In this study, we performed spatial transcriptomics (ST) to investigate the gene expression features across one normal pancreatic tissue, PC tissue, adjacent tumor tissue, and tumor stroma using 10x Genomics Visium spatial gene expression platform. We generated high-quality spatial gene expression profiles combined with histomorphological information, aiming to reveal the spatial heterogeneity of pancreatic cancer microenvironment and identify spatially restricted gene signatures associated with tumor progression. Sequencing was performed on Illumina NovaSeq 6000 platform, and data processing was conducted using SpaceRanger software with hg38 reference genome. The results revealed that KRT13+FABP5+ malignant cell subpopulation had keratinization characteristics in the tumor tissue. Fibroblasts from adjacent tumor tissue exhibited a tumor-inhibiting role such as “B-cell activation” and “positive regulation of leukocyte activation.” The FGG+CRP+ inflammatory cancer-associated fibroblasts replaced the islets in tumor stroma. During PC progression, the damage to pancreatic structure and function was heavier in the pancreatic exocrine (AMYA2+PRSS1+) than in the endocrine (INS+GCG+). Our results revealed the spatial heterogeneity of dynamic changes and highlighted the significance of impaired exocrine function in PC.
from sddb import Catalog
cohort = Catalog().collection("geo-gse327056")
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-gse327056")
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-gse327056 --dest data/Licenses: 4 NCBI GEO policy
No linked publication found — please cite the data source.