Large scale real-time PCR validation on gene expression measurements from two commercial long-oligonucleotide microarrays

Preanalytical mRNA stabilization of whole bone marrow samples

BACKGROUND

Gene expression profiling is a useful tool for cancer diagnosis and basic research. A major limitation is that, even during short-term storage of native specimens of peripheral blood or bone marrow (BM) and/or RNA isolation, significant changes of gene expression pattern can occur because of gene induction, repression, and RNA degradation.

METHODS

We investigated the effectiveness of a newly developed RNA stabilization and preparation system for BM specimens (PAXgene Bone Marrow RNA System) over time. We analyzed 256 RNA samples, processed from 64 BM specimens.

RESULTS

Although the overall RNA yield (normalized to 1 x 10(7) leukocytes) was not different, the RNA preparation using unstabilized reference samples had an approximately 3 times higher failure rate. With the PAXgene system, we observed significantly higher RNA integrity compared with the reference RNA preparation system (P <0.01). In the stabilized samples, we found very high pairwise correlation in gene expression (DeltaDeltaC(T) 0.16-0.53) for the analyzed genes (GATA1, RUNX1, NCAM1, and SPI1) after 48-h storage compared with immediate preparation of RNA (2 h after BM collection). However, we found major differences in half of the analyzed genes using the reference RNA isolation procedure (DeltaDeltaC(T) 1.07 and 1.32).

CONCLUSIONS

The PAXgene system is able to stabilize RNA from clinical BM samples and is suitable to isolate high-quality and -quantity RNA.

Pre- and postnatal hepatic gene expression profiles of two pig breeds differing in body composition: insight into pathways of metabolic regulation

The liver plays a central role in the regulation of the metabolic status, partitioning of nutrients, and expenditure of energy. To gain insight into hepatic metabolic pathways and key transcripts affecting traits related to body composition, liver expression profiles were compared of pigs of two breeds, the obese German Landrace (DL) and the lean Pietrain (Pi). Porcine oligonucleotide microarray were hybridized with liver cRNAs obtained at peripubertal age (180 days of age) and prenatal stages (35, 63, and 91 days postconception) that represent three developmental stages of liver, i.e., period of differentiation, period of metabolic activity, and period of glycogen accumulation. In terms of the number of genes regulated between DL and Pi, the most striking distinctions were found at peripubertal age with upregulation of key genes of lipid metabolism pathways (FASN, ACSS2, ACACA) in obese DL pigs and upregulation of genes of cell growth and/or maintenance, and protein syntheses, as well as cell proliferation pathways (PPARD, POU1F1, IGF2R), in lean Pi pigs. Moreover, time course analysis of breed-dependent expression profiles revealed breed-typical temporal regulation from prenatal stages to peripubertal age of genes assigned to biological processes involving lipid pathways and cell activity, i.e., breed differences are already initiated during early prenatal development. Information about mRNA expression levels of the two breeds differing in body composition, partitioning and utilization of nutrients and energy reveals functional candidate genes for traits related to obesity and leanness.

Strategies for the detection of copy number and other structural variants in the human genome

Advances in genome scanning technologies are revealing that copy number variants (CNVs) and polymorphisms, ranging from a few kilobases to several megabases in size, are present in genomes at frequencies much greater than previously known. Discoveries of additional forms of genomic variation, including inversions, insertions, deletions and complex rearrangements, are also occurring at an increased rate. Along with CNVs, these sequence alterations are collectively known as structural variants, and their discovery has had an immediate impact on the interpretation of basic research and clinical diagnostic data. This paper discusses different methods, experimental strategies and technologies that are currently available to study copy number variation and other structural variants in the human genome.

Effect of various normalization methods on Applied Biosystems expression array system data

Background

DNA microarray technology provides a powerful tool for characterizing gene expression on a genome scale. While the technology has been widely used in discovery-based medical and basic biological research, its direct application in clinical practice and regulatory decision-making has been questioned. A few key issues, including the reproducibility, reliability, compatibility and standardization of microarray analysis and results, must be critically addressed before any routine usage of microarrays in clinical laboratory and regulated areas can occur. In this study we investigate some of these issues for the Applied Biosystems Human Genome Survey Microarrays.

Results

We analyzed the gene expression profiles of two samples: brain and universal human reference (UHR), a mixture of RNAs from 10 cancer cell lines, using the Applied Biosystems Human Genome Survey Microarrays. Five technical replicates in three different sites were performed on the same total RNA samples according to manufacturer's standard protocols. Five different methods, quantile, median, scale, VSN and cyclic loess were used to normalize AB microarray data within each site. 1,000 genes spanning a wide dynamic range in gene expression levels were selected for real-time PCR validation. Using the TaqMan^® assays data set as the reference set, the performance of the five normalization methods was evaluated focusing on the following criteria: (1) Sensitivity and reproducibility in detection of expression; (2) Fold change correlation with real-time PCR data; (3) Sensitivity and specificity in detection of differential expression; (4) Reproducibility of differentially expressed gene lists.

Conclusion

Our results showed a high level of concordance between these normalization methods. This is true, regardless of whether signal, detection, variation, fold change measurements and reproducibility were interrogated. Furthermore, we used TaqMan^® assays as a reference, to generate TPR and FDR plots for the various normalization methods across the assay range. Little impact is observed on the TP and FP rates in detection of differentially expressed genes. Additionally, little effect was observed by the various normalization methods on the statistical approaches analyzed which indicates a certain robustness of the analysis methods currently in use in the field, particularly when used in conjunction with the Applied Biosystems Gene Expression System.

Statistical analysis of an RNA titration series evaluates microarray precision and sensitivity on a whole-array basis

BACKGROUND

Concerns are often raised about the accuracy of microarray technologies and the degree of cross-platform agreement, but there are yet no methods which can unambiguously evaluate precision and sensitivity for these technologies on a whole-array basis.

RESULTS

A methodology is described for evaluating the precision and sensitivity of whole-genome gene expression technologies such as microarrays. The method consists of an easy-to-construct titration series of RNA samples and an associated statistical analysis using non-linear regression. The method evaluates the precision and responsiveness of each microarray platform on a whole-array basis, i.e., using all the probes, without the need to match probes across platforms. An experiment is conducted to assess and compare four widely used microarray platforms. All four platforms are shown to have satisfactory precision but the commercial platforms are superior for resolving differential expression for genes at lower expression levels. The effective precision of the two-color platforms is improved by allowing for probe-specific dye-effects in the statistical model. The methodology is used to compare three data extraction algorithms for the Affymetrix platforms, demonstrating poor performance for the commonly used proprietary algorithm relative to the other algorithms. For probes which can be matched across platforms, the cross-platform variability is decomposed into within-platform and between-platform components, showing that platform disagreement is almost entirely systematic rather than due to measurement variability.

CONCLUSION

The results demonstrate good precision and sensitivity for all the platforms, but highlight the need for improved probe annotation. They quantify the extent to which cross-platform measures can be expected to be less accurate than within-platform comparisons for predicting disease progression or outcome.

Performance comparison of one-color and two-color platforms within the MicroArray Quality Control (MAQC) project

Microarray-based expression profiling experiments typically use either a one-color or a two-color design to measure mRNA abundance. The validity of each approach has been amply demonstrated. Here we provide a simultaneous comparison of results from one- and two-color labeling designs, using two independent RNA samples from the Microarray Quality Control (MAQC) project, tested on each of three different microarray platforms. The data were evaluated in terms of reproducibility, specificity, sensitivity and accuracy to determine if the two approaches provide comparable results. For each of the three microarray platforms tested, the results show good agreement with high correlation coefficients and high concordance of differentially expressed gene lists within each platform. Cumulatively, these comparisons indicate that data quality is essentially equivalent between the one- and two-color approaches and strongly suggest that this variable need not be a primary factor in decisions regarding experimental microarray design.

Evaluation of DNA microarray results with quantitative gene expression platforms

We have evaluated the performance characteristics of three quantitative gene expression technologies and correlated their expression measurements to those of five commercial microarray platforms, based on the MicroArray Quality Control (MAQC) data set. The limit of detection, assay range, precision, accuracy and fold-change correlations were assessed for 997 TaqMan Gene Expression Assays, 205 Standardized RT (Sta)RT-PCR assays and 244 QuantiGene assays. TaqMan is a registered trademark of Roche Molecular Systems, Inc. We observed high correlation between quantitative gene expression values and microarray platform results and found few discordant measurements among all platforms. The main cause of variability was differences in probe sequence and thus target location. A second source of variability was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes. From this analysis, we conclude that the MAQC microarray data set has been validated by alternative quantitative gene expression platforms thus supporting the use of microarray platforms for the quantitative characterization of gene expression.

Development and validation of a bovine macrophage specific cDNA microarray

Background

The response of macrophages to danger signals is an important early stage in the immune response. Our understanding of this complex event has been furthered by microarray analysis, which allows the simultaneous investigation of the expression of large numbers of genes. However, the microarray resources available to study these events in livestock animals are limited.

Results

Here we report the development of a bovine macrophage specific (BoMP) cDNA microarray. The BoMP microarray contains 5026 sequence elements (printed in duplicate) and numerous controls. The majority of the clones incorporated on the microarray were derived from the BoMP cDNA library generated from bovine myeloid cells subjected to various stimuli, including over 900 sequences unique to the library. Additional clones representing immunologically important genes have been included on the BoMP microarray. The microarray was validated by investigating the response of bovine monocytes to stimulation with interferon-γ and lipopolysaccharide using amplified RNA. At 2 and 16 hours post stimulation 695 genes exhibited statistically significant differential expression, including; 26 sequences unique to the BoMP library, interleukin 6, prion protein and toll-like receptor 4.

Conclusion

A 5 K cDNA microarray has been successfully developed to investigate gene expression in bovine myeloid cells. The BoMP microarray is available from the ARK-Genomics Centre for Functional Genomics in Farm Animals, UK.

Human astrocytes express 14-3-3 sigma in response to oxidative and DNA-damaging stresses

The 14-3-3 protein family consists of seven isoforms, most of which are expressed abundantly in neurons and glial cells, although the sigma isoform, a p53 target gene originally identified as an epithelium-specific marker, has not been identified in the human central nervous system. Here, we show that human astrocytes in culture expressed 14-3-3sigma under stress conditions. By Western blot, the expression of 14-3-3sigma, p53 and p21 was coordinately upregulated in astrocytes following exposure to hydrogen peroxide, 4-hydroxy-2-nonenal (4-HNE) or etoposide, a topoisomerase II inhibitor. 14-3-3sigma was induced by treatment with 5-aza-2'-deoxycytidine, suggesting a hypermethylated status of the gene promoter in astrocytes. In vivo, a small subset of hypertrophic reactive astrocytes, often showing a multinucleated morphology, expressed 14-3-3sigma in active demyelinating lesions of multiple sclerosis (MS) and ischemic lesions of cerebral infarction, where the expression of 4-HNE and 8-hydroxy-2'-deoxyguanosine was enhanced in reactive astrocytes. Microarray analysis of etoposide-treated astrocytes verified upregulation of p53-responsive genes and concurrent downregulation of mitotic checkpoint-regulatory genes. These observations suggest that 14-3-3sigma might serve as a marker of oxidative and DNA-damaging stresses inducing the mitotic checkpoint dysfunction in reactive astrocytes under pathological conditions.