Epigenetic Signatures in Macrophages as Biomarkers for Alzheimer's Disease (Master's Thesis)
Alzheimer's disease (AD) is the most common neurodegenerative disorder. Growing evidence suggests that innate immune cells are functionally altered in AD patients and may carry disease-associated epigenetic signatures detectable outside the central nervous system. This project investigates whether DNA methylation patterns in patient-derived macrophages can serve as a molecular readout for AD status and immune dysfunction.
The project builds on an ongoing study in which monocytes from AD patients — characterized by multiple biomarkers — and healthy controls were differentiated into macrophages, subjected to phagocytosis assays, and profiled using whole-genome bisulfite sequencing (WGBS). Preliminary analyses show that a classifier based on differentially methylated positions (DMPs) yields a meaningful predictive signal, and that patients exhibit bimodal phagocytosis rates suggesting functionally distinct macrophage subpopulations.
The student will refine and extend these findings through several interconnected tasks: improving the classifier by incorporating methylation heterogeneity and other measures; evaluating generalizability on external datasets; performing transcription factor binding site analysis to link methylation changes to upstream regulators; applying deconvolution methods to account for cellular composition differences; and running epigenetic age prediction to assess whether AD macrophages show accelerated epigenetic aging.
The findings are intended to be published as part of a peer-reviewed research article. The student should have a background in bioinformatics or a related quantitative field with experience in R and/or Python. An interest in translational research, feature engineering on high-dimensional sequencing data, and sparse learning and regularization in biomedical settings is expected
Contact: michael.lauber@tum.de