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OPEN

Received: 25 May 2017
Accepted: 27 November 2017
Published: xx xx xxxx

Discriminating single-bacterial
shape using low-aspect-ratio pores
Makusu Tsutsui1, Takeshi Yoshida1, Kazumichi Yokota1, Hirotoshi Yasaki2, Takao Yasui2,3,
Akihide Arima1, Wataru Tonomura1, Kazuki Nagashima4, Takeshi Yanagida4, Noritada Kaji2,3,
Masateru Taniguchi1, Takashi Washio1, Yoshinobu Baba2,5 & Tomoji Kawai1
Conventional concepts of resistive pulse analysis is to discriminate particles in liquid by the differencein their size through comparing the amount of ionic current blockage. In sharp contrast, we herein
report a proof-of-concept demonstration of the shape sensing capability of solid-state pore sensors byleveraging the synergy between nanopore technology and machine learning. We found ionic current
spikes of similar patterns for two bacteria reflecting the closely resembled morphology and size in an
ultra-low thickness-to-diameter aspect-ratio pore. We examined the feasibility of a machine learning
strategy to pattern-analyse the sub-nanoampere corrugations in each ionic current waveform andidentify characteristic electrical signatures signifying nanoscopic differences in the microbial shape,
thereby demonstrating discrimination of single-bacterial cells with accuracy up to 90%. This dataanalytics-driven microporescopy capability opens new applications of resistive pulse analyses forscreening viruses and bacteria by their unique morphologies at a single-particle level.
A resistive pulse method is a versatile technology widely utilized for discriminations of single-bioparticles ofvariable sizes from blood cells to polynucleotides1–4. It probes transient ionic current blockade associated with
fast translocation of an individual particle passing through a fluid flow path at which objects of higher volume
exclude more ions to render larger pulses. The last decade has witnessed resurgence of this electrical method led
by advanced nanotechnology to increase the spatial resolution of the particle analyser by employing ultra-thinmembrane materials down to single-atom level. Eventually, it led to the thickness-to-diameter aspect ratio of the
conduit to be less than unity5–12. In such short channels, the amount of ionic current blockade is anticipated to
be determined largely by the local volume of particles occupying the conduit, the characteristic of which would
enable rapid 2D-scanning of their morphologies at nanoscale. Meanwhile, although employing thinner membrane would provide higher spatial resolution to the sensor, it also poses additional challenges in interpreting theresistive pulse patterns. This is due to the concomitant increase in the relative significance of the ionic resistanceoutside the channel (access resistance) that makes the cross-membrane ionic current sensitive to physical featuresof particles including their fine shapes and the dynamical translocation motions13. Accordingly, despite the recent
efforts14,15, the potential of the envisaged sensing ability remains to be demonstrated.
We herein report a novel concept of nanopore analysis that leverages the enhanced spatial resolution ofultralow-aspect-ratio pore sensors and machine learning algorithm to elucidate the physics underlies individual resistive pulse wave patterns and discriminate single-particles by its shape instead of the whole volume. The
solid-state device consists of a micropore of diameter dpore = 3 μm sculpted in a SiN membrane of thickness
Lpore = 40 nm on a Si wafer having aspect ratio AR ~ 0.01 (Fig. 1a). We used the pore channel for detections ofEscherichia coli, potentially pathogenic bacteria ubiquitous in environment16, and Bacillus subtilis having rod-likeshape and micrometer size similar to those of E. coli (Fig. 1b), by measuring the cross-membrane ionic current
Iion in PBS buffer (10 times diluted with Milli-Q) under the applied dc voltage Vb of 0.05 V (Figs 1a and S1). The
previous work exploited intermolecular-interaction-derived cell capture dynamics for discriminations of E. colifrom other bacteria using a bionanopore system17. In contrast to the immunosensing approach, our aim here is to1

The Institute of Scientific and Industrial Research, Osaka University, Ibaraki, Osaka, 567-0047, Japan. 2Department
of Applied Chemistry, Graduate School of Engineering and ImPACT Research Center for Advanced Nanobiodevices,
Nagoya University, Furo-cho, Chikusa-ku, Nagoya, 464-8603, Japan. 3Japan Science and Technology Agency (JST),
PRESTO, 4-1-8 Honcho, Kawaguchi, Saitama, 332-0012, Japan. 4Institute for Materials Chemistry and Engineering,
Kyushu University, 6-1 Kasuga-Koen, Kasuga, Fukuoka, 816-8580, Japan. 5Health Research Institute, National
Institute of Advanced Industrial Science and Technology (AIST), Takamatsu, 761-0395, Japan. Correspondence andrequests for materials should be addressed to M.T. (email: tsutsui@sanken.osaka-u.ac.jp) or T.W. (email: washio@ar.sanken.osaka-u.ac.jp) or T.K. (email: kawai@sanken.osaka-u.ac.jp)
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Figure 1. Single-bacteria sensing using low thickness-to-diameter aspect-ratio pore channels. (a) Schematic
illustration depicting resistive pulse measurements of Escherichia coli and Bacillus subtilis using a SiN micropore
of diameter dpore and length Lpore. (b) False-colored scanning electron micrographs of E. coli (top) and B. subtilis(bottom). Scale bars denote 2 μm. Dark small circles are holes to filter ionic liquid and fix the bacteria on thesubstrate. (c,d) The cross-pore ionic current Iion versus time t two dimensional histograms constructed with 200
ionic current spike signals obtained for (c) E. coli and (d) B. subtilis using a SiN pore with dpore = 3.0 μm andLpore = 40 nm. Ip and td denote the pulse height and width, respectively.

distinguish the difference in the morphologies of the two bacteria. Ionic current spikes with height Ip and width
td were observed, the parameters of which generally denote the amount of ion blockage and time-of-flight of thenegatively-charged single-bacteria electrophoretically passing through the channel (Figs. 1c,d).

Results and Discussions

A standard approach for single-particle discriminations involve statistical comparisons of Ip and td variations18–20(Fig. S2). The distributions, however, overlap largely (Fig. S3) whereby failing to discern the two bacteria at discriminability no better than 70% (Fig. S4) due to the biologically different yet physically resembled characteristics
including the volume and the surface charge status (zeta potential: −37 mV (E. coli) and −38 mV (B. subtilis)).
Here, the negative surface charges of the gram negative and positive bacteria come largely from the phospholipids
and lipopolysaccharides covering the cell and the phosphates in teichoic acids on peptidoglycan, respectively.
On the other hand, while the plots present little difference in the average properties of the bioparticles, we at thesame time found a subtle difference in the Iion spike line-shapes, the variance of which anticipates not only the sizeand shape distributions of the two bacteria but also the stochastic nature of the capture dynamics because of thesignificant contributions of the ion transport in the external regions of the pore13,21,22, on the ionic blockage upon
particle translocation.
The above results suggest a necessity of a rational approach to first find features relevant to a bacterial morphology in the ionic current signatures to accomplish single-particle shape analysis using a low-aspect-ratio
pore sensor. Pattern recognition is an intrinsic ability of animals that spontaneously acts to match perceived
information with the memory stored in a brain. Analogously, here we used an artificial intelligence to inspect
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Figure 2. Statistical discriminations of single-bacteria by resistive pulse line shapes. (a) Definition of the pulse
bluntness βapex. The time ti at which Iion crosses the current level Yth % above the pulse top is collected. βapex isdefined as the deviations of ti. (b) Finite element analysis of ionic current blockage in a low-aspect-ratio poreby single-bacteria modelled as a microscale cylinder. (c) The accuracy Ppre for discriminating E. coli and B.
subtilis through comparing statistical distributions of βapex and Ip via 5-fold cross validation plotted against Lpore.
(d) A model used for finite element analysis of ionic current blockade by single-microbe. A bacteria-shaped
cylinder of diameter 800 nm and length 2.6 μm with different curvature R was moved along z axis wherein axistranslocation was assumed. (e) Normalized resistive pulses deduced from the finite element analysis for two
micro-rods with R = 200 nm (blue) and 1100 nm (red) that mimic the sharp-edged and rounded shapes of E. coli
and B. subtilis, respectively. Dotted line is at Inorm = 0.3 where βapex is extracted. The pulse bluntness is lower forcylinders with larger R. (f) Normalized resistive pulse bluntness βnorm plotted as a function of the roundness R of
the cylindrical model of bacteria. βnorm is deduced from βapex of the theoretical Iion spikes normalized by that at
R = 10 nm.
bacterial traits in the virtually featureless ionic current signatures obtained with a low-aspect-ratio micropore forstatistically estimating the respective numbers of E. coli and B. subtilis. Specifically, we examined a resistive pulse
line-shape analysis in a framework of a machine learning algorithm23,24 estimating non-parametric probability
density on a feature space consisting of the height Ip and bluntness βapex of the ionic spikes (Figs 2a and S5; seealso Supplementary Information 2.1). Noticeably, we found the precision Ppre exceeding 90% at Yth = 30% for
the ultra-low aspect-ratio micropore of AR ~ 0.01, where Yth denotes the current level from the pulse maxima(Fig. S5). Meanwhile, ionic transport calculations based on a Poisson-Nernst-Planck-Navier Stokes model13 predict that the bioparticles are not yet getting inside a pore but still moving at the orifice until Iion reaches Yth = 25%
from Ip (Fig. 2b)13,21. This indicates electrophoretic motions of the two bacteria at the pore exterior render nodistinctive feature in the Iion traces useful for distinguishing the microbes. Conversely, the ionic current profiles
below the level, i.e. βapex for instance, constitutes electrical fingerprints of single-bacteria wherein local shapeof the microorganisms play a dominant role on the ion exclusion inside the pore channel, the finding of which
exemplifies the potential of the data-driven analysis to discover physically valid Iion characteristics distinct to theanalytes.
In order to verify the tomographic capability, we systematically studied the roles of pore geometry on the sensing performance by carrying out the bacterial detections with 3 μm-sized micropores having variable depth from40 nm to 1500 nm (Fig. S6). The pattern recognition of Iion spikes revealed intriguing change in P that deteriorated
5-fold upon increasing Lpore to 990 nm whereas further thickening resulted in recovery of the sensor performance
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www.nature.com/scientificreports/(Fig. 2c). The non-trivial dependence can be interpreted as denoting mutual and competitive contributions of
the access resistance and the resistance inside the pore. As the Lpore is less than the length of the bacteria, longer
channels give larger change in the inner-pore resistance during single-bacterial translocation due to the larger
amount of ions excluded in the pore thereby offering better sensitivity to the bioparticle volume as denoted byhigher Ppre at Lpore ≥ 990 nm. Conversely, shorter micropores are expected to show degraded volume sensitivity.
Nevertheless, they also possess higher spatial resolution to enable discriminating bacteria by difference in theirshape. This is highlighted qualitatively in the feature parameter histograms (Figs S3 and S7) where we found lessoverlap in the distributions of βapex (Ip) with decreasing (increasing) Lpore whereby suggesting better statistical
discriminability by the microbial shape (volume) in thinner (thicker) pore channels. In other words, whereas theultra-low aspect-ratio pores and thick channels can distinguish the two bacteria with high Ppre through sensitive
detections of the bioparticle shape and volume, respectively, the intermediate range of AR leads to deteriorated
sensor performance as both the sensing capabilities become poor.
It is of interest to consider more precisely on what affects the apex line-shapes of resistive pulses. We
herein explored influence of the bacterial morphologies by running the Iion simulations for translocation ofbacteria-mimicking rod-shaped objects with various roundness R (Fig. 2d; see also S22). The estimations revealed
increasing pulse bluntness with R (Figs 2e,f and S23), which is in qualitative accordance with the sharper Iion
spikes obtained for E. coli having a rounder motif compared to that of B. subtilis (Fig. 1b). This in turn proves
the analytical power of the machine learning approach to computationally discern the bioparticles by thesubmicrometer-scale body form difference through the Iion traces.
Besides the morphologies, the bacterial motility of the flagellated microbes may have caused distinct difference in the capture-to-translocation dynamics whereby facilitated to discern E. coli and B. subtilis by the resistive pulse line-shapes. Considering the remote sensing capability of low-aspect-ratio pores to sense the analytemotions nearby the channel entrance, it is indeed worth investigating the possible influence of the swimming
ability unique to the bacteria on the ionic signatures25,26. We therefore examined resistive pulse measurements forflagellated (wild-type) and non-flagellated (gene fliC inactivated mutant) E. coli using a 3 μm-sized SiN micropore
of AR ~ 0.01 (Fig. S8). Contrary to the expectation, however, the results showed so little difference in the ionicspike forms that the machine learning based pattern analysis can discriminate the two by no better than 62%,
suggesting no conspicuous roles of flagella on the translocation speed. It can be understood by considering predominant roles of the electrophoretic forces that overwhelms the flagellar motor power to affect the translocation
motions.
Although the above results suggest the ability of ultra-thin micropores to acquire information concerning morphologies of analytes, it requires more direct evidence to assure the high-spatial resolution of the
microporescopy. Streptococcus is a suitable model system to test the geometrical sensitivity, which consists of
micrometer-sized ellipsoidal cells connected in series27. We examined a resistive pulse analysis of Streptococcus
salivarius28 (Fig. 3a), gram positive oral bacteria, using two SiN pores having same depth Lpore = 40 nm but different diameter dpore = 1.4 μm (AR ~ 0.03) or 3.0 μm (AR ~ 0.01). Whereas the large micropore provided only
featureless ionic signatures (Fig. 3b), the bacterium-sized pore yielded peculiar Iion spikes with characteristic
corrugations (Fig. 3c,d; see also Figs S3–S11). Ionic signatures with up to four bumps were detected in the courseof measurement (Figs 3e and S12). Here, each dip is naturally ascribed to translocation of one unit cell (cocci)in the bacterial chain through the ultra-thin channel as the ion transport is blocked more (less) effectively at the
moment when the cell-to-cell junctions reside in the pore. We performed multi-physics simulations13 of the ionicblockade during trafficking of the individual bacteria to verify this by modelling the ball-chain-like bacterial
shape as microscale ellipsoidal objects connected in series (Figs 3f and S18–S20). The theoretical Iion traces reproduced the characteristic Iion oscillations observed experimentally, whereby unequivocally proving the potential of
low-AR pores for 2D-scanning the nanoscale shape of single particles. Furthermore, the experimental ionic spikes
revealed current changes of about 250 pA per translocation of one cocci. The numerical simulations, on the other
hand, predicted the corresponding characteristic diameter of each ellipsoidal cell to be 800 nm and 280 nm at the
thickest and the narrowest parts, respectively (Fig. S21). The current sensitivity to the radial size of the particles
is therefore estimated to be 2 nm/pA. This yields a tentative estimation of the spatial resolution as 26 nm, which iscomparable to the pore length of 40 nm, if we only take into account the influence of the rms current noise of 13
pA on a source of error in determining the shape of bacteria (we would like to emphasize that this value is only atentative one that may change depending on the size and shape of the particles measured).
Beyond the statistical non-parametric probability density estimation using the artificially-selected features,
which has proven effective to assess the number of each bacteria species by using difference in their shapethrough comparing the sharpness of ionic signatures, we also examined a more general concept of informatics:
single-spike identification by machine learning with feature selection based on arbitrary criteria. We employed
the Waikato Environment for Knowledge Analysis (WEKA)29, a machine learning workbench widely used
for data mining, with the Rotation Forest meta-classifier30 and forty different algorithms of its base classifiers
together with 60 feature parameters including Ip, td, and βapex (Fig. 4a; see also Supplementary Information 2.2).
Intriguingly, the F-measure score FMeas calculated through a 10-fold cross validation reproduced the Lpore dependence of the bacterial discriminability (Figs. 4b,c; see Methods for definition of FMeas), whereby provided further
evidence demonstrating the intrinsic sensor capability of low-aspect-ratio pores to sensitively identify minutedifference in nanoscale morphologies of individual analytes. Moreover, it is noted that the analytical procedurecan be in principle practiced in real-time: off-line learning of the classifiers required as short as 0.003 sec to 2 sec
processing times by using a workstation equipped with Intel Core i7 CPU and 32GB RAM, and their on-line
single-spike identification spent only 1 μsec to 200 μsec.
From practical viewpoints, it is of importance to evaluate the applicability of the machine learning assisted
resistive pulse analyses to a mixture sample of various microbes. For this, we extended the WEKA-based
single-bacteria identification to a solution containing E. coli and B. subtilis at different relative concentrations
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Figure 3. Sensitivity of a low-aspect-ratio pore to particle shapes. (a) Schematic and scanning electron
microscopy images of Streptococcus salivarius. Scale bar denotes 0.5 μm. (b,c) Two-dimensional histograms ofionic spike overplots of S. salivarius in 50 nm thick pores with (b) dpore = 3.0 μm and (c) 1.2 μm. (d) Doublepeak pulse signal. The Iion corrugation depicts the characteristic motif of S. salivarius as represented in theinsets. (e) Corrugated spikes showing up to four peaks at the apex. Insets illustrate the bacterial shape deduced
from the spike forms. (f) Finite element analysis of the ionic current profiles during translocation of the beadschain-like microbes constructed with one (blue), two (red), three (green), and four (purple) cocci. The curves
are shifted vertically for the sake of clarity.
using the same teacher data used in the assessments of FMeas (Fig. 4). The result revealed a monotonic increase in
the relative number of signals assigned as B. subtilis rB with respect to that of E. coli with the nominal B. subtilis
to E. coli concentration ratio CB (Fig. 5), thus validating the efficacy of the method to count specific bacteria ina mixture. Meanwhile, the concentration dependence was found to be quite intriguing: A sharp increase in thenumber of B. subtilis was observed upon increasing the relative concentration to 2. On the other hand, further
increase in CB led to non-linear increase in the signal ratio. The non-trivial tendency would reflect the distinct
difference in the capture dynamics between the two bioparticles: Compared to E. coli, which can be drawn into
micropores relatively easily whereby giving higher rates of signal occurrence under increased bacteria concentrations, signal detections of B. subtilis is less efficient presumably due to larger entropic barrier associated with thestiffer cell walls compared to those of E. coli31.
The present findings have proven the excellent compatibility of sensor informatics to resistive pulse analysis for interfacing ionic current signals and analyte shape in low-aspect-ratio pores. Meanwhile, in light of thesensor performance of existing technologies such as cytometry32,33, the bacterial discriminability needs further
improvements for practical applications. This would be achieved in part by employing thinner membranes tofurther enhance the spatial resolution on top of the efforts to bulk test the feasibility for identifying various targets
of interest, wherein the emerging two-dimensional nanostructures such as graphene and MoS2 are expected to
be a key material34,35.
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Figure 4. Bacteria discriminations by single-shot pattern analysis. (a) Feature parameters characterizing the
spike waveforms. θ is the angle at the pulse onset. AL (orange) and AR are the area of the pulse at left and rightsides of the peak top, from which the total area A = AR + AL and the ratio rm = AL/AR are calculated. In addition,
the inertia with respect to the longitudinal (Im) and transverse (Iw) axes are deduced through Im = ∑ti2Alat,i and
Iw = ∑h i2Along, i , respectively, where Alat,i and Along,i are respectively the partial area at ti and hi. (b) F-measurescore FMeas deduced by testing 4020 combinations of feature vectors and classifiers. (c) The highest F-measurescores (FMax) plotted against Lpore. Purple and blue arrows point toward increasing sensor sensitivity to particle
shape and size, respectively. While Coulter counter principle predicts an optimal sensitivity to analyte size witha certain pore geometry, lower-aspect-ratio pore channels can benefit from the improved spatial resolution to
boost the sensor performance through exploiting the tomographic capability when combined with machinelearning based pattern analysis.

Figure 5. Single-bacteria detections in mixture solution. The relative number of signals judged as B. subtilis rB
with respect to the E. coli counter part plotted as a function of B. subtilis versus E. coli nominal concentration CB.

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Conclusion

Discriminations of single-bacteria shape was examined by analyzing individual resistive-pulse profiles in
low-thickness-to-diameter aspect-ratio pore sensors. Ionic current fingerprints of bacterial morphologies wereidentified at the signal apex whereat volume-exclusion effects inside the pore by translocating single-bacteria
becomes prominent. We also revealed excellent compatibility of a machine learning approach to extract thefeatures relevant to bioparticle shapes in resistive-pulses, which provided data-driven designs of the nanosensor for achieving better discriminability of single-particle shape. Such synergy between physical measurements
and machine learning strategies will deliver a versatile strategy to study biology using nanotechnology, wherein
biologically-important functions veiled in stochastic noise are identified by extracting relevant parameters fromthe statistical measurement results by computer in feature space.

Methods

Fabrication of low thickness-to-diameter aspect ratio micropores.

Solid-state micropore sensors
were fabricated as follows. A four-inch Si wafer (0.3 mm thick) whose both sides covered with 50 nm thick SiN
layers was first diced into 25 mm × 25 mm chips. Each piece of SiN/Si wafer was then treated with a reactive ion
etching with etchant gas of CF4 to remove SiN of a small region (0.8 mm × 0.8 mm square) on one side of thesurface, wherein a metal sheet was used as a mask. Subsequently, the SiN-removed area was exposed to aqueous
solution of KOH at 100 degrees Celsius for deep wet etching of Si. As a result, a 40 nm thick SiN membrane of size
150 μm × 150 μm was formed. Following the process, we delineated a circle pattern on the membrane coated witha resist ZEP520A by an electron beam lithography. After development, we dry-etched the SiN (CF4 etchant gas) todrill a pore. Finally, the substrate was immersed in N,N-dimethylformamide for overnight to remove the remnant
resist. For thicker pores, additional SiO2 coating was performed on the both sides of the membrane by chemical
vapor deposition to increase Lpore to 240 nm, 490 nm, 990 nm, or 1500 nm.

Resistive pulse sensing. A micropore chip was sealed with two polydimethylsiloxane (PDMS) blocks fromthe both sides. On one side of the surface, a microchannel was formed by curing PDMS on an SU-8 mold. The
pore substrate as well as the PDMS blocks were exposed to oxygen plasma in prior to the sealing, which servedto activate the surface for eternal bonding of PDMS to SiN. After that, three holes were punched in each of thepolymer blocks. Two of them were used as inlet and outlet for pouring PBS buffer in the micropore. Bacteria wereadded only on one side of the pore while the other side was filled only with buffer. The remaining pores were used
to insert Ag/AgCl electrodes. A voltage Vb was applied to these electrodes to electrophoretically drive the bacteria
to pass through a pore and record the temporal change in the cross-pore ionic current. A resistance-feedback
current amplifier was used to amplify the current and a fast digitizer (NI-5922) backed by a RAID system (NIHDD-8264) was utilized to store the output data at a sampling rate of 1 MHz. All the measurements were carriedout at room temperature in air.
Data analysis. Two dimensional histograms of ionic current spikes were prepared by first extracting the local
current minima using a computer program based on Visual Basic 6. Specifically, the open pore current was offset
to zero by subtracting the base level through linear fit to 0.5 seconds long I − t curves cut from the whole data.
A threshold of 60 pA was then used to find the current decrease onset in the offsetted ionic traces. Meanwhile, arising edge at 10 pA was utilized to determine the region wherein a resistive pulse exist. The lowest current values
within the peak region was assigned as the peak top of the ionic current spikes. Then, 20 milliseconds of the ioniccurrent data before and after the local minima were collected, thereby obtaining ionic spikes larger than 60 pA.
The current versus time two-dimensional histograms were constructed by binning the Iion and t at 1 pA and 0.1
millisecond, respectively in the Origin Pro software.
Definition of resistive pulses. An onset of a resistive pulse was searched by referring to the ionic current
level at the point where the noise increases to above 5σ. Iion data were then extracted until the current fluctuations
decreased to below 5σ. Here, 256 points long margins at both sides of the pulse were also collected. This defines
the onset time ts for each ionic spike; i.e., 0.256 ms before Iion noise rises above 5σ. The number of thus obtained
ionic spikes were 179 to 557 and differed among the tests (Table S1). For the machine learning analyses, 179
pulses were randomly selected from each set of data.
Machine learning algorithm. Several features of ionic current spikes were employed for statistical discrim-

inations of bacteria: e.g. the pulse height Ip and width td; the bluntness of resistive pulse apex βapex extracted byfirst normalizing the spike height by Ip as Inorm = Iion/Ip and width by the pulse onset te and the pulse endpoint ts
2
as tnorm = (t − ts)/(te − ts), and then deduced as βapex = 1/mtd2∑ m
i = 1(ti − ts − tave ) where ti is the time stamps Iion
curves intersect the Iion level Yth % from the peak maxima, tave denotes the arithmetic mean of ti-ts and m is thenumber of intersecting time points; the onset angle θ defined by the slope 1/r from Inorm at te to r, where r is thepulse peak position; the area A calculated by dividing a pulse into n regions and taking summation of the average
Inorm, hi, at each section; the ratio rm between the area at the peak onset and that after the peak maximum; theinertia Im and Iw calculated with respect to longitudinal and transverse axes, respectively (Detailed definitions ofeach parameter are described in Figs S14–S16). Each parameter (Figs S14 and S15) was coupled to the current
vector and the time vector (Fig. S16) to create 60 feature vectors in total. The obtained feature vectors of 161 spikes
for each of E. coli and B. subtilis (in total 322 spikes) were used as teacher data to judge the other 18 resistive pulses
for a test. This procedure is repeated ten times by interchangeably changing the teacher data and the test data
within the given 179 spike data, and the total accuracy of each classifier is provided by the average accuracy of theten tests (ten-fold cross validation method). Data classifications were implemented by using the machine learning
workbench WEKA with 67 Rotation Forest ensembles where each used a distinct base classifier. FMeas = 2PPrePRec/
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www.nature.com/scientificreports/(PPre + PRec) was deduced for all of the 60 × 67 = 4040 combinations of the classifiers and the feature vectors,
where PPre and PRec are the precision and recall calculated through TP/(TP + FP) and TP/(TP + FN), respectively,
with TP, FP, FN being respectively the number of true-positive, false-positive, false-negative outputs. Sufficiently
stable accuracies were provided over the ten tests (Supplementary Information 2.3 Statistical variability of theestimation).

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Acknowledgements

A part of this work was supported by ImPACT Program of Council for Science, Technology, and Innovation(Cabinet Office, Government of Japan), “Nanotechnology Platform Project (Nanotechnology Open Facilities in
Osaka University)” of Ministry of Education, Culture, Sports, Science and Technology, Japan [No: F-12-OS-0016],
and by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Number 16K13652.

Author Contributions

M.Tsutsui, T.W., Y.B., M. Taniguchi, and T. Kawai planned and designed experiments. K.Y., A.A., and W.T.
fabricated nanopores. M. Tsutsui, K.Y., and A.A. exhibited ionic current measurements. M. Tsutsui, K.Y., K.N.,
H.Y., W.T., T. Yanagida, and N.K. performed resistive pulse analyses. T. Yoshida and T.W. conducted machinelearning based pattern analyses. M. Tsutsui, T.W., and T.K. co-wrote paper. All the authors discussed the data andreviewed the manuscript.

Additional Information

Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-17443-6.
Competing Interests: The authors declare that they have no competing interests.
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Scientific REPOrTs | 7: 17371 | DOI:10.1038/s41598-017-17443-69