生物
巨量平行
计算生物学
细胞生物学
计算机科学
并行计算
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,José Ordovás-Montañés,Jóhann E. Guðjónsson,Robert L. Modlin,J. Christopher Love,Alex K. Shalek
出处
期刊:Immunity
[Cell Press]
日期:2020-10-01
卷期号:53 (4): 878-894.e7
被引量:224
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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