Collections / Glioma-neuronal circuit remodeling induces regional immunosuppression [visium]

Glioma-neuronal circuit remodeling induces regional immunosuppression [visium]

Neuronal activity-driven mechanisms impact glioblastoma cell proliferation and invasion (1–7), and glioblastoma remodels neuronal circuits (8,9). Distinct intratumoral regions maintain functional connectivity via a subpopulation of malignant cells that mediate tumor-intrinsic neuronal connectivity and synaptogenesis through their transcriptional programs (8). However, the effects of tumor-intrinsic neuronal activity on other cells, such as immune cells, remain unknown. Here we show that regions within glioblastomas displaying elevated connectivity are characterized by regional immunosuppression. This is accompanied by different cell compositions and inflammatory status of tumor-associated macrophages (TAMs) in the tumor microenvironment. In preclinical models, genetic knockout of Thrombospondin-1 (TSP1/Thbs1), a synaptogenic factor critical for glioma-induced neuronal circuit remodeling, in glioblastoma cells suppressed synaptogenesis and glutamatergic neuronal hyperexcitability. Moreover, this restored antigen-presentation and pro-inflammatory responses, promoted the infiltration of pro-inflammatory TAMs and CD8+ T-cells, and mitigated the immunosuppressive effect of TAMs on T-cell proliferation. Furthermore, pharmacological inhibition of glutamatergic excitatory neuronal signaling redirected TAMs toward a less immunosuppressive phenotype, resulting in prolonged mouse survival. Lastly, pharmacological inhibition of glutamatergic signaling showed potential to enhance the efficacy of immune cell-based therapy. Altogether, our results demonstrate previously unrecognized immunosuppression mechanisms resulting from glioma-neuronal circuit remodeling and suggest that targeting glioma-neuron-immune crosstalk could provide new avenues for immunotherapy.

Datasets2
Size~91 MB
AssayVisium
Samplemouse · brain
KindOther
PMID:40404658 ↗
Python · lamindb
import lamindb as ln
import spatialdata as sd

coll = ln.Collection.get(key="geo-gse289934")
coll.ordered_artifacts.df()                 # the 2 members, ordered

# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]

# …or load each straight into memory as a SpatialData object:
sdatas = {a.key: sd.read_zarr(a.cache()) for a in coll.ordered_artifacts.all()}
Visium
Mouse brain
GSM8801844
mouse brain glioblastoma 49 MB 1,975 obs bronze H&E
TechnologyVisium
Organismmouse
Tissuebrain
Conditionglioblastoma
Size49 MB
Obs1,975
bronze H&E
Open in Vitessce
Visium
Mouse brain
GSM8801845
mouse brain glioblastoma 42 MB 1,782 obs bronze H&E
TechnologyVisium
Organismmouse
Tissuebrain
Conditionglioblastoma
Size42 MB
Obs1,782
bronze H&E Open in Vitessce

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