BNP-Track: A framework for superresolved tracking.
BNP-Track: A framework for superresolved tracking.
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BNP-Track：超分辨率跟踪框架。
DOI：
10.1101/2023.04.03.535459
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Pressé,Steve
中科院分区：
文献类型：
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作者：
Sgouralis,Ioannis;Xu徐伟青,LanceWQ;Jalihal,AmeyaP;Walter,NilsG;Pressé,Steve
超分辨率工具，如Palm和Storm，通过依赖罕见的光物理事件提供纳米级的定位精度，将这些方法限制在静态样品上。相比之下，在这里，我们通过同时确定发射器数量及其轨迹(定位和链接)，将超分辨率扩展到动力学，在类似成像条件下(≈50 nm)，每帧的定位精度与固定式发射器上的宽场超分辨率相同。我们在Cello和合成数据上演示了我们的贝叶斯非参数跟踪(BNP-Track)框架。BNP-Track开发了一种联合(后验)分布，该分布学习并量化了来自散粒噪声、相机伪影、像素化、背景和散焦运动的发射器数量及其相关轨迹的不确定性。在这样做的过程中，我们将时空信息集成到我们的分布中，否则通过模块确定发射器数量以及跨帧定位和链接发射器位置会受到影响。因此，与其他单粒子跟踪工具相比，BNP-Track在拥挤方案中仍然保持准确。
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.