BNP-Track: a framework for superresolved tracking.

Autor: Sgouralis I; Department of Mathematics, University of Tennessee, Knoxville, TN, USA., Xu LWQ; Center for Biological Physics, Arizona State University, Tempe, AZ, USA.; Department of Physics, Arizona State University, Tempe, AZ, USA., Jalihal AP; Department of Cell Biology, Duke University, Durham, NC, USA., Kilic Z; Department of Structural Biology, St. Jude Children's Research Hospital, Memphis, TN, USA., Walter NG; Single Molecule Analysis Group and Center for RNA Biomedicine, Department of Chemistry, University of Michigan, Ann Arbor, MI, USA., Pressé S; Center for Biological Physics, Arizona State University, Tempe, AZ, USA. spresse@asu.edu.; Department of Physics, Arizona State University, Tempe, AZ, USA. spresse@asu.edu.; School of Molecular Sciences, Arizona State University, Tempe, AZ, USA. spresse@asu.edu.
Jazyk: angličtina
Zdroj: Nature methods [Nat Methods] 2024 Sep; Vol. 21 (9), pp. 1716-1724. Date of Electronic Publication: 2024 Jul 22.
DOI: 10.1038/s41592-024-02349-9
Abstrakt: Superresolution tools, such as PALM and STORM, provide nanoscale localization accuracy by relying on rare photophysical events, limiting these methods to static samples. By contrast, here, we extend superresolution to dynamics without relying on photodynamics by simultaneously determining emitter numbers and their tracks (localization and linking) with the same localization accuracy per frame as widefield superresolution on immobilized emitters under similar imaging conditions (≈50 nm). We demonstrate our Bayesian nonparametric track (BNP-Track) framework on both in cellulo and synthetic data. BNP-Track develops a joint (posterior) distribution that learns and quantifies uncertainty over emitter numbers and their associated tracks propagated from shot noise, camera artifacts, pixelation, background and out-of-focus motion. In doing so, we integrate spatiotemporal information into our distribution, which is otherwise compromised by modularly determining emitter numbers and localizing and linking emitter positions across frames. For this reason, BNP-Track remains accurate in crowding regimens beyond those accessible to other single-particle tracking tools.
(© 2024. The Author(s).)
Databáze: MEDLINE
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