Abstract
Single particle tracking (SPT) is one of the most widely used tools in optical microscopy to evaluate particle mobility in a variety of situations, including cellular and model membrane dynamics. Recent technological developments, such as Interferometric Scattering microscopy, have allowed recording of long, uninterrupted single particle trajectories at kilohertz framerates. The resulting data, where particles are continuously detected and do not displace much between observations, thereby do not require complex linking algorithms. Moreover, while these measurements offer more details into the short-term diffusion behaviour of the tracked particles, they are also subject to the influence of localisation uncertainties, which are often underestimated by conventional analysis pipelines. we thus developed a Python library, under the name of TRAIT2D (Tracking Analysis Toolbox - 2D version), in order to track particle diffusion at high sampling rates, and analyse the resulting trajectories with an innovative approach. The data analysis pipeline introduced is more localisation-uncertainty aware, and also selects the most appropriate diffusion model for the data provided on a statistical basis. A trajectory simulation platform also allows the user to handily generate trajectories and even synthetic time-lapses to test alternative tracking algorithms and data analysis approaches. A high degree of customisation for the analysis pipeline, for example with the introduction of different diffusion modes, is possible from the source code. Finally, the presence of graphical user interfaces lowers the access barrier for users with little to no programming experience.
Highlights
Single particle tracking (SPT) is one of the most direct and employed methods to quantify particle dynamics in a sample using optical microscopy
Given its nature as a data analysis method rather than a technique onto itself, SPT is indifferent to how the particle is detected, as long as the signal-to-noise levels are sufficient to identify it against the background
As a consequence of this, it is almost always necessary to label the sample with adequate target specificity, and it is important that the labelling should be sparse enough to enable single molecule level of detail, when employing conventional, diffraction-limited microscopy
Summary
1. Xavier Michalet , University of California, Los Angeles (UCLA), Los Angeles, USA University of California at Los Angeles, Los Angeles, USA. Any reports and responses or comments on the article can be found at the end of the article. Keywords Single Particle Tracking, Diffusion, Data analysis, Python, Graphical User Interface, Simulation, Microscopy. This article is included in the Python collection
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