Computer Vision and Image Understanding
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Fluorescence microscopy of labeled proteins yields a wealth of data on cell signaling processes. However, systems for quantitative analysis of such data have lagged behind the recent progress in data acquisition technology. As cellular protein redistribution plays a key role in proximal signaling and the establishment of cell polarity, quantitative information is critical for understanding many signaling networks. We have developed a robust automated system to analyze membrane protein redistribution based on datasets obtained via fluorescence video microscopy. Our system provides methods for cell surface segmentation and reconstruction, cell shape tracking, cell-surface parameterization, and cluster formation analysis. Our system is novel in both its integration and its surface-based approach, enabling model-free analysis of protein redistribution across the entire cell. We validate our system by measuring receptor clustering in T lymphocytes undergoing activation, obtaining clustering velocities consistent with the previously reported single-particle tracking data that serve as our reference standard. Our methods generalize to many cell-signaling phenomena, allowing quantitative measurement of these cell membrane processes and offering the ability to derive empiric parameters for spatial signaling network models.