Collections / High resolution mapping of erythroid niches in human using subcellular spatial transcriptomics

High resolution mapping of erythroid niches in human using subcellular spatial transcriptomics

Like hematopoiesis, erythropoiesis requires erythroid niches nurturing and support. We performed subcellular spatial transcriptomics to reveal the erythroid niches in human. Our results identified novel erythroid niches in human during development and stress.

Datasets3
Size~7.8 GB
Assay10x Xenium
Samplehuman · multiple
KindPublication
Flag a data issue
10.1038/s41588-026-02671-2 ↗
Python · sddb client
from sddb import Catalog

cohort = Catalog().collection("geo-gse271824")
cohort.members        # the 3 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-gse271824")
coll.ordered_artifacts.to_dataframe()   # the 3 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-gse271824 --dest data/

Licenses: 3 NCBI GEO policy

Spatial transcriptomic analyses highlight distinct erythroid niches in mice and humans.

Erythroid cells require specialized microenvironments called erythroblastic islands (EBIs), niches comprising a central macrophage surrounded by developing erythroid precursors, to complete their maturation. Understanding EBI composition and function has been limited by two-dimensional in vitro models and the unclear composition of EBIs in human hematopoietic tissues. Using spatial transcriptomic mapping in mouse and human hematopoietic tissues during development and under stress conditions, we show that EBI architecture is unexpectedly species-specific. In mice, C1q-expressing macrophages serve as a hallmark of EBI central macrophages and mediate clearance of ejected erythroid nuclei. In humans, however, EBIs are characterized by macrophage-independent erythroid clusters in fetal liver and bone marrow, whose integrity depends critically on the adhesion molecule ICAM4. These human erythroid clusters are disrupted in myeloid diseases but can be restored with therapy. These findings redefine conventional models of erythroid niche biology and establish a framework for understanding niche dynamics across species.

Open in Vitessce
10x Xenium
Human bone marrow
GSM8386297
human bone marrow myelodysplastic syndrome 1.5 GB 38,492 cells bronze NCBI GEO policy
Technology10x Xenium
Organismhuman
Tissuebone marrow
Conditionmyelodysplastic syndrome
Size1.5 GB
Cells38,492
bronze NCBI GEO policy Open in Vitessce
Open in Vitessce
10x Xenium
Human bone marrow
GSM8386296
human bone marrow 1.9 GB 40,939 cells bronze NCBI GEO policy
Technology10x Xenium
Organismhuman
Tissuebone marrow
Size1.9 GB
Cells40,939
bronze NCBI GEO policy Open in Vitessce
Open in Vitessce
10x Xenium
Human liver
GSM8386295
human liver 4.4 GB 224,957 cells bronze NCBI GEO policy
Technology10x Xenium
Organismhuman
Tissueliver
Size4.4 GB
Cells224,957
bronze NCBI GEO policy Open in Vitessce

Cite this collection

doi.org/10.1038/s41588-026-02671-2 ↗
BibTeX
@article{Han_2026,
  title = {Spatial transcriptomic analyses highlight distinct erythroid niches in mice and humans},
  volume = {58},
  ISSN = {1546-1718},
  url = {http://dx.doi.org/10.1038/s41588-026-02671-2},
  DOI = {10.1038/s41588-026-02671-2},
  number = {7},
  journal = {Nature Genetics},
  publisher = {Springer Science and Business Media LLC},
  author = {Han, Xu and Ren, Kehan and Wang, Pan and Bi, Honghao and Li, Ermin and Aydemir, Inci and Ji, Amy and Cai, Wenjie and Soleimanisardoo, Laya and Wai, Ching Man and Schipma, Matthew J. and Liu, Yijie and Goldstein, Jeffery and Sukhanova, Madina and Yang, Jing and Ji, Peng},
  year = {2026},
  month = July,
  pages = {1620–1631}
}