With so much enthusiasm around CRISPR gene editing, it’s all too easy to sweep aside those nagging concerns about technical implementation. But current CRISPR editing techniques suffer from limitations in scalability, efficiency, and access that must be overcome in order for the research community to achieve the full potential of this exciting technology.
The Inscripta team has spent a lot of time thinking about which parts of the CRISPR workflow need improvement. After all, we designed our Onyx™ digital genome engineering platform to address these issues with the idea that an automated benchtop system would be essential for scaling up CRISPR experimentation.
Based on our extensive market research and interviews with scientists involved in gene editing, we identified three main challenges that we believe must be solved so that CRISPR can be deployed to help address ongoing crises in climate change, food insecurity, and more. We’ve put together a whitepaper that goes through each of these challenges in some detail, but if you don’t have time to read the entire manuscript, check out the excerpts below.
This is the most urgent need in CRISPR pipelines today: A significant improvement in the ability to generate diversity in a scalable manner, which includes introducing both large numbers of edits as well as a broad variety of edit types. Currently, scientists must choose a small fraction of possible edits to test for the biological trait of interest; there is no feasible means of evaluating tens of thousands of genomic changes except for overly-simplistic knockout screens or single-base editors.
Recent projects have demonstrated that thousands of single-base edits can be made in one experiment. While this is impressive, it does not fully reflect the diversity of edits that should be tested in a robust editing pipeline. For a much deeper understanding of biology — and to rapidly advance genome engineering efforts — scientists must have the ability to introduce larger inserts or replace entire elements such as promoters, terminators, and transcription factor binding sites, to name just a few. Combinatorial editing must also be enabled to unlock the full potential of edit diversity, something that is not possible even at low throughput with today’s methods.
As CRISPR editing becomes more widely adopted and the scale of experiments increases, it will be important to improve efficiency in these workflows — from edit rate to trackability. With pooled editing, for instance, the fraction of cells with a desired edit can be very low. This poor conversion translates to limited success in experimental outcomes.
When it comes to trackability, there are two distinct needs: experiment-level tracking for robust scientific results, and higher-level tracking to ensure biosecurity and responsible use of CRISPR pipelines. Today’s tracking methods will not suffice as experiments scale up and become more complex.
While CRISPR technology is simpler to use than previous cell editing techniques, it is far from plug-and-play. Industrial-scale labs have little trouble committing the time and resources to set up and troubleshoot a CRISPR workflow, but many smaller labs find the challenges of implementing CRISPR to be remarkably onerous. The design processes involved for nucleases, cassettes, and guide RNAs are notoriously difficult and laborious. The cost of running CRISPR experiments — particularly as researchers scale up from a handful of edits to something more ambitious — can be prohibitive. This is true both for academic labs, where grant funding is often limited, and for biotech or bio-industrial labs, which face steep royalties for using the most well-tested CRISPR nucleases.
Inscripta’s OnyxTM digital genome engineering platform was designed with care to address all three of these challenges. It dramatically increases scale, making it possible to perform CRISPR-based forward engineering experiments through high-throughput diversity generation and expansion of the number and variety of edits that can be made. It also allows for the development of machine learning models to formulate and test new hypotheses and generate new cycles of library designs. The platform is easy to use, allowing even small labs to implement CRISPR workflows. In addition, the Onyx platform also incorporates biosecurity safeguards to promote responsible use of genome engineering.
Ultimately, the Onyx platform will give scientists a more comprehensive view of the phenotypes they study by allowing them to test far more genomic permutations and to rapidly select the most effective changes.