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.
Competing Interests: Declaration of interests Z.W., A.K., M.M., E.A.G.B., B.C., M.K.R., A.T.M., and A.E.T. are affiliated with Enable Medicine as employees and/or shareholders. J.Z. is a member of Enable Medicine’s scientific advisory board.
(Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)