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ArticleSimultaneous Recognition of Over- and Under-Expressed
MicroRNAs Using Nanopore Decoding
Sotaro Takiguchi, Fumika Kambara, Mika Tani, Tsuyoshi Sugiura, and Ryuji Kawano*
Cite This: https://doi.org/10.1021/acs.analchem.3c02560

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sı Supporting Information*

ABSTRACT: This paper describes a strategy for simultaneousrecognition of over- and under-expressed microRNAs (miRNAs)
using the method of signal classification-based nanopore decoding.
MiRNA has attracted attention as a promising biomarker forcancer diagnosis owing to its cancer-type-specific expression
patterns. While nanopore technology has emerged as a simple
and label-free method to detect miRNAs and their expression
patterns, recognizing patterns involving simultaneous over/underexpression is still challenging due to the inherent working
principles. Here, inspired by the sequence design for DNA
computation with nanopore decoding, we designed diagnostic
DNA probes targeting two individual over/under-expressed
miRNAs in the serum of oral squamous cell carcinoma. Throughnanopore measurements, our designed probes exhibited characteristic current signals depending on the hybridized miRNA species,
which were plotted on the scatter plot of duration versus current blocking ratio. The classified signals reflected the relative
abundance of target miRNAs, thereby enabling successful pattern recognition of over/under-expressed miRNAs, even when using
clinical samples. We believe that our method paves the way for miRNA-targeting simple diagnosis as a liquid biopsy.

O

multistep procedures or fluorescence detection; therefore, asimple decoding method has been in demand.
Nanopore technology is a promising tool for the electrical andlabel-free detection of oligonucleotides.10−13 Especially, αhemolysin (αHL), a pore-forming membrane protein from
Staphylococcus aureus,10 is conventionally used for oligonucleotide detection due to the size compatibility between the pore andsingle-stranded DNA. This size compatibility allows electrical
discrimination of not only four individual nucleobases12 but also
nucleic acid structures (single-stranded (ss), double-stranded(ds), and other higher-order structures).14,15 The results of
DNA computation are output as DNA/RNA molecules, leading
us to design the DNA-computed output information via nucleic
acid structural information. Combining this structural design
with αHL nanopore technology, we have developed a nanoporebased decoding method in DNA computation.16 We first
constructed a simple DNA logic gate with output 0/1
information encoded as ss/dsDNA, which resulted intransient/long-time current blocking signals which were

wing to the high programmability of nucleic acid basepairing, DNA computing has emerged as a prominent
form of molecular computation in wet environments. The initial
methodology was proposed in Adleman’s pioneering work, witha demonstration using DNA molecules to solve a directed
Hamiltonian path problem (HPP),1 which is mathematicallyclassified as NP-complete and known as the traveling salesman
problem. In his work, based on partially complementary
hybridization, the “nodes” and “paths” were associated witholigonucleotides to generate routes in a directed graph (Figure1a), successfully implementing massively parallel computation
to solve the HPP. Following this groundbreaking demonstration, extensive studies have been reported using DNA/RNA
molecules to implement mathematical models including parallel
computation,2 automaton construction,3,4 and logic-gate
operation.5,6 In addition to such mathematical modeling,
DNA computing has recently expanded the field of research to
medical applications due to the advantage of biocompatibility
and programmability of nucleic acid molecules.7 Biologicaloligonucleotides such as mRNA or miRNA are promisingbiomarkers in medical diagnosis owing to their potential as input
information in DNA computation.8,9 As described above, DNA
computing is attractive as a technology with a wide range ofapplications, but the output information is encoded in DNA/RNA molecules which then need to be decoded into a humanrecognizable signal. Conventional decoding approaches require© XXXX American Chemical Society

Received: June 13, 2023
Accepted: August 28, 2023

A

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ArticleFigure 1. (a) Sequence design principle of diagnostic DNA probes (dgDNAs) inspired by mathematical DNA computation for the simultaneous
pattern recognition of OSCC-related over/under-expressed miRNAs with nanopore-derived current signal analysis. (b) Design of diagnostic DNAs.
(c) Illustration of the three types of the output duplex structures; output 1 (dgDNA 1), output 2 (dgDNA 1 + miR-19a + dgDNA 2), and output 3(dgDNA 1 + miR-19a + dgDNA 2 + miR-5100 + dgDNA 3). Bar graphs show the abundance ratio of the three types of output duplex as the results ofthermodynamic simulations. The abundance ratios of output 1, output 2, and output 3 were, respectively, calculated to be 51, 19, and 30% for theneutral expression pattern, 0, 51, and 49% for the over-expression pattern, 51, 32, and 17% for the under-expression pattern, and 0, 76, and 24% for theover- and under-expression pattern.

successfully decoded through nanopore measurement.17 Subsequently, a logic network18 and an enzyme-involved complex
logic operation19 were also constructed using the droplet-based
nanopore system. In addition, by designing the ΔG of the output
duplex, we were able to decode Adleman’s DNA-based parallel
computation, the DNA-encoded HPP, utilizing the duration of
the current blocking signals as the decoding parameter.20 To
that end, the method of nanopore decoding has been developed
as a powerful tool for direct and simple decoding within diverse
DNA computations; the next phase of our research endeavor istoward medical applications. Our target miRNA, a shortnoncoding RNA of 18-25 nucleotides, is a promising biomarker
for cancer diagnosis because over/under-expression patterns ofmiRNAs in bodily fluids show cancer type specificity.21,22 As astrategy for miRNA expression pattern recognition, we haveused DNA computation methodology to design probes thatbind to miRNAs. In this approach, the designed probes perform
hybridization-based molecular computation with the target
miRNAs, and the output hybridized duplexes reflect the
information of the miRNA expression pattern, which is thendetected and decoded through nanopore measurement. Initially,
we constructed an AND gate using two over-expressed miRNAs
from small-cell lung cancer as inputs.23 In this system, theexpression pattern of miRNAs was described as an AND logic

operation, resulting in the formation of a four-way junction withdesigned diagnostic DNA probes only in the case of input (1, 1).
The method of nanopore decoding was able to discriminate theAND operations depending on the structural change of output
molecules, with discrimination of miRNA expression patterns.
Moreover, Takeuchi et al. have recently reported that a targetsequence-encoded diagnostic DNA probe was able to perform
parallel detection of five miRNAs, which was reported to beover-expressed in bile duct cancer (BDC) patients,24 using
pattern recognition.25 Using this method, we achieved thediscrimination of plasma miRNA expression patterns between
BDC patients and healthy volunteers. Consequently, based on
the principle of the increase in current blockage duration withincreasing miRNA concentration, we successfully demonstrated
the pattern recognition of multiple over-expressed miRNAs
using DNA computation methodology and nanopore decoding.
However, the recognition of simultaneous over/under-expression patterns is still challenging because the working principle ofthe nanopore decoding method depended on the single
decoding parameter: the current blockage duration, which
increased upon the hybridization with target miRNAs.
In this study, we report the method for pattern recognition of
two individual over/under-expressed miRNAs with current
signal classification-based nanopore decoding using diagnostic
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ArticlePreparation of the Microdevice. Microdevices were
fabricated by machining a 6.0 mm thick, 10 mm × 10 mmpoly(methyl methacrylate) (PMMA) plate (Mitsubishi Rayon,
Tokyo, Japan) using computer-aided design and manufacturingwith a three-dimensional modeling machine (MM-100, Modia
Systems, Japan) as shown in Figure S1b−d. Two wells (2.0 mm
diameter and 4.5 mm depth) and a chase between the wells were
manufactured on the PMMA plate. Each well had a through-holein the bottom and Ag/AgCl electrodes were set into this hole. A
polymeric film made of parylene C (polychloro-p-xylylene) witha thickness of 5 μm was patterned with a single pore (100 μm
diameter) using a conventional photolithography method andthen fixed between PMMA films (0.2 mm thick). The films,
including the parylene film, were inserted into the chase to
separate the wells and fixed using an adhesive bond (Acrysunday
Co., Ltd., Tokyo, Japan).
Lipid Bilayer Preparation, Channel Current Recording,and Data Analysis. Lipid bilayers were prepared in the
microdevice using the droplet contact method20,27,28 (FigureS1a), as described in the following procedures: the wells of thedevice were filled with n-decane (0.8 μL) containing DPhPC(10 mg/mL). The buffered solution (4.7 μL) with αHL (final
concentration 30 nM) and oligonucleotides were poured into
one chamber which was connected to the ground terminal. The
buffered solution (4.7 μL) was also poured into anotherchamber which was connected to the recoding terminal. In thisstudy, the buffered solution (1 M KCl, 10 mM MOPS, pH 7.0)
was used for each droplet. Within a few minutes of adding thesolutions, a lipid bilayer formed on a parylene C film that
separates the two chambers, and αHL formed a nanopore byreconstitution in the lipid bilayer. When the lipid bilayers
ruptured during this process, they were reassembled by tracingwith a hydrophobic stick at the droplet interface. The channel
current was recorded with an Axopatch 200B amplifier(Molecular Devices), filtered with a low-pass Bessel filter at 10
kHz at a sampling rate of 50 kHz. A constant voltage of +120 mV
was applied from the recording side, and the ground side was
grounded. In the event that DNA/RNA molecules become
clogged within the nanopore, resulting in a prolonged current
blocking duration exceeding our predefined threshold of 25 000
ms, we employed a transient voltage inversion to eject the
obstructing molecule from the nanopore. This procedure allows
the continued detection of other molecules. The recorded datafrom Axopatch 200B was acquired with Clampex 11.2 software(Molecular Devices) through a Digidata 1440A analog-to-digitalconverter (Molecular Devices). Data were analyzed using
Clampfit 11.2 (Molecular Devices), Excel (Microsoft, Washington), Origin pro 8.5J (Light Stone, Tokyo, Japan), andPython (Python Software Foundation, Delaware). The analyzed
data were obtained from at least three different αHL nanopores.
Oligonucleotides were detected when > 48% of open αHLchannel currents were inhibited.14 The duration time ofblocking signals was filtered between 300 μs and 25 000 ms.
300 samples were randomly extracted from all data to produce
the scatter plots of duration versus current blocking ratio. These
data sampling and analysis are described in detail in the
Supporting Information. The 95% CI of the experimentallyobtained abundance ratio (bar graph) was calculated using thefollowing equation:

DNA probes (dgDNAs), whose design is inspired by ourprevious mathematical DNA computation20 (Figure 1a). Target
miRNAs show aberrant expression (over-expression; miR-19a,
under-expression; miR-5100) in the serum of oral squamous cell
carcinoma (OSCC) patients, which is one of the most common
cancers worldwide.26 Toward simultaneous pattern recognitionof over/under-expressed miRNAs, we here utilize DNA-based
parallel computation methodology to design dgDNAs. In thismethod, as a result of molecular computation by dgDNAs andmiRNAs, three types of duplexes were generated depending on
the hybridized miRNA species, with their specific abundance
ratio reflecting the relative abundance of miRNAs. Throughnanopore measurements, the output duplexes exhibitedcharacteristic current signals, which were plotted on the scatterplot of duration versus current blocking ratio. We were able todecode information on the relative abundance of target miRNAs
from the classified signals on the scatter plot, thereby enabling
successful pattern recognition of the OSCC-specific over/under-expressed miRNAs, even when using clinical samples.

■

EXPERIMENTAL SECTION
Reagents and Chemicals. In this study, we used thefollowing reagents: 1,2-diphytanoyl-sn-glycero-3-phosphocholine (DPhPC; Avanti. Polar Lipids, Alabaster, Alabama), ndecane (Wako Pure Chemical Industries, Ltd., Osaka, Japan),potassium chloride (KCl; Nacalai Tesque), 3-morpholinopropane-1-sulfonic acid (MOPS; Nacalai Tesque, Kyoto, Japan).
Buffered electrolyte solutions (1 M KCl, 10 mM MOPS, pH 7.0)
were prepared using ultrapure water, which was obtained from a
Milli-Q system (Millipore, Billerica, Massachusetts). Wild-typeα-hemolysin (αHL; Sigma-Aldrich, St. Louis, Missouri) was
obtained as the monomer polypeptide, isolated from Staphylococcus aureus in the form of a powder and dissolved at aconcentration of 1 mg/mL in ultrapure water. For use, samples
were diluted to the designated concentration using a buffered
electrolyte solution and stored at 4 °C. HPLC-grade DNA,
RNA, and LNA-inserted DNA oligonucleotides were synthesized by Eurofins Genomics Inc. (Tokyo, Japan), FASMAC Co.,
Ltd. (Kanagawa, Japan), and GeneDesign, Inc. (Osaka, Japan),respectively, stored at −20 °C. 10× TBE buffer was obtained
from Takara Bio Inc. (Shiga, Japan) and was 10-fold diluted forgel electrophoresis. The power supply and an LED transilluminator were obtained from Bio Craft Co., Ltd. (Tokyo,
Japan) and ATTO Co., Ltd. (Tokyo, Japan), respectively.
DNA Design and Thermodynamic Simulation. To
confirm the hybridization and to simulate the DNA
computation, thermodynamic simulations were performed
using the NUPACK webserver (California Institute ofTechnology, http://www.nupack.org/). These analyses were
performed under the condition of 1 M KCl at 22 °C. The
concentrations of dgDNAs and neutral expression levels of
miRNAs were set to 500 and 250 nM, respectively. In the case ofover- or under-expression, the concentrations of miRNAs were
set to be 500 nM (2-fold) and 125 nM (0.5-fold), respectively.
Native Polyacrylamide Gel Electrophoresis. The duplex
formation was confirmed by native PAGE using 10% gels (29:1
acrylamide/bis (w/w)) in 1 × TBE buffer at a constant power of
1.0 W for 120 min at 4 °C. After electrophoresis, the gel was
stained with diluted SYBR Green II solution (Takara Bio Inc.)for 30 min and visualized under blue LED irradiation. Images
were obtained using LuminoGraph I (ATTO Co., Ltd., Tokyo,
Japan).

pC

1.96 p (1

p )/n

p

p + 1.96 p (1p )/n

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ArticleTable 1. Oligonucleotides Sequences (Underline Indicates the LNA Nucleotides)sequencemiR-19amiR-5100dgDNA 1dgDNA 2dgDNA 35′ UGUGCAAAUCUAUGCAAAACUGA 3′
5′ UUCAGAUCCCAGCGGUGCCUCU 3′
5′ GTAGAACTTTTGTTCTACCCCCCCCCCCCTCAGTTTTGCA 3′
5′ TAGATTTGCACAAGAGGCACCGC 3′
5′ TGGGATCTGAACCCCCCCCCCC 3′

Figure 2. Native PAGE to confirm the duplex formation between (a) dgDNAs and miRNAs and (b) LNA-inserted dgDNAs and miRNAs.

protocol. Eluted solutions with a final volume of 30 μL werestored at −80 °C until used. MiRNA concentrations were
quantified by reverse transcription quantitative PCR (RTqPCR) using the Mir-X miRNA qRT-PCR TB Green Kit(Takara). Reverse transcription was performed using the Mir-X
miRNA First-Strand Synthesis Kit (Takara) according to the
manufacturer’s protocol. Quantitative PCR was performed using
the Thermal Cycler Dice Real-Time System Lite (Takara) withthe following procedure: the reaction mixtures were incubated at
95 °C for 10 s, followed by 40 cycles at 95 °C for 5 s and 65 °C
for 25 s. Cycle threshold (Ct) values were calculated using
Multiplate RQ (Takara).

where p̅ is the sample ratio, p is the population ratio, and n is the
sample number.
Reference Data Preparation and Nanopore Decoding
for miRNA Pattern. To obtain the reference data, each output
duplex was prepared at the final concentration of 1 μM by thefollowing procedure. Each solution contained 1 μM oligonucleotides, 1 M KCl, and 10 mM MOPS (pH 7.0). These
solutions were heated to 95 °C for 5 min and then cooled toroom temperature gradually. To prepare four types of miRNA
expression pattern solutions, each oligonucleotide was added (1M KCl, and 10 mM MOPS (pH 7.0)) to a designatedconcentration, which is the same as the condition of thesimulation, in a buffered solution. These solutions were also
heated to 95 °C for 5 min and then cooled to room temperature
gradually. In the case of low concentrations of oligonucleotides,
the concentration of dgDNAs and miRNAs was designated to be
50 and 25 pM, respectively. The 50 pM concentration for
dgDNA was also used when using clinical samples.
Small RNA Extraction and Quantification of miRNA by
RT-qPCR. Small RNAs were extracted from 300 μL of each
serum sample using the NucleoSpin miRNA Plasma (Takara
Bio, Inc., Shiga, Japan) according to the manufacturer’s■

RESULTS AND DISCUSSION
Design of Diagnostic DNA Probes and Simulated
Results of DNA Computation. We selected miR-19a andmiR-5100 as targets because these miRNAs have been reported
to be respectively over/under-expressed in the serum of human
OSCC patients. According to the quantitative analysis, the over(miR-19a) and under- (miR-5100) expression level was
determined to be 2- and 0.5-fold, respectively.29 TowardDhttps://doi.org/10.1021/acs.analchem.3c02560
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ArticleFigure 3. Typical current blocking signals of (a) output 1, (b) output 2, and (c) output 3. (d−f) Scatter plots of duration versus the current blocking
ratio of (d) output 1 (e) output 2, and (f) output 3. (g−i) Analysis of the frequency of the blocking signals reflecting the detection of (g) output 1, (h)output 2, and (i) output 3. (j) Merged scatter plot as a reference data for nanopore decoding. Each surrounded area which reflected the detection signalof each output duplex was defined as follows; output 1: 300 μs < duration ≤ 100 ms, 48% ≤ blocking, output 2: 100 ms < duration < 25 000 ms, 48% ≤blocking < 70%, output 3: 100 ms < duration < 25 000 ms, 70% ≤ blocking. (k) Structure of αHL (PDB ID: 7AHL) and the proposed models for the
clogging of output 2 (latch) and output 3 (constriction) in the αHL nanopore.

simultaneous pattern recognition of these miRNAs, we here
utilized sequence design for DNA-based parallel computation1,20 and designed dgDNAs, which consisted of a partiallycomplementary sequence with target miRNAs (Figure 1b). As aproof of concept, four expression patterns of target miRNAs
were prepared involving over/under-expression, and wethermodynamically simulated the molecular computation
using nanomolar concentrations of oligonucleotides (describedin detail in the Experimental Section). As a result, depending on
the hybridized miRNA species, three types of duplexes (output
1−3) were generated in all patterns, and their abundance ratio
allowed us to distinguish the four miRNA expression patterns(Figure 1c). The relative abundance of the output duplexes iscorrelated with their concentration, which is validated as theindividual frequency of the current blocking event in nanopore
measurements.30 Therefore, this output information can be

decoded by nanopore-based identification of the output
duplexes. The sequences of the designed dgDNAs are listed inTable 1, and the structural design strategies are as follows:(1) Poly(dC) is added at the 3′ end of dgDNA 3 for insertion
into the αHL nanopore from the 3′ terminus of the output
duplexes. The 3′ single-stranded tail structure has beenreported to be highly efficient in the generation of current
blocking events.31(2) The current blocking duration changes drasticallydepending on the 5′/3′ direction of the nucleic acids
for insertion into the nanopore.28 To enhance the target
specificity of the current blocking signals, a hairpin
structure is added to the 5′ end of dgDNA 1 to restrict thetranslocation direction of the output duplexes through the
αHL nanopore.32
E

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ArticleFigure 4. Results of nanopore decoding using the miRNA pattern solution of (a, e, i) neutral expression, (b, f, j) over-expression, (c, g, k) underexpression, and (d, h, l) over- and under-expression. (a−d) Typical current−time traces, (e−h) scatter plots of duration versus current blocking ratio,and (i−l) the comparison of the abundance ratio between simulation results and experimental results. Error bars represent the 95% CIs.

in all measurements (Figure S4c−e). These plots wereexperimentally assigned to the clogging of duplexes in the
αHL nanopore from their hairpin terminus, by using dumbbellshaped DNA and hairpin-less output molecule (Figures S4 andS5, the sequence is listed in Table S1). Filtering the hairpin
clogging signals, we obtained the reference scatter plot of each
output duplex as shown in Figure 3d−f. The merged plotsuggested that the three types of duplexes were distinguishable
from the current blocking signals in nanopore measurements
and the plots can be classified into the framed area
corresponding to each output duplex (Figure 3j). The differencein the blocking ratio between output 2 and output 3 wasconsidered to reflect their structural difference. Regarding thelength of the single-stranded tail, which has been reported toinfluence the current blocking ratio,39 it was the same between
output 2 and output 3. As another factor, Perera et al. reported
that the helical form-dependent duplex diameter significantly
influenced the current blocking ratio.14 According to theirresults, A-form duplexes with a larger diameter (=2.4 nm)
clogged at the latch of αHL (Figure 3k), resulting in a shallow
current blockage (around 50% blocking). In contrast, B-form
duplexes with a smaller diameter (=2.0 nm) clogged at theconstriction of αHL (Figure 3k), resulting in a deep current
blockage (approximately 85% blocking). Referring to thisreport,14 our output 2 and output 3 clogged αHL at the latch
and constriction, respectively, probably as a sequence-dependent phenomenon (investigated in detail in Figure S6), allowing
for discrimination via the current blocking ratio (Figure 3k).
Using the framed area corresponding to the output duplexes, we
calculated the false positive rates as follows: 13% for output 2(3) A poly(dC) spacer is added between the hairpin structureand dgDNA 1/miR-19a binding region to form A-type(DNA-RNA) and B-type (DNA−DNA) duplexes simultaneously.33
First, we experimentally confirmed the duplex formation
between dgDNAs and miRNAs using native PAGE. Figure 2ashows the results for each binding site, in which bands of
unhybridized oligonucleotides were mainly observed, probably
owing to the short nucleotide length for hybridization.
Facilitating the duplex formation by introducing locked nucleic
acids (LNA)34,35 into dgDNAs (the insertion position is listed inTable 1), we successfully confirmed the dgDNAs/miRNAs
binding as shown in Figure 2b (additional confirmation is shownin Figure S3). LNA-inserted probes have been reported to have atarget specificity at the single-base level in nanopore measurement.36,37 By investigating the miRNA database,38 we found that
our target miR-19a has the family sequence as SNPs, but thedesigned dgDNAs have enough specificity to recognize thetarget miRNA in a variety of sequence contexts by the LNA
insertion.
Preparation of the Reference Data for Nanopore
Decoding. As mentioned above, the strategy of nanopore
decoding is premised on identifying the three types of output
duplexes. Therefore, we subsequently investigated the current
blocking signals of each duplex in nanopore measurements.
Figure 3a−c shows the representative current blocking signals ofeach output duplex. In the scatter plot of duration versus thecurrent blocking ratio of each signal, a specific plotting area
appeared in duration > 100 ms and 56% < blocking ratio < 60%Fhttps://doi.org/10.1021/acs.analchem.3c02560
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ArticleFigure 5. Results of nanopore decoding using neutral expression pattern solution (a−c) with nontarget miRNA and (d−f) at a low concentration. (a,d) Typical current−time traces, (b, e) scatter plots of duration versus current blocking ratio, and (c, f) the comparison of the abundance ratio between
simulation results and experimental results. Error bars represent the 95% CIs.

(Figure 4k), 21 ± 5, 52 ± 6, and 27 ± 5% for the over/underexpression pattern (Figure 4l). The calculated abundance ratioof the output duplexes within each pattern was significantly
different except for the case between output 2 and 3 in the overexpression pattern (Figure 4j) and between output 1 and 3 in theover/under-expression pattern (Figure 4l). Regarding thepercentage order, the experimentally obtained abundance ratio
was consistent with simulation results, discriminating the four
expression patterns of miRNA involving over/under-expression.
Although the output 1 signals appeared in the over-expression
pattern (Figure 4j) and the over- and under-expression pattern(Figure 4l), it was negligible when comparing the percentage
order with simulation results. Consequently, our proposed
method successfully demonstrated the simultaneous monitoringof over/under-expression patterns of target miRNAs.
Confirmation of the Target Specificity. Although LNAinserted probes have been reported to have single-baseresolution specificity,36,37 we experimentally confirmed thetarget specificity of our proposed method by adding a nontarget
miRNA (the sequence is listed in Table S1) to a pseudo-neutral
expression pattern solution at the same concentration as thetarget miRNAs. As shown in Figure 5a, several types of blocking
signals were observed in nanopore measurements. Producing ascatter plot of duration versus blocking ratio (Figure 5b), we
assigned each plot to each output duplex based on the reference
data. As a result, the abundance ratio of output 1, output 2, andoutput 3 was calculated to be 54 ± 6, 19 ± 4, and 27 ± 5%,
respectively, with these values being significantly different. In
terms of the percentage order, the experimentally obtained
abundance ratio was consistent with the simulation result(Figure 5c), showing the target specificity of our method.
Although the event frequency of blocking signals with duration<300 μs, which reflects the translocation of short single-stranded

identified as output 1, 9% for output 3 identified as output 1, 2%
for output 1 identified as output 2, 2% for output 3 identified as
output 2, 4% for output 1 identified as output 3, and 3% for
output 2 identified as output 3. In the range of 1 ms < duration <100 ms, which exhibited the highest level of overlap, we assigned
these signals in output 2 and output 3 measurements to thedetection of output 1 as unbound molecules to calibratemisidentification. With this reference scatter plot and framedarea assignment, we next implemented nanopore decoding using
the four types of pseudo-miRNA expression pattern solutions.
Nanopore Decoding for the Four Expression Patternsof miRNAs. The four series of pseudo-miRNA pattern solutions
were prepared using nanomolar concentrations of oligonucleotides, which were synonymous with the simulation conditions(described in detail in the Experimental Section). In thenanopore measurements, several types of current-blocking
signals were observed as shown in Figure 4a−d. Producing thescatter plots of duration versus blocking ratio (Figure 4e−h), we
assigned each plot to each output duplex based on the reference
data and framed area assignment (Figure 3j). For calculating theabundance ratio of the three types of output duplexes, wecounted the number of plots within each framed area in thegraph. To calibrate both the false positives of the misidentifications of the output molecules (overlap in Figure 3j) and theirdifferences in the frequency to be captured into nanopore, weused the frequency values of the current blocking events derived
from each output molecule (Figures 3g−i and S7). Following
the above data analysis as described in detail in the Supporting
Information, the abundance ratio of output 1, output 2, andoutput 3 were, respectively, calculated to be 47 ± 6, 22 ± 5, and
31 ± 5% for the neutral expression pattern (Figure 4i), 20 ± 5,
43 ± 6, and 37 ± 6% for the over-expression pattern (Figure 4j),
54 ± 6, 30 ± 5, and 16 ± 4% for the under-expression patternGhttps://doi.org/10.1021/acs.analchem.3c02560
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ArticleFigure 6. (a) MiRNA concentrations in pooled samples (=averages) of the three cancer patient samples with spiked-in miR-19a and the three healthy
volunteer samples. (b−g) Results of nanopore decoding using (b−d) pooled cancer sample with spiked-in miR-19a and (e−g) pooled healthy sample.
(b, e) Typical current−time traces, (c, f) scatter plots of duration versus current blocking ratio, and (d, g) experimentally obtained abundance ratio.
Error bars represent the 95% CIs.

oligonucleotides,40,41 were indeed increased by adding thenontarget miRNA (Figure S9), such signals were filteredthrough data analysis.
Estimation of the Durability against the Low miRNA
Concentration and against the Range of Concentration
Change. Considering diagnostic applications, we next confirmed the durability of our proposed method against low
concentrations of oligonucleotides using a picomolar pseudoneutral expression pattern solution (described in detail in theExperimental Section). In nanopore measurements, thefrequency of the current blocking event drastically decreased
with a reduction in oligonucleotide concentration, leading us toadopt asymmetric conditions to enhance the event frequency(Figure S10).31,42 As shown in Figure 5d, several types ofblocking signals were observed even with asymmetricconditions. Producing a scatter plot of duration versus blocking
ratio (Figure 5e), we assigned each plot to each output duplex
based on the reference data. As a result, the abundance ratio of
output 1, output 2, and output 3 was calculated to be 57 ± 6, 16± 4, and 27 ± 5%, respectively, with these values being
significantly different. In terms of percentage order, theexperimentally obtained abundance ratio was consistent withthe simulation result (Figure 5f), showing the durability of thissystem for low miRNA concentrations as secreted in bodily
fluids.
In this study, the over/under-expression levels of target
miRNAs were set to be 2- and 0.5-fold, respectively. We here
discuss the durability of our proposed method against a range ofmiRNA concentration changes using thermodynamic simulations. As shown in Figure S11, in the case of 2-, 5-, and 10-foldover-expression, the abundance ratio of the three types ofduplexes drastically changed depending on the expression level.
The differences in abundance ratio could be distinguished in

nanopore measurements, indicating that this method ispotentially robust against over-expressed miRNA recognition.
On the other hand, in the case of 0.5-, 0.2-, and 0.1-fold underexpression, there were slighter differences in the simulated
abundance ratio compared with the over-expression simulations.
Especially in the case of 0.1-fold under-expression, theabundance ratio of output 3 was calculated to be <0.1%,
which is considered to be difficult to detect and to calculate theabundance ratio by nanopore measurements. In summary, as alimitation of this method, the cancer-derived concentration
change of miRNAs should exceed a minimum threshold of 0.1fold. To overcome the above limitation in detecting the underexpressed miRNA, probe design is one of the effective strategies,
which enhances the event frequency of a specific output
molecule that shows less than 0.1-fold under-expression.31,39
When employing this method over a wide range of miRNAconcentration changes, the simulated data indicated that theidentification of the four expression patterns (shown in Figure3) was challenging due to the unchanged percentage order of theoutput duplexes (Figure S11). However, the trends of cancerderived simultaneous over/under-expression of miRNAs
appeared in the abundance ratio as an increase in output 2
coupled with a decrease in output 3 compared to the neutral
expression pattern. By capturing this change in the relative
abundance of the output duplexes, our method potentiallyperforms simultaneous recognition of over/under-expressed
miRNAs.
Operational Confirmation Using Clinical Samples. Our
targeting miR-19a and miR-5100 have been reported to be 2fold over-expressed and 0.5-fold under-expressed, respectively,in the serum of human OSCC patients.29 Since the collected
serum samples from OSCC patients (N = 3) and healthy
volunteers (N = 3) had the same clinicopathological backgroundH

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as in the previous report,29 we first attempted nanopore
measurement using pooled samples containing miRNAs
extracted from the serum samples. Employing the asymmetricelectrolyte conditions, three types of output duplex-detection
signals were observed even when using clinical samples (Figure
S12). The signals could be classified based on the reference data;
however, the experimentally obtained abundance ratio of theoutput duplexes conflicted with the miRNA profiling trends inthe previous report;29 our data indicated under-expression ofboth miR-19a and miR-5100 in OSCC patients (Figure S12).
To validate the expression level of miR-19a and miR-5100, we
performed absolute quantification of target miRNAs in theindividual serum samples using RT-qPCR (calibration curve
preparation is shown in Figure S13). Comparing the averagevalues, the cancer-induced concentration changes were
calculated to be 0.2-fold for miR-19a and 0.3-fold for miR5100 (Figure S14, miRNA concentrations in individual samples
are listed in Table S2), which profile differed from the previous
report29 but was consistent with our nanopore analysis result.
Although the relative abundance of target miRNAs was able tobe detected using pooled samples, we decided to introduce a
spike-in of miR-19a into the cancer pool to model the scenario of
2-fold over-expression (Figure 6a). Even with the spike-in
clinical sample, several types of blocking signals were observed(Figure 6b,e), which were successfully classified on the scatterplot of duration versus blocking ratio (Figure 6c,f). Based on thereference data and framed area assignment, the abundance ratioof output 1, output 2, and output 3 was calculated to be 79 ± 5,
20 ± 5, and 0.7 ± 1% (these values being significantly different)
for the cancer patients pool, and 87 ± 4, 12 ± 4, and 1 ± 1%
(these values being significantly different) for the healthy
volunteer pool, respectively (Figure 6d,g). The significant trendof an increase in output 2 coupled with a concomitant decreasein output 3, was indicative of the OSCC-induced changes in therelative abundance of target miRNAs (Figure 6d,g). To that end,
our method successfully captured the trend of simultaneous
over/under-expression at the clinical level.
Finally, we discuss a limitation inherent in this method
regarding target selection and the concentration disparities
among the target miRNAs. In our proposed method, thedetection of output 3 (miR-5100) is dependent on the presenceof output 2 (miR-19a) due to the stepwise binding principle
among dgDNAs and miRNAs. Therefore, the concentration of
miRNA proximity to dgDNA 1 (miR-19a in this study) must be
higher than another one next to itself (miR-5100 in this study).
Referring to the reported statistical data, careful selection of thetarget miRNA species is required based on the in-orderconcentration limitation. We believe that a strategic dgDNA
design can address this limitation. By preparing the reference
data of all potential hybridization products (outputs) anddesigning the dgDNAs so that all outputs are distinguishable inthe scatter plots, we can assess the relative abundance of target
miRNAs independent of their concentration order using our
proposed analysis method. Regarding the miRNA concentration
disparities, due to the correlation between the miRNAconcentration and the event frequency of current-blocking
signals, concentration differences between target miRNAs leadto a compression of the experimentally obtained abundance
ratio for a specific molecule, as shown in output 3 of our results(Figure 6d,g). To effectively monitor both over/underexpressed miRNAs, the concentration differences between thetarget miRNAs should be within a range of 500-fold, detectable
three types of duplexes simultaneously. One viable strategy to

Articleovercome this limitation is to increase the number of targeting
miRNAs, thereby mitigating the interference of miRNAs that are
secreted at aberrant concentrations compared to others. The
design of our dgDNAs can accommodate a larger number oftargeting miRNAs, suggesting the potential to achieve resilienceto the above limitation.

■

CONCLUSIONS■ASSOCIATED CONTENT

In conclusion, we proposed a strategy for the simultaneous
pattern recognition of over/under-expressed miRNAs, leveraging custom-designed DNA probes and nanopore-derived
current signal classification. Our designed probes, dgDNAs,
whose sequence design was inspired by mathematical DNA
computation, formed three types of duplexes depending on the
hybridized miRNAs as the output of the molecular computation.
By subjecting each output duplex to nanopore measurements,
we observed distinct current-blocking signals, which wereplotted on the scatter plot of duration versus current blocking
ratio and successfully classified. The classified signals reflected
the relative abundance of the target miRNAs, allowing the four
expression patterns of OSCC-related miRNAs involving over/under-expression to be distinguished using pseudo-miRNA
pattern solutions. Our data indicated that the proposed method
had target specificity, and durability in the face of low
oligonucleotide concentrations. Furthermore, this method wasable to detect the trend of simultaneous over/under-expressionof target miRNAs even when using clinical samples. Simultaneous detection of over/under-expression is generally challenging in other approaches such as surface plasmon resonance,43
electrochemical sensors,44 and fluorescence detection.45 In
addition to achieving the over/under-expression recognition,
our probe design allows for the accommodation of a larger
number of miRNAs, showing the potential for more complex
miRNA pattern recognition. With these advantages, we believethat our method provides a powerful tool for miRNA patternrecognition that can be used in simple cancer diagnosis.
In addition, commercially available nanopore devices such as
the MinION from Oxford Nanopore Technology have recently
attracted attention as a portable and high-throughput platform
for nanopore sensing.46−49 By introducing the oligonucleotidecontaining electrolyte solution into the flow cell of the MinION
as we poured it into the chamber of our microdevice, thismethod is applicable in point-of-care-testing technology as asimple and high-throughput diagnostic system.

sı Supporting Information*

The Supporting Information is available free of charge athttps://pubs.acs.org/doi/10.1021/acs.analchem.3c02560.
Detailed information on experimental and data sampling/
analysis; additional results of output duplexes formation;reference data preparation; estimation of the target
specificity and concentration durability; and clinicallevel operational confirmation of our proposed method
(PDF)

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AUTHOR INFORMATION

Corresponding Author

Ryuji Kawano − Department of Biotechnology and Life Science,
Graduate School of Engineering, Tokyo University ofAgriculture and Technology, Tokyo 184-8588, Japan;
I

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orcid.org/0000-0001-6523-0649; Email: rjkawano@cc.tuat.ac.jp
Authors

Sotaro Takiguchi − Department of Biotechnology and Life
Science, Graduate School of Engineering, Tokyo University ofAgriculture and Technology, Tokyo 184-8588, Japan
Fumika Kambara − Department of Biotechnology and Life
Science, Graduate School of Engineering, Tokyo University ofAgriculture and Technology, Tokyo 184-8588, Japan
Mika Tani − Department of Maxillofacial Diagnostic andSurgical Science, Field of Oral and Maxillofacial
Rehabilitation, Graduate School of Medical and Dental
Science, Kagoshima University, Kagoshima 890-8544, Japan
Tsuyoshi Sugiura − Department of Maxillofacial Diagnostic
and Surgical Science, Field of Oral and Maxillofacial
Rehabilitation, Graduate School of Medical and Dental
Science, Kagoshima University, Kagoshima 890-8544, Japan;
Division of Oral and Maxillofacial Oncology and Surgical
Sciences, Graduate School of Dentistry, Tohoku University,
Miyagi 980-8577, Japan
Complete contact information is available at:https://pubs.acs.org/10.1021/acs.analchem.3c02560
Author Contributions

S.T. and R.K. conceived the original idea. S.T. performed all
experiments. F.K. and M.T. conducted experiments related to
RT-qPCR, and T.S. and R.K. supervised the entire research. S.T.
and R.K. wrote the entire manuscript, and all authors approved
the final version of the manuscript.
Notes

The authors declare no competing financial interest.

■

ACKNOWLEDGMENTS
This research was partially supported by KAKENHI Grant Nos.
16H06043, 19H00901, 21H03143, 21H05229, and the WISE
Program: Doctoral Program for World-leading Innovative &Smart Education of TUAT from MEXT, Japan. The authors
thank A. Cooney for language editing.

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K

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