Recovering mixtures of fast-diffusing states from short single-particle trajectories
Single-particle tracking (SPT) directly measures the dynamics of proteins in living cells and is a powerful tool to dissect molecular mechanisms of cellular regulation. Interpretation of SPT with fast-diffusing proteins in mammalian cells, however, is complicated by technical limitations imposed by...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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eLife Sciences Publications Ltd
2022-09-01
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| Series: | eLife |
| Subjects: | |
| Online Access: | https://elifesciences.org/articles/70169 |
| _version_ | 1828090712646746112 |
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| author | Alec Heckert Liza Dahal Robert Tjian Xavier Darzacq |
| author_facet | Alec Heckert Liza Dahal Robert Tjian Xavier Darzacq |
| author_sort | Alec Heckert |
| collection | DOAJ |
| description | Single-particle tracking (SPT) directly measures the dynamics of proteins in living cells and is a powerful tool to dissect molecular mechanisms of cellular regulation. Interpretation of SPT with fast-diffusing proteins in mammalian cells, however, is complicated by technical limitations imposed by fast image acquisition. These limitations include short trajectory length due to photobleaching and shallow depth of field, high localization error due to the low photon budget imposed by short integration times, and cell-to-cell variability. To address these issues, we investigated methods inspired by Bayesian nonparametrics to infer distributions of state parameters from SPT data with short trajectories, variable localization precision, and absence of prior knowledge about the number of underlying states. We discuss the advantages and disadvantages of these approaches relative to other frameworks for SPT analysis. |
| first_indexed | 2024-04-11T06:00:58Z |
| format | Article |
| id | doaj.art-732963cee82c4d2482f9ddccbd79c962 |
| institution | Directory Open Access Journal |
| issn | 2050-084X |
| language | English |
| last_indexed | 2024-04-11T06:00:58Z |
| publishDate | 2022-09-01 |
| publisher | eLife Sciences Publications Ltd |
| record_format | Article |
| series | eLife |
| spelling | doaj.art-732963cee82c4d2482f9ddccbd79c9622022-12-22T04:41:40ZengeLife Sciences Publications LtdeLife2050-084X2022-09-011110.7554/eLife.70169Recovering mixtures of fast-diffusing states from short single-particle trajectoriesAlec Heckert0https://orcid.org/0000-0001-8748-6645Liza Dahal1Robert Tjian2Xavier Darzacq3https://orcid.org/0000-0003-2537-8395Department of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, University of California, Berkeley, Berkeley, United StatesDepartment of Molecular and Cell Biology, Li Ka Shing Center for Biomedical and Health Sciences, University of California, Berkeley, Berkeley, United States; CIRM Center of Excellence, University of California, Berkeley, Berkeley, United StatesCIRM 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, University of California, Berkeley, Berkeley, United StatesSingle-particle tracking (SPT) directly measures the dynamics of proteins in living cells and is a powerful tool to dissect molecular mechanisms of cellular regulation. Interpretation of SPT with fast-diffusing proteins in mammalian cells, however, is complicated by technical limitations imposed by fast image acquisition. These limitations include short trajectory length due to photobleaching and shallow depth of field, high localization error due to the low photon budget imposed by short integration times, and cell-to-cell variability. To address these issues, we investigated methods inspired by Bayesian nonparametrics to infer distributions of state parameters from SPT data with short trajectories, variable localization precision, and absence of prior knowledge about the number of underlying states. We discuss the advantages and disadvantages of these approaches relative to other frameworks for SPT analysis.https://elifesciences.org/articles/70169mammalian cell cultureU2OS osteosarcomasingle-particle tracking |
| spellingShingle | Alec Heckert Liza Dahal Robert Tjian Xavier Darzacq Recovering mixtures of fast-diffusing states from short single-particle trajectories eLife mammalian cell culture U2OS osteosarcoma single-particle tracking |
| title | Recovering mixtures of fast-diffusing states from short single-particle trajectories |
| title_full | Recovering mixtures of fast-diffusing states from short single-particle trajectories |
| title_fullStr | Recovering mixtures of fast-diffusing states from short single-particle trajectories |
| title_full_unstemmed | Recovering mixtures of fast-diffusing states from short single-particle trajectories |
| title_short | Recovering mixtures of fast-diffusing states from short single-particle trajectories |
| title_sort | recovering mixtures of fast diffusing states from short single particle trajectories |
| topic | mammalian cell culture U2OS osteosarcoma single-particle tracking |
| url | https://elifesciences.org/articles/70169 |
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