ArticleMolecular systems biology2018
Large-scale image-based profiling of single-cell phenotypes in arrayed CRISPR-Cas9 gene perturbation screens.
Article in Molecular systems biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 71 citations in OpenAlex.
- CRISPR/Cas9 loss-of-function screen in a neuronal model of AP-4 deficiency identifies ATG9A trafficking modulators.JCI insight · 2026Article
- A Comprehensive Review on CRISPR-Based Screening and Its Applications.Molecular biotechnology · 2026Review
- Transitive prediction of small-molecule function through alignment of high-content screening resources.Nature biotechnology · 2026Article
- Arrayed CRISPR libraries for the genome-wide activation, deletion and silencing of human protein-coding genes.Nature biomedical engineering · 2025Article
- Review
- Genome-scale requirements for dynein-based transport revealed by a high-content arrayed CRISPR screen.The Journal of cell biology · 2024Article
- A statistical simulation model to guide the choices of analytical methods in arrayed CRISPR screen experiments.PloS one · 2024Article
- Genome-scale requirements for dynein-based trafficking revealed by a high-content arrayed CRISPR screen.bioRxiv : the preprint server for biology · 2023Article
- Adding a Chemical Biology Twist to CRISPR Screening.Israel journal of chemistry · 2023Article
- Massively Parallel CRISPR-Based Genetic Perturbation Screening at Single-Cell Resolution.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- Pooled image-base screening of mitochondria with microraft isolation distinguishes pathogenic mitofusin 2 mutations.Communications biology · 2022Article
- A new era in functional genomics screens.Nature reviews. Genetics · 2022Review
- Pooled genetic perturbation screens with image-based phenotypes.Nature protocols · 2022Article
- Recent advances in the use of CRISPR/Cas for understanding the early development of molecular gaps in glial cells.Frontiers in cell and developmental biology · 2022Review
- Machine learning approach for discrimination of genotypes based on bright-field cellular images.NPJ systems biology and applications · 2021Article
- Single-cell image analysis to explore cell-to-cell heterogeneity in isogenic populations.Cell systems · 2021Review
- High-content imaging-based pooled CRISPR screens in mammalian cells.The Journal of cell biology · 2021Article
- Subcellular proteomics.Nature reviews. Methods primers · 2021Article
- Identifying chromatin features that regulate gene expression distribution.Scientific reports · 2020Article
- TheCellVision.org: A Database for Visualizing and Mining High-Content Cell Imaging Projects.G3 (Bethesda, Md.) · 2020Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.