REVIEWDNA Computing

www.biotechnology-journal.com

Nanopore Decoding of Oligonucleotides in
DNA Computing
Ryuji Kawano“Yes” or “No” depending on whether any
path remains. This type of problem displays NP complexity, requiring enormous
computational resources relative to thenumber of cities. NP problems are considered beyond the scope of von Neumanntype computers. After proposing thisgroundbreaking idea, computer scientists
collaborated with wet-lab scientists to study
1st generation DNA-based computing,[2–8]
which required human intervention to
implement each step (i.e., adding solutions, changing reaction temperatures, andobserving results via gel electrophoresis).
The 2nd generation of DNA computation allowed autonomous calculations. To
create autonomous operations, a method
involving “strand displacement” was developed[9,10] that utilized differences in freeenergy (ΔG) associated with DNA hybridization. When the hybridization energy of
DNA strand A-B is larger than that of A-C, the A-C strand isdisplaced by A-B autonomously. Using this reaction, theoperational procedure could be encoded in the DNA sequenceby designing the reaction order in terms of the differences in ΔG.
Benenson et al.[11,12] proposed a ﬁnite automaton system using
DNAs and restriction enzymes and that operated on a statetransition autonomously. This operation was implemented in a
120-mL volume without additional procedures and at room
temperature, with this calculation recorded in the Guinness
World Records as representing the “smallest biological computing device.”
Logic gate[13,14] implementation is another approach used toconstruct autonomous DNA-based calculations, given that they
are constructed according to a simple binary combination of OR,
NOT, and AND gates. This method allows higher-levelcalculations by combining a number of logic gates, with any
logic gate capable of construction through combining multiple
NAND (negative-AND) gates. Several researchers have studiedcomplex binary operations using DNAs and enzymes.[15–18] A
popular application logic gates involves a game of “tic-tac-toe”
using nine wells in a 3  3 matrix. Stojanovic et al.[19,20]
constructed a DNA-computational version of “tic-tac-toe” named“MAYA” (Molecular Array of YES and AND-AND-NOT gates)
using DNAzyme. This algorithm involved a simpliﬁed symmetry
pruned game of tic-tac-toe encompassing 19 permissible gameplays and using an array of 23 logic gates distributed over eightwells (Figure 1A).
In conventional DNA computation, recognition of output
molecules is mainly performed by four different methods

In conventional DNA-computation methods involving logic gate operations,the output molecules are detected and decoded mainly by gel electrophoresisor fluorescence measurements. To employ rapid and label-free decoding,
nanopore technology, an emerging methodology for single-molecule detection
or DNA sequencing, is proposed as a candidate for electrical and simple
decoding of DNA computations. This review describes recent approaches todecoding DNA computation using label-free and electrical nanopore measurements. Several attempts have been successful in DNA decoding with thenanopore either through enzymatic reactions or in water-in-oil droplets.
Additionally, DNA computing combined with nanopore decoding has clinicalapplications, including microRNA detection for early diagnosis of cancers.
Because this decoding methodology is still in development and not yet widely
accepted, this review aims to inform the scientific community regarding
usefulness.

1. Introduction
Polynucleotides contain information encoding amino acidsequence, with this information capable of being transferred
and/or copied using chemical and enzymatic reactions. This has
garnered the attention of computer scientists desiring to expand
their ﬁeld into wet-lab environments in order to study DNA,
RNA, and enzymes. In 1994, a computer scientist, Adleman[1]proposed “DNA-based computing” based on a directed Hamiltonian path problem (Figure 1A).[1] The problem solution
involves ﬁnding a path among several cities on a map, such thateach city is visited only once. This was initially transferred to
DNA-related problems by preparing short DNAs (20-mers) andassigning each to different cities and paths. Five steps wereexperimentally performed in order to solve this problem. Step 1:
Generate random paths through the graph; with all DNAs mixed,
hybridized, and ﬁxed by ligation. Step 2: Remove all paths that do
not begin with a start node, “0”, and end with an end node, “6”,using polymerase chain reaction (PCR). Step 3: The correct
length of the DNAs are separated and retrieved by gelelectrophoresis. Step 4: Remove any paths that repeat nodes
using magnetic bead puriﬁcation. Step 5: Supply an answer ofProf. R. Kawano
Department of Biotechnology and Life Science
Tokyo University of Agriculture and Technology
Harumicho, Fuchu, Tokyo 183-8538, Japan
E-mail: rjkawano@cc.tuat.ac.jp

DOI: 10.1002/biot.201800091

Biotechnol. J. 2018, 13, 1800091

1800091 (1 of 8)© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

www.advancedsciencenews.com

www.biotechnology-journal.com

Figure 1. Applications of nanopore technology. A) Adleman[1] described molecular computations using a Hamiltonian path presented as a problem with
NP complexity (left). Reproduced with permission.[1] Copyright 1994, the American Association for the Advancement of Science. Tic-tac-toe board
representation (right). Reproduced with permission.[20] Copyright 2003, Nature Publishing Group. B) Conventional decoding for DNA computing.
C) Schematic illustration of nanopore detection of DNA and current-time trace. D) DNA detection using an αHL nanopore. Four different
mononucleotides show individual blocking-current levels. Reproduced with permission.[43] Copyright 2006, the American Chemical Society.
E) A commercialized nanopore sequencer with small flow cells (Oxford Nanopore Technologies).

Biotechnol. J. 2018, 13, 1800091

1800091 (2 of 8)© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

www.advancedsciencenews.com

www.biotechnology-journal.com(Figure 1B and Table 1): 1) gel-electrophoretic detection
following PCR ampliﬁcation; 2) ﬂuorescence detection withisothermal ampliﬁcation; 3) ﬂuorescence detection by directlabeling without ampliﬁcation; and 4) ﬂuorescence probing
without ampliﬁcation. The pioneering works of Adleman[1] andBenenson[11] in DNA computation used these methods. Despite
the recent development of microscale rapid gel electrophoresis,[21,22] traditional gel electrophoresis is time consuming. As asubstitute, several ﬂuorescence techniques have been developed
involving speciﬁc ampliﬁcation of output DNA by isothermal
reactions and observation by ﬂuorescence labeling.[20,23] Methods 1 and 2 require an ampliﬁcation step involving enzymes,
which requires long reaction times and temperature control,
even under constant conditions at 37  C. Therefore, nonampliﬁcation methods (i.e., methods 3 and 4) can be used,
where the output DNA is labeled and detected[24] and a speciﬁc
ﬂuorescence probe is used.[25] Although neither of thesemethods require an ampliﬁcation step (considering theirimplementation at relatively high concentrations), the use of
direct labeling or speciﬁc probe molecules is required.
Nanopore technology allows the rapid and electricaldetection of oligonucleotides in the absence of labeling.
Several studies reported methods related to nanopore
decoding[26–28] and their applications in diagnosis or clinicalsettings based on DNA computing. Therefore, nanopore
methods represent potential candidate methods for decoding
DNA computations.

2. Application of Nanopore Technology for
Rapid and Label-Free Decoding
Nanopore technology involves electrical measurement of ion
current through a nanopore.[29–34] Biological (proteins) or
solid-state nanopores ranging in size from 1 nm to 10 nmshow open-pore current conductance (Figure 1C). When amolecule passes through or blocks a nanopore, the open-porecurrent reduces, thereby demonstrating current-signal blockage.
The blocking amplitude, duration time, and event frequency
provide information regarding the size, mobility, and concentration of target molecules at the single-molecule level.
α-Hemolysin (αHL), a channel toxin from Staphylococcus
aureus,[35] is conventionally used as a biological nanopore fordetecting oligonucleotides based on its having a pore size
comparable to that of single-stranded DNA (ssDNA) or ssRNA.
Extensive studies reported using this nanopore as a label-free,
rapid, and electrical method for single-oligonucleotide

determination, with one application targeting nanopore sequencing (Figure 1D).[36–40] As the ﬁrst report in 1996 by
Kasianowicz et al.,[36] enormous efforts have been undertaken to
apply this method.[37,39,41–54] In 2015, a company named Oxford
Nanopore Technologies was launched and provided the ﬁrst
commercially available nanopore sequencer for general use(Figure 1E). Currently, nanopore technology can be utilized not
only for single DNA/RNA detection but also for large-scale DNA
sequencing.[55]Nanopore methods allow recognition of oligonucleotides
rapidly, electrically, and without the necessity for labeling. Since
2016, several studies reported possible application of thistechnology for the detection of DNA-computing output. The ﬁrst
study involved detection of the output of a NAND logic operation ina micro-droplet system.[26] This method involved construction of afour-droplet system with a biological nanopore at the dropletinterface bilayer and electrodes in each droplet.[56–59] Input DNAs
are injected into two input droplets, calculations are performed in
the operation droplet, and these droplets are subsequently passed
through the output droplet, with the output monitored electricallyby the nanopore (Figure 2A). The important feature of this work
was that output “1” or “0” was deﬁned according to whether an
ssDNA translocated through a nanopore. This method harnessed
the unique property of the αHL nanopore, which allowed only
ssDNAs to pass through. This method involves conversion ofmolecular information into electrical signals in a binary system,
and while output times associated with ﬂuorescence-based logic
gates range from minutes to hours, this nanopore system requires
only 10 min without any labeling.
Following this preliminary study, Ohara et al.[27,60] proposed acomplicated logic operation using enzyme reactions in the
micro-droplet of a nanopore system in order to accommodate thenecessity for enzymatic reactions in most DNA computations.
An enzyme-free system requires rigid operational conditions,because the temperature of the reaction and/or enzyme
concentrations are strictly controlled in such reactions. However,
enzyme-free operations can be implemented in one-to-one
reaction, with one input molecule generating one output
molecule using a chain-displacement reaction. By contrast,
operations involving enzymes can be implemented in versatile
reactions, such as DNA polymerization, ampliﬁcation, andtranscription. Therefore, veriﬁcation of nanopore decoding isimportant, especially in cases of DNA calculations involving
enzymatic reactions. Ohara et al.[27] constructed an AND gate
allowing the input molecules to amplify and transcribe DNA via
T7 RNA polymerase when two DNAs are input simultaneously(Figure 2B). Their study showed that four different operations

Table 1. Comparison of conventional fluorescence and nanopore-decoding methods of DNA computation.
Gel electrophoresis[1,11]

Fluorescence (intercalator)[23]

Fluorescence (labeling)[24]

Fluorescence (probing)[25]

Nanopore[26,27]

Detection method

Optical

Optical

Optical

OpticalElectrical

Measurement time

Long

Short

Short

Long

Short

Sensitivity

Low

Low

High

High

High

Throughput

Low

Low

High

High

Potentially highPre-treatment

Multi-step

Few steps

Multi-stepMulti-step

Few steps

High

High

MediumMediumLow

Generality of method

Biotechnol. J. 2018, 13, 1800091

1800091 (3 of 8)© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

www.advancedsciencenews.com

www.biotechnology-journal.com

Figure 2. Nanopore decoder methodology. A) NAND operation in a droplet system for nanopore decoding. The input molecule moves from the input
droplet to the output droplet through the nanopore. B) Four individual operations associated with a reverse transcription AND gate involving the T7 RNApolymerase. C) Nanopore measurement enables rapid and label-free detection of the output molecules. The translocation frequency of the output
molecules through the nanopore allows discrimination between a (1, 1) system and others. Reproduced with permission.[27] Copyright 2017, theAmerican Chemical Society.

Biotechnol. J. 2018, 13, 1800091

1800091 (4 of 8)© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

www.advancedsciencenews.com

www.biotechnology-journal.com

represented by (0 0), (1 0), (0 1), and (1 1) were implemented in
multiple micro-droplet devices, and that the output could beobtained after 90 min, which included a 60-min enzymatic
reaction (Figure 2C),[28] suggesting the efﬁcacy of nanopore
decoding for operations involving enzymes in micro-droplet
systems (summarized in Table 1). However, the reaction
efﬁciency differed between that performed in conventional
plastic tubes and droplets with a surrounding lipid bilayer, with
reduced efﬁciency observed in the droplet system.[27] Therefore,
these operations need to be improved appropriately withrespect to enzymatic reactions performed in a lipid-droplet
environment.

3. Application of Nanopore Decoding in
Medical Diagnosis
The ﬁeld of DNA computing was developed largely as a curiosity
driven exercise focused on solving mathematics-related problems,
including cryptograms and constructing various types of logic
gates (AND, OR, NOT, XOR, and NAND). However, this ﬁeld
recently increased in importance due to its potential applications inmedical diagnosis.[61–63] Benenson et al.[61] reported autonomous
diagnosis and drug-release systems using DNA computing using
the following “if-then” logic: “if” certain diagnostic conditions aretrue, such as low expression levels of certain mRNAs relative tothose of others, “then” the antisense drug is released. After thispioneering study, several studies were undertaken focused on
application of this technology to diagnosis and therapy.[62] Basedon the favorable compatibility of nanopore technology witholigonucleotide detection, strategies utilizing this method for
diagnosis using nanopores and DNA have been proposed.
Wang et al.[64] demonstrated nanopore-based detection of
microRNAs (miRNAs) from patients with lung cancer(Figure 3A).[64] MiRNAs are short noncoding RNAs, the expression
levels of which correlate with various diseases and represent
potential early diagnostic markers for cancer.[65] Wang et al.[64] used
programmable oligonucleotide probes that formed partiallyhybridized structures with target miRNA (Figure 3B), resulting
in partially hybridized DNA/RNA that exhibited different blocking
levels against unzipping relative to non-hybridized oligonucleotides. They were speciﬁcally able to detect target miRNAs of thelet-7 tumor family at picomolar levels, providing a practicaldemonstration of miRNA detection in cancer patients with cancer
and emphasizing that the measurement accuracy of the αHLnanopore was higher than that of quantitative real-time PCR
assays.[64] Subsequent studies reported development of speciﬁctags, such as peptide nucleic acid (PNA)[66] or polyethylene glycol(PEG),[28] used to detect miRNA. The PNA-probe method is uniqueand involves hybridization of a cationic probe to the target miRNA
to form a double-stranded structure that can be captured bynanopores exhibiting opposite polarity.[66] Despite the efﬁcacy ofthis method, simultaneous detection of multiple miRNAs remains
challenging. Complementary DNAwith a PEGtag was later used totarget miRNAs, with PEG tags bound to target miRNAs showing
different blocking levels during translocation through the αHLnanopore.[26] Although this method achieved accurate detection offour different miRNAs, it was difﬁcult to measure the amounts ofrespective miRNAs, because differences in the blocking levels

Biotechnol. J. 2018, 13, 1800091

were too close to allow discrimination. Very recently, the analysis of
the duration of the blocking instead of the blocking current areproposed for the pattern recognition of miRNA expression using
AND logic gate with nanopore technology.[67]Another advantage of using nanopore technology for miRNAdetection is its sensitivity. In conventional analytic methods,
such as microscopic or electrochemical methods, sensitivity
relies upon signal intensity. In ﬂuorescence measurements,
low-intensity results make it difﬁcult to discriminate between
noise and signal at low concentrations. Therefore, sensitivity isdependent upon the signal-to-noise ratio (SNR). On the other
hand, nanopore measurements at low concentrations result inno changes in the SNR of the current signals, but rather adecrease in the frequency of appearance of the signal. Ideally,
even at very low target-molecule concentrations, such as those
involving single molecules, the signal will appear duringcontinuous measurement. This implies that the sensitivity ofnanopore measurements is dependent upon measurement time,
which normally ranges from several minutes to several hours.
Consequently, concentration limitations are approximately on
the order of one picomolar, even after several attempts at
enhancing translocation via the nanopore under asymmetric saltconditions.[68]A useful technique associated with DNA computation
involves ampliﬁcation, which can enhance detection of targets
at low concentrations. Zhang et al.[68] reported the successful
detection of low concentrations of miR-20a, which is secreted insmall-cell lung cancer (SCLC), by combining isothermal
ampliﬁcation of the oligonucleotide along with nanopore-based
methods (Figure 3C).[69] Their method ampliﬁed stable ssDNA
from miR-20a at concentrations ranging from one femtomolar to
10 picomolar using an isothermal enzymatic reaction, with theoutput DNA capable of quantiﬁcation by nanopore measurement according to the translocation frequency. Based on thismethodology, any cancer-speciﬁc miRNAs can potentially be
speciﬁcally ampliﬁed and detected by changing the nucleotide
sequences of the DNA template and primer according to
the target miRNA.
Another interesting aspect of DNA computing used in clinicalapplications is “theranostics,” which describes a system ofsimultaneously combining diagnosis and therapy. Benenson
et al.[61] reported an autonomous diagnosis and drug-releasesystem using DNA computing and involving a one-to-one
reaction (i.e., a single input molecule generates a single output
molecule), which is incompatible with the requirements of mosttherapies, where the concentration of drug molecule (output)
needs to be higher than that of the diagnostic molecule (input).
Hiratani et al.[70] demonstrated a theranostic system for SCLC
using isothermal ampliﬁcation from target miRNA to an
antisense oligonucleotide, which was treated as a DNA-based
drug (Figure 3D). Isothermal ampliﬁcation is an emerging
technique in DNA computation that allows DNA ampliﬁcation ata constant temperature. A previous study described generation
and ampliﬁcation of a DNA-drug molecule (output) using
enzyme-mediated strand-displacement ampliﬁcation following
detection of the target (miR-20a; input) from an SCLC patient,with the generated DNA drug (oblimersen) monitored andquantiﬁed by nanopore-based measurement in real time.[70] The
results of nanopore quantiﬁcation showed that oblimersen was

1800091 (5 of 8)© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

www.advancedsciencenews.com

www.biotechnology-journal.com

Figure 3. Nanopore detection of miRNAs. A) MiRNA detection using DNA probes. The αHL nanopore recognizes the complex according to specificcurrent signals. Reproduced with permission.[64] Copyright 2011, Nature Publishing Group. B) PEG-tagged DNA probes provide different blocking
currents to allow discrimination between specific miRNAs. Reproduced with permission.[28] Copyright 2014, the American Chemical Society.
C) Detection of ultra-low concentrations (on order of 1 fM) of miRNAs using nanopores and DNA computing. Reproduced with permission.[69] Copyright
2017, the Royal Society of Chemistry. D) Theranostic system for SCLC using DNA computing. An antisense DNA drug generated upon miR-20a
detection; followed by nanopore-based measurement to quantify drug concentration using a label-free and real time method. Reproduced withpermission.[70] Copyright 2017, the American Chemical Society.

Biotechnol. J. 2018, 13, 1800091

1800091 (6 of 8)© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

www.advancedsciencenews.com

www.biotechnology-journal.com

ampliﬁed by >20-fold from miR-20a, thereby meeting thedosage requirement for SCLC therapy and suggesting thisautonomous ampliﬁcation strategy as a potential candidate forbroad-range theranostics using antisense oligonucleotides .
Nanopore decoding might also contribute to molecularrobotics.[71,72] Molecular robots represent next-generation
biochemical machines comprised of biomaterials, such as
DNA, proteins, and lipids, with the prerequisites of sensors,
intelligence, and actuators proposed as requirements for theconstruction of such robots. To develop sensors necessary toapply a level of “intelligence” to these robots, output decoding,using nanopores will be a valuable tool used to constructing thenecessary parts.

4. Conclusions
In this review, we described recent developments in nanopore
decoding methods for DNA computation and their applications inclinical ﬁelds. Nanopore technology does not require labeling, andthe decoding time is relatively rapid compared with conventional
ﬂuorescence methods. However, for laboratory scale measurements, reconstitution of biological nanopores in lipid bilayers
requires training that might be time consuming. Although the
droplet-contact method enables rapid, reproducible, and stable
nanopore measurements, it requires experience and training.
Powerful strategies based on micro-fabrication have been recently
introduced allowing preparation of massive numbers of nanopore
chambers in a small device in order to acquire the required data
exclusively from the appropriate chambers. This strategy
addresses current nanopore-speciﬁc issues and can be potentiallyapplied to other nanopore technologies, including nanopore
decoding of DNA computation on an industrialized scale.
Nanopore technology represents a valuable methodology forenhancing the decoding of DNA computations.

Abbreviations
αHL, α-hemolysin; DNA; microRNA, miRNA; PCR, polymerase chain

reaction; PEG, polyethylene glycol; PNA, peptide nucleic acid; SCLC,
small-cell lung cancer; SNR, signal-to-noise ratio; ssDNA, single-strandedDNA.

Acknowledgments
This research was supported in part by KAKENHI, “Molecular Robotics”(Grant No. 15H00803) and Grant No. 16H06043 from Ministry of
Education, Culture, Sports, Science and Technology (MEXT), Japan.

Conflict of Interest
The authors declare no commercial or financial conflict of interest.

Keywords
DNA logic gates, DNA sequencing, lipid bilayers, nanopores, single
molecule detection

Biotechnol. J. 2018, 13, 1800091

Received: April 25, 2018
Revised: July 24, 2018
Published online: August 31, 2018

[1] L. M. Adleman, Science 1994, 266, 1021.
[2] Z. Ezziane, Nanotechnology 2006, 17, R27.
[3] R. S. Braich, N. Chelyapov, C. Johnson, P. W. K. Rothemund,L. Adleman, Science 2002, 296, 499.
[4] Q. Ouyang, P. D. Kaplan, S. M. Liu, A. Libchaber, Science 1997, 278,
446.
[5] T. Head, G. Rozenberg, R. S. Bladergroen, C. K. Breek,
PH Lommerse, HP Spaink, Biosystems 2000, 57, 87.
[6] D. Faulhammer, A. R. Cukras, R. J. Lipton, L. F. Landweber, Proc.
Natl. Acad. Sci. U. S. A. 2000, 97, 1385.
[7] A. R. Cukras, D. Faulhammer, R. J. Lipton, L. F. Landweber,
Biosystems 1999, 52, 35.
[8] M. Arita, S. Kobayashi, New Generation Computing 2002, 20, 263.
[9] D. Y. Zhang, G. Seelig, Nat. Chem. 2011, 3, 103.
[10] G. Seelig, D. Soloveichik, D. Y. Zhang, E. Winfree, Science 2006, 314,
1585.
[11] Y. Benenson, R. Adar, T. Paz-Elizur, Z. Livneh, E. Shapiro, Proc. Natl.
Acad. Sci. U. S. A. 2003, 100, 2191.
[12] Y. Benenson, T. Paz-Elizur, R. Adar, E. Keinan, Z Livneh, E. Shapiro,
Nature 2001, 414, 430.
[13] V. Balzani, A. Credi, M. Venturi, ChemPhysChem 2003, 4, 49.
[14] A. Okamoto, K. Tanaka, I. Saito, J. Am. Chem. Soc. 2004, 126, 9458.
[15] G. Chatterjee, N. Dalchau, R. A. Muscat, A. Phillips, G. Seelig, Nat.
Nanotechnol. 2017, 12, 920.
[16] L. Qian, E. Winfree, J. Bruck, Nature 2011, 475, 368.
[17] L. Qian, E. Winfree, Science 2011, 332, 1196.
[18] D. Y. Zhang, E. Winfree, J. Am. Chem. Soc. 2009, 131, 17303.
[19] M. N. Stojanovic, S. Semova, D. Kolpashchikov, J. Macdonald,
C Morgan, D. Stefanovic, J. Am. Chem. Soc. 2005, 127, 6914.
[20] M. N. Stojanovic, D. Stefanovic, Nat. Biotechnol. 2003, 21, 1069.
[21] C. T. Lo, D. J. Throckmorton, A. K. Singh, A. E. Herr, Lab Chip 2008, 8,
1273.
[22] A. E. Herr, A. V. Hatch, D. J. Throckmorton, H. M. Tran, J. S. Brennan,
W. V. Giannobile, A. K. Singh, Proc. Natl. Acad. Sci. U. S. A. 2007, 104,5268.
[23] X. M. Li, T. R. Ding, L. Sun, C. M. Mao, Biosens. Bioelectron. 2011, 30,
241.
[24] A. Saghatelian, N. H. Volcker, K. M. Guckian, V. S. Y. Lin,M. R. Ghadiri, J. Am. Chem. Soc. 2003, 125, 346.
[25] K. S. Park, M. W. Seo, C. Jung, J. Y. Lee, H. G. Park, Small 2012, 8,
2203.
[26] H. Yasuga, R. Kawano, M. Takinoue, Y. Tsuji, T Osaki, K Kamiya,
N Miki, S. Takeuchi, PLoS ONE 2016, 11, e0149667.
[27] M. Ohara, M. Takinoue, R. Kawano, ACS Synth. Biol. 2017, 6, 1427.
[28] X. Y. Zhang, Y. Wang, B. L. Fricke, L. Q. Gu, ACS Nano 2014, 8, 3444.
[29] L. Q. Gu, O. Braha, S. Conlan, S. Cheley, H. Bayley, Nature 1999, 398,686.
[30] W. Q. Shi, A. K. Friedman, L. A. Baker, Anal. Chem. 2017, 89, 157.
[31] T. Osaki, S. Takeuchi, Anal. Chem. 2017, 89, 216.
[32] D. H. Stoloff, M. Wanunu, Curr. Opin. Biotechnol. 2013, 24, 699.
[33] J. E. Reiner, A. Balijepalli, J. W. Robertson, J. Campbell, J. Suehle,
J. J. Kasianowicz, Chem. Rev. 2012, 112, 6431.
[34] Y. L. Ying, C. Cao, Y. T. Long, Analyst 2014, 139, 3826.
[35] L. Z. Song, M. R. Hobaugh, C. Shustak, S. Cheley, H. Bayley,
JE. Gouaux, Science 1996, 274, 1859.
[36] J. J. Kasianowicz, E. Brandin, D. Branton, D. W. Deamer, Proc. Natl.
Acad. Sci. U. S. A. 1996, 93, 13770.

1800091 (7 of 8)© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

www.advancedsciencenews.com

www.biotechnology-journal.com[37] M. Akeson, D. Branton, J. J. Kasianowicz, E. Brandin, D. W. Deamer,
Biophys. J. 1999, 77, 3227.
[38] D. W. Deamer, D. Branton, Acc. Chem. Res. 2002, 35, 817.
[39] D. Branton, D. W. Deamer, A. Marziali, H. Bayley, S. A. Benner,
T. Butler, M. Di Ventra, S. Garaj, A. Hibbs, X. Huang,
S. B. Jovanovich, P. S. Krstic, S. Lindsay, X. S. Ling,
C. H. Mastrangelo, A. Meller, J. S. Oliver, Y. V. Pershin,
J. M. Ramsey, R. Riehn, G. V. Soni, V. Tabard-Cossa, M. Wanunu,
M. Wiggin, J. A. Schloss, Nat. Biotechnol. 2008, 26, 1146.
[40] Y. Astier, O. Braha, H. Bayley, J. Am. Chem. Soc. 2006, 128, 1705.
[41] A. Meller, L. Nivon, D. Branton, Phys. Rev. Lett. 2001, 86, 3435.
[42] A. Meller, D. Branton, Electrophoresis 2002, 23, 2583.
[43] Y. Astier, O. Braha, H. Bayley, J. Am. Chem. Soc. 2006, 128, 1705.
[44] S. Benner, R. J. A. Chen, N. A. Wilson, R. Abu-Shumays, N. Hurt,
K. R. Lieberman, D. W. Deamer, W. B. Dunbar, M. Akeson, Nat.
Nanotechnol. 2007, 2, 718.
[45] R. J. White, E. N. Ervin, T. Yang, X. Chen, S. Daniel, P. S. Cremer,
H. S. White, J. Am. Chem. Soc. 2007, 129, 11766.
[46] T. Z. Butler, M. Pavlenok, I. M. Derrington, M. Niederweis,J. H. Gundlach, Proc. Natl. Acad. Sci. U. S. A. 2008, 105, 20647.
[47] J. Clarke, H. C. Wu, L. Jayasinghe, A. Patel, S. Reid, H. Bayley, Nat.
Nanotechnol. 2009, 4, 265.
[48] I. M. Derrington, T. Z. Butler, M. D. Collins, E. Manrao, M. Pavlenok,
M. Niederweis, J. H. Gundlach, Proc. Natl. Acad. Sci. U. S. A. 2010,
107, 16060.
[49] A. R. Hall, A. Scott, D. Rotem, K. K. Mehta, H. Bayley, C. Dekker, Nat.
Nanotechnol. 2010, 5, 874.
[50] F. Olasagasti, K. R. Lieberman, S. Benner, G. M. Cherf, J. M. Dahl,
D. W. Deamer, M. Akeson, Nat. Nanotechnol. 2010, 5, 798.
[51] G. M. Cherf, K. R. Lieberman, H. Rashid, C. E. Lam, K. Karplus,
M. Akeson, Nat. Biotechnol. 2012, 30, 344.
[52] J. Schreiber, Z. L. Wescoe, R. Abu-Shumays, J. T. Vivian, B. Baatar,
K. Karplus, M. Akeson, Proc. Natl. Acad. Sci. U. S. A. 2013, 110,
18910.

Biotechnol. J. 2018, 13, 1800091[53] M. Jain, I. T. Fiddes, K. H. Miga, H. E. Olsen, B. Paten, M. Akeson,Nat. Methods 2015, 12, 351.
[54] R. Kawano, A. Schibel, C. Cauley, H. White, Langmuir 2009, 25,
1233.
[55] H. Y. Lu, F. Giordano, Z. M. Ning, Genomics Proteomics
Bioinformatics 2016, 14, 265.
[56] K. Funakoshi, H. Suzuki, S. Takeuchi, Anal. Chem. 2006, 78, 8169.
[57] A. J. Heron, J. R. Thompson, A. E. Mason, M. I. Wallace, J. Am. Chem.
Soc. 2007, 129, 16042.
[58] H. Bayley, B. Cronin, A. Heron, M. A. Holden, W. Hwang, R. Syeda,
J. Thompson, M. Wallace, Mol. BioSyst. 2008, 4, 1191.
[59] R. Kawano, Y. Tsuji, K. Sato, T. Osaki, K. Kamiya, M. Hirano, T. Ide,
N. Miki, S. Takeuchi, Sci. Rep. 2013, 3. https://doi.org/10.1038/
srep01995[60] M. Ohara, Y. Sekiya, R. Kawano, Electrochemistry 2016, 84, 338.
[61] Y. Benenson, B. Gil, U. Ben-Dor, R. Adar, E. Shapiro, Nature 2004,
429, 423.
[62] Y. Benenson, Nat. Rev. Genet. 2012, 13, 455.
[63] C. Jung, A. D. Ellington, Acc. Chem. Res. 2014, 47, 1825.
[64] Y. Wang, D. L. Zheng, Q. L. Tan, M. X. Wang, L. Q. Gu, Nat.
Nanotechnol. 2011, 6, 668.
[65] G. Di Leva, M. Garofalo, C. M. Croce, Annu. Rev. Pathol. Mech. Dis.
2014, 9, 287.
[66] K. Tian, Z. J. He, Y. Wang, S. J. Chen, L. Q. Gu, ACS Nano 2013, 7,
3962.
[67] M. Hiratani, R. Kawano, Anal. Chem. 2018, 90, 8531.
[68] M. Wanunu, W. Morrison, Y. Rabin, A. Y. Grosberg, A. Meller, Nat.
Nanotechnol. 2010, 5, 160.
[69] H. L. Zhang, M. Hiratani, K. Nagaoka, R. Kawano, Nanoscale 2017, 9,16124.
[70] M. Hiratani, M. Ohara, R. Kawano, Anal. Chem. 2017, 89, 2312.
[71] M. Hagiya, A. Konagaya, S. Kobayashi, H. Saito, S. Murata, Acc.
Chem. Res. 2014, 47, 1681.
[72] R. Kawano, ChemPhysChem 2018, 19, 359.

1800091 (8 of 8)© 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim