Robust model-based analysis of single-particle tracking experiments with Spot-On
Abstract
Single-particle tracking (SPT) has become an important method to bridge biochemistry and cell biology since it allows direct observation of protein binding and diffusion dynamics in live cells. However, accurately inferring information from SPT studies is challenging due to biases in both data analysis and experimental design. To address analysis bias, we introduce 'Spot-On', an intuitive web-interface. Spot-On implements a kinetic modeling framework that accounts for known biases, including molecules moving out-of-focus, and robustly infers diffusion constants and subpopulations from pooled single-molecule trajectories. To minimize inherent experimental biases, we implement and validate stroboscopic photo-activation SPT (spaSPT), which minimizes motion-blur bias and tracking errors. We validate Spot-On using experimentally realistic simulations and show that Spot-On outperforms other methods. We then apply Spot-On to spaSPT data from live mammalian cells spanning a wide range of nuclear dynamics and demonstrate that Spot-On consistently and robustly infers subpopulation fractions and diffusion constants.
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Funding
National Institutes of Health (UO1-EB021236)
- Xavier Darzacq
National Institutes of Health (U54-DK107980)
- Xavier Darzacq
California Institute for Regenerative Medicine (LA1-08013)
- Xavier Darzacq
Howard Hughes Medical Institute (3061)
- Robert Tjian
Howard Hughes Medical Institute
- Luke D Lavis
Siebel Stem Cell Institute
- Anders S Hansen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- David Sherratt, University of Oxford, United Kingdom
Version history
- Received: October 27, 2017
- Accepted: January 3, 2018
- Accepted Manuscript published: January 4, 2018 (version 1)
- Version of Record published: February 12, 2018 (version 2)
Copyright
© 2018, Hansen et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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