生物
大规模并行测序
转录组
巨量平行
计算生物学
基因
表型
互补DNA
DNA测序
DNA
RNA序列
寡核苷酸
吞吐量
皮肤癌
基因表达
计算机科学
遗传学
并行计算
电信
无线
作者
Travis Hughes,Marc H. Wadsworth,Todd M. Gierahn,Tran Do,David I. Weiss,Priscila Ribeiro Andrade,Feiyang Ma,Bruno Jorge de Andrade Silva,Shuai Shao,Lam C. Tsoi,Jose Ordovas-Montanes,Johann E. Gudjonsson,Robert L. Modlin,J. Christopher Love,Alex K. Shalek
出处
期刊:Immunity
[Elsevier]
日期:2020-10-01
卷期号:53 (4): 878-894.e7
被引量:137
标识
DOI:10.1016/j.immuni.2020.09.015
摘要
High-throughput single-cell RNA-sequencing (scRNA-seq) methodologies enable characterization of complex biological samples by increasing the number of cells that can be profiled contemporaneously. Nevertheless, these approaches recover less information per cell than low-throughput strategies. To accurately report the expression of key phenotypic features of cells, scRNA-seq platforms are needed that are both high fidelity and high throughput. To address this need, we created Seq-Well S3 ("Second-Strand Synthesis"), a massively parallel scRNA-seq protocol that uses a randomly primed second-strand synthesis to recover complementary DNA (cDNA) molecules that were successfully reverse transcribed but to which a second oligonucleotide handle, necessary for subsequent whole transcriptome amplification, was not appended due to inefficient template switching. Seq-Well S3 increased the efficiency of transcript capture and gene detection compared with that of previous iterations by up to 10- and 5-fold, respectively. We used Seq-Well S3 to chart the transcriptional landscape of five human inflammatory skin diseases, thus providing a resource for the further study of human skin inflammation.
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