Stepwise Protocol for Alternative Splicing Analysis in Single-Cell SMART-Seq2 RNA-Seq Data.
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RNA alternative splicing (AS) is an essential process that expands transcriptomic and proteomic diversity in eukaryotic cells and contributes to cellular heterogeneity across physiological and pathological conditions in humans. With the advent of single-cell RNA sequencing (scRNA-seq), it has become possible to study AS at cellular resolution, although robust and standardized analytical workflows remain to be developed. Here, we present a stepwise protocol for analyzing AS in single cells from pediatric high-grade gliomas (pHGGs) harboring the histone H3.3 lysine 27-to-methionine (H3.3K27M) mutation using SMART-Seq2 scRNA-seq data. Starting from raw sequencing reads, the workflow includes read alignment, gene-level quantification, splice junction and intron quantification, and single-nucleotide variant-based mutation detection. Gene expression-based clustering and cell-type annotation are performed by using the Seurat R package. AS analysis in tumor cells is then conducted using the MARVEL R package in combination with customized scripts to calculate percent spliced-in (PSI) values, identify variable AS events, perform dimensionality reduction, cluster cells, conduct differential AS analysis, and visualize splicing patterns. This protocol provides a reproducible and comprehensive framework for dissecting AS dynamics at single-cell resolution. It is readily adaptable to other SMART-Seq2 datasets and facilitates systematic investigation of splicing heterogeneity in diverse biological contexts. Key features • Protocol for single-cell RNA alternative splicing (AS) analysis in pediatric high-grade gliomas (pHGGs) with H3.3K27M mutation using SMART-Seq2 data. • Integrates gene expression-based clustering and genetic mutation to identify tumor populations. • MARVEL plus custom scripts enable PSI computation, variable AS detection, clustering, differential splicing analysis, and visualization of splicing patterns. • Flexible workflow applicable to other full-length scRNA-seq datasets for studying AS dynamics in cancer and development.