Novel high-content and open-source image analysis tools for profiling mitochondrial morphology in neurological cell...

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Mitochondria exhibit dynamic morphological changes influenced by cellular conditions, stress, genetics, and disease.
• Accurate analysis of mitochondrial morphology is crucial for understanding its role in disease and for developing new therapeutics.

Purpose of the Study:

• To introduce a novel image analysis pipeline, MitoProfiler, for rapid and accurate profiling of mitochondrial morphology.
• To identify robust texture metrics for describing mitochondrial state using unbiased feature clustering and high-content analysis.

Main Methods:

• Employed high-content analysis to measure multiple mitochondrial morphological parameters.
• Utilized unbiased feature clustering to identify key texture metrics.
• Developed MitoProfilerHC for high-content applications and an open-source version using napari for broader accessibility.

Main Results:

• Identified a robust pair of texture metrics for describing mitochondrial morphology.
• Quantified mitochondrial morphology changes in mammalian cells and primary neurons under various conditions.
• Validated the performance and applicability of the open-source MitoProfiler pipeline.

Conclusions:

• MitoProfiler provides a high-content-based and open-source method for accurate quantification of mitochondrial morphology.
• This tool is expected to accelerate mechanistic discoveries in mitochondrial biology and disease research.