Generative machine learning unlocks the first proteome-wide image of human cells

Abstract

The spatial organization of proteins within cells governs virtually all cellular functions, yet current imaging can simultaneously visualize only tens of proteins, orders of magnitude below the thousands populating a single human cell. Here we present ProtiCelli, a deep generative model that simulates microscopy images for 12,800 human proteins from just three cellular landmark stains. Trained on 1.23 million Human Protein Atlas images, ProtiCelli outperforms existing methods in reconstruction accuracy and textural fidelity, and generalizes to unseen cell types and drug perturbations. Simulated images preserve hierarchical subcellular organization, recapitulate known protein protein interaction landscapes, and resolve compartment-specific functions of moonlighting proteins at single cell resolution. Remarkably, the model infers drug-induced changes in protein expression and localization from cell morphology alone, predicts cell cycle stage without dedicated markers, and enables unsupervised segmentation of subcellular compartments and spatial decomposition of gene sets into functional regions. We leverage ProtiCelli to generate Proteome2Cell, a dataset of 30.7 million simulated images spanning 2,400 virtual cells across 12 human cell lines, enabling hierarchical single-cell models that distinguish conserved from dynamic protein architectures. Integrated into the Human Protein Atlas, Proteome2Cell democratizes exploration of these virtual cells. By computationally bridging the experimental scalability gap, ProtiCelli establishes a foundation for spatial virtual cell modeling.