The emerging landscape of single-molecule protein sequencing technologies

Targeted therapy for rare lung cancers: Status, challenges, and prospects

Lung cancer causes the most cancer-related deaths worldwide. In recent years, molecular and immunohistochemical techniques have rapidly developed, further inaugurating an era of personalized medicine for lung cancer. The rare subset of lung cancers accounts for approximately 10%, each displaying distinct clinical characteristics. Treatments for rare lung cancers are mainly based on evidence from common counterparts, which may lead to unsolid clinical benefits considering intertumoral heterogeneity. The increasing knowledge of molecular profiling of rare lung cancers has made targeting genetic alterations and immune checkpoints a powerful strategy. Additionally, cellular therapy has emerged as a promising way to target tumor cells. In this review, we first discuss the current status of targeted therapy and preclinical models for rare lung cancers, as well as provide mutational profiles by integrating the results of existing cohorts. Finally, we point out the challenges and future directions for developing targeted agents for rare lung cancer.

Antigen-specific and cross-reactive T cells in protection and disease

Human T cells have a diverse T-cell receptor (TCR) repertoire that endows them with the ability to identify and defend against a broad spectrum of antigens. The universe of possible antigens that T cells may encounter, however, is even larger. To effectively surveil such a vast universe, the T-cell repertoire must adopt a high degree of cross-reactivity. Likewise, antigen-specific and cross-reactive T-cell responses play pivotal roles in both protective and pathological immune responses in numerous diseases. In this review, we explore the implications of these antigen-driven T-cell responses, with a particular focus on CD8+ T cells, using infection, neurodegeneration, and cancer as examples. We also summarize recent technological advances that facilitate high-throughput profiling of antigen-specific and cross-reactive T-cell responses experimentally, as well as computational biology approaches that predict these interactions.

Spatially resolved single-cell translatomics at molecular resolution

The precise control of messenger RNA (mRNA) translation is a crucial step in posttranscriptional gene regulation of cellular physiology. However, it remains a challenge to systematically study mRNA translation at the transcriptomic scale with spatial and single-cell resolution. Here, we report the development of ribosome-bound mRNA mapping (RIBOmap), a highly multiplexed three-dimensional in situ profiling method to detect cellular translatome. RIBOmap profiling of 981 genes in HeLa cells revealed cell cycle-dependent translational control and colocalized translation of functional gene modules. We mapped 5413 genes in mouse brain tissues, yielding spatially resolved single-cell translatomic profiles for 119,173 cells and revealing cell type-specific and brain region-specific translational regulation, including translation remodeling during oligodendrocyte maturation. Our method detected widespread patterns of localized translation in neuronal and glial cells in intact brain tissue networks.

Structure and dynamics of an archetypal DNA nanoarchitecture revealed via cryo-EM and molecular dynamics simulations

DNA can be folded into rationally designed, unique, and functional materials. To fully realise the potential of these DNA materials, a fundamental understanding of their structure and dynamics is necessary, both in simple solvents as well as more complex and diverse anisotropic environments. Here we analyse an archetypal six-duplex DNA nanoarchitecture with single-particle cryo-electron microscopy and molecular dynamics simulations in solvents of tunable ionic strength and within the anisotropic environment of biological membranes. Outside lipid bilayers, the six-duplex bundle lacks the designed symmetrical barrel-type architecture. Rather, duplexes are arranged in non-hexagonal fashion and are disorted to form a wider, less elongated structure. Insertion into lipid membranes, however, restores the anticipated barrel shape due to lateral duplex compression by the bilayer. The salt concentration has a drastic impact on the stability of the inserted barrel-shaped DNA nanopore given the tunable electrostatic repulsion between the negatively charged duplexes. By synergistically combining experiments and simulations, we increase fundamental understanding into the environment-dependent structural dynamics of a widely used nanoarchitecture. This insight will pave the way for future engineering and biosensing applications.

Engineered extracellular vesicles as therapeutics of degenerative orthopedic diseases

Degenerative orthopedic diseases, as a global public health problem, have made serious negative impact on patients' quality of life and socio-economic burden. Traditional treatments, including chemical drugs and surgical treatments, have obvious side effects and unsatisfactory efficacy. Therefore, biological therapy has become the focus of researches on degenerative orthopedic diseases. Extracellular vesicles (EVs), with superior properties of immunoregulatory, growth support, and drug delivery capabilities, have emerged as a new cell-free strategy for the treatment of many diseases, including degenerative orthopedic diseases. An increasing number of studies have shown that EVs can be engineered through cargo loading, surface modification, and chemical synthesis to improve efficiency, specificity, and safety. Herein, a comprehensive overview of recent advances in engineering strategies and applications of engineered EVs as well as related researches in degenerative orthopedic diseases, including osteoarthritis (OA), osteoporosis (OP), intervertebral disc degeneration (IDD) and osteonecrosis of the femoral head (ONFH), is provided. In addition, we analyze the potential and challenges of applying engineered EVs to clinical practice.

Systematic conformation-to-phenotype mapping via limited deep sequencing of proteins

Non-native conformations drive protein-misfolding diseases, complicate bioengineering efforts, and fuel molecular evolution. No current experimental technique is well suited for elucidating them and their phenotypic effects. Especially intractable are the transient conformations populated by intrinsically disordered proteins. We describe an approach to systematically discover, stabilize, and purify native and non-native conformations, generated in vitro or in vivo, and directly link conformations to molecular, organismal, or evolutionary phenotypes. This approach involves high-throughput disulfide scanning (HTDS) of the entire protein. To reveal which disulfides trap which chromatographically resolvable conformers, we devised a deep-sequencing method for double-Cys variant libraries of proteins that precisely and simultaneously locates both Cys residues within each polypeptide. HTDS of the abundant E. coli periplasmic chaperone HdeA revealed distinct classes of disordered hydrophobic conformers with variable cytotoxicity depending on where the backbone was cross-linked. HTDS can bridge conformational and phenotypic landscapes for many proteins that function in disulfide-permissive environments.

MoS2 nanopore identifies single amino acids with sub-1 Dalton resolution

The sequencing of single protein molecules using nanopores is faced with a huge challenge due to the lack of resolution needed to resolve single amino acids. Here we report the direct experimental identification of single amino acids in nanopores. With atomically engineered regions of sensitivity comparable to the size of single amino acids, MoS₂ nanopores provide a sub-1 Dalton resolution for discriminating the chemical group difference of single amino acids, including recognizing the amino acid isomers. This ultra-confined nanopore system is further used to detect the phosphorylation of individual amino acids, demonstrating its capability for reading post-translational modifications. Our study suggests that a sub-nanometer engineered pore has the potential to be applied in future chemical recognition and de novo protein sequencing at the single-molecule level.

Performance benchmarking microplate-immunoassays for quantifying target-specific cysteine oxidation reveals their potential for understanding redox-regulation and oxidative stress

The antibody-linked oxi-state assay (ALISA) for quantifying target-specific cysteine oxidation can benefit specialist and non-specialist users. Specialists can benefit from time-efficient analysis and high-throughput target and/or sample n-plex capacities. The simple and accessible "off-the-shelf" nature of ALISA brings the benefits of oxidative damage assays to non-specialists studying redox-regulation. Until performance benchmarking establishes confidence in the "unseen" microplate results, ALISA is unlikely to be widely adopted. Here, we implemented pre-set pass/fail criteria to benchmark ALISA by evaluating immunoassay performance in diverse contexts. ELISA-mode ALISA assays were accurate, reliable, and sensitive. For example, the average inter-assay CV for detecting 20%- and 40%-oxidised PRDX2 or GAPDH standards was 4.6% (range: 3.6-7.4%). ALISA displayed target-specificity. Immunodepleting the target decreased the signal by ∼75%. Single-antibody formatted ALISA failed to quantify the matrix-facing alpha subunit of the mitochondrial ATP synthase. However, RedoxiFluor quantified the alpha subunit displaying exceptional performance in the single-antibody format. ALISA discovered that (1) monocyte-to-macrophage differentiation amplified PRDX2-oxidation in THP-1 cells and (2) exercise increased GAPDH-specific oxidation in human erythrocytes. The "unseen" microplate data were "seen-to-be-believed" via orthogonal visually displayed immunoassays like the dimer method. Finally, we established target (n = 3) and sample (n = 100) n-plex capacities in ∼4 h with 50-70 min hands-on time. Our work showcases the potential of ALISA to advance our understanding of redox-regulation and oxidative stress.

aaHash: recursive amino acid sequence hashing

Motivation

K-mer hashing is a common operation in many foundational bioinformatics problems. However, generic string hashing algorithms are not optimized for this application. Strings in bioinformatics use specific alphabets, a trait leveraged for nucleic acid sequences in earlier work. We note that amino acid sequences, with complexities and context that cannot be captured by generic hashing algorithms, can also benefit from a domain-specific hashing algorithm. Such a hashing algorithm can accelerate and improve the sensitivity of bioinformatics applications developed for protein sequences.

Results

Here, we present aaHash, a recursive hashing algorithm tailored for amino acid sequences. This algorithm utilizes multiple hash levels to represent biochemical similarities between amino acids. aaHash performs ~10X faster than generic string hashing algorithms in hashing adjacent k-mers.

Availability and implementation

aaHash is available online at https://github.com/bcgsc/btllib and is free for academic use.

High sensitivity top-down proteomics captures single muscle cell heterogeneity in large proteoforms

Single-cell proteomics has emerged as a powerful method to characterize cellular phenotypic heterogeneity and the cell-specific functional networks underlying biological processes. However, significant challenges remain in single-cell proteomics for the analysis of proteoforms arising from genetic mutations, alternative splicing, and post-translational modifications. Herein, we have developed a highly sensitive functionally integrated top-down proteomics method for the comprehensive analysis of proteoforms from single cells. We applied this method to single muscle fibers (SMFs) to resolve their heterogeneous functional and proteomic properties at the single-cell level. Notably, we have detected single-cell heterogeneity in large proteoforms (>200 kDa) from the SMFs. Using SMFs obtained from three functionally distinct muscles, we found fiber-to-fiber heterogeneity among the sarcomeric proteoforms which can be related to the functional heterogeneity. Importantly, we detected multiple isoforms of myosin heavy chain (~223 kDa), a motor protein that drives muscle contraction, with high reproducibility to enable the classification of individual fiber types. This study reveals single muscle cell heterogeneity in large proteoforms and establishes a direct relationship between sarcomeric proteoforms and muscle fiber types, highlighting the potential of top-down proteomics for uncovering the molecular underpinnings of cell-to-cell variation in complex systems.

Over 30-Fold Enhancement in DNA Translocation Dynamics through Nanoscale Pores Coated with an Anionic Surfactant

Solid-state nanopores (ssNPs) are single-molecule sensors capable of label-free quantification of different biomolecules, which have become highly versatile with the introduction of different surface treatments. By modulating the surface charges of the ssNP, the electro-osmotic flow (EOF) can be controlled in turn affecting the in-pore hydrodynamic forces. Herein, we demonstrate that negative charge surfactant coating to ssNPs generates EOF that slows-down DNA translocation speed by >30-fold, without deterioration of the NP noise, hence significantly improving its performances. Consequently, surfactant-coated ssNPs can be used to reliably sense short DNA fragments at high voltage bias. To shed light on the EOF phenomena inside planar ssNPs, we introduce visualization of the electrically neutral fluorescent molecule's flow, hence decoupling the electrophoretic from EOF forces. Finite elements simulations are then used to show that EOF is likely responsible for in-pore drag and size-selective capture rate. This study broadens ssNPs use for multianalyte sensing in a single device.

Dissecting metastasis using preclinical models and methods

Metastasis has long been understood to lead to the overwhelming majority of cancer-related deaths. However, our understanding of the metastatic process, and thus our ability to prevent or eliminate metastases, remains frustratingly limited. This is largely due to the complexity of metastasis, which is a multistep process that likely differs across cancer types and is greatly influenced by many aspects of the in vivo microenvironment. In this Review, we discuss the key variables to consider when designing assays to study metastasis: which source of metastatic cancer cells to use and where to introduce them into mice to address different questions of metastasis biology. We also examine methods that are being used to interrogate specific steps of the metastatic cascade in mouse models, as well as emerging techniques that may shed new light on previously inscrutable aspects of metastasis. Finally, we explore approaches for developing and using anti-metastatic therapies, and how mouse models can be used to test them.

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.

Uncovering biology by single-cell proteomics

Recent technological advances have opened the door to single-cell proteomics that can answer key biological questions regarding how protein expression, post-translational modifications, and protein interactions dictate cell state in health and disease.

Single-molecule fingerprinting of protein-drug interaction using a funneled biological nanopore

In drug discovery, efficient screening of protein-drug interactions (PDIs) is hampered by the limitations of current biophysical approaches. Here, we develop a biological nanopore sensor for single-molecule detection of proteins and PDIs using the pore-forming toxin YaxAB. Using this YaxAB nanopore, we demonstrate label-free, single-molecule detection of interactions between the anticancer Bcl-xL protein and small-molecule drugs as well as the Bak-BH3 peptide. The long funnel-shaped structure and nanofluidic characteristics of the YaxAB nanopore enable the electro-osmotic trapping of diverse folded proteins and high-resolution monitoring of PDIs. Distinctive nanopore event distributions observed in the two-dimensional (ΔI/I_o-versus-I_N) plot illustrate the ability of the YaxAB nanopore to discriminate individual small-molecule drugs bound to Bcl-xL from non-binders. Taken together, our results present the YaxAB nanopore as a robust platform for label-free, ultrasensitive, single-molecule detection of PDIs, opening up a possibility for low-cost, highly efficient drug discovery against diverse drug targets.

Nanopore-Based Fingerprint Immunoassay Based on Rolling Circle Amplification and DNA Fragmentation

In recent years, nanopore-based sequencers have become robust tools with unique advantages for genomics applications. However, progress toward applying nanopores as highly sensitive, quantitative diagnostic tools has been impeded by several challenges. One major limitation is the insufficient sensitivity of nanopores in detecting disease biomarkers, which are typically present at pM or lower concentrations in biological fluids, while a second limitation is the general absence of unique nanopore signals for different analytes. To bridge this gap, we have developed a strategy for nanopore-based biomarker detection that utilizes immunocapture, isothermal rolling circle amplification, and sequence-specific fragmentation of the product to release multiple DNA reporter molecules for nanopore detection. These DNA fragment reporters produce sets of nanopore signals that form distinctive fingerprints, or clusters. This fingerprint signature therefore allows the identification and quantification of biomarker analytes. As a proof of concept, we quantify human epididymis protein 4 (HE4) at low pM levels in a few hours. Future improvement of this method by integration with a nanopore array and microfluidics-based chemistry can further reduce the limit of detection, allow multiplexed biomarker detection, and further reduce the footprint and cost of existing laboratory and point-of-care devices.

Nanopore Detection Using Supercharged Polypeptide Molecular Carriers

The analysis at the single-molecule level of proteins and their interactions can provide critical information for understanding biological processes and diseases, particularly for proteins present in biological samples with low copy numbers. Nanopore sensing is an analytical technique that allows label-free detection of single proteins in solution and is ideally suited to applications, such as studying protein-protein interactions, biomarker screening, drug discovery, and even protein sequencing. However, given the current spatiotemporal limitations in protein nanopore sensing, challenges remain in controlling protein translocation through a nanopore and relating protein structures and functions with nanopore readouts. Here, we demonstrate that supercharged unstructured polypeptides (SUPs) can be genetically fused with proteins of interest and used as molecular carriers to facilitate nanopore detection of proteins. We show that cationic SUPs can substantially slow down the translocation of target proteins due to their electrostatic interactions with the nanopore surface. This approach enables the differentiation of individual proteins with different sizes and shapes via characteristic subpeaks in the nanopore current, thus facilitating a viable route to use polypeptide molecular carriers to control molecular transport and as a potential system to study protein-protein interactions at the single-molecule level.

A generalizable nanopore sensor for highly specific protein detection at single-molecule precision

Protein detection has wide-ranging implications in molecular diagnostics. Substantial progress has been made in protein analytics using nanopores and the resistive-pulse technique. Yet, a long-standing challenge is implementing specific interfaces for detecting proteins without the steric hindrance of the pore interior. Here, we formulate a class of sensing elements made of a programmable antibody-mimetic binder fused to a monomeric protein nanopore. This way, such a modular design significantly expands the utility of nanopore sensors to numerous proteins while preserving their architecture, specificity, and sensitivity. We prove the power of this approach by developing and validating nanopore sensors for protein analytes that drastically vary in size, charge, and structural complexity. These analytes produce unique electrical signatures that depend on their identity and quantity and the binder-analyte assembly at the nanopore tip. The outcomes of this work could impact biomedical diagnostics by providing a fundamental basis for biomarker detection in biofluids.

Oligo-Metastatic Cancers: Putative Biomarkers, Emerging Challenges and New Perspectives

Some cancer patients display a less aggressive form of metastatic disease, characterized by a low tumor burden and involving a smaller number of sites, which is referred to as "oligometastatic disease" (OMD). This review discusses new biomarkers, as well as methodological challenges and perspectives characterizing OMD. Recent studies have revealed that specific microRNA profiles, chromosome patterns, driver gene mutations (ERBB2, PBRM1, SETD2, KRAS, PIK3CA, SMAD4), polymorphisms (TCF7L2), and levels of immune cell infiltration into metastases, depending on the tumor type, are associated with an oligometastatic behavior. This suggests that OMD could be a distinct disease with specific biological and molecular characteristics. Therefore, the heterogeneity of initial tumor burden and inclusion of OMD patients in clinical trials pose a crucial methodological question that requires responses in the near future. Additionally, a solid understanding of the molecular and biological features of OMD will be necessary to support and complete the clinical staging systems, enabling a better distinction of metastatic behavior and tailored treatments.

Sampling the proteome by emerging single-molecule and mass spectrometry methods

Mammalian cells have about 30,000-fold more protein molecules than mRNA molecules, which has major implications in the development of proteomics technologies. We review strategies that have been helpful for counting billions of protein molecules by liquid chromatography-tandem mass spectrometry (LC-MS/MS) and suggest that these strategies can benefit single-molecule methods, especially in mitigating the challenges of the wide dynamic range of the proteome.

Subcellular omics: a new frontier pushing the limits of resolution, complexity and throughput

We argue that the study of single-cell subcellular organelle omics is needed to understand and regulate cell function. This requires and is being enabled by new technology development.

Super-Resolution Detection of DNA Nanostructures Using a Nanopore

High-resolution analysis of biomolecules has brought unprecedented insights into fundamental biological processes and dramatically advanced biosensing. Notwithstanding the ongoing resolution revolution in electron microscopy and optical imaging, only a few methods are presently available for high-resolution analysis of unlabeled single molecules in their native states. Here, label-free electrical sensing of structured single molecules with a spatial resolution down to single-digit nanometers is demonstrated. Using a narrow solid-state nanopore, the passage of a series of nanostructures attached to a freely translocating DNA molecule is detected, resolving individual nanostructures placed as close as 6 nm apart and with a surface-to-surface gap distance of only 2 nm. Such super-resolution ability is attributed to the nanostructure-induced enhancement of the electric field at the tip of the nanopore. This work demonstrates a general approach to improving the resolution of single-molecule nanopore sensing and presents a critical advance towards label-free, high-resolution DNA sequence mapping, and digital information storage independent of molecular motors.

QuoteTarget: A sequence-based transformer protein language model to identify potentially druggable protein targets

The development of efficient computational methods for drug target protein identification can compensate for the high cost of experiments and is therefore of great significance for drug development. However, existing structure-based drug target protein-identification algorithms are limited by the insufficient number of proteins with experimentally resolved structures. Moreover, sequence-based algorithms cannot effectively extract information from protein sequences and thus display insufficient accuracy. Here, we combined the sequence-based self-supervised pretraining protein language model ESM1b with a graph convolutional neural network classifier to develop an improved, sequence-based drug target protein identification method. This complete model, named QuoteTarget, efficiently encodes proteins based on sequence information alone and achieves an accuracy of 95% with the nonredundant drug target and nondrug target datasets constructed for this study. When applied to all proteins from Homo sapiens, QuoteTarget identified 1213 potential undeveloped drug target proteins. We further inferred residue-binding weights from the well-trained network using the gradient-weighted class activation mapping (Grad-Cam) algorithm. Notably, we found that without any binding site information input, significant residues inferred by the model closely match the experimentally confirmed drug molecule-binding sites. Thus, our work provides a highly effective sequence-based identifier for drug target proteins, as well to yield new insights into recognizing drug molecule-binding sites. The entire model is available at https://github.com/Chenjxjx/drug-target-prediction.

Microfluidic Mixer for In Situ Ammonia Analysis of Single Cells in Mass Spectrometry

Mass spectrometry (MS) is a powerful tool for exploring single-cell heterogeneity. However, due to the ultralow absolute content of most substances in a single cell, existing methods can only analyze high-content substances; conventional methods are incompetent for quantitative analysis of important trace-amount small-molecule metabolites such as ammonia and sulfide. Herein, a method integrating single-cell extraction, online derivatization, and MS for multifunctional and more accurate MS analysis is reported. For application, ammonia content in a single cell was analyzed, where the cellular heterogeneity in ammonia metabolism was revealed. First, the extraction room of a microfluidic probe was covered on the target single cell, and the extraction fluid was allowed to flow through a single cell and dissolve cellular ammonia. Then, the ammonia was mixed and reacted with the pretreatment reagent input from another inlet to achieve the derivatization and signal amplification, enhancing the analysis sensitivity on MS. Finally, the sample was sent to MS, and the ammonia content was successfully quantitatively evaluated by analyzing its derivative urotropine, demonstrating the potential of this method to advance single-cell mass spectrometry analysis to higher sensitivity.

Systematic conformation-to-phenotype mapping via limited deep-sequencing of proteins

Non-native conformations drive protein misfolding diseases, complicate bioengineering efforts, and fuel molecular evolution. No current experimental technique is well-suited for elucidating them and their phenotypic effects. Especially intractable are the transient conformations populated by intrinsically disordered proteins. We describe an approach to systematically discover, stabilize, and purify native and non-native conformations, generated in vitro or in vivo, and directly link conformations to molecular, organismal, or evolutionary phenotypes. This approach involves high-throughput disulfide scanning (HTDS) of the entire protein. To reveal which disulfides trap which chromatographically resolvable conformers, we devised a deep-sequencing method for double-Cys variant libraries of proteins that precisely and simultaneously locates both Cys residues within each polypeptide. HTDS of the abundant E. coli periplasmic chaperone HdeA revealed distinct classes of disordered hydrophobic conformers with variable cytotoxicity depending on where the backbone was cross-linked. HTDS can bridge conformational and phenotypic landscapes for many proteins that function in disulfide-permissive environments.

Contributions from ClpS surface residues in modulating N-terminal peptide binding and their implications for NAAB development

Numerous technologies are currently in development for use in next-generation protein sequencing platforms. A notable published approach employs fluorescently-tagged binding proteins to identity the N-terminus of immobilized peptides, in-between rounds of digestion. This approach makes use of N-terminal amino acid binder (NAAB) proteins, which would identify amino acids by chemical and shape complementarity. One source of NAABs is the ClpS protein family, which serve to recruit proteins to bacterial proteosomes based on the identity of the N-terminal amino acid. In this study, a Thermosynechococcus vestitus (also known as Thermosynechococcus elongatus) ClpS2 protein was used as the starting point for direct evolution of an NAAB with affinity and specificity for N-terminal leucine. Enriched variants were analyzed and shown to improve the interaction between the ClpS surface and the peptide chain, without increasing promiscuity. Interestingly, interactions were found that were unanticipated which favor different charged residues located at position 5 from the N-terminus of a target peptide.

Quantitative Microscopic Observation of Base-Ligand Interactions via Hydrogen Bonds by Single-Molecule Counting

Chemical properties have been based on statistical averages since the introduction of Avogadro's number. The lack of suitable methods for counting identified single molecules has posed challenges to counting statistics. The selectivity, affinity, and mode of hydrogen bonding between base and small molecules that make up DNA, which is vital for living organisms, have not yet been revealed at the single molecule level. Here, we show the quantitation of the above-mentioned parameters via single-molecule counting based on the combination of single-molecule electrical measurements and AI. The binding selectivity values of five ligands to four different base molecules were evaluated quantitatively by determining the ratio of the number of aggregates in a solution mixture of base molecules and a ligand. In addition, we show the ligand dependence of the mode and number of microscopic hydrogen bonds via single-molecule counting and quantum chemical calculations.

Graphene nano-electromechanical mass sensor with high resolution at room temperature

The inherent properties of 2D materials-light mass, high out-of-plane flexibility, and large surface area-promise great potential for precise and accurate nanomechanical mass sensing, but their application is often hampered by surface contamination. Here we demonstrate a tri-layer graphene nanomechanical resonant mass sensor with sub-attogram resolution at room temperature, fabricated by a bottom-up process. We found that Joule-heating is effective in cleaning the graphene membrane surface, which results in a large improvement in the stability of the resonance frequency. We characterized the sensor by depositing Cr metal using a stencil mask and found a mass-resolution that is sufficient to weigh very small particles, like large proteins and protein complexes, with potential applications in the fields of nanobiology and medicine.

Single-molecule fluorescence methods for protein biomarker analysis

Proteins have been considered key building blocks of life. In particular, the protein content of an organism and a cell offers significant information for the in-depth understanding of the disease and biological processes. Single-molecule protein detection/sequencing tools will revolutionize clinical (proteomics) research, offering ultrasensitivity for low-abundance biomarker (protein) detection, which is important for the realization of early-stage disease diagnosis and single-cell proteomics. This improved detection/measurement capability delivers new sets of techniques to explore new frontiers and address important challenges in various interdisciplinary areas including nanostructured materials, molecular medicine, molecular biology, and chemistry. Importantly, fluorescence-based methods have emerged as indispensable tools for single protein detection/sequencing studies, providing a higher signal-to-noise ratio (SNR). Improvements in fluorescent dyes/probes and detector capabilities coupled with advanced (image) analysis strategies have fueled current developments for single protein biomarker detections. For example, in comparison to conventional ELISA (i.e., based on ensembled measurements), single-molecule fluorescence detection is more sensitive, faster, and more accurate with reduced background, high-throughput, and so on. In comparison to MS sequencing, fluorescence-based single-molecule protein sequencing can achieve the sequencing of peptides themselves with higher sensitivity. This review summarizes various typical single-molecule detection technologies including their methodology (modes of operation), detection limits, advantages and drawbacks, and current challenges with recent examples. We describe the fluorescence-based single-molecule protein sequencing/detection based on five kinds of technologies such as fluorosequencing, N-terminal amino acid binder, nanopore light sensing, and DNA nanotechnology. Finally, we present our perspective for developing high-performance fluorescence-based sequencing/detection techniques.

Evaluation of the Binding Preference of Microtubes for Nanoproteomics Sample Preparation

Nonspecific binding between the protein and the container is an often-neglected cause of sample loss in large-scale proteomics sample preparation. In nanoproteomics, due to the small sample size, this absorption loss is no longer negligible, and researchers often adopt low binding plasticware to minimize the sample loss. However, there has been little discussion in the scientific literature on the differences in microtube performance on reducing protein/peptide binding. Therefore, the exact impact of sample loss during the sample preparation is not well understood. Here, we investigated the protein/peptide loss during the nanoproteomics experiment process. Our results showed that there are significant differences in nonspecific binding among the tested microtubes, with a protein recovery rate ranging from less than 10% to over 90% for different microtubes. Interestingly, we found that the storage temperature could also be one of the key factors that contribute to protein recovery from the plastic container. In addition, we investigated the binding preferences of different microtubes by the physical characteristics of the identified proteins and peptides, such as isoelectric point, hydrophobicity, length, and charge. Our findings help to better understand protein/peptide loss in proteomics sample preparation and provide further guidance for researchers in choosing proper containers for their precious sample.

Single-Molecule Peptide Identification Using Fluorescence Blinking Fingerprints

The ability to identify peptides with single-molecule sensitivity would lead to next-generation proteomics methods for basic research and clinical applications. Existing single-molecule peptide sequencing methods can read some amino acid sequences, but they are limited in their ability to distinguish between similar amino acids or post-translational modifications. Here, we demonstrate that the fluorescence intermittency of a peptide labeled with a spontaneously blinking fluorophore contains information about the structure of the peptide. Using a deep learning algorithm, this single-molecule blinking pattern can be used to identify the peptide. This method can distinguish between peptides with different sequences, peptides with the same sequence but different phosphorylation patterns, and even peptides that differ only by the presence of epimerized residues. This study builds the foundation for a targeted proteomics method with single-molecule sensitivity.

MS/MS-Based Molecular Networking: An Efficient Approach for Natural Products Dereplication

Natural products (NPs) have historically played a primary role in the discovery of small-molecule drugs. However, due to the advent of other methodologies and the drawbacks of NPs, the pharmaceutical industry has largely declined in interest regarding the screening of new drugs from NPs since 2000. There are many technical bottlenecks to quickly obtaining new bioactive NPs on a large scale, which has made NP-based drug discovery very time-consuming, and the first thorny problem faced by researchers is how to dereplicate NPs from crude extracts. Remarkably, with the rapid development of omics, analytical instrumentation, and artificial intelligence technology, in 2012, an efficient approach, known as tandem mass spectrometry (MS/MS)-based molecular networking (MN) analysis, was developed to avoid the rediscovery of known compounds from the complex natural mixtures. Then, in the past decade, based on the classical MN (CLMN), feature-based MN (FBMN), ion identity MN (IIMN), building blocks-based molecular network (BBMN), substructure-based MN (MS2LDA), and bioactivity-based MN (BMN) methods have been presented. In this paper, we review the basic principles, general workflow, and application examples of the methods mentioned above, to further the research and applications of these methods.

Machine Learning Aided Interpretable Approach for Single Nucleotide-Based DNA Sequencing using a Model Nanopore

Solid-state nanopore-based electrical detection of DNA nucleotides with the quantum tunneling technique has emerged as a powerful strategy to be the next-generation sequencing technology. However, experimental complexity has been a foremost obstacle in achieving a more accurate high-throughput analysis with industrial scalability. Here, with one of the nucleotide training data sets of a model monolayer gold nanopore, we have predicted the transmission function for all other nucleotides with root-mean-square error scores as low as 0.12 using the optimized eXtreme Gradient Boosting Regression (XGBR) model. Further, the SHapley Additive exPlanations (SHAP) analysis helped in exploring the interpretability of the XGBR model prediction and revealed the complex relationship between the molecular properties of nucleotides and their transmission functions by both global and local interpretable explanations. Hence, experimental integration of our proposed machine-learning-assisted transmission function prediction method can offer a new direction for the realization of cheap, accurate, and ultrafast DNA sequencing.

Nanopore fingerprinting of supramolecular DNA nanostructures

DNA nanotechnology has paved the way for new generations of programmable nanomaterials. Utilizing the DNA origami technique, various DNA constructs can be designed, ranging from single tiles to the self-assembly of large-scale, complex, multi-tile arrays. This technique relies on the binding of hundreds of short DNA staple strands to a long single-stranded DNA scaffold that drives the folding of well-defined nanostructures. Such DNA nanostructures have enabled new applications in biosensing, drug delivery, and other multifunctional materials. In this study, we take advantage of the enhanced sensitivity of a solid-state nanopore that employs a poly-ethylene glycol enriched electrolyte to deliver real-time, non-destructive, and label-free fingerprinting of higher-order assemblies of DNA origami nanostructures with single-entity resolution. This approach enables the quantification of the assembly yields for complex DNA origami nanostructures using the nanostructure-induced equivalent charge surplus as a discriminant. We compare the assembly yield of four supramolecular DNA nanostructures obtained with the nanopore with agarose gel electrophoresis and atomic force microscopy imaging. We demonstrate that the nanopore system can provide analytical quantification of the complex supramolecular nanostructures within minutes, without any need for labeling and with single-molecule resolution. We envision that the nanopore detection platform can be applied to a range of nanomaterial designs and enable the analysis and manipulation of large DNA assemblies in real time.

A binary/digital approach to amino acid identification and its application to peptide sequencing and protein identification

A binary/digital method is proposed in theory for the identification of single amino acids (AAs) in the bulk or with a few molecules from a single binary measurement. Combined with Edman degradation (or other cleaving method), it can be used to sequence a peptide or identify the parent protein from a partial sequence. The approach is centered on the superspecificity property of transfer RNAs (tRNAs). Markedly different from conventional and recent single molecule (SM) sequencing methods based on analog measurements, it changes the analytical question 'Which AA is it?' to the much simpler one 'Is there an AA in the detection space?'. Each of 20 terminal residues cleaved from 20 copies of a peptide enters a different cavity with a unique tRNA; tRNA charging (or binding with AA) occurs only in the cavity with the cognate AA. The bound AA or the AA separated from the tRNA is detected with a single binary measurement; its identity is known from the position of the single high bit in the resulting 20-bit output. Alternatively, a 20-stage pipeline can be used with sparse samples. Detection of the bound AA can be done optically by tagging the AAs with a fluorescent dye, or of the freed AA electrically with a nanopore. Necessary conditions for accurate AA identification are satisfied in principle; related computations and simulation results are presented. A modified version that can be used for de novo sequencing in parallel of large numbers of peptides immobilized on a glass slide with the tRNAs carrying a fluorescent tag is also proposed. Both methods can be used for protein identification from partial sequences containing 2 or 3 AA types by using only the corresponding tRNAs. Experiments may be performed to validate them, followed by translation into practice with existing technology; potential implementation issues are discussed. Binary/digital amino acid identification for peptide sequencing.

Single-cell technologies: From research to application

In recent years, more and more single-cell technologies have been developed. A vast amount of single-cell omics data has been generated by large projects, such as the Human Cell Atlas, the Mouse Cell Atlas, the Mouse RNA Atlas, the Mouse ATAC Atlas, and the Plant Cell Atlas. Based on these single-cell big data, thousands of bioinformatics algorithms for quality control, clustering, cell-type annotation, developmental inference, cell-cell transition, cell-cell interaction, and spatial analysis are developed. With powerful experimental single-cell technology and state-of-the-art big data analysis methods based on artificial intelligence, the molecular landscape at the single-cell level can be revealed. With spatial transcriptomics and single-cell multi-omics, even the spatial dynamic multi-level regulatory mechanisms can be deciphered. Such single-cell technologies have many successful applications in oncology, assisted reproduction, embryonic development, and plant breeding. We not only review the experimental and bioinformatics methods for single-cell research, but also discuss their applications in various fields and forecast the future directions for single-cell technologies. We believe that spatial transcriptomics and single-cell multi-omics will become the next booming business for mechanism research and commercial industry.

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.

Selective Detection and Characterization of Small Cysteine-Containing Peptides with Cluster-Modified Nanopore Sensing

It was recently demonstrated that one can monitor ligand-induced structure fluctuations of individual thiolate-capped gold nanoclusters using resistive-pulse nanopore sensing. The magnitude of the fluctuations scales with the size of the capping ligand, and it was later shown one can observe ligand exchange in this nanopore setup. We expand on these results by exploring the different types of current fluctuations associated with peptide ligands attaching to tiopronin-capped gold nanoclusters. We show here that the fluctuations can be used to identify the attaching peptide through either the magnitude of the peptide-induced current jumps or the onset of high-frequency current fluctuations. Importantly, the peptide attachment process requires that the peptide contains a cysteine residue. This suggests that nanopore-based monitoring of peptide attachments with thiolate-capped clusters could provide a means for selective detection of cysteine-containing peptides. Finally, we demonstrate the cluster-based protocol with various peptide mixtures to show that one can identify more than one cysteine-containing peptide in a mixture.

Real-time dynamic single-molecule protein sequencing on an integrated semiconductor device

Studies of the proteome would benefit greatly from methods to directly sequence and digitally quantify proteins and detect posttranslational modifications with single-molecule sensitivity. Here, we demonstrate single-molecule protein sequencing using a dynamic approach in which single peptides are probed in real time by a mixture of dye-labeled N-terminal amino acid recognizers and simultaneously cleaved by aminopeptidases. We annotate amino acids and identify the peptide sequence by measuring fluorescence intensity, lifetime, and binding kinetics on an integrated semiconductor chip. Our results demonstrate the kinetic principles that allow recognizers to identify multiple amino acids in an information-rich manner that enables discrimination of single amino acid substitutions and posttranslational modifications. With further development, we anticipate that this approach will offer a sensitive, scalable, and accessible platform for single-molecule proteomic studies and applications.

Precision Targeting of Endogenous Epidermal Growth Factor Receptor (EGFR) by Structurally Aligned Dual-Modifier Labeling

Covalent modification of endogenous proteins by chemical probes is used for proteome-wide profiling of cellular protein function and drug discovery. However, probe selectivity in the complex cellular environment is a challenge, and new probes with better target selectivity are continuously needed. On the basis of the success of monocovalent activity-based and reactivity-based probes, an approach of structurally aligned dual-modifier labeling (SADL) was investigated here on its potential in improving target precision. Two reactive groups, based on the acrylamide and NHS ester chemistry, were linked with structural alignment to be under the same anilinoquinazoline ligand-directive for targeting the epidermal growth factor receptor (EGFR) protein kinase as the model system for proteome-wide profiling. The SADL approach was compared with its monocovalent precursors in a label-free MaxLFQ workflow using MDA-MB-468 triple negative breast cancer cells. The dual-modifier probe consistently showed labeling of EGFR with improved precision over both monocovalent precursors under various controls. The workflow also labeled endogenous USP34 and PKMYT1 with high selectivity. Precision labeling with two covalent modifiers under a common ligand directive may broaden protein identification opportunities in the native environment to complement genetic and antibody-based approaches for elucidating biological or disease mechanisms, as well as accelerating drug target discovery.

Extraction of Dielectric Permittivity from Atomistic Molecular Dynamics Simulations and Microwave Measurements

The design of new biotechnology depends on the prediction and measurement of the electrical properties of biomolecules. The dielectric permittivity, in particular, is highly important for the design of microwave systems for diagnostics, yet this property is rarely explicitly targeted during the development of biomolecular force fields for molecular dynamics (MD) simulations. In order to explore the ability of existing force fields to reproduce the frequency-dependent permittivity, we carried out MD simulations of various aqueous solutions, including pure water, isopropyl alcohol, alanine, and the protein ubiquitin. The TIP3P, TIP4P, TIP4P/ε, and SWM4-NDP water models were used along with the CHARMM36m and Drude protein force fields. An experimental setup using a truncated coaxial line was created to measure the permittivity of the same solutions to check for measure-model agreement. We found that one of the nonpolarizable force fields (TIP4P/ε + CHARMM36m) and the polarizable force fields (SWM4-NDP + Drude) closely agree with experimental results. This demonstrates the strength of the tuned TIP4P/ε water model, as well as the physical validity of polarizable force fields in capturing dielectric permittivity. This represents an important step toward the predictive design of biosensors.

Resolving Isomeric Posttranslational Modifications Using a Biological Nanopore as a Sensor of Molecular Shape

The chemical nature and precise position of posttranslational modifications (PTMs) in proteins or peptides are crucial for various severe diseases, such as cancer. State-of-the-art PTM diagnosis is based on elaborate and costly mass-spectrometry or immunoassay-based approaches, which are limited in selectivity and specificity. Here, we demonstrate the use of a protein nanopore to differentiate peptides─derived from human histone H4 protein─of identical mass according to the positions of acetylated and methylated lysine residues. Unlike sequencing by stepwise threading, our method detects PTMs and their positions by sensing the shape of a fully entrapped peptide, thus eliminating the need for controlled translocation. Molecular dynamics simulations show that the sensitivity to molecular shape derives from a highly nonuniform electric field along the pore. This molecular shape-sensing principle offers a path to versatile, label-free, and high-throughput characterizations of protein isoforms.

Utility of tissue-specific gene expression scores for gene prioritization in Mendelian diseases

In genetic testing of Mendelian diseases, it is a bioinformatics challenge to effectively prioritize disease-causing candidate genes listed from massively parallel sequencing. Tissue specificity of the gene expression levels may give a clue because it may reflect tissue-specific disease manifestation. However, considering poor correlations between mRNA and protein expression in some genes, it is not clear whether transcriptomics- or proteomics-based tissue specificity should be used to prioritize candidate genes. Therefore, we compared the efficiency of tissue-specific scores (TS scores) obtained from transcriptome and proteome data in prioritizing candidate genes for whole exome sequencing (WES) analysis of Mendelian disease patients. We show that both Protein and RNA TS scores are useful in prioritizing candidate genes in WES analysis, although diseases like coagulopathies get more benefit from Protein TS score. This study may provide useful evidence in developing new methods to effectively identify novel disease-causing genes.

Single-File Translocation Dynamics of SDS-Denatured, Whole Proteins through Sub-5 nm Solid-State Nanopores

The ability to routinely identify and quantify the complete proteome from single cells will greatly advance medicine and basic biology research. To meet this challenge of single-cell proteomics, single-molecule technologies are being developed and improved. Most approaches, to date, rely on the analysis of polypeptides, resulting from digested proteins, either in solution or immobilized on a surface. Nanopore biosensing is an emerging single-molecule technique that circumvents surface immobilization and is optimally suited for the analysis of long biopolymers, as has already been shown for DNA sequencing. However, proteins, unlike DNA molecules, are not uniformly charged and harbor complex tertiary structures. Consequently, the ability of nanopores to analyze unfolded full-length proteins has remained elusive. Here, we evaluate the use of heat denaturation and the anionic surfactant sodium dodecyl sulfate (SDS) to facilitate electrokinetic nanopore sensing of unfolded proteins. Specifically, we characterize the voltage dependence translocation dynamics of a wide molecular weight range of proteins (from 14 to 130 kDa) through sub-5 nm solid-state nanopores, using a SDS concentration below the critical micelle concentration. Our results suggest that proteins' translocation dynamics are significantly slower than expected, presumably due to the smaller nanopore diameters used in our study and the role of the electroosmotic force opposing the translocation direction. This allows us to distinguish among the proteins of different molecular weights based on their dwell time and electrical charge deficit. Given the simplicity of the protein denaturation assay and circumvention of the tailor-made necessities for sensing protein of different folded sizes, shapes, and charges, this approach can facilitate the development of a whole proteome identification technique.

Protein Adsorption Loss─The Bottleneck of Single-Cell Proteomics

Single-cell proteomics is a promising field to provide direct yet comprehensive molecular insights into cellular functions without averaging effects. Here, we address a grand technical challenge impeding the maturation of single-cell proteomics─protein adsorption loss (PAL). Even though widely known, there is currently no quantitation on how profoundly and selectively PAL has affected single-cell proteomics. Therefore, the mitigations to this challenge have been generic, and their efficacy was only evaluated by the size of the resolved proteome with no specificity on individual proteins. We use the existing knowledge of PAL, protein expression, and the typical surface area used in single-cell proteomics to discuss the severity of protein loss. We also summarize the current solutions to this challenge and briefly review the available methods to characterize the physical and chemical properties of protein surface adsorption. By citing successful strategies in single-cell genomics for measurement errors in individual transcripts, we pinpoint the urgency to benchmark PAL at the proteome scale with individual protein resolution. Finally, orthogonal single-cell proteomic techniques that have the potential to cross validate PAL are proposed. We hope these efforts can promote the fruition of single-cell proteomics in the near future.

Localized Nanopore Fabrication via Controlled Breakdown

Nanopore sensors provide a unique platform to detect individual nucleic acids, proteins, and other biomolecules without the need for fluorescent labeling or chemical modifications. Solid-state nanopores offer the potential to integrate nanopore sensing with other technologies such as field-effect transistors (FETs), optics, plasmonics, and microfluidics, thereby attracting attention to the development of commercial instruments for diagnostics and healthcare applications. Stable nanopores with ideal dimensions are particularly critical for nanopore sensors to be integrated into other sensing devices and provide a high signal-to-noise ratio. Nanopore fabrication, although having benefited largely from the development of sophisticated nanofabrication techniques, remains a challenge in terms of cost, time consumption and accessibility. One of the latest developed methods—controlled breakdown (CBD)—has made the nanopore technique broadly accessible, boosting the use of nanopore sensing in both fundamental research and biomedical applications. Many works have been developed to improve the efficiency and robustness of pore formation by CBD. However, nanopores formed by traditional CBD are randomly positioned in the membrane. To expand nanopore sensing to a wider biomedical application, controlling the localization of nanopores formed by CBD is essential. This article reviews the recent strategies to control the location of nanopores formed by CBD. We discuss the fundamental mechanism and the efforts of different approaches to confine the region of nanopore formation.