This master thesis is a collaborative project between the group of Prof. Ulrike Müller and Prof. Carl Herrmann, IPMB, focusing on the computational analysis of single-cell RNA-seq data from brain samples of two mouse models for Alzheimers disease.
Background: the APPsa fragment, that is derived by non-amyloidogenic cleavage of the amyloid precursor protein APP has neurotrophic, neuroprotective, synaptic plasticity and memory enhancing properties. To leverage these beneficial properties for AD treatment we follow a gene therapy approach in transgenic mouse models of AD using AAV vectors (Baltissen et al, 2023; Richter et al, 2018). We will use transcriptomics to reveal at single cell resolution the molecular pathways induced by AAV vectors expressing APPsa in the hippocampus of these mice.
The computational analysis of the single-cell RNA-seq will involve data processing and identification of differential genes and pathways between treated and control mice, using pipelines available in the group of Prof. Herrmann. The downstream analysis of regulatory processes and the identification of important transcription factors will be an important aspect of this project.
References
Baltissen D, Bold CS, Rehra L, Banicevic M, Fricke J, Just J, Ludewig S, Buchholz CJ, Korte M, Muller UC (2023) APPsalpha rescues CDK5 and GSK3beta dysregulation and restores normal spine density in Tau transgenic mice. Front Cell Neurosci 17: 1106176
Richter MC, Ludewig S, Winschel A, Abel T, Bold C, Salzburger LR, Klein S, Han K, Weyer SW, Fritz AK et al (2018) Distinct in vivo roles of secreted APP ectodomain variants APPsalpha and APPsbeta in regulation of spine density, synaptic plasticity, and cognition. EMBO J 37
In collaboration with the University Clinics Dresden (Dr. Nicole Bechmann, UKDD), we are collecting a large multiome dataset of proteome, metabolome and transcriptome data from a cohort of ~60 patients with neuro-endocrine tumors (NETs). These tumors have been found to be more prevalent in female than in male, whereas metastasis occur more frequently in males. Hence, there is a strong factor related to the sex of the patient. The goal of the project is to understand the sexual dimorphisms in these tumors, in particular the underlying molecular and metabolic differences between male and females. The goal will be to apply various statistical data integration methods to combine transcriptomics, metabolomics and proteomics. We plan to extract specific signatures and combine those with the clinical information available to reveal perturbed processes and pathways.
Please contact carl.herrmann@uni-heidelberg.de for more details or to apply.