{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T16:33:13Z","timestamp":1787070793003,"version":"build-2736575974"},"reference-count":291,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"10","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0 http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:p>Dysregulated proteome in the retina represents a promising avenue for discovering novel therapeutic targets and noninvasive diagnostic biomarkers for neurodegenerative diseases with ocular manifestations. Advanced mass spectrometry\u2013based proteomics techniques have shown considerable potential in investigating the retinal proteome in diseases such as glaucoma, age-related macular degeneration, diabetic retinopathy, retinitis pigmentosa, as well as Alzheimer\u2019s disease, amyotrophic lateral sclerosis, and Parkinson\u2019s disease. Recent proteomics innovations are overcoming challenges such as limited sample size and protein coverage that previously hindered comprehensive retinal proteome analysis. Notably, the incorporation of artificial intelligence\u2013driven computational pipelines, including Graphics Processing Unit-accelerated deep learning architectures, has markedly enhanced the precision and effectiveness of retinal proteomics. These advances facilitate high-resolution identification of novel protein signatures within large-scale multi-omics datasets. Furthermore, the integration of advanced artificial intelligence with state-of-the-art big data infrastructures supports the early detection of biomarkers and therapeutic targets in neurodegenerative diseases with ocular involvement, offering unprecedented disease specificity and sensitivity. In addition to these computational strides, emerging complementary and alternative technologies continue to provide valuable tools for retinal analysis, expanding the potential for identifying biomarker and therapeutic targets in both ophthalmic and neurodegenerative disorders. This review summarizes recent advancements in retinal proteomics, with a particular focus on neurodegenerative and ocular diseases.<\/jats:p>","DOI":"10.4103\/nrr.nrr-d-25-00291","type":"journal-article","created":{"date-parts":[[2025,10,31]],"date-time":"2025-10-31T08:00:23Z","timestamp":1761897623000},"page":"4690-4703","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":0,"title":["Retinal proteomics in neurodegeneration: Insights into ocular and brain disorders"],"prefix":"10.4103","volume":"21","author":[{"given":"Shahab","family":"Mirshahvaladi","sequence":"first","affiliation":[{"name":"Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhakta Prasad","family":"Gaire","sequence":"additional","affiliation":[{"name":"Department of Neurosurgery, Maxine Dunitz Neurosurgical Research Institute, 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