Super-resolved trajectory-derived nanoclustering analysis using spatiotemporal indexing.

Area of Science:

• Cell Biology
• Biophysics
• Neuroscience

Background:

• Single-molecule localization microscopy (SMLM) visualizes nanoscale cellular structures.
• Current SMLM analysis often overlooks temporal aspects of protein clustering, like cluster lifetime and recurrence.
• Understanding protein dynamics is crucial for cellular processes such as neuroexocytosis.

Purpose of the Study:

• To develop a novel method for analyzing spatiotemporal protein clustering in living cells.
• To incorporate temporal dimensions into the analysis of molecular trajectories from SMLM data.
• To investigate the dynamic clustering of syntaxin1a and Munc18-1 in neuroexocytosis.

Main Methods:

• Utilized the R-tree spatial indexing algorithm to identify overlapping molecular trajectory bounding boxes.
• Extended spatial indexing into the time domain to resolve spatial nanoclusters into spatiotemporal clusters.
• Developed Nanoscale Spatiotemporal Indexing Clustering (NASTIC) as a Python GUI tool.

Main Results:

• Successfully resolved spatial nanoclusters into multiple spatiotemporal clusters by incorporating temporal data.
• Demonstrated that syntaxin1a and Munc18-1 molecules form transient clusters in specific membrane 'hotspots'.
• Provided new insights into the dynamic behavior of proteins involved in neuroexocytosis.

Conclusions:

• NASTIC enhances SMLM analysis by integrating temporal information for a more comprehensive understanding of molecular organization.
• The transient clustering of syntaxin1a and Munc18-1 highlights the dynamic nature of protein interactions in neurosecretory processes.
• NASTIC is an accessible, open-source tool for advancing research in cellular dynamics and neurobiology.