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Related Concept Videos

Genetic Screens02:46

Genetic Screens

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Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
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Pooled CRISPR-Based Genetic Screens in Mammalian Cells
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Image-based pooled whole-genome CRISPRi screening for subcellular phenotypes.

Gil Kanfer1,2, Shireen A Sarraf1, Yaakov Maman3

  • 1Biochemistry Section, Surgical Neurology Branch, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD.

The Journal of Cell Biology
|January 19, 2021
PubMed
Summary

This study introduces AI-photoswitchable screening (AI-PS), a novel method using artificial intelligence and microscopy to analyze a wider range of cellular phenotypes. This powerful tool enables genome-wide interrogation of gene function by classifying subtle subcellular changes.

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Cell Surface Receptor Identification Using Genome-Scale CRISPR/Cas9 Genetic Screens
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Area of Science:

  • Cell Biology
  • Genomics
  • Bioinformatics

Background:

  • Genome-wide CRISPR screens are powerful for gene function studies but limited to specific phenotypes.
  • Current methods struggle to analyze subtle or subcellular morphological changes at a large scale.

Purpose of the Study:

  • To develop a novel pooled screening approach for a broader range of cellular and subcellular phenotypes.
  • To enable genome-wide interrogation of gene function for previously inaccessible phenotypes.

Main Methods:

  • Developed AI-photoswitchable screening (AI-PS) using machine learning and convolutional neural networks.
  • Utilized CRISPR interference (CRISPRi) with dCas9-KRAB, photoactivatable fluorescent protein, and guide RNA pools.
  • Employed microscopy, AI classification, photoactivation, flow cytometry, and sequencing for phenotype identification and gRNA isolation.

Main Results:

  • Successfully identified PINK1's role in Parkin recruitment to mitochondria in a proof-of-concept screen.
  • Discovered novel factors mediating TFEB relocation during starvation in a genome-wide screen.
  • Validated 21 of 64 hits from the neural network model, revealing new effectors of TFEB subcellular localization.

Conclusions:

  • AI-PS is a novel screening platform for classifying diverse mammalian subcellular morphologies at a genome-wide scale.
  • This approach expands the scope of functional genomics beyond current limitations.
  • AI-PS facilitates the discovery of genes influencing complex cellular phenotypes.