Visium HD
Human intestine
GSM9219222
silver Interactive viewer H&E graft versus host disease ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_hires_image': DataArray[cyx] (3, 991, 1839)
│ └── 'GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_lowres_image': DataArray[cyx] (3, 600, 281)
├── Shapes
│ ├── 'GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_002um': GeoDataFrame shape: (9192114, 1) (2D shapes)
│ ├── 'GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_008um': GeoDataFrame shape: (592669, 1) (2D shapes)
│ ├── 'GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um': GeoDataFrame shape: (153456, 1) (2D shapes)
│ └── 'GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um_for_vitessce': GeoDataFrame shape: (153456, 2) (2D shapes)
└── Tables
├── 'square_002um': AnnData (9192114, 18085)
├── 'square_008um': AnnData (592669, 18085)
└── 'square_016um': AnnData (153456, 18085)
with coordinate systems:
▸ 'GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix', with elements:
GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_hires_image (Images), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_lowres_image (Images), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_002um (Shapes), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_008um (Shapes), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um (Shapes), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um_for_vitessce (Shapes)
▸ 'GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_downscaled_hires', with elements:
GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_hires_image (Images), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_002um (Shapes), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_008um (Shapes), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um (Shapes), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um_for_vitessce (Shapes)
▸ 'GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_downscaled_lowres', with elements:
GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_lowres_image (Images), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_002um (Shapes), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_008um (Shapes), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um (Shapes), GSM9219222_SLV11_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um_for_vitessce (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("TJh4DTk27dsixJFl0000")
ds.elements() # inspect without downloading
sdata = ds.to_sdata() # cached locally, reused
print(sdata) 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")
artifact = db.Artifact.get("TJh4DTk27dsixJFl") # stem uid: Lamin resolves it to the 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 Shell
# sddb client: download the .zarr into data/
sddb-cli download TJh4DTk27dsixJFl0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/TJh4DTk27dsixJFl
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid TJh4DTk27dsixJFlLicense: NCBI GEO policy
Part of collection
Spatiotemporal Single-Cell Profiling Reveals T Cell Clonal Dynamics and Phenotypic Plasticity in Human Graft-versus-Host Disease→ 8 datasets · publication
Metadata
- Technology
- Visium HD
- Organism
- human
- Tissue
- intestine
- Condition
- graft versus host disease
- Modality
- RNA
- Observations (bins)
- 592,669
- Tier
- silver
- Resolution
- 1,839 × 991 px
- mpp
- 4.622 µm/px
- Size
- 943 MB
- Sample ID
- GSM9219222
- License
- NCBI GEO policy
- Published
- Jun 24, 2026
Cite this dataset
doi.org/10.1038/s41590-026-02631-2 ↗ BibTeX
@article{Shi_2026,
title = {Spatiotemporal single-cell profiling reveals T cell clonal dynamics and phenotypic plasticity in human graft-versus-host disease},
volume = {27},
ISSN = {1529-2916},
url = {http://dx.doi.org/10.1038/s41590-026-02631-2},
DOI = {10.1038/s41590-026-02631-2},
number = {10},
journal = {Nature Immunology},
publisher = {Springer Science and Business Media LLC},
author = {Shi, Lingting and Uzuni, Ajna and Wang, Ximi K. and Pressler, Michael and Harle, David W. and Chakrabarti, Shami and Macedo, Rodney and Belay, Kirubel and Gordillo, Christian A. and McMahon-Skates, Thomas and Raps, Erik and Zhang, Jia Yi Ady and Nazaret, Achille and Fan, Joy L. and Jin, Yinuo and Shen, Xumin and Fuller, Joshua S. and Azad, Tamjeed and Huang, Jessie and Chainani, Pranik and Nima, Jose Pomarino and Abrams, Julian A. and Del Portillo, Armando and Mapara, Markus Y. and Alhamar, Mohamed and Sykes, Megan and McFaline-Figueroa, José L. and Azizi, Elham and Reshef, Ran},
year = {2026},
month = Sept,
pages = {2156–2171}
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB