Pasteurien College, Suzhou Medical College of Soochow University, Soochow University, Suzhou 215000, China; Department of Automation, Xiamen University, Xiamen 361005, China.
Department of Automation, Xiamen University, Xiamen 361005, China.
Genomics Proteomics Bioinformatics. 2023 Jun;21(3):601-618. doi: 10.1016/j.gpb.2023.01.003. Epub 2023 Jan 18.
Alternative polyadenylation (APA) contributes to transcriptome complexity and gene expression regulation and has been implicated in various cellular processes and diseases. Single-cell RNA sequencing (scRNA-seq) has enabled the profiling of APA at the single-cell level; however, the spatial information of cells is not preserved in scRNA-seq. Alternatively, spatial transcriptomics (ST) technologies provide opportunities to decipher the spatial context of the transcriptomic landscape. Pioneering studies have revealed potential spatially variable genes and/or splice isoforms; however, the pattern of APA usage in spatial contexts remains unappreciated. In this study, we developed a toolkit called stAPAminer for mining spatial patterns of APA from spatially barcoded ST data. APA sites were identified and quantified from the ST data. In particular, an imputation model based on the k-nearest neighbors algorithm was designed to recover APA signals, and then APA genes with spatial patterns of APA usage variation were identified. By analyzing well-established ST data of the mouse olfactory bulb (MOB), we presented a detailed view of spatial APA usage across morphological layers of the MOB. We compiled a comprehensive list of genes with spatial APA dynamics and obtained several major spatial expression patterns that represent spatial APA dynamics in different morphological layers. By extending this analysis to two additional replicates of the MOB ST data, we observed that the spatial APA patterns of several genes were reproducible among replicates. stAPAminer employs the power of ST to explore the transcriptional atlas of spatial APA patterns with spatial resolution. This toolkit is available at https://github.com/BMILAB/stAPAminer and https://ngdc.cncb.ac.cn/biocode/tools/BT007320.
可变剪接（APA）有助于转录组的复杂性和基因表达调控，并与各种细胞过程和疾病有关。单细胞 RNA 测序（scRNA-seq）使在单细胞水平上对 APA 进行分析成为可能；然而，scRNA-seq 并不能保留细胞的空间信息。相反，空间转录组学（ST）技术为破译转录组景观的空间背景提供了机会。开创性的研究已经揭示了潜在的空间可变基因和/或剪接异构体；然而，APA 使用模式在空间背景下仍未被充分认识。在这项研究中，我们开发了一个名为 stAPAminer 的工具包，用于从空间条形码 ST 数据中挖掘 APA 的空间模式。从 ST 数据中鉴定和量化 APA 位点。特别是，设计了一种基于 k-最近邻算法的插补模型来恢复 APA 信号，然后鉴定具有 APA 使用变化空间模式的 APA 基因。通过分析经过充分验证的小鼠嗅球（MOB）的 ST 数据，我们呈现了 MOB 形态层之间跨空间 APA 使用的详细视图。我们编制了一份具有空间 APA 动态的基因的综合清单，并获得了几个主要的空间表达模式，这些模式代表了不同形态层中的空间 APA 动态。通过将此分析扩展到另外两个 MOB ST 数据的重复项，我们观察到几个基因的空间 APA 模式在重复项之间具有可重复性。stAPAminer 利用 ST 的强大功能，以空间分辨率探索转录组空间 APA 模式的图谱。该工具包可在 https://github.com/BMILAB/stAPAminer 和 https://ngdc.cncb.ac.cn/biocode/tools/BT007320 获得。
