wbi@bwh.harvard.edu

Single-cell RNA sequencing of meningiomas

GSE183655Choudhury et al., 2022

Choudhury/Raleigh UCSF meningioma multi-omic cohort

This UCSF cohort (Choudhury, Magill, Raleigh et al., Nature Genetics 2022, PMID 35534562) is the most heavily cross-linked dataset in this registry: 565 meningiomas profiled by DNA methylation array, 185 of the same tumors additionally bulk RNA-sequenced, and a 10-sample subset (6 patients, 57,114 cells, including matched dura and brain-tumor-interface pieces) single-cell RNA-sequenced. The paper established the widely-used three-group DNA methylation classification of meningioma (Merlin-intact, immune-enriched, hypermitotic) that several later datasets in this registry explicitly reference or build on. Strengths: large methylation cohort, real multi-modal matching (methylation + bulk RNA-seq + scRNA-seq on overlapping samples, a rare combination), open access with processed data files available. Limitations: per-sample clinical fields are only partially extracted so far. The bulk RNA-seq set has a per-sample grade breakdown (86 grade 1, 74 grade 2, 25 grade 3) and per-sample age, and the scRNA-seq set has per-sample age and a brain-tumor-interface sample count, but sex distribution remains unreported for all three records, and the 565-sample methylation series itself has no per-sample clinical fields extracted yet. Five other publications are also linked to this GEO SuperSeries family, suggesting a productive, still-active dataset with secondary reuse.

Modality
scRNA-seq
Sample count
10
Patient count
6
Institution
University of California, San Francisco
Corresponding author
Stephen T Magill, Jeremy N Rich, David R Raleigh
Platform
Illumina NovaSeq 6000 (GPL24676)
Access type
open
Tissue preservation
Not reported
WHO edition
Not reported
Grade breakdown
Not reported
Sex distribution
Not reported
Age distribution
n=10, range 38.1-86.3, mean 65.5 (extracted from GSM Sample_characteristics_ch1: age)
Anatomic location
Not location per se, but tissue type breakdown confirmed from GSM records: 6 meningioma (tumor bulk), 2 meningioma brain-tumor-interface, 2 dura (normal control) -- consistent with the 6-patient/10-sample design already described.
Brain invasion
2 of 6 patients had a paired brain-tumor-interface (BTI) sample analyzed alongside bulk tumor
Normal/control tissue
dura / normal meningeal tissue (n=2)

All 10 samples merged (unintegrated): 6 tumor-bulk, 2 brain-tumor-interface, 2 dura · 64,346 cells, 150 genes, 10 samples merged.

All 10 samples were merged by raw concatenation, not batch-corrected/integrated (same approach ScPCA documents for its own merged per-project objects) -- use "color by sample" to check whether cells separate by patient/tissue-type rather than assuming a shared embedding means removed batch effects. 21 Leiden clusters at this scale is more granular than the original 8-cluster single-sample pilot; several resolve to the same broad identity (e.g. multiple myeloid subclusters) rather than being 21 distinct cell types.

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Single-cell resolutionLinked to other modalitiesMolecular subtype annotatedPeer-reviewed
  • Choudhury A, et al. Meningioma DNA methylation groups identify biological drivers and therapeutic vulnerabilities. Nature Genetics, 2022. PMID 35534562 · DOI
  • Vasudevan HN, et al. Intratumor and informatic heterogeneity influence meningioma molecular classification. Acta neuropathologica, 2022. PMID 35759011 · DOI
  • Nguyen MP, et al. Supervised machine learning algorithms demonstrate proliferation index correlates with long-term recurrence after complete resection of WHO grade I meningioma. Journal of neurosurgery, 2023. PMID 36303473 · DOI
  • Zakimi N, et al. Gene transcript fusions are associated with clinical outcomes and molecular groups of meningiomas. Acta neuropathologica, 2024. PMID 38509407 · DOI
  • Mirchia K, et al. Meningeal solitary fibrous tumor cell states phenocopy cerebral vascular development and homeostasis. Neuro-oncology, 2025. PMID 39207122 · DOI
  • Nguyen MP, et al. Pan-cancer copy number analysis identifies optimized size thresholds and co-occurrence models for individualized risk stratification. Nature communications, 2025. PMID 40603285 · DOI

Also related: GSE183656, PRJNA761582, SRP336139

Sources

  • GEO
    https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE183655
  • PubMed
    https://pubmed.ncbi.nlm.nih.gov/35534562/
  • PubMed
    https://pubmed.ncbi.nlm.nih.gov/35759011/
  • PubMed
    https://pubmed.ncbi.nlm.nih.gov/36303473/
  • PubMed
    https://pubmed.ncbi.nlm.nih.gov/38509407/
  • PubMed
    https://pubmed.ncbi.nlm.nih.gov/39207122/
  • PubMed
    https://pubmed.ncbi.nlm.nih.gov/40603285/
  • DOI
    https://doi.org/10.1038/s41588-022-01061-8
  • ncbi.nlm.nih.gov
    https://www.ncbi.nlm.nih.gov/bioproject/PRJNA761582
  • weizmann.ac.il
    https://www.weizmann.ac.il/sites/3CA/brain