Vectorology - GEG Tech top picks

Most studies using AAVs to deliver gene therapy attempt to insert a Cas9 nuclease and a single guide RNA (sgRNA), along with their respective promoters, into the vector to create a single double-stranded break in the DNA. Given the 4.7 kb packaging limit of most AAVs, this is typically a significant challenge, especially for studies aimed at correcting genetic mutations by providing a donor DNA template for HDR. In this study, members of Professor Erik Sontheimer's lab at the University of Massachusetts Chan Medical School demonstrated the use of a cleverly designed, self-inactivating AAV vector with a compact Cas9 variant for therapeutic CRISPR genome editing in vivo. The study takes this packaging problem a step further, with the goal of creating a vector with enough cargo space to hold two sgRNAs rather than just one. In doing so, CRISPR could be used to excise pathogenic trinucleotide repeat expansions, such as that which causes Friedreich's ataxia. With the compact size of Staphylococcus aureus Cas9 (Nme1Cas9), this makes it easier to package into AAVs. It has a dinucleotide PAM sequence, which means it has a wider range of genomic targets and it displays high editing efficiencies in mammalian cells with low off-target activity, making it ideal for therapeutic applications.

In this study, the authors design a lentiviral vector to express Cas9 and gRNA to suppress ocular angiogenesis by genomic disruption of VEGF-A in human RPE cells.

Lentiviral delivery of the top-scoring gRNAs with SpCas9 resulted in indel formation in the VEGF-A gene at frequencies up to 37.0% with corresponding decreases in secreted VEGF-A protein up to 41.2%, and reduction of endothelial tube formation up to 39.4%.

The CRISPR-Cas9 endonuclease system may reduce VEGF-A secretion from human RPE cells and suppress angiogenesis, supporting the possibility of employing gene editing for antiangiogenesis therapy in ocular diseases.

When working with CRISPR, it is important to realize that there is always a difference between the gRNAs you use. Some work more efficiently and generate a high frequency of predicted modifications, while others are less efficient. Many of these differences are not obvious and are impossible to predict by simply looking at the sequences. Thus, in recent years, many efforts have been made to develop models that can help scientists select the most efficient gRNAs. Recently, researchers have created a new algorithm-based deep learning model, CRISPRon, from a novel approach that is based on studying a barcoded gRNA and its endogenous substitution site in a same lentiviral vector. In a single experiment, thousands of different lentiviral vectors can be used for transduction of human cells. The gene editing events are then studied and a massive parallel quantification of the editing activity of more than 10,000 gRNAs was obtained using this lentiviral library. This huge dataset was used to train the deep learning model, CRISPRon, and it was found that this new tool is significantly better at predicting gRNA efficiency than existing models.