from sddb import Catalog
cohort = Catalog().collection("geo-gse312698")
cohort.members # the 4 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-gse312698")
coll.ordered_artifacts.to_dataframe() # the 4 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-gse312698 --dest data/Licenses: 4 NCBI GEO policy
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a chronic condition impacting over 30% population, yet the dynamic changes in macrophage composition from steatosis to steatohepatitis (metabolic dysfunction-associated steatohepatitis, MASH) remain unclear. Here, by integrating single-nucleus transcriptomics, spatial multi-omics and proteomics on human samples, we delineate the evolving landscape of hepatic macrophages across the MASLD spectrum. Our analysis reveals a progressive depletion of Kupffer cells accompanied by the emergence of diverse, phenotypically distinct macrophage subsets. Spatial multi-omics further demonstrates that disease progression toward MASH is marked by an accumulation of antigen-presenting, phagocytic GPNMB<sup>+</sup> macrophages, supported by IL32-producing hepatocytes. These macrophages showed an adaptive metabolic and pro-inflammatory phenotype that is tightly regulated by both spatial context and disease stage. Identified macrophage markers enable patient stratification by disease activity and its stage across independent clinical cohorts. Our study sheds light on the diversity of macrophage identities and metabolic-adaptive phenotypes during the progression of MASLD.
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