Collections / Integrated single-cell and spatial transcriptomics of ovarian immune landscape reveal role of distinct macrophages in classic galactosemia associated with premature ovarian insufficiency

Integrated single-cell and spatial transcriptomics of ovarian immune landscape reveal role of distinct macrophages in classic galactosemia associated with premature ovarian insufficiency

Premature ovarian insufficiency (POI) is a complication that affects 80% of females with classic galactosemia (CG), caused by mutations in the GALT gene. Immune system regulates normal physiological processes and pathological aspects of the ovarian function. While previous animal studies have shown that accumulation of toxic galactose metabolites trigger cellular stress pathways, contribute to the disease's complications, it remains unclear how these influence the immune landscape in CG patients. Here, we investigated the molecular and cellular changes in the ovarian immune environment that may impact the ovarian function contributing to early onset of POI in prepubteral girls with CG. We noticed a marked increase in both monocyte and tissue derived macrophages, and expression of pro-inflammatory cytokines in the CG ovary. Furthermore, we noticed oxidative stress induced activation of NLRP3 inflammasome, which contributed to recruitment of macrophages, induction of pyroptotic cell-death, and overall exacerbation of the inflammatory response.

Datasets2
Size~97.0 MB
AssayVisium
Samplehuman · ovary
KindReplicates
Flag a data issue
PMID:42226209 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse308275")
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-gse308275")
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-gse308275 --dest data/

Licenses: 2 NCBI GEO policy

Open in Vitessce
Visium
Human ovary
GSM9241523
human ovary 28.0 MB 1,660 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissueovary
Size28.0 MB
Spots1,660
Magnificationother · 5.506 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human ovary
GSM9241521
human ovary 68.9 MB 1,785 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissueovary
Size68.9 MB
Spots1,785
Magnificationother · 4.276 µm/px
bronze H&E NCBI GEO policy Open in Vitessce

Cite this collection

doi.org/10.1186/s12964-026-02950-9 ↗
BibTeX
@article{Kavarthapu_2026,
  title = {Transcriptomic profiling of the ovarian immune landscape reveals distinct macrophage subsets and activation of the NLRP3 inflammasome likely contributing to accelerated follicular atresia in classic galactosemia},
  volume = {24},
  ISSN = {1478-811X},
  url = {http://dx.doi.org/10.1186/s12964-026-02950-9},
  DOI = {10.1186/s12964-026-02950-9},
  number = {1},
  journal = {Cell Communication and Signaling},
  publisher = {Springer Science and Business Media LLC},
  author = {Kavarthapu, Raghuveer and Vo, Son and Hanby, Natalie and Vanpouille, Christophe and Brunette, Margaret and Soliman, Mary E and Pham, Thang and Lou, Hong and Sierra, Maria DeLa Luz and Maher, Jacqueline C Yano and Gomez-Lobo, Veronica},
  year = {2026},
  month = May
}