Glioblastoma single-cell and spatial datasets
Review pending Version 0.1.0 · status: draft · last expert review: not yet reviewed
Research question
- What single-cell and spatial datasets exist for glioblastoma?
- What is GBmap?
- Is there enough GBM data to train a virtual cell?
Current understanding
The library includes a harmonised GBM reference atlas (GBmap, >1.1 million cells from 240 patients), primary–recurrent and spatial studies, and a GBM organoid biobank. Observational atlases are plentiful; paired perturbation data are scarce.
GBmap integrates single-cell RNA-seq from 26 datasets (over 1.1 million cells, 240 patients) into a harmonised atlas, adds high-resolution spatial transcriptomics, and reports 7 tumor niches, with hypoxia-dependent niches associated with poor prognosis. VC0060
Other key resources: the Neftel single-cell study (28 tumors), a multi-omic atlas of 86 primary–recurrent pairs, spatial transcriptomics/proteomics of glioma organisation, spatial multi-omics of tumor–host interdependence, and spatial myeloid mapping. VC0059 VC0069 VC0061 VC0062 VC0094
Data platforms and model systems: CZ CELLxGENE Discover hosts glioma datasets among 100M+ cells, and a patient-derived GBM organoid biobank recapitulates inter- and intra-tumoral heterogeneity. VC0043 VC0067
For virtual-cell training, the bottleneck is paired perturbation → state data in GBM; in vivo Perturb-seq in mouse GBM models is a rare example. VC0070
Evidence map
Primary studies & datasets
- VC0060 Charting the single-cell and spatial landscape of IDH-wild-type glioblastoma with GBmap. — Ruiz-Moreno C et al., Neuro-oncology, 2025
- VC0059 An Integrative Model of Cellular States, Plasticity, and Genetics for Glioblastoma. — Neftel C et al., Cell, 2019
- VC0069 A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. — Wang L, Jung J, Babikir H, Shamardani K, Jain S, Feng X, Gupta N, Rosi S, Chang S, Raleigh D, Solomon D, Phillips JJ, Diaz AA., Nature cancer, 2022
- VC0061 Integrative spatial analysis reveals a multi-layered organization of glioblastoma. — Greenwald AC et al., Cell, 2024
- VC0062 Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma. — Ravi VM et al., Cancer cell, 2022
- VC0094 Hypoxia coordinates the spatial landscape of myeloid cells within glioblastoma to affect survival. — Haley MJ et al., Science advances, 2024
- VC0092 · preprint Mapping the spatial architecture of glioblastoma from core to edge delineates niche-specific tumor cell states and intercellular interactions — Khan SM et al., bioRxiv, 2025
- VC0093 · preprint Spatial epigenomic niches underlie glioblastoma cell state plasticity — Kint S et al., bioRxiv, 2025
Related models & tools
- VC0043 CZ CELLxGENE Discover: a single-cell data platform for scalable exploration, analysis and modeling of aggregated data. — CZI Cell Science Program et al., Nucleic acids research, 2025
- VC0067 A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-tumoral Heterogeneity. — Jacob F et al., Cell, 2019
- VC0096 Core2Edge: a human glioblastoma organoid-brain slice model capturing infiltration and transcriptional heterogeneity from core to single-cell dispersion. — Melhem A et al., Nature protocols, 2026
Perturbation & validation evidence
- VC0070 In vivo perturb-seq of cancer and microenvironment cells dissects oncologic drivers and radiotherapy responses in glioblastoma. — Liu SJ et al., Genome biology, 2024
12 library items cited on this page; 2 are preprints (not peer reviewed).
Relations
Typed relations between entities, each backed by library items (role = how the item bears on the relation). Low-confidence rows need editorial review. All entities →
| Subject | Relation | Object | Confidence | Evidence (role) |
|---|---|---|---|---|
| GBmap | associated with | Glioblastoma | high | VC0060 dataset |
| Hypoxia | associated with | Glioblastoma | medium | VC0060 supports VC0094 supports Hypoxia-dependent niches are associated with poor prognosis (GBmap); hypoxia coordinates myeloid-cell spatial organisation. |
| Patient-derived GBM organoid biobank | associated with | Glioblastoma | high | VC0067 dataset |
Entities on this page
- CZ CELLxGENE Discover Datasets
- Patient-derived GBM organoid biobank Datasets
- Patient-derived GBM organoids Models
- GBmap Datasets
- Glioblastoma Diseases
- Hypoxia Pathways & programs
- Neftel four-state framework Methods & concepts
- Perturb-seq Methods & concepts
- Single-cell RNA sequencing Methods & concepts
- Spatial transcriptomics Methods & concepts
What remains uncertain
Accession numbers, access conditions and licences of each dataset have not been verified by GlioState; check the original papers.
Integrating 26 datasets involves batch and annotation harmonisation choices that may affect state calls. VC0060
Two spatial GBM resources in the library are preprints. VC0092 · preprint VC0093 · preprint
For research and education only. Not medical advice and not a recommendation of any diagnosis, treatment or product. Patients should consult their own physicians.