A FIJI macro for quantifying pattern in extracellular matrix.

Area of Science:

• Biophysics
• Bioinformatics
• Cell Biology

Background:

• Extracellular matrix (ECM) patterns vary significantly in biological tissues, influencing physiological and pathological processes.
• Current analytical tools often focus on single matrix patterning aspects, limiting comprehensive quantitative descriptions.
• There is a need for automated, multi-metric analysis tools to characterize diverse ECM patterns effectively.

Purpose of the Study:

• To develop and present TWOMBLI, an automated ImageJ plugin for comprehensive quantitative analysis of matrix patterns.
• To provide a versatile and user-friendly tool for scientists, including those without extensive computational expertise.
• To demonstrate the broad applicability of TWOMBLI across various biological contexts.

Main Methods:

• Development of TWOMBLI, an ImageJ plugin integrating multiple quantitative metrics.
• Included metrics encompass matrix alignment, length, branching, endpoints, gaps, fractal dimension, curvature, and fiber thickness distribution.
• The pipeline is designed for speed, versatility, and ease of use.

Main Results:

• TWOMBLI enables simultaneous quantification of a wide array of matrix pattern metrics.
• The plugin provides a comprehensive description of diverse matrix configurations.
• Demonstrated applicability to extracellular matrix, vascular networks, and cytoskeletal structures.

Conclusions:

• TWOMBLI offers a powerful, automated solution for detailed matrix pattern analysis.
• Its versatility extends beyond the extracellular matrix to other biological networks.
• The tool facilitates advanced quantitative biology for researchers across disciplines.