Mass Spectrometry Data Research Articles

Analysis of chemical constituents of different processed products of Strychni Semen based on UPLC-Q-TOF-MS technique

This study utilized ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry(UPLC-Q-TOF-MS) to rapidly analyze and identify the chemical constituents in five processed products of Strychni Semen(raw, sand-roasted, fried, urine-soaked, and vinegar-processed products). Using PeakView software to extract compound information, 50 chemical components were identified based on retention time, accurate molecular ion peaks, secondary mass spectrometry data, and comparison with reference standards and relevant literature. Specifically, 41 components were identified in raw Strychni Semen, 48 in sand-roasted, 43 in fried, 41 in urine-soaked, and 40 in vinegar-processed products. Principal component analysis(PCA) and orthogonal partial least squares discriminant analysis(OPLS-DA) were employed to identify differential components among the processed products, with variable importance in projection(VIP) values>1 and a t-test with P<0.05 as criteria. PCA revealed significant differences among the five processed products, demonstrating good clustering and separation. OPLS-DA identified 13 differential components, including brucine, strychnine, loganic acid, and chlorogenic acid. The results indicated that S. nux-vomica primarily contained alkaloids, organic acids, and glycosides, with significant compositional differences among the various processed products. Notably, the content of key components such as brucine and strychnine decreased after processing, with the most significant reduction observed in vinegar-processed Strychni Semen. In conclusion, this study provides a comprehensive comparison of the effects of different processing methods on the chemical composition of Strychni Semen, exploring the differences in the material basis of the processed products and offering data to support the scientific rationale behind the processing of Strychni Semen.

Recognition of novel proteins encoded by an aquareovirus using mass spectrometry

Aquareovirusa genus of within the family Spinareoviridae, order Reovirales, infects aquatic animals. Their genomes comprise 11 segments of double-stranded RNA, which function directly as mRNAs upon release into the cytoplasm of infected cells. Here, liquid chromatography-tandem mass spectrometry was employed to annotate small coding ORFs in the Aquareovirus-C genome. Its plus-strand RNA of segment 8 (S8) contains a novel protein-coding frame (NS15), and S5 seems to has an additional reading frame (NS18) with a putative non-AUG initiation codon. Among them, NS15 polypeptide has been proved by immunoblotting assay. Remarkably, the S4 and S11 minus-strand mRNAs may encode polypeptides, suggesting ambisense polarity of the two segmented RNAs. And the newly discovered NS12 ORF in 2019, from viral tricistronic S7 mRNA, was also confirmed by this mass-spectrometry data. Taken together, these identified new ORFs reveal the genome-coding complexity of Aquareovirus-C.

Machine learning methods for compound annotation in non-targeted mass spectrometry-A brief overview of fingerprinting, in silico fragmentation and de novo methods.

Non-targeted screenings (NTS) are essential tools in different fields, such as forensics, health and environmental sciences. NTSs often employ mass spectrometry (MS) methods due to their high throughput and sensitivity in comparison to, for example, nuclear magnetic resonance-based methods. As the identification of mass spectral signals, called annotation, is labour intensive, it has been used for developing supporting tools based on machine learning (ML). However, both the diversity of mass spectral signals and the sheer quantity of different ML tools developed for compound annotation present a challenge for researchers in maintaining a comprehensive overview of the field. In this work, we illustrate which ML-based methods are available for compound annotation in non-targeted MS experiments and provide a nuanced comparison of the ML models used in MS data analysis, unravelling their unique features and performance metrics. Through this overview we support researchers to judiciously apply these tools in their daily research. This review also offers a detailed exploration of methods and datasets to show gaps in current methods, and promising target areas, offering a starting point for developers intending to improve existing methodologies.

Proteome profiling in patients with peripartum cardiomyopathy according to ethnicity a biomarker study on the EORP cohort

Abstract Background Peripartum cardiomyopathy (PPCM) is a pregnancy-associated form of heart failure, which occurs in previously healthy women towards the end of pregnancy and in the first months postpartum. Previous reports showed a varying prevalence of PPCM across different regions and potentially a higher incidence in patients with black African ethnicity. The correct and timely diagnosis remains challenging and there is a need for novel disease-specific biomarkers. Accuracy in diagnosing PPCM can be improved by using serum-protein biomarkers such as QSOX1, adiponectin and ITIH3 in addition to NT-proBNP. However, it still needs to be ascertained whether there are ethnic differences in proteomic profiles amongst patients with PPCM with different ethnicities. Aims To delineate possible differences in serum protein biomarker profiles across different ethnic groups within the EURObservation Research Programme (EORP) multi-national cohort of women with PPCM. Methods Demographic and clinical data, as well as serum samples were collected from 84 patients with PPCM from seven EORP participating countries. Serum proteomic profiling was conducted using DIA-based label-free quantitative (LFQ) LC-MS at the time of diagnosis from depleted serum samples. Mass spectrometry data were analyzed by Spectronaut v15 using a study-specific spectral library. From the 84 patients in the EORP PPCM registry on whom we performed proteomic analyses, 82 patients self-declared their ethnicity. These 82 patients were assessed for any ethnical differences in their proteomic expression profile using unsupervised principal component analysis (PCA). Results In this cohort of 82 women with PPCM, the mean age was 30.5±6.7 years (Table 1). Three quarters of this cohort had no hypertension during pregnancy and only 13.8% had a prior diagnosis of PPCM. The mean left ventricular ejection fraction (LVEF) was 35% at time of first diagnosis, with no difference between the ethnic groups. However, women of Middle Eastern decent had more severe LV dilatation (LVEDD: 64.0 (62.0-65.0) mm). Three hundred and twenty-nine (329) proteins were identified in the serum samples. As depicted in Figure 1, PCA did not yield a distinct separation between the different ethnic groups. Principal component 1 (PC1) accounted for 15.2% of the total variability in protein expression among ethnic groups, PC2 explained a comparatively lower percentage at 12%. Conclusion The protein biomarkers identified in PPCM patients showed no discernible racial differences, demonstrating consistent performance across all ethnic groups.

Antioxidant and Anti-Inflammatory Properties of Conceivable Compounds from Glehnia littoralis Leaf Extract on RAW264.7 Cells.

Glehnia littoralis is a medicinal plant, but the scientific basis is still unclear. This study thoroughly investigated phenols from Glehnia littoralis extract (GLE) to determine their potential as anti-inflammatory and antioxidant agents. High-performance liquid chromatography (HPLC) and mass spectrometry (MS) were used to analyze the compounds in GLE. In addition, we performed GLE in vitro in macrophages after lipopolysaccharide (LPS)-induced inflammation. The extract contained eight peaks representing phenolic compounds and one peak representing riboflavin, with the corresponding mass spectrometry data documented. These biologically active compounds were purified by ultrafiltration using LC to determine their ability to target cyclooxygenase-2 (COX-2) and 2,2-diphenyl-1-picrylhydrazyl (DPPH). The results showed that significant compounds were identified, demonstrating a binding affinity for both COX-2 and DPPH. This suggests that the compounds showing excellent binding affinity for COX-2 and DPPH may be the main active ingredients. Vital inflammatory cytokines, including COX-2, inducible nitric oxide synthase (iNOS), mitogen-activated protein kinase (MAPK), and nuclear factor kappa B (NF-κB), were found to be down-regulated during the treatment. In addition, we revealed that the selected drugs exhibited potent binding capacity to inflammatory factors through molecular docking studies. In addition, we confirmed the presence of phenolic components in GLE extract and verified their possible anti-inflammatory and antioxidant properties. This study provided evidence for an efficient strategy to identify critical active ingredients from various medicinal plants. These data may serve as a baseline for further investigations of applying GLE in the pharmaceutical industry.

Experimentelle Beobachtung eines terminalen Borylen‐Distickstoff‐Addukts durch Spaltung eines 1,2,3,4,5‐Diboratriazolins

AbstractWährend Azide mit einfachen Alkenen nur unter harschen Bedingungen reagieren, reagiert ein Diboranalogon eines Alkens, das zweifach durch cyclische Alkyl(amino)carbene (CAAC) stabilisierte Dicyanodiboren 1, spontan mit organischen Aziden (7–10 Äq.) bei Raumtemperatur, um zwei Äquivalente stabiler CAAC‐stabilisierter Imino(cyano)borane (2‐R) zu ergeben. NMR‐spektroskopische Kontrollen der Reaktionsmischungen zeigen die anfängliche Bildung einer 1 : 1‐Mischung aus 2‐R und einem relativ langlebigen Zwischenprodukt (Int), welches in Gegenwart von überschüssigem Azid in ein zweites Äquivalent 2‐R umgewandelt wird. In Abwesenheit von überschüssigem Azid zersetzt sich Int jedoch zu 3, dem Produkt einer intramolekularen C−H‐Bindungsaktivierung durch eine mutmaßlich zweifach koordinierte Borylen‐Zwischenstufe, „(CAAC)B(CN)“. Mechanistische Erkenntnisse aus Abfangexperimenten, NMR‐spektroskopischen und hochauflösenden Massenspektrometrie‐Daten sowie DFT‐Berechnungen zeigen, dass Int das endständige Borylen‐Stickstoff‐Addukt [(CAAC)B(CN)(η1‐N2)] ist. Die Bildung der Iminoborane 2‐R aus Diboren 1 und RN3 erfolgt über eine Azid‐Diboren‐Huisgen‐[3+2]‐Cycloaddition, gefolgt von einer [3+2]‐Cycloreversion, wobei 2‐R und [(CAAC)B(CN)(η1‐N2)] entstehen. Letzteres durchläuft dann entweder eine N2‐Abspaltung und intramolekulare C−H‐Bindungsaktivierung zur Bildung von 3 oder eine Staudinger‐artige Reaktion mit einem zweiten Äquivalent Azid zur Bildung eines zweiten Äquivalents des Iminoborans 2‐R.

Rapid QC-MS: Interactive Dashboard for Synchronous Mass Spectrometry Data Acquisition Quality Control.

Consistently collecting high-quality liquid chromatography-coupled tandem mass spectrometry (LC-MS/MS) data is a time-consuming hurdle for untargeted workflows. Analytical controls such as internal and biological standards are commonly included in high-throughput workflows, helping researchers recognize low-integrity specimens regardless of their biological source. However, evaluating these standards as data are collected has remained a considerable bottleneck─in both person-hours and accuracy. Here we present Rapid QC-MS, an automated, interactive dashboard for assessing LC-MS/MS data quality. Minutes after a new data file is written, a browser-viewable dashboard is updated with quality control results spanning multiple performance dimensions such as instrument sensitivity, in-run retention time shifts, and mass accuracy drift. Rapid QC-MS provides interactive visualizations that help users recognize acute deviations in these performance metrics, as well as gradual drifts over periods of hours, days, months, or years. Rapid QC-MS is open-source, simple to install, and highly configurable. By integrating open-source Python libraries and widely used MS analysis software, it can adapt to any LC-MS/MS workflow. Rapid QC-MS runs locally and offers optional remote quality control by syncing with Google Drive. Furthermore, Rapid QC-MS can operate in a semiautonomous fashion, alerting users to specimens with potentially poor analytical integrity via frequently used messaging applications. Initially developed for integration with Thermo Orbitrap workflows, Rapid QC-MS offers a fast, straightforward approach to help users collect high-quality untargeted LC-MS/MS data by eliminating many of the most time-consuming steps in manual data curation. Download for free: https://github.com/czbiohub-sf/Rapid-QC-MS.

Loss of long-chain acyl-CoA dehydrogenase protects against acute kidney injury.

Proximal tubular epithelial cells (PTECs) are particularly vulnerable to acute kidney injury (AKI). While fatty acids are the preferred energy source for PTECs via fatty acid oxidation (FAO), FAO-mediated H 2 O 2 production in mitochondria has been shown to be a major source of oxidative stress. We have previously shown that a mitochondrial flavoprotein, long-chain acyl-CoA dehydrogenase (LCAD), which catalyzes a key step in mitochondrial FAO, directly produces H 2 O 2 in vitro . Further we have established that loss of a lysine deacylase, Sirtuin 5 ( Sirt5 -/- ), induces hypersuccinylation and inhibition of mitochondrial FAO genes to stimulate peroxisomal FAO and to protect against AKI. However, the role of LCAD has yet to be determined. Mass spectrometry data acquisition revealed that LCAD is hypersuccinylated in Sirt5 -/- kidneys after AKI. Following two distinct models of AKI, cisplatin treatment or renal ischemia/reperfusion (IRI), LCAD knockout mice ( LCAD -/- ) demonstrated renoprotection against AKI. Specifically, LCAD -/- kidneys displayed mitigated renal tubular injury, decreased oxidative stress, preserved mitochondrial function, enhanced peroxisomal FAO, and decreased ferroptotic cell death. LCAD deficiency confers protection against two distinct models of AKI. This suggests a therapeutically attractive mechanism whereby preserved mitochondrial respiration as well as enhanced peroxisomal FAO by loss of LCAD mediates renoprotection against AKI.

Experimental Observation of a Terminal Borylene-Dinitrogen Adduct via Cleavage of a 1,2,3,4,5-Diboratriazoline.

While azides do not react with simple alkenes except under harsh conditions, a diboron alkene analogue, the doubly cyclic alkyl(amino)carbene (CAAC)-stabilized dicyanodiborene 1, reacts spontaneously with organic azides (7-10 equiv.) at room temperature to yield two equivalents of stable CAAC-imino(cyano)boranes (2-R). NMR-spectroscopic monitoring of the reaction mixtures shows the initial formation of a 1 : 1 mixture of 2-R and a relatively long-lived intermediate (Int), which in the presence of excess azide is converted into a second equivalent of 2-R. In the absence of excess azide, however, Int decomposes to 3, the product of an intramolecular C-H activation by a putative dicoordinate borylene intermediate "(CAAC)B(CN)". Mechanistic insights from trapping experiments, NMR-spectroscopic and high-resolution mass spectrometry data, as well as DFT computations reveal that Int is the terminal borylene end-on-dinitrogen adduct [(CAAC)B(CN)(η1-N2)]. The formation of the iminoboranes 2-R from diborene 1 and RN3 proceeds via an azide-diborene Huisgen-type [3+2] cycloaddition reaction, followed by a retro-[3+2] cycloaddition, yielding 2-R and [(CAAC)B(CN)(η1-N2)]. The latter then undergoes either N2 extrusion and intramolecular C-H activation to generate 3, or a Staudinger-type reaction with a second equivalent of azide to generate a second equivalent of the iminoborane 2-R.

The Application of a Random Forest Classifier to ToF-SIMS Imaging Data.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is a potent analytical tool that provides spatially resolved chemical information on surfaces at the microscale. However, the hyperspectral nature of ToF-SIMS datasets can be challenging to analyze and interpret. Both supervised and unsupervised machine learning (ML) approaches are increasingly useful to help analyze ToF-SIMS data. Random Forest (RF) has emerged as a robust and powerful algorithm for processing mass spectrometry data. This machine learning approach offers several advantages, including accommodating nonlinear relationships, robustness to outliers in the data, managing the high-dimensional feature space, and mitigating the risk of overfitting. The application of RF to ToF-SIMS imaging facilitates the classification of complex chemical compositions and the identification of features contributing to these classifications. This tutorial aims to assist nonexperts in either machine learning or ToF-SIMS to apply Random Forest to complex ToF-SIMS datasets.

SLB-msSIM: a spectral library-based multiplex segmented SIM platform for single-cell proteomic analysis.

Mass spectrometry (MS)-based single-cell proteomics, while highly challenging, offers unique potential for a wide range of applications to interrogate cellular heterogeneity, trajectories, and phenotypes at a functional level. We report here the development of the spectral library-based multiplex segmented selected ion monitoring (SLB-msSIM) method, a conceptually unique approach with significantly enhanced sensitivity and robustness for single-cell analysis. The single-cell MS data is acquired by msSIM technique, which sequentially applies multiple isolation cycles with the quadrupole using a wide isolation window in each cycle to accumulate and store precursor ions in the C-trap for a single scan in the Orbitrap. Proteomic identification is achieved through spectral matching using a well-defined spectral library. We applied the SLB-msSIM method to interrogate cellular heterogeneity among multiple cell lines and to analyze cellular trajectories during epithelial-mesenchymal transition. Our results demonstrate that SLB-msSIM is a highly sensitive and robust platform applicable to a wide range of single-cell proteomic studies.

Identification of novel microcystins in algal extracts by a liquid chromatography–high-resolution mass spectrometry data analysis pipeline

BackgroundMicrocystins are an emergent public health problem. These toxins are secondary metabolites of harmful cyanobacterial blooms, with blooms becoming more prevalent with eutrophication of water. Exposure to microcystins can result in sickness, liver damage, and even death. Over 300 microcystins have been identified to date, with differences in toxicity based on the specific amino acid composition. Because of this diversity in microcystins, as well as the likelihood of detecting as yet undiscovered microcystins, it is vital to establish a methodological workflow to identify any microcystin in a complex sample, regardless of the availability of a reference standard. Additionally, ascribing varying levels of confidence to these identifications is critical to effectively communicate discoveries. MethodsA liquid-chromatography–high-resolution mass spectrometry method was utilized to identify microcystins present in cyanobacterial extracts from a strain of Microcystis aeruginosa and an Aphanizomenon sp. First, microcystin congeners with available standards were identified in the cyanobacterial extract. These known-unknown microcystins were considered to have the highest confidence identifications due to availability of accurate masses, retention times, and library spectra for comparison. Utilizing the spectra of these microcystins, relatively high-abundance diagnostic product-ions were identified and employed to screen the data for additional candidate microcystins. Microcystins without a standard that had an exact mass matching a microcystin published in CyanoMetDB were considered semi-known-unknown microcystins. The remaining microcystins were considered unknown-unknown microcystins. The identities of the microcystins determined herein were additionally supported by product-ion analysis, thiol reactivity, esterification reactions, neutral loss analysis, and literature contextualization. ResultsIn total, utilizing the systematic workflow presented herein, 23 microcystins were identified in the M. aeruginosa culture, including two not published previously: [d-Asp3]MC-LCit and the incompletely identified MC-L(C7H11NO3).

Annotating full-scan MS data using tandem MS libraries.

Full-scan mass spectrometry (MS) data from both liquid chromatography (LC) and MS imaging capture multiple ion forms, including their in-source fragments. Here we leverage such fragments to structurally annotate full-scan data from LC-MS or MS imaging by matching against peak intensity scaled tandem MS spectral libraries using precursor-tolerant reverse match scoring. Applied to inflammatory bowel disease and imaging datasets, we show the approach facilitates re-analyses of data in public repositories.

The translatome of glioblastoma.

Glioblastoma (GB), the most common and aggressive brain tumor, demonstrates intrinsic resistance to current therapies, resulting in poor clinical outcomes. Cancer progression can be partially attributed to the deregulation of protein translation mechanisms that drive cancer cell growth. In this study, we present the translatome landscape of GB as a valuable data resource. Eight patient-derived GB sphere cultures (GSCs) were analyzed using ribosome profiling and messenger RNA (mRNA) sequencing. We investigated inter-cell-line differences through differential expression analysis at both the translatome and transcriptome levels. Translational changes post-radiotherapy were assessed at 30 and 60 min. The translation of non-coding RNAs (ncRNAs) was validated using in-house and public mass spectrometry (MS) data, whereas RNA expression was confirmed by quantitative PCR (qPCR). Our findings demonstrate that ribosome sequencing provides more detailed information than MS or transcriptional analyses. Transcriptional similarities among GSCs correlate with translational similarities, aligning with previously defined subtypes such as proneural and mesenchymal. Additionally, we identified a broad spectrum of open reading frame types in both coding and non-coding mRNA regions, including long non-coding RNAs (lncRNAs) and pseudogenes undergoing active translation. Translation of ncRNAs into peptides was independently confirmed by in-house data and external MS data. We also observed that translational regulation of histones (downregulated) and splicing factors (upregulated) occurs in response to radiotherapy. These data offer new insights into genome-wide protein synthesis, identifying translationally regulated genes and alternative translation initiation sites in GB under normal and radiotherapeutic conditions, providing a rich resource for GB research. Further functional validation of differentially expressed genes after radiotherapy is needed. Understanding translational control in GB can reveal mechanistic insights and identify currently unknown biomarkers, ultimately enhancing the diagnosis and treatment of this aggressive brain cancer.

MSFragger-DDA+ Enhances Peptide Identification Sensitivity with Full Isolation Window Search.

Liquid chromatography-mass spectrometry (LC-MS) based proteomics, particularly in the bottom-up approach, relies on the digestion of proteins into peptides for subsequent separation and analysis. The most prevalent method for identifying peptides from data-dependent acquisition (DDA) mass spectrometry data is database search. Traditional tools typically focus on identifying a single peptide per tandem mass spectrum (MS2), often neglecting the frequent occurrence of peptide co-fragmentations leading to chimeric spectra. Here, we introduce MSFragger-DDA+, a novel database search algorithm that enhances peptide identification by detecting co-fragmented peptides with high sensitivity and speed. Utilizing MSFragger's fragment ion indexing algorithm, MSFragger-DDA+ performs a comprehensive search within the full isolation window for each MS2, followed by robust feature detection, filtering, and rescoring procedures to refine search results. Evaluation against established tools across diverse datasets demonstrated that, integrated within the FragPipe computational platform, MSFragger-DDA+ significantly increases identification sensitivity while maintaining stringent false discovery rate (FDR) control. It is also uniquely suited for wide-window acquisition (WWA) data. MSFragger-DDA+ provides an efficient and accurate solution for peptide identification, enhancing the detection of low-abundance co-fragmented peptides. Coupled with the FragPipe platform, MSFragger-DDA+ enables more comprehensive and accurate analysis of proteomics data.

Generative Adversarial Autoencoder Integration Voting Algorithm Based on Mass Spectrometry Data

Mass spectrometry technology is often used in disease prevention and diagnosis and treatment. However, the number of mass spectrometry data features is huge and the features vary greatly between different diseases, making multi-disease diagnosis. The task of judgment is complex and difficult. To address the above problems, this paper proposes a generative adversarial autoencoder integrated voting algorithm msDAGVote based on mass spectrometry data.The generative adversarial network based on dual autoencoders is used as the feature extraction framework of msDAGVote. After inputting mass spectrometry data for training, the generator subnetwork is used for feature extraction.Finally, the constructed features were screened by integrating the voting feature selection algorithm, and the optimal feature subset was used for multi-disease diagnosis.The results were evaluated on mass spectrometry datasets of different disease types. The experimental data showed that the features extracted by msDAGVote outperformed the comparison methods and significantly reduced the classification time.The required number of features and excellent disease classification and diagnosis capabilities are required. The classification AUC exceeds 0.98 on 6 datasets and The set exceeds 0.87.

CMSSP: A Contrastive Mass Spectra-Structure Pretraining Model for Metabolite Identification.

A pivotal challenge in metabolite research is the structural annotation of metabolites from tandem mass spectrometry (MS/MS) data. The integration of artificial intelligence (AI) has revolutionized the interpretation of MS data, facilitating the identification of elusive metabolites within the metabolomics landscape. Innovative methodologies are primarily focusing on transforming MS/MS spectra or molecular structures into a unified modality to enable similarity-based comparison and interpretation. In this work, we present CMSSP, a novel Contrastive Mass Spectra-Structure Pretraining framework designed for metabolite annotation. The primary objective of CMSSP is to establish a representation space that facilitates a direct comparison between MS/MS spectra and molecular structures, transcending the limitations of distinct modalities. The evaluation on two benchmark test sets demonstrates the efficacy of the approach. CMSSP achieved a remarkable enhancement in annotation accuracy, outperforming the state-of-the-art methods by a significant margin. Specifically, it improved the top-1 accuracy by 30% on the CASMI 2017 data set and realized a 16% increase in top-10 accuracy on an independent test set. Moreover, the model displayed superior identification accuracy across all seven chemical categories, showcasing its robustness and versatility. Finally, the MS/MS data of 30 metabolites from Glycyrrhiza glabra were analyzed, achieving top-1 and top-3 accuracies of 86.7 and 100%, respectively. The CMSSP model serves as a potent tool for the dissection and interpretation of intricate MS/MS data, propelling the field toward more accurate and efficient metabolite annotation. This not only augments the analytical capabilities of metabolomics but also paves the way for future discoveries in understanding of complex biological systems.

Development of Peptide Identification System for ToF-SIMS Spectra Using Supervised Machine Learning.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) data interpretation for organic materials is complicated because of various fragment ions produced from each molecule and the overlapping of certain mass peaks from different molecules. Fragmentation mechanisms in SIMS are complex because different sputtering and ionization processes can simultaneously occur. Therefore, a prediction system that can identify materials in a sample is required. A novel prediction system for peptides based on ToF-SIMS and amino-acid-based teaching information (labels) for supervised machine learning was developed. To develop the prediction system for general organic materials, the annotation of materials is crucial to creating effective labels for supervised learning. Peptides are composed of 20 amino acid residues, which can be used as labels. We previously developed a peptide prediction system using Random Forest, a supervised machine-learning method. However, only the amino acids contained in the target peptide were predicted, and the amino acid sequence was unable to be assumed. In this study, the amino acid sequence of the test peptide was determined by adding the information on two adjacent amino acids to the labels. Once the prediction system learned the target peptide spectra, the peptides in the newly obtained ToF-SIMS spectra could be identified. The new prediction system also provides useful information for the identification of unknown peptides. The prediction results indicate that two adjacent permutations of amino acids are effective pieces of teaching information for expressing the amino acid sequence of a peptide.

Peptidomics and Machine Learning–based Evaluation of Noncoding RNA–Derived Micropeptides in Breast Cancer: Expression Patterns and Functional/Therapeutic Insights

Breast cancer is a highly heterogeneous disease characterized by different subtypes arising from molecular alterations that give the disease different phenotypes, clinical behaviors, and prognostic. The noncoding RNA (ncRNA)–derived micropeptides (MPs) represent a novel layer of complexity in cancer study once they can be biologically active and can present potential as biomarkers and also in therapeutics. However, few large-scale studies address the expression of these peptides at the peptidomics level or evaluate their functions and potential in peptide-based therapeutics for breast cancer. In this study, we propose deepening the landscape of ncRNA-derived MPs in breast cancer subtypes and advance the comprehension of the relevance of these molecules to the disease. First, we constructed a 16,349 unique putative MP sequence data set by integrating 2 previously published lists of predicted ncRNA-derived MPs. We evaluated its expression on high-throughput mass spectrometry data of breast tumor samples from different subtypes. Next, we applied several machine and deep learning tools, such as AntiCP 2.0, MULocDeep, PEPstrMOD, Peptipedia, and PreAIP, to predict its functions, cellular localization, tertiary structure, physicochemical features, and other properties related to therapeutics. We identified 58 peptides expressed on breast tissue, including 27 differentially expressed MPs in tumor compared with nontumor samples and MPs exhibiting tumor or subtype specificity. These peptides presented physicochemical features compatible with the canonical proteome and were predicted to influence the tumor immune environment and participate in cell communication, metabolism, and signaling processes. In addition, some MPs presented potential as anticancer, antiinflammatory, and antiangiogenic molecules. Our data demonstrate that MPs derived from ncRNAs have expression patterns associated with specific breast cancer subtypes and tumor specificity, thus highlighting their potential as biomarkers for molecular classification. We also reinforce the relevance of MPs as biologically active molecules that play a role in breast tumorigenesis, besides their potential in peptide-based therapeutics.

Oxidative Release of Natural Glycans: Unraveling the Mechanism for Rapid N-Glycan Glycomics Analysis.

N-glycosylation is a critical post-translational modification involved in various biosynthetic pathways and disease mechanisms. In this study, we present an optimized oxidative release of natural glycans (ORNG) method using household bleach that enables the rapid and efficient release of N-glycans from biological samples. We thoroughly investigated the ORNG mechanism, identifying key intermediates and side products and providing valuable insights into the oxidative release process. The method is highly efficient, releasing a wide range of N-glycans, including high-mannose, hybrid, and complex structures, with minimal sample processing. Our ORNG-based specific N-glycan profiling approach has demonstrated high sensitivity and efficiency, particularly in releasing N-glycans resistant to enzymatic digestion, such as core α3-fucosylated N-glycans from soy protein. Validation through mass spectrometry confirmed the method's ability to accurately profile N-glycans from complex biological samples, including human serum, with results comparable to traditional PNGase F digestion. The ORNG-based method's scalability, versatility, and use of low-cost reagents make it especially suited for large-scale glycomics studies. Furthermore, the mass spectrometry data revealed that the ORNG-based method achieves high sensitivity and specificity, positioning it as a robust alternative for comprehensive glycan profiling and functional studies. Our findings highlight ORNG's potential to advance N-glycomics, offering promising improvements in speed, efficiency, and breadth of glycan analysis.