Lysosomes are essential organelles for cells that act as the“recycling center”for decomposing biomolecules and clearing out damaged organelles.The status of lysosomes is tightly regulated by cells to maintain normal...Lysosomes are essential organelles for cells that act as the“recycling center”for decomposing biomolecules and clearing out damaged organelles.The status of lysosomes is tightly regulated by cells to maintain normal homeostasis.To monitor subcellular lysosomal status,super-resolution imaging has emerged as a promising technology that surpasses conventional imaging limitations,offering extraordinary visualization capability.However,existing fluorescent probes for super-resolution imaging still suffer from significant drawbacks,such as complex synthesis,poor intracellular stability,and the lack of near-infrared(NIR)imaging capability.Besides,to quantitatively analyze fluorescence images,traditional human-driven image interpretation is time-consuming and prone to information loss and human error.To tackle these challenges,we first developed a quinoliniumbased fluorescent probe,PA-2,for NIR super-resolution imaging of lysosomes with low cytotoxicity and stable fluorescence.Harnessing PA-2’s strong resistance to photobleaching,the lysosomal dynamic statuses,encompassing autophagy,mitochondrialysosome contacts,and mitophagy,were successfully visualized.Building on this,we next demonstrate a novel approach leveraging a large multimodal model(LMM),an advanced artificial intelligence(AI)tool,for automated analysis of super-resolution images.The LMM accurately interprets images of PA-2 and predicts lysosomal status under various drug treatments with remarkable speed,precision,and explainability,significantly outperforming human experts in image analysis.To sum up,this work highlights the strong potential of combining advanced fluorescent probe design with AI-assisted image interpretation to drive revolutionary innovation in bioimaging and beyond.展开更多
基金support of the US National Science Foundation(CHE-2453603)supported by the National Institutes of Health(NIH R35GM128837).
文摘Lysosomes are essential organelles for cells that act as the“recycling center”for decomposing biomolecules and clearing out damaged organelles.The status of lysosomes is tightly regulated by cells to maintain normal homeostasis.To monitor subcellular lysosomal status,super-resolution imaging has emerged as a promising technology that surpasses conventional imaging limitations,offering extraordinary visualization capability.However,existing fluorescent probes for super-resolution imaging still suffer from significant drawbacks,such as complex synthesis,poor intracellular stability,and the lack of near-infrared(NIR)imaging capability.Besides,to quantitatively analyze fluorescence images,traditional human-driven image interpretation is time-consuming and prone to information loss and human error.To tackle these challenges,we first developed a quinoliniumbased fluorescent probe,PA-2,for NIR super-resolution imaging of lysosomes with low cytotoxicity and stable fluorescence.Harnessing PA-2’s strong resistance to photobleaching,the lysosomal dynamic statuses,encompassing autophagy,mitochondrialysosome contacts,and mitophagy,were successfully visualized.Building on this,we next demonstrate a novel approach leveraging a large multimodal model(LMM),an advanced artificial intelligence(AI)tool,for automated analysis of super-resolution images.The LMM accurately interprets images of PA-2 and predicts lysosomal status under various drug treatments with remarkable speed,precision,and explainability,significantly outperforming human experts in image analysis.To sum up,this work highlights the strong potential of combining advanced fluorescent probe design with AI-assisted image interpretation to drive revolutionary innovation in bioimaging and beyond.
