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Spatiotemporal Clustering of Repeated Super-Resolution Localizations via Linear Assignment Problem

Many fluorescence super-resolution techniques, such as (d)STORM, PALM, and DNA-PAINT, generate datasets wherein multiple localizations across many camera frames may arise from a single blinking event of an emitter. These repeated localizations not only hinder interpretation and analysis of such data...

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Detalles Bibliográficos
Autores principales: Schodt, David J., Lidke, Keith A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9581011/
https://www.ncbi.nlm.nih.gov/pubmed/36303762
http://dx.doi.org/10.3389/fbinf.2021.724325
Descripción
Sumario:Many fluorescence super-resolution techniques, such as (d)STORM, PALM, and DNA-PAINT, generate datasets wherein multiple localizations across many camera frames may arise from a single blinking event of an emitter. These repeated localizations not only hinder interpretation and analysis of such datasets, but also represent an incomplete use of the fluorescence photons. Such localizations are typically combined into a single localization either by clustering with hard distance and time thresholds, or by classical hypothesis testing assuming Gaussian localization errors. In this work, we describe a method for clustering that accounts for localization precision, local emitter density estimates, and a kinetic model for blinking which is used to optimize connections within a group of spatiotemporally colocated localizations.