Deep Learning-Enhanced Nanopore Sensing of Single-Nanoparticle Translocation Dynamics

Advancement of Next-Generation DNA Sequencing through Ionic Blockade and Transverse Tunneling Current Methods

DNA sequencing is transforming the field of medical diagnostics and personalized medicine development by providing a pool of genetic information. Recent advancements have propelled solid-state material-based sequencing into the forefront as a promising next-generation sequencing (NGS) technology, offering amplification-free, cost-effective, and high-throughput DNA analysis. Consequently, a comprehensive framework for diverse sequencing methodologies and a cross-sectional understanding with meticulous documentation of the latest advancements is of timely need. This review explores a broad spectrum of progress and accomplishments in the field of DNA sequencing, focusing mainly on electrical detection methods. The review delves deep into both the theoretical and experimental demonstrations of the ionic blockade and transverse tunneling current methods across a broad range of device architectures, nanopore, nanogap, nanochannel, and hybrid/heterostructures. Additionally, various aspects of each architecture are explored along with their strengths and weaknesses, scrutinizing their potential applications for ultrafast DNA sequencing. Finally, an overview of existing challenges and future directions is provided to expedite the emergence of high-precision and ultrafast DNA sequencing with ionic and transverse current approaches.

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.

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.

Fast and Efficient Design of Deep Neural Networks for Predicting N7-Methylguanosine Sites Using autoBioSeqpy

N⁷-Methylguanosine (m⁷G) is a crucial post-transcriptional RNA modification that plays a pivotal role in regulating gene expression. Accurately identifying m⁷G sites is a fundamental step in understanding the biological functions and regulatory mechanisms associated with this modification. While whole-genome sequencing is the gold standard for RNA modification site detection, it is a time-consuming, expensive, and intricate process. Recently, computational approaches, especially deep learning (DL) techniques, have gained popularity in achieving this objective. Convolutional neural networks and recurrent neural networks are examples of DL algorithms that have emerged as versatile tools for modeling biological sequence data. However, developing an efficient network architecture with superior performance remains a challenging task, requiring significant expertise, time, and effort. To address this, we previously introduced a tool called autoBioSeqpy, which streamlines the design and implementation of DL networks for biological sequence classification. In this study, we utilized autoBioSeqpy to develop, train, evaluate, and fine-tune sequence-level DL models for predicting m⁷G sites. We provided detailed descriptions of these models, along with a step-by-step guide on their execution. The same methodology can be applied to other systems dealing with similar biological questions. The benchmark data and code utilized in this study can be accessed for free at http://github.com/jingry/autoBioSeeqpy/tree/2.0/examples/m7G.

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.

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.

Procedural Data Processing for Single-Molecule Identification by Nanopore Sensors

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

Ionic Signal Amplification of DNA in a Nanopore

Ionic signal amplification is a key challenge for single-molecule analyses by solid-state nanopore sensing. Here, a permittivity gradient approach for amplifying ionic blockade characteristics of DNA in a nanofluidic channel is reported. The transmembrane ionic current response is found to change substantially through modifying the liquid permittivity at one side of a pore with an organic solvent. Imposing positive liquid permittivity gradients with respect to the direction of DNA electrophoresis, this study observes the resistive ionic signals to become larger due to the varying contributions of molecular counterions. On the contrary, negative gradients render adverse effects causing conductive ionic current pulses upon polynucleotide translocations. Most importantly, both the positive and negative gradients are demonstrated to be capable of amplifying the ionic signals by an order of magnitude with a 1.3-fold difference in the transmembrane liquid dielectric constants. This phenomenon allows a novel way to enhance the single-molecule sensitivity of nanopore sensing that may be useful in analyzing secondary structures and genome sequence of DNA by ionic current measurements.

AutoNanopore: An Automated Adaptive and Robust Method to Locate Translocation Events in Solid-State Nanopore Current Traces

Solid-state nanopore sequencing has shown impressive performances in several research scenarios but is still challenging, mainly due to the ultrafast speed of DNA translocation and significant noises embedded in raw signals. Hence, event detection, aiming to locate precisely these translocation events, is the fundamental step of data analysis. However, existing event detection methods use either a user-defined global threshold or an adaptive threshold determined by the data, assuming the baseline current to be stable over time. These disadvantages limit their applications in real-world application scenarios, especially considering that the results of different methods are often inconsistent. In this study, we develop an automated adaptive method called AutoNanopore, for fast and accurate event detection in current traces. The method consists of three consecutive steps: current trace segmentation, current amplitude outlier identification by straightforward statistical analyses, and event characterization. Then we propose ideas/metrics on how to quantitatively evaluate the performance of an event detection method, followed by comparing the performance of AutoNanopore against two state-of-the-art methods, OpenNanopore and EventPro. Finally, we examine if one method can detect the overlapping events detected by the other two, demonstrating that AutoNanopore has the highest coverage ratio. Moreover, AutoNanopore also performs well in detecting challenging events: e.g., those with significantly varying baselines.

Translocation of Specific DNA Nanocarrier through an Ultrasmall Nanopipette: Toward Single-Protein-Molecule Detection with Superior Signal-to-Noise Ratio

The use of functional DNA nanostructures as carriers to ship proteins through solid-state nanopores has recently seen substantial growth in single-protein-molecule detection (SPMD), driven by the potential of this methodology and implementations that it may enable. Ultrasmall nanopores have exhibited obvious advantages in spatiotemporal biological detection due to the appropriate nanoconfined spaces and unique properties. Herein, a 6.8 nm DNA tetrahedron (TDN) with a target-specific DNA aptamer (TDN-apt) was engineered to carry the representative target of acetylcholinesterase (AChE) through an ultrasmall nanopipet with a 30 nm orifice, underpinning the advanced SPMD of AChE with good performance in terms of high selectivity, low detection limit (0.1 fM), and especially superior signal-to-noise ratio (SNR). The kinetic interaction between TDN-apt and AChE was studied and the practical applicability of the as-developed SPMD toward real samples was validated using serum samples from patients with Alzheimer's disease. This work not only presented a feasible SPMD solution toward low-abundance proteins in complex samples and but also was envisioned to inspire more interest in the design and implementation of synergized DNA nanostructure-ultrasmall nanopore systems for future SPMD development.

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.

Inertial focusing and zeta potential measurements of single-nanoparticles using octet-nanochannels

Capture-to-translocation dynamics control is an important issue for single-particle and -molecule analyses by resistive pulse waveforms. Here, we report on regulated motions for accurate zeta-potential assessments of single nanoscale objects passing through an octet-nanochannel. We observed ionic spike signals consisting of eight consecutive sub-pulses signifying the ion blockage at the eight sensing zones in series upon electrophoretic translocation of individual nanoparticles. We find an exponential decrease to saturation of the channel-to-channel translocation duration as a nanobead moves forward, reflecting the more restricted radial motion degrees of freedom via inertial effects at the downstream side of the octet channel. This finding enabled a protocol for single-nanoparticle zeta potential estimation impervious to the uncertainty stemming from the stochastic nature of the translocation dynamics. The multi-channel approach presented in this study may be used as a useful tool for analyzing particles and molecules of variable sizes.