Collections / Vascular STING activation facilitates NK cell anti-tumor immunity in small cell lung cancer [Spatial_Transcriptome]

Vascular STING activation facilitates NK cell anti-tumor immunity in small cell lung cancer [Spatial_Transcriptome]

Small cell lung cancer (SCLC) typically displays a "cold" tumor microenvironment with a paucity of immune infiltrate. Neuroendocrine SCLC cells also profoundly repress MHC-I expression, rendering them vulnerable to NK cell-mediated cytotoxicity. Here, we confirm that neuroendocrine SCLC cells are sensitive to NK cell mediated attack, yet quantitative spatial profiling of the SCLC immune microenvironment in patient samples reveals that effector immune cells, including NK cells, are excluded from MHC-Ilow/neg SCLC regions. To study this biology, we develop dynamic single cell RNA sequencing of microphysiological immune tumor environments (DynaMITE-seq) and integrate findings with spatial transcriptomics in patient tissue, unveiling the microvasculature as a major checkpoint restricting NK cell extravasation/recruitment. We demonstrate that activation of vascular Stimulator of Interferon Genes (STING) signaling restores NK cell infiltration and killing of neuroendocrine SCLC, suggesting a strategy to overcome this key SCLC immunologic barrier and prime therapeutic response to DLL3-targeted CAR-NK cell therapy.

Datasets3
Size~157 MB
AssayVisium
Samplehuman · lung
KindOther
Flag a data issue
PMID:41791380 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse318867")
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-gse318867")
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-gse318867 --dest data/

Licenses: 3 NCBI GEO policy

Open in Vitessce
Visium
Human lung
GSM9505082
human lung small cell lung carcinoma 94.3 MB 4,316 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuelung
Conditionsmall cell lung carcinoma
Size94.3 MB
Spots4,316
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human lung
GSM9505080
human lung small cell lung carcinoma 33.2 MB 2,806 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuelung
Conditionsmall cell lung carcinoma
Size33.2 MB
Spots2,806
silver H&E NCBI GEO policy Open in Vitessce
Open in Vitessce
Visium
Human lung
GSM9505081
human lung small cell lung carcinoma 29.7 MB 2,589 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuelung
Conditionsmall cell lung carcinoma
Size29.7 MB
Spots2,589
silver H&E NCBI GEO policy Open in Vitessce

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