科學家利用scRNA-Seq繪製人類炎症性皮膚病轉錄圖譜
作者：
小柯機器人發布時間：2020/10/14 16:26:16
美國麻省理工學院和哈佛大學廣泛研究所Alex K. Shalek和J. Christopher Love團隊合作近日取得一項新成果。他們進行基於第二鏈合成的大規模平行單細胞RNA測序（scRNA-seq），揭示了人類炎症性皮膚病的細胞狀態和分子特徵。這一研究成果發表在2020年10月13日出版的國際學術期刊《免疫》上。
要精確研究細胞關鍵表型特徵的表達，需要高保真度和高通量的scRNA-seq平臺。為了滿足此需求，他們創建了Seq-Well S3（「第二鏈合成」），這是一種大規模並行的scRNA-seq方案，使用隨機引發的第二鏈合成來回復互補DNA（cDNA）分子，這些分子成功地被逆轉錄，但由於模板轉換效率低，未進行第二個寡核苷酸處理（隨後的整個轉錄組擴增必需）。
與以前的迭代相比，Seq-Well S3的轉錄本捕獲和基因檢測效率分別提高了10倍和5倍。他們使用Seq-Well S3繪製了五種人類炎症性皮膚病的轉錄圖譜，從而為進一步研究人類皮膚炎症提供了資源。
據悉，通量scRNA-seq方法可通過增加可同時分析的細胞數量來表徵複雜的生物樣品。然而，與低通量策略相比，這些方法在每個細胞得到的信息更少。
附：英文原文
Title: Second-Strand Synthesis-Based Massively Parallel scRNA-Seq Reveals Cellular States and Molecular Features of Human Inflammatory Skin Pathologies
Author: Travis K. Hughes, Marc H. Wadsworth, Todd M. Gierahn, Tran Do, David Weiss, Priscila R. Andrade, Feiyang Ma, Bruno J. de Andrade Silva, Shuai Shao, Lam C. Tsoi, Jose Ordovas-Montanes, Johann E. Gudjonsson, Robert L. Modlin, J. Christopher Love, Alex K. Shalek
Issue&Volume: 2020/10/13
Abstract: 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.
DOI: 10.1016/j.immuni.2020.09.015
Source: https://www.cell.com/immunity/fulltext/S1074-7613(20)30409-X