Diffraction-limited molecular cluster quantification with Bayesian nonparametrics

J. Shepard Bryan IV, Ioannis Sgouralis, Steve Pressé

Research output: Contribution to journalArticlepeer-review

Abstract

Life’s fundamental processes involve multiple molecules operating in close proximity within cells. To probe the molecular composition of such small (diffraction-limited) regions, experiments often report on the total fluorescence intensity emitted from labeled molecules within. Methods exist to enumerate total fluorophore numbers (for example, step counting by photobleaching); however, these methods cannot treat photophysical dynamics nor learn their associated kinetic rates. Here we propose a method to simultaneously enumerate fluorophores and determine their photophysical properties. Although our focus here is on photophysical dynamics, such dynamics can also serve as a proxy for other types of dynamics such as the kinetics of assembly and disassembly of clusters. As the number of active fluorescent molecules at any given time is unknown, we rely on Bayesian nonparametrics to derive our kinetic estimates. We provide a versatile framework for enumerating up to 100 fluorophores from brightness time traces, benchmarked on synthetic and real datasets.

Original languageEnglish (US)
Pages (from-to)102-111
Number of pages10
JournalNature Computational Science
Volume2
Issue number2
DOIs
StatePublished - Feb 2022

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Science (miscellaneous)

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