Back

Single-Cell Analysis of Meningiomas Reveals Mutation-Associated Tumor and Immune Cell Gene Expression Programs

Maldonado, J. A.; Mashimo, B. L.; Wang, A. Z.; Desai, R.; Khan, S. M.; Sherpa, N. D.; Anzaldua-Campos, M. I.; Zipfel, G. J.; Kim, A. H.; Dowling, J. L.; Leuthardt, E. C.; Osbun, J. W.; Vellimana, A. K.; Chicoine, M. R.; Dunn, G. P.; Petti, A. A.

2025-05-24 cancer biology
10.1101/2025.05.21.655421 bioRxiv
Show abstract

Genomic and epigenetic profiling, particularly DNA methylation analysis, have refined the molecular classification of meningiomas and revealed marked intratumoral heterogeneity. To further characterize heterogeneity within and between patients, we analyzed meningiomas using single-cell RNA sequencing (n=11), whole-exome sequencing (n=9), and spatial transcriptomics (n=3). Single-cell analysis revealed six transcriptionally distinct tumor cell states that corresponded to unique biological processes. Integration of the single-cell data with published exome and bulk RNA sequencing data from a large cohort revealed significant associations among somatic variants, tumor cell states, and immunological signatures. Notably, NF2-altered tumors were enriched for an epithelial-to-mesenchymal transition (EMT) cell state and immune cells, whereas NF2-intact tumors were enriched for a sterol metabolism cell state. Spatial transcriptomic analysis confirmed co-localization of immune cells and EMT tumor cells. Comparisons with immune cells from other brain tumors and peripheral tissues highlighted immunological cell states specific to meningioma. Collectively, these findings refine our genetic and molecular understanding of meningioma heterogeneity and underscore the link between genotype and molecular phenotype.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.