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一种通过同时分析线粒体自噬中间体来筛选线粒体自噬诱导剂的人工智能辅助荧光显微镜系统。

An AI-assisted fluorescence microscopic system for screening mitophagy inducers by simultaneous analysis of mitophagic intermediates.

作者信息

Wang Yicheng, Song Pengfei, Zhou Heqing, Wang Pengwei, Li Yan, Shao Zhiyong, Wang Lu, You Yan, Lei Zuhai, Yu Jinhua, Li Cong

机构信息

School of Pharmacy; MOE Key Laboratory of Smart Drug Delivery; MOE Innovative Center for New Drug Development of Immune Inflammatory Diseases; Zhongshan Hospital, Fudan University, Shanghai, China.

College of Biomedical Engineering, Fudan University, Shanghai, China.

出版信息

Nat Commun. 2025 Jun 4;16(1):5179. doi: 10.1038/s41467-025-60315-1.

Abstract

Mitophagy, the selective autophagic elimination of mitochondria, is essential for maintaining mitochondrial quality and cell homeostasis. Impairment of mitophagy flux, a process involving multiple sequential intermediates, is implicated in the onset of numerous neurodegenerative diseases. Screening mitophagy inducers, particularly understanding their impact on mitophagic intermediates, is crucial for neurodegenerative disease treatment. However, existing techniques do not allow simultaneous visualization of distinct mitophagic intermediates in live cells. Here, we introduce an artificial intelligence-assisted fluorescence microscopic system (AI-FM) that enables the uninterrupted recognition and quantification of key mitophagic intermediates by extracting mitochondrial pH and morphological features. Using AI-FM, we identify a potential mitophagy modulator, Y040-7904, which enhances mitophagy by promoting mitochondria transport to autophagosomes and the fusion of autophagosomes with autolysosomes. Y040-7904 also reduces amyloid-β pathologies in both in vitro and in vivo models of Alzheimer's disease. This work offers an approach for visualizing the entire mitophagy flux, advancing the understanding of mitophagy-related mechanisms and enabling the discovery of mitophagy inducers for neurodegenerative diseases.

摘要

线粒体自噬是对线粒体进行选择性自噬清除的过程,对于维持线粒体质量和细胞内稳态至关重要。线粒体自噬通量受损(这一过程涉及多个连续中间体)与众多神经退行性疾病的发病有关。筛选线粒体自噬诱导剂,尤其是了解它们对线粒体自噬中间体的影响,对于神经退行性疾病的治疗至关重要。然而,现有技术无法在活细胞中同时可视化不同的线粒体自噬中间体。在此,我们引入了一种人工智能辅助荧光显微镜系统(AI-FM),该系统通过提取线粒体pH值和形态特征,能够对关键的线粒体自噬中间体进行不间断的识别和定量分析。利用AI-FM,我们鉴定出一种潜在的线粒体自噬调节剂Y040-7904,它通过促进线粒体向自噬体的转运以及自噬体与自溶酶体的融合来增强线粒体自噬。Y040-7904在阿尔茨海默病的体外和体内模型中还能减少淀粉样β病理变化。这项工作提供了一种可视化整个线粒体自噬通量的方法,推动了对线粒体自噬相关机制的理解,并能够发现用于神经退行性疾病的线粒体自噬诱导剂。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33b7/12137556/137c53d9315d/41467_2025_60315_Fig1_HTML.jpg

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