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Updated: Aug 15, 2026
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Genome-Wide CRISPR Screen for Unveiling Radiosensitive and Radioresistant Genes
Published on: May 23, 2025
Genome-wide CRISPR screening identifies cellular factors controlling nonviral genome editing efficiency
Shivani Saxena1,2, Meha Kabra3,4, Amr A Abdeen1,2
1Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Nature Communications
|August 13, 2026
Summary
Scientists identified cellular factors that limit nonviral genome editing. Depleting these factors, like GJB2 and BET1L, significantly enhanced gene editing efficiency and restored cellular function in disease models.
Area of Science:
- Molecular Biology
- Genetics
- Cell Biology
Background:
- Nonviral genome editing technologies face cellular barriers impacting delivery and efficiency.
- Understanding these cellular constraints is crucial for advancing gene therapy.
- Genome-wide screening offers a powerful approach to identify such barriers.
Purpose of the Study:
- To systematically identify cellular factors that restrict nonviral genome editing.
- To develop a screening platform for mapping gene perturbations to editing outcomes.
- To uncover novel targets for enhancing gene editing delivery and efficiency.
Main Methods:
- Developed a genome-wide CRISPR screening platform to assess 19,114 genes.
- Linked gene perturbation to editing efficiency in human cells.
- Validated top candidate genes (GJB2, BET1L) in disease models.
Main Results:
- Identified six negative regulators of delivery that increase editing efficiency up to six-fold.
- Depletion of GJB2 and BET1L improved base editing outcomes by six-fold in human models.
- Knockdown of these genes enhanced lipid nanoparticle base editing in a retinal channelopathy model by over 3.5-fold.
Conclusions:
- Cellular barriers are significant, yet actionable, targets for improving genome editing.
- Depleting specific negative regulators enhances gene editing potency.
- This strategy holds promise for advancing genetic therapies, including functional restoration in disease models.
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