Age-related epithelial defects limit thymic function and regeneration [visium]
The thymus is essential for establishing adaptive immunity yet undergoes age-related involution that leads to compromised immune responsiveness. The thymus is also extremely sensitive to acute insult and although capable of regeneration, this capacity declines with age for unknown reasons. We applied single-cell and spatial transcriptomics, lineage-tracing and advanced imaging to define age-related changes in non-hematopoietic stromal cells and discovered the emergence of two atypical thymic epithelial cell (TEC) states. These age-associated (aa)TECs formed high-density peri-medullary epithelial clusters that were devoid of thymocytes; an accretion of non-productive thymic tissue that worsened with age, exhibited features of epithelial-to-mesenchymal transition (EMT), and was associated with downregulation of FOXN1. Interaction analysis revealed that the emergence of aaTEC drew tonic signals from other functional TEC populations at baseline acting as a sink for TEC growth factors. Following acute injury, aaTEC expanded substantially, further perturbing trophic regeneration pathways and correlating with defective repair of the involuted thymus. These findings therefore define a unique feature of thymic involution linked to immune aging and could have implications for developing immune boosting therapies in older individuals.
from sddb import Catalog
cohort = Catalog().collection("geo-gse240015")
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/") 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-gse240015")
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()] # sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse240015 --dest data/ Licenses: 2 NCBI GEO policy
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No linked publication found — please cite the data source.