Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization...

Area of Science:

• Biophysics
• Cell Biology
• Microscopy

Background:

• Super-resolution microscopy, particularly Single-Molecule Localization Microscopy (SMLM), enhances nanoscale biological imaging.
• SMLM data analysis is challenged by diverse biological sample characteristics and imaging conditions.
• Existing analytical tools for SMLM struggle with adaptability and ease of use for varied datasets.

Purpose of the Study:

• To develop an accessible and adaptable clustering analysis protocol for SMLM data.
• To address the limitations of current analytical tools in interpreting SMLM data.
• To facilitate the accurate identification of molecular clusters and subdomains from SMLM datasets.

Main Methods:

• Introduction of Diinamic, a novel software tool for SMLM data analysis.
• Integration of multiple density-based clustering algorithms within Diinamic.
• Inclusion of optional thresholding parameters for enhanced cluster detection flexibility.
• Validation using both simulated and real SMLM datasets.

Main Results:

• Diinamic successfully identified molecular clusters across various sizes and densities.
• The tool demonstrated high performance even with noisy SMLM data and multiple detections per fluorophore.
• Diinamic accurately detected subdomains ('nanodomains') within clusters exhibiting non-uniform detection distributions.

Conclusions:

• Diinamic provides an easy-to-use and versatile solution for SMLM data clustering.
• The tool enhances the interpretation of molecular organization at the nanoscale from SMLM imaging.
• Diinamic's performance in identifying clusters and nanodomains offers significant advantages for biological research.