Stepwise Protocol for Alternative Splicing Analysis in Single-Cell SMART-Seq2 RNA-Seq Data

Area of Science:

• Genomics and Molecular Biology
• Cancer Research
• Bioinformatics

Background:

• RNA alternative splicing (AS) generates proteomic diversity and contributes to cellular heterogeneity in humans.
• Single-cell RNA sequencing (scRNA-seq) enables AS study at cellular resolution, but standardized analysis workflows are needed.
• Pediatric high-grade gliomas (pHGGs) with H3.3K27M mutations exhibit complex cellular heterogeneity.

Purpose of the Study:

• To present a stepwise protocol for analyzing AS in single cells from H3.3K27M-mutant pHGGs using SMART-Seq2 scRNA-seq data.
• To provide a reproducible framework for dissecting AS dynamics at single-cell resolution.
• To facilitate systematic investigation of splicing heterogeneity in diverse biological contexts.

Main Methods:

• The workflow integrates read alignment, gene/splice junction/intron quantification, and mutation detection from raw sequencing reads.
• Gene expression-based clustering and cell-type annotation were performed using the Seurat R package.
• AS analysis utilized the MARVEL R package and custom scripts for PSI calculation, event identification, dimensionality reduction, clustering, differential analysis, and visualization.

Main Results:

• The protocol successfully analyzes AS at single-cell resolution in H3.3K27M-mutant pediatric high-grade gliomas.
• It integrates gene expression and mutation data to identify distinct tumor cell populations.
• The MARVEL package combined with custom scripts enabled comprehensive splicing pattern analysis and visualization.

Conclusions:

• This protocol offers a reproducible and comprehensive framework for single-cell AS analysis.
• The workflow is adaptable to other full-length scRNA-seq datasets, including those from cancer and developmental studies.
• It advances the systematic investigation of splicing heterogeneity in various biological contexts.