title: A diffusion model for detecting and classifying vesicle fusion and undocking events.
abstract: Fluorescently-tagged proteins located on vesicles can fuse with the surface membrane (visualised as a 'puff') or undock and return back into the bulk of the cell. Detection and quantitative measurement of these events from time-lapse videos has proven difficult. We propose a novel approach to detect fusion and undocking events by first searching for docked vesicles that 'disappear' from the field of view, and then using a diffusion model to classify them as either fusion or undocking events. We can also use the same searching method to identify docking events. We present comparative results against existing algorithms.
authors.affiliation: Department of Computer Science, University of Oxford, UK.
authors.forename: Lorenz
authors.initials: L
authors.lastname: Berger
authors.forename: Majid
authors.initials: M
authors.lastname: Mirmehdi
authors.forename: Sam
authors.initials: S
authors.lastname: Reed
authors.forename: Jeremy
authors.initials: J
authors.lastname: Tavaré
mesh_terms: D002462:Cell Membrane / Q000502:physiology / Q000648:ultrastructure*; D058948:Cell Tracking / Q000379:methods*; D003198:Computer Simulation; D004058:Diffusion; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008561:Membrane Fusion; D008856:Microscopy, Fluorescence / Q000379:methods*; D008954:Models, Biological; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D022161:Transport Vesicles / Q000648:ultrastructure*