The quantitative real time polymerase chain reaction (qPCR) has become a key molecular enabling technology with an immense range of research, clinical, forensic as well as diagnostic applications. Its relatively moderate instrumentation and reagent requirements have led to its adoption by numerous laboratories, including those located in the Arabian world, where qPCR, which targets DNA, and reverse transcription qPCR (RT-qPCR), which targets RNA, are widely used for region-specific biotechnology, agricultural and human genetic studies. However, it has become increasingly apparent that there are significant problems with both the quality of qPCR-based data as well as the transparency of reporting. This realisation led to the publication of the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines in 2009 and their more widespread adoption in the last couple of years. An analysis of the performance of biomedical research in the Arabian world between 2001–2005 suggests that the Arabian world is producing fewer biomedical publications of lower quality than other Middle Eastern countries. Hence we have analysed specifically the quality of RT-qPCR-based peer-reviewed papers published since 2009 from Arabian researchers using a bespoke iOS/Android app developed by one of the authors. Our results show that compliance with 15 essential MIQE criteria was low (median of 40%, range 0–93%) and few details on RNA quality controls (22% compliance), assays design (12%), RT strategies (32%), amplification efficiencies (30%) and the normalisation process (3%). These data indicate that one of the reasons for the poor performance of Arabian world biomedical research may be the low standard of any supporting qPCR experiments and identify which aspects of qPCR experiments require significant improvements.
Hairy root cultures produced via Agrobacterium rhizogenes-mediated transformation have emerged as practical biological models to elucidate the biosynthesis of specialized metabolites. To effectively understand the expression patterns of the genes involved in the metabolic pathways of these compounds, reference genes need to be systematically validated under specific experimental conditions as established by the MIQE (Minimum Information for Publication of Quantitative Real-Time PCR Experiments) guidelines. In the present report we describe the first validation of reference genes for RT-qPCR in hairy root cultures of peanut which produce stilbenoids upon elicitor treatments.
A total of 21 candidate reference genes were evaluated. Nineteen genes were selected based on previous qPCR studies in plants and two were from the T-DNAs transferred from A. rhizogenes. Nucleotide sequences of peanut candidate genes were obtained using their homologous sequences in Arabidopsis. To identify the suitable primers, calibration curves were obtained for each candidate reference gene. After data analysis, 12 candidate genes meeting standard efficiency criteria were selected. The expression stability of these genes was analyzed using geNorm and NormFinder algorithms and a ranking was established based on expression stability of the genes. Candidate reference gene expression was shown to have less variation in methyl jasmonate (MeJA) treated root cultures than those treated with sodium acetate (NaOAc).
This work constitutes the first effort to validate reference genes for RT-qPCR in hairy roots. While these genes were selected under conditions of NaOAc and MeJA treatment, we anticipate these genes to provide good targets for reference genes for hairy roots under a variety of stress conditions. The lead reference genes were a gene encoding for a TATA box binding protein (TBP2) and a gene encoding a ribosomal protein (RPL8C). A commonly used reference gene GAPDH showed low stability of expression suggesting that its use may lead to inaccurate gene expression profiles when used for data normalization in stress-stimulated hairy roots. Likewise the A. rhizogenes transgene rolC showed less expression stability than GAPDH. This study proposes that a minimum of two reference genes should be used for a normalization procedure in gene expression profiling using elicited hairy roots.
Quantitative PCR (qPCR) is a widely used technique for gene expression analysis. A common normalization method for accurate qPCR data analysis involves stable reference genes to determine relative gene expression. Despite extensive research in the forest tree species Populus, there is not a resource for reference genes that meet the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) standards for qPCR techniques and analysis. Since Populus is a woody perennial species, studies of seasonal changes in gene expression are important towards advancing knowledge of this important developmental and physiological trait. The objective of this study was to evaluate reference gene expression stability in various tissues and growth conditions in two important Populus genotypes (P. trichocarpa “Nisqually 1” and P. tremula x P. alba 717 1-B4) following MIQE guidelines.
We evaluated gene expression stability in shoot tips, young leaves, mature leaves and bark tissues from P. trichocarpa and P. tremula. x P. alba grown under long-day (LD), short-day (SD) or SD plus low-temperatures conditions. Gene expression data were analyzed for stable reference genes among 18S rRNA, ACT2, CDC2, CYC063, TIP4-like, UBQ7, PT1 and ANT using two software packages, geNormPLUS and BestKeeper. GeNormPLUS ranked TIP4-like and PT1 among the most stable genes in most genotype/tissue combinations while BestKeeper ranked CDC2 and ACT2 among the most stable genes.
This is the first comprehensive evaluation of reference genes in two important Populus genotypes and the only study in Populus that meets MIQE standards. Both analysis programs identified stable reference genes in both genotypes and all tissues grown under different photoperiods. This set of reference genes was found to be suitable for either genotype considered here and may potentially be suitable for other Populus species and genotypes. These results provide a valuable resource for the Populus research community.
RT-qPCR; Reference gene validation; Populus trichocarpa; Populus tremula x Populus alba
Fungal load quantification is a critical component of fungal community analyses. Limitation of current approaches for quantifying the fungal component in the human microbiome suggests the need for new broad-coverage techniques.
We analyzed 2,085 18S rRNA gene sequences from the SILVA database for assay design. We generated and quantified plasmid standards using a qPCR-based approach. We evaluated assay coverage against 4,968 sequences and performed assay validation following the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines.
We designed FungiQuant, a TaqMan® qPCR assay targeting a 351 bp region in the fungal 18S rRNA gene. Our in silico analysis showed that FungiQuant is a perfect sequence match to 90.0% of the 2,617 fungal species analyzed. We showed that FungiQuant’s is 100% sensitive and its amplification efficiencies ranged from 76.3% to 114.5%, with r2-values of >0.99 against the 69 fungal species tested. Additionally, FungiQuant inter- and intra-run coefficients of variance ranged from <10% and <20%, respectively. We further showed that FungiQuant has a limit of quantification 25 copies and a limit of detection at 5 copies. Lastly, by comparing results from human-only background DNA with low-level fungal DNA, we showed that amplification in two or three of a FungiQuant performed in triplicate is statistically significant for true positive fungal detection.
FungiQuant has comprehensive coverage against diverse fungi and is a robust quantification and detection tool for delineating between true fungal detection and non-target human DNA.
The ability to obtain accurate and reproducible data using quantitative real-time Polymerase Chain Reaction (RT-qPCR) is limited by the process of data normalization. The use of ‘housekeeping’ or ‘reference’ genes is the most common technique used to normalize RT-qPCR data. However, commonly used reference genes are often poorly validated and may change as a result of genetic background, environment and experimental intervention. Here we present an analysis of 10 reference genes in mouse skeletal muscle (Actb, Aldoa, Gapdh, Hprt1, Ppia, Rer1, Rn18s, Rpl27, Rpl41 and Rpl7L1), which were identified as stable either by microarray or in the literature. Using the MIQE guidelines we compared wild-type (WT) mice across three genetic backgrounds (R129, C57BL/6j and C57BL/10) as well as analyzing the α-actinin-3 knockout (Actn3 KO) mouse, which is a model of the common null polymorphism (R577X) in human ACTN3. Comparing WT mice across three genetic backgrounds, we found that different genes were more tightly regulated in each strain. We have developed a ranked profile of the top performing reference genes in skeletal muscle across these common mouse strains. Interestingly the commonly used reference genes; Gapdh, Rn18s, Hprt1 and Actb were not the most stable. Analysis of our experimental variant (Actn3 KO) also resulted in an altered ranking of reference gene suitability. Furthermore we demonstrate that a poor reference gene results in increased variability in the normalized expression of a gene of interest, and can result in loss of significance. Our data demonstrate that reference genes need to be validated prior to use. For the most accurate normalization, it is important to test several genes and use the geometric mean of at least three of the most stably expressed genes. In the analysis of mouse skeletal muscle, strain and intervention played an important role in selecting the most stable reference genes.
The conclusions of thousands of peer-reviewed publications rely on data obtained using fluorescence-based quantitative real-time PCR technology. However, the inadequate reporting of experimental detail, combined with the frequent use of flawed protocols is leading to the publication of papers that may not be technically appropriate. We take the view that this problem requires the delineation of a more transparent and comprehensive reporting policy from scientific journals. This editorial aims to provide practical guidance for the incorporation of absolute minimum standards encompassing the key assay parameters for accurate design, documentation and reporting of qPCR experiments (MIQE précis) and guidance on the publication of pure 'reference gene' articles.
MicroRNAs are abundantly present and surprisingly stable in multiple biological fluids. These findings have been followed by numerous reverse transcription real-time quantitative PCR (RT-qPCR)-based reports revealing the clinical potential of using microRNA levels in body fluids as a biomarker of disease. Despite a rapidly increasing body of literature, the field has failed to adopt a set of standardized criteria for reporting the methodology used in the quantification of cell-free microRNAs. Not only do many studies based on RT-qPCR fail to address the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) criteria but frequently there is also a distinct lack of detail in descriptions of sample source and RNA isolation. As a direct result, it is often impossible to compare the results of different studies, which in turn, hinders progress in the field. To address this point, we propose a simple set of criteria to be used in conjunction with MIQE to reveal the true potential of cell-free microRNAs as biomarkers.
cell-free microRNA; isolation; quantification; reporting; standard
Motivation: Quantitative real-time polymerase chain reaction (qPCR) is routinely used for RNA expression profiling, validation of microarray hybridization data and clinical diagnostic assays. Although numerous statistical tools are available in the public domain for the analysis of microarray experiments, this is not the case for qPCR. Proprietary software is typically provided by instrument manufacturers, but these solutions are not amenable to the tandem analysis of multiple assays. This is problematic when an experiment involves more than a simple comparison between a control and treatment sample, or when many qPCR datasets are to be analyzed in a high-throughput facility.
Results: We have developed HTqPCR, a package for the R statistical computing environment, to enable the processing and analysis of qPCR data across multiple conditions and replicates.
Availability: HTqPCR and user documentation can be obtained through Bioconductor or at http://www.ebi.ac.uk/bertone/software.
Many proteomics initiatives require integration of all information with uniformcriteria from collection of samples and data display to publication of experimental results. The integration and exchanging of these data of different formats and structure imposes a great challenge to us. The XML technology presents a promise in handling this task due to its simplicity and flexibility. Nasopharyngeal carcinoma (NPC) is one of the most common cancers in southern China and Southeast Asia, which has marked geographic and racial differences in incidence. Although there are some cancer proteome databases now, there is still no NPC proteome database.
The raw NPC proteome experiment data were captured into one XML document with Human Proteome Markup Language (HUP-ML) editor and imported into native XML database Xindice. The 2D/MS repository of NPC proteome was constructed with Apache, PHP and Xindice to provide access to the database via Internet. On our website, two methods, keyword query and click query, were provided at the same time to access the entries of the NPC proteome database.
Our 2D/MS repository can be used to share the raw NPC proteomics data that are generated from gel-based proteomics experiments. The database, as well as the PHP source codes for constructing users' own proteome repository, can be accessed at .
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.
The vaginal microbiome plays an important role in urogenital health. Quantitative real time Polymerase Chain Reaction (qPCR) assays for the most prevalent vaginal Lactobacillus species and bacterial vaginosis species G. vaginalis and A. vaginae exist, but qPCR information regarding variation over time is still very limited. We set up qPCR assays for a selection of seven species and defined the temporal variation over three menstrual cycles in a healthy Caucasian population with a normal Nugent score. We also explored differences in qPCR data between these healthy women and an ‘at risk’ clinic population of Caucasian, African and Asian women with and without bacterial vaginosis (BV), as defined by the Nugent score.
Temporal stability of the Lactobacillus species counts was high with L. crispatus counts of 108 copies/mL and L. vaginalis counts of 106 copies/mL. We identified 2 types of ‘normal flora’ and one ‘BV type flora’ with latent class analysis on the combined data of all women. The first group was particularly common in women with a normal Nugent score and was characterized by a high frequency of L. crispatus, L. iners, L. jensenii, and L. vaginalis and a correspondingly low frequency of L. gasseri and A. vaginae. The second group was characterized by the predominance of L. gasseri and L. vaginalis and was found most commonly in healthy Caucasian women. The third group was commonest in women with a high Nugent score but was also seen in a subset of African and Asian women with a low Nugent score and was characterized by the absence of Lactobacillus species (except for L. iners) but the presence of G. vaginalis and A. vaginae.
We have shown that the quantification of specific bacteria by qPCR contributes to a better description of the non-BV vaginal microbiome, but we also demonstrated that differences in populations such as risk and ethnicity also have to be taken into account. We believe that our selection of indicator organisms represents a feasible strategy for the assessment of the vaginal microbiome and could be useful for monitoring the microbiome in safety trials of vaginal products.
Reverse transcription quantitative real-time PCR (RT-qPCR) is a key method for measurement of relative gene expression. Analysis of RT-qPCR data requires many iterative computations for data normalization and analytical optimization. Currently no computer program for RT-qPCR data analysis is suitable for analytical optimization and user-controllable customization based on data quality, experimental design as well as specific research aims. Here I introduce an all-in-one computer program, SASqPCR, for robust and rapid analysis of RT-qPCR data in SAS. This program has multiple macros for assessment of PCR efficiencies, validation of reference genes, optimization of data normalizers, normalization of confounding variations across samples, and statistical comparison of target gene expression in parallel samples. Users can simply change the macro variables to test various analytical strategies, optimize results and customize the analytical processes. In addition, it is highly automatic and functionally extendable. Thus users are the actual decision-makers controlling RT-qPCR data analyses. SASqPCR and its tutorial are freely available at http://code.google.com/p/sasqpcr/downloads/list.
In quantitative single-cell studies, the critical part is the low amount of nucleic acids present and the resulting experimental variations. In addition biological data obtained from heterogeneous tissue are not reflecting the expression behaviour of every single-cell. These variations can be derived from natural biological variance or can be introduced externally. Both have negative effects on the quantification result. The aim of this study is to make quantitative single-cell studies more transparent and reliable in order to fulfil the MIQE guidelines at the single-cell level. The technical variability introduced by RT, pre-amplification, evaporation, biological material and qPCR itself was evaluated by using RNA or DNA standards. Secondly, the biological expression variances of GAPDH, TNFα, IL-1β, TLR4 were measured by mRNA profiling experiment in single lymphocytes. The used quantification setup was sensitive enough to detect single standard copies and transcripts out of one solitary cell. Most variability was introduced by RT, followed by evaporation, and pre-amplification. The qPCR analysis and the biological matrix introduced only minor variability. Both conducted studies impressively demonstrate the heterogeneity of expression patterns in individual cells and showed clearly today's limitation in quantitative single-cell expression analysis.
The Guideline Elements Model (GEM) was developed in 2000 to organize the information contained in clinical practice guidelines using XML and to represent guideline content in a form that can be understood by human readers and processed by computers. In this work, we systematically reviewed the literature to better understand how GEM was being used, potential barriers to its use, and suggestions for improvement. Fifty external and twelve internally produced publications were identified and analyzed. GEM was used most commonly for modeling and ontology creation. Other investigators applied GEM for knowledge extraction and data mining, for clinical decision support for guideline generation. The GEM Cutter software—used to markup guidelines for translation into XML— has been downloaded 563 times since 2000. Although many investigators found GEM to be valuable, others critiqued its failure to clarify guideline semantics, difficulties in markup, and the fact that GEM files are not usually executable.
The ImmunoDeficiency Resource (IDR), freely available at http://www.uta.fi/imt/bioinfo/idr/, is a comprehensive knowledge base on immunodeficiencies. It is designed for different user groups such as researchers, physicians and nurses as well as patients and their families and the general public. Information on immunodeficiencies is stored as fact files, which are disease- and gene-based information resources. We have developed an inherited disease markup language (IDML) data model, which is designed for storing disease- and gene-specific data in extensible markup language (XML) format. The fact files written by the IDML can be used to present data in different contexts and platforms. All the information in the IDR is validated by expert curators.
Quantitative real-time PCR (qPCR) is a commonly used validation tool for confirming gene expression results obtained from microarray analysis; however, microarray and qPCR data often result in disagreement. The current study assesses factors contributing to the correlation between these methods in five separate experiments employing two-color 60-mer oligonucleotide microarrays and qPCR using SYBR green. Overall, significant correlation was observed between microarray and qPCR results (ρ=0.708, p<0.0001, n=277) using these platforms. The contribution of factors including up- vs. down-regulation, spot intensity, ρ-value, fold-change, cycle threshold (Ct), array averaging, tissue type, and tissue preparation was assessed. Filtering of microarray data for measures of quality (fold-change and ρ-value) proves to be the most critical factor, with significant correlations of ρ>0.80 consistently observed when quality scores are applied.
Polymerase Chain Reaction; Microarray Analysis; Gene Expression; Nucleic Acid Amplification Techniques; Reverse Transcriptase Polymerase Chain Reaction; RNA
Real-time quantitative PCR (qPCR) is a method for rapid and reliable quantification of mRNA transcription. Internal standards such as reference genes are used to normalise mRNA levels between different samples for an exact comparison of mRNA transcription level. Selection of high quality reference genes is of crucial importance for the interpretation of data generated by real-time qPCR.
In this study nine commonly used reference genes were investigated in 17 different pig tissues using real-time qPCR with SYBR green. The genes included beta-actin (ACTB), beta-2-microglobulin (B2M), glyceraldehyde-3-phosphate dehydrogenase (GAPDH), hydroxymethylbilane synthase (HMBS), hypoxanthine phosphoribosyltransferase 1 (HPRT1), ribosomal protein L4 (RPL4), succinate dehydrogenase complex subunit A (SDHA), TATA box binding protein (TPB)and tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation protein zeta polypeptide (YWHAZ). The stability of these reference genes in different pig tissues was investigated using the geNorm application. The range of expression stability in the genes analysed was (from the most stable to the least stable): ACTB/RPL4, TBP, HPRT, HMBS, YWHAZ, SDHA, B2M and GAPDH.
Expression stability varies greatly between genes. ACTB, RPL4, TPB and HPRT1 were found to have the highest stability across tissues. Based on both expression stability and expression level, our data suggest that ACTB and RPL4 are good reference genes for high abundant transcripts while TPB and HPRT1 are good reference genes for low abundant transcripts in expression studies across different pig tissues.
Telomeres, the protective cap of chromosomes, have emerged as powerful markers of biological age and life history in model and non-model species. The qPCR method for telomere length estimation is one of the most common methods for telomere length estimation, but has received recent critique for being too error-prone and yielding unreliable results. This critique coincides with an increasing awareness of the potentials and limitations of the qPCR technique in general and the proposal of a general set of guidelines (MIQE) for standardization of experimental, analytical, and reporting steps of qPCR. In order to evaluate the utility of the qPCR method for telomere length estimation in non-model species, we carried out four different qPCR assays directed at humpback whale telomeres, and subsequently performed a rigorous quality control to evaluate the performance of each assay.
Performance differed substantially among assays and only one assay was found useful for telomere length estimation in humpback whales. The most notable factors causing these inter-assay differences were primer design and choice of using singleplex or multiplex assays. Inferred amplification efficiencies differed by up to 40% depending on assay and quantification method, however this variation only affected telomere length estimates in the worst performing assays.
Our results suggest that seemingly well performing qPCR assays may contain biases that will only be detected by extensive quality control. Moreover, we show that the qPCR method for telomere length estimation can be highly precise and accurate, and thus suitable for telomere measurement in non-model species, if effort is devoted to optimization at all experimental and analytical steps. We conclude by highlighting a set of quality controls which may serve for further standardization of the qPCR method for telomere length estimation, and discuss some of the factors that may cause variation in qPCR experiments.
Quantitative PCR; Telomere length; Quality control; Non-model species; Guidelines
We describe development of an absolute multiplex quantitative real-time PCR for detection of Plasmodium spp., P. falciparum and P. vivax targets in order to produce an assay amenable to high throughput but with reduced costs. Important qPCR experimental details and information that is critical to performance and reliability of assay results were investigated. Inhibition studies were performed to test and compare co-purification of PCR inhibitors in samples extracted from whole blood using either the manual or automated methods. To establish the most optimal qPCR reaction volume, volume titration of the reaction master mix was performed starting at 10 µl to 1 µl reaction master mix with 1 µl of template DNA in each reaction. As the reaction volume decreased, qPCR assays became more efficient with 1 µl reaction master mix being the most efficient. For more accurate quantification of parasites in a sample, we developed plasmid DNAs for all the three assay targets for absolute quantification. All of absolute qPCR assays performed with efficiency of more than 94%, R2 values greater than 0.99 and the STDEV of each replicate was <0.167. Linear regression plots generated from absolute qPCR assays were used to estimate the corresponding parasite density from relative qPCR in terms of parasite/µl. One copy of plasmid DNA was established to be equivalent to 0.1 parasite/µl for Plasmodium spp. assay, 0.281 parasites for P. falciparum assay and 0.127 parasite/µl for P. vivax assay. This study demonstrates for the first time use of plasmid DNA in absolute quantification of malaria parasite. The use of plasmid DNA standard in quantification of malaria parasite will be critical as efforts are underway to harmonize molecular assays used in diagnosis of malaria.
Normalization of target gene expression, measured by real-time quantitative PCR (qPCR), is a requirement for reducing experimental bias and thereby improving data quality. The currently used normalization approach is based on using one or more reference genes. Yet, this approach extends the experimental work load and suffers from assumptions that may be difficult to meet and to validate.
We developed a data driven normalization algorithm (NORMA-Gene). An analysis of the performance of NORMA-Gene compared to reference gene normalization on artificially generated data-sets showed that the NORMA-Gene normalization yielded more precise results under a large range of parameters tested. Furthermore, when tested on three very different real qPCR data-sets NORMA-Gene was shown to be best at reducing variance due to experimental bias in all three data-sets compared to normalization based on the use of reference gene(s).
Here we present the NORMA-Gene algorithm that is applicable to all biological and biomedical qPCR studies, especially those that are based on a limited number of assayed genes. The method is based on a data-driven normalization and is useful for as little as five target genes comprising the data-set. NORMA-Gene does not require the identification and validation of reference genes allowing researchers to focus their efforts on studying target genes of biological relevance.
Real-time quantitative RT-PCR (qPCR) is a powerful technique capable of accurately quantitating mRNA expression levels over a large dynamic range. This makes qPCR the most widely used method for studying quantitative gene expression. An important aspect of qPCR is selecting appropriate controls or normalization factors to account for any differences in starting cDNA quantities between samples during expression studies. Here, we report on the selection of a concise set of housekeeper genes for the accurate normalization of quantitative gene expression data in differentiating osteoblasts, osteoclasts and macrophages. We implemented the use of geNorm, an algorithm that determines the suitability of genes to function as housekeepers by assessing expression stabilities. We evaluated the expression stabilities of 18S, ACTB, B2M, GAPDH, HMBS and HPRT1 genes.
Our analyses revealed that 18S and GAPDH were regulated during osteoblast differentiation and are not suitable for use as reference genes. The most stably expressed genes in osteoblasts were ACTB, HMBS and HPRT1 and their geometric average constitutes a suitable normalization factor upon which gene expression data can be normalized. In macrophages, 18S and GAPDH were the most variable genes while HMBS and B2M were the most stably expressed genes. The geometric average of HMBS and B2M expression levels forms a suitable normalization factor to account for potential differences in starting cDNA quantities during gene expression analysis in macrophages. The expression stabilities of the six candidate reference genes in osteoclasts were, on average, more variable than that observed in macrophages but slightly less variable than those seen in osteoblasts. The two most stably expressed genes in osteoclasts were HMBS and B2M and the genes displaying the greatest levels of variability were 18S and GAPDH. Notably, 18S and GAPDH were the two most variably expressed control genes in all three cell types. The geometric average of HMBS, B2M and ACTB creates an appropriate normalization factor for gene expression studies in osteoclasts.
We have identified concise sets of genes suitable to use as normalization factors for quantitative real-time RT-PCR gene expression studies in osteoblasts, osteoclasts and macrophages.
Small ruminant lentiviruses (SRLV) are members of the Retroviridae family and infect goats and sheep worldwide. Detection of specific antibodies using AGID and ELISA is the most commonly used means of diagnosing SRLV infection. The most frequent molecular method for detecting the provirus genome is PCR, using peripheral blood leucocytes as target cells. Real time PCR has also recently been used. The aim of this study was to develop a real time PCR for detection of SRLV in order to improve molecular diagnostics of SRLV infections in sheep and goats.
Two new real time PCR assays using TaqMan probes for the specific detection of genotype A (MVV assay) and genoptype B (CAEV assay) SRLV strains and differentiation between them were developed and validated at both analytical and diagnostic levels following MIQE guidelines. The validation results showed that the new real time PCR is 100% specific, with a reliable limit of detection of 26 (CAEV assay) and 72 (MVV assay) plasmid DNA copies, while compared to ELISA the diagnostic sensitivity of both assays was 79% when tested with Slovenian SRLV field samples. Intra-assay and inter-assay coefficients of variation showed overall good repeatability and reproducibility of the new real time PCR assays, except for the highest dilutions.
Two new TaqMan probe based real time PCR assays for the specific detection of genotype A and B SRLV strains and differentiation between them were developed and validated. They can serve as an additional tool for confirming infection with SRLV and may also be useful for early detection of infected animals prior to seroconversion.
CAEV; Gag matrix; MVV; Real time PCR; Small ruminant lentiviruses
As real-time quantitative PCR (RT-QPCR) is increasingly being relied upon for the enforcement of legislation and regulations dependent upon the trace detection of DNA, focus has increased on the quality issues related to the technique. Recent work has focused on the identification of factors that contribute towards significant measurement uncertainty in the real-time quantitative PCR technique, through investigation of the experimental design and operating procedure. However, measurement uncertainty contributions made during the data analysis procedure have not been studied in detail. This paper presents two additional approaches for standardising data analysis through the novel application of statistical methods to RT-QPCR, in order to minimise potential uncertainty in results.
Experimental data was generated in order to develop the two aspects of data handling and analysis that can contribute towards measurement uncertainty in results. This paper describes preliminary aspects in standardising data through the application of statistical techniques to the area of RT-QPCR. The first aspect concerns the statistical identification and subsequent handling of outlying values arising from RT-QPCR, and discusses the implementation of ISO guidelines in relation to acceptance or rejection of outlying values. The second aspect relates to the development of an objective statistical test for the comparison of calibration curves.
The preliminary statistical tests for outlying values and comparisons between calibration curves can be applied using basic functions found in standard spreadsheet software. These two aspects emphasise that the comparability of results arising from RT-QPCR needs further refinement and development at the data-handling phase. The implementation of standardised approaches to data analysis should further help minimise variation due to subjective judgements. The aspects described in this paper will help contribute towards the development of a set of best practice guidelines regarding standardising handling and interpretation of data arising from RT-QPCR experiments.
The use of malaria-specific quantitative real-time PCR (qPCR) is increasing due to its high sensitivity, speciation and quantification of malaria parasites. However, due to the lack of consensus or standardized methods in performing qPCR, it is difficult to evaluate and/or compare the quality of work reported by different authors for a cross-study and/or cross-platform assay analysis.
The performances of seven published qPCR assays that detect Plasmodium spp or Plasmodium falciparum were compared using standard DNA and samples from a clinical trial. Amplification and qPCR measurements were performed using the Applied Biosystems 7500 Fast Real-Time PCR System. All the analyses were automatically established using the default settings. For the TaqMan probe format, the assays were performed in the background of QuantiFast Probe Master Mix whereas in SYBR Green format, the assays were performed in the background of QuantiFast SYBR Green Master Mix and QuantiTect SYBR Green Master Mix background.
Assays with high PCR efficiencies outperformed those with low efficiencies in all categories including sensitivity, precision and consistency regardless of the assay format and background. With the exception of one assay, all assays evaluated showed lower sensitivity compared to what have been published. When samples from a malaria challenge study were analysed, the qPCR assay with the overall best performance detected parasites in subjects earliest and with most consistency.
The data demonstrate the need for increased consensus and guidelines that will encourage better experimental practices, allowing more consistent and unambiguous interpretation of qPCR results.
The authors present an Electronic Healthcare Record (EHR) server, designed and developed as a proof of concept of the revised prEN13606:2005 European standard concerning EHR communications.
The development of the server includes five modules: the libraries for the management of the standard reference model, for the demographic package and for the data types; the permanent storage module, built on a relational database; two communication interfaces through which the clients can send information or make queries; the XML (eXtensible Markup Language) process module; and the tools for the validation of the extracts managed, implemented on a defined XML-Schema.
The server was subjected to four phases of trials, the first three with ad hoc test data and processes to ensure that each of the modules complied with its specifications and that the interaction between them provided the expected functionalities. The fourth used real extracts generated by other research groups for the additional purpose of testing the validity of the standard in real-world scenarios.
The acceptable performance of the server has made it possible to include it as a middleware service in a platform for the out-of-hospital follow-up and monitoring of patients with chronic heart disease which, at the present time, supports pilot projects and clinical trials for the evaluation of eHealth services.