Decoding DNA, RNA and peptides with quantum tunnelling

Journal name:
Nature Nanotechnology
Volume:
11,
Pages:
117–126
Year published:
DOI:
doi:10.1038/nnano.2015.320
Received
Accepted
Published online

Abstract

Drugs and treatments could be precisely tailored to an individual patient by extracting their cellular- and molecular-level information. For this approach to be feasible on a global scale, however, information on complete genomes (DNA), transcriptomes (RNA) and proteomes (all proteins) needs to be obtained quickly and at low cost. Quantum mechanical phenomena could potentially be of value here, because the biological information needs to be decoded at an atomic level and quantum tunnelling has recently been shown to be able to differentiate single nucleobases and amino acids in short sequences. Here, we review the different approaches to using quantum tunnelling for sequencing, highlighting the theoretical background to the method and the experimental capabilities demonstrated to date. We also explore the potential advantages of the approach and the technical challenges that must be addressed to deliver practical quantum sequencing devices.

At a glance

Figures

  1. Genomic cost and throughput over the past decade.
    Figure 1: Genomic cost and throughput over the past decade.

    The data are taken from refs 10,11,12,13,14, and the genomic costs (red) are expressed in dollars per megabases (Mb) and the throughput of different DNA sequencers (blue) in megabases per day. The quantum sequencing throughput is estimated as follows. A single DNA molecule can pass through nanogap electrodes at a speed of 1 base ms−1. The output can be calculated by assuming 1 base ms−1 times 1 day, times the number of nanogap electrodes of 1,000,000 on a single device. The number of nanogap electrodes is expected to be limited by data-handling requirements rather than the scale or extent of device integration. Considering the present data transfer rate of 3 GB s−1, 50 data transfer wires and a vertical resolution of 12 bits in current–time data, the maximum number of nanogap electrodes to be integrated is estimated as 1,000,000.

  2. Schematic of quantum sequencing.
    Figure 2: Schematic of quantum sequencing.

    Typical nanopores and nanochannels with diameters of less than 10 nm are formed on a silicon substrate covered with SiO2 or Si3N4. Nanogaps with a spacing of less than 2 nm are made within the nanopores and nanochannels. When single-stranded DNA and RNA and peptide molecules move across these channels, the device can measure tunnelling currents that are conducted via single-base and amino acid molecules passing between nanoelectrodes. On average, tunnelling currents differ for each type of base and amino acid molecule because of different molecular electronic structures.

  3. Different approaches to quantum sequencing.
    Figure 3: Different approaches to quantum sequencing.

    a, A nanogap embedded in a nanofluidic channel, where single-molecule identification has not been achieved. The movement of the DNA molecule (red) is due to the electrophoretic flow parallel to the nanochannel47. b, Single-base molecules of DNA are identified via tunnelling currents flowing between a gold probe and a gold substrate that are functionalized with recognition molecules. The movement of single-base and single-stranded DNA molecules is due to Brownian motion41. c, A nanogap embedded in a solid-state nanopore, where single-base molecules have not been identified via tunnelling currents flowing between nanoelectrodes. The nanogap can be decreased to 0.7 nm. The movement of the DNA molecule is due to the electrophoretic flow parallel to the nanopore. d, A 10 nm nanopore embedded in a silicon nanowire field-effect transistor (FET). FET conductance can detect a single DNA molecule because of changes in local potential around the nanowire–nanopore sensor, caused by fluctuations in the solution resistance of the nanopore and the trans chamber during DNA translocation. The movement of one double-stranded DNA molecule from the cis chamber to the trans chamber is due to the electrophoretic flow parallel to the nanopore54. e, A nanopore of less than 10 nm embedded in a graphene nanoribbon. The source–drain current between two electrodes can be changed by variations in the local electric potential caused by the electric charge of DNA. The movement of the DNA molecule from cis to trans chambers is due to the electrophoretic flow parallel to the nanopore56. f, Single-stranded DNA or single-stranded RNA molecules are sequenced via tunnelling currents flowing between gold nanoelectrodes, whose gap is controlled using a mechanically controllable break junction. The movement of single-base DNA and RNA molecules is due to Brownian motion42.

  4. Single-molecule identification of base molecules via tunnelling currents.
    Figure 4: Single-molecule identification of base molecules via tunnelling currents.

    a, A typical current–time profile obtained using a gold scanning tunnelling microscope tip and a gold substrate that are functionalized with 4-mercaptobenzoic acid molecules. Electric currents originate from tunnelling currents conducted via single-base molecules, which are recognized by 4-mercaptobenzoic acid via hydrogen bonds. b–d, Current histograms of dAMP, dCMP and dGMP. cps indicates counts per second. e, A typical tunnelling current–time profile obtained from mechanically controllable break junctions. Tunnelling currents are obtained via single-base molecules passing through a nanogap electrode. f, Conductance histograms obtained with quantum tunnelling of four deoxynucleoside monophosphates of DNA, constructed from 1,000 current values for each molecule. The blue, red, purple and green lines corresponding to the current histograms indicate the single-molecule conductance values of dGMP, dAMP, dCMP and dTMP, respectively, and represent Gaussian fitted lines. g, Conductance histograms of four nucleoside monophosphates of RNA. The blue, red, purple and green lines indicate the single-molecule conductance values of rGMP, rAMP, rCMP and rUMP, respectively. Figures adapted from: a–d, ref. 41, Nature Publishing Group; e–g, ref. 42, Nature Publishing Group.

  5. Sequencing single DNA oligomers using tunnelling currents.
    Figure 5: Sequencing single DNA oligomers using tunnelling currents.

    a, A current–time profile of d(ACACA) obtained using a gold scanning tunnelling microscope probe and a gold substrate functionalized with 4-mercaptobenzoic acid molecules. b, Current histogram of the DNA. The orange- and green-dashed lines correspond to adenine and cytosine, respectively. The red line is the scaled homopolymer fit. c, A relative conductance–time profile of d(GTG) obtained using a mechanically controllable break junction. The relative conductance is the individual conductance normalized by guanine's conductance. The left inset shows histograms, where the two conductance peaks correspond to guanine and thymine, respectively. d, An expanded current–time profile of c (top) and relative conductance–time profile transferred from the top panel (bottom). Different fragmented sequences are obtained due to stochastic traps of a single DNA molecule. The movement of single DNA molecules is due to Brownian motion, which causes stochastic traps. Figures adapted from: a,b, ref. 41, Nature Publishing Group; c,d, ref. 42, Nature Publishing Group.

  6. Identifying single amino acid molecules using tunnelling currents.
    Figure 6: Identifying single amino acid molecules using tunnelling currents.

    a–c, Single-molecule identification of amino acids using a palladium scanning tunnelling microscope tip and palladium substrate functionalized with recognition molecules. a, A typical current–time profile of leucine (Leu). The inset shows an expanded trace (current scale, 150 pA; timescale, 20 ms) of features that are important in the analysis of the data. b, Tunnelling current (average cluster amplitude) distributions of Leu and methylglycine (mGly). The distributions overlap considerably, resulting in an accuracy of 58% (P = 0.58) in distinguishing the two amino acids. c, Two-dimensional plot of probability densities generated using the support vector machine (SVM) algorithm. First Fourier transform is employed to obtain particular Fourier components of distributions of average cluster amplitudes of two amino acids. Red and green regions indicate data points of mGly and Leu, respectively, while the yellow regions provide ambiguous calls of amino acids. Using this plot, amino acid molecules can be distinguished with an accuracy of 95% (P = 0.95). d,e, Single-molecule identification of amino acids using a mechanically controllable break junction. d, Conductance–time profile of tyrosine (Y) and phenylaniline (F). Signals are characterized by maximum current (Ip) and duration of the current (td). e, Conductance histograms of nine amino acid molecules. The conductance histograms are constructed from at least 500 Ip data points. pY, W, P, H, F, Y, I, E and D show phosphotyrosine, tryptophan, proline, histidine, phenylaniline, tyrosine, isoleucine and glutamic acid, respectively. Figures adapted from: a–c, ref. 94, Nature Publishing Group; d,e, ref. 95, Nature Publishing Group.

  7. Sequencing data of a peptide obtained using a mechanically controllable break junction.
    Figure 7: Sequencing data of a peptide obtained using a mechanically controllable break junction.

    a, A typical conductance–time profile of a chemically modified peptide, IEEEIpYGEFD. I, E, pY, G, F and D are isoleucine, glutamic acid, phosphotyrosine, glycine, phenylaniline and aspartic acid, respectively. b, Enlarged signal of the red-dashed rectangle in a. Partial peptide sequences are assigned to XFXpYXpY and XF based on single-molecule conductance of amino acid molecules. I, E, G and D are assigned to a generic group X. B indicates the baseline. c, The intensity of the plotted data shows the signal numbers as percentages of the total signal numbers. The intensity profile demonstrates a partial sequence of the chemically modified peptide. Figures adapted from ref. 95, Nature Publishing Group.

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Affiliations

  1. Department of Physics, University of California, San Diego, California 92093, USA

    • Massimiliano Di Ventra
  2. The Institute of Scientific and Industrial Research, Osaka University, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

    • Masateru Taniguchi

Competing financial interests

M.D.V. declares that he is scientific advisor of the company Quantum Biosystems and M.T. is board director and chief scientific officer of the same company. Quantum Biosystems (www.quantumbiosystems.com) is developing quantum sequencers.

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