bims-mascan 2025-07-13 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–07–13
25 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

A Ready-to-Use Data Analysis Pipeline for BioID Experiments Using Data-Dependent or Data-Independent Acquisition Mass Spectrometry.
Triple labeling of metabolites for metabolome analysis (TLEMMA): a stable isotope labeling approach for metabolite identification and network reconstruction.
ProMix: Enhancing Protein Quantification through Experimental Design and Statistical Normalization.
An Aryl Diazonium-Based Enrichment Method for Deep Coverage of Monomethyllysine Proteomics.
Fatty acid synthase-derived lipid stores support breast cancer metastasis.
Metabolic reprogramming in osteosarcoma.
Decoding the Impact of Isolation Window Selection and QuantUMS Filtering in DIA-NN for DIA Quantification of Peptides and Proteins.
Recent advances in lung cancer lipidomics: Analytical techniques and their applications.
MAMSI: Integration of Multiassay Liquid Chromatography-Mass Spectrometry Metabolomics Data Using Multiview Machine Learning.
A photoactivatable chemical lipidomics approach for local sphingolipid metabolic analysis.
SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation.
Detection and Quantification of Biotinylated Proteins for TurboID-Based Proximity Labeling Mass Spectrometry in Arabidopsis.
An algorithm for peptide de novo sequencing from a group of SILAC labeled MS/MS spectra.
Stable Isotope In-Vivo Labeling for Mass-Spectrometry Identification of Paternal Metabolites Transferred from Sperm to Oocyte During Fertilization.
The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.
A 96-Well Ultrafiltration Approach for the High-Throughput Proteome Analysis of Extracellular Vesicles Isolated From Conditioned Medium.
Deep Exploration of the Immunopeptidome of a Pancreatic Cancer Cell Line: Implications for Clinical Immunopeptidomics and Immunotherapy.
Mass Spectrometry Reveals Novel Features of Tubulin Polyglutamylation in the Flagellum of Trypanosoma brucei.
Untargeted metabolomics revealed urinary metabolic pattern for discriminating prostate cancer from benign prostatic hyperplasia in Chinese participants.
Collisional Cross-Section Prediction for Multiconformational Peptide Ions with IM2Deep.
Targeting alveolar epithelial cell metabolism in pulmonary fibrosis: Pioneering an emerging therapeutic strategy.
Mid-Infrared Photothermal Imaging of Fatty Acid Desaturation Reaction in Cancer Cells.
Purinosomes and Lysosomes Interact to Maintain the Purine Pools.
Proteomic landscape of colorectal cancer derived liver metastasis reveals three distinct phenotypes with specific signalling and enhanced survival.
Optimizing Proximity Proteomics on the EvoSep-timsTOF LC-MS System.

Methods Mol Biol. 2025 ;2953 311-322

A Ready-to-Use Data Analysis Pipeline for BioID Experiments Using Data-Dependent or Data-Independent Acquisition Mass Spectrometry.

Sara Dufour, Teresa Mendes Maia, Laura Van Moortel, Louis Delhaye, Sven Eyckerman, Simon Devos.

  Proximity labeling combined with mass spectrometry (MS)-based proteomics has become an essential tool in interactomics. Proximity-dependent biotin identification (BioID) is a versatile method for identifying interacting and neighboring proteins within their native cellular environments. In BioID, the target (bait) protein is fused to a mutated BirA tag that biotinylates vicinal proteins (preys), which are subsequently purified and analyzed using LC-MS/MS. While data-dependent acquisition (DDA) has been the standard for MS-based proteomics, it suffers from bias toward abundant peptides, leading to missing data. In contrast, data-independent acquisition (DIA) improves the identification of low-abundant peptides, providing a more comprehensive proteomic analysis. This chapter outlines a data analysis workflow for BioID experiments in both DDA and DIA modes, using data from a study on the glucocorticoid receptor (GR) as an example. Data analysis was performed using MaxQuant, FragPipe, and DIA-NN, with downstream processing and statistical analysis conducted in R, incorporating SAINTq to enhance the reliability of bait-prey interaction identification.

Keywords:  DIA-MS; DIA-NN; FragPipe; Glucocorticoid receptor; SAINTq; TurboID

DOI:  https://doi.org/10.1007/978-1-0716-4694-6_20
Plant J. 2025 Jul;123(1): e70333

Triple labeling of metabolites for metabolome analysis (TLEMMA): a stable isotope labeling approach for metabolite identification and network reconstruction.

Yonghui Dong, Liron Feldberg, Yi Wang, Uwe Heinig, Ilana Rogachev, Asaph Aharoni.

  Metabolite identification remains a significant challenge in mass spectrometry (MS)-based metabolomics research. To address this issue, we combined a triple-labeled precursor-based isotope tracing approach (TLEMMA) with high-resolution liquid chromatography-MS for metabolite identification and metabolic network construction. As a demonstration, we fed duckweed (Spirodela polyrhiza) with four forms of phenylalanine (Phe) including unlabeled Phe, Phe-5H2, Phe-8H2, and Phe-13C9 15N1. The distinctive isotopic pattern obtained from MS spectra greatly facilitated data processing, enabling comprehensive extraction of all Phe-derived metabolites. Importantly, the labeling pattern allowed efficient metabolite identification by significantly reducing the number of structural and positional isomers. Using this approach, 47 phenylalanine-derived metabolites were putatively identified. To further evaluate the efficiency of metabolite identification in relation to the number of differently labeled precursors used, we compared the number of filtered candidates based solely on the labeling patterns obtained from unlabeled, single, dual, and triple isotope-labeled precursor tracing experiments. On average, TLEMMA eliminates the number of false candidates by 99.1% compared with unlabeled samples, 95% compared with single isotope-labeled samples, and 66.7% compared with dual isotope-labeled samples. This significant reduction in the number of false positives, along with the ability to identify previously unreported metabolites, demonstrates the power of TLEMMA in advancing the field of metabolomics and metabolic network reconstruction.

Keywords:  metabolite identification; plant metabolomics; stable isotope labeling; technical advance

DOI:  https://doi.org/10.1111/tpj.70333
J Proteome Res. 2025 Jul 07.

ProMix: Enhancing Protein Quantification through Experimental Design and Statistical Normalization.

Huaying Fang, Mei-Chiung Shih, Lihua Jiang, Felipe da Veiga Leprevost, Ruiqi Jian, Alexey I Nesvizhskii, Michael P Snyder, Hua Tang.

  Isobaric labeling of biospecimens followed by mass spectrometry (MS) has become the method of choice for large-scale, untargeted, quantitative proteomic profiling. However, subtle variation in experimental conditions can amplify sample variability and introduce systematic biases. Motivated by the challenges and opportunities arose in a recent proteogenomic study, we developed ProMix, a flexible analytical framework designed to improve protein normalization by leveraging two key experimental design features: (1) the inclusion of an additional reference sample to serve as an internal standard, and (2) the incorporation of replicates of each specimen. ProMix can utilize either or both features. Through applications to both simulated and real data sets, we demonstrate the improved performance of ProMix and highlight the advantages of the enhanced experimental design strategies.

Keywords:  experimental design; linear mixed model; multiplex mass spectrometry; protein normalization; quantitative proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.4c01108
Curr Protoc. 2025 Jul;5(7): e70170

An Aryl Diazonium-Based Enrichment Method for Deep Coverage of Monomethyllysine Proteomics.

Lufeng Yan, Manqian Zheng, Mingzhu Fan, Shan Feng, Mingxuan Wu.

  Lysine methylation is an important post-translational modification (PTM) that regulates diverse cellular processes. Proteomic analysis is a robust method to study PTMs, but a lack of good enrichment tool limits current understanding of lysine methylation. In a previous study, we demonstrated that aryl diazonium containing 2,6-dimethoxy substitutions can conjugate monomethyllysine-modified (Kme1) peptides with high selectivity and that the reaction is reversible under acidic conditions, allowing the release of Kme1 peptides. Therefore, such a warhead has great potential for the enrichment of low-abundance Kme1 peptides from biological samples. Here, we report the preparation of aryl diazonium-functionalized resins as enrichment tools and their application for mass spectrometry-based proteomic studies of Kme1 peptides. In this procedure, aniline with a PEG linker as a precursor is synthesized and then coupled to a hydrophilic solid phase. After preparation of tryptic peptides from cell samples, the aniline groups on the resin are converted to aryl diazonium for Kme1 peptide capture. After sufficient treatment and washing, the covalent linkage is broken under acidic conditions to release the original Kme1 peptides from the resin. Finally, the enriched samples are processed by mass spectrometry scanning and data analysis to identify ∼10,000 Kme1 sites in cell or mouse tissue samples. Herein, we demonstrate an efficient Kme1 peptides enrichment strategy for deep coverage of the Kme1 proteome in biological samples. © 2025 Wiley Periodicals LLC. Basic Protocol 1: Preparation of aryl aniline-functionalized Sepharose resin Basic Protocol 2: Preparation of tryptic peptides from biological samples Basic Protocol 3: Enrichment of Kme1 peptides from whole-cell lysate tryptic peptides Basic Protocol 4: Data acquisition and analysis for Kme1 proteomics.

Keywords:  aryl diazonium; mass spectrometry; monomethyllysine; proteomics; tryptic peptides

DOI:  https://doi.org/10.1002/cpz1.70170
Cancer Metab. 2025 Jul 10. 13(1): 35

Fatty acid synthase-derived lipid stores support breast cancer metastasis.

Chaylen Andolino, Eylem Kulkoyluoglu Cotul, Zilin Xianyu, Yun Li, Divya Bhat, Mitchell Ayers, Kimberly K Buhman, Stephen D Hursting, Michael K Wendt, Dorothy Teegarden.

  Lipid accumulation is associated with breast cancer metastasis. However, the mechanisms underlying how breast cancer cells increase lipid stores and their functional role in disease progression remain incompletely understood. Herein we quantified changes in lipid metabolism and characterized cytoplasmic lipid droplets in metastatic versus non-metastatic breast cancer cells. 14C-labeled palmitate was used to determine differences in fatty acid (FA) uptake and oxidation. Despite similar levels of palmitate uptake, metastatic cells increase lipid accumulation and oxidation of endogenous FAs compared to non-metastatic cells. Isotope tracing also demonstrated that metastatic cells support increased de novo lipogenesis by converting higher levels of glutamine and glucose into the FA precursor, citrate. Consistent with this, metastatic cells displayed increased levels of fatty acid synthase (FASN) and de novo lipogenesis. Genetic depletion or pharmacologic inhibition of FASN reduced cell migration, survival in anoikis assays, and in vivo metastasis. Finally, global proteomic analysis indicated that proteins involved in proteasome function, mitotic cell cycle, and intracellular protein transport were reduced following FASN inhibition of metastatic cells. Overall, these studies demonstrate that breast cancer metastases accumulate FAs by increasingde novo lipogenesis, storing TAG as cytoplasmic lipid droplets, and catabolizing these stores to drive several FAO-dependent steps in metastasis.

Keywords:  Breast cancer; FASN; Fatty acid synthase; Fatty acids; Lipid droplet; Lipid metabolism; Lipid storage; Mass spectrometry; Metastasis; TNBC

DOI:  https://doi.org/10.1186/s40170-025-00404-3
Pediatr Discov. 2023 Sep;1(2): e18

Metabolic reprogramming in osteosarcoma.

Yulu Shi, Xiaohan Yue, Qing Luo.

  Tumor cells undergo metabolic reprogramming to meet their energy and anabolic demands to maintain their malignant phenotype. Activation of oncogenes and deletion of tumor suppressors promotes metabolic reprogramming in cancer by directly or indirectly regulating enzymatic activities associated with metabolic pathways. Metabolic reprogramming in tumor cells mainly involves the glycolytic pathway, pentose phosphate pathway, serine synthesis pathway, enhanced glutamine metabolism or fatty acid anabolism, and abnormal mitochondrial oxidative phosphorylation (OXPHOS). The tricarboxylic acid (TCA) cycle is the central pathway of mitochondrial OXPHOS, and glucose, amino acid and fatty acid metabolism are associated with the TCA cycle. Metabolic abnormalities and rewiring of metabolic pathways are also present in Osteosarcoma (OS). The abnormal metabolic pattern in OS is associated with cell proliferation, migration, invasion and drug resistance. This review summarizes the current studies on glycolysis, amino acid metabolism, lipid synthesis and the TCA cycle related to OS.

Keywords:  OXPHOS; amino acid; fatty acid; glycolytic; metabolism; osteosarcoma

DOI:  https://doi.org/10.1002/pdi3.18
J Proteome Res. 2025 Jul 08.

Decoding the Impact of Isolation Window Selection and QuantUMS Filtering in DIA-NN for DIA Quantification of Peptides and Proteins.

Jorge da Cruz Moschem, Bianca Carla Silva Campitelli de Barros, Solange Maria de Toledo Serrano, Alison Felipe Alencar Chaves.

  Proteomic studies using data-independent acquisition (DIA) have gained momentum in all fields of biology. Search engines are evolving to keep up with the latest developments in instrument technology. DIA-NN is the most popular software for DIA analysis under an academic use license. The QuantUMS algorithm in DIA-NN improves quantification quality control by calculating three scores (protein group MaxLFQ quality, empirical quality, and quantity quality) that assess the agreement between MS1 and MS2 features. Here, we show that applying specific cutoffs to these scores can significantly impact the results. To enable you to make a more informed decision about what represents a reasonable trade-off (identification and quantification), we evaluated the impact of different combinations of the scores on data acquired using different isolation windows and a mixture of two species with a known ratio. To test consistency and reproducibility across the six different versions of DIA-NN, we compared them and found high reproducibility except for version 1.9. We show that filtering by QuantUMS scores removes proteins with low abundances and high coefficients of variation. Finally, we developed the QC4DIANN Shiny application in the R language for interactive quality control automation.

Keywords:  DIA; QuantUMS; isolation window; mass spectrometry; quantification

DOI:  https://doi.org/10.1021/acs.jproteome.5c00009
Clin Chim Acta. 2025 Jul 08. pii: S0009-8981(25)00349-3. [Epub ahead of print]577 120470

Recent advances in lung cancer lipidomics: Analytical techniques and their applications.

Hamada A A Noreldeen.

  Lipidomics provides a detailed qualitative and quantitative analysis of lipids in biofluids, cells, and tissues, playing a crucial role in biomarker discovery, lipid reprogramming, and precision medicine. This approach has been extensively used to assess global metabolic changes in organisms, aiding in the identification of potential cancer biomarkers, understanding cancer progression, and uncovering the underlying pathophysiology. Lung cancer, being the leading cause of cancer-related deaths worldwide, suffers from poor prognosis and challenges in early detection. While abnormalities in the lipidome have been observed in lung cancer, the comprehensive lipid reprogramming and mechanisms involved remain unclear. Thus, there is a pressing need to explore these mechanisms and identify biomarkers for early detection of different types of lung cancer. This review focuses on recent advances in lung cancer lipidomics, utilizing techniques such as "Shotgun" lipidomics, LC-MS-based lipidomics, GC-MS-based lipidomics, and imaging lipidomics for biomarker discovery, cancer subtyping, and metabolic reprogramming. Additionally, the lipidomics analysis process, including sample preparation and data interpretation, is summarized.

Keywords:  Biomarker discovery; LC-MS-based lipidomics; Lipid reprogramming; Lipidomics; Lung cancer; Metabolic reprogramming

DOI:  https://doi.org/10.1016/j.cca.2025.120470
Anal Chem. 2025 Jul 10.

MAMSI: Integration of Multiassay Liquid Chromatography-Mass Spectrometry Metabolomics Data Using Multiview Machine Learning.

Lukas Kopecky, Caroline J Sands, María Gómez-Romero, Shivani Misra, Elizabeth J Want, Timothy M D Ebbels.

Liquid chromatography-mass spectrometry (LC-MS) is a commonly used analytical technique in untargeted metabolomics. However, the diverse chemical and physical properties of metabolites often require the use of several different analytical assays for broad metabolome coverage. Conventionally, each assay is analyzed separately, but this fails to capture interassay relationships, making multiassay biomarker discovery and data interpretation difficult. Here we propose a workflow to integrate multiassay metabolomics data, designed to enable biomarker discovery and elucidation of unknown metabolites. We employ a multiblock-partial least-squares model (MB-PLS) coupled with multiblock variable importance in projection to estimate the importance of predictors to the outcome variable. Then we cluster the selected predictors and compare them to groups defined by their structural properties based on retention time and mass-to-charge ratio. To demonstrate and evaluate the approach, we used three multiassay data sets predicting biological sex, Alzheimer's disease status, and blood bilirubin levels as the outcomes of interest. The MB-PLS models outperformed single-assay models in both classification and regression tasks, indicating that modeling interblock relationships enabled an improved estimate of phenotypic outcome. Additionally, the MB-PLS models shed valuable insight into each data block's contribution to the predicted outcome. Our workflow enabled us to determine a set of potential cross-assay biomarkers. Following putative annotation, the majority of these and their signs of association agreed with results previously reported in the literature. Our workflow has the potential to benefit the metabolomics community and beyond as it offers interpretable integrative analysis of multiassay LC-MS data and facilitates discovery of potential biomarkers.

DOI: https://doi.org/10.1021/acs.analchem.5c01327
Methods. 2025 Jul 08. pii: S1046-2023(25)00149-5. [Epub ahead of print]

A photoactivatable chemical lipidomics approach for local sphingolipid metabolic analysis.

Xiaoxue Wang, Zhiyi Zhang, Shuwei Tian, Suihan Feng.

  In eukaryotic cells, lipid metabolism is tightly regulated depending on the subcellular localization, which is essential for maintaining lipid homeostasis. However, understanding compartmentalized lipid metabolism remains challenging due to limited availability of suitable techniques. In this study, we present a chemical lipidomics approach that combines photoactivatable probes with high resolution mass spectrometry and stable-isotope labelling to analyze lipid dynamics at subcellular resolution. We applied this method to analyze the metabolism of 1-deoxysphingolipid (DoxSL), a non-canonical lipid species linked to various metabolic diseases and neuropathy, whose metabolism remains largely unexplored. Using the photoactivatable probes, we selectively delivered 1-deoxysphinganine, a key DoxSL intermediate, to mitochondria upon photo-illumination and subsequently analyzed its local metabolic products over time. Our data show that most 1-deoxysphinganine delivered to mitochondria is rapidly converted into 1-deoxyceramides, while only a small fraction forms oxidized products. Further lipidomic analysis revealed that 1-deoxyceramides are transported to the extracellular space and that DoxSL is also present in mouse and human serum samples. In summary, we developed novel probes to track lipid dynamics with high spatiotemporal resolution in a non-invasive manner and provided new insights into sphingolipid metabolism.

Keywords:  1-Deoxysphingolipid; Chemical lipidomics; Lipid metabolism; Photoactivatable probes

DOI:  https://doi.org/10.1016/j.ymeth.2025.07.002
Brief Bioinform. 2025 Jul 02. pii: bbaf333. [Epub ahead of print]26(4):

SingleFrag: a deep learning tool for MS/MS fragment and spectral prediction and metabolite annotation.

Maribel Pérez-Ribera, Muhammad Faizan-Khan, Roger Giné, Josep M Badia, Alexandra Junza, Oscar Yanes, Marta Sales-Pardo, Roger Guimerà.

  Metabolite and small molecule identification via tandem mass spectrometry (MS/MS) involves matching experimental spectra with prerecorded spectra of known compounds. This process is hindered by the current lack of comprehensive reference spectral libraries. To address this gap, we need accurate in silico fragmentation tools for predicting MS/MS spectra of compounds for which empirical spectra do not exist. Here, we present SingleFrag, a novel deep learning tool that predicts individual fragments separately, rather than attempting to predict the entire fragmentation spectrum at once. Our results demonstrate that SingleFrag surpasses state-of-the-art in silico fragmentation tools, providing a powerful method for annotating unknown MS/MS spectra of known compounds. As a proof of concept, we successfully annotate three previously unidentified compounds frequently found in human samples.

Keywords:  Graph neural networks; In silico fragmentation; MS/MS; Machine learning; Metabolite

DOI:  https://doi.org/10.1093/bib/bbaf333
Methods Mol Biol. 2025 ;2953 115-126

Detection and Quantification of Biotinylated Proteins for TurboID-Based Proximity Labeling Mass Spectrometry in Arabidopsis.

Sumudu S Karunadasa, TaraBryn S Grismer, Wenxuan Zhai, Andres V Reyes, Shou-Ling Xu.

  TurboID-based proximity labeling, combined with mass spectrometry (PL-MS), has been widely adopted for studies in both plants and animals for a variety of applications. In this approach, an engineered biotin ligase, TurboID, fused to a bait protein, biotinylates proteins in close proximity to the bait protein following biotin treatment, allowing their subsequent isolation using streptavidin beads and identification by mass spectrometry. This chapter provides a detailed step-by-step protocol for sample preparation using Arabidopsis tissues, along with an overview of data analysis strategies and software tools for the reliable identification and quantification of biotinylated proteins.

Keywords:  LC-MS/MS; Label-free quantification (LFQ); Protein interaction networks; Proximity labeling mass spectrometry (PL-MS); Stable isotope labeling in Arabidopsis (SILIA); Streptavidin enrichment; TurboID

DOI:  https://doi.org/10.1007/978-1-0716-4694-6_8
J Bioinform Comput Biol. 2025 Jun 26. 2550007

An algorithm for peptide de novo sequencing from a group of SILAC labeled MS/MS spectra.

Fang Han, Kaizhong Zhang.

  Shotgun proteomics coupled with high-performance liquid chromatography and mass spectrometry has been instrumental in identifying proteins in complex mixtures. Effective computational approaches are required to automate the spectra interpretation process to handle the vast amount of data collected in a single Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) run. De novo sequencing from MS/MS has emerged as a vital technology for peptide sequencing in proteomics. To enhance the accuracy and practicality of de novo sequencing, previous algorithms have utilized multiple spectra to identify peptide sequences. Here, our study focuses on de novo sequencing of multiple tandem mass spectra of peptides with stable isotope labeling with amino acids in cell culture (SILAC) by incorporating different isotope-labeled amino acids into newly synthesized proteins. Multiple MS/MS spectra for the same peptide sequence are produced by the spectrometer after the SILAC samples undergo processing by LC-MS/MS shotgun proteomics. Taking into consideration the factors such as retention time and precursor ion mass, we aim to identify the peptide sequence with specific SILAC modifications and their locations. To do so, we propose de novo sequencing algorithms to compute the potential candidate peptide sequence by using similarity scores, followed by refinement algorithms to evaluate them. We also use real experimental data to test the algorithms.

Keywords:  Bioinformatics; SILAC; computational proteomics; de novo sequencing; mass spectrometry; multiple MS/MS spectra

DOI:  https://doi.org/10.1142/S0219720025500076
J Vis Exp. 2025 Jun 17.

Stable Isotope In-Vivo Labeling for Mass-Spectrometry Identification of Paternal Metabolites Transferred from Sperm to Oocyte During Fertilization.

Luca Roberto, Gilda Cobellis, Concetta Ambrosino, Pietro Campiglia, Mariano Stornaiuolo.

Paternal contribution to embryo genetics has so far been limited to allelic sequences for decades. A decade of results suggests, instead, that epigenetic factors-DNA methylation, histone modifications, chromosomal organization, and regulatory RNAs-play crucial roles in paternal inheritance and influence embryonic development and zygote gene expression. Together with nucleic acids, sperm metabolome (lipids, carbohydrates, free amino acids) may act as epigenetic signal. This protocol aims to identify sperm-to-oocyte transferred metabolites by mass spectrometry. The procedure includes stable isotope labeling of sperm cells with 2H2O or U13C Glucose to trace, by metabolomics, paternal metabolites transferred to the oocyte during fertilization. The overall goal of the protocol is to reveal the role of paternal sperm metabolome in offspring's susceptibility to dysmetabolism, and it may be adapted to provide insights into how environmental conditions, such as diet and exposure to pollutants, alter paternal metabolic messages, potentially affecting offspring's health and predisposition to diabetes, obesity, and cardiovascular diseases.

DOI: https://doi.org/10.3791/67765
J Mass Spectrom. 2025 Aug;60(8): e5157

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI-Powered Mass Spectrometry and Multiomics Integration.

Aivett Bilbao.

The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS-based "omics" research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI-driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS-based multiomics data. While centered on MS-based omics, this holistic AI-driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI-driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems-level understanding of biological processes and accelerating a myriad of biodiscoveries.

DOI: https://doi.org/10.1002/jms.5157
J Extracell Vesicles. 2025 Jul;14(7): e70103

A 96-Well Ultrafiltration Approach for the High-Throughput Proteome Analysis of Extracellular Vesicles Isolated From Conditioned Medium.

Jarne Pauwels, Tessa Van de Steene, Freya De Muyer, Danaë De Pauw, Femke Baeke, Sven Eyckerman, Kris Gevaert.

  Extracellular vesicles (EVs), nanoscale vesicles that are secreted by cells, are critical mediators of intercellular communication and play a crucial role in diverse pathologies such as cancer development. Therefore, EVs are regarded as having high potential in the clinic, both for diagnostic and therapeutic applications. Unfortunately, EVs reside in complex biofluids and their consistent preparation at sufficient purity for mass spectrometry-based proteomics has proven to be challenging, especially when increased high-throughput is required. Here, we describe the incorporation of our previously reported filter-aided EV enrichment (FAEVEr) strategy for the separation of EVs from conditioned medium, from harvest to proteomic analysis completely to a streamlined 96-well format. We compared our approach with ultracentrifugation, the most widely used method for EV enrichment, in terms of protein identifications, consistency, reproducibility and overall performance, including the invested time, resources and required expertise. In addition, our results show that including relative high percentages of Tween-20, a mild detergent, markedly improves the final purity of the EV proteome by removing the bulk of non-EV proteins (e.g., serum proteins) and significantly increases the number of identified transmembrane proteins. Moreover, our FAEVEr 96-well strategy improves the overall reproducibility with a consistent number of protein identifications and decreased number of missing values across replicates. This promotes the validity and comparability between results, which is essential in both a clinical and research setting, where consistency is paramount.

Keywords:  300 kDa MWCO 96‐well ultrafiltration; Tween‐20; conditioned medium; proteomics

DOI:  https://doi.org/10.1002/jev2.70103
Mol Cell Proteomics. 2025 Jul 08. pii: S1535-9476(25)00129-X. [Epub ahead of print] 101030

Deep Exploration of the Immunopeptidome of a Pancreatic Cancer Cell Line: Implications for Clinical Immunopeptidomics and Immunotherapy.

Mohammadreza Dorvash, Patricia T Illing, Nathan P Croft, Sri H Ramarathinam, Anthony W Purcell.

Pancreatic ductal adenocarcinoma (PDAC), with its devastating prognosis and limited treatment options, demands innovative therapeutic strategies. T-cell-based immunotherapy has shown promise for many cancers, including PDAC, but is limited by our knowledge of the breadth of cancer-specific T-cell epitopes available. Thus, a comprehensive assessment of the immunopeptidome of PDAC is essential to pave the way for the effective design of immunotherapy and related interventions. In this study, we immunoaffinity purified Human Leukocyte Antigen (HLA) class I-bound peptides from the Panc1 cell line grown in the absence and presence of cytokine stimulation. These peptides were subjected to an off-line high-pH reversed-phase (HPH-RP) fractionation prior to data acquisition. We demonstrate that HPH-RP fractionation followed by data-dependent acquisition (DDA) is a relatively simple and reliable technique that expands the depth of coverage of the PDAC immunopeptidome, allowing the identification of over 22,500 canonical HLA-bound peptides. In addition, the complementary separation by HPH-RP improved the identification confidence, particularly in the case of co-fragmenting precursors in data-independent acquisition (DIA) workflows. This strategy facilitated the identification of a high number of cancer-testis antigen- (CTA-) derived immunopeptides. However, given that fractionation is typically associated with an adsorptive loss, it is impractical to apply HPH-RP on often minuscule clinical specimens and biopsies. Thus, we explored the feasibility of immunopeptidome analysis with cellular inputs as low as 1 million cells (equivalent to approximately 1 mg of tissue) using either a ZenoSWATH DIA interpreted using a spectral library derived from the HPH-RP strategy, or an optimised DDA workflow on the SCIEX ZenoToF 7600 system. Both of these approaches enabled robust detection of CTA-derived and other potentially clinically actionable immunopeptides even at the lowest cellular inputs. We discuss the relative merits of both acquisition strategies and how they can form the basis for future clinical translational immunopeptidomics approaches to screen tumor antigen presentation in low cellular input PDAC biopsies and provide new opportunities for target identification in immune-based therapies. Data are available via the ProteomeXchange with identifiers PXD054360 and PXD054417.

DOI: https://doi.org/10.1016/j.mcpro.2025.101030
J Proteome Res. 2025 Jul 09.

Mass Spectrometry Reveals Novel Features of Tubulin Polyglutamylation in the Flagellum of Trypanosoma brucei.

Marija Nisavic, Thibault Chaze, Serge Bonnefoy, Carsten Janke, Philippe Bastin, Mariette Matondo, Julia Chamot-Rooke.

  Tubulin polyglutamylation is a key feature of eukaryotic cilia and flagella that is essential for their function. The diversity of enzymes catalyzing polyglutamylation with different specificities inspired the hypothesis of the tubulin code. In the protist parasite Trypanosoma brucei, nine different glutamylase enzymes are potentially involved in tubulin glutamylation. To decipher the trypanosome tubulin code generated by this diversity, we aimed at determining tubulin glutamylation patterns by robust mass spectrometry (MS)-based proteomics. MS approaches exist for many post-translational modifications but none for the chemically complex polyglutamylation. We therefore optimized a nanoLC-MS/MS pipeline from sample preparation to data analysis using synthetic peptides for quantification. Our approach enabled the quantification of C-terminal tubulin peptides with up to 11 supplementary glutamates on α-, and five on β-tubulin from the flagellum of T. brucei. In addition to the known E445 on α- and E435 on β-, a novel glutamylation site of β-tubulin was discovered at E438. Furthermore, our data revealed an increase in enzymatic detyrosination with increasing length of the glutamate chains, especially for α-tubulin. This indicates cross-talk between the modifications and different detyrosination rates of the two tubulin types. Our efficient analytical pipeline advances understanding of the tubulin code in T. brucei.

Keywords:  Trypanosoma brucei; flagellum; polyglutamylation; post-translational modifications; tubulins

DOI:  https://doi.org/10.1021/acs.jproteome.5c00107
Front Oncol. 2025 ;15 1604169

Untargeted metabolomics revealed urinary metabolic pattern for discriminating prostate cancer from benign prostatic hyperplasia in Chinese participants.

Chenye Tang, Xiao Guo, Qian Zou, Xinghao Wang, Jian Sheng, Dan Shen, Chun Sun, Shuo Li, Ruilin Shen.

   Introduction: Precise screening and discriminating of prostatic hyperplasia (PH) could avoid unnecessary biopsy and overdiagnosis. However, the metabolic pattern of patients with prostatic hyperplasia in Chinese population is rarely reported.
Methods: Urine samples of Chinese participants with prostate cancer (PCa), benign prostatic hyperplasia (BPH) and non-prostate diseases (NPD) were detected with four ultra-performance liquid chromatography/tandem mass spectrometric (UPLC-MS/MS) methods to profile the metabolic disturbance.
Results: In patients with PH, the most significant dysregulation was observed in metabolites categorized as lipid or amino acid, especially those involved in histidine metabolism, purine metabolism, tryptophan metabolism and tyrosine metabolism. For discrimination BPH from PCa, apart from previously reported metabolites related to phospholipid metabolism or tryptophan metabolism, metabolites of dipeptides and androgenic steroids, such as leucylhydroxyproline and etiocholanolone glucuronide, also exhibited potential to discriminate PCa from BPH.
Conclusion: This study conducts precise detection of urinary metabolomic pattern for patients with benign prostatic hyperplasia or prostate cancer, and could inform their potential application as discriminant biomarkers.

Keywords:  discriminant biomarkers; prostate cancer; prostatic hyperplasia; untargeted metabolomics; urinary metabolomics

DOI:  https://doi.org/10.3389/fonc.2025.1604169
Anal Chem. 2025 Jul 08.

Collisional Cross-Section Prediction for Multiconformational Peptide Ions with IM2Deep.

Robbe Devreese, Alireza Nameni, Arthur Declercq, Emmy Terryn, Ralf Gabriels, Francis Impens, Kris Gevaert, Lennart Martens, Robbin Bouwmeester.

Peptide collisional cross-section (CCS) prediction is complicated by the tendency of peptide ions to exhibit multiple conformations in the gas phase. This adds further complexity to downstream analysis of proteomics data, for example for identification or quantification through feature finding. Here, we present an improved version of IM2Deep that is trained on a carefully curated data set to predict CCS values of multiconformational peptides. The training data is derived from a large and comprehensive set of publicly available data sets. This comprehensive training data set together with a tailored architecture allows for the accurate CCS prediction of multiple peptide conformational states. Furthermore, the enhanced IM2Deep model also retains high precision for peptides with a single observed conformation. IM2Deep is publicly available under a permissive open-source license at https://github.com/compomics/IM2Deep.

DOI: https://doi.org/10.1021/acs.analchem.5c01142
Front Cell Dev Biol. 2025 ;13 1608750

Targeting alveolar epithelial cell metabolism in pulmonary fibrosis: Pioneering an emerging therapeutic strategy.

Tengkun Dai, Yidan Liang, Xin Li, Jiamin Zhao, Guangqin Li, Qihong Li, Lin Xu, Juanjuan Zhao.

  Pulmonary fibrosis (PF) is a chronic and progressive lung disease, characterized by excessive deposition of fibrotic connective tissue within the lungs. Advances in transcriptomics, proteomics, and metabolomics have enhanced our understanding of PF's pathogenesis. Recent studies have indicates that metabolic abnormalities in alveolar epithelial cells (AECs) play a central role in the pathogenesis of PF. Metabolic reprogramming of AECs affects cellular senescence, endoplasmic reticulum stress, and oxidative stress in AECs, while also promoting fibrotic progression through various signaling pathways. This review focuses on therapeutic strategies targeting the metabolism of AECs. It comprehensively explores the role of metabolic pathways through glucose metabolism, lipid metabolism, and amino acid metabolism in the pathogenesis of PF, aiming to provide novel theoretical support and research perspectives for preventing and treating pulmonary fibrosis.

Keywords:  alveolar epithelial cells; amino acid metabolism; energy metabolism; glucose metabolism; lipid metabolism; metabolic reprogramming; pathogenesis; pulmonary fibrosis

DOI:  https://doi.org/10.3389/fcell.2025.1608750
bioRxiv. 2025 Jul 05. pii: 2025.07.02.662881. [Epub ahead of print]

Mid-Infrared Photothermal Imaging of Fatty Acid Desaturation Reaction in Cancer Cells.

Xinyan Teng, Tianyu Xia, Jiaze Yin, Mingsheng Li, Jianpeng Ao, Guangrui Ding, Chinmayee V Prabhu Dessai, Ana Maria Isac, Daniela Matei, Hongjian He, Ji-Xin Cheng.

Direct visualization of metabolic conversions within living systems is essential for understanding metabolic activities yet challenging due to the absence of reaction-specific reporters and the limited sensitivity of current imaging modalities. Herein, we report an approach to monitor fatty acids (FAs) desaturation, primarily catalyzed by stearoyl-CoA desaturase, in cancer cells using deuterium (D)-labeled palmitic acid (PA-d31) as the reaction-specific reporter and mid-infrared photothermal (MIP) microscopy as the bond-selective imaging modality. The desaturation of PA-d31 produced a peak at 2246 cm-1 in the cell-silent region, corresponding to the stretching vibration of unsaturated C-D bonds (D-C=C-D) in unsaturated fatty acids. Penalized least squares fitting was employed to remove water background for enhancing the visibility of this peak. Our study revealed heterogeneous spatial distributions of both saturated FAs and their desaturated metabolites within lipid droplet pools in cancer cells. Furthermore, we observed an increase in fatty acid unsaturation level in OVCAR5 cells under cisplatin-induced stress. By directly visualizing fatty acid desaturation, this study offers new insights into fatty acid metabolism and opens avenues for evaluating new therapeutic strategies targeting fatty acid metabolism.

DOI: https://doi.org/10.1101/2025.07.02.662881
Int J Biochem Cell Biol. 2025 Jul 05. pii: S1357-2725(25)00098-6. [Epub ahead of print] 106830

Purinosomes and Lysosomes Interact to Maintain the Purine Pools.

Yubing Liu, Vidhi Pareek, Dipankar Bhowmik, Xin Zhang, Stephen J Benkovic.

  Purines are the building blocks of DNA/RNA and hence essential metabolites. While the contributions of external purine salvage as well as the de novo purine biosynthesis (DNPB) have been widely studied, the contribution of lysosome mediated DNA/RNA digestion and external reabsorption into the cytosol remains unknown. Here, we address that question as well as the role of lysosome-mediated purine recycling and its coordination with DNPB in maintaining total purine pools in human cancer cell lines. By combining in-cell stable isotope incorporation assay with quantitative metabolomics we show: cellular uptake of external purines and their internal generation are equivalent; an upregulation in lysosome biogenesis that functions in response to purine deficiency caused by methotrexate (MTX) and lometrexol (LTX) treatment. This leads to increased RNA digestion as visualized by a newly developed intracellular RNA-FRET oligo assay. Interestingly, downregulation of lysosomal RNase activity through knockdown of RNAseT2 increased RNA accumulation and a compensating increase in DNPB.

Keywords:  RNASET2; de novo purine biosynthesis; lysosome; mTORC1; purine salvage; purinosome

DOI:  https://doi.org/10.1016/j.biocel.2025.106830
Mol Cell Proteomics. 2025 Jul 03. pii: S1535-9476(25)00125-2. [Epub ahead of print] 101026

Proteomic landscape of colorectal cancer derived liver metastasis reveals three distinct phenotypes with specific signalling and enhanced survival.

Paula Nissen, Nadezhda V Popova, Antonia Gocke, Daniel J Smit, Geoffrey Yuet Mun Wong, Matthew J McKay, Thomas J Hugh, Kerstin David, Hartmut Juhl, Hannah Voß, Jens U Marquardt, Björn Nashan, Hartmut Schlüter, Mark P Molloy, Manfred Jücker.

  Colorectal carcinoma is a major global disease with the second highest mortality rate among carcinomas. The liver is the most common site for metastases which portends a poor prognosis. Nonetheless, considerable heterogeneity of colorectal cancer liver metastases (CRC-LM) exists, evidenced by varied recurrence and survival patterns in patients undergoing curative-intent resection. Our understanding of the basis for this biological heterogeneity is limited. We investigated this by proteomic analysis of 152 CRC-LM obtained from three different medical centres in Germany and Australia using mass spectrometry based differential quantitative proteomics. The proteomics data of the individual cohorts were harmonized through batch-effect correction algorithms to build a large multi-center cohort. Applying ConsensusClusterPlus to the proteome data yielded three distinct CRC-LM phenotypes (referred to as CRLM-SD, CRLM-CA and CRLM-OM). The CRLM-SD (splice-driven) phenotype showed higher abundance of key regulators of alternative splicing as well as extracellular matrix proteins commonly associated with tumour cell growth. The CRLM-CA (complement-associated) phenotype was characterized by a higher abundance of proteins involved in the classical pathway part of the complement system including the membrane attack complex proteins and those with anti-thrombotic activity. The CRLM-OM (oxidative metabolic) phenotype showed higher abundance of proteins involved in various metabolic pathways including amino acids and fatty acids metabolism, which correlated in the literature with advanced proliferation of metastases and increased recurrence. Patients classified as CRLM-OM had a significantly lower overall survival in comparison to CRLM-CA patients. Finally, we identified a set of prognosis-associated biomarkers for each group including EpCAM, CEACAM1, CEACAM5 and CEACAM6 for CRLM-SD, DCN, TIMP3 and OLFM4 for CRLM-CA and FMO3, CES2 and AGXT for CRLM-OM. In summary, the discovery of three proteomic subgroups associated with distinct signalling pathways and survival of the CRC-LM patients provides a novel classification for risk stratification, prognosis and potentially novel therapeutic targets in CRC-LM.

Keywords:  ECM; EpCAM; alternative splicing; colorectal cancer; complement system; liver metastases; prognosis; proteomics; signalling

DOI:  https://doi.org/10.1016/j.mcpro.2025.101026
Proteomics. 2025 Jul 11. e70010

Optimizing Proximity Proteomics on the EvoSep-timsTOF LC-MS System.

Julia Kitaygorodsky, Brendon Seale, Vesal Kasmaeifar, Reuben Samson, Zhen-Yuan Lin, Martina Tersigni, Saya Sedighi, Cassandra J Wong, Anne-Claude Gingras.

  Proximity-dependent biotinylation (BioID) is a powerful means of exploring the cellular environments in which proteins reside. Expressing a protein of interest (bait) fused to a biotin ligase and adding biotin induces the covalent biotinylation of proximal partners (preys), which are recovered on streptavidin beads and identified by MS. However, a major technical limitation of BioID is peptide carryover into subsequent MS runs. This is typically mitigated via lengthy intersample wash cycles, which lowers throughput considerably. The aim of this study was to optimize BioID sample acquisition using an EvoSep LC system coupled to a timsTOF mass spectrometer, which has higher throughput and sensitivity than our current system, with less carryover. Our efforts resulted in an ∼15-fold increase in throughput using the 60 samples-per-day gradient with better sensitivity, and identifying nearly double the proteins found by our previously standardized workflow. Significance scoring also revealed more sensitive detection of high-confidence proximal interactions (∼1.5-fold) for five well-characterized baits, validating the new experimental workflow. Importantly, carryover was extremely limited, even without intersample washing, and limited to abundant proteins that are easily filtered during data analyses. Without washing, the newly optimized method can process 60 samples per day, using half of the sample amount previously required. SUMMARY: Proximity-dependent biotinylation (PDB) coupled with MS is a powerful approach to characterize subcellular protein localization. However, the carry-over of peptides from the abundant proteins into subsequent MS runs is problematic. While this was previously mitigated by lengthy wash cycles of the chromatography column, this ultimately lowered throughput. The introduction of the EvoSep chromatography system and more sensitive MS instrumentation has enabled robust and fast analysis with lower sample amounts and minimal carry-over. To date, there has been no systematic evaluation of this EvoSep-timsTOF instrumentation for PDB, nor a direct comparison to a previously standardized workflow. This study compares the identifications between these acquisition setups, recommends sample loading and gradient settings for the EvoSep-timsTOF, and investigates the high-confidence proximal interactors identified. The results highlight the necessity of optimization of scoring approaches for PDB alongside faster MS methods to maximize recovery of known high-confidence proximal interactors. Importantly, the EvoSep-timsTOF system substantially increases the effective throughput of MS acquisition, as washing between samples could be eliminated without compromising the recovery of bona fide proximal interactors, likely due to both carry-over reduction from both the EvoSep chromatography system and the decreased sample load.

Keywords:  BioID; EvoSep; Significance Analysis of INTeractome; cytoskeleton; interactions; protein–protein interactions; proximity‐dependent biotinylation; spectral counts; timsTOF

DOI:  https://doi.org/10.1002/pmic.70010