Collections / Multi-omic analysis reveals retinoic acid molecular drivers for fibrosis and regenerative repair in skin fibrosis [Spatial transcriptomics]

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.

Datasets5
Size~57.4 MB
AssayVisium
Samplemouse · skin of body
KindOther
Flag a data issue
PMID:40816279 ↗
Python · sddb client
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/")
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-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()]
Shell
# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse278169 --dest data/

Licenses: 5 NCBI GEO policy

Open in Vitessce
Visium
Mouse skin of body
GSM8541074
mouse skin of body 10.3 MB 586 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueskin of body
Size10.3 MB
Spots586
Magnificationother · 4.051 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse skin of body
GSM8541072
mouse skin of body 12.3 MB 557 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueskin of body
Size12.3 MB
Spots557
Magnificationother · 4.047 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse skin of body
GSM8541068
mouse skin of body 13.7 MB 1,427 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueskin of body
Size13.7 MB
Spots1,427
Magnificationother · 3.679 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse skin of body
GSM8541073
mouse skin of body 12.0 MB 389 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueskin of body
Size12.0 MB
Spots389
Magnificationother · 4.052 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse skin of body
GSM8541070
mouse skin of body 9.2 MB 926 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissueskin of body
Size9.2 MB
Spots926
bronze H&E NCBI GEO policy Open in Vitessce

Cite this collection

doi.org/10.1016/j.stem.2025.07.010 ↗
BibTeX
@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}
}