SubCell: Vision foundation models for microscopycapture single-cell biology

Cell morphology and subcellular protein organization provide important insights into cellular function and behavior. These features of cells can be studied using large-scale protein fluorescence microscopy, and machine learning has become a powerful tool to interpret the resulting images for biological insights. Here, we introduce SubCell, a suite of self-supervised deep learning models for fluorescence microscopy designed to accurately capture cellular morphology, protein localization, cellular organization, and biological function beyond what humans can readily perceive. These models were trained on the proteome-wide image collection from the Human Protein Atlas with a novel proteome-aware learning objective. SubCell outperforms state-of-the-art methods across a variety of tasks relevant to single-cell biology and generalizes to other fluorescence microscopy datasets without any fine-tuning. Additionally, we construct the first proteome-wide hierarchical map of proteome organization that is directly learned from image data. This vision-based multiscale cell map defines cellular subsystems with high resolution of protein complexes, reveals proteins with similar functions, and distinguishes dynamic and stable behaviors within cellular compartments. Finally, Subcell enables a rich multimodal protein representation when integrated with a protein sequence model, allowing for a more comprehensive capture of gene function than either vision-only or sequence-only models alone. In conclusion, SubCell creates deep, image-driven representations of cellular architecture that are applicable across diverse biological contexts and datasets.