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BRAIN Publication-derived

Meningioma Brain Reference Datasets

Full SOP with Academic or a plan

Source Choudhury et al., 2022 · University of California, San Francisco · 10.1038/s41588-022-01061-8

Authors: Abrar Choudhury, Stephen T. Magill, Charlotte D. Eaton, Briana C. Prager, David R. Raleigh, Jeremy N. Rich, Joseph F. Costello 7 phases Cell source: Primary human meningioma tissue

Abstract

This protocol describes DNA methylation profiling of meningioma tissue samples using the Illumina EPIC BeadChip and SeSAMe preprocessing pipeline to identify three distinct DNA methylation groups with different clinical outcomes, genetic drivers, and therapeutic vulnerabilities. Integration with genetic, transcriptomic, and functional analyses reveals mechanisms underlying meningioma biology and identifies CDK4/6 inhibitors as a therapeutic strategy for high-grade meningiomas.

Cell source
Primary human meningioma tissue
Application
Disease classification and biomarker discovery

Protocol overview

27 steps across 7 phases

Phase 1 · Tissue and Sample Preparation 1-3
  1. a Meningioma tissue collection and storage
  2. b DNA extraction from meningioma tissue
  3. c DNA quality and concentration assessment
Phase 2 · DNA Methylation Profiling 4-7
  1. a Prepare DNA for EPIC BeadChip array
  2. b Bisulfite treatment of genomic DNA
  3. c Hybridization to EPIC BeadChip
  4. d EPIC BeadChip scanning and raw data generation
Phase 3 · SeSAMe Preprocessing and CNV Adjustment 8-10
  1. a Load raw EPIC BeadChip data into R
  2. b SeSAMe background correction and normalization
  3. c Quality control probe masking
  4. d β-value calculation accounting for CNVs
Phase 4 · Probe Selection and Unsupervised Clustering 11-12
  1. a Principal component analysis of β-values
  2. b Select top variable probes from each PC
  3. c Unsupervised hierarchical clustering of discovery cohort
  4. d K-means consensus clustering to validate cluster number
  5. e Validate three-group classification in discovery cohort
Phase 5 · Validation and Support Vector Machine Classifier Development 13-15
  1. a Validate three-group classification in validation cohort
  2. b Build multiclass support vector machine classifier
  3. c Apply SVM classifier to validation cohort
  4. d Assess SVM classifier performance metrics
Phase 6 · Comparative Analysis: SeSAMe vs. minfi Preprocessing 16-17
  1. a Reprocess data using minfi pipeline (without CNV adjustment)
  2. b Compare cluster assignments between SeSAMe and minfi
  3. c Calculate signal-to-noise ratios for NF2 loss across methods
Phase 7 · Quality Control and Data Integration 18-19
  1. a Perform cell-type deconvolution of DNA methylation profiles
  2. b Perform copy-number variation analysis from DNA methylation data
  3. c Verify CNV-adjusted methylation group assignments
  4. d Assess meningioma DNA methylation group reproducibility

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Attribution

This SOP was written by Organthis based on the published method in Choudhury et al., 2022. Once the originating laboratory has confirmed it, the SOP is marked Verified.

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