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Optimal inference of molecular interaction dynamics in FRET microscopy
Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the obser...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
National Academy of Sciences
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104582/ https://www.ncbi.nlm.nih.gov/pubmed/37014867 http://dx.doi.org/10.1073/pnas.2211807120 |
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author | Kamino, Keita Kadakia, Nirag Avgidis, Fotios Liu, Zhe-Xuan Aoki, Kazuhiro Shimizu, Thomas S. Emonet, Thierry |
author_facet | Kamino, Keita Kadakia, Nirag Avgidis, Fotios Liu, Zhe-Xuan Aoki, Kazuhiro Shimizu, Thomas S. Emonet, Thierry |
author_sort | Kamino, Keita |
collection | PubMed |
description | Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible—a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on Bayesian filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise. |
format | Online Article Text |
id | pubmed-10104582 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | National Academy of Sciences |
record_format | MEDLINE/PubMed |
spelling | pubmed-101045822023-10-04 Optimal inference of molecular interaction dynamics in FRET microscopy Kamino, Keita Kadakia, Nirag Avgidis, Fotios Liu, Zhe-Xuan Aoki, Kazuhiro Shimizu, Thomas S. Emonet, Thierry Proc Natl Acad Sci U S A Biological Sciences Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible—a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on Bayesian filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise. National Academy of Sciences 2023-04-04 2023-04-11 /pmc/articles/PMC10104582/ /pubmed/37014867 http://dx.doi.org/10.1073/pnas.2211807120 Text en Copyright © 2023 the Author(s). Published by PNAS. https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under Creative Commons Attribution-NonCommercial-NoDerivatives License 4.0 (CC BY-NC-ND) (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Biological Sciences Kamino, Keita Kadakia, Nirag Avgidis, Fotios Liu, Zhe-Xuan Aoki, Kazuhiro Shimizu, Thomas S. Emonet, Thierry Optimal inference of molecular interaction dynamics in FRET microscopy |
title | Optimal inference of molecular interaction dynamics in FRET microscopy |
title_full | Optimal inference of molecular interaction dynamics in FRET microscopy |
title_fullStr | Optimal inference of molecular interaction dynamics in FRET microscopy |
title_full_unstemmed | Optimal inference of molecular interaction dynamics in FRET microscopy |
title_short | Optimal inference of molecular interaction dynamics in FRET microscopy |
title_sort | optimal inference of molecular interaction dynamics in fret microscopy |
topic | Biological Sciences |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10104582/ https://www.ncbi.nlm.nih.gov/pubmed/37014867 http://dx.doi.org/10.1073/pnas.2211807120 |
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