近日，奥地利科学院分子生物技术研究所Ulrich Elling及其团队发现，CRISPR-StAR使得在复杂的体内模型中进行高分辨率遗传筛选成为可能。2024年12月16日，《自然—生物技术》杂志在线发表了这项成果。

研究人员介绍了CRISPR-StAR，这是一种筛选方法，通过在细胞克隆再扩增后仅激活一半后代的单向导RNA（sgRNA）来生成内控。该方法通过生成克隆的单细胞衍生的内在对照，克服了内在和外在的异质性以及瓶颈中的遗传漂移。研究人员使用CRISPR-StAR在小鼠黑色素瘤的全基因组筛选中识别了特异性的体内遗传依赖性。与传统筛选方法的基准比较表明，这项技术提供了改进的数据质量。

据介绍，CRISPR-Cas9的基因筛选方法已使基因与表型的全基因组高分辨率图谱成为可能，但评估特定基因扰动的效应仍需要在数百个细胞中评估每个sgRNA，以克服随机遗传漂移并获得稳健的结果。然而，在复杂的异质模型中，如移植到小鼠中的类器官或肿瘤，分辨率受到限制，因为要实现足够的代表性需要不切实际的规模化。这是由于瓶颈效应和细胞群体的生物学异质性。

附：英文原文

Title: CRISPR-StAR enables high-resolution genetic screening in complex in vivo models

Author: Uijttewaal, Esther C. H., Lee, Joonsun, Sell, Annika Charlotte, Botay, Naomi, Vainorius, Gintautas, Novatchkova, Maria, Baar, Juliane, Yang, Jiaye, Potzler, Tobias, van der Leij, Sophie, Lowden, Christopher, Sinner, Julia, Elewaut, Anais, Gavrilovic, Milanka, Obenauf, Anna, Schramek, Daniel, Elling, Ulrich

Issue&Volume: 2024-12-16

Abstract: Pooled genetic screening with CRISPR–Cas9 has enabled genome-wide, high-resolution mapping of genes to phenotypes, but assessing the effect of a given genetic perturbation requires evaluation of each single guide RNA (sgRNA) in hundreds of cells to counter stochastic genetic drift and obtain robust results. However, resolution is limited in complex, heterogeneous models, such as organoids or tumors transplanted into mice, because achieving sufficient representation requires impractical scaling. This is due to bottleneck effects and biological heterogeneity of cell populations. Here we introduce CRISPR-StAR, a screening method that uses internal controls generated by activating sgRNAs in only half the progeny of each cell subsequent to re-expansion of the cell clone. Our method overcomes both intrinsic and extrinsic heterogeneity as well as genetic drift in bottlenecks by generating clonal, single-cell-derived intrinsic controls. We use CRISPR-StAR to identify in-vivo-specific genetic dependencies in a genome-wide screen in mouse melanoma. Benchmarking against conventional screening demonstrates the improved data quality provided by this technology.

DOI: 10.1038/s41587-024-02512-9

Source: https://www.nature.com/articles/s41587-024-02512-9