Franziska Schöb

Deep learning-based analysis of stem cell differentiation pathways

Induced pluripotent stem cells (iPSC) can potentially differentiate into any cell type in the body. This gives rise to great opportunities in the field of regenerative medicine using iPSC-derived cell replacements in patients with life-threatening diseases such as diabetes mellitus. However, the dynamic process of cell differentiation in which epigenetic, transcriptional, and metabolic changes lead to new cell identities is not fully understood. Likewise, the available tools to investigate these changes are based on time consuming and expensive assays measuring gene and protein expression that calls for innovative improvement. Image-based phenotypic screening relies on the extraction of multivariate information from cells cultured under a large variety of conditions. Technical advances in high-throughput microscopy enable screening in increasingly complex and biologically relevant model systems. To this end, this project will build on recent observations that spatial relationships in the differentiating cell colonies are important for the final fate of the iPSC.

In this project, deep learning approaches will be used to analyze the spatial relationships in the images together with gene and protein expression data. Interpretation of traits of the trained network will be used to optimize and improve differentiation efficiency and functionality to achieve self-organizing systems that work towards in vitro iPSC-derived beta-cell maturation.