Electrophoretic Deformation of Individual Transfer RNA Molecules Reveals Their Identity

Angular-Inertia Regulated Stable and Nanoscale Sensing of Single Molecules Using Nanopore-In-A-Tube

Nanopore is commonly used for high-resolution, label-free sensing, and analysis of single molecules. However, controlling the speed and trajectory of molecular translocation in nanopores remains challenging, hampering sensing accuracy. Here, the study proposes a nanopore-in-a-tube (NIAT) device that enables decoupling of the current signal detection from molecular translocation and provides precise angular inertia-kinetic translocation of single molecules through a nanopore, thus ensuring stable signal readout with high signal-to-noise ratio (SNR). Specifically, the funnel-shaped silicon nanopore, fabricated at a 10-nm resolution, is placed into a centrifugal tube. A light-induced photovoltaic effect is utilized to achieve a counter-balanced state of electrokinetic effects in the nanopore. By controlling the inertial angle and centrifugation speed, the angular inertial force is harnessed effectively for regulating the translocation process with high precision. Consequently, the speed and trajectory of the molecules are able to be adjusted in and around the nanopore, enabling controllable and high SNR current signals. Numerical simulation reveals the decisive role of inertial angle in achieving uniform translocation trajectories and enhancing analyte-nanopore interactions. The performance of the device is validated by discriminating rigid Au nanoparticles with a 1.6-nm size difference and differentiating a 1.3-nm size difference and subtle stiffness variations in flexible polyethylene glycol molecules.

Photothermally Heated Asymmetric Thin Nanopores Suggest the Influence of Temperature on the Intermediate Conformational State of Cytochrome c in an Electric Field

Nanopore sensing is a label-free single-molecule technique that enables the study of the dynamical structural properties of proteins. Here, we detect the translocation of cytochrome c (Cyt c) through an asymmetric thin nanopore with photothermal heating to evaluate the influence of temperature on Cyt c conformation during its translocation in an electric field. Before Cyt c translocates through an asymmetric thin SiNx nanopore, ∼1 ms trapping events occur due to electric field-induced denaturation. These trapping events were corroborated by a control analysis with a transmission electron microscopy-drilled pore and denaturant buffer. Cyt c translocation events exhibited markedly greater broad current blockade when the pores were photothermally heated. Collectively, our molecular dynamics simulation predicted that an increased temperature facilitates denaturation of the α-helical structure of Cyt c, resulting in greater blockade current during Cyt c trapping. Our photothermal heating method can be used to study the influence of temperature on protein conformation at the single-molecule level in a label-free manner.

Unraveling RNA Conformation Dynamics in Mitochondrial Encephalomyopathy, Lactic Acidosis, and Stroke-like Episode Syndrome with Solid-State Nanopores

This study investigates transfer ribonucleic acid (tRNA) conformational dynamics in the context of MELAS (mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes) using solid-state silicon nitride (SiN) nanopore technology. SiN nanopores in thin membranes with specific dimensions exhibit high signal resolution, enabling real-time and single-molecule electronic detection of tRNA conformational changes. We focus on human mitochondrial tRNALeu(UAA) (mt-Leu(UAA)) that decodes Leu codons UUA/UUG (UUR) during protein synthesis on the mt-ribosome. The single A14G substitution in mt-Leu(UAA) is the major cause of MELAS disease. Measurements of current blockades and dwell times reveal distinct conformational dynamics of the wild-type (WT) and the A14G variant of mt-Leu(UAA) in response to the conserved post-transcriptional m1G9 methylation. While the m1G9-modified WT transcript adopts a more stable structure relative to the unmodified transcript, the m1G9-modified MELAS transcript adopts a less stable structure relative to the unmodified transcript. Notably, these differential features were observed at 0.4 M KCl, but not at 3 M KCl, highlighting the importance of experimental settings that are closer to physiological conditions. This work demonstrates the feasibility of the nanopore platform to discern tRNA molecules that differ by a single-nucleotide substitution or by a single methylation event, providing an important step forward to explore changes in the conformational dynamics of other RNA molecules in human diseases.

T-S2Inet: Transformer-based sequence-to-image network for accurate nanopore sequence recognition

MOTIVATION

Nanopore sequencing is a new macromolecular recognition and perception technology that enables high-throughput sequencing of DNA, RNA, even protein molecules. The sequences generated by nanopore sequencing span a large time frame, and the labor and time costs incurred by traditional analysis methods are substantial. Recently, research on nanopore data analysis using machine learning algorithms has gained unceasing momentum, but there is often a significant gap between traditional and deep learning methods in terms of classification results. To analyze nanopore data using deep learning technologies, measures such as sequence completion and sequence transformation can be employed. However, these technologies do not preserve the local features of the sequences. To address this issue, we propose a sequence-to-image (S2I) module that transforms sequences of unequal length into images. Additionally, we propose the Transformer-based T-S2Inet model to capture the important information and improve the classification accuracy.

RESULTS

Quantitative and qualitative analysis shows that the experimental results have an improvement of around 2% in accuracy compared to previous methods. The proposed method is adaptable to other nanopore platforms, such as the Oxford nanopore. It is worth noting that the proposed method not only aims to achieve the most advanced performance, but also provides a general idea for the analysis of nanopore sequences of unequal length.

AVAILABILITY AND IMPLEMENTATION

The main program is available at https://github.com/guanxiaoyu11/S2Inet.

Quantitative analysis of tRNA abundance and modifications by nanopore RNA sequencing

Transfer RNAs (tRNAs) play a central role in protein translation. Studying them has been difficult in part because a simple method to simultaneously quantify their abundance and chemical modifications is lacking. Here we introduce Nano-tRNAseq, a nanopore-based approach to sequence native tRNA populations that provides quantitative estimates of both tRNA abundances and modification dynamics in a single experiment. We show that default nanopore sequencing settings discard the vast majority of tRNA reads, leading to poor sequencing yields and biased representations of tRNA abundances based on their transcript length. Re-processing of raw nanopore current intensity signals leads to a 12-fold increase in the number of recovered tRNA reads and enables recapitulation of accurate tRNA abundances. We then apply Nano-tRNAseq to Saccharomyces cerevisiae tRNA populations, revealing crosstalks and interdependencies between different tRNA modification types within the same molecule and changes in tRNA populations in response to oxidative stress.

Post-Transcriptional Methylation of Mitochondrial-tRNA Differentially Contributes to Mitochondrial Pathology

Human mitochondrial tRNAs (mt-tRNAs), critical for mitochondrial biogenesis, are frequently associated with pathogenic mutations. These mt-tRNAs have unusual sequence motifs and require post-transcriptional modifications to stabilize their fragile structures. However, whether a modification that stabilizes a wild-type (WT) mt-tRNA structure would also stabilize its pathogenic variants is unknown. Here we show that the N 1 -methylation of guanosine at position 9 (m 1 G9) of mt-Leu(UAA), while stabilizing the WT tRNA, has an opposite and destabilizing effect on variants associated with MELAS (mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes). This differential effect is further demonstrated by the observation that demethylation of m 1 G9, while damaging to the WT tRNA, is beneficial to the major pathogenic variant, improving its structure and activity. These results have new therapeutic implications, suggesting that the N 1 -methylation of mt-tRNAs at position 9 is a determinant of pathogenicity and that controlling the methylation level is an important modulator of mt-tRNA-associated diseases.

Solid-State Nanopore Array: Manufacturing and Applications

Nanopore brings extraordinary properties for a variety of potential applications in various industrial sectors. Since manufacturing of solid-state nanopore is first reported in 2001, solid-state nanopore has become a hot topic in the recent years. An increasing number of manufacturing methods have been reported, with continuously decreased sizes from hundreds of nanometers at the beginning to ≈1 nm until recently. To enable more robust, sensitive, and reliable devices required by the industry, researchers have started to explore the possible methods to manufacture nanopore array which presents unprecedented challenges on the fabrication efficiency, accuracy and repeatability, applicable materials, and cost. As a result, the exploration of fabrication of nanopore array is still in the fledging period with various bottlenecks. In this article, a wide range of methods of manufacturing nanopores are summarized along with their achievable morphologies, sizes, inner structures for characterizing the main features, based on which the manufacturing of nanopore array is further addressed. To give a more specific idea on the potential applications of nanopore array, some representative practices are introduced such as DNA/RNA sequencing, energy conversion and storage, water desalination, nanosensors, nanoreactors, and dialysis.

Probing RNA Conformations Using a Polymer-Electrolyte Solid-State Nanopore

Nanopore systems have emerged as a leading platform for the analysis of biomolecular complexes with single-molecule resolution. The conformation of biomolecules, such as RNA, is highly dependent on the electrolyte composition, but solid-state nanopore systems often require high salt concentration to operate, precluding analysis of macromolecular conformations under physiologically relevant conditions. Here, we report the implementation of a polymer-electrolyte solid-state nanopore system based on alkali metal halide salts dissolved in 50% w/v poly(ethylene) glycol (PEG) to augment the performance of our system. We show that polymer-electrolyte bath governs the translocation dynamics of the analyte which correlates with the physical properties of the salt used in the bath. This allowed us to identify CsBr as the optimal salt to complement PEG to generate the largest signal enhancement. Harnessing the effects of the polymer-electrolyte, we probed the conformations of the Chikungunya virus (CHIKV) RNA genome fragments under physiologically relevant conditions. Our system was able to fingerprint CHIKV RNA fragments ranging from ∼300 to ∼2000 nt length and subsequently distinguish conformations between the co-transcriptionally folded and the natively refolded ∼2000 nt CHIKV RNA. We envision that the polymer-electrolyte solid-state nanopore system will further enable structural and conformational analyses of individual biomolecules under physiologically relevant conditions.

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

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

Active learning for efficient analysis of high-throughput nanopore data

MOTIVATION

As the third-generation sequencing technology, nanopore sequencing has been used for high-throughput sequencing of DNA, RNA, and even proteins. Recently, many studies have begun to use machine learning technology to analyze the enormous data generated by nanopores. Unfortunately, the success of this technology is due to the extensive labeled data, which often suffer from enormous labor costs. Therefore, there is an urgent need for a novel technology that can not only rapidly analyze nanopore data with high-throughput, but also significantly reduce the cost of labeling. To achieve the above goals, we introduce active learning to alleviate the enormous labor costs by selecting the samples that need to be labeled. This work applies several advanced active learning technologies to the nanopore data, including the RNA classification dataset (RNA-CD) and the Oxford Nanopore Technologies barcode dataset (ONT-BD). Due to the complexity of the nanopore data (with noise sequence), the bias constraint is introduced to improve the sample selection strategy in active learning. Results: The experimental results show that for the same performance metric, 50% labeling amount can achieve the best baseline performance for ONT-BD, while only 15% labeling amount can achieve the best baseline performance for RNA-CD. Crucially, the experiments show that active learning technology can assist experts in labeling samples, and significantly reduce the labeling cost. Active learning can greatly reduce the dilemma of difficult labeling of high-capacity nanopore data. We hope active learning can be applied to other problems in nanopore sequence analysis.

AVAILABILITY AND IMPLEMENTATION

The main program is available at https://github.com/guanxiaoyu11/AL-for-nanopore.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Oligonucleotide Discrimination Enabled by Tannic Acid-Coordinated Film-Coated Solid-State Nanopores

Discrimination of nucleotides serves as the basis for DNA sequencing using solid-state nanopores. However, the translocation of DNA is usually too fast to be detected, not to mention nucleotide discrimination. Here, we utilized polyphenolic TA and Fe³⁺, an attractive metal-organic thin film, and achieved a fast and robust surface coating for silicon nitride nanopores. The hydrophilic coating layer can greatly reduce the low-frequency noise of an original unstable nanopore, and the nanopore size can be finely tuned in situ at the nanoscale by simply adjusting the relative ratio of Fe³⁺ and TA monomers. Moreover, the hydrogen bonding interaction formed between the hydroxyl groups provided by TA and the phosphate groups of DNAs significantly increases the residence time of a short double-strand (100 bp) DNA. More importantly, we take advantage of the different strengths of hydrogen bonding interactions between the hydroxyl groups provided by TA and the analytes to discriminate between two oligonucleotide samples (oligodeoxycytidine and oligodeoxyadenosine) with similar sizes and lengths, of which the current signal patterns are significantly different using the coated nanopore. The results shed light on expanding the biochemical functionality of surface coatings on solid-state nanopores for future biomedical applications.

Fabrication of solid-state nanopores

Nanopores are valuable single-molecule sensing tools that have been widely applied to the detection of DNA, RNA, proteins, viruses, glycans, etc. The prominent sensing platform is helping to improve our health-related quality of life and accelerate the rapid realization of precision medicine. Solid-state nanopores have made rapid progress in the past decades due to their flexible size, structure and compatibility with semiconductor fabrication processes. With the development of semiconductor fabrication techniques, materials science and surface chemistry, nanopore preparation and modification technologies have made great breakthroughs. To date, various solid-state nanopore materials, processing technologies, and modification methods are available to us. In the review, we outline the recent advances in nanopores fabrication and analyze the virtues and limitations of various membrane materials and nanopores drilling techniques.

S2Snet: deep learning for low molecular weight RNA identification with nanopore

Ribonucleic acid (RNA) is a pivotal nucleic acid that plays a crucial role in regulating many biological activities. Recently, one study utilized a machine learning algorithm to automatically classify RNA structural events generated by a Mycobacterium smegmatis porin A nanopore trap. Although it can achieve desirable classification results, compared with deep learning (DL) methods, this classic machine learning requires domain knowledge to manually extract features, which is sophisticated, labor-intensive and time-consuming. Meanwhile, the generated original RNA structural events are not strictly equal in length, which is incompatible with the input requirements of DL models. To alleviate this issue, we propose a sequence-to-sequence (S2S) module that transforms the unequal length sequence (UELS) to the equal length sequence. Furthermore, to automatically extract features from the RNA structural events, we propose a sequence-to-sequence neural network based on DL. In addition, we add an attention mechanism to capture vital information for classification, such as dwell time and blockage amplitude. Through quantitative and qualitative analysis, the experimental results have achieved about a 2% performance increase (accuracy) compared to the previous method. The proposed method can also be applied to other nanopore platforms, such as the famous Oxford nanopore. It is worth noting that the proposed method is not only aimed at pursuing state-of-the-art performance but also provides an overall idea to process nanopore data with UELS.

Promising Assays for Examining a Putative Role of Ribosomal Heterogeneity in COVID-19 Susceptibility and Severity

The heterogeneity of ribosomes, characterized by structural variations, arises from differences in types, numbers, and/or post-translational modifications of participating ribosomal proteins (RPs), ribosomal RNAs (rRNAs) sequence variants plus post-transcriptional modifications, and additional molecules essential for forming a translational machinery. The ribosomal heterogeneity within an individual organism or a single cell leads to preferential translations of selected messenger RNA (mRNA) transcripts over others, especially in response to environmental cues. The role of ribosomal heterogeneity in SARS-CoV-2 coronavirus infection, propagation, related symptoms, or vaccine responses is not known, and a technique to examine these has not yet been developed. Tools to detect ribosomal heterogeneity or to profile translating mRNAs independently cannot identify unique or specialized ribosome(s) along with corresponding mRNA substrate(s). Concurrent characterizations of RPs and/or rRNAs with mRNA substrate from a single ribosome would be critical to decipher the putative role of ribosomal heterogeneity in the COVID-19 disease, caused by the SARS-CoV-2, which hijacks the host ribosome to preferentially translate its RNA genome. Such a protocol should be able to provide a high-throughput screening of clinical samples in a large population that would reach a statistical power for determining the impact of a specialized ribosome to specific characteristics of the disease. These characteristics may include host susceptibility, viral infectivity and transmissibility, severity of symptoms, antiviral treatment responses, and vaccine immunogenicity including its side effect and efficacy. In this study, several state-of-the-art techniques, in particular, chemical probing of ribosomal components or rRNA structures, proximity ligation to generate rRNA-mRNA chimeras for sequencing, nanopore gating of individual ribosomes, nanopore RNA sequencing and/or structural analyses, single-ribosome mass spectrometry, and microfluidic droplets for separating ribosomes or indexing rRNAs/mRNAs, are discussed. The key elements for further improvement and proper integration of the above techniques to potentially arrive at a high-throughput protocol for examining individual ribosomes and their mRNA substrates in a clinical setting are also presented.

Machine Learning Assisted Simultaneous Structural Profiling of Differently Charged Proteins in a Mycobacterium smegmatis Porin A (MspA) Electroosmotic Trap

The nanopore is emerging as a means of single-molecule protein sensing. However, proteins demonstrate different charge properties, which complicates the design of a sensor that can achieve simultaneous sensing of differently charged proteins. In this work, we introduce an asymmetric electrolyte buffer combined with the Mycobacterium smegmatis porin A (MspA) nanopore to form an electroosmotic flow (EOF) trap. Apo- and holo-myoglobin, which differ in only a single heme, can be fully distinguished by this method. Direct discrimination of lysozyme, apo/holo-myoglobin, and the ACTR/NCBD protein complex, which are basic, neutral, and acidic proteins, respectively, was simultaneously achieved by the MspA EOF trap. To automate event classification, multiple event features were extracted to build a machine learning model, with which a 99.9% accuracy is achieved. The demonstrated method was also applied to identify single molecules of α-lactalbumin and β-lactoglobulin directly from whey protein powder. This protein-sensing strategy is useful in direct recognition of a protein from a mixture, suggesting its prospective use in rapid and sensitive detection of biomarkers or real-time protein structural analysis.

Nanopores in Graphene and Other 2D Materials: A Decade's Journey toward Sequencing

Nanopore techniques offer a low-cost, label-free, and high-throughput platform that could be used in single-molecule biosensing and in particular DNA sequencing. Since 2010, graphene and other two-dimensional (2D) materials have attracted considerable attention as membranes for producing nanopore devices, owing to their subnanometer thickness that can in theory provide the highest possible spatial resolution of detection. Moreover, 2D materials can be electrically conductive, which potentially enables alternative measurement schemes relying on the transverse current across the membrane material itself and thereby extends the technical capability of traditional ionic current-based nanopore devices. In this review, we discuss key advances in experimental and computational research into DNA sensing with nanopores built from 2D materials, focusing on both the ionic current and transverse current measurement schemes. Challenges associated with the development of 2D material nanopores toward DNA sequencing are further analyzed, concentrating on lowering the noise levels, slowing down DNA translocation, and inhibiting DNA fluctuations inside the pores. Finally, we overview future directions of research that may expedite the emergence of proof-of-concept DNA sequencing with 2D material nanopores.

Direct Nanopore Sequencing of Individual Full Length tRNA Strands

We describe a method for direct tRNA sequencing using the Oxford Nanopore MinION. The principal technical advance is custom adapters that facilitate end-to-end sequencing of individual transfer RNA (tRNA) molecules at subnanometer precision. A second advance is a nanopore sequencing pipeline optimized for tRNA. We tested this method using purified E. coli tRNA^fMet, tRNA^Lys, and tRNA^Phe samples. 76-92% of individual aligned tRNA sequence reads were full length. As a proof of concept, we showed that nanopore sequencing detected all 43 expected isoacceptors in total E. coli MRE600 tRNA as well as isodecoders that further define that tRNA population. Alignment-based comparisons between the three purified tRNAs and their synthetic controls revealed systematic nucleotide miscalls that were diagnostic of known modifications. Systematic miscalls were also observed proximal to known modifications in total E. coli tRNA alignments, including a highly conserved pseudouridine in the T loop. This work highlights the potential of nanopore direct tRNA sequencing as well as improvements needed to implement tRNA sequencing for human healthcare applications.

Direct Nanopore Sequencing of Individual Full Length tRNA Strands

We describe a method for direct tRNA sequencing using the Oxford Nanopore MinION. The principal technical advance is custom adapters that facilitate end-to-end sequencing of individual transfer RNA (tRNA) molecules at subnanometer precision. A second advance is a nanopore sequencing pipeline optimized for tRNA. We tested this method using purified E. coli tRNA^fMet, tRNA^Lys, and tRNA^Phe samples. 76-92% of individual aligned tRNA sequence reads were full length. As a proof of concept, we showed that nanopore sequencing detected all 43 expected isoacceptors in total E. coli MRE600 tRNA as well as isodecoders that further define that tRNA population. Alignment-based comparisons between the three purified tRNAs and their synthetic controls revealed systematic nucleotide miscalls that were diagnostic of known modifications. Systematic miscalls were also observed proximal to known modifications in total E. coli tRNA alignments, including a highly conserved pseudouridine in the T loop. This work highlights the potential of nanopore direct tRNA sequencing as well as improvements needed to implement tRNA sequencing for human healthcare applications.

Combining machine learning and nanopore construction creates an artificial intelligence nanopore for coronavirus detection

High-throughput, high-accuracy detection of emerging viruses allows for the control of disease outbreaks. Currently, reverse transcription-polymerase chain reaction (RT-PCR) is currently the most-widely used technology to diagnose the presence of SARS-CoV-2. However, RT-PCR requires the extraction of viral RNA from clinical specimens to obtain high sensitivity. Here, we report a method for detecting novel coronaviruses with high sensitivity by using nanopores together with artificial intelligence, a relatively simple procedure that does not require RNA extraction. Our final platform, which we call the artificially intelligent nanopore, consists of machine learning software on a server, a portable high-speed and high-precision current measuring instrument, and scalable, cost-effective semiconducting nanopore modules. We show that artificially intelligent nanopores are successful in accurately identifying four types of coronaviruses similar in size, HCoV-229E, SARS-CoV, MERS-CoV, and SARS-CoV-2. Detection of SARS-CoV-2 in saliva specimen is achieved with a sensitivity of 90% and specificity of 96% with a 5-minute measurement.

Structural-profiling of low molecular weight RNAs by nanopore trapping/translocation using Mycobacterium smegmatis porin A

Folding of RNA can produce elaborate tertiary structures, corresponding to their diverse roles in the regulation of biological activities. Direct observation of RNA structures at high resolution in their native form however remains a challenge. The large vestibule and the narrow constriction of a Mycobacterium smegmatis porin A (MspA) suggests a sensing mode called nanopore trapping/translocation, which clearly distinguishes between microRNA, small interfering RNA (siRNA), transfer RNA (tRNA) and 5 S ribosomal RNA (rRNA). To further profit from the acquired event characteristics, a custom machine learning algorithm is developed. Events from measurements with a mixture of RNA analytes can be automatically classified, reporting a general accuracy of ~93.4%. tRNAs, which possess a unique tertiary structure, report a highly distinguishable sensing feature, different from all other RNA types tested in this study. With this strategy, tRNAs from different sources are measured and a high structural conservation across different species is observed in single molecule.

On Stochastic Reduction in Laser-Assisted Dielectric Breakdown for Programmable Nanopore Fabrication

The controlled dielectric breakdown emerged as a promising alternative toward accessible solid-state nanopore fabrication. Several prior studies have shown that laser-assisted dielectric breakdown could help control the nanopore position and reduce the possibility of forming multiple pores. Here, we developed a physical model to estimate the probability of forming a single nanopore under different combinations of the laser power and the electric field. This model relies on the material- and experiment-specific parameters: the Weibull statistical parameters and the laser-induced photothermal etching rate. Both the model and our experimental data suggest that a combination of a high laser power and a low electric field is statistically favorable for forming a single nanopore at a programmed location. While this model relies on experiment-specific parameters, we anticipate it could provide the experimental insights for nanopore fabrication by the laser-assisted dielectric breakdown method, enabling broader access to solid-state nanopores and their sensing applications.

Ribosome Fingerprinting with a Solid-State Nanopore

Nanopores hold great potential for the analysis of complex biological molecules at the single-entity level. One particularly interesting macromolecular machine is the ribosome, responsible for translating mRNAs into proteins. In this study, we use a solid-state nanopore to fingerprint 80S ribosomes and polysomes from a human neuronal cell line andDrosophila melanogaster cultured cells and ovaries. Specifically, we show that the peak amplitude and dwell time characteristics of 80S ribosomes are distinct from polysomes and can be used to discriminate ribosomes from polysomes in mixed samples. Moreover, we are able to distinguish large polysomes, containing more than seven ribosomes, from those containing two to three ribosomes, and demonstrate a correlation between polysome size and peak amplitude. This study highlights the application of solid-state nanopores as a rapid analytical tool for the detection and characterization of ribosomal complexes.

Deformation-Mediated Translocation of DNA Origami Nanoplates through a Narrow Solid-State Nanopore

With the development in DNA self-assembly technology, DNA origami nanostructures have been widely applied in biomedical research. Solid-state nanopores represent an emerging single-molecule sensing platform for studying nanostructures with arbitrary dimensions and physical characteristics, including DNA origami. Here, we employed relatively narrow silicon nitride nanopores to detect the deformation and translocation of DNA origami nanoplates with dimensions of approximately 60 × 54 nm. We performed translocation experiments using three nanopore diameters that are all smaller than the plat dimensions. Analysis of current blockade signals and the representative events reveals three types of translocation orientations for the nanoplates. Furthermore, by studying the electrical signal characteristics (current change and dwell time) for the different diameter pores, we obtained information about the translocation behaviors for the DNA nanoplates through different constrictions. Our investigation provides an approach to analyze the deformation and translocation of DNA origami structures.

How Nanopore Translocation Experiments Can Measure RNA Unfolding

Electrokinetic translocation of biomolecules through solid-state nanopores represents a label-free single-molecule technique that may be used to measure biomolecular structure and dynamics. Recent investigations have attempted to distinguish individual transfer RNA (tRNA) species based on the associated pore translocation times, ion-current noise, and blockage currents. By manufacturing sufficiently smaller pores, each tRNA is required to undergo a deformation to translocate. Accordingly, differences in nanopore translocation times and distributions may be used to infer the mechanical properties of individual tRNA molecules. To bridge our understanding of tRNA structural dynamics and nanopore measurements, we apply molecular dynamics simulations using a simplified "structure-based" energetic model. Calculating the free-energy landscape for distinct tRNA species implicates transient unfolding of the terminal RNA helix during nanopore translocation. This provides a structural and energetic framework for interpreting current experiments, which can aid the design of methods for identifying macromolecules using nanopores.

High fidelity moving Z-score based controlled breakdown fabrication of solid-state nanopore

We investigate the current transport characteristics in the electrolyte-dielectric-electrolyte structure commonly used in the in situ controlled breakdown (CBD) fabrication of solid-state nanopores. It is found that the stochastic breakdown process could lead to fidelity issues of false positives (an incorrect indication of a true nanopore formation) and false negatives (inability to detect initial nanopore formation). Robust and deterministic detection of initial physical breakdown to alleviate false positives and false negatives is critical for precise nanopore size control. To this end, we report a high fidelity moving Z-score method based CBD fabrication of solid-state nanopore. We demonstrate 100% success rate of realizing the initial nanopore conductance of 3 ± 1 nS (corresponds to size of 1.7 ± 0.6 nm) regardless of the dielectric membrane characteristics. Our study also elucidates the Joule heating is the dominant mechanism for electric field-based nanopore enlargement. Single DNA molecule sensing using nanopores fabricated by this method was successfully demonstrated. We anticipate the moving Z-score based CBD method could enable broader access to the solid state nanopore-based single molecule analysis.

Selective detections of single-viruses using solid-state nanopores

Rapid diagnosis of flu before symptom onsets can revolutionize our health through diminishing a risk for serious complication as well as preventing infectious disease outbreak. Sensor sensitivity and selectivity are key to accomplish this goal as the number of virus is quite small at the early stage of infection. Here we report on label-free electrical diagnostics of influenza based on nanopore analytics that distinguishes individual virions by their distinct physical features. We accomplish selective resistive-pulse sensing of single flu virus having negative surface charges in a physiological media by exploiting electroosmotic flow to filter contaminants at the Si₃N₄ pore orifice. We demonstrate identifications of allotypes with 68% accuracy at the single-virus level via pattern classifications of the ionic current signatures. We also show that this discriminability becomes >95% under a binomial distribution theorem by ensembling the pulse data of >20 virions. This simple mechanism is versatile for point-of-care tests of a wide range of flu types.

Nanopore-Based, Rapid Characterization of Individual Amyloid Particles in Solution: Concepts, Challenges, and Prospects

Aggregates of misfolded proteins are associated with several devastating neurodegenerative diseases. These so-called amyloids are therefore explored as biomarkers for the diagnosis of dementia and other disorders, as well as for monitoring disease progression and assessment of the efficacy of therapeutic interventions. Quantification and characterization of amyloids as biomarkers is particularly demanding because the same amyloid-forming protein can exist in different states of assembly, ranging from nanometer-sized monomers to micrometer-long fibrils that interchange dynamically both in vivo and in samples from body fluids ex vivo. Soluble oligomeric amyloid aggregates, in particular, are associated with neurotoxic effects, and their molecular organization, size, and shape appear to determine their toxicity. This concept article proposes that the emerging field of nanopore-based analytics on a single molecule and single aggregate level holds the potential to account for the heterogeneity of amyloid samples and to characterize these particles-rapidly, label-free, and in aqueous solution-with regard to their size, shape, and abundance. The article describes the concept of nanopore-based resistive pulse sensing, reviews previous work in amyloid analysis, and discusses limitations and challenges that will need to be overcome to realize the full potential of amyloid characterization on a single-particle level.

Cucurbituril mediated single molecule detection and identification via recognition tunneling

Recognition tunneling (RT) is an emerging technique for investigating single molecules in a tunnel junction. We have previously demonstrated its capability of single molecule detection and identification, as well as probing the dynamics of intermolecular bonding at the single molecule level. Here by introducing cucurbituril as a new class of recognition molecule, we demonstrate a powerful platform for electronically investigating the host-guest chemistry at single molecule level. In this report, we first investigated the single molecule electrical properties of cucurbituril in a tunnel junction. Then we studied two model guest molecules, aminoferrocene and amantadine, which were encapsulated by cucurbituril. Small differences in conductance and lifetime can be recognized between the host-guest complexes with the inclusion of different guest molecules. By using a machine learning algorithm to classify the RT signals in a hyper dimensional space, the accuracy of guest molecule recognition can be significantly improved, suggesting the possibility of using cucurbituril molecule for single molecule identification. This work enables a new class of recognition molecule for RT technique and opens the door for detecting a vast variety of small molecules by electrical measurements.

Reduction of DNA Folding by Ionic Liquids and Its Effects on the Analysis of DNA-Protein Interaction Using Solid-State Nanopore

DNA folding is not desirable for solid-state nanopore techniques when analyzing the interaction of a biomolecule with its specific binding sites on DNA since the signal derived from the binding site could be buried by a large signal from the folding of DNA nearby. To resolve the problems associated with DNA folding, ionic liquids (ILs), which are known to interact with DNA through charge-charge and hydrophobic interactions are employed. 1-n-butyl-3-methylimidazolium chloride (C4 mim) is found to be the most effective in lowering the incident of DNA folding during its translocation through solid-state nanopores (4-5 nm diameter). The rate of folding signals from the translocation of DNA-C4 mim is decreased by half in comparison to that from the control bare DNA. The conformational changes of DNA upon complexation with C4 mim are further examined using atomic force microscopy, showing that the entanglement of DNA which is common in bare DNA is not observed when treated with C4 mim. The stretching effect of C4 mim on DNA strands improves the detection accuracy of nanopore for identifying the location of zinc finger protein bound to its specific binding site in DNA by lowering the incident of DNA folding.

QuipuNet: Convolutional Neural Network for Single-Molecule Nanopore Sensing

Nanopore sensing is a versatile technique for the analysis of molecules on the single-molecule level. However, extracting information from data with established algorithms usually requires time-consuming checks by an experienced researcher due to inherent variability of solid-state nanopores. Here, we develop a convolutional neural network (CNN) for the fully automated extraction of information from the time-series signals obtained by nanopore sensors. In our demonstration, we use a previously published data set on multiplexed single-molecule protein sensing. The neural network learns to classify translocation events with greater accuracy than previously possible, while also increasing the number of analyzable events by a factor of 5. Our results demonstrate that deep learning can achieve significant improvements in single molecule nanopore detection with potential applications in rapid diagnostics.

Differential Enzyme Flexibility Probed Using Solid-State Nanopores

Enzymes and motor proteins are dynamic macromolecules that coexist in a number of conformations of similar energies. Protein function is usually accompanied by a change in structure and flexibility, often induced upon binding to ligands. However, while measuring protein flexibility changes between active and resting states is of therapeutic significance, it remains a challenge. Recently, our group has demonstrated that breadth of signal amplitudes in measured electrical signatures as an ensemble of individual protein molecules is driven through solid-state nanopores and correlates with protein conformational dynamics. Here, we extend our study to resolve subtle flexibility variation in dihydrofolate reductase mutants from unlabeled single molecules in solution. We first demonstrate using a canonical protein system, adenylate kinase, that both size and flexibility changes can be observed upon binding to a substrate that locks the protein in a closed conformation. Next, we investigate the influence of voltage bias and pore geometry on the measured electrical pulse statistics during protein transport. Finally, using the optimal experimental conditions, we systematically study a series of wild-type and mutant dihydrofolate reductase proteins, finding a good correlation between nanopore-measured protein conformational dynamics and equilibrium bulk fluorescence probe measurements. Our results unequivocally demonstrate that nanopore-based measurements reliably probe conformational diversity in native protein ensembles.

Optimal voltage for nanoparticle detection with thin nanopores

Optimal voltages were found for particle detections, at which the current blockade ratio did not depend on surface charge density.

Discriminating single-bacterial shape using low-aspect-ratio pores

Conventional concepts of resistive pulse analysis is to discriminate particles in liquid by the difference in their size through comparing the amount of ionic current blockage. In sharp contrast, we herein report a proof-of-concept demonstration of the shape sensing capability of solid-state pore sensors by leveraging the synergy between nanopore technology and machine learning. We found ionic current spikes of similar patterns for two bacteria reflecting the closely resembled morphology and size in an ultra-low thickness-to-diameter aspect-ratio pore. We examined the feasibility of a machine learning strategy to pattern-analyse the sub-nanoampere corrugations in each ionic current waveform and identify characteristic electrical signatures signifying nanoscopic differences in the microbial shape, thereby demonstrating discrimination of single-bacterial cells with accuracy up to 90%. This data-analytics-driven microporescopy capability opens new applications of resistive pulse analyses for screening viruses and bacteria by their unique morphologies at a single-particle level.

Label-Free Single-Molecule Thermoscopy Using a Laser-Heated Nanopore

When light is used to excite electronic transitions in a material, nonradiative energy during relaxation is often released in the form of heat. In this work, we show that photoexcitation of a silicon nitride nanopore using a focused visible laser results in efficient localized photothermal heating, which reduces the nearby electrolyte viscosity and increases the ionic conductance. In addition, a strong localized thermal gradient in the pore vicinity is produced, evidenced by finite-element simulations and experimental observation of both ion and DNA thermophoresis. After correcting for thermophoresis, the nanopore current can be used as a nanoscale thermometer, enabling rapid force thermoscopy. We utilize this to probe thermal melting transitions in synthetic and native biomolecules that are heated at the nanopore. Our results on single molecules are validated by correspondence to bulk measurements, which paves the way to various biophysical experiments that require rapid temperature and force control on individual molecules.

The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community

Nanopore DNA strand sequencing has emerged as a competitive, portable technology. Reads exceeding 150 kilobases have been achieved, as have in-field detection and analysis of clinical pathogens. We summarize key technical features of the Oxford Nanopore MinION, the dominant platform currently available. We then discuss pioneering applications executed by the genomics community.

The Oxford Nanopore MinION: delivery of nanopore sequencing to the genomics community

Nanopore DNA strand sequencing has emerged as a competitive, portable technology. Reads exceeding 150 kilobases have been achieved, as have in-field detection and analysis of clinical pathogens. We summarize key technical features of the Oxford Nanopore MinION, the dominant platform currently available. We then discuss pioneering applications executed by the genomics community.

An accelerated framework for the classification of biological targets from solid-state micropore data

Micro- and nanoscale systems have provided means to detect biological targets, such as DNA, proteins, and human cells, at ultrahigh sensitivity. However, these devices suffer from noise in the raw data, which continues to be significant as newer and devices that are more sensitive produce an increasing amount of data that needs to be analyzed. An important dimension that is often discounted in these systems is the ability to quickly process the measured data for an instant feedback. Realizing and developing algorithms for the accurate detection and classification of biological targets in realtime is vital. Toward this end, we describe a supervised machine-learning approach that records single cell events (pulses), computes useful pulse features, and classifies the future patterns into their respective types, such as cancerous/non-cancerous cells based on the training data. The approach detects cells with an accuracy of 70% from the raw data followed by an accurate classification when larger training sets are employed. The parallel implementation of the algorithm on graphics processing unit (GPU) demonstrates a speedup of three to four folds as compared to a serial implementation on an Intel Core i7 processor. This incredibly efficient GPU system is an effort to streamline the analysis of pulse data in an academic setting. This paper presents for the first time ever, a non-commercial technique using a GPU system for realtime analysis, paired with biological cluster targeting analysis.