Medicinal Materials Research Center, Korea Institute of Science and Technology, 5 Hwarangro-14-Gil, SeongbukGu, Seoul, 02792, Republic of Korea.
Department of Biological Sciences, Korea University, 145 AnamRo, SeongbukGu, Seoul, 02841, Republic of Korea.
BMC Biol. 2023 Feb 24;21(1):45. doi: 10.1186/s12915-023-01536-y.
CRISPR-based screens are revolutionizing drug discovery as tools to identify genes whose ablation induces a phenotype of interest. For instance, CRISPR-Cas9 screening has been successfully used to identify novel therapeutic targets in cancer where disruption of genes leads to decreased viability of malignant cells. However, low-activity guide RNAs may give rise to variable changes in phenotype, preventing easy identification of hits and leading to false negative results. Therefore, correcting the effects of bias due to differences in guide RNA efficiency in CRISPR screening data can improve the efficiency of prioritizing hits for further validation. Here, we developed an approach to identify hits from negative CRISPR screens by correcting the fold changes (FC) in gRNA frequency by the actual, observed frequency of indel mutations generated by gRNA.
Each gRNA was coupled with the "reporter sequence" that can be targeted by the same gRNA so that the frequency of mutations in the reporter sequence can be used as a proxy for the endogenous target gene. The measured gRNA activity was used to correct the FC. We identified indel generation efficiency as the dominant factor contributing significant bias to screening results, and our method significantly removed such bias and was better at identifying essential genes when compared to conventional fold change analysis. We successfully applied our gRNA activity data to previously published gRNA screening data, and identified novel genes whose ablation could synergize with vemurafenib in the A375 melanoma cell line. Our method identified nicotinamide N-methyltransferase, lactate dehydrogenase B, and polypyrimidine tract-binding protein 1 as synergistic targets whose ablation sensitized A375 cells to vemurafenib.
We identified the variations in target cleavage efficiency, even in optimized sgRNA libraries, that pose a strong bias in phenotype and developed an analysis method that corrects phenotype score by the measured differences in the targeting efficiency among sgRNAs. Collectively, we expect that our new analysis method will more accurately identify genes that confer the phenotype of interest.
基于 CRISPR 的筛选正在彻底改变药物发现，成为鉴定靶基因的工具，这些靶基因的缺失会诱导出感兴趣的表型。例如，CRISPR-Cas9 筛选已成功用于鉴定癌症中的新治疗靶点，其中基因的破坏会导致恶性细胞活力降低。然而，低活性的向导 RNA 可能会导致表型发生不同的变化，从而难以识别命中靶点，并导致假阴性结果。因此，纠正 CRISPR 筛选数据中由于向导 RNA 效率差异引起的偏倚效应可以提高命中靶点进行进一步验证的优先级。在这里，我们开发了一种通过校正由向导 RNA 诱导的缺失突变的实际观察频率来识别负 CRISPR 筛选中命中靶点的方法，从而校正 gRNA 频率的倍数变化 (FC)。
每个 gRNA 都与“报告序列”相连，该序列可以被相同的 gRNA 靶向，因此报告序列中突变的频率可以作为内源性靶基因的替代物。测量的 gRNA 活性用于校正 FC。我们发现插入缺失生成效率是导致筛选结果产生显著偏差的主要因素，与传统的倍数变化分析相比，我们的方法显著消除了这种偏差，并且更擅长鉴定必需基因。我们成功地将我们的 gRNA 活性数据应用于先前发表的 gRNA 筛选数据，并鉴定出了一些新的基因，这些基因的缺失可以与 A375 黑色素瘤细胞系中的 vemurafenib 协同作用。我们的方法鉴定出烟酰胺 N-甲基转移酶、乳酸脱氢酶 B 和多嘧啶 tract 结合蛋白 1 作为协同靶基因，其缺失使 A375 细胞对 vemurafenib 敏感。
我们确定了即使在优化的 sgRNA 文库中，目标切割效率的变化也会对表型产生强烈的偏差，并开发了一种分析方法，通过测量 sgRNA 之间靶向效率的差异来校正表型评分。总的来说，我们期望我们的新分析方法能够更准确地识别出赋予感兴趣表型的基因。
