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Human Molecular Genetics 2007 16(R2):R209-R219; doi:10.1093/hmg/ddm183
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© 2007 The Author(s)
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Insights from spatially mapped gene expression in the mouse brain

Susan M. Sunkin* and John G. Hohmann

Allen Institute for Brain Science, 551 N. 34th Street, Seattle, WA 98103, USA

* To whom correspondence should be addressed. Tel: +206 548 7000; Fax: +206 548 7071; Email: susans{at}alleninstitute.org

Received June 1, 2007; Revised June 1, 2007; Accepted July 6, 2007


   ABSTRACT
TOP
 ABSTRACT
INTRODUCTION
 CONCLUSION
 REFERENCES
 
The growing number of publicly available databases of murine gene expression arising from genomic-scale transcriptome/proteome profiling projects allows open access to information about genes potentially involved in diseases and disorders of the brain. The use of various methodologies by myriad projects provides complementary types of information, ranging from easily quantifiable microarray data for gross brain regions, to transcript tag analysis and proteomic characterization. One mode of gene expression analysis that has recently been widely adopted is the utilization of colorimetric in situ hybridization. This approach is adaptable for high throughput production, and provides a reproducible, scaleable platform for large datasets. The Allen Brain Atlas in particular has utilized this technology to produce a genomic-scale anatomical digital atlas of gene expression in the adult male mouse brain. The availability of global datasets with cellular level spatial resolution, which can be easily parsed due to accessible informatics-derived image analysis tools, can provide both high level and detailed insights into gene regulation. This article reviews various gene expression profiling projects in the mouse brain, how these data sets are increasingly used to complement other studies and applications of these datasets to further understanding of neurological disease.


   INTRODUCTION
TOP
 ABSTRACT
 INTRODUCTION
CONCLUSION
 REFERENCES
 
The mammalian brain is a structure of astounding complexity, with hundreds of different regions that have highly specialized functions and nuclei. Within these regions are a vast array of different cell types contributing to the functional properties of each distinct neuroanatomical area. Morphological, physiological, chemical, cytoarchitectural and myeloarchitectural characteristics are routinely studied to define these distinct cell populations in the central nervous system (CNS). Complementing these attributes is the recent advent of spatially mapped gene expression data. Elucidation of detailed gene expression profiles in the normal state will lead to a better understanding of the phenotypes of various CNS disorders such as schizophrenia, autism and epilepsy, many of which have poorly defined disease etiologies.

Developments in automation of histological procedures, microscopy and image analysis, as well as the availability of the mouse genome sequence (1) have facilitated the rapid production of expression data from the mouse brain with spatial and cellular resolution. These expression modalities include in situ hybridization (ISH) (2), reporter gene methods (3) and immunohistochemistry (4). In this review, we briefly describe several projects that are delineating gene expression with a variety of methodologies in the mouse brain, and the utilization of these large-scale expression data sets in diverse applications. In addition, global findings from the Allen Brain Atlas (ABA), a genome-scale spatially resolved gene expression atlas, are discussed. Using the ABA dataset, the expression profiles of example genes implicated in autism, epilepsy or schizophrenia are analysed in the context of their expression patterns in the mouse brain.

Spatially resolved expression projects
Several large-scale projects have applied a range of technologies to identify gene expression patterns in the mouse brain (Table 1). Microarray analysis on discrete brain regions has been used on one (http://symatlas.gnf.org/SymAtlas/) (5) and several adult mouse strains (http://www.teragenomics.com/) (6). Microarrays have been combined in the adult mouse with voxelation and gene expression tomography in which volumetric maps of gene expression are produced from images (http://vox.pharmacology.ucla.edu/datadownload.html) (7,8). Unlike microarrays, which are based on sequences of known transcripts, expression analysis beyond known transcripts is obtained by serial analysis of gene expression (SAGE) in numerous brain structures and developmental stages by the Mouse Atlas of Gene Expression (http://www.mouseatlas.org/) (9,10). Combinations of phenotypic data, which includes microarray expression analysis and genotype are available for mapping quantitative trait loci (QTL) by GeneNetwork (http://www.genenetwork.org) (11). Utilizing microarray analysis in assaying cell-type specific expression profiles, Sugino et al. (12) have analysed 12 distinct neuronal populations that include excitatory projection neurons and inhibitory interneurons with the resulting cell type expression data available at the Mouse Neuronal Expression Database (MNED) (http://mouse.bio.brandeis.edu/). All these gene expression data sets lack comprehensive coverage and neuroanatomic specificity throughout the brain; however, they do provide valuable expression information on a finite number of discrete brain regions or cell types.


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  		Table 1. Mouse brain gene expression databases

 
Several projects have gene expression data with high levels of spatial mapping in the developing nervous system (13,14). Each project contributes publicly available data to the community across a wide range of developmental timepoints with various degrees of resolution and anatomical mapping/annotation. Two closely related projects use two different data modalities to generate neuroanatomically mapped data. The Brain Gene Expression Map (BGEM) (http://www.stjudebgem.org/web/mainPage/mainPage.php) (15,16) utilizes radioactive ISH to identify gene candidates for the Gene Expression Nervous System Atlas (GENSAT) enhanced green fluorescent protein reporter transgenic mouse pipeline (http://www.gensat.org/ or http://www.ncbi.nlm.nih.gov/projects/gensat/) (3,17,18).

Non-isotopic ISH is another technique that has been widely employed. GenePaint (http://www.genepaint.org/) and EurExpress (http://www.eurexpress.org/ee/) (19,20) use a colorimetric ISH platform on primarily embryonic day 14.5 (E14.5). Postnatal day 7 (P7) ISH data is available at http://www.geneatlas.org (21). Various developmental stages are assayed by colorimetric ISH by the Embryo Gene Expression Patterns project (http://www.sanger.ac.uk/Teams/Team39/). The Embryonic Mouse in Bioinformative Lyceum System (EMBLYS), a project of the National Research Institute for Children Health and Development of Japan, is initially generating data for transcription factor related genes, followed by genomic-scale ISH expression profiling. Colorimetric ISH on transcription factor and RNA-binding proteins in development is available at http://mahoney.chip.org/mahoney/ (22,23). Complementing these large-scale projects is the Edinburgh Mouse Atlas Project (EMAP) (http://genex.hgu.mrc.ac.uk/), which contains substantial spatial and temporal data, including a digital mouse embryonic developmental atlas linked to a gene expression database, EMAGE (24,25). EMAGE collaborates with the Gene Expression Database (GXD) of Mouse Genome Informatics (MGI) (http://www.informatics.jax.org/mgihome/GXD/aboutGXD.shtml) (26) to acquire and map gene expression patterns throughout development (25).

Insights from the ABA
In addition to the database projects described above, a comprehensive survey of gene expression patterns obtained by colorimetric ISH has been completed by the Allen Institute for Brain Science. The ABA (http://www.brain-map.org) contains the expression patterns of approximately 20 000 genes in 56-day-old (P56) C57Bl/6J male brains from the hybridization of non-isotopic digoxigenin labeled riboprobes to uniformly spaced 25-µm thick fresh-frozen tissue sections (27). High resolution images are captured in which signal detection algorithms identify signal intensity and convert this spatial information to segmented digital representations of expressing cells (28). To create a searchable anatomical gene expression database, colorimetric ISH image data for each gene is aligned in the same three-dimensional coordinate space through registration to a reference atlas (27–29). Finally, three-dimensional representations of gene expression superimposed on the reference atlas are illustrated with the stand-alone online Brain Explorer application (C. Lau et al. submitted for publication).

From this genome-scale survey of expression in the mouse brain, one unexpected finding is that a high percentage of genes (~80%) display expression above background (27). This percentage of genes expressed in the brain is significantly higher than that predicted from microarray analysis (30), though a definitive cross platform comparison of microarray and ISH data has yet to be published. Surprisingly, relatively few genes appear to be expressed in all cells (i.e. genes with ubiquitous expression profiles). Gene expression does delineate major cell classes in the brain such as neurons, astrocytes and oligodendrocytes. At the opposite end of the expression spectrum is the very small number of genes expressed only in a single structure or nuclei.

Shifting from global to regional analyses, cellular expression patterns in defined brain structures such as the cerebral cortex may delineate potential gene markers for neuroanatomical regions and cell types. The cortex has a laminar pattern and is divided into several layers as well as motor and sensory areas. Genes with restricted expression can be found in different cortical layers (layers I, II/III, IV, V, VI and VIb) and in functionally discrete cortical regions such as the somatosensory cortex (27). However, the majority of genes expressed in the cortex are found to display relatively consistent expression patterns within a layer throughout the neocortex (27). Abnormalities of the cortex, such as cognitive impairment, degeneration and aberrant neurological wiring contribute to many neurological and degenerative nervous system disorders.

Heterogeneous gene expression profiles are also apparent in areas such as the subregions of the hippocampus (CA1, CA2, CA3, dentate gyrus and hilus) and in all hippocampal cardinal axes, with common delineations seen along the dorsal/ventral (septotemporal) axis (27). The hippocampus has been shown to be essential in certain types of learning and memory (31,32) and its relevance in several disease states has been well-described. For example, significantly larger hippocampal volumes have been correlated with autism (33,34) and smaller hippocampal volumes have been documented with schizophrenia and epilepsy (33,35). How gene expression profile diversity in the hippocampus corresponds to the function and connectivity of this structure and its role in various neurological disorders is an exciting avenue for further investigation.

Utilization of spatially resolved expression data
As the majority of large-scale expression data is recent, it is difficult to ascertain the full extent of their use by the scientific community; however, there are several published studies that showcase diverse applications addressing important biological questions in mice and humans. Papassotiropoulos et al. (36) have identified KIBRA (alias WWC1, WW and C2 domain containing 1) during a genome-wide screen of >500 000 single-nucleotide polymorphisms to find memory-related gene variants in humans. Expression of KIBRA in CA1 and the dentate gyrus of the mouse hippocampus is high in the ABA, supporting the importance of KIBRA in memory. Petyuk et al. (37) have developed a methodology for spatial proteome mapping of the mouse brain that starts with tissue voxelation and ends with peptide identification and quantitation. GENSAT and ABA data have been used to corroborate the protein abundance data from this proteomics technique (37). Mozhui et al. (38) have performed a genetic and structural analysis of the basolateral amygdala complex (BLAc) in BXD recombinant inbred mice in which BLAc volume and cell populations were quantified. A QTL for BLAc size has been identified with WebQTL (39) and the ABA expression profiles of the resulting candidate genes have been examined (38). In a study by Dugas et al. (40) in which gene expression profiles during oligodendrocyte differentiation were assessed from microarrays, ABA, BGEM and GENSAT data have been applied to validate the expression patterns of candidate genes in white matter. The majority of candidate genes (55 out of 70) are expressed in white matter in at least one database. In a study by Ponomarev et al. (41), MNED and ABA datasets have been employed to define cell type-specific expression of GABA A (gamma-aminobutyric acid) {alpha}1 mutation-associated genes in the cortex and cerebellum, respectively. Microarray expression data have been used to identify differentially expressed genes between knock-out and wild-type lines. Then, the spatially resolved expression profiles of the candidate genes have been assessed in MNED and ABA in different types of neurons and glial cells (41). In a study about corticotrophin releasing factor (CRF) and its receptors, GENSAT data confirms RT–PCR data that shows low expression levels of Type 2 CRF receptor (CRF-R2{alpha}) in the vermis and hemisphere of the cerebellum (42). A study by Morales and Hatten (43) has used GENSAT data to identify numerous genes with restricted expression in cerebellar progenitor populations, including Purkinje cell and cerebellar nuclear precursors. Finally, a study by Yaylaoglu et al. (44) has compared expression patterns of fibroblast growth factors and their receptors in E14.5 mice.

In addition to applying existing gene expression data to verify candidate genes, confirm gene expression data from microarrays, SAGE, RT–PCR, or proteomics, or ascertain cell type expression, spatially mapped expression data has been employed for comparison between wild-type and mutant states. This application of spatially mapped data can lead to a better understanding of gene regulation, cell type specificity, disease and neurodevelopment. One such example is the study of the developing hippocampus of Emx2 mutant mice by several techniques, one of which was ISH (45). In addition to examining the neuroanatomical differences in the hippocampal fissure region between wild-type and Emx2–/- mice, the downstream implications of the absence of Emx2 have been examined, specifically the affects on hippocampal Reln expressing cells (45). Continuing the study of the Emx2 mutant, Skutella et al. (46) has performed microarray analysis on wild-type and Emx2–/– hippocampus. Many microarray candidate genes that differed in expression between wild-type and mutant Emx2 hippocampus have been assayed by ISH.

Expression analysis of genes associated with neurological disorders
Cellular resolution of gene expression patterns (e.g. neuroanatomical selectivity, cell type expression and identification of co-expressed genes) in the wild-type state provides a foundation to begin to understand phenotypes in the diseased condition. In the next sections, we utilize publicly available ABA data to examine expression profiles in the cortex, hippocampus and striatum of some orthologs associated with autism, epilepsy or schizophrenia (Table 2). The list shown in Table 2 is not comprehensive, but does contain numerous candidate genes that are strongly supported in the literature (47–53). One application of spatially mapped gene expression data is assessment of the presence/absence of a candidate gene in a brain region associated with a particular disorder. In many cases, the nature of the involvement of a particular candidate gene in the pathophysiology and phenotype of neurological disease is poorly understood. While using neuroanatomical gene expression analysis for this purpose is complicated by the simultaneous needs to assess complex global expression patterns and detailed cellular resolution, insight may be gained about the circuitry affected by these disorders.


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  		Table 2. Expression analysis of genes associated with neurological disorders

 
Autism
Autism spectrum disorders (ASDs) are characterized by impairments in social, communicative and behavioral development, often accompanied by abnormal cognitive functioning, learning, attention and sensory processing (54). Autism is considered one of multiple ASDs (55) and has a prevalence of about one in every 150 children (56). Abnormalities in neural development are widely recognized as the underlying neuropathological causes of ASDs (57). Post-mortem studies of autistic brains show a variety of neuropathologies including decreased programmed cell death and/or increased cell proliferation, abnormal cell differentiation with smaller neuronal size, altered cell migration with disrupted cortical and subcortical cytoarchitectonics and modified synaptogenesis (57,58).

Table 2 lists 12 genes associated with autism. From the mouse ortholog expression profiles, 12, 11 and 10 genes are expressed in the cortex, hippocampus and striatum, respectively, although the expression is markedly lower in the striatum compared with the other two structures. Three genes, Adcyap1, Nrcam and Reln, have enriched expression in cortical layer V, but also have interesting expression profiles in other cortical layers. Reln is expressed in GABAergic interneurons, Nrcam has widespread expression in cortical layers II through VIb and Adcyap1 is enriched in cortical layers II/III (see Fig. 1 and Table 2). Expression of these genes in cortical layer V is intriguing since cortical layer V pyramidal cells are the major cortical neurons that send projections to striatal, pallidal and brainstem regions as well as projecting to other cortical layers (59). The expression of Reln in GABAergic interneurons is interesting since an imbalance between excitation and inhibition characterizes the cortex of autistic individuals, leading to hyperexcitability and unstable activity of cortical networks following sensory stimulation (60).


Figure 1
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  		Figure 1. Expression profiles in the mouse brain of genes associated with autism, epilepsy or schizophrenia. (A). Examples of cortical expression patterns of genes associated with autism. a, Adcyap1 (adenylate cyclase activating polypeptide 1) (sagittal image series ID 70523740); b, Nrcam (neuronal cell adhesion molecule) (sagittal image series ID 173) and c, Reln (reelin) (sagittal image series ID 892). Each gene has enriched expression in cortical layer 5 in addition to expression in other cortical layers. (B) Expression in hippocampal subregions of genes associated with epilepsy. d, Abat (4-aminobutyrate aminotransferase) (coronal image series ID 72079931); e, Gabrb2 (gamma-aminobutyric acid (GABA) A receptor, beta 2) (coronal image series ID 472) and f, Grik1 (glutamate receptor, ionotropic, kainate 1) (coronal image series ID 75749751). Each gene is strongly expressed in GABAergic interneurons throughout the hippocampus, but differs in their distribution and expression level in hippocampal pyramidal cells. Inset shows magnified view of expression in dentate gyrus and subgranular zone. (C) Examples of striatal expression patterns in genes associated with schizophrenia. g, Pdyn (prodynorphin) (coronal image series ID 71717084); h, Ppp1r1b [protein phosphatase 1, regulatory (inhibitor) subunit 1B (dopamine and cAMP regulated phosphoprotein, DARPP-32)] (coronal image series ID 73732146) and i, Rgs4 (regulator of G-protein signaling 4) (coronal image series ID 74511884). Each gene is strongly expressed in the striatum with either a widespread expression pattern (Ppp1r1b and Rgs4) or a pattern with strong foci of expression (Pdyn).

 
Epilepsy
Epilepsies are characterized by recurrent and often unpredictable seizures (61). Approximately 3% of the population is affected by epilepsies, with peak incidences in children and the elderly (62). Epilepsies can be caused by both acquired and genetic factors, with almost half of all epilepsies having some genetic basis (63); however, only a small percentage appears to have Mendelian inheritance. Largely genetic, idiopathic epilepsies comprise ~30% of all epilepsies (62,64).

A number of genes, including several ion channels, have been associated with rare monogenic idiopathic epilepsies (49,61). Table 2 lists 13 genes associated with epilepsy. Expression analysis of the mouse orthologs shows that 13, 13 and 12 genes are expressed in the cortex, hippocampus and striatum, respectively. Several of these epilepsy-associated genes exhibit discrete hippocampal expression patterns. Figure 1 shows the hippocampal expression of Abat, Gabrb2 and Grik1. Interestingly, all three genes are expressed in GABAergic interneurons, but differ in their distribution and expression level in hippocampal pyramidal cells (i.e. Abat has moderate expression at the CA2/CA3 boundary with heterogeneous expression levels throughout CA3 and Gabrb2 has moderate expression in CA1). In the epilepsy phenotype, memory impairment in the temporal lobe is well documented, as is hippocampal cell loss and gliosis (65,66). In addition, GABA-mediated inhibition in the hippocampus is impaired in epilepsy (67,68).

Schizophrenia
Schizophrenia is a complex, heritable psychiatric disorder with an incidence of ~1% of the population. Dysfunctions in the prefrontal and mesial temporal cortices, and in the glutamate and GABA neurotransmitter systems have been implicated in schizophrenia (69). In addition, several studies suggest a strong link with dopamine (70), hence the dopamine hypothesis (71–73). It has been suggested that delusions, a hallmark of schizophrenia psychosis, are produced by abnormally reinforced/rewarded thoughts and associations (74–76). One neuroanatomical region that has an important role in the learning of associations is the ventral striatum (77,78). In schizophrenia, aberrant striatal dopamine function has been described (79).

Several putative schizophrenia susceptibility genes have been identified (52,80). Table 2 lists 13 genes associated with schizophrenia: 13, 12 and 12 genes are expressed in the cortex, hippocampus and striatum, respectively. Figure 1 shows striatal expression profiles of mouse ortholog of PDYN, PPP1R1B and RGS4. All three genes exhibit robust expression throughout the striatum. RGS4 modulates the signaling activity of G-protein coupled receptors, which feedback to regulate RGS4 expression via dopamine receptors (81–83). Expression analysis of RGS4 in normal human brain tissue reveals dense expression in most cortical layers and lower expression in the basal ganglia (striatum), thalamus and hippocampus (pyramidal layers) (84). Examination of schizophrenia samples from medicated patients shows a decrease in RGS4 mRNA levels in the caudate (striatum) (85). PPP1R1B (DARPP-32) has a critical role in regulating dopamine signaling in the striatum (86). Previous studies have shown that PPP1R1B is strongly expressed in the mouse striatum (87,88) and the ABA ISH data concurs with this report. PDYN is the precursor for dynorphin opioid peptides and the opioid system has important functions in addiction, reward and controlling pain. Interestingly, there is a high degree of overlap between schizophrenia and addictive disorders (89). PDYN has been shown to be highly expressed in the patch/striosome compartment compared with the matrix of the dorsal caudate-putamen (90,91) and the ABA data are consistent with this description of patchy expression in the striatum.


   CONCLUSION
TOP
 ABSTRACT
 INTRODUCTION
 CONCLUSION
REFERENCES
 
A new era of neurogenomics has begun. With increasing amounts of publicly available gene expression data in the mouse, we are just beginning to reap the benefits of applying these data sets to numerous biological questions, particularly regarding neurological disease, gene regulation and cell type specificity. Expression profiling of single neurons from the rat somatosensory neocortex by multiplex RT–PCR has shown that the expression of any single gene cannot define an anatomical cell type; however, combinatorial expression profiles can predict anatomical cell types with a higher degree of accuracy (92). Further defining the molecular profile of cell types will be possible using these data sets as a starting point to define candidate cell type markers for double or multiplex fluorescent ISH studies, which can identify the extent of coexpression between marker genes. A logical extension of the mouse studies described in this review will be the future availability of cellular resolution datasets using human brain tissue in both normal and disease states, which would accelerate our understanding of neurological disorders.


   ACKNOWLEDGEMENTS
 
The authors thank Kurt Ahrens, Amy Bernard, Michael Hawrylycz, Carol Thompson, Hongkui Zeng, and particularly Ed Lein for their critical reading of the manuscript and Maureen Howell for insightful discussions about autism. This work was sponsored by the Allen Institute for Brain Science. The authors wish to thank the Allen Institute founders, Paul G. Allen and Jody Patton, for their vision, encouragement and support. ‘Funding to pay the Open Access publication charges for this article was provided by the Allen Institute for Brain Science’.

Conflict of Interest statement. None declared.


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 INTRODUCTION
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