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Comprehensive characterization of single cell full-length isoforms in human and mouse with long-read sequencing
bioRxiv - Genomics Pub Date : 2020-08-10 , DOI: 10.1101/2020.08.10.243543
Luyi Tian , Jafar S. Jabbari , Rachel Thijssen , Quentin Gouil , Shanika L. Amarasinghe , Hasaru Kariyawasam , Shian Su , Xueyi Dong , Charity W. Law , Alexis Lucattini , Jin D. Chung , Timur Naim , Audrey Chan , Chi Hai Ly , Gordon S. Lynch , James G. Ryall , Casey J.A. Anttila , Hongke Peng , Mary Ann Anderson , Andrew W. Roberts , David C.S. Huang , Michael B. Clark , Matthew E. Ritchie

Alternative splicing shapes the phenotype of cells in development and disease. Long-read RNA-sequencing recovers full-length transcripts but has limited throughput at the single-cell level. Here we developed single-cell full-length transcript sequencing by sampling (FLT-seq), together with the computational pipeline FLAMES to overcome these issues and perform isoform discovery and quantification, splicing analysis and mutation detection in single cells. With FLT-seq and FLAMES, we performed the first comprehensive characterization of the full-length isoform landscape in single cells of different types and species and identified thousands of unannotated isoforms. We found conserved functional modules that were enriched for alternative transcript usage in different cell populations, including ribosome biogenesis and mRNA splicing. Analysis at the transcript-level allowed data integration with scATAC-seq on individual promoters, improved correlation with protein expression data and linked mutations known to confer drug resistance to transcriptome heterogeneity. Our methods reveal previously unseen isoform complexity and provide a better framework for multi-omics data integration.

中文翻译:

通过长时间阅读测序全面鉴定人和小鼠中单细胞全长同工型

选择性剪接改变了发育和疾病中细胞的表型。长读RNA测序可恢复全长转录本,但单细胞水平的通量有限。在这里,我们开发了通过采样的单细胞全长转录本测序(FLT-seq),以及计算流水线FLAMES来克服这些问题,并在单细胞中进行同工型发现和定量,剪接分析和突变检测。借助FLT-seq和FLAMES,我们对不同类型和物种的单个细胞中的全长同工型景观进行了首次全面表征,并鉴定了数千个未注释的同工型。我们发现了保守的功能模块,这些功能模块丰富了不同细胞群体中替代转录物的使用,包括核糖体的生物发生和mRNA剪接。在转录水平上的分析允许在单个启动子上与scATAC-seq进行数据整合,改善与蛋白质表达数据的相关性以及已知的赋予转录组异质性耐药性的连锁突变。我们的方法揭示了以前看不见的异构体复杂性,并为多组学数据集成提供了更好的框架。
更新日期:2020-08-11
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