The spatial organization of proteins within cells underpins nearly all cellular functions. Accurate models of protein localization would bridge the gap between molecular- and cellular-scale biology, enabling a more unified understanding of how cells operate. However, current imaging techniques, such as primary immunofluorescence, can visualize only tens of protein species at a time, whereas a single human cell contains thousands. AI-driven prediction methods can close this gap, making it possible to generate comprehensive cell maps at a scale that existing imaging techniques alone cannot achieve. The goal of the CM4AI/AnVIL Cell Map Prediction DREAM Challenge is to bridge this gap. Participants will be presented reference microscopy images and requested to predict additional channels revealing where specified proteins would localize within those cells. This Challenge brings together the massive amounts of data generated by the Bridge2AI project and the NHGRI’s AnVIL cloud genomics platform. This challenge provides a unique opportunity to develop more flexible and generalizable generative image models that are capable of simulating the subcellular protein localization of a given protein based on its sequence, paving avenues for a new era of in silico microscopy experiments.
