Faculty Bibliography

Microarray-based genomic techniques allow the simultaneous determination of relative levels of expression of a large number of genes. Studies of the transcriptome in complex neurobiological systems are uniquely demanding due to the heterogeneous nature of these cells. Most brain regions contain a large variety of cell populations that are closely intermingled. The expression of any specific gene may be restricted to a subpopulation of cells, and changes in gene expression may occur in only a small fraction of the cells expressing that transcript. Due to this dilution effect, many genes of interest are expected to have relatively low levels of expression in tissue homogenates. Furthermore, biologically significant differences in expression may result in only small fold-changes. Therefore genomic approaches using brain dissections must be optimized to identify potentially regulated transcripts and differential expression should be confirmed using quantitative assays. We evaluated the effects of increasing tissue complexity on detection of regulated transcripts in focused microarray studies using a mouse cell line, mouse hypothalamus and mouse cortex. Regulated transcripts were confirmed by quantitative real-time PCR. As tissue complexity increased, distinguishing significantly regulated genes from background variation became increasingly more difficult. However, we found that cDNA microarray studies using regional brain dissections and appropriate numbers of replicates could identify genes showing less than 2-fold regulation and that most regulated genes identified fell within this range

The initial waves of gene induction caused by GnRH in the LbetaT2 gonadotrope cell line have recently been identified using microarrays. We now investigate the relationship of the concentration of GnRH to the level of biosynthesis induced. Using an optimized custom cDNA microarray, we show that a large number of genes are induced in a concentration-dependent fashion. Detailed time course studies of the induction of six induced transcripts using quantitative real-time PCR suggest that the amplitude, but not the temporal pattern, depends on the concentration of GnRH. The early genes appear to show a delay in gene induction, followed by a linear phase of increase. The relationship of rate of synthesis and GnRH concentration was studied by mathematical modeling of the induction of two genes, gly96 and tis11. In both cases, only the rates of increase, but not the lag times, are influenced by the concentration of GnRH exposure. Western blot analyses for c-Jun and Egr1 show that the levels of nuclear protein for these transcription factors also depend on the concentration of GnRH. These studies indicate that, despite the complex signaling network connecting the receptor to the activated genes, the biosynthetic rate of RNA polymerase at induced genes is correlated with the concentration of GnRH at the GnRH receptor

We compared the accuracy of microarray measurements obtained with oligonucleotide arrays (GeneChip, Affymetrix) with a laboratory-developed cDNA array by assaying test RNA samples from an experiment using a paradigm known to regulate many genes measured on both arrays. We selected 47 genes represented on both arrays, including both known regulated and unregulated transcripts, and established reference relative expression measurements for these genes in the test RNA samples using quantitative reverse transcriptase real-time PCR (QRTPCR) assays. The validity of the reproducible (average coefficient of variation = 11.8%) QRTPCR measurements were established through application of a new mathematical model. The performance of both array platforms in identifying regulated and non-regulated genes was identical. With either platform, 16 of 17 definitely regulated genes were correctly identified, and no definitely unregulated transcript was falsely identified as regulated. Accuracy of the fold-change measurements obtained with each platform was assessed by determining measurement bias. Both platforms consistently underestimate the relative changes in mRNA expression between experimental and control samples. The bias observed with cDNA arrays was predictable for fold-changes <250-fold by QRTPCR and could be corrected by the calibration function F(c) = F(a(cDNA))(q), where F(a(cDNA)) is the microarray-determined fold-change comparing experimental with control samples, q is the correction factor and F(c) is the calibrated value. The bias observed with the commercial oligonucleotide arrays was less predictable and calibration was unfeasible. Following calibration, fold-change measurements generated by custom cDNA arrays were more accurate than those obtained by commercial oligonucleotide arrays. Our study demonstrates systematic bias of microarray measurements and identifies a calibration function that improves the accuracy of cDNA array data