High-throughput super-resolution single particle trajectory analysis reconstructs organelle dynamics and membrane re-organization

Abstract

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. Here we present data analysis and algorithms that are broadly applicable to reveal local binding and trafficking interactions and organization of dynamic sub-cellular sites. We applied this analysis to the endoplasmic reticulum and neuronal membrane. The method is based on spatio-temporal time window segmentation that explores data at multiple levels and detects the architecture and boundaries of high density regions in areas that are hundreds of nanometers. By statistical analysis of a large number of datapoints, the present method allows measurements of nano-region stability. By connecting highly dense regions, we reconstructed the network topology of the ER, as well as molecular flow redistribution, and the local space explored by trajectories. Segmenting trajectories at appropriate scales extracts confined trajectories, allowing quantification of dynamic interactions between lysosomes and the ER. A final step of the method reveals the motion of trajectories relative to the ensemble, allowing reconstruction of dynamics in normal ER and the atlastin-null mutant. Our approach allows users to track previously inaccessible large scale dynamics at high resolution from massive datasets. The SPtsAnalysis algorithm is available as an ImageJ plugin that can be applied by users to large datasets of overlapping trajectories and offer a standard of SPTs metrics.