Visium
Mouse periosteum
GSM8984117
silver Interactive viewer H&E bone fracture ✓ Verified publication
print(sdata)
SpatialData object
├── Images
│ ├── 'GSM8984117_d15_full_image': DataTree[cyx] (3, 3000, 3000), (3, 1500, 1500), (3, 750, 750), (3, 375, 375), (3, 187, 187)
│ ├── 'GSM8984117_d15_hires_image': DataArray[cyx] (3, 2000, 1785)
│ └── 'GSM8984117_d15_lowres_image': DataArray[cyx] (3, 600, 536)
├── Shapes
│ └── 'GSM8984117_d15': GeoDataFrame shape: (7260, 2) (2D shapes)
└── Tables
└── 'table': AnnData (7260, 19465)
with coordinate systems:
▸ 'GSM8984117_d15', with elements:
GSM8984117_d15_full_image (Images), GSM8984117_d15_hires_image (Images), GSM8984117_d15_lowres_image (Images), GSM8984117_d15 (Shapes)
▸ 'GSM8984117_d15_downscaled_hires', with elements:
GSM8984117_d15_hires_image (Images), GSM8984117_d15 (Shapes)
▸ 'GSM8984117_d15_downscaled_lowres', with elements:
GSM8984117_d15_lowres_image (Images), GSM8984117_d15 (Shapes) Load in Python
import lamindb as ln
db = ln.DB("scverse/spatialdata-db")
artifact = db.Artifact.get("UAxRviBdEtrhXHBL") # 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/UAxRviBdEtrhXHBL
# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid UAxRviBdEtrhXHBL Load with the Python client
# pip install git+https://github.com/theislab/spatialdata-db-client
from sddb import Catalog
ds = Catalog().cohort(uids=["UAxRviBdEtrhXHBL0000"])[0]
ds.elements() # inspect without downloading
sdata = ds.load(lazy=False) # copy to the local cache and openPart of collection
Integrating spatial and single-cell transcriptomics to characterize mouse long bone fracture healing process→ 2 datasets · time course
Metadata
- Technology
- Visium
- Organism
- mouse
- Tissue
- periosteum
- Condition
- bone fracture
- Modality
- RNA
- Observations
- 7,260
- Tier
- silver
- Chemistry
- Visium CytAssist v2
- Resolution
- 1,785 × 2,000 px
- mpp
- 8.163 µm/px
- Size
- 169 MB
- Sample ID
- GSM8984117
- License
- NCBI GEO policy
- Published
- May 25, 2025
Cite this dataset
doi.org/10.1038/s42003-025-08316-0 ↗ BibTeX
@article{Wang_2025,
title = {Integrating spatial and single-cell transcriptomics to characterize mouse long bone fracture healing process},
volume = {8},
ISSN = {2399-3642},
url = {http://dx.doi.org/10.1038/s42003-025-08316-0},
DOI = {10.1038/s42003-025-08316-0},
number = {1},
journal = {Communications Biology},
publisher = {Springer Science and Business Media LLC},
author = {Wang, Hanning and He, Xuan and Ma, Mingjie and Dou, Tianxu and Wei, Yulong and Rux, Danielle and Qin, Ling and Yang, Yan and Zhu, Yue and Yao, Lutian},
year = {2025},
month = June
}Source: NCBI GEO ↗ · License: NCBI GEO policy ↗ · Reprocessed to SpatialData by SpatialData-DB