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Researchers Develop New Ways to Make CRISPR Gene Editing More Accurate

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CRISPR researchers at Aarhus University and the University of Copenhagen have developed new methods that make CRISPR gene editing more accurate than traditional methods. Researchers explain that this method selects the best molecule to help the CRISPR-Cas9 protein and performs high-precision editing at the correct location in the DNA.

When researchers first designed CRISPR as a gene-editing technique for bacterial, plant, animal, and human cells, it ultimately became a Nobel Prize-winning revolution. The potential of this technology is enormous, ranging from the treatment of genetically-induced diseases to its application to agricultural and industrial biotechnology, but with challenges.

One such challenge is to select so-called gRNA molecules that should be designed to direct the Cas9 protein to the proper location in the DNA.

There are usually multiple possible gRNAs, all of which are not equally efficient. Therefore, the challenge is to choose a small number of products that work with high efficiency, which is exactly what our new method does. “

Youngrun Luo, Associate Professor, Faculty of Biomedical Sciences, Aarhus University

New methods are developed from researchers’ new data and algorithm implementations to predict the most efficiently functioning gRNAs.

“By combining our own data with publicly available data and including knowledge of molecular interactions between gRNAs, DNAs and CRISPR-Cas9 proteins, we have succeeded in developing better methods,” said Veterinary Animals. Professor Jan Gorodkin of the Department said. Science at the University of Copenhagen.

Data, deep learning molecular interactions

Jan Gorodkin’s Giulia Corsi and Christian Anthon research groups are working with Yonglun Luo’s research group to achieve new results. The part of the study was done by Luo’s group, who was at the forefront of computer modeling.

“Our study quantified the efficiency of gRNA molecules at more than 10,000 different sites. This task used a large, high-throughput, library-based method that was not possible with traditional methods. Was achieved, “says Yonglun Luo.

Researchers have taken their starting point for data generation with the concept that viruses express gRNAs and synthetic target sites in one cell at a time. Synthetic target sites have exactly the same DNA sequences as the corresponding target sites in the genome. Therefore, these synthetic target sites are used as so-called surrogate target sites to capture CRISPR-Cas9 editing efficiency. Together with BGI Research and colleagues at the Lars Bornd Regenerative Medicine Institute at Harvard Medical School, they generated high-quality CRISPR-Cas9 activity for over 10,000 gRNAs.

Using this dataset of gRNAs with known efficiencies from low to high, researchers were able to build a model that could predict the efficiency of gRNAs that had never been seen before.

“Training algorithms accurately requires large datasets. Perfect for training deep learning algorithms to predict the efficiency of gRNAs for gene editing using a library of viruses. We’ve got the data that makes up the starting point. Our new method is more accurate than the other methods currently available, “says Jan Gorodkin.

Source:

Journal reference:

Xiang, X .., Et al. (2021) Enhanced CRISPR-Cas9 gRNA efficiency prediction through data integration and deep learning.. Nature Communications.. doi.org/10.1038/s41467-021-23576-0..

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