bioRxivCancer Biology
Human fetal cerebellar cell atlas informs medulloblastoma origin and oncogenesis
Z. Luo, M. Xia, W. Shi, C. Zhao, J. Wang, D. Xin, X. Dong, Y. Xiong, F. Zhang, K. Berry, S. Ogurek, X. Liu, R. Rao, R. Xing, L. M. N. Wu, S. Cui, L. Xu, Y. Lin, W. Ma, S. Tian, Q. Xie, L. Zhang, M. Xin, X. Wang, F. Yue, , R. Lu
Abstract
Medulloblastoma (MB) is the most common malignant childhood brain tumor1,2, yet the origin of the most aggressive subgroup-3 form remains elusive, impeding development of effective targeted treatments. Previous analyses of mouse cerebella3,4 or human counterparts from frozen tissue nuclei5 have not fully defined the compositional heterogeneity of MBs. Here, we undertook an unprecedented single-cell profiling of freshly-isolated human fetal cerebella to establish a reference map delineating hierarchical cellular states in MBs. We identified a unique transitional cerebellar progenitor connecting neural stem cells to neuronal lineages in developing fetal cerebella. Intersectional analysis revealed that the transitional progenitors were enriched in aggressive MB subgroups, including group-3 and metastatic tumors. Single-cell multi-omics revealed underlying regulatory networks in the transitional progenitor populations, including transcriptional determinants HNRNPH1 and SOX11, which are correlated with clinical prognosis in group-3 MBs. Genomic and Hi-C profiling identified de novo long-range chromatin-loops juxtaposing HNRNPH1/SOX11-targeted super-enhancers to cis-regulatory elements of MYC, an oncogenic driver for group-3 MBs. Targeting the transitional progenitor regulators inhibited MYC expression and MYC-driven group-3 MB growth. Our integrated single-cell atlases of human fetal cerebella and MBs reveal potential cell populations predisposed to transformation and regulatory circuitries underlying tumor cell states and oncogenesis, highlighting hitherto unrecognized transitional progenitor intermediates predictive of disease prognosis and potential therapeutic vulnerabilities.
- organism: human
- assay: scRNA-seq
- assay: Hi-C
- research focus: cell type annotation
- research focus: gene regulatory network inference
- research focus: differential expression
- approach: deep learning
- approach: statistical model
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