Tissue regeneration is orchestrated by macrophages that clear damaged cells and promote regenerative inflammation. How macrophages spatially adapt and diversify their functions to support the architectural requirements of actively regenerating tissue remains unknown. In this study, we reconstructed the dynamic trajectories of myeloid cells isolated from acutely injured and early-stage dystrophic muscles. We identified divergent subsets of monocytes/macrophages and dendritic cells (DCs) and validated markers (e.g., GPNMB) and transcriptional regulators associated with defined functional states. In dystrophic muscle, specialized repair-associated subsets exhibited distinct macrophage diversity and reduced DC heterogeneity. Integrating spatial transcriptomics analyses with immunofluorescence uncovered the ordered distribution of subpopulations and multilayered regenerative inflammation zones (RIZs) where distinct macrophage subsets are organized in functional zones around damaged myofibers supporting all phases of regeneration. Importantly, intermittent glucocorticoid treatment disrupted the RIZs. Our findings suggest that macrophage subtypes mediated the development of the highly ordered architecture of regenerative tissues, unveiling the principles of the structured yet dynamic nature of regenerative inflammation supporting effective tissue repair.
from sddb import Catalog
cohort = Catalog().collection("geo-gse223813")
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/")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-gse223813")
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()]# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse223813 --dest data/Licenses: 7 NCBI GEO policy
No linked publication found — please cite the data source.