Evidence map›Paper›PMID 35874114›Full record

ArticleNature computational science2022

Diffraction-Limited Molecular Cluster Quantification with Bayesian Nonparametrics.

J Shepard Bryan, Ioannis Sgouralis, Steve Pressé

Abstract read
In one paragraph

Article in Nature computational science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

22 citing papers in PubMed.

  1. Article
  2. Single-Photon Single-Particle Tracking.bioRxiv : the preprint server for biology · 2025
    Article
  3. Article
  4. Mamba time series forecasting with uncertainty quantification.Machine learning: science and technology · 2025
    Article
  5. Article
  6. Physics-Inspired Single-Particle Tracking Accelerated with Parallelism.bioRxiv : the preprint server for biology · 2025
    Article
  7. Single-molecule reaction-diffusion.Biophysical journal · 2025
    Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Cholesterol inhibits assembly and activation of the EphA2 receptor.bioRxiv : the preprint server for biology · 2024
    Article
  14. Article
  15. Article
  16. Article
  17. An accurate probabilistic step finder for time-series analysis.bioRxiv : the preprint server for biology · 2023
    Article
  18. Article
  19. Article
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

J Shepard BryanCenter for Biological Physics, Arizona State University.
Ioannis SgouralisDepartment of Mathematics, University of Tennessee Knoxville.
Steve PresséCenter for Biological Physics, Arizona State University.

Funding

A Bayesian nonparametric approach to superresolved tracking of multiple molecules inside living cellsR01GM130745 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI PRESSE, STEVE · 2019 to 2023
$1.5M
Theoretical Models of Single Molecule Dynamics from Minimal Photon NumbersR01GM134426 · NIGMS · ARIZONA STATE UNIVERSITY-TEMPE CAMPUS · PI PRESSE, STEVE · 2019 to 2022
$1.2M
NIGMS NIH HHS R01 GM130745NIGMS NIH HHS R01 GM134426
6 · The paper itself

Abstract

Life's fundamental processes involve multiple molecules operating in close proximity within cells. To probe the composition and kinetics of molecular clusters confined within small (diffraction-limited) regions, experiments often report on the total fluorescence intensity simultaneously emitted from labeled molecules confined to such regions. Methods exist to enumerate total fluorophore numbers (e.g., step counting by photobleaching). However, methods aimed at step counting by photobleaching cannot treat photophysical dynamics in counting nor learn their associated kinetic rates. Here we propose a method to simultaneously enumerate fluorophores and determine their individual photophysical state trajectories. As the number of active (fluorescent) molecules at any given time is unknown, we rely on Bayesian nonparametrics and use specialized Monte Carlo algorithms to derive our estimates. Our formulation is benchmarked on synthetic and real data sets. While our focus here is on photophysical dynamics (in which labels transition between active and inactive states), such dynamics can also serve as a proxy for other types of dynamics such as assembly and disassembly kinetics of clusters. Similarly, while we focus on the case where all labels are initially fluorescent, other regimes, more appropriate to photoactivated localization microscopy, where fluorophores are instantiated in a non-fluorescent state, fall within the scope of the framework. As such, we provide a complete and versatile framework for the interpretation of complex time traces arising from the simultaneous activity of up to 100 fluorophores.

Indexed as

Bayesian nonparametricsbiophysicsdata analysismolecular biologyphotobleachingstochastic dynamicssuperresolution

Identifiers

PMID35874114
PMCPMC9302895

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.