PDF(Pubmed)
Abstract：
Single-cell RNA-sequencing (scRNA-seq) provides unprecedented insights into cellular heterogeneity. Although scRNA-seq reads from most prevalent and popular tagged-end protocols are expected to arise from the 3\' end of polyadenylated RNAs, recent studies have shown that \"off-target\" reads can constitute a substantial portion of the read population. In this work, we introduced scCensus, a comprehensive analysis workflow for systematically evaluating and categorizing off-target reads in scRNA-seq. We applied scCensus to seven scRNA-seq datasets. Our analysis of intergenic reads shows that these off-target reads contain information about chromatin structure and can be used to identify similar cells across modalities. Our analysis of antisense reads suggests that these reads can be used to improve gene detection and capture interesting transcriptional activities like antisense transcription. Furthermore, using splice-aware quantification, we find that spliced and unspliced reads provide distinct information about cell clusters and biomarkers, suggesting the utility of integrating signals from reads with different splicing statuses. Overall, our results suggest that off-target scRNA-seq reads contain underappreciated information about various transcriptional activities. These observations about yet-unexploited information in existing scRNA-seq data will help guide and motivate the community to improve current algorithms and analysis methods, and to develop novel approaches that utilize off-target reads to extend the reach and accuracy of single-cell data analysis pipelines.
摘要：
单细胞RNA测序（scRNA-seq）为细胞异质性提供了前所未有的见解。尽管来自最普遍和流行的标记末端方案的scRNA-seq读数预计来自聚腺苷酸化RNA的3'末端，最近的研究表明，“脱靶”阅读可以构成阅读群体的很大一部分。在这项工作中,我们介绍了scCensus，全面的分析工作流程，用于系统地评估和分类scRNA-seq中的脱靶读数。我们将scCensus应用于七个scRNA-seq数据集。我们对基因间读数的分析表明，这些脱靶读数包含有关染色质结构的信息，可用于识别跨模态的相似细胞。我们对反义读段的分析表明，这些读段可用于改善基因检测并捕获有趣的转录活性，如反义转录。此外，使用拼接感知量化，我们发现剪接和未剪接的读段提供了关于细胞簇和生物标志物的不同信息，建议整合来自具有不同剪接状态的读段的信号的效用。总的来说,我们的结果表明，脱靶scRNA-seq读段包含有关各种转录活性的低估信息。这些关于现有scRNA-seq数据中尚未利用的信息的观察将有助于指导和激励社区改进当前的算法和分析方法。并开发利用脱靶读取来扩展单细胞数据分析管道的范围和准确性的新方法。
