Discovery and generalization of tissue structures from spatial omics data

Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets. [Display omitted] •SCGP is a highly flexible and efficient tool for spatial omics annotation•SCGP shows outstanding performance across 8 distinct spatial omics datasets•SCGP-Extension generalizes existing tissue structures to unseen samples•SCGP-Extension effectively addresses common data integration challenges Annotating tissue structures provides an important layer of biological interpretation from spatial molecular data. Uniform and consistent identification of structures across different batches, experiments, and diverse disease conditions remains a challenging task, often requiring manual intervention. The generalizability of annotations from a reference dataset to new or unseen data also remains a major challenge for methods in this arena. The current work introduces spatial cellular graph partitioning (SCGP) and its reference-query extension pipeline, SCGP-Extension, as unsupervised annotation tools that streamline and simplify this process, enhancing the consistency, reliability, and generalization of structure annotations across large datasets. Wu et al. proposed a universal, efficient annotation tool, SCGP, for spatial omics data, which allows unsupervised recognition of multi-cellular tissue structures. They further proposed the SCGP-Extension pipeline to gener