Zengjun "Alex" Xu and colleagues [2] employ microarray analysis in combination with a battery of bioinformatics tools and make inroads into better understanding Parkinson's Disease (PD). PD is often studied using PC12 cells, which produce dopamine, in combination with 1-methyl-4-phenylpyridinium (MPP+), which depletes dopamine content and elicits cell death in PC12 cells, much as is observed in PD. To identify the important genes affected in PC12 cells by MPP+, Xu et al identified 106 genes with differential expression levels. The genes were tied back to their ontological categories and implicated the oxidative stress and apoptosis pathways as playing a role in the observed effects. Examining these responders in terms of their literature-based associations [3], the DNA-damage pathway is identified as the likely primary culprit. Several genes are also implicated as central in this process with only loose literature ties to PD and MPP+, suggesting fruitful avenues of future experimental pursuit.
Bob Delongchamp et al [4] present the statistical design and analysis of a study to estimate gene expression differences between male and female livers. Addressing variation attributable to sample processing, arrays, hybridizations, normalization, and subjects, their statistical analysis suggested that about 224 genes of the 31,110 interrogated genes were expressed differentially depending upon gender. However, these differences were small and it was not possible to specify sets of differentially expressed genes that do not have large false discovery rates. The paper offers a comprehensive and statistically rigorous approach to summarizing genome-wide interrogation of gene expression changes.
Hong Fang et al [5] also focused upon the human liver and used a variety of bioinformatics approaches to examine microarray expression profiles from liver neoplasms that arise in albumin-SV40 transgenic rats to elucidate genes, chromosome aberrations and pathways that might be associated with human liver cancer. Their analysis implicates human chromosomes 10, 11 and 19 as regions of potential chromosomal aberrations.
Microarray-based measurements of mRNA abundance and ratio calculations assume a linear relationship between the fluorescence intensity and the dye concentration. By scanning a microarray scanner calibration slide containing known concentrations of fluorescent dyes under various PMT gains, Leming Shi et al. [6] demonstrated the dramatic differences in calibration characteristics of Cy5 and Cy3, indicating the importance of scanning microarrays at fixed, optimal gain settings under which the linearity between concentration and intensity is maximized. Combined with simulation results, they provided rational explanations to the existence of ratio underestimation, intensity-dependence of ratio bias, and anti-correlation of ratios in dye-swap replicates. Although normalization methods improve reproducibility of microarray measurements, they appear less effective in improving accuracy. A method of calculating ratios based on concentrations estimated from the calibration curves was proposed for correcting ratio bias.
In another paper, Leming Shi et al re-evaluate a study by Tan et al [7], which was extensively cited in a recent Science paper [8], that paints a very negative picture of the cross-platform comparability, and, hence, the reliability of microarray technology. Shi et al [9] reanalyzed Tan's dataset and found that the low cross-platform concordance reported in Tan's study appears to be mainly due to a combination of low intra-platform consistency and a poor choice of data analysis procedures, instead of inherent technical differences among different platforms. They emphasize the importance of establishing calibrated RNA samples and reference datasets to objectively assess the performance of different microarray platforms. They also discuss how the proficiency of individual laboratories can affect results as well as the merits of various data analysis procedures.