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Study of gene function based on spatial co-expression in a high-resolution mouse brain atlas.

Liu Z, Yan SF, Walker JR, Zwingman TA, Jiang T, Li J, Zhou Y - BMC Syst Biol (2007)

Bottom Line: Given a query brain image, HRC is a fully automated algorithm that is able to quickly mine vast number of brain images and identify a manageable subset of genes that potentially shares similar spatial co-distribution patterns for further visual inspection.A three-dimensional in situ hybridization pattern, if statistically significant, could serve as a fingerprint of certain gene function.Databases such as ABA provide valuable data source for characterizing brain-related gene functions when armed with powerful image querying tools like HRC.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of California, Riverside, 900 University Avenue, Riverside, CA 92521, USA. zliu@cs.ucr.edu

ABSTRACT

Background: The Allen Brain Atlas (ABA) project systematically profiles three-dimensional high-resolution gene expression in postnatal mouse brains for thousands of genes. By unveiling gene behaviors at both the cellular and molecular levels, ABA is becoming a unique and comprehensive neuroscience data source for decoding enigmatic biological processes in the brain. Given the unprecedented volume and complexity of the in situ hybridization image data, data mining in this area is extremely challenging. Currently, the ABA database mainly serves as an online reference for visual inspection of individual genes; the underlying rich information of this large data set is yet to be explored by novel computational tools. In this proof-of-concept study, we studied the hypothesis that genes sharing similar three-dimensional expression profiles in the mouse brain are likely to share similar biological functions.

Results: In order to address the pattern comparison challenge when analyzing the ABA database, we developed a robust image filtering method, dubbed histogram-row-column (HRC) algorithm. We demonstrated how the HRC algorithm offers the sensitivity of identifying a manageable number of gene pairs based on automatic pattern searching from an original large brain image collection. This tool enables us to quickly identify genes of similar in situ hybridization patterns in a semi-automatic fashion and consequently allows us to discover several gene expression patterns with expression neighborhoods containing genes of similar functional categories.

Conclusion: Given a query brain image, HRC is a fully automated algorithm that is able to quickly mine vast number of brain images and identify a manageable subset of genes that potentially shares similar spatial co-distribution patterns for further visual inspection. A three-dimensional in situ hybridization pattern, if statistically significant, could serve as a fingerprint of certain gene function. Databases such as ABA provide valuable data source for characterizing brain-related gene functions when armed with powerful image querying tools like HRC.

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Gene expression of Slc6a3, Slc18a2, and Ddc. All three genes are enriched in the brain substantia nigra region as indicated by the arrow. Brain images were obtained from the ABA database and the tissue gene expression data were obtained from GNF SymAtlas.
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Figure 5: Gene expression of Slc6a3, Slc18a2, and Ddc. All three genes are enriched in the brain substantia nigra region as indicated by the arrow. Brain images were obtained from the ABA database and the tissue gene expression data were obtained from GNF SymAtlas.

Mentions: The dopamine transporter (DAT), encoded by gene Slc6a3 [solute carrier family 6 (neurotransmitter transporter, dopamine), member 3], plays a critical role in the nigrostriatal dopaminergic pathway that is involved in the pathological development of Parkinson's disease [29,30]. The ABA images show that Slc6a3 expression is highly enriched in the substantia nigra (Fig. 5), in accordance with various previous studies [2,14,31]. We then applied Slc6a3 (slide position 2050) as the query pattern to search the dataset using the HRC algorithm. In the top 50 genes excluding Slc6a3, it contains Lix1, Ptpru (also known as Ptprl), Lmx1b, Aldh1a1, Slc18a2, and Ddc. This finding is consistent with a previous study that also employed mouse brain gene expression images [14]. In addition, three genes, namely Aldh1a1, Ddc, and Slc18a2, are found to be functionally annotated as "neurological disorder" by IPA with a significance value of 10-4. It is known that Ddc, Slc18a2, and Slc6a3 encode three major players in the dopaminergic nigrostriatal pathway, namely aromatic amino acid decarboxylase (AADC), vesicular monoamine transporter 2 (VMAT2), and dopamine transporter, respectively, and have been proposed to serve as biomarkers in the clinical evaluation of Parkinson's disease [29]. Also, the expression levels of these genes were found to decrease in animal models of Parkinson's disease [31].


Study of gene function based on spatial co-expression in a high-resolution mouse brain atlas.

Liu Z, Yan SF, Walker JR, Zwingman TA, Jiang T, Li J, Zhou Y - BMC Syst Biol (2007)

Gene expression of Slc6a3, Slc18a2, and Ddc. All three genes are enriched in the brain substantia nigra region as indicated by the arrow. Brain images were obtained from the ABA database and the tissue gene expression data were obtained from GNF SymAtlas.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1863433&req=5

Figure 5: Gene expression of Slc6a3, Slc18a2, and Ddc. All three genes are enriched in the brain substantia nigra region as indicated by the arrow. Brain images were obtained from the ABA database and the tissue gene expression data were obtained from GNF SymAtlas.
Mentions: The dopamine transporter (DAT), encoded by gene Slc6a3 [solute carrier family 6 (neurotransmitter transporter, dopamine), member 3], plays a critical role in the nigrostriatal dopaminergic pathway that is involved in the pathological development of Parkinson's disease [29,30]. The ABA images show that Slc6a3 expression is highly enriched in the substantia nigra (Fig. 5), in accordance with various previous studies [2,14,31]. We then applied Slc6a3 (slide position 2050) as the query pattern to search the dataset using the HRC algorithm. In the top 50 genes excluding Slc6a3, it contains Lix1, Ptpru (also known as Ptprl), Lmx1b, Aldh1a1, Slc18a2, and Ddc. This finding is consistent with a previous study that also employed mouse brain gene expression images [14]. In addition, three genes, namely Aldh1a1, Ddc, and Slc18a2, are found to be functionally annotated as "neurological disorder" by IPA with a significance value of 10-4. It is known that Ddc, Slc18a2, and Slc6a3 encode three major players in the dopaminergic nigrostriatal pathway, namely aromatic amino acid decarboxylase (AADC), vesicular monoamine transporter 2 (VMAT2), and dopamine transporter, respectively, and have been proposed to serve as biomarkers in the clinical evaluation of Parkinson's disease [29]. Also, the expression levels of these genes were found to decrease in animal models of Parkinson's disease [31].

Bottom Line: Given a query brain image, HRC is a fully automated algorithm that is able to quickly mine vast number of brain images and identify a manageable subset of genes that potentially shares similar spatial co-distribution patterns for further visual inspection.A three-dimensional in situ hybridization pattern, if statistically significant, could serve as a fingerprint of certain gene function.Databases such as ABA provide valuable data source for characterizing brain-related gene functions when armed with powerful image querying tools like HRC.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Computer Science, University of California, Riverside, 900 University Avenue, Riverside, CA 92521, USA. zliu@cs.ucr.edu

ABSTRACT

Background: The Allen Brain Atlas (ABA) project systematically profiles three-dimensional high-resolution gene expression in postnatal mouse brains for thousands of genes. By unveiling gene behaviors at both the cellular and molecular levels, ABA is becoming a unique and comprehensive neuroscience data source for decoding enigmatic biological processes in the brain. Given the unprecedented volume and complexity of the in situ hybridization image data, data mining in this area is extremely challenging. Currently, the ABA database mainly serves as an online reference for visual inspection of individual genes; the underlying rich information of this large data set is yet to be explored by novel computational tools. In this proof-of-concept study, we studied the hypothesis that genes sharing similar three-dimensional expression profiles in the mouse brain are likely to share similar biological functions.

Results: In order to address the pattern comparison challenge when analyzing the ABA database, we developed a robust image filtering method, dubbed histogram-row-column (HRC) algorithm. We demonstrated how the HRC algorithm offers the sensitivity of identifying a manageable number of gene pairs based on automatic pattern searching from an original large brain image collection. This tool enables us to quickly identify genes of similar in situ hybridization patterns in a semi-automatic fashion and consequently allows us to discover several gene expression patterns with expression neighborhoods containing genes of similar functional categories.

Conclusion: Given a query brain image, HRC is a fully automated algorithm that is able to quickly mine vast number of brain images and identify a manageable subset of genes that potentially shares similar spatial co-distribution patterns for further visual inspection. A three-dimensional in situ hybridization pattern, if statistically significant, could serve as a fingerprint of certain gene function. Databases such as ABA provide valuable data source for characterizing brain-related gene functions when armed with powerful image querying tools like HRC.

Show MeSH