Collections / Single Cell and Spatial Transcriptomics Identify Novel Immune-Stromal Interactions in Cardiac Allograft Vasculopathy

Single Cell and Spatial Transcriptomics Identify Novel Immune-Stromal Interactions in Cardiac Allograft Vasculopathy

Cardiac allograft vasculopathy (CAV) is the leading cause of mortality in heart transplant recipients. Despite the prevalence of CAV, there are no targeted therapeutic options to prevent or reverse disease progression, and patients ultimately require retransplant. CAV is characterized by progressive neointimal hyperplasia in donor heart coronary arteries, leading to luminal obliteration and ultimately allograft failure or sudden cardiac death. Although immune and stromal cell interactions are believed to play a key role in CAV pathogenesis, the specific cellular players and molecular signals driving disease remain undefined. In this study, we leverage single-cell RNA sequencing and spatial transcriptomics of human coronary arteries to transcriptionally characterize CAV and define the neointimal microenvironment. We compare arteries with CAV to atherosclerotic coronary artery disease and non-disease controls to identify a unique CAV transcriptional signature. Integration of single-cell RNA sequencing and spatial transcriptomic datasets revealed that modulated vascular smooth muscle cells and macrophage subsets dominate the CAV neointima and suggest that these cells interact to propagate type 1 interferon (IFN)-mediated inflammation. In a mouse CAV model, we demonstrate that interferon blockade with Ruxolitinib significantly reduced the incidence of CAV and prolonged allograft survival. Collectively, this study offers a novel and detailed characterization of the unique cellular and transcriptional landscape of CAV and identify a candidate pathway that may underly CAV pathogenesis, which could serve as a new therapeutic target for this devastating disease.

Datasets4
Size~104 MB
AssayVisium
Samplehuman · coronary artery
KindOther
Flag a data issue
www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE316390 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse316390")
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-gse316390")
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-gse316390 --dest data/

Licenses: 4 NCBI GEO policy

Visium
Human coronary artery
GSM9451528
human coronary artery 30.7 MB 3,146 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuecoronary artery
Size30.7 MB
Spots3,146
silver H&E NCBI GEO policy
Open in Vitessce
Visium
Human coronary artery
GSM9451527
human coronary artery 28.0 MB 4,779 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuecoronary artery
Size28.0 MB
Spots4,779
silver H&E NCBI GEO policy Open in Vitessce
Visium
Human coronary artery
GSM9451526
human coronary artery 33.5 MB 4,261 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuecoronary artery
Size33.5 MB
Spots4,261
silver H&E NCBI GEO policy
Open in Vitessce
Visium
Human coronary artery
GSM9451525
human coronary artery 11.9 MB 1,170 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissuecoronary artery
Size11.9 MB
Spots1,170
silver H&E NCBI GEO policy Open in Vitessce

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No linked publication found — please cite the data source.