Abstract 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.
Ding et al. (Thu,) studied this question.
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