Ultrafast data mining of molecular assemblies in multiplexed high-density super-resolution images

Area of Science:

• Cellular and Molecular Biology
• Biophysics
• Microscopy and Imaging

Background:

• Multicolor single-molecule localization super-resolution microscopy visualizes cellular molecular organizations.
• Current methods struggle to distinguish and quantify molecular assemblies in dense super-resolution data.
• Higher-order correlations like Triple-Correlation can reveal spatial configurations but are computationally intensive.

Purpose of the Study:

• To develop a fast algorithm for Triple-Correlation analysis of high-content multiplexed super-resolution data.
• To enable practical, high-throughput quantification of molecular complexes in dense cellular environments.
• To overcome the computational limitations of traditional Triple-Correlation methods.

Main Methods:

• Developed a novel algorithm for fast Triple-Correlation analysis.
• Computed probability density of geometric configurations for triple-wise localizations across three channels.
• Circumvented computationally expensive 4D Fourier Transforms of large images.

Main Results:

• Achieved a 100-fold enhancement in computational speed for Triple-Correlation analyses.
• Enabled robust quantification of molecular complexes in multiplexed super-resolution microscopy.
• Demonstrated feasibility of high-throughput analysis for dense super-resolution datasets.

Conclusions:

• The fast Triple-Correlation algorithm makes higher-order correlation analysis practical for super-resolution microscopy.
• This advancement allows for robust and high-throughput quantification of molecular assemblies.
• Facilitates deeper understanding of cellular organization at the nanoscale.