High-throughput imaging of brain gene expression

Gene Set Correlation Analysis and Visualization Using Gene Expression Data

Background:

Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on the identification of differentially expressed gene sets in a given phenotype.

Objective:

In this paper, we propose a gene set analytic framework, Gene Set Correlation Analysis (GSCoA), that simultaneously measures within and between gene sets variation to identify sets of genes enriched for differential expression and highly co-related pathways.

Methods:

We apply co-inertia analysis to the comparisons of cross-gene sets in gene expression data to measure the co-structure of expression profiles in pairs of gene sets. Co-inertia analysis (CIA) is one multivariate method to identify trends or co-relationships in multiple datasets, which contain the same samples. The objective of CIA is to seek ordinations (dimension reduction diagrams) of two gene sets such that the square covariance between the projections of the gene sets on successive axes is maximized. Simulation studies illustrate that CIA offers superior performance in identifying corelationships between gene sets in all simulation settings when compared to correlation-based gene set methods.

Result and Conclusion:

We also combine between-gene set CIA and GSEA to discover the relationships between gene sets significantly associated with phenotypes. In addition, we provide a graphical technique for visualizing and simultaneously exploring the associations of between and within gene sets and their interaction and network. We then demonstrate integration of within and between gene sets variation using CIA and GSEA, applied to the p53 gene expression data using the c2 curated gene sets. Ultimately, the GSCoA approach provides an attractive tool for identification and visualization of novel associations between pairs of gene sets by integrating co-relationships between gene sets into gene set analysis.

Sall2 knockdown exacerbates palmitic acid induced dysfunction and apoptosis of pancreatic NIT-1 beta cells

Spalt-like (Sall) proteins are a class of transcription factors. The role of Sall2 in beta cells remain poorly understood. Here, we aimed to explore whether Sall2 involved in lipotoxicity-mediated dysfunction and apoptosis in pancreatic NIT-1 beta cells. Our results showed that high concentrations of palmitic acid (PA) led to impaired cell viability and decreased Sall2 expression in NIT-1 cells. Knocking down of Sall2 in NIT-1 cells resulted in increased sensitivity to lipotoxicity and caused higher rates of cell apoptosis following PA treatment. Additionally, Sall2 Knockdown impaired insulin synthesis and secretion in response to glucose. Further research indicated Sall2 knockdown attenuate antioxidant capacity and decreased expression level of Peroxiredoxin 2 in NIT-1 cells. These finding implicate that Sall2 may play a significant role in NIT-1 cell function and cell apoptosis under lipotoxic conditions. Therefore, the study of Sall2 in NIT-1 cells provided a new perspective for molecular mechanism of lipotoxicity mediating dysfunction and apoptosis of beta cells.

Quantifying mRNA levels across tissue sections with 2D-RT-qPCR

Measurement of mRNA levels across tissue samples facilitates an understanding of how genes function and what their roles are in disease. Quantifying low-abundance mRNA requires a workflow that preserves spatial information, isolates RNA, and performs reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR). This is complex because these steps are typically performed in three separate platforms. In the present study, we describe two-dimensional RT-qPCR (2D-RT-qPCR), a method that quantifies RNA across tissues sections in a single integrated platform. The method uses the grid format of a multi-well plate to macrodissect tissue sections and preserve the spatial location of the RNA; this also eliminates the need for physical homogenization of the tissue. A new lysis and nucleic acid purification protocol is performed in the same multi-well plate, followed by RT-qPCR. The feasibility 2D-RT-qPCR was demonstrated on a variety of tissue types. Potential applications of the technology as a high-throughput tissue analysis platform are discussed.

The Gene Interaction Miner: a new tool for data mining contextual information for protein-protein interaction analysis

MOTIVATION

This work was motivated by the need for an automated tool for discovery of genetic networks and the availability of extensive contextual protein-protein interaction information in the iHOP repository. At the moment, this information cannot be explored to its full potential due to the lack of software tools to reliably collect, process and display that information in a way that life scientists can quickly analyze genes of interest and search for potential interaction networks. Commercial tools can perform a similar job, but results appear to be less informative than those obtained using contextual information.

RESULTS

The Gene Interaction Miner (GIM) could successfully uncover complex network structures of protein-protein interactions for a test dataset composed of genes already related to Alzheimer's disease. That same set, when examined using two other analysis tools, namely STRING and Pathway Studio, resulted in incomplete protein-protein interaction networks, which indicate that the use of curated databases only gives a partial picture of the biological processes behind the disease.

AVAILABILITY

The dataset used in this work and a running version of the software tool is available for download from the web site http://www.cs.newcastle.edu.au/~mendes/softwareGIM.html.

Statistical methods for gene set co-expression analysis

Motivation: The power of a microarray experiment derives from the identification of genes differentially regulated across biological conditions. To date, differential regulation is most often taken to mean differential expression, and a number of useful methods for identifying differentially expressed (DE) genes or gene sets are available. However, such methods are not able to identify many relevant classes of differentially regulated genes. One important example concerns differentially co-expressed (DC) genes.

Results: We propose an approach, gene set co-expression analysis (GSCA), to identify DC gene sets. The GSCA approach provides a false discovery rate controlled list of interesting gene sets, does not require that genes be highly correlated in at least one biological condition and is readily applied to data from individual or multiple experiments, as we demonstrate using data from studies of lung cancer and diabetes.

Availability: The GSCA approach is implemented in R and available at www.biostat.wisc.edu/∼kendzior/GSCA/.

Contact:kendzior@biostat.wisc.edu

Supplementary information:Supplementary data are available at Bioinformatics online.

Analysis of multiplex gene expression maps obtained by voxelation

BACKGROUND

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological disease. Researchers have previously used voxelation in combination with microarrays for acquisition of genome-wide atlases of expression patterns in the mouse brain. On the other hand, some work has been performed on studying gene functions, without taking into account the location information of a gene's expression in a mouse brain. In this paper, we present an approach for identifying the relation between gene expression maps obtained by voxelation and gene functions.

RESULTS

To analyze the dataset, we chose typical genes as queries and aimed at discovering similar gene groups. Gene similarity was determined by using the wavelet features extracted from the left and right hemispheres averaged gene expression maps, and by the Euclidean distance between each pair of feature vectors. We also performed a multiple clustering approach on the gene expression maps, combined with hierarchical clustering. Among each group of similar genes and clusters, the gene function similarity was measured by calculating the average gene function distances in the gene ontology structure. By applying our methodology to find similar genes to certain target genes we were able to improve our understanding of gene expression patterns and gene functions. By applying the clustering analysis method, we obtained significant clusters, which have both very similar gene expression maps and very similar gene functions respectively to their corresponding gene ontologies. The cellular component ontology resulted in prominent clusters expressed in cortex and corpus callosum. The molecular function ontology gave prominent clusters in cortex, corpus callosum and hypothalamus. The biological process ontology resulted in clusters in cortex, hypothalamus and choroid plexus. Clusters from all three ontologies combined were most prominently expressed in cortex and corpus callosum.

CONCLUSION

The experimental results confirm the hypothesis that genes with similar gene expression maps might have similar gene functions. The voxelation data takes into account the location information of gene expression level in mouse brain, which is novel in related research. The proposed approach can potentially be used to predict gene functions and provide helpful suggestions to biologists.

Evidence for induction of the ornithine transcarbamylase expression in Alzheimer's disease

To more rapidly identify candidate genes located within chromosomal regions of interest defined by genome scan studies in Alzheimer's disease (AD), we have developed a customized microarray containing all the ORFs (n=2741) located within nine of these regions. Levels of gene expression were assessed in total RNA from brain tissue of 12 controls and 12 AD patients. Of all genes showing differential expression, we focused on the ornithine transcarbamylase (OTC) gene on Xp21.1., a key enzyme of the urea cycle which we found to be expressed in AD brains but not in controls, as confirmed by RT-PCR. We also detected mRNA expression of all the other urea cycle enzymes in AD brains. Immunochemistry experiments revealed that the OTC expression was strictly restricted to vascular endothelial cells in brain. Furthermore, OTC activity was 880% increased in the CSF of probable AD cases compared with controls. We analysed the association of the OTC -389 G/A and -241 A/G promoter polymorphisms with the risk of developing AD. We observed that rare haplotypes may be associated with the risk of AD through a possible modulation of the methylation of the OTC promoter. In conclusion, our results suggest the involvement of a new pathway in AD brains involving the urea cycle.

Combinatorial optimization models for finding genetic signatures from gene expression datasets

The aim of this chapter is to present combinatorial optimization models and techniques for the analysis of microarray datasets. The chapter illustrates the application of a novel objective function that guides the search for high-quality solutions for sequential ordering of expression profiles. The approach is unsupervised and a metaheuristic method (a memetic algorithm) is used to provide high-quality solutions. For the problem of selecting discriminative groups of genes, we used a supervised method that has provided good results in a variety of datasets. This chapter illustrates the application of these models in an Alzheimer's disease microarray dataset.

A genome-scale map of expression for a mouse brain section obtained using voxelation

Gene expression signatures in the mammalian brain hold the key to understanding neural development and neurological diseases. We have reconstructed two-dimensional images of gene expression for 20,000 genes in a coronal slice of the mouse brain at the level of the striatum by using microarrays in combination with voxelation at a resolution of 1 mm3. Good reliability of the microarray results were confirmed using multiple replicates, subsequent quantitative RT-PCR voxelation, mass spectrometry voxelation, and publicly available in situ hybridization data. Known and novel genes were identified with expression patterns localized to defined substructures within the brain. In addition, genes with unexpected patterns were identified, and cluster analysis identified a set of genes with a gradient of dorsal/ventral expression not restricted to known anatomical boundaries. The genome-scale maps of gene expression obtained using voxelation will be a valuable tool for the neuroscience community.

Spatial mapping of protein abundances in the mouse brain by voxelation integrated with high-throughput liquid chromatography-mass spectrometry

Temporally and spatially resolved mapping of protein abundance patterns within the mammalian brain is of significant interest for understanding brain function and molecular etiologies of neurodegenerative diseases; however, such imaging efforts have been greatly challenged by complexity of the proteome, throughput and sensitivity of applied analytical methodologies, and accurate quantitation of protein abundances across the brain. Here, we describe a methodology for comprehensive spatial proteome mapping that addresses these challenges by employing voxelation integrated with automated microscale sample processing, high-throughput liquid chromatography (LC) system coupled with high-resolution Fourier transform ion cyclotron resonance (FTICR) mass spectrometer, and a "universal" stable isotope labeled reference sample approach for robust quantitation. We applied this methodology as a proof-of-concept trial for the analysis of protein distribution within a single coronal slice of a C57BL/6J mouse brain. For relative quantitation of the protein abundances across the slice, an 18O-isotopically labeled reference sample, derived from a whole control coronal slice from another mouse, was spiked into each voxel sample, and stable isotopic intensity ratios were used to obtain measures of relative protein abundances. In total, we generated maps of protein abundance patterns for 1028 proteins. The significant agreement of the protein distributions with previously reported data supports the validity of this methodology, which opens new opportunities for studying the spatial brain proteome and its dynamics during the course of disease progression and other important biological and associated health aspects in a discovery-driven fashion.

Microarrays and the microscope: balancing throughput with resolution

The cellular complexity of the brain is a major issue in the planning, execution and interpretation of microarray studies. Recent technical advances allow for high-throughput study of specific cell populations and circuits. Here we review representative examples of currently available methods that allow high resolution and specificity in brain microarray studies, while maintaining the goal of comprehensive, high-throughput analysis.

Technology for high-throughput screens: the present and future using zebrafish

The zebrafish is a popular vertebrate model organism with similar organ systems and gene sequences to humans. Zebrafish embryos are optically transparent enabling organ visualisation, which can be complemented with gene expression analysis at the transcript and protein levels. Furthermore, zebrafish can be treated with small molecules and drugs in a microtitre plate format for high-throughput analysis and for the identification and validation of drugs. High-throughput methodologies for use in zebrafish include phenotype-based visualisation, transcript studies using low-density DNA microarrays and proteomic analysis. These technologies offer significant whole-organism biological value in the drug discovery and drug development pipeline.

Investigation of different transcript quantitation tools for high-throughput mapping of brain gene expression using voxelation

Voxelation is a new approach for genome scale acquisition of brain gene expression patterns. The method employs high-throughput analysis of spatially registered voxels (cubes) to create multiple volumetric images of brain gene expression, similar to those obtained from biomedical imaging systems. The spatial resolution of voxelation depends on voxel size, with smaller voxels giving higher resolution. An important question is the applicability of different transcript profiling tools for the various levels of resolution that can be employed. Here, we describe the use of three methods to analyze voxel transcript abundance: real-time PCR, microarray analysis and linear amplification coupled with microarrays. We show statistically significant concordance between real-time PCR and microarray analysis for the myelin basic protein gene in human brain specimens at differing levels of spatial resolution. In addition, we also demonstrate the feasibility of using linear amplification coupled with microarray analysis to create voxelation maps from the mouse brain at high resolution, 1 microl. These data indicate the suitability of a number of transcript profiling tools for various levels of spatial resolution in voxelation.

Anatomical methods for voxelation of the mammalian brain

Voxelation allows high-throughput acquisition of three-dimensional gene expression patterns in the brain through analysis of spatially registered voxels (cubes). The method results in multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging techniques. An important issue for voxelation is the development of approaches to anchor correctly harvested voxels to the underlying anatomy. Here, we describe experiments to identify fixation and cryopreservation protocols for improved registration of harvested voxels with neuroanatomical structures. Paraformaldehyde fixation greatly reduced RNA recovery as judged by ribosomal RNA abundance. However, gene expression signals from paraformaldehyde-fixed samples were not appreciably diminished as judged by average signal-noise ratios from microarrays, highlighting the difficulties of accurate quantitation of cross-linked RNA. Additional use of cryoprotection helped to improve further RNA recovery and signal from fixed tissue. It appears that the best protocol to provide the necessary resolution of neuroanatomical information in voxelation entails a controlled dose of fixation and thorough cryoprotection, complemented by histological staining.

Neural system-enriched gene expression: relationship to biological pathways and neurological diseases

To understand the commitment of the genome to nervous system differentiation and function, we sought to compare nervous system gene expression to that of a wide variety of other tissues by gene expression database construction and mining. Gene expression profiles of 10 different adult nervous tissues were compared with that of 72 other tissues. Using ANOVA, we identified 1,361 genes whose expression was higher in the nervous system than other organs and, separately, 600 genes whose expression was at least threefold higher in one or more regions of the nervous system compared with their median expression across all organs. Of the 600 genes, 381 overlapped with the 1,361-gene list. Limited in situ gene expression analysis confirmed that identified genes did represent nervous system-enriched gene expression, and we therefore sought to evaluate the validity and significance of these top-ranked nervous system genes using known gene literature and gene ontology categorization criteria. Diverse functional categories were present in the 381 genes, including genes involved in intracellular signaling, cytoskeleton structure and function, enzymes, RNA metabolism and transcription, membrane proteins, as well as cell differentiation, death, proliferation, and division. We searched existing public sites and identified 110 known genes related to mental retardation, neurological disease, and neurodegeneration. Twenty-one of the 381 genes were within the 110-gene list, compared with a random expectation of 5. This suggests that the 381 genes provide a candidate set for further analyses in neurological and psychiatric disease studies and that as a field, we are as yet, far from a large-scale understanding of the genes that are critical for nervous system structure and function. Together, our data indicate the power of profiling an individual biologic system in a multisystem context to gain insight into the genomic basis of its structure and function.

Data mining of gene expression changes in Alzheimer brain

Genome-wide transcription profiling is a powerful technique for studying the enormous complexity of cellular states. Moreover, when applied to disease tissue it may reveal quantitative and qualitative alterations in gene expression that give information on the context or underlying basis for the disease and may provide a new diagnostic approach. However, the data obtained from high-density microarrays is highly complex and poses considerable challenges in data mining. The data requires care in both pre-processing and the application of data mining techniques. This paper addresses the problem of dealing with microarray data that come from two known classes (Alzheimer and normal). We have applied three separate techniques to discover genes associated with Alzheimer disease (AD). The 67 genes identified in this study included a total of 17 genes that are already known to be associated with Alzheimer's or other neurological diseases. This is higher than any of the previously published Alzheimer's studies. Twenty known genes, not previously associated with the disease, have been identified as well as 30 uncharacterized expressed sequence tags (ESTs). Given the success in identifying genes already associated with AD, we can have some confidence in the involvement of the latter genes and ESTs. From these studies we can attempt to define therapeutic strategies that would prevent the loss of specific components of neuronal function in susceptible patients or be in a position to stimulate the replacement of lost cellular function in damaged neurons. Although our study is based on a relatively small number of patients (four AD and five normal), we think our approach sets the stage for a major step in using gene expression data for disease modeling (i.e. classification and diagnosis). It can also contribute to the future of gene function identification, pathology, toxicogenomics, and pharmacogenomics.

Microarray analysis of gene expression on herbal glycoside recipes improving deficient ability of spatial learning memory in ischemic mice

In order to reveal the mechanism of herbal glycoside recipes retrieving deficient ability of spatial learning memory in mice suffering from cerebral ischemia/reperfusion, a microarray system was used to analyze gene expression in those groups with increasing ability of spatial learning memory who were different from ischemic mice. In this work, we reported a comprehensive characterization of gene expression profiles of mouse hippocampus by the use of cDNA microarray system containing 1176 known genes in middle cerebral artery occlusion (MCAO) ischemic mice after treating with different dosage recipes of glycoside herbs (30, 90, and 270 mg/kg). The ability of spatial learning memory in ischemic mice was found to be decreased. The pathological process in ischemic mouse brain showed that a complex related to 100 genes' expression yielded 1.8-fold. Dose-dependent effects showed an improvement in the deficient ability and reduction in infarct volume when treated with glycoside recipes. Many genes (38-46) in expression were found greater than 1.8-fold in those effective recipes groups, including genes in cell cycle regulation, signal transduction, nerve system transcription factors, DNA binding protein, etc. Nine genes related to retrieving deficient ability of spatial learning memory treated with glycoside recipes were also found in this study. These results suggest that microarray analysis of gene expression might be useful for elucidating the mechanisms of pharmacological function of recipes.

Voxelation and gene expression tomography for the acquisition of 3-D gene expression maps in the brain

Voxelation and gene expression tomography or GET are novel methods for the high-throughput acquisition of gene expression patterns in the mammalian brain. Voxelation employs analysis of spatially registered voxels (cubes), while GET employs analysis of sets of parallel slices rotated about multiple independent axes of rotation. Both methods employ reconstruction of the data to result in multiple volumetric maps of gene expression analogous to those obtained from biomedical imaging techniques. Here, we describe the methodologies underlying voxelation and GET and briefly outline the insights that can be obtained from these approaches.

The application of functional genomics to Alzheimer's disease

Alzheimer's disease (AD) is a polygenic/complex disorder in which more than 50 genetic loci are involved. Primary and secondary loci are potentially responsible for the phenotypic expression of the disease under the influence of both environmental factors and epigenetic phenomena. The construction of haplotypes as genomic clusters integrating the different genotype combinations of AD-related genes is a suitable strategy to investigate functional genomics in AD. It appears that AD patients show about 3-5 times higher genetic variation than the control population. The analysis of genotype-phenotype correlations has revealed that the presence of the APOE-4 allele in AD, in conjunction with other loci distributed across the genome, influence disease onset, brain atrophy, cerebrovascular perfusion, blood pressure, beta-amyloid deposition, ApoE secretion, lipid metabolism, brain bioelectrical activity, cognition, apoptosis and treatment outcome. Pharmacogenomics studies also indicate that the therapeutic response in AD is genotype-specific and that approximately 15% of the cases with efficacy and/or safety problems are associated with a defective CYP2D6 gene. Consequently, the understanding of functional genomics in AD will foster productive pharmacogenomic studies in the search for effective medications and preventive strategies in AD.

Quantitative assessment of transcriptome differences between brain territories

Transcriptome analysis of mammalian brain structures is a potentially powerful approach in addressing the diversity of cerebral functions. Here, we used a microassay for serial analysis of gene expression (SAGE) to generate quantitative mRNA expression profiles of normal adult mouse striatum, nucleus accumbens, and somatosensory cortex. Comparison of these profiles revealed 135 transcripts heterogeneously distributed in the brain. Among them, a majority (78), although matching a registered sequence, are novel regional markers. To improve the anatomical resolution of our analysis, we performed in situ hybridization and observed unique expression patterns in discrete brain regions for a number of candidates. We assessed the distribution of the new markers in peripheral tissues using quantitative RT-PCR, Northern hybridization, and published SAGE data. In most cases, expression was higher in the brain than in peripheral tissues. Because the markers were selected according to their expression level, without reference to prior knowledge, our studies provide an unbiased, comprehensive molecular signature for various mammalian brain structures that can be used to investigate their plasticity under a variety of circumstances.

Genome scale mapping of brain gene expression

Two new approaches, voxelation and gene expression tomography (GET), permit multiplex acquisition of gene expression patterns in the brain. Both methods result in volumetric images of gene expression analogous to those produced in biomedical imaging systems. Voxelation employs analysis of spatially registered cubes from the brain, whereas GET entails analysis of parallel slices obtained by rotation about multiple axes. These methods have been used to investigate neurologic diseases and their models in both humans and mice. The results of these studies are discussed, as is the future of high-throughput gene expression mapping in the brain.

High-resolution voxelation mapping of human and rodent brain gene expression

Voxelation allows high-throughput acquisition of multiple volumetric images of brain gene expression, similar to those obtained from biomedical imaging systems. To obtain these images, the method employs analysis of spatially registered voxels (cubes). For creation of high-resolution maps using voxelation, relatively small voxel sizes are necessary and instruments will be required for semiautomated harvesting of such voxels. Here, we describe two devices that allow spatially registered harvesting of voxels from the human and rodent brain, giving linear resolutions of 3.3 and 1 mm, respectively. Gene expression patterns obtained using these devices showed good agreement with known expression patterns. The voxelation instruments and their future iterations represent a valuable approach to the genome scale acquisition of gene expression patterns in the human and rodent brain.

DNA microarrays: translation of the genome from laboratory to clinic

As the complete sequences of human and other mammalian genomes become available we are faced with the challenge of understanding how variation in sequence and gene expression contributes to neurological and psychiatric disorders. DNA microarrays, or DNA chips, provide the means to measure simultaneously where and when thousands of genes are expressed. Microarrays are changing the way that researchers approach work at the bench and have already yielded new insights into brain tumours, multiple sclerosis, acute neurological insults such as stroke and seizures, and schizophrenia. The study of disease-related changes in gene expression is the first step in the long process in translation of genome research to the clinic. Eventually, the changes observed in microarray studies will need to be independently confirmed and we wil need to understand how gene expression changes translate into functional effects at the cellular level in the nervous system. Progress in these studies will translate into array-based disease classification schemes and help optimise therapy for individual patients based on gene expression patterns or their genetic background.

Genes regulated by learning in the hippocampus

The enduring changes in long-term memory probably depend on regulation of gene expression in the hippocampus. To seek genes regulated by learning, we used microarray technology to compare hippocampal gene expression in mice undergoing training in the Morris water maze and control mice forced to swim for the same period in the absence of a hidden platform. ANOVA was employed to prioritize genes for further study, and three genes were confirmed by real-time PCR as being regulated during learning. One of the genes was the alpha subunit of the platelet-derived growth factor receptor (Pdgfra); another showed homology to DnaJ and cAMP response element-binding protein 2 (CREB2); and a third was novel. These genes may provide useful insights into the molecular mechanisms of hippocampal learning.

Functional genomics of neural and behavioral plasticity

How does the environment, particularly the social environment, influence brain and behavior and what are the underlying physiologic, molecular, and genetic mechanisms? Adaptations of brain and behavior to changes in the social or physical environment are common in the animal world, either as short-term (i.e., modulatory) or as long-term modifications (e.g., via gene expression changes) in behavioral or physiologic properties. The study of the mechanisms and constraints underlying these dynamic changes requires model systems that offer plastic phenotypes as well as a sufficient level of quantifiable behavioral complexity while being accessible at the physiological and molecular level. In this article, I explore how the new field of functional genomics can contribute to an understanding of the complex relationship between genome and environment that results in highly plastic phenotypes. This approach will lead to the discovery of genes under environmental control and provide the basis for the study of the interrelationship between an individual's gene expression profile and its social phenotype in a given environmental context.

Statistical analysis of multiplex brain gene expression images

Analysis of variance (ANOVA) was employed to investigate 9,000 gene expression patterns from brains of both normal mice and mice with a pharmacological model of Parkinson's disease (PD). The data set was obtained using voxelation, a method that allows high-throughput acquisition of 3D gene expression patterns through analysis of spatially registered voxels (cubes). This method produces multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging systems. The ANOVA model was compared to the results from singular value decomposition (SVD) by using the first 42 singular vectors of the data matrix, a number equal to the rank of the ANOVA model. The ANOVA was also compared to the results from non-parametric statistics. Lastly, images were obtained for a subset of genes that emerged from the ANOVA as significant. The results suggest that ANOVA will be a valuable framework for insights into the large number of gene expression patterns obtained from voxelation.

Expression profiling with oligonucleotide arrays: technologies and applications for neurobiology

DNA microarrays have been used in applications ranging from the assignment of gene function to analytical uses in prognostics. However, the detection sensitivity, cross hybridization, and reproducibility of these arrays can affect experimental design and data interpretation. Moreover, several technologies are available for fabrication of oligonucleotide microarrays. We review these technologies and performance attributes and, with data sets generated from human brain RNA, present statistical tools and methods to analyze data quality and to mine and visualize the data. Our data show high reproducibility and should allow an investigator to discern biological and regional variability from differential expression. Although we have used brain RNA as a model system to illustrate some of these points, the oligonucleotide arrays and methods employed in this study can be used with cell lines, tissue sections, blood, and other fluids. To further demonstrate this point, we provide data generated from total RNA sample sizes of 200 ng.

Synexpression analysis of ESTs in the rat brain reveals distinct patterns and potential drug targets

The gene expression profiles of 146 novel ESTs were characterized in newborn and adult rat brains via radioactive in situ hybridization. Using Euclidean metrics and hierarchical clustering tools the brain expression profiles obtained clustered into seven synexpression groups. The groups were: I, non-detectable expression (68 ESTs); II, low expression in hippocampus (40 ESTs); III, low expression in adult, high expression in newborn (two ESTs); IV, medium expression throughout brain (31 ESTs); V, high expression throughout brain (three ESTs); VI, selective high expression in hippocampus, caudate and putamen (one EST); VII, selective high expression in hippocampus (one EST). Five ESTs were expressed in the striatum and three responded transcriptionally to neuroleptic and neuroprotective drug treatments, suggesting that this approach could be used to detect novel drug targets. These results provide a useful starting point to explore the functional genomics of genes without known functions forthcoming from various genome projects.

Multiplex three-dimensional brain gene expression mapping in a mouse model of Parkinson's disease

To facilitate high-throughput 3D imaging of brain gene expression, a new method called voxelation has been developed. Spatially registered voxels (cubes) are analyzed, resulting in multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging systems. Using microarrays, 40 voxel images for 9000 genes were acquired from brains of both normal mice and mice in which a pharmacological model of Parkinson's disease (PD) had been induced by methamphetamine. Quality-control analyses established the reproducibility of the voxelation procedure. The investigation revealed a common network of coregulated genes shared between the normal and PD brain, and allowed identification of putative control regions responsible for these networks. In addition, genes involved in cell/cell interactions were found to be prominently regulated in the PD brains. Finally, singular value decomposition (SVD), a mathematical method used to provide parsimonious explanations of complex data sets, identified gene vectors and their corresponding images that distinguished between normal and PD brain structures, most pertinently the striatum.