Plant Physiology.

[10]  Hank C Wu,et al.  A community resource for high-throughput quantitative RT-PCR analysis of transcription factor gene expression in Medicago truncatula , 2008, Plant Methods.

[11]  S. Molin,et al.  Changes in rRNA Levels during Stress Invalidates Results from mRNA Blotting: Fluorescence In Situ rRNA Hybridization Permits Renormalization for Estimation of Cellular mRNA Levels , 2001, Journal of bacteriology.

[12]  Katrin Hoffmann,et al.  Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR – how well do they correlate? , 2005, BMC Genomics.

[13]  E. Spanakis,et al.  Problems related to the interpretation of autoradiographic data on gene expression using common constitutive transcripts as controls. , 1993, Nucleic acids research.

[14]  M. Solanas,et al.  Unsuitability of using ribosomal RNA as loading control for Northern blot analyses related to the imbalance between messenger and ribosomal RNA content in rat mammary tumors. , 2001, Analytical biochemistry.

[15]  D. Redmer,et al.  Quantification of lane-to-lane loading of poly(A) RNA using a biotinylated oligo(dT) probe and chemiluminescent detection. , 1995, BioTechniques.

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

[17]  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.

[18]  M. Hendriks-Balk,et al.  Pitfalls in the normalization of real-time polymerase chain reaction data , 2007, Basic Research in Cardiology.

[19]  L. Hoffmann,et al.  Housekeeping gene selection for real-time RT-PCR normalization in potato during biotic and abiotic stress. , 2005, Journal of experimental botany.

[20]  O. Van Wuytswinkel,et al.  Towards a Systematic Validation of References in Real-Time RT-PCR , 2008, The Plant Cell Online.

[21]  C. Andersen,et al.  Sets Normalization , Applied to Bladder and Colon Cancer Data Estimation Approach to Identify Genes Suited for Transcription-PCR Data : A Model-Based Variance Normalization of Real-Time Quantitative Reverse , 2004 .

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

[23]  K Dheda,et al.  The implications of using an inappropriate reference gene for real-time reverse transcription PCR data normalization. , 2005, Analytical biochemistry.

[24]  S. Lund,et al.  An optimized grapevine RNA isolation procedure and statistical determination of reference genes for real-time RT-PCR during berry development , 2006, BMC Plant Biology.

[25]  E. Wurmbach,et al.  De-regulation of common housekeeping genes in hepatocellular carcinoma , 2007, BMC genomics.

[26]  B. Mueller‐Roeber,et al.  A quantitative RT-PCR platform for high-throughput expression profiling of 2500 rice transcription factors , 2007, Plant Methods.

[27]  K Dheda,et al.  Real-time RT-PCR normalisation; strategies and considerations , 2005, Genes and Immunity.

[28]  G. Horgan,et al.  Relative expression software tool (REST©) for group-wise comparison and statistical analysis of relative expression results in real-time PCR , 2002 .

[29]  S. Davis.  Faculty Opinions recommendation of Genome-wide identification and testing of superior reference genes for transcript normalization in Arabidopsis. , 2006 .

[30]  John Quackenbush.  Microarray data normalization and transformation , 2002, Nature Genetics.