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.

Using novel micropore technology combined with artificial intelligence to differentiate Staphylococcus aureus and Staphylococcus epidermidis.

Methods for identifying bacterial pathogens are broadly categorised into conventional culture-based microbiology, nucleic acid-based tests, and mass spectrometry. The conventional method requires several days to isolate and identify bacteria. Nucleic acid-based tests and mass spectrometry are relatively rapid and reliable, but they require trained technicians. Moreover, mass spectrometry requires expensive equipment. The development of a novel, inexpensive, and simple technique for identifying bacterial pathogens is needed. Through combining micropore technology and assembly machine learning, we developed a novel classifier whose receiver operating characteristic (ROC) curve showed an area under the ROC curve of 0.94, which rapidly differentiated between Staphylococcus aureus and Staphylococcus epidermidis in this proof-of-concept study. Morphologically similar bacteria belonging to an identical genus can be distinguished using our method, which requires no specific training, and may facilitate the diagnosis and treatment of patients with bacterial infections in remote areas and in developing countries.

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.

Self-Assembled Monolayers of Gemini-Type Amphiphilic Hexabenzocoronenes on Gold: Contribution of Their Triethylene Glycol Side Chains to Self-Assembly Formation.

We report on a two-dimensional self-assembled structure of a supramolecule with hydrophilic oligoethylene glycol (EG) units, which are capable of stronger electrostatic interactions than van der Waals (vdW) interactions between alkyl chains. For this purpose, hexabenzocoronene (HBC) with two hydrophobic dodecyl chains on one side of the HBC core and two hydrophilic triethylene glycol (TEG) chains on the other side of the HBC core (HBCGemini) and HBCGemini with a trinitrofluorenone (TNF) added to the end of one TEG chain (HBCTNFGemini) were employed. Scanning tunneling microscopy (STM) revealed the presence of multiple two-dimensional self-assembled structures in each of HBCGemini and HBCTNFGemini deposited on the gold substrate in vacuum. The role of polar functional groups in these observations is discussed based on semiempirical molecular orbital simulations. Two types of 2D organized structures of HBC-TEG were observed: one with rectangular and relatively dense unit cells and the other with nearly square and relatively sparse unit cells. In both organized structures, the phenyl group TEG units and alkyl chains were considered to be the main molecular interactions with each other. On the other hand, in HBCTNFGemini, three types of organized structures were observed, which could be explained by the mechanism of interdigitation of the TEG-containing side-chain moieties to form a dimeric core. The EG units are more flexible than the alkyl chains and thus can interact flexibly with the hydrophobic HBC core, and the glycol side chains facilitate the intermolecular interactions as well as the alkyl chains.

High-precision rapid testing of omicron SARS-CoV-2 variants in clinical samples using AI-nanopore.

A digital platform that can rapidly and accurately diagnose pathogenic viral variants, including SARS-CoV-2, will minimize pandemics, public anxiety, and economic losses. We recently reported an artificial intelligence (AI)-nanopore platform that enables testing for Wuhan SARS-CoV-2 with high sensitivity and specificity within five minutes. However, which parts of the virus are recognized by the platform are unknown. Similarly, whether the platform can detect SARS-CoV-2 variants or the presence of the virus in clinical samples needs further study. Here, we demonstrated the platform can distinguish SARS-CoV-2 variants. Further, it identified mutated Wuhan SARS-CoV-2 expressing spike proteins of the delta and omicron variants, indicating it discriminates spike proteins. Finally, we used the platform to identify omicron variants with a sensitivity and specificity of 100% and 94%, respectively, in saliva specimens from COVID-19 patients. Thus, our results demonstrate the AI-nanopore platform is an effective diagnostic tool for SARS-CoV-2 variants.

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.

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.

Regulating Nonlinear Ion Transport through a Solid-State Pore by Partial Surface Coatings.

Using functional nanofluidic devices to manipulate ion transport allows us to explore the nanoscale development of blue energy harvesters and iontronic building blocks. Herein, we report on a method to alter the nonlinear ionic current through a pore by partial dielectric coatings. A variety of dielectric materials are examined on both the inner and outer surfaces of the channel with four different patterns of coated or uncoated surfaces. Through controlling the specific part of the surface charge, the pore can behave like a resistor, diode, and bipolar junction transistor. We use numerical simulations to find out the reason for the asymmetric ion transport in the pore and illustrate the relationship between specifically charged surfaces and electroosmotic flow. These findings help understand the role of the corresponding surface composition in ion transport, which provides a direct approach to modify the electroosmotic-flow-driven ionic current rectification in the channel-based device via dielectric coatings.

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.

Interference of electrochemical ion diffusion in nanopore sensing.

Stable and fast-responding ionic current is a prerequisite for reliable measurements of small objects with a nanopore. Here, we report on the interference of ion diffusion kinetics at liquid-electrode interfaces in nanopore sensing. Using platinum as electrodes, we observed a slow and large decrease in the ionic current through a nanopore in a salt solution suggestive of the considerable influence of the growing impedance at the liquid-metal interfaces via Cottrell diffusion. When detecting nanoparticles, the resistive pulses became weaker following the steady increase in the resistance at the partially polarizable electrodes. The interfacial impedance was also demonstrated to couple with the nanopore chip capacitance thereby degraded the temporal resolution of the ionic current measurements in a time-varying manner. These findings can be useful for choosing the suitable size and material of electrodes for the single-particle and -molecule analyses by ionic current.

Establishment of a reference single-cell RNA sequencing dataset for human pancreatic adenocarcinoma.

Single-cell RNA sequencing (scRNAseq) has been used to assess the intra-tumor heterogeneity and microenvironment of pancreatic ductal adenocarcinoma (PDAC). However, previous knowledge is not fully universalized. Here, we built a single cell atlas of PDAC from six datasets containing over 70 samples and >130,000 cells, and demonstrated its application to the reanalysis of the previous bulk transcriptomic cohorts and inferring cell-cell communications. The cell decomposition of bulk transcriptomics using scRNAseq data showed the cellular heterogeneity of PDAC; moreover, high levels of tumor cells and fibroblasts were indicative of poor-prognosis. Refined tumor subtypes signature indicated the tumor cell dynamics in intra-tumor and their specific regulatory network. We further identified functionally distinct tumor clusters that had close interaction with fibroblast subtypes via different signaling pathways dependent on subtypes. Our analysis provided a reference dataset for PDAC and showed its utility in research on the microenvironment of intra-tumor heterogeneity.

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.

Review of the use of nanodevices to detect single molecules.

The development of methodologies to identify single molecules and/or to detect/monitor molecular behavior at the single-molecule level is one of the important research topics in chemistry and biology. In this review, we summarized the state-of-the-art of single molecule measurement methods and its latest applications using nanodevices integrated with molecular-size functional nanostructures, nanopores, nanogaps, and nanofluidic channels, which detect differences in chemical species, presence or absence of translational modifications, changes in steric structure, and changes in interactions between molecules. Besides these fundamental analytical achievements of molecular identification abilities, the latest applications include the single-molecule electrical sequencing, disease diagnosis, viral testing, single-molecule drug screening, and environmental monitoring. Finally, we added some discussion on the current status of single-molecule measurement as a method and technology to solve the problems to expand the future application needs of single-molecule measurement.

Salt Gradient Control of Translocation Dynamics in a Solid-State Nanopore.

Tuning capture rates and translocation time of analytes in solid-state nanopores are one of the major challenges for their use in detecting and analyzing individual nanoscale objects via ionic current measurements. Here, we report on the use of salt gradient for the fine control of capture-to-translocation dynamics in 300 nm sized SiNx nanopores. We demonstrated a decrease up to a factor of 3 in the electrophoretic speed of nanoparticles at the pore exit along with an over 3-fold increase in particle detection efficiency by subjecting a 5-fold ion concentration difference across the dielectric membrane. The improvement in the sensor performance was elucidated to be a result of the salt-gradient-mediated electric field and electroosmotic flow asymmetry at nanochannel orifices. The present findings can be used to enhance nanopore sensing capability for detecting biomolecules such as amyloids and proteins.

Computational healthcare: Present and future perspectives (Review).

Artificial intelligence (AI) has been developed through repeated new discoveries since around 1960. The use of AI is now becoming widespread within society and our daily lives. AI is also being introduced into healthcare, such as medicine and drug development; however, it is currently biased towards specific domains. The present review traces the history of the development of various AI-based applications in healthcare and compares AI-based healthcare with conventional healthcare to show the future prospects for this type of care. Knowledge of the past and present development of AI-based applications would be useful for the future utilization of novel AI approaches in healthcare.

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.