Methods for imaging mammalian mitochondrial morphology: A prospective on MitoGraph

Area of Science:

• Cell Biology
• Biophysics
• Computational Biology

Background:

• Mitochondria exhibit diverse morphologies, crucial for cellular function.
• Previous qualitative methods for mitochondrial morphology quantification lacked consistency.
• Computational tools are emerging for more objective analysis.

Purpose of the Study:

• To evaluate MitoGraph, an open-source platform, for quantitative mitochondrial morphology analysis.
• To assess MitoGraph's performance across different mammalian cell types.
• To provide imaging recommendations for reproducible mitochondrial morphology studies.

Main Methods:

• MitoGraph software was applied to analyze mitochondrial morphology in five mammalian cell types.
• Confocal imaging of labeled mitochondria (mito-YFP, MitoTracker, immunostaining) was performed.
• MitoGraph's segmentation accuracy and ability to differentiate morphologies were assessed.

Main Results:

• MitoGraph accurately segmented mitochondria in all tested mammalian cell types.
• The platform successfully distinguished between fragmented and hyper-elongated mitochondrial morphologies.
• General recommendations for optimal confocal imaging parameters were established.

Conclusions:

• MitoGraph provides a robust and reproducible method for quantifying mitochondrial morphology.
• Widespread adoption of MitoGraph can standardize mitochondrial network analysis across research fields.
• This tool facilitates consistent and accurate assessment of mitochondrial dynamics.