Automated identification of cell-type-specific genes in the mouse brain by image computing of expression patterns

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.

The Allen Brain Atlas (ABA) project aims to create a cellular-resolution, genome-wide map of gene expression in the adult mouse brain. The resulting in situ hybridization (ISH) image data will be available free-of-charge to the public. Additionally, we are developing an informatics pipeline to support searching of the data by anatomic region and expression level and/or pattern. This paper describes a robust, high-throughput registration scheme to automatically annotate hierarchical brain structures in the ISH imagery.

ObjectiveTo compare the gene expression patterns of synovial cells from inflamed or normal/reactive areas of synovial membrane obtained from the same patient with osteoarthritis (OA).MethodsAt the time of total knee replacement, synovial tissues were obtained from 12 patients with knee OA. The inflammation status of the synovial membrane was characterized according to macroscopic criteria and classified as normal/reactive or inflamed. Biopsy samples were cultured separately for 7 days. Microarray gene expression profiling was performed on normal/reactive and inflamed areas. Western blot and immunohistochemistry were used to confirm the identified genes that were differentially expressed.ResultsWe identified 896 genes that were differentially expressed between normal/reactive and inflamed areas. The key pathways were related to inflammation, cartilage metabolism, Wnt signaling, and angiogenesis. In the inflammation network, the genes TREM1 and S100A9 were strongly up-regulated. The genes MMP3, MMP9, CTSH (cathepsin H), and CTSS (cathepsin S) were significantly up-regulated in the cartilage catabolism pathway, while the most up-regulated anabolism enzyme gene was HAS1. In the Wnt signaling pathway, the genes for Wnt-5a and low-density lipoprotein receptor–related protein 5 were up-regulated, while the gene FZD2 and the gene for Dkk-3 were down-regulated. Finally, STC1, which codes for a protein involved in angiogenesis, was identified as the most up-regulated gene in inflamed compared with normal/reactive areas.ConclusionThis study is the first to identify different expression patterns between 2 areas of the synovial membrane from the same patient. These differences concern several key pathways involved in OA pathogenesis. This analysis also provides information regarding new genes and proteins as potential targets of treatment.

Event Abstract Back to Event Reconstruction of the mouse brain regions from the spatial gene expression data Alexey Tsvetkov1* 1 Russian Scientific Centre, Kurchatov Institute, Nano-, Bio-, Info-, Cognitive (NBIC) Center Understanding the structural basis of nervous system still remains one of the primary challenges in neuroscience. New perspectives for the studies of brain anatomy are emerging with the availability of large-scale spatial gene expression data. Exploration of these data promises to deliver new insights into the understanding of relations between genes and brain structure. We performed an analysis of the gene expression for more than 4000 genes in the mouse brain with the spatial resolution of 200 micrometers. These data are freely available from the Allen Brain Atlas [1] project. After data filtering and scaling, distance matrices between all brain voxels (volumetric pixels) were calculated for a number of common distance measures. We also proposed and utilized a novel distance measure based on the locality of gene expression. Then the brain volume was hierarchically clustered with a variety of techniques [2]. In order to verify the resulting brain parcellations we compared them by means of a quantitative measure to the histologically-defined anatomical reference atlas and found significant overlap between automatically derived spatial clusters and anatomical structures. We also compared brain regions by the number of genes expressed. Here the number of simultaneously activated genes was considered to be proportional to the amount of accumulated evolutionary changes. This allows us to identify dentate gyrus and adjoining hippocampal regions as the structures with the richest expression and, hence, less conservative in the evolutionary sense. Other regions of the cerebral cortex show intermediate levels of expression variability while other brain structures have accumulated fewer changes in the process of evolution, with olfactory bulbs and cerebellum being most conservative. This work was supported by Russian FTP "Scientific and scientific-pedagogical personnel". [1] Ng L., Bernard A., Lau C. et. al., "An anatomic gene expression atlas of the adult mouse brain" (2009) Nature Neuroscience, 12 (3): 356-362. [2] Xu R. and Wunsch D., "Survey of clustering algorithms" (2005) IEEE Transactions on Neural Networks, 16 (3): 645-678. Keywords: General neuroinformatics Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: General neuroinformatics Citation: Tsvetkov A (2011). Reconstruction of the mouse brain regions from the spatial gene expression data. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00043 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Alexey Tsvetkov, Russian Scientific Centre, Kurchatov Institute, Nano-, Bio-, Info-, Cognitive (NBIC) Center, Moscow, alexis.tsvetkov@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Alexey Tsvetkov Google Alexey Tsvetkov Google Scholar Alexey Tsvetkov PubMed Alexey Tsvetkov Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Understanding the geography of genetic expression in the mouse brain has opened previously unexplored avenues in neuroinformatics. The Allen Brain Atlas (www.brain-map.org) (ABA) provides genome-wide colorimetric in situ hybridization (ISH) gene expression images at high spatial resolution, all mapped to a common three-dimensional 200μm3 spatial framework defined by the Allen Reference Atlas (ARA) and is a unique data set for studying expression based structural and functional organization of the brain. The goal of this study was to facilitate an unbiased data-driven structural partitioning of the major structures in the mouse brain. We have developed an algorithm that uses nonnegative matrix factorization (NMF) to perform parts based analysis of ISH gene expression images. The standard NMF approach and its variants are limited in their ability to flexibly integrate prior knowledge, in the context of spatial data. In this paper, we introduce spatial connectivity as an additional regularization in NMF decomposition via the use of Markov Random Fields (mNMF). The mNMF algorithm alternates neighborhood updates with iterations of the standard NMF algorithm to exploit spatial correlations in the data. We present the algorithm and show the sub-divisions of hippocampus and somatosensory-cortex obtained via this approach. The results are compared with established neuroanatomic knowledge. We also highlight novel gene expression based sub divisions of the hippocampus identified by using the mNMF algorithm.

This report contains a gene expression summary of the ventral posterior complex of the thalamus (VP), derived from the Allen Brain Atlas (ABA) in-situ hybridization (ISH) mouse data set. The structure’s location and morphological characteristics in the mouse brain are described using the Nissl data found in the Allen Reference Atlas. Using an established algorithm, the expression values of the VP were compared to the values of the macro/parent-structure, in this case the thalamus, for the purpose of extracting regionally specific gene expression data. The highest ranking ratios were then manually curated and verified. The 50 Select Genes were compiled for expression characterization. The experimental data for each gene may be accessed via the links provided; complementary sagittal data may also be accessed using the ABA. Correlation between gene expression in the VP and the rest of the brain, across all genes in the coronal dataset (~4300 genes), were derived computationally and are presented below. A gene ontology table (derived from DAVID Bioinformatics Resources 2007) is also included, highlighting possible functions of these 50 Select Genes.

This report contains a gene expression summary of the inferior colliculus (IC), derived from the Allen Brain Atlas (ABA) in-situ hybridization (ISH) mouse data set. The structure’s location and morphological characteristics in the mouse brain are described using the Nissl data found in the Allen Reference Atlas. Using an established algorithm, the expression values of the IC were compared to the values of the macro/parent-structure, in this case the midbrain, for the purpose of extracting regionally specific gene expression data. The highest ranking ratios were then manually curated and verified. The 50 Select Genes were compiled for expression characterization. The experimental data for each gene may be accessed via the links provided; complementary sagittal data may also be accessed using the ABA. Correlation between gene expression in the IC and the rest of the brain, across all genes in the coronal dataset (~4300 genes), were derived computationally and are presented below. A gene ontology table (derived from DAVID Bioinformatics Resources 2007) is also included, highlighting possible functions of these 50 Select Genes.

Human pancreatic ribonuclease (RNase 1) is a pancreatic enzyme that is present at high levels in the serum of most patients with pancreatic adenocarcinoma. For this reason, the authors studied its patterns of expression at the single-cell level in pancreatic adenocarcinoma tissues by immunohistochemical analysis and in situ hybridization (ISH). Immunohistochemical analysis with polyclonal antibodies against RNase 1 and by ISH with digoxigenin-labeled RNase 1 probe were used to detect RNase 1 in the neoplastic cells of ductal type pancreatic adenocarcinomas. Fifteen of 18 carcinoma samples were positive for RNase 1, demonstrating that the expression of ribonuclease that the authors observed previously in human pancreatic adenocarcinoma cell lines was not an artifact of cell culture. The authors also found RNase 1 in some of the metaplastic ducts and atrophic islets in 4 of 6 chronic pancreatitis samples, and they observed RNase 1 immunostaining in hyperplastic ducts adjacent to one of the well-differentiated adenocarcinomas. The expression levels of RNase 1 by tumor cells from pancreatic adenocarcinomas are consistent with the high RNase 1 levels found in the serum of most patients with pancreatic adenocarcinoma. This expression of RNase 1, which is an acinar protein, demonstrates that the patterns of gene expression in pancreatic adenocarcinoma are distinct from those of normal pancreatic duct cells. Conversely, RNase 1 expression levels in altered ductal cells from some chronic pancreatitis tissues and hyperplastic ducts from carcinoma tissues suggest that abnormal expression levels may be an early event in pancreatic tumorigenesis.

The Allen Brain Atlas is a repository of neuroanatomical data concerning the mouse brain. The core of the database is a Nissl-stained reference atlas of the brain accompanied by in situ hybridization data for essentially the entire mouse genome. This database is freely available at the Allen Institute for Brain Science website, as is an innovative tool to explore the database, the Brain Explorer. This tool is downloaded and installed on your own computer. I have developed a self-study tutorial, “Explorations with the Allen Brain Explorer”, which uses the Brain Explorer and the Brain Atlas to teach fundamentals of mammalian neuroanatomy and brain function. In this tutorial background information and step-by-step exercises on the use of the Brain Explorer are given using PowerPoint as a platform. To do the tutorial both the PowerPoint and the Brain Explorer are opened on the computer and the students switch from one program to the other as they go, in a step-wise fashion, through the various exercises. There are two main groups of exercises, titled “The Basics” and “Explorations”, with both groups accessed from a PowerPoint “Start Menu” by clicking on dynamic links to the appropriate exercises. Most exercises have a number of dynamic links to PowerPoint slides where background information for the exercises is given or the neuroanatomical data collected from the Brain Atlas is discussed.

Alternative splicing (AS) occurs in more than 90% of human genes. The expression pattern of an alternatively spliced exon is often regulated in a cell type-specific fashion. AS expression patterns are typically analyzed by RT-PCR and RNA-seq using RNA samples isolated from a population of cells. In situ examination of AS expression patterns for a particular biological structure can be carried out by RNA in situ hybridization (ISH) using exon-specific probes. However, this particular use of ISH has been limited because alternative exons are generally too short to design exon-specific probes. In this report, the use of BaseScope, a recently developed technology that employs short antisense oligonucleotides in RNA ISH, is described to analyze AS expression patterns in mouse brain sections. Exon 23a of neurofibromatosis type 1 (Nf1) is used as an example to illustrate that short exon-exon junction probes exhibit robust hybridization signals with high specificity in RNA ISH analysis on mouse brain sections. More importantly, signals detected with exon inclusion- and skipping-specific probes can be used to reliably calculate the percent spliced in values of Nf1 exon 23a expression in different anatomical areas of a mouse brain. The experimental protocol and calculation method for AS analysis are presented. The results indicate that BaseScope provides a powerful new tool to assess AS expression patterns in situ.

The large-scale automation of neuroscience has enabled the construction of genome-wide atlases, of which the Allen Brain Atlas (ABA), which allows the 3D visualization of the expression profile of 21,500 genes in the male mouse brain down to single-cell level resolution, is the most comprehensive (1). In PNAS, Mahfouz et al. (2) use the ability of the ABA to pinpoint the anatomical locations of expressed genes to uncover transcripts whose expression profiles correlate with those of steroid receptors, to begin to understand their function and specificity of action in different brain regions. This study shows that mapping combinatorial interactions among specific sets of genes represents a significant leap forward in our understanding of how tissue specificity for a given signaling pathway is determined, and in identifying the potential relationship between otherwise unrelated brain areas in terms of the adaptive response to specific biological and environmental challenges. Steroid receptors are pleiotropic transcription factors belonging to the superfamily of nuclear receptors, whose activity is induced by steroid hormones: lipophilic signaling molecules derived from cholesterol and primarily produced by the gonads and the adrenal cortex. In the mammalian brain, steroid hormones mediate the feedback from these steroid-generating organs on the neuroendocrine hypothalamus to control bodily functions (reproduction, metabolism, stress, inflammation, osmoregulation), but also play a fundamental organizational role during brain development, trigger adult brain plasticity, and are involved in cognitive and emotional regulation (3⇓⇓⇓⇓⇓⇓–10). The idea of having an anatomical map combined with a quantitative expression map of nuclear receptor genes dates back to 2007, when Gofflot et al. created an interactive database of 49 nuclear receptor genes spanning more than 100 different regions of the mouse brain (11). These researchers used two complementary approaches to meet the challenge of obtaining both cell-level resolution … [↵][1]1To whom correspondence should be addressed. Email: vincent.prevot{at}inserm.fr. [1]: #xref-corresp-1-1

A consistent feature of varicella-zoster virus (VZV) latency is the restricted pattern of viral gene expression in human ganglionic tissues. To understand further the significance of this gene restriction, we used in situ hybridization (ISH) to detect the frequency of RNA expression for nine VZV genes in trigeminal ganglia (TG) from 35 human subjects, including 18 who were human immunodeficiency virus (HIV) positive. RNA for VZV gene 21 was detected in 7 of 11 normal and 6 of 10 HIV-positive subjects, RNA for gene 29 was detected in 5 of 14 normal and 11 of 11 HIV-positive subjects, RNA for gene 62 was detected in 4 of 10 normal and 6 of 9 HIV-positive subjects, and RNA for gene 63 was detected in 8 of 17 normal and 12 of 15 HIV-positive subjects. RNA for VZV gene 4 was detected in 2 of 13 normal and 4 of 9 HIV-positive subjects, and RNA for gene 18 was detected in 4 of 15 normal and 5 of 15 HIV-positive subjects. By contrast, RNAs for VZV genes 28, 40, and 61 were rarely or never detected. In addition, immunocytochemical analysis detected the presence of VZV gene 63-encoded protein in five normal and four HIV-positive subjects. VZV RNA was also analyzed in explanted fresh human TG and dorsal root ganglia from five normal human subjects over a period of up to 11 days in culture. We found a very different pattern of gene expression in these explants, with transcripts for VZV genes 18, 28, 29, 40, and 63 all frequently detected, presumably as a result of viral reactivation. Taken together, these data provide further support for the notion of significant and restricted viral gene expression in VZV latency.

For pearl culture, nucleus and mantle grafts are implanted into the gonad of the host oyster. The epithelial cells of the implanted mantle graft elongate and surround the nucleus, and a pearl sac is formed. Shell matrix proteins secreted by the pearl sac play an important role in pearl formation. We studied the gene expression patterns of six shell matrix proteins (msi60, n16, nacrein, msi31, prismalin-14, and aspein) in the epithelial cells associated with pearl sac formation. There were differences in the expression patterns of the six genes in the epithelial cells, and the relative expression levels for msi60 and aspein differed between the mantle graft and pearl sac (48days after implantation). Therefore, the gene expression patterns of the epithelial cells were genetically undetermined, and changed between before and after pearl sac formation. The gene expression patterns of the epithelial cells of the pearl sac may be regulated by the host oysters.

Disruption of the circadian rhythm is a risk factor for cancer, while glioma is a leading contributor to mortality worldwide. Substantial efforts are being undertaken to decrypt underlying molecular pathways. Our understanding of the mechanisms through which disrupted circadian rhythm induces glioma development and progression is incomplete. We, therefore, examined changes in the expression of glioma-related genes in the mouse brain after chronic jetlag (CJL) exposure. A total of 22 candidate tumor suppressor (n= 14) and oncogenes (n= 8) were identified and analyzed for their interaction with clock genes. Both the control and CJL groups were investigated for the expression of candidate genes in the nucleus accumbens, hippocampus, prefrontal cortex, hypothalamus, and striatum of wild type, Bmal1-/- and Cry1/2 double knockout male mice. We found significant variations in the expression of candidate tumor suppressor and oncogenes in the brain tissues after CJL treatment in the wild type, Bmal1-/- and Cry1/2 double knockout mice. In response to CJL treatment, some of the genes were regulated in the wild type, Bmal1-/- and Cry1/2 similarly. However, the expression of some of the genes indicated their association with the functional clock. Overall, our result suggests a link between CJL and gliomas risk at least partially dependent on the circadian clock. However, further studies are needed to investigate the molecular mechanism associated with CJL and gliomas.

This report contains a summary of expression patterns for genes that are enriched in the motor nucleus of the trigeminal nerve (V) of the pons. All data is derived from the Allen Brain Atlas (ABA) in situ hybridization mouse project. The structure's location and morphological characteristics in the mouse brain are described using the Nissl data found in the Allen Reference Atlas. Using an established algorithm, the expression values of the motor nucleus of the trigeminal nerve were compared to the values of its larger parent structure, in this case the pons, for the purpose of extracting regionally selective gene expression data. The highest ranking genes were manually curated and verified. 50 genes were then selected and compiled for expression analysis. The experimental data for each gene may be accessed via the links provided; additional data in the sagittal plane may also be accessed using the ABA. Correlations between gene expression in the motor nucleus of the trigeminal nerve and the rest of the brain, across all genes in the coronal dataset (~4300 genes), were derived computationally. A gene ontology table (derived from DAVID Bioinformatics Resources 2007) is also included, highlighting possible functions of the 50 genes selected for this report.