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Optimization of AsCas12a for combinatorial genetic screens in human cells.
Nature Biotechnology ( IF 33.1 ) Pub Date : 2020-07-13 , DOI: 10.1038/s41587-020-0600-6
Peter C DeWeirdt 1 , Kendall R Sanson 1 , Annabel K Sangree 1 , Mudra Hegde 1 , Ruth E Hanna 1 , Marissa N Feeley 1 , Audrey L Griffith 1 , Teng Teng 2 , Samantha M Borys 1 , Christine Strand 1 , J Keith Joung 3, 4, 5, 6 , Benjamin P Kleinstiver 6, 7, 8 , Xuewen Pan 2 , Alan Huang 2 , John G Doench 1
Affiliation  

Cas12a RNA-guided endonucleases are promising tools for multiplexed genetic perturbations because they can process multiple guide RNAs expressed as a single transcript, and subsequently cleave target DNA. However, their widespread adoption has lagged behind Cas9-based strategies due to low activity and the lack of a well-validated pooled screening toolkit. In the present study, we describe the optimization of enhanced Cas12a from Acidaminococcus (enAsCas12a) for pooled, combinatorial genetic screens in human cells. By assaying the activity of thousands of guides, we refine on-target design rules and develop a comprehensive set of off-target rules to predict and exclude promiscuous guides. We also identify 38 direct repeat variants that can substitute for the wild-type sequence. We validate our optimized AsCas12a toolkit by screening for synthetic lethalities in OVCAR8 and A375 cancer cells, discovering an interaction between MARCH5 and WSB2. Finally, we show that enAsCas12a delivers similar performance to Cas9 in genome-wide dropout screens but at greatly reduced library size, which will facilitate screens in challenging models.



中文翻译:


用于人类细胞组合遗传筛选的 AsCas12a 优化。



Cas12a RNA 引导的核酸内切酶是用于多重遗传扰动的有前途的工具,因为它们可以处理表达为单个转录本的多个向导 RNA,并随后切割目标 DNA。然而,由于活性低且缺乏经过充分验证的汇总筛选工具包,它们的广泛采用落后于基于 Cas9 的策略。在本研究中,我们描述了氨基酸球菌增强型 Cas12a (enAsCas12a) 的优化,用于人类细胞中的汇总组合遗传筛选。通过分析数千个向导的活动,我们完善了目标设计规则并开发了一套全面的脱靶规则来预测和排除混杂的向导。我们还鉴定了 38 个可以替代野生型序列的同向重复变异体。我们通过筛选 OVCAR8 和 A375 癌细胞的合成致死率来验证我们优化的 AsCas12a 工具包,发现MARCH5WSB2之间的相互作用。最后,我们证明 enAsCas12a 在全基因组丢失筛选中具有与 Cas9 类似的性能,但文库大小大大减小,这将有助于在具有挑战性的模型中进行筛选。

更新日期:2020-07-13
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