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Spot-On: robust model-based analysis of single-particle tracking experiments

View ORCID ProfileAnders S Hansen, View ORCID ProfileMaxime Woringer, Jonathan B Grimm, View ORCID ProfileLuke D Lavis, View ORCID ProfileRobert Tjian, View ORCID ProfileXavier Darzacq
doi: https://doi.org/10.1101/171983
Anders S Hansen
1Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, CA 94720, USA.
2Howard Hughes Medical Institute, Berkeley, CA 94720, USA
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Maxime Woringer
1Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, CA 94720, USA.
3Unité Imagerie et Modélisation, Institut Pasteur, 25 rue du Docteur Roux, 75015 Paris, France
4Sorbonne Universités, UPMC Univ Paris 06, IFD, 4 Place Jussieu, 75252 Paris cedex 05, France
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Jonathan B Grimm
5Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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Luke D Lavis
5Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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Robert Tjian
1Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, CA 94720, USA.
2Howard Hughes Medical Institute, Berkeley, CA 94720, USA
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Xavier Darzacq
1Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, CA 94720, USA.
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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.

IMPACT STATEMENT Spot-On is an easy-to-use website that makes a rigorous and bias-corrected modeling framework for analysis of single-molecule tracking experiments available to all.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted October 26, 2017.
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Spot-On: robust model-based analysis of single-particle tracking experiments
Anders S Hansen, Maxime Woringer, Jonathan B Grimm, Luke D Lavis, Robert Tjian, Xavier Darzacq
bioRxiv 171983; doi: https://doi.org/10.1101/171983
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Spot-On: robust model-based analysis of single-particle tracking experiments
Anders S Hansen, Maxime Woringer, Jonathan B Grimm, Luke D Lavis, Robert Tjian, Xavier Darzacq
bioRxiv 171983; doi: https://doi.org/10.1101/171983

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