Present Address: Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China.
Department of Biological Sciences, Center for Systems Biology, The University of Texas at Dallas, Richardson, TX, 75080, USA.
BMC Biol. 2021 Jul 23;19(1):144. doi: 10.1186/s12915-021-01076-3.
Alternative polyadenylation (APA) is emerging as an important mechanism in the post-transcriptional regulation of gene expression across eukaryotic species. Recent studies have shown that APA plays key roles in biological processes, such as cell proliferation and differentiation. Single-cell RNA-seq technologies are widely used in gene expression heterogeneity studies; however, systematic studies of APA at the single-cell level are still lacking.
Here, we described a novel computational framework, SAPAS, that utilizes 3'-tag-based scRNA-seq data to identify novel poly(A) sites and quantify APA at the single-cell level. Applying SAPAS to the scRNA-seq data of phenotype characterized GABAergic interneurons, we identified cell type-specific APA events for different GABAergic neuron types. Genes with cell type-specific APA events are enriched for synaptic architecture and communications. In further, we observed a strong enrichment of heritability for several psychiatric disorders and brain traits in altered 3' UTRs and coding sequences of cell type-specific APA events. Finally, by exploring the modalities of APA, we discovered that the bimodal APA pattern of Pak3 could classify chandelier cells into different subpopulations that are from different laminar positions.
We established a method to characterize APA at the single-cell level. When applied to a scRNA-seq dataset of GABAergic interneurons, the single-cell APA analysis not only identified cell type-specific APA events but also revealed that the modality of APA could classify cell subpopulations. Thus, SAPAS will expand our understanding of cellular heterogeneity.
可变多聚腺苷酸化（APA）作为真核生物基因表达转录后调控的一种重要机制正在兴起。最近的研究表明，APA 在细胞增殖和分化等生物过程中发挥着关键作用。单细胞 RNA-seq 技术广泛应用于基因表达异质性研究；然而，在单细胞水平上对 APA 的系统研究仍然缺乏。
在这里，我们描述了一种新的计算框架 SAPAS，该框架利用基于 3' 标签的 scRNA-seq 数据来鉴定新的多聚腺苷酸化位点，并在单细胞水平上定量 APA。将 SAPAS 应用于表型特征化 GABA 能中间神经元的 scRNA-seq 数据，我们鉴定了不同 GABA 能神经元类型的细胞类型特异性 APA 事件。具有细胞类型特异性 APA 事件的基因富含突触结构和通讯。此外，我们观察到几个精神障碍和大脑特征在改变的 3'UTR 和细胞类型特异性 APA 事件的编码序列中的遗传力有很强的富集。最后，通过探索 APA 的模式，我们发现 Pak3 的双峰 APA 模式可以将 Chandelier 细胞分为来自不同层位置的不同亚群。
我们建立了一种在单细胞水平上描述 APA 的方法。当应用于 GABA 能中间神经元的 scRNA-seq 数据集时，单细胞 APA 分析不仅鉴定了细胞类型特异性 APA 事件，还揭示了 APA 的模式可以对细胞亚群进行分类。因此，SAPAS 将扩展我们对细胞异质性的理解。
