Myocarditis is a severe inflammatory cardiac disease that can progress to dilated cardiomyopathy and heart failure. Experimental autoimmune myocarditis (EAM) represents an animal model of acute myocarditis, which is followed by the development of post-inflammatory cardiac fibrosis and systolic dysfunction. In this model, cardiac inflammation is characterized by the massive influx of myeloid infiltrates and is paralleled by the activation of extracellular matrix-producing cardiac fibroblasts. 6–8-week-old BALB/c mice were immunized with αMyHC peptide together with Complete Freunds’ Adjuvant to induce EAM. Heart tissues were isolated, embedded in OTC, sectioned into 10 µm-thick fragments, and placed on a 10x Genomics Visium Gene Expression Slide. Single-cell spatial libraries were prepared using 10x Genomics Visium protocols, and the sequencing was performed using Illumina Chemistry. Single-cell RNA sequencing data was analyzed using RStudio software. In our analysis, we included three biological time points: a healthy heart (d0), acute myocarditis (d19). Following sequencing, we analyzed 3789 spots under the tissue. We identified the main cell populations present in the mouse heart, including fibroblasts, immune cells, endothelial cells, smooth muscle cells, and cardiomyocytes. Subset analysis on cardiac fibroblasts resulted in the presence of four different subsets (FB1–FB6). Subset FB3 has a myofibroblastic signature defined by the expression of, among others, Acta2, Postn, Vim, Col1a1 and Col3a1. This subset is transiently present at d19 of EAM. Spatial analysis of infiltrating immune cells characterized by the expression of, e.g., Ly6c1 and Lyz2 resulted in the presence of five subsets (INF1–INF5). The INF4 subset is present only at acute myocarditis phase (d19) and is spatiotemporally colocalized with the myofibroblastic subset(FB3). Our data indicate that myofibroblasts and infiltrating immune cells colocalize within the tissue during acute phase of myocarditis. This may indicate active molecular interplay between inflammatory cells and cardiac fibroblasts.
from sddb import Catalog
cohort = Catalog().collection("geo-gse248347")
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/")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-gse248347")
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()]# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse248347 --dest data/Licenses: 2 NCBI GEO policy
@article{Stefa_ska_2025,
title = {Elucidating cardiac fibroblasts heterogeneity and activation during experimental autoimmune myocarditis using spatial transcriptomics},
volume = {44},
ISSN = {2405-5808},
url = {http://dx.doi.org/10.1016/j.bbrep.2025.102344},
DOI = {10.1016/j.bbrep.2025.102344},
journal = {Biochemistry and Biophysics Reports},
publisher = {Elsevier BV},
author = {Stefańska, Monika and Sarad, Katarzyna and Kot, Marta and Ruciński, Marcin and Strzelec, Martyna and Krzysztofik, Daria and Lindberg, Eric L.},
year = {2025},
month = Dec,
pages = {102344}
}