Article

Mammalian Reverse Genetics without Crossing
Reveals Nr3a as a Short-Sleeper Gene
Graphical Abstract

Authors
Genshiro A. Sunagawa, Kenta Sumiyama,
Maki Ukai-Tadenuma, Dimitri Perrin, ...,
Koji L. Ode, Shigehiro Kuraku,
Hiroki R. Ueda

Correspondence
uedah-tky@umin.ac.jp

In Brief
Sunagawa et al. present an application of
mammalian reverse genetics withoutcrossing by developing two methods. The
authors improve KO mice production
using a triple-target CRISPR and combine
this with a non-invasive respirationbased automated sleep phenotyping
system, the Snappy Sleep Stager.
Combining these methods, the authors
found that the Nr3a KO is a short-sleeper.

HighlightsdA triple-target CRISPR method achieved almost perfectknockout efficiencyd

SSS achieved non-invasive fully automated highperformance sleep phenotypingdGenetics without crossing revealed Nr3a as a short-sleeper
gene

Sunagawa et al., 2016, Cell Reports 14, 1–16
January 26, 2016 ª2016 The Authors
http://dx.doi.org/10.1016/j.celrep.2015.12.052

Please cite this article in press as: Sunagawa et al., Mammalian Reverse Genetics without Crossing Reveals Nr3a as a Short-Sleeper Gene, Cell Reports (2016), http://dx.doi.org/10.1016/j.celrep.2015.12.052

Cell Reports

ArticleMammalian Reverse Genetics without Crossing
Reveals Nr3a as a Short-Sleeper Gene
Genshiro A. Sunagawa,1,8 Kenta Sumiyama,2,8 Maki Ukai-Tadenuma,1,8 Dimitri Perrin,1,6,8 Hiroshi Fujishima,1
Hideki Ukai,1 Osamu Nishimura,5 Shoi Shi,3 Rei-ichiro Ohno,3 Ryohei Narumi,1 Yoshihiro Shimizu,7 Daisuke Tone,3
Koji L. Ode,3 Shigehiro Kuraku,5 and Hiroki R. Ueda1,3,4,*
1Laboratory for Synthetic Biology, RIKEN Quantitative Biology Center, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan
2Laboratory for Mouse Genetic Engineering, RIKEN Quantitative Biology Center, 1-3 Yamadaoka, Suita, Osaka 565-0871, Japan
3Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo
113-0033, Japan
4CREST, Japan Science and Technology Agency, 4-1-8 Honcho, Kawaguchi, Saitama 332-0012, Japan
5Phyloinformatics Unit, RIKEN Center for Life Science Technologies, 2-2-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo
650-0047, Japan
6School of Electrical Engineering and Computer Science, Science and Engineering Faculty, Queensland University of Technology, GPO Box
2434, Brisbane, QLD 4001, Australia
7Laboratory for Cell-Free Protein Synthesis, RIKEN Quantitative Biology Center, 6-2-3, Furuedai, Suita, Osaka 565-0874, Japan
8Co-first author*Correspondence: uedah-tky@umin.ac.jp
http://dx.doi.org/10.1016/j.celrep.2015.12.052
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

SUMMARY

The identification of molecular networks at the system level in mammals is accelerated by next-generation mammalian genetics without crossing, which
requires both the efficient production of whole-bodybiallelic knockout (KO) mice in a single generationand high-performance phenotype analyses. Here,
we show that the triple targeting of a single geneusing the CRISPR/Cas9 system achieves almost
perfect KO efficiency (96%–100%). In addition, wedeveloped a respiration-based fully automated noninvasive sleep phenotyping system, the Snappy
Sleep Stager (SSS), for high-performance (95.3% accuracy) sleep/wake staging. Using the triple-target
CRISPR and SSS in tandem, we reliably obtained
sleep/wake phenotypes, even in double-KO mice.
By using this system to comprehensively analyze allof the N-methyl-D-aspartate (NMDA) receptor family
members, we found Nr3a as a short-sleeper gene,which is verified by an independent set of triple-target
CRISPR. These results demonstrate the applicationof mammalian reverse genetics without crossing toorganism-level systems biology in sleep research.
INTRODUCTION
The system-level identification of molecular networks in organisms is an important challenge in biology (Kitano, 2002a,
2002b). Classical reverse genetics requires several generationsof animal crosses to produce mutant animals of sufficient quality
and quantity for phenotype analysis. Conventional methods for
producing knockout (KO) mice usually involve targeting-vector

construction (2 weeks), the introduction of target mutations
into embryonic stem cells (ESCs) by homologous recombination(a few weeks), and the injection of the mutant ESCs into wildtype blastocysts to produce chimera mice (3 weeks). If themutant ESCs contribute to the germline of the newborn chimera
mice, their next-generation offspring will possess a heterozygous mutation (3 months). Further crossings of the offspring(several months to years; at least 3 months per generation) willproduce mice with completely homozygous KO mutations on
an inbred genomic background, which is required for reliablephenotype analysis. Thus, conventional methods require substantial amounts of time, space, and effort to knock out even asingle gene (Nagy, 2003). Therefore, identification of molecular
networks comprehensively in organisms will require ‘‘next-generation’’ genetics, i.e., genetic alterations without crossing. Toward this goal, both highly efficient (>90%) mutant production
in a single generation and highly accurate (>90%) phenotype analyses of mutant animals are needed.
The efficient production of biallelic KO mice can be facilitatedby recently developed genome editing techniques, including
ZFNs (zinc-finger nucleases) (Carbery et al., 2010), TALENs (transcription-activator-like effector nucleases) (Sung et al., 2013),
and the CRISPR/Cas nuclease systems (Hsu et al., 2014; Wanget al., 2013). Unlike the protein-based ZFNs and TALENs, the
CRISPR/Cas system uses RNA-based DNA recognition derived
from a bacterial adaptive immune system (Horvath and Barrangou, 2010; Wiedenheft et al., 2012). This method accelerates
the generation of KO animals via the co-injection of RNA encoding the Cas9 protein and target-locus-specific guide RNAs(gRNAs) into embryos (Fujii et al., 2013; Wang et al., 2013).
Several modifications of the CRISPR/Cas9 system have been
also introduced to improve the efficiency and specificity of targeted mutations in a genome (Fu et al., 2014; Ran et al., 2013;
Zhou et al., 2014). However, two problems remain: (1) first-generation mice often contain a mosaic of wild-type and KO cells, andCell Reports 14, 1–16, January 26, 2016 ª2016 The Authors 1

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Please cite this article in press as: Sunagawa et al., Mammalian Reverse Genetics without Crossing Reveals Nr3a as a Short-Sleeper Gene, Cell Reports (2016), http://dx.doi.org/10.1016/j.celrep.2015.12.052(2) the rate of complete biallelic mutant mice generated is relatively low (usually 60%–80% at best). Therefore, the highly efficient (>90%) production of whole-body biallelic KO in a single
generation remains a fundamental challenge for organism-levelsystems biology.
The comprehensive identification of molecular circuits at theorganism level also requires accurate (>90%) phenotype analysis. In neuroscience, sleep/wake behavior is an intriguing
phenotype, because sleep disorders (e.g., insomnia or hypersomnia) are sensitive and informative symptoms of almost all
psychological disorders. Sleep/wake states have been characterized in humans by electroencephalography (EEG) and electromyography (EMG). For example, during sleep, EEG mostly
displays high-amplitude, low-frequency fluctuations, whereas
during waking, it exhibits low-amplitude, high-frequency fluctuations. On the other hand, EMG displays low-amplitude fluctuations during sleep and high-amplitude fluctuations during
waking. These characteristic EEG/EMG patterns during sleep
and waking are preserved in mammals and can be measured
by electrodes surgically implanted in the brain and muscles.
However, such recording requires special surgical skills, andthe surgery is highly invasive, requiring a long recovery period(more than 10 days) after implantation before sleep/wakerecording. Furthermore, the EEG/EMG data are often manuallyannotated and classified into sleep/wake phenotypes by visualassessment, which can be time consuming and somewhat
subjective. Therefore, sleep/wake phenotyping has been alow-throughput method; for comprehensive studies, a scalable,
non-invasive, fully automated sleep/wake recording method isneeded.
In this study, we developed a simple theory to predict theminimum efficacy of different CRISPR methods for the highly
efficient production of whole-body biallelic KO mice. As
we predicted, triple-target CRISPR elicited almost perfect(96%–100%) whole-body KO of the Tyrosinase (Tyr) gene,which is functionally evaluated by animal coat color. This KO efficiency was confirmed using three independent sets of gRNAs.
The highly efficient production of whole-body KO by the tripletarget CRISPR method also enabled us to obtain clock mutant
phenotypes reliably, not only of Bmal1 single-KO mice but alsoof Cry1/Cry2 or Per1/Per2 double-KO mice. For accurate phenotype analysis, we developed a respiration-based, non-invasive,
fully automated system, the Snappy Sleep Stager (SSS), whichenabled the high-performance analysis (95.3% accuracy) of
sleep/wake phenotypes. Using the triple-target CRISPR and
SSS methods, we analyzed the N-methyl-D-aspartate (NMDA)
receptor (NMDAR) family comprehensively and found that the
Nr3a KO mouse is a short sleeper (96.7 min/day less than wildtype). Finally, we developed a freely accessible web-based database that contains the triple-target candidates for 81.2% of thegenes in the mouse genome.
RESULTS
Triple-Target CRISPR Achieves Almost Perfect KO
Efficiency
To obtain a highly efficient method for producing biallelic KO
mice, we first constructed a simple computational model to es-

timate the minimum efficacy of different CRISPR methods (Supplemental Experimental Procedures). According to this computational model, a multiple-target CRISPR strategy, in which
multiple gRNAs target the same gene, is more efficient thanthe high-concentration CRISPR strategy, in which a multiplefold higher concentration of a single gRNA targets the gene of interest (Figures 1A and 1B; Figures S1A and S1B). This finding is
consistent with a previous report on dual-target CRISPR (Zhouet al., 2014). Our model further predicted that triple-target
CRISPR would achieve 79%–97% efficiency for the biallelic
KO of a single gene (Figure S1C). Importantly, if it achieves
more than 90% efficiency for the biallelic KO of a single gene,it can also achieve more than 80% efficiency even for the biallelic
KO of dual genes, which is sufficient for reliable analysis ofbehavioral phenotypes.
To test the model’s predictions, we selected Tyr as a target
gene and C57BL/6N as a basal inbred strain, because if thisgene is knocked out biallelically, the black coat color of
C57BL/6N becomes white. We designed three gRNAs for threetarget sequences of the Tyr gene (gRNA1, gRNA2, and gRNA3
for targets Tyr-1, Tyr-2, and Tyr-3, respectively; Figures 1C
and S1D). We confirmed that the designed gRNAs had highcleavage efficiency (7-fold to 40-fold compared to control)at least in cellulo by using a single strand annealing (SSA) assay(Figures 1D and S1E), in which the DNA cleavage of a target
sequence by the CRISPR/Cas9 system induced the recombination of incomplete fragments of firefly Luciferase (Luc), resulting
in an entire Luc gene and enhanced bioluminescence.
We then estimated two parameters of the model, an effective
total gRNA concentration compared to the dissociation constantof gRNA (S) and a recovery rate of a mutation (a). When injected
into fertilized eggs, the single-target strategy using each of thethree gRNAs alone at 150 ng/ml exhibited only moderate efficacy(36.0%, 54.2%, and 64.7%, for gRNA1, gRNA2, and gRNA3,
respectively; Figures 1F and S1G). Based on these results, S
and a were estimated to be 5.72 and 0.136, respectively. This allowed us to predict the minimum efficacy of the triple-target
CRISPR method for whole-body biallelic KO mice as 82.6%(Figure S1F). In fact, the triple-target strategy using the mixtureof Cas9 mRNA (100 ng/ml) and three gRNAs (gRNA1, gRNA2,
and gRNA3, each at 50 ng/ml) achieved almost perfect efficiency(97.5%; Figures 1E and 1F). We also performed qPCR of thetarget region including the CRISPR-target sequence and confirmed that five out of six CRISPR-target regions from two Tyr
KO mice were not detectable. These qPCR results matchedwith each other among the genomic DNAs extracted from brains,
scalps, and tails (Figure S1H), which is consistent with theobserved whole-body biallelic KO phenotype (i.e., white coat color) of the Tyr gene. We noted that observed KO efficacy (97.5%)
is more than the predicted minimum efficiency (82.6%) (Figure S1F). We speculated that the increased KO efficacy would
be because of irreversible mutations such as deletion between
multiple CRISPR target sites.
To investigate its genomic effect, we performed exome
sequencing; results were that 91.6%–95.6% of target bases
were covered more than ten times (Table S5). As a result, it
demonstrated on-target deletions of genomic stretches with variable lengths (Figure 2) and no off-target mutation. Thus, the2 Cell Reports 14, 1–16, January 26, 2016 ª2016 The Authors
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gRNA-1 gRNA-2

gRNA-3B

Cytoplasmic
Injection

α = 0.10
Single-allelic knockout
1.0

Gene

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Gene

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A

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10
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Tyr target sequence-3
gaagaagaagcaaccccagg (+)

Tyr-2

Tyr target sequence-2
gttattgctgcagctctctc (+)

20

Tyr-3

Tyr exon530(-)

Tyr target sequence-1
gtgtcaagggacacactgct (+)

40

pX330

Tyr exon1

50

Tyr-1

87,430,000

(-)

-1

87,460,000

pX330

chr7 (-) 87,490,000

Relative Luc Activity

C

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Total gRNA concentration (S)pSSA-Tyr-1 pSSA-Tyr-2/3

E

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Triple target (Tyr-1,2,3)Single(Tyr-1)

Single(Tyr-2)Single(Tyr-3)

Triple(Tyr-1,2,3)gRNA1(ng/µl)

150

0

0

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gRNA2
(ng/µl)

0

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(ng/µl)

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Embryos

73

58

198

251

Pups

25

24

34

40

Albino9(36.0%)

13(54.2%)

22(64.7%)

39(97.5%)

Pigmented

16(64.0%)

11(45.8%)

12(35.3%)

1(2.5%)

Figure 1. A Triple-Target CRISPR Method for Highly Efficient Production of Whole-Body Biallelic KO Mice(A) Schematic diagram of triple-target and single-target CRISPR methods. Each method uses the same amount of total gRNA.
(B) Computational simulation predicting how efficiently one or more alleles are cut out when different numbers of gRNAs are used against the same target gene. The
triple-target (red line) and single-target (blue line) methods were compared. The line and shaded area around the line indicate the mean and the SD of 1,000 simulations, respectively. The difference in efficiency between triple-target and single-target strategies became more apparent as the number of target alleles increased.
(C) Target sequences of the gRNAs for KO of the Tyrosinase (Tyr) gene. The short colored bars (blue, orange, and green) indicate the 20-base target sequences.
The target sequences were on the sense strand of the genomic DNA (plus sign refers to the gene’s sense strand).
(D) SSA assay for the gRNAs of the Tyr gene. Relative luciferase activities from the SSA vectors (pSSA-Tyr-1 and pSSA-Tyr-2) were measured. The SSA vector
was transfected into 293T cells with empty vector (-), pX330 Cas9 vector without any gRNA, or pX330 Cas9 and Tyr gRNA (Tyr-1, Tyr-2, or Tyr-3). The relative
Luciferase activity for each sample was scaled so that the activity with empty vector (-) was defined as 1. Error bars represent SD (n = 3).
(E) Tyr KO mice created by the triple-target CRISPR method. The coat color showed the biallelically knocked out mice.
(F) Comparison of the single- and triple-target CRISPR methods. The table shows the gRNA injection condition and resulting phenotypes (Figure S1G). Embryos,
number of injected and transferred embryos; Pups, number of pups born (total); Albino, pups with albino coat color (biallelic knockout); Pigmented, pups withmosaic or wild-type coat color.

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Tyr exon1
Chr7 (-)

Tyr exon5

87,493,000 bp

87,492,500 bp

87,429,500 bp

Target-1

87,429,000 bp

Target-2

Wild-type 100

50

Tyr #1 100

50

Target-3

Inter-exon deletion
and inversion
Intra-exon deletion and inversion

Tyr #2

Short deletion

50

Intra-exon deletion
and inversion

50

Intra-exon deletion
and inversion

Short deletion

Short insertion(legend on next page)

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triple-target CRISPR method could produce biallelic KO micewith >90% efficiency in a single generation. Furthermore, undetectable expression of TYR protein was confirmed by mass
spectrometry (MS) comparing with another independently produced triple-target CRISPR KO strain. TYR protein was notdetectable in two Tyr KO mice, whereas it was detected in one
Nr3a KO mouse (Figure S1I). Importantly, since a triple-target
CRISPR method achieves high efficiency (>90%) for the biallelic
KO of a single gene, it has a potential to achieve efficiency higher
than 80% even for the biallelic KO of dual genes, which is sufficient for reliable analysis of behavioral phenotypes.
The SSS Enables Non-invasive, Fully Automated,
High-Performance Sleep/Wake Phenotyping
To identify system-level molecular circuits in organisms, boththe efficient production of KO mice and simple but accurate
phenotyping are necessary. The standard method for phenotyping mammals’ sleep/wake states is to record EEG/EMG
data. Each short-period segment of EEG/EMG data, called an‘‘epoch,’’ is then annotated into one of three sleep/wake states(‘‘REM [rapid eye movement] sleep,’’ ‘‘NREM [non-REM] sleep,’’
and ‘‘awake’’), determined by the EEG/EMG pattern (Figure S2A).
Here, to obtain a non-invasive and fully automated method forsimple and accurate sleep/wake phenotype analysis, we developed the SSS (Figure 3A). For this method, we first focused on
the non-invasive phenotyping of sleep/wake states by recording
the respiration of an animal, since the different states exhibit
unique respiration patterns (Figure S2A).
Among the current methods for non-invasively recording the
respiration of mice (Glaab et al., 2007; Gordon and Ali, 1984),
whole-body plethysmography (WBP) is one of the most popularand promising. The correlation between respiration patterns and
EEG/EMG-based sleep/wake states was previously analyzed(Hernandez et al., 2012). However, previous studies mainly
examined short recording periods (less than 1 day) during
different states of consciousness, so the volume of the WBPchamber could be relatively small. Although a smaller chamber
size increases the sensitivity of the respiration recording, too
small a chamber decreases the animal’s comfort and can affectsleep/wake behavior. Therefore, no system has been developed
for recording for a week or more, which is critical for accurate
sleep/wake phenotyping. Increasing the size of the chamber requires a compensatory increase in the signal/noise ratio of the
WBP system itself.
To optimize the WBP system, we modeled it as an equivalent
electrical circuit (Figure 3B; Supplemental Experimental Procedures for the detailed equations)—essentially a band-pass filter
for a certain frequency of pressure change. By computer simulation, we optimized the resistance and capacitance in the WBPsystem to band-pass the 1- to 10-Hz flow (Figure 3C), which isthe physiological range for mouse respiration frequency. Basedon this analysis, we constructed a high-performance WBP sys-tem (Figure 3A), which exhibited a high signal/noise ratio forthe 1- to 10-Hz flow and allowed us to record detailed, longterm respiration patterns of mice non-invasively.
For simple and accurate sleep/wake phenotype analyses, fully
automated annotation of the recorded data is also critical. We
previously developed FASTER (an unsupervised fully automatedsleep staging method for mice based on EEG/EMG recordings)(Sunagawa et al., 2013), which avoids the subjective manual
annotation of EEG/EMG data. We applied the FASTER method
to the recorded respiration data to create a fully automatedannotation pipeline (Figure S2B; Supplemental Experimental
Procedures). Each epoch is annotated as either ‘‘sleep’’ or‘‘awake’’ fully automatically. Since REM state represented only
5% of the total epochs, which agrees with past documentation(Chemelli et al., 1999), we focused on a two-state classification
between sleep and awake states as the first-order approximation in this study. Simultaneous recordings of the respiration
flow and EEG/EMG enabled us to estimate the performance of
the SSS system (Figure 3D), resulting in the high accuracy of
95.3 ± 0.4% (n = 3) for the respiration-based sleep/wake phenotyping (Figures 3E and S2C). Importantly, the non-invasivenessof the recording and fully automated data analysis minimized
the variability and subjectivity of the procedures, thereby making
the sleep/wake phenotyping highly reproducible, as indicatedby the overlapping probability densities of sleep/wake statesamong three individuals (Figure 3E). We also note that even‘‘quiet awake,’’ which was defined in this study as the awake
state accompanied by the lower 33.3% of EMG values, wasannotated accurately (95.3 ± 1.3%; n = 3) by the SSS system(Figure S2D). Therefore, the SSS provides a non-invasive, fully
automated method for sleep/wake phenotyping.
High-performance in the SSS system enabled us to obtainvarious sleep/wake phenotypes based not only on daily sleep
time (or awake time) but also on the transition probabilities between sleep and awake states (s and w, respectively, in Pws
and Psw; Figure 3F) and on the sleep/wake amplitude, which isrelated to the sleep-time variation within the daily sleep cycle(Figure 3G; Supplemental Experimental Procedures). We notethat Pws and Psw were proposed to represent sleep drive andwake drive in a previous study (De Valck and Cluydts, 2003)
and that they are informative sleep/wake phenotypes. We also
defined the sleep/wake amplitude as the coefficient of variation(CV, the SD divided by the mean) of sleep time for each 10-min
bin for 24 hr (Figure 3G).
To evaluate the performance of the SSS system, we recorded
108 individual C57BL/6N male mice (6 weeks old) in the SSS
chamber for a week (Figure 3H; Table S1). To avoid the firstday effect, the recorded data from the second day to the seventh
day were analyzed by the SSS annotation pipeline. The results
revealed a daily sleep time of 735.7 ± 50.8 min (mean ± SD;
n = 108), which corresponded to a CV of 6.9%. The Pws and
Psw were 0.0379 ± 0.0068 and 0.0363 ± 0.0067, respectively,

Figure 2. Exome Analysis of Whole-Body Biallelic Tyr KO Mice Produced by Triple-Target CRISPR Method
Genome alignments of exome sequence reads. The genomic regions targeted by the three gRNAs were shown for wild-type, Tyr KO #1, and Tyr KO #2. The upper
part of each panel shows read coverage, and the lower part shows read alignments (rectangles). Horizontal lines between rectangles indicate read pairing.
Different types of mutations occurring at the target sites are highlighted as follows: inter-exon deletion and inversion (red rectangles); intra-exon deletion andinversion mutation (green rectangles); short deletion (red dots); and short insertion (black dots).

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A

B

C
I1

C2

I2

I

G1

G2

Gain (Pa·s/ml)

101

R2

R1

C1

C2

10-3.510-4.010-4.510-5.0100
10-1
10 0.5R1

10 0.0

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10-2 10-1 100 101 102
Frequency (Hz)

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24 0

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Time (hr)612 18Time (hr)

24 0

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Time (hr)F

Sens. 94.2 ± 1.4%
Spec. 97.1 ± 1.0%

−5
−10

Sleep

1 23

Wake

Sens. 97.6 ± 1.0%
Spec. 93.7 ± 1.5%

−20

24

0
PC1

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0.10

Sleep time

0.05

Sleep
Wake
Sleep transition rate
Pws

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Count

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Psw
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C57BL/6N, male (n = 108)

100%

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10

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Accuracy 95.3 ± 0.4%
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10

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24

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Amplitude

0.025

0.075
Psw

Figure 3. The SSS Enables Non-invasive, Fully Automated, High-Performance Sleep/Wake Phenotyping(A) The SSS system. Upper left: one mouse is housed in an SSS chamber. The chamber is connected to a water outlet, and food is stored in its ceiling, whichallows 14 days of continuous recording without additional handling of the chamber. Upper right: up to 36 SSS chambers are set in an SSS rack. The rack is(legend continued on next page)

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and the sleep/wake amplitude was 0.4462 ± 0.0530. To evaluate
the SSS system for other inbred strains and in females, we recorded C57BL/6J male mice, C57BL/6N female mice, andC57BL/6J female mice (n = 178, 72, and 173, respectively; Figure S2E; Table S1). Their tight distributions of sleep time showedthat the SSS provides a simple and high-performance method
for sleep/wake phenotyping.
To further validate the SSS, we recorded various known
sleep/wake phenotypes in mice. First, we tested mice whose
sleep/wake behavior was perturbed pharmacologically. Methamphetamine (MAP), a potent psychostimulant that promotes
wakefulness (Edgar and Seidel, 1997), was injected intraperitoneally (i.p.) into mice during the day, at zeitgeber time 2 (ZT2;
Figure S3A). The administration of 3 mg/kg MAP significantly
decreased sleep time and Pws (Figures S3B and S3C). These
results indicated that the decreased sleep time was due
to increased stabilization of the awake state rather than bydecreased stabilization of the sleep state.
To test the SSS system for the opposite phenotype (sleep-timeincrease), we injected i.p. a sleep-inducing drug, diazepam(DZP) (Radulovacki et al., 1984), at nighttime, ZT13 (Figure S3D).
Indeed, the administration of 5 mg/kg DZP significantly
increased the sleep time and Pws (Figures S3E and S3F), whichis the exact opposite phenotype observed in MAP injection. To
further confirm observed sleep-phenotype changes, we performed the drug administration experiment under simultaneousrecording of SSS and EEG/EMG (Figures S3H and S3I). The accuracy, sleep sensitivity, sleep specificity, wake sensitivity, andwake specificity of SSS against EEG/EMG-based sleep staging
were as high as those for wild-type animals (Figures 3D and 3E).
These results clearly show that SSS is highly sensitive to sleep/
wake pattern, even in animals under drug administration.
Furthermore, to test the SSS system’s ability to detect thesleep/wake phenotypes of genetically modified animals, we recorded the behavior of circadian mutants that lose their circadianrhythm under constant darkness (DD) (Laposky et al., 2005; Shiromani et al., 2004; Wisor et al., 2002). Three KO strains, Bmal1
KO (Shimba et al., 2011), Cry1/Cry2 (van der Horst et al.,1999), and Per1/Per2 double-KO mice (Bae et al., 2001) were re-corded for a week under LD conditions (light on at ZT0, light off at
ZT12; Figure S3J). All strains exhibited less sleep during the lightphase (an inactive phase of wild-type mice) and more sleep during the dark phase (an active phase for wild-type mice; Figure S3K). Both Pws during the light phase and Psw during thedark phase are decreased in Bmal1 KO and Cry1/Cry2 doubleKOs (Figures S3L and S3M). Although Per1/Per2 double-KOmice did not exhibit a significant decrease of sleep/wake amplitude, their daily sleep/wake patterns differed from those of wildtype mice (Figure S3K). In all strains, the total sleep time was not
significantly different from that of the C57BL/6N mice (Figure S3M; Table S1). Overall, these results also indicate that the
SSS can sensitively detect various sleep/wake phenotypes,
including the total sleep/wake time, transition probabilities between sleep and awake states, and sleep/wake amplitude.
Combined Use of a Triple-Target CRISPR
and SSS Enables Sleep/Wake Phenotyping, Even in
Double-KO MiceTo test the feasibility of combining efficient KO mouse production with automated sleep/wake phenotyping, we analyzed
sleep/wake phenotypes in different KO mouse lines producedby the triple-target CRISPR method described earlier. First, we
designed three gRNAs against the Bmal1 gene (Figure S4A).
Sleep/wake phenotyping was performed for animals with the
KO genotype, which was confirmed by qPCR and/or genomic
sequencing (Figures S4D and S4E; Tables S1 and S2). As expected, the generated Bmal1 KO mice showed a significantly
decreased sleep/wake amplitude under LD conditions compared with wild-type mice, and the Tyr KO mice did not (Figures
4A–4C; Figure S4H). In contrast, the daily sleep times did not
differ between the Tyr KO and Bmal1 KO mice or in comparison
with wild-type mice (Figure 4C). Notably, the Tyr KO mice
had sleep/wake patterns similar to those of the wild-typemice (Figure 4B), indicating that the triple-target CRISPR method
itself had little effect on sleep/wake phenotypes. Next, we generated double-KO mice for Cry1/Cry2 and Per1/Per2, which are
well-known mouse clock genes. We designed three gRNAs
each for each gene (Figures S4B and S4C), and sleep/wakeequipped with continuous air circulation units, which provides fresh air to each chamber. Lower panel: we used six SSS racks for recording and thus could
determine the sleep/wake phenotypes of 216 mice per week.
(B) Equivalent electric circuit model of the SSS system. The SSS chamber (red dashed box) was modeled with a capacitor (C1) and resister (R1) in parallel, and thepressure sensor (blue dashed box) was modeled with the resister (R2) and capacitor (C2) in series (Supplemental Experimental Procedures).
(C) Calculation of the conductance over R2, which corresponds to the amplification factor of the pressure detected with the sensor and optimization of C2 and R1
to maximize the gain of the frequency band of the physiological respiration of mice (1 to 10 Hz, pink shaded area). Based on the simulation results, C2 was fixed to
6.12 3 10 5 ml/Pa, and R1 was fixed to 172 Pa , s/ml.
(D) SSS-based sleep/wake staging results compared with conventional EEG/EMG-based sleep/wake staging results. Data from three mice were used. Uppergraph shows the sleep time for every 30 min evaluated by SSS. Lower graph shows the accuracy based on conventional EEG/EMG-based staging, for each
30 min during recording.
(E) The performance of SSS. Sleep (red) and awake (blue) states of three individual mice are shown as probability densities on a two-dimesional principalcomponent map in the SSS annotation pipeline. Values represent the mean ± SD (n = 3) of the sensitivity and specificity of sleep (red) and awake (blue) states
when compared with EEG/EMG-based sleep/wake staging. The overall accuracy reached 95.3% ± 0.4% (mean ± SD, green).
(F) Illustration of sleep/wake transitions. Upper left: schematic view of the transition probabilities between sleep and awake states. Upper right: sleep time atdifferent values of Pws and Psw. When both Pws and Psw decrease (green circle), both sleep and awake episodes will stabilize. Note that when both Pws and Psw
increase (red diamond), the stability of each state is dramatically decreased (bar chart below) without changing the total sleep (or awake) time.
(G) Illustration of sleep/wake amplitude. In the chart, different amplitudes are distinguished by color, and the points represent sleep times at various times of day.
(H) Distribution of sleep/wake parameters of C57BL/6N male mice recorded in the SSS. The pink and sky blue dashed lines show the mean and the mean ± SD ofeach distribution, respectively.
See also Table S1 and Supplemental Experimental Procedures.

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Bmal1 (n = 6)

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E

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Time (Hour)phenotyping was performed for animals with the KO genotype(Figures S4F and S4G; Tables S1 and S2). Both strains showedsimilar phenotypes with conventionally generated KO mice (Figures 4A–4C; Table S1). All three circadian KO strains were also
recorded for a week under DD conditions and confirmed to
exhibit arrhythmic phenotype (Figures S4I and S4J).
To further validate the functionality of our strategy, we designed three gRNAs against the Hcrt gene (Figure S4K) and produced animals by triple-target CRISPR method. Sleep/wakephenotyping was performed for animals with KO genotype (Figure S4L; Tables S1 and S2). Consistent with the conventional
Hcrt KO mice (Chemelli et al., 1999), the mice showed significant
increase in both transition probabilities, Pws and Psw, without any
significant change in total sleep time (Figures 4D–4F and S4M).
Overall, the combinatorial use of triple-target CRISPR andSSS enables the efficient generation of KO mice and their accurate sleep/wake phenotyping not only in single-KO mice but alsoin double-KO mice.
Reverse Genetics without Crossing Reveals Nr3a
Mutant to Be a Short-Sleeper Mouse
Mammalian reverse genetics without crossing can be applied tothe comprehensive analysis of gene functions at the systems
and whole-organism level. To demonstrate proof of concept,

60
Sleep time (min)A

0Figure 4. Combined Use of a Triple-Target
CRISPR and SSS Enables Sleep/Wake Phenotyping in Single- and Double-KO Mice(A) Sleep time (per hour) phenotype for individual
mice in Tyr KO mice and circadian-clock mutants
produced by triple-target CRISPR.
(B) Sleep time (per hour) over 24 hr averaged over
6 days in Tyr KO mice and circadian-clock mutants
produced by triple-target CRISPR. Red lines show
the mean sleep time at each time of day for eachstrain. Gray line shows the data for C57BL/6N malemice (n = 108). The shaded area around the line is
the SEM for each time point.
(C) Distributions of sleep/wake parameters of Tyr
KO mice and circadian-clock mutants. Dunnett’s
test compared to C57BL/6N male mice.
(D) Sleep-time (per hour) phenotype of Hcrt KO
mice.
(E) Sleep time (per hour) over 24 hr averaged over
6 days in Hcrt KO mice. Red line, gray line, and theshaded area are the same as described in (B).
(F) Distributions of sleep/wake parameters of Hcrt
KO mice. Dunnett’s test compared to C57BL/6N
male mice. See also Table S1.

we comprehensively targeted the members of the NMDAR family. We focused
Amplitude
on this family because the NMDAR is
implicated in several psychological disorders, including schizophrenia (Belforte
et al., 2010; Moghaddam and Javitt,
0.02
0.04
0.06
P sw
2012; Mohn et al., 1999), Alzheimer’s disease (Snyder et al., 2005), and depression(Autry et al., 2011; Li et al., 2010), all ofwhich have associated sleep disorders.
There are seven genes in the NMDAR family: Nr1, Nr2a, Nr2b,
Nr2c, Nr2d, Nr3a, and Nr3b. For triple-target CRISPR, we
designed three gRNAs for each of these genes (Figures S5A–
S5G) and used them to generate KO mice for each one.
Sleep/wake phenotyping was performed for animals with the
KO genotype, which was confirmed by qPCR and/or genomic
sequencing (Figures S5H–S5L; Tables S1 and S2). First, we
confirmed that the lethal phenotype seen in Nr1 and Nr2b biallelic KO mice that was generated by conventional methods (Forrest et al., 1994; Li et al., 1994) also occurred in Nr1 and Nr2b KO
mice generated by the triple-target CRISPR method, because
we noted that there were no survivors of Nr1 and Nr2b KO
genotypes (Table S2). KO mice for the other five NMDAR genes,
Nr2a, Nr2c, Nr2d, Nr3a, and Nr3b, survived for at least 6 to
7 weeks (Table S2), at which time they were placed in SSS chambers, and their sleep/wake behavior was recorded for at least
1 week under LD conditions. For the phenotype analysis, weused the 6-day data from the second recording day to avoid
the first-day effect (Figure 5A). The sleep/wake phenotype analysis was performed fully automatically, and the statistical evaluation used sleep time, Pws, Psw, and sleep/wake amplitude asparameters (Figures 5B, 5C, and S5M; Table S1).
Among the five KO mice, the Nr3a KO mice showed a significant ‘‘short-sleeper’’ phenotype, with a daily sleep time of

8 Cell Reports 14, 1–16, January 26, 2016 ª2016 The Authors
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639.0 ± 17.5 min (n = 8), which is 96.7 min shorter than that ofwild-type mice (p < 0.0001). Nr3a KO mice displayed a lower
Pws than wild-type mice, whereas their Psw was normal (Figure 5C), indicating that their short-sleeper phenotype was due
to an increase in awake-state stabilization (i.e., longer episodedurations of the awake state) but not a decrease in sleep-statestabilization. To preclude the possibility of an off-target effectof CRISPR/Cas9 gene editing, we generated another group of
Nr3a KO mice (n = 12) by an independent CRISPR probe set(set 2; Figures S6A and S6B; Table S2) and confirmed theobserved short-sleeper phenotype compared with wild-typemice (p < 0.0001) (Figures 5D–5F and S6C). Furthermore, toensure that the short-sleeper phenotype is not due to the modality of sleep evaluation, we produced another series of Nr3a (set 1)
KO mice (Figure S6D) and used EEG and EMG recordings toevaluate their sleep. Consistent with the SSS results, EEG recordings showed a significant short-sleeper phenotype in KOmice (p < 0.05; Figure S6E). For genomic validation, four of thegenerated Nr3a KO mice (Figures S5K and S6B) mentioned
earlier were analyzed with exome sequencing (Table S5). In
both set 1 and set 2 animals, the analysis revealed deletions ofgenome stretches with variable lengths at the target site (Figure 6; Figure S7A), with its extent for at least one target site of
up to 100% (see Figure S7B for a magnifiable view). Importantly,
no off-target mutation was identified in this exome sequencing.
These results strongly suggested that the observed shortsleeper phenotype of Nr3a KO mice cannot be attributed to thepossible off-target effect of CRISPR but rather to the common
genomic defects in the Nr3a gene. Finally, to confirm theabsence of NR3A protein in the KO animals, we have performed
MS analysis in these animals (Figure S6F). Two Tyr KO animals(Figure S4D) were used as controls for NR3A protein detection,
which showed a clear peak at the position of internal standardof NR3A protein. By contrast, none of the Nr3a KO mice (Figures
S5K and S6B) had a detectable peak at the position of internal
NR3A protein. These results suggest that Nr3a is knocked
out not only at the genomic level but also at the protein expression level. Overall, these results demonstrate that the use of
mammalian reverse genetics without crossing, and specificallycombining this efficient KO mouse production with accurate,
automated phenotyping, enables the comprehensive identification of shared and diverse functions of gene family members inorganisms.
Developing a Database for the Triple-Target CRISPR
MethodAs described earlier, triple-target CRISPR enabled us to efficiently produce biallelic KO mice. However, as we produced
several KO mice using this method, we noticed that manuallydesigning triple targets for the same gene is still time consuming,
even using existing tools to extract candidate target sequences
for a given genomic sequence (http://cas9.cbi.pku.edu.cn/
index.jsp) (Ma et al., 2013) and to evaluate the off-target riskfor each candidate (http://tools.genome-engineering.org) (Ranet al., 2013). This manual design procedure takes up to a few
hours for a single gene and, therefore, limits the ability to performlarge-scale screenings. Therefore, we sought to develop an
automated method that performs all of the gRNA selection steps.

We scanned the whole mouse genome once, and all of the suitable targets are now in an online database (Figure 7A; Supplemental Experimental Procedures). This gRNA database provides
at least three target sequences (one set for triple-target CRISPR)
for 81.2% of mouse genes. In addition, 71.9% of all mouse genes
have more than six target sequences (multiple sets for tripletarget CRISPR), which are also included (Figures 7B and 7C).
To validate the usefulness of the triple-target CRISPR database, we selected two independent sets of triple targets for
the Tyr gene (Tyr-4, Tyr-5, and Tyr-6 for set 2, and Tyr-7, Tyr-8,
and Tyr-9 for set 3; Figure 7D), which were distinct from Tyr-1,
Tyr-2, and Tyr-3 (set 1) used in the previous experiments (Figure 1C). When injected into fertilized eggs, the triple-target
method using both sets of gRNAs showed almost perfect efficacy (96.6% and 100%, respectively; Figures 7E and 7F), supporting the usefulness of the triple-target CRISPR database.
Therefore, we made this database public (http://crispr.riken.jp/).
DISCUSSION
Triple-Target CRISPR Enables the Efficient Productionof Biallelic KO Mice in a Single Generation
In this study, we sought to achieve the highly efficient (>90%)
production of whole-body biallelic KO mice in a single generation.
We used a simple computational model to estimate the minimum
efficacy of different CRISPR methods and discovered that multiple-target CRISPR methods would generate mutant mice withmuch higher efficiency than single-target CRISPR. We designed
three different gRNAs against the Tyr gene and confirmed thatthe triple-target CRISPR method—using a mixture of three gRNAs(Tyr-1, Tyr-2, and Tyr-3)—achieved almost perfect efficacy(97.5%), whereas the single-target method with any of the gRNAs
at a 3-fold higher concentration had only moderate efficiency.
Genomic verification revealed no off-target mutation, at least inexons (Figure 2; Table S5). The total number of gigabase pairs(or Gbp) read in the exome sequencing including Tyr (set 1),
Nr3a (set 1), and Nr3a (set 2) KOs was 65.6 Gbp. This is morethan 24 times greater than the whole genome base pairs of mouse(2.7 Gbp). This, therefore, gives us enough coverage to be confident that off-target effect of the triple-target CRISPR strategy is
not a problem in practice. Importantly, the DNA-cleavage efficiency of Tyr-3 was more than 3-fold higher than that of Tyr-2 (Figure 1D), whereas the whole-body biallelic KO efficiency for Tyr-3(64.7%) was similar to that for Tyr-2 (54.2%) (Figure 1F), indicatingthat a limiting factor in vivo is not DNA cleavage by the CRISPR/
Cas9 system but other factors such as DNA repair, consistent
with the prediction by the computational model. The reproducibility and robustness of the triple-target method were further
confirmed by additional experiments with two independent setsof three gRNAs against the Tyr gene (Figures 7E and 7F). We
also confirmed the KO efficiency by genotyping all mice used
for SSS analysis in this study. We genotyped 102 mice producedby the triple-target CRISPR method, including 11 single-KO and 2
double-KO strains (Table S2). At least 92.2% (n = 94) of the animals were confirmed as KO mice by qPCR or sequencing. Thisefficacy is confirmed to be higher than the minimum efficacy predicted by our model. In addition, the triple-target CRISPR method
is scalable and can be applied to the identification of common andCell Reports 14, 1–16, January 26, 2016 ª2016 The Authors 9

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Nr2c (n = 15)

Nr2d (n = 13)

Nr3a (set 1) (n = 8)60

Nr3b (n = 11)

Mouse

Nr2a (n = 8)

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Sleep time (min)A

Time (Day)0

Sleep time (min)B
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Nr2c (n = 15)

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Nr3b (n = 11)

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700

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900

Sleep time (min)

0.2

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Time (Hour)
Figure 5. Reverse Genetics without Crossing Reveals Nr3a Mutant to Be a Short-Sleeper(A) Sleep-time (per hour) phenotype for all individual KO mice of five NMDA receptor family members generated by triple-target CRISPR.
(B) Sleep time (per hour) over 24 hr, averaged over six days in KO mice of five NMDA receptor family members. Red lines show the mean sleep time at each time of
day for each strain. Gray line shows the data for C57BL/6N male mice (n = 108). The shaded area around the line is the SEM for each time point.
(C) Distributions of sleep/wake parameters of comprehensive KO mice of NMDA receptor family members. Dunnett’s test or Dunnett’s modified Tukey-Kramer
pairwise multiple comparison test compared to C57BL/6N male mice.
(D) Sleep-time (per hour) phenotype of Nr3a KO mice (set 2) generated by triple-target CRISPR.
(E) Sleep time (per hour) over 24 hr, averaged over 6 days in Nr3a KO mice (set 2). Red line, gray line, and the shaded area are the same as described in (B).
(F) Distributions of sleep/wake parameters of Nr3a KO mice (set 2). Dunnett’s test or Dunnett’s modified Tukey-Kramer pairwise multiple comparison testcompared to C57BL/6N male mice.
See also Table S1.

diverse functions of a gene family of interest (Figure 5). Furthermore, the triple-target CRISPR method is applicable to the phenotype analysis of double-KO mice (Figures 4A–4C). We also

created a publicly available CRISPR database that provides triple-target candidate gRNAs for more than 81.2% of the genes
in the mouse genome (Figure 7A).

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The SSS Provides a Simple and Accurate Phenotyping
Method for a Complex Behavior
Along with the efficient production of biallelic KO mice, accurate
phenotype analysis is also important to make full use of mammalian reverse genetics without crossing. In this study, we focusedon the phenotype analysis of sleep/wake states, because sleep
disorders are often associated with psychiatric diseases. The
development of a non-invasive and fully automated method forsleep/wake phenotyping (the SSS) allowed us to sensitively
detect the sleep-time effects of drugs targeting the CNS,
including a psychostimulant, MAP (dopamine transporter antagonist), and a known sleep-inducing drug, DZP (GABAA receptor
agonist) (Figures S3A–S3F). In addition, the SSS enabled us toaccurately and quantitatively analyze other sleep/wake phenotypes, such as sleep/wake amplitude, which was shown by thedecreased amplitude of clock mutants (Figure 4C), as well as
the transition probabilities from sleep to awake states, andfrom awake to sleep states (Figures 3F–3H), which was clearlyshown by the significant increase of Pws and Psw in Hcrt KO
mice (Figure 4F). This ability is useful for dissecting the effectsof CNS drugs on daily sleep time. For example, we found thatthe decreased sleep time caused by MAP was due to an increase
in awake-state stabilization (low Pws) rather than to a decrease in
sleep-state stabilization (Figure S3B). In contrast, the increased
sleep time caused by DZP for 5 hr after the injection was due to adecrease in awake-state stabilization (high Pws) (Figure S3E),
suggesting that the sleep/wake phenotype induced by DZP is
functionally the opposite of that induced by MAP. Interestingly,
after DZP increased the sleep time (0 to 5 hr after injection), it
conversely decreased the sleep time (5 to 10 hr after injection)(Figure S3G). This compensatory decrease in sleep time by
DZP was associated with a slight decrease in sleep-state stabilization (high Psw) (Figure S3G; p = 0.0869). These results suggestthat the SSS can sensitively detect and carefully dissect
the common and diverse sleep/wake effects of drugs targeting
the CNS.
The sleep time of two circadian KO animals, Bmal1 KO and
Cry1/Cry2 double-KO mice, recorded in the SSS is not exactly
the same as in past reports (Laposky et al., 2005; Wisor et al.,2002). This might be partly because of the functional advantages of the SSS system. First, because of its convenience,
the number of recorded mutant mice is larger in this study,
where 34 Bmal1 KO and 27 Cry1/Cry2 KO mice and morethan 100 wild-type mice are recorded. In contrast, nine
Bmal1 KO mice against eight wild-type mice are recorded in
the Laposky et al. (2005) paper, and six Cry1/2 double-KOmice against eight wild-type mice are recorded in the Wisor
et al. (2002) paper. Second, in both studies, the sleep phenotypes were evaluated by electrodes implanted in the skull,
which is inevitably invasive. In fact, three out of nine KO mice
were removed from the study after EEG/EMG recording in the
Laposky et al. paper because EEG/EMG signals were deterio-

rating, which implies some invasiveness inherited in the conventional EEG/EMG recording methods.
Several other approaches have been used in the past to
develop automated non-invasive sleep staging systems. The
video-based sleep stager is non-invasive and can discriminate
between REM and NREM (McShane et al., 2012). However, theexpected drawback is difficulty in parallelization due to thehuge amount of data to be handled. Furthermore, this method
requires a user to set a parameter, which makes it distinct
from the SSS, which requires no manual optimization. Piezoelectric-based movement detection technology allowed for estimation of the sleep/wake status from the animal motion (Flores
et al., 2007). The simplicity of this system is attractive, althoughwhen performing a high-throughput phenotyping in this system,
we expect its inevitable interference, i.e., the difficulty in assigning the movement information to the recoding animal against theinevitable noise coming from the neighboring chambers. In
contrast, the SSS only uses single-channel respiration flow information per animal, which is a very compact-sized dataset andthe respiration is detected only by the pressure inside the chamber, which is much less prone to be affected from the neighboring chambers. These characteristics make the SSS a much
useful technology.
Regulation of Sleep/Wake States by NMDAR Family
Members
To test the use of our combined method to comprehensively
identify the common and diverse functions of gene family members, we separately knocked out all of the NMDAR family members. We found that Nr3a KO mice are short-sleeper mutants.
Nr3a expression peaks during early postnatal life and decreases
to lower levels in adulthood (Henson et al., 2010). Unlike thetraditional NMDARs, Nr3a-containing NMDARs have less Ca2+
permeability and are almost completely insensitive to Mg2+ blockat hyperpolarized potentials (Sasaki et al., 2002; Tong et al.,2008). Recently, adult Nr3a KO mice were reported to haveincreased pain sensation and enhanced abilities in learningand memory tasks (Mohamad et al., 2013). These KO mice hadgreater long-term potentiation (LTP) in adult hippocampal slices
compared with wild-type slices. Based on these previous findings, we can assume two possible hypotheses to explain the
short-sleeper phenotype in Nr3a KO animals. One is the developmental circuit influence of Nr3a-containing neurons. Since
Nr3a defect impairs fundamental NMDAR responses that are
deeply related to formation of synaptic networks, the absenceof Nr3a may alter the original sleep regulation network of thebrain. The other is the direct neural activity modulation by
NR3A-containing neurons. Even Nr3a impairment during development may be complemented by another mechanism; the influence of Nr3a modulation against neural activity remains until
adulthood. The enhanced LTP can be explained by decreased
blunting of the NMDAR response by Nr3a. Because the NMDAR

Figure 6. Exome Analysis of Whole-Body Biallelic Nr3a, Set 1, KO Mice Produced by Triple-Target CRISPR Method
The genomic regions targeted by the three gRNAs were shown for two mice in Nr3a (set 1) KO group (Figure S5K). The upper part of each panel shows readcoverage, and the lower part shows read alignments (rectangles). Horizontal lines between rectangles indicate read pairing. Different types of mutations
occurring at the target sites are highlighted as follows: inter-exon deletion (red rectangles); intra-exon deletion mutation (green rectangles); short deletion (reddots); and short insertion (blue dots).

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AMatch #
in genome
GCAGCTGGGGACACAGCAGCTGG >1GCAGGTGATGAAGCCACAGGATTCTGG >1GTGA
GTTTTTCTATGCAGGTAAGAAGG
GTTT1GTGCGCTACGCGTGCGCGGTGGG
GTGC
1GCAAAGACCTCATGGGCTTCGGG
GCAA
1GAGCAGATTGTGGTGGCTCTCGG
GAGC
1GATGGGTAACTGAGAAATACGGG
GATG
1
GAGGTATATTGATCTGCTTCAGG
GAGG
1Reverse primer
AAAAGCACCGACTCGGTGCCAAAAAAGCACCGACTCGG-TGCC
AAA
A
GCAAAG-ACCTCATGGGCTTCGGGGCAAAGACCTCATGGGCTTCGGG

GATGGGTAACTGAGAAATACGGG
AACTGAGAAATGAGCAGATTGTGGTGGCTCTCGG
GAG
GATGGGTAACTGAGAAATACGGGGAT
GAGGTATATTGATCTGCTTCAGG
GAG

Multiple match removal

TargetPrimer Homologs RemovalPri

Off-target

Off-target Filtering
GAGGTATATTGATCTGCTTCAGG
ATTGATC
ATCTGCT

Pattern based extraction

Low AT, TTTT Removal

PatternG
AnnnnnnnnnnnnnnnnnnnnGG
C
GCAGCTGGGGACACAGCAGCTGG
GTGATGAAGCCACAGGATTCTGG
GTGA
GTTTTTCTATGCAGGTAAGAAGGGTTTGTGCGCTACGCGTGCGCGGTGGGGTGC
GCAAAGACCTCATGGGCTTCGGG
G
CAA
GAGCAGATTGTGGTGGCTCTCGG
G
AGC
GATGGGTAACTGAGAAATACGGG
G
ATG
GAGGTATATTGATCTGCTTCAGG
GAGG

Structure based Filtering

GTTTTTCTATGCAGGTAAGAAGG
GTTTGTGCGCTACGCGTGCGCGGTGGG
GT
TGC
U
U
G

GCAAAGACCTCATGGGCTTCGGG
GCAA
GAGCAGATTGTGGTGGCTCTCGG
GAGC
GATGGGTAACTGAGAAATACGGG
GATG
GAGGTATATTGATCTGCTTCAGG
GAGG

A

CU

A U

U G
C
G G A
G C
U C
G A
U U UC C
G
U
G
U
G
A
G
A
U C
A
U U
G G A A UU G A A
C
A A
A U U
U A

C
U G
U
U
G AA GUC U AGU AUUU AA
A G AU AUCGGG A U G CA A
A
UA
A A

G
C
C

U G
C A
G G
G C
U C
G A
U U UCGCUGU G A A AA
A U G
GU AU
A C U U A
G
U
G
A
G
A
C
C
C
GU
G
C C CGUU AU
G
G CU A GU U
C G A AG
U
U AU
U
U AA
A
A G UAUGC C AGAU
G A AGUA
A A

Database StorageGATGGGTAACTGAGAAATACGGGGTAACTGAGAAGAGCAGATTGTGGTGGCTCTCGG
GTGGCTCTCGG GAGGTATATTGATCTGCTTCAGG
ATATTGATCTG

B

C
Number of Genes
15000

20000

25000

2500
2000

14

>14

12−

6−8

9.3%

1
9−1

3−5

18.8%

10000

1500

Count

5000

0−2

Number ofTargets

0

71.9%

1000
500

Less than
two targets

A single set of
triple target

Multiple sets of triple targets

0
0

25
50
75
Number of targets

100

D
Tyr target sequence-4
ggacctcagttccccttcaa (+)

chr7 (-)

Tyr target sequence-5
agtttgacccagtatgaatc (+)

87,490,000

Tyr target sequence-6
gagtctctgttatggccgat (-)

87,460,000

Set 2

87,430,000
Tyrggtcatccacccctttgaag (-)
Tyr target sequence-7

E

Triple target
Set 2 (Tyr-4,5,6)

aagccatggccagatacgac (-)
Tyr target sequence-9

gtgttgacccattgttcatt (-)
Tyr target sequence-8

Triple target
Set 3 (Tyr-7,8,9)

Set 3

F

Triple Set 2(Tyr-4,5,6)

Triple Set 3(Tyr-7,8,9)

gRNA1(ng/µl)

50

50

gRNA2
(ng/µl)

50

50

gRNA3
(ng/µl)50

50

Embryos

145

105

Pups

32

14

Albino31 (96.9%)

14 (100%)

Pigmented

1 (3.1%)0 (0%)(legend on next page)

Cell Reports 14, 1–16, January 26, 2016 ª2016 The Authors 13
CELREP 2332

Please cite this article in press as: Sunagawa et al., Mammalian Reverse Genetics without Crossing Reveals Nr3a as a Short-Sleeper Gene, Cell Reports (2016), http://dx.doi.org/10.1016/j.celrep.2015.12.052

family can be strongly blocked by MK-801, which results in hyperlocomotion when administered to mice (Koek et al., 1988),
it is possible that, without Nr3a, the NMDAR response is weakened, and this may result in shorter sleep. To test these hypotheses, conditional perturbation of Nr3a-containing neurons isnecessary in future investigations.
Although the detailed molecular mechanism, in which Nr3acontributes to the sleep/wake cycle, remains to be elucidated,the discovery of Nr3a KO mice as a strong short sleeper provides
an interesting entry point for mammalian sleep research. We also
note that, in the fruit fly, forward genetic screenings have shownthat other ion channels play important roles in sleep homeostasis. Mutation in the Shaker gene, which encodes a voltagedependent potassium channel, and in the Redeye gene, which
encodes a nicotinic acetylcholine receptor a subunit, both resultin a short-sleeping phenotype (Cirelli et al., 2005; Shi et al., 2014).
Therefore, it will be interesting to investigate the relationship ofthe mammalian homologs of these channels to NR3A-containing
NMDARs in mammalian sleep.
Toward Organism-Level Systems Biology
In this study, we sought to develop mammalian reverse genetics
without crossing by eliciting the efficient (>90%) production andaccurate (>90%) phenotype analysis of mutant animals in a single generation. Mammalian reverse genetics without crossing(called ‘‘next-generation’’ mammalian reverse genetics) now allows us to test—in whole organisms—hypotheses that arederived from the past knowledge. For example, a computationalmodel of slow-wave sleep that provides explanations for theobserved complex dynamics of sleep/wake states, along withpredictions for the important molecular pathways regulatingthese states, could be extremely powerful. Previously, it wasdifficult to test such predictions, at least without years of labor.
However, the present study demonstrated that the importanceof a predicted molecular pathway can be efficiently tested using
next-generation reverse genetics. Therefore, one of the nextchallenges will be to construct a simple computational modeland experimentally validate its predictions using triple-target
CRISPR and/or the SSS. New research fields, including the system-level identification and analysis of molecular circuits inwhole organisms, lie ahead.

12-hr/12-hr light/dark cycle. All experimental procedures and housing conditions involving animals and their care were in accordance with the RIKEN Regulations for Animal Experiments and the Animal Care and the Use Committeeof the Graduate School of Medicine, The University of Tokyo.
One-Cell Embryo Microinjection
C57BL/6N females (4–6 weeks old) were superovulated and mated with
C57BL/6N males. Fertilized eggs were collected from the ampulla of theoviduct of plugged C57BL/6N females by micro-dissection and kept in
KSOM medium (Merck Millipore) in a 5% CO2 incubator at 37 C. Cas9mRNA (100 ng/ml) and gRNAs (150 ng/ml in total) were co-injected into the cytoplasm of fertilized eggs in M2 medium (Merck Millipore) at room temperature.
Details of the cytoplasmic injection were described previously (Sumiyama
et al., 2010). After microinjection, the injected embryos were cultured for 1 hr
in KSOM medium (Merck Millipore) in a 5% CO2 incubator at 37 C, and
15–30 embryos were then transferred to the oviducts of pseudopregnant
ICR female mice.
Non-invasive Sleep/Wake Phenotype Analysis by SSS Recordings
Mice were placed in the SSS chamber (Figure 3A, upper left panel; Minamide
Rika Shokai), which had an air flow of at least 0.4 l/min. The chamber was connected to the SSS sensor (PMD-8203-2G, Biotex, light brown box on top of thechamber in Figure 3A), which detected the pressure difference between theoutside and inside of the chamber. The analog signal output from the sensor
was digitized at 250 Hz by an AD converter (ADS-0128aK-64MB, Microscience) and stored on a computer by data logging software (Lab-DAQ Pro,
Matsuyama Advance). The analysis of the digitized data was fully automatedas follows: first, the respiration time domain data were converted to frequency
domain data by fast Fourier transformation. The power spectrum was thensubjected to principal-component analysis. The first three principal components were used for clustering by probability density clustering (Azzalini andTorelli, 2007). Finally, the clusters were annotated according to the power
spectrum of each cluster. The details in each step of SSS annotation pipeline
are described in the Supplemental Experimental Procedures. One set of data
recording in the SSS chamber was for 2 weeks. During the experiment, neither
the chamber nor the rack was opened unless the injection was performed i.p.
The light condition of the SSS rack was set to a 12-hr/12-hr light/dark cycle,
and food and water were given ad libitum. Data analysis was conducted
6 days from the second recording date.
SUPPLEMENTAL INFORMATION
Supplemental Information includes Supplemental Experimental Procedures,
seven figures, five tables, and a data file showing the source code of the
SSS analysis and can be found with this article online at http://dx.doi.org/10.
1016/j.celrep.2015.12.052.
AUTHOR CONTRIBUTIONSEXPERIMENTAL PROCEDURES
Animals
C57BL/6NJcl mice were purchased from CLEA Japan, and C57BL/6J mice
were purchased from Oriental Yeast. All mice were given food and water ad
libitum. They were kept in an environment at an ambient temperatureof 21 C, with a relative humidity of 50%. The light was controlled under a

H.R.U. and G.A.S. designed the study. G.A.S. developed the SSS system andperformed the sleep data analysis. G.A.S. and S.S. performed the SSS
recording. G.A.S., D.T., and K.L.O. performed EEG/EMG recordings. K.S.
developed the triple-target CRISPR method. M.U.-T. designed the CRISPR
probes. M.U.-T. and S.S. produced CRISPR probes. K.S. and R.O. produced
the mice. D.P. implemented the whole-genome detection method and the

Figure 7. A Publicly Available Database for the Triple-Target CRISPR Method(A) The pipeline for automatically selecting targets for the triple-target CRISPR method.
(B) Distribution of the number of the targets per gene.
(C) Histogram of the number of targets per gene.
(D) Alternative target sequences for the Tyr gene.
(E) Experimental validation of the automatically selected targets for the Tyr gene. Two sets of additional triple gRNAs (Tyr-4, Tyr-5, and Tyr-6 for set 2 and Tyr-7,
Tyr-8, and Tyr-9 for set 3) were tested. Photographs show the coat color of the generated mice.
(F) Table shows the injection conditions of the gRNAs and resulting phenotype. Embryos, number of injected and transferred embryos; Pups, number of born
pups (total); Albino, pups with albino coat color (biallelic knockout); Pigmented, pups with mosaic or wild-type coat color.

14 Cell Reports 14, 1–16, January 26, 2016 ª2016 The Authors
CELREP 2332

Please cite this article in press as: Sunagawa et al., Mammalian Reverse Genetics without Crossing Reveals Nr3a as a Short-Sleeper Gene, Cell Reports (2016), http://dx.doi.org/10.1016/j.celrep.2015.12.052

database for the triple-target CRISPR method. H.U. developed the SSA assay.
M.U.-T. and H.F. performed the genotyping of animals. O.N. and S.K. performed the exome sequencing analysis. R.N. and Y.S. performed the protein
quantification. H.R.U. and G.A.S. wrote the manuscript. All authors discussed
the results and commented on the manuscript text.
ACKNOWLEDGMENTS
We thank T. Mikami for the enthusiastic support for developing the SSS. We
also thank all the lab members at RIKEN CDB, QBiC, and The University ofTokyo, in particular, S. Fujino, J. Hara, J. Yoshida-Garcon, M. Shiokawa, A.
Kishimoto, A. Nishiyama, A. Sato, Y. Wada, and M. Nomura for their kind
help in preparing the materials and supporting experiments; the LARGE,
RIKEN CDB for housing the mice; Kaori Tatsumi in the Phyloinformatics Unit
(GRAS), RIKEN Center for Life Science Technologies, for exome library preparation and sequencing; T. Ueno for running the initial experiment for tripletarget CRISPR; F. Tatsuki and G. Kanda for helpful discussion; and K. Yoshida
for improving the SSS algorithm. This work was supported by the Innovative
Cell Biology by Innovative Technology Program from the Ministry of Education,
Culture, Sports, Science and Technology (MEXT) of Japan (H.R.U.); a Grant-inAid for Scientific Research (S) (grant no. 25221004; H.R.U.) and a Grant-in-Aid
for Scientific Research on Innovative Areas (grant no. 23115006) from MEXT/
Japan Society for the Promotion of Science (JSPS; H.R.U.); a Grant-in-Aid forYoung Scientists (B) (grant no. 26870858; G.A.S.); a Grant-in-Aid for Scientific
Research on Innovative Areas (grant no. 26113720; K.S.); the strategic
programs for R&D (President’s discretionary fund) of RIKEN (H.R.U.); an
intramural Grant-in-Aid from the RIKEN Center for Developmental Biology
and RIKEN Quantitative Biology Center (H.R.U.); a grant from AMED Core
Research for Evolutionary Medical Science and Technology (AMED-CREST),Japan AMED (H.R.U.), and Core Research for Evolutionary Science and Technology (CREST), Japan Science and Technology Agency (JST; H.R.U.); theProgram for the Brain Mapping by Integrated Neurotechnologies for Disease
Studies (Brain/MINDS) from the MEXT (H.R.U.); the Basic Science and Platform Technology Program for Innovative Biological Medicine from the Japan
Agency for Medical Research and Development (H.R.U.); and by the RIKEN
Foreign Postdoctoral Researcher Program (D.P.).

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Received: September 27, 2015
Revised: October 29, 2015
Accepted: December 8, 2015
Published: January 7, 2016

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Cell Reports
Supplemental Information

Mammalian Reverse Genetics without Crossing
Reveals Nr3a as a Short-Sleeper Gene
Genshiro A. Sunagawa, Kenta Sumiyama, Maki Ukai-Tadenuma, Dimitri Perrin, Hiroshi
Fujishima, Hideki Ukai, Osamu Nishimura, Shoi Shi, Rei-ichiro Ohno, Ryohei Narumi,
Yoshihiro Shimizu, Daisuke Tone, Koji L. Ode, Shigehiro Kuraku, and Hiroki R. Ueda

SUPPLEMENTAL EXPERIMENTAL PROCEDURES
A Computational Model to Estimate the Minimum Efficiency to Generate
Whole-body KO Mice by Multiple-target CRISPR Methods
A computational model estimated the efficiency of different CRISPR methods forgenerating whole-body knockout mice as follows. When sufficient Cas9 are provided,
the efficiency of the single-gene mono-allelic knockout P can be described as:
P  =

S
  K+S

, where total gRNA concentration and the dissociation constant between the gRNAand its target site are defined as S and K, respectively. This simple phenomenologicalmodel is adopted as the first-order approximation of the observed saturation in
CRISPR-mediated DNA cutting. It describes the net effect, which is therefore
intended to mechanistically include the known biochemical processes carried on
during DNA cutting (e.g. PAM site binding, as well as, Cas9 remaining bonded to thecut DNA site on both strands following a cut).
When a single allele is targeted by three different gRNAs at the concentration
S/3 with dissociation constants (K1, K2, and K3) between each gRNA and its target
site, the single-gene mono-allelic KO efficiency P can be described as:
P  =  1  -  K1K1 +S/3

K2
K2 +S/3

K3
K3 +S/3

The concentration of each gRNA is S/3 to keep total gRNA concentration as S.
Now, to include the recovery rate from single-site damage (α) is introduced
as the probability to recover of a single target site from a damaged state to an intact
1

state. When α is considered, the single-gene mono-allelic KO efficiency P for asingle-target method (Figure 1B) can be described as:
P  =  

S(1-α)
K+S

Similarly, the single-gene mono-allelic KO efficiency P for a triple-target CRISPR
method (Figure 1B) can be described as:
S K1 K2 +K2 K3 +K3 K1 1-α +S2 K1 +K2 +K3 1-α2 +S3 1-α3P  =  
K1 +S/3 K2 +S/3 K3 +S/3
When a single allele is targeted by N different sites, the dissociation constant
between each gRNA and its target site is defined as Ki (i=1⋯N). The single-genemono-allelic KO efficiency P for a N-tuple-target method (Figure S1A) can be thendescribed as:
P  =  S K1 ⋯KN-1 +⋯+K2 ⋯KN 1-α +⋯+SN-1 K1 +⋯+KN 1-αN-1 +SN 1-αNN
i=1 Ki +S

In our simulations, the single-gene mono-allelic KO efficiency P was calculated 1,000
times for each given α, N and S. In every time of calculation, one set of K (1, 3, and N
for single-, triple-, and N-tuple-target methods, respectively) were picked up randomly
from a log-normal distribution whose geometric mean and geometric standard
deviation as 1.0 and 2.5, respectively (Figure S1B). Without loss of generality, we
can use 1.0 for the geometric mean of the distribution of the dissociation constant K.
In order to estimate its geometric standard deviation, we first estimated three gRNA
dissociation constants from the SSA assay for three gRNAs for Tyr gene (K1 =2.32203, K2 = 1.16562 and K3 = 0.369466 for Tyr-1, Tyr-2 and Tyr-3, respectively,
Figure 1D). We note that K is estimated as an inverse-proportional value to the
DNA-cleavage efficiency measured from SSA assays. Since the geometric standard2

deviation of these estimated K values was 2.53097, we used 2.5 for the geometricstandard deviation. In order to estimate the efficiency of single-gene biallelic KO,
double-gene biallelic KO, and triple-gene biallelic KO, we calculated P2, P4 and P6,
respectively when α = 0.10 (Figure S1A). Since there is uncertainty for α and S invivo, we also calculated single-gene biallelic KO efficiency P2 with different values of
α and S (Figure S1C, α = 0.05, 0.10, 0.15, and S = 5, 10, 15, respectively). We notethat calculated KO efficiencies for multiple-target methods (N > 1) predict the“minimum” efficiency of KO since we did not include, for its simplicity, the probabilityof other type of mutations in the multiple-target CRISPR models (e.g. large deletions
induced by multiple-target CRISPR methods), which is difficult for a DNA repair
system to recover back to the intact state.
To estimate the realistic values for 𝛼  and S, we constructed three
single-target CRISPR models with different dissociation constants (K1 = 2.32203, K2= 1.16562 and K3 = 0.369466) estimated from the SSA assay results for Tyr-1, Tyr-2,
and Tyr-3, respectively. We compared that these models with the experimental
results of single-target CRISPR (36.0%, 54.2% and 64.7% for Tyr-1, Tyr-2 and Tyr-3,
respectively), and used a least squares method to estimate the realistic values for arecovery rate of mutation (α = 0.136428) and total gRNA concentration (S = 5.7248),
respectively. We then performed 1,000 simulations to calculate single-gene biallelic
KO efficiency (P2) for single-target (N = 1) and triple-target (N = 3) CRISPR models
with α = 0.136 and K, which are randomly picked up from the log-normal distribution
in Figure S1B. As a results, single-gene biallelic KO efficiency (P2) for single-target(N = 1) and triple-target (N = 3) CRISPR models at S = 5.7248 are 0.512 ± 0.138 and0.826 ± 0.098 (mean ± SD), respectively (Figure S1F), indicating that a triple-target
CRISPR method is much more efficient than a single-target CRISPR method when
we used the realistic parameter values for α and S. In addition, the triple-target3

strategy using the mixture of Cas9 mRNA and three gRNAs achieved almost perfect
efficiency (97.5%, Figures 1E and 1F), which is even more efficient than thepredicted minimum efficiency (82.6%) (Figure S1F).

Sleep Analysis by EEG/EMG
We anesthetized the animals and implanted telemetry transmitters (F20-EET, DSI)
into them for EEG/EMG recording. The detailed methods were described previously(Sunagawa et al., 2013). The recorded data were analyzed by the FASTER method
followed by a 16-second-rule filter. This rule only allows sleep or wake epochs lasting
longer than 16 seconds (two epochs in this study). If the automatically staged results
include sleep or wake episodes sized less than 16 seconds, these epochs will be
converted to the stage just before the episode started.

Equivalent Electrical Circuit Simulation of the SSS System.
Introducing the Laplace transform, the SSS system can be modeled as an electricalcircuit (Figure 3B). Reducing the physical properties of the SSS components to thoseof electrical circuit elements, the pressure difference detected by the sensor can bepredicted through simulation. Using these results, we optimized two variable
components in the SSS system, i.e., a resistance (R1) and a capacitance (C2).
The mathematical descriptions of the model are as follows. When the total
air flow is I, the flow can be divided into I1 and I2, which are the flow to the chamber
and the sensor (Figure 3B), respectively. How I is divided into I1 and I2 is determined4

by the ratio of conductance of the chamber and the sensor unit. This conductancecan be described in the Laplace domain as:
G1 s   =  

1
C1 s+

G2 (s)  =  R2 +

1
R1

1
C2 s

where G1 and G2 denote the conductance of the SSS chamber and the pressure
sensor unit, respectively. C1 is the capacitance of the SSS chamber, R1 is theresistance of the SSS chamber, R2 is the resistance of the pressure sensor, and C2 isthe capacitance of the sensor. Using G1 and G2, the air flow to the sensor can be
described as:
I2   =  I  -  I1   =  G1
IG1 +G2This equation can be interpreted as I2 is a value that I is amplified by
G1/(G1+G2). Now, R2 is the resistance of the sensor and C1 depends on the volume of
the chamber. Using a simple flow meter and a flow pump and the pressure sensor,
we estimated R2 and C1 as 152 Pa·s/ml and 0.018 ml/Pa, respectively. Because thiswhole circuit can be treated as a band-pass filter, our goal was to optimize R1 and C2
to maximize the gain of G1/(G1+G2) over the physiological respiration frequency ofmice. We ran computer simulations and selected 172 Pa·s/ml and 6.12 × 10-5 ml/Pa
as an optimal value for R1 and C2 in our environment (Figure 3C).

5

Fully-Automated Sleep/wake Phenotype Analysis by SSS Annotation Pipeline
The SSS annotation pipeline realizes fully automated sleep/wake phenotype analysis
from respiration data of mice. The basic staging strategy was based on the FASTER
method, which automatically stages the sleep state from EEG/EMG recordings(Sunagawa et al., 2013) (Figure S2B).
The SSS annotation pipeline is composed of three major steps (character
extraction, clustering, and annotation). In the character extraction step, the recorded
respiratory flow data are split into 8-second-length segments (called “epochs”). Eachepoch of respiratory-flow time-domain data are next converted by fast Fouriertransform (FFT) into frequency-domain data, the respiration power spectra. We used
the power up to the maximum frequency, called Nyquist frequency (i.e. half of thesampling frequency), which corresponds to 125 Hz in this study because therespiratory flow was recorded at 250 Hz. Characters of respiration power spectra arethen extracted by principal component analysis (PCA). The top three principal
components were used in the following analysis.
In the following clustering step, we used the nonparametric density estimation
clustering method (Azzalini and Torelli, 2007) to cluster the epochs. This clustering
method automatically selects the number of clusters, and therefore it is powerful tocluster certain datasets without a priori model or without information on the number ofclusters. A 6-day length data includes 64800 epochs. Entire epochs are divided into
12 group. Each group including 5400 epochs is used for a single-run of clustering.
There are two parameters used for this clustering. One is the smoothing factor hmult
which is used when the probability density is estimated from the given data. The otheris Ngrid, which affects the grid density for scanning the cluster core from the estimatedprobability density. We used 0.80 and 270 as hmult and Ngrid, respectively.

6

In the final annotation step, each cluster is annotated according to thestatistical value of characteristics for each cluster. During sleep the respiratory patternrepresents a slow and regular flow while during wake the flow become fast and highly
fluctuated (Figure S2A). This time-domain observation in sleep turns out to have a
peaky shape in power spectrum due to the regular respiration patterns. On the other
hand, during waking, the power spectrum of the respiratory flow tends to include wide
range of spectra, therefore showing relatively more uniform power spectrum due to its
fast and fluctuated respiration patterns. Focusing on these characteristic patterns in
the frequency domain, we classified “sleep” and “awake” states by using larger andsmaller skewness of the epoch’s power, respectively. “Sleep” state exhibits larger
skewness than awake state because of its higher power for a narrower range of
frequency and lower power for other range of frequency, which will make the
distribution of sleep-epoch’s power positively skewed. Therefore, we defined whenthe median of skewness of epoch’s power spectrum in one cluster is within the 49th
percentile (from the lowest value) of skewness of power spectrum of entire epochs,
the cluster is annotated as “awake,” otherwise as “sleep”. After each cluster isannotated by skewness of power spectrum, the two epoch rule (one state continues
at least two epochs) is applied to the results and the staging completes. All steps
were fully-automated and unsupervised, which allows a highly-objective andreproducible sleep/wake phenotype analysis. The source code of the SSS analysis isavailable at Supplemental Code S1 (https://goo.gl/teteJM).
The SSS technology is applied only to mice in this study. However, it might bepossible to extend this method to larger organisms such as primates and human.
SSS only uses a simple pressure sensor to detect the respiration pattern. Therefore,
the subject needs to be placed in a semi-closed area for monitoring. Applications
could be sleep monitors for babies in the incubator, or in the car for drivers.
7

Calculation of Sleep/wake Parameters
Four sleep/wake parameters are mainly used in this study. One is the “sleep time.”
This is the length of time that an animal’s status is staged as sleep over a given
period of time. The second is the “amplitude of sleep,” or simply the “amplitude.”
When an animal’s sleep time is recorded for D days and summed in the
B-minutes-bin, the sleep time of the kth bin is expressed as Tk , where
1  <  k  <  DLB
and
1440
B

LB   =  The average sleep time of the mth bin of the day  is expressed as1Tm   =  D

D

Tm+LB (i-1)
i=1When the mean of all bins is expressed as
1
ET   =  
LB

LB

Ti
i=1

the amplitude A is defined as
1
A  =  ET LB

LB

Ti -ET
i=1

82

The amplitude value differs with the bin “size” (i.e., duration) used to integrate
the sleep time. We used a 10-min bin size in this study for amplitude calculation.
Circadian and/or daily rhythmicity is expressed as a larger sleep/wake amplitude
value.
The third and fourth statistics are the transition probability between sleep andawake states. In this study, sleep staging is undergone every 8 seconds. This minimal
unit of sleep/wake state is called a segment (or an epoch). When we focus on two
consecutive epochs, there are four possible sets of state. They are sleep to awake,
awake to sleep, two sleep repeats and two wake repeats. When Nmn(X) is defined asa number of transitions from state m to n (m,n ⊆ {sleep, awake}) within the period X,
Pws   =  Nws
Nsw
, Psw   =  .  
Nws +Nww
Nss +Nsw

The two transition probabilities, Pws and Psw, which are independent of eachother, are indirect stabilization parameters that negatively reflect the stabilization of
the awake state (Pww) and sleep state (Pss), respectively (Pws = 1 - Pww, and Psw = 1 Pss). We think PWS and PSW are useful to experimentally test the sleep models which
try to provide sleep-wake temporal architecture (Diniz Behn and Booth, 2011)
because the transition probabilities can be quantified by SSS.
Pharmacological Administration
Methamphetamine (Philopon, Sumitomo Dainippon Pharma, Lot #006281) was
diluted with natural saline to be 3 mg/kg at 100 µl of solution. The vehicle was naturalsaline. Each mouse received an i.p. injection at ZT2.
Diazepam (Horizon, Maruishi Pharmaceutical, Lot #3Y140) was diluted withnatural saline to be 5 mg/kg at 300 µl of solution. The vehicle was natural saline. Eachmouse received an i.p. injection at ZT13 under dark conditions.
9Every animal used for pharmacological experiment was C57BL/6N male mice
(6 to 8-week-old). Mice were assigned arbitrarily to control or treatment groups except
the animals used in dual recording for EEG/EMG and SSS. In the dual recording, thecontrol animals used in DZP i.p. injection were used for MAP i.p. injection after 4 daysof recovery period.

Design of Target Sequences for gRNA
The target sequences for Tyr (Figure 1C) were designed using Jack Lin's on-line
CRISPR gRNA finder (http://spot.colorado.edu/~slin/cas9.html). The target
sequences for Bmal1, Cry1, Cry2, Per1, Per2, Hcrt (Figures S4A-C and S4K), Nr1,
Nr2a, Nr2b, Nr2c, Nr2d, Nr3a (set 1 and 2), and Nr3b (Figures S5A-G and S6A)
were designed using the on-line CRISPR guide RNA Design tool
(http://cas9.cbi.pku.edu.cn/index.jsp)(Ma et al., 2013). Possible off-target sequences
within the mouse genome for each target sequence were checked using the CRISPR
Design Tool (http://tools.genome-engineering.org) (Ran et al., 2013).
The alternative target sequences for Tyr (set 2 and 3, Figure 7D) wereselected from the list resulting from the mm10 CRISPR/Cas9 database(http://www.crispr.riken.jp/).

Construction of the pGL3-SSA Plasmid for the Single-strand Annealing (SSA)Assay
Two partial fragments of the pGL3-control vector (Promega), containing the 5'- or 3'partial sequence of the Luciferase gene, were amplified using PCR with the following
10

primers: 1) forward 5'-GTAAAATCGATAAGGATCCGTCGAC-3', (Hokkaido System
Science), and 2) reverse:
5'-CAGCTGAAACTGCAGAAAGATATCAAAGAATTCTTAATCCAGATCCACAACCT
TCGCTTC-3' as primers amplifying part of the vector backbone and the 5' portion of
the Luciferase gene, and 3) forward
5'-GATATCTTTCTGCAGTTTCAGCTGCCAATCATCCAAAAAATTATTATCATGG-3',
and 4) reverse 5'-CATCGGTCGACGGATCCTTATCG-3' as primers amplifying part ofthe vector backbone and the 3' portion of the Luciferase gene. Both PCR products
were digested by PstI and BamHI, and mutually ligated. The resulting vector, whichcontained multiple cloning sequences(5'-TAAGAATTCTTTGATATCTTTCTGCAGTTTCAGCTG-3':
Stop-EcoRI-EcoRV-PstI-PvuII) between the 5'- and 3'-partial Luciferase sequences,
was designated as pGL3-SSA.

Construction of the pSSA-Tyr-1 and pSSA-Tyr-2/3 Plasmids.
We amplified 199-base and 200-base fragments containing the target sequences for
Tyrosinase (Tyr-1, and both Tyr-2 and Tyr-3, respectively, Figure S1D) from
C57BL/6 mouse genomic DNA by PCR. The PCR products were 5'-endphosphorylated with the Mighty Cloning kit (TaKaRa), and inserted into the EcoRV
sites of the pGL3-SSA plasmid (see above). The resulting vectors were designatedas pSSA-Tyr-1 and pSSA-Tyr-2/3, respectively.

11Oligonucleotide Sequences for the Target Sequences (Hokkaido System
Science)pSSA-Tyr-1:
Forward oligonucleotide: 5'-GGCACCTATGGCCAAATGAACAATGGG-3'
Reverse oligonucleotide: 5'-GTTCCCACAATAACAAGAAAAGTCTGTGCC-3'
pSSA-Tyr-2/3:
Forward oligonucleotide: 5'-TGGAACAAGCCAGTCGTATCTGGCC-3'
Reverse oligonucleotide: 5'-TCACAGATGGCTCTGATACAGCAAGCTG-3'

Construction of the pX330-Tyr-1, pX330-Tyr-2, pX330-Tyr-3, pX330-Bmal1-1,
pX330-Bmal1-2, and pX330-Bmal1-3 Plasmids.
Oligonucleotides (Hokkaido System Science) containing the target sequences for
Tyrosinase and Bmal1 (Tyr-1, Tyr-2, Tyr-3, Bmal1-1, Bmal1-2, and Bmal1-3,
respectively) were annealed and inserted into the BbsI sites downstream of the U6
promoter on the pX330 plasmid (Cong et al., 2013; Ran et al., 2013) [Addgene#42230]. The resulting vectors were designated as pX330-Tyr-1, pX330-Tyr-2,
pX330-Tyr-3, pX330-Bmal1-1, pX330-Bmal1-2, and pX330-Bmal1-3, respectively.

Oligonucleotide Sequences for the Target Sequences
Tyr-1:
Forward oligonucleotide: 5'-CACCGTGTCAAGGGACACACTGCT-3'
Reverse oligonucleotide: 5'-AAACAGCAGTGTGTCCCTTGACAC-3'

12

Tyr-2:
Forward oligonucleotide: 5'-CACCGTTATTGCTGCAGCTCTCTC-3'
Reverse oligonucleotide: 5'-AAACGAGAGAGCTGCAGCAATAAC-3'
Tyr-3:
Forward oligonucleotide: 5'-CACCGAAGAAGAAGCAACCCCAGG-3'
Reverse oligonucleotide: 5'-AAACCCTGGGGTTGCTTCTTCTTC-3'
Bmal1-1:
Forward oligonucleotide: 5'-CACCGTGTGGACTGCAATCGCAAG-3'
Reverse oligonucleotide: 5'-AAACCTTGCGATTGCAGTCCACAC-3'
Bmal1-2:
Forward oligonucleotide: 5'-CACCGTGTTCTGCATATTCTAACC-3'
Reverse oligonucleotide: 5'-AAACGGTTAGAATATGCAGAACAC-3'
Bmal1-3:
Forward oligonucleotide: 5'-CACCGTCAGATTGAAAAGAGGCGT-3'
Reverse oligonucleotide: 5'-AAACACGCCTCTTTTCAATCTGAC-3'

SSA Assay
293T cells were maintained in DMEM (Life Technologies) supplemented with 10%
FBS (JRH Biosciences) and antibiotics (100 U/ml penicillin and 100 µg/ml
streptomycin; Life Technologies). One day prior to transfection, the cells were plated
onto six-well plates at a density of 4×105 cells per well. The following day, the cells
were co-transfected using FuGene6 (Roche) with 1 µg of pSSA-Tyr-1 or
pSSA-Tyr-2/3 reporter plasmids in the presence of the following constructs, as
13

indicated in Figure S1E: 0 or 2 µg of pX330, pX330-Tyr-1, pX330-Tyr-2, or
pX330-Tyr-3 plasmid, according to the manufacturer's instructions. Empty vector wasused to bring the total amount of DNA to 3 µg per well. In addition, 50 ng of aphRL-CMV plasmid [Renilla luciferase (RLuc) reporter vector, Promega] was includedin each transfection as an internal control for transfection efficiency. Forty-eight hours
after transfection, the cells were harvested and assayed using the Dual-Luciferase
Reporter Assay System (Promega). The Luciferase activity was normalized to the
Rluc activity.

Cas9 mRNA Synthesis
p3s-Cas9HC plasmid(Cho et al., 2013) [Addgene #43945], which includes a T7
promoter-fused Cas9 coding region, was digested with XbaI (TaKaRa), and used as
the template for in vitro transcription using the mMESSAGE mMACHINE T7 kit (LifeTechnologies). The Cas9 mRNA was purified using the MEGAclear kit (LifeTechnologies).

gRNA Synthesis
The gRNA templates for Tyr (Figure 1C) and Bmal1 (Figure S4A) were
simultaneously fused to the T7 promoter and amplified from the pX330-Tyr and
pX330-Bmal1 vectors, respectively (see above) by PCR using the primers listed
below (Hokkaido System Science) (Wang et al., 2013). Alternatively, the gRNA
templates for Cry1, Cry2, Per1, Per2, Hcrt (Figures S4B, S4C and S4K), Nr1, Nr2a,
Nr2b, Nr2c, Nr2d, Nr3a (set 1 and 2), Nr3b (Figures S5A-G and S6A), and Tyr(Figure 7D) were directly synthesized and fused to the T7 promoter by PCR. First,
14the partial fragments of the gRNA templates including each target sequence were
amplified from pX330 plasmids (Addgene #42230) using PCR with the Common
Reverse primer and Forward primer-1 (Hokkaido System Science) for each target
sequence (Table S3). Subsequently, the T7 promoter-fused gRNA templates were
amplified from the diluted PCR products using PCR with the Common Reverse primer
and Forward primer-2 (Hokkaido System Science) for each target sequence (Table
S3).
The T7 promoter-fused gRNA PCR fragments were used as the template forin vitro transcription using the MEGAshortscript T7 kit (Life Technologies). The
gRNAs were purified using the MEGAclear kit (Life Technologies).

Common Reverse Oligonucleotide for T7-gRNAs:
5'-AAAAGCACCGACTCGGTGCC-3' (Wang et al., 2013)

Oligonucleotide Sequences for T7-gRNAs of Tyr (set 1) and Bmal1
Tyr-1 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGTGTCAAGGGACACACTGCT-3'
Tyr-2 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGTTATTGCTGCAGCTCTCTC-3'
Tyr-3 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGAAGAAGAAGCAACCCCAGG-3'
Bmal1-1 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGTGTGGACTGCAATCGCAAG-3'
15

Bmal1-2 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGTGTTCTGCATATTCTAACC-3'
Bmal1-3 Forward oligonucleotide:
5'-GGGCCTAATACGACTCACTATAGGTCAGATTGAAAAGAGGCGT-3'

Oligonucleotide Sequences for Other T7-gRNAs
Oligonucleotide sequences of triple-target gRNAs (T7-gRNAs) for Cry1, Cry2, Per1,
Per2, Hcrt, Nr1, Nr2a, Nr2b, Nr2c, Nr2d, Nr3a (set 1 and 2), Nr3b, and Tyr (set 2 and
3) genes are described in Table S3.

Genotyping of KO Mice by Quantitative PCR (qPCR) and Sequencing
We first prepared the genomic DNA of wild type and Tyr KO mice from their brains,
scalps, and tails using the Wizard Genomic DNA Purification Kit (Promega, wild type,
Tyr KO mice, Figure S1H) according to the manufacturer's instructions, thenperformed qPCR analysis for genotyping of these mice using the ABI PRISM 7900
(Applied Biosystems)/ QuantStudio7 Real-Time PCR System (Life Technologies),
SYBR Premix Ex Taq GC (TaKaRa) and primers for qPCR (Table S4, Hokkaido
System Science) and confirmed that the quantitative qPCR results matched with eachother among the genomic DNAs extracted from brains, scalps and tails. Therefore,
we hereafter prepared the genomic DNA only from the tails using the DNeasy Blood& Tissue Kit (QIAGEN) for genotyping of other KO mice generated for sleep/wakephenotyping (Bmal1, Cry1/Cry2, Per1/Per2, Hcrt, Nr2a, Nr2b, Nr2c, Nr2d, Nr3a, Nr3b
and Tyr KO mice). The absolute target site abundance was calculated using a
16

standard curve obtained from wild-type genomic DNA. The amount of Tbp(Tsujino etal., 2013) was quantified and used as an internal control.
When the amplified intact DNA by qPCR is less than 0.2% of wild-type
genome, we judged as the target DNA is not detectable. When either three targets ofthe gene were not detectable, we classified the animal as a knock-out. When all threetargets were detectable, genotyping by sequencing was performed. 0.6 ~ 1 kb aroundthe each target sequence was amplified by PCR with Ex Taq (TaKaRa) and theprimers (Table S4, Hokkaido System Science). The fragment was subcloned into
pGEM-T-easy vector (Promega), and sequenced. When every subcloned sequence
has deletion and/or insertion which is not an in-frame, the target was labeled asknock-out. When the target gene has at least one knocked-out target by sequence,
the animal was classified as KO. Otherwise the animal was classified as non-KO. For
double-KO animals, we classified the animal as KO when both genes were knocked
out by the criteria written above.

Protein Quantification
Absolute amount of TYR and NR3A proteins were quantified by selected reaction
monitoring (SRM) MS. Sample processing for the MS analysis was performed
according to a phase-transfer surfactant (PTS) protocol (Masuda et al.) with several
modifications. To analyze TYR abundance, ears from the mutant mice Tyr #3, Tyr #4,
and Nr3a (set 2) # 10 were used. To analyze NR3A abundance, brains from themutant mice Tyr #3, Tyr #4, Nr3a (set 1) # 1 - # 8, and Nr3a (set 2) # 1 - # 12 wereused. In brief, the tissues were homogenized by sonication in PTS buffer (12 mM
sodium deoxycholate, 12 mM sodium N-lauroylsarcosinate, and 50 mM NH4HCO3)
containing a phosphatase inhibitor cocktail (Nacalai Tesque) and a protease inhibitor
17

cocktail (Nacalai Tesque) and were clarified by centrifugation at 10,000xg for 10 min.
The resulting homogenates were frozen in liquid nitrogen and stored at -80°C until
use. Protein concentrations were determined by Quick Start Bradford Dye Reagent(Bio-Rad).
As internal standards, we added the synthetic peptides with the isotope
labeled lysine and arginine residue (13C6-Lys and 13C615N4-Arg, respectively). The
synthetic peptides were pre-quantified and digested to produce specific sequences
for the quantification of target proteins (DTLLGGSEIWR for the quantification of Tyr
protein and VVTLIEHPFVFTR for the quantification of NR3A protein) according to our
newly developed method described elsewhere. The proteins/standards mixture were
subjected to cysteine reduction and alkylation (10 mM TCEP at 37°C for 1 hr and 15
mM iodoacetamide at 37°C for 30 min in dark) followed by 5-fold dilution with 50 mM
NH4HCO3 solution. Then, enzymatic cleavage of proteins was performed by 8
hr-incubation with 1:50 (w/w) LysC followed by 16 hr-incubation with 1:50 (w/w)
trypsin at 37°C. The digestion was stopped by mixing an equal volume of ethyl
acetate in the presence of 0.5% TFA. The surfactants in the sample were removed bydiscarding the ethyl acetate phase. The left aqueous phase containing peptides wasdried with SpeedVac (Thermo Scientific). The dried peptide mixture was thendissolved in an analytical buffer (2% acetonitrile and 0.1% TFA). The resulting
peptide solution was desalted by using StageTip (Rappsilber et al.). Twenty µg of thepeptide mixture prepared from brain was dissolved in 20 µL of analytical buffer. 100µg of the peptide mixture prepared from ear was pre-fractionated into 6 fractions byStageTip-based fractionation (Wisniewski et al.), and then the fractions weredissolved in 14 - 30µL of analytical buffer.
The resulting peptide mixture corresponding to approximately 1µg of proteins
was analyzed by liquid chromatography (LC)-MS using a triple quadruple mass18

spectrometer (TSQ Vantage EMR mass spectrometer, Thermo Scientific) equippedwith a captive spray ionization source (Michrom Bioresources), a nano-Advance
UHPLC system (Bruker Daltonics), and an HTC-PAL autosampler (CTC Analytics)
with a trap column (0.3 x 5 mm, L-column, ODS, Chemicals Evaluation and ResearchInstitute, Japan). Analytical samples were separated by reversed phase
chromatography, in which we used a home-made capillary column (length of 200 mm
and inner diameter of 100 µm) packed with 2 µm C18 resin (L-column2, Chemicals
Evaluation and Research Institute, Japan), a gradient of acetonitrile from 4 to 36% in
0.5% acetic acid over 105 min at a flow late 300 nL/min. Elution was directly
electrosprayed (1.6 kV) into the MS.
To analyze TYR protein in ear samples, the heavy and light versions of
DTLLGGSEIWR were monitored by LC-SRM-MS by using the SRM transitions of m/z
623.8 to 804.4 (for the light version) and m/z 628.8 to 814.4 (for the heavy version).
To analyze NR3a protein in brain samples, the heavy and light versions of
VVTLIEHPFVFTR were monitored by LC-SRM-MS by using the SRM transitions ofm/z 520.0 to 766.4 (for the light version) and m/z 523.3 to 766.4 (for the heavy
version).

Library Preparation for Exome Sequencing
Exome libraries were constructed using SureSelectQXT Reagent Kit (Cat. No.
G9681A, Agilent Technologies) and SureSelectXT Mouse All Exon Kit V1 (Cat. No.
5190-4641, Agilent Technologies) according to the manufacturer’s instructions.
Fragmentation and adapter-tagging of purified genomic DNA samples were
performed by transposase-based reaction. After an amplification of adapter-tagged
DNA library, purification was performed with AMPure XP beads (Beckman Coulter).
19

The purified libraries were hybridized to the SureSelect capture library, and collectedhybridized DNA using streptavidin-coated beads. The enriched DNA libraries were
amplified with PCR using the appropriate pair of dual indexing primers to add index
tags for the Illumina TruSeq system. In each purification step, the length distribution
and concentration of DNA molecules in libraries were analyzed using the 2100
Bioanalyzer (Agilent Technologies).

Exome Sequencing and Indel Detection
The libraries were subjected to on-board cluster generation using TruSeq Rapid PE
Cluster Kit (Cat. No. PE-402-4001) and sequenced on Rapid Run Mode of Illumina
HiSeq 1500 (Illumina) to obtain paired-end reads with 126 cycles using three 50-cycle
SBS kits (Cat. No. FC-402-4002), taking advantage of surplus reagents asdemonstrated previously (Tatsumi K, 2015, in press.). 5% PhiX spike-in was added toeach lane as control. Image analysis and base calling were executed using thestandard Illumina software consisting of HiSeq Control Software (HCS) ver. 2.0.12.0
and Real-Time Analysis (RTA) ver. 1.17.21.3. Quality of the raw sequence data were
controlled by FastQC ver. 0.11.1(http://www.bioinformatics.bbsrc.ac.uk/projects/fastqc/). Removal of adapter
sequences and low-quality reads was performed with Trim Galore ver. 0.3.3 with theparameters ‘-e 0.1 -q 30’(http://www.bioinformatics.babraham.ac.uk/projects/trim_galore/). Additionally,reads with the average per-base quality score of no more than 30 were removed withan original script. The reads filtered as above were aligned with the genome
sequences GRCm38/mm10 by BWA ver. 0.7.10-r789 using the BWA-MEM algorithm(Li and Durbin, 2010). Potential PCR duplicates among the mapped paired reads
20were removed using the MarkDuplicates function of Picard Tools ver. 1.122(http://picard.sourceforge.net/). The sequencing statistics including the number ofreads, mapping rate and exome on-bait coverages were summarized by the
CalculateHsMetrics function of Picard Tools.
Local realignment of the mapped reads was performed by
RealignerTargetCreator and IndelRealigner of the Genome Analysis Toolkit (GATK)
package ver. 3.2-2 (McKenna et al., 2010), and recalibration based on per-basequality score were performed with the BaseRecalibrator and PrintReads functions.
For these processes, the mm9-based exome region data with 100 bp padding
provided by Agilent Technologies was converted to mm10 using the UCSC LiftOver
program (https://genome.ucsc.edu/util.html) (Rhead et al., 2010), and insertionand deletion mutations (indels) from NCBI dbSNP (build 137) were used as known
sites. The sample-specific indels were called using the HaplotypeCaller in GATK after
applying local realignment and recalibration performed above.
SnpEff package ver. 4.0E (Cingolani et al., 2012) was used to filter andannotate the indels causing high-impact coding variants. We defined as high-impact
mutations both the indels categorized in ‘HIGH’ by the SnpEff definition and thein-frame indels. The indels in both the samples and the control were removed, incomparison with the indels in the control. On- and off-target indels were visualized
and evaluated on the UCSC Integrative Genomics Viewer (IGV) ver. 2.3.36(Thorvaldsdottir et al., 2013).
The exome sequencing data have been deposited and are available in the
DNA Data Bank of Japan (DDBJ). The accession numbers for Tyr KO and Nr3a KO
data are DRA003478 and DRA003920 respectively.

21

CRISPR gRNA Database Development
The first step in automatically detecting gRNA sites was to extract candidate targets.
The process is described here for a single gene, and is repeated for all genes whenrunning the method. Using the genome annotation file and the genomic sequence, we
extracted the sequence of all exons shared by all known isoforms of the gene. This
was necessary to ensure that we targeted a gene, rather than only a few of its
isoforms. We then extracted all the sequences that matched the [G, C, or A]N20GG
pattern and those for which the reverse-complement matched this pattern. This list
represented all the possible candidates for the gene. The next steps were used tofilter this list and to keep only suitable targets. Each target needed to pass all thesteps, so the order did not have any impact on the selection. The steps were
therefore performed in the most computationally efficient order.
The candidate gRNAs needed to target a unique site. Using Bowtie2(Langmead and Salzberg, 2012), we eliminated all the candidates that had multiple
exact matches in the genome (irrespective of location). We also removed targets witha low AT percentage (below 45%), which have a risk of binding strongly to off-target
sites, and targets that contained TTTT, which tend to break the gRNA’s secondary
structure. Because the targets appeared in the forward primer during synthesis, we
also needed to make sure they were not too similar to the reverse primer (See above“Common Reverse oligonucleotide for T7-gRNAs”). This was achieved using the
Needleman-Wunsch algorithm (Needleman and Wunsch, 1970). We then used theVienna RNA-fold package (Lorenz et al., 2011) to compute the gRNA’s secondary
structure. We eliminated all candidates for which the stem loop structure for Cas9
recognition could not fold (Nishimasu et al., 2014), except if the folding energy wasabove -18 (indicating that the ‘wrong’ structure was very unstable). Finally, weevaluated the off-target risk using our own implementation of the Zhang tool (Ran et22

al., 2013). To ensure that all targets stored in the database were as safe as possible,
we used a very strict threshold and rejected candidates with a score below 75.
Candidates that passed all the filtering steps were saved and stored in the
database, which is accessible online at http://crispr.riken.jp.

Statistical Analyses
Statistical analyses were performed by Microsoft Excel or R version 3.1.0(2015).
For sleep phenotype analyses of CRISPR KO mice and conventional
circadian KO mice, normality was evaluated by Kolmogorov-Smirnov test at thesignificance level of 0.05. The homogeneity of variance for each group was evaluatedby Bartlett's test when all the member of the group was normal, or Levene's test whennot. In both tests, significance level was set to 0.05. The sleep phenotype of every KO
strain was compared to the control (C57BL/6N, male, n=108) by Dunnett’s test when
the group has homogenous variance and Dunnett's Modified Tukey-Kramer pairwise
multiple comparison test when not.
The histogram at the top of each sleep/wake parameter chart (Figures 4C, F,
5C, F, and S3M) shows the results of C57BL/6N male mice (n = 108). The error bar is
the SEM for each strain, and the black dashed vertical line and gray shaded area are
the mean and 1 SD range from the recordings of C57BL/6N male mice.
To estimate the sample size for phenotype evaluation, we compared thesleep time control dataset Xc (C57BL/6N, male, n=108) to a simulated dataset Xs
which have the same distribution except a mean of 1 SD (50.8 min) less or much thancontrol (Xs = Xc ± 50.8). We randomly sampled k (2 ≤ k ≤ 20) data from Xs andcompared with Xc by Dunnett’s test with the significance level of 0.05. This test was23repeated 1000 times for one k. We calculated the rate of p < 0.05 as sensitivity foreach k from 2 to 20. According to this calculation, we found that the sample size k ≥ 2provides more than 25% sensitivity whereas the sample size k ≥ 6 provides morethan 50% sensitivity. In this study, we selected the sample size k ≥ 8 to achieve morethan 75% sensitivity for the CRISPR knockout study of NMDA receptor family
members.
For pharmacological studies, means of two sets of data were evaluated. First,
the normality was tested by Shapiro-Wilk test at the significant level of 0.05. When thenormality is confirmed in both groups, the homogeneity of variance was tested by
F-test at the significance level of 0.05. When two groups were normal distributions
with equal variance, Student's t-test, when the groups are normal distributions without
equal variance, Welch's t-test, otherwise two-sample Wilcoxon test was applied.
For the sleep time evaluation of Nr3a KO mice by EEG/EMG, daily sleep time
was compared between Nr3a KO group (n=2) and wild type group (n=6). The
normality for wild type group was tested by Shapiro-Wilk test at the significance levelof 0.05 and turned out to be normal (p = 0.9195). Since there were only two micetested in the Nr3a KO group, they were estimated to be a normal distribution becauseof normality confirmed in other EEG recording done in this study (the Shapiro-Wilk
test for wild-type, DZP-control group, DZP-treated group, MAP-control group and
MAP-treated group were 0.6609, 0.1003, 0.4282, 0.4709 and 0.3382, respectively).
F-test at the significance level of 0.05 showed these groups do not have different
variance (p = 0.3014). Therefore, the mean sleep time was tested by Student's t-test(p = 0.0201, Figure S6E).
The i.p. injection was performed blindly without informing the injector thecontent of the drug. Randomization was not performed for processing data.
24

In this study, p < 0.05 was considered as significant (*p < 0.05, **p < 0.01,***p < 0.001 and n.s. for not significant evaluations).

25

SUPPLEMENTAL DATA
Figure S1. A triple-target CRISPR method for highly efficient production ofwhole-body biallelic KO mice, Related to Figure 1.
(A) Computational simulation predicting how efficiently one or more alleles are cut out
when different numbers of gRNAs are used for the same target gene. Knockout
efficiency was calculated for an increasing number of target alleles: single genemono-allelic (one allele), single gene biallelic (two alleles), double gene biallelic (fouralleles), and triple gene biallelic KO (six alleles). In each simulation, we comparedsingle- (one target per gene), dual- (two targets), triple- (three targets), sextuple- (sixtargets), and decuple- (ten targets) target methods. Each method uses the sameamount of total gRNA. A computational model of the different methods (except thesingle-target method) predicts the minimum efficiency.
(B) Distribution of the gRNA dissociation constant (K) used in the computational
simulation of CRISPR-based KO (Figure S1A). Distribution of the gRNA dissociation
constant (K) follows the log-normal distribution with its geometric mean and geometricstandard deviation as 1.0 and 2.5, respectively. Without loss of generality, we can use
1.0 for the geometric mean in the log-normal distribution of the gRNA dissociation
constant. We then estimated three gRNA dissociation constants from the SSA assay
for three gRNAs for Tyr gene (K1 = 2.32203, K2 = 1.16562 and K3 = 0.369466, Figure1D). Since the geometric standard deviation of these values was 2.53097, we used
2.5 for the geometric standard deviation in the log-normal distribution of the gRNA
dissociation constant.
(C) Computer simulation predicting the minimum efficiency (mean ± SD) of
single-gene biallelic KO with different recovery rates of mutations (α = 0.05, 0.10 and26

0.15) and total gRNA concentrations (S = 5, 10 and 15). We note that total gRNA
concentration (S) is a relative value to the geometric mean of gRNA dissociation
constants (1.0).
(D) Sequences of the inserted fragment for the SSA assay of gRNAs for the Tyr gene.
The fragment sequences for the SSA vector are shown with three target sequences(blue, orange, and green) for Tyr-1, Tyr-2 and Tyr-3 gRNAs, respectively. Mousegenomic sequence data were obtained from GRCm38/mm10 via the UCSC Genome
Browser (http://genome.ucsc.edu/) (Rhead et al., 2010).
(E) Schematic diagram of the single-strand annealing (SSA) assay. The SSA-reporter
vector contained 5’ and 3’ Luciferase gene fragments that shared 702 bp of direct
repeats. These fragments were separated by stop codon and gRNA target site. A
gRNA/Cas9-mediated double-strand break at the target site induced an SSA reaction
between the homologous regions, producing an active Luciferase gene.
(F) Computational simulation with estimated values for a recovery rate of mutation (α= 0.136) and total gRNA concentration (S = 5.72) predicting how efficiently two allelesof a gene are cut out when single or triple gRNAs are used for the same target gene.
We first constructed three single-target CRISPR models with three different
dissociation constants (K1 = 2.32203, K2 = 1.16562 and K3 = 0.369466) estimatedfrom the SSA assay results for Tyr-1, Tyr-2, and Tyr-3, respectively. We note that
estimated values of dissociation constants (K1, K2 and K3) are inverse proportional to
the DNA-cleavage efficiency in SSA assay, and geometric mean of dissociation
constants is set to be 1.0 without loss of generality. The single-target CRISPR
efficiencies of the experiments are shown in blue, orange and green solid circles for
Tyr-1 (36.0%), Tyr-2 (54.2%) and Tyr-3 (64.7%), respectively. We then compared thesingle-target CRISPR models and experiments to estimate the values for a recovery
27

rate of mutation (α = 0.136), total gRNA concentration (S = 5.72) by least squares
method. The triple-target CRISPR efficiencies of the experiment (97.5%) andsimulation (82.6%) are shown in solid and open circles (red), respectively.
(G) Coat color of the Tyr KO mice generated with three different single-target gRNAs.
See also Figure 1E.
(H) The genotyping of Tyr KO mice. The relative amount of intact DNA for each target
sequence was measured by quantitative PCR (qPCR). The genomic DNA was
purified from the brain, scalp and tail of each mouse. The relative amount of intact
DNA for each target sequence was scaled so that the level in one wild-type mouse
was defined as 100%.
(I) An MS analysis of Tyr protein in mice skin. Mass spectrometric signals obtained bymonitoring a tryptic peptide of Tyr protein (upper) in Nr3a (set 2) #10 (Figure S6B),
Tyr #3 and #4 (Figure S4D) in the presence of isotopically labeled internal standard(lower) are shown. The peptide was absolutely quantified using a triple quadruple
mass spectrometer (TSQ Vantage EMR mass spectrometer, Thermo Scientific). The
peptide concentration, which represents the Tyr protein concentration, wasdetermined to be 1.7 atmol/µg in Nr3a (set 2) #10, whereas the peak was notobserved in Tyr #3 and #4.

28

Figure S2. The Snappy Sleep Stager (SSS) enables non-invasive, fully
automated, high-performance sleep/wake phenotyping, Related to Figure 3.
(A) Representative wave patterns of EEG, EMG, and the pressure change in the SSS
chamber, reflecting the animal’s respiration flow, during NREM sleep, REM sleep,
and wake states in the mouse. During NREM sleep, the respiration rate was slow andits period was stable, whereas during waking, the respiration rate was faster and its
period and amplitude fluctuated more.
(B) Schematic diagram of the SSS annotation pipeline. After the mouse is set in thechamber and the respiration flow recording started, no subjective decision isnecessary until the sleep staging finishes. The recorded respiration flow is sampled at
250 Hz, then digitized, and stored in a computer. The recorded data are divided into
8-second epochs. Each epoch is transformed to a power spectrum, and its characters
extracted by principal component analysis. The top three principal components areused as characters, and time-series of the characters are automatically clustered byprobability density clustering. Finally, the clusters are annotated as sleep or awake
based on their characteristics. See Supplemental Experimental Procedures fordetail.
(C) Sleep/wake phenotyping using simultaneously recorded EEG/EMG andrespiration. Three animals were used to estimate the SSS performance. Top row
shows the principal components from the SSS analysis. Bottom row shows the timecourse of sleep time (per hour) determined by both EEG/EMG based staging (blackline) and SSS results (red line). Note that even in different individuals, the first andsecond principal components clearly formed two clusters. Choosing which cluster
was awake state and which was sleep state was simpler than judging all of theepoch’s states in conventional analyses.
29

(D) Accuracy of the SSS for different levels of wakefulness. Each awake epoch wascategorized as “quiet awake”, “normal awake”, or “active awake”, which were defined
as 0 to 33.3%, 33.3 to 66.6%, and 66.6 to 100% EMG power, respectively. Upper
panel shows representative “quiet awake” and “active awake” recordings. Lower
panel shows the accuracy of the SSS results for each level of wakefulness. Note thateven in “quiet awake” state the accuracy exceeded 90%.
(E) Distribution of sleep/wake parameters recorded in C57BL/6J male and female
mice and C57BL/6N female mice. The upper, middle and the lower rows show thedata of C57BL/6J male (n = 178), C57BL/6N female (n = 72) and C57BL/6N male (n =173) mice, respectively. The C57BL/6J male mice slept 699.3 ± 49.6 min (mean ± SD,
n = 178, 7.1% CV), which is 35 minutes less than the daily sleep time of C57BL/6N
mice. In females, C57BL/6N and C57BL/6J showed 636.4 ± 57.7 min and 613.2 ±50.1 min, respectively. They had shorter sleep time than males, which presumably isan effect of the estrus cycle. The pink and sky blue dashed lines show the mean andthe mean ± SD. See Table S1.

30

Figure S3. SSS can detect various sleep/wake phenotypes, Related to Figure 4.
(A) SSS detected the sleep-time reduction induced by a single i.p. administration of
methamphetamine (MAP) at ZT2. Heatmaps show the sleep time (per hour) for all
mice used in this experiment. The MAP group (n = 6) received 3 mg/kg of MAP i.p. on
ZT2 of the second day, and the control group (n = 6) received vehicle i.p. at the sametime. The MAP group showed less sleep than the control group for the first 3 hr after
i.p. injection. Red bar at the top of the plot shows the period used for comparing theparameters in Figure S3B.
(B) MAP reduced the sleep time and Pws significantly for the following 3 hr (ZT2 to 4)
after the i.p. injection of MAP. (Left) Average sleep time. (Middle and right) Average
Pws and Psw. Error bars represent SEM (n = 6 for each group). Welch’s two sample
t-test.
(C) A single i.p. administration of MAP reduced sleep. Plot shows the average sleep
time for the MAP and control groups over time. Red bar at top indicates the 3-hr
period used for comparing the groups in Figure S3B.
(D) SSS detects the sleep-time increase caused by a single i.p. injection of diazepam(DZP) at ZT13. Heatmaps show the sleep time (per hour) of six mice from the DZP
group and the control group. The DZP group received 5 mg/kg of DZP i.p. on ZT13 of
the second day, while the control group received vehicle i.p. at the same time. The
DZP group showed increased sleep time for the first 5 hr after i.p. injection. Red barat the top of the plot shows the period used for comparing the parameters in Figure
S3E.
(E) DZP increased the sleep time and Pws significantly during the 5 hr (ZT13 to 17)
after i.p. administration. (Left) Total sleep time. (Middle and right) Average Pws and31

Psw. Error bars represent SEM (n = 6 for each group). Student's t-test or two-sample
Wilcoxon test.
(F) A single i.p. administration of DZP increased sleep. Plot shows the average sleep
time for the DZP and control groups. Red bar at top indicates the 5-hr period used forcomparing the groups in Figure S3E.
(G) After DZP increased the sleep time significantly during the first 5 hr (ZT13 to 17),
DZP conversely decreased the sleep time during the second 5 hr (ZT18 to 22) after
i.p. administration. (Left) Total sleep time (p < 0.05). (Middle and right) average Pws
and Psw. Error bars represent SEM (n = 6 for each group). Student's t-test, Welch's
t-test or two-sample Wilcoxon test.
(H) Performance of SSS during MAP induced sleep decrease. When 3 mg/kg of MAP
i.p. was given on ZT2, the accuracy, sleep sensitivity, sleep specificity, wakesensitivity and wake specificity against EEG derived sleep status were over 90%.
Error bars represent SEM (n = 4 for each group). The light and dark blue bars show
the saline and MAP administered group respectively. The dashed line denotes 90%.
(I) Performance of SSS during DZP induced sleep increase. When 5 mg/kg of DZP i.p.
was given on ZT13, the accuracy, sleep sensitivity, sleep specificity, wake sensitivity
and wake specificity against EEG derived sleep status were over 90%. Error bars
represent SEM (n = 4 for each group). The light and dark blue bars show the saline
and DZP administered group respectively. The dashed line denotes 90%.
(J) Sleep time of circadian-clock mutants evaluated by SSS. Altered sleep/wakephenotypes were detected in circadian-clock mutants. Heatmaps show the sleep time(per hour) for a week under light/dark conditions for C57BL/6N (n = 108), Bmal1 KO

32

mice (n = 34), Cry1/Cry2 double-KO mice (n = 27) and Per1/Per2 double-KO mice (n= 26), respectively.
(K) Sleep time (per hour) over 24 hr averaged over six days in circadian-clock
mutants. Red lines show the mean sleep time at each time of day for the mutants.
Gray line shows the data for C57BL/6N male mice (n = 108). The shaded area aroundthe line is the SEM for each time point.
(L) Averaged Pws and Psw over the course of one day in circadian-clock mutants. Redlines represent the mean transition probabilities at each time of day for the mutant
strains. Gray line shows the results for C57BL/6N male mice (n = 108).
(M) Distributions of sleep/wake parameters for circadian-clock mutants. Dunnett’s testor Dunnett's modified Tukey-Kramer pairwise multiple comparison test compared to
C57BL/6N male mice. See also Table S1.

33

Figure S4. Combined use of a triple-target CRISPR and SSS enables
sleep/wake phenotyping in single- and double-knockout mice, Related to
Figure 4.
(A-C) Target sequences of the gRNAs for knocking out clock genes. Bmal1 (A), Cry1
(B, left), Cry2 (B, right), Per1 (C, left), and Per2 (C, right); each gene had three target
sequences. Mouse genomic sequence data were obtained from GRCm38/mm10 via
the UCSC Genome Browser (http://genome.ucsc.edu/) (Rhead et al., 2010). The
colored letters (blue, orange, and green) show the 20-base target sequences. The
target sequences were designed on the sense (+) or the antisense (-) strand of
genomic DNA.
(D-G) The genotype of Tyr KO and clock KO mice. Tyr (D), Bmal1 (E), Cry1/Cry2 (f)and Per1/Per2 (G); each gene had three targets. The relative amount of intact DNA
for each target sequence was measured by quantitative PCR (qPCR). The genomic
DNA was purified from the tail of each mouse. The relative amount of intact DNA foreach target sequence was scaled so that the level in a wild-type mouse was definedas 100%. The numbers and wt below the bar plot denotes the mouse id and thewild-type mouse, respectively. Genotyping by sequencing was done for three Tyr KO
mouse (D) and Per2 gene of one Per1/Per2 KO mice (G). The sequences of mutant
alleles are shown. The target sites are colored and underlined. The mutations arelabeled in red. Red texts show the description of the sequence with the number ofdetected allele in parentheses.
(H) Average time course of the Pws and Psw for Tyr KO and circadian-clock mutants.
Red lines and red shaded area represent the mean and SEM of the transition
probabilities for each time of day for the mutants. Gray line shows the results of
C57BL/6N male mice (n = 108).
34

(I) Sleep time (per hour) phenotype under constant darkness for individual mice incircadian mutants produced by triple-target CRISPR. Sleep-time phenotype over six
days is shown for the three circadian-clock mutant mice, Bmal1 KO mice (n = 6),
Cry1/Cry2 (n = 5) and Per1/Per2 KO mice (n = 2).
(J) Sleep time (per hour) under constant darkness over 24 hr averaged over six daysin circadian mutants produced by triple-target CRISPR. Red lines and red shaded
area represent the mean and SEM of sleep time at each time of day for each strain.
The light gray and black bar written at the bottom of the panel denotes the subjective
time of the animal.
(K) Target sequences of the gRNAs for knocking out Hcrt gene. Mouse genomic
sequence data were obtained from GRCm38/mm10 via the UCSC Genome Browser(http://genome.ucsc.edu/) (Rhead et al., 2010). The colored letters (blue, orange,and green) show the 20-base target sequences. The target sequences were
designed on the sense (+) or the antisense (-) strand of genomic DNA.
(L) The genotype of Hcrt KO mice. The relative amount of intact DNA for each target
sequence was measured by qPCR. The genomic DNA was purified from the tail ofeach mouse. The relative amount of intact DNA for each target sequence was scaled
so that the level in a wild-type mouse was defined as 100%. The numbers and wt
below the bar plot denotes the mouse id and the wild-type mouse, respectively.
(M) Average time course of the Pws and Psw for Hcrt KO mice. Red lines and red
shaded area represent the mean and SEM of the transition probabilities for each timeof day for the mutants. Gray line shows the results of C57BL/6N male mice (n = 108).

35

Figure S5. Reverse genetics without crossing revealed Nr3a mutant to be ashort-sleeper, Related to Figure 5.
(A-G) Target sequences of the gRNAs for the knockout of NMDA receptor family. Nr1(A), Nr2a (B), Nr2b (C), Nr2c (D), Nr2d (E), Nr3a (F), and Nr3b (G); each had threetarget sequences. The mouse genomic sequence data were obtained from
GRCm38/mm10 via the UCSC Genome Browser (http://genome.ucsc.edu/) (Rhead
et al., 2010). The colored letters (blue, orange, and green) show the 20-base target
sequences. The target sequences were designed on the sense or antisense strand of
genomic DNA.
(H-L) The genotype of NMDA receptor family KO mice. Nr2a (H), Nr2c (I), Nr2d (J),
Nr3a (set 1) (K), and Nr3b (L); each had three targets. The relative amount of intact
DNA for each target sequence was measured by quantitative PCR (qPCR). The
genomic DNA was purified from the tail of each mouse. The relative amount of intact
DNA for each target sequence was scaled so that the level in a wild-type mouse wasdefined as 100%. The numbers and wt below the bar plot denotes the mouse id andthe wild-type mouse, respectively. Genotyping by sequencing was done for one Nr3a
KO mouse (K) and one Nr3b KO mice (L). The sequences of mutant alleles areshown. The target sites are colored and underlined. The mutations are labeled in red.
Red texts show the description of the sequence with the number of detected allele inparentheses.
(M) Average time course of the Pws and Psw of the NMDA receptor family systematic
KOs for one day. Red lines and red shaded area in each chart represent the mean
and SEM of the Pws or Psw for each time of day for the mutants. Gray line shows theresults of C57BL/6N male mice (n = 108).

36

Figure S6. Nr3a mutant is a short-sleeper in multiple KO strains and inconventional sleep analysis, Related to Figure 5.
(A) The second set of target sequences of the gRNAs for knocking out Nr3a gene.
Note that these sequences are distinct from set 1 sequences (Figure S5F). Mousegenomic sequence data were obtained from GRCm38/mm10 via the UCSC Genome
Browser (http://genome.ucsc.edu/) (Rhead et al., 2010). The colored letters (blue,
orange, and green) show the 20-base target sequences. The target sequences were
designed on the sense (+) or the antisense (-) strand of genomic DNA.
(B) The genotype of Nr3a (set 2) KO mice. The relative amount of intact DNA for eachtarget sequence was measured by quantitative PCR (qPCR). The genomic DNA was
purified from the tail of each mouse. The relative amount of intact DNA for each target
sequence was scaled so that the level in a wild-type mouse was defined as 100%.
The numbers and wt below the bar plot denotes the mouse id and the wild-type
mouse, respectively.
(C) Average time course of the Pws and Psw for Nr3a KO mice (set 2). Red lines andred shaded area represent the mean and SEM of the transition probabilities for eachtime of day for the mutants. Gray line shows the results of C57BL/6N male mice (n =108).
(D) The genotype of Nr3a (set 1) KO mice used for EEG/EMG analysis. The relative
amount of intact DNA for each target sequence was measured by qPCR. The
genomic DNA was purified from the tail of each mouse. The relative amount of intact
DNA for each target sequence was scaled so that the level in a wild-type mouse wasdefined as 100%. The numbers and wt below the bar plot denotes the mouse id andthe wild-type mouse, respectively.

37

(E) The sleep time of Nr3a (set 1) KO evaluated by EEG/EMG based sleep staging.
Nr3a KO group (n = 2) showed a significant short-sleeper phenotype (p = 0.02005,
Student's t-test) than the wild-type group (n = 6). Error bars represent SEM.
(F) Mass spectrometric signals obtained by monitoring a tryptic peptide of Nr3a
protein (upper) in each strain in the presence of isotopically labeled internal standard(lower) are shown. The peptide was absolutely quantified using a triple quadruple
mass spectrometer (TSQ Vantage EMR mass spectrometer, Thermo Scientific). The
peptide concentrations, which represents the Nr3a protein concentrations, were
determined to be 2.2 atmol/µg in Tyr KO #3 and 5.7 atmol/µg in Tyr KO #4, whereas
the peak was not observed in any Nr3a KO mice.

38

Figure S7. Exome analysis of whole-body biallelic Nr3a (set 2) KO mice
produced by triple-target CRISPR method, Related to Figure 6.
(A) The genomic regions targeted by the three gRNAs were shown for two mice in
Nr3a (set 2) KO group (Figure S6B). The upper part of each panel shows readcoverage, and the lower part shows read alignments (rectangles). Horizontal lines
between rectangles indicate read pairing. Different types of mutations occurring at thetarget sites are highlighted as follows: inter-exon deletion (red rectangles); intra-exon
deletion mutation (green rectangles); short deletion (red dots); and short insertion(blue dots).
(B) Enlarged View of Read Coverage of Exome Sequence of Nr3a KO. Diminisheddepth shows a deletion mutation with the most severely affected site pointed by a redarrow. A blue vertical bar indicates an insertion mutation whose proportion is shownby its length. Percentages in parentheses are the proportions of indel mutations thatcan cause loss of gene function.

39

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40

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41

A

B

1000

α = 0.10750Single-gene biallelic KO
Count

Predicted total knockout probability

Single-gene mono-allelic KO
1.0
0.5

# of
targets
10
6
3
2
1

0.0
1.0

Double-gene biallelic KO

Triple-gene biallelic KO

500
250
0

0.1 1.0 10.0
gRNA dissociation constant (K)C

Single-gene biallelic KO efficiency
Total gRNA concentration (S)
5
10
15

0.5
0.05
Recovery rate
0.10
of mutation (α)
0.15

0.0
0.0

2.5

5.0

7.5 10.0

0.0

2.5

5.0

7.5 10.0

0.85 ± 0.11

0.95 ± 0.05

0.97 ± 0.03

0.83 ± 0.11

0.93 ± 0.06

0.96 ± 0.03

0.79 ± 0.12

0.90 ± 0.07

0.93 ± 0.04

Total gRNA concentration (S)D
chr7 (-) 87,490,000-187,460,000

87,430,000

Tyr exon1

Tyr exon5

SSA vector-1

ggcacctatggccaaatgaacaatgggtcaacacccatgtttaatgatatcaacatctacgacctctttgtatggatgcattactatgtgtcaagggacacactgcttgggggctctgaaatatggagggacattgattttgcccatgaagcaccagggtttctgccttggcacagacttttcttgttattgtgggaac

Tyr target sequence-1
SSA vector-2/3
tggaacaagccagtcgtatctggccatggcttcttggggcagcactggtgggagct

gttattgctgcagctctctctgggcttagcagtaggctatgccttcagaagaagaagaagaagaagcaaccccaggaggaaaggcagccactcctcatggacaaagacgactaccacagcttgctgtatcagagccatctgtga

Tyr target sequence-2

E

Tyr target sequence-3

F

Luc (N) 1-1131

STOP

Luc (N) 1-1131

STOP

Target seq

Luc (C) 430-1653

seq

Luc (C) 430-1653

Target

Luc (

Luc (
SV40 promoter

C) 43

N) 1-

1131

0-16

α = 0.13643
Single-gene biallelic KO

Predicted total
knockout probability

Homologous sequence

SV40 promoter

53

1.0

0.975
0.826

Single-target
Tyr-1 Tyr-2 Tyr-3
Triple-target (Tyr-1,2,3)Experiment
Simulation

0.5
S = 5.7248
0.0 2.5 5.0 7.5 10.0
Total gRNA concentration (S)0.0# of targets
3 1

Luc 1-1653

G

Single target (Tyr-2)

Single target (Tyr-1)

H

Single target (Tyr-3)

I
Genome DNA / Tbp(Wild-type =100%)

125

Scalp

Target-1

75

Target-2

50

Target-3

25
Wildtype#1#2

Tyr knockout

Wildtype#1#2

Tyr knockout

Wildtype#1

Tyr #3

Tyr #4

300

100

0

Nr3a (set 2) #10

400

Tail

#2

Tyr knockout

Signal intensity

Brain

200
100
0
40000
30000
20000
10000
0

69

Figure S1 G.A. Sunagawa

70

71

72

69

70
71
Time (min)

72

69

70

71

72

A
NREM Sleep

Voltages (mV)
EMG
EEG

0.5

REM Sleep

Wake

0.0
-0.5
0.5
0.0

0.2
0.0

Non-invasive respiratory recording

C
5
PC2

2

Relative Power (A.U.)
0
80

10

4
Time (sec)Power spectrum analysis

0

0
PC1

Principal component analysis

Mouse #2

Mouse #3
Accuracy 95.6%

10

Sleep Sens. 93.5%
Spec. 97.7%

5

−20
SSS

0
PC1

Wake

−10

20

Sens. 98.3%
Spec. 93.1%−20

EEG/EMG

SSS

60

0
PC1

−5

20

EEG/EMG

30

0

0

D

0

Sens. 96.4%
Spec. 95.4%

20

male (n = 178)

Count

C57BL/6J

20
10
0

0.5

0.2
-0.2

0

C57BL/6J
female (n = 173)

20
10

10

10

0
10

0

0

10

10

0
30

0
20

0

0

30

20

20

20

10

10
0

0
0 500 1000
Sleep time (min)10

10
0.0 0.2 0.4 0.6
Amplitude

0
0.000

0.050
P ws

Figure S2 G.A. Sunagawa

4

8

0

Time (sec)

4

8

90

75
50
25
0

24 48 72 96 120 144
Time (hr)Countfemale (n = 72)

Active
awake

-0.5

10

Count

C57BL/6N

20

Sleep staging

100

EEG/EMG

Sleep
0
PC1

-0.5

E
30

Wake

0

Quiet
awake

0.5

8

e
ak
aw ke
e
tiv waAc al arm ake
No aw
iet
Qu

24 48 72 96 120 144
Time (hr)

SSS

60

30
0

6

−10
−20

20

−30 −20 −10 0 10 20
PC10

24 48 72 96 120 144
Time (hr)

Wake

−10

30
0

0
PC1

0

−5

Sens. 98.1%
Spec. 92.5%

4

10

Sleep Sens. 95.8%
Spec. 95.9%

5

0

Wake

2

Propability density clustering

Accuracy 95.3%
10

0

0

−10
−20

20

Mouse #1

060

8Accuracy 94.9%

−5
−10

6

10

−10
−20

30 60 90
Time (min)Sleep Sens. 93.3%
Spec. 97.6%

10

Sleep time (min)

0

20
15
10
5
0
0

8

PC2

Frequency (Hz)B

6

Accuracy (%)

4

Voltages(mV)
Resp.
(Pa) EMG EEG

2

PC2

-0.2
0

PC2

Resp. (Pa)

-0.5

0
0.000

0.050
P sw

B

D

Sleep time (min)

0

12

24

36

48

60

Time (hr)

60

0

0.00

0

5

0

0.025
0.000

5

0.000

5

0

0

0 5

0

Psw

0.25
0.00

3

0

Bmal1 (n = 34)

Cry1/Cry2 (n = 27)

Per1/Per2 (n = 26)

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

1 2 3 4 5 6 7

Cry1/Cry2 (n = 27) Per1/Per2 (n = 26)

0

0

L

30
0

6

12 18 246

12 18 240

6

12 18 24

P ws

Bmal1 (n = 34)

0.2
0.1
0.0

P sw

60

0.2
0.1
0.0

Time (hr)Bmal1 (n = 34)

06

12 18 24

0

5

Bmal1

**
**

Per1/Per2
600
800
1000
Sleep time (min)

0.2

0.4
0.6
Amplitude

0.8

0.04
Pws

Figure S3 G.A. Sunagawa

0.06

72

Cry1/Cry2 (n = 27)

0

6 12 18 24
Time (hr)

0.03

0.04
Psw

Per1/Per2 (n = 26)

0

**
**
***
0.02

5

60

***
*
***
0.02

60

0 5
0
DZP (mg/kg)

M
Cry1/Cry2

48

Sleep time (min)B6N (n = 108)
Mouse
Sleep time (min)K

36Time (hr)

Sleep
Wake
Wake
Sleep
sensitivity specificity sensitivity specificity

5

JTime (Day)24

Accuracy

0.25
0DZP (mg/kg)
05

SSS statistics against EEG based stages

0.50

3

12

I

0.75

0 3
0
MAP (mg/kg)

72

0

0.50

3

60

20

1.00

0

48

40

0.75

3

36

Time (hr)

60

1.00

0

24

i.p.

5

SSS statistics against EEG based stages

0.00

12

F

0.04

0.00

20

0

0.02

0 5

40

3

0.06

0.050

60

Sleep
Wake
Wake
Sleep
Accuracy
sensitivity specificity sensitivity specificity

0.075

0

0

0.025

50

H

Psw

Pws

0
DZP (mg/kg)

3

0.075

100

0.050

0.02

0.00
0

150

n.s.

0.04

50

3

n.s.

0
DZP (mg/kg)

n.s.

100

0.05

0.00
0

0.06

150

0.02
0.01

0
MAP (mg/kg)

Sleep time (min)

0

30

0.10

E

5

DZP (mg/kg)
Second 5 hours after i.p.
Sleep time (min)

60

i.p.

0

G

48

0.03

60

MAP (mg/kg)
0
3

i.p.

Sleep time (min)36Time (hr)

0.15

Psw

24

0.04

90

Pws

12

First 5 hours after i.p.
Sleep time (min)

0

60

C

n.s.
120

First 3 hours after i.p.
Sleep time (min)Sleep time (min)03

MAP (mg/kg)

i.p.

Pws

A

0.05

6

12 18 24

0

5

A

Bmal1
chr7(+)

113,250,000

113,300,000
Bmal1
Bmal1

gtgtggactgcaatcgcaag (+)
Bmal1 target sequence-1

B

Cry1

chr10(-)

gtgttctgcatattctaacc (-)
Bmal1 target sequence-2

gtcagattgaaaagaggcgt (+)
Bmal1 target sequence-3

85,160,000

85,140,000

85,180,000

Cry292,270,000

chr2(-)

92,260,000

92,250,000
Cry2
Cry2

Cry1

gaaatatggcgttccttccc (+)
Cry1 target sequence-1

C

gtcaaagcgtgggttgtttg (-)
Cry1 target sequence-2

gtcctcctaccaaaacccac (-)
Cry1 target sequence-3

69,1050,000

69,110,000
Per1
Per1

gtcatattcaaaggtcaaac (-)
Cry2 target sequence-1

Per1

gtctctgcatgattgacgat (-)
Cry2 target sequence-3

Per2

chr11(+) 69,100,000

gacatgagtggtcccctaga (+) ggacatgtctacttacaccc (+)
Per1 target sequence-1
Per1 target sequence-2

D

chr1(-)

ggtagcgaatctcattctcg (-)
Per2 target sequence-1

gtctatatttcggagcaggc (+)
Per1 target sequence-3

Target-1
Target-2
Target-3
wt

3

4

5

Tyr #46

7

8

9

Target-1
Target-3

Genome DNA / Tbp(Wild-type =100%)

Target-2Reference: GGTGGGAGCTGTTATTGCTGCAGCTCTCTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCCC
AGGAGGAAAGGCAGCC
Mouse #4: GGTGGGAGCTGTTATTGCTGCAGCTCCCTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGAA---------------AGGCAGCC 1 bp mutation, -16 bp (5)GGTGGGAGCTGTTATTGCT-----------------------------ATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCCCCTAAAGGAGGAAAGGCAGCC -29 bp, +4 bp (10)

Tyr #5

Target-1

Reference: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACT
GCTTGGGGGCTCTGAAATATGGAGGGACATTG
Mouse #5: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACT
GCTTGGGGGCTCTGAAATATGGAGGGACATTG wild type (3)ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACA--CTTGGGGGCTCTGAAATATGGAGGGACATTG -3 bp [TLL→TL](1)
ACGACCTCTTTGTATGGATGCATTACTATGTGTCA---------GA
GCTTGGGGGCTCTGAAATATGGAGGGACATTG -11 bp+2 bp [VSRDTLLG→VSELG] (8)ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGA------(+60base)GCTTGGGGGCTCTGAAATATGGAGGGACATTG -6 bp+60 bp [including stop codon] (3)Target-2

Target-3

Genome DNA / Tbp(Wild-type =100%)

Target-1
Reference: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACT GCTTGGGGGCTCTGAAATATGGAGGGACATTG
Mouse #4: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACT GCTTGGGGGCTCTGAAATATGGAGGGACATTG wild type (3)ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACTTGCTTGGGGGCTCTGAAATATGGAGGGACATTG +1 bp (3)
ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGA------ ---------CTCTGAAATATGGAGGGACATTG -15 bp [∆ TLLGG] (7)Target-2

Target-3

Reference: GGTGGGAGCTGTTATTGCTGCAGCTCT CTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCCC
AGGAGGAAAGGCAGCCACTCCTCATGG
Mouse #6: GGTGGGAGCTGTTATTGCTGCAGCTCTTCTCTGGGCTTAGCAGTAGGCTACGCCTTCAGAAGAAGAAGAAGAAG--------------------CAACCACTCCTCATGG +1 bp, -24 bp+3 bp (4)
GGTGGGAGCTGTTATTGCTGCAGCTCTTCTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAG-----------------------CAACCACTCCTCATGG +1 bp, -27 bp+3 bp(1)
GGTGGGAGCTGTTATTGCT-------- ---------------------ATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCCCCTAAAGGAGGAAAGGCAGCCACTCCTCATGG -29 bp, +4 bp (1)
GGTGGGAGCTGTTATTGCTGCAG---- --------------------------------------------------------GAAAGGCAGGAAAGGCAGCCACTCCTCATGG -63 bp+7 bp (2)
GGTGGGAGCTGTTATTGCTGCAGCTCT C------------------------------------------------------AGGAGGAAAGGCAGCCACTCCTCATGG -55 bp (6)

P ws

0.2
0.1
0.0

P sw

H
0.2
0.1
0.0

0

wt

1

2

120
100
80
60
40
20
0

3

4

5

wt

1

2

3

4

5

wt

1

5

Target-1-6 bp (5)
+1 bp (4)Target-2

wt

1

2

wt

1

Reference: GGTTGATGAAGCTGGACCAGCTAGTGTCCAGTGTGATGTACTCCCCG TTGCGGGTGCGGAATCGAATGGGAGAATAATC
Mouse #2: GGTTGATGAAGCTGGACCAGCTAGTGTCCAGTGTGATGTACTCCC-- ---CGGGTGCGGAATCGAATGGGAGAATAATC -5 bp (2)
GGTTGATGAAGCTGGACCAGCTAGTGTCCAGTGTGATGTACTCCCCGTTTGCGGGTGCGGAATCGAATGGGAGAATAATC +1 bp (6)Per1/Per2 (n = 2)

6

0

0

0

6

12 18 24

-5 bp (15)Target-3

2

Cry1/Cry2 (n = 5)

12 18 24

4

Reference: GGAGCTGCACGAGCACAGGCGTCTCGATCAGATCCTGAGGTAGATAGCCCAGGAGGGGGACCGCCCTGCGAGGCAAGGG
Mouse #2: GGAGCTGCACGAGCACAGGCGTCTCGATCAGATCCTGAGGTAGAT-----AGGAGGGGGACCGCCCTGCGAGGCAAGGG

Bmal1 (n = 6)63

Reference: TTGACCAGGTAGGGTGTCATGCGGAAGGGCTGGTAGCGAATCTCATTC TCGTGGTGTTTCCCAACACTGTGGAGAGAG
Mouse #2: TTGACCAGGTAGGGTGTCATGCGGAAGGGCTGGTAGCGAATC------ TCGTGGTGTTTCCCAACACTGTGGAGAGAGTTGACCAGGTAGGGTGTCATGCGGAAGGGCTGGTAGCGAATCTCATTCATCGTGGTGTTTCCCAACACTGTGGAGAGAG

Tyr (n = 7)

12 18 24

2

Per2 #2

Per2

Per1

6

Cry2

Cry1

G

Reference: GGTGGGAGCTGTTATTGCTGCAGCTCT CTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCCCAGGAGGAAAGGCAGCCACTCCTCATGGACAAAGACGAMouse #5: GGTGGGAGCTGTTATTGCTGCAGCTCTACTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGAAGCAACCC-----------------ACTCCTCATGGACAAAGACGA +1 bp, -17 bp (6)
GGTGGGAGCTGTTATTGCTGCAGCTCT --------------------------------------------------------AGGAGGAAAGGCAGCCACTCCTCATGGACAAAGACGA -56 bp (4)
GGTGGGAGCTGTTATTGCTGC--TAAG CTCTGGGCTTAGCAGTAGGCTATGCCTTCAGAAGAAGAAGAAGAAGA---------AGGAGGAAAGGCAGCCACTCCTCATGGACAAAGACGA -6 bp+4 bp, -9 bp (8)Tyr #6

120
100
80
60
40
20
0

gtgtgatgtactccccgttg (-)
Per2 target sequence-3

Bmal1

120
100
80
60
40
20
0

F

Reference: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACAC TGCTTGGGGGCTCTGAAATATGGAGGGACATTG
Mouse #2: ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACT TGCTTGGGGGCTCTGAAATATGGAGGGACATTG +1 bp (5)
ACGACCTCTTTGTATGGATGCATTACTATGTGTCAAGGGACACACTGTGCTTGGGGGCTCTGAAATATGGAGGGACATTG +2 bp (9)91,420,000

gatcctgaggtagatagccc (-)
Per2 target sequence-2

E

Tyr

91,435,000

91,450,000

Per2Genome DNA / Tbp(Wild-type =100%)

120
100
80
60
40
20
0

gcgagaattcttctacacag (+)
Cry2 target sequence-2

6

12 18 24

1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7Time (Day)K Hcrtchr11(-)

J60

0100,762,500

100,762,000
Hcrt

catgaactttccttctacaa (+)
Hcrt target sequence-1

caacagttcgtagagacggc (-)
Hcrt target sequence-2

aaagcggtggtagttacggt (-)
Hcrt target sequence-3

0

0

6

12 18 24

1

Per1/Per2 (n = 2)

0

0

2

Figure S4 G.A. Sunagawa

3

4

6

12 18 24

Time (Hour)

M

Hcrt

wt

Cry1/Cry2 (n = 5)

30

L
120
100
80
60
40
20
0

Bmal1 (n = 6)

60

5

6

7

P ws

Per1/Per2 (n = 2)

0.2
0.1
0.0

P sw

Cry1/Cry2 (n = 5)

Mouse

Bmal1 (n = 6)

Sleep time (min)I

Sleep time (min)Time (Hour)

0.2
0.1
0.0

6

12 18 24

Hcrt (n = 7)06

12 18 24

Time (Hour)A Nr1chr2(-)

25,305,000

25,315,000

B Nr2a25,295,000

chr16(-)

Nr1
Nr1
Nr1os
Nr1
gaccccaagattgtcaacat (+) gtgggagtgaagtggtcgtt (-)
Nr1 target sequence-1
Nr1 target sequence-2

C Nr2bchr6(-)

9,600,000
Nr2agtgaagtggtcgttgggagt (-)
Nr1 target sequence-3

gaaattctcctacattcccg (+)
Nr2a target sequence-1

ggtcatgctgaagatcatgc (+)
Nr2a target sequence-2

D Nr2c135,800,000

136,100,000

9,800,000

9,950,000

chr11(-) 115,265,000

115,260,000

gatcttgacaaacttccgac (-)
Nr2a target sequence-3

115,255,000
Nr2c
Nr2c

Nr2b

ggccaacaccaaccagaact (-) gatggaagtcatctttctcg (-)
Nr2b target sequence-1 Nr2b target sequence-2

E Nr2dchr7(-) 45,865,000

gttcacgaagtcctggtagc (-)
Nr2b target sequence-3

45,850,000

gcatcctcacccaaatctgt (+)
Nr2c target sequence-1

F Nr3a (set 1)
chr4(-)

45,835,000

gttctcctacgacttgtacc (+)
Nr2c target sequence-2

gtggaatggtatgatcggtg (+)
Nr2c target sequence-3

49,750,000

49,800,000

49,700,000
Nr3a
Nr3a
Pp3r2

Nr2d

Nr3b

chr10(+)

79,974,000

79,972,000H

79,977,000
Nr3bgctacggctattgcagttcc (+)
Nr3b target sequence-1

gagattcatgaggaagcgtc (-)
Nr3b target sequence-3

2

160
140
120
100
80
60
40
20
0

3

4

5

6

7

8

9

Genome DNA / Tbp(Wild-type =100%)

1

10 11 12 13 14 15

L

Nr3a (set 1)

wt

1

2

Nr3a (set 1) #6

3

4

5

6

7

8

gatagactcgaggtggaact (-) gctaaacaggctatccagta (-)
Nr3a target sequence-2 Nr3a target sequence-3

Nr2a
Target-1
Target-2
Target-3
wt

120
100
80
60
40
20
0

1

2

3

4

5

6

7

8

Nr2d

wt

200
180
160
140
120
100
80
60
40
20
0

1

2

3

4

5

6

7

8

9

10 11 12 13

Nr3b

wt

1

2

3

4

5

6

7

8

9

10 11

Target-1

Reference: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCA TCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT
Mouse #6: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCATTCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT +1 bp (3)CCTACAACCTGTCTTTGGAAGTAGTGATGG--- -------------------CCTGCCACTTATGCCTT -22 bp (4)Reference: CTATAACCTTTCCCGAGGCGCGGCCTGAGCTACGGCTATTGCAGTTCCTGGCAGCTGCCACAGAGACCCCGGTGCTGAGCGTCCTACGGAGG
Mouse #7: CTATAACCTTTCCCGAGGCGCGGCCTGAGCTACGGCTATTGCAG----------CTGCCACAGAGACCCCGGTGCTGAGCGTCCTACGGAGG
CTATAACCTTTCCCGAGGCGCGGCCTGAGCTACGGCTATTGC----------------CACAGAGACCCCGGTGCTGAGCGTCCTACGGAGG
CTATAACCTTTCCCGAGGCGCGGCCTGAGCTACGGCTATTGCAG-TCCTGGCAGCTGCCACAGAGACCCCGGTGCTGAGCGTCCTACGGAGG

Reference: TAGCAGTGATGTTGATGATAGACTCGAGGTGGA ACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG
Mouse #6: TAGCAGTGATGTTGATGATAGACTCGAGGTGGACACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG +1 bp (4)TAGCAGTGATGTTGATGATAGACTCGAGG---- ACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG -4 bp (3)Target-2

Target-3

0.2
0.1
0.0
0.2
0.1
0.0

Target-3+1 bp mutation (5)-5 bp (2)Nr2a (n = 8)0

6

-10 bp (3)-16 bp (4)-1 bp (3)Reference: ATGACTCGGCCAGGCTGGATGCGCTCTTCACTGCATTGGAGACTACCGGACGCTTCCTCATGAATCTCTGGGCAATCTTCTGCCTG
Mouse #7: ATGACTCGGCCAGGCTGGATGCGCTCTTCACT------------(Δ547base)------------TCCTCATGAATCTCTGGGCAATCTTCTGCCTG -547 bp (16)≈Reference: TACTGCTATGTAGGCTGCTAAACAGGCTATCCAGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA
Mouse #6: TACTGCTATGTAGGCTGCTAAACAGGCTATCCTGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGATACTGCTATGTAGGCTGCTAAACAGGC-----AGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA

M

Target-1

Nr3b #7

Target-2

P ws

wt

120
100
80
60
40
20
0

J

Nr2c

P sw

K

120
100
80
60
40
20
0

Genome DNA / Tbp(Wild-type =100%)

Genome DNA / Tbp(Wild-type =100%)I

ggatgcgctcttcactgcat (+)
Nr3b target sequence-2

Genome DNA / Tbp(Wild-type =100%)G

ggaagtagtgatggccatcg (+)
Nr3a target sequence-1

gactactccttcaatgagga(+) ggcgtctggaatggcatgat(+)
Nr2d target sequence-2 Nr2d target sequence-3

Genome DNA / Tbp(Wild-type =100%)

ggatctgcgcactgacacta(-)
Nr2d target sequence-1

Nr2c (n = 15)

12 18 24 0

6

12 18 24 0

Nr2d (n = 13) Nr3a (set 1) (n = 8) Nr3b (n = 11)6

12 18 24 0

6Time (Hour)

Figure S5 G.A. Sunagawa

12 18 24 0

6

12 18 24

A
Nr3a (set 2)
49,800,000

gaaaacctgaaccgtgtgga (+)
Nr3a target sequence-4

Nr3a (set 1)

ggccacatgaaggctccaat (-)
Nr3a target sequence-6

C

Nr3a (set 2)

120
100
80
60
40
20
0

Target-1
Target-2
Target-3
wt

1

Nr3a (set 1) #9
120
100
80
60
40
20
0

gactaatgactcttccatac (+)
Nr3a target sequence-5

2

3

4

5

6

7

8

9

0.2
0.1
0.0

Nr3a (set 2)(n = 12)0

6

12 18 24

Time (Hour)E

Nr3a (set 1) #10

Reference: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCA TCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT
Mouse #9: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCATTCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT +1 bp (16)Reference: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCA TCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT
Mouse #10: CCTACAACCTGTCTTTGGAAGTAGTGATGGCCATTCGAGGCGGGCCTGGGCGACCTGCCACTTATGCCTT +1 bp (14)
CCTACAACCTGTCTTTGGAAGTAGTGAT----- --------------GGCGACCTGCCACTTATGCCTT -19 bp (2)Target-2Reference: TAGCAGTGATGTTGATGATAGACTCGAGGTGGAACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG
Mouse #10: TAGCAGTGATGTTGATGATAGACTCGAGGTGGAACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGGTAGCAGTGATGTTGATGA --------------AACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG

wild type (16)Target-3

wild type (6)-14 bp (10)Target-3

Reference: TACTGCTATGTAGGCTGCTAAACAGGCTATCCAGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA
Mouse #9: TACTGCTATGTAGGCTGCTAAACAGGCTATCCAGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA

EEG based sleep timecomparison

Target-1

Target-2

9 10

0.2
0.1
0.0

10 11 12Target-1

Reference: TAGCAGTGATGTTGATGATAGACTCGAGGTGGAACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG
Mouse #9: TAGCAGTGATGTTGATGATAGACTCGAGGTGGAACTTGGAGTTATTCTCTGTAAGGAGTAGGAAGTCGG

wt

Nr3a
Nr3a
Pp3r2

Sleep time (min)D

49,700,000

P ws

Genome DNA / Tbp(Wild-type =100%)

B

49,750,000

P sw

chr4(-)

Reference: TACTGCTATGTAGGCTGCTAAACAGGCTATCCA GTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA
Mouse #10: TACTGCTATGTAGGCTGCTAAACAGGCTATCCA GTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA wild type (7)TACTGCTATGTAGGCTGCTAAACAGGCTATCCAAGTATGGAAGAGTCATTAGTCATAGGGTCTAGACAGA +1 bp (8)wild type (15)

800
600
400
200
0
WT

F

Tyr

Nr3a (set 1)

3

4

1

1500

1500

1000

1000

500

500

Signal intensity

Signal intensity

#

0
30000
67.46

20000

67.50

3

67.28

67.09

4

5

67.62

66.21

68

66

6

7

8

0
30000

67.20

66.87

20000

67.19
66.31

10000

10000
0

2

66

68

66

Time (min)

0

68

66

68 66

68

66

68

Time (min)

66

68

66

66

68

Nr3a (set 2)
#

1

2

3

67.40

66.65

66.42

4

5

6

7

8

9

66.31

67.22

10

11

12

1500

Signal intensity

1000
500
0
30000

66.06

66.56

20000

67.35

67.46

66.24

66.50
65.50

10000
0

66

68

66

68

66

66

66

68

66

66

66

Time (min)Figure S6 G.A. Sunagawa

68 66

68

66

68

64

66

66

68

Nr3a

A

Nr3a exon1
Chr4 (-)

Nr3a exon3
49,844 kb

49,846 kb

49,773 kb

49,842 kb

Target-4

49,771 kb

49,769 kb

49,767 kb

Target-5 Target-6

Wild-typeNr3a (set 2) #1

Intra-exon deletion
and inversion

Short insertion

Nr3a (set 2) #7

Intra-exon deletion
and inversion

Inter-exon deletion and inversion

long deletion

Short deletion

Short deletion
Short deletion
Short insertion

B

Nr3a exon1

Chr4 (-)

Nr3a exon2

49,844,600 bp

49,844,650 bp

49,792,850 bp

Target-1

Nr3a exon3
49,771,100 bp

49,792,800 bp

Target-2

49,771,050 bp

Target-3

Wild-type
350

400

200

Nr3a (set 1) #6
Coverage: 247 (97.6% affected)

400

200

Coverage: 129 (99.2% affected)

Deletion: 136

Deletion: 71

Insertion: 105

Insertion: 59

300

Coverage: 146 (58.9% affected)
Deletion: 86

Nr3a (set 1) #8
Coverage: 200 (97.0% affected)

300

80

Coverage: 46 (95.7% affected)

300

Deletion: 194

Coverage: 153 (100.0% affected)
Deletion: 153

Insertion: 44

Nr3a exon1
Chr4 (-)

49,844,700 bp

Nr3a exon3
49,771,150 bp

49,844,650 bp

Target-4

Nr3a exon3

49,771,100 bp

49,770,800 bp

Target-5

49,770,750 bp

Target-6

Wild-type
350

350

350

Nr3a (set 2) #1
300

Coverage: 187 (20.1% affected)

200

Coverage: 100 (100.0% affected)

200

Coverage: 64 (95.3% affected)

Deletion: 20
Deletion: 100

Deletion: 61

Insertion: 19

Nr3a (set 2) #7
300

Coverage: 140 (41.4% affected)
Deletion: 38

100

Coverage: 56 (100.0% affected)
Deletion: 56

Insertion: 20

Figure S7 G.A. Sunagawa

100

Coverage: 71 (91.5% affected)
Deletion: 65

Table S1. Sleep/wake phenotype, Related to Figure 3, 4 and 5. Every mouse phenotyped in SSS under LD
conditions. The values are mean ± SD for wild-type and mean ± SEM for KOs.
Amplitude

Pws

Psw

108

Daily sleep
time (min)735.7 ± 50.8

0.4462 ± 0.0530

0.0379 ± 0.0068

0.0363 ± 0.0067

C57BL/6N female 72

636.4 ± 57.7

0.5764 ± 0.0823

0.0282 ± 0.0066

0.0353 ± 0.0064

C57BL/6J male 178
C57BL/6J female 173
Bmal1
34
Conventional
Cry1/Cry227
KO
Per1/Per2
26
Tyr
7
Bmal1
6
Cry1/Cry2
5
Per1/Per2
2
Hcrt
7
Triple-target
Nr2a
8
CRISPR KO
Nr2c
15
Nr2d
13
Nr3a (set 1)
8
Nr3a (set 2)
12
Nr3b
11

699.3 ± 49.6
613.2 ± 50.1
719.1 ± 15.0
748.8 ± 11.5
742.9 ± 14.7
734.5 ± 17.2
758.5 ± 14.6
725.8 ± 14.5
716.2 ± 2.3
720.1 ± 24.1
711.9 ± 29.5
715.0 ± 11.6
755.8 ± 10.6
639.0 ± 17.5
627.1 ± 11.2
683.7 ± 11.3

0.4948 ± 0.0610
0.5716 ± 0.0815
0.3871 ± 0.0132
0.3586 ± 0.0119
0.4308 ± 0.0119
0.4249 ± 0.0097
0.3735 ± 0.0189
0.3489 ± 0.0092
0.3699 ± 0.0253
0.3925 ± 0.0278
0.5328 ± 0.0290
0.4557 ± 0.0188
0.4052 ± 0.0131
0.5826 ± 0.0342
0.5737 ± 0.0169
0.5004 ± 0.0149

0.0387 ± 0.0064
0.0300 ± 0.0050
0.0312 ± 0.0014
0.0331 ± 0.0012
0.0456 ± 0.0021
0.0368 ± 0.0023
0.0307 ± 0.0022
0.0388 ± 0.0018
0.0364 ± 0.0009
0.0548 ± 0.0022
0.0407 ± 0.0035
0.0343 ± 0.0012
0.0408 ± 0.0011
0.0250 ± 0.0015
0.0254 ± 0.0009
0.0310 ± 0.0015

0.0411 ± 0.0076
0.0406 ± 0.0075
0.0310 ± 0.0012
0.0307 ± 0.0014
0.0431 ± 0.0021
0.0350 ± 0.0012
0.0274 ± 0.0015
0.0381 ± 0.0013
0.0368 ± 0.0007
0.0556 ± 0.0048
0.0410 ± 0.0026
0.0348 ± 0.0013
0.0369 ± 0.0010
0.0310 ± 0.0009
0.0363 ± 0.0006
0.0342 ± 0.0015

Wild-type

Strain/gene#

C57BL/6N male

Table S2. KO mice production by triple-target CRISPR method, Related to Figure 1, 4 and 5. Cas9 mRNAand gRNAs targeting Tyr gene and circadian/sleep related genes (Bmal1, Cry1/Cry2, Per1/Per2 and Hcrt) and
NMDA receptor family (Nr1, Nr2a, Nr2b, Nr2c, Nr2d, Nr3a, and Nr3b) were injected into fertilized C57BL/6N
eggs. Male mice, which surveived at least the age of 6 weeks, were used for SSS analysis. The table shows thenumber of transferred embryos, number of offsprings and the results of genotyping for all mice used in SSS
analysis.
Non-KO
NotTransferred
Offspring
Genotyped KO confirmed
Gene
confirmed determinedmale micemale miceembryos
Male
Femalemale micemale mice
Tyr
150
9
11
7
7
0
0
Bmal1
111
6
8
6
6
0
0
Cry1/Cry2
105
6
8
6
5
0
1
Per1/Per2
169
3
5
3
2
0
1
Hcrt
96
11
8
10
7
0
3
Nr1
205
0
0
0
0
0
0
Nr2a
131
8
12
8
8
0
0
Nr2b
236
1
0
1
0
1
0
Nr2c
143
16
8
15
15
0
0
Nr2d
140
13
20
13
13
0
0
Nr3a (set 1)
135
8
9
8
8
0
0
Nr3a (set 2)
124
13
7
13
12
0
1
Nr3b
205
12
9
12
11
0
1
Total
1950
106
105
102
94
1
7
100.0%
92.2%
1.0%
6.9%

Table S5. Exome sequencing statistics for Tyr and Nr3a KO mice, Related to Figure 2 and 6. See
Methods for the detail of library preparation and sequencing. 'Target bases' are those in bait regions designed
for capturing exons.

Proportionof on-baitbases inuniquely
mappedreads (%)

Targetbases
achieving
10X orgreatercoverage(%)

Sample namein DDBJ

LibraryTotal
number of
reads

Number of
uniquely
mappedreads

Wildtype used in
Tyr KO analysis

50,520,378

46,336,283

91.7

3,032,197,583

53.7

95.0

tyr-wild-type

Tyr #1

53,635,574

49,124,972

91.6

3,196,839,130

53.5

95.6

tyr-01

Tyr #250,975,220

47,102,814

92.4

2,258,383,004

39.4

91.6

tyr-02

Wildtype used in
Nr3a KO analysis

237,830,634

217,074,710

91.3

13,453,459,028

51.0

99.0

nr3a-wild-type

Nr3a (set 1) #6

182,374,702

172,485,979

94.6

14,927,922,341

69.9

98.9

nr3a-set1-06

Nr3a (set 1) #8

194,956,048

184,558,103

94.7

15,510,369,211

67.8

99.0

nr3a-set1-08

Nr3a (set 2) #1

175,181,378

167,133,655

95.4

14,427,136,154

69.7

98.5

nr3a-set2-01

Nr3a (set 2) #7

187,343,854

177,780,377

94.9

15,194,075,796

69.0

98.8

nr3a-set2-07

Uniquely
Number of
mappedreads (%) on-bait bases

Data S1. The Source Code of the SSS Analysis, Written in R Language
The code can be downloaded from https://goo.gl/teteJM.