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Neuroinformatics for Genome-Wide 3-D Gene Expression Mapping in the Mouse Brain
July-September 2007 (vol. 4 no. 3)
pp. 382-393
Large scale gene expression studies in the mammalian brain offer the promise of understanding the topology, networks and ultimately the function of its complex anatomy, opening previously unexplored avenues in neuroscience. High-throughput methods permit genome-wide searches to discover genes that are uniquely expressed in brain circuits and regions that control behavior. Previous gene expression mapping studies in model organisms have employed situ hybridization (ISH), a technique that uses labeled nucleic acid probes to bind to specific mRNA transcripts in tissue sections. A key requirement for this effort is the development of fast and robust algorithms for anatomically mapping and quantifying gene expression for ISH. We describe a neuroinformatics pipeline for automatically mapping expression profiles of ISH data and its use to produce the first genomic scale 3-D mapping of gene expression in a mammalian brain. The pipeline is fully automated and adaptable to other organisms and tissues. Our automated study of over 20,000 genes indicates that at least 78.8% are expressed at some level in the adult C56BL/6J mouse brain. In addition to providing a platform for genomic scale search, high-resolution images and visualization tools for expression analysis are available at the Allen Brain Atlas web site (

[1] A.I. Su et al., “Large-Scale Analysis of the Human and Mouse Transcriptomes,” Proc. Nat'l Academy of Sciences USA, vol. 99, pp.4465-4470, 2002.
[2] S. Gong et al., “A Gene Expression Atlas of the Central Nervous System Based on Bacterial Artificial Chromosomes,” Nature, vol. 425, pp. 917-925, 2003.
[3] R. Sandberg et al., “Regional and Strain-Specific Gene Expression Mapping in the Adult Mouse Brain,” Proc. Nat'l Academy of Sciences USA, vol. 97, pp. 11038-11043, 2000.
[4] P. Carninci et al., “The Transcriptional Landscape of the Mammalian Genome,” Science, vol. 309, pp. 1559-1563, 2005.
[5] D.H. Geschwind and J.P. Gregg, Microarrays for the Neurosciences: An Essential Guide. MIT Press, 2002.
[6] R.L. Rotundo et al., Advances in Gene Technology: Molecular Neurobiology and Neuropharmacology. Oxford Univ. Press, 1989.
[7] A.S. Siddiqui et al., “A Mouse Atlas of Gene Expression: Large-Scale Digital Gene-Expression Profiles from Precisely Defined Developing C57BL/6J Mouse Tissues and Cells,” Proc. Nat'l Academy of Sciences USA, vol. 102, p. 18485, 2005.
[8] A. MacKenzie-Graham et al., “The Informatics of a C57BL/6J Mouse Brain Atlas,” Neuroinformatics, vol. 1, pp. 397-410, 2003.
[9] E.L. Schwartz et al., “Applications of Computer-Graphics and Image-Processing to 2D and 3D Modeling of the Functional Architecture of Visual-Cortex,” IEEE Computer Graphics and Applications, vol. 8, pp. 13-23, 1988.
[10] M.S. Boguski and A.R. Jones, “Neurogenomics: At the Intersection of Neurobiology and Genome Sciences,” Nature Neuroscience, vol. 7, pp. 429-433, 2004.
[11] J.D. Lieb, “Genome-Wide Mapping of Protein-DNA Interactions by Chromatin Immunoprecipitation and DNA Microarray Hybridization,” Functional Genomics: Methods and Protocols, vol. 224, M.J. Brownstein and A.B. Khodursky, eds., pp. 99-110, Humana Press, 2003.
[12] V.E. Velculescu et al., “Analysis of Human Transcriptomes,” Nature Genetics, vol. 23, pp. 387-388, 1999.
[13] E.S. Lein, X.Y. Zhao, and F.H. Gage, “Defining a Molecular Atlas of the Hippocampus Using DNA Microarrays and High-Throughput In Situ Hybridization,” J. Neuroscience, vol. 24, pp.3879-3889, 2004.
[14] S. Koslow and S. Subramanian, Databasing the Brain: From Data to Knowledge (Neuroinformatics). John Wiley and Sons, 2005.
[15] A.W. Toga and J.C. Mazziotta, Brain Mapping: The Methods. Academic Press, 1996.
[16] J.A. Blake et al., “MGD: The Mouse Genome Database,” Nucleic Acids Research, vol. 31, pp. 193-195, 2003.
[17] A. Visel, C. Thaller, and G. Eichele, “ An Atlas of Gene Expression Patterns in the Mouse Embryo,” Nucleic Acids Research, vol. 32, pp. D552-D556, 2004.
[18] U. Herzig et al., “Development of High-Throughput Tools to Unravel the Complexity of Gene Expression Patterns in the Mammalian Brain,” Proc. Symp. Complexity in Biological Information Processing, 2001.
[19] E.R. Kandel, J.H. Schwartz, and T.M. Jessell, Principles of Neural Science. McGraw-Hill, 2000.
[20] P.A. Yushkevich et al., “Using MRI to Build a 3D Reference Atlas of the Mouse Brain from Histology Images,” Proc. Int'l Soc. Magnetic Resonance in Medicine, 2005.
[21] S.P. Raya and J.K. Udupa, “Shape-Based Interpolation of Multidimensional Objects,” IEEE Trans. Medical Imaging, vol. 9, pp. 32-42, 1990.
[22] T. Ju et al., “A Geometric Database for Gene Expression Data,” Proc. Symp. Geometry Processing, 2003.
[23] I.A. Kakadiaris et al., “Landmark-Driven, Atlas-Based Segmentation of Mouse Brain Tissue Images Containing Gene Expression Data,” Proc. Int'l Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI '04), C. Barillot, D.R. Haynor, and P.Hellier, eds., pp. 192-199, 2004.
[24] M. Bello et al., “Hybrid Segmentation Framework for Tissue Images Containing Gene Expression Data.,” Proc. Int'l Conf. Medical Image Computing and Computer-Assisted Intervention (MICCAI '05), 2005.
[25] P. Viola and W.M. Wells, “Alignment by Maximization of Mutual Information,” Int'l J. Computer Vision, vol. 24, pp. 137-154, 1997.
[26] J.P.W. Pluim, J.B.A. Maintz, and M.A. Viergever, “Mutual-Information-Based Registration of Medical Images: A Survey,” IEEE Trans. Medical Imaging, vol. 22, p. 986, 2003.
[27] J.V. Hajnal, D.L.G. Hill, and D.J. Hawkes, Medical Image Registration. CRC Press, 2001.
[28] J.M. Fitzpatrick, D.L.G. Hill, and C.R. Maurer, “Image Registration,” Handbook of Medical Imaging: Medical Image Processing and Analysis, vol. 2, M. Sonka and J.M. Fitzpatrick, eds., pp. 447-514, SPIE Press, 2000.
[29] B. Jahne, Digital Image Processing, fifth ed. Springer, 2002.
[30] D. Mattes et al., “PET-CT Image Registration in the Chest Using Free-Form Deformations,” IEEE Trans. Medical Imaging, vol. 22, p.120, 2003.
[31] H. Zreiqat et al., “Quantitative Aspects of an In Situ Hybridization Procedure for Detecting mRNAs in Cells Using 96-Well Microplates,” Molecular Biotechnology, vol. 10, pp. 107-113, 1998.
[32] J.P. Carson, G. Eichele, and W. Chiu, “A Method for Automated Detection of Gene Expression Required for the Establishment of a Digital Transcriptome-Wide Gene Expression Atlas,” J. Microscopy-Oxford, vol. 217, pp. 275-281, 2005.
[33] N. Ostu, “A Threshold Selection Method from Gray Level Histograms,” IEEE Trans. Systems, Man, and Cybernetics, vol. 9, pp. 62-66, 1979.
[34] R. Gonsalez and R. Woods, Digital Image Processing, second ed. Prentice Hall, 2001.
[35] P. Fagergren et al., “Temporal Upregulation of Prodynorphin mRNA in the Primate Striatum after Cocaine Self-Administration,” European J. Neuroscience, vol. 17, pp. 2212-2218, 2003.
[36] N. Mataga et al., “Experience-Dependent Plasticity of Mouse Visual Cortex in the Absence of the Neuronal Activity-Dependent Marker egr1/zif268,” J. Neuroscience, vol. 21, pp. 9724-9732, 2001.
[37] M. Peters et al., “Loss of Ca2+/Calmodulin Kinase Kinase Beta Affects the Formation of Some, but Not All, Types of Hippocampus-Dependent Long-Term Memory,” J. Neuroscience, vol. 23, pp.9752-9760, 2003.
[38] B.G. Wen et al., “Inositol (1,4,5) Trisphosphate 3 Kinase B Controls Positive Selection of T Cells and Modulates Erk Activity,” Proc. Nat'l Academy of Sciences USA, vol. 101, pp. 5604-5609, 2004.
[39] Z. Rahman et al., “RGS9 Modulates Dopamine Signaling in the Basal Ganglia,” Neuron, vol. 38, pp. 941-952, 2003.
[40] S. Sunkin, “Towards Integration of Murine Spatial Resolution Expression Databases,” Trends in Genetics, vol. 22, pp. 211-217, 2006.
[41] J.P. Carson et al., “A Digital Atlas to Characterize the Mouse Brain Transcriptome,” PLoS Computational Biology, vol. 1, p. e41, 2005.
[42] J. Warren and H. Weimer, Subdivision Meshes for Geometric Design: A Constructive Approach. Morgan Kaufman, 2002.
[43] A. Visel, J. Ahdidan, and G. Eichele, “A Gene Expression Map of the Mouse Brain:—A Database of Gene Expression Patterns,” Neuroscience Databases: A Practical Guide, R. Kotter, ed., pp. 19-36, 2002.
[44] D.E. Rex, J.Q. Ma, and A.W. Toga, “The LONI Pipeline Processing Environment,” Neuroimage, vol. 19, pp. 1033-1048, 2003.
[45] Scientific Computing and Imaging Inst., “The SCIRun Problem Solving Environment,” http://software.sci.utah.eduscirun.html, 2004.

Index Terms:
Bioinformatics (genome or protein) databases, Data mining, Registration, Segmentation, Information Visualization
Lydia Ng, Sayan Pathak, Chihchau Kuan, Chris Lau, Hong-wei Dong, Andrew Sodt, Chinh Dang, Brian Avants, Paul Yushkevich, James Gee, David Haynor, Ed Lein, Allan Jones, Mike Hawrylycz, "Neuroinformatics for Genome-Wide 3-D Gene Expression Mapping in the Mouse Brain," IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 4, no. 3, pp. 382-393, July-Sept. 2007, doi:10.1109/tcbb.2007.1035
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