Spatial transcriptomics (ST) has revolutionized our understanding of tissue architecture, yet constructing comprehensive three-dimensional (3D) cell atlases remains challenging due to technical limitations and high cost. Current approaches typically capture only sparsely sampled two-dimensional sections, leaving substantial gaps that limit our understanding of continuous organ organization. Here, we present SpatialZ, a computational framework that bridges these gaps by generating virtual slices between experimentally measured sections, enabling the construction of dense 3D cell atlases from planar ST data. SpatialZ is designed to operate at single-cell resolution and function independently of gene coverage limitations inherent to specific spatial technologies. Comprehensive validation demonstrates that SpatialZ accurately preserves cell identities, gene expression patterns and spatial relationships. Leveraging the BRAIN Initiative Cell Census Network data, we constructed a 3D hemisphere atlas comprising over 38 million cells. This dense atlas enables new capabilities, including in silico sectioning at arbitrary angles, explorations of gene expression across both 3D volumes and surfaces, 3D mapping of query tissue sections, and discovery of 3D spatial molecular architectures through new synthesized views. To demonstrate its extensibility beyond transcriptomics, we applied SpatialZ to imaging mass cytometry data from human breast cancer, successfully deciphering 3D spatial gradients within the tumor microenvironment. Our approach generates cell atlases that provide previously unattainable 3D resolution of spatial molecular landscapes.
© 2025. The Author(s), under exclusive licence to Springer Nature America, Inc.