bioRxivBiophysics
Single molecule tracking and analysis framework including theory-predicted parameter settings
Abstract
Imaging, tracking and analyzing individual biomolecules in living systems is a powerful technology to obtain quantitative kinetic and spatial information such as reaction rates, diffusion coefficients and localization maps. Common tracking tools often operate on single movies and require additional manual steps to analyze whole data sets or to compare different experimental conditions. We report a fast and comprehensive single molecule tracking and analysis framework (TrackIt) to simultaneously process several multi-movie data sets. A user-friendly GUI offers convenient tracking visualization, multiple state-of-the-art analysis procedures, display of results, and data im- and export at different levels to utilize external software tools. We applied our framework to quantify dissociation rates of a transcription factor in the nucleus and found that tracking errors, similar to fluorophore photobleaching, have to be considered for reliable analysis. Accordingly, we developed an algorithm, which accounts for both tracking losses and suggests optimized tracking parameters when evaluating reaction rates. Our versatile and extensible framework facilitates quantitative analysis of single molecule experiments at different experimental conditions.
- assay: imaging
- research focus: cell signaling & trafficking
- approach: deep learning
- approach: statistical model
Reproducibility ledger
Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.
Sign in to file a reproducibility report.
Related papers
- Slitflow: a Python framework for single-molecule dynamics and localization analysis
Y. Ito, M. Hirose, M. Tokunaga·bioRxiv·Mar 1, 2023
- Variational algorithms for analyzing noisy multi-state diffusion trajectories
Martin Lindén, Johan Elf·bioRxiv·Mar 8, 2018
- QuantiTrack: A unified software to study protein dynamics in living cells
David A Ball, Kaustubh Wagh, … Tatiana Karpova·bioRxiv·Sep 15, 2026
- ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks
F. Simon, J.-Y. Tinevez, S. van Teeffelen·bioRxiv·Jul 13, 2022
- Extracting transition rates in single-particle tracking using analytical diffusion distribution analysis
J. Vink, S. J. J. Brouns, J. Hohlbein·Biophysical JournalPeer-reviewed·Nov 1, 2020
- FISIK: Framework for the Inference of in Situ Interaction Kinetics from single-molecule imaging data
L. R. de Oliveira, K. Jaqaman·bioRxiv·Jun 28, 2019