title: Bayesian inference for improved single molecule fluorescence tracking.
abstract: Single molecule tracking is widely used to monitor the change in position of lipids and proteins in living cells. In many experiments in which molecules are tagged with a single or small number of fluorophores, the signal/noise ratio may be limiting, the number of molecules is not known, and fluorophore blinking and photobleaching can occur. All these factors make accurate tracking over long trajectories difficult and hence there is still a pressing need to develop better algorithms to extract the maximum information from a sequence of fluorescence images. We describe here a Bayesian-based inference approach, based on a trans-dimensional sequential Monte Carlo method that utilizes both the spatial and temporal information present in the image sequences. We show, using model data, where the real trajectory of the molecule is known, that our method allows accurate tracking of molecules over long trajectories even with low signal/noise ratio and in the presence of fluorescence blinking and photobleaching. The method is then applied to real experimental data.
authors.affiliation: Department of Engineering and Department of Chemistry, University of Cambridge, Cambridge, United Kingdom.
authors.forename: Ji Won
authors.initials: JW
authors.lastname: Yoon
authors.forename: Andreas
authors.initials: A
authors.lastname: Bruckbauer
authors.forename: William J
authors.initials: WJ
authors.lastname: Fitzgerald
authors.forename: David
authors.initials: D
authors.lastname: Klenerman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001704:Biopolymers / Q000737:chemistry*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D018715:Microscopy, Video / Q000379:methods*; D009038:Motion; D010363:Pattern Recognition, Automated / Q000379:methods*
chemical_list: D001704:Biopolymers