Robust model-based analysis of single-particle tracking experiments with Spot-On
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 analy...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2018-01-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/33125 |
| _version_ | 1829097614322171904 |
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| author | Anders S Hansen Maxime Woringer Jonathan B Grimm Luke D Lavis Robert Tjian Xavier Darzacq |
| author_facet | Anders S Hansen Maxime Woringer Jonathan B Grimm Luke D Lavis Robert Tjian Xavier Darzacq |
| author_sort | Anders S Hansen |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T09:16:55Z |
| format | Article |
| id | doaj.art-3e0631103f5547218092c96c47f94a83 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-11T09:16:55Z |
| publishDate | 2018-01-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-3e0631103f5547218092c96c47f94a832022-12-22T04:32:18ZengeLife Sciences Publications LtdeLife2050-084X2018-01-01710.7554/eLife.33125Robust model-based analysis of single-particle tracking experiments with Spot-OnAnders S Hansen0https://orcid.org/0000-0001-7540-7858Maxime Woringer1https://orcid.org/0000-0003-2581-9808Jonathan B Grimm2Luke D Lavis3Robert Tjian4https://orcid.org/0000-0003-0539-8217Xavier Darzacq5https://orcid.org/0000-0003-2537-8395Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, Berkeley, United StatesDepartment of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, Berkeley, United States; Unité Imagerie et Modélisation, Institut Pasteur, Paris, France; UPMC Univ Paris 06, Sorbonne Universités, Paris, FranceJanelia Research Campus, Howard Hughes Medical Institute, Ashburn, United StatesJanelia Research Campus, Howard Hughes Medical Institute, Ashburn, United StatesDepartment of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, Berkeley, United States; Howard Hughes Medical Institute, Berkeley, United StatesDepartment of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, CIRM Center of Excellence, University of California, Berkeley, Berkeley, United StatesSingle-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.https://elifesciences.org/articles/33125single particle trackingtranscription factordynamicssingle moleculekinetic modelingsuper-resolution |
| spellingShingle | Anders S Hansen Maxime Woringer Jonathan B Grimm Luke D Lavis Robert Tjian Xavier Darzacq Robust model-based analysis of single-particle tracking experiments with Spot-On eLife single particle tracking transcription factor dynamics single molecule kinetic modeling super-resolution |
| title | Robust model-based analysis of single-particle tracking experiments with Spot-On |
| title_full | Robust model-based analysis of single-particle tracking experiments with Spot-On |
| title_fullStr | Robust model-based analysis of single-particle tracking experiments with Spot-On |
| title_full_unstemmed | Robust model-based analysis of single-particle tracking experiments with Spot-On |
| title_short | Robust model-based analysis of single-particle tracking experiments with Spot-On |
| title_sort | robust model based analysis of single particle tracking experiments with spot on |
| topic | single particle tracking transcription factor dynamics single molecule kinetic modeling super-resolution |
| url | https://elifesciences.org/articles/33125 |
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