AI-enabled reconstruction of 3D spatial multi-omics at single-cell resolution

Three-dimensional (3D) spatial multi-omics provides unparalleled insights into biological activities, yet remains technically prohibitive. Here, we introduce Histo3D-MO, a hybrid experimental-computational pipeline for reconstructing single-cell-resolution 3D spatial multi-omics maps. Notably, Histo3D-MO integrates sparse, omics-disjoint spatial measurements with dense Hematoxylin and Eosin (H&E) histology through SPatial multi-Omics from h&E imaGEs (SPONGE), achieving cell-level 3D mapping across multiple omics layers. Validated using held-out slices, SPONGE substantially outperforms existing omics prediction methods. We further developed an algorithmic suite for 3D cell-type propagation and tissue-domain annotation, enabling whole-volume characterization of the tumor microenvironment. Applied to the in-house hepatocellular carcinoma data, Histo3D-MO revealed spatially organized patterns of translation efficiency, volumetric decoupling between malignant cells and monocytes, and depth-associated monocyte differentiation trajectories. Together, these results establish Histo3D-MO as a scalable framework for reconstructing single-cell-resolution 3D spatial multi-omics and interrogating tissue organization across complex biological systems.