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

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.

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.