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.

光学显微镜技术的新近突破，使得研究者能够以空前的空间与时间分辨率，从活体细胞中获取超大规模数据集。当前，对这类数据集的处理能力，已成为解析诸多生命过程的核心前提。本研究提出一种自动化粒子检测算法，可在低信噪比荧光显微镜成像环境下稳定运行，并支持超大规模数据集处理。当与我们自研的粒子关联框架结合使用时，该算法可实现此前难以企及的定量测量，用以描述从细胞器到单分子等各类细胞组分的大型群体动力学特征。我们首先基于合成图像数据验证了所提方法的性能，随后将验证范围拓展至带有真实标注（ground truth）的实验图像。最后，我们将该算法应用于两类单粒子追踪光激活定位显微镜（single-particle-tracking photo-activated localization microscopy）生物数据集，这类数据集采集自具有极高时间采样速率的活原代细胞。我们对数以万计膜相关蛋白分子组成的大型群体开展动力学分析，结果显示这些分子的行为恰似被束缚于纳米域中。研究证实，本方法的鲁棒性与计算效率，可为以空前空间分辨率与高采集速率开展单分子行为研究提供有力工具。