bioRxivMolecular BiologyPreprint
Leveraging single-cell transcriptomics of developing rat ocular outflow tissues to prioritize congenital glaucoma candidate genes
Sean M. Martin, Kristina N. WhisenhuntiD, Stuart W. J. TompsoniD
Abstract
Primary congenital glaucoma (PCG) is a severe, early-onset eye disease most often caused by abnormal development of the aqueous humor outflow pathway (AHOP). Located circumferentially at the anterior chamber angle where the iris meets the cornea, the AHOP comprises the trabecular meshwork (TM) and Schlemms canal (SC), which together regulate intraocular fluid drainage. Malformation of this pathway leads to elevated intraocular pressure, painful ocular enlargement, and retinal damage that can result in blindness. While single-gene mutations account for approximately 25% of PCG cases across diverse populations, most molecular causes remain unknown and are likely due to rare or complex genetic factors. Progress in identifying these mechanisms has been limited by a lack of detailed gene expression data during AHOP development. To address this gap, we generated the first single-cell RNA sequencing dataset from developing AHOP tissue, using rat eyes at three key stages of TM and SC formation. This high-resolution dataset contains the transcriptomic profiles of 29,626 genes across 86,653 cells clustered into 13 general cell types, which included over 10,000 cells related to TM/SC subtypes. Analysis of 44 genes previously linked to Mendelian childhood glaucoma showed that 36 (82%) were expressed in these TM/SC-related populations, validating the datasets relevance. Notably, this study identified 395 genes selectively upregulated in developing TM/SC subtypes, revealing numerous candidates potentially involved in the formation and function of TM/SC structures. This resource will support the discovery of rare Mendelian disease genes and inform the development of polygenic risk scores for complex genetics underlying early-onset forms of glaucoma.
- organism: rat
- assay: scRNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- Citations
- 0
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