美国亚利桑那州立大学Steve Pressé团队近期取得重要工作进展，他们研究开发了BNP-Track——一个超分辨跟踪框架。相关研究成果2024年7月22日在线发表于《自然—方法学》杂志上。

据介绍，超分辨率工具，如PALM和STORM，依靠罕见的光物理事件提供纳米级定位精度，将这些方法限制在静态样品上。

相比之下，研究人员将超分辨率扩展到动力学，而不依赖于光动力学，通过同时确定发射器数量及其轨迹（定位和链接），每帧的定位精度与在类似成像条件下（≈50 nm）固定发射器的宽场超分辨率相同。研究人员在细胞和合成数据上展示了贝叶斯非参数跟踪（BNP-track）框架。

BNP Track开发了一种联合（后验）分布，可以学习和量化发射器数量及其相关轨迹的不确定性，这些轨迹由散粒噪声、相机伪影、像素化、背景和离焦运动传播而来。在此过程中，研究人员将时空信息整合到分布中，否则，通过模块化确定发射器数量以及跨帧定位和链接发射器位置会损害分布

因此，与其他单粒子跟踪工具相比，BNP-Track在拥挤情况下仍然准确。

附：英文原文

Title: BNP-Track: a framework for superresolved tracking

Author: Sgouralis, Ioannis, Xu, Lance W. Q., Jalihal, Ameya P., Kilic, Zeliha, Walter, Nils G., Press, Steve

Issue&Volume: 2024-07-22

Abstract: 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 (≈50nm). 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.

DOI: 10.1038/s41592-024-02349-9

Source: https://www.nature.com/articles/s41592-024-02349-9