Related Experiment Video
Updated: May 20, 2026

A Rhodopsin Transport Assay by High-Content Imaging Analysis
Published on: January 16, 2019
Next-generation sequencing in health-care delivery: lessons from the functional analysis of rhodopsin
Wayne I L Davies1, Susan M Downes, Josephine K Fu
1Nuffield Department of Clinical Neuroscience, Nuffield Laboratory of Ophthalmology, John Radcliffe Hospital, University of Oxford, UK.
Purpose:
The interpretation of genetic information has always been challenging, but next-generation sequencing produces data on such a vast scale that many more variants of uncertain pathogenicity will be found. We exemplify this issue with reference to human rhodopsin, in which pathogenic mutations can lead to autosomal dominant retinitis pigmentosa.
Methods:
Rhodopsin variants, with unknown pathogenicity, were found in patients by next-generation and Sanger sequencing and a multidisciplinary approach was used to determine their functional significance.
Results:
Four variants in rhodopsin were identified: F45L, P53R, R69H, and M39R, with the latter two substitutions being novel. We investigated the cellular transport and photopigment function of all four human substitutions and found that the F45L and R69H variants behave like wild-type and are highly unlikely to be pathogenic. By contrast, P53R (a de novo change) and M39R were retained in the endoplasmic reticulum with significantly reduced functionality and are clearly pathogenic.
Conclusion:
Potential pathogenicity of variants requires careful assessment using clinical, genetic, and functional data. We suggest that a multidisciplinary pathway of assessment, using several functional assays, will be required if next-generation sequencing is to be used effectively, reliably, and safely in the clinical environment.
More Related Videos
Related Concept Videos
Channel Rhodopsins
Rhodopsins belong to the family of cell surface proteins called G-protein coupled receptors,...
Next-generation Sequencing
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.

