10x Xenium
Human kidney
GSM8933095
bronze Interactive viewer Provisional
print(sdata)
SpatialData object
├── Images
│ └── 'morphology_mip': DataTree[cyx] (1, 105405, 51140), (1, 52702, 25570), (1, 26351, 12785), (1, 13175, 6392), (1, 6587, 3196)
├── Labels
│ ├── 'cell_labels': DataTree[yx] (104623, 50083), (52311, 25041), (26155, 12520), (13077, 6260), (6538, 3130)
│ └── 'nucleus_labels': DataTree[yx] (104482, 49949), (52241, 24974), (26120, 12487), (13060, 6243), (6530, 3121)
├── Points
│ └── 'transcripts': DataFrame with shape: (20632983, 10) (3D points)
├── Shapes
│ ├── 'cell_boundaries': GeoDataFrame shape: (307653, 1) (2D shapes)
│ ├── 'cell_circles': GeoDataFrame shape: (307653, 2) (2D shapes)
│ └── 'nucleus_boundaries': GeoDataFrame shape: (307653, 2) (2D shapes)
└── Tables
└── 'table': AnnData (307653, 480)
with coordinate systems:
▸ 'global', with elements:
morphology_mip (Images), cell_labels (Labels), nucleus_labels (Labels), transcripts (Points), cell_boundaries (Shapes), cell_circles (Shapes), nucleus_boundaries (Shapes) Python · sddb client
from sddb import Catalog
ds = Catalog().dataset("9LJCZD7Y0xHUJQdG0000")
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("9LJCZD7Y0xHUJQdG") # 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 9LJCZD7Y0xHUJQdG0000 --dest data/
# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/9LJCZD7Y0xHUJQdG
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid 9LJCZD7Y0xHUJQdGLicense: NCBI GEO policy
Part of collection
Spatio-temporal interaction of immune and renal cells determines glomerular crescent formation in autoimmune kidney disease→ 9 datasets · publication
Metadata
- Technology
- 10x Xenium
- Organism
- human
- Tissue
- kidney
- Modality
- RNA
- Observations (cells)
- 307,653
- Transcripts
- 20,632,983
- Tier
- bronze
- Size
- 6.3 GB
- Sample ID
- GSM8933095
- License
- NCBI GEO policy
- Published
- May 30, 2025
Cite this dataset
doi.org/10.1038/s41467-025-59819-7 ↗ BibTeX
@article{Wang_2025,
title = {Type I interferon drives T cell cytotoxicity by upregulation of interferon regulatory factor 7 in autoimmune kidney diseases in mice},
volume = {16},
ISSN = {2041-1723},
url = {http://dx.doi.org/10.1038/s41467-025-59819-7},
DOI = {10.1038/s41467-025-59819-7},
number = {1},
journal = {Nature Communications},
publisher = {Springer Science and Business Media LLC},
author = {Wang, Huiying and Engesser, Jonas and Khatri, Robin and Schaub, Darius P. and Paust, Hans-Joachim and Sultana, Zeba and Jauch-Speer, Saskia-Larissa and Peters, Anett and Kaffke, Anna and Bonn, Stefan and Huber, Tobias B. and Mittrücker, Hans-Willi and Krebs, Christian F. and Panzer, Ulf and Asada, Nariaki},
year = {2025},
month = May
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB