Neuroinformatics for Genome-Wide 3-D Gene Expression Mapping in the Mouse Brain

University of Washington Seattle, Seattle, Washington, United States
IEEE/ACM Transactions on Computational Biology and Bioinformatics (Impact Factor: 1.44). 08/2007; 4(3):382-93. DOI: 10.1109/tcbb.2007.1035
Source: PubMed


Large scale gene expression studies in the mammalian brain offer the promise of understanding the topology, networks and ultimately the function of its complex anatomy, opening previously unexplored avenues in neuroscience. High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions that control behavior. Previous gene expression mapping studies in model organisms have employed situ hybridization (ISH), a technique that uses labeled nucleic acid probes to bind to specific mRNA transcripts in tissue sections. A key requirement for this effort is the development of fast and robust algorithms for anatomically mapping and quantifying gene expression for ISH. We describe a neuroinformatics pipeline for automatically mapping expression profiles of ISH data and its use to produce the first genomic scale 3-D mapping of gene expression in a mammalian brain. The pipeline is fully automated and adaptable to other organisms and tissues. Our automated study of over 20,000 genes indicates that at least 78.8 percent are expressed at some level in the adult C56BL/6J mouse brain. In addition to providing a platform for genomic scale search, high-resolution images and visualization tools for expression analysis are available at the Allen Brain Atlas web site (

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    • "The IDP has also been described in detail previously (Ng et al., 2007), and was adopted for use in the Transgenic Characterization pipeline. Briefly, each ISH image series undergoes pre-processing (e.g., white-balancing, cropping, and quality control assessment) and then registration to the 3D Allen Reference Atlas (ARA, Dong, 2008) which contains ∼800 annotated structures. "
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    ABSTRACT: Significant advances in circuit-level analyses of the brain require tools that allow for labeling, modulation of gene expression, and monitoring and manipulation of cellular activity in specific cell types and/or anatomical regions. Large-scale projects and individual laboratories have produced hundreds of gene-specific promoter-driven Cre mouse lines invaluable for enabling genetic access to subpopulations of cells in the brain. However, the potential utility of each line may not be fully realized without systematic whole brain characterization of transgene expression patterns. We established a high-throughput in situ hybridization (ISH), imaging and data processing pipeline to describe whole brain gene expression patterns in Cre driver mice. Currently, anatomical data from over 100 Cre driver lines are publicly available via the Allen Institute's Transgenic Characterization database, which can be used to assist researchers in choosing the appropriate Cre drivers for functional, molecular, or connectional studies of different regions and/or cell types in the brain.
    Frontiers in Neural Circuits 07/2014; DOI:10.3389/fncir.2014.00076 · 3.60 Impact Factor
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    • "These image slices are subsequently processed by an informatics data processing pipeline to generate grid-level voxel data in the Allen Reference Atlas space [18]. The output of the pipeline is quantified expression values at a grid voxel level [19,20]. The voxel-level data have been used to identify cell-type-specific genes based on correlation search [13]. "
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    ABSTRACT: Background Differential gene expression patterns in cells of the mammalian brain result in the morphological, connectional, and functional diversity of cells. A wide variety of studies have shown that certain genes are expressed only in specific cell-types. Analysis of cell-type-specific gene expression patterns can provide insights into the relationship between genes, connectivity, brain regions, and cell-types. However, automated methods for identifying cell-type-specific genes are lacking to date. Results Here, we describe a set of computational methods for identifying cell-type-specific genes in the mouse brain by automated image computing of in situ hybridization (ISH) expression patterns. We applied invariant image feature descriptors to capture local gene expression information from cellular-resolution ISH images. We then built image-level representations by applying vector quantization on the image descriptors. We employed regularized learning methods for classifying genes specifically expressed in different brain cell-types. These methods can also rank image features based on their discriminative power. We used a data set of 2,872 genes from the Allen Brain Atlas in the experiments. Results showed that our methods are predictive of cell-type-specificity of genes. Our classifiers achieved AUC values of approximately 87% when the enrichment level is set to 20. In addition, we showed that the highly-ranked image features captured the relationship between cell-types. Conclusions Overall, our results showed that automated image computing methods could potentially be used to identify cell-type-specific genes in the mouse brain.
    BMC Bioinformatics 06/2014; 15(1):209. DOI:10.1186/1471-2105-15-209 · 2.58 Impact Factor
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    • "This map is comprised of high-resolution images from ISH data spanning across approximately 20,000 genes, generated by using non-radioactive, digoxigenin-labeled anti-sense riboprobes (Lein et al., 2007; Jones et al., 2009; Sunkin et al., 2013). The Allen Reference Atlas (Dong, 2008) made registration and alignment of this map with anatomical information possible, producing an integrated suite of sophisticated data search and visualization tools that help their users discover where each gene is expressed in the adult mouse brain (Ng et al., 2007, 2009; Lau et al., 2008). "
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    ABSTRACT: The Allen Brain Atlas-Driven Visualizations (ABADV) is a publicly accessible web-based tool created to retrieve and visualize expression energy data from the Allen Brain Atlas (ABA) across multiple genes and brain structures. Though the ABA offers their own search engine and software for researchers to view their growing collection of online public data sets, including extensive gene expression and neuroanatomical data from human and mouse brain, many of their tools limit the amount of genes and brain structures researchers can view at once. To complement their work, ABADV generates multiple pie charts, bar charts and heat maps of expression energy values for any given set of genes and brain structures. Such a suite of free and easy-to-understand visualizations allows for easy comparison of gene expression across multiple brain areas. In addition, each visualization links back to the ABA so researchers may view a summary of the experimental detail. ABADV is currently supported on modern web browsers and is compatible with expression energy data from the Allen Mouse Brain Atlas in situ hybridization data. By creating this web application, researchers can immediately obtain and survey numerous amounts of expression energy data from the ABA, which they can then use to supplement their work or perform meta-analysis. In the future, we hope to enable ABADV across multiple data resources.
    Frontiers in Neuroinformatics 05/2014; 8:51. DOI:10.3389/fninf.2014.00051 · 3.26 Impact Factor
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