CRISPR, or a “clustered regularly interspaced short palindromic repeat,” was first discovered in E. coli in 1987. But its interaction with the endonuclease Cas9, and the recognition that this odd quirk of the prokaryotic immune system could be used to precisely and permanently modify the genetic code in mammalian cells, came years later (Charpentier et al. 2012, Mali et al. 2012). This was the start of the gene editing revolution.
Since then, CRISPR/Cas9 based gene editing has become the staple technology for the genetic manipulation of cells in both academic and commercial labs. It’s a technology that has enormous potential and has already started to impact the entire scientific spectrum from drug discovery to direct therapeutic applications, but it’s not without its issues.
Generating a CRISPR-modified cell that’s grown from a single-cell clone remains time consuming, inefficient and is routinely carried out by hand, which gives plenty of opportunities for errors to creep in. These same limitations complicate the commercial or clinical scaling of this technology.
And scale isn’t the only issue. There are still questions remaining around the precision and efficiency of CRISPR/Cas9 for genome editing.
Despite its profound impact on biotechnology, the CRISPR system is relatively simple: A guide RNA directs a Cas9 protein to a DNA sequence that is complimentary to the RNA. The Cas9 then cuts, creating a double strand break (DSB). The cell then repairs the cut through either non-homologous end joining, which frequently leads to knock-out mutations, or homology directed repair, which can be used to knock-in mutations. However, regardless of the method of repair, this traditional style of CRISPR/Cas9 editing relies heavily on the guide RNA directing the Cas9 only to the specified target site. Where Cas9 binds nonspecifically, off-target mutations can occur, resulting in undesirable genome modifications.
New gene editing innovations over the last few years aim to improve the precision of gene editing. Base editing takes elements of the CRISPR/Cas9 system, but works together with other enzymes to directly insert individual point mutations in the DNA of nondividing cells without creating double strand breaks (Komor et al. 2016). This significantly reduces the number of unintentional edits.
More recently, prime editing took the world—and the world’s media—by storm. This involves using a form of Cas9 that just nicks the DNA, rather than making a full double-strand break. And instead of providing a guide RNA to target the molecular cut alongside a new template DNA to spell out the new genetic code, the guide RNA in prime editing does both. The DNA nick then triggers reverse transcription of the edited guide RNA into the DNA target site, again resulting in far fewer off-target mutations (Anzalone et al. 2019).
However, neither of these innovations addresses the difficulty of scaling this technology. That’s where OXGENE™ takes the lead.
Our highly optimized and quality assured automated CRISPR cell line engineering workflow is built on the foundations of a strong multidisciplinary framework. We bring together expertise in biology, informatics, and automation to create a robust and efficient high-throughput genetic engineering platform that routinely edits hundreds of cell lines a year, with impressive pass rates of KO success at the protein level based on genotype predictions.
OXGENE’s platform automates many of the routine, repetitive, or time-consuming elements of gene editing, those parts of the protocol most at risk of human error.
Group Leader of Laboratory Automation, Simon Pollack, explains how OXGENE set about building the platform’s infrastructure: “First we automated the process of scanning plates at high throughput. Then we built the IT infrastructure and procedures to deal with clone verification. The next challenge was getting the clone-picking properly automated. But the best bit was building and optimizing the user interfaces so the scientists can be completely in control of their own work, without needing input from the automation team.”
The success of OXGENE’s genome engineering capability clearly doesn’t rest solely on their robots. In fact, the strong collaborations between OXGENE’s automation experts, the in-house bioinformatics team designing highly accurate guides and primers, the biologists running the protocols, and the project managers working closely with the teams to ensure tight operational planning and resourcing are all key elements of OXGENE’s platform.
Yet despite their current successes, OXGENE isn’t finished innovating yet.
Pela Derizioti, who leads our Gene Editing team notes, “We’re currently optimizing conditions for more complex cell types, like primary cells and stem cells on our high-throughput platform, and also for more complex modifications, such as knock-ins.”
Simon sums up OXGENE’s ethos: “Within the year, we’ll have doubled our capacity again.“ “We’ll never finish innovating,” Pela concludes.