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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
Published on: March 3, 2023
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Machine learning-based classification of mitochondrial morphology in primary neurons and brain
Garrett M Fogo1,2, Anthony R Anzell1,3,4, Kathleen J Maheras1
1Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, 48109, USA.
Scientific Reports
|March 5, 2021
Summary
Researchers developed a semi-automated image analysis pipeline to accurately quantify mitochondrial morphology. This method is sensitive and specific for classifying physiological and pathological mitochondrial changes in various biological models.
Area of Science:
- Cell Biology
- Neuroscience
- Biophysics
Background:
- Mitochondria undergo continuous fission and fusion, maintaining a dynamic equilibrium.
- Physiological stress can disrupt the mitochondrial network's morphology.
- Accurate measurement of mitochondrial morphology is crucial for understanding cellular health and disease.
Purpose of the Study:
- To develop a robust, semi-automated image analysis pipeline for quantifying mitochondrial morphology.
- To validate the pipeline for both in vitro and in vivo applications.
- To provide a customizable workflow for diverse biological models.
Main Methods:
- Developed a semi-automated image analysis pipeline.
- Validated the pipeline using primary cortical neurons from transgenic mice with genetically ablated mitochondrial dynamics components.
- Extended the application to in vivo studies using immunolabeling and serial block-face scanning electron microscopy.
Main Results:
- Demonstrated a highly specific and sensitive method for classifying mitochondrial morphologies.
- Successfully applied the pipeline to both in vitro and in vivo samples.
- The pipeline accurately distinguishes between physiological and pathological mitochondrial states.
Conclusions:
- The developed image analysis pipeline offers a reliable and accurate method for assessing mitochondrial morphology.
- This open-source, high-throughput workflow is adaptable to various biological systems.
- The tool facilitates research into mitochondrial dynamics in health and disease.
