High-throughput, image-based screening of pooled genetic-variant libraries

Emanuel, George; Moffitt, Jeffrey R.; Zhuang, Xiaowei

doi:10.1038/nmeth.4495

Cited by 59 publications

(56 citation statements)

References 29 publications

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“…Alternatively, image-based screens have been used to access a rich set of spatially and temporally defined phenotypes across diverse processes in eukaryotic cells, including mitosis (8,9), endocytosis (10), viral infection (11), differentiation (12), metabolism (13), DNA damage (14), autophagy (15) and synaptogenesis (16), but have been restricted to arrayed formats. Pooled screens of image-based phenotypes have thus far only been demonstrated in bacterial cells (17,18).…”

Section: Introductionmentioning

confidence: 99%

Pooled optical screens in human cells

Feldman

Singh

Schmid-Burgk

et al. 2018

Preprint

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†These authors contributed equally to this work.Large-scale genetic screens play a key role in the systematic discovery of genes underlying cellular phenotypes. Pooling of genetic perturbations greatly increases screening throughput, but has so far been limited to screens of enrichments defined by cell fitness and flow cytometry, or to comparatively lowthroughput single cell gene expression profiles. Although microscopy is a rich source of spatial and temporal information about mammalian cells, high-content imaging screens have been restricted to much less efficient arrayed formats. Here, we introduce an optical method to link perturbations and their phenotypic outcomes at the singlecell level in a pooled setting. Barcoded perturbations are read out by targeted in situ sequencing following image-based phenotyping. We apply this technology to screen a focused set of 952 genes across >3 million cells for involvement in NF-κB activation by imaging the translocation of RelA (p65) to the nucleus, recovering 20 known pathway components and 3 novel candidate positive regulators of IL-1β and TNFα-stimulated immune responses.

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Section: Introductionmentioning

confidence: 99%

Pooled optical screens in human cells

Feldman

Singh

Schmid-Burgk

et al. 2018

Preprint

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“…Image‐based phenotyping using automated microscopy is ideally suited to study such phenotypes. Recently, methods to perturb cells in a pooled format, followed by image‐based phenotyping and in situ genotyping were developed for prokaryotic model systems (Emanuel et al , ; Lawson et al , ). An alternative screening strategy involves seeding cells in multi‐well plates that contain reagents that perturb one specific gene per well.…”

Section: Introductionmentioning

confidence: 99%

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

Groot

Lüthi

Lindsay

et al. 2018

Molecular Systems Biology

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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.

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“…[ 58 ] Recent proof‐of‐concept studies have already explored live cell screening of barcoded bacteria and matching the resulting genotype and phenotype data. [ 59,60 ] In developing mouse embryos, the mechanisms and proteins involved in cell migration have been studied using gene knockouts of several cellular components and classes of actin, vinculin, integrins and cadherins among others. [ 61 ] Whether the same proteins and migration mechanisms are also involved in cell migration within P19C5 aggregates requires further investigation.…”

Section: Figurementioning

confidence: 99%