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Cell morphology, learned from image-based spatial proteomics, is shown to predict molecular phenotypes — bridging single-cell imaging and proteomics.

A collaborative community roadmap surveying how deep learning is applied across microscopy — image restoration, detection, segmentation, classification, tracking, and multimodal fusion — with its possibilities and limitations.

Large-scale immunofluorescence profiling maps how SARS-CoV-2 reshuffles the host protein landscape across 602 proteins, then screens approved drugs against the changed targets — yielding a 100,000-image public resource and pinpointing elesclomol as an antiviral modulator.

A universal deep-learning approach for accurate FISH spot detection across imaging-based spatial-omics datasets.

A framework that makes deep-learning pipelines for microscopy flexible, shareable, and reproducible across computing environments.