Language

English

Publication Date

7-30-2025

Journal

Neuro-Oncology

DOI

10.1093/neuonc/noaf020

PMID

39957311

PMCID

PMC13223746

PubMedCentral® Posted Date

2-17-2025

PubMedCentral® Full Text Version

Post-print

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.

Keywords

Humans, Medulloblastoma, Cerebellar Neoplasms, DNA Copy Number Variations, Transcriptome, Biomarkers, Tumor, Genomics, Neoplastic Stem Cells, Gene Expression Profiling, Genetic Heterogeneity, Multiomics, cellular state program, medulloblastoma, spatial organization, trajectorial transition

Published Open-Access

yes

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