生物
转录组
胶质母细胞瘤
生态位
空间异质性
计算生物学
利基
癌症研究
生态学
遗传学
基因
栖息地
基因表达
作者
Cristian Ruiz-Moreno,Sergio Marco Salas,Erik Samuelsson,Mariia Minaeva,Ignacio Ibarra,Marco Grillo,Sebastian Brandner,Ananya Roy,Karin Forsberg‐Nilsson,Mariëtte E.G. Kranendonk,Fabian J. Theis,Mats Nilsson,Hendrik G. Stunnenberg
标识
DOI:10.1093/neuonc/noaf113
摘要
Abstract Background 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. Methods 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. Results 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. Conclusions 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.
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