Diabetes is a life-threatening chronic metabolic disease which impacts daily life and can have long term severe consequences for the patients due to serious secondary complications that can lead to premature death.
Unfortunately, there is no cure for the disease yet, and therapy predominantly focuses on management to mimic the fine-tuning of glucose regulation in our body. Replacement therapies, based on introducing functional healthy insulin producing cells to patients requires donor material that is extremely sparse and requires tissue from deceased donors.
Induced pluripotent stem cells (iPS) can potentially differentiate into any cell type in the body. Although researchers have made some progress in developing protocols for differentiating iPS cells into functional human insulin producing beta cells, there is currently no protocol that can differentiate human iPS cells into fully glucose responsive cells.
The dynamic process in which epigenetic, transcriptional, and metabolic changes lead to new cell identities is not fully understood. Likewise, the available tool to investigate these changes is based on time consuming and expensive assays measuring gene and protein expression that calls for innovative improvement. One pathway to improvement is that in addition to the molecular changes, iPSC differentiation is followed by important transformations identifiable by microscopy.
The goal of this project is to use frequent, systematic microscopy imaging during iPS differentiation into true insulin producing cells by applying deep learning and new modeling approaches. We will optimize and improve differentiation efficiency and functionality to achieve self-organizing systems that work towards in vitro iPS-derived beta-cell maturation. Interpretation of traits of the trained network will be used to choose key changes to be modelled. The analysis and modelling will be important tools in the development of new differentiation protocols with the potential to generate insulin producing cells as a cure for diabetes.
The project is highly interdisciplinary with supervision of expert in physics and stem cell biology.
- CompSci: Training in Computational Science
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Deep-learning based analysis and modelling of stem cell differentiation pathways