Distinct Molecular Patterns in R6/2 HD Mouse Brain: Insights from Spatiotemporal Transcriptomics [Spatial Transcriptomics]
Huntington’s disease (HD) is a progressive neurodegenerative disease marked by widespread cellular disruption. To understand disease mechanisms by investigating transcriptional differences, we and others have utilized bulk and single cell transcriptomics which provide cell type information but limited spatial information. We therefore utilized 10x Genomics Visium spatial transcriptomics integrated with matched single nuclei-RNA-seq in the HD R6/2 mouse brain (postnatal day 0, 4- and 12-week) to examine disease trajectories across regions in a rapidly progressing model. Our data suggests regional, temporal and cell type specific regulatory pathways that establish distinct gene expression changes in brain. Synaptic dysfunction is observed broadly throughout the brain. We observed early dysregulation of the transcription factor Tcf-4 that may drive cortical changes. Mitochondrial deficits are the earliest changes, beginning at P0 in striatum prior to overt symptom onset together with increased expression of striatal identity genes that then become progressively downregulated. Finally, we identified a time-dependent dysregulation of neuropeptide Y signaling and potential interaction with the cAMP/PKA pathway, which may be involved in early imbalance between Drd1 and Drd2 neuron vulnerability.
from sddb import Catalog
cohort = Catalog().collection("geo-gse285858")
cohort.members # the 16 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-gse285858")
coll.ordered_artifacts.to_dataframe() # the 16 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-gse285858 --dest data/ Licenses: 16 NCBI GEO policy
Cite this collection
doi.org/10.1016/j.neuron.2025.05.014 ↗@article{Burns_2025,
title = {Distinct molecular patterns in R6/2 HD mouse brain: Insights from spatiotemporal transcriptomics},
volume = {113},
ISSN = {0896-6273},
url = {http://dx.doi.org/10.1016/j.neuron.2025.05.014},
DOI = {10.1016/j.neuron.2025.05.014},
number = {15},
journal = {Neuron},
publisher = {Elsevier BV},
author = {Burns, Mara S. and Miramontes, Ricardo and Wu, Jie and Gulia, Ravinder and Saddala, Madhu S. and Lau, Alice L. and Quach, Tiffany and Reidling, John C. and Swarup, Vivek and La Spada, Albert R. and Lim, Ryan G. and Thompson, Leslie M.},
year = {2025},
month = Aug,
pages = {2416–2437.e6}
}