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Using Single-Cell Multiplexed Imaging and Manifold Learning to Visualize Cell Cycle Plasticity in Health and Disease
November 17 @ 12:00 pm - 1:00 pm UTC-5
A Presentation by Wayne Michael Stallaert, Ph.D. Postdoctoral Researcher in Computational Medicine, University of North Carolina at Chapel Hill
The recent development of single-cell approaches to study the cell cycle (e.g. time-lapse imaging of fluorescent cell cycle biosensors) has revealed that cells do not always take the same molecular path through the cell cycle. This nascent field of cell cycle plasticity offers exciting new opportunities to understand how the cell cycle changes in different states of health and disease, including cancer. I have developed an approach that combines highly multiplexed, single-cell imaging and machine learning to obtain a “bird’s-eye view” of the entire cell cycle. These cell cycle maps reveal the mechanistic trajectories that cells can take from one cell division to the next, and the changes in protein expression/activity that accompany progression along each of these trajectories. These maps also reveal the points at which cells exit from the proliferative cell cycle into states of reversible (“quiescent”) or irreversible (“senescent”) arrest, and the mechanisms that govern these proliferation/arrest decisions. I will demonstrate how we can use these maps to reveal how the cell cycle responds to different environments and stresses. I will also show how this approach can be used to identify the mechanistic “detours” that cancer cells take through the cell cycle to escape drug-induced arrest.