High-resolution gene expression atlases for adult and developing mouse brain and spinal cord

Knowledge of the structure, genetics, circuits, and physiological properties of the mammalian brain in both normal and pathological states is ever increasing as research labs worldwide probe the various aspects of brain function. Until recently, however, comprehensive cataloging of gene expression across the central nervous system has been lacking. The Allen Institute for Brain Science, as part of its mission to propel neuroscience research, has completed several large gene-mapping projects in mouse, nonhuman primate, and human brain, producing informative online public resources and tools. Here we present the Allen Mouse Brain Atlas, covering ~20,000 genes throughout the adult mouse brain; the Allen Developing Mouse Brain Atlas, detailing expression of approximately 2,000 important developmental genes across seven embryonic and postnatal stages of brain growth; and the Allen Spinal Cord Atlas, revealing expression for ~20,000 genes in the adult and neonatal mouse spinal cords. Integrated data-mining tools, including reference atlases, informatics analyses, and 3-D viewers, are described. For these massive-scale projects, high-throughput industrial techniques were developed to standardize and reliably repeat experimental goals. To verify consistency and accuracy, a detailed analysis of the 1,000 most viewed genes for the adult mouse brain (according to website page views) was performed by comparing our data with peer-reviewed literature and other databases. We show that our data are highly consistent with independent sources and provide a comprehensive compendium of information and tools used by thousands of researchers each month. All data and tools are freely available via the Allen Brain Atlas portal (www.brain-map.org).     

Surface-based mapping of gene expression and probabilistic expression maps in the mouse cortex

The Allen Brain Atlas (ABA, www.brain-map.org) is a genome wide, spatially registered collection of cellular resolution in situ hybridization gene expression image data of the C57Bl/6J mouse brain. Derived from the ABA, the Anatomic Gene Expression Atlas (AGEA, http://mouse.brain-map.org/agea) has demonstrated both laminar and areal spatial gene expression correlations in the mouse cortex. While the mouse cortex is lissencephalic, its curvature and substantial bending in boundary areas renders it difficult to visualize and analyze laminar versus areal effects in a rectilinear coordinate framework. In context of human and non-human primate cortex, surface-based representation has proven useful for understanding relative locations of laminar, columnar, and areal features. In this paper, we describe a methodology for constructing surface-based flatmaps of the mouse cortex that enables mapping of gene expression data from individual genes in the ABA, or probabilistic expression maps from the AGEA, to identify and visualize genetic relationships between layers and areas.     

If I only had a brain: exploring mouse brain images in the Allen Brain Atlas

The combination of a powerful well-designed user interface with detailed high-quality data sets can create new possibilities for data exploration and analysis. The Allen Brain Atlas (http://www.brain-map.org) provides a collection of tools for examining a set of images that detail gene expression in the mouse brain. Powerful web-based viewers for individual images and parallel examination of related images interact with an external application for three-dimensional views. The underlying dataset, generated via high-throughput analysis of expression patterns of more than 21,000 genes in adult mouse brains, provides three-dimensional views of gene expression patterns displayed in the context of an anatomical ontology. Facilities for filtering views, saving views of interest, annotating images and sharing views via email support the ongoing process of analysis and provide a model for the future of integrated tools for analysing large image data sets.     

Clustering of spatial gene expression patterns in the mouse brain and comparison with classical neuroanatomy

Spatial gene expression profiles provide a novel means of exploring the structural organization of the brain. Computational analysis of these patterns is made possible by genome-scale mapping of the C57BL/6J mouse brain in the Allen Brain Atlas. Here we describe methodology used to explore the spatial structure of gene expression patterns across a set of 3041 genes chosen on the basis of consistency across experimental observations (N = 2). The analysis was performed on smoothed, co-registered 3D expression volumes for each gene obtained by aggregating cellular resolution image data. Following dimensionality and noise reduction, voxels were clustered according to similarity of expression across the gene set. We illustrate the resulting parcellations of the mouse brain for different numbers of clusters (K) and quantitatively compare these parcellations with a classically-defined anatomical reference atlas at different levels of granularity, revealing a high degree of correspondence. These observations suggest that spatial localization of gene expression offers substantial promise in connecting knowledge at the molecular level with higher-level information about brain organization.     

Integrated view and comparative analysis of baseline protein expression in mouse and rat tissues

The increasingly large amount of proteomics data in the public domain enables, among other applications, the combined analyses of datasets to create comparative protein expression maps covering different organisms and different biological conditions. Here we have reanalysed public proteomics datasets from mouse and rat tissues (14 and 9 datasets, respectively), to assess baseline protein abundance. Overall, the aggregated dataset contained 23 individual datasets, including a total of 211 samples coming from 34 different tissues across 14 organs, comprising 9 mouse and 3 rat strains, respectively.In all cases, we studied the distribution of canonical proteins between the different organs. The number of canonical proteins per dataset ranged from 273 (tendon) and 9,715 (liver) in mouse, and from 101 (tendon) and 6,130 (kidney) in rat. Then, we studied how protein abundances compared across different datasets and organs for both species. As a key point we carried out a comparative analysis of protein expression between mouse, rat and human tissues. We observed a high level of correlation of protein expression among orthologs between all three species in brain, kidney, heart and liver samples, whereas the correlation of protein expression was generally slightly lower between organs within the same species. Protein expression results have been integrated into the resource Expression Atlas for widespread dissemination.     

Co-expression Profiling of Autism Genes in the Mouse Brain

Autism spectrum disorder (ASD) is one of the most prevalent and highly heritable neurodevelopmental disorders in humans. There is significant evidence that the onset and severity of ASD is governed in part by complex genetic mechanisms affecting the normal development of the brain. To date, a number of genes have been associated with ASD. However, the temporal and spatial co-expression of these genes in the brain remain unclear. To address this issue, we examined the co-expression network of 26 autism genes from AutDB (http://mindspec.org/autdb.html), in the framework of 3,041 genes whose expression energies have the highest correlation between the coronal and sagittal images from the Allen Mouse Brain Atlas database (http://mouse.brain-map.org). These data were derived from in situ hybridization experiments conducted on male, 56-day old C57BL/6J mice co-registered to the Allen Reference Atlas, and were used to generate a normalized co-expression matrix indicating the cosine similarity between expression vectors of genes in this database. The network formed by the autism-associated genes showed a higher degree of co-expression connectivity than seen for the other genes in this dataset (Kolmogorov–Smirnov P = 5×10⁻²⁸). Using Monte Carlo simulations, we identified two cliques of co-expressed genes that were significantly enriched with autism genes (A Bonferroni corrected P<0.05). Genes in both these cliques were significantly over-expressed in the cerebellar cortex (P = 1×10⁻⁵) suggesting possible implication of this brain region in autism. In conclusion, our study provides a detailed profiling of co-expression patterns of autism genes in the mouse brain, and suggests specific brain regions and new candidate genes that could be involved in autism etiology.     

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

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.     

Quantitative methods for genome-scale analysis of in situ hybridization and correlation with microarray data

With the emergence of genome-wide colorimetric in situ hybridization (ISH) data sets such as the Allen Brain Atlas, it is important to understand the relationship between this gene expression modality and those derived from more quantitative based technologies. This study introduces a novel method for standardized relative quantification of colorimetric ISH signal that enables a large-scale cross-platform expression level comparison of ISH with two publicly available microarray brain data sources.     

Relationships between gene expression and brain wiring in the adult rodent brain

We studied the global relationship between gene expression and neuroanatomical connectivity in the adult rodent brain. We utilized a large data set of the rat brain "connectome" from the Brain Architecture Management System (942 brain regions and over 5000 connections) and used statistical approaches to relate the data to the gene expression signatures of 17,530 genes in 142 anatomical regions from the Allen Brain Atlas. Our analysis shows that adult gene expression signatures have a statistically significant relationship to connectivity. In particular, brain regions that have similar expression profiles tend to have similar connectivity profiles, and this effect is not entirely attributable to spatial correlations. In addition, brain regions which are connected have more similar expression patterns. Using a simple optimization approach, we identified a set of genes most correlated with neuroanatomical connectivity, and find that this set is enriched for genes involved in neuronal development and axon guidance. A number of the genes have been implicated in neurodevelopmental disorders such as autistic spectrum disorder. Our results have the potential to shed light on the role of gene expression patterns in influencing neuronal activity and connectivity, with potential applications to our understanding of brain disorders. Supplementary data are available at http://www.chibi.ubc.ca/ABAMS.     

Comparative analysis of selenocysteine machinery and selenoproteome gene expression in mouse brain identifies neurons as key functional sites of selenium in mammals

Although dietary selenium (Se) deficiency results in phenotypes associated with selenoprotein depletion in various organs, the brain is protected from Se loss. To address the basis for the critical role of Se in brain function, we carried out comparative gene expression analyses for the complete selenoproteome and associated biosynthetic factors. Using the Allen Brain Atlas, we evaluated 159 regions of adult mouse brain and provided experimental analyses of selected selenoproteins. All 24 selenoprotein mRNAs were expressed in the mouse brain. Most strikingly, neurons in olfactory bulb, hippocampus, cerebral cortex, and cerebellar cortex were exceptionally rich in selenoprotein gene expression, in particular in GPx4, SelK, SelM, SelW, and Sep15. Over half of the selenoprotein genes were also expressed in the choroid plexus. A unique expression pattern was observed for one of the highly expressed selenoprotein genes, SelP, which we suggest to provide neurons with Se. Cluster analysis of the expression data linked certain selenoproteins and selenocysteine machinery genes and suggested functional linkages among selenoproteins, such as that between SelM and Sep15. Overall, this study suggests that the main functions of selenium in mammals are confined to certain neurons in the brain.     

Computational neuroanatomy and co-expression of genes in the adult mouse brain,analysis tools for the Allen Brain Atlas

We review quantitative methods and software developed to analyze genome-scale, brain-wide spatially-mapped gene-expression data. We expose new methods based on the underlying high-dimensional geometry of voxel space and gene space, and on simulations of the distribution of co-expression networks of a given size. We apply them to the Allen Atlas of the adult mouse brain, and to the co-expression network of a set of genes related to nicotine addiction retrieved from the NicSNP database. The computational methods are implemented in BrainGeneExpressionAnalysis (BGEA), a Matlab toolbox available for download.     

Harnessing Gene Expression Networks to Prioritize Candidate Epileptic Encephalopathy Genes

We apply a novel gene expression network analysis to a cohort of 182 recently reported candidate Epileptic Encephalopathy genes to identify those most likely to be true Epileptic Encephalopathy genes. These candidate genes were identified as having single variants of likely pathogenic significance discovered in a large-scale massively parallel sequencing study. Candidate Epileptic Encephalopathy genes were prioritized according to their co-expression with 29 known Epileptic Encephalopathy genes. We utilized developing brain and adult brain gene expression data from the Allen Human Brain Atlas (AHBA) and compared this to data from Celsius: a large, heterogeneous gene expression data warehouse. We show replicable prioritization results using these three independent gene expression resources, two of which are brain-specific, with small sample size, and the third derived from a heterogeneous collection of tissues with large sample size. Of the nineteen genes that we predicted with the highest likelihood to be true Epileptic Encephalopathy genes, two (GNAO1 and GRIN2B) have recently been independently reported and confirmed. We compare our results to those produced by an established in silico prioritization approach called Endeavour, and finally present gene expression networks for the known and candidate Epileptic Encephalopathy genes. This highlights sub-networks of gene expression, particularly in the network derived from the adult AHBA gene expression dataset. These networks give clues to the likely biological interactions between Epileptic Encephalopathy genes, potentially highlighting underlying mechanisms and avenues for therapeutic targets.     

Brain-Derived Neurotrophic Factor and Antidepressive Effect of Electroconvulsive Therapy: Systematic Review and Meta-Analyses of the Preclinical and Clinical Literature

Emerging data suggest that Electro-Convulsive Treatment (ECT) may reduce depressive symptoms by increasing the expression of Brain-Derived Neurotrophic Factor (BDNF). Yet, conflicting findings have been reported. For this reason we performed a systematic review and meta-analysis of the preclinical and clinical literature on the association between ECT treatment (ECS in animals) and changes in BDNF concentrations and their effect on behavior. In addition, regional brain expression of BDNF in mouse and human brains were compared using Allen Brain Atlas. ECS, over sham, increased BDNF mRNA and protein in animal brain (effect size [Hedge’s g]: 0.38―0.54; 258 effect-size estimates, N = 4,284) but not in serum (g = 0.06, 95% CI = -0.05―0.17). In humans, plasma but not serum BDNF increased following ECT (g = 0.72 vs. g = 0.14; 23 effect sizes, n = 281). The gradient of the BDNF increment in animal brains corresponded to the gradient of the BDNF gene expression according to the Allen brain atlas. Effect-size estimates were larger following more ECT sessions in animals (r = 0.37, P < .0001) and in humans (r = 0.55; P = 0.05). There were some indications that the increase in BDNF expression was associated with behavioral changes in rodents, but not in humans. We conclude that ECS in rodents and ECT in humans increase BDNF concentrations but this is not consistently associated with changes in behavior.