from sddb import Catalog
cohort = Catalog().collection("geo-gse282123")
cohort.members # the 7 datasets as a table
print(cohort.licenses()) # check the terms before you download
sdatas = cohort.to_dict() # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/")Needs pip install lamindb and network access to the Lamin database, which is limited during the alpha. If it fails, use the Client tab.
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
coll = db.Collection.get(key="geo-gse282123")
coll.ordered_artifacts.to_dataframe() # the 7 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse282123 --dest data/Licenses: 7 NCBI GEO policy
Imaging-based, single-cell, spatial transcriptomics (iSCST) of FFPE tissue enables comprehensive analysis of archived specimens while preserving spatial context, critical to an understanding of ulcerative colitis (UC) pathology. Here, we deployed a robust framework for applying iSCST to clinical FFPE mucosal biopsies from patients with UC or immune checkpoint inhibitor-induced colitis, as well as patients serving as healthy controls. iSCST using custom Xenium gene panels enabled precise detection of diverse cell subsets and disease-specific genes. We mapped transcriptionally distinct fibroblast subsets within mucosal niches, including inflammation-associated fibroblasts (IAFs), and identified colitis-specific neighborhoods formed by IAFs, monocytes, and neutrophils. Transcriptional signatures and spatial neighborhoods uncovered through iSCST were associated with vedolizumab (VDZ) response, with nonresponders exhibiting either an innate IAF-monocyte-neutrophil signature or adaptive gut-associated lymphoid tissue signature, while responders showed enrichment of an epithelial cellular neighborhood. These signatures were validated in an internal and an external dataset, supporting the existence of 2 distinct archetypes of treatment resistance to VDZ in UC. This iSCST framework provides a powerful approach for analyzing FFPE tissues, offering insights into colitis-associated cellular networks and identifying biomarkers to enhance patient risk stratification in routine clinical workflows.
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