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An interactive deep learning-based approach reveals mitochondrial cristae topologies.

Shogo Suga1, Koki Nakamura1, Yu Nakanishi1

  • 1Department of Chemistry and Biotechnology, School of Engineering, The University of Tokyo, Tokyo, Japan.

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We developed PHILOW, a deep learning tool, to analyze mitochondrial cristae 3D structure. This reveals Optic Atrophy 1 (OPA1) regulates cristae shape, impacting mitochondrial function.

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Area of Science:

  • Cell Biology
  • Mitochondrial Biology
  • Biophysics

Background:

  • Mitochondrial cristae are vital for cellular energy production.
  • Cristae structure varies across cell types, adapting to metabolic needs.
  • Previous 3D structural analysis was limited by complex volumetric requirements.

Purpose of the Study:

  • To develop a novel deep learning platform for quantitative 3D mitochondrial cristae analysis.
  • To reveal the complex nanostructure of mitochondrial membranes.
  • To identify novel regulators of cristae morphology.

Main Methods:

  • Development of the Python-based human-in-the-loop workflow (PHILOW) platform.
  • Utilizing deep learning (DL) and human-in-the-loop (HITL) algorithms.
  • Analysis of focused ion beam-scanning electron microscopy (FIB-SEM) data for 3D nanostructure.

Main Results:

  • PHILOW enabled quantitative analysis of cristae structure in large volumes of mitochondria.
  • Detailed nanometer-scale features like surface area, orientation, and junction density were quantified.
  • Unbiased clustering identified Optic Atrophy 1 (OPA1) as a regulator of lamellar/tubular cristae balance.

Conclusions:

  • PHILOW provides a powerful tool for unbiased, quantitative 3D analysis of mitochondrial nanostructure.
  • The study uncovers a novel role for OPA1 in modulating cristae morphology.
  • Understanding cristae structure-function relationships is crucial for metabolic adaptation research.