Solid-State Nanopore Platform Integrated with Machine Learning for Digital Diagnosis of Virus Infection

Nanopore-based sensors for DNA sequencing: a review

Nanopore sensors, owing to their distinctive structural properties, can be used to detect biomolecular translocation events. These sensors operate by monitoring variations in electric current amplitude and duration, thereby enabling the calibration and distinction of various biomolecules. As a result, nanopores emerge as a potentially powerful tool in the field of deoxyribonucleic acid (DNA) sequencing. However, the interplay between testing bandwidth and noise often leads to the loss of part of the critical translocation signals, presenting a substantial challenge for the precise measurement of biomolecules. In this context, innovative detection mechanisms have been developed, including optical detection, tunneling current detection, and nanopore field-effect transistor (FET) detection. These novel detection methods are based on but beyond traditional nanopore techniques and each of them has unique advantages. Notably, nanopore FET sensors stand out for their high signal-to-noise ratio (SNR) and high bandwidth measurement capabilities, overcoming the limitations typically associated with traditional solid-state nanopore (SSN) technologies and thus paving the way for new avenues to biomolecule detection. This review begins by elucidating the fundamental detection principles, development history, applications, and fabrication methods for traditional SSNs. It then introduces three novel detection mechanisms, with a particular emphasis on nanopore FET detection. Finally, a comprehensive analysis of the advantages and challenges associated with both SSNs and nanopore FET sensors is performed, and then insights into the future development trajectories for nanopore FET sensors in DNA sequencing are provided. This review has two main purposes: firstly, to provide researchers with a preliminary understanding of advancements in the nanopore field, and secondly, to offer a comprehensive analysis of the fabrication techniques, transverse current detection principles, challenges, and future development trends in the field of nanopore FET sensors. This comprehensive analysis aims to help give researchers in-depth insights into cutting-edge advancements in the field of nanopore FET sensors.

Characterizing Prion-Like Protein Aggregation: Emerging Nanopore-Based Approaches

Prion-like protein aggregation is characteristic of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. This process involves the formation of aggregates ranging from small and potentially neurotoxic oligomers to highly structured self-propagating amyloid fibrils. Various approaches are used to study protein aggregation, but they do not always provide continuous information on the polymorphic, transient, and heterogeneous species formed. This review provides an updated state-of-the-art approach to the detection and characterization of a wide range of protein aggregates using nanopore technology. For each type of nanopore, biological, solid-state polymer, and nanopipette, discuss the main achievements for the detection of protein aggregates as well as the significant contributions to the understanding of protein aggregation and diagnostics.

Discrimination of Disaccharide Isomers of Different Glycosidic Linkages Using a Modified MspA Nanopore

Disaccharides are composed of two monosaccharide subunits joined by a glycosidic linkage in an α or β configuration. Different combinations of isomeric monosaccharide subunits and different glycosidic linkages result in different isomeric disaccharide products. Thus, direct discrimination of these disaccharide isomers from a mixture is extremely difficult. In this paper, a hetero-octameric Mycobacterium smegmatis porin A (MspA) nanopore conjugated with a phenylboronic acid (PBA) adapter was applied for disaccharide sensing, with which three most widely known disaccharides in nature, including sucrose, lactose and maltose, were clearly discriminated. Besides, all six isomeric α-D-glucopyranosyl-D-fructoses, differing only in their glycosidic linkages, were also well resolved. Assisted by a custom machine learning algorithm, a 0.99 discrimination accuracy is achieved. Nanopore discrimination of disaccharide isomers with different glycosidic linkages, which has never been previously demonstrated, is inspiring for nanopore saccharide sequencing. This sensing capacity was also applied in direct identification of isomaltulose additives in a commercial sucrose-free yogurt, from which isomaltulose, lactose and L-lactic acid were simultaneously detected.

A perspective on computer vision in biosensing

Computer vision has become a powerful tool in the field of biosensing, aiding in the development of innovative and precise systems for the analysis and interpretation of biological data. This interdisciplinary approach harnesses the capabilities of computer vision algorithms and techniques to extract valuable information from various biosensing applications, including medical diagnostics, environmental monitoring, and food health. Despite years of development, there is still significant room for improvement in this area. In this perspective, we outline how computer vision is applied to raw sensor data in biosensors and its advantages to biosensing applications. We then discuss ongoing research and developments in the field and subsequently explore the challenges and opportunities that computer vision faces in biosensor applications. We also suggest directions for future work, ultimately underscoring the significant impact of computer vision on advancing biosensing technologies and their applications.

Impact of nanotechnology on conventional and artificial intelligence-based biosensing strategies for the detection of viruses

Recent years have witnessed the emergence of several viruses and other pathogens. Some of these infectious diseases have spread globally, resulting in pandemics. Although biosensors of various types have been utilized for virus detection, their limited sensitivity remains an issue. Therefore, the development of better diagnostic tools that facilitate the more efficient detection of viruses and other pathogens has become important. Nanotechnology has been recognized as a powerful tool for the detection of viruses, and it is expected to change the landscape of virus detection and analysis. Recently, nanomaterials have gained enormous attention for their value in improving biosensor performance owing to their high surface-to-volume ratio and quantum size effects. This article reviews the impact of nanotechnology on the design, development, and performance of sensors for the detection of viruses. Special attention has been paid to nanoscale materials, various types of nanobiosensors, the internet of medical things, and artificial intelligence-based viral diagnostic techniques.

Nano/microfluidic device for high-throughput passive trapping of nanoparticles

We present a design and a fabrication method for devices designed for rapid collection of nanoparticles in a fluid. The design uses nanofluidic channels as a passive size-based barrier trap to isolate particles near a central point in the channel, which is also covered by a thin membrane. Particles that enter the collection region are trapped with 100% efficiency within a 6-12 μ m radius from a central point. Flow rates for particle-free fluid range from 1.88 to 3.69 nl/s for the pressure and geometries tested. Particle trapping tests show that high trapped particle counts significantly impact flow rates. For suspensions as dilute as 30-300 aM (20-200 particles/μ l), 8-80 particles are captured within 500 s.

High Accuracy Protein Identification: Fusion of Solid-State Nanopore Sensing and Machine Learning

Proteins are arguably one of the most important class of biomarkers for health diagnostic purposes. Label-free solid-state nanopore sensing is a versatile technique for sensing and analyzing biomolecules such as proteins at single-molecule level. While molecular-level information on size, shape, and charge of proteins can be assessed by nanopores, the identification of proteins with comparable sizes remains a challenge. Here, solid-state nanopore sensing is combined with machine learning to address this challenge. The translocations of four similarly sized proteins is assessed using amplifiers with bandwidths (BWs) of 100 kHz and 10 MHz, the highest bandwidth reported for protein sensing, using nanopores fabricated in <10 nm thick silicon nitride membranes. F-values of up to 65.9% and 83.2% (without clustering of the protein signals) are achieved with 100 kHz and 10 MHz BW measurements, respectively, for identification of the four proteins. The accuracy of protein identification is further enhanced by classifying the signals into different clusters based on signal attributes, with F-value and specificity of up to 88.7% and 96.4%, respectively, for combinations of four proteins. The combined use of high bandwidth instruments, advanced clustering and machine learning methods allows label-free identification of proteins with high accuracy.

Spiers Memorial Lecture: Towards understanding of iontronic systems: electroosmotic flow of monovalent and divalent electrolyte through charged cylindrical nanopores

In many practical applications, ions are the primary charge carrier and must move through either semipermeable membranes or through pores, which mimic ion channels in biological systems. In analogy to electronic devices, the "iontronic" ones use electric fields to induce the charge motion. However, unlike the electrons that move through a conductor, motion of ions is usually associated with simultaneous solvent flow. A study of electroosmotic flow through narrow pores is an outstanding challenge that lies at the interface of non-equilibrium statistical mechanics and fluid dynamics. In this paper, we will review recent works that use dissipative particle dynamics simulations to tackle this difficult problem. We will also present a classical density functional theory (DFT) based on the hypernetted-chain approximation (HNC), which allows us to calculate the velocity of electroosmotic flows inside nanopores containing 1 : 1 or 2 : 1 electrolyte solution. The theoretical results will be compared with simulations. In simulations, the electrostatic interactions are treated using the recently introduced pseudo-1D Ewald summation method. The zeta potentials calculated from the location of the shear plane of a pure solvent are found to agree reasonably well with the Smoluchowski equation. However, the quantitative structure of the fluid velocity profiles deviates significantly from the predictions of the Smoluchowski equation in the case of charged pores with 2 : 1 electrolyte. For low to moderate surface charge densities, the DFT allows us to accurately calculate the electrostatic potential profiles and the zeta potentials inside the nanopores. For pores with 1 : 1 electrolyte, the agreement between theory and simulation is particularly good for large ions, for which steric effects dominate over the ionic electrostatic correlations. The electroosmotic flow is found to depend very strongly on the ionic radii. In the case of pores containing 2 : 1 electrolyte, we observe a reentrant transition in which the electroosmotic flow first reverses and then returns to normal as the surface change density of the pore is increased.

Analysis of Nanopore Data: Classification Strategies for an Unbiased Curation of Single-Molecule Events from DNA Nanostructures

Nanopores are versatile single-molecule sensors that are being used to sense increasingly complex mixtures of structured molecules with applications in molecular data storage and disease biomarker detection. However, increased molecular complexity presents additional challenges to the analysis of nanopore data, including more translocation events being rejected for not matching an expected signal structure and a greater risk of selection bias entering this event curation process. To highlight these challenges, here, we present the analysis of a model molecular system consisting of a nanostructured DNA molecule attached to a linear DNA carrier. We make use of recent advances in the event segmentation capabilities of Nanolyzer, a graphical analysis tool provided for nanopore event fitting, and describe approaches to the event substructure analysis. In the process, we identify and discuss important sources of selection bias that emerge in the analysis of this molecular system and consider the complicating effects of molecular conformation and variable experimental conditions (e.g., pore diameter). We then present additional refinements to existing analysis techniques, allowing for improved separation of multiplexed samples, fewer translocation events rejected as false negatives, and a wider range of experimental conditions for which accurate molecular information can be extracted. Increasing the coverage of analyzed events within nanopore data is not only important for characterizing complex molecular samples with high fidelity but is also becoming essential to the generation of accurate, unbiased training data as machine-learning approaches to data analysis and event identification continue to increase in prevalence.

Nanopore Impedance Spectroscopy Reveals Electrical Properties of Single Nanoparticles for Detecting and Identifying Pathogenic Viruses

In the conventional nanopore method, direct current (DC) is used to study molecules and nanoparticles; however, it cannot easily discriminate between materials with similarly sized particles. Herein, we developed an alternating current (AC)-based nanopore method to measure the impedance of a single nanoparticle and distinguish between particles of the same size based on their material characteristics. We demonstrated the performance of this method using impedance measurements to determine the size and frequency characteristics of various particles, ranging in diameter from 200 nm to 1 μm. Furthermore, the alternating current method exhibited high accuracy for biosensing applications, identifying viruses with over 85% accuracy using single-particle measurement and machine learning. Therefore, this novel nanopore method is useful for applications in materials science, biology, and medicine.

Development of an Artificially Intelligent Nanopore for High-Throughput DNA Sequencing with a Machine-Learning-Aided Quantum-Tunneling Approach

Solid-state nanopore-based single-molecule DNA sequencing with quantum tunneling technology poses formidable challenges to achieve long-read sequencing and high-throughput analysis. Here, we propose a method for developing an artificially intelligent (AI) nanopore that does not require extraction of the signature transmission function for each nucleotide of the whole DNA strand by integrating supervised machine learning (ML) and transverse quantum transport technology with a graphene nanopore. The optimized ML model can predict the transmission function of all other nucleotides after training with data sets of all the orientations of any nucleotide inside the nanopore with a root-mean-square error (RMSE) of as low as 0.062. Further, up to 96.01% accuracy is achieved in classifying the unlabeled nucleotides with their transmission readouts. We envision that an AI nanopore can alleviate the experimental challenges of the quantum-tunneling method and pave the way for rapid and high-precision DNA sequencing by predicting their signature transmission functions.

Identification of tagged glycans with a protein nanopore

Structural complexity of glycans derived from the diversities in composition, linage, configuration, and branching considerably complicates structural analysis. Nanopore-based single-molecule sensing offers the potential to elucidate glycan structure and even sequence glycan. However, the small molecular size and low charge density of glycans have restricted direct nanopore detection of glycan. Here we show that glycan sensing can be achieved using a wild-type aerolysin nanopore by introducing a facile glycan derivatization strategy. The glycan molecule can induce impressive current blockages when moving through the nanopore after being connected with an aromatic group-containing tag (plus a carrier group for the neutral glycan). The obtained nanopore data permit the identification of glycan regio- and stereoisomers, glycans with variable monosaccharide numbers, and distinct branched glycans, either independently or with the use of machine learning methods. The presented nanopore sensing strategy for glycans paves the way towards nanopore glycan profiling and potentially sequencing.

Large-scale nano-biosensing technologies

Nanoscale technologies have brought significant advancements to modern diagnostics, enabling unprecedented bio-chemical sensitivities that are key to disease monitoring. At the same time, miniaturized biosensors and their integration across large areas enabled tessellating these into high-density biosensing panels, a key capability for the development of high throughput monitoring: multiple patients as well as multiple analytes per patient. This review provides a critical overview of various nanoscale biosensing technologies and their ability to unlock high testing throughput without compromising detection resilience. We report on the challenges and opportunities each technology presents along this direction and present a detailed analysis on the prospects of both commercially available and emerging biosensing technologies.

Nanopore Detection Assisted DNA Information Processing

The deoxyribonucleotide (DNA) molecule is a stable carrier for large amounts of genetic information and provides an ideal storage medium for next-generation information processing technologies. Technologies that process DNA information, representing a cross-disciplinary integration of biology and computer techniques, have become attractive substitutes for technologies that process electronic information alone. The detailed applications of DNA technologies can be divided into three components: storage, computing, and self-assembly. The quality of DNA information processing relies on the accuracy of DNA reading. Nanopore detection allows researchers to accurately sequence nucleotides and is thus widely used to read DNA. In this paper, we introduce the principles and development history of nanopore detection and conduct a systematic review of recent developments and specific applications in DNA information processing involving nanopore detection and nanopore-based storage. We also discuss the potential of artificial intelligence in nanopore detection and DNA information processing. This work not only provides new avenues for future nanopore detection development, but also offers a foundation for the construction of more advanced DNA information processing technologies.

Nanopore Electrochemistry for Pathogen Detection

Pathogen infections have seriously threatened human health, and there is an urgent demand for rapid and efficient pathogen identification to provide instructions in clinical diagnosis and therapeutic intervention. Recently, nanopore technology, a rapidly maturing technology which delivers ultrasensitive sensing and high throughput in real-time and at low cost, has achieved success in pathogen detection. Furthermore, the remarkable development of nanopore sequencing, for example, the MinION sequencer from Oxford Nanopore Technologies (ONT) as a competitive sequencing technology, has facilitated the rapid analysis of disease-related microbiomes at the whole-genome level and on a large scale. Here, we highlighted recent advances in nanopore approaches for pathogen detection at the single-molecule level. We also overviewed the applications of nanopore sequencing in pathogenic bacteria identification and diagnosis. In the end, we discussed the challenges and future developments of nanopore technology as promising tools for the management of infections, which may be helpful to aid understanding as well as decision-making.

Focus on using nanopore technology for societal health, environmental, and energy challenges

With an increasing global population that is rapidly ageing, our society faces challenges that impact health, environment, and energy demand. With this ageing comes an accumulation of cellular changes that lead to the development of diseases and susceptibility to infections. This impacts not only the health system, but also the global economy. As the population increases, so does the demand for energy and the emission of pollutants, leading to a progressive degradation of our environment. This in turn impacts health through reduced access to arable land, clean water, and breathable air. New monitoring approaches to assist in environmental control and minimize the impact on health are urgently needed, leading to the development of new sensor technologies that are highly sensitive, rapid, and low-cost. Nanopore sensing is a new technology that helps to meet this purpose, with the potential to provide rapid point-of-care medical diagnosis, real-time on-site pollutant monitoring systems to manage environmental health, as well as integrated sensors to increase the efficiency and storage capacity of renewable energy sources. In this review we discuss how the powerful approach of nanopore based single-molecule, or particle, electrical promises to overcome existing and emerging societal challenges, providing new opportunities and tools for personalized medicine, localized environmental monitoring, and improved energy production and storage systems.

Pushing the limits of nanopore transport performance by polymer functionalization

Inspired by the design and performance of biological pores, polymer functionalization of nanopores has emerged as an evolving field to advance transport performance within the last few years. This feature article outlines developments in nanopore functionalization and the resulting transport performance including gating based on electrostatic interaction, wettability and ligand binding, gradual transport controlled by polymerization as well as functionalization-based asymmetric nanopore and nanoporous material design going towards the transport direction. Pushing the limits of nanopore transport performance and thus reducing the performance gap between biological and technological pores is strongly related to advances in polymerization chemistry and their translation into nanopore functionalization. Thereby, the effect of the spatial confinement has to be considered for polymer functionalization as well as for transport regulation, and mechanistic understanding is strongly increased by combining experiment and theory. A full mechanistic understanding together with highly precise nanopore structure design and polymer functionalization is not only expected to improve existing application of nanoporous materials but also opens the door to new technologies. The latter might include out of equilibrium devices, ionic circuits, or machine learning based sensors.

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.

Reversal of Electroosmotic Flow in Charged Nanopores with Multivalent Electrolyte

We study the reversal of electroosmotic flow in charged cylindrical nanopores containing multivalent electrolyte. Dissipative particle dynamics is used to simulate the hydrodynamics of the electroosmotic flow. The electrostatic interactions are treated using 3D Ewald summation, corrected for a pseudo-one-dimensional geometry of the pore. We observe that, for sufficiently large surface charge density, condensation of multivalent counterions leads to the reversal of the pore's surface charge. This results in the reversal of electroosmotic flow. Our simulations show that the Smoluchowski equation is able to quantitatively account for the electroosmotic flow through the nanopore, if the shear plane is shifted from the position of the Stern contact surface.

Gold Nanoparticle-Labeled CRISPR-Cas13a Assay for the Sensitive Solid-State Nanopore Molecular Counting

A gold nanoparticle (AuNP) labeled CRISPR-Cas13a nucleic acid assay has been developed for sensitive solid-state nanopore sensing. Instead of directly detecting the translocation of RNA through a nanopore, our system utilizes non-covalent conjugates of AuNPs and RNA targets. Upon CRISPR activation, the AuNPs are liberated from the RNA, isolated, and passed through a nanopore sensor. Detection of the AuNPs can be observed as increasing ionic current in the chip. Each AuNP that is detected is enumerated as an event, leading to quantitative of molecular targets. Leveraging the high signal-to-noise ratio enabled by the AuNPs, a detection limit of 50 fM before front-end target amplification is achieved using SARS-CoV-2 RNA segments as a Cas13 target. Furthermore, a dynamic range of six orders of magnitude is demonstrated for quantitative RNA sensing. This simplified AuNP-based CRISPR assay is performed at the physiological temperature without relying on thermal cyclers. In addition, the nanopore reader is similar in size to a smartphone, making the assay system suitable for rapid and portable nucleic acid biomarker detection in either low-resource settings or hospitals.

Nanodevices for Biological and Medical Applications: Development of Single-Molecule Electrical Measurement Method

A comprehensive detection of a wide variety of diagnostic markers is required for the realization of personalized medicine. As a sensor to realize such personalized medicine, a single molecule electrical measurement method using nanodevices is currently attracting interest for its comprehensive simultaneous detection of various target markers for use in biological and medical application. Single-molecule electrical measurement using nanodevices, such as nanopore, nanogap, or nanopipette devices, has the following features:; high sensitivity, low-cost, high-throughput detection, easy-portability, low-cost availability by mass production technologies, and the possibility of integration of various functions and multiple sensors. In this review, I focus on the medical applications of single- molecule electrical measurement using nanodevices. This review provides information on the current status and future prospects of nanodevice-based single-molecule electrical measurement technology, which is making a full-scale contribution to realizing personalized medicine in the future. Future prospects include some discussion on of the current issues on the expansion of the application requirements for single-mole-cule measurement.

Two-dimensional material-based virus detection

Cost-effective, rapid, and accurate virus detection technologies play key roles in reducing viral transmission. Prompt and accurate virus detection enables timely treatment and effective quarantine of virus carrier, and therefore effectively reduces the possibility of large-scale spread. However, conventional virus detection techniques often suffer from slow response, high cost or sophisticated procedures. Recently, two-dimensional (2D) materials have been used as promising sensing platforms for the high-performance detection of a variety of chemical and biological substances. The unique properties of 2D materials, such as large specific area, active surface interaction with biomolecules and facile surface functionalization, provide advantages in developing novel virus detection technologies with fast response and high sensitivity. Furthermore, 2D materials possess versatile and tunable electronic, electrochemical and optical properties, making them ideal platforms to demonstrate conceptual sensing techniques and explore complex sensing mechanisms in next-generation biosensors. In this review, we first briefly summarize the virus detection techniques with an emphasis on the current efforts in fighting again COVID-19. Then, we introduce the preparation methods and properties of 2D materials utilized in biosensors, including graphene, transition metal dichalcogenides (TMDs) and other 2D materials. Furthermore, we discuss the working principles of various virus detection technologies based on emerging 2D materials, such as field-effect transistor-based virus detection, electrochemical virus detection, optical virus detection and other virus detection techniques. Then, we elaborate on the essential works in 2D material-based high-performance virus detection. Finally, our perspective on the challenges and future research direction in this field is discussed.

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

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

Solid-state and polymer nanopores for protein sensing: A review

In two decades, the solid state and polymer nanopores became attractive method for the protein sensing with high specificity and sensitivity. They also allow the characterization of conformational changes, unfolding, assembly and aggregation as well the following of enzymatic reaction. This review aims to provide an overview of the protein sensing regarding the technique of detection: the resistive pulse and ionic diodes. For each strategy, we report the most significant achievement regarding the detection of peptides and protein as well as the conformational change, protein-protein assembly and aggregation process. We discuss the limitations and the recent strategies to improve the nanopore resolution and accuracy. A focus is done about concomitant problematic such as protein adsorption and nanopore lifetime.

Nanopore sensors for viral particle quantification: current progress and future prospects

Rapid, inexpensive, and laboratory-free diagnostic of viral pathogens is highly critical in controlling viral pandemics. In recent years, nanopore-based sensors have been employed to detect, identify, and classify virus particles. By tracing ionic current containing target molecules across nano-scale pores, nanopore sensors can recognize the target molecules at the single-molecule level. In the case of viruses, they enable discrimination of individual viruses and obtaining important information on the physical and chemical properties of viral particles. Despite classical benchtop virus detection methods, such as amplification techniques (e.g., PCR) or immunological assays (e.g., ELISA), that are mainly laboratory-based, expensive and time-consuming, nanopore-based sensing methods can enable low-cost and real-time point-of-care (PoC) and point-of-need (PoN) monitoring of target viruses. This review discusses the limitations of classical virus detection methods in PoN virus monitoring and then provides a comprehensive overview of nanopore sensing technology and its emerging applications in quantifying virus particles and classifying virus sub-types. Afterward, it discusses the recent progress in the field of nanopore sensing, including integrating nanopore sensors with microfabrication technology, microfluidics and artificial intelligence, which have been demonstrated to be promising in developing the next generation of low-cost and portable biosensors for the sensitive recognition of viruses and emerging pathogens.

The C2 entity of chitosugars is crucial in molecular selectivity of the Vibrio campbellii chitoporin

The marine bacterium Vibrio campbellii expresses a chitooligosaccharide-specific outer-membrane channel (chitoporin) for the efficient uptake of nutritional chitosugars that are externally produced through enzymic degradation of environmental host shell chitin. However, the principles behind the distinct substrate selectivity of chitoporins are unclear. Here, we employed black lipid membrane (BLM) electrophysiology, which handles the measurement of the flow of ionic current through porins in phospholipid bilayers for the assessment of porin conductivities, to investigate the pH dependency of chitosugar-chitoporin interactions for the bacterium's natural substrate chitohexaose and its deacetylated form, chitosan hexaose. We show that efficient passage of the N-acetylated chitohexaose through the chitoporin is facilitated by its strong affinity for the pore. In contrast, the deacetylated chitosan hexaose is impermeant; however, protonation of the C2 amino entities of chitosan hexaose allows it to be pulled through the channel in the presence of a transmembrane electric field. We concluded from this the crucial role of C2-substitution as the determining factor for chitoporin entry. A change from N-acetylamino- to amino-substitution effectively abolished the ability of approaching molecules to enter the chitoporin, with deacetylation leading to loss of the distinctive structural features of nanopore opening and pore access of chitosugars. These findings provide further understanding of the multistep pathway of chitin utilization by marine Vibrio bacteria and may guide the development of solid-state or genetically engineered biological nanopores for relevant technological applications.

Pipette-Tip-Enabled Digital Nucleic Acid Analyzer for COVID-19 Testing with Isothermal Amplification

Herein, a pipette-tip-enabled digital nucleic acid analyzer for high-performance COVID-19 testing is demonstrated. This is achieved by digital loop-mediated isothermal amplification (digital LAMP or dLAMP) using common laboratory equipment and materials. It is shown that simply fixing a glass capillary inside conventional pipette tips enables the generation of monodisperse, water-in-oil microdroplets with benchtop centrifugation. It is shown that using LAMP, the ORF1a/b gene, a standard test region for COVID-19 screening, can be amplified without a thermal cycler. The amplification allows counting of fluorescent microdroplets so that Poisson analysis can be performed to allow quantification with a limit of detection that is 1 order of magnitude better than those of nondigital techniques and comparable to those of commercial dLAMP platforms. It is envisioned that this work will inspire studies on ultrasensitive digital nucleic acid analyzers demanding both sensitivity and accessibility, which is pivotal to their large-scale applications.

Detection of Amyloid-β Fibrils Using Track-Etched Nanopores: Effect of Geometry and Crowding

Several neurodegenerative diseases have been linked to proteins or peptides that are prone to aggregate in different brain regions. Aggregation of amyloid-β (Aβ) peptides is recognized as the main cause of Alzheimer's disease (AD) progression, leading to the formation of toxic Aβ oligomers and amyloid fibrils. The molecular mechanism of Aβ aggregation is complex and still not fully understood. Nanopore technology provides a new way to obtain kinetic and morphological aspects of Aβ aggregation at a single-molecule scale without labeling by detecting the electrochemical signal of the peptides when they pass through the hole. Here, we investigate the influence of nanoscale geometry (conical and bullet-like shape) of a track-etched nanopore pore and the effect of molecular crowding (polyethylene glycol-functionalized pores) on Aβ fibril sensing and analysis. Various Aβ fibril samples that differed by their length were produced by sonication of fibrils obtained in the presence of epigallocatechin gallate. The conical nanopore functionalized with polyethylene glycol (PEG) 5 kDa is suitable for discrimination of the fibril size from relative current blockade. The bullet-like-shaped nanopore enhances the amplitude of the current and increases the dwell time, allowing us to well discern the fibrils. Finally, the nanopore crowded with PEG 20 kDa enhances the relative current blockade and increases the dwell time; however, the discrimination is not improved compared to the "bullet-shaped" nanopore.

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.

Nanoparticle-assisted detection of nucleic acids in a polymeric nanopore with a large pore size

Rapid and accurate detection of nucleic acids is of paramount importance in many fields, including medical diagnosis, gene therapy and virus identification. In this work, by taking advantage of two DNA hybridization probes, one of which was immobilized on the surface of gold nanoparticles, while the other was free in solution, detection of short length nucleic acids was successfully achieved using a large size (20 nm tip diameter) polyethylene terephythalate (PET) nanopore. The sensor was sensitive and selective: DNA samples with concentrations at as low as 0.5 nM could be detected within minutes and the number of mismatches can be discerned from the translocation frequency. Furthermore, the nanopore can be repeatedly used many times. Our developed large-size nanopore sensing platform offers the potential for fieldable/point-of-care diagnostic applications.

Electroosmotic Flow in Polarizable Charged Cylindrical Nanopores

We present a simulation method to study electroosmotic flow in charged nanopores with dielectric contrast between their interior and the surrounding medium. To perform simulations, we separate the electrostatic energy into the direct Coulomb and the polarization contributions. The polarization part is obtained using periodic Green functions and can be expressed as a sum of fast converging modified Bessel functions. On the other hand, the direct Coulomb part of the electrostatic energy is calculated using fast converging three-dimensional (3D) Ewald summation method, corrected for a pseudo one-dimensional (1D) geometry. The effects of polarization are found to be particularly important for systems with multivalent counterions and narrow nanopores. Depending on the surface charge density, polarization can increase the volumetric flow rate by 200%. For systems with 3:1 electrolyte, we observe that there is a saturation of the volumetric flow rate. In this case, for polarizable pores, the flow rate is 100% higher than for nonpolarizable pores.

Discrimination of α-Thrombin and γ-Thrombin Using Aptamer-Functionalized Nanopore Sensing

Protein detection and identification at the single-molecule level are major challenges in many biotechnological fields. Solid-state nanopores have raised attention as label-free biosensors with high sensitivity. Here, we use solid-state nanopore sensing to discriminate two closely related proteins, α-thrombin and γ-thrombin. We show that aptamer functionalization improves protein discrimination thanks to a significant difference in the relative current blockade amplitude. To enhance discrimination, we postprocessed the signals using machine learning and training algorithms and we were able to reach an accuracy of 98.8% using seven features and ensemble methods.

Single-Entity Detection With TEM-Fabricated Nanopores

Nanopore-based single-entity detection shows immense potential in sensing and sequencing technologies. Solid-state nanopores permit unprecedented detail while preserving mechanical robustness, reusability, adjustable pore size, and stability in different physical and chemical environments. The transmission electron microscope (TEM) has evolved into a powerful tool for fabricating and characterizing nanometer-sized pores within a solid-state ultrathin membrane. By detecting differences in the ionic current signals due to single-entity translocation through the nanopore, solid-state nanopores can enable gene sequencing and single molecule/nanoparticle detection with high sensitivity, improved acquisition speed, and low cost. Here we briefly discuss the recent progress in the modification and characterization of TEM-fabricated nanopores. Moreover, we highlight some key applications of these nanopores in nucleic acids, protein, and nanoparticle detection. Additionally, we discuss the future of computer simulations in DNA and protein sequencing strategies. We also attempt to identify the challenges and discuss the future development of nanopore-detection technology aiming to promote the next-generation sequencing technology.