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

Mol Syst Biol. 2018 Jan 23;14(1):e8064. doi: 10.15252/msb.20178064.

Abstract

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.

Keywords: CRISPR‐Cas9; arrayed library; functional genomics; nuclear pore complex; single‐cell phenotypic profiling.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • CRISPR-Cas Systems*
  • Gene Knockout Techniques
  • Gene Library*
  • HeLa Cells
  • Humans
  • Machine Learning
  • Nuclear Pore / genetics*
  • Phenotype
  • Single-Cell Analysis / methods*