Simulation of single-protein nanopore sensing shows feasibility for whole-proteome identification

Single molecular profile of proteins sensing by nanopore technology

The characterization of biological macromolecules such as proteins and their interactions are crucial to understanding biological processes, disease diagnosis, and drug design. With the rapid development of proteomics, nanopore technology has emerged potentially as a single-molecule profile for huge amounts of peptides and proteins defined in the biological system, particularly for protein sequencing. This review focuses on recent advances in nanopore sensing of proteins and peptides, involving protein dynamic interactions, protein fingerprinting, and protein sequencing. These progresses will provide new perspectives to decipher the mechanisms of protein structure and function, and serve much more possible applications.

Nanofluidic Lab-On-A-Chip Systems for Biosensing in Healthcare

Biosensing plays a vital role in healthcare monitoring, disease detection, and treatment planning. In recent years, nanofluidic technology has been increasingly explored to be developed into lab-on-a-chip biosensing systems. Given now the possibility of fabricating geometrically defined nanometric channels that are commensurate with the size of many biomolecules, nanofluidic-based devices are likely to become a key technology for the analysis of various clinical biomarkers, including DNA (deoxyribonucleic acid) and proteins in liquid biopsies. This review summarizes the fundamentals and technological advances of nanofluidics from the purview of single-molecule analysis, detection of low-abundance molecules, and single-cell analysis at the subcellular level. The extreme confinement and dominant surface charge effects in nanochannels provide unique advantages to nanofluidic devices for the manipulation and transport of target biomarkers. When coupled to a microfluidic network to facilitate sample introduction, integrated micro-nanofluidic biosensing devices are proving to be more sensitive and specific in molecular analysis compared to conventional assays in many cases. Based on recent progress in nanofluidics and current clinical trends, the review concludes with a discussion of near-term challenges and future directions for the development of nanofluidic-based biosensing systems toward enabling a new wave of lab-on-a-chip technology for personalized and preventive medicine.

Towards improved biofilm models

Biofilms are complex microbial communities that have a critical function in many natural ecosystems, industrial settings as well as in recurrent and chronic infections. Biofilms are highly heterogeneous and dynamic assemblages that display complex responses to varying environmental factors, and those properties present substantial challenges for their study and control. In recent years, there has been a growing interest in developing improved biofilm models to offer more precise and comprehensive representations of these intricate systems. However, an objective assessment for ascertaining the ability of biofilms in model systems to recapitulate those in natural environments has been lacking. In this Perspective, we focus on medical biofilms to delve into the current state-of-the-art in biofilm modelling, emphasizing the advantages and limitations of different approaches and addressing the key challenges and opportunities for future research. We outline a framework for quantitatively assessing model accuracy. Ultimately, this Perspective aims to provide a comprehensive and critical overview of medically focused biofilm models, with the intent of inspiring future research aimed at enhancing the biological relevance of biofilm models.

Full-Length Single Protein Molecules Tracking and Counting in Thin Silicon Channels

Emerging single-molecule protein sensing techniques are ushering in a transformative era in biomedical research. Nevertheless, challenges persist in realizing ultra-fast full-length protein sensing, including loss of molecular integrity due to protein fragmentation, biases introduced by antibodies affinity, identification of proteoforms, and low throughputs. Here, a single-molecule method for parallel protein separation and tracking is introduced, yielding multi-dimensional molecular properties used for their identification. Proteins are tagged by chemo-selective dual amino-acid specific labels and are electrophoretically separated by their mass/charge in custom-designed thin silicon channel with subwavelength height. This approach allows analysis of thousands of individual proteins within a few minutes by tracking their motion during the migration. The power of the method is demonstrated by quantifying a cytokine panel for host-response discrimination between viral and bacterial infections. Moreover, it is shown that two clinically-relevant splice isoforms of Vascular endothelial growth factor (VEGF) can be accurately quantified from human serum samples. Being non-destructive and compatible with full-length intact proteins, this method opens up ways for antibody-free single-protein molecule quantification.

Nanopore DNA sequencing technologies and their applications towards single-molecule proteomics

Sequencing of nucleic acids with nanopores has emerged as a powerful tool offering rapid readout, high accuracy, low cost and portability. This label-free method for sequencing at the single-molecule level is an achievement on its own. However, nanopores also show promise for the technologically even more challenging sequencing of polypeptides, something that could considerably benefit biological discovery, clinical diagnostics and homeland security, as current techniques lack portability and speed. Here we survey the biochemical innovations underpinning commercial and academic nanopore DNA/RNA sequencing techniques, and explore how these advances can fuel developments in future protein sequencing with nanopores.

Recent methodological advances towards single-cell proteomics

Studying the central dogma at the single-cell level has gained increasing attention to reveal hidden cell lineages and functions that cannot be studied using traditional bulk analyses. Nonetheless, most single-cell studies exploiting genomic and transcriptomic levels fail to address information on proteins that are central to many important biological processes. Single-cell proteomics enables understanding of the functional status of individual cells and is particularly crucial when the specimen is composed of heterogeneous entities of cells. With the growing importance of this field, significant methodological advancements have emerged recently. These include miniaturized and automated sample preparation, multi-omics analyses, and combined analyses of multiple techniques such as mass spectrometry and microscopy. Moreover, artificial intelligence and single-molecule detection technologies have advanced throughput and improved sensitivity limitations, respectively, over conventional methods. In this review, we summarize cutting-edge methodologies for single-cell proteomics and relevant emerging technologies that have been reported in the last 5 years, and provide an outlook on this research field.

Engineering Biological Nanopore Approaches toward Protein Sequencing

Biotechnological innovations have vastly improved the capacity to perform large-scale protein studies, while the methods we have for identifying and quantifying individual proteins are still inadequate to perform protein sequencing at the single-molecule level. Nanopore-inspired systems devoted to understanding how single molecules behave have been extensively developed for applications in genome sequencing. These nanopore systems are emerging as prominent tools for protein identification, detection, and analysis, suggesting realistic prospects for novel protein sequencing. This review summarizes recent advances in biological nanopore sensors toward protein sequencing, from the identification of individual amino acids to the controlled translocation of peptides and proteins, with attention focused on device and algorithm development and the delineation of molecular mechanisms with the aid of simulations. Specifically, the review aims to offer recommendations for the advancement of nanopore-based protein sequencing from an engineering perspective, highlighting the need for collaborative efforts across multiple disciplines. These efforts should include chemical conjugation, protein engineering, molecular simulation, machine-learning-assisted identification, and electronic device fabrication to enable practical implementation in real-world scenarios.

Portable nanopore-sequencing technology: Trends in development and applications

Sequencing technology is the most commonly used technology in molecular biology research and an essential pillar for the development and applications of molecular biology. Since 1977, when the first generation of sequencing technology opened the door to interpreting the genetic code, sequencing technology has been developing for three generations. It has applications in all aspects of life and scientific research, such as disease diagnosis, drug target discovery, pathological research, species protection, and SARS-CoV-2 detection. However, the first- and second-generation sequencing technology relied on fluorescence detection systems and DNA polymerization enzyme systems, which increased the cost of sequencing technology and limited its scope of applications. The third-generation sequencing technology performs PCR-free and single-molecule sequencing, but it still depends on the fluorescence detection device. To break through these limitations, researchers have made arduous efforts to develop a new advanced portable sequencing technology represented by nanopore sequencing. Nanopore technology has the advantages of small size and convenient portability, independent of biochemical reagents, and direct reading using physical methods. This paper reviews the research and development process of nanopore sequencing technology (NST) from the laboratory to commercially viable tools; discusses the main types of nanopore sequencing technologies and their various applications in solving a wide range of real-world problems. In addition, the paper collates the analysis tools necessary for performing different processing tasks in nanopore sequencing. Finally, we highlight the challenges of NST and its future research and application directions.

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-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.

Current Flow in a Cylindrical Nanopore with an Object–Implications for Virus Sensing

Interest is growing in nanopores as real-time, low-cost, label-free virus size sensors. To optimize their performance, we evaluate how external electric field and ion concentrations and pore wall charges influence currents and object (disk) radius-current relationship using simulations. The physics was described using the Poisson-Nernst-Planck and Navier–Stokes equations. In a charged cylindrical nanopore with a charged disk, elevated external electric field produces higher (and polarity independent) ion concentrations and greater ion current (largely migratory). Elevated external ion concentrations also lead to higher concentrations (mainly away from the pore wall), greater axial electric field especially in the disk-pore wall space, and finally larger current. At low concentrations, current is disk radius independent. The current rises as concentrations increase. Interestingly, the rise is greater for larger disks (except when the pore is blocked mechanically). Smaller cross-sectional area for current flow or volume exclusion of electrolyte by object thus cannot be universally accepted as explanations of current blockage. Ion current rises when pore wall charge density increases, but its direction is independent of charge sign. Current-disk radius relationship is also independent of pore wall charge sign. If the pore wall and disk charges have the same sign, larger current with bigger disk is due to higher counter-ion accumulation in the object-pore wall space. However, if their signs are opposite, it is largely due to elevated axial electric field in the object-pore wall space. Finally in uncharged nanopores, current diminishes when disk radius increases making them better sensors of virus size.

Protein Sequencing, One Molecule at a Time

Despite tremendous gains over the past decade, methods for characterizing proteins have generally lagged behind those for nucleic acids, which are characterized by extremely high sensitivity, dynamic range, and throughput. However, the ability to directly characterize proteins at nucleic acid levels would address critical biological challenges such as more sensitive medical diagnostics, deeper protein quantification, large-scale measurement, and discovery of alternate protein isoforms and modifications and would open new paths to single-cell proteomics. In response to this need, there has been a push to radically improve protein sequencing technologies by taking inspiration from high-throughput nucleic acid sequencing, with a particular focus on developing practical methods for single-molecule protein sequencing (SMPS). SMPS technologies fall generally into three categories: sequencing by degradation (e.g., mass spectrometry or fluorosequencing), sequencing by transit (e.g., nanopores or quantum tunneling), and sequencing by affinity (as in DNA hybridization-based approaches). We describe these diverse approaches, which range from those that are already experimentally well-supported to the merely speculative, in this nascent field striving to reformulate proteomics.

Distinguishing between Similar Miniproteins with Single-Molecule Nanopore Sensing: A Computational Study

A nanopore is a tool in single-molecule sensing biotechnology that offers label-free identification with high throughput. Nanopores have been successfully applied to sequence DNA and show potential in the study of proteins. Nevertheless, the task remains challenging due to the large variability in size, charges, and folds of proteins. Miniproteins have a small number of residues, limited secondary structure, and stable tertiary structure, which can offer a systematic way to reduce complexity. In this computational work, we theoretically evaluated sensing two miniproteins found in the human body using a silicon nitride nanopore. We employed molecular dynamics methods to compute occupied-pore ionic current magnitudes and electronic structure calculations to obtain interaction strengths between pore wall and miniprotein. From the interaction strength, we derived dwell times using a mix of combinatorics and numerical solutions. This latter approach circumvents typical computational demands needed to simulate translocation events using molecular dynamics. We focused on two miniproteins potentially difficult to distinguish owing to their isotropic geometry, similar number of residues, and overall comparable structure. We found that the occupied-pore current magnitudes not to vary significantly, but their dwell times differ by 1 order of magnitude. Together, these results suggest a successful identification protocol for similar miniproteins.

Biological nanopores for single-molecule sensing

Evolution has found countless ways to transport material across cells and cellular compartments separated by membranes. Protein assemblies are the cornerstone for the formation of channels and pores that enable this regulated passage of molecules in and out of cells, contributing to maintaining most of the fundamental processes that sustain living organisms. As in several other occasions, we have borrowed from the natural properties of these biological systems to push technology forward and have been able to hijack these nano-scale proteinaceous pores to learn about the physical and chemical features of molecules passing through them. Today, a large repertoire of biological pores is exploited as molecular sensors for characterizing biomolecules that are relevant for the advancement of life sciences and application to medicine. Although the technology has quickly matured to enable nucleic acid sensing with transformative implications for genomics, biological pores stand as some of the most promising candidates to drive the next developments in single-molecule proteomics.

Nanopore-Based Protein Identification

The implementation of a reliable, rapid, inexpensive, and simple method for whole-proteome identification would greatly benefit cell biology research and clinical medicine. Proteins are currently identified by cleaving them with proteases, detecting the polypeptide fragments with mass spectrometry, and mapping the latter to sequences in genomic/proteomic databases. Here, we demonstrate that the polypeptide fragments can instead be detected and classified at the single-molecule limit using a nanometer-scale pore formed by the protein aerolysin. Specifically, three different water-soluble proteins treated with the same protease, trypsin, produce different polypeptide fragments defined by the degree by which the latter reduce the nanopore's ionic current. The fragments identified with the aerolysin nanopore are consistent with the predicted fragments that trypsin could produce.

Lifetime-based analysis of binary fluorophores mixtures in the low photon count limit

Single biomolecule sensing often requires the quantification of multiple fluorescent species. Here, we theoretically and experimentally use time-resolved fluorescence via Time Correlated Single Photon Counting (TCSPC) to accurately quantify fluorescent species with similar chromatic signatures. A modified maximum likelihood estimator is introduced to include two fluorophore species, with compensation of the instrument response function. We apply this algorithm to simulated data of a simplified two-fluorescent species model, as well as to experimental data of fluorophores' mixtures and to a model protein, doubly labeled with different fluorophores' ratio. We show that 100 to 200 photons per fluorophore, in a 10-ms timescale, are sufficient to provide an accurate estimation of the dyes' ratio on the model protein. Our results provide estimation for the desired photon integration time toward implementation of TCSPC in systems with fast occurring events, such as translocation of biomolecules through nanopores or single-molecule burst analyses experiments.

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Exact ratios of emission-similar dyes in binary mixtures were quantified by TCSPC

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MLE-based analysis with IRF compensation was implemented for two fluorescent dyes

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Dual dye bioconjugation on a model protein was quantified at limited photon counts

Machine Learning Assisted Simultaneous Structural Profiling of Differently Charged Proteins in a Mycobacterium smegmatis Porin A (MspA) Electroosmotic Trap

The nanopore is emerging as a means of single-molecule protein sensing. However, proteins demonstrate different charge properties, which complicates the design of a sensor that can achieve simultaneous sensing of differently charged proteins. In this work, we introduce an asymmetric electrolyte buffer combined with the Mycobacterium smegmatis porin A (MspA) nanopore to form an electroosmotic flow (EOF) trap. Apo- and holo-myoglobin, which differ in only a single heme, can be fully distinguished by this method. Direct discrimination of lysozyme, apo/holo-myoglobin, and the ACTR/NCBD protein complex, which are basic, neutral, and acidic proteins, respectively, was simultaneously achieved by the MspA EOF trap. To automate event classification, multiple event features were extracted to build a machine learning model, with which a 99.9% accuracy is achieved. The demonstrated method was also applied to identify single molecules of α-lactalbumin and β-lactoglobulin directly from whey protein powder. This protein-sensing strategy is useful in direct recognition of a protein from a mixture, suggesting its prospective use in rapid and sensitive detection of biomarkers or real-time protein structural analysis.

Single-molecule mechanical fingerprinting with DNA nanoswitch calipers

Decoding the identity of biomolecules from trace samples is a longstanding goal in the field of biotechnology. Advances in DNA analysis have substantially affected clinical practice and basic research, but corresponding developments for proteins face challenges due to their relative complexity and our inability to amplify them. Despite progress in methods such as mass spectrometry and mass cytometry, single-molecule protein identification remains a highly challenging objective. Towards this end, we combine DNA nanotechnology with single-molecule force spectroscopy to create a mechanically reconfigurable DNA nanoswitch caliper capable of measuring multiple coordinates on single biomolecules with atomic resolution. Using optical tweezers, we demonstrate absolute distance measurements with ångström-level precision for both DNA and peptides, and using multiplexed magnetic tweezers, we demonstrate quantification of relative abundance in mixed samples. Measuring distances between DNA-labelled residues, we perform single-molecule fingerprinting of synthetic and natural peptides, and show discrimination, within a heterogeneous population, between different posttranslational modifications. DNA nanoswitch calipers are a powerful and accessible tool for characterizing distances within nanoscale complexes that will enable new applications in fields such as single-molecule proteomics.

Evaluation of FRET X for single-molecule protein fingerprinting

Single-molecule protein identification is an unrealized concept with potentially ground-breaking applications in biological research. We propose a method called FRET X (Förster Resonance Energy Transfer via DNA eXchange) fingerprinting, in which the FRET efficiency is read out between exchangeable dyes on protein-bound DNA docking strands and accumulated FRET efficiencies constitute the fingerprint for a protein. To evaluate the feasibility of this approach, we simulated fingerprints for hundreds of proteins using a coarse-grained lattice model and experimentally demonstrated FRET X fingerprinting on model peptides. Measured fingerprints are in agreement with our simulations, corroborating the validity of our modeling approach. In a simulated complex mixture of >300 human proteins of which only cysteines, lysines, and arginines were labeled, a support vector machine was able to identify constituents with 95% accuracy. We anticipate that our FRET X fingerprinting approach will form the basis of an analysis tool for targeted proteomics.

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We propose a FRET-based single-molecule protein identification method

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Peptides are experimentally distinguishable by their fingerprints

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Our approach can classify the constituents of complex samples with 95% accuracy

In silico assessment of a novel single-molecule protein fingerprinting method employing fragmentation and nanopore detection

The identification of proteins at the single-molecule level would open exciting new venues in biological research and disease diagnostics. Previously, we proposed a nanopore-based method for protein identification called chop-n-drop fingerprinting, in which the fragmentation pattern induced and measured by a proteasome-nanopore construct is used to identify single proteins. In the simulation study presented here, we show that 97.1% of human proteome constituents are uniquely identified under close to ideal measuring circumstances, using a simple alignment-based classification method. We show that our method is robust against experimental error, as 69.4% can still be identified if the resolution is twice as low as currently attainable, and 10% of proteasome restriction sites and protein fragments are randomly ignored. Based on these results and our experimental proof of concept, we argue that chop-n-drop fingerprinting has the potential to make cost-effective single-molecule protein identification feasible in the near future.

A Guide to Signal Processing Algorithms for Nanopore Sensors

Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network

Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net is evaluated on simulated data sets and further applied to experimental data of DNA and protein translocation. The B-Net results are characterized by small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to 1, an impossibility for threshold-based algorithms. The B-Net presents a generic architecture applicable to pulse-like signals beyond nanopore currents.

Herding cats: Label-based approaches in protein translocation through nanopore sensors for single-molecule protein sequence analysis

Proteins carry out life's essential functions. Comprehensive proteome analysis technologies are thus required for a full understanding of the operating principles of biological systems. While current proteomics techniques suffer from limitations in sensitivity and/or throughput, nanopore technology has the potential to enable de novo protein identification through single-molecule sequencing. However, a significant barrier to achieving this goal is controlling protein/peptide translocation through the nanopore sensor for processive strand analysis. Here, we review recent approaches that use a range of techniques, from oligonucleotide conjugation to molecular motors, aimed at driving protein strands and peptides through protein nanopores. We further discuss site-specific protein conjugation chemistry that could be combined with these translocation approaches as future directions to achieve single-molecule protein detection and sequencing of native proteins.

The emerging landscape of single-molecule protein sequencing technologies

Single-cell profiling methods have had a profound impact on the understanding of cellular heterogeneity. While genomes and transcriptomes can be explored at the single-cell level, single-cell profiling of proteomes is not yet established. Here we describe new single-molecule protein sequencing and identification technologies alongside innovations in mass spectrometry that will eventually enable broad sequence coverage in single-cell profiling. These technologies will in turn facilitate biological discovery and open new avenues for ultrasensitive disease diagnostics.

Proteomics, Personalized Medicine and Cancer

As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome.

Theoretical Considerations for Next-Generation Proteomics

While mass spectrometry still dominates proteomics research, alternative and potentially disruptive, next-generation technologies are receiving increased investment and attention. Most of these technologies aim at the sequencing of single peptide or protein molecules, typically labeling or otherwise distinguishing a subset of the proteinogenic amino acids. This note considers some theoretical aspects of these future technologies from a bottom-up proteomics viewpoint, including the ability to uniquely identify human proteins as a function of which and how many amino acids can be read, enzymatic efficiency, and the maximum read length. This is done through simulations under ideal and non-ideal conditions to set benchmarks for what may be achievable with future single-molecule sequencing technology. The simulations reveal, among other observations, that the best choice of reading N amino acids performs similarly to the average choice of N+1 amino acids, and that the discrimination power of the amino acids scales with their frequency in the proteome. The simulations are agnostic with respect to the next-generation proteomics platform, and the results and conclusions should therefore be applicable to any single-molecule partial peptide sequencing technology.

Nanopores: a versatile tool to study protein dynamics

Proteins are the active workhorses in our body. These biomolecules perform all vital cellular functions from DNA replication and general biosynthesis to metabolic signaling and environmental sensing. While static 3D structures are now readily available, observing the functional cycle of proteins - involving conformational changes and interactions - remains very challenging, e.g., due to ensemble averaging. However, time-resolved information is crucial to gain a mechanistic understanding of protein function. Single-molecule techniques such as FRET and force spectroscopies provide answers but can be limited by the required labelling, a narrow time bandwidth, and more. Here, we describe electrical nanopore detection as a tool for probing protein dynamics. With a time bandwidth ranging from microseconds to hours, nanopore experiments cover an exceptionally wide range of timescales that is very relevant for protein function. First, we discuss the working principle of label-free nanopore experiments, various pore designs, instrumentation, and the characteristics of nanopore signals. In the second part, we review a few nanopore experiments that solved research questions in protein science, and we compare nanopores to other single-molecule techniques. We hope to make electrical nanopore sensing more accessible to the biochemical community, and to inspire new creative solutions to resolve a variety of protein dynamics - one molecule at a time.

Plasmonic Biosensors for Single-Molecule Biomedical Analysis

The rapid spread of epidemic diseases (i.e., coronavirus disease 2019 (COVID-19)) has contributed to focus global attention on the diagnosis of medical conditions by ultrasensitive detection methods. To overcome this challenge, increasing efforts have been driven towards the development of single-molecule analytical platforms. In this context, recent progress in plasmonic biosensing has enabled the design of novel detection strategies capable of targeting individual molecules while evaluating their binding affinity and biological interactions. This review compiles the latest advances in plasmonic technologies for monitoring clinically relevant biomarkers at the single-molecule level. Functional applications are discussed according to plasmonic sensing modes based on either nanoapertures or nanoparticle approaches. A special focus was devoted to new analytical developments involving a wide variety of analytes (e.g., proteins, living cells, nucleic acids and viruses). The utility of plasmonic-based single-molecule analysis for personalized medicine, considering technological limitations and future prospects, is also overviewed.

Proteome-wide Systems Genetics to Identify Functional Regulators of Complex Traits

Proteomic technologies now enable the rapid quantification of thousands of proteins across genetically diverse samples. Integration of these data with systems-genetics analyses is a powerful approach to identify new regulators of economically important or disease-relevant phenotypes in various populations. In this review, we summarize the latest proteomic technologies and discuss technical challenges for their use in population studies. We demonstrate how the analysis of correlation structure and loci mapping can be used to identify genetic factors regulating functional protein networks and complex traits. Finally, we provide an extensive summary of the use of proteome-wide systems genetics throughout fungi, plant, and animal kingdoms and discuss the power of this approach to identify candidate regulators and drug targets in large human consortium studies.

On-chip protein separation with single-molecule resolution

Accurate identification of both abundant and rare proteins hinges on the development of single-protein sensing methods. Given the immense variation in protein expression levels in a cell, separation of proteins by weight would improve protein classification strategies. Upstream separation facilitates sample binning into smaller groups while also preventing sensor overflow, as may be caused by highly abundant proteins in cell lysates or clinical samples. Here, we scale a bulk analysis method for protein separation, sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE), to the single-molecule level using single-photon sensitive widefield imaging. Single-molecule sensing of the electrokinetically moving proteins is achieved by in situ polymerization of the PAGE in a low-profile fluidic channel having a depth of only ~ 0.6 µm. The polyacrylamide gel restricts the Brownian kinetics of the proteins, while the low-profile channel ensures that they remain in focus during imaging, allowing video-rate monitoring of single-protein migration. Calibration of the device involves separating a set of Atto647N-covalently labeled recombinant proteins in the size range of 14-70 kDa, yielding an exponential dependence of the proteins' molecular weights on the measured mobilities, as expected. Subsequently, we demonstrate the ability of our fluidic device to separate and image thousands of proteins directly extracted from a human cancer cell line. Using single-particle image analysis methods, we created detailed profiles of the separation kinetics of lysine and cysteine -labeled proteins. Downstream coupling of the device to single-protein identification sensors may provide superior protein classification and improve our ability to analyze complex biological and medical protein samples.

Solid-Phase Peptide Capture and Release for Bulk and Single-Molecule Proteomics

The field of proteomics has expanded recently with more sensitive techniques for the bulk measurement of peptides as well as single-molecule techniques. One limiting factor for some of these methods is the need for multiple chemical derivatizations and highly pure proteins free of contaminants. We demonstrate a solid-phase capture-release strategy suitable for the proteolysis, purification, and subsequent chemical modification of peptides. We use this resin on an HEK293T cell lysate and perform one-pot proteolysis, capture, and derivatization to survey peptide capture biases from over 40 000 unique peptides from a cellular proteome. We also show that this capture can be reversed in a traceless manner, such that it is amenable for single-molecule proteomics techniques. With this technique, we perform a fluorescent labeling and C-terminal derivatization on a peptide and subject it to fluorosequencing, demonstrating that washing the resin is sufficient to remove excess dyes and other reagents prior to single-molecule protein sequencing.

Protein unfolding by SDS: the microscopic mechanisms and the properties of the SDS-protein assembly

The effects of detergent sodium dodecyl sulfate (SDS) on protein structure and dynamics are fundamental to the most common laboratory technique used to separate proteins and determine their molecular weights: polyacrylamide gel electrophoresis. However, the mechanism by which SDS induces protein unfolding and the microstructure of protein-SDS complexes remain largely unknown. Here, we report a detailed account of SDS-induced unfolding of two proteins-I27 domain of titin and β-amylase-obtained through all-atom molecular dynamics simulations. Both proteins were found to spontaneously unfold in the presence of SDS at boiling water temperature on the time scale of several microseconds. The protein unfolding was found to occur via two distinct mechanisms in which specific interactions of individual SDS molecules disrupt the protein's secondary structure. In the final state of the unfolding process, the proteins are found to wrap around SDS micelles in a fluid necklace-and-beads configuration, where the number and location of bound micelles changes dynamically. The global conformation of the protein was found to correlate with the number of SDS micelles bound to it, whereas the number of SDS molecules directly bound to the protein was found to define the relaxation time scale of the unfolded protein. Our microscopic characterization of SDS-protein interactions sets the stage for future refinement of SDS-enabled protein characterization methods, including protein fingerprinting and sequencing using a solid-state nanopore.

Current Blockades of Proteins inside Nanopores for Real-Time Metabolome Analysis

Biological nanopores are emerging as powerful and low-cost sensors for real-time analysis of biological samples. Proteins can be incorporated inside the nanopore, and ligand binding to the protein adaptor yields changes in nanopore conductance. In order to understand the origin of these conductance changes and develop sensors for detecting metabolites, we tested the signal originating from 13 different protein adaptors. We found that the quality of the protein signal depended on both the size and charge of the protein. The engineering of a dipole within the surface of the adaptor reduced the current noise by slowing the protein dynamics within the nanopore. Further, the charge of the ligand and the induced conformational changes of the adaptor defined the conductance changes upon metabolite binding, suggesting that the protein resides in an electrokinetic minimum within the nanopore, the position of which is altered by the ligand. These results represent an important step toward understanding the dynamics of the electrophoretic trapping of proteins inside nanopores and will allow developing next-generation sensors for metabolome analysis.

Beyond mass spectrometry, the next step in proteomics

Proteins can be the root cause of a disease, and they can be used to cure it. The need to identify these critical actors was recognized early (1951) by Sanger; the first biopolymer sequenced was a peptide, insulin. With the advent of scalable, single-molecule DNA sequencing, genomics and transcriptomics have since propelled medicine through improved sensitivity and lower costs, but proteomics has lagged behind. Currently, proteomics relies mainly on mass spectrometry (MS), but instead of truly sequencing, it classifies a protein and typically requires about a billion copies of a protein to do it. Here, we offer a survey that illuminates a few alternatives with the brightest prospects for identifying whole proteins and displacing MS for sequencing them. These alternatives all boast sensitivity superior to MS and promise to be scalable and seem to be adaptable to bioinformatics tools for calling the sequence of amino acids that constitute a protein.