Visium

Human neocortex

GSM9643337

silver Interactive viewer H&E ✓ Verified publication

Preview Download via Python Download via CLI Load via sddb

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9643337_KS169_D56_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│     ├── 'GSM9643337_KS169_D56_hires_image': DataArray[cyx] (3, 3000, 3000)
│     └── 'GSM9643337_KS169_D56_lowres_image': DataArray[cyx] (3, 600, 600)
├── Shapes
│     └── 'GSM9643337_KS169_D56': GeoDataFrame shape: (1953, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (1953, 18085)
with coordinate systems:
    ▸ 'GSM9643337_KS169_D56', with elements:
        GSM9643337_KS169_D56_full_image (Images), GSM9643337_KS169_D56_hires_image (Images), GSM9643337_KS169_D56_lowres_image (Images), GSM9643337_KS169_D56 (Shapes)
    ▸ 'GSM9643337_KS169_D56_downscaled_hires', with elements:
        GSM9643337_KS169_D56_hires_image (Images), GSM9643337_KS169_D56 (Shapes)
    ▸ 'GSM9643337_KS169_D56_downscaled_lowres', with elements:
        GSM9643337_KS169_D56_lowres_image (Images), GSM9643337_KS169_D56 (Shapes)

Load in Python

import lamindb as ln

db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("hNC5CXBFuwrMn1rq")   # latest version

sdata = artifact.load()   # cache, then load into memory
group = artifact.open()   # stream without downloading
path  = artifact.cache()  # sync into the local cache, returns the path

Load from the CLI

# one-shot: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/hNC5CXBFuwrMn1rq

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid hNC5CXBFuwrMn1rq

Load with the Python client

# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog

ds = Catalog().cohort(uids=["hNC5CXBFuwrMn1rq0000"])[0]
ds.elements()                 # inspect without downloading
sdata = ds.load(lazy=False)   # copy to the local cache and open

Docs: finding & loading data →

View on Lamin ↗ Open in Vitessce ↗ Flag a data issue

Part of collection

Polarized neocortical organoids with areal signatures upon the formation of anterior and posterior organizers. [Spatial Transcriptomics]→

3 datasets · time course

Metadata

Technology: Visium
Organism: human
Tissue: neocortex
Modality: RNA
Observations: 1,953
Tier: silver
Chemistry: Visium CytAssist v2
Resolution: 3,000 × 3,000 px
mpp: 4.282 µm/px
Size: 75 MB
Sample ID: GSM9643337
License: NCBI GEO policy
Published: Sep 9, 2026

Cite this dataset

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}
}

Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB