生物
转录组
空间组织
干细胞
祖细胞
计算生物学
祖细胞
进化生物学
染色质
髓母细胞瘤
电池类型
细胞分化
遗传学
细胞
神经科学
基因
基因表达
作者
Jiankang Li,Hailong Liu,Ziwei Wang,Jiao Zhang,Xuan Chen,Craig Daniels,Xiaochong Wu,Olivier Saulnier,Hiromichi Suzuki,Pasqualino De Antonellis,Alexandra Rasnitsyn,Winnie Ong,Evan Y. Wang,Liam D. Hendrikse,Yu Su,Yu Tian,Dongming Han,Ruohan Wang,Jialin Mo,Fei Liu
出处
期刊:Neuro-oncology
[Oxford University Press]
日期:2025-02-17
卷期号:27 (6): 1611-1627
被引量:1
标识
DOI:10.1093/neuonc/noaf020
摘要
Abstract Background Despite numerous studies on medulloblastoma (MB) cell heterogeneity, the spatial characteristics of cellular states remain unclear. Methods We analyze single-nucleus and spatial transcriptomes and chromatin accessibility from human MB spanning four subgroups, to identify malignant cell populations and describe the spatial evolutionary trajectories. The spatial copy number variations (CNVs) patterns and niches were analyzed to investigate the cellular interactions. Results Three main malignant cell populations were identified, including progenitor-like, cycling, and differentiated populations. Gene signatures of cell populations strongly correlate to clinical outcomes. These tumor cell populations are geographically organized as stem-like and mature regions, highlighting their spatially heterogeneous nature. Progenitor-like and cycling cells are mainly concentrated in stem-like regions, whereas various differentiated populations are primarily distributed in mature regions. By analyzing chromosomal alterations, we find that stem-like regions typically harbor a single pattern of CNVs, reflecting high originality and uniformity, which is in stark contrast to mature regions exhibiting multiple patterns with a broader range of biological functions. Projecting cellular state programs onto spatial sections fully illustrates the evolution from stem-like regions to various functional zones in mature regions, which is correlated to microenvironmental components along the paths to maintain stemness or promote differentiation. Conclusions This multi-omics database comprehensively facilitates the understanding of MB spatial evolutionary organization.
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