UCLA

The recent developments of the CRISPR/Cas9 gene-editing system have made way for large-scale, loss-of-function genetic screens that can identify genes underlying a given phenotype, known as high-throughput CRISPR screens. By leveraging the precision of CRISPR/Cas9 and the capacity to capture millions of cells in one library preparation, these screens enrich and deplete the expression of various specific genes, identifying gene functions that help elucidate genotype-phenotype relationships. Furthermore, by modulating genetic interactions, these screens can uncover gene regulatory mechanisms, revealing genetic dependencies. Although these screens have shown to be incredibly effective, they are often prohibitively expensive. Additionally, there is a lack of information and tools to determine the optimal experimental design, such that the most informative data is produced, given experimental constraints.

Here, we introduce a statistical model that simulates high-throughput in vivo CRISPR screens to provide insight into optimizing the experimental protocol. We first demonstrate our model successfully simulates such screens by comparing the generated data with real experimental data. Then, we simulate screens across varying parameter inputs and investigate their influence on statistical power. Given our findings, we conclude with general guidelines and suggestions for effectively designing high-throughput in vivo CRISPR screens.