A quantitative comparison of cell-type-specific microarray gene expression profiling methods in the mouse brain

Expression profiling of restricted neural populations using microarrays can facilitate neuronal classification and provide insight into the molecular bases of cellular phenotypes. Due to the formidable heterogeneity of intermixed cell types that make up the brain, isolating cell types prior to microarray processing poses steep technical challenges that have been met in various ways. These methodological differences have the potential to distort cell-type-specific gene expression profiles insofar as they may insufficiently filter out contaminating mRNAs or induce aberrant cellular responses not normally present in vivo. Thus we have compared the repeatability, susceptibility to contamination from off-target cell-types, and evidence for stress-responsive gene expression of five different purification methods--Laser Capture Microdissection (LCM), Translating Ribosome Affinity Purification (TRAP), Immunopanning (PAN), Fluorescence Activated Cell Sorting (FACS), and manual sorting of fluorescently labeled cells (Manual). We found that all methods obtained comparably high levels of repeatability, however, data from LCM and TRAP showed significantly higher levels of contamination than the other methods. While PAN samples showed higher activation of apoptosis-related, stress-related and immediate early genes, samples from FACS and Manual studies, which also require dissociated cells, did not. Given that TRAP targets actively translated mRNAs, whereas other methods target all transcribed mRNAs, observed differences may also reflect translational regulation.     

A comparison of four clustering methods for brain expression microarray data

Background  

DNA microarrays, which determine the expression levels of tens of thousands of genes from a sample, are an important research tool. However, the volume of data they produce can be an obstacle to interpretation of the results. Clustering the genes on the basis of similarity of their expression profiles can simplify the data, and potentially provides an important source of biological inference, but these methods have not been tested systematically on datasets from complex human tissues. In this paper, four clustering methods, CRC, k-means, ISA and memISA, are used upon three brain expression datasets. The results are compared on speed, gene coverage and GO enrichment. The effects of combining the clusters produced by each method are also assessed.     

Trypanosoma brucei brucei: A comparison of gene expression in the liver and spleen of infected mice utilizing cDNA microarray technology

Trypanosoma brucei brucei, the infectious agent of the disease known as Nagana, is a pathogenic trypanosome occurring in Africa, where it causes significant economic loss to domesticated livestock. Although many studies on the histopathology of organs of mice infected with T. b. brucei have been reported, little work has been done regarding gene expression in these organs in infected mice. In this paper, we describe the use of cDNA microarray to determine gene expression profiles in the liver and spleen of mice infected with T. b. brucei (STIB 920) at peak parasitaemia (12 days after infection). Our results showed that a total of 123 genes in the liver and 389 genes in the spleen were expressed differentially in T. b. brucei infected mice. In contrast, however, in an acute infection in mice caused by Trypanosoma brucei evansi, a species genetically related to T. b. brucei, 336 genes in the liver and 190 genes in the spleen were expressed, differentially, indicating that the liver of mice was more affected by the acute T. b. evansi infection whilst the spleen was more affected by the subacute T. b. brucei infection. Our results provide a number of possible reasons why mice infected with T. b. evansi die sooner than those infected with T. b. brucei: (1) mice infected with T. b. evansi may need more stress response proteins to help them pass through the infection and these are probably excessively consumed; (2) proliferating cell nuclear antigen was more down-regulated in the liver of mice infected with T. b. evansi, which indicated that the inhibition of proliferation of hepatocytes in mice infected with T. b. evansi might be more severe than that in T. b. brucei infection; and (3) more hepatocyte apoptosis occurred in the mice infected with T. b. evansi and this might be probably the most important reason why mice died sooner than those infected with T. b. brucei. Studies of the changes in the gene expression profile in the liver and spleen of mice infected with T. b. brucei may be helpful in understanding the mechanisms of pathogenesis in Nagana disease at the molecular level. By comparing the gene profiles of the liver and spleen of mice infected with T. b. brucei with T. b. evansi, we have identified a number of factors that could explain the differences in pathogenesis in mice infected with these two African trypanosomes.     

FGDP: functional genomics data pipeline for automated, multiple microarray data analyses

Gene expression microarrays and oligonucleotide GeneChips have provided biologists with a means of measuring, in a single experiment, the expression levels of entire genomes under a variety of conditions. As with any nascent field, there is no single accepted method for analyzing the new data types, with new methods appearing monthly. Investigators using the new technology must constantly seek access to the latest tools and explore their data in multiple ways. The functional genomics data pipeline provides an integrated, extendable analysis environment permitting multiple, simultaneous analyses to be automatically performed and provides a web server and interface for presenting results. AVAILABILITY: Source code and executables are available under the GNU public license at http://bioinformatics.fccc.edu/     

A comparison of cDNA, oligonucleotide, and Affymetrix GeneChip gene expression microarray platforms.

We have conducted a study to compare the variability in measured gene expression levels associated with three types of microarray platforms. Total RNA samples were obtained from liver tissue of four male mice, two each from inbred strains A/J and C57BL/6J. The same four samples were assayed on Affymetrix Mouse Genome Expression Set 430 GeneChips (MOE430A and MOE430B), spotted cDNA microarrays, and spotted oligonucleotide microarrays using eight arrays of each type. Variances associated with measurement error were observed to be comparable across all microarray platforms. The MOE430A GeneChips and cDNA arrays had higher precision across technical replicates than the MOE430B GeneChips and oligonucleotide arrays. The Affymetrix platform showed the greatest range in the magnitude of expression levels followed by the oligonucleotide arrays. We observed good concordance in both estimated expression level and statistical significance of common genes between the Affymetrix MOE430A GeneChip and the oligonucleotide arrays. Despite their apparently high precision, cDNA arrays showed poor concordance with other platforms.     

Differences in gene expression between first and third trimester human placenta: a microarray study

Background

The human placenta is a rapidly developing organ that undergoes structural and functional changes throughout the pregnancy. Our objectives were to investigate the differences in global gene expression profile, the expression of imprinted genes and the effect of smoking in first and third trimester normal human placentas.

Materials and Methods

Placental samples were collected from 21 women with uncomplicated pregnancies delivered at term and 16 healthy women undergoing termination of pregnancy at 9–12 weeks gestation. Placental gene expression profile was evaluated by Human Genome Survey Microarray v.2.0 (Applied Biosystems) and real-time polymerase chain reaction.

Results

Almost 25% of the genes spotted on the array (n = 7519) were differentially expressed between first and third trimester placentas. Genes regulating biological processes involved in cell proliferation, cell differentiation and angiogenesis were up-regulated in the first trimester; whereas cell surface receptor mediated signal transduction, G-protein mediated signalling, ion transport, neuronal activities and chemosensory perception were up-regulated in the third trimester. Pathway analysis showed that brain and placenta might share common developmental routes. Principal component analysis based on the expression of 17 imprinted genes showed a clear separation of first and third trimester placentas, indicating that epigenetic modifications occur throughout pregnancy. In smokers, a set of genes encoding oxidoreductases were differentially expressed in both trimesters.

Conclusions

Differences in global gene expression profile between first and third trimester human placenta reflect temporal changes in placental structure and function. Epigenetic rearrangements in the human placenta seem to occur across gestation, indicating the importance of environmental influence in the developing feto-placental unit.     

Bivariate microarray analysis: statistical interpretation of two-channel functional genomics data

Conventional statistical methods for interpreting microarray data require large numbers of replicates in order to provide sufficient levels of sensitivity. We recently described a method for identifying differentially-expressed genes in one-channel microarray data 1. Based on the idea that the variance structure of microarray data can itself be a reliable measure of noise, this method allows statistically sound interpretation of as few as two replicates per treatment condition. Unlike the one-channel array, the two-channel platform simultaneously compares gene expression in two RNA samples. This leads to covariation of the measured signals. Hence, by accounting for covariation in the variance model, we can significantly increase the power of the statistical test. We believe that this approach has the potential to overcome limitations of existing methods. We present here a novel approach for the analysis of microarray data that involves modeling the variance structure of paired expression data in the context of a Bayesian framework. We also describe a novel statistical test that can be used to identify differentially-expressed genes. This method, bivariate microarray analysis (BMA), demonstrates dramatically improved sensitivity over existing approaches. We show that with only two array replicates, it is possible to detect gene expression changes that are at best detected with six array replicates by other methods. Further, we show that combining results from BMA with Gene Ontology annotation yields biologically significant results in a ligand-treated macrophage cell system.     

A gene expression bar code for microarray data

The ability to measure genome-wide expression holds great promise for characterizing cells and distinguishing diseased from normal tissues. Thus far, microarray technology has been useful only for measuring relative expression between two or more samples, which has handicapped its ability to classify tissue types. Here we present a method that can successfully predict tissue type based on data from a single hybridization. A preliminary web-tool is available online (http://rafalab.jhsph.edu/barcode/).     

Tandem gene arrays: a challenge for functional genomics

In sequenced plant genomes, 15% or more of the identified genes are members of tandem-arrayed gene families. Because mutating only one gene in a duplicated pair often produces no measurable phenotype, this poses a particular challenge for functional analysis. To generate phenotypic knockouts, it is necessary to create deletions that affect multiple genes, select for rare meiotic recombination between tightly linked loci, or perform sequential mutant screens in the same plant line. Successfully implemented strategies include PCR-based screening for fast neutron-induced deletions, selection for recombination between herbicide resistance markers, and localized transposon mutagenesis. Here, we review the relative merits of current genetic approaches and discuss the prospect of site-directed mutagenesis for generating elusive knockouts of tandem-arrayed gene families.     

A quality-controlled microarray method for gene expression profiling

Gene expression profiling on microarrays is widely used to measure the expression of large numbers of genes in a single experiment. Because of the high cost of this method, feasible numbers of replicates are limited, thus impairing the power of statistical analysis. As a step toward reducing technically induced variation, we developed a procedure of sample preparation and analysis that minimizes the number of sample manipulation steps, introduces quality control before array hybridization, and allows recovery of the prepared mRNA for independent validation of results. Sample preparation is based on mRNA separation using oligo(dT) magnetic beads, which are subsequently used for first-strand cDNA synthesis on the beads. cDNA covalently bound to the magnetic beads is used as template for second-strand cDNA synthesis, leaving the intact mRNA in solution for further analysis. The quality of the synthesized cDNA can be assessed by quantitative polymerase chain reaction using 3'- and 5'-specific primer pairs for housekeeping genes such as glyceraldehyde-3-phosphate dehydrogenase. Second-strand cDNA is chemically labeled with fluorescent dyes to avoid dye bias in enzymatic labeling reactions. After hybridization of two differently labeled samples to microarray slides, arrays are scanned and images analyzed automatically with high reproducibility. Quantile-normalized data from five biological replica display a coefficient of variation 45% for 90% of profiled genes, allowing detection of twofold changes with false positive and false negative rates of 10% each. We demonstrate successful application of the procedure for expression profiling in plant leaf tissue. However, the method could be easily adapted for samples from animal including human or from microbial origin.     

GEPAS: A web-based resource for microarray gene expression data analysis

We present a web-based pipeline for microarray gene expression profile analysis, GEPAS, which stands for Gene Expression Profile Analysis Suite (http://gepas.bioinfo.cnio.es). GEPAS is composed of different interconnected modules which include tools for data pre-processing, two-conditions comparison, unsupervised and supervised clustering (which include some of the most popular methods as well as home made algorithms) and several tests for differential gene expression among different classes, continuous variables or survival analysis. A multiple purpose tool for data mining, based on Gene Ontology, is also linked to the tools, which constitutes a very convenient way of analysing clustering results. On-line tutorials are available from our main web server (http://bioinfo.cnio.es).     

Exploring the functional landscape of gene expression: directed search of large microarray compendia

MOTIVATION: The increasing availability of gene expression microarray technology has resulted in the publication of thousands of microarray gene expression datasets investigating various biological conditions. This vast repository is still underutilized due to the lack of methods for fast, accurate exploration of the entire compendium. RESULTS: We have collected Saccharomyces cerevisiae gene expression microarray data containing roughly 2400 experimental conditions. We analyzed the functional coverage of this collection and we designed a context-sensitive search algorithm for rapid exploration of the compendium. A researcher using our system provides a small set of query genes to establish a biological search context; based on this query, we weight each dataset's relevance to the context, and within these weighted datasets we identify additional genes that are co-expressed with the query set. Our method exhibits an average increase in accuracy of 273% compared to previous mega-clustering approaches when recapitulating known biology. Further, we find that our search paradigm identifies novel biological predictions that can be verified through further experimentation. Our methodology provides the ability for biological researchers to explore the totality of existing microarray data in a manner useful for drawing conclusions and formulating hypotheses, which we believe is invaluable for the research community. AVAILABILITY: Our query-driven search engine, called SPELL, is available at http://function.princeton.edu/SPELL. SUPPLEMENTARY INFORMATION: Several additional data files, figures and discussions are available at http://function.princeton.edu/SPELL/supplement.     

Systematic variation normalization in microarray data to get gene expression comparison unbiased

Normalization removes or minimizes the biases of systematic variation that exists in experimental data sets. This study presents a systematic variation normalization (SVN) procedure for removing systematic variation in two channel microarray gene expression data. Based on an analysis of how systematic variation contributes to variability in microarray data sets, our normalization procedure includes background subtraction determined from the distribution of pixel intensity values from each data acquisition channel and log conversion, linear or non-linear regression, restoration or transformation, and multiarray normalization. In the case when a non-linear regression is required, an empirical polynomial approximation approach is used. Either the high terminated points or their averaged values in the distributions of the pixel intensity values observed in control channels may be used for rescaling multiarray datasets. These pre-processing steps remove systematic variation in the data attributable to variability in microarray slides, assay-batches, the array process, or experimenters. Biologically meaningful comparisons of gene expression patterns between control and test channels or among multiple arrays are therefore unbiased using normalized but not unnormalized datasets.     

Advances in pig genomics and functional gene discovery

Advances in pig gene identification, mapping and functional analysis have continued to make rapid progress. The porcine genetic linkage map now has nearly 3000 loci, including several hundred genes, and is likely to expand considerably in the next few years, with many more genes and amplified fragment length polymorphism (AFLP) markers being added to the map. The physical genetic map is also growing rapidly and has over 3000 genes and markers. Several recent quantitative trait loci (QTL) scans and candidate gene analyses have identified important chromosomal regions and individual genes associated with traits of economic interest. The commercial pig industry is actively using this information and traditional performance information to improve pig production by marker-assisted selection (MAS). Research to study the co-expression of thousands of genes is now advancing and methods to combine these approaches to aid in gene discovery are under way. The pig's role in xenotransplantation and biomedical research makes the study of its genome important for the study of human disease. This review will briefly describe advances made, directions for future research and the implications for both the pig industry and human health.     

Technology for microarray analysis of gene expression

The past year has demonstrated the versatility of microarrays for the analysis of whole model-organism genomes and has seen the development of chips to measure the expression of 40,000 human genes. Microarray technology has also become considerably more robust and sensitive. Technology enhancements include the use of noncontact printing methods, improved 2-color sample preparation, and statistically based software for data analysis.     

Angiogenic profiling and comparison of immortalized endothelial cells for functional genomics

Genomics efforts of the past decade have resulted in the identification of numerous genes with putative roles in disease processes, including tumor angiogenesis. To functionally validate these genes, cultured endothelial cells are indispensable tools, though these may not completely mimic the phenotype of tissue endothelial cells as the proper microenvironment is lacking. To obtain experimental data representative of normal physiology, the use of primary endothelial cells is preferred. However, these cells are usually limited in passage number, can be difficult to obtain and show great interindividual variety. Furthermore, transfection efficiency is very limited in primary cells, hampering applications in functional genomics and gene function analysis. The use of properly characterized alternative endothelial cell sources is therefore warranted. Here, we compared immortalized endothelial cells - HMEC, RF24 and EVLC2 - with primary HUVEC. We show that RF24, and to a slightly lesser extent HMEC, resembles primary HUVEC most on all facets examined. RF24, in contrast to EVLC2, express the endothelial markers CD31, CD34, CD105, vWF and VE-cadherin, and are capable of migration and tube formation in vitro. Furthermore, the expression levels of angiogenic growth factors and their receptors are comparable to that of primary EC. In addition, whereas primary HUVEC are resistant to transfection using common lipophilic transfection reagents, HMEC and RF24 could be readily transfected. Hence, these cells pose a valuable tool for functional genomics in angiogenesis research.