Collections / Profiling the Microenvironment in Tuberous Sclerosis Complex-Associated Angiomyolipoma: Insights from Spatial Gene Expression Analysis

Profiling the Microenvironment in Tuberous Sclerosis Complex-Associated Angiomyolipoma: Insights from Spatial Gene Expression Analysis

Tuberous sclerosis complex (TSC) is a multisystem genetic disorder caused by mutations in TSC1 or TSC2, leading to constitutive activation of the mTORC1 pathway. Among its manifestations, renal angiomyolipomas (AMLs) are a common and distinctive tumor type. However, the molecular differences between TSC-associated AMLs (TSC-AMLs) and sporadic AMLs remain poorly understood. To address this, we performed spatial transcriptomic profiling (CytAssist Visium) on FFPE tissue sections from one case each of TSC-AML, sporadic AML, and renal cell carcinoma (RCC). This approach enabled in situ analysis of gene expression while preserving tissue architecture and cellular context. Our findings provide insight into the unique molecular landscape of TSC-associated tumors and offer a valuable resource for identifying disease-specific biomarkers and therapeutic targets.

Datasets3

Size~368 MB

AssayVisium

Samplehuman · kidney

KindComparison

Flag a data issue

PMID:41309810 ↗

Download the whole collection

Client Lamin CLI

Python · sddb client

from sddb import Catalog

cohort = Catalog().collection("geo-gse299165")
cohort.members        # the 3 datasets as a table

print(cohort.licenses())              # check the terms before you download

sdatas = cohort.to_dict()             # {uid: SpatialData}, cached locally
# or keep the .zarr files in a folder of your choice:
# sdatas = cohort.to_dict(dest="data/")

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")
coll = db.Collection.get(key="geo-gse299165")
coll.ordered_artifacts.to_dataframe()   # the 3 members, ordered

# download every member .zarr to the local cache:
paths = [a.cache() for a in coll.ordered_artifacts.all()]

Install (Python 3.12+) Docs →

Shell

# sddb client: download every member .zarr into data/
sddb-cli download --collection geo-gse299165 --dest data/

Install (Python 3.12+) Docs →

Licenses: 3 NCBI GEO policy

Member datasets · 3

Visium

Human kidney

GSM9034733

human kidney tuberous sclerosis 125 MB H&E NCBI GEO policy

TechnologyVisium

Organismhuman

Tissuekidney

Conditiontuberous sclerosis

Size125 MB

H&E NCBI GEO policy

Visium

Human kidney

GSM9034734

human kidney tuberous sclerosis 88.4 MB 3,428 spots silver H&E NCBI GEO policy

TechnologyVisium

Organismhuman

Tissuekidney

Conditiontuberous sclerosis

Size88.4 MB

Spots3,428

silver H&E NCBI GEO policy

Visium

Human kidney

GSM9034732

human kidney tuberous sclerosis 154 MB 4,938 spots silver H&E NCBI GEO policy

TechnologyVisium

Organismhuman

Tissuekidney

Conditiontuberous sclerosis

Size154 MB

Spots4,938

silver H&E NCBI GEO policy

Cite this collection

doi.org/10.1038/s41598-025-26541-9 ↗

BibTeX

@article{Watanabe_2025,
  title = {Spatial gene expression profiling reveals the distinct microenvironment of tuberous sclerosis complex-associated angiomyolipoma},
  volume = {15},
  ISSN = {2045-2322},
  url = {http://dx.doi.org/10.1038/s41598-025-26541-9},
  DOI = {10.1038/s41598-025-26541-9},
  number = {1},
  journal = {Scientific Reports},
  publisher = {Springer Science and Business Media LLC},
  author = {Watanabe, Ryuta and Fukumoto, Tetsuya and Miura, Noriyoshi and Kurata, Mie and Kitazawa, Riko and Kikugawa, Tadahiko and Saika, Takashi},
  year = {2025},
  month = Nov
}