Methods & Protocols

This page provides detailed methodology for the spatiotemporal transcriptomic analysis of epileptogenesis in the GAERS model. Our approach combines two complementary transcriptomic technologies to map gene expression changes during the critical window when GAERS rats transition from seizure-free to seizure-prone states.

GAERS Model

Genetic Absence Epilepsy Rats from Strasbourg (GAERS) is a well-established genetic model for absence epilepsy. GAERS rats spontaneously develop spike-and-wave discharge (SWD) seizures starting around postnatal day 30 (P30).

Experimental Design

• Time Points:
  • P15 (Pre-seizure): Before onset of seizure activity - developmental baseline
  • P30 (Seizure-onset): At the onset of absence seizures
• Brain Regions: Thalamocortical circuits (cortex and thalamus)
• Sample Handling: Rapid tissue collection, snap-freezing in liquid nitrogen, storage at -80°C

LCM Protocol

Laser capture microdissection enables precise isolation of specific cell populations from heterogeneous tissue sections.

1. Tissue Sectioning: 10 μm cryosections mounted on PEN membrane slides
2. Rapid Staining: Cresyl violet staining (30 seconds) to visualize tissue architecture
3. Laser Capture: Region-specific cell capture using LCM system
4. RNA Extraction: Immediate lysis and RNA extraction from captured cells

Library Preparation & Sequencing

• Library Kit: Takara SMARTer Stranded Total RNA-Seq Kit v3 - Pico Input Mammalian
  • Optimized for low-input samples (250 pg - 10 ng total RNA)
  • Ribosomal RNA depletion
  • Strand-specific library construction
• Sequencing Platform: Illumina sequencing
  • Paired-end sequencing (2 × 150 bp)
  • Target depth: ~30-40 million reads per sample
• Sample Size:
  • Ga15 (P15): n = 2 biological replicates
  • Ga30 (P30): n = 5 biological replicates

Visium Spatial Gene Expression Platform

The Visium platform captures transcriptome-wide gene expression while preserving tissue architecture, enabling spatial mapping of gene expression patterns.

Protocol Overview

1. Tissue Preparation:
   • Fresh-frozen tissue embedded in OCT compound
   • 10 μm cryosections placed directly on Visium slides
   • Tissue permeabilization to release mRNA
2. Library Construction:
   • mRNA capture on spatially barcoded oligonucleotides
   • Reverse transcription and cDNA synthesis
   • Illumina-compatible library preparation
3. Sequencing & Imaging:
   • High-resolution brightfield H&E imaging for tissue architecture
   • Next-generation sequencing (Illumina)
   • Target depth: ~50,000 reads per spot

Platform Specifications

• Spot Size: 55 μm diameter (~10 cells per spot)
• Spot Spacing: 100 μm center-to-center
• Total Spots: ~5,000 per capture area
• Genes Detected: Typically 5,000-10,000 genes per tissue section

Sample Information

• Total Samples: n = 8 (4 at P15, 4 at P30)
• Sample IDs:
  • P15: GA1, GA2, WH1, WH3
  • P30: GA1, GA2, WH1, WH2

Bulk RNA-seq Pipeline

1. Raw Read QC: FastQC for read quality assessment
2. Adapter Trimming: Trimmomatic or Cutadapt
3. Read Alignment: STAR aligner to Rattus norvegicus reference genome (Rnor_6.0/rn6)
4. Gene Quantification: FeatureCounts or HTSeq for read counting
5. Quality Metrics:
   • Mapping rate > 80%
   • Uniquely mapped reads > 70%
   • Genes detected (≥10 reads) > 10,000

Spatial Transcriptomics Pipeline

1. Space Ranger: 10X Genomics official pipeline
   • FASTQ processing and alignment
   • Tissue detection and spot assignment
   • UMI counting and gene expression matrix generation
2. Seurat: R package for spatial data analysis
   • Data normalization (SCTransform)
   • Dimensionality reduction (PCA, UMAP)
   • Spatial clustering and visualization

Bulk RNA-seq: DESeq2

DESeq2 (Love et al., 2014) is the gold standard for differential expression analysis of count data.

• Normalization: Median-of-ratios method to account for library size and composition
• Dispersion Estimation: Gene-wise and shrinkage estimation
• Statistical Testing: Wald test for pairwise comparisons (Ga30 vs Ga15)
• Multiple Testing Correction: Benjamini-Hochberg FDR adjustment
• Significance Thresholds:
  • Adjusted p-value (padj) < 0.1
  • |log2 Fold Change| > 0.5

Spatial Transcriptomics: Seurat/Limma

• Normalization: SCTransform for spatial data
• Statistical Testing: Wilcoxon rank-sum test or limma-voom
• Spatial Context: Consideration of spatial dependencies
• Significance Thresholds:
  • Adjusted p-value < 0.1
  • |log2 Fold Change| > 0.5

Gene Ontology (GO) Enrichment

Functional enrichment analysis using clusterProfiler R package (Yu et al., 2012).

• Input: List of differentially expressed genes (up or downregulated)
• Background: All genes detected in the experiment
• Ontology Categories:
  • Biological Process (BP)
  • Molecular Function (MF)
  • Cellular Component (CC)
• Statistical Test: Hypergeometric test with Benjamini-Hochberg correction
• Significance Threshold: Adjusted p-value < 0.05

Reactome Pathway Analysis

Pathway enrichment using the Reactome database.

• Curated biological pathways
• Focus on molecular mechanisms and signaling cascades
• Complementary to GO enrichment

Key Enriched Pathways

Visualization Tools

• R:
  • ggplot2 for publication-quality figures
  • EnhancedVolcano for volcano plots
  • pheatmap for heatmaps
  • Seurat for spatial plots
• Web Visualization:
  • Chart.js for interactive plots on gaers.bio
  • Custom JavaScript for gene search and filtering

Plot Types