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Time-resolved burst variance analysis

Quantifying biomolecular dynamics has become a major task of single-molecule fluorescence spectroscopy methods. In single-molecule Förster resonance energy transfer (smFRET), kinetic information is extracted from the stream of photons emitted by attached donor and acceptor fluorophores. Here, we des...

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Detalles Bibliográficos
Autores principales: Terterov, Ivan, Nettels, Daniel, Makarov, Dmitrii E., Hofmann, Hagen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406964/
https://www.ncbi.nlm.nih.gov/pubmed/37559939
http://dx.doi.org/10.1016/j.bpr.2023.100116
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author Terterov, Ivan
Nettels, Daniel
Makarov, Dmitrii E.
Hofmann, Hagen
author_facet Terterov, Ivan
Nettels, Daniel
Makarov, Dmitrii E.
Hofmann, Hagen
author_sort Terterov, Ivan
collection PubMed
description Quantifying biomolecular dynamics has become a major task of single-molecule fluorescence spectroscopy methods. In single-molecule Förster resonance energy transfer (smFRET), kinetic information is extracted from the stream of photons emitted by attached donor and acceptor fluorophores. Here, we describe a time-resolved version of burst variance analysis that can quantify kinetic rates at microsecond to millisecond timescales in smFRET experiments of diffusing molecules. Bursts are partitioned into segments with a fixed number of photons. The FRET variance is computed from these segments and compared with the variance expected from shot noise. By systematically varying the segment size, dynamics at different timescales can be captured. We provide a theoretical framework to extract kinetic rates from the decay of the FRET variance with increasing segment size. Compared to other methods such as filtered fluorescence correlation spectroscopy, recurrence analysis of single particles, and two-dimensional lifetime correlation spectroscopy, fewer photons are needed to obtain reliable timescale estimates, which reduces the required measurement time.
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spelling pubmed-104069642023-08-09 Time-resolved burst variance analysis Terterov, Ivan Nettels, Daniel Makarov, Dmitrii E. Hofmann, Hagen Biophys Rep (N Y) Article Quantifying biomolecular dynamics has become a major task of single-molecule fluorescence spectroscopy methods. In single-molecule Förster resonance energy transfer (smFRET), kinetic information is extracted from the stream of photons emitted by attached donor and acceptor fluorophores. Here, we describe a time-resolved version of burst variance analysis that can quantify kinetic rates at microsecond to millisecond timescales in smFRET experiments of diffusing molecules. Bursts are partitioned into segments with a fixed number of photons. The FRET variance is computed from these segments and compared with the variance expected from shot noise. By systematically varying the segment size, dynamics at different timescales can be captured. We provide a theoretical framework to extract kinetic rates from the decay of the FRET variance with increasing segment size. Compared to other methods such as filtered fluorescence correlation spectroscopy, recurrence analysis of single particles, and two-dimensional lifetime correlation spectroscopy, fewer photons are needed to obtain reliable timescale estimates, which reduces the required measurement time. Elsevier 2023-07-07 /pmc/articles/PMC10406964/ /pubmed/37559939 http://dx.doi.org/10.1016/j.bpr.2023.100116 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Terterov, Ivan
Nettels, Daniel
Makarov, Dmitrii E.
Hofmann, Hagen
Time-resolved burst variance analysis
title Time-resolved burst variance analysis
title_full Time-resolved burst variance analysis
title_fullStr Time-resolved burst variance analysis
title_full_unstemmed Time-resolved burst variance analysis
title_short Time-resolved burst variance analysis
title_sort time-resolved burst variance analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406964/
https://www.ncbi.nlm.nih.gov/pubmed/37559939
http://dx.doi.org/10.1016/j.bpr.2023.100116
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