Spatiotemporal hepatocyte heterogeneity impacts in vivo gene engineering [Visium Spatial Transcriptomics]
The liver is a central organ for physiology and metabolism, with hepatocytes being the main cell type responsible for most of its functions. Several previous studies investigated the cell types involved in tissue homeostasis and regeneration, however the mechanisms underlying post-natal liver growth and establishment of the mature hepatocyte phenotypes remain to be fully understood. Moreover, genetic modification of hepatocytes is emerging as a promising therapeutic approach, particularly for genetic diseases of the coagulation system and hepatic metabolism. Here, we investigate liver tissue dynamics in mice during post-natal growth and turnover in adulthood, by spatial transcriptomics, clonal analysis, and lineage tracing. We observe progressive establishment of metabolic zonation of hepatocytes and that only a fraction of hepatocytes proliferates in the newborn liver, generating most of the adult tissue. We assess the impact of these changes on both in vivo liver gene transfer and gene editing. We show that the age at treatment affects both the efficiency and lobule distribution of lentiviral vector-mediated gene transfer, and that preferential targeting of the more proliferating hepatocytes allows expansion of the genetically modified liver area. Overall, our findings provide new insights into the spatio-temporal dynamics of the liver during post-natal growth and hepatocyte heterogeneity, with broad implications for liver biology and therapeutic applications.
from sddb import Catalog
cohort = Catalog().collection("geo-gse236424")
cohort.members # the 21 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-gse236424")
coll.ordered_artifacts.to_dataframe() # the 21 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-gse236424 --dest data/ Licenses: 21 NCBI GEO policy
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