So3D: a comprehensive three-dimensional spatial omics resource for decoding tissue architecture in physiology and disease.

Abstract

Cells function within intricate three-dimensional (3D) architectures to form tissues and organs. Constructing 3D tissue structure is critical for understanding cellular states, biological processes, and intercellular interactions. Herein, we describe a comprehensive 3D spatial omics resource, So3D (http://bio-bigdata.hrbmu.edu.cn/So3D or https://so3d.bio-database.com/), which aims to construct 3D tissue architecture and dissect biological processes occurring in 3D space from multiple perspectives. We systematically collected 72 sets of 3D spatial transcriptome datasets, involving 1 132 902 spots across 882 slices from four species, and matched reference single-cell RNA-sequencing data, covering 763 893 cells from 28 datasets. So3D also provides multiple flexible analysis modules for retrieving and analyzing 3D tissue, such as inference of 3D spatial domains and gene expression patterns across 3D tissue regions, 2D spatial slices, and single-cell datasets; mapping of cell type distribution and cell-cell communication networks to explore intercellular crosstalk in 3D space; and functional annotation of biological pathways and signaling networks within 3D tissue contexts. Collectively, So3D provides comprehensive insights for investigating biologically meaningful 3D spatial domains, mapping local gene expression landscapes, functional state, and communication networks in tissue, and may also serve as an efficient and reliable tool for understanding the tissue microenvironment and discovering biomarkers.