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Development of high-throughput tools to unravel the complexity of gene expression patterns in the mammalian brain.

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Eichele,  G.
Department of Molecular Embryology, Max Planck Institute for Experimental Endocrinology, Max Planck Society;

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Citation

Herzig, U., Cadenas, C., Sieckmann, F., Sierralta, W., Thaller, C., Visel, A., et al. (2001). Development of high-throughput tools to unravel the complexity of gene expression patterns in the mammalian brain. In G. Bock, & J. Goode (Eds.), Complexity in Biological Information Processing (pp. 129-149). Chicester: John Wiley and Sons.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-2583-8
Abstract
Genomes of animals contain between 15 000 (e.g. Drosophila) and 50 000 (human, mouse) genes, many of which encode proteins involved in regulatory processes. The availability of sequence data for many of these genes opens up opportunities to study complex genetic and protein interactions that underlie biological regulation. Many examples demonstrate that an understanding of regulatory networks consisting of multiple components is significantly advanced by a detailed knowledge of the spatiotemporal expression pattern of each of the components. Gene expression patterns can readily be determined by RNA in situ hybridization. The unique challenge emerging from the knowledge of the sequence of entire genomes is that assignment of biological functions to genes needs to be carried out on an appropriately large scale. In terms of gene expression analysis by RNA insitu hybridization, efficient technologies need to be developed that permit determination and representation of expression patterns of thousands of genes within an acceptable time-scale. We set out to determine the spatial expression pattern of several thousand genes encoding putative regulatory proteins. To achieve this goal we have developed high-throughput technologies that allow the determination and visualization of gene expression patterns by RNA in situ hybridization on tissue sections at cellular resolution. In particular, we have invented instrumentation for robotic in situ hybridization capable of carrying out in a fully automated fashion, all steps required for detecting sites of gene expression in tissue sections. In addition, we have put together hardware and software for automated microscopic scanning of gene expression data that are produced by RNA in situ hybridization. The potential and limitations of these techniques ind out efforts to build a Web-based database of gene expression patterns are discussed.