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Generation of mouse conditional knockout alleles in one step using the i-GONAD method
Renjie Shang1,2, Haifeng Zhang1, Pengpeng Bi1,2
1Center for Molecular Medicine, University of Georgia, Athens, Georgia 30602, USA.
Genome Research
|December 17, 2020
Summary
Researchers developed a simpler method to create gene-edited mice using CRISPR and oviduct electroporation (i-GONAD). This technique efficiently generates floxed alleles for studying gene function, overcoming limitations of traditional methods.
Area of Science:
- Genetics and Genomics
- Molecular Biology
- Animal Models
Background:
- The Cre/loxP system enables precise genetic modifications in vivo for gene function studies.
- Generating conditional knockout mouse models is labor-intensive, requiring ES cell targeting and embryo manipulation.
- Current methods for creating loxP alleles are complex, limiting the broader application of conditional genetics.
Purpose of the Study:
- To develop an alternative, simplified approach for generating mouse loxP alleles.
- To overcome the technical challenges and high costs associated with traditional conditional genetics.
- To facilitate wider gene function studies through novel murine models.
Main Methods:
- Integration of a CRISPR donor with a unique design.
- Application of the novel oviduct electroporation technique, termed i-GONAD.
- Simultaneous generation of floxed alleles for multiple genes in a single experimental run.
Main Results:
- Successfully generated floxed alleles for five different genes in one attempt using i-GONAD.
- Achieved efficient allele generation with relatively low costs and minimal equipment.
- Constitutive knockout alleles were obtained as byproducts, demonstrating versatility.
Conclusions:
- The i-GONAD technique offers a simplified and cost-effective alternative for generating mouse loxP alleles.
- This method significantly reduces the technical barriers for creating conditional genetics models.
- Wider application of i-GONAD is expected to accelerate gene function studies using novel mouse models.
