Mitochondrial segmentation and function prediction in live-cell images with deep learning

Yang Ding, Jintao Li, Jiaxin Zhang, Panpan Li, Hua Bai, Bin Fang, Haixiao Fang, Kai Huang, Guangyu Wang, Cameron J. Nowell, Nicolas H. Voelcker, Bo Peng (), Lin Li () and Wei Huang ()
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Yang Ding: Northwestern Polytechnical University
Jintao Li: Northwestern Polytechnical University
Jiaxin Zhang: Northwestern Polytechnical University
Panpan Li: Northwestern Polytechnical University
Hua Bai: Northwestern Polytechnical University
Bin Fang: Xiamen University
Haixiao Fang: Xiamen University
Kai Huang: Future Display Institute in Xiamen
Guangyu Wang: Beijing University of Posts and Telecommunications
Cameron J. Nowell: Monash University
Nicolas H. Voelcker: Monash University
Bo Peng: Northwestern Polytechnical University
Lin Li: Northwestern Polytechnical University
Wei Huang: Northwestern Polytechnical University

Nature Communications, 2025, vol. 16, issue 1, 1-15

Abstract: Abstract Mitochondrial morphology and function are intrinsically linked, indicating the opportunity to predict functions by analyzing morphological features in live-cell imaging. Herein, we introduce MoDL, a deep learning algorithm for mitochondrial image segmentation and function prediction. Trained on a dataset of 20,000 manually labeled mitochondria from super-resolution (SR) images, MoDL achieves superior segmentation accuracy, enabling comprehensive morphological analysis. Furthermore, MoDL predicts mitochondrial functions by employing an ensemble learning strategy, powered by an extended training dataset of over 100,000 SR images, each annotated with functional data from biochemical assays. By leveraging this large dataset alongside data fine-tuning and retraining, MoDL demonstrates the ability to precisely predict functions of heterogeneous mitochondria from unseen cell types through small sample size training. Our results highlight the MoDL’s potential to significantly impact mitochondrial research and drug discovery, illustrating its utility in exploring the complex relationship between mitochondrial form and function within a wide range of biological contexts.

Date: 2025
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DOI: 10.1038/s41467-025-55825-x

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