Haile Simon, Corbett Richard D, LeBlanc Veronique G, Wei Lisa, Pleasance Stephen, Bilobram Steve, Nip Ka Ming, Brown Kirstin, Trinh Eva, Smith Jillian, Trinh Diane L, Bala Miruna, Chuah Eric, Coope Robin J N, Moore Richard A, Mungall Andrew J, Mungall Karen L, Zhao Yongjun, Hirst Martin, Aparicio Samuel, Birol Inanc, Jones Steven J M, Marra Marco A
Canada's Michael Smith Genome Sciences Centre, BC Cancer, Vancouver, BC, Canada.
Department of Molecular Oncology, BC Cancer, Vancouver, BC, Canada.
Front Genet. 2021 Jun 3;12:665888. doi: 10.3389/fgene.2021.665888. eCollection 2021.
RNA sequencing (RNAseq) has been widely used to generate bulk gene expression measurements collected from pools of cells. Only relatively recently have single-cell RNAseq (scRNAseq) methods provided opportunities for gene expression analyses at the single-cell level, allowing researchers to study heterogeneous mixtures of cells at unprecedented resolution. Tumors tend to be composed of heterogeneous cellular mixtures and are frequently the subjects of such analyses. Extensive method developments have led to several protocols for scRNAseq but, owing to the small amounts of RNA in single cells, technical constraints have required compromises. For example, the majority of scRNAseq methods are limited to sequencing only the 3' or 5' termini of transcripts. Other protocols that facilitate full-length transcript profiling tend to capture only polyadenylated mRNAs and are generally limited to processing only 96 cells at a time. Here, we address these limitations and present a novel protocol that allows for the high-throughput sequencing of full-length, total RNA at single-cell resolution. We demonstrate that our method produced strand-specific sequencing data for both polyadenylated and non-polyadenylated transcripts, enabled the profiling of transcript regions beyond only transcript termini, and yielded data rich enough to allow identification of cell types from heterogeneous biological samples.
RNA测序(RNAseq)已被广泛用于生成从细胞群体中收集的大量基因表达测量数据。直到最近,单细胞RNA测序(scRNAseq)方法才为单细胞水平的基因表达分析提供了机会,使研究人员能够以前所未有的分辨率研究细胞的异质混合物。肿瘤往往由异质细胞混合物组成,并且经常是此类分析的对象。广泛的方法开发已经产生了几种scRNAseq方案,但是由于单细胞中的RNA量很少,技术限制需要做出妥协。例如,大多数scRNAseq方法仅限于对转录本的3'或5'末端进行测序。其他有助于全长转录本分析的方案往往只能捕获多聚腺苷酸化的mRNA,并且通常一次只能处理96个细胞。在这里,我们解决了这些限制,并提出了一种新的方案,该方案允许在单细胞分辨率下对全长总RNA进行高通量测序。我们证明,我们的方法为多聚腺苷酸化和非多聚腺苷酸化转录本都产生了链特异性测序数据,能够对仅转录本末端之外的转录本区域进行分析,并产生了足够丰富的数据,以允许从异质生物样本中识别细胞类型。