Data from: Automated single particle detection and tracking for large microscopy datasets

Recent advances in optical microscopy have enabled the acquisition of very large datasets from living cells with unprecedented spatial and temporal resolutions. Our ability to process these datasets now plays an essential role in order to understand many biological processes. In this paper we present an automated particle detection algorithm capable of operating in low signal-to-noise fluorescence microscopy environments and handling large datasets. When combined with our particle linking framework it can provide hitherto intractable quantitative measurements describing the dynamics of large cohorts of cellular components from organelles to single molecules. We begin with validating the performance of our method on synthetic image data and then extend the validation to include experiment images with ground truth. Finally, we apply the algorithm to two single-particle-tracking photo-activated localization microscopy biological datasets, acquired from living primary cells with very high temporal rates. Our analysis of the dynamics of very large cohorts of 10,000s of membrane-associated protein molecules show that they behave as if caged in nano-domains. We show the robustness and efficiency of our method provides a tool for the examination of single molecule behaviour with unprecedented spatial detail and high acquisition rates.

光学显微镜（optical microscopy）领域的最新进展，使得科研人员得以从活体细胞中获取超大规模数据集，且空间与时间分辨率达到前所未有的水准。如今，对这类数据集的处理能力，已成为解析诸多生物过程的核心关键。本文中，我们提出一款自动化粒子检测算法，该算法可在低信噪比荧光显微镜（fluorescence microscopy）环境下稳定运行，且能够处理超大规模数据集。将该算法与我们自研的粒子关联框架结合后，可提供此前无法实现的定量测量结果，用以描述从细胞器到单分子的各类细胞组分的大规模群体动力学特征。我们首先在合成图像数据集上验证了本方法的性能，随后将验证范围拓展至带有真值（ground truth）标注的实验图像。最后，我们将该算法应用于两个单粒子追踪光激活定位显微镜（photo-activated localization microscopy）生物数据集，这些数据集采集自时间采样率极高的活原代细胞。我们对数以万计的膜相关蛋白分子的大规模群体动力学的分析显示，这些分子的运动行为如同被束缚在纳米结构域中。我们证实，本方法的鲁棒性与高效性，可为以超高空间细节与高采集速率开展单分子行为研究提供有力工具。