bioRxivCancer BiologyPreprint
Single-Cell Analysis of Meningiomas Reveals Mutation-Associated Tumor and Immune Cell Gene Expression Programs
José MaldonadoiD, Bryce L. MashimoiD, Anthony Z. WangiD, Rupen R. DesaiiD, Saad M. KhaniD, Ngima D. SherpaiD, Markus I. Anzaldua-CamposiD, Gregory Joseph ZipfeliD, Albert H. KimiD, Joshua L. DowlingiD, Eric C. LeuthardtiD, Joshua William OsbuniD, Ananth Kesav VellimanaiD, Michael R. ChicoineiD, Gavin P. DunniD, Allegra A. PettiiD
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.
- organism: human
- assay: scRNA-seq
- assay: whole-exome sequencing
- assay: spatial transcriptomics
- research focus: cell type annotation
- research focus: differential expression
- research focus: cell-cell communication
- approach: deep learning
- approach: statistical model
- Citations
- 0
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