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BNP-Track: A framework for multi-particle superresolved tracking
When tracking fluorescently labeled molecules (termed “emitters”) under widefield microscopes, point spread function overlap of neighboring molecules is inevitable in both dilute and especially crowded environments. In such cases, superresolution methods leveraging rare photophysical events to disti...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104013/ https://www.ncbi.nlm.nih.gov/pubmed/37066179 http://dx.doi.org/10.1101/2023.04.03.535440 |
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author | Xu, Lance W.Q. Sgouralis, Ioannis Kilic, Zeliha Pressé, Steve |
author_facet | Xu, Lance W.Q. Sgouralis, Ioannis Kilic, Zeliha Pressé, Steve |
author_sort | Xu, Lance W.Q. |
collection | PubMed |
description | When tracking fluorescently labeled molecules (termed “emitters”) under widefield microscopes, point spread function overlap of neighboring molecules is inevitable in both dilute and especially crowded environments. In such cases, superresolution methods leveraging rare photophysical events to distinguish static targets nearby in space introduce temporal delays that compromise tracking. As we have shown in a companion manuscript, for dynamic targets, information on neighboring fluorescent molecules is encoded as spatial intensity correlations across pixels and temporal correlations in intensity patterns across time frames. We then demonstrated how we used all spatiotemporal correlations encoded in the data to achieve superresolved tracking. That is, we showed the results of full posterior inference over both the number of emitters and their associated tracks simultaneously and self-consistently through Bayesian nonparametrics. In this companion manuscript we focus on testing the robustness of our tracking tool, BNP-Track, across sets of parameter regimes and compare BNP-Track to competing tracking methods in the spirit of a prior Nature Methods tracking competition. We explore additional features of BNP-Track including how a stochastic treatment of background yields greater accuracy in emitter number determination and how BNP-Track corrects for point spread function blur (or “aliasing”) introduced by intraframe motion in addition to propagating error originating from myriad sources (such as criss-crossing tracks, out-of-focus particles, pixelation, shot and camera artefact, stochastic background) in posterior inference over emitter numbers and their associated tracks. While head-to-head comparison with other tracking methods is not possible (as competitors cannot simultaneously learn molecule numbers and associated tracks), we can give competing methods some advantages in order to perform approximate head-to-head comparison. We show that even under such optimistic scenarios, BNP-Track is capable of tracking multiple diffraction-limited point emitters conventional tracking methods cannot resolve thereby extending the superresolution paradigm to dynamical targets. |
format | Online Article Text |
id | pubmed-10104013 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-101040132023-04-15 BNP-Track: A framework for multi-particle superresolved tracking Xu, Lance W.Q. Sgouralis, Ioannis Kilic, Zeliha Pressé, Steve bioRxiv Article When tracking fluorescently labeled molecules (termed “emitters”) under widefield microscopes, point spread function overlap of neighboring molecules is inevitable in both dilute and especially crowded environments. In such cases, superresolution methods leveraging rare photophysical events to distinguish static targets nearby in space introduce temporal delays that compromise tracking. As we have shown in a companion manuscript, for dynamic targets, information on neighboring fluorescent molecules is encoded as spatial intensity correlations across pixels and temporal correlations in intensity patterns across time frames. We then demonstrated how we used all spatiotemporal correlations encoded in the data to achieve superresolved tracking. That is, we showed the results of full posterior inference over both the number of emitters and their associated tracks simultaneously and self-consistently through Bayesian nonparametrics. In this companion manuscript we focus on testing the robustness of our tracking tool, BNP-Track, across sets of parameter regimes and compare BNP-Track to competing tracking methods in the spirit of a prior Nature Methods tracking competition. We explore additional features of BNP-Track including how a stochastic treatment of background yields greater accuracy in emitter number determination and how BNP-Track corrects for point spread function blur (or “aliasing”) introduced by intraframe motion in addition to propagating error originating from myriad sources (such as criss-crossing tracks, out-of-focus particles, pixelation, shot and camera artefact, stochastic background) in posterior inference over emitter numbers and their associated tracks. While head-to-head comparison with other tracking methods is not possible (as competitors cannot simultaneously learn molecule numbers and associated tracks), we can give competing methods some advantages in order to perform approximate head-to-head comparison. We show that even under such optimistic scenarios, BNP-Track is capable of tracking multiple diffraction-limited point emitters conventional tracking methods cannot resolve thereby extending the superresolution paradigm to dynamical targets. Cold Spring Harbor Laboratory 2023-04-15 /pmc/articles/PMC10104013/ /pubmed/37066179 http://dx.doi.org/10.1101/2023.04.03.535440 Text en https://creativecommons.org/licenses/by-nc-nd/4.0/This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nc-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator. |
spellingShingle | Article Xu, Lance W.Q. Sgouralis, Ioannis Kilic, Zeliha Pressé, Steve BNP-Track: A framework for multi-particle superresolved tracking |
title | BNP-Track: A framework for multi-particle superresolved tracking |
title_full | BNP-Track: A framework for multi-particle superresolved tracking |
title_fullStr | BNP-Track: A framework for multi-particle superresolved tracking |
title_full_unstemmed | BNP-Track: A framework for multi-particle superresolved tracking |
title_short | BNP-Track: A framework for multi-particle superresolved tracking |
title_sort | bnp-track: a framework for multi-particle superresolved tracking |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104013/ https://www.ncbi.nlm.nih.gov/pubmed/37066179 http://dx.doi.org/10.1101/2023.04.03.535440 |
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