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BNP-Track: a framework for superresolved tracking
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DOI：10.1038/s41592-024-02349-9.png)
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摘要
En 中文
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 (approximate to 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. Bayesian nonparametric Track (BNP-Track) simultaneously determines emitter numbers and their tracks alongside uncertainty, extending the superresolution paradigm from static samples to single-particle tracking even in dense environments.
Keyword:
SINGLE-PARTICLE TRACKING
FLUORESCENCE MICROSCOPY
RESOLUTION LIMIT
IN-CELLULO
DYNAMICS
PLATFORM
RECONSTRUCTION
LOCALIZATION
REVEALS
LIGHT
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