Inscripta Publishes a Paper on Characterizing Edited Cell Libraries
An integral part of Inscripta’s mission is helping to educate our customers about genome engineering, which includes establishing a common terminology that would allow scientists to exchange ideas and accelerate technology development. We also want to introduce new ways of thinking about gene-edited cell populations to encourage more scientists to feel comfortable with massively parallel editing tools.
In that spirit, Inscripta scientists collaborated to produce a paper recently submitted to bioRxiv. The paper, titled “A framework for evaluating edited cell libraries created by massively parallel genome engineering,” describes how large genome-edited cell libraries, such as those created using our Onyx™ platform, should be evaluated and characterized.
Currently, researchers use different methods (both in scale and approach) for generating edited cell libraries. As a result, there is no common framework for assessing these libraries. The first step in overcoming this challenge is to establish a common vocabulary. The paper defines some useful terms for library characterization, such as complete and intended edit, burden population, edit fraction, edit richness, screener’s and selector’s score, and more.
The authors also describe the framework and methods for estimating the edit fraction, edit richness, and other metrics to help assess the quality of edited cell libraries and to provide guidelines for determining the appropriate phenotyping approach and sample size. They also provide examples of how one can implement these frameworks for forward engineering and genome discovery experiments.
Many of the statistical methods and concepts used for characterizing genome-edited cell libraries are derived from the fields of ecology and have been extended to studying edited microbial populations. The authors take into consideration that both biologists and bioinformaticians work on constructing and evaluating these libraries. Therefore, the terms and metrics are defined to be useful for all scientists working in the developing field of genome editing.
To learn more about how large-scale genome editing works, check out this and our other publications on the Inscripta website.