Multi-omic analysis reveals retinoic acid molecular drivers for fibrosis and regenerative repair in skin fibrosis [Spatial transcriptomics]
In the skin, tissue injury results in fibrosis in the form of scars composed of dense extracellular matrix deposited by fibroblasts. The therapeutic goal of regenerative wound healing has remained elusive, in part because principles of fibroblast programming and adaptive response to injury remain incompletely understood. Here, we present a multimodal -omics platform for the comprehensive study of cell populations in complex tissue, which has allowed us to characterize the cells involved in wound healing using a bleomycin induced ifibrosis model. We employ a bleomycin skin models that recapitulates human tissue repair kinetics. Through integrated analysis of single cell chromatin landscapes and gene expression states, coupled with spatial transcriptomic profiling, we are able to impute fibroblast epigenomes with temporospatial resolution. This has allowed us to reveal potential mechanisms controlling fibroblast fate during migration, proliferation, and differentiation following skin injury, and thereby reexamine the canonical phases of wound healing. These findings have broad implications for the study of tissue repair in complex organ systems.
from sddb import Catalog
cohort = Catalog().collection("geo-gse278169")
cohort.members # the 5 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-gse278169")
coll.ordered_artifacts.to_dataframe() # the 5 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-gse278169 --dest data/ Licenses: 5 NCBI GEO policy
Cite this collection
doi.org/10.1016/j.stem.2025.07.010 ↗@article{Griffin_2025,
title = {Multi-omic analysis reveals retinoic acid molecular drivers for dermal fibrosis and regenerative repair in the skin},
volume = {32},
ISSN = {1934-5909},
url = {http://dx.doi.org/10.1016/j.stem.2025.07.010},
DOI = {10.1016/j.stem.2025.07.010},
number = {9},
journal = {Cell Stem Cell},
publisher = {Elsevier BV},
author = {Griffin, Michelle and Guo, Jason L. and Parker, Jennifer B.L. and Kuhnert, Maxwell and Li, Dayan J. and Valencia, Caleb and Morgan, Annah and Downer, Mauricio and Cotterell, Asha C. and Lu, John M. and Dilorio, Sarah and Bauer-Rowe Ramos, Khristian Eric and Januszyk, Michael and Chang, Howard Y. and Wan, Derrick C. and Longaker, Michael T.},
year = {2025},
month = Sept,
pages = {1421–1437.e6}
}