Analysis of critical transitions in Parkinson's disease

Background
Parkinson’s disease is the most common neurodegenerative movement disorder and is clinically characterized by resting tremor, bradykinesia and cogwheel rigidity. The disease affects 1-2% of the global population with prevalence in the people above 65 years of age. The main pathological hallmark of Parkinson’s disease is a progressive loss of dopaminergic neurons in the substantia nigra. Therefore, one important challenge is to improve the understanding of regime shifts between health and disease states.
Improving predictions of critical transitions triggering the onset of parkinsonian phenotypes could contribute to the improvement of preventive treatments.
Methods
Based on cellular models, we will use the mathematical concept of critical transitions to create a toolbox for potentially predicting tipping points towards cellular Parkinson’s disease phenotypes, e.g. mitochondrial dysfunction. Experimentally, we will induce and analyze potential critical transitions in the SH-SY5Y cell line. To do this, we will apply Parkinson’s disease relevant chemical and genetic perturbations and analyze multiple scales of the resulting temporal system behavior. We will combine high content imaging with genetic and biochemical data. A significant informatics challenge arises from the aim to perform the analysis of high time-resolved 3D imaging data. We are therefore developing an automated image analysis pipeline that relies on latest technologies and techniques, such as 3D deconvolution and 3D particle tracking. This pipeline will be applied to study parameters, such as mitochondrial dynamics, which include for instance velocity, morphology, and spatial organization.