Multi-scale correlation structure of gene expression in the brain

Cortical gradient perturbation in attention deficit hyperactivity disorder correlates with neurotransmitter-, cell type-specific and chromosome- transcriptomic signatures

AIMS

This study aimed to illuminate the neuropathological landscape of attention deficit hyperactivity disorder (ADHD) by a multiscale macro-micro-molecular perspective from in vivo neuroimaging data.

METHODS

The "ADHD-200 initiative" repository provided multi-site high-quality resting-state functional connectivity (rsfc-) neuroimaging for ADHD children and matched typically developing (TD) cohort. Diffusion mapping embedding model to derive the functional connectome gradient detecting biologically plausible neural pattern was built, and the multivariate partial least square method to uncover the enrichment of neurotransmitomic, cellular and chromosomal gradient-transcriptional signatures of AHBA enrichment and meta-analytic decoding.

RESULTS

Compared to TD, ADHD children presented connectopic cortical gradient perturbations in almost all the cognition-involved brain macroscale networks (all pBH <0.001), but not in the brain global topology. As an intermediate phenotypic variant, such gradient perturbation was spatially enriched into distributions of GABAA/BZ and 5-HT2A receptors (all pBH <0.01) and co-varied with genetic transcriptional expressions (e.g. DYDC2, ATOH7, all pBH <0.01), associated with phenotypic variants in episodic memory and emotional regulations. Enrichment models demonstrated such gradient-transcriptional variants indicated the risk of both cell-specific and chromosome- dysfunctions, especially in enriched expression of oligodendrocyte precursors and endothelial cells (all pperm <0.05) as well enrichment into chromosome 18, 19 and X (pperm <0.05).

CONCLUSIONS

Our findings bridged brain macroscale neuropathological patterns to microscale/cellular biological architectures for ADHD children, demonstrating the neurobiologically pathological mechanism of ADHD into the genetic and molecular variants in GABA and 5-HT systems as well brain-derived enrichment of specific cellular/chromosomal expressions.

Topological clustering of multilayer networks

Multilayer networks continue to gain significant attention in many areas of study, particularly due to their high utility in modeling interdependent systems such as critical infrastructures, human brain connectome, and socioenvironmental ecosystems. However, clustering of multilayer networks, especially using the information on higher-order interactions of the system entities, still remains in its infancy. In turn, higher-order connectivity is often the key in such multilayer network applications as developing optimal partitioning of critical infrastructures in order to isolate unhealthy system components under cyber-physical threats and simultaneous identification of multiple brain regions affected by trauma or mental illness. In this paper, we introduce the concepts of topological data analysis to studies of complex multilayer networks and propose a topological approach for network clustering. The key rationale is to group nodes based not on pairwise connectivity patterns or relationships between observations recorded at two individual nodes but based on how similar in shape their local neighborhoods are at various resolution scales. Since shapes of local node neighborhoods are quantified using a topological summary in terms of persistence diagrams, we refer to the approach as clustering using persistence diagrams (CPD). CPD systematically accounts for the important heterogeneous higher-order properties of node interactions within and in-between network layers and integrates information from the node neighbors. We illustrate the utility of CPD by applying it to an emerging problem of societal importance: vulnerability zoning of residential properties to weather- and climate-induced risks in the context of house insurance claim dynamics.

Topological clustering of multilayer networks

Multilayer network clustering is used in such diverse areas as optimal islanding of critical infrastructures, analysis of trade agreements, and monitoring ecological interaction patterns. We propose a perspective on multilayer network clustering based on the concept of shape. By invoking the machinery of topological data analysis, we first study a shape of each node neighborhood and then group nodes based on how similar shapes of their local neighborhoods are. The significance of this methodology can be viewed through an emerging problem of sustainability of house insurance to climate risks. The topological perspective opens possibilities for more systematic, robust, and mathematically rigorous integration of higher-order network properties and their interplay to the analysis of complex networks.

Distinct Genetic Signatures of Cortical and Subcortical Regions Associated with Human Memory

Despite the discovery of gene variants linked to memory performance, understanding the genetic basis of adult human memory remains a challenge. Here, we devised an unsupervised framework that relies on spatial correlations between human transcriptome data and functional neuroimaging maps to uncover the genetic signatures of memory in functionally-defined cortical and subcortical memory regions.

Despite the discovery of gene variants linked to memory performance, understanding the genetic basis of adult human memory remains a challenge. Here, we devised an unsupervised framework that relies on spatial correlations between human transcriptome data and functional neuroimaging maps to uncover the genetic signatures of memory in functionally-defined cortical and subcortical memory regions. Results were validated with animal literature and showed that our framework is highly effective in identifying memory-related processes and genes compared to a control cognitive function. Genes preferentially expressed in cortical memory regions are linked to memory-related processes such as immune and epigenetic regulation. Genes expressed in subcortical memory regions are associated with neurogenesis and glial cell differentiation. Genes expressed in both cortical and subcortical memory areas are involved in the regulation of transcription, synaptic plasticity, and glutamate receptor signaling. Furthermore, distinct memory-associated genes such as PRKCD and CDK5 are linked to cortical and subcortical regions, respectively. Thus, cortical and subcortical memory regions exhibit distinct genetic signatures that potentially reflect functional differences in health and disease, and nominates gene candidates for future experimental investigations.

Application of Computational Biology to Decode Brain Transcriptomes

The rapid development of high-throughput sequencing technologies has generated massive valuable brain transcriptome atlases, providing great opportunities for systematically investigating gene expression characteristics across various brain regions throughout a series of developmental stages. Recent studies have revealed that the transcriptional architecture is the key to interpreting the molecular mechanisms of brain complexity. However, our knowledge of brain transcriptional characteristics remains very limited. With the immense efforts to generate high-quality brain transcriptome atlases, new computational approaches to analyze these high-dimensional multivariate data are greatly needed. In this review, we summarize some public resources for brain transcriptome atlases and discuss the general computational pipelines that are commonly used in this field, which would aid in making new discoveries in brain development and disorders.

Supervised and Unsupervised End-to-End Deep Learning for Gene Ontology Classification of Neural In Situ Hybridization Images

In recent years, large datasets of high-resolution mammalian neural images have become available, which has prompted active research on the analysis of gene expression data. Traditional image processing methods are typically applied for learning functional representations of genes, based on their expressions in these brain images. In this paper, we describe a novel end-to-end deep learning-based method for generating compact representations of in situ hybridization (ISH) images, which are invariant-to-translation. In contrast to traditional image processing methods, our method relies, instead, on deep convolutional denoising autoencoders (CDAE) for processing raw pixel inputs, and generating the desired compact image representations. We provide an in-depth description of our deep learning-based approach, and present extensive experimental results, demonstrating that representations extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Our methods improve the previous state-of-the-art classification rate (Liscovitch, et al.) from an average AUC of 0.92 to 0.997, i.e., it achieves 96% reduction in error rate. Furthermore, the representation vectors generated due to our method are more compact in comparison to previous state-of-the-art methods, allowing for a more efficient high-level representation of images. These results are obtained with significantly downsampled images in comparison to the original high-resolution ones, further underscoring the robustness of our proposed method.

Effects of Chronic Spinal Cord Injury on Relationships among Ion Channel and Receptor mRNAs in Mouse Lumbar Spinal Cord

Spinal cord injury (SCI) causes widespread changes in gene expression of the spinal cord, even in the undamaged spinal cord below the level of the lesion. Less is known about changes in the correlated expression of genes after SCI. We investigated gene co-expression networks among voltage-gated ion channel and neurotransmitter receptor mRNA levels using quantitative RT-PCR in longitudinal slices of the mouse lumbar spinal cord in control and chronic SCI animals. These longitudinal slices were made from the ventral surface of the cord, thus forming slices relatively enriched in motor neurons or interneurons. We performed absolute quantitation of mRNA copy number for 50 ion channel or receptor transcripts from each sample, and used multiple correlation analyses to detect patterns in correlated mRNA levels across all pairs of genes. The majority of channels and receptors changed in expression as a result of chronic SCI, but did so differently across slice levels. Furthermore, motor neuron-enriched slices experienced an overall loss of correlated channel and receptor expression, while interneuron slices showed a dramatic increase in the number of positively correlated transcripts. These correlation profiles suggest that spinal cord injury induces distinct changes across cell types in the organization of gene co-expression networks for ion channels and transmitter receptors.

Adar3 Is Involved in Learning and Memory in Mice

The amount of regulatory RNA encoded in the genome and the extent of RNA editing by the post-transcriptional deamination of adenosine to inosine (A-I) have increased with developmental complexity and may be an important factor in the cognitive evolution of animals. The newest member of the A-I editing family of ADAR proteins, the vertebrate-specific ADAR3, is highly expressed in the brain, but its functional significance is unknown. In vitro studies have suggested that ADAR3 acts as a negative regulator of A-I RNA editing but the scope and underlying mechanisms are also unknown. Meta-analysis of published data indicates that mouse Adar3 expression is highest in the hippocampus, thalamus, amygdala, and olfactory region. Consistent with this, we show that mice lacking exon 3 of Adar3 (which encodes two double stranded RNA binding domains) have increased levels of anxiety and deficits in hippocampus-dependent short- and long-term memory formation. RNA sequencing revealed a dysregulation of genes involved in synaptic function in the hippocampi of Adar3-deficient mice. We also show that ADAR3 transiently translocates from the cytoplasm to the nucleus upon KCl-mediated activation in SH-SY5Y cells. These results indicate that ADAR3 contributes to cognitive processes in mammals.

Brain Transcriptome Databases: A User's Guide

Transcriptional programs instruct the generation and maintenance of diverse subtypes of neural cells, establishment of distinct brain regions, formation and function of neural circuits, and ultimately behavior. Spatiotemporal and cell type-specific analyses of the transcriptome, the sum total of all RNA transcripts in a cell or an organ, can provide insights into the role of genes in brain development and function, and their potential contribution to disorders of the brain. In the previous decade, advances in sequencing technology and funding from the National Institutes of Health and private foundations for large-scale genomics projects have led to a growing collection of brain transcriptome databases. These valuable resources provide rich and high-quality datasets with spatiotemporal, cell type-specific, and single-cell precision. Most importantly, many of these databases are publicly available via user-friendly web interface, making the information accessible to individual scientists without the need for advanced computational expertise. Here, we highlight key publicly available brain transcriptome databases, summarize the tissue sources and methods used to generate the data, and discuss their utility for neuroscience research.

BrainScope: interactive visual exploration of the spatial and temporal human brain transcriptome

Spatial and temporal brain transcriptomics has recently emerged as an invaluable data source for molecular neuroscience. The complexity of such data poses considerable challenges for analysis and visualization. We present BrainScope: a web portal for fast, interactive visual exploration of the Allen Atlases of the adult and developing human brain transcriptome. Through a novel methodology to explore high-dimensional data (dual t-SNE), BrainScope enables the linked, all-in-one visualization of genes and samples across the whole brain and genome, and across developmental stages. We show that densities in t-SNE scatter plots of the spatial samples coincide with anatomical regions, and that densities in t-SNE scatter plots of the genes represent gene co-expression modules that are significantly enriched for biological functions. We also show that the topography of the gene t-SNE maps reflect brain region-specific gene functions, enabling hypothesis and data driven research. We demonstrate the discovery potential of BrainScope through three examples: (i) analysis of cell type specific gene sets, (ii) analysis of a set of stable gene co-expression modules across the adult human donors and (iii) analysis of the evolution of co-expression of oligodendrocyte specific genes over developmental stages. BrainScope is publicly accessible at www.brainscope.nl.

Comparing the Expression of Genes Related to Serotonin (5-HT) in C57BL/6J Mice and Humans Based on Data Available at the Allen Mouse Brain Atlas and Allen Human Brain Atlas

Brain atlases are tools based on comprehensive studies used to locate biological characteristics (structures, connections, proteins, and gene expression) in different regions of the brain. These atlases have been disseminated to the point where tools have been created to store, manage, and share the information they contain. This study used the data published by the Allen Mouse Brain Atlas (2004) for mice (C57BL/6J) and Allen Human Brain Atlas (2010) for humans (6 donors) to compare the expression of serotonin-related genes. Genes of interest were searched for manually in each case (in situ hybridization for mice and microarrays for humans), normalized expression data (z-scores) were extracted, and the results were graphed. Despite the differences in methodology, quantification, and subjects used in the process, a high degree of similarity was found between expression data. Here we compare expression in a way that allows the use of translational research methods to infer and validate knowledge. This type of study allows part of the relationship between structures and functions to be identified, by examining expression patterns and comparing levels of expression in different states, anatomical correlations, and phenotypes between different species. The study concludes by discussing the importance of knowing, managing, and disseminating comprehensive, open-access studies in neuroscience.

Insulin Resistance as a Link between Amyloid-Beta and Tau Pathologies in Alzheimer's Disease

Current hypotheses and theories regarding the pathogenesis of Alzheimer’s disease (AD) heavily implicate brain insulin resistance (IR) as a key factor. Despite the many well-validated metrics for systemic IR, the absence of biomarkers for brain-specific IR represents a translational gap that has hindered its study in living humans. In our lab, we have been working to develop biomarkers that reflect the common mechanisms of brain IR and AD that may be used to follow their engagement by experimental treatments. We present two promising biomarkers for brain IR in AD: insulin cascade mediators probed in extracellular vesicles (EVs) enriched for neuronal origin, and two-dimensional magnetic resonance spectroscopy (MRS) measures of brain glucose. As further evidence for a fundamental link between brain IR and AD, we provide a novel analysis demonstrating the close spatial correlation between brain expression of genes implicated in IR (using Allen Human Brain Atlas data) and tau and beta-amyloid pathologies. We proceed to propose the bold hypotheses that baseline differences in the metabolic reliance on glycolysis, and the expression of glucose transporters (GLUT) and insulin signaling genes determine the vulnerability of different brain regions to Tau and/or Amyloid beta (Aβ) pathology, and that IR is a critical link between these two pathologies that define AD. Lastly, we provide an overview of ongoing clinical trials that target IR as an angle to treat AD, and suggest how biomarkers may be used to evaluate treatment efficacy and target engagement.

Insulin resistance in Alzheimer's disease

The links between systemic insulin resistance (IR), brain-specific IR, and Alzheimer's disease (AD) have been an extremely productive area of current research. This review will cover the fundamentals and pathways leading to IR, its connection to AD via cellular mechanisms, the most prominent methods and models used to examine it, an introduction to the role of extracellular vesicles (EVs) as a source of biomarkers for IR and AD, and an overview of modern clinical studies on the subject. To provide additional context, we also present a novel analysis of the spatial correlation of gene expression in the brain with the aid of Allen Human Brain Atlas data. Ultimately, examining the relation between IR and AD can be seen as a means of advancing the understanding of both disease states, with IR being a promising target for therapeutic strategies in AD treatment. In conclusion, we highlight the therapeutic potential of targeting brain IR in AD and the main strategies to pursue this goal.

Brain transcriptome atlases: a computational perspective

The immense complexity of the mammalian brain is largely reflected in the underlying molecular signatures of its billions of cells. Brain transcriptome atlases provide valuable insights into gene expression patterns across different brain areas throughout the course of development. Such atlases allow researchers to probe the molecular mechanisms which define neuronal identities, neuroanatomy, and patterns of connectivity. Despite the immense effort put into generating such atlases, to answer fundamental questions in neuroscience, an even greater effort is needed to develop methods to probe the resulting high-dimensional multivariate data. We provide a comprehensive overview of the various computational methods used to analyze brain transcriptome atlases.

Progress Towards Mammalian Whole-Brain Cellular Connectomics

Neurons are the fundamental structural units of the nervous system-i.e., the Neuron Doctrine-as the pioneering work of Santiago Ramón y Cajal in the 1880's clearly demonstrated through careful observation of Golgi-stained neuronal morphologies. However, at that time sample preparation, imaging methods and computational tools were either nonexistent or insufficiently developed to permit the precise mapping of an entire brain with all of its neurons and their connections. Some measure of the "mesoscopic" connectional organization of the mammalian brain has been obtained over the past decade by alignment of sparse subsets of labeled neurons onto a reference atlas or via MRI-based diffusion tensor imaging. Neither method, however, provides data on the complete connectivity of all neurons comprising an individual brain. Fortunately, whole-brain cellular connectomics now appears within reach due to recent advances in whole-brain sample preparation and high-throughput electron microscopy (EM), though substantial obstacles remain with respect to large volume electron microscopic acquisitions and automated neurite reconstructions. This perspective examines the current status and problems associated with generating a mammalian whole-brain cellular connectome and argues that the time is right to launch a concerted connectomic attack on a small mammalian whole-brain.

Differential expression of transcriptional regulatory units in the prefrontal cortex of patients with bipolar disorder: potential role of early growth response gene 3

Bipolar disorder (BD) is a severe mental illness with a strong genetic component. Despite its high degree of heritability, current genetic studies have failed to reveal individual loci of large effect size. In lieu of focusing on individual genes, we investigated regulatory units (regulons) in BD to identify candidate transcription factors (TFs) that regulate large groups of differentially expressed genes. Network-based approaches should elucidate the molecular pathways governing the pathophysiology of BD and reveal targets for potential therapeutic intervention. The data from a large-scale microarray study was used to reconstruct the transcriptional associations in the human prefrontal cortex, and results from two independent microarray data sets to obtain BD gene signatures. The regulatory network was derived by mapping the significant interactions between known TFs and all potential targets. Five regulons were identified in both transcriptional network models: early growth response 3 (EGR3), TSC22 domain family, member 4 (TSC22D4), interleukin enhancer-binding factor 2 (ILF2), Y-box binding protein 1 (YBX1) and MAP-kinase-activating death domain (MADD). With a high stringency threshold, the consensus across tests was achieved only for the EGR3 regulon. We identified EGR3 in the prefrontal cortex as a potential key target, robustly repressed in both BD signatures. Considering that EGR3 translates environmental stimuli into long-term changes in the brain, disruption in biological pathways involving EGR3 may induce an impaired response to stress and influence on risk for psychiatric disorders, particularly BD.

Cell-type-specific neuroanatomy of cliques of autism-related genes in the mouse brain

Two cliques of genes identified computationally for their high co-expression in the mouse brain according to the Allen Brain Atlas, and for their enrichment in genes related to autism spectrum disorder (ASD), have recently been shown to be highly co-expressed in the cerebellar cortex, compared to what could be expected by chance. Moreover, the expression of these cliques of genes is not homogeneous across the cerebellar cortex, and it has been noted that their expression pattern seems to highlight the granular layer. However, this observation was only made by eye, and recent advances in computational neuroanatomy allow to rank cell types in the mouse brain (characterized by their transcriptome profiles) according to the similarity between their spatial density profiles and the spatial expression profiles of the cliques. We establish by Monte Carlo simulation that with probability at least 99%, the expression profiles of the two cliques are more similar to the density profile of granule cells than 99% of the expression of cliques containing the same number of genes (Purkinje cells also score above 99% in one of the cliques). Thresholding the expression profiles shows that the signal is more intense in the granular layer. Finally, we work out pairs of cell types whose combined expression profiles are more similar to the expression profiles of the cliques than any single cell type. These pairs predominantly consist of one cortical pyramidal cell and one cerebellar cell (which can be either a granule cell or a Purkinje cell).

Exploration and visualization of connectivity in the adult mouse brain

The Allen Mouse Brain Connectivity Atlas is a mesoscale whole brain axonal projection atlas of the C57Bl/6J mouse brain. All data were aligned to a common template in 3D space to generate a comprehensive and quantitative database of inter-areal and cell-type-specific projections. A suite of computational tools were developed to search and visualize the projection labeling experiments, available at http://connectivity.brain-map.org. We present three use cases illustrating how these publicly-available tools can be used to perform analyses of long range brain region connectivity. The use cases make extensive use of advanced visualization tools integrated with the atlas including projection density histograms, 3D computed anterograde and retrograde projection paths, and multi-specimen projection composites. These tools offer convenient access to detailed axonal projection information in the adult mouse brain and the ability to perform data analysis and visualization of projection fields and neuroanatomy in an integrated manner.

A high-resolution spatiotemporal atlas of gene expression of the developing mouse brain

To provide a temporal framework for the genoarchitecture of brain development, we generated in situ hybridization data for embryonic and postnatal mouse brain at seven developmental stages for ∼2,100 genes, which were processed with an automated informatics pipeline and manually annotated. This resource comprises 434,946 images, seven reference atlases, an ontogenetic ontology, and tools to explore coexpression of genes across neurodevelopment. Gene sets coinciding with developmental phenomena were identified. A temporal shift in the principles governing the molecular organization of the brain was detected, with transient neuromeric, plate-based organization of the brain present at E11.5 and E13.5. Finally, these data provided a transcription factor code that discriminates brain structures and identifies the developmental age of a tissue, providing a foundation for eventual genetic manipulation or tracking of specific brain structures over development. The resource is available as the Allen Developing Mouse Brain Atlas (http://developingmouse.brain-map.org).

Whole brain expression of bipolar disorder associated genes: structural and genetic analyses

Studies of bipolar disorder (BD) suggest a genetic basis of the illness that alters brain function and morphology. In recent years, a number of genetic variants associated with BD have been identified. However, little is known about the associated genes, or brain circuits that rely upon their function. Using an anatomically comprehensive survey of the human transcriptome (The Allen Brain Atlas), we mapped the expression of 58 genes with suspected involvement in BD based upon their relationship to SNPs identified in genome wide association studies (GWAS). We then conducted a meta-analysis of structural MRI studies to identify brain regions that are abnormal in BD. Of 58 BD associated genes, 22 had anatomically distinct expression patterns that could be categorized into one of three clusters (C1–C3). Brain regions with the highest and lowest expression of these genes did not overlap strongly with anatomical sites identified as abnormal by structural MRI except in the parahippocampal gyrus, the inferior/superior temporal gyrus and the cerebellar vermis, regions where overlap was significant. Using the 22 genes in C1–C3 as reference points, additional genes with correlated expression patterns were identified and organized into sets based on similarity. Further analysis revealed that five of these gene sets were significantly associated with BD, suggesting that anatomical expression profile is correlated with genetic susceptibility to BD, particularly for genes in C2. Our data suggest that expression profiles of BD-associated genes do not explain the majority of structural abnormalities observed in BD, but may be useful in identifying new candidate genes. Our results highlight the complex neuroanatomical basis of BD, and reinforce illness models that emphasize impaired brain connectivity.

Evidence for the role of EPHX2 gene variants in anorexia nervosa

Anorexia nervosa (AN) and related eating disorders are complex, multifactorial neuropsychiatric conditions with likely rare and common genetic and environmental determinants. To identify genetic variants associated with AN, we pursued a series of sequencing and genotyping studies focusing on the coding regions and upstream sequence of 152 candidate genes in a total of 1205 AN cases and 1948 controls. We identified individual variant associations in the Estrogen Receptor-ß (ESR2) gene, as well as a set of rare and common variants in the Epoxide Hydrolase 2 (EPHX2) gene, in an initial sequencing study of 261 early-onset severe AN cases and 73 controls (P=0.0004). The association of EPHX2 variants was further delineated in: (1) a pooling-based replication study involving an additional 500 AN patients and 500 controls (replication set P=0.00000016); (2) single-locus studies in a cohort of 386 previously genotyped broadly defined AN cases and 295 female population controls from the Bogalusa Heart Study (BHS) and a cohort of 58 individuals with self-reported eating disturbances and 851 controls (combined smallest single locus P<0.01). As EPHX2 is known to influence cholesterol metabolism, and AN is often associated with elevated cholesterol levels, we also investigated the association of EPHX2 variants and longitudinal body mass index (BMI) and cholesterol in BHS female and male subjects (N=229) and found evidence for a modifying effect of a subset of variants on the relationship between cholesterol and BMI (P<0.01). These findings suggest a novel association of gene variants within EPHX2 to susceptibility to AN and provide a foundation for future study of this important yet poorly understood condition.

Cell-type-based model explaining coexpression patterns of genes in the brain

Spatial patterns of gene expression in the vertebrate brain are not independent, as pairs of genes can exhibit complex patterns of coexpression. Two genes may be similarly expressed in one region, but differentially expressed in other regions. These correlations have been studied quantitatively, particularly for the Allen Atlas of the adult mouse brain, but their biological meaning remains obscure. We propose a simple model of the coexpression patterns in terms of spatial distributions of underlying cell types and establish its plausibility using independently measured cell-type-specific transcriptomes. The model allows us to predict the spatial distribution of cell types in the mouse brain.

Quantitative trait loci mapping and gene network analysis implicate protocadherin-15 as a determinant of brain serotonin transporter expression

Presynaptic serotonin (5-hydroxytryptamine, 5-HT) transporters (SERT) regulate 5-HT signaling via antidepressant-sensitive clearance of released neurotransmitter. Polymorphisms in the human SERT gene (SLC6A4) have been linked to risk for multiple neuropsychiatric disorders, including depression, obsessive-compulsive disorder and autism. Using BXD recombinant inbred mice, a genetic reference population that can support the discovery of novel determinants of complex traits, merging collective trait assessments with bioinformatics approaches, we examine phenotypic and molecular networks associated with SERT gene and protein expression. Correlational analyses revealed a network of genes that significantly associated with SERT mRNA levels. We quantified SERT protein expression levels and identified region- and gender-specific quantitative trait loci (QTLs), one of which associated with male midbrain SERT protein expression, centered on the protocadherin-15 gene (Pcdh15), overlapped with a QTL for midbrain 5-HT levels. Pcdh15 was also the only QTL-associated gene whose midbrain mRNA expression significantly associated with both SERT protein and 5-HT traits, suggesting an unrecognized role of the cell adhesion protein in the development or function of 5-HT neurons. To test this hypothesis, we assessed SERT protein and 5-HT traits in the Pcdh15 functional null line (Pcdh15(av-) (3J) ), studies that revealed a strong, negative influence of Pcdh15 on these phenotypes. Together, our findings illustrate the power of multidimensional profiling of recombinant inbred lines in the analysis of molecular networks that support synaptic signaling, and that, as in the case of Pcdh15, can reveal novel relationships that may underlie risk for mental illness.

Molecular neuroanatomy: a generation of progress

The neuroscience research landscape has changed dramatically over the past decade. Specifically, an impressive array of new tools and technologies have been generated, including but not limited to: brain gene expression atlases, genetically encoded proteins to monitor and manipulate neuronal activity, and new methods for imaging and mapping circuits. However, despite these technological advances, several significant challenges must be overcome to enable a better understanding of brain function and to develop cell type-targeted therapeutics to treat brain disorders. This review provides an overview of some of the tools and technologies currently being used to advance the field of molecular neuroanatomy, and also discusses emerging technologies that may enable neuroscientists to address these crucial scientific challenges over the coming decade.

FuncISH: learning a functional representation of neural ISH images

MOTIVATION

High-spatial resolution imaging datasets of mammalian brains have recently become available in unprecedented amounts. Images now reveal highly complex patterns of gene expression varying on multiple scales. The challenge in analyzing these images is both in extracting the patterns that are most relevant functionally and in providing a meaningful representation that allows neuroscientists to interpret the extracted patterns.

RESULTS

Here, we present FuncISH--a method to learn functional representations of neural in situ hybridization (ISH) images. We represent images using a histogram of local descriptors in several scales, and we use this representation to learn detectors of functional (GO) categories for every image. As a result, each image is represented as a point in a low-dimensional space whose axes correspond to meaningful functional annotations. The resulting representations define similarities between ISH images that can be easily explained by functional categories. We applied our method to the genomic set of mouse neural ISH images available at the Allen Brain Atlas, finding that most neural biological processes can be inferred from spatial expression patterns with high accuracy. Using functional representations, we predict several gene interaction properties, such as protein-protein interactions and cell-type specificity, more accurately than competing methods based on global correlations. We used FuncISH to identify similar expression patterns of GABAergic neuronal markers that were not previously identified and to infer new gene function based on image-image similarities.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evaluation of heritable determinants of blood and brain serotonin homeostasis using recombinant inbred mice

The biogenic amine serotonin (5-HT, 5-hydroxytryptamine) exerts powerful, modulatory control over multiple physiological functions in the brain and periphery, ranging from mood and appetite to vasoconstriction and gastrointestinal motility. In order to gain insight into shared and distinct molecular and phenotypic networks linked to variations in 5-HT homeostasis, we capitalized on the stable genetic variation present in recombinant inbred mouse strains. This family of strains, all derived from crosses between C57BL/6J and DBA/2J (BXD) parents, represents a unique, community resource with approximately 40 years of assembled phenotype data that can be exploited to explore and test causal relationships in silico. We determined levels of 5-HT and 5-hydroxyindoleacetic acid from whole blood, midbrain and thalamus/hypothalamus (diencephalon) of 38 BXD lines and both sexes. All 5-HT measures proved highly heritable in each region, although both gender and region significantly impacted between-strain correlations. Our studies identified both expected and novel biochemical, anatomical and behavioral phenotypes linked to 5-HT traits, as well as distinct quantitative trait loci. Analyses of these loci nominate a group of genes likely to contribute to gender- and region-specific capacities for 5-HT signaling. Analysis of midbrain mRNA variations across strains revealed overlapping gene expression networks linked to 5-HT synthesis and metabolism. Altogether, our studies provide a rich profile of genomic, molecular and phenotypic networks that can be queried for novel relationships contributing risk for disorders linked to perturbed 5-HT signaling.

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.

Localizing genes to cerebellar layers by classifying ISH images

Gene expression controls how the brain develops and functions. Understanding control processes in the brain is particularly hard since they involve numerous types of neurons and glia, and very little is known about which genes are expressed in which cells and brain layers. Here we describe an approach to detect genes whose expression is primarily localized to a specific brain layer and apply it to the mouse cerebellum. We learn typical spatial patterns of expression from a few markers that are known to be localized to specific layers, and use these patterns to predict localization for new genes. We analyze images of in-situ hybridization (ISH) experiments, which we represent using histograms of local binary patterns (LBP) and train image classifiers and gene classifiers for four layers of the cerebellum: the Purkinje, granular, molecular and white matter layer. On held-out data, the layer classifiers achieve accuracy above 94% (AUC) by representing each image at multiple scales and by combining multiple image scores into a single gene-level decision. When applied to the full mouse genome, the classifiers predict specific layer localization for hundreds of new genes in the Purkinje and granular layers. Many genes localized to the Purkinje layer are likely to be expressed in astrocytes, and many others are involved in lipid metabolism, possibly due to the unusual size of Purkinje cells.