bims-mascan 2025-04-13 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–04–13
thirty-one papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

QuickProt: A bioinformatics and visualization tool for DIA and PRM mass spectrometry-based proteomics datasets.
Protocol for dual metabolomics and proteomics using nanoflow liquid chromatography-tandem mass spectrometry.
Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome.
TMT-based Multiplexed (Chemo)proteomics on the Orbitrap Astral Mass Spectrometer.
MSFragger-DDA+ enhances peptide identification sensitivity with full isolation window search.
Multispectrum ModiFinder Site Localization Performance.
In-depth characterization of acylcarnitines: utilizing nitroxide radical-directed dissociation in tandem mass spectrometry.
Tumor acidosis supports cancer cell lipid uptake via a rapid, transporter-independent mechanism.
RapidMass: A Graphical Data Processing Application for Automated Species Authentication in High-Throughput Mass Spectrometry.
Amino acids in cancer: Understanding metabolic plasticity and divergence for better therapeutic approaches.
Comprehensive PTM profiling with SCASP-PTM uncovers mechanisms of p62 degradation and ALDOA-mediated tumor progression.
The perspective of targeting cancer cell metabolism: combination therapy approaches.
MSIght: A Modular Platform for Improved Confidence in Global, Untargeted Mass Spectrometry Imaging Annotation.
An integrated proteomic classifier to distinguish benign from malignant pulmonary nodules.
Evaluation of ion source parameters and liquid chromatography methods for plasma untargeted metabolomics using orbitrap mass spectrometer.
Deep Proteome Profiling of Primary Skin Fibroblasts.
Seminal Plasma Proteomics Using Filter-Aided Sample Preparation.
Quantitative Analysis of Coupled Carbon and Energy Metabolism for Lignin Carbon Utilization in Pseudomonas putida.
omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation applied to bulk- and single cell proteomics data.
Unraveling the nexus: Genomic instability and metabolism in cancer.
Dissecting Branch-Specific Unfolded Protein Response Activation in Drug-Tolerant BRAF-Mutant Melanoma using Data-Independent Acquisition Mass Spectrometry.
Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.
Overcoming Analytical Challenges in Proximity Labeling Proteomics.
Time-lapse tryptic digestion: a proteomic approach to improve sequence coverage of extracellular matrix proteins.
Acoustic ejection Mass spectrometry: the potential for personalized medicine.
A reference database enabling in-depth proteome and PTM analysis of mouse immune cells.
Quantitative proteomics and phosphoproteomics analysis of patient-derived ovarian cancer stem cells.
Towards Generalizable In Silico Predictions of Differential Ion Mobility Using Machine Learning and Customized Fingerprint Engineering.
G-protein coupled receptors in metabolic reprogramming and cancer.
Online Alkaline-pH Reversed-Phase Nanoelectrospray-Tandem Mass Spectrometry Complements Traditional Phosphoproteomic Analysis via Influencing Charge State Distribution of Phosphopeptides.
Isomer-resolved characterization of acylcarnitines reveals alterations in type 2 diabetes.

bioRxiv. 2025 Mar 28. pii: 2025.03.24.645047. [Epub ahead of print]

QuickProt: A bioinformatics and visualization tool for DIA and PRM mass spectrometry-based proteomics datasets.

Omar Arias-Gaguancela, Carmen Palii, Mehar Un Nissa, Marjorie Brand, Jeff Ranish.

Mass spectrometry (MS)-based proteomics focuses on identifying and quantifying peptides and proteins in biological samples. Processing of MS-derived raw data, including deconvolution, alignment, and peptide-protein prediction, has been achieved through various software platforms. However, the downstream analysis, including quality control, visualizations, and interpretation of proteomics results remains challenging due to the lack of integrated tools to facilitate the analyses. To address this challenge, we developed QuickProt, a series of Python-based Google Colab notebooks for analyzing data-independent acquisition (DIA) and parallel reaction monitoring (PRM) proteomics datasets. These pipelines are designed so that users with no coding expertise can utilize the tool. Furthermore, as open-source code, QuickProt notebooks can be customized and incorporated into existing workflows. As proof of concept, we applied QuickProt to analyze in-house DIA and stable isotope dilution (SID)-PRM MS proteomics datasets from a time-course study of human erythropoiesis. The analysis resulted in annotated tables and publication-ready figures revealing a dynamic rearrangement of the proteome during erythroid differentiation, with the abundance of proteins linked to gene regulation, metabolic, and chromatin remodeling pathways increasing early in erythropoiesis. Altogether, these tools aim to automate and streamline DIA and PRM-MS proteomics data analysis, making it more efficient and less time-consuming.

DOI: https://doi.org/10.1101/2025.03.24.645047
STAR Protoc. 2025 Apr 05. pii: S2666-1667(25)00151-0. [Epub ahead of print]6(2): 103745

Protocol for dual metabolomics and proteomics using nanoflow liquid chromatography-tandem mass spectrometry.

Weiwei Lin, Fatemeh Mousavi, Benjamin C Blum, Christian F Heckendorf, Matthew Lawton, Noah Lampl, Ryan Hekman, Mark McComb, Andrew Emili.

  Nanoflow liquid chromatography-tandem mass spectrometry (nLC-MS) benefits untargeted metabolomics by enhancing sensitivity and integrating proteomics for the same sample. Here, we present a protocol to enable nLC-MS for dual metabolomics and proteomics. We describe steps for solid-phase micro-extraction (SPME)-assisted metabolite cleaning and enrichment, which avoids capillary column blockage. We then detail nLC-MS data acquisition and analysis. This protocol has been applied in diverse specimens including biofluids, cell lines, and tissues. For complete details on the use and execution of this protocol, please refer to Lin et al.1.

Keywords:  Bioinformatics; Mass Spectrometry; Metabolomics; Proteomics; Systems biology

DOI:  https://doi.org/10.1016/j.xpro.2025.103745
Proteomics. 2025 Apr 10. e202400187

Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome.

Andrew T Rajczewski, J Alfredo Blakeley-Ruiz, Annaliese Meyer, Simina Vintila, Matthew R McIlvin, Tim Van Den Bossche, Brian C Searle, Timothy J Griffin, Mak A Saito, Manuel Kleiner, Pratik D Jagtap.

  Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being data-dependent acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-independent acquisition mass spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. In this study, DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory for the particular instruments and software parameters chosen. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation.

Keywords:  artificial microbial community; metaproteome; microbiome; microbiota; synthetic community

DOI:  https://doi.org/10.1002/pmic.202400187
Mol Cell Proteomics. 2025 Apr 08. pii: S1535-9476(25)00066-0. [Epub ahead of print] 100968

TMT-based Multiplexed (Chemo)proteomics on the Orbitrap Astral Mass Spectrometer.

Yuchen He, Ka Yang, Shaoxian Li, Martin Zeller, Graeme C McAlister, Hamish I Stewart, Christian Hock, Eugen Damoc, Vlad Zabrouskov, Steven P Gygi, Joao A Paulo, Qing Yu.

Ongoing advancements in instrumentation has established mass spectrometry (MS) as an essential tool in proteomics research and drug discovery. The newly released Asymmetric Track Lossless (Astral) analyzer represents a major step forward in MS instrumentation. Here, we evaluate the Orbitrap Astral mass spectrometer in the context of tandem mass tag-based multiplexed proteomics and activity-based proteome profiling, highlighting its sensitivity boost relative to the Orbitrap Tribrid platform-50% at the peptide and 20% at the protein level. We compare TMT DDA and label-free DIA on the same instrument, both of which quantify over 10,000 human proteins per sample within one hour. TMT offers higher quantitative precision and data completeness, while DIA is free of ratio compression and is thereby more accurate. Our results suggest that ratio compression is prevalent with the high-resolution MS2-based quantification on the Astral, while real-time search-based MS3 quantification on the Orbitrap Tribrid platform effectively restores accuracy. Additionally, we benchmark TMT-based activity-based proteome profiling by interrogating cysteine ligandability. The Astral measures over 30,000 cysteines in a single-shot experiment, a 54% increase relative to the Orbitrap Eclipse. We further leverage this remarkable sensitivity to profile the target engagement landscape of FDA-approved covalent drugs, including Sotorasib and Adagrasib. We herein provide a reference for the optimal use of the advanced MS platform.

DOI: https://doi.org/10.1016/j.mcpro.2025.100968
Nat Commun. 2025 Apr 08. 16(1): 3329

MSFragger-DDA+ enhances peptide identification sensitivity with full isolation window search.

Fengchao Yu, Yamei Deng, Alexey I Nesvizhskii.

Liquid chromatography-mass spectrometry 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 mass spectrometry data is database search. Traditional tools typically focus on identifying a single peptide per tandem mass spectrum, often neglecting the frequent occurrence of peptide co-fragmentations leading to chimeric spectra. Here, we introduce MSFragger-DDA+, a 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 tandem mass spectrum, 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 control. It is also uniquely suited for wide-window acquisition 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.

DOI: https://doi.org/10.1038/s41467-025-58728-z
J Am Soc Mass Spectrom. 2025 Apr 09.

Multispectrum ModiFinder Site Localization Performance.

Mohammad Reza Zare Shahneh, James Pyke, Emma E Rennie, Mingxun Wang.

  Tandem mass spectrometry (MS/MS) is a powerful technique for structural identification of small molecules, yet a significant portion of MS/MS spectra from untargeted experiments remain unidentifiable through spectrum library matching. ModiFinder, a computational tool, tackles this issue by predicting the site of chemical modifications on known analogs of the unidentified compounds using MS/MS data. However, ModiFinder's performance is limited by insufficient peak data and fragmentation annotation ambiguities. In this study, we investigate how incorporating MS/MS spectra from multiple collision energies and mass spectrometry adducts can enhance ModiFinder's localization accuracy. Using a data set from Agilent Technologies comprising 2150 data-rich compounds (five times larger than previously available data sets), we evaluated the impact of complementary spectral information. Our results show that combining spectra from different adducts and collision energies expands ModiFinder's localization abilities to more compounds and improves the overall performance.

Keywords:  library search; mass spectrometry; modification site localization; structural identification

DOI:  https://doi.org/10.1021/jasms.4c00464
Anal Bioanal Chem. 2025 Apr 08.

In-depth characterization of acylcarnitines: utilizing nitroxide radical-directed dissociation in tandem mass spectrometry.

Xiangyu Gao, Xue Zhao.

  Acylcarnitines (ACs) are metabolic intermediates of fatty acids playing important roles in regulating cellular energy and lipid metabolism. The large structural diversity of ACs arises from variations in acyl chain length and the presence of chemical modifications, such as methyl branching, desaturation, hydroxylation, and carboxylation. Numerous studies have demonstrated that these structural isomers of ACs function as biomarkers for a variety of diseases. However, conventional tandem mass spectrometry (MS/MS) via low-energy collision-induced dissociation (CID) faces challenges in distinguishing these isomers. In this study, we report a radical-directed dissociation (RDD) approach for characterization of the intrachain modifications within ACs. The method involves derivatizing ACs with O-benzylhydroxylamine (O-BHA), followed by MS2 CID to produce a nitroxide radical for subsequent RDD along the fatty acyl chain. The above RDD approach was employed on a cyclic ion mobility spectrometry (cIMS) and reversed-phase liquid chromatography (RPLC), enabling the identification and relative quantification of branched chain isomers of ACs. By derivatizing carboxylated ACs with O-BHA, their mass is shifted to a higher region, thereby facilitating their separation from the isobars of hydroxylated ACs. Furthermore, this RDD method effectively allows for the assignment and localization of C = C and hydroxylation positions. This RDD approach has been applied for in-depth profiling of ACs in mice plasma extracts.

Keywords:  Acylcarnitine; Liquid chromatography; Radical-directed dissociation; Tandem mass spectrometry

DOI:  https://doi.org/10.1007/s00216-025-05868-2
J Cell Sci. 2025 Apr 07. pii: jcs.263688. [Epub ahead of print]

Tumor acidosis supports cancer cell lipid uptake via a rapid, transporter-independent mechanism.

Marc Severin, Rikke K Hansen, Michala G Rolver, Tove Hels, Kenji Maeda, Luis A Pardo, Stine F Pedersen.

  Tumor acidosis alters cancer cell metabolism and favors aggressive disease progression. Cancer cells in acidic environments increase lipid droplet (LD) accumulation and oxidative phosphorylation, characteristics of aggressive cancers. Here, we use live imaging, shotgun lipidomics, and immunofluorescence analyses of mammary and pancreatic cancer cells to demonstrate that both acute acidosis and adaptation to acidic growth drive rapid uptake of fatty acids (FA), which are converted to triacylglycerols (TAG) and stored in LDs. Consistent with its independence of de novo synthesis, TAG- and LD accumulation in acid-adapted cells is unaffected by FA-synthetase inhibitors. Macropinocytosis, which is upregulated in acid-adapted cells, partially contributes to FA uptake, which is independent of other protein-facilitated lipid uptake mechanisms, including CD36, FATP2, and caveolin- and clathrin-dependent endocytosis. We propose that a major mechanism by which tumor acidosis drives FA uptake is through neutralizing protonation of negatively charged FAs allowing their diffusive, transporter-independent uptake. We suggest that this could be a major factor triggering acidosis-driven metabolic rewiring.

Keywords:  CD36; FASN; Lipid diffusion; Macropinocytosis; Membrane contact sites; Protonation

DOI:  https://doi.org/10.1242/jcs.263688
Anal Chem. 2025 Apr 11.

RapidMass: A Graphical Data Processing Application for Automated Species Authentication in High-Throughput Mass Spectrometry.

Chunxiang Liu, Qian Meng, Yun Li, Hanze Wang, Huanya Yang, Huiting Ou, Saiyi Ye, Changliang Yao, Qirui Bi, Wenlong Wei, Jianqing Zhang, Dean Guo.

High-throughput mass spectrometry (HT-MS) facilitates rapid, large-scale data acquisition, providing a fast and efficient solution for various analytical challenges. However, the increasing volume of data generated by MS requires automation and easy-to-use processing tools. Currently, there is no freely available software that is compatible with most instruments for species authentication or classification. Here, we introduce RapidMass, a cutting-edge software platform designed to automate the handling, evaluation, presentation, and management of HT-MS data for species identification. Key features include a streamlined workflow for processing spectra from various instruments (e.g., DI-MS, ASAP-MS, DART-MS), three specialized algorithms for scoring unknown samples, peak annotation review, visualization of MS1 and MS2 spectra, and an expandable personal database for customized data management. Performance validation conducted on nine data sets covering diverse sample compositions, instrument types, suppliers, resolutions, and MS acquisition modes, demonstrated RapidMass's excellent performance, with processing times of 12 to 20 s per sample and authentication accuracies ranging from 97% to 100% for easily confused plants. With its user-friendly interface, RapidMass empowers users to create and manage personalized databases, presenting significant prospects for broad applications across various fields.

DOI: https://doi.org/10.1021/acs.analchem.4c05062
Cell Rep. 2025 Apr 05. pii: S2211-1247(25)00300-6. [Epub ahead of print]44(4): 115529

Amino acids in cancer: Understanding metabolic plasticity and divergence for better therapeutic approaches.

Linda K Do, Hyun Min Lee, Yun-Sok Ha, Chan-Hyeong Lee, Jiyeon Kim.

  Metabolic reprogramming is a hallmark of malignant transformation. While initial studies in the field of cancer metabolism focused on central carbon metabolism, the field has expanded to metabolism beyond glucose and glutamine and uncovered the important role of amino acids in tumorigenesis and tumor immunity as energy sources, signaling molecules, and precursors for (epi)genetic modification. As a result of the development and application of new technologies, a multifaceted picture has emerged, showing that context-dependent heterogeneity in amino acid metabolism exists between tumors and even within distinct regions of solid tumors. Understanding the complexity and flexibility of amino acid metabolism in cancer is critical because it can influence therapeutic responses and predict clinical outcomes. This overview discusses the current findings on the heterogeneity in amino acid metabolism in cancer and how understanding the metabolic diversity of amino acids can be translated into more clinically relevant therapeutic interventions.

Keywords:  CP: Cancer; CP: Metabolism; amino acids; cancer metabolism; metabolic heterogeneity

DOI:  https://doi.org/10.1016/j.celrep.2025.115529
Cell Rep. 2025 Apr 03. pii: S2211-1247(25)00271-2. [Epub ahead of print]44(4): 115500

Comprehensive PTM profiling with SCASP-PTM uncovers mechanisms of p62 degradation and ALDOA-mediated tumor progression.

Zhan-Peng Lin, Guohong Gan, Xiao Xu, Chengwen Wen, Xin Ding, Xiang-Yu Chen, Kaijie Zhang, Wen-Yu Guo, Mingxin Lin, Yu-Yang Wang, Xi Chen, Changchuan Xie, Jinling Wang, Minjie Li, Chuan-Qi Zhong.

  Multiple post-translational modification (PTM) proteomics typically combines PTM enrichment with multiplex isobaric labeling and peptide fractionation. However, effective methods for sequentially enriching multiple PTMs from a single sample for data-independent acquisition mass spectrometry (DIA-MS) remain lacking. We present SDS-cyclodextrin-assisted sample preparation (SCASP)-PTM, an approach that enables desalting-free enrichment of diverse PTMs, including phosphopeptides, ubiquitinated peptides, acetylated peptides, glycopeptides, and biotinylated peptides. SCASP-PTM uses SDS for protein denaturation, which is sequestered by cyclodextrins before trypsin digestion, facilitating sequential PTM enrichment without additional purification steps. Combined with DIA-MS, SCASP-PTM quantifies the proteome, ubiquitinome, phosphoproteome, and glycoproteome in HeLa-S3 cell samples, identifying serine 28 phosphorylation as a key driver of poly(I:C)-induced p62 degradation. This method also quantifies PTMs in clinical tissue samples, revealing the critical role of ALDOA K330 ubiquitination/acetylation in tumor progression. SCASP-PTM offers a streamlined workflow for comprehensive PTM analysis in both basic research and clinical applications.

Keywords:  ALDOA; CP: Cancer; CP: Molecular biology; data-independent acquisition; diaPASEF; post-translational modifications

DOI:  https://doi.org/10.1016/j.celrep.2025.115500
Mol Biol Rep. 2025 Apr 09. 52(1): 375

The perspective of targeting cancer cell metabolism: combination therapy approaches.

Arezu Karimpur-Zahmatkesh, Mohammad Khalaj-Kondori.

  Cancer cells are considered the most adaptable for their metabolic status, which supports growth, survival, rapid proliferation, invasiveness, and metastasis in a nutrient-deficient microenvironment. Since the discovery of altered glucose metabolism (aerobic glycolysis), which is generally known as a part of metabolic reprogramming and an innate trait of cancer cells, in 1930 via Dr. Otto Warburg, numerous studies have endeavored to recognize various aspects of cancer cell metabolism and find new methods for efficiently eradicating described cells by targeting their energy metabolism. In this way, the outcomes have mainly been promising. Accordingly, outlining the related results will indeed assist us in making a definitive path for developing targeted therapy strategies based on cancer cell-altered metabolism. The present study reviews the key features of cancer cell metabolism and treatment strategies based on them. It emphasizes the importance of targeting cancer cell dysregulated metabolic pathways that influence the cell energy supply and manage cancer cell growth and survival. This trial also introduces a multimodal therapeutic strategy hypothesis, a potential next-generation combination therapy approach, and suggests interdisciplinary research to recognize the complexities of cancer metabolism and exploit them for designing more efficacious cancer therapeutic strategies.

Keywords:  Cancer; Combination therapy; Metabolic pathway; Multimodal approach; Targeted therapy

DOI:  https://doi.org/10.1007/s11033-025-10472-9
J Proteome Res. 2025 Apr 08.

MSIght: A Modular Platform for Improved Confidence in Global, Untargeted Mass Spectrometry Imaging Annotation.

Lauren Fields, Hannah N Miles, Alexis E Adrian, Elliot Patrenets, William A Ricke, Lingjun Li.

  Mass spectrometry imaging (MSI) has gained popularity in clinical analyses due to its high sensitivity, specificity, and throughput. However, global profiling experiments are often still restricted to LC-MS/MS analyses that lack spatial localization due to low-throughput methods for on-tissue peptide identification and confirmation. Additionally, the integration of parallel LC-MS/MS peptide confirmation, as well as histological stains for accurate mapping of identifications, presents a large bottleneck for data analysis, limiting throughput for untargeted profiling experiments. Here, we present a novel platform, termed MSIght, which automates the integration of these multiple modalities into an accessible and modular platform. Histological stains of tissue sections are coregistered to their respective MSI data sets to improve spatial localization and resolution of identified peptides. MS/MS peptide identifications via untargeted LC-MS/MS are used to confirm putative MSI identifications, thus generating MS images with greater confidence in a high-throughput, global manner. This platform has the potential to enable large-scale clinical cohorts to utilize MSI in the future for global proteomic profiling that uncovers novel biomarkers in a spatially resolved manner, thus widely expanding the utility of MSI in clinical discovery.

Keywords:  MALDI; Python; histology; imaging software; mass spectrometry imaging; multimodal imaging; spatial omics

DOI:  https://doi.org/10.1021/acs.jproteome.4c01140
Clin Proteomics. 2025 Apr 07. 22(1): 11

An integrated proteomic classifier to distinguish benign from malignant pulmonary nodules.

Bin Jia, Tingting Wang, Liangxuan Pan, Xiaoyao Du, Jing Yang, Fei Gao, Lujian Liao, Bianqin Guo, Junqiang Dong.

   BACKGROUND: Pulmonary nodule with diameters ranging 8-30 mm has a high occurrence rate, and distinguishing benign from malignant nodules can greatly improve the patient outcome of lung cancer. However, sensitive and specific liquid-biopsy methods have yet to achieve satisfactory clinical goals.
METHODS: We enrolled three cohorts and a total of 185 patients diagnosed with benign (BE) and malignant (MA) pulmonary nodules. Utilizing data-independent acquisition (DIA) mass spectrometry, we quantified plasma proteome from these patients. We then performed logistic regression analysis to classify benign from malignant nodules, using cohort 1 as discovery data set and cohort 2 and 3 as independent validation data sets. We also developed a targeted multi-reaction monitoring (MRM) method to measure the concentration of the selected six peptide markers in plasma samples.
RESULTS: We quantified a total of 451 plasma proteins, with 15 up-regulated and 5 down-regulated proteins from patients diagnosed as having malignant nodules. Logistic regression identified a six-protein panel comprised of APOA4, CD14, PFN1, APOB, PLA2G7, and IGFBP2 that classifies benign and malignant nodules with improved accuracy. In cohort 1, the area under curve (AUC) of the training and testing reached 0.87 and 0.91, respectively. We achieved a sensitivity of 100%, specificity of 40%, positive predictive value (PPV) of 62.5%, and negative predictive value (NPV) of 100%. In two independent cohorts, the 6-biomarker panel showed a sensitivity, specificity, PPV, and NPV of 96.2%, 35%, 65.8%, and 87.5% respectively in cohort 2, and 91.4%, 54.2%, 74.4%, and 81.3% respectively in cohort 3. We performed a targeted LC-MS/MS method to quantify plasma concentration of the six peptides and applied logistic regression to classify benign and malignant nodules with AUC of the training and testing reached 0.758 and 0.751, respectively.
CONCLUSIONS: Our study identified a panel of plasma protein biomarkers for distinguishing benign from malignant pulmonary nodules that worth further development into a clinically valuable assay.

Keywords:  Biomarker; Classification; Lung cancer; Plasma; Pulmonary nodule

DOI:  https://doi.org/10.1186/s12014-025-09532-w
J Chromatogr B Analyt Technol Biomed Life Sci. 2025 Mar 19. pii: S1570-0232(25)00116-3. [Epub ahead of print]1257 124564

Evaluation of ion source parameters and liquid chromatography methods for plasma untargeted metabolomics using orbitrap mass spectrometer.

Hailemariam Abrha Assress, Ahsan Hameed, Lindsay M Pack, Mario G Ferruzzi, Renny S Lan.

  Although untargeted metabolomics holds promise for study of metabolites in human health and disease, robust method development and optimization are needed to reduce potential analytical biases and to ensure comprehensive, high-throughput results. In this study, the effect of mass spectrometer (MS) ion source parameters on the signal reproducibility and number of metabolite annotations during untargeted metabolomics is shown. Furthermore, different mobile phase gradients and columns (five reversed phase (RP)-C18 and two hydrophilic interaction liquid chromatography (HILIC) columns) were evaluated for untargeted metabolomics of blood plasma extracts. Positioning the electrospray needle at the farthest on the Z-direction and the closest tested position on the Y-direction with respect to the mass spectrometry inlet produced the best signal reproducibility and the greatest number of metabolite annotations. Moreover, optimal ion source conditions included a positive spray voltage between 2.5 and 3.5 kV, a negative spray voltage between 2.5 and 3.0 kV, vaporization and ion transfer tube (ITT) temperature between 250 and 350 °C, 30 to 50 arbitrary units of sheath gas, and at least 10 auxiliary gas units. Despite the differences in chromatographic characteristics, the different RP columns assessed showed comparable performance in terms of number of metabolites annotated. For HILIC columns, a zwitterionic column demonstrated better performance than an amide column. Finally, as compared with use of a RP column alone, use of both the optimal RP and HILIC approaches expanded metabolome coverage: the number of metabolites annotated increased by 60 %. This study highlights the significance of fine-tuning the MS ion source parameters and optimizing chromatographic conditions on metabolome coverage during untargeted metabolomics of plasma samples.

Keywords:  ESI needle position; ESI parameters; HILIC columns; Liquid chromatography gradient; Reversed phase columns; Untargeted metabolomics

DOI:  https://doi.org/10.1016/j.jchromb.2025.124564
Methods Mol Biol. 2025 ;2922 197-207

Deep Proteome Profiling of Primary Skin Fibroblasts.

Sibilla Sander, Alexandre Leytens, Jörn Dengjel.

  Skin-derived primary cells and skin tissue are prime model systems to study molecular disease mechanisms on cell and tissue level. Cells and tissue can be cultivated ex vivo and protocols for both, simple 2D as well as 3D in vitro cultures reflecting different levels of complexity exist. Mass spectrometry (MS)-based proteomics is a prime approach to link molecular mechanisms to observable disease phenotypes. In this chapter, we describe in detail the analysis of 2D and 3D primary skin fibroblast cultures by MS. We focus on automated sample processing to increase throughput and usage of limited cell numbers to reduce costs. The described workflow supports the study of proteome regulation in large scale screening approaches, be it for drug discovery or in clinical studies.

Keywords:  Fibroblasts; Mass spectrometry; Proteomics; SP3; Skin; Spheroid

DOI:  https://doi.org/10.1007/978-1-0716-4510-9_15
Methods Mol Biol. 2025 ;2897 637-645

Seminal Plasma Proteomics Using Filter-Aided Sample Preparation.

Taylor Pini, Simon P de Graaf.

  The advent of proteomics has enabled the identification of individual proteins within seminal plasma. Here, we describe the steps required to isolate seminal plasma from semen and subsequently create a high-quality peptide mixture for bottom-up proteomic analysis using Filter-Aided Sample Preparation.

Keywords:  LC-MS/MS; Seminal plasma; accessory sex glands; mass spectrometry; semen; seminal fluid

DOI:  https://doi.org/10.1007/978-1-0716-4406-5_43
bioRxiv. 2025 Mar 24. pii: 2025.03.24.645021. [Epub ahead of print]

Quantitative Analysis of Coupled Carbon and Energy Metabolism for Lignin Carbon Utilization in Pseudomonas putida.

Nanqing Zhou, Rebecca A Wilkes, Xinyu Chen, Kelly P Teitel, James A Belgrave, Gregg T Beckham, Allison Z Werner, Yanbao Yu, Ludmilla Aristilde.

Soil Pseudomonas species, which can thrive on lignin-derived phenolic compounds, are widely explored for biotechnology applications. Yet, there is limited understanding of how the native metabolism coordinates phenolic carbon processing with cofactor generation. Here, we achieve quantitative understanding of this metabolic balance through a multi-omics investigation of Pseudomonas putida KT2440 grown on four common phenolic substrates: ferulate, p- coumarate, vanillate, and 4-hydroxybenzoate. Relative to succinate as a non-aromatic reference, proteomics data reveal >140-fold increase in proteins for transport and initial catabolism of each phenolic substrate, but metabolomics profiling reveals that bottleneck nodes in initial phenolic compound catabolism maintain more favorable cellular energy state. Up to 30-fold increase in pyruvate carboxylase and glyoxylate shunt proteins implies a metabolic remodeling confirmed by kinetic 13 C-metabolomics. Quantitative analysis by 13 C-fluxomics demonstrates coupling of this remodeling with cofactor production. Specifically, anaplerotic carbon recycling via pyruvate carboxylase promotes fluxes in the tricarboxylic acid cycle to provide 50-60% NADPH yield and 60-80% NADH yield, resulting in 2-fold higher ATP yield than for succinate metabolism; the glyoxylate shunt sustains cataplerotic flux through malic enzyme for the remaining NADPH yield. The quantitative blueprint elucidated here explains deficient versus sufficient cofactor rebalancing during manipulations of key metabolic nodes in lignin valorization.

DOI: https://doi.org/10.1101/2025.03.24.645021
bioRxiv. 2025 Mar 28. pii: 2025.03.24.644996. [Epub ahead of print]

omicsGMF: a multi-tool for dimensionality reduction, batch correction and imputation applied to bulk- and single cell proteomics data.

Alexandre Segers, Cristian Castiglione, Christophe Vanderaa, Elfride De Baere, Lennart Martens, Davide Risso, Lieven Clement.

The unprecedented speed and sensitivity of mass spectrometry (MS) unlocked large-scale applications of proteomics and even enabled proteome profiling of single cells. However, this fast-evolving field is hindered by a lack of scalable dimensionality reduction tools that can compensate for substantial batch effects and missingness across MS runs. Therefore, we present omicsGMF, a fast, scalable, and interpretable matrix factorization method, tailored for bulk and single-cell proteomics data. Unlike current workflows that sequentially apply imputation, batch correction, and principal component analysis, omicsGMF integrates these steps into a unified framework, dramatically enhancing data processing and dimensionality reduction. Additionally, omicsGMF provides robust imputation of missing values, outperforming bespoke state-of-the-art imputation tools. We further demonstrate how this integrated approach increases statistical power to detect differentially abundant proteins in the downstream data analysis. Hence, omicsGMF is a highly scalable approach to dimensionality reduction in proteomics, that dramatically improves many important steps in proteomics data analysis.

DOI: https://doi.org/10.1101/2025.03.24.644996
Cell Rep. 2025 Apr 08. pii: S2211-1247(25)00311-0. [Epub ahead of print]44(4): 115540

Unraveling the nexus: Genomic instability and metabolism in cancer.

Vaibhavi Gujar, Haojian Li, Tanya T Paull, Carola A Neumann, Urbain Weyemi.

  The DNA-damage response (DDR) is a signaling network that enables cells to detect and repair genomic damage. Over the past three decades, inhibiting DDR has proven to be an effective cancer therapeutic strategy. Although cancer drugs targeting DDR have received approval for treating various cancers, tumor cells often develop resistance to these therapies, owing to their ability to undergo energetic metabolic reprogramming. Metabolic intermediates also influence tumor cells' ability to sense oxidative stress, leading to impaired redox metabolism, thus creating redox vulnerabilities. In this review, we summarize recent advances in understanding the crosstalk between DDR and metabolism. We discuss combination therapies that target DDR, metabolism, and redox vulnerabilities in cancer. We also outline potential obstacles in targeting metabolism and propose strategies to overcome these challenges.

Keywords:  CP: Cancer; DNA damage response; DNA repair; cancer therapy; metabolism; redox metabolism; therapy resistance

DOI:  https://doi.org/10.1016/j.celrep.2025.115540
bioRxiv. 2025 Mar 24. pii: 2025.03.20.644425. [Epub ahead of print]

Dissecting Branch-Specific Unfolded Protein Response Activation in Drug-Tolerant BRAF-Mutant Melanoma using Data-Independent Acquisition Mass Spectrometry.

Lea A Barny, Jake N Hermanson, Sarah K Garcia, Philip E Stauffer, Lars Plate.

Cells rely on the Unfolded Protein Response (UPR) to maintain ER protein homeostasis (proteostasis) when faced with elevated levels of misfolded and aggregated proteins. The UPR is comprised of three main branches-ATF6, IRE1, and PERK-that coordinate the synthesis of proteins involved in folding, trafficking, and degradation of nascent proteins to restore ER function. Dysregulation of the UPR is linked to numerous diseases, including neurodegenerative disorders, cancer, and diabetes. Despite its importance, identifying UPR targets has been challenging due to their heterogeneous induction, which varies by cell type and tissue. Additionally, defining the magnitude and range of UPR-regulated genes is difficult because of intricate temporal regulation, feedback between UPR branches, and extensive cross-talk with other stress-signaling pathways. To comprehensively identify UPR-regulated proteins and determine their branch specificity, we developed a data-independent acquisition (DIA) liquid-chromatography mass spectrometry (LC-MS) pipeline. Our optimized workflow improved identifications of low-abundant UPR proteins and leveraged an automated SP3-based protocol on the Biomek i5 liquid handler for label-free peptide preparation. Using engineered stable cell lines that enable selective pharmacological activation of each UPR branch without triggering global UPR activation, we identified branch-specific UPR proteomic targets. These targets were subsequently applied to investigate proteomic changes in multiple patient-derived BRAF-mutant melanoma cell lines treated with a BRAF inhibitor (PLX4720, i.e., vemurafenib). Our findings revealed differential regulation of the XBP1s branch of the UPR in the BRAF-mutant melanoma cell lines after PLX4720 treatment, likely due to calcium activation, suggesting that the UPR plays a role as a non-genetic mechanism of drug tolerance in melanoma. In conclusion, the validated branch-specific UPR proteomic targets identified in this study provide a robust framework for investigating this pathway across different cell types, drug treatments, and disease conditions in a high-throughput manner.

DOI: https://doi.org/10.1101/2025.03.20.644425
Proteomics. 2025 Apr 10. e202400398

Peptide Property Prediction for Mass Spectrometry Using AI: An Introduction to State of the Art Models.

Jesse Angelis, Eva Ayla Schröder, Zixuan Xiao, Wassim Gabriel, Mathias Wilhelm.

  This review explores state of the art machine learning and deep learning models for peptide property prediction in mass spectrometry-based proteomics, including, but not limited to, models for predicting digestibility, retention time, charge state distribution, collisional cross section, fragmentation ion intensities, and detectability. The combination of these models enables not only the in silico generation of spectral libraries but also finds many additional use cases in the design of targeted assays or data-driven rescoring. This review serves as both an introduction for newcomers and an update for experienced researchers aiming to develop accessible and reproducible models for peptide property predictions. Key limitations of the current models, including difficulties in handling diverse post-translational modifications and instrument variability, highlight the need for large-scale, harmonized datasets, and standardized evaluation metrics for benchmarking.

Keywords:  deep learning; machine learning; mass spectrometry; peptide property prediction; proteomics

DOI:  https://doi.org/10.1002/pmic.202400398
J Mass Spectrom. 2025 May;60(5): e5134

Overcoming Analytical Challenges in Proximity Labeling Proteomics.

Haorong Li, Wan Nur Atiqah Binti Mazli, Ling Hao.

  Proximity labeling (PL) proteomics has emerged as a powerful tool to capture both stable and transient protein interactions and subcellular networks. Despite the wide biological applications, PL still faces technical challenges in robustness, reproducibility, specificity, and sensitivity. Here, we discuss major analytical challenges in PL proteomics and highlight how the field is advancing to address these challenges by refining study design, tackling interferences, overcoming variation, developing novel tools, and establishing more robust platforms. We also provide our perspectives on best practices and the need for more robust, scalable, and quantitative PL technologies.

Keywords:  biotinylation; mass spectrometry; proximity labeling

DOI:  https://doi.org/10.1002/jms.5134
bioRxiv. 2025 Mar 28. pii: 2025.03.26.645502. [Epub ahead of print]

Time-lapse tryptic digestion: a proteomic approach to improve sequence coverage of extracellular matrix proteins.

Fred Lee, Xinhao Shao, James M Considine, Yu Tom Gao, Alexandra Naba.

The extracellular matrix (ECM) is a complex and dynamic meshwork of proteins providing structural support to cells. It also provides biochemical signals governing cellular processes, including proliferation, adhesion, and migration. Alterations of ECM structure and/or composition have been linked to many pathological processes, including cancer and fibrosis. Over the past decade, mass-spectrometry-based proteomics has become the state-of-the-art method to profile the protein composition of ECMs. However, existing methods do not fully capture the broad dynamic range of protein abundances in the ECM. They also do not permit to achieve the high coverage needed to gain finer biochemical on ECM proteoforms ( e.g. , isoforms, post-translational modifications) and topographical information critical to better understand ECM protein functions. Here, we present the development of a time-lapsed proteomic pipeline using limited tryptic proteolysis and sequential release of peptides over time. This experimental pipeline was combined with data-independent acquisition mass spectrometry and the assembly of a custom matrisome spectral library to enhance peptide-to-spectrum matching. This pipeline shows superior protein identification, peptide-to-spectrum matching, and significantly increased sequence coverage against standard ECM proteomic pipelines. Exploiting the spatio-temporal resolution of this method, we further demonstrate how time-resolved 3-dimensional peptide mapping can identify protein regions differentially susceptible to trypsin, which may aid in identifying protein-protein interaction sites.

DOI: https://doi.org/10.1101/2025.03.26.645502
Expert Rev Proteomics. 2025 Apr 09.

Acoustic ejection Mass spectrometry: the potential for personalized medicine.

Chang Liu.

   INTRODUCTION: The emergence of personalized medicine (PM) has shifted the focus of healthcare from the traditional 'one-size-fits-all' approach to strategies tailored to individual patients, accounting for genetic, environmental, and lifestyle factors. Acoustic ejection mass spectrometry (AEMS) is a novel technology that offers a robust and scalable platform for high-throughput MS readout. AEMS achieves analytical speeds of one sample per second while maintaining high data quality, broad compound coverage, and minimal sample preparation, making it an invaluable tool for PM.Areas covered: This article explores the potential of AEMS in critical PM applications, including therapeutic drug monitoring (TDM), proteomics, metabolomics, and mass spectrometry imaging. AEMS simplifies conventional workflows by minimizing sample preparation, enhancing automation compatibility, and enabling direct analysis of complex biological matrices.
EXPERT OPINION: Integrating AEMS with orthogonal separation techniques such as differential mobility spectrometry (DMS) further addresses challenges in isomer discrimination, expanding the platform's analytical capabilities. Additionally, the development of high-throughput data processing tools could further enable AEMS to accelerate the development of personalized medicine.

Keywords:  High-throughput analysis; Metabolomics; Personalized medicine; Proteomics; acoustic ejection mass spectrometry; mass spectrometry imaging; therapeutic drug monitoring

DOI:  https://doi.org/10.1080/14789450.2025.2491356
Sci Data. 2025 Apr 10. 12(1): 596

A reference database enabling in-depth proteome and PTM analysis of mouse immune cells.

Devon Siemes, Hannah Voss, Federica Benvenuti, Francesca Simoncello, Dominik Kopczynski, Bente Siebels, Hartmut Schlüter, Laxmikanth Kollipara, Albert Sickmann, Daniel Robert Engel, Olga Shevchuk.

Spectral libraries fulfill multiple functions in biological and analytical applications. For biologists, these libraries provide a valuable resource to verify the presence and abundance of proteins or pathways within a selected cell type thus determine the feasibility of further experiments. Despite advances, existing libraries are incomplete and provide researchers only a limited amount of information. To address this, we introduce the reference database - Spectral Library of Immune Cells (SpLICe), a resource covering B-cells, CD4 and CD8 T-cells, macrophages and dendritic cells containing nearly 9,000 protein groups and 110,346 proteotypic peptides. Additionally, the database provides data on > 20,000 post-translationally modified proteotypic peptides (oxidation, phosphorylation, methylation, acetylation, deamidation and N-glycosylation) across the selected immune cell populations. SpLICe supports the quantification of more than half of total murine proteins annotated by UniProtKB/Swiss-Prot, enabling monitoring of selected proteins or pathways from Reactome pathways and Gene Ontology databases. The platform provides relative protein abundances and supports the generation of targeted mass spectrometry assays by identifying and scoring proteotypic peptides.

DOI: https://doi.org/10.1038/s41597-025-04829-9
Mol Cell Proteomics. 2025 Apr 07. pii: S1535-9476(25)00063-5. [Epub ahead of print] 100965

Quantitative proteomics and phosphoproteomics analysis of patient-derived ovarian cancer stem cells.

Giulia Franciosa, Valentina Nieddu, Chiara Battistini, Miriam Caffarini, Michela Lupia, Nicoletta Colombo, Nicola Fusco, Jesper V Olsen, Ugo Cavallaro.

  High-grade serous ovarian carcinoma (HGSOC) is the deadliest gynecologic cancer. Key to the progression and ultimate lethality of this subtype is the intra-tumoral heterogeneity (ITH), which is defined as the coexistence of different cell types and populations within a single tumor. Among those, ovarian cancer stem cells (OCSCs) are a distinct subpopulation of tumor cells endowed with stem-like properties, which can survive current standard therapies, resulting in tumor recurrence. Here, we generated ex vivo primary OCSC-enriched three-dimensional (3D) spheres from ten distinct treatment naive patient-derived adherent (2D) cultures. We used state-of-the-art quantitative mass spectrometry to characterize the molecular events associated with OCSCs by analyzing their phosphoproteome and proteome. Our data revealed a stemness-related protein signature, shared within a heterogeneous patient cohort, which correlates with chemo-refractoriness in a clinical proteomics dataset. Moreover, we identified targetable deregulated kinases and aberrant PDGF receptor activation in OCSCs. Pharmacological inhibition of PDGFR in adherent OC cells reduced the stemness potential, measured by sphere formation assay. Overall, we provide a valuable resource to identify new OCSC markers and putative targets for OCSC-directed therapies.

Keywords:  HGSOC; Ovarian cancer; cancer stem cells; phosphoproteomics; proteomics

DOI:  https://doi.org/10.1016/j.mcpro.2025.100965
Anal Chem. 2025 Apr 10.

Towards Generalizable In Silico Predictions of Differential Ion Mobility Using Machine Learning and Customized Fingerprint Engineering.

Cailum M K Stienstra, Christopher R M Ryan, Daniel Demczuk, Justine R Bissonnette, Anish Arjuna, J Larry Campbell, W Scott Hopkins.

Differential mobility spectrometry (DMS), a tool for separating chemically similar species (including isomers), is readily coupled to mass spectrometry to improve selectivity in analytical workflows. DMS dispersion curves, which describe the dynamic mobility experienced by an ion in a gaseous environment, show the maximum ion transmission for an analyte through the DMS instrument as a function of the separation voltage (SV) and compensation voltage (CV) conditions. To date, there exists no fast, general prediction tool for the dispersion behavior of ions. Here, we demonstrate a machine learning (ML) model that achieves generalized dispersion prediction using an in silico feature addition pipeline. We employ a data set containing 1141 dispersion curve measurements of anions and cations recorded in pure N2 environments and in N2 environments doped with 1.5% methanol (MeOH). Our feature addition pipeline can compute 1591 RDKit and Mordred descriptors using only SMILES codes, which are then normalized to sampled molecular distributions (n = 100 000) using cumulative density functions (CDFs). This tool can be thought of as a "learned" feature fingerprint generation pipeline, which could be applied to almost any molecular (bio)cheminformatics tasks. Our best performing model, which for the first time considers solvent-modified environments, has a mean absolute error (MAE) of 2.1 ± 0.2 V for dispersion curve prediction, a significant improvement over the previous state-of-the-art work. We use explainability techniques (e.g., SHAP analysis) to show that this feature addition pipeline is a semideterministic process for feature sets, and we discuss "best practices" to understand feature sets and maximize model performance. We expect that this tool could be used for prescreening to accelerate or even automate the use of DMS in complex analytical workflows (e.g., 2D LC×DMS separation) and perform automated identification of transmission windows and increase the "self-driving" potential of the instrument. We make our models available as a free and accessible tool at https://github.com/HopkinsLaboratory/DispersionCurveGUI.

DOI: https://doi.org/10.1021/acs.analchem.5c00737
Pharmacol Ther. 2025 Apr 07. pii: S0163-7258(25)00061-0. [Epub ahead of print] 108849

G-protein coupled receptors in metabolic reprogramming and cancer.

Songyeon Ahn, Benny Abraham Kaipparettu.

  G-protein coupled receptors (GPCR) are one of the frequently investigated drug targets. GPCRs are involved in many human pathophysiologies that lead to various disease conditions, such as cancer, diabetes, and obesity. GPCR receptor activates multiple signaling pathways depending on the ligand and tissue type. However, this review will be limited to the GPCR-mediated metabolic modulations and the activation of relevant signaling pathways in cancer therapy. Cancer cells often have reprogrammed cell metabolism to support tumor growth and metastatic plasticity. Many aggressive cancer cells maintain a hybrid metabolic status, using both glycolysis and mitochondrial metabolism for better metabolic plasticity. In addition to glucose and glutamine pathways, fatty acid is a key mitochondrial energy source in some cancer subtypes. Recently, targeting alternative energy pathways like fatty acid beta-oxidation (FAO) has attracted great interest in cancer therapy. Several in vitro and in vivo experiments in different cancer models reported encouraging responses to FAO inhibitors. However, due to the potential liver toxicity of FAO inhibitors in clinical trials, new approaches to indirectly target metabolic reprogramming are necessary for in vivo targeting of cancer cells. This review specifically focused on free fatty acid receptors (FFAR) and β-adrenergic receptors (β-AR) because of their reported significance in mitochondrial metabolism and cancer. Further understanding the pharmacology of GPCRs and their role in cancer metabolism will help repurpose GPCR-targeting drugs for cancer therapy and develop novel drug discovery strategies to combine them with standard cancer therapy to increase anticancer potential and overcome drug resistance.

Keywords:  Free fatty acid receptors; Mitochondria metabolism; cancer; Β-Adrenergic receptors

DOI:  https://doi.org/10.1016/j.pharmthera.2025.108849
J Proteome Res. 2025 Apr 10.

Online Alkaline-pH Reversed-Phase Nanoelectrospray-Tandem Mass Spectrometry Complements Traditional Phosphoproteomic Analysis via Influencing Charge State Distribution of Phosphopeptides.

Yuqiu Wang, Jing Gao, Wenfan Xie, Minchu Tang, Xin Chen, Lv Chen, Hongxu Chen, Zhicheng Yang, Qiang Gao, Yansheng Liu, Hu Zhou.

  Phosphorylation (O-linked and N-linked) plays an important role in biological functions and cell signaling. Here, we employed a one-dimensional online alkaline-pH reversed-phase nanoelectrospray-tandem mass spectrometry (alkaline-pH-MS/MS) for the investigation of global phosphorylation. In this method, phosphopeptides were separated on a nanoflow C18 column with an alkaline-pH gradient and directly introduced to the mass spectrometer through nanoelectrospray ionization. Although the phosphosites and phosphopeptides identified by alkaline-pH-MS/MS were slightly lower than those of traditional online low-pH reversed-phase tandem MS (low-pH-MS/MS), these two methods were highly complementary to each other. This alkaline-pH-MS/MS may affect the actual polarity and CSD of phosphopeptides, consequently improving the identification of multiply phosphorylated peptides. Moreover, alkaline-pH-MS/MS was compatible with other peptide fractionation and phosphopeptide enrichment techniques, such as offline high-pH or low-pH reversed-phase liquid chromatography fractionation. The complementarity of alkaline-pH-MS/MS and low-pH-MS/MS was further demonstrated by the tandem mass tag (TMT)-based quantitative phosphoproteomic analysis of five pairs of hepatocellular carcinoma (HCC) tumors and normal adjacent tissues (NATs). Furthermore, unique information on significantly changed phosphosites was observed by alkaline-pH-MS/MS. This study provided an alternative and complementary tool for global analysis of both O- and N-phosphoproteome, which may be beneficial for the discovery of phosphoproteins with significant biological functions.

Keywords:  alkaline-pH-MS/MS; charge state; fractionation; mass spectrometry; phosphoproteomics

DOI:  https://doi.org/10.1021/acs.jproteome.4c01091
Anal Chim Acta. 2025 May 15. pii: S0003-2670(25)00250-8. [Epub ahead of print]1351 343856

Isomer-resolved characterization of acylcarnitines reveals alterations in type 2 diabetes.

Xiangyu Gao, Chunli Liu, Xue Zhao.

   BACKGROUND: Acylcarnitines (CARs) are metabolites of fatty acids that play crucial roles in various cellular energy metabolism pathways. The structural diversity of CAR species arises from several modifications localized on the fatty acyl chain and there is currently a lack of reports characterizing these detailed structures. High-performance liquid chromatography (HPLC)-electrospray mass spectrometry (ESI-MS) is the common tool for CARs analysis.
RESULTS: In this study, we improved the MS detection signals of CARs by adding NH4HCO3 as buffer in the mobile phase of LC system. We demonstrated that electron activated dissociation (EAD) on the ZenoTOF 7600 system is capable of localizing the hydroxyl group and methyl branching position in CARs. The benzophenone Paternò-Büchi (PB) reaction was used for derivatizing the carbon-carbon double bond (CC). The capability of profiling CARs with detailed structural information was demonstrated by analyzing complex lipid extracts from mouse plasma. Our results also provided visualization of isomers composition, including branched chain isomers of CAR 4:0 and CAR 5:0 and CC location isomers of unsaturated CARs. Notably, we observed significant changes in the relative compositions of branched-chain isomers of CAR 5:0 and CC location isomers of several unsaturated CARs in mouse plasma samples from type 2 diabetes (T2D) compared to normal controls, suggesting their potential as diagnostic indicators for T2D.
SIGNIFICANCE: In this work, we enhanced the limit of detection for acylcarnitine species by incorporating ammonium bicarbonate into the LC system. The CC positions in the acyl chain of CARs were identified using Paternò-Büchi (PB) derivatization coupled with tandem mass spectrometry. Modifications such as methyl branching and hydroxyl groups along the acyl chain were localized through Electron-Activated Dissociation (EAD) on the Zeno-TOF 7600 system.

Keywords:  Acylcarnitine; Electron activated dissociation; Mass spectrometry; Paternò–büchi

DOI:  https://doi.org/10.1016/j.aca.2025.343856