Ottawa Hospital Research Institute

Published purpose

Stem cells harbour tremendous potential for tissue regeneration after injury or in the face of chronic/degenerative disease. Yet, clinical application of stem cell therapies for muscle degenerative diseases has been limited due in part to the complexities of manipulating or augmenting stem cell systems in vivo. Designing optimal stem cell therapies is therefore extremely challenging because testing of every design is highly time-consuming and costly. In silico design using machine learning models can provide a high performance alternative solution. The overall objective of this Project is the development of novel AI-driven methodologies for optimal cell therapy design. This will be achieved by accurately predicting the outcomes of targeted perturbations of a stem cell system based on advanced single-cell genomic data. Researchers will collect publicly available skeletal muscle-relevant single-cell data and in-house cell data from the Ottawa Hospital Research Institute, as well as from the NRC’s Disruptive Technology Solutions for Cell and Gene Therapy Program. The AI-methodologies and simulation models developed in this Project will help to better understand the mechanisms of how stem cells differentiate to specific cell types with desired properties, speeding up the optimal strategy for selection while reducing the costs and facilitating the safe and efficient stem cell therapy. The outcome of this Project will provide an efficient and robust tool for optimal stem cell therapy selection.