Mass Spectrometry Data Research Articles

SILP Type Catalyst Based on H3PMo12O40: Composition of Heteropolyanions According to Mass Spectrometry Data and Activity in Oxidation of Sulfur-Containing Substrates

SILP Type Catalyst Based on H3PMo12O40: Composition of Heteropolyanions According to Mass Spectrometry Data and Activity in Oxidation of Sulfur-Containing Substrates

Review and Practical Guide for Getting Started With Single-Cell Proteomics.

Single-cell proteomics (SCP) has advanced significantly in recent years, with new tools specifically designed for the preparation and analysis of single cells now commercially available to researchers. The field is sufficiently mature to be broadly accessible to any lab capable of isolating single cells and performing bulk-scale proteomic analyses. In this review, we highlight recent work in the SCP field that has significantly lowered the barrier to entry, thus providing a practical guide for those who are newly entering the SCP field. We outline the fundamental principles and report multiple paths to accomplish the key steps of a successful SCP experiment including sample preparation, separation, and mass spectrometry data acquisition and analysis. We recommend that researchers start with a label-free SCP workflow, as achieving high-quality and quantitatively accurate results is more straightforward than label-based multiplexed strategies. By leveraging these accessible means, researchers can confidently perform SCP experiments and make meaningful discoveries at the single-cell level.

OPTIMIZATION OF ROBUST HPLC APPROACH FOR ASSESSING PROCESS RELATED IMPURITIES OF LEMBOREXANT AND ELUCIDATION OF STRESS-INDUCED DEGRADATION PRODUCTS THROUGH LCMS/MS

This study primarily aimed to establish an uncomplicated yet highly responsive HPLC technique to effectively separate and quantify process-related impurities of Lemborexant. Additionally, it aimed to explore the forced degradation behavior of lemborexant through systematic assessments by utilizing LCMS. The chromatographic separation of drug substance, process related impurities and its degradation products (DPs) was achieved on Kinetex C18 (150×4.6 mm, 5 μm) column at that was maintained at 350C temperature using 10 mM ammonium formate buffer pH 4.2, acetonitrile and methanol in 65:25:10 (v/v) isocratic elution at 0.7 mL/min. Detection wavelength was selected as 265 nm. In the proposed conditions, lemborexant is identified at 4.06 and 6.19, 9.33 min and 1.60 min respectively for impurity 1, 2 and 3 min with acceptable system suitability and specificity. The method produces LOD at 0.009 for impurities with calibration range of 40–280 µg/mL for lemborexant and 0.04 - 0.28 µg/mL for impurities. The remaining validation parameters were observed to fall within acceptable ranges for both lemborexant and its impurities. The compound underwent exposure to various stress conditions (acid, base, peroxide, thermal, and UV light) as outlined in accordance with ICH Q1A (R2) guidelines. The degradation products formed during the stress study were detected and characterized using LCMS/MS in ESI positive mode. This involved a thorough comparison of collision-induced dissociation mass spectrometry data between the degradation products and lemborexant. Consequently, potential structures for five degradation compounds were proposed. The outcomes of supplementary validation investigations were equally satisfactory, confirming their appropriateness for the regular quantification of lemborexant and its related impurities in both bulk drug and pharmaceutical formulations. Furthermore, these findings have the potential to shed light on the mechanism of stress-induced degradation in lemborexant

JPOST environment accelerates the reuse and reanalysis of public proteome mass spectrometry data.

jPOST (https://jpostdb.org/) comprises jPOSTrepo (https://repository.jpostdb.org/) (over 2000 projects), a repository for proteome mass spectrometry data, the reanalysis of raw proteome data based on a standardised protocol using UniScore, and jPOSTdb (https://globe.jpostdb.org/) (over 600 datasets), a database that integrates the reanalysed data. The jPOST reanalysis protocol rescores MS/MS spectra using a new scale, UniScore, to evaluate the extent to which the spectral peaks correspond to the amino acid sequences identified by search engines. However, the metadata registered in the repository database is insufficient for conducting the reanalysis. To address this issue, the Japanese Proteomics Society launched a data journal, the Journal of Proteome Data and Methods (JPDM), which accepts data descriptor articles detailing metadata that can be reanalysed. Within jPOST, raw proteome data is reanalysed based on the metadata described in the JPDM data descriptor articles, utilising UniScore. The reanalysed data is deposited in jPOSTdb, and a link to the JPDM articles is added to jPOSTrepo. These reanalysis accelerations within the jPOST environment will promote FAIR data principles and open science.

A Streamlined Workflow for Microscopy-Driven MALDI Imaging Mass Spectrometry Data Collection.

Matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) is a rapidly advancing technology for biomedical research. As spatial resolution increases, however, so do acquisition time, file size, and experimental cost, which increases the need to perform precise sampling of targeted tissue regions to optimize the biological information gleaned from an experiment and minimize wasted resources. The ability to define instrument measurement regions based on key tissue features and automatically sample these specific regions of interest (ROIs) addresses this challenge. Herein, we demonstrate a workflow using standard software that allows for direct sampling of microscopy-defined regions by MALDI IMS. Three case studies are included, highlighting different methods for defining features from common sample types─manual annotation of vasculature in human brain tissue, automated segmentation of renal functional tissue units across whole slide images using custom segmentation algorithms, and automated segmentation of dispersed HeLa cells using open-source software. Each case minimizes data acquisition from unnecessary sample regions and dramatically increases throughput while uncovering molecular heterogeneity within targeted ROIs. This workflow provides an approachable method for spatially targeted MALDI IMS driven by microscopy as part of multimodal molecular imaging studies.

Age-Associated Alterations in the Metabolome of Human Vitreous in Bacterial Endophthalmitis.

Endophthalmitis is a severe inflammatory condition due to intraocular infections that often leads to irreversible blindness. This study aimed to understand the age-dependent metabolic alterations in the vitreous of patients with bacterial endophthalmitis. The study included the vitreous metabolome of patients with bacterial endophthalmitis (group 1, n = 15) and uninfected controls (group 2, n = 14), which were further stratified into three groups according to their age: young (0-30years), middle (31-60years), and elderly (>60years). Vitreous samples were subjected to untargeted metabolomic analysis using high-resolution mass spectrometry (HRMS)m and acquired mass spectrometry data were analyzed using MetaboAnalyst 6.0. The altered metabolites with log2FC of ≥2/≤2, P < 0.05, and variable importance in projection > 1 were considered significant. In a total of 109 endogenous metabolites identified, young and elderly patients with endophthalmitis showed 52 (elevated, 25; reduced, 27; P < 0.05) and 27 (elevated, 19; reduced, 8; P < 0.05) significantly altered metabolites, respectively, compared to their age-matched controls. Additionally, 27 metabolites were differentially expressed in young patients with endophthalmitis compared to the older group. The crucial metabolic pathways dysregulated in the older infected population were de novo purine synthesis and salvage, carnitine, polyamine (spermidine), lipids (prostaglandins), and amino acid (taurine, methionine, histidine) which could possibly be attributed to the increased disease severity and inflammation observed in a clinical setting. Despite the erratic metabolic changes observed in the younger group infected with endophthalmitis when compared to age-matched controls, dysregulation in the specific pathways such as purine, carnitine, arachidonic acid, and polyamine metabolism could possibly alter the immunological exacerbation observed in the older group.

Aromatic Abietane-Type Diterpenoids from the Roots of Salvia prattii and their Protective Activity Against Alcoholic Liver Disease.

Eighteen aromatic abietane-type diterpenes, including three previously unreported compounds, Salkanoids A-C (1-3), were isolated from the roots of Salvia prattii. Their stuctures were extensively elucidated using 1D/2D NMR, high-resolution electrospray ionization mass spectrometry (HRESIMS) data, and ECD calculation. Among these, compounds 1, 6 and 7 belong to a class of diterpenes featuring a [5, 5]-oxaspirolactones moiety, a rare structure isolated from the Salvia plants. All the isolates were assessed for their protective effects against alcoholic liver disease using ethanol-induced AML-12 cell lines. The findings revealed that compounds 2, 5, 8 and 15 demonstrated potential protective activity.

Untargeted metabolomics reveals the formation pathways of bound sulfite in sodium metabisulfite-soaked Hemerocallis citrina Baroni

Hemerocallis citrina Baroni (HC) is usually soaked with sodium metabisulfite solution after harvest to keep its freshness. However, this will introduce bound sulfites with potential safety risks. In this study, formation pathways of bound sulfite in HC were explored using ultra-high performance liquid chromatography-Q-Exactive HF mass spectrometry (UHPLC-QE-MS) and statistical analysis. The results indicated that the chemical composition of HC changed the most at soaking times of 6–8 h and concentrations of 1.00–2.50 mg/mL. Primary metabolites and phenols in secondary metabolites were greatly affected during soaking. 71 compounds were identified in HC and 8 characteristic markers including 3 bound sulfites were screened which can be used to distinguish between non-soaked HC (NSHC) and sodium metabisulfite-soaked HC (SSHC). Among the 19 commercial HC samples analyzed, 79 % had been treated with sulfites. Mass spectrometry data further revealed two pathways of formation of bound sulfite in HC: one is the sulfonation of amino acids at the amino, the content initially increased with sulfite soaking time and concentration and then decreased until it disappeared; the other is that the addition of aldehyde at the carbonyl, the content increased continuously with sulfite soaking concentration. This study provides insight into bound sulfite formation in HC post‑sodium metabisulfite treatment.

Talaketides A−G, linear polyketides with prostate cancer cytotoxic activity from the mangrove sediment-derived fungus Talaromyces sp. SCSIO 41027

Seven novel linear polyketides, talaketides A−G (1−7), were isolated from the rice media cultures of the mangrove sediment-derived fungus Talaromyces sp. SCSIO 41027. Among these, talaketides A−E (1−5) represented unprecedented unsaturated linear polyketides with an epoxy ring structure. The structures, including absolute configurations of these compounds, were elucidated through detailed analyses of nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HR-MS) data, as well as electronic custom distributors (ECD) calculations. In the cytotoxicity screening against prostate cancer cell lines, talaketide E (5) demonstrated a dose-dependent inhibitory effect on prostate cancer PC-3 cell lines, with an IC50 value of 14.44 μmol·L−1 . Moreover, compound 5 significantly inhibited the cloning formation of PC-3 cell lines and arrested the cell cycle in S-phase, ultimately inducing apoptosis. These findings indicate that compound 5 may serve as a promising lead compound for the development of a potential treatment for prostate cancer.

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.

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.

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.

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.