Single-Cell Alternative Splicing Analysis with Expedition Reveals Splicing Dynamics during Neuron Differentiation - Song, Yan; Botvinnik, Olga B.; Lovci, Michael T.; Kakaradov, Boyko; Liu, Patrick; Xu, Jia L.; Yeo, Gene W. (Molecular cell, 2017)
Alternative splicing (AS) generates isoform diversity for cellular identity and homeostasis in multicellular life. Although AS variation has been observed among single cells, little is known about the biological or evolutionary significance of such variation. We developed Expedition, a computational framework consisting of outrigger, a de novo splice graph transversal algorithm to detect AS; anchor, a Bayesian approach to assign modalities; and bonvoyage, a visualization tool using non-negative matrix factorization to display modality changes. Applying Expedition to single pluripotent stem cells undergoing neuronal differentiation, we discover that up to 20% of AS exons exhibit bimodality. Bimodal exons are flanked by more conserved intronic sequences harboring distinct cis-regulatory motifs, constitute much of cell-type-specific splicing, are highly dynamic during cellular transitions, preserve reading frame, and reveal intricacy of cell states invisible to conventional gene expression analysis. Systematic AS characterization in single cells redefines our understanding of AS complexity in cell biology. [Display omitted] •Expedition suite quantifies, classifies, and monitors AS events in single cells•Up to a fifth of AS events exhibit bimodality in homogeneous single-cell populations•Different AS modalities have distinct sequence and evolutionary properties•High-variance AS events reveal cell states invisible to gene expression analysis Variation of post-transcriptional RNA changes in single-cell populations is unappreciated. Song et al. develop the Expedition software suite, which enables systematic analysis of alternative splicing from single-cell RNA-seq data. Expedition classifies distributions into modalities, revealing modality-specific sequence and evolutionary properties and cell states hidden by conventional expression analysis.