Evidence map›Paper›PMID 40312969›Full record

ArticleNeuro-oncology2025

Charting the single-cell and spatial landscape of IDH-wild-type glioblastoma with GBmap.

Cristian Ruiz-Moreno, Sergio Marco Salas, Erik Samuelsson, Mariia Minaeva, Ignacio Ibarra, Marco Grillo, Sebastian Brandner, Ananya Roy, Karin Forsberg-Nilsson, Mariette E G Kranendonk and 3 more

Abstract read
In one paragraph

Article in Neuro-oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

44 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Cristian Ruiz-MorenoDepartment of Molecular Biology, Faculty of Science, Radboud University, Nijmegen, The Netherlands.ORCID 0000-0001-9689-7964
Sergio Marco SalasScience for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
Erik SamuelssonScience for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
Mariia MinaevaInstitute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Ignacio IbarraInstitute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Marco GrilloScience for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.
Sebastian BrandnerDivision of Neuropathology and Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, UK.ORCID 0000-0002-8635-7195
Ananya RoyDepartment of Immunology, Genetics and Pathology, Science for Life Laboratory; Uppsala University, Uppsala, Sweden.
Karin Forsberg-NilssonDepartment of Immunology, Genetics and Pathology, Science for Life Laboratory; Uppsala University, Uppsala, Sweden.ORCID 0000-0003-0692-6245
Mariette E G KranendonkPrinses Máxima Centrum for Pediatric Oncology, Utrecht, The Netherlands.
Fabian J TheisInstitute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
Mats NilssonScience for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Stockholm, Sweden.ORCID 0000-0001-9985-0387
Hendrik G StunnenbergDepartment of Molecular Biology, Faculty of Science, Radboud University, Nijmegen, The Netherlands.ORCID 0000-0002-0066-1576

Funding

European Union's Horizon 2020
6 · The paper itself

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.

Indexed as

Biomarkers, TumorBrainBrain NeoplasmsGlioblastomaCell SeparationDatasets as TopicDisease ProgressionFlow CytometryHumansIsocitrate DehydrogenaseKaplan-Meier EstimateMicrogliaNeutrophilsPrognosisSingle-Cell Gene Expression AnalysisSpatial AnalysisBiomarkers, TumorIsocitrate Dehydrogenaseglioblastomahypoxiasingle-cell atlasspatial transcriptomicstumor organization

Identifiers

PMID40312969
PMCPMC12526130

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.