Visium HD

Human right lobe of liver

GSM9324330

silver Interactive viewer H&E liver disorder ✓ Verified publication

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_hires_image': DataArray[cyx] (3, 3000, 3200)
│     └── 'GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_lowres_image': DataArray[cyx] (3, 563, 600)
├── Shapes
│     ├── 'GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_002um': GeoDataFrame shape: (7182157, 1) (2D shapes)
│     ├── 'GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_008um': GeoDataFrame shape: (452402, 1) (2D shapes)
│     ├── 'GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um': GeoDataFrame shape: (114262, 1) (2D shapes)
│     └── 'GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um_for_vitessce': GeoDataFrame shape: (114262, 2) (2D shapes)
└── Tables
      ├── 'square_002um': AnnData (7182157, 18085)
      ├── 'square_008um': AnnData (452402, 18085)
      └── 'square_016um': AnnData (114262, 18085)
with coordinate systems:
    ▸ 'GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix', with elements:
        GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_hires_image (Images), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_lowres_image (Images), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_002um (Shapes), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_008um (Shapes), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um (Shapes), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um_for_vitessce (Shapes)
    ▸ 'GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_downscaled_hires', with elements:
        GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_hires_image (Images), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_002um (Shapes), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_008um (Shapes), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um (Shapes), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um_for_vitessce (Shapes)
    ▸ 'GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_downscaled_lowres', with elements:
        GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_lowres_image (Images), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_002um (Shapes), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_008um (Shapes), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um (Shapes), GSM9324330_C113_only_binned_outputs_square_002um_filtered_feature_bc_matrix_square_016um_for_vitessce (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("jE9V0TYZpOL7VSIX0000")
ds.elements()          # inspect without downloading
sdata = ds.to_sdata()  # cached locally, reused
print(sdata)

Install (Python 3.12+) Docs →

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("jE9V0TYZpOL7VSIX")   # 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

Install (Python 3.12+) Docs →

Shell

# sddb client: download the .zarr into data/
sddb-cli download jE9V0TYZpOL7VSIX0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/jE9V0TYZpOL7VSIX

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid jE9V0TYZpOL7VSIX

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

High-resolution spatial transcriptomics of human liver with VisiumHD→

3 datasets · other

Metadata

Technology: Visium HD
Organism: human
Tissue: right lobe of liver
Condition: liver disorder
Modality: RNA
Observations (bins): 452,402
Tier: silver
Resolution: 3,200 × 3,000 px
mpp: 4.622 µm/px
Size: 662 MB
Sample ID: GSM9324330
License: NCBI GEO policy
Published: Apr 17, 2026

Cite this dataset

doi.org/10.1038/s41597-026-07597-2 ↗

BibTeX

@article{Hasan_2026,
  title = {High-resolution spatial transcriptomics of adult and pediatric human liver with Visium HD},
  ISSN = {2052-4463},
  url = {http://dx.doi.org/10.1038/s41597-026-07597-2},
  DOI = {10.1038/s41597-026-07597-2},
  journal = {Scientific Data},
  publisher = {Springer Science and Business Media LLC},
  author = {Hasan, Faizan and Edgar, Rachel D. and Atif, Jawairia and Nakib, Diana and Thoeni, Cornelia and Ricciuto, Amanda and Sayed, Blayne A. and McGilvray, Ian and Bader, Gary D. and MacParland, Sonya A.},
  year = {2026},
  month = June
}

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