Biomedical subjects

Alex Ossadtchi

Publications and source records attributed to Alex Ossadtchi.

8 recordsLinked to original sources

PubMed2003

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.

Animals↗

PubMed2003

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.

Activating Transcription Factor 4↗

PubMed2003

Error-correcting microarray design.

We describe a microarray design based on the concept of error-correcting codes from digital communication theory. Currently, microarrays are unable to efficiently deal with "drop-outs," when one or more spots on the array are corrupted. The resulting information loss may lead to decoding errors in which no quantitation of expression can be extracted for the corresponding genes. This issue is expected to become increasingly problematic as the number of spots on microarrays expands to accommodate the entire genome. The error-correcting approach employs multiplexing (encoding) of more than one gene onto each spot to efficiently provide robustness to drop-outs in the array. Decoding then allows fault-tolerant recovery of the expression information from individual genes. The error-correcting method is general and may have important implications for future array designs in research and diagnostics.

Data Interpretation, Statistical↗

PubMed2002

Finding new candidate genes for learning and memory.

The genetic mechanisms underlying learning and memory remain mysterious, but many of the genes are likely to be expressed in the hippocampus, a region pivotal to this process. We used a 9,000 gene microarray to examine differences in hippocampal gene expression between two F1 hybrid mouse strains that perform well on the Morris water maze and two inbred strains that perform poorly. This resulted in identification of 27 differentially expressed genes, which could be used to place the F1 hybrid and inbred strains into separate clusters based on singular value decomposition. Most of the genes have unknown function, but those with known functions may provide clues to the molecular mechanisms of learning. Using multiple strains to narrow down the number of candidate genes should be a useful general approach to genome-wide studies of behavioral and other complex traits.

Animals↗

PubMed2002

Gene expression tomography.

Gene expression tomography, or GET, is a new method to increase the speed of three-dimensional (3-D) gene expression analysis in the brain. The name is evocative of the method's dual foundations in high-throughput gene expression analysis and computerized tomographic image reconstruction, familiar from techniques such as positron emission tomography (PET) and X-ray computerized tomography (CT). In GET, brain slices are taken using a cryostat in conjunction with axial rotation about independent axes to create a series of "views" of the brain. Gene expression information obtained from the axially rotated views can then be used to recreate 3-D gene expression patterns. GET was used to successfully reconstruct images of tyrosine hydroxylase gene expression in the mouse brain, using both RNase protection and real-time quantitative reverse transcription PCR (QRT-PCR). A Monte-Carlo analysis confirmed the good quality of the GET image reconstruction. By speeding acquisition of gene expression patterns, GET may help improve our understanding of the genomics of the brain in both health and disease.

Animals↗

PubMed2002

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.

Analysis of Variance↗

PubMed2002

High-throughput imaging of brain gene expression.

Voxelation is a new method for acquisition of three dimensional (3D) gene expression patterns in the brain. It employs high-throughput analysis of spatially registered voxels (cubes) to produce multiple volumetric maps of gene expression analogous to the images reconstructed in biomedical imaging systems. Using microarrays, 24 voxel images of coronal hemisections at the level of the hippocampus of both the normal human brain and Alzheimer's disease brain were acquired for 2000 genes. The analysis revealed a common network of coregulated genes, and allowed identification of putative control regions. In addition, singular value decomposition (SVD), a mathematical method used to provide economical explanations of complex data sets, produced images that distinguished between brain structures, including cortex, caudate, and hippocampus. The results suggest that voxelation will be a useful approach for understanding how the genome constructs the brain.

Adult↗

PubMed2002

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.

Animals↗