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      Genomic Analysis of Mouse Retinal Development

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          Abstract

          The vertebrate retina is comprised of seven major cell types that are generated in overlapping but well-defined intervals. To identify genes that might regulate retinal development, gene expression in the developing retina was profiled at multiple time points using serial analysis of gene expression (SAGE). The expression patterns of 1,051 genes that showed developmentally dynamic expression by SAGE were investigated using in situ hybridization. A molecular atlas of gene expression in the developing and mature retina was thereby constructed, along with a taxonomic classification of developmental gene expression patterns. Genes were identified that label both temporal and spatial subsets of mitotic progenitor cells. For each developing and mature major retinal cell type, genes selectively expressed in that cell type were identified. The gene expression profiles of retinal Müller glia and mitotic progenitor cells were found to be highly similar, suggesting that Müller glia might serve to produce multiple retinal cell types under the right conditions. In addition, multiple transcripts that were evolutionarily conserved that did not appear to encode open reading frames of more than 100 amino acids in length (“noncoding RNAs”) were found to be dynamically and specifically expressed in developing and mature retinal cell types. Finally, many photoreceptor-enriched genes that mapped to chromosomal intervals containing retinal disease genes were identified. These data serve as a starting point for functional investigations of the roles of these genes in retinal development and physiology.

          Abstract

          Spatial and temporal patterns of expression for over 1000 genes in identified retinal cells invites functional investigations into the role of these genes in development and physiology

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          Most cited references110

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          Identifying biological themes within lists of genes with EASE.

          EASE is a customizable software application for rapid biological interpretation of gene lists that result from the analysis of microarray, proteomics, SAGE and other high-throughput genomic data. The biological themes returned by EASE recapitulate manually determined themes in previously published gene lists and are robust to varying methods of normalization, intensity calculation and statistical selection of genes. EASE is a powerful tool for rapidly converting the results of functional genomics studies from 'genes' to 'themes'.
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            Electroporation and RNA interference in the rodent retina in vivo and in vitro.

            The large number of candidate genes made available by comprehensive genome analysis requires that relatively rapid techniques for the study of function be developed. Here, we report a rapid and convenient electroporation method for both gain- and loss-of-function studies in vivo and in vitro in the rodent retina. Plasmid DNA directly injected into the subretinal space of neonatal rodent pups was taken up by a significant fraction of exposed cells after several pulses of high voltage. With this technique, GFP expression vectors were efficiently transfected into retinal cells with little damage to the operated pups. Transfected GFP allowed clear visualization of cell morphologies, and the expression persisted for at least 50 days. DNA-based RNA interference vectors directed against two transcription factors important in photoreceptor development led to photoreceptor phenotypes similar to those of the corresponding knockout mice. Reporter constructs carrying retinal cell type-specific promoters were readily introduced into the retina in vivo, where they exhibited the appropriate expression patterns. Plasmid DNA was also efficiently transfected into retinal explants in vitro by high-voltage pulses.
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              Open source clustering software.

              We have implemented k-means clustering, hierarchical clustering and self-organizing maps in a single multipurpose open-source library of C routines, callable from other C and C++ programs. Using this library, we have created an improved version of Michael Eisen's well-known Cluster program for Windows, Mac OS X and Linux/Unix. In addition, we generated a Python and a Perl interface to the C Clustering Library, thereby combining the flexibility of a scripting language with the speed of C. The C Clustering Library and the corresponding Python C extension module Pycluster were released under the Python License, while the Perl module Algorithm::Cluster was released under the Artistic License. The GUI code Cluster 3.0 for Windows, Macintosh and Linux/Unix, as well as the corresponding command-line program, were released under the same license as the original Cluster code. The complete source code is available at http://bonsai.ims.u-tokyo.ac.jp/mdehoon/software/cluster. Alternatively, Algorithm::Cluster can be downloaded from CPAN, while Pycluster is also available as part of the Biopython distribution.
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                Author and article information

                Journal
                PLoS Biol
                pbio
                PLoS Biology
                Public Library of Science (San Francisco, USA )
                1544-9173
                1545-7885
                September 2004
                29 June 2004
                : 2
                : 9
                : e247
                Affiliations
                [1] 1Department of Genetics and Howard Hughes Medical Institute, Harvard Medical School Boston, Massachusetts, United States of America
                [2] 2Dana-Farber Cancer Institute, Harvard Medical School Boston, MassachusettsUnited States of America
                [3] 3Department of Statistics, University of California Berkeley, CaliforniaUnited States of America
                [4] 4Children's Hospital Informatics Program, Boston MassachusettsUnited States of America
                [5] 5Decision Systems Group, Brigham and Women's Hospital Boston, MassachusettsUnited States of America
                [6] 6Department of Biostatistics, Harvard School of Public Health Boston, MassachusettsUnited States of America
                Article
                10.1371/journal.pbio.0020247
                439783
                15226823
                2a4d5e9a-b377-4024-b908-8c2a15ad7600
                Copyright: © 2004 Blackshaw et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
                History
                : 16 December 2003
                : 26 May 2004
                Categories
                Research Article
                Development
                Genetics/Genomics/Gene Therapy
                Neuroscience
                Mus (Mouse)

                Life sciences
                Life sciences

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