Collections / Age-related epithelial defects limit thymic function and regeneration [visium]

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.

Datasets2
Size~138 MB
AssayVisium
Samplemouse · thymus
KindTime course
Flag a data issue
PMID:39112630 ↗
Python · sddb client
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/")
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-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()]
Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse240015 --dest data/

Licenses: 2 NCBI GEO policy

Open in Vitessce
Visium
Mouse thymus
GSM7679868
mouse thymus 12.1 MB 1,392 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuethymus
Size12.1 MB
Spots1,392
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse thymus
GSM7679867
mouse thymus 126 MB 3,010 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuethymus
Size126 MB
Spots3,010
silver H&E NCBI GEO policy Open in Vitessce

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