Developing tissue grafts and models from stem cells is a complex process. Mimicking the structure and function of natural tissue requires coordination of signals from many cell types. How these signals interact to direct development of complex tissues is not well understood, and effective predictive tools are lacking. The goal of this project is to monitor, model, and modulate the differentiation of stem cells to produce tissues. This project will provide a clearer picture of dynamic tissue development and new tools to predict and manipulate stem cell fate. This will lead to a strategy for engineering of accurate and reproducible tissues, leading to improved tissue engineered therapies and better models for drug development. This project will involve undergraduate research experiences, a capstone design component, a student summer internship program, and an industry immersion program for graduate students.
This RECODE project will apply a systems reverse engineering of stochasticity (SRES) approach to monitor, model, and modulate heterotypic co-differentiation of progenitor cells in 3D organoids. SRES will advance the science which underlies the co-differentiation of cells in complex tissues. Crucial to this project is the development of nanoscale biosensors providing dynamic multigene expression analysis in single cells within complex heterotypic organoids. Spatiotemporal differentiation marker expression, combined with proliferation measures in sorted populations will be monitored. The data will be used to develop stochastic agent-based models of mesenchymal stem cell (MSC) function and differentiation. The models will predict the relative proportions of osteogenic, endotheliogenic, chondrogenic and adipogenic lineages based on multiplexed stimuli, and be updatable using real-time biosensor data. The predictive models will be tested by modulating heterotypic differentiation trajectories with temporally controlled siRNA differentiation factors to create heterotypic bone and cartilage organoids. This new knowledge will provide a systematic means for engineering heterotypic 3D tissue models necessary for realizing intricate skeletal architectures. The technical goals of the project are to: (i) develop new multiplex real-time intracellular reporters for differentiation markers, (ii) develop stochastic models of progenitor cell proliferation and fate based on dynamic differentiation marker expression and (iii) validate the agent-based stochastic models of organoid development using siRNA targeting pathways known to de novo regulate differentiation.
This project is being jointly supported by the Engineering Biology and Health Cluster in ENG/CBET and the Cellular Dynamics and Function Cluster in BIO/MCB.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
