Single Mitochondrion Morphology‐Function Relationship Analysis Using Fluorescent Probes and Artificial Intelligence Article Swipe
Yang Ding
,
Bin Fang
,
Q. Li
,
Biying Zhang
,
Jintao Li
,
Hua Bai
,
Nicolas H. Voelcker
,
Bo Peng
,
Xuekang Yang
,
Lin Li
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202509140
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/advs.202509140
The ability to decode the relationship between mitochondrial morphology and function at the level of individual organelles is central to understanding cellular responses to stress, such as hypoxia. Herein, a comprehensive strategy is presented that integrates tailored fluorescent probes with artificial intelligence (AI) for single mitochondrion analysis. Focus is on three interrelated biomarkers, reactive oxygen species (ROS), viscosity, and mitochondrial membrane potential (MMP), that together form a pathophysiological axis indicative of mitochondrial state under hypoxic stress. A functional probe set is used to image these features simultaneously, including a newly developed dual‐cationic probe, MitoVP , which enhances mitochondrial targeting and resolution for viscosity sensing. Mitochondrial morphological features are then extracted using a deep learning‐based algorithm, which further classified individual mitochondria into dot, rod, and network morphotypes. This analysis enabled quantitative mapping between mitochondrial morphology and functional states, revealing significant heterogeneity across diverse physiological conditions. Based on this characterization, a random forest classifier trained on over 10,000 mitochondria accurately distinguished normoxic from hypoxic states and identified viscosity as a primary contributor to mitochondrial status under hypoxia. This integrated approach provides a powerful platform for single organelle investigations and advances the understanding of mitochondrial dysfunction in complex biological systems.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/advs.202509140
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202509140
- OA Status
- gold
- Cited By
- 1
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413437323
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4413437323Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/advs.202509140Digital Object Identifier
- Title
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Single Mitochondrion Morphology‐Function Relationship Analysis Using Fluorescent Probes and Artificial IntelligenceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-08-21Full publication date if available
- Authors
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Yang Ding, Bin Fang, Q. Li, Biying Zhang, Jintao Li, Hua Bai, Nicolas H. Voelcker, Bo Peng, Xuekang Yang, Lin LiList of authors in order
- Landing page
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https://doi.org/10.1002/advs.202509140Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202509140Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/advs.202509140Direct OA link when available
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Morphology (biology), Fluorescence, Function (biology), Mitochondrion, Biological system, Chemistry, Biophysics, Biology, Cell biology, Biochemistry, Zoology, Physics, OpticsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.systems. | 197 |
| abstract_inverted_index.tailored | 37 |
| abstract_inverted_index.together | 65 |
| abstract_inverted_index.analysis. | 47 |
| abstract_inverted_index.developed | 91 |
| abstract_inverted_index.extracted | 110 |
| abstract_inverted_index.including | 88 |
| abstract_inverted_index.organelle | 185 |
| abstract_inverted_index.potential | 62 |
| abstract_inverted_index.presented | 34 |
| abstract_inverted_index.responses | 23 |
| abstract_inverted_index.revealing | 138 |
| abstract_inverted_index.targeting | 99 |
| abstract_inverted_index.viscosity | 103, 166 |
| abstract_inverted_index.accurately | 158 |
| abstract_inverted_index.algorithm, | 115 |
| abstract_inverted_index.artificial | 41 |
| abstract_inverted_index.biological | 196 |
| abstract_inverted_index.classified | 118 |
| abstract_inverted_index.classifier | 152 |
| abstract_inverted_index.functional | 78, 136 |
| abstract_inverted_index.identified | 165 |
| abstract_inverted_index.indicative | 70 |
| abstract_inverted_index.individual | 16, 119 |
| abstract_inverted_index.integrated | 177 |
| abstract_inverted_index.integrates | 36 |
| abstract_inverted_index.morphology | 9, 134 |
| abstract_inverted_index.organelles | 17 |
| abstract_inverted_index.resolution | 101 |
| abstract_inverted_index.viscosity, | 58 |
| abstract_inverted_index.biomarkers, | 53 |
| abstract_inverted_index.conditions. | 144 |
| abstract_inverted_index.contributor | 170 |
| abstract_inverted_index.dysfunction | 193 |
| abstract_inverted_index.fluorescent | 38 |
| abstract_inverted_index.significant | 139 |
| abstract_inverted_index.intelligence | 42 |
| abstract_inverted_index.interrelated | 52 |
| abstract_inverted_index.mitochondria | 120, 157 |
| abstract_inverted_index.morphotypes. | 126 |
| abstract_inverted_index.quantitative | 130 |
| abstract_inverted_index.relationship | 6 |
| abstract_inverted_index.Mitochondrial | 105 |
| abstract_inverted_index.comprehensive | 31 |
| abstract_inverted_index.distinguished | 159 |
| abstract_inverted_index.heterogeneity | 140 |
| abstract_inverted_index.mitochondrial | 8, 60, 72, 98, 133, 172, 192 |
| abstract_inverted_index.mitochondrion | 46 |
| abstract_inverted_index.morphological | 106 |
| abstract_inverted_index.physiological | 143 |
| abstract_inverted_index.understanding | 21, 190 |
| abstract_inverted_index.investigations | 186 |
| abstract_inverted_index.dual‐cationic | 92 |
| abstract_inverted_index.simultaneously, | 87 |
| abstract_inverted_index.learning‐based | 114 |
| abstract_inverted_index.characterization, | 148 |
| abstract_inverted_index.pathophysiological | 68 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5100412926, https://openalex.org/A5036493016, https://openalex.org/A5018699150 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 10 |
| corresponding_institution_ids | https://openalex.org/I17145004, https://openalex.org/I191208505, https://openalex.org/I4210158926, https://openalex.org/I9916479 |
| citation_normalized_percentile.value | 0.8440644 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |