Liver zonation characterizes the separation of metabolic pathways along the lobules and is required for optimal hepatic function. Wnt signaling is a master regulator of spatial liver zonation. A perivenous-periportal Wnt activity gradient orchestrates metabolic zonation by activating genes in perivenous hepatocytes, while suppressing genes in their periportal counterparts. The understanding of liver gene zonation and zonation regulators in disease was limited. Non-alcoholic steatohepatitis (NASH) is a chronic liver disease characterized by fat accumulation, inflammation, and fibrosis. Here, we investigated the perturbation of liver gene zonation in a mouse NASH model by combination of spatial transcriptomics, bulk RNAseq and in situ hybridization. Wnt target genes represented a major subset of genes showing altered spatial expression in the NASH liver. The altered Wnt target gene expression level and zonation spatial pattern were in line with the upregulation of Wnt regulators and the augmentation of Wnt signaling. Particularly, we found that the Wnt activator Rspo3 expression was restricted to the perivenous zone in control livers, whereas expanded to the periportal zone in NASH livers. AAV8-mediated RSPO3 overexpression in controls resulted in a zonation change, and further amplified the disturbed zonation of Wnt target genes in NASH, but had no impacts on steatosis, inflammation and fibrosis. In summary, our study demonstrated the alteration of Wnt signaling in a mouse NASH model, leading to perturbed liver zonation.
from sddb import Catalog
cohort = Catalog().collection("geo-gse248077")
cohort.members # the 13 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-gse248077")
coll.ordered_artifacts.to_dataframe() # the 13 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-gse248077 --dest data/Licenses: 13 NCBI GEO policy
No linked publication found — please cite the data source.