Imaging biological structures across scales, from individual molecules up to complex organs is possible using electron microscopy (EM). Two EM technologies have advanced rapidly in the past decade: 1) cryo-electron tomography (cryoET), which resolves molecules inside cells at near-atomic detail, and 2) volume electron microscopy (VEM), which maps cellular architecture of tissues across millimeter scales. Used together, they could reveal how molecules are organized from cells to tissues to support essential processes and functions. However, cryoET and VEM have evolved within separate research communities supported by incompatible software and data standards. The absence of a shared framework prevents connecting observations across different scales, leaving the field unable to construct integrated, multiscale models of tissue organization. This project will develop the imaging datasets, computational tools, standardized analysis pipelines, and unified metadata framework needed to close this gap. The approach combines artificial intelligence (AI)-enabled segmentation and classification with state-of-the-art cryoET and VEM imaging of tissues of organs central to neurological, metabolic, and cardiovascular health. The project will also produce the first publicly available multiscale 3D reference image libraries for these tissues, establishing open benchmark resources for the global research community. This work will build partnerships that unite US investigators at the University