Identifying core biological processes distinguishing human eye tissues with precise systems-level gene expression analyses and weighted correlation networks. (22nd June 2018)

• Record Type: Journal Article
• Authors: Bryan, John M; Fufa, Temesgen D; Bharti, Kapil; Brooks, Brian P; Hufnagel, Robert B; McGaughey, David M
• Is Part Of: Human molecular genetics. Volume 27:Number 19(2018:Oct. 01)
• Page Start: 3325
• Page End: 3339
• Publication Date: 2018-06-22
• Subjects: Human molecular genetics -- Periodicals; Human chromosome abnormalities -- Periodicals; 572.8
• DOI: 10.1093/hmg/ddy239
• Languages: English
• ISSNs: 0964-6906

Abstract

Abstract: The human eye is built from several specialized tissues which direct, capture and pre-process information to provide vision. The gene expression of the different eye tissues has been extensively profiled with RNA-seq across numerous studies. Large consortium projects have also used RNA-seq to study gene expression patterning across many different human tissues, minus the eye. There has not been an integrated study of expression patterns from multiple eye tissues compared with other human body tissues. We have collated all publicly available healthy human eye RNA-seq datasets as well as dozens of other tissues. We use this fully integrated dataset to probe the biological processes and pan expression relationships between the cornea, retina, retinal pigment epithelium (RPE)−choroid complex, and the rest of the human tissues with differential expression, clustering and gene ontology term enrichment tools. We also leverage our large collection of retina and RPE−choroid tissues to build the first human weighted gene correlation networks and use them to highlight known biological pathways and eye gene disease enrichment. We also have integrated publicly available single-cell RNA-seq data from mouse retina into our framework for validation and discovery. Finally, we make all these data, analyses and visualizations available via a powerful interactive web application (https://eyeintegration.nei.nih.gov/ ).