From Tear Drops to Diagnostics: Profiling Tear RNA for Early Biomarker Discovery in Alzheimer's Disease.
Description: The rising prevalence of Alzheimer's disease (AD) increasingly demands faster and unambiguous diagnosis. Due to their developmental and physical relatedness to the brain, tear fluid presents itself as an ideal non-invasive biomarker candidate. Although the tears' RNA content could fill this dire biomarker gap, they remain surprisingly underexploited. Therefore, this project aims to tackle this unmet need by mapping the total RNA content of tear fluid and employing these results to detect improved early AD biomarkers. We hypothesize that tear fluid offers a unique, non-invasive view into the brain and that the RNA within these tears provides signs of early AD and other neurodegenerative disorders that can be employed as a future non-invasive biomarker. This project builds on the expansive experience of the promoter in RNA analysis and on the AD expertise of the Sleegers Lab at VIB-CMN. In Task 1, we will extract and sequence the RNA of tears from 100 elders who are included in a separate longitudinal study. In Task 2, we will analyze these samples' circular RNA and linear RNA content and find transcriptomic differences related to dementia risk factors, genetic risk scores, and cognitive test scores. This project will (1) develop a new tool for sequencing the total tear-derived transcriptome, (2) provide the first line of evidence for phenotype-induced changes in the tear RNA, and (3) generate unique datasets ready for valorization. In its role as a seed project, the granting of this proposal would carve out a unique niche to kick-start the promoter's career and initiate a promising line of up-and-coming research in the lab.
Funder: Vlaamse regering
Funding type: grant
Funder scheme: BOF - projects
Start date: 2025-04-01
End date: 2026-03-30
Investigator: Jasper Monique Robert Verwilt
Institution: UAntwerpen
Funded outputs count: 0
Funder award ID: vlaamse-13fa77c4-ef9c-43f6-a7a2-9c7e1ed4e454
OpenAlex ID: G131968976