Multiplex Three-Dimensional Brain Gene Expression Mapping in a Mouse Model of Parkinson's Disease

Brown, Vanessa M.; Ossadtchi, Alexei; Ah, Khan; Yee, Simon; Laćan, Goran; Melega, William P.; Cherry, Simon R.; Leahy, Richard M.; Smith, Desmond J.

doi:10.1101/gr.229002

Cited by 51 publications

(34 citation statements)

References 45 publications

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“…Permutation testing was used to assess the similarity of correlation matrices (6,8). To obtain the null distribution, we randomly permuted the columns of the matrices and determined the Frobenius norm by subtracting one permuted matrix from the other.…”

Section: Methodsmentioning

confidence: 99%

“…The voxelation images were mapped to a 1-mm coronal slice of the mouse brain (0.02 mm bregma, 3.82 mm interaural) at the level of the striatum (10). Smoothed gene expression images were constructed using MATLAB as described previously (5)(6)(7)(8)22). The pixelated expression images (Fig.…”

Section: Methodsmentioning

confidence: 99%

“…To complement ISH and transgenic methods, we developed an approach combining voxelation with microarrays to create genome-wide atlases of expression patterns in the mouse brain (6,8,22). Voxelation involves dicing the brain into spatially registered voxels (cubes).…”

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confidence: 99%

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

Chin

Geng

et al. 2007

Physiological Genomics

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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

Chin

Geng

et al. 2007

Physiological Genomics

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“…71 The samples were lysed in 80 μL of 5 mM PBS with 80 μL TFE with intermittent sonication in an ice-water bath. The lysate was reduced with 5 mM TBP and digested by trypsin overnight, and the digests were lyophilized immediately after digestion without further cleanup.…”

Section: Preparation Of Mouse Brain Tissue and Voxel Samplesmentioning

confidence: 99%

Improved Peptide Elution Time Prediction for Reversed-Phase Liquid Chromatography-MS by Incorporating Peptide Sequence Information

et al. 2006

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We describe an improved artificial neural network (ANN)-based method for predicting peptide retention times in reversed phase liquid chromatography. In addition to the peptide amino acid composition, this study investigated several other peptide descriptors to improve the predictive capability, such as peptide length, sequence, hydrophobicity and hydrophobic moment, and nearest neighbor amino acid, as well as peptide predicted structural configurations (i.e., helix, sheet, coil). An ANN architecture that consisted of 1052 input nodes, 24 hidden nodes, and 1 output node was used to fully consider the amino acid residue sequence in each peptide. The network was trained using ~345,000 non-redundant peptides identified from a total of 12,059 LC-MS/MS analyses of more than 20 different organisms, and the predictive capability of the model was tested using 1303 confidently identified peptides that were not included in the training set. The model demonstrated an average elution time precision of ~1.5% and was able to distinguish among isomeric peptides based upon the inclusion of peptide sequence information. The prediction power represents a significant improvement over our earlier report (Petritis et al., Anal. Chem. 2003, 75, 1039-1048 and other previously reported models.

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“…Each of the 40 voxels was analyzed using a 9000-gene microarray. This experiment results in multiple volumetric maps of gene expression in the brain [2]. Microarray data are available at GEO, http://www.ncbi.nlm.nih.gov/geo, under the series accession no.…”

Section: A Microarray Datamentioning

confidence: 99%

A Correlation Analysis on Multiplex Three-Dimensional Brain Gene Expression Mapping of Parkinson Disease

Liu

2007

2007 IEEE/ICME International Conference on Complex Medical Engineering

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This paper is aimed to predict Parkinson disease (PD) genes from multiplex Three-Dimensional brain gene expression mapping. First, we select the available data from the original datasets and use log ratio as the object that we would analysis. Second, correlation coefficient calculated provides information on the correlation between genes based on gene expression levels. Later, classification and hierarchical cluster analysis can help us to find the relevant genes of Parkinson disease. The 27 genes selected by the statistical method are worthy of further experiments to assess the results of microarray.

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Multiplex Three-Dimensional Brain Gene Expression Mapping in a Mouse Model of Parkinson's Disease

Cited by 51 publications

References 45 publications

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

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

Improved Peptide Elution Time Prediction for Reversed-Phase Liquid Chromatography-MS by Incorporating Peptide Sequence Information

A Correlation Analysis on Multiplex Three-Dimensional Brain Gene Expression Mapping of Parkinson Disease

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