Rhythmic nephron formation in the developing kidney
The mammalian kidney achieves massive parallelization of function by exponentially duplicating nephron-forming niches during development. Each niche caps a tip of the ureteric bud epithelium (the future urinary collecting duct tree) as it undergoes branching morphogenesis, while nephron progenitors within niches balance self-renewal and differentiation to early nephron cells. Nephron formation rate approximately matches branching rate over a large fraction of mouse gestation, yet the nature of this apparent pace-maker is unknown. Here we correlate spatial transcriptomics data with branching ‘life-cycle’ to discover rhythmically alternating signatures of nephron progenitor differentiation and renewal across Wnt, Hippo/Yap, retinoic acid (RA), and other pathways. Our data bring temporal resolution to the renewal vs. differentiation balance in the nephrogenic niche and inform new strategies to achieve self-sustaining nephron formation in synthetic human kidney tissues.
from sddb import Catalog
cohort = Catalog().collection("geo-gse315435")
cohort.members # the 11 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-gse315435")
coll.ordered_artifacts.to_dataframe() # the 11 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-gse315435 --dest data/ Licenses: 11 NCBI GEO policy
Cite this collection
No linked publication found — please cite the data source.