1. Mitochondrial nanoprobe for precise cellular and drug analysis via graph Neural network.
- Author
-
He, Hua, Qin, Guangyong, Xue, Minmin, Feng, Zhenzhen, Mao, Jian, Tao, Wenpeng, Chen, Hongqi, Wang, Xiaojuan, Yu, Daoyong, and Huang, Fang
- Subjects
- *
GRAPH neural networks , *DRUG analysis , *CELL analysis , *MITOCHONDRIA , *HIGH resolution imaging - Abstract
[Display omitted] • Engineered near-infrared carbon dots enhance mitochondrial imaging signal-to-noise ratio by 2–3 folds. • Charge-driven dense blinking of carbon dots enables rapid super-resolution mitochondrial imaging with minimal frames. • Novel graph structure with GNN analysis achieves 98% accuracy in cellular typing and drug toxicity prediction using mitochondrial morphology. • Pioneering interpretability in GNN models elucidates mitochondrial features' impact on predictive outcomes in cellular functions. 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF