Serial spatial omics technologies capture genome-wide gene expression patterns in thin tissue sections but lose spatial continuity along the third dimension. Reconstructing these two-dimensional measurements into coherent three-dimensional volumes is necessary to relate molecular domains, gradients, and tissue architecture within whole organs or embryos. sc3D is an open-source Python framework that registers consecutive spatial transcriptomic sections, interpolates bead coordinates in three dimensions, and stores the result in an AnnData object compatible with Scanpy. The workflow performs slice alignment, 3D reconstruction, optional downsampling, and interactive visualization in a napari-sc3D-viewer, enabling virtual in situ hybridization and spatial differential gene expression analysis. We tested sc3D on Slide-seq and Stereo-seq datasets, including E8.5 and E16.5 mouse embryos, recovering continuous tissue morphologies, cardiac anatomical markers, and the expected anterior-posterior gradients of Hox gene expression. These results show that sc3D allows reproducible reconstruction and analysis of volumetric spatial omics data across different samples and experimental platforms. Key features • 3D reconstruction: sc3D aligns serial Slide-seq arrays and interpolates between sections to generate volumetric transcriptomic datasets preserving tissue geometries. • Portable data formats: Outputs a .h5ad object storing coordinates, expression, and metadata, readable in Python or napari-sc3D-viewer. • Integrated visualization: The napari plugin enables interactive 3D exploration, virtual in situ hybridization, and gene expression profiling along developmental axes. • Spatial differential expression: Built-in functions detect regionally enriched genes and export quantitative 3D heatmaps and ranked tables.
Sendra et al. (Thu,) studied this question.