Achieving subtemporal resolution in the analysis of two-state single-molecule trajectories
Clarkson, Erik LU
and Ambjörnsson, Tobias
LU
(2026)
In Physical Review Research
8(1).
p.013115-013115
- Abstract
- Although spatial resolution in fluorescence microscopy and related fields has advanced to the nanometer scale, time resolution has remained essentially unchanged and is set by the camera system’s imaging time. Yet adequate time resolution is crucial for information acquisition about, for instance, dynamical processes in cells. This acquisition typically proceeds by analyzing biomolecular trajectories from single-particle tracking experiments, in terms of a standard discrete-time hidden Markov model. This type of analysis assumes, however, that subsampling time events that happen during imaging time can be neglected, which is rarely the case. To remedy this, we here introduce an algorithm that efficiently calculates the exact contribution... (More)
- Although spatial resolution in fluorescence microscopy and related fields has advanced to the nanometer scale, time resolution has remained essentially unchanged and is set by the camera system’s imaging time. Yet adequate time resolution is crucial for information acquisition about, for instance, dynamical processes in cells. This acquisition typically proceeds by analyzing biomolecular trajectories from single-particle tracking experiments, in terms of a standard discrete-time hidden Markov model. This type of analysis assumes, however, that subsampling time events that happen during imaging time can be neglected, which is rarely the case. To remedy this, we here introduce an algorithm that efficiently calculates the exact contribution of state switches to the likelihood of observed trajectories. This is made possible by our analytic derivation of generalized transition probabilities—which we call transition-accretion probabilities—that probabilistically capture unseen switching behavior during data acquisition. We do in-silico Bayesian model selection and parameter inference, and demonstrate that our subsampling time hidden Markov model approach outperforms the standard variant (applicable to slow kinetics). Our method and associated free-to-use software opens up for precise and reliable parameter estimation across a variety of single-molecule experiments, irrespective of the temporal resolution of the setup. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/485710cb-65de-40b0-9a5c-fe6d300ed62e
- author
- Clarkson, Erik
LU
and Ambjörnsson, Tobias
LU
- organization
- publishing date
- 2026-01-30
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Physical Review Research
- volume
- 8
- issue
- 1
- pages
- 18 pages
- publisher
- American Physical Society
- ISSN
- 2643-1564
- DOI
- 10.1103/ccdh-hhx8
- project
- Probabilistic analysis of fluorescence trajectories
- language
- English
- LU publication?
- yes
- id
- 485710cb-65de-40b0-9a5c-fe6d300ed62e
- date added to LUP
- 2026-01-31 10:04:12
- date last changed
- 2026-02-03 08:19:16
@article{485710cb-65de-40b0-9a5c-fe6d300ed62e,
abstract = {{Although spatial resolution in fluorescence microscopy and related fields has advanced to the nanometer scale, time resolution has remained essentially unchanged and is set by the camera system’s imaging time. Yet adequate time resolution is crucial for information acquisition about, for instance, dynamical processes in cells. This acquisition typically proceeds by analyzing biomolecular trajectories from single-particle tracking experiments, in terms of a standard discrete-time hidden Markov model. This type of analysis assumes, however, that subsampling time events that happen during imaging time can be neglected, which is rarely the case. To remedy this, we here introduce an algorithm that efficiently calculates the exact contribution of state switches to the likelihood of observed trajectories. This is made possible by our analytic derivation of generalized transition probabilities—which we call transition-accretion probabilities—that probabilistically capture unseen switching behavior during data acquisition. We do in-silico Bayesian model selection and parameter inference, and demonstrate that our subsampling time hidden Markov model approach outperforms the standard variant (applicable to slow kinetics). Our method and associated free-to-use software opens up for precise and reliable parameter estimation across a variety of single-molecule experiments, irrespective of the temporal resolution of the setup.}},
author = {{Clarkson, Erik and Ambjörnsson, Tobias}},
issn = {{2643-1564}},
language = {{eng}},
month = {{01}},
number = {{1}},
pages = {{013115--013115}},
publisher = {{American Physical Society}},
series = {{Physical Review Research}},
title = {{Achieving subtemporal resolution in the analysis of two-state single-molecule trajectories}},
url = {{http://dx.doi.org/10.1103/ccdh-hhx8}},
doi = {{10.1103/ccdh-hhx8}},
volume = {{8}},
year = {{2026}},
}