Characterising patient-based iPS cells during differentiation and maturation by cutting-edge single cell omics methods, to investigate how mutations in Parkinson’s disease related genes affect the cellular phenotype
Michela Bernini, PhD supervisor: Alexander Skupin, external partner Sui Huang
This project will generate temporal integrative omics and imaging data to identify disease-relevant transitions based on the theoretical tools developed. Cellular heterogeneity is an important biological attribute responsible for the pathogenesis and progression of several developmental and non-developmental associated diseases, including neurodegenerative diseases like Parkinson’s disease and cancer. The sources of heterogeneity are associated with several hierarchical levels of biochemical process, such as genetics, transcriptomics, proteomics and metabolomics (Moignard 2013; Shi 2012; Yoon 2011). Importantly, the phenotypic state of a cell is the direct consequence of close system-level stochastic interactions of different levels of these biochemical interactions. Thus, for critical understanding of the role of cellular stochasticity in the pathogenesis and development of complex diseases, it is imperative to deconvolute and identify different sources of stochasticity existing in the different facets of biochemical processes (Elowitz 2002). This would necessitate development of platforms capable of sensitively teasing out multi-omics features of cellular states in a single quantitation platform. The past decade has seen advances in the development of quantification platforms of omics at single-cell level. However, these developments have largely focused on single-omics quantification. Currently, a research focus of the ICS group (AS) is the development of multi-omics strategy for single-cell quantification, to address specific discovery questions related to aetiology and progression of breast cancer and neurodegenerative disease such as PD. Specifically, the proposed subproject would use micro-fabrication technologies to isolate and quantify the proteomics and transcriptomics level at single-cell resolution (Shi 2012). Further, the aim would be to use clinical samples such as patient-derived fibroblast biopsies or surrogate systems such as the transformed iPSC cell lines obtained from the the patients to understand the progression and state of the disease and possibly design personalised intervention strategies for alleviating the pathology based on the network-level interaction/perturbation studies based on the data obtained from the single-cell-omics studies. Together with the imaging data, this will allow identifying early warning signals preceding critical transitions (Morales 2015; Kuehn 2014; Aguirre 2015) with relation to PD based on the RB group experience in loss-and-gain-of-function experiments, using both cellular and animal models. Furthermore, for the CT framework, the temporal data obtained from these single-cell-omics and imaging studies will provide a statistically rich data source for modelling the bifurcation dynamics of the disease systems and testing of the developed theoretical frameworks for classification of different modes of CTs (Lovecchio 2012 Kuehn 2014; Tsuchiya 2015; D’Souza 2015). The subproject will interact with the melanoma project of Stephanie Kreiss by exchanging cancer-specific analyses on the protein level at single-cell resolution.