Genome Editing in Drug Discovery. Группа авторов

generation experiments with a CRO, it is important to focus on the formula Model × Assay × Perturbation (M × A × P), as clearly defining all three will ensure success. Following, discussions on the science, economics, and timelines need to be considered. Sometimes, providers may offer optimistic timelines that work if everything goes well according to plan and no repeats need to be made. However, this may not always be the case, so it is important to discuss this and ensure that timelines are realistic. If possible, reach out to other groups that have used the same provider and learn how long the execution of their project took. Alongside timeline discussions, you will need to build milestone checkpoints as part of the service agreement. The importance of milestone checkpoints is to provide you (the client) with the opportunity to review the data at appropriate progress checks and terminate the programme if it is unsatisfactory. Also, one should mutually agree on the format and frequency of updates on the project and ensure that the CRO is willing to provide not only analyzed, but also raw data, so that you can independently review and assess the quality of the results. If material transfer is needed (i.e. a host cell line), a material transfer agreement (MTA) should be part of the service contract. Figure 4.1 outlines key steps involved in working with CRO on CRISPR genome editing or screen projects.

4.5 Considerations on Experimental Design and Controls Required when Outsourcing

For most experiments, CRISPR outsourcing falls into three broad categories: (i) CRISPR genetic screening; (ii) custom cell model generation; (iii) animal model generation. Animal model generation is out of scope for this chapter. For several commonly used cell lines, KO clones may already be commercially available, e.g. the human haploid HAP1 collection comprising over 7500 knockout cell lines. However, in the majority of cases, a custom edit will need to be generated. So far, we have provided a general description of the overall process of working with CROs. The remaining discussion will focus on specific aspects of the experimental design that are critical to discuss when outsourcing cell line generation or CRISPR genetic screens.

Figure 4.1 Process workflow for project externalization of genome editing to CROs.

      4.5.1 Considerations on Selecting the Appropriate Cellular Host

      Selecting appropriate host cells for genetic manipulation is a key aspect of experimental design and applies both to CRISPR screens and custom cell model generation. A trade‐off may need to be considered between physiological relevance (tissue origin and/or differentiation state) and technical feasibility. This is because most primary cells can only be cultured in the laboratory for a limited number of days and therefore researchers often choose immortalized cell lines as the next best alternative to conduct their research. For example, Yeung and colleagues used the THP‐1 cell line as a proxy for macrophages and performed a genome‐wide screen to identify loss‐of‐function mutations conferring resistance to Salmonella uptake (Yeung et al. 2019). Shang and colleagues used Jurkat cells as proxy for primary T cells and performed a genome‐wide screen to identify genes that regulate T cell activation upon anti‐T cell receptor (TCR) stimulation. Recently, strategies like gRNA lentiviral infection with Cas9 protein electroporation (SLICE) were developed to overcome the requirement for stably expressing Cas9 cells and have enabled genetic screens to occur in primary human cells (Shifrut et al. 2018). However, not many CROs have the ability to conduct such technically demanding types of genetic screens.

      When work is to be carried out in non‐primary cells, several aspects should be considered to help you choose the right cellular host. These have been extensively discussed in a review by Kimberland and colleagues (Kimberland et al. 2018). They include information on chromosome ploidy, genetic stability, and clonality. For most cancer cell lines, multi‐omic information can already be accessed through datasets, such as the Cancer Cell Line Encyclopedia (https://portals.broadinstitute.org/ccle/about) (Ghandi et al. 2019), the Cell Model Passports portal (https://cellmodelpassports.sanger.ac.uk/) (van der Meer et al. 2019), or the Cancer Dependency Map portals (https://depmap.org/portal/ & https://depmap.sanger.ac.uk/). This information enables researchers to bioinformatically interogate their cell line of interest and make informed decisions on how similar it is to their tissue of interest. Moreover, online tools such as Cellector faciliate the selection of the most appropriate model to use based on similarity to patient samples (https://ot‐cellector.shinyapps.io/CELLector_App/). We would also recommend discussing with the CRO what assays they routinely use to validate the cell lines used in their work, and if needed draft additional testing as part of the contract research. In our experience, array comparative genomic hybridization (aCGH) plus SNP genome analysis provides a cost‐effective and quick survey of the host cell lines. aCGH detects structural and numeric chromosomal alterations, while SNP analysis allows detection of polyploidy, loss of heterozygosity, and uniparental disomy (Wiszniewska et al. 2014). Such information ensures that the genetic makeup of the cell line used matches the parental cell line.

      4.5.2 Considerations on the Gene/Locus to be Edited

Another key aspect to consider when planning CRISPR screens or custom cell line editing experiments is to determine if the target gene‐of‐interest (GOI) is expressed in the host cell and whether it is essential for cellular viability. Online tools are available and can be used (Chen et al. 2017) to help determine this. However, essentiality for some genes is context dependent (Wang et al. 2015) and as such caution on data interpretation is advised (Kimberland et al. 2018). Regardless, highly amplified genes in cancer cell lines appear to be unsuitable for gene KO studies, since simultaneous on‐target cleavage of a high number of alleles may affect cell viability. This has been shown to result in false positives/negatives in library screens where cell viability was used as an enrichment step (Aguirre et al. 2016; Munoz et al. 2016). One approach to address the challenge of essentiality for CRISPR screens is to use CRISPRi, which relies on dCas9 that does not alter the sequence of genomic DNA. However, CRISPRi may not be suitable for all targets, as it can downregulate multiple genes at bidirectional promoters (Rosenbluh et al. 2017).

      To separate technical artifacts from true hits and to obtain a deeper understanding of how the target(s) of interest function, one can adopt a dual screening strategy, where two complementary screens (e.g. CRISPRi and CRISPRa) are performed in parallel (Jost et al. 2017; le Sage et al. 2020). Combining screening platforms substantially augments the quality and value of data derived from the screening campaigns, as well as providing novel insights not accessible when using one technology alone. For example, whereas CRISPRko will reveal targets where complete protein removal is required for a phenotype to emerge, CRISPRi could be used to investigate levels of repression needed to elicit a phenotypic response.

      4.5.3 Controlling CRISPR Off‐Target Effects (OTEs) and Clonal Variations

      Despite the remarkable specificity of the first and commonly used CRISPR/Cas9 system derived from Streptococcus pyogenes (SpCas9), varying levels of OTEs have been observed (Cho et al. 2014; Fu et al. 2013; Hsu et al. 2013; Lin et al. 2014; Pattanayak et al. 2013). These effects can be exacerbated when combined with additional sources of experimental variation, such as clonal variation in the cellular system. In cases where gene KO efficiency is high (e.g. >90%), bulk cell populations can be readily assayed without further enrichment; this is analogous to experimental approaches applied to siRNA/shRNA gene knockdown. Certain CROs offer gene KO in bulk for cell lines and ship the KO cell populations as frozen stocks (e.g. Synthego). However, when the editing efficiencies are lower and bulk assays are not feasible, especially