A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells.
A Scalable Strand-Specific Protocol Enabling Full-Length Total RNA Sequencing From Single Cells.
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DOI：
10.3389/fgene.2021.665888
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发表时间：
2021
影响因子：
3.7
通讯作者：
Marra MA
中科院分区：
文献类型：
--
作者：
Haile S;Corbett RD;LeBlanc VG;Wei L;Pleasance S;Bilobram S;Nip KM;Brown K;Trinh E;Smith J;Trinh DL;Bala M;Chuah E;Coope RJN;Moore RA;Mungall AJ;Mungall KL;Zhao Y;Hirst M;Aparicio S;Birol I;Jones SJM;Marra MA
RNA测序（RNAseq）已被广泛用于产生从细胞池收集的批量基因表达测量。直到最近，单细胞RNAseq（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导致了scRNAseq的几种方案，但是由于单细胞中RNA的量很小，技术限制需要妥协。例如，大多数scRNAseq方法仅限于对转录物的3′或5′末端进行测序。其他促进全长转录物谱分析的方案倾向于仅捕获聚腺苷酸化的mRNA，并且通常限于一次仅处理96个细胞。在这里，我们解决这些限制，并提出了一种新的协议，允许高通量测序的全长，总RNA在单细胞分辨率。我们证明，我们的方法产生的多聚腺苷酸化和非多聚腺苷酸化的转录物的链特异性测序数据，使超过只有转录物末端的转录区域的分析，并产生足够丰富的数据，以允许从异质性生物样品的细胞类型的识别。
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.
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影响因子：
64.8
作者：
Cao, Junyue;Spielmann, Malte;Shendure, Jay
通讯作者：
Shendure, Jay
DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
7
作者：
Cieslik M;Chugh R;Wu YM;Wu M;Brennan C;Lonigro R;Su F;Wang R;Siddiqui J;Mehra R;Cao X;Lucas D;Chinnaiyan AM;Robinson D
通讯作者：
Robinson D
影响因子：
16.6
作者：
Hayashi T;Ozaki H;Sasagawa Y;Umeda M;Danno H;Nikaido I
通讯作者：
Nikaido I
影响因子：
3.7
作者：
Butterfield YS;Kreitzman M;Thiessen N;Corbett RD;Li Y;Pang J;Ma YP;Jones SJ;Birol İ
通讯作者：
Birol İ