Project Abstract Recent advances in imaging and genomics are revolutionizing our understanding of human tissues. As these two technologies become more intertwined and high-throughput, there is an increasing need for scalable methods to interpret these data with single-cell resolution. In this Supplement, we seek to build on ongoing work to develop scalable and accurate algorithms for single-cell analysis of spatial genomics data. We propose to develop annotation software for annotating individual cells, their cell types, and functional tissue units in multiplexed imaging data. This software will be essential to produce training data to power the next generation of deep learning models for analyzing multiplexed imaging data.