The use of digital PCR for quantification of nucleic acids is rapidly growing. A major drawback remains the lack of flexible data analysis tools. Published analysis approaches are either tailored to specific problem settings or fail to take into account sources of variability. We propose the generalized linear mixed models framework as a flexible tool for analyzing a wide range of experiments. We also introduce a method for estimating reference gene stability to improve accuracy and precision of copy number and relative expression estimates. We demonstrate the usefulness of the methodology on a complex experimental setup.
Fragmented RNA from formalin-fixed paraffin-embedded (FFPE) tissue is a known obstacle to gene expression analysis. In this study, the impact of RNA integrity, gene-specific reverse transcription and targeted cDNA preamplification was quantified in terms of reverse transcription polymerase chain reaction (RT-qPCR) sensitivity by measuring 48 protein coding genes on eight duplicate cultured cancer cell pellet FFPE samples and twenty cancer tissue FFPE samples. More intact RNA modestly increased gene detection sensitivity by 1.6 fold (earlier detection by 0.7 PCR cycles, 95% CI = 0.593– 0.850). Application of gene-specific priming instead of whole transcriptome priming during reverse transcription further improved RT-qPCR sensitivity by a considerable 4.0 fold increase (earlier detection by 2.0 PCR cycles, 95% CI = 1.73–2.32). Targeted cDNA preamplification resulted in the strongest increase of RT-qPCR sensitivity and enabled earlier detection by an average of 172.4 fold (7.43 PCR cycles, 95% CI = 6.83–7.05). We conclude that gene-specific reverse transcription and targeted cDNA preamplification are adequate methods for accurate and sensitive RT-qPCR based gene expression analysis of FFPE material. The presented methods do not involve expensive or complex procedures and can be easily implemented in any routine RT-qPCR practice.
Quantitative PCR (qPCR) is the method of choice in gene expression analysis. However, the number of groups or treatments, target genes and technical replicates quickly exceeds the capacity of a single run on a qPCR machine and the measurements have to be spread over more than 1 plate. Such multi-plate measurements often show similar proportional differences between experimental conditions, but different absolute values, even though the measurements were technically carried out with identical procedures. Removal of this between-plate variation will enhance the power of the statistical analysis on the resulting data. Inclusion and application of calibrator samples, with replicate measurements distributed over the plates, assumes a multiplicative difference between plates. However, random and technical errors in these calibrators will propagate to all samples on the plate. To avoid this effect, the systematic bias between plates can be removed with a correction factor based on all overlapping technical and biological replicates between plates. This approach removes the requirement for all calibrator samples to be measured successfully on every plate. This paper extends an already published factor correction method to the use in multi-plate qPCR experiments. The between-run correction factor is derived from the target quantities which are calculated from the quantification threshold, PCR efficiency and observed Cq value. To enable further statistical analysis in existing qPCR software packages, an efficiency-corrected Cq value is reported, based on the corrected target quantity and a PCR efficiency per target. The latter is calculated as the mean of the PCR efficiencies taking the number of reactions per amplicon per plate into account. Export to the RDML format completes an RDML-supported analysis pipeline of qPCR data ranging from raw fluorescence data, amplification curve analysis and application of reference genes to statistical analysis.
Background: The universal qPCR data exchange file format RDML is today well accepted by the scientific community, part of the MIQE guidelines and implemented in many qPCR instruments. With the increased use of RDML new challenges emerge. The flexibility of the RDML format resulted in some implementations that did not meet the expectations of the consortium in the level of support or the use of elements.
Results: In the current RDML version 1.2 the description of the elements was sharpened. The open source editor RDML-Ninja was released (http://sourceforge.net/projects/qpcr-ninja). RDML-Ninja allows to visualize, edit and validate RDML files and thus clarifies the use of RDML elements. Furthermore RDML-Ninja serves as reference implementation for RDML and enables migration between RDML versions independent of the instrument software. The database RDMLdb will serve as an online repository for RDML files and facilitate the exchange of RDML data (http://www.rdmldb.org). Authors can upload their RDML files and reference them in publications by the unique identifier provided by RDMLdb. The MIQE guidelines propose a rich set of information required to document each qPCR run. RDML provides the vehicle to store and maintain this information and current development aims at further integration of MIQE requirements into the RDML format.
Conclusions: The editor RDML-Ninja and the database RDMLdb enable scientists to evaluate and exchange qPCR data in the instrument-independent RDML format. We are confident that this infrastructure will build the foundation for standardized qPCR data exchange among scientists, research groups, and during publication.
The selection and validation of stably expressed reference genes is a critical issue for proper RT-qPCR data normalization. In zebrafish expression studies, many commonly used reference genes are not generally applicable given their variability in expression levels under a variety of experimental conditions. Inappropriate use of these reference genes may lead to false interpretation of expression data and unreliable conclusions. In this study, we evaluated a novel normalization method in zebrafish using expressed repetitive elements (ERE) as reference targets, instead of specific protein coding mRNA targets. We assessed and compared the expression stability of a number of EREs to that of commonly used zebrafish reference genes in a diverse set of experimental conditions including a developmental time series, a set of different organs from adult fish and different treatments of zebrafish embryos including morpholino injections and administration of chemicals. Using geNorm and rank aggregation analysis we demonstrated that EREs have a higher overall expression stability compared to the commonly used reference genes. Moreover, we propose a limited set of ERE reference targets (hatn10, dna15ta1 and loopern4), that show stable expression throughout the wide range of experiments in this study, as strong candidates for inclusion as reference targets for qPCR normalization in future zebrafish expression studies. Our applied strategy to find and evaluate candidate expressed repeat elements for RT-qPCR data normalization has high potential to be used also for other species.
Reference genes have become the method of choice for normalization of qPCR data. It has been demonstrated in many studies that reference gene validation is essential to ensure accurate and reliable results. This chapter describes how a pilot study can be set up to identify the best set of reference genes to be used for normalization of qPCR data. The data from such a pilot study should be analyzed with dedicated algorithms such as geNorm to rank genes according to their stability-a measure for how well they are suited for normalization. geNorm also provides insights into the optimal number of reference genes and the overall quality of the selected set of reference genes. Importantly, these results are always in function of the sample type being studied. Guidelines are provided on the interpretation of the results from geNorm pilot studies as well as for the continued monitoring of reference gene quality in subsequent studies. For screening studies including a large, unbiased set of genes (e.g., complete miRNome) an alternative normalization method can be used: global mean normalization. This chapter also describes how the data from such studies can be used to identify reference genes for subsequent validation studies on smaller sets of selected genes.
Two surveys of over 1,700 publications whose authors use quantitative real-time PCR (qPCR) reveal a lack of transparent and comprehensive reporting of essential technical information. Reporting standards are significantly improved in publications that cite the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines, although such publications are still vastly outnumbered by those that do not.
Background: Genome-sequencing studies have led to an immense increase in the number of known singlenucleotide polymorphisms (SNPs). Designing primers that anneal to regions devoid of SNPs has therefore become challenging. We studied the impact of one or more mismatches in primer-annealing sites on different quantitative PCR (qPCR)-related parameters, such as quantitative cycle (Cq), amplification efficiency, and reproducibility. METHODS: We used synthetic templates and primers to assess the effect of mismatches at primer-annealing sites on qPCR assay performance. Reactions were performed with 5 commercially available master mixes. We studied the effects of the number, type, and position of priming mismatches on Cq value, PCR efficiency, reproducibility, and yield.
Results: The impact of mismatches was most pronounced for the number of mismatched nucleotides and for their distance from the 3 end of the primer. In addition, having 4 mismatches in a single primer or having 3 mismatches in one primer and 2 in the other was required to block a reaction completely. Finally, the degree of the mismatch effect was concentration independent for single mismatches, whereas concentration independence failed at higher template concentrations as the number of mismatches increased.
Conclusions: Single mismatches located 5 bp from the 3 end have a moderate effect on qPCR amplification and can be tolerated. This finding, together with the concentration independence for single mismatches and the complete blocking of the PCR reaction for 3 mismatches, can help to chart mismatch behavior in qPCR reactions and increase the rate of successful primer design for sequences with a high SNP density or for homologous regions of sequence.
Background: Measuring messenger RNA (mRNA) levels using the reverse transcription quantitative polymerase chain reaction (RT-qPCR) is common practice in many laboratories. A specific set of mRNAs as internal control reference genes is considered as the preferred strategy to normalize RT-qPCR data. Proper selection of reference genes is a critical issue, especially in cancer cells that are subjected to different in vitro manipulations. These manipulations may result in dramatic alterations in gene expression levels, even of assumed reference genes. In this study, we evaluated the expression levels of 11 commonly used reference genes as internal controls for normalization of 19 experiments that include neuroblastoma, TALL, melanoma, breast cancer, non small cell lung cancer (NSCL), acute myeloid leukemia (AML), prostate cancer, colorectal cancer, and cervical cancer cell lines subjected to various perturbations.
Results: The geNorm algorithm in the software package qbase+ was used to rank the candidate reference genes according to their expression stability. We observed that the stability of most of the candidate reference genes varies greatly in perturbation experiments. Expressed Alu repeats show relatively stable expression regardless of experimental condition. These Alu repeats are ranked among the best reference assays in all perturbation experiments and display acceptable average expression stability values (M,0.5).
Conclusions: We propose the use of Alu repeats as a reference assay when performing cancer cell perturbation experiments.
Gene expression quantification on cultured cells using the reverse transcription quantitative polymerase chain reaction (RT-qPCR) typically involves an RNA purification step that limits sample processing throughput and precludes parallel analysis of large numbers of samples. An approach in which cDNA synthesis is carried out on crude cell lysates instead of on purified RNA samples can offer a fast and straightforward alternative. Here, we evaluate such an approach, benchmarking Ambion’s Cells-to-CT kit with the classic workflow of RNA purification and cDNA synthesis, and demonstrate its good accuracy and superior sensitivity.
RNA transcripts such as mRNA or microRNA are frequently used as biomarkers to determine disease state or response to therapy. Reverse transcription (RT) in combination with quantitative PCR (qPCR) has become the method of choice to quantify small amounts of such RNA molecules. In parallel with the democratization of RT-qPCR and its increasing use in biomedical research or biomarker discovery, we witnessed a growth in the number of gene expression data analysis methods. Most of these methods are based on the principle that the position of the ampliﬁcation curve with respect to the cycle-axis is a measure for the initial target quantity: the later the curve, the lower the target quantity. However, most methods differ in the mathematical algorithms used to determine this position, as well as in the way the efﬁciency of the PCR reaction (the fold increase of product per cycle) is determined and applied in the calculations. Moreover, there is dispute about whether the PCR efﬁciency is constant or continuously decreasing. Together this has lead to the development of different methods to analyze ampliﬁcation curves. In published comparisons of these methods, available algorithms were typically applied in a restricted or outdated way, which does not do them justice. Therefore, we aimed at development of a framework for robust and unbiased assessment of curve analysis performance whereby various publicly
available curve analysis methods were thoroughly compared using a previously published large clinical data set (Vermeulen et al., 2009) . The original developers of these methods applied their algorithms and are co-author on this study. We assessed the curve analysis methods’ impact on transcriptional biomarker identiﬁcation in terms of expression level, statistical signiﬁcance, and patient-classiﬁcation accuracy. The concentration series per gene, together with data sets from unpublished technical performance experiments, were analyzed in order to assess the algorithms’ precision, bias, and resolution. While large differences exist between methods when considering the technical performance experiments, most methods perform relatively well on the biomarker data. The data and the analysis results per method are made available to serve as benchmark for further development and evaluation of qPCR curve analysis methods (http://qPCRDataMethods.hfrc.nl).
Real-time quantitative PCR (qPCR) is the gold standard for fast, accurate, sensitive and costefficient gene expression analysis. Despite its conceptual simplicity and ease of use, the multi-step qPCR workflow contains many potential pitfalls. An intelligent experiment design and setup, high quality reagents and assays, quality controls in each step of the workflow, proper quantification models and appropriate bio-statistical analyses pave the way to successful gene expression results. This chapter will cover all data analysis aspects from the evaluation of pilot studies and quality controls, through universally applicable quantification models and bio-statistics, to the reporting of experiment results.
Compromised RNA quality is suggested to lead to unreliable results in gene expression studies. Therefore, assessment of RNA integrity and purity is deemed essential prior to including samples in the analytical pipeline. This may be of particular importance when diagnostic, prognostic or therapeutic conclusions depend on such analyses. In this study, the comparative value of six RNA quality parameters was determined using a large panel of 740 primary tumour samples for which real-time quantitative PCR gene expression results were available. The tested parameters comprise of microfluidic capillary electrophoresis based 18S/28S rRNA ratio and RNA Quality Index value, HPRT1 5'–3' difference in quantification cycle (Cq) and HPRT1 3' Cq value based on a 5'/3' ratio mRNA integrity assay, the Cq value of expressed Alu repeat sequences and a normalization factor based on the mean expression level of four reference genes. Upon establishment of an innovative analytical framework to assess impact of RNA quality, we observed a measurable impact of RNA quality on the variation of the reference genes, on the significance of differential expression of prognostic marker genes between two cancer patient risk groups, and on risk classification performance using a multigene signature. This study forms the basis for further rational assessment of reverse transcription quantitative PCR based results in relation to RNA quality.
MicroRNAs (miRNAs) are an important class of gene regulators, acting on several aspects of cellular function such as differentiation, cell cycle control, and stemness. These master regulators constitute an invaluable source of biomarkers, and several miRNA signatures correlating with patient diagnosis, prognosis, and response to treatment have been identiﬁ ed. Within this exciting ﬁ eld of research, whole-genome RT-qPCRbased miRNA proﬁ ling in combination with a global mean normalization strategy has proven to be the most sensitive and accurate approach for high-throughput miRNA proﬁ ling (Mestdagh et al., Genome Biol 10:R64, 2009). In this chapter, we summarize the power of the previously described global mean normalization method in comparison to the multiple reference gene normalization method using the most stably expressed small RNA controls. In addition, we compare the original global mean method to a modiﬁed global mean normalization strategy based on the attribution of equal weight to each individual miRNA during normalization. This modiﬁed algorithm is implemented in Biogazelle’s qbasePLUS software and is presented here for the ﬁrst time.
Copy number changes are known to be involved in numerous human genetic disorders. In this context, qPCR-based copy number screening may serve as the method of choice for targeted screening of the relevant disease genes and their surrounding regulatory landscapes. qPCR has many advantages over alternative methods, such as its low consumable and instrumentation costs, fast turnaround and assay development time, high sensitivity and open format (independent of a single supplier). In this chapter we provide all relevant information for a successfully implement of qPCR-based copy number analysis. We emphasize the signiﬁcance of thorough in silico and empirical validation of the primers, the need for a well thought-out experiment design, and the importance of quality controls along the entire workﬂow. Furthermore, we suggest an appropriate and practical way to calculate copy numbers and to objectively interpret the results.
The provided guidelines will most certainly improve the quality and reliability of your qPCR-based copy number screening.
Reverse transcription quantitative PCR (RT-qPCR) is considered today as the gold standard for accurate, sensitive and fast measurement of gene expression. Unfortunately, what many users fail to appreciate is that numerous critical issues in the workﬂow need to be addressed before biologically meaningful and trustworthy conclusions can be drawn. Here, we review the entire workﬂow from the planning and preparation phase, over the actual real-time PCR cycling experiments to data-analysis and reporting steps. This process can be captured with the appropriate acronym PCR: plan/prepare, cycle and report. The key message is that quality assurance and quality control are essential throughout the entire RT-qPCR workﬂow; from living cells, over extraction of nucleic acids, storage, various enzymatic steps such as DNase treatment, reverse transcription and PCR ampliﬁcation, to data-analysis and ﬁnally reporting.
In this paper, we summarized the different quality control steps while handling post- qPCR data. We state that it is essential to evaluate the quality of the post-qPCR data and to discard data points that do not meet the predefined criteria prior to drawing valid conclusions. User-friendly data analysis software is an invaluable tool within this process. These tools help applying rigorous MIQE-compliant procedures and guide the experimenter to highest quality results.
Gene expression analysis of microRNA molecules is becoming increasingly important. In this study we assess the use of the mean expression value of all expressed microRNAs in a given sample as a normalization factor for microRNA real-time quantitative PCR data and compare its performance to the currently adopted approach. We demonstrate that the mean expression value outperforms the current normalization strategy in terms of better reduction of technical variation and more accurate appreciation of biological changes.
Background: The quantitative polymerase chain reaction (qPCR) is a widely utilized method for gene-expression analysis. However, insufficient material often compromises large-scale geneexpression studies. The aim of this study is to evaluate an RNA pre-amplification method to
produce micrograms of cDNA as input for qPCR.
Findings: The linear isothermal Ribo-SPIA pre-amplification method (WT-Ovation; NuGEN) was first evaluated by measuring the expression of 20 genes in RNA samples from six neuroblastoma cell lines and of 194 genes in two commercially available reference RNA samples before and after pre-amplification, and subsequently applied on a large panel of 738 RNA samples extracted from neuroblastoma tumours. All RNA samples were evaluated for RNA integrity and purity. Starting from 5 to 50 nanograms of total RNA the sample pre-amplification method was applied, generating approximately 5 microgams of cDNA, sufficient to measure more than 1000 target genes. The results obtained from this study show a constant yield of pre-amplified cDNA independent of the amount of input RNA; preservation of differential gene-expression after pre-amplification without introduction of substantial bias; no co-amplification of contaminating genomic DNA; no necessity to purify the pre-amplified material; and finally the importance of good RNA quality to enable preamplification.
Conclusion: Application of this unbiased and easy to use sample pre-amplification technology offers great advantage to generate sufficient material for diagnostic and prognostic work-up and enables large-scale qPCR gene-expression studies using limited amounts of sample material.
The quantitative polymerase chain reaction (qPCR) is widely utilized for gene expression analysis. However, the lack of robust strategies for cross laboratory data comparison hinders the ability to collaborate or perform large multicentre studies conducted at different sites. In this study we introduced and validated a workflow that employs universally applicable, quantifiable external oligonucleotide standards to address this question. Using the proposed standards and data-analysis procedure, we obtained a perfect concordance between expression values from eight different genes in 366 patient samples measured on three different qPCR instruments and matching software, reagents, plates and seals, demonstrating the power of this strategy to detect and correct inter-run variation and to enable exchange of data between different laboratories, even when not using the same qPCR platform.
BACKGROUND: Currently, a lack of consensus exists on how best to perform and interpret quantitative realtime PCR (qPCR) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader’s ability to evaluate critically the quality of the results presented or to repeat the experiments.
CONTENT: The Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. MIQE is a set of guidelines that describe the minimum information necessary for evaluating qPCR experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. MIQE details should be published either in abbreviated form or as an online supplement.
The XML-based Real-Time PCR Data Markup Language (RDML) has been developed by the RDML consortium (http://www.rdml.org) to enable straightforward exchange of qPCR data and related information between qPCR instruments and third party data analysis software, between colleagues and collaborators and between experimenters and journals or public repositories. We here also propose data related guidelines as a subset of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) to guarantee inclusion of key data information when reporting experimental results.
RTPrimerDB (http://www.rtprimerdb.org) is a freely accessible database and analysis tool for real-time quantitative PCR assays. RTPrimerDB includes records with user submitted assays that are linked to genome information from reference databases and quality controlled using an in silico assay evaluation system. The primer evaluation tools intended to assess the specificity and to detect features that could negatively affect the amplification efficiency are combined into a pipeline to test customdesigned primer and probe sequences. An improved user feedback system guides users and submitters to enter practical remarks and details about experimental evaluation analyses. The database is linked with reference databases to allow the submission of assays for all genes and organisms officially registered in Entrez Gene and RefSeq. Records in
RTPrimerDB are assigned unique and stable identifiers. The content is provided via an interactive webbased search system and is available for download in the recently developed RDML format and as bulk export file. RTPrimerDB is a one-stop portal for high-quality and highly annotated real-time PCR assays.
Real-time PCR is the method of choice for expression analysis of a limited number of genes. The measured gene expression variation between subjects is the sum of the true biological variation and several confounding factors resulting in non-specific variation. The purpose of normalization is to remove the non-biological variation as much as possible. Several normalization strategies have been proposed, but the use of one or more reference genes is currently the preferred way of normalization. While these reference genes constitute the best possible normalizers, a major problem is that these genes have no constant expression under all experimental conditions. The experimenter therefore needs to carefully assess whether a certain reference gene is stably expressed in the experimental system under study. This is not trivial and represents a circular problem. Fortunately, several algorithms and freely available software have been developed to address this problem. This chapter aims to provide an overview of the different concepts.
Gene expression analysis by quantitative reverse transcription PCR (qRT–PCR) allows accurate quantiﬁcations of messenger RNA (mRNA) levels over different samples. Corrective methods for different steps in the qRT–PCR reaction have been reported; however, statistical analysis and presentation of substantially variable biological repeats present problems and are often not meaningful, for example, in a biological system such as mouse embryonic stem cell differentiation. Based on a series of sequential corrections, including log transformation, mean centering, and autoscaling, we describe a robust and powerful standardization method that can be used on highly variable data sets to draw statistically reliable conclusions.
Although quantitative PCR (qPCR) is becoming the method of choice for expression profiling of selected genes, accurate and straightforward processing of the raw measurements remains a major hurdle. Here we outline advanced and universally applicable models for relative quantification and inter-run calibration with proper error propagation along the entire calculation track. These models and algorithms are implemented in qBase, a free program for the management and automated analysis of qPCR data.
The RTPrimerDB (http://medgen.ugent.be/rtprimerdb) project provides a freely accessible data retrieval system and an in silico assay evaluation pipeline for real-time quantitative PCR assays. Over the last year the number of user submitted assays has grown to 3500. Data conveyance from Entrez Gene by establishing an assay-to-gene relationship enables the addition of new primer assays for one of the 1.5 million different genes from 2300 species stored in the system. Easy access to the primer and probe data is possible by using multiple search criteria. Assay
reports contain gene information, assay details (such as oligonucleotide sequences, detection chemistry and reaction conditions), publication information, users’ experimental evaluation feedback and submitter’s contact details. Gene expression assays are extended with a scalable assay viewer that provides detailed information on the alignment of primer and probe sequences on the known transcript variants of a gene, along with Single Nucleotide Polymorphisms (SNP) positions and peptide domain information. Furthermore, an mfold module is implemented to predict the secondary structure of the amplicon sequence, as this has been reported to impact the efficiency of the PCR. RTPrimerDB is also extended with an in silico analysis pipeline to streamline the evaluation of custom designed primer and probe sequences prior to ordering and experimental evaluation. In a secured environment, the pipeline performs automated BLAST specificity searches, mfold secondary structure prediction, SNP or plain sequence error identification, and graphical visualization of the aligned primer and probe sequences on the target gene.
Changes in copy number of genes contribute to the pathogenesis of various genetic disorders and cancer. The status of a gene has not only diagnostic value but sometimes directs treatment stratification. Although, for many years, Southern blot and fluorescence in situ hybridization were the standard methods for the detection of deletion, duplication, or amplification of a gene, both methods have their own important limitations. Recently, realtime quantitative PCR has proven to be a good alternative for the detection of gene copy number changes. Its main advantages are the large dynamic range of accurate quantification, the absence of post-PCR manipulations, its high-throughput screening capacity and degree of automation, and the possibility to perform the assay on minimal amounts of sample DNA in just a few hours of time. In this chapter, we outline the procedure of how to develop an assay for the detection of gene copy number changes for your gene of interest. We illustrate the approach by describing a validated assay for the detection of germline VHL exon deletions and for determination of MYCN copy numbers in tumor samples.
The objective of this study was to analyze the influence of RNA degradation on the stability and expression pattern of candidate reference genes. To this purpose, 10 commonly used reference genes were quantified in both intact and degraded RNA from clinical specimens obtained from ethmoidal and maxillary sinuses collected from patients with nasal polyposis (NP) and chronic rhinosinusitis (CRS). In view of the observed difference in reference gene expression stability between intact and degraded RNA samples from the same tissue and the higher gene-specific variation in degraded samples, we propose performing RNA quality control prior to downstream quantification assays and discarding degraded samples, especially if one aims to accurately quantify small expression differences. Indeed, as it is of utmost importance to normalize samples using the same set of reference genes, our results suggest that it is inappropriate to compare degraded and intact samples.
Various types of mutations exist that exert an effect on the normal function of a gene. Among these, exon/gene deletions often remain unnoticed in initial mutation screening. Until recently, no fast and efficient methods were available to detect this type of mutation. Molecular detection methods for gene copy number changes included Southern blot (SB) and fluorescence in situ hybridisation, both with their own intrinsic limitations. In this paper, we report the development and application of a fast, sensitive and high-resolution method for the detection of single exon or larger deletions in the VHL gene based on real-time quantitative PCR (Q-PCR). These deletions account for approximately one-fifth of all patients with the von Hippel–Lindau syndrome, a dominantly inherited highly penetrant familial cancer syndrome predisposing to specific malignancies including phaeochromocytomas and haemangioblastomas. Our VHL exon quantification strategy is based on SYBR Green I detection and normalisation using two reference genes with a normal copy number, that is, ZNF80 (3q13.31) and GPR15 (3q12.1). Choice of primer sequences and the use of two reference genes appears to be critical for accurate discrimination between 1 and 2 exon copies. In a blind Q-PCR study of 29 samples, all 14 deletions were detected, which is in perfect agreement with previously determined SB results. We propose Q-PCR as the method of choice for fast (within 3.5 h), accurate and sensitive (ng amount of input DNA) exon deletion screening in routine DNA diagnosis of VHL disease. Similar assays can be designed for deletion screening in other genetic disorders.
Background: Gene-expression analysis is increasingly important in biological research, with realtime reverse transcription PCR (RT-PCR) becoming the method of choice for high-throughput and accurate expression profiling of selected genes. Given the increased sensitivity, reproducibility and large dynamic range of this methodology, the requirements for a proper internal control gene for normalization have become increasingly stringent. Although housekeeping gene expression has been reported to vary considerably, no systematic survey has properly determined the errors related to the common practice of using only one control gene, nor presented an adequate way of working around this problem.
Results: We outline a robust and innovative strategy to identify the most stably expressed control genes in a given set of tissues, and to determine the minimum number of genes required to calculate a reliable normalization factor. We have evaluated ten housekeeping genes from different abundance and functional classes in various human tissues, and demonstrated that the conventional use of a single gene for normalization leads to relatively large errors in a significant proportion of samples tested. The geometric mean of multiple carefully selected housekeeping genes was validated as an accurate normalization factor by analyzing publicly available microarray data.
Conclusions: The normalization strategy presented here is a prerequisite for accurate RT-PCR expression profiling, which, among other things, opens up the possibility of studying the biological relevance of small expression differences.
A reliable and robust method for measuring the expression of alternatively spliced transcripts is an important step in investigating the significance of each variant. So far, accurate quantification of splice variants has been laborious and difficult due to the intrinsic limitations of conventional methods. The many advantages of real-time PCR have made this technique attractive to study its application in quantification of splice isoforms. We use skipping of exon 37 in the NF1 gene as a model to compare and evaluate the different strategies for quantitiating splice variants using real-time PCR. An overview of three different possibilities for detecting alternative transcript is given. We propose the use of a boundary-spanning primer to quantify isoforms that differ greatly in abundance. We describe here a novel method for creating a reliable standard curve using one plasmid containing both alternative transcripts. In addition, we validate the use of an absolute standard curve based on a dilution series of fluorometrically quantified PCR products.