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CRISPR screens in plants: approaches, guidelines, and future prospects
Christophe Gaillochet1,2, Ward Develtere1,2, Thomas B Jacobs1,2
1Department of Plant Biotechnology and Bioinformatics, Ghent University, Ghent 9052, Belgium.
The Plant Cell
|April 6, 2021
Summary
CRISPR screens revolutionize plant genome research by enabling large-scale gene function analysis. This technology offers new ways to study gene networks and advance plant functional and synthetic biology.
Area of Science:
- Plant Biology
- Genomics
- Genome Engineering
Background:
- CRISPR-associated systems have transformed genome engineering and functional genomics.
- CRISPR screens allow for high-throughput gene function testing at a genomic scale.
- Implementation of CRISPR screens in plants is a developing field with significant potential.
Purpose of the Study:
- To discuss concepts, tools, and workflows for establishing CRISPR screens in plants.
- To analyze recent reports on CRISPR screens for generating mutant collections and diversifying DNA sequences.
- To provide insights into designing CRISPR knockout screens in plants, considering challenges and limitations.
Main Methods:
- Review of general concepts, tools, and workflows for CRISPR screens in plants.
- Analysis of existing literature on CRISPR-based mutant collections and DNA diversification.
- Examination of design options for CRISPR knockout screens, including multiplexing strategies.
Main Results:
- CRISPR screens offer powerful new methods for interrogating gene regulatory networks in plants.
- Recent reports demonstrate the use of CRISPR screens for generating mutant collections and diversifying DNA sequences.
- The multiplexing capabilities of CRISPR screens are particularly valuable for studying gene redundancy in complex plant genomes.
Conclusions:
- CRISPR screens are poised to significantly advance plant functional and synthetic biology.
- The integration of CRISPR screens with genomic data will enable deeper analysis of plant genomes.
- Combinatorial mutant screens can routinely generate higher-order mutant collections for gene network characterization.
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