Collections / Seeing the whole transcriptomics picture: Spatial gene expression analysis reveals photoreceptor stress as modulator of retinal vascular changes

Seeing the whole transcriptomics picture: Spatial gene expression analysis reveals photoreceptor stress as modulator of retinal vascular changes

Photoreceptor cell death is a hallmark feature of many retinal degenerative diseases including age-related macular degeneration. At the molecular level, oxidative stress and inflammation are known central pathogenetic drivers, but the spatial and cellular origins of the underlying molecular mechanisms remain unclear. We used spatial transcriptomics to investigate the spatio-temporal gene expression changes in the mouse retina in response to photo-oxidative damage. In both the healthy and degenerating retina, we uncover regionally distinct transcriptomes, with higher expression of immunity related genes in the superior retina. Exposure to photo-oxidative damage increased the expression of genes involved in inflammatory processes, innate immune responses, and cytokine production in a highly localised manner. This focal region seems a key driver of molecular changes, including upregulation of angiogenic pathways. Consequently, we show substantial alterations in vacular endothelial growth factor, pleiotrophin and midkine signalling between retinal layers, some of which are novel angiogenic signalling pathways within the retina.

Datasets7
Size~70.2 MB
AssayVisium
Samplemouse · retina
KindComparison
Flag a data issue
PMID:40251274 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse248517")
cohort.members        # the 7 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-gse248517")
coll.ordered_artifacts.to_dataframe()   # the 7 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-gse248517 --dest data/

Licenses: 7 NCBI GEO policy

Open in Vitessce
Visium
Mouse retina
GSM7916287
mouse retina 11.1 MB 4,992 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueretina
Size11.1 MB
Spots4,992
Magnificationother · 3.814 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse retina
GSM7916289
mouse retina 7.1 MB 733 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueretina
Size7.1 MB
Spots733
Magnificationother · 3.673 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse retina
GSM7916286
mouse retina 11.6 MB 4,992 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueretina
Size11.6 MB
Spots4,992
Magnificationother · 3.839 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse retina
GSM7916290
mouse retina 11.3 MB 975 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueretina
Size11.3 MB
Spots975
Magnificationother · 3.815 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Visium
Mouse retina
GSM7916292
mouse retina 9.7 MB 566 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueretina
Size9.7 MB
Spots566
bronze H&E NCBI GEO policy
Open in Vitessce
Visium
Mouse retina
GSM7916291
mouse retina 9.6 MB 575 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueretina
Size9.6 MB
Spots575
Magnificationother · 3.817 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse retina
GSM7916293
mouse retina 9.8 MB 573 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueretina
Size9.8 MB
Spots573
Magnificationother · 3.674 µm/px
bronze H&E NCBI GEO policy Open in Vitessce

Cite this collection

doi.org/10.1038/s42003-025-07887-2 ↗
BibTeX
@article{Schumann_2025,
  title = {Spatial transcriptomics reveals regionally altered gene expression that drives retinal degeneration},
  volume = {8},
  ISSN = {2399-3642},
  url = {http://dx.doi.org/10.1038/s42003-025-07887-2},
  DOI = {10.1038/s42003-025-07887-2},
  number = {1},
  journal = {Communications Biology},
  publisher = {Springer Science and Business Media LLC},
  author = {Schumann, Ulrike and Liu, Lixinyu and Aggio-Bruce, Riemke and Cioanca, Adrian V. and Shariev, Artur and Madigan, Michele C. and Valter, Krisztina and Wen, Jiayu and Natoli, Riccardo},
  year = {2025},
  month = Apr
}