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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

Time-resolved neurotransmitter detection in mouse brain tissue using an artificial intelligence-nanogap.

The analysis of neurotransmitters in the brain helps to understand brain functions and diagnose Parkinson's disease. Pharmacological inhibition experiments, electrophysiological measurement of action potentials, and mass analysers have been applied for this purpose; however, these techniques do not allow direct neurotransmitter detection with good temporal resolution by using nanometre-sized electrodes. Hence, we developed a method for direct observation of a single neurotransmitter molecule with a gap width of ≤ 1 nm and on the millisecond time scale. It consists of measuring the tunnelling current that flows through a single-molecule by using nanogap electrodes and machine learning analysis. Using this method, we identified dopamine, serotonin, and norepinephrine neurotransmitters with high accuracy at the single-molecule level. The analysis of the mouse striatum and cerebral cortex revealed the order of concentration of the three neurotransmitters. Our method will be developed to investigate the neurotransmitter distribution in the brain with good temporal resolution.

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.

Thermally activated charge transport in carbon atom chains.

Charge transport through single molecules is at the heart of molecular electronics for realizing the practical use of the rich quantum characteristics of electrode-molecule-electrode systems. Despite the extensive studies reported in the past, little experimental efforts have been focused on the electron transport mechanism at a temperature higher than the ambient temperature. In this work, we have reported the observation of the subtle interplay between electron tunneling and charge hopping in carbon chains connected to two Au electrodes at elevated temperatures. We measured the single-molecule conductance of Au-alkanedithiol-Au molecular junctions at various temperatures from 300 K to 420 K in vacuum. The temperature dependence of conductance suggested substantial roles of superexchange with inter-chain charge hopping under elevated temperatures for alkane chains longer than heptane. This finding provides a guide to design functional molecular junctions under practical conditions.

Key aurophilic motif for robust quantum-tunneling-based characterization of a nucleoside analogue marker.

A quantum sequencer offers a scalable electrical platform for single-molecule analysis of genomic events. A thymidine (dT) analog exhibiting uniquely high single-molecule conductance is a key element in capturing DNA synthesis dynamics by serving as a decodable marker for enzymatic labeling of nascent strands. However, the current design strategies of dT analogs that focus on their molecular orbital energy levels require bulky chemical modifications to extend the π-conjugation, which hinders polymerase recognition. We report herein a polymerase-compatible dT analog that is highly identifiable in quantum sequencing. An ethynyl group is introduced as a small gold-binding motif to differentiate the nucleobase-gold electronic coupling, which has been an overlooked factor in modifying nucleobase conductance. The resulting C5-ethynyl-2'-deoxyuridine exhibits characteristic signal profiles that allowed its correct identification at a 93% rate while maintaining polymerase compatibility. This study would expand the applicability of quantum sequencing by demonstrating a robust nucleoside marker with high identifiability.

Evaluation of the Electronic Structure of Single-Molecule Junctions Based on Current-Voltage and Thermopower Measurements: Application to C60 Single-Molecule Junction.

The electronic structure of molecular junctions has a significant impact on their transport properties. Despite the decisive role of the electronic structure, a complete characterization of the electronic structure remains a challenge. This is because there is no straightforward way of measuring electron spectroscopy for an individual molecule trapped in a nanoscale gap between two metal electrodes. Herein, a comprehensive approach to obtain a detailed description of the electronic structure in single-molecule junctions based on the analysis of current-voltage (I-V) and thermoelectric characteristics is described. It is shown that the electronic structure of the prototypical C60 single-molecule junction can be resolved by analyzing complementary results of the I-V and thermoelectric measurement. This combined approach confirmed that the C60 single-molecule junction was highly conductive with molecular electronic conductances of 0.033 and 0.003 G0 and a molecular Seebeck coefficient of -12 µV K-1 . In addition, we revealed that charge transport was mediated by a LUMO whose energy level was located 0.5≈0.6 eV above the Fermi level of the Au electrode.