Ying Yuan, Pooja Biswas, Nathan R. Zemke, Kelsey Dang, Yue Wu, Matteo D’Antonio, Yang Xie, Qian Yang, Keyi Dong, Pik Ki Lau, Daofeng Li, Chad Seng, Weronika M. Bartosik, Justin Buchanan, Lin Lin, Ryan Lancione, Kangli Wang, Seo Yeon Lee, Zane A. Gibbs, Bing Yang, Joseph R. Ecker, Kelly A. Frazer, Ting Wang, Sebastian Preißl, Allen Wang, Radha Ayyagari, Bing Ren
Most genetic risk variants linked to ocular diseases are nonprotein coding and presumably contribute to disease through dysregulation of gene expression; however, understanding their mechanisms has been impeded by incomplete annotation of transcriptional regulatory elements across retinal cell types. To address this, we carried out single-cell multiomics assays to investigate gene expression, chromatin accessibility, DNA methylome, and three-dimensional (3D) chromatin architecture in human retina, macula, and retinal pigment epithelium/choroid. We identified 420,824 unique candidate regulatory elements and characterized their chromatin states in 23 retinal cell types. Comparative analysis of chromatin landscapes between human and mouse retina cells further revealed both evolutionarily conserved and divergent retinal gene-regulatory programs. Leveraging the advancements in deep-learning techniques, we developed sequence-based predictors to interpret noncoding risk variants of retinal diseases. Our study establishes retina-wide, single-cell transcriptome, epigenome, and 3D genome atlases and provides a resource for studying the gene regulatory programs of the human retina and ocular diseases.