A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods.

Area of Science:

• Biophysics
• Computational Biology
• Microscopy

Background:

• Single-molecule localization microscopy (SMLM) offers super-resolution imaging below 20 nm, enabling subcellular molecular interaction studies.
• SMLM generates large point cloud datasets, posing challenges for traditional imaging analysis.
• Advancements in SMLM imaging outpace current data quantification and interpretation methods.

Purpose of the Study:

• To survey and critically examine state-of-the-art clustering methods for analyzing and quantifying SMLM data.
• To classify SMLM analysis methods based on biological application, data acquisition, and analysis details.
• To identify limitations of current methods and propose future research directions for SMLM data analysis.

Main Methods:

• Review and classification of existing clustering techniques for SMLM data.
• Analysis of methods based on biological context, imaging parameters (dimension, resolution, localization count), and analytical approaches (2D/3D, ML, multi-scale).
• Evaluation of method capabilities and shortcomings, focusing on noise sensitivity, 3D applicability, machine learning integration, and scalability.

Main Results:

• Most existing SMLM analysis methods based on second-order statistics are sensitive to noise and artifacts.
• Current methods are often limited to 2D data, lack machine learning integration, and are not scalable for big data.
• Significant gaps exist in computational tools for effective SMLM data quantification and interpretation.

Conclusions:

• There is a critical need for advanced computational methods to analyze the big data generated by SMLM.
• Future research should focus on developing scalable, 3D-compatible, and machine learning-integrated analysis pipelines.
• Addressing these challenges will enhance the extraction of biosignatures and accelerate discoveries in biology and medicine using SMLM.