Accurate Data Process for Nanopore Analysis

A Robust Parallel Computing Data Extraction Framework for Nanopore Experiments

The success of a nanopore experiment relies not only on the quality of the experimental design but also on the performance of the analysis program utilized to decipher the ionic perturbations necessary for understanding the fundamental molecular intricacies. An event extraction framework is developed that leverages parallel computing, efficient memory management, and vectorization, yielding significant performance enhancement. The newly developed abf-ultra-simple function extracts key parameters from the header critical for the operation of open-seek-read-close data loading architecture running on multiple cores. This underpins the swift analysis of large files where an ≈ × 18 improvement is found for a 100 min-long file (≈4.5 GB) compared to the more traditional single (cell) array data loading method. The application is benchmarked against five other analysis platforms showcasing significant performance enhancement (>2 ×-1120 ×). The integrated provisions for batch analysis enable concurrently analyzing multiple files (vital for high-bandwidth experiments). Furthermore, the application is equipped with multi-level data fitting based on abrupt changes in the event waveform. The application condenses the extracted events to a single binary file improving data portability (e.g., 16 GB file with 28 182 events reduces to 47.9 MB-343 × size reduction) and enables a multitude of post-analysis extractions to be done efficiently.

Emerging Data Processing Methods for Single-Entity Electrochemistry

Single-entity electrochemistry is a powerful tool that enables the study of electrochemical processes at interfaces and provides insights into the intrinsic chemical and structural heterogeneities of individual entities. Signal processing is a critical aspect of single-entity electrochemical measurements and can be used for data recognition, classification, and interpretation. In this review, we summarize the recent five-year advances in signal processing techniques for single-entity electrochemistry and highlight their importance in obtaining high-quality data and extracting effective features from electrochemical signals, which are generally applicable in single-entity electrochemistry. Moreover, we shed light on electrochemical noise analysis to obtain single-molecule frequency fingerprint spectra that can provide rich information about the ion networks at the interface. By incorporating advanced data analysis tools and artificial intelligence algorithms, single-entity electrochemical measurements would revolutionize the field of single-entity analysis, leading to new fundamental discoveries.

Direct Determination of the Structure of Single Biopolymer Molecules Using Nanopore Sequencing

This review highlights operational principles, features, and modern aspects of the development of third-generation sequencing technology of biopolymers focusing on the nucleic acids analysis, namely the nanopore sequencing system. Basics of the method and technical solutions used for its realization are considered, from the first works showing the possibility of creation of these systems to the easy-to-handle procedure developed by Oxford Nanopore Technologies company. Moreover, this review focuses on applications, which were developed and realized using equipment developed by the Oxford Nanopore Technologies, including assembly of whole genomes, methagenomics, direct analysis of the presence of modified bases.

Combining iontronic, chromatography and nanopipette for Aβ42 aggregates detection and separation

In this work, we aim to capture, detect and analysis at single molecule level Aβ42 aggregates. To this end, two strategies of track-etched nanopore membranes functionalization were investigated. The first one uses an aptamer and requires only three steps, whereas the second strategy uses Lecanemab antibodies and requires six steps. Out of the two presented strategies, the second one was found to be the most suitable to detect Aβ42 aggregates using a quick current-voltage readout. The resulting single nanopore was then upscale to multipore membranes to capture the Aβ42 aggregates before analysis through them through a single-molecule approach. By comparing the species present in the retentate and filtrate, we confirmed the membrane's affinity for the larger Aβ42 aggregates present in the sample. We found that chromatographic membranes combined with an ionic diode for binary on/off readout are powerful tools for detecting rare biomarkers before single molecule analysis.

Stochastic Sensing of Dynamic Interactions and Chemical Reactions with Nanopores/Nanochannels

Nanopore sensing technology is an emerging analysis method with the advantages of simple operation, high sensitivity, fast output and being label free, and it is widely used in protein analysis, gene sequencing, biomarker detection, and other fields. The confined space of the nanopore provides a place for dynamic interactions and chemical reactions between substances. The use of nanopore sensing technology to track these processes in real time is helpful to understand the interaction/reaction mechanism at the single-molecule level. According to nanopore materials, we summarize the development of biological nanopores and solid-state nanopores/nanochannels in the stochastic sensing of dynamic interactions and chemical reactions. The goal of this paper is to stimulate the interest of researchers and promote the development of this field.

SmartImage: A Machine Learning Method for Nanopore Identifying Chemical Modifications on RNA

RNA modifications modulate essential cellular functions. However, it is challenging to quantitatively identify the differences in RNA modifications. To further improve the single-molecule sensing ability of nanopores, we propose a machine-learning algorithm called SmartImage for identifying and classifying nanopore electrochemical signals based on a combination of improved graph conversion methods and deep neural networks. SmartImage is effective for nearly all ranges of signal duration, which breaks the limitation of the current nanopore algorithm. The overall accuracy (OA) of our proposed recognition strategy exceeded 90% for identifying three types of RNAs. Prediction experiments show that the SmartImage owns the ability to recognize one modified RNA molecule from 1000 normal RNAs with OA >90%. Thus our proposed model and algorithm hold the potential application in clinical applications.

Procedural Data Processing for Single-Molecule Identification by Nanopore Sensors

Nanopores are promising single-molecule sensing devices that have been successfully used for DNA sequencing, protein identification, as well as virus/particles detection. It is important to understand and characterize the current pulses collected by nanopore sensors, which imply the associated information of the analytes, including the size, structure, and surface charge. Therefore, a signal processing program, based on the MATLAB platform, was designed to characterize the ionic current signals of nanopore measurements. In a movable data window, the selected current segment was analyzed by the adaptive thresholds and corrected by multi-functions to reduce the noise obstruction of pulse signals. Accordingly, a set of single molecular events was identified, and the abundant information of current signals with the dwell time, amplitude, and current pulse area was exported for quantitative analysis. The program contributes to the efficient and fast processing of nanopore signals with a high signal-to-noise ratio, which promotes the development of the nanopore sensing devices in various fields of diagnosis systems and precision medicine.

Enhanced Signal to Noise Ratio Enables High Bandwidth Nanopore Recordings and Molecular Weight Profiling of Proteins

Fast protein translocations often lead to bandwidth-limited amplitude-attenuated event signatures. In this study, we developed a protein- and electrolyte chemistry-centric pathway to construct a readily executable decision tree for the detection of non-attenuated protein translocations using conventional electronics. Each optimization encompasses increasing capture rate (C_R), signal-to-noise ratio (SNR), and minimizing irreversible analyte clogging to collect >10⁴ events/pipette spanning a host of electric fields. This was demonstrated using 11 proteins ranging from ∼12 kDa to ∼720 kDa. Moreover, both symmetric and asymmetric electrolyte conditions (cis and trans chamber electrolyte concentration ratios _<^> 1) were explored. As a result, asymmetric electrolyte conditions were favorable on the extreme ends of the size spectrum (i.e., larger, and smaller proteins) and while the remainder of proteins were best sensed under symmetric electrolyte conditions. Under these optimal conditions, only ≲10% of events were attenuated at 500 mV (≲ 5% for most proteins at 500 mV with only ≲1-5% of the population faster than ∼7 μs, which is the theoretical attenuation threshold for 100 kHz bandwidth). Finally, applied voltage (V_app), peak current drop (ΔI_p), electrolyte conductivity (K), and open-pore conductance (G₀) were used to generate a linear relationship to evaluate the molecular weight of the protein (M_w) using plots of (dΔI_p)/(dV_app) vs M_w/(G₀/K).

A Generalized Transformer-Based Pulse Detection Algorithm

Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse recognition. Here, we propose a generalized machine-learning based method, named pulse detection transformer (PETR), for pulse detection. PETR determines the start and end time points of individual pulses, thereby singling out pulse segments in a time-sequential trace. It is objective without needing to specify any threshold. It provides a generalized interface for downstream algorithms for specific application scenarios. PETR is validated using both simulated and experimental nanopore translocation data. It returns a competitive performance in detecting pulses through assessing them with several standard metrics. Finally, the generalization nature of the PETR output is demonstrated using two representative algorithms for feature extraction.

Aerolysin nanopore-based identification of proteinogenic amino acids using a bipolar peptide probe

Nanopore technology has attracted extensive attention due to its rapid, highly sensitive, and label-free performance. In this study, we aimed to identify proteinogenic amino acids using a wild-type aerolysin nanopore. Specifically, bipolar peptide probes were synthesised by linking four aspartic acid residues to the N-terminal and five arginine residues to the C-terminal of individual amino acids. With the help of the bipolar peptide carrier, 9 proteinogenic amino acids were reliably recognised based on current blockade and dwell time using an aerolysin nanopore. Furthermore, by changing the charge of the peptide probe, two of the five unrecognized amino acids above mentioned were identified. These findings promoted the application of aerolysin nanopores in proteinogenic amino acid recognition.

Seeing Is Not Believing: Filtering Effects on Random Nature in Electrochemical Measurements of Single-Entity Collision

To clarify the discrete nature of electrochemistry, single-entity electrochemistry of collision (SEEC) utilizes a confinement space in a nanoscale local electric field at a microscale electrode interface for characterizing single freely diffusing entities. This promising method provides new insights at the single entity level. However, the precise measurement is challenging because of the short residence time and wide current fluctuations caused by the dynamic and stochastic motion of a single entity at the interface of the electrode. Moreover, the enormous noise in the electrochemical system would submerge these weak transient electrochemical signals. To increase the signal-to-noise ratio, the low-pass filter (LPF) is often used but at the cost of lower temporal resolution. Therefore, a deeper understanding of the filtering effects on the electrochemical signal is required in SEEC. Here, we build a random walk model to simulate the dynamic electrochemical oxidation of individual silver nanoparticles (AgNPs) in the local electric field near the electrode. This model considers the effect of the effective potential during the interaction between NP and electrode. Results reveal that the shape of the signal is seriously distorted as the cutoff frequency (f _c) of LPF is set at <20 kHz. Due to the filtering effects, hundreds of subpeaks originating from the dynamic motion of NP are merged in a simple peak, which muddies our "believing" from the "seeing" signals. However, the entire interaction time of single NPs with the electrodes can be acquired at f _c ≥ 10 kHz. Moreover, an integral charge of the signal is conserved at any LPF, which enables quantitative analysis of SEEC. Our understanding of the filtering effect on single AgNPs oxidation is generally applicable to nano-electrochemical techniques (e.g., nanopore electrochemistry and nanopipette sensing) that generate transient current signals.

Unbiased Data Analysis for the Parameterization of Fast Translocation Events through Nanopores

Single-molecule nanopore electrophysiology is an emerging technique for the detection of analytes in aqueous solutions with high sensitivity. These detectors have proven applicable for the enzyme-assisted sequencing of oligonucleotides. There has recently been an increased interest in the use of nanopores for the fingerprinting of peptides and proteins, referred to as single-molecule nanopore spectrometry. However, the analysis of the resulting electrophysiology traces remains complicated due to the fast unassisted translocation of such analytes, usually in the order of micro- to milliseconds, and the small ion current signal produced (in the picoampere range). Here, we present the application of a generalized normal distribution function (gNDF) for the characterization of short-lived ion current signals (blockades). We show that the gNDF can be used to determine if the observed blockades have adequate time to reach their maximum current plateau while also providing a description of each blockade based on the open pore current (I _O), the difference caused by the pore blockade (ΔI _B), the position in time (μ), the standard deviation (σ), and a shape parameter (β), leaving only the noise component. In addition, this method allows the estimation of an ideal range of low-pass filter frequencies that contains maximum information with minimal noise. In summary, we show a parameter-free and generalized method for the analysis of short-lived ion current blockades, which facilitates single-molecule nanopore spectrometry with minimal user bias.

The application of single molecule nanopore sensing for quantitative analysis

Nanopore-based sensors typically work by monitoring transient pulses in conductance via current-time traces as molecules translocate through the nanopore. The unique property of being able to monitor single molecules gives nanopore sensors the potential as quantitative sensors based on the counting of single molecules. This review provides an overview of the concepts and fabrication of nanopore sensors as well as nanopore sensing with a view toward using nanopore sensors for quantitative analysis. We first introduce the classification of nanopores and highlight their applications in molecular identification with some pioneering studies. The review then shifts focus to recent strategies to extend nanopore sensors to devices that can rapidly and accurately quantify the amount of an analyte of interest. Finally, future prospects are provided and briefly discussed. The aim of this review is to aid in understanding recent advances, challenges, and prospects for nanopore sensors for quantitative analysis.

An ultra-low noise amplifier array system for high throughput single entity analysis

Electrochemical measurements at the single entity level provide ultra-sensitive tools for the precise diagnosis and understanding of basic biological and chemical processes. By decoding current signatures, single-entity electrochemistry provides abundant information on charges, sizes, shapes, catalytic performances and compositions. The accuracy of single-entity electrochemistry highly relies on advanced instrumentation to achieve the amperometric resolution at the sub-picoampere level and the temporal resolution at the sub-microsecond level. Currently, it is still a challenge for paralleling amplifiers to allow low-noise and high bandwidth single-entity electrochemical measurements. Herein, we developed a low-noise four-channel electrochemical instrumentation that integrates an Au electrode array with amplifiers in the circuit board. With this amplifier array, we achieved a high bandwidth (>100 kHz) electrochemical measurement. The further practical experiments proved the capability of this amplifier array system in acquiring transient signals from both single-molecule detection with an aerolysin nanopore and single Pt nanoparticle catalysis during the dynamic collision process. Paired with appropriate microfluidic array systems, our instrumentation will enable an extraordinarily high-throughput feature for single-entity sensing.

An advanced optical-electrochemical nanopore measurement system for single-molecule analysis

Nanopore measurement has advanced in single-molecule analysis by providing a transient time and confined space window that only allows one interested molecule to exist. By optimization and integration of the electrical and optical analysis strategies in this transient window, the acquisition of comprehensive information could be achieved to resolve the intrinsic properties and heterogeneity of a single molecule. In this work, we present a roadmap to build a unified optical and electrochemical synchronous measurement platform for the research of a single molecule. We design a low-cost ultralow-current amplifier with low noise and high-bandwidth to measure the ionic current events as a single molecule translocates through a nanopore and combine a multi-functional optical system to implement the acquisition of the fluorescence, scattering spectrum, and photocurrent intensity of single molecule events in a nanopore confined space. Our system is a unified and unique platform for the protein nanopore, the solid-state nanopore, and the glass capillary nanopore, which has advantages in the comprehensive research of nanopore single-molecule techniques.

Monitoring single Au38 nanocluster reactions via electrochemiluminescence

Herein, we report for the first time single Au₃₈ nanocluster reaction events of highly efficient electrochemiluminescence (ECL) with tri-n-propylamine radicals as a reductive co-reactant at the surface of an ultramicroelectrode (UME). The statistical analyses of individual reactions confirm stochastic single ones influenced by the applied potential.

A Guide to Signal Processing Algorithms for Nanopore Sensors

Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network

Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net is evaluated on simulated data sets and further applied to experimental data of DNA and protein translocation. The B-Net results are characterized by small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to 1, an impossibility for threshold-based algorithms. The B-Net presents a generic architecture applicable to pulse-like signals beyond nanopore currents.

Nanopore Data Analysis: Baseline Construction and Abrupt Change-Based Multilevel Fitting

Solid-state nanopore technology delivers single-molecule resolution information, and the quality of the deliverables hinges on the capability of the analysis platform to extract maximum possible events and fit them appropriately. In this work, we present an analysis platform with four baseline fitting methods adaptive to a wide range of nanopore traces (including those with a step or abrupt changes where pre-existing platforms fail) to maximize extractable events (2× improvement in some cases) and multilevel event fitting capability. The baseline fitting methods, in the increasing order of robustness and computational cost, include arithmetic mean, linear fit, Gaussian smoothing, and Gaussian smoothing and regressed mixing. The performance was tested with ultra-stable to vigorously fluctuating current profiles, and the event count increased with increasing fitting robustness prominently for vigorously fluctuating profiles. Turning points of events were clustered using the dbscan method, followed by segmentation into preliminary levels based on abrupt changes in the signal level, which were then iteratively refined to deduce the final levels of the event. Finally, we show the utility of clustering for multilevel DNA data analysis, followed by the assessment of protein translocation profiles.

Rapid and Accurate Data Processing for Silver Nanoparticle Oxidation in Nano-Impact Electrochemistry

In recent years, nano-impact electrochemistry (NIE) has attracted widespread attention as a new electroanalytical approach for the analysis and characterization of single nanoparticles in solution. The accurate analysis of the large volume of the experimental data is of great significance in improving the reliability of this method. Unfortunately, the commonly used data analysis approaches, mainly based on manual processing, are often time-consuming and subjective. Herein, we propose a spike detection algorithm for automatically processing the data from the direct oxidation of sliver nanoparticles (AgNPs) in NIE experiments, including baseline extraction, spike identification and spike area integration. The resulting size distribution of AgNPs is found to agree very well with that from transmission electron microscopy (TEM), showing that the current algorithm is promising for automated analysis of NIE data with high efficiency and accuracy.

Instrumentational implementation for parallelized nanopore electrochemical measurements

Nanopore electrochemistry, as one of the promising tools for single molecule sensing, has proved its capability in DNA sequencing and protein analysis. To achieve a high resolution for obtaining molecular information, the nanopore electrochemical technique not only urgently requires an appropriate nanopore sensing interface with atomic resolution but also requires advanced instrumentation and its related data processing methods. In order to reveal the fundamental biological process and process the point-of-care diagnosis, it is necessary to use a nanopore sensing instrument with a high amperometric and temporal resolution as well as high throughput. The development of the instrumentation requires multi-disciplinary collaboration involving preparing a sensitive nanopore interface, low-noise circuit design, and intelligent data analysis. In this review, we have summarized the recent improvements in the nanopore sensing interface as well as discussed the higher throughput achieved by nanopore arrays and intelligent nanopore data analysis methods. The parallelized nanopore instrumentation could be popularized to all ranges of single-molecule applications.

Study on the Functionalization and Signaling Efficiency of the Hybridization Chain Reaction Using Traditional and Single Molecular Characterizations

As an important enzyme-free amplifier, the hybridization chain reaction (HCR) uses an ssDNA to trigger cycled displacement interactions between substrate hairpins and finally form elongated dsDNA concatamer mixtures. In many cases, to provide a signal probe or advanced function, additional oligonucleotides (named hairpin tails) have to be extended upon classic HCR hairpin substrates, but by doing so the HCR assembly efficiency and signal-to-noise ratio (SNR) may get seriously reduced. In this Article, a rational and general model that may guide the study on HCR functionalization and signaling efficiency is provided. We rationally design a four-hairpin model HCR system (4H-HCR) in which one or more hairpin substrates are appended with additional tails as a signaling probe. After HCR assembly, two adjacent tails are supposedly integrating into a full G-quadruplex structure to provide the evidence or signal for the assembly. A systematic study has been applied to reveal the relationship between the "tail-design" with assembly efficiency and SNR. A clear design rule-set guiding the optimized assembly and signal has been provided for traditional electrophoresis and G-quadruplex-enhanced fluorescence signal. Importantly, solid-state nanopore single molecular detection has been innovatively introduced and recommended as an "antirisk" and "mutual benefit" readout to traditional G-quadruplex signaling. Nanopore detection can provide a clear signal distinguished before and after the HCR reaction, especially when the traditional G-quadruplex-enhanced signal only provides low SNR. The G-quadruplex, in turn, may enhance the nanopore signal amplitude via increasing the diameter of the HCR products.

Snapshotting the transient conformations and tracing the multiple pathways of single peptide folding using a solid-state nanopore

A fundamental question relating to protein folding/unfolding is the time evolution of the folding of a protein into its precisely defined native structure. The proper identification of transition conformations is essential for accurately describing the dynamic protein folding/unfolding pathways. Owing to the rapid transitions and sub-nm conformation differences involved, the acquisition of the transient conformations and dynamics of proteins is difficult due to limited instrumental resolution. Using the electrochemical confinement effect of a solid-state nanopore, we were able to snapshot the transient conformations and trace the multiple transition pathways of a single peptide inside a nanopore. By combining the results with a Markov chain model, this new single-molecule technique is applied to clarify the transition pathways of the β-hairpin peptide, which shows nonequilibrium fluctuations among several blockage current stages. This method enables the high-throughput investigation of transition pathways experimentally to access previously obscure peptide dynamics, which is significant for understanding the folding/unfolding mechanisms and misfolding of peptides or proteins.

A solid-state nanopore based method is described for resolving protein-folding-related problems via snapshotting the folding intermediates and characterizing the kinetics of a single peptide.

A Fourier Transform-Induced Data Process for Label-Free Selective Nanopore Analysis under Sinusoidal Voltage Excitations

Nanopore analysis based on a resistive-pulse technique is an attractive tool for single-molecule detection in different fields, but it suffers a great drawback in selectivity. A common solution to this challenge is to add extra sensing aptamers and labels to analytes by improving the sensitivity of their pulses for distinguishing. Compared to the labeling methods, we alternatively develop and demonstrate a novel data process for label-free nanopore analysis that enables the conversion of resistive current signals to more specific frequency domain phase angle features with the contribution from both sinusoidal voltage excitation and Fourier transform. In particular, we find that the transmural capacitance induced by nanoparticle translocations under a sinusoidal voltage plays an important role in this process, making phase angle features more pronounced. In practical applications, the method is successfully applied to directly distinguish the translocation events through a nanopipette by their unique phase angles for similarly sized SiO₂, Ag, and Au nanoparticles and soft living organisms of HeLa and LoVo and even in a more complicated case of a SiO₂, Ag, and Au nanoparticle mixture.

Low-Noise Nanopore Enables In-Situ and Label-Free Tracking of a Trigger-Induced DNA Molecular Machine at the Single-Molecular Level

Solid-state nanopores have shown special high potential in a label-free molecular assay, structure identification, and target-index at the single-molecular level, even though frustrating electrical baseline noise is still one of the major factors that limit the spatial resolution and signaling reliability of solid-state nanopores, especially in small target detection. Here we develop a significant and easy-operating noise-reduction approach via mixing organic solvents with high dielectric constants into a traditional aqueous electrolyte. The strategy is generally effective for pores made of different materials, such as the most commonly used conical glass (CGN) or SiN_x. While the mechanism should be multisourced, MD simulations suggest the noise reduction may partially arise from the even ionic distribution caused by the addition of higher dielectric species. Among all solvents experimentally tested, the two with the highest dielectric constants, formamide and methylformamide, exhibit the best noise reduction effect for target detection of CGN. The power spectral density at the low-frequency limit is reduced by nearly 3 orders with the addition of 20% formamide. Our work qualifies the reliability of solid-state nanopores into much subtler scales of detection, such as dsDNAs under 100 bp. As a practical example, bare CGN is innovatively employed to perform in-situ tracking of trigger-responsive DNA machine forming oligomers.

Learning Shapelets for Improving Single-Molecule Nanopore Sensing

The nanopore technique employs a nanoscale cavity to electrochemically confine individual molecules, achieving ultrasensitive single-molecule analysis based on evaluating the amplitude and duration of the ionic current. However, each nanopore sensing interface has its own intrinsic sensing ability, which does not always efficiently generate distinctive blockade currents for multiple analytes. Therefore, analytes that differ at only a single site often exhibit similar blockade currents or durations in nanopore experiments, which often produces serious overlap in the resulting statistical graphs. To improve the sensing ability of nanopores, herein we propose a novel shapelet-based machine learning approach to discriminate mixed analytes that exhibit nearly identical blockade current amplitudes and durations. DNA oligomers with a single-nucleotide difference, 5'-AAAA-3' and 5'-GAAA-3', are employed as model analytes that are difficult to identify in aerolysin nanopores at 100 mV. First, a set of the most informative and discriminative segments are learned from the time-series data set of blockade current signals using the learning time-series shapelets (LTS) algorithm. Then, the shapelet-transformed representation of the signals is obtained by calculating the minimum distance between the shapelets and the original signals. A simple logistic classifier is used to identify the two types of DNA oligomers in accordance with the corresponding shapelet-transformed representation. Finally, an evaluation is performed on the validation data set to show that our approach can achieve a high F₁ score of 0.933. In comparison with the conventional statistical methods for the analysis of duration and residual current, the shapelet-transformed representation provides clearly discriminated distributions for multiple analytes. Taking advantage of the robust LTS algorithm, one could anticipate the real-time analysis of nanopore events for the direct identification and quantification of multiple biomolecules in a complex real sample (e.g., serum) without labels and time-consuming mutagenesis.

Selective Single Molecule Nanopore Sensing of microRNA Using PNA Functionalized Magnetic Core-Shell Fe3O4-Au Nanoparticles

Solid-state nanopores have been employed as useful tools for single molecule analysis due to their advantages of easy fabrication and controllable diameter, but selectivity is always a big concern for complicated samples. In this work, functionalized magnetic core-shell Fe₃O₄-Au nanoparticles, which acted as a molecular carrier, were introduced into nanopore electrochemical system for microRNA sensing in complicated samples with high sensitivity, selectivity and signal-to-noise ratio (SNR). This strategy is based on the specific affinity between neutral peptide nucleic acids (PNA)-modified Fe₃O₄-Au nanoparticles and negative miRNA, and the formation of negative Fe₃O₄-Au-PNA-miRNA complex, which can pass through the nanopore by application of a positive potential and eliminate neutral Fe₃O₄-Au-PNA complex. To detect miRNA in complicated samples, a magnet has been used to separate Fe₃O₄-Au-PNA-miRNA complex with good selectivity. We think this is a facile and effective method for the detection of different targets at single molecular level, including nucleic acids, proteins, and other small molecules, which will open up a new approach in the nanopore sensing field.

An investigation of solid-state nanopores on label-free metal-ion signalling via the transition of RNA-cleavage DNAzyme and the hybridization chain reaction

Recent advances have proven solid-state nanopores as a powerful analysis platform that enables label-free and separation-free single-molecule analysis. However, the relatively low resolution still limits its application because many chemicals or targets with small sizes could not be recognized in a label-free condition. In this paper, we provide a possible solution that uses solid-state nanopores for small species signaling via the transition of huge DNA assembly products. DNAzyme responding to metal ions and the hybridization chain reaction (HCR) generating nanopore-detectable dsDNA concatamers are used as the transition model set. By the two-step DNAzyme-HCR transition, Pb2+ that was too tiny to be sensed was successfully recognized by the nanopore. The whole process happened in a completely homogeneous solution without any chemical modification. During condition optimization, we also discussed one possible application challenge that may affect the HCR signal-background distinction. Solid-state nanopores provide a potential solution to this challenge due to its ability to profile product length or even 3D structure information.

Nanopore-Based Strategy for Sensing of Copper(II) Ion and Real-Time Monitoring of a Click Reaction

A straightforward yet efficient, nanopore-based strategy that enables the sensitive detection of copper(II) ion (Cu²⁺) and real-time monitoring of a click reaction is provided. Two single-stranded DNAs (ssDNAs) are designed to act as the preprobes, one being modified with an azide and the other an alkyne. The presence of Cu²⁺ induces the ligation of two ssDNAs via click reaction, leading to the formation of a forked DNA which can quantitatively generate characteristic current signatures when interacts with α-hemolysin (α-HL) nanopore. The assay facilitates a highly selective and sensitive measurement of Cu²⁺ without the need for labels or signal amplification. More importantly, this nanopore platform exhibits excellent performance in real-time monitoring of a copper(I) ion (Cu⁺)-catalyzed click reaction at the single-molecule level, by recording the current signals of the forked DNA generated by click chemistry. The proposed strategy is believed to play an important role in both nanopore sensing and characterization of chemistry reactions, especially coupling reactions.

Fabrication and practical applications of molybdenum disulfide nanopores

Among the different developed solid-state nanopores, nanopores constructed in a monolayer of molybdenum disulfide (MoS₂) stand out as powerful devices for single-molecule analysis or osmotic power generation. Because the ionic current through a nanopore is inversely proportional to the thickness of the pore, ultrathin membranes have the advantage of providing relatively high ionic currents at very small pore sizes. This increases the signal generated during translocation of biomolecules and improves the nanopores' efficiency when used for desalination or reverse electrodialysis applications. The atomic thickness of MoS₂ nanopores approaches the inter-base distance of DNA, creating a potential candidate for DNA sequencing. In terms of geometry, MoS₂ nanopores have a well-defined vertical profile due to their atomic thickness, which eliminates any unwanted effects associated with uneven pore profiles observed in other materials. This protocol details all the necessary procedures for the fabrication of solid-state devices. We discuss different methods for transfer of monolayer MoS₂, different approaches for the creation of nanopores, their applicability in detecting DNA translocations and the analysis of translocation data through open-source programming packages. We present anticipated results through the application of our nanopores in DNA translocations and osmotic power generation. The procedure comprises four parts: fabrication of devices (2-3 d), transfer of MoS₂ and cleaning procedure (24 h), the creation of nanopores within MoS₂ (30 min) and performing DNA translocations (2-3 h). We anticipate that our protocol will enable large-scale manufacturing of single-molecule-analysis devices as well as next-generation DNA sequencing.

Aerolysin, a Powerful Protein Sensor for Fundamental Studies and Development of Upcoming Applications

The nanopore electrical approach is a breakthrough in single molecular level detection of particles as small as ions, and complex as biomolecules. This technique can be used for molecule analysis and characterization as well as for the understanding of confined medium dynamics in chemical or biological reactions. Altogether, the information obtained from these kinds of experiments will allow us to address challenges in a variety of biological fields. The sensing, design, and manufacture of nanopores is crucial to realize these objectives. For some time now, aerolysin, a pore forming toxin, and its mutants have shown high potential in real time analytical chemistry, size discrimination of neutral polymers, oligosaccharides, oligonucleotides and peptides at monomeric resolution, sequence identification, chemical modification on DNA, potential biomarkers detection, and protein folding analysis. This review focuses on the results obtained with aerolysin nanopores on the fields of chemistry, biology, physics, and biotechnology. We discuss and compare as well the results obtained with other protein channel sensors.

Exploration of solid-state nanopores in characterizing reaction mixtures generated from a catalytic DNA assembly circuit

Recent advances have proven that using solid-state nanopores is a promising single molecular technique to enrich the DNA assembly signaling library. Other than using them for distinguishing structures, here we innovatively adapt solid-state nanopores for use in analyzing assembly mixtures, which is usually a tougher task for either traditional characterization techniques or nanopores themselves. A trigger induced DNA step polymerization (SP-CHA), producing three-way-DNA concatemers, is designed as a model. Through counting and integrating the translocation-induced current block when each concatemer passes through a glass conical glass nanopore, we propose an electrophoresis-gel like, but homogeneous, quantitative method that can comprehensively profile the "base-pair distribution" of SP-CHA concatemer mixtures. Due to the higher sensitivity, a number of super long concatemers that were previously difficult to detect via gel electrophoresis are also revealed. These ultra-concatemers, longer than 2 kbp, could provide a much enhanced signal-to-noise ratio for nanopores and are thus believed to be more accurate indicators for the existence of a trigger, which may be of benefit for further applications, such as molecular machines or biosensors.

Nanopore-Based Confined Spaces for Single-Molecular Analysis

The field of nanopore sensing at the single-molecular level is in a "boom" period. Such nanopores, which are either composed of biological materials or are fabricated from solid-state substrates, offer a unique confined space that is compatible with the single-molecular scale. Under the influence of an electrical field, such single-biomolecular interfaces can read single-molecular information and, if appropriately fine-tuned, each molecule plays its individual ionic rhythm to compose a "molecular symphony". Over the past few decades, many research groups have worked on nanopore-based single-molecular sensors for a range of thrilling chemical and clinical applications. Furthermore, for the past decade, we have also focused on nanopore-based sensors. In this Minireview, we summarize the recent developments in fundamental research and applications in this area, along with data algorithms and advances in hardware, which act as infrastructure for the electrochemical analysis.

Integrated Solid-State Nanopore Electrochemistry Array for Sensitive, Specific, and Label-Free Biodetection

Nanopore ionic current measurement is currently a prevailing readout and offers considerable opportunities for bioassays. Extending conventional electrochemistry to nanoscale space, albeit noteworthy, remains challenging. Here, we report a versatile electrochemistry array established on a nanofluidic platform by controllably depositing gold layers on the two outer sides of anodic aluminum oxide (AAO) nanopores, leading to form an electrochemical microdevice capable of performing amperometry in a label-free manner. Electroactive species ferricyanide ions passing through gold-decorated nanopores act as electrochemical indicator to generate electrolytic current signal. The electroactive species flux that dominates current signal response is closely related to the nanopore permeability. Such well-characteristic electrolytic current-species flux correlation lays a premise for quantitative electrochemical analysis. As a proof-of-concept demonstration, we preliminarily verify the analytical utility by detection of nucleic acid and protein at picomolar concentration levels. Universal surface modification and molecule assembly, specific target recognition and reliable signal output in nanopore enable direct electrochemical detection of biomolecules without the need of cumbersome probe labeling and signal amplification.

Group Behavior of Nanoparticles Translocating Multiple Nanopores

Nanopores have been implemented as nanosensors for DNA sequencing, biomolecule inspection, chemical analysis, nanoparticle detection, etc. For high-throughput and parallelized measurement using nanopore arrays, individual addressability has been a crucial technological solution in order to enable scrutiny of signals generated at each and every nanopore. Here, an alternative pathway of employing arrayed nanopores to perform sensor functions is investigated by examining the group behavior of nanoparticles translocating multiple nanopores. As no individual addressability is required, fabrication of nanopore devices along with microfluidic cells and readout circuits can be greatly simplified. Experimentally, arrays of less than 10 pores are shown to be capable of analyzing translocating nanoparticles with a good signal-to-noise margin. According to theoretical predictions, more pores (than 10) per array can perform high-fidelity analysis if the noise level of the measurement system can be better controlled. More pores per array would also allow for faster measurement at lower concentration because of larger capture cross sections for target nanoparticles. By experimentally varying the number of pores, the concentration of nanoparticles, or the applied bias voltage across the nanopores, we have identified the basic characteristics of this multievent process. By characterizing average pore current and associated standard deviation during translocation and by performing physical modeling and extensive numerical simulations, we have shown the possibility of determining the size and concentration of two kinds of translocating nanoparticles over 4 orders of magnitude in concentration. Hence, we have demonstrated the potential and versatility of the multiple-nanopore approach for high-throughput nanoparticle detection.

From current trace to the understanding of confined media

Nanopores constitute devices for the sensing of nano-objects such as ions, polymer chains, proteins or nanoparticles. We describe what information we can extract from the current trace. We consider the entrance of polydisperse chains into the nanopore, which leads to a conductance drop. We describe the detection of these current blockades according to their shape. Finally, we explain how data analysis can be used to enhance our understanding of physical processes in confined media.

Nano-channel of viral DNA packaging motor as single pore to differentiate peptides with single amino acid difference

Detection, differentiation, mapping, and sequencing of proteins are important in proteomics for the assessment of cell development such as protein methylation or phosphorylation as well as the diagnosis of diseases including metabolic disorder, mental illness, immunological ailments, and malignant cancers. Nanopore technology has demonstrated the potential for the sequencing or sensing of DNA, RNA, chemicals, or other macromolecules. Due to the diversity of protein in shape, structure and charge and the composition versatility of 20 amino acids, the sequencing of proteins remains challenging. Herein, we report the application of the channel of bacteriophage T7 DNA packaging motor for the differentiation of an assortment of peptides of a single amino acid difference. Explicit fingerprints or signatures were obtained based on current blockage and dwell time of individual peptide. Data from the clear mapping of small proteins after protease digestion suggests the potential of using T7 motor channel for proteomics including protein sequencing.