Visium

Mouse calcaneal tendon

GSM7836300

bronze Interactive viewer H&E Provisional

Preview Client Lamin CLI

print(sdata)

SpatialData object
├── Images
│     ├── 'GSM7836300_INMPP1-2_hires_image': DataArray[cyx] (3, 466, 2000)
│     └── 'GSM7836300_INMPP1-2_lowres_image': DataArray[cyx] (3, 140, 600)
├── Shapes
│     └── 'GSM7836300_INMPP1-2': GeoDataFrame shape: (2751, 2) (2D shapes)
└── Tables
      └── 'table': AnnData (2751, 32285)
with coordinate systems:
    ▸ 'GSM7836300_INMPP1-2', with elements:
        GSM7836300_INMPP1-2_hires_image (Images), GSM7836300_INMPP1-2_lowres_image (Images), GSM7836300_INMPP1-2 (Shapes)
    ▸ 'GSM7836300_INMPP1-2_downscaled_hires', with elements:
        GSM7836300_INMPP1-2_hires_image (Images), GSM7836300_INMPP1-2 (Shapes)
    ▸ 'GSM7836300_INMPP1-2_downscaled_lowres', with elements:
        GSM7836300_INMPP1-2_lowres_image (Images), GSM7836300_INMPP1-2 (Shapes)

Install (Python 3.12+) Docs →

Python · sddb client

from sddb import Catalog

ds = Catalog().dataset("kgHNU98f284ohR5v0000")
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("kgHNU98f284ohR5v")   # 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 kgHNU98f284ohR5v0000 --dest data/

# or lamin: cache the dataset and print its local path
lamin load https://lamin.ai/scverse/spatialdata-db/artifact/kgHNU98f284ohR5v

# or connect once, then load by uid
lamin connect scverse/spatialdata-db
lamin load artifact --uid kgHNU98f284ohR5v

Install (Python 3.12+) Docs →

License: NCBI GEO policy

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

Part of collection

Spatial transcriptomics of the achilles tendon harvested from the injury site with and without TrkA neuronal inhibition→

4 datasets · comparison

Metadata

Technology: Visium
Organism: mouse
Tissue: calcaneal tendon
Modality: RNA
Observations (spots): 2,751
Tier: bronze
Chemistry: Visium v1
Magnification: other
Resolution: 2,000 × 466 px
mpp: 14.939 µm/px
Size: 35.0 MB
Sample ID: GSM7836300
License: NCBI GEO policy
Published: Nov 27, 2023

Cite this dataset

doi.org/10.1126/scitranslmed.ade4619 ↗

BibTeX

@article{Cherief_2023,
  title = {TrkA-mediated sensory innervation of injured mouse tendon supports tendon sheath progenitor cell expansion and tendon repair},
  volume = {15},
  ISSN = {1946-6242},
  url = {http://dx.doi.org/10.1126/scitranslmed.ade4619},
  DOI = {10.1126/scitranslmed.ade4619},
  number = {727},
  journal = {Science Translational Medicine},
  publisher = {American Association for the Advancement of Science (AAAS)},
  author = {Cherief, Masnsen and Xu, Jiajia and Li, Zhao and Tower, Robert J. and Ramesh, Sowmya and Qin, Qizhi and Gomez-Salazar, Mario and Yea, Ji-Hye and Lee, Seungyong and Negri, Stefano and Xu, Mingxin and Price, Theodore and Kendal, Adrian R. and Fan, Chen-Ming and Clemens, Thomas L. and Levi, Benjamin and James, Aaron W.},
  year = {2023},
  month = Dec
}

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