Super-resolved trajectory-derived nanoclustering analysis using spatiotemporal indexing

Tristan P. Wallis (), Anmin Jiang, Kyle Young, Huiyi Hou, Kye Kudo, Alex J. McCann, Nela Durisic, Merja Joensuu, Dietmar Oelz, Hien Nguyen, Rachel S. Gormal () and Frédéric A. Meunier ()
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Tristan P. Wallis: The University of Queensland
Anmin Jiang: The University of Queensland
Kyle Young: The University of Queensland
Huiyi Hou: The University of Queensland
Kye Kudo: The University of Queensland
Alex J. McCann: The University of Queensland
Nela Durisic: The University of Queensland
Merja Joensuu: The University of Queensland
Dietmar Oelz: The University of Queensland
Hien Nguyen: The University of Queensland
Rachel S. Gormal: The University of Queensland
Frédéric A. Meunier: The University of Queensland

Nature Communications, 2023, vol. 14, issue 1, 1-16

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-38866-y

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