Human neocortex
GSM9643336
human neocortex 66.5 MB 1,859 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissueneocortex
Spots1,859
silver H&E NCBI GEO policy
Collections / Polarized neocortical organoids with areal signatures upon the formation of anterior and posterior organizers. [Spatial Transcriptomics]
Polarized neocortical organoids with areal signatures upon the formation of anterior and posterior organizers. [Spatial Transcriptomics]
Most methods to generate neocortical organoids from human pluripotent stem cells lack clear evidence for anterior and posterior polarization, which eventually parcellate into different areal identities with unique cytoarchitecture, cell composition, connectivity, and gene expression patterns in vivo. This study demonstrates the polarization of neocortical organoids. By adding rostral (FGF8) and caudal (BMP4/WNT agonists) morphogens, neocortical organoids upregulate and downregulate anterior or posterior homeodomain transcription factors, including SP8 and COUPTF1. With the application of FGF8 or BMP4/WNT agonists, the formation of anterior or posterior organizers was observed in neocortical organoids. Single-cell RNA-sequencing analyses exhibited molecular signatures associated with different lobes/areas found in multiple studies using human fetal tissue.
Datasets3
Size~242 MB
AssayVisium
Samplehuman · neocortex
KindTime course
Download the whole collection
Python · sddb client
from sddb import Catalog
cohort = Catalog().collection("geo-gse326903")
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-gse326903")
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-gse326903 --dest data/Licenses: 3 NCBI GEO policy
Member datasets · 3
Human neocortex
GSM9643337
human neocortex 79.2 MB 1,953 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissueneocortex
Spots1,953
silver H&E NCBI GEO policy
Human neocortex
GSM9643338
human neocortex 96.5 MB 3,888 spots silver H&E NCBI GEO policy
TechnologyVisium
Organismhuman
Tissueneocortex
Spots3,888
silver H&E NCBI GEO policy
Cite this collection
doi.org/10.1016/j.stem.2026.07.014 ↗ BibTeX
@article{Tsai_2026,
title = {Morphogen-guided neocortical organoids with anteroposterior areal identity},
volume = {33},
ISSN = {1934-5909},
url = {http://dx.doi.org/10.1016/j.stem.2026.07.014},
DOI = {10.1016/j.stem.2026.07.014},
number = {9},
journal = {Cell Stem Cell},
publisher = {Elsevier BV},
author = {Tsai, Yuan-Chen and Ozaki, Hajime and Almet, Axel A. and Fleming, Isabel and Nie, Caihao and Luo, Songhao and Wang, Xinyi and Shiraiwa, Kaori and Noh, Matthew Jung Min and Trejo, Sunnyana and Sugita, Bret Kiyoshi and Dalal, Jiya and Gonzalez, Ruben Alberto and De Jesus, Briana and Branco, Nate and Chen, Gregory Li-Min and Gandal, Michael J. and Nie, Qing and Watanabe, Momoko},
year = {2026},
month = Sept,
pages = {1540–1552.e9}
}