Identification of Novel Genes Differentially Expressed in PMA-induced HL-60 Cells Using cDNA Microarrays

Identification of normal growth and differentiation-inducing proteins and their interaction in normal development have made it possible to elucidate the molecular basis of normal development and the mechanisms uncoupling growth and differentiation during tumor development. The development of cancer and the experimental reversal of tumorigenicity are accompanied by complex changes in patterns of gene expression. cDNA microarrays provide a powerful tool for studying these phenomena. In the present study, a high-density microarray of human cDNA elements was used to search for differences in gene expression associated with differentiation of human promyelic leukemia HL-60 cells. Microarrays containing 3,063 human cDNAs were printed on glass slides with high-speed robotics. These DNA 'chips' were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. The identification of known and novel phorbol ester-regulated genes in hematopoietic progenitor cells demonstrates the sensitivity of the assay.     

Exploiting microarrays to reveal differential gene expression in the nervous system

Microarrays have been used in a wide variety of experimental systems, but realizing their full potential is contingent on sophisticated and rigorous experimental design and data analysis. This article highlights what is needed to get the most out of microarrays in terms of accurately and effectively revealing differential gene expression and regulation in the nervous system.     

Microarray analysis: basic strategies for successful experiments

Microarrays offer a powerful approach to the analysis of gene expression that can be used for a wide variety of experimental purposes. However, there are several types of microarray platforms that are available. In addition, microarray experiments are expensive and generate complicated data sets that can be difficult to interpret. Success with microarray approaches requires a sound experimental design and a coordinated and appropriate use of statistical tools. Here, the advantages and pitfalls of utilizing microarrays are discussed, as are practical strategies to help novice users succeed with this method that can empower them with the ability to assay changes in gene expression at the whole genome level.     

Microarray challenges in ecology

Microarrays are used to measure simultaneously the amount of mRNAs transcribed from many genes. They were originally designed for gene expression profiling in relatively simple biological systems, such as cell lines and model systems under constant laboratory conditions. This poses a challenge to ecologists who increasingly want to use microarrays to unravel the genetic mechanisms underlying complex interactions among organisms and between organisms and their environment. Here, we discuss typical experimental and statistical problems that arise when analyzing genome-wide expression profiles in an ecological context. We show that experimental design and environmental confounders greatly influence the identification of candidate genes in ecological microarray studies, and that following several simple recommendations could facilitate the analysis of microarray data in ecological settings.     

Comparative genomics and experimental promoter analysis reveal functional liver-specific elements in mammalian hepatic lipase genes

Background

Array-based comparative genomic hybridization (aCGH) is a high-throughput method for measuring genome-wide DNA copy number changes. Current aCGH methods have limited resolution, sensitivity and reproducibility. Microarrays for aCGH are available only for a few organisms and combination of aCGH data with expression data is cumbersome.

Results

We present a novel method of using commercial oligonucleotide expression microarrays for aCGH, enabling DNA copy number measurements and expression profiles to be combined using the same platform. This method yields aCGH data from genomic DNA without complexity reduction at a median resolution of approximately 17,500 base pairs. Due to the well-defined nature of oligonucleotide probes, DNA amplification and deletion can be defined at the level of individual genes and can easily be combined with gene expression data.

Conclusion

A novel method of gene resolution analysis of copy number variation (graCNV) yields high-resolution maps of DNA copy number changes and is applicable to a broad range of organisms for which commercial oligonucleotide expression microarrays are available. Due to the standardization of oligonucleotide microarrays, graCNV results can reliably be compared between laboratories and can easily be combined with gene expression data using the same platform.     

Identification of Novel Genes Differentially Expressed in PMA-induced HL-60 Cells Using cDNA Microarrays

Identification of normal growth and differentiation-inducing proteins and their interaction in normal development have made it possible to elucidate the molecular basis of normal development and the mechanisms uncoupling growth and differentiation during tumor development. The development of cancer and the experimental reversal of tumorigenicity are accompanied by complex changes in patterns of gene expression. cDNA microarrays provide a powerful tool for studying these phenomena. In the present study, a high-density microarray of human cDNA elements was used to search for differences in gene expression associated with differentiation of human promyelic leukemia HL-60 cells. Microarrays containing 3,063 human cDNAs were printed on glass slides with high-speed robotics. These DNA chips were used to quantitatively monitor differential expression of the cognate human genes using a highly sensitive two-color hybridization assay. The identification of known and novel phorbol ester-regulated genes in hematopoietic progenitor cells demonstrates the sensitivity of the assay.     

Stripping custom microRNA microarrays and the lessons learned about probe-slide interactions

Microarrays have been used extensively in gene expression profiling and genotyping studies. To reduce the high cost and enhance the consistency of microarray experiments, it is often desirable to strip and reuse microarray slides. Our genome-wide analysis of microRNA expression involves the hybridization of fluorescently labeled nucleic acids to custom-made, spotted DNA microarrays based on GAPSII-coated slides. We describe here a simple and effective method to regenerate such custom microarrays that uses a very low-salt buffer to remove labeled nucleic acids from microarrays. Slides can be stripped and reused multiple times without significantly compromising data quality. Moreover, our analyses of the performance of regenerated slides identifies parameters that influence the attachment of oligonucleotide probes to GAPSII slides, shedding light on the interactions between DNA and the microarray surface and suggesting ways in which to improve the design of oligonucleotide probes.     

Comparative microarray analysis

Microarrays enable high-throughput parallel gene expression analysis, and their use has grown exponentially during the past decade. We are now in a position where individual experiments could benefit from using the swelling public data repositories to allow microarrays to progress from being a hypothesis-generating tool to a powerful resource that can be used to test hypothesis about biology. Comparative microarray analysis could better distinguish phenotypes from associated phenotypes; identify valid differentially expressed genes by combining many studies; test new hypothesis; and discover fundamental patterns of gene regulation. This review aims to describe the additional methodology needed for such comparative microarray analysis, and we identify and discuss a number of problems such as loss of published data, lack of annotations, and variable array quality, which need to be solved before comparative microarray analysis can be used in a more systematic and powerful manner.     

Analysis of variance for gene expression microarray data.

Spotted cDNA microarrays are emerging as a powerful and cost-effective tool for large-scale analysis of gene expression. Microarrays can be used to measure the relative quantities of specific mRNAs in two or more tissue samples for thousands of genes simultaneously. While the power of this technology has been recognized, many open questions remain about appropriate analysis of microarray data. One question is how to make valid estimates of the relative expression for genes that are not biased by ancillary sources of variation. Recognizing that there is inherent "noise" in microarray data, how does one estimate the error variation associated with an estimated change in expression, i.e., how does one construct the error bars? We demonstrate that ANOVA methods can be used to normalize microarray data and provide estimates of changes in gene expression that are corrected for potential confounding effects. This approach establishes a framework for the general analysis and interpretation of microarray data.     

Transcriptional signatures of normal human mammary epithelial cells in response to benzo[a]pyrene exposure: a comparison of three microarray platforms

Microarrays are used to study gene expression in a variety of biological systems. A number of different platforms have been developed, but few studies exist that have directly compared the performance of one platform with another. The goal of this study was to determine array variation by analyzing the same RNA samples with three different array platforms. Using gene expression responses to benzo[a]pyrene exposure in normal human mammary epithelial cells (NHMECs), we compared the results of gene expression profiling using three microarray platforms: photolithographic oligonucleotide arrays (Affymetrix), spotted oligonucleotide arrays (Amersham), and spotted cDNA arrays (NCI). While most previous reports comparing microarrays have analyzed pre-existing data from different platforms, this comparison study used the same sample assayed on all three platforms, allowing for analysis of variation from each array platform. In general, poor correlation was found with corresponding measurements from each platform. Each platform yielded different gene expression profiles, suggesting that while microarray analysis is a useful discovery tool, further validation is needed to extrapolate results for broad use of the data. Also, microarray variability needs to be taken into consideration, not only in the data analysis but also in specific probe selection for each array type.     

A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Gene expression studies generate large quantities of data with the defining characteristic that the number of genes (whose expression profiles are to be determined) exceed the number of available replicates by several orders of magnitude. Standard spot-by-spot analysis still seeks to extract useful information for each gene on the basis of the number of available replicates, and thus plays to the weakness of microarrays. On the other hand, because of the data volume, treating the entire data set as an ensemble, and developing theoretical distributions for these ensembles provides a framework that plays instead to the strength of microarrays. We present theoretical results that under reasonable assumptions, the distribution of microarray intensities follows the Gamma model, with the biological interpretations of the model parameters emerging naturally. We subsequently establish that for each microarray data set, the fractional intensities can be represented as a mixture of Beta densities, and develop a procedure for using these results to draw statistical inference regarding differential gene expression. We illustrate the results with experimental data from gene expression studies on Deinococcus radiodurans following DNA damage using cDNA microarrays.     

Gene expression profiling in the rhesus macaque: Experimental design considerations

The development of species-specific gene microarrays has greatly facilitated gene expression profiling in nonhuman primates. However, to obtain accurate and physiologically meaningful data from these microarrays, one needs to consider several factors when designing the studies. This article focuses on effective experimental design while the companion article focuses on methodology and data analysis. Biological cycles have a major influence on gene expression, and at least 10% of the expressed genes are likely to show a 24-h expression pattern. Consequently, the time of day when RNA samples are collected can influence detection of significant changes in gene expression levels. Similarly, when photoperiodic species such as the rhesus macaque are housed outdoors, some of their genes show differential expression according to the time of year. In addition, the sex-steroid environment of humans and many nonhuman primates changes markedly across the menstrual cycle, and so phase of the cycle needs to be considered when studying gene expression in adult females.