GO-CRISPR: a highly controlled workflow to improve discovery of gene essentiality

Genome-wide CRISPR screens aid researchers in discovering genes that function across a broad range of cellular mechanisms. Loss-of-function screens are particularly useful for identifying essential genes through loss of cell fitness measurements. However, loss-of-function screens are more challenging compared to gain-of-function screens due to the limited dynamic range of decreased sgRNA sequence detection. Here we describe Guide-Only control CRISPR (GO-CRISPR), an improved loss-of-function screening workflow, and its companion software package, Toolset for the Ranked Analysis of GO-CRISPR Screens (TRACS; https://github.com/developerpiru/TRACS). We demonstrate a typical GO-CRISPR workflow in a non-proliferative 3D spheroid model of dormant high grade serous ovarian cancer. We compare the results from our GO-CRISPR screen and demonstrate the discovery of novel pathways that were not possible using the currently established CRISPR screening method. Overall design: GO-CRISPR and its accompanying software, TRACS, robustly improve the discovery of essential genes in challenging biological scenarios.

全基因组CRISPR（成簇规律间隔短回文重复序列）筛选技术可助力科研人员发掘参与各类细胞调控机制的功能基因。功能丧失型筛选可通过检测细胞适合度损失情况，精准识别必需基因，应用价值尤为突出。然而，由于sgRNA（单向导RNA）序列检测的动态范围受限，功能丧失型筛选的实施难度显著高于功能获得型筛选。本研究介绍了仅向导对照CRISPR（Guide-Only control CRISPR，缩写为GO-CRISPR）这一优化后的功能丧失型筛选流程，及其配套分析软件包——GO-CRISPR筛选分级分析工具集（Toolset for the Ranked Analysis of GO-CRISPR Screens，缩写为TRACS；访问链接：https://github.com/developerpiru/TRACS）。我们以休眠性高级别浆液性卵巢癌的非增殖三维球体模型为实验体系，完整展示了标准GO-CRISPR筛选流程的实操方案。通过与当前主流CRISPR筛选技术的结果对比，我们验证了GO-CRISPR筛选能够发掘传统方法无法识别的全新细胞通路。整体实验设计：GO-CRISPR技术及其配套软件TRACS可显著提升复杂生物学场景下必需基因的筛选效能。