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Adv."],"published-print":{"date-parts":[[2026,10,2]]},"abstract":"<jats:p>Lipid homeostasis is orchestrated by rapid exchange and remodeling across the endoplasmic reticulum (ER), lipid droplets (LDs), and mitochondria. However, live-cell visualization of this triorganelle network remains limited by subdiffraction structures, multiplexed labeling burden, and the ambiguity of intensity-only readouts. Here, we introduce a single-shot stimulated emission depletion-fluorescence lifetime imaging (STED-FLIM) workflow that combines Nile Red analogs with deep learning\u2013based demultiplexing to generate compartment-resolved maps of lipid-organelle organization and dynamics. By combining the STED-resolved nanoscale ultrastructure with lifetime-encoded microenvironmental contrast, our approach separates ER, LDs, and mitochondria from a single acquisition and enables automated tricompartment quantification using a lightweight VGG16-UNet segmentation model. This platform captures coordinated remodeling across the ER-LD-mitochondria axis during lipid stress, including ferroptosis- and apoptosis-associated transitions, while simultaneously reporting nanoscale organization and microenvironmental shifts. Together, this strategy provides a practical route to high-spatiotemporal-resolution, lifetime-encoded multiorganelle lipid imaging in living cells.<\/jats:p>","DOI":"10.1126\/sciadv.aeh3416","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T17:58:26Z","timestamp":1790791106000},"update-policy":"https:\/\/doi.org\/10.34133\/aaas_crossmark","source":"Crossref","is-referenced-by-count":0,"title":["Deep learning\u2013enhanced single-shot triorganelle STED-FLIM imaging of lipid dynamics in living cells"],"prefix":"10.1126","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7930-0032","authenticated-orcid":true,"given":"Lu","family":"Gao","sequence":"first","affiliation":[{"name":"Med-X Research Institute and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Beibei","family":"Gao","sequence":"additional","affiliation":[{"name":"Med-X 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