Highly parallel profiling of the activities and specificities of Cas12a variants in human cells
Peng Chen1,2, Yankang Wu2, Hongjian Wang2
1Department of Pediatric Research Institute; Ministry of Education Key Laboratory of Child Development and Disorders, National Clinical Research Center for Child Health and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Children's Hospital of Chongqing Medical University, School of Basic Medical Sciences, Chongqing Medical University, Chongqing, China.
Abstract:
Several Cas12a variants have been developed to broaden its targeting range, improve the gene editing specificity or the efficiency. However, selecting the appropriate Cas12a among the many orthologs for a given target sequence remains difficult. Here, we perform high-throughput analyses to evaluate the activity and compatibility with specific PAMs of 24 Cas12a variants and develop deep learning models for these Cas12a variants to predict gene editing activities at target sequences of interest. Furthermore, we reveal and enhance the truncation in the integrated tag sequence that may hinder off-targeting detection for Cas12a by GUIDE-seq. This enhanced system, which we term enGUIDE-seq, is used to evaluate and compare the off-targeting and translocations of these Cas12a variants.
Insights
This study evaluates 24 Cas12a variants for gene editing, developing deep learning models to predict activity and enhance off-targeting detection. The findings aid in selecting optimal Cas12a variants for precise gene editing applications.
Area of Science:
- Molecular Biology
- Genomics
- Bioinformatics
Background:
- Cas12a variants offer diverse gene editing capabilities but selecting the right one is challenging.
- Existing methods for evaluating Cas12a variants are limited in scope and predictive power.
Purpose of the Study:
- To comprehensively analyze the activity and PAM compatibility of 24 Cas12a variants.
- To develop deep learning models for predicting Cas12a gene editing activity.
- To enhance the GUIDE-seq method for improved off-targeting detection and introduce enGUIDE-seq.
Main Methods:
- High-throughput screening of 24 Cas12a variants.
- Development and validation of deep learning models for activity prediction.
- Utilizing and enhancing the GUIDE-seq technique to create enGUIDE-seq for off-target analysis.
Main Results:
- Detailed characterization of activity and PAM compatibility across 24 Cas12a variants.
- Validated deep learning models capable of predicting gene editing activity.
- Demonstrated improved off-targeting detection and translocation analysis using enGUIDE-seq.
Conclusions:
- The study provides a valuable resource for selecting appropriate Cas12a variants for gene editing.
- Deep learning models offer a predictive tool for Cas12a gene editing efficiency.
- The enhanced enGUIDE-seq method improves the assessment of Cas12a variant safety and specificity.
