ArticleNature communications2025
Mitochondrial segmentation and function prediction in live-cell images with deep learning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed.
- Reversible one-step acylation facilitates mitochondrial delivery of functional RNA.Chemical science · 2026Article
- Advanced Open-Source Experimental-Design Tools for Microplate-Based Assays with Acoustic Liquid Handling.bioRxiv : the preprint server for biology · 2026Article
- Artificial Intelligence for Discovery in Life Sciences.Bioconjugate chemistry · 2026Review
- Light-Activated Isolation of High-Quality Mitochondria for Therapeutic Transplantation.Angewandte Chemie (International ed. in English) · 2026Article
- Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights.Chemical & biomedical imaging · 2026Review
- WeakMitoSAM: competitive prompt aggregation for point-supervised mitochondria segmentation in electron microscopy images.Biomedical optics express · 2026Article
- 3D Mitochondria Shape Library for Optical Microscopy (3DMSL): A multimodal dataset for deep learning based mitochondrial analysis.Data in brief · 2026Article
- Fluorescent TAPY-bodipy dyads as tools for imaging fungal mitochondria by confocal microscopy and flow cytometry.Analytical and bioanalytical chemistry · 2026Article
- From Microscopy to Nanoscopy: Contemporary Physical Methods in Mitochondrial Structural Biology.International journal of molecular sciences · 2026Review
- Mitochondrial quantity-quality imbalance in cellular senescence: practical readouts and minimal assay bundles.BMB reports · 2026Review
- Metabolic Regulation in the Maintenance ofInternational journal of molecular sciences · 2026Review
- β-Cell Mitochondrial Dysfunction: Underlying Mechanisms and Potential Therapeutic Strategies.Cells · 2025Review
- Single Mitochondrion Morphology-Function Relationship Analysis Using Fluorescent Probes and Artificial Intelligence.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Mitochondrial Dysfunction in Aging, HIV, and Long COVID: Mechanisms and Therapeutic Opportunities.Pathogens (Basel, Switzerland) · 2025Review
- AI-assisted SERS imaging method for label-free and rapid discrimination of clinical lymphoma.Journal of nanobiotechnology · 2025Article
- Mitochondrial segmentation and function prediction in live-cell images with deep learning.Nature communications · 2025Article
- Artificial Intelligence for Organelle Segmentation in Live-Cell Imaging.Research (Washington, D.C.) · 2025Review
- MitoLandscape, a semi-automated pipeline for subcellular localization and quantification of mitochondria.Frontiers in cell and developmental biology · 2025Article
Corrections and comments
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Authors and funding
14 authors.
Funding
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.
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Registered trials
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