Mouse strain specific gene expression differences for illumina microarray expression profiling in embryos

ABSTRACT: BACKGROUND: In the field of mouse genetics the advent of technologies like microarray based expression profiling dramatically increased data availability and sensitivity, yet these advanced methods are often vulnerable to the unavoidable heterogeneity of in vivo material and might therefore reflect differentially expressed genes between mouse strains of no relevance to a targeted experiment. The aim of this study was not to elaborate on the usefulness of microarray analysis in general, but to expand our knowledge regarding this potential "background noise" for the widely used Illumina microarray platform surpassing existing data which focused primarily on the adult sensory and nervous system, by analyzing patterns of gene expression at different embryonic stages using wild type strains and modern transgenic models of often non-isogenic backgrounds. RESULTS: Wild type embryos of 11 mouse strains commonly used in transgenic and molecular genetic studies at three developmental time points were subjected to Illumina microarray expression profiling in a strain-by-strain comparison. Our data robustly reflects known gene expression patterns during mid-gestation development. Decreasing diversity of the input tissue and/or increasing strain diversity raised the sensitivity of the array towards the genetic background. Consistent strain sensitivity of some probes was attributed to genetic polymorphisms or probe design related artifacts. CONCLUSION: Our study provides an extensive reference list of gene expression profiling background noise of value to anyone in the field of developmental biology and transgenic research performing microarray expression profiling with the widely used Illumina microarray platform. Probes identified as strain specific background noise further allow for microarray expression profiling on its own to be a valuable tool for establishing genealogies of mouse inbred strains.     

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 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.     

Improved data normalization methods for reverse phase protein microarray analysis of complex biological samples

Members of the heat shock protein-90 (Hsp90) family are key regulators of biological processes through dynamic interaction with a multitude of protein partners. However, the transient nature of these interactions hinders the identification of Hsp90 interactors. Here we show that chemical cross-linking with ethylene glycolbis (succinimidylsuccinate), but not shorter cross-linkers, generated an abundant 240-kDa heteroconjugate of the molecular chaperone Hsp90 in different cell types. The combined use of pharmacological and genetic approaches allowed the characterization of the subunit composition and subcellular compartmentalization of the multimeric protein complex, termed p240. The in situ formation of p240 did not require the N-terminal domain or the ATPase activity of Hsp90. Utilizing subcellular fractionation techniques and a cell-impermeant cross-linker, subpopulations of p240 were found to be present in both the plasma membrane and the mitochondria. The Hsp90-interacting proteins, including Hsp70, p60Hop and the scaffolding protein filamin A, had no role in governing the formation of p240. Therefore, chemical cross-linking combined with proteomic methods has the potential to unravel the protein components of this p240 complex and, more importantly, may provide an approach to expand the range of tools available to the study of the Hsp90 interactome.     

An oligonucleotide microarray for mouse imprinted genes profiling

Genomic imprinting is an epigenetic phenomenon unique to mammals that causes some genes to be expressed according to their parental origin. It results in developmental asymmetry in the function of the parental genomes. We describe here a method for the profiling of imprinted genes based on the development of a mouse imprinting microchip containing oligonucleotides corresponding to 493 genes, including most of the known imprinted genes (IG = 63), genes involved in epigenetic processes (EPI = 15), in metabolism (= 147), in obesity (= 10) and in neurotransmission (= 256) and housekeeping reference genes (= 2). This custom oligonucleotide microarray has been constructed to make data analysis and handling more manageable than pangenomic microarrays. As a proof of concept we present the differential expression of these 493 genes in different tissues (liver, placenta, embryo) of C57BL6/J mice fed different diets. Appropriate experimental strategies and statistical tools were defined at each step of the data analysis process with regard to the different sources of constraints. Data were confirmed by expression analyses based on quantitative real-time PCR. These oligochips should make it possible to increase our understanding of the involvement of imprinted genes in the timing of expression programs, tissue by tissue, stage by stage, in response to nutrients, lifestyles and other as yet unknown critical environmental factors in a variety of physiopathological situations, and in animals of different strains, ages and sexes. The use of oligonucleotides makes it possible to expand this microchip to include the increasing number of imprinted genes discovered.     

Statistical methods for microarray assays

The paper shortly reviews statistical methods used in the area of DNA microarray studies. All stages of the experiment are taken into account: planning, data collection, data preprocessing, analysis and validation. Among the methods of data analysis, the algorithms for estimating differential expression, multivariate approaches, clustering methods, as well as classification and discrimination are reviewed. The need is stressed for routine statistical data processing protocols and for the search of links of microarray data analysis with quantitative genetic models.     

Gibberella zeae ascospore production and collection for microarray experiments

Fusarium graminearum Schwabe (teleomorph Gibberella zeae) is a plant pathogen causing scab disease on wheat and barley that reduces crop yield and grain quality. F. graminearum also causes stalk and ear rots of maize and is a producer of mycotoxins such as the trichothecenes that contaminate grain and are harmful to humans and livestock (Goswami and Kistler, 2004). The fungus produces two types of spores. Ascospores, the propagules resulting from sexual reproduction, are the main source of primary infection. These spores are forcibly discharged from mature perithecia and dispersed by wind (Francl et al 1999). Secondary infections are mainly caused by macroconidia which are produced by asexual means on the plant surface. To study the developmental processes of ascospores in this fungus, a procedure for their collection in large quantity under sterile conditions was required. Our protocol was filmed in order to generate the highest level of information for understanding and reproducibility; crucial aspects when full genome gene expression profiles are generated and interpreted. In particular, the variability of ascospore germination and biological activity are dependent on the prior manipulation of the material. The use of video for documenting every step in ascospore production is proposed in order to increase standardization, complying with the increasingly stringent requirements for microarray analysis. The procedure requires only standard laboratory equipment. Steps are shown to prevent contamination and favor time synchronization of ascospores.     

Advances in spotted microarray resources for expression profiling.

The new millennium has ushered in a new era in human biology. The elucidation of the human genome sequence, together with those of model organisms, provides us with an unprecedented insight into the makeup of our genetic blueprint. The challenge now is to figure out how all the constituent pieces fit together to form the whole picture, and the consequences of what happens when the process goes awry. One experimental tool that has the potential to provide enormous insights into this complex process is expression profiling using microarrays. The past few years have seen a considerable growth in the availability and use of microarrays. Fuelled in part by the many genome projects currently underway, there has been a large increase in the number of organisms for which microarray reagents are available from both commercial and academic sources. In addition to the increasing number of genome-wide probe sets that are available, a significant amount of attention has been focussed on generating more targeted probe sets that focus in on specific pathways or biological processes. Finally, the microarray field is starting to see a shift away from the use of cDNAs or polymerase chain reaction (PCR) products as probes towards the use of 50-70mer oligonucleotide probes with all of the potential advantages that they offer. The aim of this review is to provide an overview of what is currently available in terms of spotted microarray reagents both with respect to pre-made arrays and to probe sets available for arraying.     

Custom microarray for glycobiologists: considerations for glycosyltransferase gene expression profiling

The development of microarray technology offers the unprecedented possibility of studying the expression of thousands of genes in one experiment. Its exploitation in the glycobiology field will eventually allow the parallel investigation of the expression of many glycosyltransferases, which will ultimately lead to an understanding of the regulation of glycoconjugate synthesis. While numerous gene arrays are available on the market, e.g. the Affymetrix GeneChip arrays, glycosyltransferases are not adequately represented, which makes comprehensive surveys of their gene expression difficult. This chapter describes the main issues related to the establishment of a custom glycogenes array.     

Evaluation of microarray surfaces and arraying parameters for autoantibody profiling

Autoantigen microarrays are being used increasingly to study autoimmunity. Significant variation has been observed when comparing microarray surfaces, printing methods, and probing conditions. In the present study, 24 surfaces and several arraying parameters were analyzed using >500 feature autoantigen microarrays printed with quill pins. A small subset of slides, including FAST, PATH, and SuperEpoxy2, performed well while maintaining the sensitivity and specificity of autoantigen microarrays previously demonstrated by our laboratory. By optimizing the major variables in our autoantigen microarray platform, subtle differences in serum samples can be identified that will shed light on disease pathogenesis.     

Tissue collection methods for antler research

The rapid growth of deer antlers makes them potentially excellent models for studying tissue regeneration. In order to facilitate this, we have developed and refined antler tissue sampling methods through years of antler research. In the study, antler tissues were divided into three main groups: antler stem tissue, antler blastema and antler growth centre. For sampling stem tissue, entire initial antlerogenic periosteum (around 22 mm in diameter) could be readily peeled off from the underlying bone using a pair of rat-toothed forceps after delineating the boundary. Apical and peripheral periosteum/ perichondrium of pedicle and antler could only be peeled off intact when they were cut into 4 quadrants and 0.5 cm-wide strips respectively. Antler blastema included blastema per se, and potentiated and dormant periostea. Blastema per se was sampled after it was divided into 4 quadrants using a disposable microtome blade. Potentiated and dormant periostea were collected following the same method used for sampling peripheral periosteum of pedicle and antler. The antler growth centre was divided with a scalpel into 5 layers according to distinctive morphological markers. The apical skin layer could be further separated into dermis and epidermis using enzyme digestion for the study of tissue interaction. We believe that the application of modern techniques coupled with the tissue collection methods reported here will greatly facilitate the establishment of these valuable models.     

Clustering methods for microarray gene expression data

Within the field of genomics, microarray technologies have become a powerful technique for simultaneously monitoring the expression patterns of thousands of genes under different sets of conditions. A main task now is to propose analytical methods to identify groups of genes that manifest similar expression patterns and are activated by similar conditions. The corresponding analysis problem is to cluster multi-condition gene expression data. The purpose of this paper is to present a general view of clustering techniques used in microarray gene expression data analysis.     

Evaluation of normalization methods for microarray data

Background

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. This novel technique helps us to understand gene regulation as well as gene by gene interactions more systematically. In the microarray experiment, however, many undesirable systematic variations are observed. Even in replicated experiment, some variations are commonly observed. Normalization is the process of removing some sources of variation which affect the measured gene expression levels. Although a number of normalization methods have been proposed, it has been difficult to decide which methods perform best. Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization.

Results

In this paper, we use the variability among the replicated slides to compare performance of normalization methods. We also compare normalization methods with regard to bias and mean square error using simulated data.

Conclusions

Our results show that intensity-dependent normalization often performs better than global normalization methods, and that linear and nonlinear normalization methods perform similarly. These conclusions are based on analysis of 36 cDNA microarrays of 3,840 genes obtained in an experiment to search for changes in gene expression profiles during neuronal differentiation of cortical stem cells. Simulation studies confirm our findings.

     

Comparing transformation methods for DNA microarray data

Background  

When DNA microarray data are used for gene clustering, genotype/phenotype correlation studies, or tissue classification the signal intensities are usually transformed and normalized in several steps in order to improve comparability and signal/noise ratio. These steps may include subtraction of an estimated background signal, subtracting the reference signal, smoothing (to account for nonlinear measurement effects), and more. Different authors use different approaches, and it is generally not clear to users which method they should prefer.     

New normalization methods for cDNA microarray data

MOTIVATION: The focus of this paper is on two new normalization methods for cDNA microarrays. After the image analysis has been performed on a microarray and before differentially expressed genes can be detected, some form of normalization must be applied to the microarrays. Normalization removes biases towards one or other of the fluorescent dyes used to label each mRNA sample allowing for proper evaluation of differential gene expression. RESULTS: The two normalization methods that we present here build on previously described non-linear normalization techniques. We extend these techniques by firstly introducing a normalization method that deals with smooth spatial trends in intensity across microarrays, an important issue that must be dealt with. Secondly we deal with normalization of a new type of cDNA microarray experiment that is coming into prevalence, the small scale specialty or 'boutique' array, where large proportions of the genes on the microarrays are expected to be highly differentially expressed. AVAILABILITY: The normalization methods described in this paper are available via http://www.pi.csiro.au/gena/ in a software suite called tRMA: tools for R Microarray Analysis upon request of the authors. Images and data used in this paper are also available via the same link.     

Design of a seven-genome Escherichia coli microarray for comparative genomic profiling

We describe the design and evaluate the use of a high-density oligonucleotide microarray covering seven sequenced Escherichia coli genomes in addition to several sequenced E. coli plasmids, bacteriophages, pathogenicity islands, and virulence genes. Its utility is demonstrated for comparative genomic profiling of two unsequenced strains, O175:H16 D1 and O157:H7 3538 (Deltastx(2)::cat) as well as two well-known control strains, K-12 W3110 and O157:H7 EDL933. By using fluorescently labeled genomic DNA to query the microarrays and subsequently analyze common virulence genes and phage elements and perform whole-genome comparisons, we observed that O175:H16 D1 is a K-12-like strain and confirmed that its phi3538 (Deltastx(2)::cat) phage element originated from the E. coli 3538 (Deltastx(2)::cat) strain, with which it shares a substantial proportion of phage elements. Moreover, a number of genes involved in DNA transfer and recombination was identified in both new strains, providing a likely explanation for their capability to transfer phi3538 (Deltastx(2)::cat) between them. Analyses of control samples demonstrated that results using our custom-designed microarray were representative of the true biology, e.g., by confirming the presence of all known chromosomal phage elements as well as 98.8 and 97.7% of queried chromosomal genes for the two control strains. Finally, we demonstrate that use of spatial information, in terms of the physical chromosomal locations of probes, improves the analysis.