Collections / Histological Signatures Reveal Anti-Fibrotic Factors in Mouse and Human Lungs [Visium]

Histological Signatures Reveal Anti-Fibrotic Factors in Mouse and Human Lungs [Visium]

Fibrosis, the replacement of healthy tissue with collagen-rich matrix, can occur following injury in almost every organ. Mouse lungs follow stereotyped sequences of fibrogenesis-to-resolution after bleomycin injury, and we reasoned that profiling post-injury histological progression could uncover pro- vs. anti-fibrotic features with functional value for human fibrosis. We mapped spatiotemporally-resolved transformations in lung extracellular matrix (ECM) architecture to spatially-resolved, multi-omic data. First, we charted stepwise trajectories of matrix aberration vs. resolution using unsupervised machine learning, denoting a reversible transition in uniform-to-disordered histological architecture. Single-cell sequencing along these trajectories identified temporally-enriched “ECM-secreting” (Csmd1+) and “pro-resolving” (Cd248+) fibroblasts, for which Visium inferred divergent histological signatures and spatial-transcriptional “neighborhoods”. Critically, pro-resolving fibroblast instillation helped ameliorate fibrosis in vivo. Further, fibroblast neighborhood-associated moieties, Serpine2 and Pi16, functionally modulated human lung fibrosis ex vivo. Spatial phenotyping of idiopathic pulmonary fibrosis further uncovered analogous fibroblast subtypes and neighborhoods in human disease. Collectively, these findings establish an atlas of pro-/anti-fibrotic factors underlying lung matrix architecture and implicate fibroblast-centered moieties in modulating fibrotic progression vs. resolution.

Datasets8
Size~97.4 MB
AssayVisium
Samplemouse · lung
KindTime course
Flag a data issue
PMID:40108456 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse250395")
cohort.members        # the 8 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-gse250395")
coll.ordered_artifacts.to_dataframe()   # the 8 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-gse250395 --dest data/

Licenses: 8 NCBI GEO policy

Open in Vitessce
Visium
Mouse lung
GSM7978620
mouse lung pulmonary fibrosis 10.0 MB 2,013 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuelung
Conditionpulmonary fibrosis
Size10.0 MB
Spots2,013
Magnificationother · 4.049 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse lung
GSM7978623
mouse lung pulmonary fibrosis 16.9 MB 3,464 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuelung
Conditionpulmonary fibrosis
Size16.9 MB
Spots3,464
Magnificationother · 4.047 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse lung
GSM7978617
mouse lung pulmonary fibrosis 10.7 MB 2,831 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuelung
Conditionpulmonary fibrosis
Size10.7 MB
Spots2,831
Magnificationother · 3.680 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse lung
GSM7978618
mouse lung pulmonary fibrosis 13.3 MB 3,048 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuelung
Conditionpulmonary fibrosis
Size13.3 MB
Spots3,048
Magnificationother · 3.681 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse lung
GSM7978621
mouse lung 10.2 MB 2,086 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuelung
Size10.2 MB
Spots2,086
Magnificationother · 4.049 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse lung
GSM7978624
mouse lung pulmonary fibrosis 12.0 MB 2,848 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuelung
Conditionpulmonary fibrosis
Size12.0 MB
Spots2,848
Magnificationother · 4.047 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse lung
GSM7978619
mouse lung pulmonary fibrosis 12.5 MB 2,775 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuelung
Conditionpulmonary fibrosis
Size12.5 MB
Spots2,775
Magnificationother · 4.049 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Mouse lung
GSM7978622
mouse lung pulmonary fibrosis 11.8 MB 2,436 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismmouse
Tissuelung
Conditionpulmonary fibrosis
Size11.8 MB
Spots2,436
Magnificationother · 4.051 µm/px
bronze H&E NCBI GEO policy Open in Vitessce

Cite this collection

doi.org/10.1038/s41586-025-08727-3 ↗
BibTeX
@article{Guo_2025,
  title = {Histological signatures map anti-fibrotic factors in mouse and human lungs},
  volume = {641},
  ISSN = {1476-4687},
  url = {http://dx.doi.org/10.1038/s41586-025-08727-3},
  DOI = {10.1038/s41586-025-08727-3},
  number = {8064},
  journal = {Nature},
  publisher = {Springer Science and Business Media LLC},
  author = {Guo, Jason L. and Griffin, Michelle and Yoon, Jung-Ki and Lopez, David M. and Zhu, Yili and Lu, John M. and Mikos, Georgios and Parker, Jennifer B. L. and Mascharak, Shamik and Brenac, Camille and Guardino, Nicholas J. and Abbas, Darren B. and Li, Dayan J. and Valencia, Caleb and Liang, Norah E. and Januszyk, Michael and Chang, Howard Y. and Wan, Derrick C. and Desai, Tushar J. and Longaker, Michael T.},
  year = {2025},
  month = Mar,
  pages = {993–1004}
}