Abstract

Mitochondrial morphology is crucial for cell identification and drug toxicity evaluation, yet it is challenged by mitochondrial complexities and limitations in imaging technologies. We develop an ultrasmall (∼1.5 nm) fluorescent carbon dot (CD), specifically designed to target mitochondria in live cells. These CDs exhibit near-infrared fluorescence at 685 nm, enhancing fluorescence imaging's signal-to-noise ratio by 2–3 times. Their dense-blinking behavior enables rapid fluctuation-based super-resolution imaging with as few as 10 frames, thereby allowing for detailed visualization of mitochondrial morphology and dynamics. We further propose a graph-based deep learning framework that integrates multidimensional mitochondrial features, which are updated using a Graph Neural Network (GNN), to achieve precise mitochondria-based cellular typing and drug toxicity analysis with up to 98 % accuracy. Additionally, we pioneer the use of interpretability algorithms to elucidate the GNN model, revealing how the depicted mitochondrial features by the CDs drive these predictions. This approach has significant implications in cellular and toxicological research, offering a unique tool for deciphering cellular behaviors and drug interactions.