Collections / A single-nucleus and spatial transcriptomic atlas of the Syrian hamster preoptic area across hibernation states

A single-nucleus and spatial transcriptomic atlas of the Syrian hamster preoptic area across hibernation states

Hibernation is an extreme physiological state in certain mammals that is characterized by a reversible reduction in metabolic and thermogenic demands. Although the preoptic area has been implicated in the regulation of hibernation, comprehensive molecular and spatial resources for this brain region in hibernating mammals remain limited. Here, we present an integrated transcriptomic resource of the preoptic area generated using single-nucleus RNA sequencing (snRNA-seq) and Xenium-based spatial transcriptomics in a mammalian hibernator, Syrian hamster. The resource includes cross-species transcriptomic annotations relative to mouse preoptic area datasets, seasonal snRNA-seq datasets obtained under non-hibernating and hibernating conditions, and spatially resolved cell-type annotations generated from Xenium profiling. Together, these datasets provide a reference framework for identifying neuronal populations, examining molecular conservation across species and conditions, and integrating transcriptomic identity with spatial organization.

Datasets4
Size~15.2 GB
Assay10x Xenium
Samplegolden hamster · brain
KindOther
Flag a data issue
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse345359")
cohort.members        # the 4 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-gse345359")
coll.ordered_artifacts.to_dataframe()   # the 4 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-gse345359 --dest data/

Licenses: 4 NCBI GEO policy

Open in Vitessce
10x Xenium
Golden hamster brain
GSM10002162
golden hamster brain 1.8 GB 486,207 cells silver NCBI GEO policy
Technology10x Xenium
Organismgolden hamster
Tissuebrain
Size1.8 GB
Cells486,207
silver NCBI GEO policy Open in Vitessce
Open in Vitessce
10x Xenium
Golden hamster brain
GSM10002164
golden hamster brain 1.9 GB 498,713 cells silver NCBI GEO policy
Technology10x Xenium
Organismgolden hamster
Tissuebrain
Size1.9 GB
Cells498,713
silver NCBI GEO policy Open in Vitessce
Open in Vitessce
10x Xenium
Golden hamster brain
GSM10002163
golden hamster brain 2.0 GB 460,753 cells silver NCBI GEO policy
Technology10x Xenium
Organismgolden hamster
Tissuebrain
Size2.0 GB
Cells460,753
silver NCBI GEO policy Open in Vitessce
Open in Vitessce
10x Xenium
Golden hamster brain
GSM10002165
golden hamster brain 9.5 GB 510,268 cells silver NCBI GEO policy
Technology10x Xenium
Organismgolden hamster
Tissuebrain
Size9.5 GB
Cells510,268
silver NCBI GEO policy Open in Vitessce