Identifying virus-cell fusion in two-channel fluorescence microscopy image sequences based on a layered probabilistic approach.
The entry process of virus particles into cells is decisive for infection. In this work, we investigate fusion of virus particles with the cell membrane via time-lapse fluorescence microscopy. To automatically identify fusion for single particles based on their intensity over time, we have developed a layered probabilistic approach. The approach decomposes the action of a single particle into three abstractions: the intensity over time, the underlying temporal intensity model, as well as a high level behavior. Each abstraction corresponds to a layer and these layers are represented via stochastic hybrid systems and hidden Markov models. We use a maxbelief strategy to efficiently combine both representations. To compute estimates for the abstractions we use a hybrid particle filter and the Viterbi algorithm. Based on synthetic image sequences, we characterize the performance of the approach as a function of the image noise. We also characterize the performance as a function of the tracking error. We have also successfully applied the approach to real image sequences displaying pseudotyped HIV-1 particles in contact with host cells and compared the experimental results with ground truth obtained by manual analysis.
Department Bioinformatics and Functional Genomics, University of Heidelberg, Heidelberg, Germany. wgodinez@ieee.org
William J
WJ
Godinez
Marko
M
Lampe
Peter
P
Koch
Roland
R
Eils
Barbara
B
Müller
Karl
K
Rohr
D000465:Algorithms; D001499:Bayes Theorem; D002459:Cell Fusion; D058948:Cell Tracking; D015497:HIV-1 / Q000502:physiology / Q000648:ultrastructure; D006367:HeLa Cells; D054884:Host-Pathogen Interactions / Q000502:physiology*; D006801:Humans; D008390:Markov Chains; D008856:Microscopy, Fluorescence / Q000379:methods*; D008954:Models, Biological; D013269:Stochastic Processes; D014771:Virion / Q000502:physiology* / Q000648:ultrastructure; D053585:Virus Attachment; D053586:Virus Internalization