Automated Multi-Peak Tracking Kymography (AMTraK): A Tool to Quantify Sub-Cellular Dynamics with Sub-Pixel Accuracy

Abstract

Kymographs or space-time plots are widely used in cell biology to reduce the dimensions of a time-series in microscopy for both qualitative and quantitative insight into spatio-temporal dynamics. While multiple tools for image kymography have been described before, quantification remains largely manual. Here, we describe a novel software tool for automated multi-peak tracking kymography (AMTraK), which uses peak information and distance minimization to track and automatically quantify kymographs, integrated in a GUI. The program takes fluorescence time-series data as an input and tracks contours in the kymographs based on intensity and gradient peaks. By integrating a branch-point detection method, it can be used to identify merging and splitting events of tracks, important in separation and coalescence events. In tests with synthetic images, we demonstrate sub-pixel positional accuracy of the program. We test the program by quantifying sub-cellular dynamics in rod-shaped bacteria, microtubule (MT) transport and vesicle dynamics. A time-series of E. coli cell division with labeled nucleoid DNA is used to identify the time-point and rate at which the nucleoid segregates. The mean velocity of microtubule (MT) gliding motility due to a recombinant kinesin motor is estimated as 0.5 μm/s, in agreement with published values, and comparable to estimates using software for nanometer precision filament-tracking. We proceed to employ AMTraK to analyze previously published time-series microscopy data where kymographs had been manually quantified: clathrin polymerization kinetics during vesicle formation and anterograde and retrograde transport in axons. AMTraK analysis not only reproduces the reported parameters, it also provides an objective and automated method for reproducible analysis of kymographs from in vitro and in vivo fluorescence microscopy time-series of sub-cellular dynamics.

Kymograph analysis with high temporal resolution reveals new features of neurofilament transport kinetics

“…We then pooled these data to analyze the distributions of the run velocities, run times, run lengths and pause times for the filament population. Several other laboratories have described automated computational algorithms for tracking moving objects in kymographs, including edge detection methods (Zhang et al 2011; Chaphalkar et al 2016), but our procedure differs in that it is a data-driven solution to the specific problem of identifying runs and pauses in pixelated and noisy traces. Our analysis of this new data set revealed new features and complexity to the transport kinetics which are discussed below.…”

A new kymogram-based method reveals unexpected effects of marker protein expression and spatial anisotropy of cytoskeletal dynamics in plant cell cortex

“…Another drawback of the image-to-image correlation method is its dependence on the costly commercial MATLAB software package, as no version for free data analysis packages such as SciLab is available to date. Recently published ImageJ-compatible or standalone tools that enable semi-automated or automated extraction of quantitative information from kymograms [37, 38] might provide an interesting high-throughput alternative to manual single filament tracking, but their suitability for plant cells remains to be tested.…”

A Bayesian Framework for the Analog Reconstruction of Kymographs From Fluorescence Microscopy Data

“…They are used to infer parameters quantifying the dynamics at the microtubule ends [12], [13], [39], [7]. Moreover, they are used to reconstruct trajectories of motor proteins, cargos and vesicles moving along the microtubule lattice [3], [40], [10], estimate parameters quantifying their dynamics [7], [8], [9], and study the underlying regulatory mechanisms [3], [2]. Indeed, the analysis of kymographs provides a wealth of quantitative information such as the orientation, the velocity, and the pausing times of the transported particles [4], the co-localization of motor proteins and microtubule-associated proteins [2], [5].…”