Stochastic tracking of migrating live cells interacting with 3D gel environment using augmented-space particle filters

This paper presents an integrated stochastic approach to tracking multiple live cells that interact with the surrounding gel matrix. Cells migrate in a stochastic manner, forming a functional structure. Tracking the trajectory of each migrating cell and its interactions with the gel and other cells provide useful insights into how a vascular structure is formed as a collection of migratory cells. In micro-fluidic 3-D angiogenic sprouting experiments, two types of images are obtained at discrete time steps using confocal microscopy: a) three-dimensional fluorescent images of stained cell nuclei and b) two dimensional visible light images of the gel matrix. These two sources of images provide supplementary information as the outline of the conduit or lumen formed in the matrix by the migrating cells can be seen in the images of the gel. A Bayesian filtering framework is developed which augments both the cell and conduit parameters to the same state vector, allowing mathematically consistent simultaneous observation updates from both channels. Issues encountered include the high dimensional state vector and non-Gaussian cell state updates. Results demonstrate that our method based on Rao-Blackwellized particle filtering treats these issues effectively.