Background & Aims: Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest cancers with poor prognosis and bad therapy outcome. Therefore, mouse models are frequently used to study PDAC and pancreatic preneoplastic lesions. This study aims to evaluate a proposed progression model from acinar cells via acinar-to-ductal metaplasia (ADM) to mucinous tubular complexes (MTC) at transcriptomic level and to investigate the molecular changes behind acinar-cell derived preneoplastic lesions in a mouse model of PDAC. Methods: Spatial transcriptomics was combined with pathological characterization of preneoplastic lesions in p48Cre/+-LSL-KrasG12D/+ mice (n=5) using the 10x Visium® platform. Integration with the Mouse Cell Atlas (MCA) 2.0 single-cell dataset was performed. Bioinformatic analysis was conducted using different Python toolkits and by calculating a pseudotime along the progression axis. Findings were validated at the protein level using immunohistochemistry (IHC). Results: Transcriptional data aligned well with pathological annotations and single-cell data confirmed a decrease in acinar cells and an increase in ductal cells during ADM. Pseudotime analysis supported the progression from acinar cells via ADM to MTC, with marker gene expression patterns along this axis. Upregulation of Keratin 19 and Tetraspanin 8, and downregulation of Amylase 1 and Claudin 10, were observed. Claudin 18 was upregulated specifically in MTC, while Mesothelin and Cathepsin L peaked during ADM. Validation by IHC confirmed the expression of Keratin 19, Amylase 1, Claudin 18 and Mesothelin on protein level. Conclusions: This study confirms the development of MTC from ADM and identifies novel biomarkers along the preneoplastic progression axis in a mouse model of PDAC, advancing our understanding of early pancreatic cancer development.
from sddb import Catalog
cohort = Catalog().collection("geo-gse279507")
cohort.members # the 5 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-gse279507")
coll.ordered_artifacts.to_dataframe() # the 5 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-gse279507 --dest data/Licenses: 5 NCBI GEO policy
No linked publication found — please cite the data source.