Collections / Single cell and spatial sequencing analysis of cancer associated fibroblasts in the brain metastasis tumor microenvironment

Single cell and spatial sequencing analysis of cancer associated fibroblasts in the brain metastasis tumor microenvironment

Brain metastasis (BM) remains largely incurable. Cancer-associated fibroblasts (CAFs) can either support or inhibit tumor growth in the tumor microenvironment (TME), yet their roles in BM remain under-described. Here we report a single-cell and spatial sequencing analysis of human BM tissues and define four transcriptionally distinct CAF subpopulations. BM CAF subpopulations are characterized by either extracellular matrix (ECM), immune, contractile, or neural features, and show distinct spatial distributions within the BM TME. Further analyses reveal that BM CAFs engage extensively in cell-cell communication and adopt distinct cell states, including an ECM CAF cell state marked by high levels of immunoglobulin superfamily containing leucine rich repeat expression (ISLR-CAFs). Functionally, ISLR-CAFs reduce BM tumor cell viability in vitro, consistent with a tumor-inhibitory role. These findings highlight the heterogeneity and plasticity of CAFs in BM, emphasizing the importance of precision in defining stromal contributions to disease progression and therapeutic response.

Datasets4
Size~155 MB
AssayVisium
Samplehuman · brain
KindOther
Flag a data issue
PMID:41917370 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse320505")
cohort.members        # the 4 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-gse320505")
coll.ordered_artifacts.to_dataframe()   # the 4 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-gse320505 --dest data/

Licenses: 4 NCBI GEO policy

Open in Vitessce
Visium
Human brain
GSM9544606
human brain lung carcinoma 9.8 MB 809 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionlung carcinoma
Size9.8 MB
Spots809
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human brain
GSM9544605
human brain breast carcinoma 61.8 MB 3,623 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionbreast carcinoma
Size61.8 MB
Spots3,623
bronze H&E NCBI GEO policy Open in Vitessce
Visium
Human brain
GSM9544608
human brain lung carcinoma 46.6 MB 1,244 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionlung carcinoma
Size46.6 MB
Spots1,244
bronze H&E NCBI GEO policy
Open in Vitessce
Visium
Human brain
GSM9544607
human brain lung carcinoma 36.6 MB 1,986 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionlung carcinoma
Size36.6 MB
Spots1,986
bronze H&E NCBI GEO policy Open in Vitessce

Cite this collection

No linked publication found — please cite the data source.