PURPOSE: Construction of a canine retinal custom cDNA microarray for comprehensive retinal gene expression profiling and application for the identification of genes that are preferentially expressed in the retina and brain lobes using a brain pool reference tissue. METHODS: A cDNA microarray was constructed utilizing clones obtained from a normalized canine retinal expressed sequence tag library. Gene expression profiles were analyzed for normal retina, as well as the cortex of the frontal, occipital, and temporal brain regions. Each sample was studied against a reference sample of pooled brain RNA. Data from a quantified scanned image were normalized using the loess subgrid procedure. Retina-enriched genes were identified using the Significance Analysis of Microarrays (SAM) algorithm, and confirmed by northern blot analyses for selected genes. Differences between biological samples were displayed using principal component analysis (PCA). RESULTS: Expression profiles for each tissue set were analyzed against the common reference of pooled brain. Changes in expression between the sample and the reference were higher in the retina (27.9%) than the individual brain tissues (2-6.6%). Furthermore, all individual retinal samples were clearly separated from any of the hybridizations using brain tissue in the PCA. The accuracy of observed changes in expression has been confirmed by northern blot analysis using five randomly chosen genes that represented a wide range of different expression levels between retina and brain. CONCLUSIONS: We have established an accurate and robust microarray system suitable for the investigation of expression patterns in the retina and brain. Characterization of the gene expression profiles in normal retina will facilitate the understanding of the processes that underline differences between normal and diseased retinas.
PURPOSE: Construction of a canine retinal custom cDNA microarray for comprehensive retinal gene expression profiling and application for the identification of genes that are preferentially expressed in the retina and brain lobes using a brain pool reference tissue. METHODS: A cDNA microarray was constructed utilizing clones obtained from a normalized canine retinal expressed sequence tag library. Gene expression profiles were analyzed for normal retina, as well as the cortex of the frontal, occipital, and temporal brain regions. Each sample was studied against a reference sample of pooled brain RNA. Data from a quantified scanned image were normalized using the loess subgrid procedure. Retina-enriched genes were identified using the Significance Analysis of Microarrays (SAM) algorithm, and confirmed by northern blot analyses for selected genes. Differences between biological samples were displayed using principal component analysis (PCA). RESULTS: Expression profiles for each tissue set were analyzed against the common reference of pooled brain. Changes in expression between the sample and the reference were higher in the retina (27.9%) than the individual brain tissues (2-6.6%). Furthermore, all individual retinal samples were clearly separated from any of the hybridizations using brain tissue in the PCA. The accuracy of observed changes in expression has been confirmed by northern blot analysis using five randomly chosen genes that represented a wide range of different expression levels between retina and brain. CONCLUSIONS: We have established an accurate and robust microarray system suitable for the investigation of expression patterns in the retina and brain. Characterization of the gene expression profiles in normal retina will facilitate the understanding of the processes that underline differences between normal and diseased retinas.
Authors: Sem Genini; Barbara Zangerl; Julianna Slavik; Gregory M Acland; William A Beltran; Gustavo D Aguirre Journal: Invest Ophthalmol Vis Sci Date: 2010-06-23 Impact factor: 4.799
Authors: Rebekkah J Hitti; James A C Oliver; Ellen C Schofield; Anina Bauer; Maria Kaukonen; Oliver P Forman; Tosso Leeb; Hannes Lohi; Louise M Burmeister; David Sargan; Cathryn S Mellersh Journal: Genes (Basel) Date: 2019-05-21 Impact factor: 4.096