Group of Data Modeling and Computational Biology, IBENS, Ecole Normale Supérieure, 75005 Paris, France.
Research Group Functional Neurobiology at the Institute of Developmental Biology and Neurobiology, Johannes Gutenberg University Mainz, Mainz, Germany.
Cell Rep Methods. 2022 Aug 22;2(8):100277. doi: 10.1016/j.crmeth.2022.100277.
Super-resolution imaging can generate thousands of single-particle trajectories. These data can potentially reconstruct subcellular organization and dynamics, as well as measure disease-linked changes. However, computational methods that can derive quantitative information from such massive datasets are currently lacking. We present data analysis and algorithms that are broadly applicable to reveal local binding and trafficking interactions and organization of dynamic subcellular sites. We applied this analysis to the endoplasmic reticulum and neuronal membrane. The method is based on spatiotemporal segmentation that explores data at multiple levels and detects the architecture and boundaries of high-density regions in areas measuring hundreds of nanometers. By connecting dense regions, we reconstructed the network topology of the endoplasmic reticulum (ER), as well as molecular flow redistribution and the local space explored by trajectories. The presented methods are available as an ImageJ plugin that can be applied to large datasets of overlapping trajectories offering a standard of single-particle trajectory (SPT) metrics.
超分辨率成像可以生成数千条单粒子轨迹。这些数据有可能重建亚细胞结构和动态，以及测量与疾病相关的变化。然而，目前缺乏能够从这些海量数据集中提取定量信息的计算方法。我们提出了数据分析和算法，这些方法广泛适用于揭示局部结合和运输相互作用以及动态亚细胞位点的组织。我们将这种分析应用于内质网和神经元膜。该方法基于时空分割，它在多个层次上探索数据，并检测测量数百纳米的区域中高密度区域的结构和边界。通过连接密集区域，我们重建了内质网 (ER) 的网络拓扑结构，以及分子流动再分配和轨迹所探索的局部空间。所提出的方法可用作 ImageJ 插件，可应用于重叠轨迹的大型数据集，提供单粒子轨迹 (SPT) 度量的标准。
