Predicting Proximity to Pathology for Single-Cell Data in Alzheimer’s Disease
G.S. Vergieva (TU Delft - Electrical Engineering, Mathematics and Computer Science)
MJT Reinders – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
T. Verlaan – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
R. Lardenoije – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
G.A. Bouland – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
R. Guerra Marroquim – Graduation committee member (TU Delft - Computer Graphics and Visualisation)
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Abstract
Alzheimer’s Disease is a complex neurodegenerative disorder marked by the abnormal build-up of proteins in the brain. As no cure currently exists, understanding the disease’s cellular mechanisms is essential for advancing diagnostics and treatment. To this end, single-cell RNA sequencing (scRNA-seq) is a method that offers detailed information about the gene activity of individual cells but lacks their spatial context. Conversely, spatial transcriptomics technology preserves the localization of the cells but provides more limited transcriptomic information. To resolve this, we provide a model that predicts a cell’s distance to pathology from single-cell RNA-sequencing data. Additionally, we identify APOE, LYVE1, and SLC17A7 as genes potentially associated with AD-related microglial clustering around plaques.