MOTIVATION: Subcellular protein localization data are critical to the quantitative understanding of cellular function and regulation. Such data are acquired via observation and quantitative analysis of fluorescently labeled proteins in living cells. Differentiation of labeled protein from cellular artifacts remains an obstacle to accurate quantification. We have developed a novel hybrid machine-learning-based method to differentiate signal from artifact in membrane protein localization data by deriving positional information via surface fitting and combining this with fluorescence-intensity-based data to generate input for a support vector machine. RESULTS: We have employed this classifier to analyze signaling protein localization in T-cell activation. Our classifier displayed increased performance over previously available techniques, exhibiting both flexibility and adaptability: training on heterogeneous data yielded a general classifier with good overall performance; training on more specific data yielded an extremely high-performance specific classifier. We also demonstrate accurate automated learning utilizing additional experimental data.

The relocalization of plasma membrane proteins is critical for establishing cellular polarity and regulating cell signaling. Three-dimensional fluorescence video microscopy allows the dynamic visualization of proteins in living cells. We have developed a robust and automated method to employ fluorescence data acquired in this manner for quantitative analysis of membrane protein movements across the cell surface. Our method utilizes level-set-based surface reconstruction followed by a maximum likelihood surface registration algorithm for rigid-body alignment of noisy images. A surface-walking technique yields distance maps for the cell surface, which are then used to measure changes in protein surface distribution over time. Applying this method to signaling in T lymphocytes, we have used it to monitor receptor movements and have validated these results against previously reported single-particle tracking data.