Diatrack particle tracking software: Review of applications and performance evaluation

Abstract

Object tracking is an instrumental tool supporting studies of cellular trafficking. There are three challenges in object tracking: the identification of targets; the precise determination of their position and boundaries; and the assembly of correct trajectories. This last challenge is particularly relevant when dealing with densely populated images with low signal-to-noise ratios-conditions that are often encountered in applications such as organelle tracking, virus particle tracking or single-molecule imaging. We have developed a set of methods that can handle a wide variety of signal complexities. They are compiled into a free software package called Diatrack. Here we review its main features and utility in a range of applications, providing a survey of the dynamic imaging field together with recommendations for effective use. The performance of our framework is shown to compare favorably to a wide selection of custom-developed algorithms, whether in terms of localization precision, processing speed or correctness of tracks.

Keywords: biological imaging; cell tracking; dynamic imaging; live cell imaging; organelle tracking; particle tracking; single molecule; super-resolution; vesicle tracking.

Figure 1

A) The workflow in Diatrack guides the user naturally, from particle production (see panel B) and particle selection (see panel C), down to track production (see panel D), selection, and analysis. B) Immediately after particle production, a large number of particles do not correspond to genuine objects. C) Spurious particles must be systematically eliminated using a combination of selection tools such as the “remove blurred” slider, until only genuine particles are retained. D) The ability to identify and track spots with high precision even when they are in close proximity to each other is illustrated. A particle is seen passing near another one less than 3 pixels away. High precision tracking allows reporting on molecular events at the single-molecule level. E) Illustration of the notion of assignment conflict. In order to construct trajectories, particles 1a, 2a, 3a, 4a and 5a observed in time frame marked by “a” must be associated with particles seen at positions 1b, 2b, 3b, 4b and 5b one frame later. Given the maximum jump amplitude shown by dashed circles, all possible particle motions are indicated by dashed arrows. Particles compete with each other for assignment. F) In order to form tracks, one might first want to assign particle 3a with particle 2b because they lie in closest proximity. However, proceeding in order of proximity will eventually leave particle 1a and 5b unpaired (the suboptimal solution is shown). G) Using Diatrack’s graph-based algorithm a solution that maximizes the pairing of particles may be found. Note that when particles are quite likely to disappear (e.g. in the presence of out of focus motion), the “suboptimal” solution shown in F) should probably be preferred over the “optimal solution” because the latter imposes a much greater total overall particle displacement. The best algorithm must strike a delicate balance between maximal matching of particles between frames and the ability to deal gracefully with particle appearance and disappearances (see Figure 2).

Figure 2. Tracking performance

The ground truth data from the work of Chenouard et al. were compared to the tracking results produced using Diatrack. As described in Chenouard et al., Alpha is a measure of track quality, while RMSE represents the root mean square discrepancy between true positions of particles and those corresponding positions found using Diatrack. The software performed really well, especially in light of the fact that it was not modified or tuned for the purpose of dealing with these particular data sets. It was also very fast in 2D (last row). Additional results may be found in the supplementary information.

Figure 3. Panel A) Example of 4D tracking

With the advances in computer performance, ever larger 4D data sets can be analyzed rapidly. Colored surfaces represent watershed-segmented objects and the tracks shown in red color connect the center of gravity of these surfaces over time. B) Analysis of in vitro motility data. Here, actin filaments were segmented using the “in vitro motility assay” particle production mode (time along tracks is color-coded from blue to red). The inset demonstrates in this situation a relatively narrow distribution of actin filament speed. C) Diatrack can also track objects segmented by third party methods. Here, bacteria were segmented as described in and then tracked within our software using the “pre-segmented” mode. On the basis of this type of data, flow movies may be generated automatically that show locally coherent streaming of bacteria in the biofilm (see supplementary movie 2 and inset). D) Automating identification of mRNA export events. PP7-labelled mRNA particles (green channel) in yeast were tracked over time using both manual tracking (white trace), and using Diatrack (blue trace) -showing excellent overlap. Together with custom image analysis to detect the nuclear envelope (red channel), this high-throughput tool now routinely allows the identification of rare mRNA export events as well as their kinetic characterization (e.g. export time, nuclear envelope scanning time etc.). E) Characterizing fluid flows. Top view images of a planar cross-section of a liquid drop captured using a high-speed video camera. 5 μm fluorescent microparticles are observed to either follow smooth concentric trajectories associated with the acoustic streaming flow in a high viscosity drop (top), or exponentially divergent trajectories associated with chaotic acoustic streaming flow in a low viscosity drop (bottom). Each particle trajectory is labelled in a different color. F) Tracking GFP-Rab5 endosomes in RPE cells. A subpopulation of these organelles is transported rapidly and efficiently along microtubules. In the representation used here, time is coded as the vertical axis. The insets characterize the effectiveness of the displacements i.e. the ratio of straight-line trajectory length to total trajectory length, and the total track lengths, respectively. Original movie was obtained from Flores-Rodriguez et al.