MuscleJ: a high-content analysis method to study skeletal muscle with a new Fiji tool

Area of Science:

• Biomedical Engineering
• Bioinformatics
• Skeletal Muscle Physiology

Background:

• Skeletal muscle adaptation and regeneration are crucial processes studied via immunostained muscle images.
• Key parameters include muscle cross-sectional area, fiber typing, nuclear localization, vessel count, and stem cell assessment.
• Manual quantification is labor-intensive and lacks reproducibility; existing software cannot analyze all parameters simultaneously.

Purpose of the Study:

• To develop a novel bioinformatics tool, MuscleJ, for comprehensive and automated analysis of skeletal muscle images.
• To overcome limitations of current software in simultaneously analyzing multiple physiological parameters.

Main Methods:

• MuscleJ runs on the Fiji open-source platform, automating the analysis of immunofluorescent staining data.
• It performs automated segmentation of muscle fibers and can analyze up to three additional channels concurrently.
• User-friendly dialog boxes and in situ color cartographies facilitate visualization and analysis.

Main Results:

• MuscleJ provides results comparable to manual analysis by multiple experts.
• The tool enables robust, multi-parameter analysis of skeletal muscle physiology and pathology.
• It enhances statistical analysis for reliable comparisons across different laboratories and imaging systems.

Conclusions:

• MuscleJ offers a fast, robust, and automated solution for analyzing skeletal muscle images.
• It significantly improves the efficiency and reliability of physiological and pathological assessments.
• MuscleJ is freely available to the scientific community, supporting skeletal muscle research.