Department of Automation, Xiamen University, Xiamen 361005, China.
Xiamen Health and Medical Big Data Center, Xiamen 361008, China.
Int J Mol Sci. 2022 Jul 23;23(15):8123. doi: 10.3390/ijms23158123.
Alternative polyadenylation (APA) is a key layer of gene expression regulation, and APA choice is finely modulated in cells. Advances in single-cell RNA-seq (scRNA-seq) have provided unprecedented opportunities to study APA in cell populations. However, existing studies that investigated APA in single cells were either confined to a few cells or focused on profiling APA dynamics between cell types or identifying APA sites. The diversity and pattern of APA usages on a genomic scale in single cells remains unappreciated. Here, we proposed an analysis framework based on a Gaussian mixture model, scAPAmod, to identify patterns of APA usage from homogeneous or heterogeneous cell populations at the single-cell level. We systematically evaluated the performance of scAPAmod using simulated data and scRNA-seq data. The results show that scAPAmod can accurately identify different patterns of APA usages at the single-cell level. We analyzed the dynamic changes in the pattern of APA usage using scAPAmod in different cell differentiation and developmental stages during spermatogenesis and found that even the same gene has different patterns of APA usages in different differentiation stages. The preference of patterns of usages of APA sites in different genomic regions was also analyzed. We found that patterns of APA usages of the same gene in 3' UTRs (3' untranslated region) and non-3' UTRs are different. Moreover, we analyzed cell-type-specific APA usage patterns and changes in patterns of APA usages across cell types. Different from the conventional analysis of single-cell heterogeneity based on gene expression profiling, this study profiled the heterogeneous pattern of APA isoforms, which contributes to revealing the heterogeneity of single-cell gene expression with higher resolution.
可变多聚腺苷酸化 (APA) 是基因表达调控的一个关键层面，其 APA 选择在细胞中受到精细调控。单细胞 RNA 测序 (scRNA-seq) 的进展为研究细胞群体中的 APA 提供了前所未有的机会。然而，现有的研究要么局限于少数细胞，要么专注于细胞类型之间的 APA 动态分析或 APA 位点鉴定。单个细胞中 APA 用法的多样性和模式在基因组范围内仍未得到充分认识。在这里，我们提出了一个基于高斯混合模型的分析框架，即 scAPAmod，用于在单细胞水平上从同质或异质细胞群体中识别 APA 用法模式。我们使用模拟数据和 scRNA-seq 数据系统地评估了 scAPAmod 的性能。结果表明，scAPAmod 可以在单细胞水平上准确识别不同的 APA 用法模式。我们使用 scAPAmod 分析了在精子发生过程中不同细胞分化和发育阶段的 APA 用法模式的动态变化，发现即使是同一个基因在不同的分化阶段也有不同的 APA 用法模式。还分析了不同基因组区域 APA 位点用法模式的偏好。我们发现，同一个基因在 3'UTR（3'非翻译区）和非 3'UTR 中的 APA 用法模式是不同的。此外，我们分析了细胞类型特异性 APA 用法模式以及跨细胞类型 APA 用法模式的变化。与基于基因表达谱的传统单细胞异质性分析不同，这项研究对 APA 异构体的异质模式进行了分析，有助于以更高的分辨率揭示单细胞基因表达的异质性。
