Real-time computer vision tracking of stemness

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DESCRIPTION (provided by applicant): This proposal addresses the need for new stem cell engineering toolsets that can regulate adult stem cell expansion (self-renewal, without differentiation) ex vivo in order to create sufficient numbers of cells for clinical applications. The goals of this proposal are to: 1) develop a computer vision system that tracks in real-time the spatiotemporal histories of cell divisions in vitro in phase-contrast imagery; 2) use this data to automatically derive real-time metrics of symmetry, division times, confluence, and predictive population growth models; and, 3) apply this system to develop novel strategies that maximize the yield of adult stem cell expansion in growth factor-mediated conditions. 'Real-time' means that processing is completed 'on-line' in the 15 minute intervals between image acquisitions, and this information is available during cell culture as feedback for process monitoring and control. In Aim 1, robust image segmentation and tracking of dense cell populations will be achieved using multi-modal cell tracking modules that represent and reason about cell states and motion from different perspectives, and then fuse and coordinate their respective outputs to make collaborative decisions. The multi-modal modules will be formulated for fast implementation on vector-based graphics processing hardware. The system will measure: proliferation, lineage (i.e., parent-daughter relationship), quiescence, and apoptotic states of each cell in culture within developing confluent populations of muscle-derived stem cells (MDSCs), including immortalized pluripotent C2C12, primary mouse MDSCs, and primary human MDSCs. In Aim 2, the system will be applied to efficient discovery and characterization of cell expansion behaviors and determination of nominal culture conditions to maximize symmetry using bio-printed combinatorial arrays of immobilized growth factors on extracellular matrix substrates. In Aim 3, real-time monitoring and control of cell expansion will be demonstrated using an adaptive subculturing strategy; the predictive growth model will signal when to suspend cell culture, followed by fluorescent-activated cell sorting to exclude apoptotic or differentiating cells, thus selectively enriching subcultures for cells exhibiting the highest proliferation rates. Maintenance of differentiative potential will be monitored between subcultures using in vitro osteogenesis and myogenesis as paradigm differentiation indices.