ArticleGenome research2016
Integrative subcellular proteomic analysis allows accurate prediction of human disease-causing genes.
Article in Genome research, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 22 citations in OpenAlex.
- Mass spectrometry-based retina proteomics.Mass spectrometry reviews · 2023Review
- Arginylation Regulates G-protein Signaling in the Retina.Frontiers in cell and developmental biology · 2021Article
- SARM1 depletion rescues NMNAT1-dependent photoreceptor cell death and retinal degeneration.eLife · 2020Article
- Simple Targeted Assays for Metabolic Pathways and Signaling: A Powerful Tool for Targeted Proteomics.Analytical chemistry · 2020Article
- Identification of potential markers for type 2 diabetes mellitus via bioinformatics analysis.Molecular medicine reports · 2020Article
- Review
- Single-nuclei RNA-seq on human retinal tissue provides improved transcriptome profiling.Nature communications · 2019Article
- Defining the layers of a sensory cilium with STORM and cryoelectron nanoscopy.Proceedings of the National Academy of Sciences of the United States of America · 2019Article
- NudC regulates photoreceptor disk morphogenesis and rhodopsin localization.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2019Article
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Authors and funding
14 authors at 1 institution in 1 country.
Funding
Abstract
Proteomic profiling on subcellular fractions provides invaluable information regarding both protein abundance and subcellular localization. When integrated with other data sets, it can greatly enhance our ability to predict gene function genome-wide. In this study, we performed a comprehensive proteomic analysis on the light-sensing compartment of photoreceptors called the outer segment (OS). By comparing with the protein profile obtained from the retina tissue depleted of OS, an enrichment score for each protein is calculated to quantify protein subcellular localization, and 84% accuracy is achieved compared with experimental data. By integrating the protein OS enrichment score, the protein abundance, and the retina transcriptome, the probability of a gene playing an essential function in photoreceptor cells is derived with high specificity and sensitivity. As a result, a list of genes that will likely result in human retinal disease when mutated was identified and validated by previous literature and/or animal model studies. Therefore, this new methodology demonstrates the synergy of combining subcellular fractionation proteomics with other omics data sets and is generally applicable to other tissues and diseases.
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