Collections / Spatial transcriptomics reveal signatures of histopathological changes in muscular sarcoidosis

Spatial transcriptomics reveal signatures of histopathological changes in muscular sarcoidosis

Sarcoidosis is a multisystemic disease characterized by non-caseating granuloma infiltrating various organs. The form with symptomatic muscular involvement is called muscular sarcoidosis. The impact of immune cells composing the granuloma on the skeletal muscle is misunderstood. Here, we investigated the granuloma-skeletal muscle interactions through spatial transcriptomics. Five major transcriptomic clusters corresponding to perigranuloma, granuloma, and three successive muscle tissue areas (proximal, intermediate and distal) around the granuloma were identified. Analyses revealed upregulated pathways in the granuloma corresponding to the activation of T-lymphocytes and monocytes/macrophages cytokines and upregulation of extracellular matrix signatures and induction of the TGF-β signaling in the perigranuloma. Comparison between proximal and distal muscles to the granuloma revealed an inverse correlation between distance to the granuloma and upregulation of cellular response to interferon-γ/α, TNF-α, IL-1,4,6, fibroblast proliferation, epithelial to mesenchymal cell transition and downregulation of muscle gene expression. These data shed light on the intercommunications between granulomas and the muscle tissue and provides pathophysiological mechanisms by showing that granuloma immune-cells have a direct impact on proximal muscle tissue by promoting its progressive replacement by fibrosis via the expression of pro-inflammatory and pro-fibrosing signatures. These data could possibly explain the evolution towards a state of disability for some patients.

Datasets2
Size~81 MB
AssayVisium
Samplehuman · skeletal muscle tissue
KindOther
PMID:38067175 ↗
Python · lamindb
import lamindb as ln
import spatialdata as sd

coll = ln.Collection.get(key="geo-gse243291")
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()}
Open in Vitessce
Visium
Human skeletal muscle tissue
GSM7782915
human skeletal muscle tissue sarcoidosis 42 MB 2,723 obs bronze H&E
TechnologyVisium
Organismhuman
Tissueskeletal muscle tissue
Conditionsarcoidosis
Size42 MB
Obs2,723
bronze H&E Open in Vitessce
Open in Vitessce
Visium
Human skeletal muscle tissue
GSM7782914
human skeletal muscle tissue sarcoidosis 39 MB 2,299 obs bronze H&E
TechnologyVisium
Organismhuman
Tissueskeletal muscle tissue
Conditionsarcoidosis
Size39 MB
Obs2,299
bronze H&E Open in Vitessce

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