The emerging landscape of single-molecule protein sequencing technologies

DNA-PAINT Imaging with Hydrogel Imprinting and Clearing

Hydrogel-embedding is a versatile technique in fluorescence microscopy, offering stabilization, optical clearing, and the physical expansion of biological specimens. DNA-PAINT is a super-resolution microscopy approach based on the diffusion and transient binding of fluorescently labeled oligos, but its feasibility in hydrogels has not yet been explored. In this study, we demonstrate that polyacrylamide hydrogels support sufficient diffusion for effective DNA-PAINT imaging. Using acrydite-anchored oligonucleotides imprinted from patterned DNA origami nanostructures and microtubule filaments in fixed cells, we find that hydrogel embedding preserves docking strand positioning at the nanoscale. Sample clearing via protease treatment had minor structural effects on the microtubule structure and enhanced diffusion and accessibility to hydrogel-imprinted docking strands. Our work demonstrates promising potential for diffusion and binding-based fluorescence imaging applications in hydrogel-embedded samples.

Exploring a solid-state nanopore approach for single-molecule protein detection from single cells

Direct protein analysis from complex cellular samples is crucial for understanding cellular diversity and disease mechanisms. Here, we explored the potential of SiN x solid-state nanopores for single-molecule protein analysis from complex cellular samples. Using the LOV2 protein as a model, we designed a nanopore electrophoretic driver protein and fused it with LOV2, thereby enhancing the capture efficiency of the target protein. Then, we performed ex situ single-cell protein analysis by directly extracting the contents of individual cells using glass nanopipette-based single-cell extraction and successfully identified and monitored the conformational changes of the LOV2 protein from single-cell extracts using SiN x nanopores. Our results reveal significant differences between proteins measured directly from single cells and those obtained from purified samples. This work demonstrates the potential of solid-state nanopores as a powerful tool for single-cell, single-molecule protein analysis, opening avenues for investigating protein dynamics and interactions at the cellular level.

Developments of the mDZ-RhoBAST Probe for Super-Resolution Imaging of FTO Demethylation in Live Cells

Single-molecule localization microscopy (SMLM) provides precise, high-resolution visualization of target biomolecules in cellular imaging. Fluorescent light-up aptamers (FLAPs) offer a versatile approach for labeling RNA of interest in live cells by binding to fluorogens through adaptive recognition. Several FLAPs can generate blinking fluorescent signals in conjunction with fluorogens, enabling single-molecule imaging of RNA in living cells. An example of this is RhoBAST, which has been successfully used for single-molecule imaging of RNA due to its fast exchange kinetics with fluorogens. However, single-molecule imaging of RNA activities regulated by proteins in living cells presents a greater challenge. Here, we introduce mDZ-RhoBAST, a nucleic acid probe, to visualize the demethylation process by fat mass and obesity-associated protein (FTO) in living cells. This probe combines the 6mA-modified DNAzyme with the RhoBAST FLAP, activated by FTO, restoring the DNAzyme's cleavage activity and releasing RhoBAST to bind the target fluorogen. Our approach achieved super-resolution imaging of the demethylation process of endogenous FTO, providing real-time insights into its demethylation processes in vivo.

A series of reviews in familial cancer: genetic cancer risk in context variants of uncertain significance in MMR genes: which procedures should be followed?

Interpreting variants of uncertain significance (VUS) in mismatch repair (MMR) genes remains a major challenge in managing Lynch syndrome and other hereditary cancer syndromes. This review outlines recommended VUS classification procedures, encompassing foundational and specialized methodologies tailored for MMR genes by expert organizations, including InSiGHT and ClinGen's Hereditary Colorectal Cancer/Polyposis Variant Curation Expert Panel (VCEP). Key approaches include: (1) functional data, encompassing direct assays measuring MMR proficiency such as in vitro MMR assays, deep mutational scanning, and MMR cell-based assays, as well as techniques like methylation-tolerant assays, proteomic-based approaches, and RNA sequencing, all of which provide critical functional evidence supporting variant pathogenicity; (2) computational data/tools, including in silico meta-predictors and models, which contribute to robust VUS classification when integrated with experimental evidence; and (3) enhanced variant detection to identify the actual causal variant through whole-genome sequencing and long-read sequencing to detect pathogenic variants missed by traditional methods. These strategies improve diagnostic precision, support clinical decision-making for Lynch syndrome, and establish a flexible framework that can be applied to other OMIM-listed genes.

Orientation dependence of current blockade in single amino acid translocation through a graphene nanopore

After the successful commercialization of DNA sequencing using biological nanopores, the next frontier for nanopore technology is protein sequencing, a significantly more complex task. Molecules passing through the solid-state nanopores produce current blockades that correlate linearly with their volume in the simplest model. As thinner membranes provide better volume sensitivity, 2D materials such as graphene and MoS2 membranes have been explored. Molecular dynamics studies have primarily focused on the translocation of the homogeneous polypeptide chains through 2D membranes. In this study, we investigated the translocation of 20 single amino acids through the monolayer and bilayer graphene nanopores using the all-atom molecular dynamics. These studies were motivated by the fact that single amino acids, as fundamental units of peptide chains, provide a simpler model for understanding pore-molecule interactions during translocations by eliminating the neighbour effects found in chains. Herein, it is shown that the correlation between the ionic current blockade and the volume of single amino acids is strongly affected by their orientation at the pore, especially when the molecule is static at the pore. We explained this phenomenon by the fact that with increasing vdW volume, the amino acid in a particular orientation has a longer projection along the perpendicular direction of the pore plane. We demonstrated distinctive current and force signals for different amino-acid translocations. We observed that some of the smaller amino acids with low molecular volume produced disproportionately high current blockades in a particular orientation due to their lower structural fluctuations during translocation. We investigated how the dipole moment (of the translocating amino acids) and its alignment with the electric field in the pores were linked with our observations.

How did we get there? AI applications to biological networks and sequences

The rapidly advancing field of artificial intelligence (AI) has transformed numerous scientific domains, including biology, where a vast and complex volume of data is available for analysis. This paper provides a comprehensive overview of the current state of AI-driven methodologies in genomics, proteomics, and systems biology. We discuss how machine learning algorithms, particularly deep learning models, have enhanced the accuracy and efficiency of embedding sequences, motif discovery, and the prediction of gene expression and protein structure. Additionally, we explore the integration of AI in the embedding and analysis of biological networks, including protein-protein interaction networks and multi-layered networks. By leveraging large-scale biological data, AI techniques have enabled unprecedented insights into complex biological processes and disease mechanisms. This work underlines the potential of applying AI to complex biological data, highlighting current applications and suggesting directions for future research to further explore AI in this rapidly evolving field.

Proteomics as a complementary approach to measure norovirus infection in clinical samples

Norovirus (NoV) is a leading cause of global acute gastroenteritis, particularly in young children, with no current licensed vaccine. Epidemiological studies have shown that asymptomatic cases are common, and infected patients may test positive for prolonged periods; however, the impact of these phenomena on transmission and public health measures remains unclear. A major limiting factor is our ability to measure infection, which is constrained to real-time reverse transcription polymerase chain reaction or antibody-based assays, both of which are susceptible to loss of detection by rapid NoV evolution. This review highlights the potential for proteomics to overcome current technical limitations and advance basic science discovery and clinical research. Importantly, proteomics-based protein detection can span NoV, host, and microbiome proteins and could help identify host or microbiome factors that correlate with disease outcome. Further developing proteomics tools to complement existing diagnostic technologies will improve our ability to assess NoV pathogenesis and transmission, as well as therapeutic efficacy.

The Emergence of Nanofluidics for Single-Biomolecule Manipulation and Sensing

Driven by recent advancements in nanofabrication techniques, single-molecule sensing and manipulations in nanofluidic devices are rapidly evolving. These sophisticated biosensors have already had significant impacts on basic research as well as on applications in molecular diagnostics. The nanoscale dimensions of these devices introduce new physical phenomena by confining the biomolecules in at least one dimension, creating effects such as biopolymer linearization, stretching, and separation by mass that are utilized to enhance the biomolecule sensing resolutions. At the same time, the suppressed diffusional motion allows for better single-molecule SNR (signal-to-noise ratio) sensing over time. In particular, nanofluidic devices based on nanochannels have been established as promising technologies for the linearization of ultralong genomic DNA molecules and for optical genome mapping, opening a window to directly observe and infer genome organization. More recently, nanochannels have shown promising capabilities for single-molecule protein sizing, separation, and identification. Consequently, this technology is attracting remarkable interest for applications in single-molecule proteomics. In this review, we discuss the recent advancements of nanochannel-based technologies, focusing on their applications for single-molecule sensing and the characterization of a wide range of biomolecules.

Photolysis of the peptide bond at 193 and 222 nm

Ultraviolet (UV) light is a well-established tool for fragmenting peptides in vacuum. This study investigates the fragmentation of peptides using 193 and 222 nm light in aqueous solution. Changes in the absorption spectra of solutions of the model dipeptide glycylglycine are monitored using a combination of real-time in situ transmission measurements and UV-Vis spectroscopy to report peptide bond scission following UV irradiation. Irradiation by a broadband ultraviolet light source flattens the absorbance peak centered near 193 nm, indicating cleavage of peptide bonds. Irradiation with low-intensity, monochromatic 193 and 222 nm light enabled measurements of the single-photon quantum yield of peptide bond scission, found to be (1.50 ± 0.12)% at 193 nm and (0.16 ± 0.03)% at 222 nm. These findings indicate that peptides may be fragmented in solution prior to emission into a mass spectrometer for new types of single-molecule analyses. The susceptibility of peptide bonds to ultraviolet radiation also suggests limited lifetimes for peptides on the early Earth's surface, which are relevant to theories of the origins-of-life, and suggests a role for protein damage in explanations of the germicidal effect of 222 nm light exposure.

Nanochannel functionalization using POFs: Progress and prospects

Biomimetic nanochannels, inspired by natural ion channels found in living organisms, are synthetic systems designed to replicate the highly selective and efficient ion/molecule transport processes essential for various biological functions. These artificial channels mimic the structural and functional properties of their biological counterparts, offering precise control over ion and molecular transport. Porous organic framework materials (POFs), including metal-organic frameworks (MOFs) and covalent organic frameworks (COFs), have emerged as promising materials for functionalizing nanochannels due to their unique structures and exceptional properties. This functionalization strategy not only enhances the performance of synthetic nanochannels but also broadens their application potential across various fields. This review comprehensively examines the recent progress in the preparation and application of POFs stereoscopic-functionalized solid nanochannels. Special emphasis is placed on their practical applications, including proton conduction, ion-selective membranes, photo-responsive materials, sensing and detection, chiral separation, and catalysis. Finally, the future development prospects and challenges in this research area are discussed, highlighting opportunities for advancing the design and application of biomimetic nanochannels.

After 75 Years, an Alternative to Edman Degradation: A Mechanistic and Efficiency Study of a Base-Induced Method for N-Terminal Peptide Sequencing

The sequencing of peptides via N-terminal amino acid removal is a classic reaction termed Edman degradation. This method involves repeated treatment of the N-terminal amino group of a peptide with phenyl isothiocyanate (PITC), followed by treatment with trifluoroacetic acid. Spurred by the need for an alternative non-acid-based chemistry for next-generation protein sequencing technologies, we developed a base-induced N-terminal degradation method. Several N-terminal derivatization reagents carrying supernucleophiles were tested. After rounds of iterative designs, compound DR3, with a N-hydroxysuccinimide as a leaving group and hydrazinecarboxamide as the supernucleophile, demonstrated the highest yield for the peptide derivatization step and the most efficient elimination of the N-terminal amino acid in just 1% of a hydroxide salt. The method successfully removed all 20 amino acids at the N-terminus in high yield. The technique demonstrates compatibility with oligonucleic acids, which differs from Edman degradation due to their inherent sensitivity to acidic environments. To demonstrate the practical application of our approach, we sequenced amino acids sequentially from a peptide, effectively determining the sequence of an unknown peptide. Notably, our methodology was successfully applied to mixtures of peptides derived from protein samples, where a significant fraction of the peptides derivatized with DR3 underwent elimination of their N-terminal amino acid upon addition of base. Overall, although our method does not outperform Edman degradation in efficiency, it serves as a valuable alternative in cases where base-induced cleavage is advantageous, particularly for preserving acid-sensitive functionalities.

Multidimensional Investigations of Single Molecule Unfolding of Bovine Serum Albumin Using Plasmonic Nanopores

Direct detection of proteins, especially their conformation and configuration information, at the single molecule level, is challenging in various biotechnological fields. Plasmonic nanopores have raised attention as multidimensional biosensors with single molecule (SM) sensitivity. Here, we employ a gold plasmonic nanopore to monitor the unfolding of SM bovine serum albumin (BSA). The gradual collapse of the BSA structure induced by high bias voltages is demonstrated through an increase in the fraction current blockade. Surface-enhanced Raman scattering (SERS) spectra provide structural evidence for protein unfolding, while the optical force is verified as an additional factor contributing to BSA deformation. The effect of the optical force on the dwell time of BSA in a nanopore is also investigated. The present study reveals that plasmonic nanopores offer multidimensional observations on the structure and conformation of SM proteins, which will drive further innovations in protein detection and analysis.

Nanopore sensing of protein and peptide conformation for point-of-care applications

The global population's aging and growth will likely result in an increase in chronic aging-related diseases. Early diagnosis could improve the medical care and quality of life. Many diseases are linked to misfolding or conformational changes in biomarker peptides and proteins, which affect their function and binding properties. Current clinical methods struggle to detect and quantify these changes. Therefore, there is a need for sensitive conformational sensors that can detect low-concentration analytes in biofluids. Nanopore electrical detection has shown potential in sensing subtle protein and peptide conformation changes. This technique can detect single molecules label-free while distinguishing shape or physicochemical property changes. Its proven sensitivity makes nanopore sensing technology promising for ultra-sensitive, personalized point-of-care devices. We focus on the capability of nanopore sensing for detecting and quantifying conformational modifications and enantiomers in biomarker proteins and peptides and discuss this technology as a solution to future societal health challenges.

Discrimination and Translocation of Charged Proteinogenic Amino Acids through a Single-Walled Carbon Nanotube

Nanopore sensing relies on associating the measured current signals with specific features of the target molecules. The diversity of amino acids presents significant challenges in detecting and sequencing peptides and proteins. The hollow and uniform tubular structure of single-walled carbon nanotubes (SWCNTs) makes them ideal candidates for nanopore sensors. Here, we demonstrate by molecular dynamics simulations the discrimination and translocation of charged proteinogenic amino acids through the nanopore sensor formed by inserting a SWCNT into lipid bilayers. Moreover, our analysis suggests that the current blockade is influenced not only by excluded atomic volume but also by noncovalent interactions between amino acids and SWCNT during similar helical translocation. The presence of noncovalent interactions enhances the understanding of current differences in nanopore translocation of molecules with similar excluded atomic volume. This finding provides new perspectives and applications for the optimal design of SWCNT nanopore sensors.

Interpretable Identification of Single-Molecule Charge Transport via Fusion Attention-Based Deep Learning

Interpretability is fundamental in the precise identification of single-molecule charge transport, and its absence in deep learning models is currently the major barrier to the usage of such powerful algorithms in the field. Here, we have pioneered a novel identification method employing fusion attention-based deep learning technologies. Central to our approach is the innovative neural network architecture, SingleFACNN, which integrates convolutional neural networks with a fusion of multihead self-attention and spatial attention mechanisms. Our findings demonstrate that SingleFACNN accurately classifies the three-type and four-type STM-BJ data sets, leveraging the convolutional layers' robust feature extraction and the attention layers' capacity to capture long-range interactions. Through comprehensive gradient-weighted class activation mapping and ablation studies, we identified and analyzed the critical features impacting classification outcomes with remarkable accuracy, thus enhancing the interpretability of our deep learning model. Furthermore, SingleFACNN's application was extended to mixed samples with varying proportions, achieving commendable prediction performance at low computational cost. Our study underscores the potential of SingleFACNN in advancing the interpretability and credibility of deep learning applications in single-molecule charge transport, opening new avenues for single-molecule detection in complex systems.

Surface-Enhanced Raman Spectroscopy for Biomedical Applications: Recent Advances and Future Challenges

The year 2024 marks the 50th anniversary of the discovery of surface-enhanced Raman spectroscopy (SERS). Over recent years, SERS has experienced rapid development and became a critical tool in biomedicine with its unparalleled sensitivity and molecular specificity. This review summarizes the advancements and challenges in SERS substrates, nanotags, instrumentation, and spectral analysis for biomedical applications. We highlight the key developments in colloidal and solid SERS substrates, with an emphasis on surface chemistry, hotspot design, and 3D hydrogel plasmonic architectures. Additionally, we introduce recent innovations in SERS nanotags, including those with interior gaps, orthogonal Raman reporters, and near-infrared-II-responsive properties, along with biomimetic coatings. Emerging technologies such as optical tweezers, plasmonic nanopores, and wearable sensors have expanded SERS capabilities for single-cell and single-molecule analysis. Advances in spectral analysis, including signal digitalization, denoising, and deep learning algorithms, have improved the quantification of complex biological data. Finally, this review discusses SERS biomedical applications in nucleic acid detection, protein characterization, metabolite analysis, single-cell monitoring, and in vivo deep Raman spectroscopy, emphasizing its potential for liquid biopsy, metabolic phenotyping, and extracellular vesicle diagnostics. The review concludes with a perspective on clinical translation of SERS, addressing commercialization potentials and the challenges in deep tissue in vivo sensing and imaging.

Vectorial Discrimination of Small Molecules with a Macrocycle Adaptor-Protein Nanopore System and Nanocavity-Dependent, pH Gradient-Controlled Analyte Kinetics

Owing to their intrinsic qualities, protein nanopores became game-changers in the realm of analyte sensing, as they offer an inexpensive and label-free method for sophisticated recognition at the single-molecule level. Here, we exploit the complexation capability of nonfunctionalized γ-cyclodextrin (γ-CD), coupled with its propensity to get reversibly captured inside a wild-type α-hemolysin nanopore (α-HL), and achieve a hybrid construct endowing specific sensing of selected, 5 bases-long oligonucleotides. We find that the molecular discrimination capability of the system has a vectorial-like sensitivity and is influenced by the sidedness and geometry of γ-CD. We showcase that asymmetrical pH changes across the γ-CD-α-HL hybrid and the ensuing electro-osmotic flow offer a simple yet powerful method to control γ-CD capture and residence time inside the nanopore, highlighting the capability of programmable sensing of spatially separated analytes. Unexpectedly, the electro-osmotic flow ensued via pH changes exerted a negligible effect on host (γ-CD)-guest (analyte) interactions, suggesting the complexity arising from a combination of hydrodynamic effects in a restricted environment and electrostatics screening in hydrophobic nanoconfinement. We present evidence that the asymmetric, low pH-mediated, electro-osmotic stabilization of a γ-CD molecule inside α-HL enables probing of β-lactam antibiotic azlocillin encapsulation inside γ-CD under distinct ionization states.

Slowing Down Peptide Translocation through MoSi2N4 Nanopores for Protein Sequencing

Precise identification and quantification of amino acids are crucial for numerous biological applications. A significant challenge in the development of high-throughput, cost-effective nanopore protein sequencing technology is the rapid translocation of protein through the nanopore, which hinders accurate sequencing. In this study, we explore the potential of nanopore constructed from a novel two-dimensional (2D) material MoSi2N4 in decelerating the velocity of protein translocation using molecular dynamics simulations. The translocation velocity of the peptide through the MoSi2N4 nanopore can be reduced by nearly an order of magnitude compared to the MoS2 nanopore. Systematic analysis reveals that this reduction is due to stronger interaction between the peptide and MoSi2N4 membrane surface, particularly for aromatic residues, as they contain aromatic rings composed of relatively nonpolar C-C and C-H bonds. By adjusting the proportion of aromatic residues in peptides, further control over peptide translocation velocity can be achieved. Additionally, the system validates the feasibility of using an appropriate nanopore diameter for protein sequencing. The theoretical investigations presented herein suggest a potential method for manipulating protein translocation kinetics, promising more effective and economical advancements in nanopore protein sequencing technology.

Enhanced sensitivity and scalability with a Chip-Tip workflow enables deep single-cell proteomics

Single-cell proteomics (SCP) promises to revolutionize biomedicine by providing an unparalleled view of the proteome in individual cells. Here, we present a high-sensitivity SCP workflow named Chip-Tip, identifying >5,000 proteins in individual HeLa cells. It also facilitated direct detection of post-translational modifications in single cells, making the need for specific post-translational modification-enrichment unnecessary. Our study demonstrates the feasibility of processing up to 120 label-free SCP samples per day. An optimized tissue dissociation buffer enabled effective single-cell disaggregation of drug-treated cancer cell spheroids, refining overall SCP analysis. Analyzing nondirected human-induced pluripotent stem cell differentiation, we consistently quantified stem cell markers OCT4 and SOX2 in human-induced pluripotent stem cells and lineage markers such as GATA4 (endoderm), HAND1 (mesoderm) and MAP2 (ectoderm) in different embryoid body cells. Our workflow sets a benchmark in SCP for sensitivity and throughput, with broad applications in basic biology and biomedicine for identification of cell type-specific markers and therapeutic targets.

Emerging protein sequencing technologies: proteomics without mass spectrometry?

INTRODUCTION

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has been a leading method for proteomics for 30 years. Advantages provided by LC-MS/MS are offset by significant disadvantages, including cost. Recently, several non-mass spectrometric methods have emerged, but little information is available about their capacity to analyze the complex mixtures routine for mass spectrometry.

AREAS COVERED

We review recent non-mass-spectrometric methods for sequencing proteins and peptides, including those using nanopores, sequencing by degradation, reverse translation, and short-epitope mapping, with comments on bioinformatics challenges, fundamental limitations, and areas where new technologies will be more or less competitive with LC-MS/MS. In addition to conventional literature searches, instrument vendor websites, patents, webinars, and preprints were also consulted to give a more up-to-date picture.

EXPERT OPINION

Many new technologies are promising. However, demonstrations that they outperform mass spectrometry in terms of peptides and proteins identified have not yet been published, and astute observers note important disadvantages, especially relating to the dynamic range of single-molecule measurements of complex mixtures. Still, even if the performance of emerging methods proves inferior to LC-MS/MS, their low cost could create a different kind of revolution: a dramatic increase in the number of biology laboratories engaging in new forms of proteomics research.

Sub-5 nm Silicon Nanopore Sensors: Scalable Fabrication via Self-Limiting Metal-Assisted Chemical Etching

Solid-state nanopores offer unique possibilities for biomolecule sensing; however, scalable production of sub-5 nm pores with precise diameter control remains a manufacturing challenge. In this work, we developed a scalable method to fabricate sub-5 nm nanopores in silicon (Si) nanomembranes through metal-assisted chemical etching (MACE) using gold nanoparticles. Notably, we present a previously unreported self-limiting effect that enables sub-5 nm nanopore formation from both 10 and 40 nm nanoparticles in the 12 nm thick monocrystalline device layer of a silicon-on-insulator substrate. This effect reveals distinctive etching dynamics in ultrathin Si nanomembranes, enabling precise control over nanopore dimensions. The resulting nanopore sensor, suspended over self-aligned spheroidal oxide undercuts with diameters of just a few hundred nanometers, exhibited low electrical noise and high stability due to encapsulation within dielectric layers. In DNA translocation experiments, our nanopore platform could distinguish folded and unfolded DNA conformations and maintained stable baseline conductance for up to 6 h, demonstrating both sensitivity and robustness. Our scalable nanopore fabrication method is compatible with wafer-level and batch processing and holds promise for advancing biomolecular sensing and analysis.

Next-Generation Protein Sequencing and individual ion mass spectrometry enable complementary analysis of interleukin-6

The vast complexity of the proteome currently overwhelms any single analytical technology in capturing the full spectrum of proteoform diversity. In this study, we evaluated the complementarity of two cutting-edge proteomic technologies-single-molecule protein sequencing and individual ion mass spectrometry-for analyzing recombinant human IL-6 (rhIL-6) at the amino acid, peptide, and intact proteoform levels. For single-molecule protein sequencing, we employ the recently released Platinum® instrument. Next-Generation Protein Sequencing™ (NGPS™) on Platinum utilizes cycles of N-terminal amino acid recognizer binding and aminopeptidase cleavage to enable parallelized sequencing of single peptide molecules. We found that NGPS produces single amino acid coverage of multiple key regions of IL-6, including two peptides within helices A and C which harbor residues that reportedly impact IL-6 function. For top-down proteoform evaluation, we use individual ion mass spectrometry (I2MS), a highly parallelized orbitrap-based charge detection MS platform. Single ion detection of gas-phase fragmentation products (I2MS2) gives significant sequence coverage in key regions in IL-6, including two regions within helices B and D that are involved in IL-6 signaling. Together, these complementary technologies deliver a combined 52% sequence coverage, offering a more complete view of IL-6 structural and functional diversity than either technology alone. This study highlights the synergy of complementary protein detection methods to more comprehensively cover protein segments relevant to biological interactions.

Machine learning approaches for predicting protein-ligand binding sites from sequence data

Proteins, composed of amino acids, are crucial for a wide range of biological functions. Proteins have various interaction sites, one of which is the protein-ligand binding site, essential for molecular interactions and biochemical reactions. These sites enable proteins to bind with other molecules, facilitating key biological functions. Accurate prediction of these binding sites is pivotal in computational drug discovery, helping to identify therapeutic targets and facilitate treatment development. Machine learning has made significant contributions to this field by improving the prediction of protein-ligand interactions. This paper reviews studies that use machine learning to predict protein-ligand binding sites from sequence data, focusing on recent advancements. The review examines various embedding methods and machine learning architectures, addressing current challenges and the ongoing debates in the field. Additionally, research gaps in the existing literature are highlighted, and potential future directions for advancing the field are discussed. This study provides a thorough overview of sequence-based approaches for predicting protein-ligand binding sites, offering insights into the current state of research and future possibilities.

Detection and distinction of amino acids and post-translational modifications with gold nanojunctions

Amino acids are fundamental building blocks of proteins, playing critical roles in medical diagnostics, environmental monitoring, and biomarker identification. The development of nanoscale electronic sensors capable of single-amino-acid recognition has gained significant attention due to their potential for label-free, real-time detection. In this study, we investigate the electronic transport properties of amino acids in two gold-based nanodevices with distinct architectures: a gold nanojunction and a gold-capacitor system. Using density functional theory (DFT) in combination with nonequilibrium Green's function (NEGF) calculations, we explore the sensing mechanism and conductance variations induced by different amino acids, including select phosphorylated variants. Each device was assigned a reference amino acid, F (M), for a capacitor (nanojunction) to differentiate its conductance from other molecules. Our results reveal distinct conductance that enables amino acid classification based on their electronic signatures, demonstrating that molecular discrimination is primarily governed by conductance variations as a function of the binding energy differences. The nanojunction exhibited remarkable differentiation for the amino acids S, pS, Y, and pY, rendering it especially proficient in distinguishing between structurally analogous molecules. These findings highlight the strong potential of gold-based nanodevices for precise amino acid detection, paving the way for advancements in biosensing technologies, molecular diagnostics, and biomedical applications.

Engineering GID4 for use as an N-terminal proline binder via directed evolution

Nucleic acid sequencing technologies have gone through extraordinary advancements in the past several decades, significantly increasing throughput while reducing cost. To create similar advancement in proteomics, numerous approaches are being investigated to advance protein sequencing. One of the promising approaches uses N-terminal amino acid binders (NAABs), also referred to as recognizers, that selectively can identify amino acids at the N-terminus of a peptide. However, there are only a few engineered NAABs currently available that bind to specific amino acids and meet the requirements of a biotechnology reagent. Therefore, additional NAABs need to be identified and engineered to enable confident identification and, ultimately, de novo protein sequencing. To fill this gap, a human protein GID4 was engineered to create a NAAB for N-terminal proline (Nt-Pro). While native GID4 binds Nt-Pro, its binding is weak (µmol/L) and greatly influenced by the identity of residues following the Nt-Pro. Through directed evolution, yeast-surface display, and fluorescence-activated cell sorting, we identified sequence variants of GID4 with increased binding response to Nt-Pro. Moreover, variants with an A252V mutation showed a reduced influence from residues in the second and third positions of the target peptide when binding to Nt-Pro. The workflow outlined here is shown to be a viable strategy for engineering NAABs, even when starting from native Nt-binding proteins whose binding is strongly impacted by the identity of residues following Nt-amino acid.

Programmable DNA Reactions for Advanced Fluorescence Microscopy in Bioimaging

Biological organisms are composed of billions of molecules organized across various length scales. Direct visualization of these biomolecules in situ enables the retrieval of vast molecular information, including their location, species, and quantities, which is essential for understanding biological processes. The programmability of DNA interactions has made DNA-based reactions a major driving force in extending the limits of fluorescence microscopy, allowing for the study of biological complexity at different scales. This review article provides an overview of recent technological advancements in DNA-based fluorescence microscopy, highlighting how these innovations have expanded the technique's capabilities in terms of target multiplexity, signal amplification, super-resolution, and mechanical properties. These advanced DNA-based fluorescence microscopy techniques have been widely used to uncover new biological insights at the molecular level.

scTrends: A living review of commercial single-cell and spatial 'omic technologies

Understanding the rapidly evolving landscape of single-cell and spatial omic technologies is crucial for advancing biomedical research and drug development. We provide a living review of both mature and emerging commercial platforms, highlighting key methodologies and trends shaping the field. This review spans from foundational single-cell technologies such as microfluidics and plate-based methods to newer approaches like combinatorial indexing; on the spatial side, we consider next-generation sequencing and imaging-based spatial transcriptomics. Finally, we highlight emerging methodologies that may fundamentally expand the scope for data generation within pharmaceutical research, creating opportunities to discover and validate novel drug mechanisms. Overall, this review serves as a critical resource for navigating the commercialization and application of single-cell and spatial omic technologies in pharmaceutical and academic research.

A major step forward toward high-resolution nanopore sequencing of full-length proteins

In a recent publication in Nature, Motone et al.1 report the development of a protein sequencing method using nanopores that enables the reading of long protein strands. This method allows for multi-pass re-reading and can detect single amino acid substitutions as well as post-translational modifications (PTMs).

Engineered serum markers for non-invasive monitoring of gene expression in the brain

Measurement of gene expression in the brain requires invasive analysis of brain tissue or non-invasive methods that are limited by low sensitivity. Here we introduce a method for non-invasive, multiplexed, site-specific monitoring of endogenous gene or transgene expression in the brain through engineered reporters called released markers of activity (RMAs). RMAs consist of an easily detectable reporter and a receptor-binding domain that enables transcytosis across the brain endothelium. RMAs are expressed in the brain but exit into the blood, where they can be easily measured. We show that expressing RMAs at a single mouse brain site representing approximately 1% of the brain volume provides up to a 100,000-fold signal increase over the baseline. Expression of RMAs in tens to hundreds of neurons is sufficient for their reliable detection. We demonstrate that chemogenetic activation of cells expressing Fos-responsive RMA increases serum RMA levels >6-fold compared to non-activated controls. RMAs provide a non-invasive method for repeatable, multiplexed monitoring of gene expression in the intact animal brain.

Profiling protein-protein interactions to predict the efficacy of B-cell-lymphoma-2-homology-3 mimetics for acute myeloid leukaemia

B-cell-lymphoma-2 (BCL2) homology-3 (BH3) mimetics are inhibitors of protein-protein interactions (PPIs) that saturate anti-apoptotic proteins in the BCL2 family to induce apoptosis in cancer cells. Despite the success of the BH3-mimetic ABT-199 for the treatment of haematological malignancies, only a fraction of patients respond to the drug and most patients eventually develop resistance to it. Here we show that the efficacy of ABT-199 can be predicted by profiling the rewired status of the PPI network of the BCL2 family via single-molecule pull-down and co-immunoprecipitation to quantify more than 20 types of PPI from a total of only 1.2 × 106 cells per sample. By comparing the obtained multidimensional data with BH3-mimetic efficacies determined ex vivo, we constructed a model for predicting the efficacy of ABT-199 that designates two complexes of the BCL2 protein family as the primary mediators of drug effectiveness and resistance, and applied it to prospectively assist therapeutic decision-making for patients with acute myeloid leukaemia. The characterization of PPI complexes in clinical specimens opens up opportunities for individualized protein-complex-targeting therapies.

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.

Single-Cell RNA Sequencing and Combinatorial Approaches for Understanding Heart Biology and Disease

By directly measuring multiple molecular features in hundreds to millions of single cells, single-cell techniques allow for comprehensive characterization of the diversity of cells in the heart. These single-cell transcriptome and multi-omic studies are transforming our understanding of heart development and disease. Compared with single-dimensional inspections, the combination of transcriptomes with spatial dimensions and other omics can provide a comprehensive understanding of single-cell functions, microenvironment, dynamic processes, and their interrelationships. In this review, we will introduce the latest advances in cardiac health and disease at single-cell resolution; single-cell detection methods that can be used for transcriptome, genome, epigenome, and proteome analysis; single-cell multi-omics; as well as their future application prospects.

Nanopore ion sources deliver individual ions of amino acids and peptides directly into high vacuum

Electrospray ionization is widely used to generate vapor phase ions for analysis by mass spectrometry in proteomics research. However, only a small fraction of the analyte enters the mass spectrometer due to losses that are fundamentally linked to the use of a background gas to stimulate the generation of ions from electrosprayed droplets. Here we report a nanopore ion source that delivers ions directly into high vacuum from aqueous solutions. The ion source comprises a pulled quartz pipette with a sub-100 nm opening. Ions escape an electrified meniscus by ion evaporation and travel along collisionless trajectories to the ion detector. We measure mass spectra of 16 different amino acid ions, post-translationally modified variants of glutathione, and the peptide angiotensin II, showing that these analytes can be emitted as desolvated ions. The emitted current is composed of ions rather than charged droplets, and more than 90% of the current can be recovered in a distant collector.

A multi-AS-PCR-coupled CRISPR/Cas12a assay for the detection of ten single-base mutations

Single-nucleotide polymorphism (SNP) detection is critical for diagnosing diseases, and the development of rapid and accurate diagnostic tools is essential for treatment and prevention. Allele-specific polymerase chain reaction (AS-PCR) is widely used for detecting SNPs with multiplexing capabilities, while CRISPR-based technologies provide high sensitivity and specificity in targeting mutation sites through specific guide RNAs (gRNAs). In this study, we have integrated the high sensitivity and specificity of CRISPR technology with the multiplexing capabilities of AS-PCR, achieving the simultaneous detection of ten single-base mutations. As for Multi-AS-PCR, our research identified that competitive inhibition of primers targeting the same loci, coupled with divergent amplification efficiencies of these primers, could result in diminished amplification efficiency. Consequently, we adjusted and optimized primer combinations and ratios to enhance the amplification efficacy of Multi-AS-PCR. Finally, we successfully developed a novel nested Multi-AS-PCR-Cas12a method for multiplex SNPs detection. To evaluate the clinical utility of this method in a real-world setting, we applied it to diagnose rifampicin-resistant tuberculosis (TB). The limit of detection (LoD) for the nested Multi-AS-PCR-Cas12a was 102 aM, achieving sensitivity, specificity, positive predictive value, and negative predictive value of 100 %, 93.33 %, 90.00 %, and 100 %, respectively, compared to sequencing. In summary, by employing an innovative design that incorporates a universal reverse primer alongside ten distinct forward allele-specific primers, the nested Multi-AS-PCR-Cas12a technique facilitates the parallel detection of ten rpoB gene SNPs. This method also holds broad potential for the detection of drug-resistant gene mutations in infectious diseases and tumors, as well as for the screening of specific genetic disorders.

Exploring the unmapped cysteine redox proteoform landscape

Cysteine redox proteoforms define the diverse molecular states that proteins with cysteine residues can adopt. A protein with one cysteine residue must adopt one of two binary proteoforms: reduced or oxidized. Their numbers scale: a protein with 10 cysteine residues must assume one of 1,024 proteoforms. Although they play pivotal biological roles, the vast cysteine redox proteoform landscape comprising vast numbers of theoretical proteoforms remains largely uncharted. Progress is hampered by a general underappreciation of cysteine redox proteoforms, their intricate complexity, and the formidable challenges that they pose to existing methods. The present review advances cysteine redox proteoform theory, scrutinizes methodological barriers, and elaborates innovative technologies for detecting unique residue-defined cysteine redox proteoforms. For example, chemistry-enabled hybrid approaches combining the strengths of top-down mass spectrometry (TD-MS) and bottom-up mass spectrometry (BU-MS) for systematically cataloguing cysteine redox proteoforms are delineated. These methods provide the technological means to map uncharted redox terrain. To unravel hidden redox regulatory mechanisms, discover new biomarkers, and pinpoint therapeutic targets by mining the theoretical cysteine redox proteoform space, a community-wide initiative termed the "Human Cysteine Redox Proteoform Project" is proposed. Exploring the cysteine redox proteoform landscape could transform current understanding of redox biology.

Multi-pass, single-molecule nanopore reading of long protein strands

The ability to sequence single protein molecules in their native, full-length form would enable a more comprehensive understanding of proteomic diversity. Current technologies, however, are limited in achieving this goal1,2. Here, we establish a method for the long-range, single-molecule reading of intact protein strands on a commercial nanopore sensor array. By using the ClpX unfoldase to ratchet proteins through a CsgG nanopore3,4, we provide single-molecule evidence that ClpX translocates substrates in two-residue steps. This mechanism achieves sensitivity to single amino acids on synthetic protein strands hundreds of amino acids in length, enabling the sequencing of combinations of single-amino-acid substitutions and the mapping of post-translational modifications, such as phosphorylation. To enhance classification accuracy further, we demonstrate the ability to reread individual protein molecules multiple times, and we explore the potential for highly accurate protein barcode sequencing. Furthermore, we develop a biophysical model that can simulate raw nanopore signals a priori on the basis of residue volume and charge, enhancing the interpretation of raw signal data. Finally, we apply these methods to examine full-length, folded protein domains for complete end-to-end analysis. These results provide proof of concept for a platform that has the potential to identify and characterize full-length proteoforms at single-molecule resolution.

Improved Conductance Blockage Modeling of Cylindrical Nanopores, from 2D to Thick Membranes

The ionic current blockage from a nanopore sensor is a fundamental metric for characterizing its dimensions and identifying molecules translocating through it. Yet, most analytical models predicting the conductance of a nanopore in both open and obstructed states remain inaccurate. Here, using an oblate spheroidal coordinate framework to study the electrical response of nanopore access regions, we reveal that the widely used model from Kowalczyk et al. significantly overestimates access region contributions when blocked by a cylindrical object, like DNA. To address this, we present an improved analytical model for the obstructed access resistance, which we establish as highly accurate through finite-element simulations, especially for ultrathin membranes and long narrow channels. Equipped with an improved nanopore conductance model, this work provides tools for more accurate calculation of the pore size and for the expected blockade from DNA, of high practical value for many biosensing applications.

Simple, Efficient, and Scalable Structure-Aware Adapter Boosts Protein Language Models

Fine-tuning pretrained protein language models (PLMs) has emerged as a prominent strategy for enhancing downstream prediction tasks, often outperforming traditional supervised learning approaches. As a widely applied powerful technique in natural language processing, employing parameter-efficient fine-tuning techniques could potentially enhance the performance of PLMs. However, the direct transfer to life science tasks is nontrivial due to the different training strategies and data forms. To address this gap, we introduce SES-Adapter, a simple, efficient, and scalable adapter method for enhancing the representation learning of PLMs. SES-Adapter incorporates PLM embeddings with structural sequence embeddings to create structure-aware representations. We show that the proposed method is compatible with different PLM architectures and across diverse tasks. Extensive evaluations are conducted on 2 types of folding structures with notable quality differences, 9 state-of-the-art baselines, and 9 benchmark data sets across distinct downstream tasks. Results show that compared to vanilla PLMs, SES-Adapter improves downstream task performance by a maximum of 11% and an average of 3%, with significantly accelerated convergence speed by a maximum of 1034% and an average of 362%, the training efficiency is also improved by approximately 2 times. Moreover, positive optimization is observed even with low-quality predicted structures. The source code for SES-Adapter is available at https://github.com/tyang816/SES-Adapter.

Acoustically targeted measurement of transgene expression in the brain

Gene expression is a critical component of brain physiology, but monitoring this expression in the living brain represents a major challenge. Here, we introduce a new paradigm called recovery of markers through insonation (REMIS) for noninvasive measurement of gene expression in the brain with cell type, spatial, and temporal specificity. Our approach relies on engineered protein markers that are produced in neurons but exit into the brain's interstitium. When ultrasound is applied to targeted brain regions, it opens the blood-brain barrier and releases these markers into the bloodstream. Once in blood, the markers can be readily detected using biochemical techniques. REMIS can noninvasively confirm gene delivery and measure endogenous signaling in specific brain sites through a simple insonation and a subsequent blood test. REMIS is reliable and demonstrated consistent improvement in recovery of markers from the brain into the blood. Overall, this work establishes a noninvasive, spatially specific method of monitoring gene delivery and endogenous signaling in the brain.

Shallow conductance decay along the heme array of a single tetraheme protein wire

Multiheme cytochromes (MHCs) are the building blocks of highly conductive micrometre-long supramolecular wires found in so-called electrical bacteria. Recent studies have revealed that these proteins possess a long supramolecular array of closely packed heme cofactors along the main molecular axis alternating between perpendicular and stacking configurations (TST = T-shaped, stacked, T-shaped). While TST arrays have been identified as the likely electron conduit, the mechanisms of outstanding long-range charge transport observed in these structures remain unknown. Here we study charge transport on individual small tetraheme cytochromes (STCs) containing a single TST heme array. Individual STCs are trapped in a controllable nanoscale tunnelling gap. By modulating the tunnelling gap separation, we are able to selectively probe four different electron pathways involving 1, 2, 3 and 4 heme cofactors, respectively, leading to the determination of the electron tunnelling decay constant along the TST heme motif. Conductance calculations of selected single-STC junctions are in excellent agreement with experiments and suggest a mechanism of electron tunnelling with shallow length decay constant through an individual STC. These results demonstrate that an individual TST motif supporting electron tunnelling might contribute to a tunnelling-assisted charge transport diffusion mechanism in larger TST associations.

Single-cell RNA sequencing in tuberculosis: Application and future perspectives

Tuberculosis (TB) has one of the highest mortality rates among infectious diseases worldwide. The immune response in the host after infection is proposed to contribute significantly to the progression of TB, but the specific mechanisms involved remain to be elucidated. Single-cell RNA sequencing (scRNA-seq) provides unbiased transcriptome sequencing of large quantities of individual cells, thereby defining biological comprehension of cellular heterogeneity and dynamic transcriptome state of cell populations in the field of immunology and is therefore increasingly applied to lung disease research. Here, we first briefly introduce the concept of scRNA-seq, followed by a summarization on the application of scRNA-seq to TB. Furthermore, we underscore the potential of scRNA-seq for clinical biomarker exploration, host-directed therapy, and precision therapy research in TB and discuss the bottlenecks that need to be overcome for the broad application of scRNA-seq to TB-related research.

Single-Cell Patch-Clamp/Proteomics of Human Alzheimer's Disease iPSC-Derived Excitatory Neurons Versus Isogenic Wild-Type Controls Suggests Novel Causation and Therapeutic Targets

Standard single-cell (sc) proteomics of disease states inferred from multicellular organs or organoids cannot currently be related to single-cell physiology. Here, a scPatch-Clamp/Proteomics platform is developed on single neurons generated from hiPSCs bearing an Alzheimer's disease (AD) genetic mutation and compares them to isogenic wild-type controls. This approach provides both current and voltage electrophysiological data plus detailed proteomics information on single-cells. With this new method, the authors are able to observe hyperelectrical activity in the AD hiPSC-neurons, similar to that observed in the human AD brain, and correlate it to ≈1400 proteins detected at the single neuron level. Using linear regression and mediation analyses to explore the relationship between the abundance of individual proteins and the neuron's mutational and electrophysiological status, this approach yields new information on therapeutic targets in excitatory neurons not attainable by traditional methods. This combined patch-proteomics technique creates a new proteogenetic-therapeutic strategy to correlate genotypic alterations to physiology with protein expression in single-cells.

Electroosmotic Sensing of Uncharged Peptides and Differentiating Their Phosphorylated States Using Nanopores

The correct characterization and identification of different kinds of proteins is crucial for the survival and development of living organisms, and proteomics research promotes the analysis and understanding of future genome functions. Nanopore technique has been proved to accurately identify individual nucleotides. However, accurate and rapid protein sequencing is difficult due to the variability of protein structures that contains more than 20 amino acids, and it remains very challenging especially for uncharged peptides as they can not be electrophoretically driven through the nanopore. Graphene nanopores have the advantages of high accuracy, sensitivity and low cost in identifying protein phosphorylation modifications. Here, by using all-atom molecular dynamics simulations, charged graphene nanopores are employed to electroosmotically capture and sense uncharged peptides. By further mimicking AFM manipulation of single molecules, it is also found that the uncharged peptides and their phosphorylated states could also be differentiated by both the ionic current and pulling force signals during their pulling processes through the nanopore with a slow and constant velocity. The results shows ability of using nanopores to detect and discriminate single amino acid and its phosphorylation, which is essential for the future low-cost and high-throughput sequencing of protein residues and their post-translational modifications.

All the single cells: if you like it then you should put some virus on it

Across a rich 70-year history, single-cell virology has revealed the impact of host and pathogen heterogeneity during virus infections. Recent technological innovations have enabled higher-resolution analyses of cellular and viral heterogeneity. Furthermore, single-cell analysis has revealed extreme phenotypes and provided additional insights into host-pathogen dynamics. Using a single-cell approach to explore fundamental virology questions, contemporary researchers have contributed to a revival of interest in single-cell virology with increased insights and enthusiasm.

Site-Specific Integration of Hexagonal Boron Nitride Quantum Emitters on 2D DNA Origami Nanopores

Optical emitters in hexagonal boron nitride (hBN) are promising probes for single-molecule sensing platforms. When engineered in nanoparticle form, they can be integrated as detectors in nanodevices, yet positional control at the nanoscale is lacking. Here we demonstrate the functionalization of DNA origami nanopores with optically active hBN nanoparticles (NPs) with nanometer precision. The NPs are active under three wavelengths of visible illumination and display both stable and blinking emission, enabling their accurate localization by using wide-field optical nanoscopy. Correlative opto-structural characterization reveals deterministic binding of bright, multicolor hBN NPs at the pore rim due to π-π stacking interactions at site-specific locations on the DNA origami. Our work provides a scalable, bottom-up approach toward deterministic assembly of solid-state emitters on arbitrary structural elements based on DNA origami. Such a nanoscale arrangement of optically active components can advance the development of single-molecule platforms, including optical nanopores and nanochannel sensors.

Electro-osmotic Flow Generation via a Sticky Ion Action

Selective transport of ions through nanometer-sized pores is fundamental to cell biology and central to many technological processes such as water desalination and electrical energy storage. Conventional methods for generating ion selectivity include placement of fixed electrical charges at the inner surface of a nanopore through either point mutations in a protein pore or chemical treatment of a solid-state nanopore surface, with each nanopore type requiring a custom approach. Here, we describe a general method for transforming a nanoscale pore into a highly selective, anion-conducting channel capable of generating a giant electro-osmotic effect. Our molecular dynamics simulations and reverse potential measurements show that exposure of a biological nanopore to high concentrations of guanidinium chloride renders the nanopore surface positively charged due to transient binding of guanidinium cations to the protein surface. A comparison of four biological nanopores reveals the relationship between ion selectivity, nanopore shape, composition of the nanopore surface, and electro-osmotic flow. Guanidinium ions are also found to produce anion selectivity and a giant electro-osmotic flow in solid-state nanopores via the same mechanism. Our sticky-ion approach to generate electro-osmotic flow can have numerous applications in controlling molecular transport at the nanoscale and for detection, identification, and sequencing of individual proteins.