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INTRODUCTION The monoclonal antibodies Aducanumab, Lecanemab, Gantenerumab, and Donanemab were developed for the treatment of Alzheimer's disease (AD). METHODS We used single‐molecule detection and super‐resolution imaging to characterize the binding of these antibodies to diffusible amyloid beta (Aβ) aggregates generated in‐vitro and harvested from human brains. RESULTS Lecanemab showed the best performance in terms of binding to the small‐diffusible Aβ aggregates, affinity, aggregate coating, and the ability to bind to post‐translationally modified species, providing an explanation for its therapeutic success. We observed a Braak stage–dependent increase in small‐diffusible aggregate quantity and size, which was detectable with Aducanumab and Gantenerumab, but not Lecanemab, showing that the diffusible Aβ aggregates change with disease progression and the smaller aggregates to which Lecanemab preferably binds exist at higher quantities during earlier stages. DISCUSSION These findings provide an explanation for the success of Lecanemab in clinical trials and suggests that Lecanemab will be more effective when used in early‐stage AD. Highlights Anti amyloid beta therapeutics are compared by their diffusible aggregate binding characteristics. In‐vitro and brain‐derived aggregates are tested using single‐molecule detection. Lecanemab shows therapeutic success by binding to aggregates formed in early disease. Lecanemab binds to these aggregates with high affinity and coats them better.

In recent decades, there has been a significant increase in the application of single‐molecule electrical analysis platforms in studying proteins and peptides. These advanced analysis methods have the potential for deep investigation of enzymatic working mechanisms and accurate monitoring of dynamic changes in protein configurations, which are often challenging to achieve in ensemble measurements. In this work, the prominent research progress in peptide and protein‐related studies are surveyed using electronic devices with single‐molecule/single‐event sensitivity, including single‐molecule junctions, single‐molecule field‐effect transistors, and nanopores. In particular, the successful commercial application of nanopores in DNA sequencing has made it one of the most promising techniques in protein sequencing at the single‐molecule level. From single peptides to protein complexes, the correlation between their electrical characteristics, structures, and biological functions is gradually being established. This enables to distinguish different molecular configurations of these biomacromolecules through real‐time electrical monitoring of their life activities, significantly improving the understanding of the mechanisms underlying various life processes. This work presents a timely overview of the prominent research progress in peptide and protein‐related studies through single‐molecule electrical techniques, including single‐molecule junction, single‐molecule field‐effect transistor (FET), and nanopore. The charge transport mechanism, detection of biological interactions, visualization of dynamic biological processes, and single‐molecule accurate sequencing are discussed in depth, respectively.

Single‐molecule detection at a nanometric interface in a femtomolar solution, can take weeks as the encounter rate between the diffusing molecule to be detected and the transducing nanodevice is negligibly small. On the other hand, several experiments prove that macroscopic label‐free sensors based on field‐effect‐transistors, engaging micrometric or millimetric detecting interfaces are capable to assay a single‐molecule in a large volume within few minutes. The present work demonstrates why at least a single molecule out of a few diffusing in a 100 µL volume has a high probability to hit a large capturing and detecting electronic interface. To this end, sensing data, measured with an electrolyte‐gated FET whose gate is functionalized with 1012 capturing anti‐immunoglobulin G, are here provided along with a Brownian diffusion‐based modeling. The EG‐FET assays solutions down to some tens of zM in concentrations with volumes ranging from 25 µL to 1 mL in which the functionalized gates are incubated for times ranging from 30 s to 20 min. The high level of accordance between the experimental data and a model based on the Einstein's diffusion‐theory proves how the single‐molecule detection process at large‐capturing interfaces is controlled by Brownian diffusion and yet is highly probable and fast. A single‐molecule out of few in 100 µL diffusing according to Einstein's theory, can impinge on a large‐area (0.2 cm2) gate within 10 min. This is demonstrated by modeling the data gathered with a bioelectronic platform whose gate is functionalized with a highly packed layer of capturing‐antibodies. A general equation reproduces the data measured at different incubation volume and times.

Observing chemical reactions in complex structures such as zeolites involves a major challenge in precisely capturing single‐molecule behavior at ultra‐high spatial resolutions. To address this, a sophisticated deep learning framework tailored has been developed for integrated Differential Phase Contrast Scanning Transmission Electron Microscopy (iDPC‐STEM) imaging under low‐dose conditions. The framework utilizes a denoising super‐resolution model (Denoising Inference Variational Autoencoder Super‐Resolution (DIVAESR)) to effectively mitigate shot noise and thereby obtain substantially clearer atomic‐resolved iDPC‐STEM images. It supports advanced single‐molecule detection and analysis, such as conformation matching and elemental clustering, by incorporating object detection and Density Functional Theory (DFT) configurational matching for precise molecular analysis. the model's performance is demonstrated with a significant improvement in standard image quality evaluation metrics including Peak Signal‐to‐Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The test conducted using synthetic datasets shows its robustness and extended applicability to real iDPC‐STEM images, highlighting its potential in elucidating dynamic behaviors of single molecules in real space. This study lays a critical groundwork for the advancement of deep learning applications within electron microscopy, particularly in unraveling chemical dynamics through precise material characterization and analysis. An innovative deep learning framework utilizes a two‐stage DIVAESR model based on noise reconstruction and detailed enhancement incorporating domain knowledge to achieve atomic‐level clarity in low‐dose iDPC‐STEM images. This approach significantly optimizes shot noise handling and enables precise single‐molecule detection and analysis in complex structures, highlighting its potential for real‐time elucidation of single‐molecule dynamics.

For nearly 50 years, the vision of using single molecules in circuits has been seen as providing the ultimate miniaturization of electronic chips. An advanced example of such a molecular electronics chip is presented here, with the important distinction that the molecular circuit elements play the role of general-purpose singlemolecule sensors. The device consists of a semiconductor chip with a scalable array architecture. Each array element contains a synthetic molecular wire assembled to span nanoelectrodes in a current monitoring circuit. A central conjugation site is used to attach a single probe molecule that defines the target of the sensor. The chip digitizes the resulting picoamp-scale current-versus-time readout from each sensor element of the array at a rate of 1,000 frames per second. This provides detailed electrical signatures of the single-molecule interactions between the probe and targets present in a solution-phase test sample. This platform is used to measure the interaction kinetics of single molecules, without the use of labels, in a massively parallel fashion. To demonstrate broad applicability, examples are shown for probe molecule binding, including DNA oligos, aptamers, antibodies, and antigens, and the activity of enzymes relevant to diagnostics and sequencing, including a CRISPR/Cas enzyme binding a target DNA, and a DNA polymerase enzyme incorporating nucleotides as it copies a DNA template. All of these applications are accomplished with high sensitivity and resolution, on a manufacturable, scalable, all-electronic semiconductor chip device, thereby bringing the power of modern chips to these diverse areas of biosensing.

Nanopore-based sensors have established themselves as a prominent tool for solution-based, single-molecule analysis of the key building blocks of life, including nucleic acids, proteins, glycans and a large pool of biomolecules that have an essential role in life and healthcare. The predominant molecular readout method is based on measuring the temporal fluctuations in the ionic current through the pore. Recent advances in materials science and surface chemistries have not only enabled more robust and sensitive devices but also facilitated alternative detection modalities based on field-effect transistors, quantum tunnelling and optical methods such as fluorescence and plasmonic sensing. In this Review, we discuss recent advances in nanopore fabrication and sensing strategies that endow nanopores not only with sensitivity but also with selectivity and high throughput, and highlight some of the challenges that still need to be addressed. Nanopore sensors enable the solution-based analysis of nucleic acids, proteins and other biomolecules at the single-molecule level. This Review discusses new fabrication and sensing strategies — including field-effect transistors, quantum tunnelling and optical methods — that enhance the sensitivity and selectivity of nanopores.

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) has the potential to detect single molecules in a non-invasive, label-free manner with high-throughput. SM-SERS can detect chemical information of single molecules without statistical averaging and has wide application in chemical analysis, nanoelectronics, biochemical sensing, etc. Recently, a series of unprecedented advances have been realized in science and application by SM-SERS, which has attracted the interest of various fields. In this review, we first elucidate the key concepts of SM-SERS, including enhancement factor (EF), spectral fluctuation, and experimental evidence of single-molecule events. Next, we systematically discuss advanced implementations of SM-SERS, including substrates with ultra-high EF and reproducibility, strategies to improve the probability of molecules being localized in hotspots, and nonmetallic and hybrid substrates. Then, several examples for the application of SM-SERS are proposed, including catalysis, nanoelectronics, and sensing. Finally, we summarize the challenges and future of SM-SERS. We hope this literature review will inspire the interest of researchers in more fields.

Techniques to analyze proteins often involves complex workflows and/or sophisticated equipment with modest limits-of-detection. While fluorescence spectroscopy can interrogate single molecules, it often requires fluorescence labeling with lasers and microscopes. We report herein a label-free approach for analyzing intact proteins using resistive pulse sensing (RPS). RPS data were secured using a unique RPS device, which we call a dual in-plane nanopore sensor, fabricated in a thermoplastic. The nanopore sensor was produced via nano-injection molding with critical structures of 30 nm, enabling the detection of individual protein molecules and providing an approach toward their identification. Following nano-injection molding, the pore size could be reduced to ∼ 10 nm using thermal fusion bonding of a cover plate to the molded substrate. The device architecture contained two in-plane nanopores flanking a nanochannel (50 × 50 nm width × depth and 5 µm length) that facilitated the measurement of the apparent electrophoretic mobilities of protein molecules in a label free manner via their molecular-dependent time-of-flight (ToF; time-difference between two consecutive RPS events—peak pair). We investigated four model proteins and collected multiple characteristics including RPS peak amplitude and dwell time, as well as an RPS-independent value, which was the ToF. Furthermore, we analyzed the temporal profiles of RPS events revealing distinct peak shapes for spherical and non-spherical proteins that were influenced by their rotational motion when resident within the nanopore.

Nanopores have emerged as powerful tools for single-molecule detection, enabling real-time analysis across diverse applications in genomics and molecular diagnostics. While natural pores laid the foundation for single-molecule detection, their limited diversity has driven advances in protein engineering and, more recently, de novo design to create customizable nanopore sensors. Computational approaches now allow for the design of nanopores with tailored geometries, enhanced stability, and specific molecular recognition functions. Together, these advances are ushering in a new era of programmable nanopore sensors with broad applications in diagnostics and molecular biotechnology.