Brief Communication
https://doi.org/10.1038/s41587-020-0509-0

Base editors for simultaneous introductionof C-to-T and A-to-G mutations
Rina C. Sakata 1,2,13, Soh Ishiguro 1,3,4,13, Hideto Mori 1,3,4,13, Mamoru Tanaka 1, Kenji Tatsuno 5,
Hiroki Ueda 6, Shogo Yamamoto 5, Motoaki Seki 1, Nanami Masuyama 1,3,4, Keiji Nishida 7,8,
Hiroshi Nishimasu 9, Kazuharu Arakawa 3,4,10, Akihiko Kondo 7,8,11, Osamu Nureki 9,
Masaru Tomita 1,3,4,10, Hiroyuki Aburatani 5 and Nozomu Yachie 1,2,3,4,9,12 ✉We describe base editors that combine both cytosine and adenine base-editing functions. A codon-optimized fusion of thecytosine deaminase PmCDA1, the adenosine deaminase TadA
and a Cas9 nickase (Target-ACEmax) showed a high mediansimultaneous C-to-T and A-to-G editing activity at 47 genomic
targets. On-target as well as DNA and RNA off-target activities of Target-ACEmax were similar to those of existing
single-function base editors.
CRISPR–Cas9 is a genome editing tool in which Cas9 is recruitedby a guide RNA (gRNA) to its target DNA region upstream of a3′ protospacer adjacent motif (PAM) to induce a double-stranded
DNA break (DSB)1,2. This method has rapidly expanded our ability to knock genes out through error-prone DNA repair and insert
transgenes into chromosomes through DSB-induced homologous recombination. In contrast, base editors derived by tethering
deoxynucleoside deaminase to a nuclease-deficient or nickase Cas9
(dCas9 or nCas9, respectively)–gRNA complex induce efficientand direct base substitutions in the genomic sequence3. Among theavailable base editors, cytosine base editors (CBEs)4,5 and adenine
base editors (ABEs)6 enable highly efficient and precise base substitutions in a narrow window of gRNA-targeting sites. CBEs consistof a cytidine deaminase (such as rAPOBEC1 used in base editors5
and PmCDA1 used in Target-AID4) that converts cytidines of the
non-gRNA bound DNA strand into uridines, and a uracil glycosylase inhibitor (UGI) that inhibits base excision repair, allowing
uracils to be replaced with thymines through DNA replication.
Similarly, ABEs use a heterodimer complex of WT and engineered
TadA adenosine deaminases that convert adenines to inosines thatare then replicated as guanines6. Currently, single-function base editors enable only two transition mutations, C·G→T·A and A·T→G·C,
and have limited diversity of editing patterns that they can generate
at a target site. A single base editor with both C→T and A→G basesubstitution activities would thus broaden the capabilities of baseediting for various applications.
To this end, we developed and tested three dual-function baseeditors, Target-ACE, Target-ACEmax and ACBEmax, which haveboth cytidine deaminase and adenosine deaminase fused to asingle nCas9 (D10A) (Extended Data Fig. 1). Target-ACE consistsof nCas9 fused to PmCDA1 from Target-AID4 and the TadA heterodimer from ABE7.10 (ref. 6) at its C and N termini, respectively,
along with other functional domains present in the constituent
single-function base editors. Because GenScript codon optimization and the addition of an N-terminal bipartite nuclear localization
signal (NLS) have previously led the development of enhanced baseeditors BE4max and ABEmax7, we applied the same optimizations
to Target-ACE to derive Target-ACEmax (Fig. 1a). ACBEmax wasconstructed by replacing the codon-optimized PmCDA1 domain
of Target-ACEmax with the codon-optimized cytidine deaminasedomain rAPOBEC1 from BE4max. As single-function base editor
controls, in addition to Target-AID, BE4max, ABE and ABEmax,
we constructed codon-optimized Target-AIDmax and BE4max(C)in which the C-terminal PmCDA1 domain of Target-AIDmax was
replaced with the rAPOBEC1 domain from BE4max.
To test the base-editing activities of the single- and dual-function
base editors in living cells, we constructed C→T and A→Gbase-editing reporter cells in which the corresponding base substitutions were designed to activate EGFP protein expression(Extended Data Fig. 2a,b). All single-function CBEs were only
able to activate C→T reporter cells, whereas ABEs were only able
to activate A→G reporter cells (Fig. 1b, Extended Data Fig. 2cand Supplementary Data 1). Both C→T and A→G reporter cells
were activated by all three dual-function base editors, Target-ACE,
Target-ACEmax and ACBEmax, as well as their corresponding
enzyme mix controls, Target-AID+ABE, Target-AIDmax+ABEmax,
BE4max(C)+ABEmax, and BE4max+ABEmax. These results wereconfirmed by amplicon sequencing of the gRNA target regions in
the reporter cells (Extended Data Fig. 2d–g and Supplementary
Data 2 and 3).
To characterize the C→T and A→G base-editing activities of different base-editing methods, we analyzed the base-editing spectra
at 47 genomic target sites in human embryonic kidney (HEK293Ta)
cells by amplicon sequencing in triplicate (1,833 assays)(Supplementary Data 4). By taking the average C→T or A→G editing frequencies at each cytosine or adenine position relative to theSynthetic Biology Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan. 2College of Arts and Sciences,
University of Tokyo, Tokyo, Japan. 3Institute for Advanced Biosciences, Keio University, Tsuruoka, Japan. 4Graduate School of Media and Governance,
Keio University, Fujisawa, Japan. 5Genome Science Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan.
6
Biological Data Science Division, Research Center for Advanced Science and Technology, University of Tokyo, Tokyo, Japan. 7Engineering Biology
Research Center, Kobe University, Kobe, Japan. 8Graduate School of Science, Technology and Innovation, Kobe University, Kobe, Japan. 9Departmentof Biological Sciences, School of Science, University of Tokyo, Tokyo, Japan. 10Faculty of Environment and Information Studies, Keio University, Fujisawa,
Japan. 11Department of Chemical Science and Engineering, Graduate School of Engineering, Kobe University, Kobe, Japan. 12PRESTO, Japan Scienceand Technology Agency (JST), Tokyo, Japan. 13These authors contributed equally: Rina C. Sakata, Soh Ishiguro, Hideto Mori✉e-mail: yachie@synbiol.rcast.u-tokyo.ac.jp
1

Nature Biotechnology | www.nature.com/naturebiotechnology

Brief Communication

Target DNA
SH3 linker

SV40
NLS
PmCDA1
UGI

ax
m

ax

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C
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–10

–1

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-A

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30

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40

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BE

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40

**
***

***

***
***

4m

0
30

Ta

0
30

m

0
30

4m

0
40

40

rg

ACBEmax

0
40

n.s.

Ta

Target-ACEmax

0
40

0
30

n.s.

***

60

ax

Target-ACE

+

0
40

0
30

BE

BE4max+ABEmax

040

A→G editing

***

m

+

0
30

ID

BE4max(C)+ABEmax

0
40

-A

+

0
30

ID

Target-AIDmax+ABEmax

0
40

n.s.

80

et

+

0
30

Average A→G editing frequency (%)

Target-AID+ABE

Average C→T editing frequency (%)

BE4max

n.s.

Ta

0
40
BE4max(C)C→T editing
100

ax

0
30

m

0
40

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Target-AIDmaxdA→G editing
30-A

Target-AID

ABEmax+BE

C→T editing
40

et

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ABE

+

EGFP/HoechstrgNBipartite NLS

Ta

TadA

Ta

C

A→G reporter cells

3′

Editing frequency (%)

XTEN
linker

C →T reporter cells

5′
5′

+

NT gRNA OT gRNA NT gRNA OT gRNA

+

sgRNA

3′

Ta
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et

Guide:target
heteroduplex

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a

NATuRE BiOTEChnOlOGy

–20

–10

–1

Position from the PAM (bp)Fig. 1 | C→T and A→G base-editing activities of single- and dual-function base editors. a, Structural overview of Target-ACEmax. b, Microscopy
images of C→T and A→G base-editing reporter cells transiently transfected with different base editor reagents and corresponding on-target (OT) or
non-targeting (NT) gRNAs. Scale bar, 40 µm. Consistent results were obtained independently for four cell culture replicates. c, Average C→T and A→Gbase-editing spectra of a genomic target site. Vertical grey lines indicate the peak editing positions of the different base-editing conditions. d, C→T and
A→G editing frequencies of 47 different endogenous target sites in the human genome. Horizontal black bars represent the average C→T or A→G editing
frequencies. Two-sided Mann–Whitney’s U-test was performed to compare arbitrary pairs of two datasets from three cell culture replicates. Arrowheads
indicate datasets with a higher average editing frequency than the other in the pair (*P < 0.05; **P < 0.01; ***P < 0.001).

PAM, we found that dual-function base editors commonly inherited similar base-editing characteristics from their corresponding
single-function base editors (Fig. 1c and Supplementary Data 5).
The C→T and A→G editing frequencies of 47 endogenous target
sites containing different numbers of cytosines and adenines varied
widely, but were overall consistent with the base-editing spectrum
data (Fig. 1d and Supplementary Data 6).
In the amplicon sequencing data of 47 genomic target sites, 722
editing outcome patterns with both C→T and A→G edits wereobserved at read frequencies of 0.1% or more by the dual-function
base editors and four combinations of base editor mix controls(Fig. 2a). Overall, we found that Target-ACEmax, Target-ACE
and their corresponding enzyme mixes displayed a cluster ofmulti-base-editing patterns distinct from those of ACBEmax andits corresponding enzyme mix. To observe the co-editing patterns
in the 47 different target sites, we next investigated dinucleotide

homologous and heterologous co-editing spectra for each of the 13
base editor conditions. For each cytosine–cytosine, adenine–adenine or cytosine–adenine pair in different positions relative to the
PAM, the average co-editing frequency was calculated using amplicon sequencing results of the relevant target sites (Supplementary
Fig. 1). Among the different base editor reagents, the multiple C→T
and A→G edit spectra recaptured the same trends of the average
C→T and A→G frequencies of the 47 target sites (Supplementary
Fig. 2 and Supplementary Data 7). For simultaneous C→T and
A→G editing, Target-ACEmax and Target-AIDmax+ABEmax
showed similarly efficient co-editing spectra around cytosine at–18 bp and adenine at –15 bp relative to the PAM with peak heights
of 19.2% and 21.0%, respectively (Fig. 2b,c and Supplementary
Fig. 3). ACBEmax and BE4max+ABEmax demonstrated anotherefficient co-editing spectrum: their peak frequencies at cytosine–
adenine positional combinations at –12 and –15 bp were the two
Nature Biotechnology | www.nature.com/naturebiotechnology

NATuRE BiOTEChnOlOGy
highest among all of the base editor reagents (23.2% and 23.6%,
respectively).
To examine DNA off-target effects of the different base-editing
methods caused by non-specific binding of the gRNA, we used
three gRNAs and performed amplicon sequencing of their on-target
and commonly observed off-target sites8,9 (Extended Data Fig. 3).
Based on the amplicon sequencing data, the off-target risk scores
were calculated for the different base-editing methods (Fig. 2d andSupplementary Data 8). The off-target risk scores of dual-function
base editors were within the range of those for the single-function
base editors, whereas the off-target risk scores of base editor mix
controls were markedly higher than those of their corresponding dual-function base editors. We also extensively surveyed
genome-wide DNA and RNA off-target activities of the 13 base editor conditions by whole exome sequencing (WES) and transcriptome
sequencing (RNA-seq) (Supplementary Table 1). While no elevatedlevel of single-nucleotide variation (SNV) induction was detected in
the WES dataset (Supplementary Fig. 4), the numbers of C→U RNA
edits found using rAPOBEC1-related, but not PmCDA1-related,base-editing methods were strikingly higher (an average of
3,403) than those of the other samples (322), which was consistent with previous reports10,11 (Fig. 2e and Supplementary Fig. 5).
The non-specific A→I RNA-editing activities were overall significantly higher using ABEmax and base editor mixes containing ABEor ABEmax compared with the other methods (P = 0.00019), as
reported previously10,12,13. Notably, non-specific A→I editing activity of Target-ACEmax (an average of 3,359 across two replicates)
was relatively lower than that of Target-AIDmax+ABEmax (averageof 4,179).
Similar to the recent machine learning approaches to predict
WT Cas9-mediated genome editing outcomes14–16, we developed
a base-editing prediction method that trains amplicon sequencing
data and predicts base-editing patterns and their frequencies for agiven target sequence (Extended Data Fig. 4a). In brief, we foundthat our method successfully predicted base-editing outcomes ofuntrained targets with Pearson’s correlation coefficients of 0.70 and
0.71 for Target-ACEmax and ACBEmax, respectively (Extended
Data Fig. 4b). We also demonstrated that the amplicon sequencing
data obtained in this study were sufficient for the training procedure (Supplementary Fig. 6). Because the machine learning method
enabled prediction of multi-nucleotide co-editing (Extended Data
Fig. 5 and Supplementary Figs. 7 and 8), we used it to predict
the frequencies of all possible codon conversion patterns in the

Brief Communication
human genome obtained by the different base-editing methods.
When bystander mutations were not allowed to occur, this analysis
showed that Target-ACEmax and its corresponding base editor mix
Target-AIDmax+ABEmax had the highest potentials for diversifying genomic codons (Extended Data Figs. 6 and 7, Supplementary
Fig. 9 and Supplementary Data 9). We then repeated the same
analysis by allowing bystander mutations to occur (Extended
Data Figs. 8 and 9) and estimated bystander risks of generating
unwanted mutations for all of the base-editing methods (Fig. 2f).
The bystander mutation risks of Target-ACEmax and ACBEmax
were within the risk range of commonly used single-function baseeditors, of which BE4max was the highest. Although the bystander
mutation risk scores of ACBEmax were significantly lower thanthose of Target-ACEmax, this was largely consistent with ACBEmax
not showing marked improvement in expanding genome-widecodon conversion patterns. Finally, our model predicted that
Target-ACEmax had the highest potentials to correct pairs of heterologous disease mutations reported in the ClinVar database17 as asingle base editor enzyme (Supplementary Fig. 10).
Target-ACEmax and ACBEmax both showed high heterologous co-editing efficiencies at their favorable positional combinations upstream of the PAM. While the dual-function base editors
were evaluated using HEK293Ta cells in this study and remain
to be tested in other systems, they could complementarily serve
as genome editing tools to induce heterologous multiple baseedits in various settings including therapeutics with the advantagesof high delivery efficiency considering their compact gene sizes(Supplementary Note 1). CRISPR–X18 involving a mutant human
AID has been developed as a sequence diversification tool to induce
C→A/G/T substitutions at a multiple hundred base pair region surrounding a gRNA target site, but showed low editing efficiency per
position. Target-ACEmax induces a higher efficiency of heterologous base editing than CRISPR–X. Thus, Target-ACEmax could beapplied as a complementary tool for in vivo diversification of targeted sequences for mutational scanning analysis of protein functions and directed protein evolution as examples.
Target-ACEmax could also be a powerful tool for recent cell lineage tracing using CRISPR genome editing19,20. Most of the current
implementations employ WT Cas9 that induces DSBs. These DSBs
result in cytotoxicity and rapidly saturate the mutation patterns inDNA barcodes following target site deletions, which theoretically limit
the resolution of cell lineage reconstruction21. While base editors could
minimize these detrimental effects caused by DSBs, unidirectional

Fig. 2 | Simultaneous C→T and A→G base-editing activities and unwanted mutation risks of dual-function base editors. a, Editing outcome
patterns with both C→T and A→G edits. A total of 722 heterologous co-editing patterns obtained by either base-editing condition with a readfrequency threshold of 0.1% (top) were hierarchically clustered based on the frequency produced by the different base-editing conditions (bottom).
The total number of editing outcome patterns observed in three independent replicate experiments are shown on the right for each base-editing
condition. b, Average frequencies of simultaneous C→T and A→G editing at different positional combinations in the –20 to −1 bp region upstreamof the PAM. c, Co-editing frequencies of dual-function base editors and single-function enzyme mixes for cytosines and adenines located at
specific combinatorial positions relative to the PAM. Positional combinations with an average co-editing frequency within the top five ranked forany one base-editing condition are shown. Each bar shows the average co-editing frequencies measured for target sites with cytosine and adenine
in the respective combinatorial positions in three cell culture replicates (dots). Two-sided Mann–Whitney’s U-test was performed to compare
Target-ACEmax with its corresponding enzyme mix and the other two dual-function base editors for positional combinations with sufficient sample
sizes (*P < 0.05; **P < 0.01; ***P < 0.001). d, DNA off-target risk estimation. The jitter plot shows relative off-/on-target editing frequencies measured
for three gRNAs in three cell culture replicates experiments and horizontal black bars represent medians as their DNA off-target risk scores. The
pink-shaded area shows the score range for single-function base editors. Two-sided Welch’s t-test was performed to compare the dual-function
base editors with their corresponding single-function enzyme mixes. Arrowhead indicates a dataset with a higher average score than the other(**P < 0.01). e, Genome-wide C→U and A→I RNA variants detected in cells that were subjected to different base-editing conditions. Each bar shows
the number of variants identified by RNA-seq and the jitter plot shows their variant allele frequencies. Experiments were performed in duplicate foreach base-editing condition. f, Bystander mutation risk scores of codon conversion for the different base-editing methods. Horizontal bars represent
the median of risk scores for different codon conversion types represented by dots. The pink shaded area shows the range of average risk scores forthe single-function base editors excluding BE4max(C). Two-sided Mann–Whitney’s U-test was performed to compare arbitrary pairs of two datasets.
Arrowheads indicate the dataset with a higher average score than the other in the pair (***P < 0.001).
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Brief Communication

NATuRE BiOTEChnOlOGy
Online content

mutations induced by single-function base editors would also cause
saturation in the diversity of mutated DNA barcode patterns. In contrast, Target-ACEmax could alleviate this saturation issue because of
its reversible C·G↔T·A activity and contribute to high-resolution celllineage tracing. Dual-function base editors with their expanded baseconversion ability and further improvements would have the potential
to promote development in therapeutics and biotechnology.

aAny methods, additional references, Nature Research reporting
summaries, source data, extended data, supplementary information, acknowledgements, peer review information; details of author
contributions and competing interests; and statements of data andcode availability are available at https://doi.org/10.1038/s41587020-0509-0.

C→T editing

722 heterologous co-editing patterns
Position from the
PAM (bp)–20

A→G editing

–15
–10
–5

–1
Target-ACEmax
Target-AIDmax+ABEmax
Target-ACE
Target-AID+ABE
ACBEmax
BE4max(C)+ABEmax
BE4max+ABEmax

408 patterns
428 patterns
271 patterns
181 patterns
147 patterns
139 patterns
296 patterns
Read pattern frequency (%)
0.1bTarget-AID+ABE

Target-AIDmax+ABEmax

BE4max(C)+ABEmax

BE4max+ABEmax

+

+

+

+

Target-ACE

1.0

10

Target-ACEmax

ACBEmax

–15
–10
–5
–1
–1

–20

–15

–10
C

Position from the PAM (bp)

30

30

30

20

20

20

20

20

10

10

10

10

10

0

0

0

0

0

40

C→U editing
Numberof variants

5,000

Variant allele
frequency (%)

0
100
80
60
40
20
0

n.s.

***

Target-AID+ABE
Target-AIDmax+ABEmax
BE4max(C)+ABEmax
BE4max+ABEmax
Target-ACE
Target-ACEmax
ACBEmax

40

f

10,000
5,000
0
100
80
60
40
20
ACBEmax
EGFP

Target-ACE

Target-ACEmax

BE4max+ABEmax

BE4max(C)+ABEmax

Target-AID+ABE

Target-AIDmax+ABEmax

ABE

ABEmaxBE4maxBE4max(C)Target-AID

Target-AIDmax

ACBEmax
EGFP

Target-ACE

Target-ACEmax

BE4max+ABEmax

BE4max(C)+ABEmax

Target-AID+ABE

Target-AIDmax+ABEmax

ABE

ABEmaxBE4maxBE4max(C)Target-AIDmax

0
Target-AID–1

0 12.5 25
103

n.s.

102

**
n.s.

101
100
10–1
10–2
10–3
10–4
10–5

A→I editing

15,00010,000***A

Target-AID+ABE
Target-AIDmax+ABEmax
BE4max(C)+ABEmax
BE4max+ABEmax
Target-ACE
Target-ACEmax
ACBEmax***

Target-AID+ABE
Target-AIDmax+ABEmax
BE4max(C)+ABEmax
BE4max+ABEmax
Target-ACE
Target-ACEmax
ACBEmax

Target-AID+ABE
Target-AIDmax+ABEmax
BE4max(C)+ABEmax
BE4max+ABEmax
Target-ACE
Target-ACEmax
ACBEmax

***

40

–5

105

ACBEmax

30

**

–10

***
n.s.
***

104
***

103
2

10

101
100

***

***
***

10–1

***

ACBEmax

30

n.s.

–15

Target-ACE

40

–20

Target-ACEmax

40

50

–1

Target-ACEmax

50–5

Average co-editing frequency (%)

d–19C:–15A
n.s.

–10

BE4max+ABEmax

50–18C:–15A
n.s.
n.s.

–15

Target-ACE

n.s.

50

15,000
Numberof variants–17C:–16A

n.s.

–20

n.a. or n.d

50e

Variant allele
frequency (%)–17C:–15A–1

Bystander mutation risk score forcodon conversion–12C:–15A

Target-AID+ABE
Target-AIDmax+ABEmax
BE4max(C)+ABEmax
BE4max+ABEmax
Target-ACE
Target-ACEmax
ACBEmax

Averageco-editing frequency (%)

c–5BE4max(C)+ABEmax–5

BE4max+ABEmax–10

BE4max(C)+ABEmax–15

Target-AID+ABE–20

Target-AIDmax+ABEmax–1

Target-AID+ABE–5

Target-AIDmax+ABEmax

–10

ABE

–15

ABEmax–20

ABEmax–1

BE4max

–5

ABE

–10

BE4max(C)–15

BE4max–20

BE4max(C)–1

Target-AID–5Target-AIDmax–10

Target-AID–15

Target-AIDmax–20

Relative off-target/on-target
mutation frequency

Position from the PAM (bp)–20

Nature Biotechnology | www.nature.com/naturebiotechnology

NATuRE BiOTEChnOlOGy
Received: 7 August 2019; Accepted: 3 April 2020;
Published: xx xx xxxx

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21. Salvador-Martinez, I., Grillo, M., Averof, M. & Telford, M. J. Is it possible toreconstruct an accurate cell lineage using CRISPR recorders? eLife 8,
e40292 (2019).
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims inpublished maps and institutional affiliations.
© The Author(s), under exclusive licence to Springer Nature America, Inc. 2020

Brief Communication
Methods

Oligonucleotides. The oligonucleotides used in this study are listed in
Supplementary Table 2.
Plasmid preparation. Base editor expression plasmids. All base editor expression
plasmids were prepared with the same backbone sequence used in pCMV-BE3(Addgene 73021). BE4 (pCMV-BE4), BE4max (pCMV-BE4max), ABE7.10(pCMV-ABE7.10), and ABEmax (pCMV-ABEmax) plasmids with the same
backbone were obtained from Addgene (100802, 112093, 102919, and 112095,
respectively) and the other single- and dual-function base editors were constructedby Gibson assembly of PCR fragments as follows. The Target-AID plasmid(pCMV-Target-AID) was constructed by assembling two fragments encoding
the N- and C-terminus halves of Target-AID, which were both amplified
from pcDNA3.1_pCMV-nCas-PmCDA1-ugi pH1-gRNA(HPRT) (Addgene
79620) using primer pairs RS045/HM129 and HM128/RS046, respectively,
with a backbone fragment amplified from pCMV-ABE7.10 using RS047/
RS048. To construct the Target-AIDmax plasmid (pCMV-Target-AIDmax),
the pUC-optimized-PmCDA1-ugi plasmid encoding the codon-optimized
C-terminal region of Target-AIDmax was first constructed by the genesynthesis service of GenScript. This C terminus fragment was then amplified
with primer pair SI1304/SI1307 and assembled with a nCas9 fragment
amplified from pCMV-BE4max using SI945/SI1308 and a backbone fragment
amplified from pCMV-ABEmax using SI1310/SI1309. The BE4max(C) plasmid(pCMV-BE4max(C)) was constructed to replace the C-terminal region of
Target-AIDmax with the codon-optimized rAPOBEC1 and 2×UGI domains of
BE4max. To this end, an nCas9 fragment obtained from pCMV-Target-AIDmax
using SI447/SI1105 was assembled with rAPOBEC1 and 2×UGI fragments
obtained from BE4max using SI1352/SI1357 and SI1359/SI1350, respectively,
and a backbone obtained from pCMV-BE4max using SI1351/SI448. The
Target-ACE plasmid (pCMV-Target-ACE) was constructed with a fragment
encoding a plasmid backbone as well as ABE7.10 amplified from pCMV-ABE7.10
using RS047/RS052 and a fragment encoding the C-terminus region of
Target-AID amplified from pcDNA-pCMV-nCas9 using RS051/RS046. The
Target-ACEmax plasmid (pCMV-Target-ACEmax) was constructed by assemblingan ABEmax fragment obtained from pCMV-ABEmax using SI945/SI1305, afragment encoding the C-terminus region of Target-AIDmax obtained from
pUC-optimized-PmCDA1-ugi using SI1304/SI1307, and a plasmid backbone
obtained from pCMV-ABEmax using SI1310/SI1309. Finally, the ACBEmax
plasmid (pCMV-ACBEmax) was constructed by assembling an ABEmax
fragment obtained from pCMV-Target-ACEmax using SI447/SI1105 with thethree fragments encoding the rAPOBEC1 domain, 2×UGI domain, and the two
backbone fragments that were prepared to construct pCMV-BE4max(C). Notethat the 2×UGI domain used in this study had a non-synonymous nucleotide
substitution in the GS linker between the tandem UGIs (SGGSG[G > E]SGGS).
gRNA expression plasmids. gRNA spacer inserts were prepared by a single pot
reaction to phosphorylate and anneal ssDNA pairs. To prepare each spacer
fragment (Supplementary Table 2), a T4 polynucleotide kinase reaction sample
was prepared with two ssDNAs in accordance with the manufacturer’s protocol(Takara) and placed in a thermal cycler with the following conditions: 37 °C for
30 min; 95 °C for 5 min; 70 cycles of 12 s starting with 95 °C and −1 °C per cycle,
and then maintained at 25 °C. The annealed spacer inserts were then ligatedinto a pU6-gRNA cloning backbone (pSI-356) by Golden Gate Assembly using
BsmBI (NEB) and T4 DNA ligase (NEB). The assembly was performed under
the following thermal cycler conditions: 15 cycles of 37 °C for 5 min and 20 °C for
5 min, 55 °C for 30 min, and then maintained at 4 °C.
Lentiviral base-editing reporter plasmids. The C→T reporter circuit was designed
to restore a mutated GTG start codon to ATG by C→T base editing of its antisense
strand (Extended Data Fig. 2a). In the A→G reporter circuit, EGFP translation was
designed to be released by destruction of a TAA stop codon to CAA by A→G baseediting of the antisense strand (Extended Data Fig. 2b). To construct the lentiviralC→T base-editing reporter plasmid (pLV-SI-112) and its positive control plasmid(pRS112), reporter cassette fragments were amplified from pLV-eGFP (Addgene
36083) using primer sets 112-V4-BC2-FW/SI680 and RS204/SI627, respectively,
and cloned into the EcoRI and BamHI sites of the pLVSIN-CMV-Puro backbone
vector (Takara) using T4 DNA ligase (NEB). The lentiviral A→G base-editing
reporter plasmid (pLV-SI-121) and its positive control plasmid (pLV-SI-122) wereconstructed similarly, where reporter cassettes were amplified using primer sets
SI760/SI680 and SI761/SI680, respectively.
The plasmid sequences were confirmed by Sanger sequencing. Other than
the gRNA plasmids for genomic target sites, we submitted the newly constructed
plasmids to Addgene. The list of plasmids used in this study is shown in
Supplementary Table 3.
Selection of gRNA target sites. To select gRNA target sites with cytosines andadenines for amplicon sequencing assays, we first searched for target sites with
poly-cytosine repeats (poly-C), poly-adenine repeats (poly-A), alternating
poly-adenine/cytosine repeats (poly-AC), and alternating poly-cytosine/adenine

NATuRE BiOTEChnOlOGy
repeats (poly-CA) in the human genome (hg19). Poly-C target sites were required
to have cytosines filling a 7-bp sliding window for which the 5′ end shifted atintervals of 2 bp from −24 bp to −16 bp relative to the PAM. Poly-A target sites
were required to have adenines filling a 6-bp sliding window for which the 5′ end
shifted at intervals of 2 bp from −21 bp to −13 bp relative to the PAM. Poly-AC andpoly-CA target sites were required to have the corresponding patterns in a 6-bp
sliding window for which the 5′ end shifted at intervals of 2 bp from −24 bp to−14 bp relative to PAM. The candidate target sites that contained a homopolymerof ≥4 bp long within a gRNA seed region spanning from −8 bp to −1 bp to the
PAM and those overlapping with annotated exons were excluded. For each sliding
window position of poly-C and poly-A, we selected two candidate sites each withthe highest predicted gRNA activity scores22. Similarly, for each sliding window
position of poly-AC and poly-CA, we selected one candidate site each. Using
amplicon sequencing primers designed for these target sites, we further screened
seven poly-C, seven poly-A, six poly-AC, and four poly-CA target sites that wererobustly amplified by our amplicon sequencing library preparation protocol. Notethat poly-C and poly-A target sites that were screened also contained both cytosine
and adenine bases. In addition to these 24 target sites, we screened 24 target sites
from our gRNA library collections that we previously prepared for other assays.
Each of these target sites was required to contain one or more cytosines and one
or more adenines in a region spanning from –20 bp to –14 bp. We failed to obtain
the EGFP control data for the amplicon sequencing assays of CUL3-NGG-site 2
and therefore analyzed a total of 47 on-target sites. For off-target site analysis byamplicon sequencing, we selected two on-target sites for FANCF and one on-target
site for EMX1 genes which had both cytosine and adenine bases in the region
spanning from –20 bp to –14 bp and many off-target sites that were commonly
identified by GUIDE-seq in multiple previous studies8,9. While we selected five of
the previously reported off-target sites for each on-target site, one of the EMX1
off-target sites was omitted from the analysis because it was not amplified by ouramplicon sequencing library preparation protocol. All on-target and off-target sites
analyzed by amplicon sequencing assays are shown in Supplementary Table 2.
Cell culture. HEK293Ta cells were purchased from GeneCopoeia and maintainedin Dulbecco’s Modified Eagle’s Medium (DMEM) (Sigma) supplemented with
10% fetal bovine serum (FBS) (Thermo Fisher Scientific) and 1% penicillin–
streptomycin (Sigma) at 37 °C with 5% CO2. Cells were routinely tested for
mycoplasma contamination by nested PCR using culture medium as a template.
Base-editing reporter cell lines. The C→T and A→G reporter circuits andtheir respective positive control circuits, containing the expected mutations,were introduced into human embryonic kidney HEK293Ta cells by lentiviraltransduction. For lentiviral packaging, ~2 × 105 cells per well were seeded in
a 6-well plate 1 day before transfection. In each packaging reaction, 489 ng oflentiviral plasmid was cotransfected with two helper plasmids, psPAX2 (Addgene
12260) and pMD2.G (Addgene 12259), at 366 ng and 122 ng, respectively, and9.38 µl of 1 mg ml−1 polyethyleneimine MAX (PEI) (Polysciences) in 300 µl
phosphate-buffered saline (PBS). The next day, the culture medium was changed
to fresh medium, and 2 days later the culture supernatant containing lentiviralparticles was harvested and aliquoted into 1.5-ml tubes. The viral sample wasthen stored at −80 °C before infection. For lentiviral infection, ~2 × 105 cells
per well were seeded on a 6-well plate with 2 ml DMEM and incubated for 24 h.
The viral supernatant was then thawed at room temperature, mixed with 1 µlof 8 mg ml−1 polybrene (Sigma), and added to each cell sample. One day after
infection, ~5 × 103 infected cells were reseeded on a 96-well culture plate forfunctional titer measurement by the CellTiter-Glo assay (Promega). Two days
after infection, 2.0 µg ml−1 puromycin (Thermo Fisher Scientific) was added to theculture medium, followed by incubation for 3 days to select successfully transduced
cells. After puromycin selection, absence of background fluorescence or EGFP
expression was confirmed for the reporter cell lines and their corresponding
positive control cell lines, respectively.
Transfection. EGFP reporter activation assays. Base-editing reporter cells andcorresponding control cells were seeded in a collagen-I-coated 24-well plate(Asone) with 500 µl DMEM at a density of ~5 × 104 cells per well. The next day,
1.2 µl of 1 mg ml−1 PEI, 50 µl PBS, 300 ng base editor plasmid, and 100 ng gRNA
expression plasmid were mixed and then incubated at room temperature for
20 min before application to each well for transfection. For each base editor mix
experiment, two base editor plasmids were mixed at a 1:1 mass ratio and 300 ng
was used (see Supplementary Note 2 for detail discussion about of the base editor
mix experiments). Fluorescence imaging was performed 3 days after transfection
using a confocal microscopy InCellAnalyzer6000 (GE Healthcare) with a ×20
objective lens. The experiment was performed in quadruplicates. For one of thereplicates, cell nuclei were stained with 10 mg ml−1 Hoechst 33342 (Thermo
Fisher Scientific).
Genomic on-target and off-target assays. HEK293Ta cells were seeded in acollagen-I-coated 96-well plate (Asone) with 200 µl DMEM at a density of ~5 × 103
cells per well. The next day, 0.48 µl PEI, 50 µl 1× PBS, 120 ng base editor expression
or control EGFP expression (pLV-eGFP) plasmid, and 40 ng gRNA expression
plasmid were mixed and then incubated at room temperature for 15 min before
Nature Biotechnology | www.nature.com/naturebiotechnology

NATuRE BiOTEChnOlOGy
application to each well for transfection. The experiments were performed as three
independent replicates.
Genome-wide DNA and RNA off-target assays. HEK293Ta cells were seeded in acollagen-I-coated 6-well plate (Asone) with 2 ml DMEM at a density of ~2 × 105
cells per well. The next day, 3.0 µl of 1 mg ml−1 PEI, 200 µl 1× PBS, 666 ng baseeditor expression or control EGFP expression (pLV-eGFP) plasmid, and 333 ng
EMX1-targeting gRNA plasmid were mixed and then transferred to each well after
15 min of incubation at room temperature. The experiments were performed as
two independent replicates.
Amplicon sequencing. EGFP reporter activation assays. After confocal imaging,the culture medium was aspirated and 200 µl of freshly prepared 50 mM NaOH
was added to each cell sample in a 24-well plate. Then, 100 µl of the sample wastransferred to a 96-well PCR plate (Nippon Genetics) for cell lysis and heated at
95 °C for 15 min and cooled to 4 °C, followed by neutralization with 20 µl of 1 M
Tris-HCl (pH 8.0). From each sample, the target regions were amplified using
the cell lysate as the PCR template with its corresponding first HTS primer pair
(Supplementary Table 2). The PCR was performed in a 20 µl volume including 1 µl
template, 1 µl of 10 µM each primer, 0.2 µl Phusion DNA Polymerase, 5× Phusion
HF Buffer (NEB), and 1.6 µl of 2.5 mM dNTPs with the following thermal cycler
conditions: 98 °C for 30 s, 30 cycles of 98 °C for 10 s, 60 °C for 10 s, and 72 °C for
10 s, and then 72 °C for 5 min for final extension. The PCR product was thenelectrophoresed on a 2% agarose gel and reamplified using 1 µl of a 10-fold dilution
as the template and custom Illumina index primers (Supplementary Tables 2 and 4)
in a 20 µl volume reaction with the following thermal cycler conditions: 98 °C for
30 s, 15 cycles of 98 °C for 10 s, 65 °C for 10 s, and 72 °C for 30 s, and then 72 °C for
5 min for final extension. Each indexed library was electrophoresed in a 2% agarose
gel and the expected band was extracted using FastGene Gel/PCR Extraction Kit(Nippon Genetics).
Genomic on-target and off-target assays. Three days after transfection, the culture
medium was removed and 50 µl of freshly prepared 50 mM NaOH was added toeach cell sample in a 96-well plate. The samples were transferred to a 96-well qPCR
plate (BioRad), sealed with an optically clear adhesive PCR seal (BioRad), andcentrifuged at 2,400 rpm for 2 min, heated at 95 °C for 15 min, and cooled to 4 °C,
followed by neutralization with 5 µl of 1 M Tris-HCl (pH 8.0). The cell lysate plates
were centrifuged again and stored at –20 °C. Each target region was amplified
with its corresponding first HTS primer pair (Supplementary Table 1). The PCR
was performed in a 20 µl volume including 2 µl genomic DNA template, 1.20 µl of
8.3 µM each primer, 0.2 µl Phusion DNA Polymerase, 5× Phusion HF Buffer, and1.6 µl of 2.5 mM dNTPs with the following thermal cycler conditions: 98 °C for
30 s, 30 cycles of 98 °C for 10 s, 60 °C for 10 s, and 72 °C for 60 s, and then 72 °C for
5 min for final extension. For each replicate experiment, 3 µl of each PCR productof the same base editor reagent was pooled and purified using a 1.8× volume of
Agencourt AMPure XP magnetic beads (Beckman Coulter). The purified productwas reamplified using 1 µl of 10 ng µl−1 of the first PCR product as the template andcustom Illumina index primers (Supplementary Table 4) in a 20 µl volume reaction
with the following thermal cycler conditions: 98 °C for 30 s, 15 cycles of 98 °C for
10 s, 65 °C for 10 s, and 72 °C for 90 s, and then 72 °C for 5 min for final extension.
Each indexed library was electrophoresed on a 2% agarose gel and the expectedband was extracted using FastGene Gel/PCR Extraction Kit.
The sequencing libraries were quantified by qPCR using KAPA Library
Quantification Kit Illumina (KAPA Biosystems) for multiplexing. The multiplexed
libraries were quantified by the same qPCR protocol and sequenced with 20–30%
PhiX control using Illumina HiSeq2500 (TruSeq rapid SBS kit; 2 × 151 bp pairedend) or MiSeq (MiSeq v3 kit; 2 × 200 bp paired end).
Preparation of RNA-seq and WES libraries. Cells were trypsinized 3 days after
transfection and divided into two 1.5-ml tubes for transcriptome sequencing(RNA-seq) and whole exome sequencing (WES). For RNA-seq library preparation,
cells were centrifuged at 1,000 rpm for 5 min and the culture supernatant wasremoved. Total RNA was then extracted by ISOSPIN Cell & Tissue RNA (Nippon
Gene) and an RNA-seq library was prepared with TruSeq Stranded mRNA
Library Prep Kit (Illumina). For WES library preparation, genomic DNA wasextracted using NucleoSpin Tissue (Macherey Nagel). We then sheared 500 ng ofgenomic DNA in a 50 µl volume to an average size of 150–300 bp using Covalis
E-220, followed by end-repair, A-tailing, and SureSelect adapter ligation (Agilent).
The adapter-ligated DNA was enriched using KAPA HyperPrep kit (KAPA
Biosystems). We then hybridized 750 ng of the pre-amplified DNA to SureSelectXT
Human All Exon V3 kit probes (Agilent) for 20 h. Post-capture DNA library
amplification was then performed using KAPA DNA Polymerase and SureSelectIndexing Post-Capture Polymerase Chain Reaction Primers for library indexing.
The library was finally purified with Agencourt AMPure XP beads. The libraryindex information of RNA-seq and WES is shown in Supplementary Table 4. The
fragment size distributions and yields of the RNA-seq and WES libraries were
quantified using the LabChip GX electrophoresis system (Perkin Elmer). After
multiplexing, the final library was sequenced by Illumina NovaSeq 6000 (S2 Flow
Cells; 2 × 101 bp paired end).
Nature Biotechnology | www.nature.com/naturebiotechnology

Brief Communication
Amplicon sequencing analysis. Common adapter sequences were first mapped
onto the amplicon sequencing reads using NCBI BLAST+ (version 2.7.0)23 with theblastn-short option to identify custom sample indices and demultiplex paired-end
reads. The paired-end reads of each sample were then merged using FLASH(version 1.2.0)24 to generate merged sequencing reads that were further mapped tothe corresponding reference sequence of the target region using EMBOSS needle
package (version 6.6.0)25 with an identity threshold of 80%. For single and multiple
editing spectrum analyses using the genomic on-target and off-target ampliconsequencing data, the EGFP transfection control data were always treated in thesame manner to subtract background signals.
RNA and DNA variant calling pipelines. RNA-seq and WES base call files were
demultiplexed using bcl2fastq2 (version v2.20.0). To eliminate sequencing coveragebias for variant calling, all RNA-seq and WES libraries were randomly subsampled
to 74 million and 94 million reads per sample, respectively, using seqtk (version
1.3-r107-dirty) as described previously11. The subsampled RNA-seq reads werethen mapped to the reference human genome GRCh38.d1.vd1 using STAR (version
2.7.3a)26 with a transcript annotation GTF (GENCODE Release 22 GRCh38.p2)
and deduplicated using Picard MarkDuplicates (version 2.0.1). The subsampled
DNA reads were also processed for mapping according to the National Cancer
Institute Genomic Data Commons DNA-Seq analysis pipeline. In brief, the reads
were aligned by BWA-MEM (version 0.7.15)27 with the reference human genome
GRCh38.d1.vd1 and PCR duplicates were removed using Picard MarkDuplicates(version 2.0.1). GATK HaplotypeCaller (version 4.1.4.1)28 was used to call both
DNA and RNA variants for the reference human genome GRCh38.d1.vd1. The
mutation positions called by GATK HaplotypeCaller were further filtered as
described previously11.
DNA off-target risk estimation. To examine the DNA off-target effects of thedifferent base-editing methods caused by non-specific binding of targeting gRNA,
we used three gRNAs targeting EMX1 and FANCF genes, whose off-target sites
are commonly observed by GUIDE-seq8. After transfection of base editors andon-target gRNA reagents into HEK293Ta cells, we analyzed the base-editing
efficiencies of five off-target sites for the FANCF target site 1, five for the FANCF
target site 2, and four for an EMX1 target site by amplicon sequencing (Extended
Data Fig. 3 and Supplementary Data 8). Among all tested off-target sites, themedian of their relative off-target activities normalized to the corresponding
on-target activities were calculated as the DNA off-targeting risk score for eachbase-editing method.
Base-editing prediction model. Model training. Amplicon sequencing datasetsof different target sites were used to train the conditional probability model. To
minimize the effects of potential sequencing errors in the training procedure,observed editing outcomes with relative read frequencies of less than 1 × 10−4 werefirst eliminated from the dataset. Let si be the nucleotide base transition status at
i bp position relative to the PAM at the target site and P(si) be the probability ofsi. For each target region, P(si) and P(sj|si) were calculated for each combinationof i and j in a given area (i ≠ j). The training model was finally constructed by
the average of P(si) and P(sj|si) across different training target sites for which si sj jsi , ðsi Þ and P
is observed in 100 reads and more, which is represented by P
I
respectively.
I
Base-editing outcome prediction. Let Sm,n be a base-editing pattern in a window
spanning from m bp n bp relative to the PAM, which can be alternatively
represented by a string of transition statuses, sm ; smþ1 ; ¼ ; sn�1 ; sn. Using the
I Sm,n in a test target site wastraining model, the frequency of a given outcome
predicted using the following equation:
!! E1
j j
Y
Y
 ðsi Þ j jsi ÞPPðsPðSm;n Þ ¼
i2E

j2R

where R :¼ fx 2 Zjm ≤ x ≤ ng, E :¼ fx 2 positions with base transitionsg,
 sIj jsi ¼ 1 unless defined.
 ðsi Þ ¼I0 unless defined, and PP
I Essentially, to predict the frequency
of a given editing outcome with multiple
Ibase transitions, this prediction model calculates a geometric mean of probabilities
of base transitions at all edited positions, each considering the other independentbase transition patterns. Any specific P(sj|si) that was devoid of training data wasignored (treated as 1) and P(si) that was devoid of training data was 0.
Validation of base-editing prediction model. The base-editing prediction model was
evaluated by 5-fold, 15-fold and leave-one-out cross-validation experiments. For atest target site, the frequencies of all of the base-editing outcome patterns detectedin the amplicon sequencing dataset for a window of −25 bp to −5 bp relative to the
PAM were predicted by training the amplicon sequencing data of other target sitesthat did not overlap with the test target site. In k-fold cross-validation experiments,
47/k target sites were randomly selected as test samples from the 47 target sites andtheir editing outcomes were predicted by training the amplicon sequencing dataof the remaining target sites, which was iterated 100 times by randomly changing

Brief Communication
the test samples. For the leave-one-out cross-validation, we predicted the editing
outcomes of each target site using the amplicon sequencing data of the other 46
target regions. The prediction performance was measured by first transforming thepredicted editing frequencies to relative editing frequencies among all edited reads,
randomly selecting one prediction result for each editing outcome pattern if there
were multiple, and calculating the Pearson’s correlation coefficient between theprediction and experimental measurement.
Simulation of co-editing frequencies on synthetic target sequences. To predictthe multidimensional co-editing spectra of different base-editing methods using
the base-editing prediction model, we simulated 100 synthetic target sequences
consisting of only cytosine and/or adenine bases in the region from −20 bp to−1 bp relative to the PAM. For each target region, all possible outcomes with C→T
and A→G edits (220 outcomes in total) were predicted using the base-editing
prediction model trained from all 47 amplicon sequencing data. The average
homologous and heterologous dinucleotide-editing spectra were then calculatedusing all predicted frequencies. The trinucleotide-editing spectra were also
predicted for a simulated sequence in which poly-AC stretched from −20 bp to−1 bp relative to the PAM.
Codon convertibility matrix and bystander mutation risk. To estimate thecodon conversion potential and bystander mutation risk of each base-editing
method, codon convertibility matrices (CCMs) were generated with and without
allowing bystander mutations to generate unwanted mutations alongside thetarget codon conversion. First, for each of the 11,250,496 source codons in thehuman genome (hg38), possible gRNA target sites were screened in the area of±25 bp from a target codon. For all gRNAs, base-editing outcome probabilitiesof all possible C→T and/or A→G editing patterns in the ±15 bp region of
the target codon triplet were predicted using the base-editing prediction
model trained by the amplicon sequencing data of all 47 genomic sites. The
conversion potential of the target source codon to each destination codon
without bystander mutations was then defined as the maximum probability ofgenerating the target outcome among those generated by all possible gRNAs.
When bystander mutations were allowed to occur, all predicted probabilities ofbase-editing outcomes with the target source codon conversion were summed
for each gRNA, and the maximum integrated probability among the possible
gRNAs was defined as the conversion potential. Conversion potentials forcodons that had no targetable gRNA were defined as 0 for any destination
codon type. After calculating the conversion potentials to different destination
codons for all genomic codons, a CCM was finally generated to show thefrequency of each source-destination codon conversion type with a conversion
potential threshold of 5%. The bystander mutation risk scores for different
source-destination codon types were calculated by dividing theCCM frequencies allowing bystander mutations by those not allowing
bystander mutations.
ClinVar analysis. For pathological C·G→T·A and A·T→G·C SNVs reported in
the ClinVar database, the possible gRNA target sites to correct each mutation werefirst screened within the ±25 bp region from the target mutation. The probabilities
of correcting mutations by these different gRNA target sites without inducing
unwanted bystander mutations in the ±15 bp region from the target mutation
were then predicted using the base-editing prediction model trained by all of
the amplicon sequencing datasets. For each mutation, its correction potential
was defined as the maximum probability of the target codon conversion among
those induced by different gRNAs. Finally, to estimate the global potential ofthe dual-function base editors to correct two heterologous disease mutations
simultaneously, we counted the combinations of two heterologous mutations thatwere both predicted to be correctable with a correction potential threshold of 5%.
We limited the combinatorial heterologous mutation space to ones in the samegenes to reduce the calculation cost.
Statistical analysis. All of the genome editing experiments were repeated atleast three times with independent cell culture samples. The statistical test was
performed by Scipy (version 1.4.1.)29 on Python (version 3.7.4) or R (version
3.6.0.). Statistical methods, exact sample sizes and P values are available in
Supplementary Table 5.
Reporting Summary. Further information on research design is available in the
Nature Research Reporting Summary linked to this article.

NATuRE BiOTEChnOlOGy
Data availability

The high-throughput sequencing data of this study are available at the Sequence
Read Archive (PRJNA596330) of the NCBI. The original fluorescent microscopy
image data are available at https://doi.org/10.6084/m9.figshare.12016785.v1.

Code availability

The source codes for the base-editing prediction model are available at https://github.com/yachielab/base-editing-prediction. The other codes used in this study
are available upon request.

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Acknowledgements

We thank members of the Yachie lab for useful discussions and critical assessment of thiswork, especially A. Adel for reviewing the manuscript. We also thank K. Shiina, Y. Takai
and N. Ishii for technical supports of high-throughput sequencing. This study was mainly
funded by the Uehara Memorial Foundation (to N.Y.), the NOVARTIS Foundation (Japan)
for the Promotion of Science (to N.Y.), and the Japan Agency for Medical Research andDevelopment (AMED) Platform Project for Supporting Drug Discovery and Life Science
Research (to N.Y., H.N. and O.N.), and partly supported by the New Energy and Industrial
Technology Development Organization (NEDO), AMED PRIME program (17gm6110007),the Japan Science and Technology Agency (JST) PRESTO program (10814), the Naito
Foundation, the SECOM Science and Technology Foundation (all to N.Y.), the Japan Society
for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (16J06287) (toS.I.) and research funds from the Yamagata Prefectural Government and Tsuruoka City,
Japan (to K.A. and M. Tomita). S.I. was supported by a JSPS DC1 Fellowship; S.I., H.M. and
N.M. were supported by TTCK Fellowships; H.M. and N.M. were supported by the Mori
Memorial Foundation; and N.M. was supported by the Yamagishi Student Project Support
Program of Keio University.

Author contributions

R.C.S., S.I., H.M. and N.Y. conceived and designed the study. R.C.S., S.I. and M. Tanaka
constructed the plasmids. R.C.S., S.I., M. Tanaka and N.M. performed the base editor
assays and the library construction for high-throughput sequencing. S.I. established thebase editor reporter cell lines. R.C.S. and S.I. performed the fluorescence microscopy
imaging. S.I. and H.M. performed most of the data analysis. K.T., H.U., S.Y., K.A., M.S.
and H.A. performed the high-throughput sequencing and data analysis. K.N., A.K., H.N.
and O.N. supported the design of Target-ACE and provided materials. M. Tomita helped
the computational analyses. R.C.S., S.I., H.M. and N.Y. wrote the manuscript.

Competing interests

K.N. and A.K. are shareholders and board members of BioPalette Co., Ltd.

Additional information

Extended data is available for this paper at https://doi.org/10.1038/s41587-020-0509-0.
Supplementary information is available for this paper at https://doi.org/10.1038/
s41587-020-0509-0.
Correspondence and requests for materials should be addressed to N.Y.
Reprints and permissions information is available at www.nature.com/reprints.

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Extended Data Fig. 1 | Single- and dual-function base editors used in this study. Developmental lineages of single- and dual-function base editors used inthis study are represented by arrows. Base editor mix controls for dual-function base editors are indicated by dashed lines.

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Extended Data Fig. 2 | Base-editing activity in base-editing reporter cells. a, Schematic representation of the C→T base-editing reporter. C→T baseediting of the antisense strand followed by DNA replication restores the translation of EGFP by converting a mutated start codon GTG (valine) to ATG(methionine). b, Schematic representation of the A→G base-editing reporter. A→G base editing of the antisense strand followed by DNA replication
converts the stop codon, TAA, to CAA (glutamine) releases the translation of its downstream EGFP. c, Microscopy images of the positive control cells
for C→T and A→G base-editing reporters transiently transfected with different base editor reagents and non-targeting (NT) gRNAs. Scale bar, 40 µm.
d, Frequency of start codon restoration in C→T editing reporter cells. Each bar shows the mean of three independent transfection experiments represented
by dots. e, Frequency of stop codon destruction in A→G editing reporter cells. f, Frequency of amplicon sequencing reads showing C→T editing at any
position of the gRNA target site of C→T editing reporter cells (from –30 to +10 bp relative to the PAM). g, Frequency of amplicon sequencing reads
showing A→G editing at any position of the gRNA target site of A→G editing reporter cells (from –30 to +10 bp relative to the PAM).

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Extended Data Fig. 3 | DNA off-target editing activity. Editing frequencies of EMX1 site 1 and FANCF site 1 and site 2 and their corresponding off-target
sites. Amplicon sequencing experiments were performed in triplicate.

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Extended Data Fig. 4 | Prediction of base-editing outcome frequencies. a, Schematic diagram of the model to predict the frequencies of eachbase-editing outcome. In brief, to train a given base editor model using a training amplicon sequencing dataset for different target sites, probabilities
of single base transition events and their conditional probabilities given each of the other single events are thoroughly calculated for different positions
relative to the PAM. The frequency of a given editing outcome in a new test target site is then predicted as a geometric mean of probabilities of basetransitions at all edited positions, each given by the other independent base transition patterns. b, Correlation of measured and predicted relative editing
outcome frequencies in the 5-fold cross-validation experiment.

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Extended Data Fig. 5 | Heterologous trinucleotide co-editing frequencies predicted by the computational model. To predict the multidimensionalco-editing spectra of the different base-editing methods using the base-editing prediction model, 100 synthetic target sequences consisting of only
cytosine and/or adenine bases in the region from −20 to −1 bp relative to the PAM were generated in silico. For each target sequence, all possible
outcomes with C→T and/or A→G edits (220 outcomes in total) were predicted using the base-editing prediction model trained from all 47 ampliconsequencing data. The average homologous trinucleotide-editing spectra shown by the bubble charts were then calculated using all predicted frequencies.

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Extended Data Fig. 6 | Codon convertibility matrices (CCMs) of single-function base editors without allowing bystander mutations to occur. For
each codon in the human genome (hg38), possible gRNA target sites were first screened in the area of ±25 bp. For all gRNAs, base-editing outcome
probabilities of all possible C→T and/or A→G editing patterns in the ±15 bp region of the target codon were predicted using the base-editing prediction
model trained by the amplicon sequencing data for all 47 genomic sites. The conversion potential of the target source codon to each destination codon
without allowing bystander mutations to occur was then defined as the maximum probability of generating the target outcome among those inducedby all possible gRNAs. After calculating conversion potentials to different destination codons for all genomic codons, a CCM was generated to show the
genome-wide frequency of each source-destination codon conversion type with a conversion potential threshold of 5%.

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Extended Data Fig. 7 | Codon convertibility matrices (CCMs) of base editor mixes and dual-function base editors without allowing bystander
mutations to occur.

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Extended Data Fig. 8 | Codon conversion matrices (CCMs) of single-function base editors with allowing bystander mutations to occur.

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Extended Data Fig. 9 | Codon conversion matrices (CCMs) of base editor mixes and dual-function base editors with allowing bystander
mutations to occur.

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Last updated by author(s): Mar 25, 2020

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For data acquisition, we used software tools supplied by the manufacturers for InCellAnalyzer6000 and Illumina MiSeq, HiSeq 2500 and
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For computational data analysis, we used NCBI Blast 2.7.0, EMBOSS 6.0.0, FLASH 1.2.0, bcl2fastq2 (version v2.20.0), seqtk version 1.3r107-dirty, STAR version 2.7.3a, Picard MarkDuplicates version 2.0.1, BWA-MEM version 0.7.15, and GATK HaplotypeCaller version
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The amplicon sequencing datasets were obtained for 47 genomic loci each with 13 different base editor reagents in triplicate (1,833 datasetsin total). The number of data points generated from this data for different analyses are clarified in Supplementary Fig. 2. The base editing
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