Automatic Tracking of Escherichia Coli Bacteria

Automated Tracking of Biopolymer Growth and Network Deformation with TSOAX

Studies of how individual semi-flexible biopolymers and their network assemblies change over time reveal dynamical and mechanical properties important to the understanding of their function in tissues and living cells. Automatic tracking of biopolymer networks from fluorescence microscopy time-lapse sequences facilitates such quantitative studies. We present an open source software tool that combines a global and local correspondence algorithm to track biopolymer networks in 2D and 3D, using stretching open active contours. We demonstrate its application in fully automated tracking of elongating and intersecting actin filaments, detection of loop formation and constriction of tilted contractile rings in live cells, and tracking of network deformation under shear deformation.

Detecting cell division of Pseudomonas aeruginosa bacteria from bright-field microscopy images with hidden conditional random fields

An approach to automatically detect bacteria division with temporal models is presented. To understand how bacteria migrate and proliferate to form complex multicellular behaviours such as biofilms, it is desirable to track individual bacteria and detect cell division events. Unlike eukaryotic cells, prokaryotic cells such as bacteria lack distinctive features, causing bacteria division difficult to detect in a single image frame. Furthermore, bacteria may detach, migrate close to other bacteria and may orientate themselves at an angle to the horizontal plane. Our system trains a hidden conditional random field (HCRF) model from tracked and aligned bacteria division sequences. The HCRF model classifies a set of image frames as division or otherwise. The performance of our HCRF model is compared with a Hidden Markov Model (HMM). The results show that a HCRF classifier outperforms a HMM classifier. From 2D bright field microscopy data, it is a challenge to separate individual bacteria and associate observations to tracks. Automatic detection of sequences with bacteria division will improve tracking accuracy.

Interplay between type IV pili activity and exopolysaccharides secretion controls motility patterns in single cells of Myxococcus xanthus

Myxococcus xanthus performs coordinated social motility of cell groups through the extension and retraction of type IV pili (TFP) on solid surfaces, which requires both TFP and exopolysaccharides (EPS). By submerging cells in a liquid medium containing 1% methylcellulose, M. xanthus TFP-driven motility was induced in isolated cells and independently of EPS. We measured and analyzed the movements of cells using community tracking algorithms, which combine single-cell resolution with statistics from large sample populations. Cells without significant multi-cellular social interactions have surprisingly complex behaviors: EPS⁻ cells exhibited a pronounced increase in the tendency to stand vertically and moved with qualitatively different characteristics than other cells. A decrease in the EPS secretion of cells correlates with a higher instantaneous velocity, but with lower directional persistence in trajectories. Moreover, EPS⁻ cells do not adhere to the surface as strongly as wild-type and EPS overproducing cells, and display a greater tendency to have large deviations between the direction of movement and the cell axis, with cell velocity showing only minimal dependence on the direction of movement. The emerging picture is that EPS does not simply provide rheological resistance to a single mechanism but rather that the availability of EPS impacts motility pattern.

A matching model based on earth mover's distance for tracking Myxococcus xanthus

Tracking the motion of Myxococcus xanthus is a crucial step for fundamental bacteria studies. Large number of bacterial cells involved, limited image resolution, and various cell behaviors (e.g., division) make tracking a highly challenging problem. A common strategy is to segment the cells first and associate detected cells into moving trajectories. However, known detection association algorithms that run in polynomial time are either ineffective to deal with particular cell behaviors or sensitive to segmentation errors. In this paper, we propose a polynomial time hierarchical approach for associating segmented cells, using a new Earth Mover's Distance (EMD) based matching model. Our method is able to track cell motion when cells may divide, leave/enter the image window, and the segmentation results may incur false alarm, detection lost, and falsely merged/split detections. We demonstrate it on tracking M. xanthus. Applied to error-prone segmented cells, our algorithm exhibits higher track purity and produces more complete trajectories, comparing to several state-of-the-art detection association algorithms.