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 10 000 s of membrane-associated protein molecules show that they behave as if caged in nanodomains. We show that 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.

光学显微镜领域的近期进展，使得科研人员能够以前所未有的空间与时间分辨率，从活体细胞中获取超大规模数据集。如今，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要素。本文提出一种自动化粒子检测算法，可在低信噪比（signal-to-noise）荧光显微镜成像环境下运行，并支持超大规模数据集处理。将其与我们的粒子关联框架结合后，该算法可提供此前难以实现的定量测量结果，用以描述从细胞器到单分子的大规模细胞组分群组的动态特征。我们首先在合成图像数据集上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（photo-activated localization microscopy）生物数据集，这些数据集采自具有极高时间采样率的原代活体细胞。我们对逾万余个膜相关蛋白分子的大规模群组动态特征进行分析后发现，这些分子的行为宛如被束缚于纳米域中。研究表明，本方法所具备的鲁棒性与高效性，可为以空前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。