Staff and Researcher Publications
Language
English
Publication Date
8-19-2024
Journal
Cell Reports Methods
DOI
10.1016/j.crmeth.2024.100838
PMID
39127044
PMCID
PMC11384092
PubMedCentral® Posted Date
8-9-2024
PubMedCentral® Full Text Version
Post-print
Abstract
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.
Keywords
Humans, Animals, Computational Biology, Diabetic Nephropathies, Mice, Skin Diseases, spatial omics, artificial intelligence, unsupervised annotation
Published Open-Access
yes
Recommended Citation
Wu, Zhenqin; Kondo, Ayano; McGrady, Monee; et al., "Discovery and Generalization of Tissue Structures From Spatial Omics Data" (2024). Staff and Researcher Publications. 20.
https://digitalcommons.library.tmc.edu/clinic_pub/20
Graphical Abstract
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