Morphological multiparameter filtration and persistent homology in mitochondrial image analysis

Area of Science:

• Cell Biology
• Computational Biology
• Biophysics

Background:

• Mitochondrial networks exhibit complex branching and curvilinear morphology, posing challenges for quantitative analysis.
• Understanding mitochondrial structure is crucial for deciphering cellular function and disease mechanisms.

Purpose of the Study:

• To develop a novel image analysis technique for quantifying complex mitochondrial network morphology.
• To investigate the impact of Optineurin (OPTN) gene mutations on mitochondrial structure using this new method.

Main Methods:

• Utilized persistent homology with a multiparameter filtration framework, integrating mathematical morphology for image processing.
• Developed a connectivity index to characterize the branching patterns of mitochondrial networks.
• Applied the technique to analyze mitochondrial morphology in cells with altered OPTN gene expression.

Main Results:

• The developed filtration framework successfully extracts topological and geometric information from cellular organelle structures.
• The connectivity index quantifies mitochondrial network morphology, differentiating normal from altered states.
• Observed that OPTN gene mutations lead to fragmentation of the normally interconnected mitochondrial network.

Conclusions:

• The persistent homology and mathematical morphology approach provides a robust method for analyzing complex mitochondrial networks.
• This technique quantitatively demonstrates that OPTN gene mutations alter mitochondrial structure, potentially explaining disease pathologies like glaucoma and ALS.
• The findings offer a new tool for studying organelle dynamics and their role in human diseases.