Kampmann Martin, Horlbeck Max A, Chen Yuwen, Tsai Jordan C, Bassik Michael C, Gilbert Luke A, Villalta Jacqueline E, Kwon S Chul, Chang Hyeshik, Kim V Narry, Weissman Jonathan S
Department of Cellular and Molecular Pharmacology, California Institute for Quantitative Biomedical Research, University of California, San Francisco, CA 94158; Howard Hughes Medical Institute, University of California, San Francisco, CA 94158;
Center for RNA Research, Institute for Basic Science, Seoul 151-742, South Korea; School of Biological Sciences, Seoul National University, Seoul 151-742, South Korea.
Proc Natl Acad Sci U S A. 2015 Jun 30;112(26):E3384-91. doi: 10.1073/pnas.1508821112. Epub 2015 Jun 15.
Genetic screening based on loss-of-function phenotypes is a powerful discovery tool in biology. Although the recent development of clustered regularly interspaced short palindromic repeats (CRISPR)-based screening approaches in mammalian cell culture has enormous potential, RNA interference (RNAi)-based screening remains the method of choice in several biological contexts. We previously demonstrated that ultracomplex pooled short-hairpin RNA (shRNA) libraries can largely overcome the problem of RNAi off-target effects in genome-wide screens. Here, we systematically optimize several aspects of our shRNA library, including the promoter and microRNA context for shRNA expression, selection of guide strands, and features relevant for postscreen sample preparation for deep sequencing. We present next-generation high-complexity libraries targeting human and mouse protein-coding genes, which we grouped into 12 sublibraries based on biological function. A pilot screen suggests that our next-generation RNAi library performs comparably to current CRISPR interference (CRISPRi)-based approaches and can yield complementary results with high sensitivity and high specificity.
基于功能丧失表型的基因筛选是生物学中一种强大的发现工具。尽管近年来基于成簇规律间隔短回文重复序列（CRISPR）的筛选方法在哺乳动物细胞培养中的发展具有巨大潜力，但在多种生物学背景下，基于RNA干扰（RNAi）的筛选仍然是首选方法。我们之前证明，超复杂混合短发夹RNA（shRNA）文库在很大程度上可以克服全基因组筛选中RNAi脱靶效应的问题。在此，我们系统地优化了shRNA文库的几个方面，包括shRNA表达的启动子和微小RNA背景、引导链的选择以及与深度测序的筛选后样本制备相关的特征。我们展示了针对人类和小鼠蛋白质编码基因的下一代高复杂性文库，我们根据生物学功能将其分为12个子文库。一项预筛选表明，我们的下一代RNAi文库与当前基于CRISPR干扰（CRISPRi）的方法表现相当，并且能够以高灵敏度和高特异性产生互补结果。
