A Highly Conductive Nucleotide Analogue Facilitates Base-Calling in

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A Highly Conductive Nucleotide Analogue Facilitates BaseCalling in Quantum Tunneling-Based DNA Sequencing Takafumi Furuhata, Takahito Ohshiro, Gaku Akimoto, Ryosuke Ueki, Masateru Taniguchi, and Shinsuke Sando ACS Nano, Just Accepted Manuscript • DOI: 10.1021/acsnano.9b01250 • Publication Date (Web): 19 Mar 2019 Downloaded from http://pubs.acs.org on March 19, 2019

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A Highly Conductive Nucleotide Analogue Facilitates Base-Calling in Quantum TunnelingBased DNA Sequencing Takafumi Furuhata,†, § Takahito Ohshiro,‡, § Gaku Akimoto, † Ryosuke Ueki, † Masateru Taniguchi*,‡ and Shinsuke Sando*,†, ‖ †Department

of Chemistry and Biotechnology, Graduate School of Engineering, The University

of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan. ‡

The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki,

Osaka 567-0047, Japan. ‖Department

of Bioengineering, Graduate School of Engineering, The University of Tokyo, 7-3-1

Hongo, Bunkyo-ku, Tokyo, 113-8656, Japan.

ACS Paragon Plus Environment

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ABSTRACT: Quantum tunneling-based DNA sequencing is a single molecular technology that has a great potential for achieving facile and high throughput DNA sequencing. In principle, sequence of DNA could be read out by the time trace of tunnel current that can be changed according to molecular conductance of nucleobases passing through nano-sized gap electrodes. However, efficient base-calling of four genetic alphabets has been seriously impeded due to the similarity of molecular conductance among canonical nucleotides. In this article, we demonstrated that replacement of canonical 2’-deoxyadenosine (dA) with a highly conductive dA analog, 7-deaza dA could expand the difference of molecular conductance between four genetic alphabets. Additionally, systematic evaluation of molecular conductance using a series of dA and dG analogues revealed that molecular conductance of the nucleotide is highly dependent on the HOMO level. Thus, the present study demonstrating signal characteristics of the nucleotide can be modulated based on HOMO level provides a widely applicable chemical approach and insight for facilitation of single molecular sensing as well as DNA sequencing based on quantum tunneling.

KEYWORDS: DNA sequencing, quantum tunneling, non-canonical nucleoside, HOMO level, molecular conductance


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Electrical detection of DNA using a nano-sized pore or gap has attracted much attention as a next-generation technology for DNA sequencing.1–4 Among existing methods, quantum tunneling-based DNA sequencing has emerged as a powerful strategy.5–9 In this method, DNA passes through a nano-sized gap created between conductive materials such as metal and graphene (Figure 1a). The sequence information can be read out by tracing tunnel current as the current intensity is changed according to molecular conductance of nucleotides passing close to a pair of electrodes.10,11 Quantum tunneling-based sequencing provides a single nucleotide resolution if the thickness of gap-electrodes is on the same order with the size of single nucleobase. Since this method utilizes solid material-based electrodes, it has several advantages over existing protein pore-based sequencing methods because it would offer industrial scalability for high throughput analysis, and higher durability and robustness that would be amenable to repetitive analysis.1,4,5 In order to achieve facile and accurate DNA sequencing by quantum tunneling, there remains a big issue to be solved; the molecular conductance of four canonical nucleotides (dT, dC, dA and dG) exhibits a severe overlap (Figure 1b). This problem is caused by the following two factors. First is the wide distribution of molecular conductance of canonical nucleotides.12,13 The molecular orientation and motion of DNA between gap electrodes has not been strictly regulated in this system so that the molecular conductance between the gap electrodes widely fluctuated. This problem will be overcome technically by developing a method to regulate the dynamics of DNA in a nano-sized space.14–24 Another critical factor is the similarity of molecular conductance between nucleotides that is governed by their electrochemical properties. For high throughput DNA sequencing, current signals have to be interpreted into sequence information from a minimal set of signal


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characteristics that are obtained in a short time. However, the signal characteristics of canonical nucleotides are similar to each other due to their close conductivity, which makes it difficult to interpret each of signals into genetic alphabets efficiently. Especially, as demonstrated in a previous work, dA and dC possess similar molecular conductance so that base-calling of all four genetic alphabets is still a daunting challenge, while sequencing of 7-mer RNA and 22-mer DNA containing only three kinds of nucleotides (T, A, and G) has been demonstrated.12,25 Herein we propose a chemical approach that expands the difference of molecular conductance between four genetic alphabets (Figure 1b). We envisioned that replacement of the canonical dA with a highly conductive dA analogue (dA*) that exhibits higher molecular conductance over canonical nucleotides would offer an opportunity to accurately discriminate four genetic alphabets. If such dA* is recognized as dA in enzymatic reaction such as PCR, primer extension, and reverse transcription, one would be able to prepare decodable oligonucleotide samples that retain the genetic information of original oligonucleotide templates (Figure 1c). For exploring such a highly conductive dA analogue, we firstly investigated the chemical property that affects molecular conductance of the nucleotide using a 0.6 nm gap of gold electrodes fabricated by mechanically controlled break junction (MCBJ) (Figure 2 and Figure S1). 26,27 So far, the knowledge about the correlations between chemical properties and molecular conductance of the nucleotide has been lacking. Systematic evaluation of molecular conductance using a properly designed series of dA and dG analogues clearly showed molecular conductance of the nucleotide is highly correlated with the energy level of the highest occupied molecular orbital (HOMO). Then, we found that 7-deaza dA (dzdA) exhibits a highly conductive character over canonical nucleotides due to its HOMO level being closer to the Fermi level of electrodes. Current measurement of nucleotides demonstrated that the difference in molecular conductance


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between four genetic alphabets could be expanded by replacement of canonical dA with dzdA. This finding that molecular conductance of the nucleotide can be tuned based on the HOMO level provides mechanism-based insight that would serve to facilitate single molecular sensing including DNA sequencing based on quantum tunnelling.

Results Design of non-natural nucleotides with different HOMO levels. To explore a highly conductive dA analogue, it is essential to understand which chemical properties can affect molecular conductance of the nucleotide. However, there was no systematic evaluation to clarify the correlations between chemical properties and molecular conductance of the nucleotide in quantum tunneling through a nucleobase between gap electrodes. We first tried to probe the effect of a chemical property on molecular conductance of the nucleotide, especially focusing on the HOMO level. Theoretical studies have shown that the energy level of a molecular orbital (MO) that is the closest to the Fermi level of a electrode, which is gold in this study, affects molecular conductance (Figure 3a).28 Molecular conductance (G) at low bias voltage can be described by the following equation: 𝐺=

4Γ𝐿Γ𝑅 2𝑒2 h (𝐸𝐹 ― 𝜀𝑀𝑂)2 + (Γ𝐿 + Γ𝑅)2

(1)

where 𝜀𝑀𝑂 is a MO level that is the closest to the Fermi level of gold (EF) and Γ𝐿(𝑅) represents coupling strength of a molecule to the left (right) gold electrodes, respectively.29 Under the


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condition where the coupling strength is small as would be the case for nucleotides, equation 1 can be approximated as the followings.30

2𝑒2 4Γ𝐿Γ𝑅 𝐺= h (𝐸𝐹 ― 𝜀𝑀𝑂)2

(2)

In the case of canonical nucleosides, the HOMO level (𝜀𝐻𝑂𝑀𝑂) are the MO level that is the closest to the Fermi level of gold (EF, –5.3 – –4.7 eV)31 while the LUMO level (𝜀𝐿𝑈𝑀𝑂) is relatively farther from 𝐸𝐹 (Table S2). Based on the consideration above, we assumed that molecular conductance of the nucleotide should have a positive correlation with 1/(𝐸𝐹–𝜀𝐻𝑂𝑀𝑂)2. This hypothesis can be tested by systematically evaluating the molecular conductance of a series of nucleotides that have different 𝜀𝐻𝑂𝑀𝑂. To elucidate the effect of the HOMO level on molecular conductance, other chemical properties of nucleotides should be analogous, such as the size and exocyclic substituents of molecules, to minimalize the difference in Γ𝐿(𝑅) values (the coupling strength with electrodes). Along this line, we carefully selected and synthesized a series of dA and dG analogues as model compounds that satisfied the structural requirements described above. Figure 3b shows the chemical structures of the nucleobases and their 𝜀𝐻𝑂𝑀𝑂 in their nucleoside forms that were calculated based on density function theory. It was shown that 𝜀𝐻𝑂𝑀𝑂 of 7-deaza-8-aza (azdA and azdG) and 7-deaza (dzdA and dzdG) of dA and dG analogues covered lower and higher energy levels than those of canonical dA and dG.

Systematic evaluation of the correlation between molecular conductance and HOMO level using a series of dA and dG analogues. To study molecular conductance of dA and dG analogues and its HOMO level dependency, 3-mer oligonucleotides containing nucleotides of


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interest flanked by two abasic mimics (1,2-dideoxy-d-ribose, ddR) were applied to current measurement (Figure 3c). Figure 3d shows typical current traces of oligonucleotides containing a dG or dG analogues. Spike peaks emerged during the measurement of nucleotide samples, while such peaks were rarely observed in the background measurement of deionized water (Figure S2). The signal intensity (Ip) was defined by the maximum current value of each peak (Figure 3d) and molecular conductance (G) was calculated by Ip/V where V was bias voltage applied between gap electrodes. As shown in Figure 3e, the conductance at the lower conductance region were highly overlapped between nucleotides, but the profiles in the higher conductance region were quite different. The percentages of molecular conductance values >300 pS were calculated as follows: azdA (17.0%) < dA (23.0%) < dzdA (37.2%) for dA analogues and azdG (13.8%) < dG (34.6%) < dzdG (46.6%) for dG analogues. The increase of percentages is in the same order with HOMO levels.

For quantitative evaluation of molecular conductance of nucleotides, we further conducted statistical analysis. As the conductance histogram (Figure 3e) could not be described by a single Gaussian fitting due to their tailing structure to the high conductance region, we hypothesized the histogram was composed of multiple distributions of conductance. Actually, all the conductance histograms taken in a logarithmic scale were well fitted by two Gaussian fitting curves, which provided two conductance peaks, GL and GH with lower and higher values. The differences in GL values were relatively small but GH values were substantially varied between nucleotides. Therefore, it was rational to consider that the GH value reflected the conductive character of the nucleotide that was governed by its specific chemical properties. We adopted the GH value as a measure of molecular conductance and plotted it against 1/(𝐸𝐹–𝜀𝐻𝑂𝑀𝑂)2to see HOMO level dependency of molecular conductance (Figure 3f). Although the GH value was not completely


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proportional to 1/(𝐸𝐹–𝜀𝐻𝑂𝑀𝑂)2 presumably due to small differences in Γ𝐿(𝑅) values between nucleotides (equation 2), GH values clearly increased with 1/(𝐸𝐹–𝜀𝐻𝑂𝑀𝑂)2. This results indicated the strong correlation between the HOMO level and the molecular conductance of the nucleotide, which provided a general guideline to differentiate the conductivity of nucleotides.

The two conductance peaks, GL and GH that were provided by the fittings indicated that there were different modes of electron transport through a nucleotide. One is HOMO conducting process that was represented by GH values as suggested above. DFT calculation showed electron clouds of HOMO were localized on nucleobases so that GH values would represent the molecular conductance of nucleobase (Figure 3g, Figure S3). On the other hand, the GL value which is lower than the GH value was not highly dependent on the HOMO level. This suggested that GL represented another electron transport process that was not mediated by the nucleobase. One possibility was the electron transport through ribose backbone where electrons at HOMO–1 were found by DFT calculation (Figure 3g, Figure S3). It was plausible that the electrons with low energy mediated the weak electron transport through the ribose and resulted in low conductance signals. This contention was supported by the fact that ddR also had low molecular conductance (Figure S4).

7-deaza dA (dzdA) as a highly conductive dA alternative to expand the difference in molecular conductance between four genetic alphabets. Systematic evaluation of molecular conductance of dA and dG analogues showed that molecular conductance of the nucleotide was greatly affected by its HOMO level (Figure 3f). It was also found that dzdA had higher molecular conductance than canonical dA and dG due to its HOMO level being closer to the Fermi level of the electrode (Figure 3e). This result suggested that dzdA was promising as a


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conductive dA alternative that expands the difference of molecular conductance between four genetic alphabets to facilitate their discrimination. In addition to its high molecular conductance, dzdA can be recognized as dA by DNA polymerases, thereby it can be used to replace dA in DNA strands via enzymatic reaction.32–34 We also experimentally confirmed that dzdA could be enzymatically incorporated into DNA samples in place of dA with high efficiency and accuracy (Figure S5). Thus dzdA is promising as a conductive dA alternative that is amenable to enzymatic replacement of canonical dA. We next conducted current measurement to compare molecular conductance of dzdA with those of canonical nucleotides (Figure 4a). For this purpose, we conducted current measurement of dT and dC in addition to dA, dG and dzdA whose molecular conductance had been already evaluated in the previous section (Figure 3e). Figure 4b shows typical current traces for oligonucleotides containing dzdA, dA and dC. Two Gaussian fittings were again applied to conductance histograms to provide two conductance peaks, GL and GH (Figure 4c). Comparison of GH values showed that the conductance values were substantially similar between the canonical nucleobases. In particular, the difference in GH values between dC and dA was only 10 pS, thereby making it difficult to distinguish the signals from these two nucleotides. On the other hand, the difference in the GH value between dC and dzdA reached 64 pS, which was much larger than the difference between dC and dA. The relative values of GH (Rel. GH) for each nucleotide were followings; dzdA (1.15) > dG (1.00) > dA (0.96) > dC (0.93) > dT (0.83) (Figure 4c). These results demonstrated that the difference of molecular conductance between four genetic alphabets could be expanded by replacing dA with dzdA. For primary evaluation of the utility of dzdA in quantum tunneling-based DNA sequencing, we conducted current measurement of the hetero-oligonucleotide that has all four genetic alphabets


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(Figure 5a). In the conductance histograms of 6-mer oligonucleotides 5’-d(TGTACT)-3’ (dAseq) or 5’-d(TGTdzACT)-3’ (dzdA-seq), there obviously increased the frequency of high conductance values in dzdA-seq compared to that in dA-seq (Figure 5b). The percentages of molecular conductance values over 281 pS, where the conductance values were larger than the GH value of dG, were 30.5% for dzdA-seq and 19.7% for dA-seq. This difference in the frequency of high conductance values (11%) could be attributed to the existence of conductive dzdA since it was comparable to the proportion of dA or dzdA (17%) in 6-mer heterooligonucleotides. This result indicated that replacement of dA with dzdA differetiates signal characteristics of four genetic alphabets in hetero-oligonucleotides as well.

Discussion In the present work, we have demonstrated that replacement of dA with its highly conductive analogue, 7-deaza dA (dzdA) could expand the difference in molecular conductance between four genetic alphabets (Figure 4c). For exploring such a conductive dA analogue, we firstly investigated the correlation between HOMO level and molecular conductance of the nucleotide by the systematic evaluation of molecular conductance using a series of dA and dG analogues with different HOMO levels (Figure 3f). In the previous work, the effect of HOMO level on molecular conductance of the nucleotide was indicated in the case of canonical12 and epigenetically modified nucleotides such as dC and C5-methyl dC.35 However, there was no systematic experimental evaluation to prove the correlation between HOMO level and molecular conductance of the nucleotide. The present work presented the systematic evidence that the HOMO level has a strong correlation with molecular conductance of the nucleotide (Figure 3f).


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This result provides a chemical insight that is generally applicable to modulate conductivity of molecules to facitate quantum tunneling-based sensing.

In the course of the evaluation described above, we found dzdA as a highly conductive dA alternative. We found dzdA brought the expanded difference in molecular conductance between four genetic alphabets as a dA alternative (Figure 4c) and exhibited high conductivity in a hetero-oligonucleotide as well (Figure 5b). These result indictated that the replacement of dA with dzdA would be helpful for statistical discrimination of the nucleotides. To estimate how dzdA would potentially facilitate base assignment, we estimated the amount of signal data points that is required to distinguish a pair of nucleotides with a certain accuracy rate (For detailed procedure, see Supporting Information). For simplcity, we assumed that the probability density functions of molecular conductance used in this study were corresponding to the higher side of fitting curve in two Gaussian fitting in Figure 4c. The simulation showed that discrimination of dzdA from the other nucleotides, dC and dG can be achieved with fewer amounts of signal points than the case of canonical dA (Figure S6). For example, 4 milliseconds of a signal (corresponding to 40 data points in this system) was estimated to be enough to distinguish dzdA from dC at 99 % accuracy, while the same length of signal guaranteed only less than 55 % accuracy in the discrimination of dA from dC. Actually, the sensing system still requires tens of data points corresponding to several milliseconds of signal time for meaningful interpretation of signals. However, this result clearly indicated that expanding the difference in one of signal characteristics is contributable to statistically classify the signals in a more accurate manner. The accuracy and efficiency of statistical signal characterization would be further improved if this system is sucessfully combined with multi-dimentional analysis of signal characteristics.36-38


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Though dzdA potentially facilitates the discrimination of nucleobases as discussed above, it is the fact that accurate interpretation of signals into genetic alphabets is still not feasible in the current system. In principle, base-assignment is possible if the current change in the signal can be unequivocally associated with each trapping event of a nucleobase between gap electrodes (Figure 1c). However, there were no signals with clear current plateaus that undoubtedly yielded a full sequence of dzdA-seq. Actually, the majority of signals exhibited undecodable current profiles due to large fluctuation and/or an insufficient number of steps to assign all genetic alphabets (Figure S7). Some fractions of multi-step signals seemed to agree with full or partial sequences of dzdA-seq, but it should be noted that the amount of data points in each current plateau is not sufficient to statistically assign a genetic alphabet (Figure S8). To overcome these current limitations, an improved nanotechnology that can precisely regulate the orientation and the velocity of nucleotides around the gap would be required. This kind of nanotechnology should enable a strand of nucleobases to be stably held against gap electrodes for a time that is adequate to collect data points for statistical base assignment. Additionally, a strand of nucleobases should pass close to the gap in the appropriate order of sequence. Demanded here is the molecular control that makes nucleobases slide through the space opening just near the gap. This would allow a strand of nucleobases to electronically couple with 0.6 nm gap electrodes one by one in the order of DNA sequence, even for the purine nucleobase whose size (0.7 nm) is larger than 0.6 nm (Figure 1 and Figure S1). Electrode-embedded nanopore39 with a one-way fluidic system40,41 would be a solution in conbination with nanotechnologies that provide molecular scale control of DNA dynamics in the vicinity of electrodes.16,17,24


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Conclusions Here we proposed a simple chemical strategy that would serve to facilitate discrimination of four genetic alphabets in quantum tunneling-based DNA sequencing. We demonstrated that (1) molecular conductance of the nucleotide is highly correlated with the energy gap between HOMO level and the Fermi level of the electrode by systematic evaluation of molecular conductance using a well-designed series of nucleotide analogues, (2) dA analogue (dzdA) with a HOMO level being closer to the Fermi level of the electrode possessed higher conductivity over canonical nucleotides, and (3) replacement of dA with dzdA expanded the difference of molecular conductance between four genetic alphabets. Importantly, our finding offers a solution to overcome one of the intrinsic issues, similarity of conductivity among canonical nucleotides that has limited the applicability of quantum tunneling-based sequencing. It is still a fact that statistical information of signals is required to interpret a signal into a nucleobase due to the remaining overlaps of the conductance histograms. This indicates the need for development of nanotechnologies that enable a nucleobase to stably couple with a pair of electrodes for a time adequate to obtain a statistically valid amount of signal data points. In summary, this study presented a strategy to modulate signal characteristics of molecules of interest based on the observation that molecular conductance is highly correlated with the HOMO level. In addition to biologically relevant DNA samples, we note that this strategy is also applicable to DNA storage,42–45 which is promising for a next generation information storage. Molecular design based on HOMO levels allowed us to prepare decodable sets of nucleotides, over canonical four


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nucleotides, to expand the capacity of information encoded by the certain length of DNA sequence.

Methods/Experimental Experimental settings for current measurements. Gold nano-gap electrodes were fabricated in gold-embedded SiO2 plate by a mechanically controlled break junction (MCBJ) method as previously described.26,27 Briefly, free-standing gold junction was subjected to repetitive threepoint bending until it was broken to generate a gap. The size of the gap was controlled under fine-tuning of a lifting bar regulated by a piezoelectric device (Figure 2). Nucleotide samples were prepared as a 10 µM of deionized aqueous solution. For measurement, a 20 µL of sample solution was loaded on the sensor plate and the bias voltage was fixed at 100 mV. The gap size was 0.6 nm, which is comparable to the size of nucleobases (Figure S1). The measurement was repeated until around one thousand signals were obtained (Actual numbers of events are listed in Table S1). No electrophoretic force was applied to nucleotides, therefore access of nucleotides towards the gap electrodes depended on the Brownian motion (Figure 2). More detailed information about preparation of gap electrodes and current measurement is available in Supporting Information.

Signal pick and statistical analysis for nucleotides. For evaluating molecular conductance of nucleotides, the data points that exceeded threshold were picked as the signal. The maximum current value from basal current was defined as signal intensity (Ip = maximum current value – basal current). After current intensity was converted to molecular conductance, they were


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combined to make the conductance histogram for each nucleotide. The conductance histograms were taken in a logarithmic scale and bin size of Log10(G) was 0.02 where G is the absolute value of conductance (pS). Then two Gaussian fittings were applied to the conductance histograms to extract GL and GH values. ASSOCIATED CONTENT Supplementary Information The Supporting Information is available free of charge on the ACS Publications website. Additional procedures including experimental settings of the electrical device, current measurement, analytical procedures, quantum chemical calculation, enzymatic incorporation of nucleotides and chemical synthesis (PDF)

AUTHOR INFORMATION Corresponding Author *[email protected] *[email protected]

Author Contributions †T.F.

and T.O. contributed equally to this work.

ACKNOWLEDGMENT The authors thank Dr. Y. Asai and Dr. M. Buerkle for useful discussions. The authors thank Prof. T. Suzuki and Dr. A. Nagao for letting us use a gel imager (FLA-7000, FUJIFILM). This work


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was supported by JST Core Research for Evolutional Science and Technology (CREST) of Molecular Technology (No. JPMJCR13L4), Japan Science and Technology Agency, grant to S.S. and KAKENHI (No. 17J10215), Japan Society for the Promotion of Science, Grant-in-Aid for JSPS Fellows to T.F., KAKENHI (No. 26220603) granted to M.T., and SCREEN Cooperative Research Division of Single Molecular Analysis. The MCBJ device was supplied by SCREEN Holdings Co. Ltd.. The MCBJ instrument had been developed in CREST of Intelligent Measurement Analysis (No. JPMJCR1666). This work is dedicated to Professor Isao Saito on the occasion of his 77th birthday.


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References 1. Venkatesan, B. M.; Bashir, R. Nanopore Sensors for Nucleic Acid Analysis. Nat. Nanotechnol. 2011, 6, 615–624. 2. Ying, Y.; Zhang, J., Gao, R.; Long, Y. Nanopore-Based Sequencing and Detection of Nucleic Acids. Angew. Chem. Int. Ed. 2013, 52, 13154–13161. 3. Branton, D.; Deamer, D. W.; Marziali, A.; Bayley, H.; Benner, S. A.; Butler, T.; Di Ventra; Garaj, S.; Hibbs, A.; Huang, X. ; Jovanivich, S. B.; Krstic, P. S.; Lindsay, S.; Ling, X. S.; Mastrangelo, C. H.; Meller, A.; Oliver, J. S.; Pershin, Y. V.; Ramsey, J. M.; Riehn, R. et al. The Potential and Challenges of Nanopore Sequencing. Nat. Biotechnol. 2008, 26, 1146–1153. 4. Reiner, J. E.; Balijepalli, A.; Robertson, J. W. F.; Campbell, J.; Suehle, J.; Kasianowicz, J. J. Disease Detection and Management via Single Nanopore-Based Sensors. Chem. Rev. 2012, 112, 6431–6451. 5. Di Ventra; Taniguchi, M. Decoding DNA, RNA and Peptides With Quantum Tunnelling. Nat. Nanotechnol. 2016, 11, 117–126. 6. Lindsay, S. The Promises and Challenges of Solid-State Sequencing. Nat. Nanotechnol. 2016, 11, 109–111. 7. Tsutsui, M.; Taniguchi M.; Yokota, K.; Kawai, T. Identifying Single Nucleotides by Tunnelling Current. Nat. Nanotechnol. 2010, 5, 286–290. 8. Huang, S.; He, J.; Chang, S.: Zhang, P.; Liang, F.; Li, S.; Tuchband, M.; Fuhrmann, A.; Ros, R.; Lindsay, S. Identifying Single Bases in a DNA Oligomer With Electron Tunnelling. Nat. Nanotechnol. 2010, 5, 868–873. 9. Heerema, S. J.; Dekker, C. Graphene Nanodevices for DNA Sequencing. Nat, Nanotechnol. 2016, 11, 127–136. 10. Zwolak, M.; Di Ventra, Electronic Signature of DNA Nucleotides via Transverse Transport. Nano Lett. 2005, 5, 421–424. 11. Lagerqvist, J.; Zwolak, M.; Di Ventra, Fast DNA Sequencing via Transverse Electronic Transport. Nano Lett. 2006, 6, 779–782. 12. Ohshiro, T.; Matsubara, K.; Tsutsui, M.; Furuhashi, M.; Taniguchi, M.; Kawai, T. SingleMolecule Electrical Random Resequencing of DNA and RNA. Sci. Rep. 2012, 2, 501. 13. Chang, S.; Huang, S.; He, J.; Liang, F.; Zhang, P.; Li, S.; Chen, X.; Sankey, O.; Lindsay, S. Electronic Signatures of All Four DNA Nucleosides in a Tunnelling Gap. Nano Lett. 2010, 10, 1070–1075. 14. Keyser, U. F. Controlling Molecular Transport Through Nanopores. J. R. Soc. Interface 2011, 8, 1369–1378. 15. Luan, B.; Stolovitzky, G.; Martyna, G. Slowing and Controlling the Translocation of DNA in a Solid-State Nanopore. Nanoscale 2012, 4, 1068–1077. 16. Keyser, U. F.; Koeleman, B. N.; Dorp, S. V.; Krapf, D.; Smeets, R. M. M.; Lemay, S. G.; Dekker, N. H.; Dekker, C. Direct Force Measurements on DNA in a Solid-State Nanopore. Nat. Phys. 2006, 2, 473–477. 17. Peng, H.; Ling, X. S. Reverse DNA Translocation Through a Solid-State Nanopore by Magnetic Tweezers. Nanotechnology 2009, 20, 185101.


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18. Waugh, M.; Carlsen, A.; Sean, D.; Slater, G. W.; Briggs, K.; Kwok, H.; Tabard-Cossa, V. Interfacing Solid-State Nanopores With Gel Media to Slow DNA Translocations. Electrophoresis 2015, 36, 1759–1767. 19. Fiori, N. D.; Squires, A.; Bar, D.; Bilboa, T.; Moustakas, T. D.; Meller, A. Optoelectronic Control of Surface Charge and Translocation Dynamics in Solid-State Nanopores. Nat. Nanotechnol. 2013, 8, 946–951. 20. Liu, Y.; Yobas, L. Slowing DNA Translocation in a Nanofluidic Field-Effect Transistor. ACS Nano 2016, 10, 3985–3994. 21. Fologea, D.; Uplinger, J.; Thomas, B.; McNabb, D. S.; Li, J. Slowing DNA Translocation in a Solid-State Nanopore. Nano. Lett. 2005, 9, 1734–1737. 22. Kishnakumar, P.; Gyarfas, B.; Song, W.; Sen, S.; Zhang, P.; Krstic, P.; Lindsay, S. Slowing DNA Translocation Through a Nanopore Using a Functionalized Electrode. ACS Nano 2013, 11, 10319–10326. 23. He, Y.; Tsutsui, M.; Fan, C.; Taniguchi, M.; Kawai, T. Controlling DNA Translocation Through Gate Modulation of Nanopore Wall Surface Charges. ACS Nano 2011, 5, 5509–5518. 24. He, Y.; Tsutsui, M.; Taniguchi, M.; Kawai, T. DNA Capture in Nanopores for Genome Sequencing: Challenges and Opportunities. J. Mater. Chem. 2012, 22, 13423–13427. 25. Ohshiro, T.; Tsutsui, M.; Yokota, K.; Taniguchi, M.; Quantitative Analysis of DNA with Single-Molecule Sequencing. Sci. Rep. 2018, 8, 8517. 26. Agrait, N.; Yeyati, A. L.; van Ruitenbeek, J. M. Quantum Properties of Atomic-Sized Conductors. Phys. Rep. 2003, 377, 81–279. 27. Tsutsui, M.; Taniguchi, M.; Kawai, T. Fabrication of 0.5 nm Electrode Gaps Using SelfBreaking Technique. Appl. Phys. Lett. 2008, 93, 163115. 28. Albrecht, T. Electrochemical Tunnelling Sensors and Their Potential Applications. Nat. Commun. 2012, 3, 829. 29. Chen, F.; Tao, N. J. Electron Tansport in Single Molecules: From Benzene to Graphene. Acc. Chem. Res. 2009, 42, 429–438. 30. Zwolak, M.; Di Ventra, Colloquium: Physical Approaches to DNA Sequencing and Detection. Rev. Mod. Phys. 2008, 80, 141–165. 31. Yamashita, D.; Ishizuka, A. In Situ Measurements of Change in Work Function of Pt, Pd and Au Surfaces During Desorption of Oxygen by Using Photoemission Yield Spectrometer in Air. Appl. Surf. Sci. 2016, 363, 240–244. 32. Eremeeva, E.; Abramov, M.; Margamuliana, L.; Rozenski, J.; Pezo, V.; Marliere, P.; Herdewijn, P. Chemical Morphing of DNA Containing Four Noncanonical Bases. Angew Chem. Int. Ed. 2016, 55, 7515–7519. 33. Yamakawa, H.; Ohara, O. A DNA Cycle Sequencing Reaction That Minimizes Compressions on Automated Fluorescent Sequencers. Nucl. Acid. Res. 1997, 25, 1311–1312. 34. Fletcher, T. M., Salazar, M.; Chen, S. F. Human Telomerase Inhibition by 7-Deaza-2’Deoxypurine Nucleoside Triphosphates. Biochemistry 1996, 35, 15611–15617.


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35. Tsutsui, M.; Matsubara, K.; Ohshiro, T.; Furuhashi, M.; Taniguchi, M.; Kawai, T. Electrical Detection of Single Methylcytosines in a DNA Oligomer. J. Am. Chem. Soc. 2011, 133, 9124–9128. 36. Korshoj, L. E.; Afsari, S.; Chatterjee, A.; Nagpal, P. Conformational Smear Characterization and Binning of Single-Molecule Conductance Measurements for Enhanced Molecular Recognition. J. Am. Chem. Soc. 2017, 139, 15420–15428. 37. Korshoj, L. E.; Afsari, S.; Khan, S.; Chatterjee, A.; Nagpal, P. Single Nucleobase Identification Using Biophysical Signatures From Nanoelectronic Quantum Tunneling. Small 2017, 13, 1603033. 38. Biswas, S.; Sen, S.; Im, J. O.; Biswis, S.; Krstic, P.; Ashcroft, B.; Borges, C.; Zhao, Y.; Lindsay, S.; Zhang, P. Universal Readers Based on Hydrogen Bonding or π−π Stacking for Identification of DNA Nucleotides in Electron Tunnel Junctions. ACS Nano 2016, 10, 11304– 11316. 39. Yokota, K.; Tsutsui, M.; Taniguchi, M. Electrode-Embedded Nanopores for Label-Free Single-Molecule Sequencing by Electric Currents. RSC Adv. 2014, 4, 15886–15899. 40. Tsutsui, M.; Rahong, S.; Iizumi, Y.; Okazaki, T.; Taniguchi, M.; Kawai, T. SingleMolecule Sensing Electrode Embedded in-Plane Nanopore. Sci. Rep. 2011, 1, 46. 41. Pang, P.; Ashcroft, B. A.; Song, W.; Zhang, P.; Biswas, S.; Qing, Q.; Yang, J.; Nemanich, R. J.; Bai, J.; Smith, J. T.; Kathleen, R.; Balagurusamy, V. S. K.; Astier, Y.; Stolovitzky, G.; Lindsay, S. Fixed-Gap Tunnel Junction for Reading DNA Nucleotides. ACS Nano 2014, 8, 11994–12003. 42. Zhirnov, V.; Zadegan, R. M.; Sandhu, G. S.; Church, G. M.; Hughes, W. L. Nucleic Acid Memory. Nat. Meter. 2016, 15, 366–370. 43. Grass, R. N.; Heckel, R.; Puddu, M.; Paunescu, D.; Stark, W. J. Robust Chemical Preservation of Digital Information on DNA in Silica With Error-Correcting Codes. Angew. Chem. Int. Ed. 2015, 54, 2552–2555. 44. Goldman, N.; Bertone, P.; Chen, S.; Dessimoz, C.; LeProust, E. M.; Sipos, B.; Birney, E. Towards Practical, High-Capacity, Low-Maintenance Information Storage in Synthesized DNA. Nature 2013, 494, 77–80. 45. Church, G. M.; Gao, Y.; Kosuri, S. Next-Generation Digital Information Storage in DNA. Science 2012, 337, 1628.


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Figures and Captions

Figure 1. DNA sequencing based on quantum tunneling and a chemical strategy to expand the signal difference between four genetic alphabets using a highly conductive dA analogue. (a) A schematic illustration of quantum tunneling-based DNA sequencer with bare gold gap electrodes. A DNA passing through a pair of gap electrodes can be detected by an electrical signal of quantum tunneling through a nucleobase. Each nucleotide can be distinguished from each other according to their difference of tunnel current output. (b) A schematic illustration of the present strategy to expand the difference of molecular conductance between four genetic alphabets. Xaxis is the molecular conductance and the Y-axis shows its frequency. Solid lines represent the distribution of molecular conductance of each nucleotide. Replacement of dA (purple) with a highly conductive dA analogue, dA* (red) would reduce the overlaps of molecular conductance between four genetic alphabets. (c) A schematic representation of enzymatic replacement of canonical dA (purple) with dA* (red) to produce decodable oligonucleotide samples.


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Figure 2. Schematic illustration of an experimental setup for current measurement of nucleotides. The nano-sized gap electrodes for detection of a nucleotide were fabricated by a mechanically controlled break junction (MCBJ) method. The gap size was adjusted to 0.6 nm by controlling the pushing bar using a piezoelectric device. A sample solution was applied at the center of a sensor chip where gap electrodes were fabricated. A 100 mV of bias voltage was applied between the gap electrodes during current measurement.


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Figure 3. Molecular conductance of dA and dG analogues and the correlation with HOMO level. (a) Schematic illustration of electron transport in quantum tunneling across gold-nucleotide-gold junction. Γ𝐿(𝑅) represents the coupling strength of nucleotides with the gap electrodes. The right


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panel shows the electron density of the left electrode (fL) and the right electrode (fR). The Fermi level of the left electrode (μL) and the right electrode (μR) differs by –eV where V is the bias voltage applied to the gap. Electron transport would be the most efficient around the Fermi level of gold (EF) because the difference between fL and fR is the largest at EF. (b) Chemical structures of nucleobases of dA and dG analogues and their HOMO levels calculated in their nucleoside forms. HOMO levels were calculated by density function theory (DFT) using B3LYP/6311+G(d,p)//B3LYP/6-31+G(d). (c) A schematic illustration of current measurement of a 3-mer oligonucleotide. X is a nucleobase of interest which was flanked by two abasic ribose mimics (ddR). (d) Typical current traces of oligonucleotides containing dG and its analogues. The difference between the maximum current value and the basal current value was defined as signal intensity (Ip). (e) Histograms of molecular conductance for dA and dG analogues. X-axis is molecular conductance taken in the logarithmic scale and y-axis shows its relative frequency. Blue area and red area show the Gaussian fittings representing the distribution of lower and higher molecular conductance. Black lines are the integrated curves of two Gaussian fittings. GL and GH are the peak conductance of the Gaussian fitting curves at lower conductance side (blue) and higher conductance side (red) respectively. The interval of auxiliary scale lines is 100 pS. Tables show GL and GH values. Rel. GH is the relative GH value for each nucleotide normalized by the GH of dG. (f) The plots of the GH values against 1/(𝐸𝐹–𝜀𝐻𝑂𝑀𝑂)2 for dA analogues (upper panel) and dG analogues (lower panel). EF was assumed to be –4.9 eV. (g) Electron clouds of HOMO and HOMO–1 in dzdA and their putative contribution to the conductance histogram. Quantum chemical calculation was conducted by DFT using B3LYP/6-311+G(d,p)//B3LYP/631+G(d).


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Figure 4. Current measurement of oligonucleotides containing canonical nucleotides and 7deaza dA (dzdA). (a) Chemical structures of canonical and dzdA nucleobases and their HOMO levels calculated in their nucleoside forms. Quantum chemical calculations were conducted by DFT using B3LYP/6-311+G(d,p)//B3LYP/6-31+G(d). (b) Typical time traces of tunnel current during the measurement of oligonucleotides containing dC, dA or dzdA. (c) Conductance histograms of nucleotides and two Gaussian fittings to the histograms. X-axis is conductance taken in a logarithmic scale. Black points represent the experimental frequency of molecular conductance of oligonucleotides observed in the current measurement. Blue area and red area show the Gaussian fittings representing the distribution of lower molecular conductance and that of higher molecular conductance. Black lines are integrated curves of two Gaussian fittings. GLand GH are peak conductance values of the Gaussian fitting curves at lower conductance side (blue) and higher conductance side (red) respectively. The interval of auxiliary scale lines is 100


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pS. Right table shows GL and GH values and relative values of GH (Rel. GH) normalized by the GH value of dG.


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Figure 5. Conductance histograms of hetero-oligonucleotides containing four genetic alphabets. (a) Typical signal peaks during the current measurement of 6-mer hetero-oligonucleotides, 5’d(TGTACT)-3’ (dA-seq, upper panel) and 5’-d(TGTdzACT)-3’ (dzdA-seq, lower panel). All data points over threshold (baseline + 6×standard deviations, red line) were picked up to construct the conductance histograms. (b) Conductance histograms for dA-seq (upper panel) and dzdA-seq (lower panel). The numbers on the right side of the histograms were the percentages of conductance values that exceeded 281 pS, the GH value of dG provided in Figures 3e and 4c.


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TOC Graphic


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A Highly Conductive Nucleotide Analogue Facilitates Base-Calling in

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