Artificial Biomimetic systems – the Niche of Islet Organoids
The convergence environment wants to develop future models for diabetes research by using new strategies for stem cell differentiation.
Project leader: Hanne Bjørnson Scholz, Faculty of Medicine, Institute of Basic Medical Sciences (IMB), UiO, Division of Surgery, Inflammatory medicine, and Transplantation, OUS and Centre for Hybrid Technology Hub (CoE-HTH)
- Prof. Anne Danielsen, Department of Musicology and Centre for Interdisciplinary Studies in Rhythm, Time and Motion (CoE-RITMO), UiO
- Assoc. Prof. Alexander Refsum Jensenius, Department of Musicology and Centre for Interdisciplinary Studies in Rhythm, Time and Motion (CoE-RITMO), UiO
- Prof. Anders Malthe-Sørenssen, Department of Physics and Centre for Computing in Science Education (CoE-CCSE), UiO
- Prof. Simon Rayner, Department of Medical Genetics, OUS and CoE-HTH
- Prof. Stefan Krauss, Institute of Basic Medical Sciences, UiO and CoE-HTH
Our mission is to conduct cutting-edge research in life science disciplines to develop future models for treatment and cure of diabetes. Diabetes is a global chronic disease, impacting daily life and can have long term severe consequences for patients such as blindness, kidney failure, stroke, leading to premature death. Restoring the body insulin by introducing functional healthy insulin producing islets to patients is an effective solution, but donor material is extremely limited and accessing the pancreas tissue from deceased donors is a complex and lengthy process.
This convergence environment will integrate our knowledge of islet biology and differentiation pathways, together with expertise in matrices and acoustic-mechanical stimuli to develop novel differentiation protocols. By applying deep learning and modeling approaches, we will optimize these protocols to improve differentiation efficiency and functionality. Development of such model systems could enable (i) therapeutic transplantation to cure diabetes, (ii) studies of diabetes development and progression, (iii) drug-screening for more effective treatments of diabetes.
We will apply advanced analytical tools (such as deep learning) to investigate the different data types that will be generated in the work to aid optimization of protocols. Finally, in spite of the central role in development, stem cells remain a controversial and often misunderstood concept. We will therefore encapsulate collected knowledge into an education platform in an endeavour to foster better communication related to this topic.
To develop future models for diabetes research by using new strategies for stem cell differentiation.
- Develop islet organoids from human iPS by direct differentiation in combination with pathway sensors and novel methods generated from controlled mechanical/acoustic-based perturbations
- Apply deep learning approaches to predict islet organoids reproducibility
- Develop an education app to aid education related to stem cell-based research for diabetes
- Establish a life science consortium of researchers from the department of musicology and physics at UiO, computational and cell biology at OUS, and three Center of Excellence (CoE) (RITMO, HTH, CCSE).
- Prof. Anne Danielsen and Ass. Prof. Alexander Jensenius UiO-RITMO (Centre for Interdisciplinary Studies in Rhythm, Time and Motion)
- Prof. Anders Malthe-Sørenssen UiO-Department of Physics and CCSE (Centre for Computing in Science Education)
- Prof. Simon Rayner and Prof. Stefan Krauss UiO-HTH (Centre for Hybrid Technology Hub)