Super-resolved trajectory-derived nanoclustering analysis using spatiotemporal indexing

Single-molecule localization microscopy techniques are emerging as vital tools to unravel the nanoscale world of living cells by understanding the spatiotemporal organization of protein clusters at the nanometer scale. Current analyses define spatial nanoclusters based on detections but neglect important temporal information such as cluster lifetime and recurrence in hotspots on the plasma membrane. Spatial indexing is widely used in video games to detect interactions between moving geometric objects. Here, we use the R-tree spatial indexing algorithm to determine the overlap of the bounding boxes of individual molecular trajectories to establish membership in nanoclusters. Extending the spatial indexing into the time dimension allows the resolution of spatial nanoclusters into multiple spatiotemporal clusters. Using spatiotemporal indexing, we found that syntaxin1a and Munc18-1 molecules transiently cluster in hotspots, offering insights into the dynamics of neuroexocytosis. Nanoscale spatiotemporal indexing clustering (NASTIC) has been implemented as a free and open-source Python graphic user interface.

Automated summary

Single-molecule localization microscopy techniques are essential for understanding the organization of protein clusters at the nanometer scale in living cells. In this study, the R-tree spatial indexing algorithm was used to determine the membership of individual molecular trajectories in nanoclusters. By extending the spatial indexing into the time dimension, spatiotemporal clusters were identified, revealing the dynamics of neuroexocytosis. The findings have been incorporated into a free and open-source Python graphic user interface called NASTIC.