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

Emerging Data Processing Methods for Single-Entity Electrochemistry

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

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.

Saliva-based detection of SARS-CoV-2: a bibliometric analysis of global research

Saliva has emerged as a promising noninvasive biofluid for the diagnosis of oral and systemic diseases, including viral infections. During the coronavirus disease 2019 (COVID-19) pandemic, a growing number of studies focused on saliva-based detection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Taking advantage of the WoS core collection (WoSCC) and CiteSpace, we retrieved 1021 articles related to saliva-based detection of SARS-CoV-2 and conducted a comprehensive bibliometric analysis. We analyzed countries, institutions, authors, cited authors, and cited journals to summarize their contribution and influence and analyzed keywords to explore research hotspots and trends. From 2020 to 2021, research focused on viral transmission via saliva and verification of saliva as a reliable specimen, whereas from 2021 to the present, the focus of research has switched to saliva-based biosensors for SARS-CoV-2 detection. By far, saliva has been verified as a reliable specimen for SARS-CoV-2 detection, although a standardized procedure for saliva sampling and processing is needed. Studies on saliva-based detection of SARS-CoV-2 will promote the development of saliva-based diagnostics and biosensors for viral detection. Collectively, our findings could provide valuable information to help scientists perceive the basic knowledge landscapes on saliva-based detection of SARS-CoV-2, the past and current research hotspots, and future opportunities.

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

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

Nanopore analysis of salvianolic acids in herbal medicines

Natural herbs, which contain pharmacologically active compounds, have been used historically as medicines. Conventionally, the analysis of chemical components in herbal medicines requires time-consuming sample separation and state-of-the-art analytical instruments. Nanopore, a versatile single molecule sensor, might be suitable to identify bioactive compounds in natural herbs. Here, a phenylboronic acid appended Mycobacterium smegmatis porin A (MspA) nanopore is used as a sensor for herbal medicines. A variety of bioactive compounds based on salvianolic acids, including caffeic acid, protocatechuic acid, protocatechualdehyde, salvianic acid A, rosmarinic acid, lithospermic acid, salvianolic acid A and salvianolic acid B are identified. Using a custom machine learning algorithm, analyte identification is performed with an accuracy of 99.0%. This sensing principle is further used with natural herbs such as Salvia miltiorrhiza, Rosemary and Prunella vulgaris. No complex sample separation or purification is required and the sensing device is highly portable.

A Universal Approximation for Conductance Blockade in Thin Nanopore Membranes

Nanopore-based sensing platforms have transformed single-molecule detection and analysis. The foundation of nanopore translocation experiments lies in conductance measurements, yet existing models, which are largely phenomenological, are inaccurate in critical experimental conditions such as thin and tightly fitting pores. Of the two components of the conductance blockade, channel and access resistance, the access resistance is poorly modeled. We present a comprehensive investigation of the access resistance and associated conductance blockade in thin nanopore membranes. By combining a first-principles approach, multiscale modeling, and experimental validation, we propose a unified theoretical modeling framework. The analytical model derived as a result surpasses current approaches across a broad parameter range. Beyond advancing our theoretical understanding, our framework's versatility enables analyte size inference and predictive insights into conductance blockade behavior. Our results will facilitate the design and optimization of nanopore devices for diverse applications, including nanopore base calling and data storage.

Single-molecule RNA sizing enables quantitative analysis of alternative transcription termination

Transcription, a critical process in molecular biology, has found many applications in RNA synthesis, including mRNA vaccines and RNA therapeutics. However, current RNA characterization technologies suffer from amplification and enzymatic biases that lead to loss of native information. Here, we introduce a strategy to quantitatively study both transcription and RNA polymerase behaviour by sizing RNA with RNA nanotechnology and nanopores. To begin, we utilize T7 RNA polymerase to transcribe linear DNA lacking termination sequences. Surprisingly, we discover alternative transcription termination in the origin of replication sequence. Next, we employ circular DNA without transcription terminators to perform rolling circle transcription. This allows us to gain valuable insights into the processivity and transcription behaviour of RNA polymerase at the single-molecule level. Our work demonstrates how RNA nanotechnology and nanopores may be used in tandem for the direct and quantitative analysis of RNA transcripts. This methodology provides a promising pathway for accurate RNA structural mapping by enabling the study of full-length RNA transcripts at the single-molecule level.

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.

Deciphering DNA nucleotide sequences and their rotation dynamics with interpretable machine learning integrated C3N nanopores

A solid-state nanopore combined with the quantum transport method has garnered substantial attention and intrigue for DNA sequencing due to its potential for providing rapid and accurate sequencing results, which could have numerous applications in disease diagnosis and personalized medicine. However, the intricate and multifaceted nature of the experimental protocol poses a formidable challenge in attaining precise single nucleotide analysis. Here, we report a machine learning (ML) framework combined with the quantum transport method to accelerate high-throughput single nucleotide recognition with C3N nanopores. The optimized eXtreme Gradient Boosting Regression (XGBR) algorithm has predicted the fingerprint transmission of each unknown nucleotide and their rotation dynamics with root mean square error scores as low as 0.07. Interpretability of ML black box models with the game theory-based SHapley Additive exPlanation method has provided a quasi-explanation for the model working principle and the complex relationship between electrode-nucleotide coupling and transmission. Moreover, a comprehensive ML classification of nucleotides based on binary, ternary, and quaternary combinations shows maximum accuracy and F1 scores of 100%. The results suggest that ML in tandem with a nanopore device can potentially alleviate the experimental hurdles associated with quantum tunneling and facilitate fast and high-precision DNA sequencing.

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

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

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.

Emerging Applications of Nanobiosensors in Pathogen Detection in Water and Food

Food and waterborne illnesses are still a major concern in health and food safety areas. Every year, almost 0.42 million and 2.2 million deaths related to food and waterborne illness are reported worldwide, respectively. In foodborne pathogens, bacteria such as Salmonella, Shiga-toxin producer Escherichia coli, Campylobacter, and Listeria monocytogenes are considered to be high-concern pathogens. High-concern waterborne pathogens are Vibrio cholerae, leptospirosis, Schistosoma mansoni, and Schistosima japonicum, among others. Despite the major efforts of food and water quality control to monitor the presence of these pathogens of concern in these kinds of sources, foodborne and waterborne illness occurrence is still high globally. For these reasons, the development of novel and faster pathogen-detection methods applicable to real-time surveillance strategies are required. Methods based on biosensor devices have emerged as novel tools for faster detection of food and water pathogens, in contrast to traditional methods that are usually time-consuming and are unsuitable for large-scale monitoring. Biosensor devices can be summarized as devices that use biochemical reactions with a biorecognition section (isolated enzymes, antibodies, tissues, genetic materials, or aptamers) to detect pathogens. In most cases, biosensors are based on the correlation of electrical, thermal, or optical signals in the presence of pathogen biomarkers. The application of nano and molecular technologies allows the identification of pathogens in a faster and high-sensibility manner, at extremely low-pathogen concentrations. In fact, the integration of gold, silver, iron, and magnetic nanoparticles (NP) in biosensors has demonstrated an improvement in their detection functionality. The present review summarizes the principal application of nanomaterials and biosensor-based devices for the detection of pathogens in food and water samples. Additionally, it highlights the improvement of biosensor devices through nanomaterials. Nanomaterials offer unique advantages for pathogen detection. The nanoscale and high specific surface area allows for more effective interaction with pathogenic agents, enhancing the sensitivity and selectivity of the biosensors. Finally, biosensors' capability to functionalize with specific molecules such as antibodies or nucleic acids facilitates the specific detection of the target pathogens.

Advances in miniaturized nanosensing platforms for analysis of pathogenic bacteria and viruses

Pathogenic bacteria and viruses are the main causes of infectious diseases all over the world. Early diagnosis of such infectious diseases is a critical step in management of their spread and treatment of the infection in its early stages. Therefore, the innovation of smart sensing platforms for point-of-care diagnosis of life-threatening infectious diseases such as COVID-19 is a prerequisite to isolate the patients and provide them with suitable treatment strategies. The developed diagnostic sensors should be highly sensitive, specific, ultrafast, portable, cheap, label-free, and selective. In recent years, different nanosensors have been developed for the detection of bacterial and viral pathogens. We focus here on label-free miniaturized nanosensing platforms that were efficiently applied for pathogenic detection in biological matrices. Such devices include nanopore sensors and nanostructure-integrated lab-on-a-chip sensors that are characterized by portability, simplicity, cost-effectiveness, and ultrafast analysis because they avoid the time-consuming sample preparation steps. Furthermore, nanopore-based sensors could afford single-molecule counting of viruses in biological specimens, yielding high-sensitivity and high-accuracy detection. Moreover, non-invasive nanosensors that are capable of detecting volatile organic compounds emitted from the diseased organ to the skin, urine, or exhaled breath were also reviewed. The merits and applications of all these nanosensors for analysis of pathogenic bacteria and viruses in biological matrices will be discussed in detail, emphasizing the importance of artificial intelligence in advancing specific nanosensors.

Improvement of Sensitivity and Speed of Virus Sensing Technologies Using nm- and μm-Scale Components

Various viral diseases can be widespread and cause severe disruption to global society. Highly sensitive virus detection methods are needed to take effective measures to prevent the spread of viral infection. This required the development of rapid virus detection technology to detect viruses at low concentrations, even in the biological fluid of patients in the early stages of the disease or environmental samples. This review describes an overview of various virus detection technologies and then refers to typical technologies such as beads-based assay, digital assay, and pore-based sensing, which are the three modern approaches to improve the performance of viral sensing in terms of speed and sensitivity.

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.

Label-Free Electrochemical Methods for Disease Detection

Label-free electrochemical biosensing leverages the advantages of label-free techniques, low cost, and fewer user steps, with the sensitivity and portability of electrochemical analysis. In this review, we identify four label-free electrochemical biosensing mechanisms: (a) blocking the electrode surface, (b) allowing greater access to the electrode surface, (c) changing the intercalation or electrostatic affinity of a redox probe to a biorecognition unit, and (d) modulating ion or electron transport properties due to conformational and surface charge changes. Each mechanism is described, recent advancements are summarized, and relative advantages and disadvantages of the techniques are discussed. Furthermore, two avenues for gaining further diagnostic information from label-free electrochemical biosensors, through multiplex analysis and incorporating machine learning, are examined.

Microfluidic Organ-Chips and Stem Cell Models in the Fight Against COVID-19

SARS-CoV-2, the virus underlying COVID-19, has now been recognized to cause multiorgan disease with a systemic effect on the host. To effectively combat SARS-CoV-2 and the subsequent development of COVID-19, it is critical to detect, monitor, and model viral pathogenesis. In this review, we discuss recent advancements in microfluidics, organ-on-a-chip, and human stem cell-derived models to study SARS-CoV-2 infection in the physiological organ microenvironment, together with their limitations. Microfluidic-based detection methods have greatly enhanced the rapidity, accessibility, and sensitivity of viral detection from patient samples. Engineered organ-on-a-chip models that recapitulate in vivo physiology have been developed for many organ systems to study viral pathology. Human stem cell-derived models have been utilized not only to model viral tropism and pathogenesis in a physiologically relevant context but also to screen for effective therapeutic compounds. The combination of all these platforms, along with future advancements, may aid to identify potential targets and develop novel strategies to counteract COVID-19 pathogenesis.

Nanopore Single-molecule Analysis of Biomarkers: Providing Possible Clues to Disease Diagnosis

Biomarker detection has attracted increasing interest in recent years due to the minimally or non-invasive sampling process. Single entity analysis of biomarkers is expected to provide real-time and accurate biological information for early disease diagnosis and prognosis, which is critical to the effective disease treatment and is also important in personalized medicine. As an innovative single entity analysis method, nanopore sensing is a pioneering single-molecule detection technique that is widely used in analytical bioanalytical fields. In this review, we overview the recent progress of nanopore biomarker detection as new approaches to disease diagnosis. In highlighted studies, nanopore was focusing on detecting biomarkers of different categories of communicable and noncommunicable diseases, such as pandemic Covid-19, AIDS, cancers, neurologic diseases, etc. Various sensitive and selective nanopore detecting strategies for different types of biomarkers are summarized. In addition, the challenges, opportunities, and direction for future development of nanopore-based biomarker sensors are also discussed.

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.

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.

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.

Nanopores: synergy from DNA sequencing to industrial filtration - small holes with big impact

Nanopores in thin membranes play important roles in science and industry. Single nanopores have provided a step-change in portable DNA sequencing and understanding nanoscale transport while multipore membranes facilitate food processing and purification of water and medicine. Despite the unifying use of nanopores, the fields of single nanopores and multipore membranes differ - to varying degrees - in terms of materials, fabrication, analysis, and applications. Such a partial disconnect hinders scientific progress as important challenges are best resolved together. This Viewpoint suggests how synergistic crosstalk between the two fields can provide considerable mutual benefits in fundamental understanding and the development of advanced membranes. We first describe the main differences including the atomistic definition of single pores compared to the less defined conduits in multipore membranes. We then outline steps to improve communication between the two fields such as harmonizing measurements and modelling of transport and selectivity. The resulting insight is expected to improve the rational design of porous membranes. The Viewpoint concludes with an outlook of other developments that can be best achieved by collaboration across the two fields to advance the understanding of transport in nanopores and create next-generation porous membranes tailored for sensing, filtration, and other applications.

Simultaneous identification of viruses and viral variants with programmable DNA nanobait

Respiratory infections are the major cause of death from infectious disease worldwide. Multiplexed diagnostic approaches are essential as many respiratory viruses have indistinguishable symptoms. We created self-assembled DNA nanobait that can simultaneously identify multiple short RNA targets. The nanobait approach relies on specific target selection via toehold-mediated strand displacement and rapid readout via nanopore sensing. Here we show that this platform can concurrently identify several common respiratory viruses, detecting a panel of short targets of viral nucleic acids from multiple viruses. Our nanobait can be easily reprogrammed to discriminate viral variants with single-nucleotide resolution, as we demonstrated for several key SARS-CoV-2 variants. Last, we show that the nanobait discriminates between samples extracted from oropharyngeal swabs from negative- and positive-SARS-CoV-2 patients without preamplification. Our system allows for the multiplexed identification of native RNA molecules, providing a new scalable approach for the diagnostics of multiple respiratory viruses in a single assay.

Nanopore Discrimination of Coagulation Biomarker Derivatives and Characterization of a Post-Translational Modification

One of the most important health challenges is the early and ongoing detection of disease for prevention, as well as personalized treatment management. Development of new sensitive analytical point-of-care tests are, therefore, necessary for direct biomarker detection from biofluids as critical tools to address the healthcare needs of an aging global population. Coagulation disorders associated with stroke, heart attack, or cancer are defined by an increased level of the fibrinopeptide A (FPA) biomarker, among others. This biomarker exists in more than one form: it can be post-translationally modified with a phosphate and also cleaved to form shorter peptides. Current assays are long and have difficulties in discriminating between these derivatives; hence, this is an underutilized biomarker for routine clinical practice. We use nanopore sensing to identify FPA, the phosphorylated FPA, and two derivatives. Each of these peptides is characterized by unique electrical signals for both dwell time and blockade level. We also show that the phosphorylated form of FPA can adopt two different conformations, each of which have different values for each electrical parameter. We were able to use these parameters to discriminate these peptides from a mix, thereby opening the way for the potential development of new point-of-care tests.

Portable nanopore-sequencing technology: Trends in development and applications

Sequencing technology is the most commonly used technology in molecular biology research and an essential pillar for the development and applications of molecular biology. Since 1977, when the first generation of sequencing technology opened the door to interpreting the genetic code, sequencing technology has been developing for three generations. It has applications in all aspects of life and scientific research, such as disease diagnosis, drug target discovery, pathological research, species protection, and SARS-CoV-2 detection. However, the first- and second-generation sequencing technology relied on fluorescence detection systems and DNA polymerization enzyme systems, which increased the cost of sequencing technology and limited its scope of applications. The third-generation sequencing technology performs PCR-free and single-molecule sequencing, but it still depends on the fluorescence detection device. To break through these limitations, researchers have made arduous efforts to develop a new advanced portable sequencing technology represented by nanopore sequencing. Nanopore technology has the advantages of small size and convenient portability, independent of biochemical reagents, and direct reading using physical methods. This paper reviews the research and development process of nanopore sequencing technology (NST) from the laboratory to commercially viable tools; discusses the main types of nanopore sequencing technologies and their various applications in solving a wide range of real-world problems. In addition, the paper collates the analysis tools necessary for performing different processing tasks in nanopore sequencing. Finally, we highlight the challenges of NST and its future research and application directions.

Quantitative Microscopic Observation of Base-Ligand Interactions via Hydrogen Bonds by Single-Molecule Counting

Chemical properties have been based on statistical averages since the introduction of Avogadro's number. The lack of suitable methods for counting identified single molecules has posed challenges to counting statistics. The selectivity, affinity, and mode of hydrogen bonding between base and small molecules that make up DNA, which is vital for living organisms, have not yet been revealed at the single molecule level. Here, we show the quantitation of the above-mentioned parameters via single-molecule counting based on the combination of single-molecule electrical measurements and AI. The binding selectivity values of five ligands to four different base molecules were evaluated quantitatively by determining the ratio of the number of aggregates in a solution mixture of base molecules and a ligand. In addition, we show the ligand dependence of the mode and number of microscopic hydrogen bonds via single-molecule counting and quantum chemical calculations.

Evaluating the habitat suitability modeling of Aceria alhagi and Alhagi maurorum in their native range using machine learning techniques.. [DOI: 10.21203/rs.3.rs-2441475/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Spatial locational modeling techniques are increasingly used in species distribution modeling. However, the implemented techniques differ in their modeling performance. In this study, we tested the predictive accuracy of three algorithms, namely "random forest (RF)," "support vector machine (SVM)," and "boosted regression trees (BRT)" to prepare habitat suitability mapping of an invasive species, Alhagi maurorum, and its potential biological control agent, Aceria alhagi. Location of this study was in Fars Province, southwest of Iran. The spatial distributions of the species were forecasted using GPS devices and GIS software. The probability values of occurrence were then checked using three algorithms. The predictive accuracy of the machine learning (ML) techniques was assessed by computing the “area under the curve (AUC)” of the “receiver-operating characteristic” plot. When the Aceria alhagi was modeled, the AUC values of RF, BRT and SVM were 0.89, 0.81, and 0.79, respectively. However, in habitat suitability models (HSMs) of Alhagi maurorum the AUC values of RF, BRT and SVM were 0.89, 0.80, and 0.73, respectively. The RF model provided significantly more accurate predictions than other algorithms. The importance of factors on the growth and development of Alhagi maurorum and Aceria alhagi was also determined using the partial least squares (PLS) algorithm, and the most crucial factors were the road and slope. Habitat suitability modeling based on algorithms may significantly increase the accuracy of species distribution forecasts, and thus it shows considerable promise for different conservation biological and biogeographical applications.

Functionalized electrodes embedded in nanopores: read-out enhancement?

In this review, functionalized nanogaps embedded in nanopores are discussed in view of their high biosensitivity in detecting biomolecules, their length, type, and sequence. Specific focus is given on nanoelectrodes functionalized with tiny nanometer-sized diamond-like particles offering vast functionalization possibilities for gold junction electrodes. This choice of the functionalization, in turn, offers nucleotide-specific binding possibilities improving the detection signals arising from such functionalized electrodes potentially embedded in a nanopore. The review sheds light onto the use and enhancement of the tunnelling recognition in functionalized nanogaps towards sensing DNA nucleotides and mutation detection, providing important input for a practical realization.

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.

Nanopore fingerprinting of supramolecular DNA nanostructures

DNA nanotechnology has paved the way for new generations of programmable nanomaterials. Utilizing the DNA origami technique, various DNA constructs can be designed, ranging from single tiles to the self-assembly of large-scale, complex, multi-tile arrays. This technique relies on the binding of hundreds of short DNA staple strands to a long single-stranded DNA scaffold that drives the folding of well-defined nanostructures. Such DNA nanostructures have enabled new applications in biosensing, drug delivery, and other multifunctional materials. In this study, we take advantage of the enhanced sensitivity of a solid-state nanopore that employs a poly-ethylene glycol enriched electrolyte to deliver real-time, non-destructive, and label-free fingerprinting of higher-order assemblies of DNA origami nanostructures with single-entity resolution. This approach enables the quantification of the assembly yields for complex DNA origami nanostructures using the nanostructure-induced equivalent charge surplus as a discriminant. We compare the assembly yield of four supramolecular DNA nanostructures obtained with the nanopore with agarose gel electrophoresis and atomic force microscopy imaging. We demonstrate that the nanopore system can provide analytical quantification of the complex supramolecular nanostructures within minutes, without any need for labeling and with single-molecule resolution. We envision that the nanopore detection platform can be applied to a range of nanomaterial designs and enable the analysis and manipulation of large DNA assemblies in real time.

Advances in Biosensing Technologies for Diagnosis of COVID-19

The COVID-19 pandemic has severely impacted normal human life worldwide. Due to its rapid community spread and high mortality statistics, the development of prompt diagnostic tests for a massive number of samples is essential. Currently used traditional methods are often expensive, time-consuming, laboratory-based, and unable to handle a large number of specimens in resource-limited settings. Because of its high contagiousness, efficient identification of SARS-CoV-2 carriers is crucial. As the advantages of adopting biosensors for efficient diagnosis of COVID-19 increase, this narrative review summarizes the recent advances and the respective reasons to consider applying biosensors. Biosensors are the most sensitive, specific, rapid, user-friendly tools having the potential to deliver point-of-care diagnostics beyond traditional standards. This review provides a brief introduction to conventional methods used for COVID-19 diagnosis and summarizes their advantages and disadvantages. It also discusses the pathogenesis of COVID-19, potential diagnostic biomarkers, and rapid diagnosis using biosensor technology. The current advancements in biosensing technologies, from academic research to commercial achievements, have been emphasized in recent publications. We covered a wide range of topics, including biomarker detection, viral genomes, viral proteins, immune responses to infection, and other potential proinflammatory biomolecules. Major challenges and prospects for future application in point-of-care settings are also highlighted.

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.

High-sensitivity and point-of-care detection of SARS-CoV-2 from nasal and throat swabs by magnetic SERS biosensor

The outbreak of COVID-19 caused by SARS-CoV-2 urges the development of rapidly and accurately diagnostic methods. Here, one high-sensitivity and point-of-care detection method based on magnetic SERS biosensor composed of Fe₃O₄-Au nanocomposite and Au nanoneedles array was developed to detect SARS-CoV-2 directly. Among, the magnetic Fe₃O₄-Au nanocomposite is applied to capture and separate virus from nasal and throat swabs and enhance the Raman signals of SARS-CoV-2. The magnetic SERS biosensor possessed high sensitivity by optimizing the Fe₃O₄-Au nanocomposite. More significantly, the on-site detection of inactivated SARS-CoV-2 virus was achieved based on the magnetic SERS biosensor with ultra-low limit of detection of 100 copies/mL during 15 mins. Furthermore, the contaminated nasal and throat swabs samples were identified by support vector machine, and the diagnostic accuracy of 100% was obtained. The magnetic SERS biosensor combined with support vector machine provides giant potential as the point-of-care detection tool for SARS-CoV-2.

Nanostructures for the Prevention, Diagnosis, and Treatment of SARS-CoV-2: A Review

Scientists, doctors, engineers, and even entire societies have become aware of the seriousness of the COVID-19 infection and are taking action quickly, using all the tools from protection to treatment against coronavirus SARS-CoV-2. Especially in this sense, scientific approaches and materials using nanotechnology are frequently preferred. In this review, we focus on how nanoscience and nanotechnology approaches can be used for protective equipment, diagnostic and treatment methods, medicine, and vaccine applications to stop the coronavirus SARS-CoV-2 and prevent its spread. SARS-CoV-2, which itself can be considered as a core-shell nanoparticle, can interact with various materials around it and remain bound for variable periods of time while maintaining its bioactivity. These applications are especially critical for the controlled use of disinfection systems. One of the most important processes in the fight against coronavirus is the rapid diagnosis of the virus in humans and the initiation of isolation and treatment processes. The development of nanotechnology-based test and diagnostic kits is another important research thrust. Nanotechnological therapeutics based on antiviral drug design and nanoarchitecture vaccines have been vital. Nanotechnology plays critical roles in the production of protective film surfaces for self-cleaning and antiviral masks, gloves, and laboratory clothes. An overview of literature studies highlighting nanotechnology and nanomaterial-based approaches to combat SARS-CoV-2 is presented.

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.

Methodology to Detect Biological Particles Using a Biosensing Surface Integrated in Resistive Pulse Sensing

Resistive pulse sensing (RPS) is an analytical method that can be used to individually count particles from a small sample. RPS simply monitors the physical characteristics of particles, such as size, shape, and charge density, and the integration of RPS with biosensing is an attractive theme to detect biological particles such as virus and bacteria. In this report, a methodology of biosensing on RPS was investigated. Polydopamine (PD), an adhesive component of mussels, was used as the base material to create a sensing surface. PD adheres to most materials, such as noble metals, metal oxides, semiconductors, and polymers; as a result, PD is a versatile intermediate layer for the fabrication of a biosensing surface. As an example of a biological particle, human influenza A virus (H1N1 subtype) was used to monitor translocation of particles through the pore membrane. When virus-specific ligands (6'-sialyllactose) were immobilized on the pore surface, the translocation time of the virus particles was considerably extended. The detailed translocation data suggest that the viral particles were trapped on the sensing surface by specific interactions. In addition, virus translocation processes on different pore surfaces were distinguished using machine learning. The result shows that the simple and versatile PD-based biosensor surface design was effective. This advanced RPS measurement system could be a promising analytical technique.

Application of Nanopore Sequencing in the Detection of Foodborne Microorganisms

Foodborne pathogens have become the subject of intense interest because of their high incidence and mortality worldwide. In the past few decades, people have developed many methods to solve this challenge. At present, methods such as traditional microbial culture methods, nucleic acid or protein-based pathogen detection methods, and whole-genome analysis are widely used in the detection of pathogenic microorganisms in food. However, these methods are limited by time-consuming, cumbersome operations or high costs. The development of nanopore sequencing technology offers the possibility to address these shortcomings. Nanopore sequencing, a third-generation technology, has the advantages of simple operation, high sensitivity, real-time sequencing, and low turnaround time. It can be widely used in the rapid detection and serotyping of foodborne pathogens. This review article discusses foodborne diseases, the principle of nanopore sequencing technology, the application of nanopore sequencing technology in foodborne pathogens detection, as well as its development prospects.

Tailoring Nanoporous Graphene via Machine Learning: Predicting Probabilities and Formation Times of Arbitrary Nanopore Shapes

Nanopores in graphene, a 2D material, are currently being explored for various applications, such as gas separation, water desalination, and DNA sequencing. The shapes and sizes of nanopores play a major role in determining the performance of devices made out of graphene. However, given an arbitrary nanopore shape, anticipating its creation probability and formation time are challenging inverse problems, solving which could help develop theoretical models for nanoporous graphene and guide experiments in tailoring pore sizes/shapes. In this work, we develop a machine learning (ML) framework to predict these target variables, based on data generated using kinetic Monte Carlo simulations and chemical graph theory. Thereby, we enable the rapid quantification of the ease of formation of a given nanopore shape in graphene via silicon-catalyzed electron-beam etching and provide an experimental handle to realize it in practice. We use structural features such as the number of carbon atoms removed, the number of edge atoms, the diameter of the nanopore, and its shape factor, which can be readily extracted from the nanopore shape. We show that the trained models can accurately predict nanopore probabilities and formation times with R2 values on the test set of 0.97 and 0.95, respectively. Not only that, we obtain physical insight into the working of the model and discuss the role played by the various structural features in modulating nanopore formation. Overall, our work provides a solid foundation for experimental studies to manipulate nanopore sizes/shapes and for theoretical studies to consider realistic structures of nanopores in graphene.

Biological nanopores for single-molecule sensing

Evolution has found countless ways to transport material across cells and cellular compartments separated by membranes. Protein assemblies are the cornerstone for the formation of channels and pores that enable this regulated passage of molecules in and out of cells, contributing to maintaining most of the fundamental processes that sustain living organisms. As in several other occasions, we have borrowed from the natural properties of these biological systems to push technology forward and have been able to hijack these nano-scale proteinaceous pores to learn about the physical and chemical features of molecules passing through them. Today, a large repertoire of biological pores is exploited as molecular sensors for characterizing biomolecules that are relevant for the advancement of life sciences and application to medicine. Although the technology has quickly matured to enable nucleic acid sensing with transformative implications for genomics, biological pores stand as some of the most promising candidates to drive the next developments in single-molecule proteomics.

New Insights for Biosensing: Lessons from Microbial Defense Systems

Microorganisms have gained defense systems during the lengthy process of evolution over millions of years. Such defense systems can protect them from being attacked by invading species (e.g., CRISPR-Cas for establishing adaptive immune systems and nanopore-forming toxins as virulence factors) or enable them to adapt to different conditions (e.g., gas vesicles for achieving buoyancy control). These microorganism defense systems (MDS) have inspired the development of biosensors that have received much attention in a wide range of fields including life science research, food safety, and medical diagnosis. This Review comprehensively analyzes biosensing platforms originating from MDS for sensing and imaging biological analytes. We first describe a basic overview of MDS and MDS-inspired biosensing platforms (e.g., CRISPR-Cas systems, nanopore-forming proteins, and gas vesicles), followed by a critical discussion of their functions and properties. We then discuss several transduction mechanisms (optical, acoustic, magnetic, and electrical) involved in MDS-inspired biosensing. We further detail the applications of the MDS-inspired biosensors to detect a variety of analytes (nucleic acids, peptides, proteins, pathogens, cells, small molecules, and metal ions). In the end, we propose the key challenges and future perspectives in seeking new and improved MDS tools that can potentially lead to breakthrough discoveries in developing a new generation of biosensors with a combination of low cost; high sensitivity, accuracy, and precision; and fast detection. Overall, this Review gives a historical review of MDS, elucidates the principles of emulating MDS to develop biosensors, and analyzes the recent advancements, current challenges, and future trends in this field. It provides a unique critical analysis of emulating MDS to develop robust biosensors and discusses the design of such biosensors using elements found in MDS, showing that emulating MDS is a promising approach to conceptually advancing the design of biosensors.

Overcoming the limitations of COVID-19 diagnostics with nanostructures, nucleic acid engineering, and additive manufacturing

The COVID-19 pandemic revealed fundamental limitations in the current model for infectious disease diagnosis and serology, based upon complex assay workflows, laboratory-based instrumentation, and expensive materials for managing samples and reagents. The lengthy time delays required to obtain test results, the high cost of gold-standard PCR tests, and poor sensitivity of rapid point-of-care tests contributed directly to society's inability to efficiently identify COVID-19-positive individuals for quarantine, which in turn continues to impact return to normal activities throughout the economy. Over the past year, enormous resources have been invested to develop more effective rapid tests and laboratory tests with greater throughput, yet the vast majority of engineering and chemistry approaches are merely incremental improvements to existing methods for nucleic acid amplification, lateral flow test strips, and enzymatic amplification assays for protein-based biomarkers. Meanwhile, widespread commercial availability of new test kits continues to be hampered by the cost and time required to develop single-use disposable microfluidic plastic cartridges manufactured by injection molding. Through development of novel technologies for sensitive, selective, rapid, and robust viral detection and more efficient approaches for scalable manufacturing of microfluidic devices, we can be much better prepared for future management of infectious pathogen outbreaks. Here, we describe how photonic metamaterials, graphene nanomaterials, designer DNA nanostructures, and polymers amenable to scalable additive manufacturing are being applied towards overcoming the fundamental limitations of currently dominant COVID-19 diagnostic approaches. In this paper, we review how several distinct classes of nanomaterials and nanochemistry enable simple assay workflows, high sensitivity, inexpensive instrumentation, point-of-care sample-to-answer virus diagnosis, and rapidly scaled manufacturing.

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.

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.

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.