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.

Rapid Discrimination of Extracellular Vesicles by Shape Distribution Analysis.

A rapid and simple cancer detection method independent of cancer type is an important technology for cancer diagnosis. Although the expression profiles of biological molecules contained in cancer cell-derived extracellular vesicles (EVs) are considered candidates for discrimination indexes to identify any cancerous cells in the body, it takes a certain amount of time to examine these expression profiles. Here, we report the shape distributions of EVs suspended in a solution and the potential of these distributions as a discrimination index to discriminate cancer cells. Distribution analysis is achieved by low-aspect-ratio nanopore devices that enable us to rapidly analyze EV shapes individually in solution, and the present results reveal a dependence of EV shape distribution on the type of cells (cultured liver, breast, and colorectal cancer cells and cultured normal breast cells) secreting EVs. The findings in this study provide realizability and experimental basis for a simple method to discriminate several types of cancerous cells based on rapid analyses of EV shape distributions.

Site-Selection in Single-Molecule Junction for Highly Reproducible Molecular Electronics.

Adsorption sites of molecules critically determine the electric/photonic properties and the stability of heterogeneous molecule-metal interfaces. Then, selectivity of adsorption site is essential for development of the fields including organic electronics, catalysis, and biology. However, due to current technical limitations, site-selectivity, i.e., precise determination of the molecular adsorption site, remains a major challenge because of difficulty in precise selection of meaningful one among the sites. We have succeeded the single site-selection at a single-molecule junction by performing newly developed hybrid technique: simultaneous characterization of surface enhanced Raman scattering (SERS) and current-voltage (I-V) measurements. The I-V response of 1,4-benzenedithiol junctions reveals the existence of three metastable states arising from different adsorption sites. Notably, correlated SERS measurements show selectivity toward one of the adsorption sites: "bridge sites". This site-selectivity represents an essential step toward the reliable integration of individual molecules on metallic surfaces. Furthermore, the hybrid spectro-electric technique reveals the dependence of the SERS intensity on the strength of the molecule-metal interaction, showing the interdependence between the optical and electronic properties in single-molecule junctions.

Rectifying Electron-Transport Properties through Stacks of Aromatic Molecules Inserted into a Self-Assembled Cage.

Aromatic stacks formed through self-assembly are promising building blocks for the construction of molecular electronic devices with adjustable electronic functions, in which noncovalently bound π-stacks act as replaceable modular components. Here we describe the electron-transport properties of single-molecule aromatic stacks aligned in a self-assembled cage, using scanning probe microscopic and break junction methods. Same and different modular aromatic pairs are noncovalently bound and stacked within the molecular cage holder, which leads to diverse electronic functions. The insertion of same pairs induces high electronic conductivity (10(-3)-10(-2) G0, G0 = 2e(2)/h), while different pairs develop additional electronic rectification properties. The rectification ratio was, respectively, estimated to be 1.4-2 and >10 in current-voltage characteristics and molecular orientation-dependent conductance measurements at a fixed bias voltage. Theoretical calculations demonstrate that this rectification behavior originates from the distinct stacking order of the internal aromatic components against the electron-transport direction and the corresponding lowest unoccupied molecular orbital conduction channels localized on one side of the molecular junctions.

Highly stable Au atomic contacts covered with benzenedithiol under ambient conditions.

The stability of Au atomic contacts under ambient conditions is investigated by measuring the electrical conductance during the self-breaking process. Free standing Au atomic contacts can be kept for more than 100 s after immersion in a 1,4-benzenedithiol (BDT) solution. The average lifetime, that is the amount of time in which the junction remains stable before breaking, is increased from 1.5 s to 12 s due to the metal chemical modification with BDT. By comparing the lifetime of the Au atomic contact covered with BDT and with benzenethiol, we found that the stabilization of the metal atomic contacts stems from the charge transfer from the gold to the molecule. The present results have important implications on the preparation of stable metal atomic contacts and open new directions to fabricate stable nanojunctions at room temperature.

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.

Single-molecule detection of modified amino acid regulating transcriptional activity.

Acetylation of lysine, a component of histones, regulates transcriptional activity. Simple detection methods for acetyl lysine are essential for early diagnosis of diseases and understanding of the physiological effects. We have detected and recognized acetyl lysine at the single-molecule level by combining MCBJ measurement and machine learning.

Enhanced Nanoparticle Sensing in a Highly Viscous Nanopore.

Slowing down translocation dynamics is a crucial challenge in nanopore sensing of small molecules and particles. Here, it is reported on nanoparticle motion-mediated local viscosity enhancement of water-organic mixtures in a nanofluidic channel that enables slow translocation speed, enhanced capture efficiency, and improved signal-to-noise ratio by transmembrane voltage control. It is found that higher detection rates of nanoparticles under larger electrophoretic voltage in the highly viscous solvents. Meanwhile, the strongly pulled particles distort the liquid in the pore at high shear rates over 103 s-1 which leads to a counterintuitive phenomenon of slower translocation speed under higher voltage via the induced dilatant viscosity behavior. This mechanism is demonstrated as feasible with a variety of organic molecules, including glycerol, xanthan gum, and polyethylene glycol. The present findings can be useful in resistive pulse analyses of nanoscale objects such as viruses and proteins by allowing a simple and effective way for translocation slowdown, improved detection throughput, and enhanced signal-to-noise ratio.

Machine learning and analytical methods for single-molecule conductance measurements.

Single-molecule measurements of single-molecule conductance between metal nanogap electrodes have been actively investigated for molecular electronics, biomolecular analysis, and the search for novel physical properties at the nanoscale level. While it is a disadvantage that single-molecule conductance measurements exhibit easily fluctuating and unreliable conductance, they offer the advantage of rapid, repeated acquisition of experimental data through the repeated breaking and forming of junctions. Owing to these characteristics, recently developed informatics and machine learning approaches have been applied to single-molecule measurements. Machine learning-based analysis has enabled detailed analysis of individual traces in single-molecule measurements and improved its performance as a method of molecular detection and identification at the single-molecule level. The novel analytical methods have improved the ability to investigate for new chemical and physical properties. In this review, we focus on the analytical methods for single-molecule measurements and provide insights into the methods used for single-molecule data interrogation. We present experimental and traditional analytical methods for single-molecule measurements, provide examples of each type of machine learning method, and introduce the applicability of machine learning to single-molecule measurements.

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.

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-Molecule Classification of Aspartic Acid and Leucine by Molecular Recognition through Hydrogen Bonding and Time-Series Analysis.

Amino acid detection/identification methods are important for understanding biological systems. In this study, we developed single-molecule measurements for investigating quantum tunneling enhancement by chemical modification and carried out machine learning-based time series analysis for developing accurate amino acid discrimination. We performed single-molecule measurement of L-aspartic acid (Asp) and L-leucine (Leu) with a mercaptoacetic acid (MAA) chemical modified nano-gap. The measured current was investigated by a machine learning-based time series analysis method for accurate amino acid discrimination. Compared to measurements using a bare nano-gap, it is found that MAA modification improves the difference in the conductance-time profiles between Asp and Leu through the hydrogen bonding facilitated tunneling phenomena. It is also found that this method enables determination of relative concentration. even in the mixture of Asp and Leu. It improves selective analysis for amino acids and therefore would be applicable in medicine, diagnosis, and single-molecule peptide sequencing.

Dependence of Molecular Diode Behaviors on Aromaticity.

A molecule-scale diode is an essential component for the concept of molecular electronics. Here we report on heterogeneous contact-mediated rectifying behavior in single-molecule junctions. We performed massive current versus voltage characteristics measurements of metal-molecule-metal structures under stretching by a mechanical break junction method. In-situ deformations of the molecular bridges were revealed to induce stochastic switching of the rectifying direction to varying rectification ratio derived from the induced asymmetry in the contact motifs at the molecule termini. Aromatic molecules were found to enable stronger rectifications via the more pronounced Fermi pinning effect to shift the molecular orbital levels by the applied voltage. Dissimilar anchoring groups also served to stabilize the single-molecule diode properties by bestowing a chemically defined difference in the electronic coupling strengths at the electrode-molecule links. The present findings provide a guide to design diodes with the smallest and simplest structures.

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.

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.

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.

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.

Single-Molecule Counting of Nucleotide by Electrophoresis with Nanochannel-Integrated Nano-Gap Devices.

We utilized electrophoresis to control the fluidity of sample biomolecules in sample aqueous solutions inside the nanochannel for single-molecule detection by using a nanochannel-integrated nanogap electrode, which is composed of a nano-gap sensing electrode, nanochannel, and tapered focusing channel. In order to suppress electro-osmotic flow and thermal convection inside this nanochannel, we optimized the reduction ratios of the tapered focusing channel, and the ratio of inlet 10 µm to outlet 0.5 µm was found to be high performance of electrophoresis with lower concentration of 0.05 × TBE (Tris/Borate/EDTA) buffer containing a surfactant of 0.1 w/v% polyvinylpyrrolidone (PVP). Under the optimized conditions, single-molecule electrical measurement of deoxyguanosine monophosphate (dGMP) was performed and it was found that the throughput was significantly improved by nearly an order of magnitude compared to that without electrophoresis. In addition, it was also found that the long-duration signals that could interfere with discrimination were significantly reduced. This is because the strong electrophoresis flow inside the nanochannels prevents the molecules' adsorption near the electrodes. This single-molecule electrical measurement with nanochannel-integrated nano-gap electrodes by electrophoresis significantly improved the throughput of signal detection and identification accuracy.

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.

Dissecting Time-Evolved Conductance Behavior of Single Molecule Junctions by Nonparametric Machine Learning.

Improved understanding of charge transport in single molecules is essential for utilizing their potential as circuit components at the nanosize limit. However, reliable analyses of varying tunneling current acquired by break junction experiments remain an ongoing challenge to find molecular feature structure-property relationships. In this work, we report on an unsupervised learning approach for investigating molecular signatures in conductance traces. Our hybrid machine learning algorithm compares grids of data in conductance-time domains and judges the similarity without any researcher-crafted parameters to identify fine molecular components that may otherwise be obscured by background fluctuations. We demonstrate its ability for classifying Au-alkanedithiol-Au conductance traces acquired with microfabricated mechanically controllable break junctions. The unbiased procedure was able to not only judge the presence or absence of the carbon chains in the electrode gap but also to identify multiple conductance states of the molecular tunneling junctions with different conformations. This finding may offer a useful tool for studying single-molecule properties using break junction methods.