Decoding DNA, RNA and peptides with quantum tunnelling

Electromigrated Gold Nanogap Tunnel Junction Arrays: Fabrication and Electrical Behavior in Liquid and Gaseous Media

Tunnel junctions have been suggested as high-throughput electronic single molecule sensors in liquids with several seminal experiments conducted using break junctions with reconfigurable gaps. For practical single molecule sensing applications, arrays of on-chip integrated fixed-gap tunnel junctions that can be built into compact systems are preferable. Fabricating nanogaps by electromigration is one of the most promising approaches to realize on-chip integrated tunnel junction sensors. However, the electrical behavior of fixed-gap tunnel junctions immersed in liquid media has not been systematically studied to date, and the formation of electromigrated nanogap tunnel junctions in liquid media has not yet been demonstrated. In this work, we perform a comparative study of the formation and electrical behavior of arrays of gold nanogap tunnel junctions made by feedback-controlled electromigration immersed in various liquid and gaseous media (deionized water, mesitylene, ethanol, nitrogen, and air). We demonstrate that tunnel junctions can be obtained from microfabricated gold nanoconstrictions inside liquid media. Electromigration of junctions in air produces the highest yield (61-67%), electromigration in deionized water and mesitylene results in a lower yield than in air (44-48%), whereas electromigration in ethanol fails to produce viable tunnel junctions due to interfering electrochemical processes. We map out the stability of the conductance characteristics of the resulting tunnel junctions and identify medium-specific operational conditions that have an impact on the yield of forming stable junctions. Furthermore, we highlight the unique challenges associated with working with arrays of large numbers of tunnel junctions in batches. Our findings will inform future efforts to build single molecule sensors using on-chip integrated tunnel junctions.

Machine Learning-Assisted Direct RNA Sequencing with Epigenetic RNA Modification Detection via Quantum Tunneling

RNA sequence information holds immense potential as a drug target for diagnosing various RNA-mediated diseases and viral/bacterial infections. Massively parallel complementary DNA (c-DNA) sequencing helps but results in a loss of valuable information about RNA modifications, which are often associated with cancer evolution. Herein, we report machine learning (ML)-assisted high throughput RNA sequencing with inherent RNA modification detection using a single-molecule, long-read, and label-free quantum tunneling approach. The ML tools achieve classification accuracy as high as 100% in decoding RNA and 98% for decoding both RNA and RNA modifications simultaneously. The relationships between input features and target values have been well examined through Shapley additive explanations. Furthermore, transmission and sensitivity readouts enable the recognition of RNA and its modifications with good selectivity and sensitivity. Our approach represents a starting point for ML-assisted direct RNA sequencing that can potentially decode RNA and its epigenetic modifications at the molecular level.

Precision Basecalling of Single DNA Nucleotide from Overlapped Transmission Readouts with Machine Learning Aided Solid-State Nanogap

DNA sequencing with the quantum tunneling technique heralds a paradigm shift in genetic analysis, promising rapid and accurate identification for diverging applications ranging from personalized medicine to security issues. However, the widespread distribution of molecular conductance, conduction orbital alignment for resonant transport, and decoding crisscrossing conductance signals of isomorphic nucleotides have been persistent experimental hurdles for swift and precise identification. Herein, we have reported a machine learning (ML)-driven quantum tunneling study with solid-state model nanogap to determine nucleotides at single-base resolution. The optimized ML basecaller has demonstrated a high predictive basecalling accuracy of all four nucleotides from seven distinct data pools, each containing complex transmission readouts of their different dynamic conformations. ML classification of quaternary, ternary, and binary nucleotide combinations is also performed with high precision, sensitivity, and F1 score. ML explainability unravels the evidence of how extracted normalized features within overlapped nucleotide signals contribute to classification improvement. Moreover, electronic fingerprints, conductance sensitivity, and current readout analysis of nucleotides have promised practical applicability with significant sensitivity and distinguishability. Through this ML approach, our study pushes the boundaries of quantum sequencing by highlighting the effectiveness of single nucleotide basecalling with promising implications for advancing genomics and molecular diagnostics.

Next-Generation Sequencing and Emerging Technologies

Genetic sequencing technologies are evolving at a rapid pace with major implications for research and clinical practice. In this review, the authors provide an updated overview of next-generation sequencing (NGS) and emerging methodologies. NGS has tremendously improved sequencing output while being more time and cost-efficient in comparison to Sanger sequencing. The authors describe short-read sequencing approaches, such as sequencing by synthesis, ion semiconductor sequencing, and nanoball sequencing. Third-generation long-read sequencing now promises to overcome many of the limitations of short-read sequencing, such as the ability to reliably resolve repeat sequences and large genomic rearrangements. By combining complementary methods with massively parallel DNA sequencing, a greater insight into the biological context of disease mechanisms is now possible. Emerging methodologies, such as advances in nanopore technology, in situ nucleic acid sequencing, and microscopy-based sequencing, will continue the rapid evolution of this area. These new technologies hold many potential applications for hematological disorders, with the promise of precision and personalized medical care in the future.

Total variation denoising-based method of identifying the states of single molecules in break junction data

Break junction (BJ) measurements provide insights into the electrical properties of diverse molecules, enabling the direct assessment of single-molecule conductances. The BJ method displays potential for use in determining the dynamics of individual molecules, single-molecule chemical reactions, and biomolecules, such as deoxyribonucleic acid and ribonucleic acid. However, conductance data obtained via single-molecule measurements may be susceptible to fluctuations due to minute structural changes within the junctions. Consequently, clearly identifying the conduction states of these molecules is challenging. This study aims to develop a method of precisely identifying conduction state traces. We propose a novel single-molecule analysis approach that employs total variation denoising (TVD) in signal processing, focusing on the integration of information technology with measured single-molecule data. We successfully applied this method to simulated conductance traces, effectively denoise the data, and elucidate multiple conduction states. The proposed method facilitates the identification of well-defined plateau lengths and supervised machine learning with enhanced accuracies. The introduced TVD-based analytical method is effective in elucidating the states within the measured single-molecule data. This approach exhibits the potential to offer novel perspectives regarding the formation of molecular junctions, conformational changes, and cleavage.

Deciphering DNA nucleotide sequences and their rotation dynamics with interpretable machine learning integrated C3N nanopores

A solid-state nanopore combined with the quantum transport method has garnered substantial attention and intrigue for DNA sequencing due to its potential for providing rapid and accurate sequencing results, which could have numerous applications in disease diagnosis and personalized medicine. However, the intricate and multifaceted nature of the experimental protocol poses a formidable challenge in attaining precise single nucleotide analysis. Here, we report a machine learning (ML) framework combined with the quantum transport method to accelerate high-throughput single nucleotide recognition with C3N nanopores. The optimized eXtreme Gradient Boosting Regression (XGBR) algorithm has predicted the fingerprint transmission of each unknown nucleotide and their rotation dynamics with root mean square error scores as low as 0.07. Interpretability of ML black box models with the game theory-based SHapley Additive exPlanation method has provided a quasi-explanation for the model working principle and the complex relationship between electrode-nucleotide coupling and transmission. Moreover, a comprehensive ML classification of nucleotides based on binary, ternary, and quaternary combinations shows maximum accuracy and F1 scores of 100%. The results suggest that ML in tandem with a nanopore device can potentially alleviate the experimental hurdles associated with quantum tunneling and facilitate fast and high-precision DNA sequencing.

Tunnel junction sensing of TATP explosive at the single-molecule level

Triacetone triperoxide (TATP) is a highly potent homemade explosive commonly used in terrorist attacks. Its detection poses a significant challenge due to its volatility, and the lack of portability of current sensing techniques. To address this issue, we propose a novel approach based on single-molecule TATP detection in the air using a device where tunneling current in N-terminated carbon-nanotubes nanogaps is measured. By employing the density functional theory combined with the non-equilibrium Green's function method, we show that current of tens of nanoamperes passes through TATP trapped in the nanogap, with a discrimination ratio of several orders of magnitude even against prevalent indoor volatile organic compounds (VOCs). This high tunneling current through TATP's highest occupied molecular orbital (HOMO) is facilitated by the strong electric field generated by N-C polar bonds at the electrode ends and by the hybridization between TATP and the electrodes, driven by oxygen atoms within the probed molecule. The application of the same principle is discussed for graphene nanogaps and break-junctions.

Influence of nanopore coating patterns on the translocation dynamics of polyelectrolytes

Polyelectrolytes can electrophoretically be driven through nanopores in order to be detected. The respective translocation events are often very fast and the process needs to be controlled to promote efficient detection. To this end, we attempt to control the translocation dynamics by coating the inner surface of a nanopore. For this, different charge distributions are chosen that result in substantial variations of the pore-polymer interactions. In addition and in view of the existing detection modalities, experimental settings, and nanopore materials, different types of sensors inside the nanopore have been considered to probe the translocation process and its temporal spread. The respective transport of polyelectrolytes through the coated nanopores is modeled through a multi-physics computational scheme that incorporates a mesoscopic/electrokinetic description for the solvent and particle-based scheme for the polymer. This investigation could underline the interplay between sensing modality, nanopore material, and detection accuracy. The electro-osmotic flow and electrophoretic motion in a pore are analyzed together with the polymeric temporal and spatial fluctuations unraveling their correlations and pathways to optimize the translocation speed and dynamics. Accordingly, this work sketches pathways in order to tune the pore-polymer interactions in order to control the translocation dynamics and, in the long run, errors in their measurements.

Solid-State Single-Molecule Sensing with the Electronic Life-Detection Instrument for Enceladus/Europa (ELIE)

Growing evidence of the potential habitability of Ocean Worlds across our solar system is motivating the advancement of technologies capable of detecting life as we know it-sharing a common ancestry or physicochemical origin with life on Earth-or don't know it, representing a distinct emergence of life different than our one known example. Here, we propose the Electronic Life-detection Instrument for Enceladus/Europa (ELIE), a solid-state single-molecule instrument payload that aims to search for life based on the detection of amino acids and informational polymers (IPs) at the parts per billion to trillion level. As a first proof-of-principle in a laboratory environment, we demonstrate the single-molecule detection of the amino acid L-proline at a 10 μM concentration in a compact system. Based on ELIE's solid-state quantum electronic tunneling sensing mechanism, we further propose the quantum property of the HOMO-LUMO gap (energy difference between a molecule's highest energy-occupied molecular orbital and lowest energy-unoccupied molecular orbital) as a novel metric to assess amino acid complexity. Finally, we assess the potential of ELIE to discriminate between abiotically and biotically derived α-amino acid abundance distributions to reduce the false positive risk for life detection. Nanogap technology can also be applied to the detection of nucleobases and short sequences of IPs such as, but not limited to, RNA and DNA. Future missions may utilize ELIE to target preserved biosignatures on the surface of Mars, extant life in its deep subsurface, or life or its biosignatures in a plume, surface, or subsurface of ice moons such as Enceladus or Europa. One-Sentence Summary: A solid-state nanogap can determine the abundance distribution of amino acids, detect nucleic acids, and shows potential for detecting life as we know it and life as we don't know it.

Single-molecular protein-based bioelectronics via electronic transport: fundamentals, devices and applications

Biomolecular electronics is a rapidly growing multidisciplinary field that combines biology, nanoscience, and engineering to bridge the two important fields of life sciences and molecular electronics. Proteins are remarkable for their ability to recognize molecules and transport electrons, making the integration of proteins into electronic devices a long sought-after goal and leading to the emergence of the field of protein-based bioelectronics, also known as proteotronics. This field seeks to design and create new biomolecular electronic platforms that allow for the understanding and manipulation of protein-mediated electronic charge transport and related functional applications. In recent decades, there have been numerous reports on protein-based bioelectronics using a variety of nano-gapped electrical devices and techniques at the single molecular level, which are not achievable with conventional ensemble approaches. This review focuses on recent advances in physical electron transport mechanisms, device fabrication methodologies, and various applications in protein-based bioelectronics. We discuss the most recent progress of the single or few protein-bridged electrical junction fabrication strategies, summarise the work on fundamental and functional applications of protein bioelectronics that enable high and dynamic electron transport, and highlight future perspectives and challenges that still need to be addressed. We believe that this specific review will stimulate the interdisciplinary research of topics related to protein-related bioelectronics, and open up new possibilities for single-molecule biophysics and biomedicine.

Single-Molecule Identification of Nucleotides Using a Quantum Computer

Genomic information is essential for human health. Due to its large volume, genomic information can be potentially computed using quantum computers, which are rapidly developing. Genome analysis using quantum computers can accelerate the development of personalized medicine, innovative drugs, and novel diagnostics based on genomic information. However, genomic analysis, including nucleotide identification, has not yet been performed using quantum computers. Here, we demonstrate single-molecule identification of nucleotides using a quantum computer. We have designed a quantum gate that explains the single-molecule conductance of adenosine electronically bonded between electrodes. The quantum circuit consists of a reverse and an encoding quantum gate that can strongly distinguish adenosine among the four nucleotides. Our results are the first step toward the realization of genome analysis using quantum computers.

Fabrication of electron tunneling probes for measuring single-protein conductance

Studying the electrical properties of individual proteins is a prominent research area in the field of bioelectronics. Electron tunnelling or quantum mechanical tunnelling (QMT) probes can act as powerful tools for investigating the electrical properties of proteins. However, current fabrication methods for these probes often have limited reproducibility, unreliable contact or inadequate binding of proteins onto the electrodes, so better solutions are required. Here, we detail a generalizable and straightforward set of instructions for fabricating simple, nanopipette-based, tunnelling probes, suitable for measuring conductance in single proteins. Our QMT probe is based on a high-aspect-ratio dual-channel nanopipette that integrates a pair of gold tunneling electrodes with a gap of less than 5 nm, fabricated via the pyrolytic deposition of carbon followed by the electrochemical deposition of gold. The gold tunneling electrodes can be functionalized using an extensive library of available surface modifications to achieve single-protein-electrode contact. We use a biotinylated thiol modification, in which a biotin-streptavidin-biotin bridge is used to form the single-protein junction. The resulting protein-coupled QMT probes enable the stable electrical measurement of the same single protein in solution for up to several hours. We also describe the analysis method used to interpret time-dependent single-protein conductance measurements, which can provide essential information for understanding electron transport and exploring protein dynamics. The total time required to complete the protocol is ~33 h and it can be carried out by users trained in less than 24 h.

Direct biomolecule discrimination in mixed samples using nanogap-based single-molecule electrical measurement

In single-molecule measurements, metal nanogap electrodes directly measure the current of a single molecule. This technique has been actively investigated as a new detection method for a variety of samples. Machine learning has been applied to analyze signals derived from single molecules to improve the identification accuracy. However, conventional identification methods have drawbacks, such as the requirement of data to be measured for each target molecule and the electronic structure variation of the nanogap electrode. In this study, we report a technique for identifying molecules based on single-molecule measurement data measured only in mixed sample solutions. Compared with conventional methods that require training classifiers on measurement data from individual samples, our proposed method successfully predicts the mixing ratio from the measurement data in mixed solutions. This demonstrates the possibility of identifying single molecules using only data from mixed solutions, without prior training. This method is anticipated to be particularly useful for the analysis of biological samples in which chemical separation methods are not applicable, thereby increasing the potential for single-molecule measurements to be widely adopted as an analytical technique.

Advances in single-molecule junctions as tools for chemical and biochemical analysis

The development of miniaturized electronics has led to the design and construction of powerful experimental platforms capable of measuring electronic properties to the level of single molecules, along with new theoretical concepts to aid in the interpretation of the data. A new area of activity is now emerging concerned with repurposing the tools of molecular electronics for applications in chemical and biological analysis. Single-molecule junction techniques, such as the scanning tunnelling microscope break junction and related single-molecule circuit approaches have a remarkable capacity to transduce chemical information from individual molecules, sampled in real time, to electrical signals. In this Review, we discuss single-molecule junction approaches as emerging analytical tools for the chemical and biological sciences. We demonstrate how these analytical techniques are being extended to systems capable of probing chemical reaction mechanisms. We also examine how molecular junctions enable the detection of RNA, DNA, and traces of proteins in solution with limits of detection at the zeptomole level.

Development of an Artificially Intelligent Nanopore for High-Throughput DNA Sequencing with a Machine-Learning-Aided Quantum-Tunneling Approach

Solid-state nanopore-based single-molecule DNA sequencing with quantum tunneling technology poses formidable challenges to achieve long-read sequencing and high-throughput analysis. Here, we propose a method for developing an artificially intelligent (AI) nanopore that does not require extraction of the signature transmission function for each nucleotide of the whole DNA strand by integrating supervised machine learning (ML) and transverse quantum transport technology with a graphene nanopore. The optimized ML model can predict the transmission function of all other nucleotides after training with data sets of all the orientations of any nucleotide inside the nanopore with a root-mean-square error (RMSE) of as low as 0.062. Further, up to 96.01% accuracy is achieved in classifying the unlabeled nucleotides with their transmission readouts. We envision that an AI nanopore can alleviate the experimental challenges of the quantum-tunneling method and pave the way for rapid and high-precision DNA sequencing by predicting their signature transmission functions.

Highly efficient and robust π-FISH rainbow for multiplexed in situ detection of diverse biomolecules

In the unprecedented single-cell sequencing and spatial multiomics era of biology, fluorescence in situ hybridization (FISH) technologies with higher sensitivity and robustness, especially for detecting short RNAs and other biomolecules, are greatly desired. Here, we develop the robust multiplex π-FISH rainbow method to detect diverse biomolecules (DNA, RNA, proteins, and neurotransmitters) individually or simultaneously with high efficiency. This versatile method is successfully applied to detect gene expression in different species, from microorganisms to plants and animals. Furthermore, we delineate the landscape of diverse neuron subclusters by decoding the spatial distribution of 21 marker genes via only two rounds of hybridization. Significantly, we combine π-FISH rainbow with hybridization chain reaction to develop π-FISH+ technology for short nucleic acid fragments, such as microRNA and prostate cancer anti-androgen therapy-resistant marker ARV7 splicing variant in circulating tumour cells from patients. Our study provides a robust biomolecule in situ detection technology for spatial multiomics investigation and clinical diagnosis.

Quantitative Microscopic Observation of Base-Ligand Interactions via Hydrogen Bonds by Single-Molecule Counting

Chemical properties have been based on statistical averages since the introduction of Avogadro's number. The lack of suitable methods for counting identified single molecules has posed challenges to counting statistics. The selectivity, affinity, and mode of hydrogen bonding between base and small molecules that make up DNA, which is vital for living organisms, have not yet been revealed at the single molecule level. Here, we show the quantitation of the above-mentioned parameters via single-molecule counting based on the combination of single-molecule electrical measurements and AI. The binding selectivity values of five ligands to four different base molecules were evaluated quantitatively by determining the ratio of the number of aggregates in a solution mixture of base molecules and a ligand. In addition, we show the ligand dependence of the mode and number of microscopic hydrogen bonds via single-molecule counting and quantum chemical calculations.

A Step toward Amino Acid-Labeled DNA Sequencing: Boosting Transmission Sensitivity of Graphene Nanogap

Existing obstacles in next-generation DNA sequencing techniques, for instance, high noise, high translocation speed, and configurational fluctuations, call for approaches capable of reaching the goal and accelerating the process of personalized medicine development. The labeling nucleotide approach has the potential to overcome these barriers and boost the recognition sensitivity of a solid-state nanodevice. In this theoretical report, the first-principles density functional theory calculations have been employed to study the role of three different labels, tyrosine (Tyr), aspartic acid (Asp), and arginine (Arg), for labeling DNA nucleotides and study their effect in rapid and controlled DNA sequencing at atomic resolution. Remarkable differences in interaction energy values are noticed in all three cases of differently labeled nucleotides. The zero-bias transmission spectra confirm that proposed labels have the ability to detect the individual nucleotide, amplifying the tunneling current sensitivity by several orders of magnitude. The current-voltage characteristics of Arg-labeled nucleotides are found to be promising for single nucleotide recognition even at a very low bias voltage of 0.1 V.

Electronic analysis of hydrogen-bonded molecular complexes: the case of DNA sensed in a functionalized nanogap

DNA nucleotides can be interrogated by nanomaterials in order to be detected. With the aid of quantum-mechanical simulations, we unravel the intrinsic details of the electronic transport across nanoelectrodes functionalized with tiny modified diamond-like molecules. These electrodes generate a gap in which DNA nucleotides are placed and can be identified. The identification is strongly affected by the hydrogen bonding characteristics of the diamond-like particle and the nucleotides. The results point to the connection of the electronic transmission across the functionalized nanogap and the electronic and bonding characteristics of the molecular complexes within the nanogap. Specifically, our discussion focuses on the influence of the DNA dynamics on the electronic signals across the nanogap. We identify the molecular complex's details that hinder or promote the electronic transport through an analysis that moves from the bonding within the molecular complex up to the electronic current that this can accommodate. Accordingly, our work discusses pathways for analyzing hydrogen-bonded molecular complexes or molecules hydrogen-bonded to a material part having the optimization of the design of biosensing nanogaps and read-out nanopores in mind. The presented approach, though, is applicable to a wide range of applications utilizing exactly the bio/nano interface.

Towards a graphene semi/hybrid-nanogap: a new architecture for ultrafast DNA sequencing

The tremendous upsurge in the research of next-generation sequencing (NGS) methods has broadly been driven by the rise of the wonder material graphene and continues to dominate the futuristic approaches for fast and accurate DNA sequencing. The success of graphene has also triggered the search for many new potential NGS methods capable of ultrafast, reliable, and controlled DNA sequencing. The present study delves into the potential of a new NGS architecture utilizing graphene, namely, a 'semi/hybrid-nanogap' for the identification of DNA nucleobases with single-base resolution. In the framework of first-principles density functional theory methods, we have calculated the transmission function and current-voltage (I-V) characteristics which are of particular significance for DNA sequencing applications. It is noted that the interaction energy values are significantly reduced compared to the previously reported graphene nanodevices, which can lead to a controlled translocation during experimental measurements. Based on the transmission function, each nucleobase can be identified with pertinent sensitivity. It is noticed that the use of highly conductive nucleobase analogs can facilitate improved single nucleobase sensing by increasing the transmission sensitivity. Therefore, we believe that the present study opens up promising frontiers for sequencing applications.

Functionalized electrodes embedded in nanopores: read-out enhancement?

In this review, functionalized nanogaps embedded in nanopores are discussed in view of their high biosensitivity in detecting biomolecules, their length, type, and sequence. Specific focus is given on nanoelectrodes functionalized with tiny nanometer-sized diamond-like particles offering vast functionalization possibilities for gold junction electrodes. This choice of the functionalization, in turn, offers nucleotide-specific binding possibilities improving the detection signals arising from such functionalized electrodes potentially embedded in a nanopore. The review sheds light onto the use and enhancement of the tunnelling recognition in functionalized nanogaps towards sensing DNA nucleotides and mutation detection, providing important input for a practical realization.

Machine Learning Aided Interpretable Approach for Single Nucleotide-Based DNA Sequencing using a Model Nanopore

Solid-state nanopore-based electrical detection of DNA nucleotides with the quantum tunneling technique has emerged as a powerful strategy to be the next-generation sequencing technology. However, experimental complexity has been a foremost obstacle in achieving a more accurate high-throughput analysis with industrial scalability. Here, with one of the nucleotide training data sets of a model monolayer gold nanopore, we have predicted the transmission function for all other nucleotides with root-mean-square error scores as low as 0.12 using the optimized eXtreme Gradient Boosting Regression (XGBR) model. Further, the SHapley Additive exPlanations (SHAP) analysis helped in exploring the interpretability of the XGBR model prediction and revealed the complex relationship between the molecular properties of nucleotides and their transmission functions by both global and local interpretable explanations. Hence, experimental integration of our proposed machine-learning-assisted transmission function prediction method can offer a new direction for the realization of cheap, accurate, and ultrafast DNA sequencing.

Ab Initio Properties of Hybrid Cove-Edged Graphene Nanoribbons as Metallic Electrodes for Peptide Sequencing via Transverse Tunneling Current

Recently synthesized metallic cove-edged graphene nanoribbons are considered for use as one-dimensional (1D) electrodes for ideal atomistically resolved recognition of amino acids. To this purpose a narrow nanogap device is considered, and the transversal tunneling current flowing across it is calculated during the translocation of a model Gly homopeptide using the nonequilibrium Green function scheme, based on density functional theory. We show that the signal collected from the metallic spin states is characterized by a double peak per residue in analogy with the results obtained with 1D graphene nanoribbon template electrodes. The presented results pave the way for experimentally feasible atomistically resolved tunneling current recognition using metallic edge engineered graphene electrodes obtained by bottom-up fabrication strategies.

Sub-5 nm nanogap electrodes towards single-molecular biosensing

Nanogap electrodes (NGEs) with sub-5 nm gap has been widely used in single-molecule sensing and sequencing, with the characteristics of label-free, high sensitivity, rapid detection and low-cost. However, the fabrication of sub-5 nm gap electrodes with high controllability and reproducibility still remains a great challenge that impedes the experimental research and the commercialization of the nanogap device. Here, we review the common currently used fabrication methods of nanogap electrodes, such as gap narrowing deposition, mechanical controllable break junctions and the fabrication methods combined with nanopore or nanochannel. We then highlight the typical applications of nanogap electrodes in biological/chemical sensing fields, including single molecule recognition, single molecule sequencing and chemical kinetics analysis. Finally, the challenges of nanogap electrodes in single molecule sensing/sequencing are outlined and the future directions for sensing perspectives are suggested.

Advances of nanopore-based sensing techniques for contaminants evaluation of food and agricultural products

Food safety assurance systems are becoming more stringent in response to the growing food safety problems. Rapid, sensitive, and reliable detection technology is a prerequisite for the establishment of food safety assurance systems. Nanopore technology has been taken as one of the emerging technology capable of dealing with the detection of harmful contaminants as efficiently as possible due to the advantage of label-free, high-throughput, amplification-free, and rapid detection features. Start with the history of nanopore techniques, this review introduced the underlying knowledge of detection mechanism of nanopore-based sensing techniques. Meanwhile, sensing interfaces for the construction of nanopore sensors are comprehensively summarized. Moreover, this review covers the current advances of nanopore techniques in the application of food safety screening. Currently, the establishment of nanopore sensing devices is mainly based on the blocking current phenomenon. Sensing interfaces including biological nanopores, solid-state nanopores, DNA origami, and de novo designed nanopores can be used in the manufacture of sensing devices. Food harmful substances, including heavy metals, veterinary drugs, pesticide residues, food toxins, and other harmful substances can be quickly determined by nanopore-based sensors. Moreover, the combination of nanopore techniques with advanced materials has become one of the most effective methods to improve sensing properties.

Measuring conductance switching in single proteins using quantum tunneling

Interpreting the electrical signatures of single proteins in electronic junctions has facilitated a better understanding of the intrinsic properties of proteins that are fundamental to chemical and biological processes. Often, this information is not accessible using ensemble and even single-molecule approaches. In addition, the fabrication of nanoscale single-protein junctions remains challenging as they often require sophisticated methods. We report on the fabrication of tunneling probes, direct measurement, and active control (switching) of single-protein conductance with an external field in solution. The probes allowed us to bridge a single streptavidin molecule to two independently addressable, biotin-terminated electrodes and measure single-protein tunneling response over long periods. We show that charge transport through the protein has multiple conductive pathways that depend on the magnitude of the applied bias. These findings open the door for the reliable fabrication of protein-based junctions and can enable their use in future protein-embedded bioelectronics applications.

Protein Sequencing, One Molecule at a Time

Despite tremendous gains over the past decade, methods for characterizing proteins have generally lagged behind those for nucleic acids, which are characterized by extremely high sensitivity, dynamic range, and throughput. However, the ability to directly characterize proteins at nucleic acid levels would address critical biological challenges such as more sensitive medical diagnostics, deeper protein quantification, large-scale measurement, and discovery of alternate protein isoforms and modifications and would open new paths to single-cell proteomics. In response to this need, there has been a push to radically improve protein sequencing technologies by taking inspiration from high-throughput nucleic acid sequencing, with a particular focus on developing practical methods for single-molecule protein sequencing (SMPS). SMPS technologies fall generally into three categories: sequencing by degradation (e.g., mass spectrometry or fluorosequencing), sequencing by transit (e.g., nanopores or quantum tunneling), and sequencing by affinity (as in DNA hybridization-based approaches). We describe these diverse approaches, which range from those that are already experimentally well-supported to the merely speculative, in this nascent field striving to reformulate proteomics.

Direct observation of DNA alterations induced by a DNA disruptor

DNA alterations, such as base modifications and mutations, are closely related to the activity of transcription factors and the corresponding cell functions; therefore, detection of DNA alterations is important for understanding their relationships. Particularly, DNA alterations caused by exposure to exogenous molecules, such as nucleic acid analogues for cancer therapy and the corresponding changes in cell functions, are of interest in medicine for drug development and diagnosis purposes. However, detection of comprehensive direct evidence for the relationship of DNA modifications/mutations in genes, their effect on transcription factors, and the corresponding cell functions have been limited. In this study, we utilized a single-molecule electrical detection method for the direct observation of DNA alterations on transcription factor binding motifs upon exposure to a nucleic acid analogue, trifluridine (FTD), and evaluated the effects of the DNA alteration on transcriptional activity in cancer cell line cells. We found ~ 10% FTD incorporation at the transcription factor p53 binding regions in cancer cells exposed to FTD for 5 months. Additionally, through single-molecule analysis of p53-enriched DNA, we found that the FTD incorporation at the p53 DNA binding regions led to less binding, likely due to weaken the binding of p53. This work suggests that single-molecule detection of DNA sequence alterations is a useful methodology for understanding DNA sequence alterations.

Nanodevices for Biological and Medical Applications: Development of Single-Molecule Electrical Measurement Method

A comprehensive detection of a wide variety of diagnostic markers is required for the realization of personalized medicine. As a sensor to realize such personalized medicine, a single molecule electrical measurement method using nanodevices is currently attracting interest for its comprehensive simultaneous detection of various target markers for use in biological and medical application. Single-molecule electrical measurement using nanodevices, such as nanopore, nanogap, or nanopipette devices, has the following features:; high sensitivity, low-cost, high-throughput detection, easy-portability, low-cost availability by mass production technologies, and the possibility of integration of various functions and multiple sensors. In this review, I focus on the medical applications of single- molecule electrical measurement using nanodevices. This review provides information on the current status and future prospects of nanodevice-based single-molecule electrical measurement technology, which is making a full-scale contribution to realizing personalized medicine in the future. Future prospects include some discussion on of the current issues on the expansion of the application requirements for single-mole-cule measurement.

Scalable Fabrication of Metallic Nanogaps at the Sub-10 nm Level

Metallic nanogaps with metal-metal separations of less than 10 nm have many applications in nanoscale photonics and electronics. However, their fabrication remains a considerable challenge, especially for applications that require patterning of nanoscale features over macroscopic length-scales. Here, some of the most promising techniques for nanogap fabrication are evaluated, covering established technologies such as photolithography, electron-beam lithography (EBL), and focused ion beam (FIB) milling, plus a number of newer methods that use novel electrochemical and mechanical means to effect the patterning. The physical principles behind each method are reviewed and their strengths and limitations for nanogap patterning in terms of resolution, fidelity, speed, ease of implementation, versatility, and scalability to large substrate sizes are discussed.

Nanopore chip with self-aligned transverse tunneling junction for DNA detection

To achieve better signal quality and resolution in nanopore sequencing, there has been strong interest in quantum tunneling based detection which requires integration of tunneling junctions in nanopores. However, there has been very limited success due to precision and reproducibility issues. Here we report a new strategy based on feedback-controlled electrochemical processes in a confined nanoscale space to construct nanopore devices with self-aligned transverse tunneling junctions, all embedded on a nanofluidic chip. We demonstrate high-yield (>93%) correlated detection of translocating DNAs from both the ionic channel and the tunneling junction with enriched event rate. We also observed events attributed to non-translocating DNA making contact with the transverse electrodes. Existing challenges for precise sequencing are discussed, including fast translocation speed, and interference from transient electrostatic signals from fast-moving DNAs. Our work can serve as a first step to provide an accessible, and reproducible platform enabling further optimizations for tunneling-based DNA detection, and potentially sequencing.

Planar Boronic Graphene and Nitrogenized Graphene Heterostructure for Protein Stretch and Confinement

Single-molecule techniques such as electron tunneling and atomic force microscopy have attracted growing interests in protein sequencing. For these methods, it is critical to refine and stabilize the protein sample to a “suitable mode” before applying a high-fidelity measurement. Here, we show that a planar heterostructure comprising boronic graphene (BC₃) and nitrogenized graphene (C₃N) sandwiched stripe (BC₃/C₃N/BC₃) is capable of the effective stretching and confinement of three types of intrinsically disordered proteins (IDPs), including amyloid-β (1–42), polyglutamine (Q42), and α-Synuclein (61–95). Our molecular dynamics simulations demonstrate that the protein molecules interact more strongly with the C₃N stripe than the BC₃ one, which leads to their capture, elongation, and confinement along the center C₃N stripe of the heterostructure. The conformational fluctuations of IDPs are substantially reduced after being stretched. This design may serve as a platform for single-molecule protein analysis with reduced thermal noise.

Predicting Finite-Bias Tunneling Current Properties from Zero-Bias Features: The Frontier Orbital Bias Dependence at an Exemplar Case of DNA Nucleotides in a Nanogap

The electrical current properties of single-molecule sensing devices based on electronic (tunneling) transport strongly depend on molecule frontier orbital energy, spatial distribution, and position with respect to the electrodes. Here, we present an analysis of the bias dependence of molecule frontier orbital properties at an exemplar case of DNA nucleotides in the gap between H-terminated (3, 3) carbon nanotube (CNT) electrodes and its relation to transversal current rectification. The electronic transport properties of this simple single-molecule device, whose characteristic is the absence of covalent bonding between electrodes and a molecule between them, were obtained using density functional theory and non-equilibrium Green's functions. As in our previous studies, we could observe two distinct bias dependences of frontier orbital energies: the so-called strong and the weak pinning regimes. We established a procedure, from zero-bias and empty-gap characteristics, to estimate finite-bias electronic tunneling transport properties, i.e., whether the molecular junction would operate in the weak or strong pinning regime. We also discuss the use of the zero-bias approximation to calculate electric current properties at finite bias. The results from this work could have an impact on the design of new single-molecule applications that use tunneling current or rectification applicable in high-sensitivity sensors, protein, or DNA sequencing.

Single-molecule RNA sequencing for simultaneous detection of m6A and 5mC

Epitranscriptomics is the study of RNA base modifications involving functionally relevant changes to the transcriptome. In recent years, epitranscriptomics has been an active area of research. However, a major issue has been the development of sequencing methods to map transcriptome-wide RNA base modifications. We have proposed a single-molecule quantum sequencer for mapping RNA base modifications in microRNAs (miRNAs), such as N6-methyladenosine (m6A) or 5-methylcytidine (5mC), which are related to cancer cell propagation and suppression. Here, we investigated 5mC and m6A in hsa-miR-200c-5p extracted from colorectal cancer cells and determined their methylation sites and rates; the data were comparable to those determined by mass spectrometry. Furthermore, we evaluated the methylation ratio of cytidine and adenosine at each site in the sequences and its relationship. These results suggest that the methylation ratio of cytidine and adenosine is facilitated by the presence of vicinal methylation. Our work provides a robust new tool for sequencing various types of RNA base modifications in their RNA context.

Synchronized resistive-pulse analysis with flow visualization for single micro- and nanoscale objects driven by optical vortex in double orifice

Resistive-pulse analysis is a powerful tool for identifying micro- and nanoscale objects. For low-concentration specimens, the pulse responses are rare, and it is difficult to obtain a sufficient number of electrical waveforms to clearly characterize the targets and reduce noise. In this study, we conducted a periodic resistive-pulse analysis using an optical vortex and a double orifice, which repetitively senses a single micro- or nanoscale target particle with a diameter ranging from 700 nm to 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu$$\end{document}μm. The periodic motion results in the accumulation of a sufficient number of waveforms within a short period. Acquired pulses show periodic ionic-current drops associated with the translocation events through each orifice. Furthermore, a transparent fluidic device allows us to synchronously average the waveforms by the microscopic observation of the translocation events and improve the signal-to-noise ratio. By this method, we succeed in distinguishing single particle diameters. Additionally, the results of measured signals and the simultaneous high-speed observations are used to quantitatively and systematically discuss the effect of the complex fluid flow in the orifices on the amplitude of the resistive pulse. The synchronized resistive-pulse analysis by the optical vortex with the flow visualization improves the pulse-acquisition rate for a single specific particle and accuracy of the analysis, refining the micro- and nanoscale object identification.

Accurate diagnosis of prostate cancer using logistic regression

A new logistic regression-based method to distinguish between cancerous and noncancerous RNA genomic data is developed and tested with 100% precision on 595 healthy and cancerous prostate samples. A logistic regression system is developed and trained using whole-exome sequencing data at a high-level, i.e., normalized quantification of RNAs obtained from 495 prostate cancer samples from The Cancer Genome Atlas and 100 healthy samples from the Genotype-Tissue Expression project. We could show that both sensitivity and specificity of the method in the classification of cancerous and noncancerous cells are perfectly 100%.

Development of Single-Molecule Electrical Identification Method for Cyclic Adenosine Monophosphate Signaling Pathway

Cyclic adenosine monophosphate (cAMP) is an important research target because it activates protein kinases, and its signaling pathway regulates the passage of ions and molecules inside a cell. To detect the chemical reactions related to the cAMP intracellular signaling pathway, cAMP, adenosine triphosphate (ATP), adenosine monophosphate (AMP), and adenosine diphosphate (ADP) should be selectively detected. This study utilized single-molecule quantum measurements of these adenosine family molecules to detect their individual electrical conductance using nanogap devices. As a result, cAMP was electrically detected at the single molecular level, and its signal was successfully discriminated from those of ATP, AMP, and ADP using the developed machine learning method. The discrimination accuracies of a single cAMP signal from AMP, ADP, and ATP were found to be 0.82, 0.70, and 0.72, respectively. These values indicated a 99.9% accuracy when detecting more than ten signals. Based on an analysis of the feature values used for the machine learning analysis, it is suggested that this discrimination was due to the structural difference between the ribose of the phosphate site of cAMP and those of ATP, ADP, and AMP. This method will be of assistance in detecting and understanding the intercellular signaling pathways for small molecular second messengers.

Light-Nucleotide versus Ion-Nucleotide Interactions for Single-Nucleotide Resolution

Several parallel reads of ionic currents through multiple CsgG nanopores provide information about ion-nucleotide interactions for sequencing single-stranded DNA (ss-DNA) using base-calling algorithms. However, the information in ion-nucleotide interactions seems insufficient for single-read nanopore DNA sequencing. Here we report discriminative light-nucleotide interactions calculated from density functional theory (DFT), which are compared with ionic currents obtained from molecular dynamics (MD) simulations. The MD simulations were performed on a system containing a transverse nanochannel and a longitudinal solid state nanopore. We show that both of the transverse and longitudinal ionic currents during the translocation of A₁₆, G₁₆, T₁₆, and C₁₆ through the nanopore, overlapped widely. On the other hand, the UV-vis and Raman spectra of different types of single nucleotides, nucleosides, and nucleobases show relatively higher resolution than the ionic currents. Light-nucleotide interactions provide better information for characterizing the nucleotides in comparison to ion-nucleotide interactions for nanopore DNA sequencing. This can be realized by using optical techniques including surface-enhanced Raman spectroscopy (SERS) or tip-enhanced Raman spectroscopy (TERS), while plasmon excitation can be used to localize light and control the rate of nucleotide flow.

Combined quantum tunnelling and dielectrophoretic trapping for molecular analysis at ultra-low analyte concentrations

Quantum tunnelling offers a unique opportunity to study nanoscale objects with atomic resolution using electrical readout. However, practical implementation is impeded by the lack of simple, stable probes, that are required for successful operation. Existing platforms offer low throughput and operate in a limited range of analyte concentrations, as there is no active control to transport molecules to the sensor. We report on a standalone tunnelling probe based on double-barrelled capillary nanoelectrodes that do not require a conductive substrate to operate unlike other techniques, such as scanning tunnelling microscopy. These probes can be used to efficiently operate in solution environments and detect single molecules, including mononucleotides, oligonucleotides, and proteins. The probes are simple to fabricate, exhibit remarkable stability, and can be combined with dielectrophoretic trapping, enabling active analyte transport to the tunnelling sensor. The latter allows for up to 5-orders of magnitude increase in event detection rates and sub-femtomolar sensitivity.

Probes that effectively utilize quantum tunneling are sought after for high-resolution study of nanoscale objects. Here the authors present an easily fabricated probe of two nanoelectrodes that enables highly sensitive quantum-tunneling-based sensing of single molecules.

Field Effect and Local Gating in Nitrogen-Terminated Nanopores (NtNP) and Nanogaps (NtNG) in Graphene

Functionalization of electrodes is a wide-used strategy in various applications ranging from single-molecule sensing and protein sequencing, to ion trapping, to desalination. We demonstrate, employing non-equilibrium Green's function formalism combined with density functional theory, that single-species (N, H, S, Cl, F) termination of graphene nanogap electrodes results in a strong in-gap electrostatic field, induced by species-dependent dipoles formed at the electrode ends. Consequently, the field increases or decreases electronic transport through a molecule (benzene) placed in the nanogap by shifting molecular levels by almost 2 eV in respect to the electrode Fermi level via a field effect akin to the one used for field-effect transistors. We also observed the local gating in graphene nanopores terminated with different single-species atoms. Nitrogen-terminated nanogaps (NtNGs) and nanopores (NtNPs) show the strongest effect. The in-gap potential can be transformed from a plateau-like to a saddle-like shape by tailoring NtNG and NtNP size and termination type. In particular, the saddle-like potential is applicable in single-ion trapping and desalination devices.

Slowing down DNA translocation through solid-state nanopores by edge-field leakage

Solid-state nanopores allow high-throughput single-molecule detection but identifying and even registering all translocating small molecules remain key challenges due to their high translocation speeds. We show here the same electric field that drives the molecules into the pore can be redirected to selectively pin and delay their transport. A thin high-permittivity dielectric coating on bullet-shaped polymer nanopores permits electric field leakage at the pore tip to produce a voltage-dependent surface field on the entry side that can reversibly edge-pin molecules. This mechanism renders molecular entry an activated process with sensitive exponential dependence on the bias voltage and molecular rigidity. This sensitivity allows us to selectively prolong the translocation time of short single-stranded DNA molecules by up to 5 orders of magnitude, to as long as minutes, allowing discrimination against their double-stranded duplexes with 97% confidence.

Length Discrimination of Homo-oligomeric Nucleic Acids with Single-molecule Measurement

Single-molecule DNA/RNA sequencing based on single-molecule measurement is a prominent method for higher throughput sequencing. In a previous report, the single-molecule DNA/RNA sequencing method has applied to detect each base-conductance difference in the tunneling current time profiles, and determined the sequence. However, discrimination of identical base lengths has not yet been achieved. The number of the identical contiguous bases has importance in biology because some homopolymers of nucleic acid control gene expression. In this study, we aimed to develop a method for discriminating the length of homopolymer of nucleic acids of adenosine monophosphate (AMP) using a single-molecule sequencing technique. We carried out single-molecule conductance measurements of adenine pentamer, hexamer and heptamer. The single-molecule signals of the oligomers are not distinguishable from current and duration time histograms. The three oligomers were discriminated by our developed machine learning-based analysis with accuracy of 0.54 for a single signal, and 99% for 40 signals. This method will be applied to the single signals and identify the contiguous bases in the sequence and provide new biological insights.

Advancing Biosensors with Machine Learning

Chemometrics play a critical role in biosensors-based detection, analysis, and diagnosis. Nowadays, as a branch of artificial intelligence (AI), machine learning (ML) have achieved impressive advances. However, novel advanced ML methods, especially deep learning, which is famous for image analysis, facial recognition, and speech recognition, has remained relatively elusive to the biosensor community. Herein, how ML can be beneficial to biosensors is systematically discussed. The advantages and drawbacks of most popular ML algorithms are summarized on the basis of sensing data analysis. Specially, deep learning methods such as convolutional neural network (CNN) and recurrent neural network (RNN) are emphasized. Diverse ML-assisted electrochemical biosensors, wearable electronics, SERS and other spectra-based biosensors, fluorescence biosensors and colorimetric biosensors are comprehensively discussed. Furthermore, biosensor networks and multibiosensor data fusion are introduced. This review will nicely bridge ML with biosensors, and greatly expand chemometrics for detection, analysis, and diagnosis.

Detection of an alcohol-associated cancer marker by single-molecule quantum sequencing

Alcoholic beverages are a well-known risk factor for cancer. N²-Ethyl-2'-deoxyguanosine (N²-Et-dG) is a promising biomarker for alcohol-associated cancers. However, the lack of a convenient detection method for N²-Et-dG hinders the development of practical DNA damage markers. Herein, we develop a detection method for N²-Et-dG using a single-molecule quantum sequencing (SMQS) method and machine learning analysis. Our method succeeded in discriminating between N²-Et-dG and dG with an accuracy of 99%, using 20 signals. Our developed method quantified the mixing ratio of N²-Et-dG from a mixed solution of N²-Et-dG and dG. It is shown that our method has the potential to facilitate the development of DNA damage markers, and thus the early detection and prevention of cancers.