BioMassSpec

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3 weeks ago

Systematic Evaluation of the Impact of Storage Time on Label-Free Proteomics of Colorectal Adenocarcinoma Formalin-Fixed Paraffin-Embedded Tissues Mass spectrometry (MS)-based proteomics has empowered comprehensive protein profiling of biological specimens. However, formalin-fixed paraffin-embedded (FFPE) tissues─critical resources for clinical biomarker discovery-remain underexplored in the setting of long-term storage (>15 years). Herein, we systematically evaluated the impact of storage time on proteomic analyses of 80 colorectal adenocarcinoma (CRC) FFPE samples, which were stratified by two key variables: storage time (>15 years vs <1 year) and tissue type (tumor vs adjacent normal tissue). We adopted a standardized protein extraction strategy, and subsequent proteomic profiling was performed via data-dependent acquisition and data-independent acquisition MS workflows. Our results demonstrated that FFPE tissue storage time impacts protein extraction efficiency, peptide yields, PTM identification, and protein quantification. The impacts were more pronounced on the peptide level. However, the biological enrichments (Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis) from the global proteome profile and from differentially expressed proteins in CRC tissues were independent of archival time. Five clinically relevant biomarkers of CRC were further validated via immunohistochemistry. Collectively, our findings confirm that FFPE tissues retain stability for proteomic analyses even following >15 years of storage, thereby providing critical insights for leveraging archival FFPE biobanks to advance clinical proteomics and archival pathology research.

Systematic Evaluation of the Impact of Storage Time on Label-Free Proteomics of Colorectal Adenocarcinoma Formalin-Fixed Paraffin-Embedded Tissues #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

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4 weeks ago

Proteomics and Lipidomics Analysis Reveal That Membrane Remodeling and Extracellular Matrix Alterations Are Crucial for Cisplatin Resistance in Triple-Negative Breast Cancer Cisplatin is a widely used chemotherapeutic agent for triple-negative breast cancer (TNBC), but resistance remains a major challenge. Understanding the molecular alterations driving this resistance is essential for identifying therapeutic targets. In this study, we employed an integrated proteomics and lipidomics approach to elucidate key pathways associated with cisplatin resistance. Employing high-resolution mass spectrometry, we conducted a comparative analysis between cisplatin-resistant (cisR) and cisplatin-sensitive (cisS) TNBC cell lines to discover resistance-associated alterations in protein and lipid expression. Proteomic analysis revealed overexpression of extracellular matrix (ECM) remodeling proteins, COL6A1, COL6A2, COL6A3, and VTN, that support epithelial-mesenchymal transition (EMT) and chemoresistance. Membrane-associated proteins such as TIMP2, MMP14, and APP were also elevated, indicating enhanced invasive and pro-survival signaling. Lipidomic alterations, including upregulation of FABP3, FABP4, LPL, and downregulation of PLA2G4A, indicated increased lipid uptake, metabolic rewiring, and membrane restructuring. Notably, elevated long-chain phosphatidylcholines and decreased sphingomyelins suggested increased membrane rigidity and reduced cisplatin permeability. Additionally, dysregulation of CDK activity through CCND2, CCND3, and CCNB2 overexpression indicated accelerated cell cycle progression and evasion of DNA damage checkpoints. Together, this integrative analysis highlights ECM remodeling, cytoskeletal dynamics, and lipid metabolism as major contributors to cisplatin resistance and identifies potential therapeutic markers for TNBC.

Proteomics and Lipidomics Analysis Reveal That Membrane Remodeling and Extracellular Matrix Alterations Are Crucial for Cisplatin Resistance in Triple-Negative Breast Cancer #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

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4 weeks ago

Combined Quantitative and Qualitative Statistical Analyses Improve Benzodiazepine Target Discovery in Label-free Affinity-Based Protein Profiling Data Affinity-based protein profiling (AfBPP) allows us to identify target proteins that bind drugs or other small molecules of interest in complex samples. As an enrichment technique, label-free AfBPP often generates data with high missingness, particularly in negative control samples. We developed an R package, chemoprotR, which enables both quantitative and qualitative statistical analyses of chemoproteomic data, and applied it to the identification of specific benzodiazepine drug targets in the brain. Benzodiazepines comprise a class of drugs that affect GABAA receptors through positive allosteric modulation, but benzodiazepine interactions with other proteins are not fully understood. To this end, we synthesized benzodiazepine affinity-based probes (AfBPs) and applied them to rat brain synaptosomes. Our benzodiazepine AfBPs identified GABAA receptor subunits and other proteins with ion channel functions. Across the three probes, there was minimal overlap in protein targets identified by competitive labeling with flurazepam, and FR-DA, the probe based on flurazepam, yielded more significant protein targets than the probes based on flunitrazepam. These results demonstrate the ability of benzodiazepine AfBPs to identify protein targets when used with an authentic benzodiazepine to compete for binding sites and highlight the utility of combined statistical analyses for the interpretation of presence–absence data in AfBPP data sets.

Combined Quantitative and Qualitative Statistical Analyses Improve Benzodiazepine Target Discovery in Label-free Affinity-Based Protein Profiling Data #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Site-Specific and Quantitative O-GlcNAc Proteomics for Hepatocellular Carcinoma O-linked β-N-acetylglucosamine (O-GlcNAc) modification (O-GlcNAcylation) underlies the pathogenesis of multiple cancers, including hepatocellular carcinoma (HCC). However, comprehensive and quantitative characterization of site-specific O-GlcNAcylation at the proteome scale remains technically challenging. Here, we employed an integrated workflow for the quantitative O-GlcNAc proteomics of HCC and controls. Proteins from liver samples were subjected to chemoenzymatic labeling, photocleavable alkyne-biotin-based enrichment, proteolytic digestion, and isotopic labeling with tandem mass tags. The O-GlcNAc peptides were analyzed by a nanoUPLC-MS/MS system in HCD product-dependent EThcD (HCD-pd-EThcD) mode for site mapping and quantification. A total of 440 O-GlcNAc peptides, representing 305 sites on 196 proteins, were confidently identified. Differential analysis revealed 190 O-GlcNAc peptides from 121 proteins significantly upregulated in HCC after normalization to their corresponding protein abundance. Functional enrichment and protein–protein interaction analyses indicate that proteins with increased levels of O-GlcNAcylation are involved in nuclear transport, transcriptional regulation, and ATP-dependent chromatin remodeling. Our work provides quantitative proteomic insights into O-GlcNAcylation in HCC, revealing global upregulation and functional clustering of O-GlcNAc-modified proteins. These findings will help elucidate the functional roles of O-GlcNAcylation in liver cancer, facilitating the development of novel therapeutics and sensitive biomarkers.

Site-Specific and Quantitative O-GlcNAc Proteomics for Hepatocellular Carcinoma #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Untargeted Metabolomic and Lipidomic Profiling in Cystic Fibrosis Patients Using UPLC-QTOF-MS Cystic fibrosis (CF), also known as mucoviscidosis, is a rare, autosomal recessive genetic disease. It is caused by various mutations in the CFTR (Cystic Fibrosis Transmembrane Conductance Regulator) gene, which disrupt the normal function of the chloride ion channel. Clinical manifestations of CF typically include recurrent respiratory infections, chronic airway inflammation, a progressive decline in lung function, and intermittent pulmonary exacerbations. The primary aim of our study is to identify plasma biomarkers in patients with cystic fibrosis through untargeted metabolomic and lipidomic analyses, with the goal of enabling early detection, accurate diagnosis, and effective monitoring of the disease. Liquid chromatography (LC) coupled with time-of-flight mass spectrometry (TOF-MS) was employed to discriminate the 24 cystic fibrosis patients from the 26 age- and gender-matched healthy controls. Multivariate statistical and pathway enrichment analyses revealed dysregulation in galactose metabolism, glycolysis/gluconeogenesis, bile acid metabolism, fatty acid metabolism, steroid hormone biosynthesis, and amino acid catabolism. The quantification of the targeted cystic fibrosis biomarkers identified by combined lipidomic and metabolomic analyses will be valuable for early diagnosis and treatment.

Untargeted Metabolomic and Lipidomic Profiling in Cystic Fibrosis Patients Using UPLC-QTOF-MS #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

A Sensitive, Specific, and Cost-Effective Lauroylation-Assisted Workflow for Profiling Peptide-Level Protease Specificity Using Proteomic Identification of Cleavage Sites (PICS), with Applicability to Protein-Level N-Terminomics Proteases play crucial roles in numerous biological processes through specific protein cleavage, and their dysregulation has been implicated in various diseases. To better understand protease specificity, we developed a lauroylation-assisted proteomic identification of protease cleavage sites (PICS) workflow that labels and enriches targeted protease-generated neo-N-termini using economical reagents and standard laboratory equipment. The lauroylation enables both discrimination of the neo-N-termini in LC-MS/MS and efficient enrichment on a C18 StageTip by exploiting its hydrophobicity. Among tested acylations, we found lauroylation to be optimal for PICS and improved enrichment and fractionation conditions. We demonstrated that this method can profile specificities of multiple proteases with high sensitivity. Furthermore, we extended this concept to N-terminomics to examine proteolysis at the protein level. Protein N-terminal dimethylation is used for labeling, and tryptic internal peptides are lauroylated for removal. This approach identified over 1500 cleavages induced by etoposide, including 912 Asp-cleaved sites consistent with caspase-3 motifs and sensitive to inhibition by Z-DEVD-FMK. Additionally, 2286 protein N-termini were identified in untreated cells, including 1794 non-ORF N-termini with 665 previously annotated processing sites. These results demonstrate that our workflow provides a simple, economical, and widely applicable method for characterizing protease cleavage at both peptide and protein levels.

A Sensitive, Specific, and Cost-Effective Lauroylation-Assisted Workflow for Profiling Peptide-Level Protease Specificity Using Proteomic Identification of Cleavage Sites (PICS), with Applicability to Protein-Level N-Terminomics #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Glucocorticoid-Induced Proteome and Phosphoproteome Changes in Breast Cancer Cell Lines Glucocorticoids (GCs) are steroid hormones that bind to the glucocorticoid receptor (GR) as ligands to initiate systemic anti-inflammatory effects. GCs are commonly administered alongside chemotherapy to reduce treatment-related side effects in breast cancer patients. However, GC administration has been shown to promote metastasis in breast cancer. In this study, we used quantitative mass-spectrometry-based approaches to analyze proteome and phosphoproteome of three breast cancer cell lines following treatment of a clinically approved synthetic GC, dexamethasone (Dex). By comparing MCF7, MDA-MB-231, and MDA-MB-436 cells, we suggest that the level of GR significantly affects Dex-mediated responses. Additionally, we identify noncanonical transcription factors (TFs) and kinases that are regulated by GR in different cell lines. Together, our data present Dex-induced protein modulations and modifications involving several TFs and kinases that regulate cytoskeletal remodeling and migration in breast cancer cell lines. These findings highlight the need for careful consideration of GC use in breast cancer therapy and identify potential molecular targets for mitigating adverse effects.

Glucocorticoid-Induced Proteome and Phosphoproteome Changes in Breast Cancer Cell Lines #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Salivary Proteomics Reveals Oxidative Markers in E-Cigarette Users Electronic cigarettes (e-cigs) have become increasingly popular, particularly among younger populations. This study aimed to evaluate the salivary proteome of e-cig users and identify potential altera...

Salivary Proteomics Reveals Oxidative Markers in E-Cigarette Users #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Exploring the Impact of Two DNA Minor Groove Binder Compounds on HCT-116 Cells: A Comprehensive Multiomics Analysis Using Mass Spectrometry Colorectal cancer (CRC) remains a major global health burden, necessitating innovative therapeutic approaches with improved selectivity and reduced toxicity. DNA minor groove binders (MGBs) represent a promising class of agents that modulate DNA-associated processes without inducing permanent DNA damage. In this study, two previously reported distamycin-like DNA minor groove binders, MGB30 and MGB32, were investigated to elucidate their molecular mechanisms of action in HCT-116 human colorectal cancer cells. An integrated multiomics approach combining metabolomics and proteomics was employed using TIMS-QTOF-UHPLC-MS. Four biological replicates were used for each treatment condition. Following MGB30 treatment, 12 metabolites and 187 proteins were significantly dysregulated, whereas MGB32 treatment resulted in alterations of 41 metabolites and 409 proteins using a Student’s t-test with q-value <0.05. Pathway enrichment analysis revealed that both compounds significantly disrupted purine metabolism, while MGB32 additionally affected beta-alanine metabolism, glutathione metabolism, and spermidine and spermine biosynthesis. Proteomic analysis further demonstrated deactivation of RNA processing, translation, and ribosome biogenesis, leading to impaired protein synthesis and reduced cancer cell proliferation. This study provides mechanistic insights into the downstream molecular effects of MGB30 and MGB32 that disrupt key mechanisms underlying tumor growth, offering new avenues for CRC treatment.

Exploring the Impact of Two DNA Minor Groove Binder Compounds on HCT-116 Cells: A Comprehensive Multiomics Analysis Using Mass Spectrometry #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Optimization of the Identification Rate and Reproducibility of an Untargeted diaPASEF Method Applicable for Quantitative Peptide Profiling of Hydrolyzed Infant Formula Quality control of hydrolyzed infant formula (HIF) requires comprehensive and precise quantification of its peptide components. Quantitative peptidome analysis by liquid chromatography–tandem mass spectrometry (LC–MS/MS) with data-independent acquisition (DIA) and parallel accumulation–serial fragmentation (PASEF) is used for this application. Here, an optimization strategy was developed to increase the peptide identification rate and the qualitative and quantitative reproducibility of this approach. To expand the peptide identification rate, the originally assigned equidistant ion mobility (IM) windows were transferred to variable ion mobility windows with manually adjusted window placement. To improve the reproducibility, major acquisition parameters, such as the number of diaPASEF scans and ion mobility windows as well as the resulting cycle time, were systematically optimized. Thus, the approach was modified from 17 equidistant windows with a cycle time of 1.8 s to 30 variable windows with a cycle time of 1.7 s. The optimization process led to the identification of 628 peptides versus 522 peptides, increasing the identification rate by 20.3%. Concurrently, the coefficient of variation (CV) for peptide identification was reduced from 10.9 to 0.8%, and for quantitative reproducibility, it was reduced from 24.3 to 17.2%. Based on these results, an optimization workflow is presented to systematically improve the identification rate and reproducibility for other sample types and instruments.

Optimization of the Identification Rate and Reproducibility of an Untargeted diaPASEF Method Applicable for Quantitative Peptide Profiling of Hydrolyzed Infant Formula #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Enrichment and Preservation of Urine Metabolites Using Styrene Divinylbenzene Reversed Phase Sulfonate (SDB-RPS) Disks for Enhanced Biomarker Discovery and Disease Monitoring Urine metabolomics plays a crucial role in biomarker discovery and disease monitoring, but challenges in metabolite preservation remain. This study evaluates the use of styrene divinylbenzene reversed phase sulfonate (SDB-RPS) disks for enriching and preserving urine metabolites utilizing ultraperformance liquid chromatography–mass spectrometry (UPLC–MS) for analysis. We compared SDB-RPS-enriched urine samples with untreated urine across three experimental parts: (1) metabolic profiling using C18 and HILIC chromatography under both positive and negative ion modes; (2) degradation kinetics, where SDB-RPS and untreated urine samples were incubated at 55, 65, and 75 °C with constant humidity (75%); and (3) disease classification using hepatitis (n = 72) and cirrhosis (n = 72) samples. The results revealed that metabolite identification was highly consistent between SDB-RPS and urine samples, with an overlapping rate of 88.26%. Additionally, in the disease classification task, the SDB-RPS panel demonstrated consistent performance, with AUC values of 0.867 and 0.828 in training and validation data sets, respectively, outperforming the urine panel (AUC: 0.765 and 0.691, respectively). These findings suggest that SDB-RPS disks significantly enhance the enrichment and long-term preservation of urine metabolites, offering a promising tool for clinical sample analysis and biomarker discovery.

Enrichment and Preservation of Urine Metabolites Using Styrene Divinylbenzene Reversed Phase Sulfonate (SDB-RPS) Disks for Enhanced Biomarker Discovery and Disease Monitoring #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Targeted Quantitative Analysis of Specific Proteins in Cytosolic, Mitochondrial, and Nuclear Fractions Using PRM Mitochondria play a central role in liver physiology by regulating key metabolic processes. Consequently, mitochondrial dysfunction is a hallmark of multiple liver diseases, including steatosis, steatohepatitis, and liver failure following hepatectomy. Subcellular fractionation is widely used to isolate mitochondria from liver cells or tissue; however, the enrichment and purity of isolated fractions are critical to ensure reliable downstream functional and proteomic analyses. Conventional validation methods, such as immunoblotting of organelle-specific markers, are limited by low throughput, restricted sensitivity, and variability. In this study, we present a targeted proteomics strategy based on parallel reaction monitoring (PRM) to quantitatively assess the enrichment of cytosolic, mitochondrial, and nuclear fractions obtained from liver samples using commercial isolation kits. PRM analyses demonstrated robust and compartment-specific enrichment in both PLC/PRF/5 cells and mouse liver tissue. In PLC/PRF/5 cells, high nuclear/cytosolic enrichment was observed for Prelamin A/C, while mitochondrial markers such as ATPase showed strong mitochondrial/cytosolic ratios. Cytosolic markers consistently displayed enrichment in the cytosolic fraction. Similar trends were observed in mouse liver tissue, confirming applicability across biological systems. Overall, these results highlight PRM as a sensitive, reproducible, and cost-effective alternative to immunodetection approaches for evaluating subcellular fraction purity, supporting high-quality mitochondrial preparations for translational hepatology studies.

Targeted Quantitative Analysis of Specific Proteins in Cytosolic, Mitochondrial, and Nuclear Fractions Using PRM #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Systematic Evaluation of Data-independent Acquisition Workflows for High-Throughput and Low-Input Proteomics Analysis with an Astral Mass Spectrometer There is a growing interest in developing high-throughput and high-sensitivity mass spectrometry methods for proteomic profiling of low-input samples, such as sorted cells or spatially resolved tissue samples. Data-independent acquisition mass spectrometry (DIA-MS) coupled with short-gradient liquid chromatography (LC) is gaining significant attention for providing deep proteome coverage in low-input samples, particularly with the recent release of high-speed mass spectrometers. However, the quantification performance of existing DIA workflows for low-input samples has not been extensively evaluated, and there is no consensus on optimal informatics workflows to obtain high-quality quantitative data. As such, we systematically evaluated multiple factors in low-input DIA workflows on an Astral MS, including MS acquisition parameters, data analysis software (DIA-NN, Spectronaut, and FragPipe), LC separation gradient lengths, database searching algorithms, and protein quantification approaches. Using three-species proteome samples (human, yeast, and Escherichia coli) with total input ranging from 0.1 ng to 10 ng and predefined quantity ratios, we focused on proteome coverage, quantification accuracy, and precision, which are the most important considerations when applying these methods in biological applications. Our evaluation suggested a preferred DIA workflow for low-input samples, which involves using a FAIMS interface, DIA-NN-based library-free database search with the enabled match between runs (MBR) function, and MS1-level protein quantification with the maxLFQ algorithm.

Systematic Evaluation of Data-independent Acquisition Workflows for High-Throughput and Low-Input Proteomics Analysis with an Astral Mass Spectrometer #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Definition of Metabolite: Size as a Critical Criterion Metabolites have traditionally been defined as organic molecules smaller than 1500 daltons (Da). However, recent advances in analytical technologies and chemoinformatics have uncovered a wider chemical diversity, including biologically significant metabolites exceeding this conventional size cutoff─such as polypeptides, glycosphingolipids, and bacterial lipopolysaccharides. Detecting these larger metabolites challenges standard metabolomics approaches and necessitates optimized mass spectrometry acquisition parameters. In this perspective, the analysis of multiple databases (HMDB blood, GNPS, Plant Molecular Network [PMN], Natural Product Atlas [NPA] fungi, NPA bacteria, and MiMeDB) confirms that although most metabolites are below 1000 Da, notable populations exceed this threshold, particularly in bacterial data sets. Furthermore, reanalysis of liquid chromatography–mass spectrometry (LC-MS2) data sets from diverse biological samples, especially bacteria-rich matrices like feces and skin, reveals features (peaks with m/z and retention time) extending beyond 1500 m/z. These findings underscore that metabolites are often larger than commonly recognized in the literature. Therefore, the definition of metabolites should evolve to accommodate their size diversity, ensuring accurate knowledge dissemination to new generations of metabolomics researchers.

Definition of Metabolite: Size as a Critical Criterion #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Scalable Incremental Clustering for Tandem Mass Spectra in Untargeted Metabolomics Tandem mass spectrometry (MS/MS) has become the analytical backbone of large-scale untargeted metabolomics, routinely generating millions of spectra per study. However, existing clustering methods struggle to process this scale due to computational and memory bottlenecks, limiting the utility of clustering in downstream analysis. This bottleneck is especially acute in long-term studies and public repositories, where new data are continuously added over time. Here we present a scalable clustering framework for MS/MS metabolomics data. Our method incrementally incorporates new spectra batches while preserving clustering performance through a novel spectrum pooling strategy, which propagates local density structure across batches. Using both database-search–based evaluation on proteomics data sets and the MS1-retention time (MS-RT) method on metabolomics data sets, we show that incremental clustering achieves comparable performance to the state-of-the-art clustering methods in terms of cluster purity and completeness. Critically, our approach scales up to clustering tasks consisting of 368 million spectra clustering task and millions of clusters, completing in under 10,000 CPU hours, while traditional methods could not scale to this data volume and failed to complete due to excessive memory or time requirements. Our method offers a practical solution for large-scale, continuously growing MS/MS studies and is well suited for integration into public metabolomics platforms such as GNPS2.

Scalable Incremental Clustering for Tandem Mass Spectra in Untargeted Metabolomics #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

MetaboliteAnnotator: AI-Assisted Name Harmonization and Metadata Enrichment Tool for Metabolomics Metabolite metadata enrichment remains a significant challenge in metabolomics due to the limitations of static databases, incomplete metabolite coverage, and the labor-intensive nature of manual verification. Here, we present MetaboliteAnnotator, an R Shiny-based application for AI-assisted metabolite name harmonization and metadata enrichment. MetaboliteAnnotator implements a hierarchical procedure, including preprocessing of input metabolite names, matching against a curated local resource (covering information on ∼640,000 metabolites names), PubChem-based real-time retrieval, and AI-assisted matching for ambiguous compounds, followed by real-time integration of KEGG, CTD, Reactome, and ChEBI. Compared with MetaboAnalyst 6.0 and MetaboliteIDmapping, MetaboliteAnnotator achieved significantly higher name hit rates across all six MetaboLights data sets 93.2% in positive mode (4021/4314 names) and 93.5% in negative mode (2344/2510 names). MetaboliteAnnotator outputs standardized identifiers (e.g., InChIKey, PubChem CID), endogenous/exogenous information, pathway mappings, and metabolite-gene/phenotype associations for downstream biological interpretation.

MetaboliteAnnotator: AI-Assisted Name Harmonization and Metadata Enrichment Tool for Metabolomics #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

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1 month ago

Charge Detection Mass Spectrometry and a Glu-C/Lys-C Digestion-Based Data-Dependent Approach Suggest Mono-PEGylation of a Heterogenous Therapeutic Protein There have been various strategies to increase protein’s half-life in blood, such as glycosylation or protein fusion, but one of the most widely used approaches is the addition of PEG (polyethylene-glycol). Attaching a large, polydisperse PEG moiety to the therapeutics makes the analytical characterization challenging due to conjugation chemistry and increased heterogeneity. In this study, we aimed to develop a mass-spectrometry-based workflow to mitigate challenges. Using Orbitrap-based charge detection mass spectrometry, we determined the intact mass of a heavily glycosylated protein modality which is likely to carry a 30 kDa PEG. It has been demonstrated that the technique is suitable for impurity analysis, such as the remaining underivatized PEG in the formulated drug. Our data suggest mono-PEGylation of glycoprotein. Analyzing the digests of the protein generated by Lys-C and Glu-C and the combination of both enzymes in data-dependent acquisition allowed us to identify PEGylation sites on K45 and K52 residues and the N-terminus. Software-assisted data processing of PEGylation from digests yielded by the three digestion conditions generated results complementary to each other and led to a highly confident assignment of PEGylation sites. The methodology introduced here overcomes bottlenecks caused by PEGylation and can be routinely used for the comprehensive characterization of PEGylated therapeutics.

Charge Detection Mass Spectrometry and a Glu-C/Lys-C Digestion-Based Data-Dependent Approach Suggest Mono-PEGylation of a Heterogenous Therapeutic Protein #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

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2 months ago

Spatial Proteomics of the Normal Breast Collagen Stroma: Links to Density and Body Mass Index Collagen breast stroma can become a breast cancer risk factor, yet proteomic regulation of normal breast stroma remains poorly defined. This study evaluates the spatial regulation of the collagen proteome from normal breast tissue. Normal breast tissue sections from the Susan G. Komen tissue bank were used (n = 40), with data including genetic ancestry (n = 20 African ancestry; n = 20 European ancestry), body-mass-index (BMI), age, and mammogram density by the Breast Imaging Reporting and Data System (BI-RADS). 10-plex cell marker staining showed CD44 and COL1A1 markers modulated with BMI. Collagen fiber widths by second harmonic generation microscopy contrasted in BMI categories by genetic ancestry. Targeted extracellular matrix proteomics mass spectrometry imaging showed the collagen alpha-1(I) chain proteome was spatially heterogeneous across the normal breast microenvironment with site-specific post-translational modification of proline hydroxylation. Signatures computationally extracted from stroma-rich regions reported that 47 collagen peptides distinguished BI-RADS categories (area under the receiver operating curve >0.7; p-value >0.05). Multivariate modeling of collagen peptides, fiber metrics, and clinical features supported a strong positive association with BMI as a determinant of collagen alterations in the normal breast. This study provides a foundation for larger studies investigating the clinical value of spatial collagen proteome alterations in human breast.

Spatial Proteomics of the Normal Breast Collagen Stroma: Links to Density and Body Mass Index #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

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2 months ago

An Investigation into the Differentiation of Lactyllysine and Carboxyethyllysine Peptide Modifications by Liquid Chromatography/Mass Spectrometry Post-translational modifications (PTMs) are key drivers in the regulation of protein activity. Therefore, the ability to measure and identify them accurately is critical to understanding the function and regulation of these modifications. Nε-carboxyethyllysine (CEL) and lactyllysine (LactylLys) are two modifications that share the same chemical composition and thus mass shift, making traditional LC-MS/MS approaches unsuitable for distinguishing them. Standard LC-MS/MS approaches utilizing HCD show that both modifications display extremely similar fragmentation, with no distinguishing features observed for either modification. Furthermore, cyclic and linear immonium ions, which have recently been reported as unique to LactylLys, were also observed in MS/MS from carboxyethylated peptides. We show that carboxyethylated and lactylated peptides can be chromatographically resolved on in-house packed and commercial C18 columns, and retention time alignment with isotopically labeled peptides can be used for discrimination. Furthermore, we observed differences in the MS/MS spectra obtained from EAD fragmentation of the two PTM-containing peptides. Our results highlight the analytical challenges associated with distinguishing CEL- and LactylLys-modified peptides and provide a proof of concept in which retention time alignment of endogenous peptides with isotopically labeled standards and electron activated dissociation (EAD) fragmentation is applied to accurately assign lactylation at K147 of aldolase A across various cell lines/tissues.

An Investigation into the Differentiation of Lactyllysine and Carboxyethyllysine Peptide Modifications by Liquid Chromatography/Mass Spectrometry #JProteomeRes pubs.acs.org/doi/10.1021/...

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2 months ago

Molecular Portraits of Aqueous Humor in Primary and Pseudoexfoliative Open-Angle Glaucoma Glaucoma is a neurodegenerative disease that affects the optic nerve and retinal ganglion cells. The two most common forms, primary open-angle glaucoma (POAG) and pseudoexfoliative glaucoma (PEG), are associated with ocular hypertension caused by impaired aqueous humor (AH) outflow but have specific ocular and systemic features that may determine their diagnosis and treatment. To identify these features, we compared AH composition in patients using a multiomics approach. Total reflection X-ray fluorescence analysis revealed an increase in zinc and calcium concentrations more pronounced in PEG. Nuclear magnetic resonance study identified common metabolomic changes involving amino acids (Ala, Glu, His, Lys, Tre) and tricarboxylic acid cycle components (citrate and succinate). Targeted liquid chromatography-tandem mass spectrometry data demonstrated shared elevation of oxidative stress-related oxylipins (9/13-HODE, 9-HOTrE) and POAG-specific changes in phospholipase A2 products (DHA, lyso-PAF) and inflammatory prostaglandins (PGE2 and 15d-PGJ2). POAG featured secretion of proteins controlling intraocular pressure (angiotensinogen) and trabecular meshwork outflow (myocilin and cystatin C) and altered levels of neurotrophic factors (PEDF, VGF). Our data highlight novel AH biomarkers distinguishing POAG from PEG (15d-PGJ2, VGF) and demonstrate that they have specific triggers and responses to ocular hypertension but share common mechanisms, such as oxidative stress, mitochondrial dysfunction, and zinc and calcium toxicity.

Molecular Portraits of Aqueous Humor in Primary and Pseudoexfoliative Open-Angle Glaucoma #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

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2 months ago

Mass Spectrometry-Based Multi-Protein Panel Assay for Detecting Immune Checkpoint Proteins in Immune Cells of Oral Cancer Patients Cancer immunotherapy represents a transformative approach to cancer treatment, paving the way for personalized and precision medicine strategies. Despite the significant advances in immunotherapy, the treatment response rate remains a major clinical challenge. The stratification of patients into responders and nonresponders is paramount for immunotherapy treatment response, which reduces the likelihood of treatment challenges, expenses, and potentially severe adverse effects in cases where patients are not likely to respond. Therefore, in the present study, we optimized a mass spectrometry-based analytical assay deploying data-independent acquisition analysis (DIA) to simultaneously detect a panel of six well-known immune checkpoint proteins. Further, the optimized assay was analytically validated to assess the technical performance, and preliminary clinical feasibility testing was conducted using the clinical samples of immune cells from oral cancer patients. Altogether, we tested the method for the detection of the candidate proteins in samples from 20 oral cancer patients and healthy individuals. A proof-of-concept method using DIA-MS was optimized with a shorter turnaround time, facilitating a multiplexed detection and quantitation of protein targets within the panel. This DIA-MS method could be applied to detect potential immune checkpoint proteins without additional enrichment techniques. Furthermore, this optimized method can be used in clinical settings to guide appropriate drug selection based on the abundance of target protein(s) in each patient. The assay may also be adapted for other cancer types using the same multiprotein panel, or expanded in the future to include additional proteins validated as potential immunotherapeutic targets.

Mass Spectrometry-Based Multi-Protein Panel Assay for Detecting Immune Checkpoint Proteins in Immune Cells of Oral Cancer Patients #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

@realbiomassspec.bsky.social

2 months ago

Toward Robust Machine Learning Models for MALDI-TOF MS: Novel Approaches for Mycobacterium abscessus Subspecies Identification Distinguishing Mycobacterium abscessus subspecies presents significant diagnostic challenges due to their genetic homogeneity and variability in analytical platforms. Our research combines matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry with machine learning (ML) approaches to enhance discrimination accuracy, utilizing 325 spectra profiles from diverse European hospitals. The analytical pipeline incorporates specialized techniques for geographical data harmonization, feature selection, and balancing class representation. The best model employs support vector machines (SVMs) with ComBat correction, Boruta feature selection, and centroid clustering for class imbalance, achieving a discrimination performance of 97% F1 score and 97.17% AUC-ROC on test samples. Noteworthily, most tested models improved their discrimination performance with the approach and demonstrated consistent performance metrics with high geometric mean (GEO) and index balanced accuracy (IBA) metrics (>0.90), ensuring consistent sensitivity and specificity across all subspecies. SHAP (SHapley Additive exPlanations) validated the biological relevance of selected spectral features, particularly improving discrimination of the diagnostically challenging M. abscessus subsp. bolletii. This work advances the state-of-the-art in M. abscessus classification, providing a scalable analytical framework for enhanced microbial diagnostics and targeted antimicrobial therapy selection.

Toward Robust Machine Learning Models for MALDI-TOF MS: Novel Approaches for Mycobacterium abscessus Subspecies Identification #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

@realbiomassspec.bsky.social

2 months ago

Expanding the Proteomics and Metabolomics Toolkit with Methods for Differential Expression Analysis from Transcriptomics With the increasing adoption of discovery -omics in the life sciences, a large number of analysis tools for differential expression analysis (DEA) have been introduced over the years. While such tools tend to be developed with one particular -omics modality in mind, they can often be applied across technologies to solve common issues. This is particularly the case when -omics data share statistical and distributional properties. Herein, we showcase how tools originally developed for transcriptomics analysis are especially well-suited to solving problems in discovery proteomics and metabolomics. Using data from our own experimental work as examples of real-world implementation, we demonstrate how these methods can be used to tackle common DEA issues, such as variable sample quality, hidden batch effects, normalization, and small sample size. We believe this can be useful to novices and seasoned practitioners alike by expanding their toolkits. As multiomic and integrative analyses become commonplace, it is especially useful to capitalize on the similarities of otherwise different -omics.

Expanding the Proteomics and Metabolomics Toolkit with Methods for Differential Expression Analysis from Transcriptomics #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

@realbiomassspec.bsky.social

2 months ago

Complete Data Analysis Workflow for Quantitative DIA Mass Spectrometry Using Nextflow Data-independent acquisition (DIA) mass spectrometry is a technique used in proteomics to identify and quantify proteins in complex biological samples. While this comprehensive approach yields more co...

Complete Data Analysis Workflow for Quantitative DIA Mass Spectrometry Using Nextflow #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

@realbiomassspec.bsky.social

2 months ago

Proteomic Signature in Men with Central Serous Chorioretinopathy To explore systemic contributors to central serous chorioretinopathy (CSCR) pathogenesis, we performed untargeted serum proteomics in 60 male CSCR patients (30 acute, 30 chronic) and 60 age-matched controls using label-free LC-MS/MS with stringent statistical pairing. Among 242 abundant proteins identified, 27 (11.5%) were significantly different in CSCR, converging on pathways of complement activation, coagulation, oxidative stress, immune regulation, and response to external stimuli. Complement cascade components (C1QA, C1S, C3, C4B, C8A/B/G, CFB) were upregulated, while the regulators CFHR1 and CFHR2 were decreased, contrary to age-related macular degeneration. Oxidative stress-related proteins (haptoglobin, hemoglobin subunits, peroxiredoxin-2) were elevated, consistent with prior evidence of systemic redox imbalance in CSCR. Tetranectin (CLEC3B) decreased and attractin (ATRN) increased in CSCR were validated by ELISA. Multiplex immunofluorescence on the human retina localized tetranectin to Müller cells, including the outer limiting membrane, and to the RPE and attractin to photoreceptor segments, retinal pigment epithelium, Bruch’s membrane, and the choriocapillaris, supporting potential roles of both proteins at the retina–choroid interface. A distinct systemic proteomic signature in patients with CSCR highlights complement dysregulation, oxidative stress, and stress responses to external stimuli and identifies tetranectin and attractin as candidate biomarkers, which should further be validated in other cohorts.

Proteomic Signature in Men with Central Serous Chorioretinopathy #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

@realbiomassspec.bsky.social

2 months ago

Molecular Signatures of Pyomelanin Production in MDR Acinetobacter baumannii: A Proteomic Perspective Acinetobacter baumannii causes severe nosocomial opportunistic infections and is rapidly emerging as a global health threat. Despite reports on the rare pigment production in A. baumannii and genomic insights, the molecular mechanisms and their functional implications in virulence driving this rare phenotype remain inadequately characterized. Recognizing this limitation, we performed comparative proteomic profiling of six clinical pyomelanin-producing A. baumannii isolates with four nonpyomelanin-producing isolates. Among the total proteins detected, 66 were up-regulated, and 52 were down-regulated, forming a highly interactive regulatory network in pyomelanin production. Comparative proteomics identified perturbed pathways, including activation of amino acid and pyruvate metabolism, cell membrane biosynthesis, stress response, and virulence factor association, such as biofilm formation, T6 secretion system, highlighting their roles in host adaptation and pathogenicity. Nevertheless, no significant change was noticed in terms of adhesion, cytotoxicity in A549 cell lines, and virulence in Galleria mellonella. Overall, this study expands our understanding of pyomelanin-driven proteomic perturbations in clinical isolates of A. baumannii and helps us to monitor the marked alterations in pigment-producing phenotype, which may provide critical insights for diagnostic and treatment approaches.

Molecular Signatures of Pyomelanin Production in MDR Acinetobacter baumannii: A Proteomic Perspective #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

@realbiomassspec.bsky.social

2 months ago

Quantifying the ∼75–95% of Peptides in DIA-MS Data Sets that Were Not Previously Quantified We have developed a novel algorithm termed GoldenHaystack (GH) that was designed for enhanced peptide quantification of data-independent acquisition liquid-mass spectrometry (DIA-LC-MS) data files reg...

Quantifying the ∼75–95% of Peptides in DIA-MS Data Sets that Were Not Previously Quantified #JProteomeRes pubs.acs.org/doi/10.1021/...

BioMassSpec

@realbiomassspec.bsky.social

2 months ago

Proteomic Landscape of H2S-Releasing Peptide Mediated Neuroprotection in Traumatic Brain Injury Traumatic brain injury (TBI) constitutes a health burden, with outcomes shaped by the primary injury and the progressive secondary injury cascades leading to the impairment of neuronal integrity, brai...

Proteomic Landscape of H2S-Releasing Peptide Mediated Neuroprotection in Traumatic Brain Injury #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

@realbiomassspec.bsky.social

2 months ago

Cross-Kingdom Global Proteomics Reveals Specific Modulation of Disease Signaling in Multi-Host Fungal Pathogen Infection in Chickpea and Worm An interconnected loop of messages and counter-messages determine the outcome of host–pathogen interactions. Multihost pathogenicity across plants and animals, particularly nematode, is a major source...

Cross-Kingdom Global Proteomics Reveals Specific Modulation of Disease Signaling in Multi-Host Fungal Pathogen Infection in Chickpea and Worm #JProteomeRes #MassSpec pubs.acs.org/doi/10.1021/...

BioMassSpec

@realbiomassspec.bsky.social

2 months ago

Identifying Diabetic Cardiomyopathy Biomarkers via Proteomic and Glycation Modification Analysis Using DIA and PRM Diabetic cardiomyopathy (DCM), a severe complication of type 2 diabetes mellitus (T2DM), lacks specific and effective biomarkers for early diagnosis. This study constructed a plasma-specific spectral ...

Identifying Diabetic Cardiomyopathy Biomarkers via Proteomic and Glycation Modification Analysis Using DIA and PRM #JProteomeRes pubs.acs.org/doi/10.1021/...