Human liver
GSM9521683
human liver 672 MB 333,667 bins bronze H&E NCBI GEO policy
TechnologyVisium HD
Organismhuman
Tissueliver
Bins333,667
bronze H&E NCBI GEO policy
Collections / Inference of cell-cell communication in human fetal liver from high definition spatial profiling using Renoir
Inference of cell-cell communication in human fetal liver from high definition spatial profiling using Renoir
We developed Renoir, an algorithm for charting the ligand-target activities across a spatial topology. To validate this method, we generated 10x Genomics Visium HD datasets from human fetal liver samples (13 PCW and 14 PCW) and applied Renoir on these datasets. Renoir identified hepatocyte-macrophage interactions in these samples.
Datasets2
Size~1.7 GB
AssayVisium HD
Samplehuman · liver
KindPublication
Download the whole collection
Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse319617")
cohort.members # the 2 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/") Python · lamindb
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-gse319617")
coll.ordered_artifacts.to_dataframe() # the 2 members, ordered
# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()] Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse319617 --dest data/Licenses: 2 NCBI GEO policy
About the study
Charting spatial ligand-target activity using Renoir.
The advancement of single-cell RNA sequencing and spatial transcriptomics has enabled the inference of cellular interactions in a tissue microenvironment. Despite advances in cell-cell interaction inference, methods capable of mapping the influence of ligands on downstream target genes across spatial niches harboring specific cell type composition, crucial for resolving niche-specific relationship between ligands and their downstream targets are still lacking. Here, we present Renoir for charting the ligand-target activities across a spatial topology, delineating spatial communication niches harboring specific ligand-target activities and spatially mapping pathway-level activity of genesets. Across spatial datasets with varying resolution (spot to single-cell) ranging from development to disease, Renoir infers cellular niches with distinct ligand-target interactions, spatially maps pathway activities, and identifies context-specific cell-cell interactions, including hepatocyte-macrophage interactions in fetal liver and interactions between onco-fetal and bipotent cells in hepatocellular carcinoma. Renoir uncovers biological insights and therapeutically-relevant cellular crosstalk from spatial transcriptomics data.
Member datasets · 2
Human liver
GSM9521682
human liver 1.1 GB 500,570 bins bronze H&E NCBI GEO policy
TechnologyVisium HD
Organismhuman
Tissueliver
Bins500,570
bronze H&E NCBI GEO policy
Cite this collection
doi.org/10.1038/s41467-026-72388-7 ↗ BibTeX
@article{Rao_2026,
title = {Charting spatial ligand-target activity using Renoir},
volume = {17},
ISSN = {2041-1723},
url = {http://dx.doi.org/10.1038/s41467-026-72388-7},
DOI = {10.1038/s41467-026-72388-7},
number = {1},
journal = {Nature Communications},
publisher = {Springer Science and Business Media LLC},
author = {Rao, Narein and Kumar, Tanush and Kazemi, Dina and Hou, Shaozhi and Khakpoor, Atefeh and Eapen, Merrin Mary and Pai, Rhea and Qiao, Liang and Mishra, Archita and Ginhoux, Florent and Chan, Jerry Kok Yen and George, Jacob and Sharma, Ankur and Zafar, Hamim},
year = {2026},
month = May
}