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.

The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements.

Over the last decade, the introduction of microarray technology has had a profound impact on gene expression research. The publication of studies with dissimilar or altogether contradictory results, obtained using different microarray platforms to analyze identical RNA samples, has raised concerns about the reliability of this technology. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and data analysis issues. Expression data on four titration pools from two distinct reference RNA samples were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. Here we describe the experimental design and probe mapping efforts behind the MAQC project. We show intraplatform consistency across test sites as well as a high level of interplatform concordance in terms of genes identified as differentially expressed. This study provides a resource that represents an important first step toward establishing a framework for the use of microarrays in clinical and regulatory settings.

Validation of oligonucleotide microarray data using microfluidic low-density arrays: a new statistical method to normalize real-time RT-PCR data.

Profiling studies using microarrays to measure messenger RNA (mRNA) expression frequently identify long lists of differentially expressed genes. Differential expression is often validated using real-time reverse transcription PCR (RT-PCR) assays. In conventional real-time RT-PCR assays, expression is normalized to a control, or housekeeping gene. However, no single housekeeping gene can be used for all studies. We used TaqMan Low-Density Arrays, a medium-throughput method for real-time RT-PCR using microfluidics to simultaneously assay the expression of 96 genes in nine samples of chronic lymphocytic leukemia (CLL). We developed a novel statistical method, based on linear mixed-effects models, to analyze the data. This method automatically identifies the genes whose expression does not vary significantly over the samples, allowing them to be used to normalize the remaining genes. We compared the normalized real-time RT-PCR values with results obtained from Affymetrix Hu133A GeneChip oligonucleotide microarrays. We found that real-time RT-PCR using TaqMan Low-Density Arrays yielded reproducible measurements over seven orders of magnitude. Our model identified numerous genes that were expressed at nearly constant levels, including the housekeeping genes PGK1, GAPD, GUSB, TFRC, and 18S rRNA. After normalizing to the geometric mean of the unvarying genes, the correlation between real-time RT-PCR and microarrays was high for genes that were moderately expressed and varied across samples.

Gene expression profiling in mitochondrial disease: assessment of microarray accuracy by high-throughput Q-PCR.

Mitochondrial diseases are a heterogeneous array of disorders with a complex etiology. Use of microarrays as a tool to investigate complex human disease is increasingly common, however, a principle drawback of microarrays is their limited dynamic range, due to the poor quantification of weak signals. Although it is generally understood that low-intensity microarray 'spots' may be unreliable, there exists little documentation of their accuracy. Quantitative PCR (Q-PCR) is frequently used to validate microarray data, yet few Q-PCR validation studies have focused on the accuracy of low-intensity microarray signals. Hence, we have used Q-PCR to systematically assess microarray accuracy as a function of signal strength in a mouse model of mitochondrial disease, the superoxide dismutase 2 (SOD2) nullizygous mouse. We have focused on a unique category of data--spots with only one weak signal in a two-dye comparative hybridization--and show that such 'high-low' signal intensities are common for differentially expressed genes. This category of differential expression may be more important in mitochondrial disease in which there are often mosaic expression patterns due to the idiosyncratic distribution of mutant mtDNA in heteroplasmic individuals. Using RNA from the SOD2 mouse, we found that when spotted cDNA microarray data are filtered for quality (low variance between many technical replicates) and spot intensity (above a negative control threshold in both channels), there is an excellent quantitative concordance with Q-PCR (R2 = 0.94). The accuracy of gene expression ratios from low-intensity spots (R2 = 0.27) and 'high-low' spots (R2 = 0.32) is considerably lower. Our results should serve as guidelines for microarray interpretation and the selection of genes for validation in mitochondrial disorders.