TRAIT2D: a Software for Quantitative Analysis of Single Particle Diffusion Data

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.

Anomalous diffusion, aging, and nonergodicity of scaled Brownian motion with fractional Gaussian noise: overview of related experimental observations and models

“…The flurry of new single-particle-tracking (SPT) datasets reporting on and novel theoretical-analysis tools assessing the properties of anomalous diffusion 1–27 has established an unprecedented need for novel theoretical models of diffusion and transport. Such models should desirably embody certain characteristic features of different “standard” anomalous-diffusion processes 28–33 such as, i.e.…”

Quantification of live cell membrane compartmentalization with high-speed single lipid tracking through interferometric Scattering Microscopy

“…For a more complete overview of the methodology adopted, see Supplemental Note 2. All the analysis routines herein described, can be easily reconstructed using the Python package reported in (61).…”

“…Monte Carlo simulations were performed using custom written routines in MatLab as previously described (8), which can be easily replicated using the Python package described in (61). In brief, in these simulations, we generated fluorescence time traces of 2-dimensional diffusion of single molecules in a heterogeneous corralled environment.…”

Slitflow: a Python framework for single-molecule dynamics and localization analysis

“…To solve these problems, several software framework packages with graphical user interfaces have been developed [10, 12, 13, 14]. These software packages allow users to construct a robust and reproducible analysis workflow using a well-organized process scheme.…”