Unveiling spatial heterogeneity in medulloblastoma: a multi-omics analysis of cellular state and geographical organization

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 Peitee 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]
标识
DOI:10.1093/neuonc/noaf020
摘要

Despite numerous studies on medulloblastoma (MB) cell heterogeneity, the spatial characteristics of cellular states remain unclear. 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 CNVs patterns and niches were analyzed to investigate the cellular interactions. 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 region typically harbors a single pattern of CNVs, reflecting high originality and uniformity, which is in stark contrast to mature region exhibiting multiple patterns with a broader range of biological functions. Projecting cellular state program onto spatial sections fully illustrates the evolution from stem-like region to various functional zones in mature region, 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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