Direct Identification Research Articles

A study on the collapse characteristics of loess based on energy spectrum superposition method

Mineral types form the basis for studying the structural stability of loess, and identifying mineral types at the microscopic scale has always been a difficult task. Identifying mineral types at the microscopic scale is very helpful in understanding the differential role that different minerals play in the structural stability of loess, and it can also clarify the specific mineral changes that occur during the process of humidification and dehumidification. Using an innovative energy spectrum superposition method, this article combines backscattered electron imaging and X-ray energy spectrum analysis results to achieve direct identification of the eight main minerals in loess, including quartz, illite, and chlorite, within SEM images. The mineral identification results provide a basis for statistical analysis of mineral water sensitivity and morphological changes under wetting conditions. The results demonstrate that chlorite and hematite, which account for no more than 23% of the loess composition, play a crucial role in binding. Furthermore, these minerals exhibit significant hydrolysis phenomena. Particularly, the intense decomposition of chlorite leads to the displacement of non-binding quartz and feldspar particles, thereby altering the pore structure of loess. These findings are of great significance in understanding the multi-level collapsibility of loess.

MSBooster: improving peptide identification rates using deep learning-based features

Peptide identification in liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments relies on computational algorithms for matching acquired MS/MS spectra against sequences of candidate peptides using database search tools, such as MSFragger. Here, we present a new tool, MSBooster, for rescoring peptide-to-spectrum matches using additional features incorporating deep learning-based predictions of peptide properties, such as LC retention time, ion mobility, and MS/MS spectra. We demonstrate the utility of MSBooster, in tandem with MSFragger and Percolator, in several different workflows, including nonspecific searches (immunopeptidomics), direct identification of peptides from data independent acquisition data, single-cell proteomics, and data generated on an ion mobility separation-enabled timsTOF MS platform. MSBooster is fast, robust, and fully integrated into the widely used FragPipe computational platform.

Viral Instant Mutation Viewer: A Tool to Speed Up the Identification and Analysis of New SARS-CoV-2 Emerging Variants and Beyond.

The appearance of genetic variants impacts vaccination efficiency and therapeutic options, generating a need to map and relate mutations observed in the proteome and the genome. We develop an user-friendly web service software (Viral Instant Mutation Viewer or VIMVer) which allows a direct identification of mutations in the genome and its counterpart in the viral proteome. Since its emergence in 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, has generated an overwhelming amount of data while becoming one of the most studied viruses of the Nidovirales order. We originally developed this tool during the COVID pandemic; thus, for any SARS-CoV-2 nucleotide sequence, the web service gives a fast identification, mapping, and display of new mutations simultaneously at the nucleotide and amino acid level in comparison to a reference sequence (Wuhan-1). Furthermore, the lineage or the relative position to the known lineage of the variant of interest is available on the link to Phylogenetic Assignment of Named Global Outbreak LINeages (PANGOLIN COVID-19). The workflow presented here is available online. The source code is released under public license and can be easily adapted for further development to other viruses.

Electrochemistry/mass spectrometry (EC/MS) for fast generation and identification of novel reactive metabolites of two unsymmetrical bisacridines with anticancer activity

The development of a new drug requires knowledge about its metabolic fate in a living organism, regarding the comprehensive assessment of both drug therapeutic activity and toxicity profiles. Electrochemistry (EC) coupled with mass spectrometry (MS) is an efficient tool for predicting the phase I metabolism of redox-sensitive drugs. In particular, EC/MS represents a clear advantage for the generation of reactive drug transformation products and their direct identification compared to biological matrices. In this work, we focused on the characterization of novel electrochemical products of two representative unsymmetrical bisacridines (C-2028 and C-2045) with demonstrated high anticancer activity. The electrochemical thin-layer flow-through cell μ-PrepCell 2.0 (Antec Scientific) was used here for the effective metabolite electrosynthesis. The electrochemical simulation of C-2028 reductive and C-2045 oxidative metabolism resulted in the generation of new products that were not observed before. The formation of nitroso [M-O+H]+ and azoxy [2M-3O+H]+ species from C-2028, as well as a series of hydroxylated and/or dehydrogenated products, including possible quinones [M-2H+H]+ and [M+O-2H+H]+ from C-2045, was demonstrated. For the latter, a glutathione S-conjugate (m/z 935.3130) was also obtained in measurements supplemented with the excess of reduced glutathione. For the identification of the products of interest, structural confirmation based on MS/MS fragmentation experiments was performed. Novel products of electrochemical conversions of unsymmetrical bisacridines were discussed in the context of their possible biological effect on the human organism.

Setup for the Ionic Lifetime Measurement of the 229mTh3+ Nuclear Clock Isomer

For the realization of an optical nuclear clock, the first isomeric excited state of thorium-229 (229mTh) is currently the only candidate due to its exceptionally low-lying excitation energy (8.338±0.024 eV). Such a nuclear clock holds promise not only to be a very precise metrological device but also to extend the knowledge of fundamental physics studies, such as dark matter research or variations in fundamental constants. Considerable progress was achieved in recent years in characterizing 229mTh from its first direct identification in 2016 to the only recent observation of the long-sought-after radiative decay channel. So far, nuclear resonance as the crucial parameter of a nuclear frequency standard has not yet been determined with laser-spectroscopic precision. To determine another yet unknown basic property of the thorium isomer and to further specify the linewidth of its ground-state transition, a measurement of the ionic lifetime of the isomer is in preparation. Theory and experimental investigations predict the lifetime to be 103–104 s. To precisely target this property using hyperfine structure spectroscopy, an experimental setup is currently being commissioned at LMU Munich. It is based on a cryogenic Paul trap providing long-enough storage times for 229mTh ions, that will be sympathetically cooled with 88Sr+. This article presents a concept for an ionic lifetime measurement and discusses the laser-optical part of a setup specifically developed for this purpose.

Minimum input design for direct data-driven property identification of unknown linear systems

In a direct data-driven approach, this paper studies the property identification (ID) problem to analyze whether an unknown linear system has a property of interest, e.g., stabilizability and structural properties. In sharp contrast to the model-based analysis, we approach it by directly using the input and state feedback data of the unknown system. Via a new concept of sufficient richness of input sectional data, we first establish the necessary and sufficient condition for the minimum input design to excite the system for property ID. Specifically, the input sectional data is sufficiently rich for property ID if and only if it spans a linear subspace that contains a property dependent minimum linear subspace, any basis of which can also be easily used to form the minimum excitation input. Interestingly, we show that many structural properties can be identified with the minimum input that is however unable to identify the explicit system model. Overall, our results rigorously quantify the advantages of the direct data-driven analysis over the model-based analysis for linear systems in terms of data efficiency.

Direct mapping of ligandable tyrosines and lysines in cells with chiral sulfonyl fluoride probes.

Advances in chemoproteomic technology have revealed covalent interactions between small molecules and protein nucleophiles, primarily cysteine, on a proteome-wide scale. Most chemoproteomic screening approaches are indirect, relying on competition between electrophilic fragments and a minimalist electrophilic probe with inherently limited proteome coverage. Here we develop a chemoproteomic platform for direct electrophile-site identification based on enantiomeric pairs of clickable arylsulfonyl fluoride probes. Using stereoselective site modification as a proxy for ligandability in intact cells, we identify 634 tyrosines and lysines within functionally diverse protein sites, liganded by structurally diverse probes. Among multiple validated sites, we discover a chiral probe that modifies Y228 in the MYC binding site of the epigenetic regulator WDR5, as revealed by a high-resolution crystal structure. A distinct chiral probe stimulates tumour cell phagocytosis by covalently modifying Y387 in the recently discovered immuno-oncology target APMAP. Our work provides a deep resource of ligandable tyrosines and lysines for the development of covalent chemical probes.

Analysis of DIA proteomics data using MSFragger-DIA and FragPipe computational platform

Liquid chromatography (LC) coupled with data-independent acquisition (DIA) mass spectrometry (MS) has been increasingly used in quantitative proteomics studies. Here, we present a fast and sensitive approach for direct peptide identification from DIA data, MSFragger-DIA, which leverages the unmatched speed of the fragment ion indexing-based search engine MSFragger. Different from most existing methods, MSFragger-DIA conducts a database search of the DIA tandem mass (MS/MS) spectra prior to spectral feature detection and peak tracing across the LC dimension. To streamline the analysis of DIA data and enable easy reproducibility, we integrate MSFragger-DIA into the FragPipe computational platform for seamless support of peptide identification and spectral library building from DIA, data-dependent acquisition (DDA), or both data types combined. We compare MSFragger-DIA with other DIA tools, such as DIA-Umpire based workflow in FragPipe, Spectronaut, DIA-NN library-free, and MaxDIA. We demonstrate the fast, sensitive, and accurate performance of MSFragger-DIA across a variety of sample types and data acquisition schemes, including single-cell proteomics, phosphoproteomics, and large-scale tumor proteome profiling studies.

A prospective study evaluating the effect of a ‘Diagnostic Stewardship Care-Bundle’ for automated blood culture diagnostics

We prospectively implemented a diagnostic stewardship care-bundle checklist, 'Sepsis-48 DSB', with the aim of reducing intervening duration of key steps of automated blood culture diagnostics (aBCD). Sepsis-48 DSB was implemented for automated blood culture bottles (BCBs) received from adult intensive care units (AICUs) during the intervention period (P2; July 2020-June 2021) and intervening durations were compared with those during the retrospective, pre-intervention period (P1; March-June 2020). During both periods, provisional blood culture reports (pBCR) were issued wherein direct microbial identification (dID) was performed in BCBs with Gram-negatives by directly inoculating conventional biochemical tests and direct antimicrobial susceptibility testing (dAST) using EUCAST RAST method. The results were compared with the standard of care (SoC) method (i.e. full incubation followed by identification and AST by VITEKⓇ-2 Compact). During P2, significant reductions in loading time (LT) [median: 63.5 vs. 32 minutes, P < 0.001], time to dID+dAST performance (TTD) [186 vs. 115 minutes, P=0.0018] and an increase in compliance to bundle targets [LT ≤45: 44% vs. 66%, P=0.006 and TTD ≤120: 34% vs. 51.7%, P=0.03] were observed. Using dID+dAST method, results were read 694 minutes earlier than SoC method. Of 176 pBCR, 165 (94%) were concordant with SoC in microbial identification of species. Categorical agreement for any drug-bug combination was 92.7% (1079/1164) and corresponding major, very major, and minor error rates were 8.8% (19/216), 4.9% (45/921), and 1.8% (21/1164), respectively. The 'diagnostic stewardship care-bundle' strategy was successfully implemented with considerable diagnostic accuracy leading to significant reductions in duration of targeted steps of aBCD.

Identifying SARS-CoV-2 infected cells with scVDN.

Single-cell RNA sequencing (scRNA-seq) is a powerful tool for understanding cellular heterogeneity and identifying cell types in virus-related research. However, direct identification of SARS-CoV-2-infected cells at the single-cell level remains challenging, hindering the understanding of viral pathogenesis and the development of effective treatments. In this study, we propose a deep learning framework, the single-cell virus detection network (scVDN), to predict the infection status of single cells. The scVDN is trained on scRNA-seq data from multiple nasal swab samples obtained from several contributors with varying cell types. To objectively evaluate scVDN's performance, we establish a model evaluation framework suitable for real experimental data. Our results demonstrate that scVDN outperforms four state-of-the-art machine learning models in identifying SARS-CoV-2-infected cells, even with extremely imbalanced labels in real data. Specifically, scVDN achieves a perfect AUC score of 1 in four cell types. Our findings have important implications for advancing virus research and improving public health by enabling the identification of virus-infected cells at the single-cell level, which is critical for diagnosing and treating viral infections. The scVDN framework can be applied to other single-cell virus-related studies, and we make all source code and datasets publicly available on GitHub at https://github.com/studentiz/scvdn.

Direct Identification and Quantitation of Protein Peptide Powders Based on Multi-Molecular Infrared Spectroscopy and Multivariate Data Fusion.

Given that protein peptide powders (PPPs) from different biological sources were inherited with diverse healthcare functions, which aroused adulteration of PPPs. A high-throughput and rapid methodology, united multi-molecular infrared (MM-IR) spectroscopy with data fusion, could determine the types and component content of PPPs from seven sources as examples. The chemical fingerprints of PPPs were thoroughly interpreted by tri-step infrared (IR) spectroscopy, and the defined spectral fingerprint region of protein peptide, total sugar, and fat was 3600-950 cm-1, which constituted MIR finger-print region. Moreover, the mid-level data fusion model was of great applicability in qualitative analysis, in which the F1-score reached 1 and the total accuracy was 100%, and a robust quantitative model was established with excellent predictive capacity (Rp: 0.9935, RMSEP: 1.288, and RPD: 7.97). MM-IR coordinated data fusion strategies to achieve high-throughput, multi-dimensional analysis of PPPs with better accuracy and robustness which meant a significant potential for the comprehensive analysis of other powders in food as well.

Identification of pegmatites zones in Muiane and Naipa (Mozambique) from Sentinel-2 images, using band combinations, band ratios, PCA and supervised classification

Remote sensing has been widely used in Geological Sciences for different applications, such as to identify geological and mineralogical objects and surface alteration changes. This study aimed to analyze the Sentinel-2 potential to detect pegmatite bodies and associated alteration zones in Muiane and Naipa in Mozambique. Different remote sensing techniques were applied to a Sentinel-2 image: RGB combinations, band ratios, principal component analysis (PCA), and supervised image classification algorithms such as the Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). MLC was used as a benchmark classifier to evaluate the performance of SVM because MLC is the predominant algorithm employed in remote sensing classification studies. For that, several statistical metrics based on the confusion matrices were computed, namely accuracy, Kappa index, precision, recall, and f-score, among others. This study allows identifying the location of pegmatites by direct identification and segregating between hydrothermally altered zones and non-altered areas through remote sensing data/techniques, supported by field data. The field campaigns allowed for validating the results obtained and verifying the pegmatites identified using Sentinel-2 data that were not previously mapped. Moreover, reflectance spectroscopy studies in the laboratory were conducted on the samples collected in the field campaigns allow to validate the adequacy of the methodology proposed in this study. The results show that the precise identification of pegmatite targets requires a high spatial resolution such as Sentinel-2 images. Thus, with the integration of high spatial and spectral resolution data, a potential level of precision and accuracy can be achieved in the study areas.

Gene-allele system of shade tolerance in southern China soybean germplasm revealed by genome-wide association study using gene-allele sequence as markers.

Fifty-three shade tolerance genes with 281 alleles in the SCSGP were identified directly using gene-allele sequence as markers in RTM GWAS, from which optimized crosses, evolutionary motivators, and gene-allele networks were explored. Shade tolerance is a key for optimal cultivation of soybean inter/relay-cropped with corn. To explore the shade tolerance gene-allele system in the southern China soybean germplasm, we proposed using gene-allele sequence markers (GASMs) in a restricted two-stage multi-locus model genome-wide association study (GASM-RTM-GWAS). A representative sample with 394 accessions was tested for their shade tolerance index (STI), in Nanning, China. Through whole-genome re-sequencing, 47,586 GASMs were assembled. From GASM-RTM-GWAS, 53 main-effect STI genes with 281 alleles (2-13 alleles/gene) (totally 63 genes with 308 alleles, including 38 G × E genes with 191 alleles) were identified and then organized into a gene-allele matrix composed of eight submatrices corresponding to geo-seasonal subpopulations. The population featured mild STI changes (1.69 → 1.56-1.82) and mild gene-allele changes (92.5% alleles inherited, 0% alleles excluded, 7.5% alleles emerged) from the primitive (SAIII) to the derived seven subpopulations, but large transgressive recombination potentials and optimal crosses were predicted. The 63 STI genes were annotated into six biological categories (metabolic process, catalytic activity, response to stresses, transcription and translation, signal transduction and transport and unknown functions), interacted as gene networks. From the STI gene-allele system, 38 important alleles of 22 genes were nominated for further in-depth study. GASM-RTM-GWAS performed powerful and efficient in germplasm population genetic study comparing to other procedures through facilitating direct and thorough identification of its gene-allele system, from which genome-wide breeding by design could be achieved, and evolutionary motivators and gene-allele networks could be explored.

First isolation of Francisella halioticida strains from blue mussel (Mytilus edulis) in Normandy, France

Mass mortality events affecting the blue mussels Mytilus edulis have been observed in France since 2014. The DNA of the bacterium Francisella halioticida, reported as pathogen of giant abalone (Haliotis gigantea) and Yesso scallop (Mizuhopecten yessoensis) has been detected recently in mussels from areas suffering mortalities. Isolation of this bacterium was attempted from individuals collected during mortality events. Identification was performed by 16S rRNA gene sequencing, real-time specific PCR and MALDI-ToF using spectra produced from the strain 8472-13A isolated from diseased Yesso scallop in Canada. Five isolates were identified as F. halioticida by real-time specific PCR and 16S rRNA sequencing. MALDI-ToF allowed the direct identification of four isolates (FR22a,b,c,d) which had 100% identity on the 16S rRNA gene with the known strains. On the other hand, one isolate (FR21) was not recognized by MALDI-ToF and had 99.9% identity on the 16S rRNA gene. The FR22 isolates showed difficult growth and required media optimization, which was not the case with the FR21 isolate. For these reasons, it was hypothesized that two type strains are present on French coasts, named FR21 and FR22. The FR21 isolate was selected for phenotypic analysis (growth curve, biochemical characteristics, electron microscopy), phylogenetic analysis and an experimental challenge. This isolate showed distinct differences compared to published F. halioticida strains, both at phenotypic and genotypic levels. Experimental infections of adult mussels led to 36% mortalities in 23 days following intramuscular injection with 3 × 107 CFU while a lower dose (3 × 103 CFU) did not lead to significant mortalities. In the context of this study, the strain FR21 was not virulent towards adult mussels.

Identification of the High-Order Rational Function of Bridge Decks by SDOF Forced Vibration Testing

The high-order rational function provides an accurate and effective model of self-excited forces for flutter analysis of bridges. This study proposes a direct identification method for the rational function of a bridge deck, which enables high-order modeling. In this method, the single degree of freedom (SDOF) forced vibration test is first carried out on the sectional model of the bridge deck. The tested results are then processed by a harmonic function-based filter, yielding force time histories with high signal-to-noise ratios. Formulas are derived to identify parameters of the high-order rational function from these time histories. To deal with the numerical difficulties of the formulas, two practical optimal nonlinear fitting procedures were proposed, which simplify the identification but at the same time maintain a reasonable accuracy. A thin flat plate and a streamlined box section under various wind angles of attack were used as examples to elucidate the effectiveness and feasibility of the proposed identification method. Results showed that the identified high-order rational function could accurately replicate the self-excited force. In contrast, a significant deviation was observed if the first-order rational function was utilized as an alternative. Finally, the high-order rational function model identified was applied in the flutter analysis by a state-space method. The yielded critical flutter wind speed matched satisfactorily with the free-vibration wind tunnel test results, confirming the advantage of the proposed model.

Contact Mass in the Direct Synthesis of Alkoxysilanes. Raman Spectroscopy Study

Raman spectroscopy was used to study the composition of contact masses (CMs) CuCl + Si obtained by different methods, which are utilized in the direct synthesis of alkoxysilanes. The CMs prepared by the classical method were shown to include copper oxides in a mixture with silicon and on the surface of metallic copper and Cu5Si intermetallide. Along with copper oxides, an active phase containing terminal Cu–Cl groups was also observed. In the case of mechanochemical synthesis, the formation of copper oxides was not observed. In view of the metallic conductivity of Cu3Si and Cu5Si, their direct identification using Raman spectra is not possible. All the mentioned copper-containing phases are involved in the reaction with alcohols and esters as catalysts. A difference between the two methods for obtaining CMs comes down to the presence or absence of a "catalytic cocktail", which significantly affects the parameters of the direct synthesis course.

A characteristic bacterial SERS marker for direct identification of Salmonella in real samples assisted by a high-performance SERS chip and a selective culture medium

Salmonella should be absent in pharmaceutical preparations and foods according to the regulations. However, up to now, rapid and convenient identification of Salmonella is still full of challenge. Herein, we reported a label-free surface-enhanced Raman scattering (SERS) method for direct identification of Salmonella spiked in drug samples based on a characteristic bacterial SERS marker assisted by a high-performance SERS chip and a selective culture medium. The SERS chip being fabricated through in situ growth of bimetallic Au-Ag nanocomposites on silicon wafer within 2 h, featured a high SERS activity (EF > 107), good uniformity and batch-to-batch consistency (RSD < 10 %), and satisfactory chemical stability. The directly-visualized SERS marker at 1222 cm−1 originated from bacterial metabolite hypoxanthine was robust and exclusive for discrimination of Salmonella with other bacterial species. Moreover, the method was successfully used for direct discrimination of Salmonella in mixed pathogens by using a selective culture medium, and could identify Salmonella contaminant at ∼1 CFU spiked level in a real sample (Wenxin granule, a botanical drug) after 12 h of enrichment. The combined results showed that developed SERS method is practical and reliable, and could be a promising alternative for rapid identification of Salmonella contamination in pharmaceutical and foods industries.

A Wild Pseudomonas has appeared: An Exercise in Bacterial Isolation and Identification

<span id="docs-internal-guid-8e962b78-7fff-c5ee-bc79-1ba2a6c149e3"><span>The aim of our research was to isolate and identify wild type </span><span>Pseudomonas putida </span><span>from soil in various cities of San Gabriel Valley. </span><span>P. putida </span><span>is</span><span>capable of biomineralization. Biomineralization can potentially be used as a method of phosphorus recovery by using bacteria to produce phosphate rich struvite. In isolating bacteria for further observation, fluorescence was used as a primary determinant in identifying possible </span><span>Pseudomonas </span><span>strains as fluorescence is a common trait shared among varying </span><span>Pseudomonas </span><span>species; </span><span>P. putida, P. aeruginosa</span><span>,</span><span> P. fluorescens</span><span>,</span><span> P. cichorii</span><span>,</span><span> P. chlororaphis</span><span>,</span><span> P. syringae</span><span>,</span><span>and </span><span>P. aureofaciens</span><span>. King’s B agar was used to promote the production of pyoverdine in these strains (allowing for direct identification based on a green fluorescence under UV light) as this medium has specific ingredients that enhance pigment production.</span><span>These fluorescent bacteria were then further isolated from each other and identified using biochemical methods including catalase, oxidase, nitrate reduction, and gelatin hydrolysis tests to differentiate </span><span>P. putida </span><span>from the six other fluorescent </span><span>Pseudomonas </span><span>species. Of the 21 total samples isolated based on fluorescence, 5 of the samples were determined to be potential </span><span>P. putida. </span><span>While the biochemical assays were conducted, the isolated samples were placed in refrigeration for 3 weeks. After the biochemical tests were completed and 3 weeks had passed, visible crystals had formed in the potential </span><span>P. putida</span><span>. Albert’s metachromatic staining was performed to determine the presence of polyphosphate granules. Ultimately, each potential </span><span>P. putida</span><span> that produced crystals also showed polyphosphate granules when Albert’s stained, which further connects crystal formation with prior polyphosphate formation.</span></span>

A combined diagnostic approach for the investigation of lesions resembling aspergillosis in Caribbean sea fans (Gorgonia spp.).

Aspergillosis of gorgonian sea fans is a Caribbean-wide disease characterized by focal, annular purple pigmentation with central tissue loss. We applied a holistic diagnostic approach including histopathology and a combination of culture and direct molecular identification of fungi to evaluate these lesions with the goal of determining the diversity of associated micro-organisms and pathology. Biopsies were collected from 14 sea fans without gross lesions and 44 sea fans with lesions grossly consistent with aspergillosis in shallow fringing reefs of St. Kitts. Histologically, the tissue loss margin had exposure of the axis and amoebocyte encapsulation with abundant mixed micro-organisms. Polyp loss, gastrodermal necrosis, and coenenchymal amoebocytosis were at the lesion interface (purpled area transitioning to grossly normal tissue) with algae (n = 21), fungus-like hyphae (n = 20), ciliate protists (n = 16), cyanobacteria (n = 15), labyrinthulomycetes (n = 5), or no micro-organisms (n = 8). Slender, septate hyaline hyphae predominated over other morphological categories, but were confined to the axis with little host response other than periaxial melanization. Hyphae were absent in 6 lesioned sea fans and present in 5 control biopsies, questioning their pathogenicity and necessary role in lesion causation. From cultivation, different fungi were isolated and identified by sequencing of the nuclear ribosomal internal transcribed spacer region. In addition, 2 primer pairs were used in a nested format to increase the sensitivity for direct amplification and identification of fungi from lesions, thereby circumventing cultivation. Results suggest mixed and opportunistic infections in sea fans with these lesions, requiring longitudinal or experimental studies to better determine the pathogenesis.

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.