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

Agarose Gel-Coated Nanochannel Biosensor for Detection of Prostate-Specific Antigen in Unprocessed Whole Blood Samples

Solid-state nanopore/nanochannel biosensors have rapidly advanced due to their high sensitivity, label-free detection, and fast response. However, detecting biomarkers directly in complex biological environments, particularly whole blood, remains challenging because of nonspecific protein adsorption and nanopore/nanochannel clogging. Here, a DNA aptamer functionalized nanochannel biosensor is developed with excellent antifouling properties, achieved by coating the nanochannel surface with agarose gel. This gel coating effectively mitigates fouling in diverse biological environments while maintaining comparable sensitivity to uncoated nanochannels for detecting prostate-specific antigen (PSA) in buffer solutions within 20 min. The biosensor exhibits a detection limit of 1 ng mL-1 for PSA in human serum, matching the performance of commercial enzyme-linked immunosorbent assay (ELISA) kits. Importantly, it successfully differentiates whole blood samples from prostate cancer patients and healthy individuals. The superior antifouling behavior is attributed to the electrically neutral, highly hydrophilic nature, and porous structure of the agarose gel, which prevents the adsorption of large biomolecules while facilitating the diffusion of PSA for aptamer-based capture. This DNA aptamer functionalized nanochannel biosensor with agarose gel coating offers reliable protein detection in complex biological environments, showing great promise in biomedical applications.

Amplification-Free Quantification of Endogenous Mitochondrial DNA Copy Number Using Solid-State Nanopores

Mitochondrial DNA (mtDNA) quantification is crucial in understanding mitochondrial dysfunction, which is linked to a variety of diseases, including cancer and neurodegenerative disorders. Traditional methods often rely on amplification-based techniques, which can introduce bias and lack the precision needed for clinical diagnostics. Solid-state nanopores, an emerging biosensing platform, have the advantage of offering single-molecule and label-free approaches by enabling the direct counting of DNA molecules without amplification. The ion-current signatures obtained from each DNA molecule contain rich information on the molecules' lengths and origin. In this study, we present an amplification-free method for mtDNA quantification using solid-state nanopores and machine learning. Intriguingly, we find that native (unamplified) mtDNA translocations harbor structurally distinctive features that can be exploited to specifically detect and quantify mtDNA copies over the background of genomic DNA fragments. By combining selective degradation of linear genomic DNA (gDNA) via exonuclease V with a support vector machine (SVM)-based model, we isolate and quantify mtDNA directly from biological samples. We validate our method using plasmids or isolated mtDNAs by spiking in predetermined quantities. We then quantify endogenous mtDNAs in a cancer cell line and in blood cells and compare our results with qPCR-based quantification of the mtDNA/nuclear DNA ratios. To elucidate the source of the ion-current signatures from the native mtDNA molecules, we perform synchronous electro-optical sensing of mtDNAs during passage through the nanopore after NHS ester reaction with fluorophore compounds. Our results show correlated electro-optical events, indicating that the mtDNA is complexed with packaging proteins. Our assay is robust, with a high classification accuracy and is capable of detecting mtDNA at picomolar levels, making it suitable for low-abundance samples. This technique requires minimal sample preparation and eliminates the need for amplification or purification steps. The developed approach has significant potential for point-of-care applications, offering a low-cost and scalable solution for accurate mtDNA quantification in clinical settings.

Nanopore toward Genuine Single-Molecule Sensing: Molecular Ping-Pong Technology

Nanopore sensing is a so-called label-free, single-molecule technology; however, multiple events of different molecules are recorded to obtain statistically robust data, which can limit both efficiency and sample use. To overcome these challenges, nanopore molecular ping-pong technology enables precise single-molecule manipulation, reducing systematic and stochastic errors by repeatedly measuring the same molecule. This review introduces the fundamentals and advancements of ping-pong technology, highlighting a recent breakthrough achieving over 10,000 recaptures of a single dsDNA molecule within minutes. This innovation not only minimizes sample requirements, which is critical for nonamplifiable samples, but also significantly enhances experimental precision. While current applications focus on dsDNA, extending this technology to protein and glycan analysis could transform nanopore research. Just as nanopore technology revolutionized DNA sequencing, it holds the potential to drive the development of nanopore-based protein and glycan sequencers, paving the way for groundbreaking advancements in molecular biology and biomedicine.

Machine Learning Assisted Nanofluidic Array for Multiprotein Detection

Solid-state nanopore and nanochannel biosensors have revolutionized protein detection by offering label-free, highly sensitive analyses. Traditional sensing systems (1st and 2nd stages) primarily focus on inner wall (IW) interactions, facing challenges such as complex preparation processes, variable protein entry angles, and conformational changes, leading to irregular detection events. To address these limitations, recent advancements (3rd stage) have shifted toward outer surface (OS) functionalization but are constrained by single-protein recognition models. Herein, we show a machine learning assisted nanofluidic array (MANY) sensing system (4th stage) that integrates a supervised dimensionality reduction strategy with photoresponsive MoS2 nanofluidic array functionalized with nonspecific functional elements (FEarray) at the OS. This approach serves as a proof-of-concept for label-free, probe-free detection of multiple proteins with 100% accuracy, highlighting its significant potential for rapid diagnostics in future disease detection applications.

Dynamic Features Driven by Stochastic Collisions in a Nanopore for Precise Single-Molecule Identification

Nanopore technology holds great potential for single-molecule identification. However, extracting meaningful features from ionic current signals and understanding the molecular mechanisms underlying the specific features remain unresolved. In this study, we uncovered a distinctive ionic current pattern in a K238Q aerolysin nanopore, characterized by transient spikes superimposed on two stable transition states. By employing a neural network model, we demonstrated that these previously overlooked dynamic spike features exhibit superior discriminative power, improving the accuracy from 44% to 93%. We identified that the stable transition states result from simultaneous interactions of ssDNA with the two sensitive sites of the nanopore. The proposed stochastic collision model offers a mechanistic framework for interpreting the generation of the dynamic spike features. This model indicates that the continuous transitions facilitate iterative, comprehensive snapshots of molecular interactions by nanopores. Our findings introduce a new approach for optimizing nanopore technology to capture complex dynamic features and substantially improve the accuracy of single-molecule identification.

Pioneering Role of Nanopore Single-Molecule Sensing in Environmental and Food Surveillance

In recent years, environmental and food safety have garnered substantial focus due to their intimate connection with human health. Numerous biosensors have been developed for identifying deleterious compounds; however, these biosensors reveal certain limitations. Nanopore sensors, featuring nano-scaled pore size, have demonstrated outstanding performance in terms of rapidity, sensitivity, and selectivity as a single-molecule technique for environmental and food surveillance. In this review, we present a comprehensive overview of nanopore applications in these two fields. To elucidate the pioneering roles of nanopores, analytes are categorized into three distinct groups, including metal ions, synthetic contaminants, and biotoxins. Moreover, a variety of strategies are involved, such as the coalescence with ligand probes, the implementation of chemical reactions, the functionalization of nanopores, etc. These scientific studies showcase the versatility and diversity of the nanopore technique, paving the way for further developments of nanopore technology in environmental and food safety.

A Robust Parallel Computing Data Extraction Framework for Nanopore Experiments

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

Single-Molecule Bioelectronic Sensors with AI-Aided Data Analysis: Convergence and Challenges

Single-molecule bioelectronic sensing, a groundbreaking domain in biological research, has revolutionized our understanding of molecules by revealing deep insights into fundamental biological processes. The advent of emergent technologies, such as nanogapped electrodes and nanopores, has greatly enhanced this field, providing exceptional sensitivity, resolution, and integration capabilities. However, challenges persist, such as complex data sets with high noise levels and stochastic molecular dynamics. Artificial intelligence (AI) has stepped in to address these issues with its powerful data processing capabilities. AI algorithms effectively extract meaningful features, detect subtle changes, improve signal-to-noise ratios, and uncover hidden patterns in massive data. This review explores the synergy between AI and single-molecule bioelectronic sensing, focusing on how AI enhances signal processing and data analysis to boost accuracy and reliability. We also discuss current limitations and future directions for integrating AI, highlighting its potential to advance biological research and technological innovation.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.

Pulley Effect in the Capture of DNA Translocation through Solid-State Nanopores

Nanopores are powerful single-molecule sensors for analyzing biomolecules such as DNA and proteins. Understanding the dynamics of DNA capture and translocation through nanopores is essential for optimizing their performance. In this study, we examine the effects of applied voltage and pore diameter on current blockage, translocation time, collision, and capture location by translocating λ-DNA through 5.7 and 16 nm solid-state nanopores. Ionic current changes are used to infer DNA conformations during translocation. We find that translocation time increases with pore diameter, which can be attributed to the decrease of the stall force. Linear and exponential decreases of collision frequency with voltage are observed in the 16 and 5.7 nm pores, respectively, indicating a free energy barrier in the small pore. Moreover, the results reveal a voltage-dependent bias in the capture location toward the DNA ends, which is explained by a "pulley effect" deforming the DNA as it approaches the pore. This study provides insights into the physics governing DNA capture and translocation, which can be useful for promoting single-file translocation to enhance nanopore sensing.

Lithium Chloride Effects Field-Induced Protein Unfolding and the Transport Energetics Inside a Nanopipette

The tapered geometry of nanopipettes offers a unique perspective on protein transport through nanopores since both a gradual and fast confinement are possible depending on the translocation direction. The protein capture rate, unfolding, speed of translocation, and clogging probability are studied by toggling the LiCl concentration between 2 and 4 M. Interestingly, the proteins in this study could be transported with or against electrophoresis and offer vastly different attributes of sensing. Herein, a ruleset for studying proteins is developed that prevents irreversible pore clogging and yields upward of >100,000 events/nanopore. The extended duration of experiments further revealed that the capture rate takes ∼2 h to reach a steady state, emphasizing the importance of reaching equilibrated transport for studying the energetics and kinetics of protein transport (i.e., diffusion vs barrier-limited). Even in the equilibrated transport state, improper lowpass filtering was shown to distort the classification of diffusion-limited vs barrier-limited transport. Finally, electric-field-induced protein unfolding was found to be most prominent in electroosmotic-dominant transport, whereas electrophoretic-dominant events show no evidence of unfolding. Thus, our findings showcase the optimal conditions for protein translocations and the impact on studying protein unfolding, transporting energetics, and acquiring high bandwidth data.

Nanopore Fabrication Made Easy: A Portable, Affordable Microcontroller-Assisted Approach for Tailored Pore Formation via Controlled Breakdown

With growing interest in solid-state nanopore sensing─a single-molecule technique capable of profiling a host of analyte classes─establishing facile and scalable approaches for fabricating molecular-size pores is becoming increasingly important. The introduction of nanopore fabrication by controlled breakdown (CBD) has transformed the economics and accessibility of nanopore fabrication. Here, we introduce the design of an Arduino-based, portable USB-powered CBD device, with an estimated cost of <150 USD, which is ≈10-100× cheaper than most commercial solutions, capable of fabricating single nanopores conducive for single molecule sensing experiments. We demonstrate the facile fabrication of 60 tailored nanopores (∼2.6-12.6 nm) with ∼80% of the pores within 1 nm of the target diameter. Selected pores were then tested with double-stranded DNA, the canonical molecular ruler, demonstrating their performance for single-molecule sensing applications. The device is constructed with off-the-shelf readily available components and controlled using a highly customizable MATLAB application, which has capabilities encompassing pore fabrication, pore enlargement, and current-voltage acquisition for pore size estimation. When combined with a portable amplifier, this device also provides a fully portable sensing platform, an important step toward portable solid-state nanopore sensing applications.