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

Autor:	Reina F; Leibniz-Institut für Photonische Technologien e.V, Jena, Germany., Wigg JMA; Institute of Applied Optics and Biophysics, Friedrich-Schiller-Universität, Jena, Germany., Dmitrieva M; Department of Engineering Science, University of Oxford, Oxford, UK., Vogler B; Institute of Applied Optics and Biophysics, Friedrich-Schiller-Universität, Jena, Germany., Lefebvre J; Département d'informatique, University of Quebec at Montreal, Montreal, Canada., Rittscher J; Department of Engineering Science, University of Oxford, Oxford, UK., Eggeling C; Leibniz-Institut für Photonische Technologien e.V, Jena, Germany.; Institute of Applied Optics and Biophysics, Friedrich-Schiller-Universität, Jena, Germany.; MRC Human Immunology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, Oxford, UK.
Jazyk:	angličtina
Zdroj:	F1000Research [F1000Res] 2021 Aug 20; Vol. 10, pp. 838. Date of Electronic Publication: 2021 Aug 20 (Print Publication: 2021).
DOI:	10.12688/f1000research.54788.2
Abstrakt:	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. Competing Interests: No competing interests were disclosed. (Copyright: © 2022 Reina F et al.)
Databáze:	MEDLINE
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