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ArticlePattern Recognition of microRNA Expression in Body Fluids Using
Nanopore Decoding at Subfemtomolar Concentrations
Nanami Takeuchi, Moe Hiratani, and Ryuji Kawano*
Cite This: https://doi.org/10.1021/jacsau.2c00117

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sı Supporting Information*

ABSTRACT: This paper describes a method for detecting
microRNA (miRNA) expression patterns using the nanoporebased DNA computing technology. miRNAs have shown promise
as markers for cancer diagnosis due to their cancer type speciﬁcity,and therefore simple strategies for miRNA pattern recognition are
required. We propose a system for pattern recognition of ﬁve typesof miRNAs overexpressed in bile duct cancer (BDC). The
information of miRNAs from BDC is encoded in diagnostic
DNAs (dgDNAs) and decoded electrically by nanopore analysis.
With this system, we succeeded in the label-free detection of
miRNA expression patterns from the plasma of BDC patients.
Moreover, our dgDNA−miRNA complexes can be detected at subfemtomolar concentrations, which is a signiﬁcant improvementcompared to previously reported limits of detection (∼10−12 M) for similar analytical platforms. Nanopore decoding of dgDNAencoded information represents a promising tool for simple and early cancer diagnosis.
KEYWORDS: DNA computing technology, nanopore, microRNA, cancer, membrane■

INTRODUCTION
DNA computing uses the biochemical reactions of information-encoding DNA molecules to solve problems. The
autonomous calculations are implemented in parallel in a wet
environment. Initially, DNA computing was developed largely
as a curiosity-driven exercise focused on solving mathematicsrelated problems. The power of DNA computing was ﬁrst
demonstrated in the early pioneering work of Adleman in
1994,1 in which he presented a method to solve the
Hamiltonian path problema mathematical problem also
known as the traveling salesman problem. After proposing thisgroundbreaking idea, extensive methods of DNA computation
have been studied, including logic gates.2 The logic operation
has been one of the most attractive informational processes for
DNA computation, given that logic operations are constructedby simple binary combinations of OR, NOT, and AND gates,
for instance. This method allows higher-level calculations to be
performed by combining a number of logic gates, with any
logic gate capable of construction by combining multiple
NAND (negative-AND) gates.3
In conventional DNA computation, the recognition of
output molecules is mainly performed by combining several
methodologies such as gel electrophoresis or ﬂuorescence
detection, followed by polymerase chain reaction (PCR)
ampliﬁcation.1,4,5 To improve the speed of decoding, we haverecently proposed nanopore decoding for the detection of theoutput molecules directly and electrically. We constructed
several logic gates including AND, OR, NOT, and NAND, and© XXXX The Authors. Published byAmerican Chemical Society

the output molecule was detected by nanopore measurementof the electrical signals in a droplet-based nanopore device.6−9
In addition, we have also studied nanopore decoding forsolving the Hamiltonian path problem with parallel computation, as mentioned above.10 Based on experiences from theseprevious studies, we are convinced that nanopore decoding isappropriate for rapid and simple decoding in DNA computing.
Recently, the potential of DNA computing in medical
diagnostics has also been realized.11 Benenson et al. reported
autonomous diagnosis and drug-release systems with DNA
computing using the following “if-then” logic: “if” certain
diagnostic conditions are true, such as low expression levels ofcertain mRNAs relative to those of others, “then” the antisense
drug is released.12 After this pioneering study, several studies
were undertaken focused on the application of this technology
to diagnosis and therapy. Based on the favorable compatibilityof nanopore technology with oligonucleotide detection,13−15
strategies utilizing this method for diagnosis using nanoporesand DNA have been proposed.16 MicroRNA, which is a shortnoncoding RNA that has about 18−25 nucleotides, is an
important target in terms of diagnosis for cancers because its
Received: February 22, 2022
Revised: May 23, 2022
Accepted: May 24, 2022A

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ArticleFigure 1. Design of the miRNA detection system using DNA computing technology and nanopore decoding. (a) Photograph of the device used fornanopore analysis. (b) Schematic illustration of lipid bilayer preparation. (c) Design of HP-dgDNA. (d) Structure of the duplex of HP-dgDNA andmiR-193, miR-106a, miR-15a, and miR-374, simulated by NUPACK. When we simulated the structure, we converted the sequence of HP-dgDNA
into the corresponding RNA sequence and replaced the “A” of HP-dgDNA sequence with “U”. (e) Schematic illustration of the technical principleof nanopore decoding of HP-dgDNA.

the ﬁve miRNAs, HP-dgDNA performs information processing
to convert the expression pattern of the miRNAs into a nucleic
acid structure that can be decoded using the nanopore. Duringtranslocation through the pore, the hybridized miRNAs will be“unzipped” from the diagnostic DNA, which will result in acurrent inhibition of characteristic amplitude and duration. We
analyzed the unzipping time of the miRNA pattern and wereable to recognize the BDC-speciﬁc expression pattern of themiRNAs even from clinical samples. In addition, we found that
our method can detect the miRNA pattern at the attomolar(10−18 M) level using an excess of the HP-dgDNA.

expression is regulated with cancer types from early stages, andthe high-cancer speciﬁcity of the pattern of miRNA expression
is attracting attention as a form of liquid biopsy.17−27 In recent
years, several approaches based on logic operations have beendeveloped, including conventional DNA computation and gold
nanoparticle strategies.28,29 We have also constructed the ANDgate for the detection of two overexpressed miRNAs (miR-20a
and miR-17-5p) that are secreted from small cell lung cancer(SCLC).30 In this system, two diagnostic DNAs wereencapsulated in input droplets and formed a four-way junction
with the miRNAs only when the two miRNAs were present at
the same time (equivalent to an AND gate operation). The
structure of the four-way junction blocked the nanopore andgenerated a long current inhibition as the output signal,
resulting in current block durations speciﬁc for each system of(1, 1), (0, 1), (1, 0), and (0, 0). This logic operation enables
simple and rapid diagnostic applications using nanopore
analysis. However, although parallel operation is the mostintriguing characteristic of DNA computing, implementing
logic operations in multiplex diagnosis remains an unsolved
challenge.
Here, we report a method for the identiﬁcation of theexpression patterns of ﬁve diﬀerent types of miRNAs (miR193, miR-106a, miR-15a, miR-374, and miR-224) based on
DNA computing combined with nanopore decoding. ThesemiRNAs are overexpressed in bile duct cancer (BDC), which isone of the highest mortality cancers.31,32 A diagnostic DNA
with a hairpin structure (HP-dgDNA), which has the ability todetect multiple miRNAs simultaneously, is employed as thecomputational molecule. By forming a duplex structure with■

MATERIAL AND METHOD

Reagents and Chemicals

All aqueous solutions were prepared with ultrapure water froma Milli-Q system (Millipore, Billerica, MA). The reagents wereas follows: 1,2-diphytanoyl-sn-glycero-3-phosphocholine(DPhPC; Avanti Polar Lipids, Alabaster, AL), n-decane(Wako Pure Chemical Industries, Ltd., Osaka, Japan),potassium chloride (KCl; Nacalai Tesque), 3-(N-morpholino)propanesulfonic acid (MOPS; Nacalai Tesque, Kyoto, Japan).
Wild-type α-hemolysin (αHL; List Biological Laboratories,
Campbell, CA and Sigma-Aldrich, St Louis, MO) was obtained
as a monomer polypeptide, isolated from Staphylococcus aureus
in powder form, and dissolved at a concentration of 1 mg/mL
in ultrapure water. For use, samples were diluted to thedesignated concentration using a buﬀered electrolyte solution
and stored at 4 °C. High-performance liquid chromatography(HPLC)-grade DNA oligonucleotides and miRNA wereB

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Articlesynthesized by FASMAC Co., Ltd. (Kanagawa, Japan) andstored at −20 and −80 °C, respectively.

ﬁlms, including the parylene ﬁlm, were inserted into the chaseto separate the wells.

Patients and Healthy Samples

Bilayer Lipid Membrane (BLMs) Preparation andReconstitution of αHL

Plasma samples were supplied by a biobank operated by the
National Center for Global Health and Medicine (Tokyo,
Japan). Patient samples were obtained from six patients withhistologically proven BDC. All of the samples were obtained
from patients who had undergone surgical resection in May
2010−November 2016. The diagnosis of these patients was
based on histological assessment after surgical resection.
Additionally, plasma samples were collected from 11 healthy
volunteers (HVs) and the plasma of 6 HVs was pooled foranalysis. The clinicopathological backgrounds of the patientsand HVs are shown in Supporting Information Table S1. After
sample collection, blood samples were centrifuged at 3000 rpm
for 10 min at 15 °C to spin down the blood cells. Plasma
samples were then transferred into fresh collection tubes andstored at −80 °C until further processing.

Bilayer lipid membranes (BLMs) were prepared using the
microdevice fabricated as described above (Figure 1a). BLMs
can be spontaneously formed in this device by the droplet
contact method (Figure 1b).6,7,33 In this method, the two lipid
monolayers contact each other and form BLMs on the 100 μm
aperture in the parylene C ﬁlm that separates the two wells.
BLMs were formed as follows: the wells of the device were
ﬁlled with n-decane (2.5 μL) containing DPhPC (10 mg/mL).
Next, the aqueous recording solutions (4.7 μL, 1 M KCl, and10 mM MOPS, pH 7.0) were added to both of the wells. αHL
was reconstituted in BLMs to form a nanopore from theground side. The diagnostic solution after miRNAs/HPdgDNA hybridization was also added to the ground side.
Within a few minutes of adding the solutions, BLMs wereformed and αHL reconstructed nanopores within them. If theBLMs ruptured during this process, they were recreated bytracing with a hydrophobic stick at the interface of thedroplets.

Small RNA Extraction

Small RNAs were extracted from 300 μL of plasma with a
NucleoSpin miRNA Plasma (Takara Bio, Inc., Shiga, Japan)
according to the manufacturer’s protocol. At the beginning ofeach extraction procedure, exogenous control cel-miR-39-3p(FASMAC) was spiked into samples before the addition oflysis buﬀer. The ﬁnal volume was 30 μL. All eluted RNA
samples were stored at −80 °C until used.

Channel Current Measurements and Data Analysis

The channel current was recorded with an Axopatch 200B
ampliﬁer (Molecular Devices), ﬁltered with a low-pass Bessel
ﬁlter at 10 kHz with a sampling rate of 50 kHz. A constantvoltage of +200 mV was applied from the recording side, while
the other side was grounded. The recorded data from
Axopatch 200B were acquired with Clampex 9.0 software(Molecular Devices) through a Digidata 1440A analog-todigital converter (Molecular Devices). Data were analyzed
using Clampﬁt 10.6 (Molecular Devices), Excel (Microsoft,
Washington), and Origin Pro 8.5J (Light Stone, Tokyo,
Japan). DNA or miRNA translocation and blocking weredetected when >80% of open αHL channel currents were
inhibited. Between 251 and 322 translocating or blocking
events were recorded. From these data, we generated
histograms of unzipping time for each sample using a bootstrap
method. The event frequency was counted for each 1 min
interval when a single αHL pore was open. Nanopore
measurements were conducted at 22 ± 2 °C.

Quantiﬁcation of miRNA by Quantitative Real-Time PCR(RT-qPCR)

Amounts of miRNAs were quantiﬁed by quantitative real-time
PCR (RT-qPCR) using the SYBR Advantage qPCR Premix(Takara). The reverse transcription reaction was carried out
with the Mir-X miRNA First-Strand Synthesis Kit (Takara)according to the manufacturer’s instructions. Quantitative PCR
was performed on the Thermal Cycler Dice Real-Time System
Lite (Takara), and 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. The cycle threshold (Ct) values were calculated with
Multiplate RQ (Takara).
miRNA/HP-dgDNA Hybridization

Diagnostic solutions consisted of each extracted miRNA with
500 nM HP-dgDNA in MOPS buﬀer (pH 7.0, 10 mM)
containing 1 M potassium chloride. These solutions were
heated to 95 °C for 5 min and then cooled to room
temperature gradually.

Stability Prediction of the miRNA/HP-dgDNA Duplex

The Gibbs free energy (ΔGsim) of each of the miRNA/HPdgDNA duplex was predicted by the nearest-neighbor (NN)model with NN parameters for RNA/DNA hybrids.34■

Preparation of the Microdevice for Nanopore Analysis

The microdevice used for nanopore analysis was fabricated bymachining a 6.0 mm thick, 10 × 10 mm polymethyl
methacrylate (PMMA) plate (Mitsubishi Rayon, Tokyo,
Japan) using a computer-aided design and computer-aided
manufacturing three-dimensional modeling machine (MM100, Modia Systems, Japan). Two wells (2.0 mm in diameterand 4.5 mm in depth) and a chase between the wells were
manufactured on the PMMA plate. Each well had a throughhole in the bottom, through which Ag/AgCl electrodes were
installed to the bottom of the wells (Figure 1a). A polymericﬁlm made of parylene C (polychloro-p-xylylene) with athickness of 5 μm was patterned with a single pore (100 μm
in diameter) using a conventional photolithography method
and then ﬁxed between PMMA ﬁlms (0.2 mm thick) using anadhesive (Super X, Cemedine Co., Ltd, Tokyo, Japan). The

RESULTS

Design of Diagnostic DNA (HP-dgDNA)

We selected miR-193, miR-106a, miR-15a, miR-374, and miR224 as target miRNAs for BDC diagnosis because thesemiRNAs have been reported to be overexpressed in humanintrahepatic cholangiocarcinoma.31 We designed HP-dgDNA,
a diagnostic DNA with a hairpin structure and a sequence thatenables the linear binding of all of the ﬁve target miRNAs(Figure 1c and Supporting Information Table S2). The
detailed design rationale is as follows:(i) The complementary strands of the ﬁve miRNAs areinserted into the main sequence to encode the miRNA
patterns (dgDNA).
(ii) Poly(dC)20 is added at the 3′ end of dgDNA to be
exclusively inserted into αHL pore. The length ofC

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ArticleFigure 2. Results of nanopore analysis of each miRNA pattern. (a)−(c) Characteristic current signals of diﬀerent combinations of miRNAs boundto HP-dgDNA: (a) miR-374; (b) miR-15a, miR-224, and miR-193; and (c) miR-15a, miR-224, miR-193, miR-106a, and miR-374. (d) Histogramsof unzipping time of each miRNA pattern. (e) Histogram of unzipping time of translocation of HP-dgDNA without bound miRNAs. (f) Mean of
the unzipping time of each miRNA pattern. (g) Mean of the unzipping time as a function of the free energy of each duplex of HP-dgDNA andmiRNAs. Error bars represent the mean ± standard deviation (SD) after the bootstrap analysis.

ΔGsim values of each miRNA/HP-dgDNA hybridization arelisted in Supporting Information Table S3.
Two cytosines were set in between the complementary
sequence of each miRNA as spacers. The thermodynamic
simulation showed that the two-cytosine spacers make theduplex suﬃciently stabilized (Supporting Information FigureS1). Next, we determined the order of complementary strandsof the ﬁve miRNAs in HP-dgDNA using simulations. To
inhibit the unpredicted formation of secondary structures, thesequence order was selected to have the smallest hybridizationenergy from the 120 (=5!) possible sequence orders. The
optimal strand order resulted in a minimum ΔGsim of −94.8
kJ/mol (Supporting Information, Figure S2). The HP-dgDNA

poly(dC)20 is 8.4 nm; therefore, the HP-dgDNA canpenetrate the αHL pore because the length from theentrance to the β-barrel structure of the pore is 4.8 nm.
(iii) The HP-dgDNA hairpin structure is added at the 5′ endof dgDNA to prevent insertion into the αHL pore fromthe 5′ end.35
All designed structures were checked by thermodynamic
simulation (NUPACK: http://www.nupack.org/, and nearestneighbor (NN) model). The duplex formation of HP-dgDNA
and the miRNAs at 500 nM each were simulated
thermodynamically (Figure 1d). The NUPACK simulation
can calculate ΔGsim of a secondary structure composed of only
DNA or RNA alone. Therefore, ΔGsim of RNA/DNA binding
was calculated by the NN model using NN parameters.34 AllDhttps://doi.org/10.1021/jacsau.2c00117
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ArticleFigure 3. Results of RT-qPCR and nanopore analysis of real plasma samples. (a) miRNA concentration of the ﬁve cancer patient plasma samples
and the ﬁve healthy volunteer plasma samples quantiﬁed by RT-qPCR. (b) Average miRNA concentration of the ﬁve cancer patient samples andthe ﬁve healthy volunteer samples. Characteristic current signal of cancer patient plasma samples (c) and healthy volunteer plasma samples (d). (e)Histograms of the unzipping time of the six cancer patient samples and the six healthy volunteer samples. (f) Average unzipping time of plasma
samples. All error bars are mean ± SE.

structure fulﬁlling the above requirements is shown in Figure
1c,d.
The long cytosine homo-sequence was selected as the 3′end-tail because it enables the detection of HP-dgDNA
insertion based on the unique current blocking ratio ofpoly(dC) (Ib = 70%) to open pore current.36 The blocking
ratio of poly(dC) is likely to be higher than 70%, while theblocking ratio of poly(dA) and poly(dT) is likely to be 50%.

S3a). Histograms of the unzipping time, the duration ofunzipping events, were made using the bootstrap method, astatistical method, and the mean value of the unzipping time at
200 mV was shorter than that at 150 mV (Supporting
Information, Figure S3b). The unzipping time was shortenedby increasing the applied voltage, suggesting that the duplex of
miR-193/HP-dgDNA entered the pore was pushed out by thevoltage and then passed through the pore with the unzippingof miR-193. The unzipping signal ratio has been reported to
become higher with an increase in applied voltage.38,39
Therefore, to facilitate the unzipping, the applied voltage of
200 mV was adopted for the following experiments.
To validate the proof of concept, we prepared a cancer
miRNA pattern (with all 5 miRNAs present) and two healthy
miRNA patterns (with 1 or 3 miRNAs present) using synthetic
miRNAs. In each pattern, unzipping signals with over 80%

Nanopore Analysis of Each miRNA Pattern

In the nanopore analysis of the miRNA/HP-dgDNA duplex,
there were two possibilities: that the duplex passed through thenanopore resulting in the unzipping of the complementarystrands (Figure 1e) or that the duplex returned to the cis
solution.37 To conﬁrm the translocation of HP-dgDNA of theduplex, we measured the complex of miR-193/HP-dgDNA
using αHL at 150 or 200 mV (Supporting Information FigureEhttps://doi.org/10.1021/jacsau.2c00117
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ArticleFigure 4. Results of subfemtomolar miRNA detection. (a) Average capture frequency of the six cancer samples and the six healthy samples in thepresence of HP-dgDNA. (b) Characteristic current signals of 500 nM HP-dgDNA and miRNAs at each concentration. (c) Capture frequency of
500 nM HP-dgDNA and miRNAs for each condition. Using 5 fM miRNAs without HP-dgDNA, we observed a characteristic signal (d) and its
capture frequency (e). (f) Histograms of the unzipping time of each miRNA pattern. HP-dgDNA was 500 nM in each. (g) Mean of the unzipping
time as a function of the total miRNA concentrations. All error bars are mean ± SD. (h) Schematic illustration of the femtomolar miRNA detection
with/without 500 nM HP-dgDNA. (i) The capture frequency calculated by the theoretical model.

blocking were observed; hence, we propose that HP-dgDNA
was inserted from poly(dC)20 and subsequently passed
through the αHL pore with unzipping of the miRNAs andits hairpin structure (Figure 2a−c). The mean current blocking
ratio (Ib,mean) was [Ib,mean ± 2.2%] (n = 37). A previous study
showed that the standard error (SE) of the blocking current ofpoly(dC)50 was 2−4%, which is approximately consistent with
our result. We analyzed the unzipping time with theassumption that unzipping time reﬂects the number ofmiRNAs bound to HP-dgDNA. Histograms of the unzipping
time were also made using the bootstrap method (Figure 2d).

The peak values of unzipping time histogram of miR-374, miR15a, miR-224, miR-106a, miR-193, three miRNAs, and ﬁvemiRNAs were 709, 736, 1081, 1349, 2052, 4517, and 5841 ms,
respectively. Each peak value was larger than that of HPdgDNA itself, at 369 ms (Figure 2e). This result shows thatmiRNAs were unzipped from HP-dgDNA during the translocation through the αHL pore, with a characteristic unzipping
time (Figure 2e). The unzipping time became larger as thenumber of miRNAs binding to HP-dgDNA increased, andcorrelated with the ΔGsim of the duplex (Figure 2f,g). In thecase of the singly bound miRNA, the unzipping time also
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Articlewas possible that HP-dgDNA and the ﬁve miRNAs comprised
32 (=25) types of secondary structure in the hybridized
combinations with diﬀerent concentrations. We observed
various unzipping times on the order of 10−2 to 106 ms(Supporting Information Figure S6), probably due to the wide
variety of hybridized structures. We assumed that distributionsof unzipping time represented the overall miRNA pattern ineach sample since we considered the ratio of miRNAs/HPdgDNA duplex in a crowd of HP-dgDNA to be reﬂected in the
distributions. Therefore, the histogram of the unzipping time
was prepared by the bootstrap method, and the peak values ofeach of the cancer patients and HVs were obtained (Figure3e). The average unzipping times of the cancer patients and
HVs were 1487 ms and 856 ms, respectively (Figure 3f). The
miRNA expression level was higher in the cancer patients thanin the HVs, a result in line with the RT-qPCR analysis.
However, we cannot ﬁnd a clear relationship between theunzipping time and the total concentration of miRNAs in theplasma sample probably due to the complex binding pattern.
The cutoﬀ value of unzipping time to distinguish between
cancer patients and HVs calculated in this study was 1011 ms(Supporting Information Figure S7). As a result, our nanopore
technique using HP-dgDNA with a multiplexing property
distinguished the cancer patients from the HVs without
generating a calibration curve for each miRNA.

became longer with increasing ΔGsim (Figure 2e,f). These
results indicate that complexes with a larger ΔGsim require alonger time to be unzipped from HP-dgDNA in the αHL pore.
The unzipping time showed an exponential dependence on
ΔGsim (R2 = 0.82), and this result is consistent with previous
studies40,41 (Figure 2g and Supporting Information, FigureS4). Our system could be used to recognize other miRNA
patterns of diﬀerent cancers by adjusting ΔGsim and controllingthe unzipping time. Regarding the speciﬁcity of this method,
there are already several reports on the speciﬁcity/selectivity of
the hybridization of oligonucleotides including miRNAs.37,42,43
As demonstrated in several reports of miRNA detection
techniques, our system would have enough speciﬁcity for
miRNAs.23,44,45
RT-qPCR of Clinical Samples

Based on the results of the proof-of-concept study, we next
attempted to recognize miRNA patterns using clinical plasma
samples. The ﬁve miRNAs, miR-15a, miR-193, miR-224, miR106a, and miR-374, are known to be overexpressed incholangiocytes microdissected from the tissue of BDC
patients.31 To conﬁrm the expression level in plasma before
performing the nanopore decoding, absolute quantiﬁcation of
miRNAs extracted from the plasma samples was conducted by
RT-qPCR. First, we prepared calibration curves of the ﬁvemiRNAs and cel-miR-39-3p, as spike-in miRNA (Supporting
Information Figure S5). The diﬀerence in the eﬃciency ofreverse transcription and ampliﬁcation between the ﬁvemiRNAs was normalized using the calibration curves of the
ﬁve miRNAs. The diﬀerence in the eﬃciency of reverse
transcription and ampliﬁcation between the samples was
normalized by the calibration curve of the spike-in miRNA.
The miRNA concentration in each of the samples was between
approximately 30 aM to 7.0 nM (Figure 3a). To distinguishthe cancer patients and the healthy volunteers (HVs), we had
to interpret the ﬁve-dimensional data (= concentration value ofthe ﬁve miRNAs). One way to distinguish cancer patients fromhealthy controls using a combination of values attributed tomultiple miRNAs is by logistic regression.46 However, it has
been reported that for logistic regression, the sample size
should be at least 5 times larger than the explanatory
variables.47 For this study, we would therefore need morethan 5 × 5 = 25 samples because there were ﬁve types ofmiRNAs. Since clinical samples of BDC are diﬃcult to obtain
due to the small patient numbers in Japan, we instead analyzed
the diﬀerence in the expression level of each miRNA of the ﬁvecancer patients and the ﬁve HVs. The concentration of each of
the miRNAs in the cancer patients was higher compared to the
HVs. In the cancer patients, miR-193, miR-106a, and miR-15a
were signiﬁcantly overexpressed compared to the HVs with p <0.01. Although other two miRNAs (miR-224 and miR-374)
did not show a signiﬁcant diﬀerence, the mean concentrations
were higher in the cancer patients (Figure 3b and Supporting
Information). To the best of our knowledge, this is the ﬁrst
report revealing that the miRNAs known to increase in BDC
tissue are also overexpressed in plasma.

Investigation of Subfemtomolar Detection with a Model
System Using Synthetic miRNAs

Generally, the frequency of DNA/RNA translocation via a
nanopore (= capture frequency) is used to estimate thedetection capability against a target concentration. We also
calculated the capture frequency of the plasma samples in
addition to the unzipping times. However, as shown in Figure
4a, monitoring the capture frequency did not allow forrecognition of the miRNA patterns in the BDC because theHP-dgDNA was present at a higher concentration (500 nM)
and therefore dominated the frequency events. To investigate
detection at low concentrations, we designed model experiments with similar experimental conditions to the clinicalexperiments: 0.1, 1, 10, and 100 fM of each of the ﬁve miRNAs
were used with 500 nM HP-dgDNA and the capture frequency
was analyzed (Figure 4b). Although the capture frequencies ofthe femtomolar conditions were similar to each other, the 500
nM miRNAs/HP-dgDNA duplex showed a lower frequency(Figure 4c). These results suggest that miRNAs/HP-dgDNA
with residual HP-dgDNA show higher frequency, andmiRNAs/HP-dgDNA itself shows lower frequency. Besides,in the presence of only miRNAs without HP-dgDNA, thefrequency of 1 fM 5 miRNAs (total 5 fM) with over 80%
inhibition was around 0.05 s−1; this value was less than onehundredth of 500 nM 5 miRNAs/HP-dgDNA (5.68 s−1,
Figure 4d,e).
We next analyzed the unzipping time of the modelexperiments. The unzipping times of the femtomolar
concentrations of miRNAs with 500 nM HP-dgDNA were
measured and compared with each other. The unzipping timeincreased with the increase in the concentration of themiRNAs, suggesting that miRNA can be detected at
subfemtomolar concentrations using the HP-dgDNA (Figure4f,g). The calculated detection limit was 0.3 fM in eachmiRNA (Supporting Information Figure S8).

Nanopore Analysis of Clinical Samples

HP-dgDNA hybridized with miRNAs extracted from theplasma samples were examined by nanopore analysis. In bothcancer and HV samples, over 80% inhibition of the ion current
ﬂowing through the pore was observed, as also observed withsynthetic miRNAs (Figure 3c,d), indicating that miRNAdetection is possible even with the use of clinical samples. It
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DISCUSSION

equilibrium. When calculating f for 5 miRNAs/HP-dgDNA
and HP-dgDNA, respectively, the parameters that diﬀer
between them are q and ΔG0. If we ﬁrst consider q: if theratio of the 5 miRNAs/HP-dgDNA signal was 10 out of 300
signals for the experiments using 0.5 fM miRNAs, f for 5
miRNAs/HP-dgDNA would become more than 106 times
larger than that for HP-dgDNA. However, using the elemental
charge e in this assumption, we calculated that q = 270e for 5
miRNAs/HP-dgDNA and q = 160e for HP-dgDNA, indicatingthat q is unlikely to have a signiﬁcant eﬀect on f. Therefore, we
assumed that the free energy barrier at equilibrium ΔG0
between 5 miRNAs/HP-dgDNA and HP-dgDNA is considerably diﬀerent and aﬀects f. Although proper modeling topredict ΔG0 is required, ΔG0 can be represented as

Low-Concentration Detection of miRNA/HP-dgDNA in the
Nanopore Measurements

Our results when using clinical samples were a pleasantsurprise since nanopore measurements have generally been
considered unable to detect nucleic acids at subpicomolar
concentrations.37 To measure the subpicomolar or femtomolar
targets, several elaborate methods have been proposed; Wanget al. showed the detection of 0.1 pM miR-155 with the αHLnanopore using asymmetric solution conditions of 0.2 M/3 M(cis/trans) KCl salt gradient, to increase the capture frequencyof the target molecule.37 Zhang et al. used the αHL nanopore
with isothermal ampliﬁcation of nucleic acids, resulting in thedetection of 1 fM miR-20a.48 In contrast, our method was able
to detect subfemtomolar miRNAs directly without any
modiﬁcations to the basic nanopore detection setup.
We consider that the key to this low-concentration detectionis the excess amount of HP-dgDNA as the complementaryprobe for the target miRNAs. Our results in the in vitro
experiments support this hypothesis:(1) In the case of the equimolar condition between the HPdgDNA and miRNAs or the miRNAs themselves, thetotal capture frequency is constant or decreased (Figure4c).
(2) In the case of the excess amount of the HP-dgDNA, theunzipping time showed a linear relationship forconcentrations ranging from 0.1 fM to 1 pM (Figure
4g).
Consequently, excess HP-dgDNA surrounding target
miRNAs/HP-dgDNA caused an increase in the sensitivity, as
schematically shown in Figure 4h. To support this hypothesis
from a theoretical view, we referred to a nanopore capture
model as we have recently proposed.49 The modeling of thecapture frequency f of a particle captured into a nanopore is
described by the following equation
1
1
1
= +
f
fa
fe

ΔG0 = c × kBT■

CONCLUSIONSIn conclusion, we propose a system for miRNA expression
pattern recognition using DNA computing and nanopore
decoding. The information encoded in miRNA hybridized to
HP-dgDNA is decoded by nanopore sensing in the form of
unzipping time of the hybridized strands as the diagnostic
construct translocates through the nanopore. We successfully
distinguished miRNA expression patterns in clinical plasma
samples of bile duct cancer patients and HVs. Our nanopore
system was able to detect very low concentrations (∼10−16 M)
of miRNA from the plasma samples, which is a signiﬁcant
improvement compared to the previously reported limit ofdetections for nanopore analysis of DNA/RNA (∼10−12 M).
Based on a theoretical estimation, we found that the higher
concentration of our diagnostic DNA compared to the target
RNA molecules plays a critical role in this phenomenon. Ourﬁnding should be an intriguing physicochemical phenomenon
that has never been proposed, and it will be an importantconcept for low-concentration detection using the nanopore
technology.
Regarding the multiplex of our diagnosis system, this systemdoes not require a calibration curve for the quantiﬁcation ofeach miRNA. Certainly, our method is possible to quantify theconcentration of miRNAs using a calibration curve for eachmiRNA (Supporting Information Figure S9), and it can be
multiplex using the nanopore array device.7 This approach issimilar to conventional multiplex measurements. For instance,a PCR technique requires a large number of sample tubes, andthey need each calibration curve for quantiﬁcation of miRNAs.
In this study, however, the multiplexing property of HPdgDNA enabled the pattern recognition of the ﬁve types ofmiRNAs with a single device.
The proposed system would be potentially useful for
medical applications if this system will integrate into acommercially available nanopore sequencer such as MinION(Oxford Nanopore Technologies). Moreover, several biological nanopores other than αHL have also been used fornanopore sensing.33,50−56 The nanopore decoding will havethe potential to apply to the diﬀerent types of nanopores.

(1)

2πa 2C0
1 − e−a2 / reD

(2)

Q
μqI
+ f
2πσ
2π

(3)

with
a2 =

(5)

where c is a target-speciﬁc constant. Using this model for ourcase, we plotted f against ΔG0/kBT (=c) in Figure 4i. As aresult, c values of ssDNA and dsDNA captured into a nanopore
were expected to be signiﬁcantly diﬀerent, suggesting that ΔG0
diﬀers between ssDNA and dsDNA (Figure 4i). Therefore, alarge excess of ssDNA could promote the migration of dsDNA
to the pore.

with fa an approach frequency related to the migration ofparticles from the bulk to the pore inlet and fe an entrance
frequency related to the actual entry of particles into the poreregion; fa and fe are deﬁned as follows
fa =Articlewhere C0 is the particle concentration, re is the pore entrance
radius, D denotes the diﬀusion coeﬃcient, μ is the particle
mobility, q is the particle charge, I is the ion current ﬂowing
through the pore, σ is the electrolyte conductivity, and Qf is thevolumetric ﬂow rate entering the pore, andÅÄÅ
ÑÉ
ΔG0 ÑÑÑ
kTÅÑÑfe = 2πC0re3 B expÅÅÅÅ2φ(re) −ÅÅÇhkBT ÑÑÑÖ
(4)
where kB is the Boltzmann constant, T is the temperature, h is
Planck’s constant, φ(re) is the dimensionless eﬀective potential
evaluated at re, and ΔG0 is the free energy barrier atH

https://doi.org/10.1021/jacsau.2c00117
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ASSOCIATED CONTENT

sı Supporting Information*

The Supporting Information is available free of charge athttps://pubs.acs.org/doi/10.1021/jacsau.2c00117.

■

Additional results of unzipping time and thermodynamic
simulation, additional information on miRNA detection
methods and DNA computing technique, detailed
results of RT-qPCR and nanopore measurement forplasma samples, clinicopathological backgrounds ofpatients and HVs, and DNA/miRNA sequences(PDF)

AUTHOR INFORMATION

Corresponding Author

Ryuji Kawano − Department of Biotechnology and Life
Science, Tokyo University of Agriculture and Technology,
Tokyo 184-8588, Japan; orcid.org/0000-0001-65230649; Email: rjkawano@cc.tuat.ac.jp
Authors

Nanami Takeuchi − Department of Biotechnology and Life
Science, Tokyo University of Agriculture and Technology,
Tokyo 184-8588, Japan
Moe Hiratani − Department of Biotechnology and Life Science,
Tokyo University of Agriculture and Technology, Tokyo 1848588, Japan
Complete contact information is available at:https://pubs.acs.org/10.1021/jacsau.2c00117
Author Contributions

R.K. conceived the original idea. M.H. designed HP-dgDNA
and performed the initial experiments. N.T. performed all of
the other experiments and analyses. N.T. and R.K. wrote theentire manuscript. All authors have given approval to the ﬁnal
version of the manuscript.
Notes

The authors declare no competing ﬁnancial interest.

■

ACKNOWLEDGMENTS
The authors thank A. Tamotsu, A. Tada, and M. Yamaji forconstructive discussion and advice on data analysis. The
biological samples utilized for this research were provided by
the Biobank of National Center for Global Health andMedicine in Japan. This research was partially supported by
JSPS KAKENHI Grant Numbers 16H06043 (R.K.),
19H00901 (R.K.), 21H05229 (R.K.), and 21H03143 (N.T.).

■

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