Large‐scale image‐based profiling of single‐cell phenotypes in arrayed CRISPR‐Cas9 gene perturbation screens

High‐content imaging using automated microscopy and computer vision allows multivariate profiling of single‐cell phenotypes. Here, we present methods for the application of the CISPR‐Cas9 system in large‐scale, image‐based, gene perturbation experiments. We show that CRISPR‐Cas9‐mediated gene perturbation can be achieved in human tissue culture cells in a timeframe that is compatible with image‐based phenotyping. We developed a pipeline to construct a large‐scale arrayed library of 2,281 sequence‐verified CRISPR‐Cas9 targeting plasmids and profiled this library for genes affecting cellular morphology and the subcellular localization of components of the nuclear pore complex (NPC). We conceived a machine‐learning method that harnesses genetic heterogeneity to score gene perturbations and identify phenotypically perturbed cells for in‐depth characterization of gene perturbation effects. This approach enables genome‐scale image‐based multivariate gene perturbation profiling using CRISPR‐Cas9. Synopsis The CRISPR‐Cas9 system is applied in high‐content image‐based gene perturbation screens. Computational classifiers trained between wild‐type cells and cells expressing Cas9 and gRNA enable the profiling of multivariate single cell phenotypes. CRISPR‐Cas9 mediated gene perturbation by transient transfection of a single targeting plasmid is combined with large‐scale, image‐based profiling. Methods are developed for the construction of arrayed CRISPR‐Cas9 screening libraries. Single cell phenotypes are profiled by training computational classifiers between transfected and non‐transfected cells from the same well. Profiling of a marker of the nuclear pore complex identifies several classes of phenotypic perturbations. Graphical Abstract The CRISPR‐Cas9 system is applied in high‐content image‐based gene perturbation screens. Computational classifiers trained between wild‐type cells and cells expressing Cas9 and gRNA enable the profiling of multivariate single cell phenotypes.