Spatiotemporal dynamics of the cardioimmune niche during lesion repair [spatial]
[Note: Some files can only be opened in R or Python. If you have trouble opening them, please don't hesitate to contact Andy Chan, email: shing-fung.chan@uni-wuerzburg.de] The heart is one of the least regenerative organs in humans, and heart disease is the leading cause of death worldwide. Understanding the cellular and molecular processes during cardiac wound healing is an essential prerequisite to reduce health burden and improve cardiac function after myocardial tissue damage. By integrating single-cell RNA-sequencing with imaging-based spatial transcriptomics, we reconstructed the spatiotemporal dynamics of the fibrotic niche after ventricular injury in adult mice. Our analysis revealed dynamic regulation of local cell communication niches over time. We identified interactions that regulate cardiac repair, including fibroblast proliferation silencing by Trem2high macrophages that prevents excessive fibrosis. Moreover, we discovered a rare population of dedifferentiating cardiomyocytes early post-lesioning repair, sustained by signals from myeloid and lymphoid cells. Culturing non-regenerative mouse cardiomyocytes or human heart tissue with these niche factors reactivated progenitor gene expression and cell cycle activity. In summary, this spatiotemporal cell type atlas offers valuable insights into the heterocellular interactions that control cardiac repair.
from sddb import Catalog
cohort = Catalog().collection("geo-gse280376")
cohort.members # the 7 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-gse280376")
coll.ordered_artifacts.to_dataframe() # the 7 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-gse280376 --dest data/ Licenses: 7 NCBI GEO policy
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