|
|
||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Topical Review |
1 Developmental Neurobiology, St Jude Children's Research Hospital, 332 N Lauderdale, Memphis, TN 38105-2729, USA
| Abstract |
|---|
|
|
|---|
(Received 1 May 2006;
accepted after revision 24 May 2006;
first published online 25 May 2006)
Corresponding author T. Curran: Developmental Neurobiology, St Jude Children's Research Hospital, 332 N Lauderdale, Memphis, TN 38105-2729, USA. Email: thomas.curran{at}stjude.org
Introduction
The completion of the mouse genome presented scientists with the challenge of understanding the function of each gene and how these functions integrate in the context of the whole animal. A critical first step in this process is to determine when and where each is expressed at the mRNA level. To accomplish this formidable task, it is essential to utilize high throughput technologies and to develop bioinformatic approaches for collecting and disseminating the enormous volume of gene expression image data. The large number of cell types and complex developmental processes in the brain make it the most challenging and rewarding of organs for this type of genomic science. Several groups are pioneering this approach in the developing and adult mouse nervous system since there is a great need for such systematic gene expression analysis in the neuroscience community (Heintz 2004; Visel et al. 2004; Magdaleno et al. 2006; Christiansen et al. 2006).
There are three major collections of developmental brain gene expression: BGEM, the Brain Gene Expression Map (http://www.stjudebgem.org); Gene Paint (http://www.genepaint.org); and the Embryo Gene Expression Patterns Project (http://www.sanger.ac.uk/Teams/Team39/) which is also hosted by the Edinburgh Mouse Atlas Project (EMAP) (http://genex.hgu.mrc.ac.uk/). There are many are other smaller collections, but most of these are not actively accumulating data (Sunkin 2006). In addition, the Allen Brain Atlas (ABA) (http://www.brain-map.org) contains an extensive collection of adult mouse gene expression images. Recently, the adult databases were reviewed (Sunkin 2006). Therefore, we will focus primarily on the attributes of the major mouse developmental expression databases.
Development
Comparative analyses of the spatial and temporal patterns of gene expression have the potential to uncover structural boundaries and gene interactions that occur during development. Together with published information on specific gene functions, these data provide an opportunity for generating hypotheses concerning the cellular and molecular mechanisms responsible for formation of the neural architecture. At each stage of development distinct biological events predominate. For example, at embryonic day 11 (E11) there is active neurogenesis and migration of several neuronal populations in many regions of the brain. At E15, neuronal differentiation and the generation of a vast array of specific cell types is happening throughout the nervous system, whereas at postnatal day 7 (P7) neurite outgrowth, myelination, synaptic pruning and apoptosis come into play. Thus, the patterns of gene expression observed are extremely dynamic until the adult nervous system is functionally mature. Not all genes expressed during development are detected in the adult brain and the reverse is also true. By examining gene expression patterns at these different time points, it is possible to garner clues about the underlying events and ultimately to understand how sets of genes work together. In this regard, it is particularly useful to compare the embryonic expression patterns with those of the adult as some genes may exhibit distinct functions in the developing and adult brain.
BGEM
BGEM is a growing atlas of genes expressed in the developing and adult mouse brain (Jensen et al. 2004). BGEM is part of the GENSAT project (http://www.gensat.org/), which uses gene expression data to examine gene function (Gong et al. 2003; Heintz 2004). GENSAT is supported by the National Institutes of Neurological Disorders and Stroke and the National Institutes of health (NIH) Neurosciences Blueprint (http://neuroscienceblueprint.nih.gov/). An advisory committee selects a large number of candidate genes each year for in situ hybridization analysis (http://www.stjudebgem.org and (http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=gensat&cmd=search&term=). The database and website are continuously updated from links to the National Center for Biotechnology Information (NCBI; http://www.ncbi.nlm.nih.gov/). This ensures that the gene identification information is as current as possible. In addition to images of gene expression patterns, each entry includes the latest GeneID, accession numbers, Unigene numbers, gene names, identifications and links. For each gene entry there is also a gene ontology list and a convenient link to the respective ontology category. This makes it possible to investigate candidate interacting genes, possible binding proteins and potential functions. Ultimately, we hope that BGEM, and the other databases, combine in a virtual biology laboratory that is freely available to all investigators.
The images displayed on the BGEM website include sagittal as well as coronal orientations of brain and spinal cord. The ages analysed range from E11 and E15 to P7 and P42. For each developmental age several sets of images are posted (Christiansen et al. 2006). Currently approximately 2500 genes are posted on the website with over 1100 being added annually. Detailed methods and background information on how the work was performed are provided on the website. A particular strength of BGEM is that it can accommodate large numbers of candidate genes in the high-throughput in situ hybridization format. Typically data from 1000 slides or 2600 sections of tissue, are generated each month. The ultimate aim of BGEM is to successfully map all the genes expressed in the mouse brain.
Since BGEM includes embryonic data from E11 and E15, many genes that are primarily expressed during development can be visualized. These may include genes that are up-regulated early and then down-regulated in the adult brain. Using the search function on the website, it is easy to assemble and compare expression patterns from sets of genes involved in mitosis (11), migration (52), cell cycle (130), transcription (353), induction (26), development (518), differentiation (215), proliferation (100), cell fate (25), Drosophila homologues (96), pattern formation (27), growth factors (90), apoptosis (103), signalling (505), G-protein (257), kinase (279) and microtubule associated protein (MAP, 193). Though many of these processes continue in the P7 and adult brain, BGEM features many genes that are only expressed during development. Figure 1A shows the expression pattern of abnormal spindle-like, microcephaly associated (Drosophila homologue) gene Aspm. Aside from its role as a calmodulin binding protein and cell cycle regulator, its biological function is largely unknown. Aspm is expressed in the brain at E1115, but is limited to the cerebellum and hippocampal formation at P7. BGEM also provides insights into transcription regulation, which can greatly influence gene expression during differentiation. Figure 1B shows the paired box gene 6 (Pax6). Pax6 has multiple roles, but it functions as a regulator of transcription during neurogenesis of sensory regions of the brain, including the eye. It also has roles in cell fate, patterning and cell migration. It is present in the brain and spinal cord at E11, in the cortex, hippocampus, thalamus, hypothalamus and cerebellum at E15, and at P7 it is heavily expressed in the cerebellum, as well as other regions including the olfactory bulb. In adult mice the cerebellar signal is retained but at a reduced level. Analysis of signal transduction during neuronal migration, mitosis, differentiation, apoptosis and communication is also possible by querying gene sets in BGEM. For example, fibroblast growth factor receptor 3 (Fgfr3; Fig. 1C) is a receptor that transduces critical signals during development; it shows precise localization in the brain and spinal cord at E11 and E15. In this case the special control of signalling is also influenced by the distribution of the ligands and genes that influence ligand processing and distribution. Fgfr1, -2 and -4 are also included on the website.
|
|
Future directions
Taken together these brain maps compliment each other very well. To date expression data for a large number of genes is presented and the numbers are continuing to grow. Although annotation is still ongoing, many genes have been identified in different categories that are available for investigation. Thus, the data can be used immediately and even be incorporated in manuscripts or grant applications. However, the databases will be much more useful when the majority of the genome has been completed and when anatomical annotations are complete. This will allow comprehensive searching of brain regions and gene categories. Ultimately, it may be possible to assemble three-or even four-dimensional brain images with gene expression data at a level of resolution approaching that of single cells. However, this is beyond the capabilities of any of the current collections of data.
| References |
|---|
|
|
|---|
Gong S, Zheng C, Doughty ML, Losos K, Didkovsky N et al. (2003). A gene expression atlas of the central nervous system based on bacterial artificial chromosomes. Nature 425, 917925.[CrossRef][Medline]
Heintz N (2004). Gene Expression Nervous System Atlas (GENSAT). Nat Neurosci 7, 483.[CrossRef][Medline]
Jensen P, Magdaleno S, Lehman KM, Rice DS, Lavallie ER et al. (2004). A neurogenomics approach to gene expression analysis in the developing brain. Brain Res Mol Brain Res 132, 116127.[Medline]
Magdaleno S, Jensen P, Brumwell CL, Seal A, Lehman K, Asbury A, Cheung T, Cornelius T, Batten DM, Eden C, Norland S, Rice DS, Dosooye N, Shakya S, Mehta P & Curran T (2006). The Brain Gene Expression Map (BGEM): a database containing in situ hybridization data of gene expression in the developing and adult mouse nervous system. Plos Biol 4, e86.[CrossRef][Medline]
Sunkin SM (2006). Towards the integration of spatially and temporally resolved murine gene expression databases. Trends Genet 22, 211217.[CrossRef][Medline]
Visel
A, Thaller
C
&
Eichele
G (2004). GenePaint.org: an atlas of gene expression patterns in the mouse embryo. Nucl Acids Res
32, D552556.
This article has been cited by other articles:
![]() |
S. M. Sunkin and J. G. Hohmann Insights from spatially mapped gene expression in the mouse brain Hum. Mol. Genet., October 15, 2007; 16(R2): R209 - R219. [Abstract] [Full Text] [PDF] |
||||
![]() |
E. Cherubini, S. Gustincich, and H. Robinson The mammalian transcriptome and the cellular complexity of the brain J. Physiol., September 1, 2006; 575(2): 319 - 320. [Full Text] [PDF] |
||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |