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Guide RNA (gRNA) is an essential part of the CRISPR system, as it serves to direct the Cas nuclease to specific genomic locations defined by its complementation to the target DNA sequence of interest. In the case of SpCas9, the gRNA is composed of two parts: a) a 20‐nucleotide sequence complementary to the target DNA named CRISPR RNA (crRNA), and b) a 67‐nucleotide sequence which serves as a binding scaffold for the Cas nuclease, named trans‐activating CRISPR RNA (tracrRNA). These two essential gRNA components can be kept separate as two‐piece reagents, or manufactured as a simpler alternative that combines both the crRNA and tracrRNA elements into a chimeric single‐guide RNA molecule (sgRNA).

Guide RNA design is critical to achieving efficient gene knockout. In the first few years after the emergence of CRISPR, multiple groups studied gRNA design and found that while many gRNAs will cut on‐target with a reasonably high rate, a substantial portion will produce a low or zero cutting rate, or alternatively bind promiscuously in the genome, which can lead to off‐target mutagenesis (Fu et al. 2013; Kim et al. 2019; Wienert et al. 2019). To address these issues, research focused on identifying the sequence and structural features that contribute to effective (and ineffective) gRNAs has led to noticeable improvements to the system (Filippova et al. 2019; Wu and Yin 2019). The production and use of chemically modified gRNAs, which are more resistant to degradation by cellular RNases, is now the norm, with different providers offering their own proprietary modification solutions. Major providers of gRNAs, for bespoke or library screening applications, are listed in Table 4.1.

As in the case of the CRISPR enzymes, there is high flexibility with regard to gRNA formats, and reagents can be purchased as synthetic single‐ or two‐component gRNA for use with Cas9 RNP complexes, or within a plasmid or lentiviral vector backbone usually expressed under the control of a human U6 promoter. Determining the most appropriate type of gRNA for your experiment will depend on your particular application and cell type. As a general rule of thumb, for single‐target KOs, we recommend the RNP approach and in our hands single‐ and two‐component gRNAs appear to work equally well. Choice of format is more dependent on delivery time, sample handling automation, and cost considerations. Selecting good‐quality gRNAs is critical for the success of your CRISPR experiments and now all major commercial providers offer predesigned gRNAs (single or pools). They have spent years optimizing their designs with proprietary algorithms and extensive internal R&D testing, so their gRNAs generally work well for simple gene KOs. It is still best practice though to purchase a minimum of 4 gRNAs and test their editing efficiencies before selecting the best two or three for your actual experiment, as demonstrated by Martufi and colleagues (Martufi et al. 2019). In addition, for gene KO experiments exploring target biology, to achieve a high level of editing we would recommend a strategy described by Seki and Rutz (Seki and Rutz 2018), where a combination of two or three gRNAs against the same target are added in one transfection. The rationale behind this is that it increases the chances of knocking down protein expression efficiently, as gene editing will occur in multiple regions of the gene in addition to the indel mutations induced by each gRNA. This strategy has been adopted by Synthego and Horizon Discovery as part of their arrayed CRISPR libraries portfolio and has the added benefit of alleviating the need for testing multiple gRNAs individually. Joberty and colleagues further demonstrated that if gRNA pairs target sequences in close proximity (40–300bp apart), then they work in synergy and edit close to 100% of the targeted alleles. They hypothesize that the binding of the most efficient (driver) gRNA (in RNP complex format) alters the local chromatin context, which in turn helps recruit a less efficient (helper) gRNA in its vicinity (Joberty et al. 2020).

For situations where a gRNA needs to be positioned to a particular genomic location, for example, to enable KIs, or to edit genomes other than human or mouse, custom gRNA design is still required. An abundance of freely available computational tools have been generated by academic institutions, although some, like the popular CHOPCHOP (Labun et al. 2019), are reserved for nonprofit and academic use only. All major commercial providers also offer custom gRNA design tools at their websites. To assist researchers in making better‐informed decisions, several recent publications have sought to benchmark gRNA design and make recommendations on the best tools available (Bradford and Perrin 2019a; Bradford and Perrin 2019b; Liu et al. 2020). Nevertheless, these studies advise caution in choosing the right tool as experimental datasets used to build models for predicting gRNA specificity or efficiency are disparate. Moreover, some tools are specific to a particular organism or genome build.