1.6 Chromatin
Accessibility Affects
Genome Editing
Efficiency
Epigenetic modifications influence chromatin state and thus accessibility of DNA; hence, they are a major factor affecting genome
targeting ability. Restrictions due to epigenetic repression were
already hypothesized for previous site-specific nucleases such as
zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) [30, 31]. For SpCas9, in vitro and in vivo
analysis demonstrated no restriction in cleavage activity when targeting methylated DNA [9, 32]. However, in vitro analysis revealed
an impact of nucleosome occupancy on Cas9-mediated cleavage.
A correlation between Cas9 binding and low nucleosome occupancy also indicates toward a contribution in vivo [33, 34]. Further
experiments in human cells also demonstrated an impairment of
genome editing at an epigenetically repressed reporter locus and
data from zebrafish also suggests a negative correlation between
chromatin accessibility and genome editing efficiency [35–
37]. Additionally, the open chromatin state associated with transcriptionally active regions can have its own positive effect on Cas9
editing by displacing Cas9, thereby increasing the rate at which
cleaved ends are exposed and accessible for DNA repair. On the
other hand, this might have a negative effect for dCas9-based
applications where extended binding is beneficial [32].
1.7 CRISPR
Prediction Tools
for Approving Target
Selection
Applying in silico tools may assist in predicting on-target and
minimizing off-target activity. However, some tools do not necessarily cover all contributing factors by the current state of knowledge. Furthermore, depending on the experimental system the data
are based on, discrepancies between prediction and outcome can
occur. To obtain an optimal consensus, the use of multiple prediction tools is recommended. For RNA secondary structure prediction, free-available online tools, such as Mfold [38] and RNAfold
[39], are reliable to exclude potential issues from RNA structure.
Computational prediction tools for the identification of optimal
guide sequences are available on a large scale; however, they might
differ concerning their parameters. While SSC [25] only allows for
variation between different guide lengths, CRISPR-P 2.0 [40] and
CCTop [41] allow the choice of a variety of CRISPR orthologs and
target organisms. CRISPR RGEN Tools [42, 43] additionally
offers crucial off-target prediction criteria such as RNA and DNA
bulges. While these tools are highly useful to assist in target site
selection, their limitations should always be kept in mind. Predictive power is often limited and efficiency prediction is based solely
on the target sequence, whereas local chromatin context cannot be
taken into account [44].
1.8 Nontrivial
Considerations
for Designing CRISPR
Knockout Experiments
As extensively described above, designing efficient gRNAs is one of
the major concerns when conducting CRISPR experiments. However, depending on the experimental goal, further criteria have to
be taken into account. As the perhaps most frequent used CRISPR
application, the following paragraph concentrates on the
Guidelines for gRNA Design
335
Accessibility Affects
Genome Editing
Efficiency
Epigenetic modifications influence chromatin state and thus accessibility of DNA; hence, they are a major factor affecting genome
targeting ability. Restrictions due to epigenetic repression were
already hypothesized for previous site-specific nucleases such as
zinc-finger nucleases (ZFNs) and transcription activator-like effector nucleases (TALENs) [30, 31]. For SpCas9, in vitro and in vivo
analysis demonstrated no restriction in cleavage activity when targeting methylated DNA [9, 32]. However, in vitro analysis revealed
an impact of nucleosome occupancy on Cas9-mediated cleavage.
A correlation between Cas9 binding and low nucleosome occupancy also indicates toward a contribution in vivo [33, 34]. Further
experiments in human cells also demonstrated an impairment of
genome editing at an epigenetically repressed reporter locus and
data from zebrafish also suggests a negative correlation between
chromatin accessibility and genome editing efficiency [35–
37]. Additionally, the open chromatin state associated with transcriptionally active regions can have its own positive effect on Cas9
editing by displacing Cas9, thereby increasing the rate at which
cleaved ends are exposed and accessible for DNA repair. On the
other hand, this might have a negative effect for dCas9-based
applications where extended binding is beneficial [32].
1.7 CRISPR
Prediction Tools
for Approving Target
Selection
Applying in silico tools may assist in predicting on-target and
minimizing off-target activity. However, some tools do not necessarily cover all contributing factors by the current state of knowledge. Furthermore, depending on the experimental system the data
are based on, discrepancies between prediction and outcome can
occur. To obtain an optimal consensus, the use of multiple prediction tools is recommended. For RNA secondary structure prediction, free-available online tools, such as Mfold [38] and RNAfold
[39], are reliable to exclude potential issues from RNA structure.
Computational prediction tools for the identification of optimal
guide sequences are available on a large scale; however, they might
differ concerning their parameters. While SSC [25] only allows for
variation between different guide lengths, CRISPR-P 2.0 [40] and
CCTop [41] allow the choice of a variety of CRISPR orthologs and
target organisms. CRISPR RGEN Tools [42, 43] additionally
offers crucial off-target prediction criteria such as RNA and DNA
bulges. While these tools are highly useful to assist in target site
selection, their limitations should always be kept in mind. Predictive power is often limited and efficiency prediction is based solely
on the target sequence, whereas local chromatin context cannot be
taken into account [44].
1.8 Nontrivial
Considerations
for Designing CRISPR
Knockout Experiments
As extensively described above, designing efficient gRNAs is one of
the major concerns when conducting CRISPR experiments. However, depending on the experimental goal, further criteria have to
be taken into account. As the perhaps most frequent used CRISPR
application, the following paragraph concentrates on the
Guidelines for gRNA Design
335