Center for Computational Biology, University of California, Berkeley, California 94720, USA.
Department of Electrical Engineering and Computer Science, University of California, Berkeley, California 94720, USA.
Genome Res. 2022 Jul 27;32(7):1385-1397. doi: 10.1101/gr.276109.121.
Alternative splicing shapes the transcriptome and contributes to each cell's unique identity, but single-cell RNA sequencing (scRNA-seq) has struggled to capture the impact of alternative splicing. We previously showed that low recovery of mRNAs from single cells led to erroneous conclusions about the cell-to-cell variability of alternative splicing. Here, we present a method, Psix, to confidently identify splicing that changes across a landscape of single cells, using a probabilistic model that is robust against the data limitations of scRNA-seq. Its autocorrelation-inspired approach finds patterns of alternative splicing that correspond to patterns of cell identity, such as cell type or developmental stage, without the need for explicit cell clustering, labeling, or trajectory inference. Applying Psix to data that follow the trajectory of mouse brain development, we identify exons whose alternative splicing patterns cluster into modules of coregulation. We show that the exons in these modules are enriched for binding by distinct neuronal splicing factors and that their changes in splicing correspond to changes in expression of these splicing factors. Thus, Psix reveals cell type-dependent splicing patterns and the wiring of the splicing regulatory networks that control them. Our new method will enable scRNA-seq analysis to go beyond transcription to understand the roles of post-transcriptional regulation in determining cell identity.
可变剪接塑造了转录组，并为每个细胞的独特身份做出了贡献，但单细胞 RNA 测序（scRNA-seq）一直难以捕捉可变剪接的影响。我们之前曾表明，从单细胞中回收 mRNA 的效率低下，导致对可变剪接的细胞间变异性的错误结论。在这里，我们提出了一种方法 Psix，可以使用一种对 scRNA-seq 数据限制具有鲁棒性的概率模型，自信地识别跨越单细胞景观的剪接变化。其受自相关启发的方法可以找到与细胞身份模式（例如细胞类型或发育阶段）相对应的可变剪接模式，而无需显式的细胞聚类、标记或轨迹推断。将 Psix 应用于遵循小鼠大脑发育轨迹的数据中，我们鉴定了其可变剪接模式聚类为核心调控模块的外显子。我们表明，这些模块中的外显子富含由不同神经元剪接因子结合的外显子，并且它们的剪接变化对应于这些剪接因子表达的变化。因此，Psix 揭示了依赖于细胞类型的剪接模式以及控制它们的剪接调控网络的连接。我们的新方法将使 scRNA-seq 分析超越转录，从而了解转录后调控在确定细胞身份中的作用。
