10x Xenium data from: A spatially resolved human glioblastoma atlas reveals distinct cellular and molecular patterns of anatomical niches

Glioblastoma is an aggressive brain cancer with limited treatment options and poor patient survival, driven in part by extensive cellular diversity within tumors. While individual cell types have been catalogued, how malignant, vascular, and immune cells are spatially organized inside human tumors remains incompletely understood. Here we show a spatially resolved, multi-modal atlas of human glioblastoma that integrates gene expression profiling across tissue sections with matched single-cell and protein measurements at subcellular resolution. Using a targeted 348 gene panel enriched for vascular and stromal markers, we identify previously unrecognized less well-characterized endothelial, perivascular, and fibroblast-like cell states and define their spatial associations with malignant and immune compartments. We further identify a distinct oligodendrocyte population restricted to tumor core and perivascular regions that exhibits gene expression patterns associated with tumor recurrence and poor clinical outcome. This publicly accessible atlas provides a high-resolution framework for studying the spatial organization of glioblastoma and highlights region-specific cellular interactions that may represent therapeutically actionable vulnerabilities.

Due to size constraints remaining data is uploaded here:

Source data for the figures

10x spatial xenium: Bucket2, Bucket3

10x spatial visium: Bucket 1, Bucket 2, Bucket 3, Bucket 4, Bucket 5, Bucket 6, Bucket 7, Bucket 8

Spatial Transcriptomics & Interactive Browsers

Explore our spatial transcriptome and single-cell datasets via the following portals:

Spatial Datasets (Visium, Xenium, IHC; Our Cohort & Public Data)Available at: Amaranth Insights

UCSC Multi-Omic GBM Browser – A comprehensive portal for exploring integrated multi-omic glioblastoma datasets.

Code & Video Tutorials All analysis code and walkthrough tutorials are available on GitHub: 👉 TSKGA Repository