Collections / Spatial transcriptomics reveals injury-responsive compartments and coordinated immune–fibrotic signaling in ANCA-associated renal vasculitis

Spatial transcriptomics reveals injury-responsive compartments and coordinated immune–fibrotic signaling in ANCA-associated renal vasculitis

Immune dysregulation is a hallmark of anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV), particularly in the kidney; however, the spatial organization of renal microenvironments remains poorly understood. Here, we applied spatial transcriptomics to renal biopsies from pediatric patients with AAV representing distinct histological classes defined by the Berden classification (4 control, 2 focal, and 3 non-focal cases), generating an in situ map of localized disease programs. Four disease-associated compartments—immune/interstitial fibroblasts (IM/Fib), glomeruli, myofibroblasts, and vascular compartments—showed distinct compartment-specific signatures that correlated with patient-level histopathology. We further identified coordinated immune–fibrotic signaling associated with different histopathological classes. Within IM/Fib, the CXCR4–CD74 receptor complex co-localized with IgM⁺ cells, and lumican (LUM) co-localized with collagen I/III; both were validated by immunofluorescence and associated with fibrotic injury. These tissue-anchored signatures represent potential disease-associated molecular features with possible diagnostic relevance, and highlight the value of this foundational spatial transcriptomics map for generating testable hypotheses to be further evaluated in larger, stratified, treatment-annotated cohorts.

Datasets3
Size~52.9 MB
AssayVisium
Samplehuman · cortex of kidney
KindComparison
Flag a data issue
PMID:42602048 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse302677")
cohort.members        # the 3 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-gse302677")
coll.ordered_artifacts.to_dataframe()   # the 3 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-gse302677 --dest data/

Licenses: 3 NCBI GEO policy

Open in Vitessce
Visium
Human cortex of kidney
GSM9108684
human cortex of kidney anti-neutrophil cytoplasmic antibody-associated vasculitis 11.8 MB 892 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuecortex of kidney
Conditionanti-neutrophil cytoplasmic antibody-associated vasculitis
Size11.8 MB
Spots892
Magnificationother · 3.420 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human cortex of kidney
GSM9108683
human cortex of kidney 14.7 MB 1,413 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuecortex of kidney
Size14.7 MB
Spots1,413
Magnificationother · 3.738 µm/px
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human cortex of kidney
GSM9108685
human cortex of kidney anti-neutrophil cytoplasmic antibody-associated vasculitis 26.4 MB 1,277 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuecortex of kidney
Conditionanti-neutrophil cytoplasmic antibody-associated vasculitis
Size26.4 MB
Spots1,277
bronze H&E NCBI GEO policy Open in Vitessce

Cite this collection

doi.org/10.3389/fimmu.2026.1884100 ↗
BibTeX
@article{Tang_2026,
  title = {Spatial transcriptomics reveals injury-responsive compartments and coordinated immune–fibrotic signaling in ANCA-associated renal vasculitis},
  volume = {17},
  ISSN = {1664-3224},
  url = {http://dx.doi.org/10.3389/fimmu.2026.1884100},
  DOI = {10.3389/fimmu.2026.1884100},
  journal = {Frontiers in Immunology},
  publisher = {Frontiers Media SA},
  author = {Tang, Yucheng and Zhu, Chunhua and Chen, Shuang and Chen, Huimei and Qian, Hai and Zhang, Aihua and Petretto, Enrico},
  year = {2026},
  month = July
}