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.
Authors
- Monee McGrady
- James Zou (ORCID: https://orcid.org/0000-0001-8880-4764)
- Benjamin Chidester (ORCID: https://orcid.org/0000-0003-3261-1167)
- Ethan A. G. Baker (ORCID: https://orcid.org/0000-0003-1833-8053)
- Nathan A. Bracey (ORCID: https://orcid.org/0000-0001-7938-1691)
- Vivek Charu (ORCID: https://orcid.org/0000-0002-7147-9026)
- Jeffrey B. Cheng (ORCID: https://orcid.org/0000-0001-9821-4549)
- Alexandro E. Trevino (ORCID: https://orcid.org/0000-0002-9763-1540)
- Raymond J. Cho (ORCID: https://orcid.org/0000-0001-6090-6603)
- Aaron T. Mayer (ORCID: https://orcid.org/0000-0001-6429-0420)
- Zhenqin Wu (ORCID: https://orcid.org/0000-0002-0148-7055)
- Maryam Afkarian (ORCID: https://orcid.org/0000-0001-8428-8264)
- Ayano Kondo (ORCID: https://orcid.org/0000-0002-2845-0279)
- Eric Q. Wu (ORCID: https://orcid.org/0000-0001-8379-3373)
- Maha K. Rahim (ORCID: https://orcid.org/0000-0002-6756-5802)
Institutions
- San Francisco VA Medical Center (US)
- University of California, San Francisco (US)
- Menlo School (US)
- Enable Biosciences (United States) (US)
- University of California, Davis (US)
- Stanford University (US)
Publication Details
- Journal
- Cell Reports Methods
- Published
- 2024-08-01
- DOI
- https://doi.org/10.1016/j.crmeth.2024.100838
- Citations
- 8
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 1.87
Funders
- National Science Foundation
- National Sleep Foundation