geo-gse329811
from sddb import Catalog
cohort = Catalog().collection("geo-gse329811")
cohort.members # the 10 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-gse329811")
coll.ordered_artifacts.to_dataframe() # the 10 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-gse329811 --dest data/ Licenses: 10 NCBI GEO policy
Multi-stromal organoid co-culture modelling reveals epithelial-fibroblast heterogeneity in pancreatic cancer and pancreatitis.
Interactions between epithelial cells and fibroblasts influence disease progression and treatment response in pancreatic ductal adenocarcinoma (PDAC). While the diversity of fibroblasts in PDAC is increasingly recognized, it remains unclear how these cells differ from fibroblasts found in pancreatic inflammation. Chronic pancreatitis is a stroma-rich inflammatory disease and a risk factor for PDAC, making it a useful setting to study how epithelial cells and fibroblasts change during disease. Here we compare fibroblast diversity and epithelial-stromal interactions in pancreatitis and PDAC using human samples, mouse models and mouse pancreatitis-derived epithelial organoids. We also developed pancreatitis and PDAC organoid co-cultures containing pancreatic stellate cells, fibroblasts and mesothelial cells. Combining in vitro and in vivo models better reflected human disease than mouse models alone. Overall, our findings reveal distinct epithelial and fibroblast features in pancreatitis and PDAC and provide models to identify disease-specific markers and therapeutic vulnerabilities.
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