MOLECULAR VIDEOGAMING: SUPER-RESOLVED TRAJECTORY-BASED NANOCLUSTERING ANALYSIS USING SPATIO-TEMPORAL INDEXING

Single-molecule localization microscopy (SMLM) techniques are emerging as vital tools to unravel the nanoscale world of living cells. However, current analysis methods primarily focus on defining spatial nanoclusters based on detection density, but neglect important temporal information such as cluster lifetime and recurrence in "hotspots" on the plasma membrane. Spatial indexing is widely used in videogames to effectively detect interactions between moving geometric objects. Here, we use the R-tree spatial indexing algorithm to perform SMLM data analysis and determine whether the bounding boxes of individual molecular trajectories overlap, as a measure of their potential membership in nanoclusters. Extending the spatial indexing into the time dimension allows unique resolution of spatial nanoclusters into multiple spatiotemporal clusters. We have validated this approach using synthetic and SMLM-derived data. Quantitative characterization of recurring nanoclusters allowed us to demonstrate that both syntaxin1a and Munc18-1 molecules transiently cluster in hotspots on the neurosecretory plasma membrane, offering unprecedented insights into the dynamics of these protein which are essential to neuronal communication. This new analytical tool, named Nanoscale Spatiotemporal Indexing Clustering (NASTIC), has been implemented as a free and open-source Python graphic user interface.