Charting the single-cell and spatial landscape of IDH-wild-type glioblastoma with GBmap
Charting the single-cell and spatial landscape of IDH-wild-type glioblastoma with GBmap is a dataset published in Neuro-Oncology (2025). On theSindex it has a DataRank of 0.734, placing it in the top 44.8% of the data-sharing corpus. It has been cited 44 times, with 42 citing works in its 1-hop citation network. Its calibrated FAIR score is 52/100.
Abstract
BackgroundGlioblastoma (GB), particularly IDH-wild type, is the most aggressive brain malignancy with a dismal prognosis. Despite advances in molecular profiling, the complexity of its tumor microenvironment and spatial organization remains poorly understood. This study aimed to create a comprehensive single-cell and spatial atlas of GB to unravel its cellular heterogeneity, spatial architecture, and clinical relevance.MethodsWe integrated single-cell RNA sequencing data from 26 datasets, encompassing over 1.1 million cells from 240 patients, to construct GBmap, a harmonized single-cell atlas. High-resolution spatial transcriptomics was employed to map the spatial organization of GB tissues. We developed the Tumor Structure Score (TSS) to quantify tumor organization and correlated it with patient outcomes.ResultsWe showcase the applications of GBmap for reference mapping, transfer learning, and biological discoveries. GBmap revealed extensive cellular heterogeneity, identifying rare populations such as tumor-associated neutrophils and homeostatic microglia. Spatial analysis uncovered 7 distinct tumor niches, with hypoxia-dependent niches strongly associated with poor prognosis. The TSS demonstrated that highly organized tumors, characterized by well-defined vasculature and hypoxic niches, correlated with worse survival outcomes.ConclusionsThis study provides a comprehensive resource for understanding glioblastoma heterogeneity and spatial organization. GBmap and the TSS provide an integrative view of tumor architecture in GB, highlighting hypoxia-driven niches that may represent avenues for further investigation. Our resource can facilitate exploratory analyses and hypothesis generation to better understand disease progression.
›Data sources & pipeline
FAIR Checklist
Context only (not used in score)- Has DOI
- Open Access
- Dataset classification
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Calibrated FAIR score — a parallel quality metric, independent of the DataRank citation score. See the full evaluation →
DataRank Breakdown
Base Score Contribution
0.470
From this paper's citation signal
Citation Network Contribution
0.264
From 16 citing papers with measurable signal
Top 5 citers driving the network score
Ranked by citation count — the same ordering the engine uses when summing log1p(Cq) over citers.
- Integrated Genomic Analysis Identifies Clinically Relevant Subtypes of Glioblastoma Characterized by Abnormalities in PDGFRA, IDH1, EGFR, and NF1Cancer Cell20107,682 citationsDataRank 1.3
- An Integrative Model of Cellular States, Plasticity, and Genetics for GlioblastomaCell20192,765 citationsDataRank 1.2
- Benchmarking atlas-level data integration in single-cell genomicsNature Methods20211,376 citationsDataRank 10.3Top 21%
- Interactions between cancer cells and immune cells drive transitions to mesenchymal-like states in glioblastomaCancer Cell2021575 citationsDataRank 0.953
- Inhibitory CD161 receptor identified in glioma-infiltrating T cells by single-cell analysisCell2021394 citationsDataRank 0.897
Why this DataRank?
DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 64% comes from its base citations and 36% from the citation network (16 citing papers contributed measurable signal).
- Base score B(p)
- log1p(citation_count) — grows sub-linearly, so a paper with 1,000 citations is not 10× a paper with 100.
- Network N(p)
- Σ over citers of log1p(Cq) ÷ max(outdegreeq, 1). Being cited by a highly-cited paper with few references counts most.
- Damping factor d = 0.85
- DataRank = (1−d)·B(p) + d·N(p) — the two cards above are each already multiplied by their share.
- Self-citations excluded
- Citers sharing any OpenAlex author ID with this paper are filtered out before the network sum.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.
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