Collections / A blueprint for local and distal glioblastoma invasion programs

A blueprint for local and distal glioblastoma invasion programs

Glioblastoma (GBM) invasion into brain parenchyma presents significant challenges for treatment but remains poorly understood. In this study, we combined single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and multiplexed imaging of orthotopic xenograft models to investigate GBM invasion. We first screened 20 patient-derived gliomasphere models for their distal (i.e., extending to the contralateral hemisphere) and local invasive potential in mice. We found that models with distal invasion potential are enriched with oligodendrocyte progenitor-like (OPC-like) cells, while models with only local invasion potential are enriched with mesenchymal-like (MES-like) cells. These patterns reflect predominantly peri-axonal vs peri-vascular invasion routes, respectively. Next, we analyzed the transcriptomes of invading cells within models (compared to tumor core) and identified novel programs associated with distal and local invasion. Thus, we decouple transcriptional features associated with invasion potential from those associated with the process of invasion. We validated our findings by spatial transcriptomics and multiplexed imaging, further describing the spatial niche of invasive cells. Taken together, our results provide a blueprint for the invasive potential of GBM cell states and of the programs associated with invasion across different scales.

Datasets2
Size~57.8 MB
AssayVisium
Samplehuman · brain
KindComparison
Flag a data issue
PMID:41844611 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse281796")
cohort.members        # the 2 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-gse281796")
coll.ordered_artifacts.to_dataframe()   # the 2 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-gse281796 --dest data/

Licenses: 2 NCBI GEO policy

Open in Vitessce
Visium
Human brain
GSM8630048
human brain glioblastoma 26.7 MB 1,986 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionglioblastoma
Size26.7 MB
Spots1,986
bronze H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human brain
GSM8630049
human brain glioblastoma 31.1 MB 2,330 spots bronze H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuebrain
Conditionglioblastoma
Size31.1 MB
Spots2,330
bronze H&E NCBI GEO policy Open in Vitessce

Cite this collection

No linked publication found — please cite the data source.