Sequence features associated with the cleavage efficiency of CRISPR/Cas9 system

Abstract

The CRISPR-Cas9 system has recently emerged as a versatile tool for biological and medical research. In this system, a single guide RNA (sgRNA) directs the endonuclease Cas9 to a targeted DNA sequence for site-specific manipulation. In addition to this targeting function, the sgRNA has also been shown to play a role in activating the endonuclease activity of Cas9. This dual function of the sgRNA likely underlies observations that different sgRNAs have varying on-target activities. Currently, our understanding of the relationship between sequence features of sgRNAs and their on-target cleavage efficiencies remains limited, largely due to difficulties in assessing the cleavage capacity of a large number of sgRNAs. In this study, we evaluated the cleavage activities of 218 sgRNAs using in vitro Surveyor assays. We found that nucleotides at both PAM-distal and PAM-proximal regions of the sgRNA are significantly correlated with on-target efficiency. Furthermore, we also demonstrated that the genomic context of the targeted DNA, the GC percentage, and the secondary structure of sgRNA are critical factors contributing to cleavage efficiency. In summary, our study reveals important parameters for the design of sgRNAs with high on-target efficiencies, especially in the context of high throughput applications.

Full text

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Scientific RepoRts | 6:19675 | DOI: 10.1038/srep19675
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Sequence features associated with
the cleavage eciency of CRISPR/
Cas9 system
Xiaoxi Liu1, Ayaka Homma1, Jamasb Sayadi1,2, Shu Yang3, Jun Ohashi4 & Toru Takumi1,5
The CRISPR-Cas9 system has recently emerged as a versatile tool for biological and medical research. In
this system, a single guide RNA (sgRNA) directs the endonuclease Cas9 to a targeted DNA sequence for
site-specic manipulation. In addition to this targeting function, the sgRNA has also been shown to play
a role in activating the endonuclease activity of Cas9. This dual function of the sgRNA likely underlies
observations that dierent sgRNAs have varying on-target activities. Currently, our understanding of
the relationship between sequence features of sgRNAs and their on-target cleavage eciencies remains
limited, largely due to diculties in assessing the cleavage capacity of a large number of sgRNAs. In
this study, we evaluated the cleavage activities of 218 sgRNAs using in vitro Surveyor assays. We found
that nucleotides at both PAM-distal and PAM-proximal regions of the sgRNA are signicantly correlated
with on-target eciency. Furthermore, we also demonstrated that the genomic context of the targeted
DNA, the GC percentage, and the secondary structure of sgRNA are critical factors contributing to
cleavage eciency. In summary, our study reveals important parameters for the design of sgRNAs with
high on-target eciencies, especially in the context of high throughput applications.
e clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR - associated protein (Cas) sys-
tem, an adaptive immune system found in many archaea and bacteria, has recently emerged as an ecient and
precise tool for genome engineering1–3. e system has been further repurposed to regulate gene expression by
transcriptional activation or repression4,5, modify the local chromatin epigenetic status of various loci6,7, and even
target single stranded RNA8. Rapid advances in CRISPR-Cas based technology such as these are transforming
biological research and hold tremendous potential for future therapeutic applications.
To date, three CRISPR-Cas subtypes have been classified in prokaryotes9. Among them, the type II
CRISPR-Cas system derived from Streptococcus pyogenes is the most commonly used based on its relative simplic-
ity10. In particular, the type II CRISPR system utilizes a single endonuclease protein Cas9 to induce DNA cleavage
while multiple proteins are required in other subtypes11. When coupled with Cas9, two non-coding RNAs: the
CRISPR associated RNA (crRNA), required for DNA targeting, and the trans-activating RNA (tracrRNA), neces-
sary for nuclease activity, are sucient to induce DNA cleavage. ese two RNAs can be fused as a chimeric single
guide RNA (sgRNA) and further cloned with Cas9 into an expression vector, allowing convenient and ecient
delivery of the whole system12,13.
To direct the Cas9 complex to a desired locus for genetic manipulation, a 20-nucleotide guide sequence
found within the sgRNA must be complementary to the target DNA14. In addition, a protospacer-adjacent motif
(PAM) (3 nucleotides NGG for SpCas9) sequence must be present in the targeted genomic locus. Once bound
to the target DNA, two nuclease domains in Cas9, HNH and RuvC, cleave the DNA strands complementary and
non-complementary to the guide sequence, leaving a blunt-ended DNA double strand break (DSB)15. us, in
theory, any specic 20 nt genomic sequence followed by a PAM can be targeted. e exibility of this RNA-guided
system enables researchers to perform genome editing for virtually any locus of interest in an easy and quick
manner by simply changing the sgRNA in the expression vector.
1RIKEN Brain Science Institute, Wako, Saitama, Japan. 2Harvard College, Cambridge, Massachusetts 02138, United
States. 3Department of Computer Science, University of British Columbia, Vancouver, Canada. 4Department of
Biological Sciences, Graduate School of Science, University of Tokyo, Bunkyo, Tokyo, Japan. 5Core Research for
Evolutional Science and Technology (CREST), Japan Science and Technology Agency (JST), 7 Gobancho, Chiyoda-ku,
Tokyo 102-0076, Japan. Correspondence and requests for materials should be addressed to T.T. (email: toru.
takumi@riken.jp)
Received: 28 September 2015
Accepted: 16 December 2015
Published: 27 January 2016
OPEN

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Scientific RepoRts | 6:19675 | DOI: 10.1038/srep19675
Given the relatively short length of the guide sequence in the sgRNA (20 nt), targeting specicity has become
one major concern in using CRISPR-Cas9, and the o-target eects of the system have been extensively inves-
tigated16. It has been proposed that the 8–12 PAM-proximal bases, known as the seed sequence, determine tar-
geting specicity by making contacting with the arginine-rich bridge helix (BH) within the recognition (REC)
lobe of the Cas9 protein17; therefore, selecting sites predicted to have the most specic seed regions with the
fewest possible o target mismatches may be crucial to improving on-target eciency. In contrast, the PAM distal
sequence has been suggested to be less important for specicity, and mismatches in this region are more likely to
be tolerated.
Despite extensive research on o-target eects, only a limited number of studies have focused on analyzing
the on-target cleavage eciency of the sgRNA/Cas9 complex. It has been observed that the mutagenesis rate of
the CRISPR/Cas9 system varies greatly18. Further studies have implicated that on-target eciency of site-directed
mutation is highly dependent on the sgRNA given that sgRNAs targeting the same genomic locus show dierent
activities19. Moreover, several recent studies have attempted to identify sgRNA sequence determinants that may
underlie sgRNA cleavage activity20–24. Doench et al. evaluated the eciencies of a total of 1,841 sgRNAs in induc-
ing complete loss of a protein and demonstrated that the nucleotide composition at specic positions, especially
the one adjacent to the protospacer-adjacent motif (PAM), contributes to the activity of the sgRNA21. Based on
previous published datasets, Hu et al. analyzed the eects of sequence context on sgRNA eciency and generated
models that achieved reasonable predicative power in which the Area Under Curve (AUC) scores were greater
than 0.7 in Receiver Operating Characteristic (ROC)22. Despite such advances, a large fraction of inecient sgR-
NAs are still not predictable with current models, which emphasizes the need to further optimize the design
principle of sgRNAs. In addition, many sequence features that are highly likely to be relevant to sgRNA activity,
such as the genomic context of the targeted region and the stability of the secondary structure of the sgRNA, have
not yet been explored and incorporated into a statistical model. In this study, we comprehensively analyzed the
sequence features of sgRNAs and their eects on cleavage activity based on the Surveyor assay system.
Among the host of in vitro systems available to evaluate sgRNA performance, the Surveyor nuclease assay
is the most commonly used and reliable method. is assay utilizes an enzyme mismatch cleavage system in
which heteroduplex DNA with mismatches and indels are cleaved. However, despite its high reliability, the pro-
cedure is tedious and time-consuming: it usually takes 10 days from the design of sgRNA to obtain the nal assay
results. Additionally, it is dicult to multiplex the procedure since cell culture, transfection, and genomic PCR are
required for each individual sgRNA assay. Currently, systematic evaluations of sgRNA on-target eciency based
on Surveyor assay are still limited, especially using mammalian cell lines. In this study, we reported the evaluation
of the on-target activity for 218 sgRNAs based on the mouse Neuro2A cell line.
Figure 1. (a) Outline of the procedure of the current study. (b) Distribution of sgRNAs in various genomic
contexts represented by dierent colors. (c) Distribution of sgRNAs across chromosomes. (d) e average GC
percentages of sgRNAs. e error bar indicated the 95% condence intervals.

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Scientific RepoRts | 6:19675 | DOI: 10.1038/srep19675
Results
We designed and successfully cloned 218 sgRNAs into expression vectors. e experimental design and proce-
dure are briey outlined in Fig.1a. e insertion of all guide sequences into the expression vectors was conrmed
using Sanger sequencing. Together, these 218 sgRNAs target 153 distinct genomic loci across 18 chromosomes
in the mouse genome. e sequences of all sgRNAs as well as detailed annotations including targeted genomic
locations, guanine-cytosine (GC) percentages, and genomic contexts are provided in Supplementary Table 1 and
illustrated in Fig.1(b–d). We then performed Surveyor assays using Neuro2A cell line to evaluate the on-target
eciency of these sgRNAs. Representative gel images of the Surveyor assays for seven sgRNA samples are shown
in Fig.2. For each assay, we included one negative control in which a pMax-GFP vector was used for transfection.
By comparing PCR bands amplied from the negative controls and sgRNA-transfected samples, we classied the
guide sequences in the sgRNAs as Surveyor positive sequences if their cleavage pattern was clearly visible in the
sgRNA-transfected samples. rough this analysis, a total of 129 sgRNAs (59%) were determined to be Surveyor
positive. Meanwhile, no cleavage was observed for 89 sgRNAs (41%).
Nucleotide preferences of high ecient sgRNA. We next set out to explore whether the nucleotide
composition of the sgRNAs aected the cleavage results. First, we separated the sgRNA sequences into two
groups: Surveyor positive and Surveyor negative. For each group, the occurrences and frequencies of nucleo-
tides (A, C, T, G) at each position were calculated (Table1). We then compared the nucleotide frequencies in
the Surveyor positive sequences with those in the Surveyor negative sequences. A heatmap was subsequently
generated to visualize the frequency change between the two groups (Fig.3). In Surveyor positive sequences, we
observed an elevated frequency of thymine (T) at positions 3 and 6, an increased frequency of cytosine (C) and
decreased frequency of adenine (A) at position 20, and a host of other nucleotide frequency changes compared
to Surveyor negative sequences.
We next asked whether these frequency changes are statistically meaningful or merely represent chance obser-
vations. Chi-square analysis was performed for each position of the guide sequence to test if the overall nucleotide
composition is dierent between Surveyor positive and negative sequences. Statistically signicant changes were
observed at positions 3 and 20 with P values of 0.031 and 0.022, respectively (Table1). Position 3 is located at
the PAM-distal region, while position 20 is the base immediately upstream of the PAM sequence. We further
calculated the permutation adjusted P value for each position based on 10,000 times randomization of the sample
labels. e associations of positions 3 and 20 were not signicant aer corrected by permutation test (permuta-
tion P value = 0.4762 and 0.371 respectively).
Evaluation of GC percentage dierence. Since the GC percentage of sgRNAs, particularly that of the
6 PAM-proximal bases, has been previously reported to be positively correlated with on-target eciency20,25,26,
we further examined the potential association between GC content and cleavage outcome. We calculated the
overall GC percentage for the whole guide sequence, as well as the GC percentages for positions from 1–6, 7–14,
and 15–20, in a sliding window manner. Finally, we conducted a Welch two-sample T test, non-parametric
Kolmogorov-Smirnov test and logistic regression analysis but did not observe any significant associations
(Supplementary Table 2).
Logistic regression analysis. As a follow-up to the Chi-square test, we tried to evaluate the eect of each
nucleotide on cleavage eciency through logistic regression analysis. e nucleotides in each position were coded
Figure 2. e representative Surveyor assay results of 7 independent sgRNAs. e ID indicates the
sgRNA ID shown in the supplementary Table 1. e minus sign indicates the negative control sample that
was transfected with pMaxGFP. e plus sign indicates the sample that was transfected with the PX330 vector
containing the sgRNA. Among 7 sgRNAs, 5 sgRNAs (No. 133, 149, 166, 174, 175) showed positive cleavage
activity and 2 (No. 135, 163) showed no cleavage activity.

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Scientific RepoRts | 6:19675 | DOI: 10.1038/srep19675
as dummy variables, and the nucleotide that showed the lowest frequency change at each given position was set
as the baseline level. Similarly, the genomic contexts of the target sequence were also included in the regression
analysis where the intergenic region was set as the baseline level. In addition to the sequential features of the sgR-
NAs, we also evaluated the impacts of several structural features on cleavage eciency. We assessed the overall
secondary structure of each sgRNA measured as single minimum free energy (MFE). Additionally, we analyzed
the local secondary structure of the seed region and the eects of the guide sequence on tracrRNA structure. We
further speculated that the relationship between GC percentage and cleavage eciency is likely to be a non-linear
one, where sgRNAs with GC percentages that are too high or too low are unfavorable. us, we labeled sgRNAs
with GC percentages within the range of 40%–60% as “GC normal” and those with GC percentages below 40% or
above 60% as “GC abnormal.” ese variables were incorporated into the logistic regression model.
Aer logistic regression analysis, a total of 14 variables were found to be signicantly correlated with the
cleavage results, including the nucleotide present at 10 distinct positions, the condition of being targeted to a
promoter-transcription start site (TSS), having a normal range GC, as well as several features of the second-
ary structures of sgRNA. e position-dependent nucleotide P values are illustrated in Fig.4. e results of
signicantly associated variables are shown in Table2, and the complete logistic analysis results are listed in
Supplementary Table 3.
To evaluate the performance of the current model, we rst tried to examine how well the cleavage activities of
sgRNAs used in this study can be predicted by previous methods. We calculated “on-target scores” for our sgR-
NAs using the standalone Python soware proposed in Doench et al.’s study21. To do so, we updated our sgRNA
sequences as the program requires a 30nt sequence including the anking sequence of the guide sequence. By
using the logistic regression, we found a positive correlation between the “on-target score” and the Surveyor cleav-
age result, though the P value is marginal at 0.07. We then assessed the ROC curve of the model tted based on
this score and calculate the area under the ROC curve (AUC) to be 0.57. As a comparison, the AUC score based
Position
Surveyor Positive Sequences Surveyor Negative Sequences
A C G T A C G T
P
value
P value
(perm)
135 (27.1%) 25 (19.4%) 43 (33.3%) 26 (20.2%) 26 (29.2%) 19 (21.3%) 23 (25.8%) 21 (23.6%) 0.694 1
233 (25.6%) 32 (24.8%) 33 (25.6%) 31 (24%) 24 (27%) 16 (18%) 21 (23.6%) 28 (31.5%) 0.51 1
333 (25.6%) 21 (16.3%) 24 (18.6%) 51 (39.5%) 22 (24.7%) 20 (22.5%) 27 (30.3%) 20 (22.5%) 0.031* 0.4762
420 (15.5%) 41 (31.8%) 45 (34.9%) 23 (17.8%) 21 (23.6%) 24 (27%) 23 (25.8%) 21 (23.6%) 0.213 0.9931
528 (21.7%) 33 (25.6%) 43 (33.3%) 25 (19.4%) 24 (27%) 21 (23.6%) 18 (20.2%) 26 (29.2%) 0.107 0.9048
632 (24.8%) 26 (20.2%) 29 (22.5%) 42 (32.6%) 32 (36%) 18 (20.2%) 24 (27%) 15 (16.9%) 0.054 0.6909
726 (20.2%) 32 (24.8%) 36 (27.9%) 35 (27.1%) 20 (22.5%) 21 (23.6%) 20 (22.5%) 28 (31.5%) 0.774 1
833 (25.6%) 28 (21.7%) 33 (25.6%) 35 (27.1%) 22 (24.7%) 19 (21.3%) 32 (36%) 16 (18%) 0.283 0.9991
933 (25.6%) 34 (26.4%) 37 (28.7%) 25 (19.4%) 19 (21.3%) 31 (34.8%) 19 (21.3%) 20 (22.5%) 0.39 0.9999
10 25 (19.4%) 28 (21.7%) 41 (31.8%) 35 (27.1%) 20 (22.5%) 27 (30.3%) 21 (23.6%) 21 (23.6%) 0.348 0.9996
11 33 (25.6%) 26 (20.2%) 35 (27.1%) 35 (27.1%) 12 (13.5%) 20 (22.5%) 27 (30.3%) 30 (33.7%) 0.185 0.9869
12 29 (22.5%) 34 (26.4%) 40 (31%) 26 (20.2%) 17 (19.1%) 24 (27%) 24 (27%) 24 (27%) 0.648 1
13 28 (21.7%) 37 (28.7%) 33 (25.6%) 31 (24%) 15 (16.9%) 26 (29.2%) 26 (29.2%) 22 (24.7%) 0.825 1
14 31 (24%) 37 (28.7%) 39 (30.2%) 22 (17.1%) 13 (14.6%) 36 (40.4%) 25 (28.1%) 15 (16.9%) 0.205 0.9923
15 16 (12.4%) 44 (34.1%) 39 (30.2%) 30 (23.3%) 15 (16.9%) 27 (30.3%) 32 (36%) 15 (16.9%) 0.468 1
16 30 (23.3%) 41 (31.8%) 30 (23.3%) 28 (21.7%) 16 (18%) 27 (30.3%) 24 (27%) 22 (24.7%) 0.745 1
17 38 (29.5%) 33 (25.6%) 33 (25.6%) 25 (19.4%) 16 (18%) 21 (23.6%) 27 (30.3%) 25 (28.1%) 0.167 0.9803
18 36 (27.9%) 37 (28.7%) 29 (22.5%) 27 (20.9%) 17 (19.1%) 24 (27%) 27 (30.3%) 21 (23.6%) 0.366 0.9997
19 38 (29.5%) 34 (26.4%) 40 (31%) 17 (13.2%) 22 (24.7%) 19 (21.3%) 25 (28.1%) 23 (25.8%) 0.126 0.9394
20 13 (10.1%) 63 (48.8%) 26 (20.2%) 27 (20.9%) 21 (23.6%) 29 (32.6%) 20 (22.5%) 19 (21.3%) 0.022* 0.371
21 44 (34.1%) 8 (6.2%) 20 (15.5%) 57 (44.2%) 31 (34.8%) 4 (4.5%) 20 (22.5%) 34 (38.2%) 0.545 1
Table 1. e occurrences and frequencies of nucleotides at each position in Surveyor positive and negative
sequences. P value (perm): P value obtained based on 10,000 times permutation test.
Figure 3. Heatmap plot showing nucleotide frequency change in each position of Surveyor positive
sgRNAs compared with that of Surveyor negative sgRNAs. e value of the color scale in each cell of the
heatmap indicates the nucleotide frequency dierence and is calculated as Frequencypositive − Frequencynegative.

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on our current logistic model is 0.91 and 0.67 when the tted model is applied to the total training data and from
a 20-fold cross-validation, respectively.
Discussion
In this study, we performed Surveyor assays to evaluate the on-target eciency of 218 sgRNA sequences. We
found that 41% of the sgRNAs showed no cleavage eects based on our assays. To understand what sequence
features inuence the cleavage outcome, we performed comprehensive statistical analyses that revealed the
position-dependent nucleotide preferences associated with positive cleavage results. We further revealed that the
genomic contexts of target DNA as well as the GC percentage and secondary structure of sgRNAs also contribute
to sgRNA performance. As such, these factors should be considered when designing guide sequences.
Based on Chi-square analysis, we found that position 3 and position 20, a base adjunct to the PAM, are associ-
ated with cleavage eciency. e signicant association at position 20 is in line with previous reports20,21, further
supporting the validity of our ndings. Studies on the crystal structure of CRISPR/Cas9 reveal that the nucleotide
at position 20 induces DNA double strand separation and is responsible for initiating R-loop formation27. Using
logistic regression analysis, we further revealed that the presence of an adenine at this position has a negative
impact on targeting eciency. Similarly, previous study has observed that possessing an adenine at position 20
resulted in a nearly 50% decrease in the cut rate26. Furthermore, other positions of the PAM-proximal seed region
were also found as signicant variables correlated with on-target eciency, which supports the importance of the
seed region for the proper functioning of the sgRNA/Cas9 complex.
In addition to the PAM-proximal region, we also observed signicant correlations between positions in the
PAM-distal region and cleavage eciency. Unlike the proximal region, the PAM-distal region has been consid-
ered less important in determining the sgRNA specicity. However, in our study, we show that this region may
actually contribute to the on-target eciencies of sgRNAs. At position 2 and 3, e T and G were found to have
a negative eect on cleavage eciency. Additionally, A at position 6 was identied as signicant nucleotide cor-
related with the cleavage outcome. It has been shown that the backbones of position 2 and 4–6 interact with the
REC1 domain of the sgRNA, which is critical for sgRNA:DNA recognition17. e nucleotides at these positions
might inuence this recognition process and thereby aect cleavage performance.
Figure 4. P-values of nucleotides from position 1 to 21 assessed by the logistic regression analysis. e
y-axis direction indicates whether a given nucleotide is favored or disfavored for cleavage activity.
Estimate Std. Error ZP value
Unpairing probability of guide sequence − 9.054 2.505 − 3.614 0.0003
GC normal 3.143 0.950 3.311 0.0009
Pos_2_T − 2.419 0.900 − 2.688 0.0072
Pos_3_G − 2.464 0.847 − 2.911 0.0036
Pos_8_G − 2.110 0.804 − 2.625 0.0087
Pos_17_G − 2.419 0.814 − 2.970 0.0030
Context: promoter TSS 3.862 1.256 3.075 0.0021
Pos_6_A − 2.049 0.898 − 2.281 0.0225
Pos_11_G − 1.861 0.854 − 2.178 0.0294
Pos_14_A 2.535 1.007 2.518 0.0118
Pos_18_G − 1.798 0.816 − 2.205 0.0275
Pos_19_A 1.785 0.768 2.326 0.0200
Pos_19_C 1.827 0.891 2.051 0.0403
Pos_20_A − 2.216 0.959 − 2.311 0.0208
Table 2. Variables signicant associated with on-target eciency of sgRNA. TSS: transcription start site;
Estimate: estimated eect doses. Std.Error: Standard error; Pos is abbreviated for position.

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Previous studies have shown that the GC percentage of overall sgRNAs26 and the 6 PAM-proximal nucleo-
tides25 are positively correlated with eciency in zebrash and Drosophila, respectively. However, in our study,
which is based on a mammalian cell line, we found that sgRNAs with very high or low GC contents are less eec-
tive rather than a simple model in which a greater GC percentage always lead to higher activity. In our logistic
regression analysis, we demonstrate that sgRNAs with a GC percentage within the range of 40%–60% are favored
for ecient on-target cleavage.
Intriguingly, we found that if a target DNA sequence is located at the promoter-TSS region, the sequence
has a greater chance to be successfully cleaved compared with a sequence located in the intergenic region. is
result is likely related to local chromatin accessibilities in dierent types of genomic loci. A recent genome-wide
Cas9 binding analysis based on Chip-Sequencing demonstrated that chromatin inaccessibility decrease the dCas9
binding and genomic loci commonly accessible in large number of cell types have a signicantly higher probabil-
ity binding to the sgRNA/Cas9 complex28,29.
Furthermore, our analysis also suggested that the secondary structure of the guide sequence is also an impor-
tant parameter that should be considered for designing sgRNAs. In particular, the probability of the seed region to
form an unfolded structure was identied as the most signicant feature. Counterintuitively, our analysis revealed
that if the seed sequence is more likely to form secondary structure, the sgRNA has a higher chance of cleaving
the target sequence. Loading of the guide RNA into Cas9 has been demonstrated to be a crucial step in converting
Cas9 into an active conformation capable of executing its nuclease function30. us, the secondary structure of
the seed region might have a role in facilitating the loading process and may potentially improve the cleavage
activity of Cas9.
Recently, two large-scale studies have been reported with the aim to improve the sgRNA design21,24. Our cur-
rent study diers from these two studies in several aspects and has unique advantages. In the rst study, Doench
et al. evaluated 1,841 sgRNAs’ eciency in inducing complete loss of the protein21. ese sgRNAs were designed
to target six cell surface marker genes. By FACS analysis using antibodies specic to these cell surface proteins, the
marker-negative cells were isolated and the sequencing was followed to determine highly active sgRNAs in these
cells. Given that sgRNAs targeting at intron or UTR regions are unlikely to aect the coding sequence, only sgR-
NAs targeting the coding sequences (CDS) were analyzed and were used to build the predictive model. However
this design has several potential limitations, for example, the sgRNA that induces in-frame mutation is unlikely to
be labeled as high-eective despite it may have a high cleavage eciency; additionally, if the frame-shi mutation
induced by the sgRNA occurred downstream of the epitope sites, the sgRNA might show less eects in abol-
ishing the recognition by antibody. In our study, we systemically designed sgRNAs targeting various loci with
dierent genomic contexts across the genome, and most importantly, rather than measuring the eects induced
by sgRNA, we directly measured the cleavage eciency of sgRNAs. In another study, 133 high-activity sgRNAs
and 146 low-activity sgRNAs for Cas9Sp together with 82 and 69 sgRNAs for Cas9St1 were determined and were
used to build the predictive model24. Since the support vector machine (SVM) model was adopted in this study,
it is dicult to compare the parameters with the current study. Despite dierences in the methodology and study
design, there was a striking similarity that the most dramatic nucleotide frequency changes were observed at
position 20 in all three studies. At this position, either C or G was found with an elevated frequency. e G/C
may be preferred to allow RNA/DNA hybridization and might be important for the initiation of the R-loop.
Furthermore, in the second study, a strong correlation was observed between the DNase I values of the targeting
sites and sgRNA eciency, supporting the locus accessibility is a critical determinant for the sgRNA activity.
Since the DNase I data was not available for the Neuro2A cell, we alternatively retrieved the DNaseI hypersensi-
tivity sites (DHS) of whole mouse brain available at ENCODE project31. We merged DHSs from an adult (week
8) and embryonic (day 14.5) mouse and used this collection to represent DHS sites specic to the brain. We
then examined how many sgRNA targets overlap with the DHS sites and found that a total of 47 sgRNAs out of
218 overlapped with the brain-specic DHSs. Among them, 32 sgRNAs were located in Promoter/TSS region.
Statistical analysis revealed a signicant positive correlation between being located at Promoter/TSS and being
located in DHS (P = 2.7 × 10−11). is observation conrms that the promoter/TSS regions have a higher level
of chromatin accessibility. is link was further supported by a genome-wide survey of chromatin accessibility
of human genome using 125 diverse cell and tissue types, in which it was found that promoters typically exhibit
high accessibility across various cell types32. In our study we revealed that the secondary structure of the guide
sequence of sgRNA is associated with the on-target eciency and the inclusion of secondary structure variables
greatly improves the prediction power of the model. We showed that our logistic regression model performs rea-
sonably well. e detailed parameters of the model are provided and may prove valuable for future studies. e
full dataset is also available and can be used as a source for meta-analysis in future studies.
Although our study oers key insights into sgRNA design, attention should be paid for interpreting the results.
First, we used cleavage outcome data, which is binary in nature, for our statistical analysis. Although binary
responses are easy to understand and interpret, and by this criterion we can clearly separate the sgRNAs into 2
distinct groups, the eciencies of individual guide sequences might dier within the same group of sgRNAs that
showed positive cleavage results. us, quantitative outcomes such as cleavage percentage and number of muta-
tions induced by each sgRNA are needed to provide further insight into sgRNA optimization. Secondly, we used
800 ng plasmids for each transfection, which is commonly used for 24-well plate33. Based on in vivo mutagenesis
study of CRISPR/Cas9 in Drosophila, the protein level of Cas9 is unlikely to be a critical factor for mutagenesis
eciency, while the amount sgRNA has a more profound impact25. us, the sgRNA amount may need to be
optimized depending on specic experiment condition and cell type.
Conclusion
Here we report a systematic evaluation of on-target performance of 218 sgRNAs based on in vitro Surveyor
assay. We found that 41% of sgRNAs in our study showed negative results for cleavage, further emphasizing the

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Scientific RepoRts | 6:19675 | DOI: 10.1038/srep19675
need to improve the design of the sgRNA. rough statistical analysis, we found that nucleotide preferences at
positions both adjunct and distal to the PAM sequence are signicantly correlated with on-target performance.
Furthermore, we showed that the genomic contexts of the target region, the optimal GC percentage, and second-
ary structure of sgRNA are important factors contributing to the cleavage eciency. Taken together, our study
reveals crucial parameters for the design of sgRNAs to achieve high on-target eciency, particularly in the con-
text of high throughput applications. Future studies are warranted to further replicate our study and improve the
state-of-the-art CRISPR/Cas9 technology.
Methods
Design and cloning of sgRNA. e sgRNAs were designed to target the anking sites of various loci that
harbor copy number variations (CNVs) associated with autism spectrum disorder (ASD). e top 100 most fre-
quently occurred ASD CNVs were retrieved from the SFARI CNV database34. We then used Ensembl Compara
API to determine the syntenic regions in the corresponding mouse genome. e sgRNAs were designed at the
anking sites of such mouse loci regardless their genomic contexts. e DNA sequences of selected regions were
obtained from the Ensembl database (GRCm38.p3) and were subsequently used as inputs for the CRISPR design
tool (http://crispr.mit.edu). en, candidate sgRNAs with the highest scores (generally indicating fewest potential
o-targets) were selected and synthesized. Two complementary oligonucleotides of sgRNAs were annealed, phos-
phorylated, and cloned into the BbsI sites of pX330 CRISPR/Cas9 vector (Addgene plasmid ID 42230).
Cell culture and transfection. Neuro2A (N2A) cells were cultured in Dulbecco’s modied Eagle’s Medium
(DMEM) supplemented with 10% fetal bovine serum (Life Technology), 100 units penicillin, and 100 μ g
Streptomycin (Nacalai) and incubated at 37 °C with 5% CO2. e cells were seeded into 24-well plates (FALCON)
to reach 1 × 105 cells per well. Plasmids (800 ng) were transfected using Lipo3000 reagents. N2A cells were har-
vested 48 hours post-transfection.
Surveyor assay. N2A cells transfected with both empty and sgRNA-containing PX330 vectors were treated
with buer containing proteinase K, and genomics DNA was then extracted by ethanol precipitation. Genomic
PCR was conducted to amplify a 400–700 bp region containing the sgRNA target. PCR products were gel puri-
ed with Wizard SV Gel and the PCR CleanUp kit (Promega). 800 ng of each puried PCR product was mixed
and re-annealed to form heteroduplexes, which were subsequently treated with SURVEYOR nuclease and
SURVEYOR enhancer S (Transgenomics) following the manufacturer’s recommended protocol. e nal product
was separated on a 3% TAE Agarose gel and stained with ethidium bromide.
Statistical Analysis. e R environment (version 3.1.3) was used for statistical analyses35. e two-sided
P value < 0.05 was regarded as the level of statistical signicance. Categorical variables were analyzed using
Chi-square test. Independent two-sample t-tests and Kolmogorov-Smirnov test were used in the comparison of
means between groups. Logistic regression was used to determine factors independently correlated with cleavage
eciency. To adjust for multiple testing, we further calculate permutation P values based on 10,000 times ran-
domization. In each cycle of the permutation test, 129 and 89 sgRNAs were randomly assigned as positive and
negative sequences, standard Chi-square test was followed and the smallest P value among all 21 positions was
recorded to construct an empirical frequency distribution of the smallest P values. Aer 10,000 repeats of this
procedure, the permutated P value is determined by comparing the original P value from the real data with the
empirical P value distribution. We used annotatePeaks.pl program from the Homer Chip-Seq soware to anno-
tate the genomic context of each sgRNA target36 based on the following categories: 3′ UTR, Promoter-TSS, TTS
(Transcription termination site), 5′ UTR, intron, exon and intergenic region. To evaluate the performance of the
logistic regression model, we performed Receiver operating characteristics (ROC) analysis in two settings. In the
rst setting, we trained the model using all samples and then examined how well the model can predict the cleav-
age results of the input samples. To prevent over-tting, in the second setting, we repeated the modeling based on
a 20-fold cross-validation (CV) and calculated the mean AUC value from the 20 times iteration.
Secondary structure analysis of sgRNAs. e MFE of each sgRNA was predicted using RNAfold with
the default parameters37. RNAplfold can compute local pair probabilities and has been used to model RNA
co-transcriptional folding by estimating the relative stabilities of all local structures based on a sliding window
approach38. As such, we used RNAplfold to assess the probability that the entire seed sequence is unpaired (i.e.
no folding structure) by scanning the seed region using a sliding window and averaging the probability over
all windows which contain the seed region. We set the window size W = 21 which is the length of the guide
sequence appended with an additional G used for U6 promoter (GN20), and U = 12 which is the length of the seed
sequence. Finally, we also estimated the eect of the guide sequence on the tracrRNA structure using the dot plot
of the base-pairing matrix predicted by RNAfold. In brief, for each nucleotide on the tracrRNA, we calculated
its maximum and average base pairing probability with nucleotides on the guide sequence from the base-pairing
matrix. We then averaged each individual probability over all nucleotides on tracrRNA and calculated the overall
probability that the tracrRNA structure interacts with the guide sequences.
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Acknowledgements
We thank the excellent technical supports from stas of the Takumi laboratory. We appreciate Dr. Tomomi Aida
for critical reading the manuscript and insightful suggestions. is work was funded in part by KAKENHI, Japan
Society of Promotion of Science and Ministry of Education, Culture, Sports, Science, and Technology, Strategic
International Cooperative Program (SICP) and CREST, Japan Science and Technology Agency, Intramural
Research Grant for Neurological and Psychiatric Disorders of NCNP, and Takeda Pharmaceutical Co. Ltd.

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Scientific RepoRts | 6:19675 | DOI: 10.1038/srep19675
Author Contributions
X.L. and T.T. conceived and designed the study. A.H. conducted the experiments. X.L., J.S., S.Y. and J.O.
performed the analyses. X.L. and J.S. draed the manuscript. All authors participated in the revision of the initial
manuscript and approved the nal manuscript.
Additional Information
Supplementary information accompanies this paper at http://www.nature.com/srep
Competing nancial interests: e authors declare no competing nancial interests.
How to cite this article: Liu, X. et al. Sequence features associated with the cleavage eciency of CRISPR/Cas9
system. Sci. Rep. 6, 19675; doi: 10.1038/srep19675 (2016).
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