The liver plays a critical role in metabolism and immune function. These crucial functions are diminished in chronic liver diseases, leading to over two million deaths annually worldwide due to liver failure. Single-cell transcriptomics has provided insights into the cellular composition of the liver in health and disease, but is inherently biased due to cell type-specific enrichment and destruction during the single-cell dissociation process. Previous work has highlighted difficulties in capturing specific populations such as cholangiocytes and hepatocytes. Spatial transcriptomics is a promising approach that does not have inherent bias for cell populations and adds important spatial context. Until recently, spatial transcriptomics technologies have only been at a multi-cellular resolution leading to mixed signals from different cell types. The latest spatial transcriptomic technology from 10X Genomics, VisiumHD, enables high-resolution spatial mapping of gene expression in tissue samples, offering a sophisticated platform for exploring the cellular composition of the liver. With a bin width of 2um, it can quantify transcripts at a sub-cellular resolution. Samples from three healthy human liver donors were sequenced and cells were clustered into cell types by integrating spatial transcriptomic data with existing single-cell reference maps. Spatially distinct cell signatures were identified through differential expression analyses and a high-resolution map of the liver was created. This resource provides cell-level and spatially-resolved insights into the cellular and geographical heterogeneity of the liver to serve as a resource for researchers to identify disease-specific spatial signatures and novel therapeutic targets.
from sddb import Catalog
cohort = Catalog().collection("geo-gse311383")
cohort.members # the 3 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-gse311383")
coll.ordered_artifacts.to_dataframe() # the 3 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-gse311383 --dest data/Licenses: 3 NCBI GEO policy
@article{Hasan_2026,
title = {High-resolution spatial transcriptomics of adult and pediatric human liver with Visium HD},
ISSN = {2052-4463},
url = {http://dx.doi.org/10.1038/s41597-026-07597-2},
DOI = {10.1038/s41597-026-07597-2},
journal = {Scientific Data},
publisher = {Springer Science and Business Media LLC},
author = {Hasan, Faizan and Edgar, Rachel D. and Atif, Jawairia and Nakib, Diana and Thoeni, Cornelia and Ricciuto, Amanda and Sayed, Blayne A. and McGilvray, Ian and Bader, Gary D. and MacParland, Sonya A.},
year = {2026},
month = June
}