CRISPulator: a discrete simulation tool for pooled genetic screens

Abstract

BackgroundThe rapid adoption of CRISPR technology has enabled biomedical researchers to conduct CRISPR-based genetic screens in a pooled format. The quality of results from such screens is heavily dependent on the selection of optimal screen design parameters, which also affects cost and scalability. However, the cost and effort of implementing pooled screens prohibits experimental testing of a large number of parameters.ResultsWe present CRISPulator, a Monte Carlo method-based computational tool that simulates the impact of screen parameters on the robustness of screen results, thereby enabling users to build intuition and insights that will inform their experimental strategy.CRISPulator enables the simulation of screens relying on either CRISPR interference (CRISPRi) or CRISPR nuclease (CRISPRn). Pooled screens based on cell growth/survival, as well as fluorescence-activated cell sorting according to fluorescent reporter phenotypes are supported. CRISPulator is freely available online (http://crispulator.ucsf.edu).ConclusionsCRISPulator facilitates the design of pooled genetic screens by enabling the exploration of a large space of experimental parameters in silico, rather than through costly experimental trial and error. We illustrate its power by deriving non-obvious rules for optimal screen design.

gscreend: modelling asymmetric count ratios in CRISPR screens to decrease experiment size and improve phenotype detection

“…To achieve high data quality and accurate analysis, we need to understand how the experimental design influences the results. Previously reported simulations of CRISPR-based screens highlighted the importance of coverage for reducing the signal-to-noise ratio [29]. Our study is the first to systematically explore the influence of experimental design, including the quality of the gRNA library-as measured by the library distribution widthon phenotype detection in pooled screens.…”

“…Published recommendations on optimal library coverage selection range from 200 [28] to 500 [23]. Nagy et al used computational simulations to investigate the impact of screen parameters on the robustness of screening results, highlighting how coverage and screen duration can influence signal-to-noise ratios [29]. Further optimizing such experimental choices is a major thrust of this work, since they have substantial consequences on the size, costs, and outcomes of an experiment.…”

Mitochondrial stress is relayed to the cytosol by an OMA1–DELE1–HRI pathway

“…On day 2, after 16 h of oligomycin treatment, both untreated and oligomycin-treated cells were FACS-sorted based on the ratio of mApple to GFP fluorescence intensity, and population corresponding to the top 30% and bottom 30% of cells were collected. This experimental design is optimal for FACS-based screen based on our previous simulations 46 . The representation in each sorted population was ~500 cells per sgRNA element.…”

Cell Reprogramming With CRISPR/Cas9 Based Transcriptional Regulation Systems

“…In contrast to RNAi, CRISPRn is not limited to RNA transcripts, so it can be used for establishing the role of any genomic region, not only the coding ones. Nonetheless, CRISPRn screening has several important limitations such as its inability to study essential genes and to perform sensitive analysis of regulatory elements and epigenetic modifications ( Nagy and Kampmann, 2017 ). Another issue is that CRISPRn provides genetically heteromorphous cell populations, parts of which have normal non-knockout phenotype ( González et al, 2014 ).…”