AMTraK

AMTraK automates multi-peak tracking and quantitative analysis of kymographs to extract spatio-temporal dynamics from fluorescence time-series microscopy.

Key Features:

• Multi-peak detection: Detects intensity and gradient peaks within kymographs to identify features for tracking.
• Distance-minimization tracking: Uses distance minimization algorithms to link peaks and track contours across time.
• Contour tracking: Tracks contours within kymographs based on detected peak information to follow spatio-temporal trajectories.
• Branch-point detection: Identifies merging and splitting events in tracks to detect coalescence and separation.
• Sub-pixel positional accuracy: Achieves sub-pixel localization precision validated using synthetic images.
• Quantitative outputs: Produces quantitative track parameters including velocities and event timings (e.g., mean velocity estimates).
• Input data compatibility: Operates on kymographs derived from fluorescence time-series microscopy (in vitro and in vivo).
• Reanalysis capability: Reproduces reported parameters when applied to previously manually quantified datasets such as clathrin polymerization kinetics and axonal transport.

Scientific Applications:

• Nucleoid segregation in E. coli: Quantifies nucleoid segregation during E. coli cell division using labeled nucleoid DNA time-series.
• Microtubule gliding motility: Measures microtubule gliding by recombinant kinesin, including reported mean velocity of ~0.5 μm/s.
• Vesicle dynamics and clathrin polymerization: Analyzes vesicle formation dynamics and clathrin polymerization kinetics from time-series microscopy.
• Axonal transport: Quantifies anterograde and retrograde transport events in axons.
• Sub-cellular dynamics in rod-shaped bacteria: Characterizes spatio-temporal behaviors of sub-cellular components in rod-shaped bacteria.

Methodology:

Detects intensity and gradient peaks in kymographs, applies distance-minimization algorithms to link peaks and track contours, employs a branch-point detection method to identify merging and splitting events, and validated positional accuracy on synthetic images.