bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2025–12–28
sixteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. MoSAIC: An Integrated and Modular Workflow for Confident Analysis of Protein Post-Translational Modification Landscapes.
  2. Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput.
  3. Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC-MS Plasma Proteomics.
  4. Cross-platform clinical proteomics using the Charité open standard for plasma proteomics (OSPP).
  5. A Polymeric Nanocarrier Platform for Rapid and Precise Fatty Acid Tracing in Cells.
  6. A Perspective on Unintentional Fragments and Their Impact on the Dark Metabolome, Untargeted Profiling, Molecular Networking, Public Data, and Repository Scale Analysis.
  7. Simple, Fast, and Reliable Analysis of Label-Free Proteomics Data With the Proteomics Eye (ProtE).
  8. Assessing the Performance of Mass Spectrometry Search Strategies in Identifying Translational Errors Using PDX Proteomics Data.
  9. Proteome-Wide Multipoint Internal Calibration Curves for Evaluating Peptide-Level Linearity in Relative Quantitative Proteomics.
  10. Comparative Analysis of Lysine-Specific Peptidases for Optimizing Proteomics Workflows.
  11. Optimized Secretome Sample Preparation From High Volume Cell Culture Media for LC-MS/MS Proteomic Analysis.
  12. Streamlining feature elaboration and statistics analysis in metabolomics: the GetFeatistics R-package.
  13. Untargeted metabolomics and lipidomics to study autophagy induction in mouse embryonic fibroblasts.
  14. Perspectives in computational mass spectrometry: recent developments and key challenges.
  15. Identification of diagnostic biomarkers for relapsing-remitting multiple sclerosis in plasma by mass spectrometry-based proteomics.
  16. Benchmarking Software for DDA-PASEF Immunopeptidomics.
  1. Mol Cell Proteomics. 2025 Dec 24. pii: S1535-9476(25)00601-2. [Epub ahead of print] 101502
    MoSAIC: An Integrated and Modular Workflow for Confident Analysis of Protein Post-Translational Modification Landscapes.
    Yuanwei Xu, Lijun Chen, T Mamie Lih, Yingwei Hu, Hui Zhang.
      Investigating multiple protein post-translational modifications (PTMs) is critical for unraveling the complexities of protein regulation and the dynamic interplay among PTMs, a growing focus in proteomics. However, simultaneous analysis of diverse PTMs remains a significant technical challenge, as existing workflows struggle to balance throughput, sensitivity, and reproducibility, particularly when sample amounts are limited. To address these limitations, we present MoSAIC, a multi-PTM workflow integrating co-enrichment strategies, multiplexing, fractionation, hybrid data acquisition, and unified data analysis, optimized for clinically relevant biological samples. This approach targets phosphorylation, glycosylation, acetylation, and ubiquitination, enabling comprehensive interrogation of these modifications simultaneously. Compared to the traditional CPTAC workflow, MoSAIC doubles PTM coverage (4 vs. 2 PTMs) while maintaining the same instrument time (24 MS runs), achieving increased identifications of PTM-modified peptides. By leveraging fractionation and tandem mass tag (TMT) labeling, we achieved concurrent identification and quantification of PTM-specific peptides from the same sample, enhancing throughput and data consistency. This robust workflow addresses key limitations in multi-PTM proteomics, providing a cost-effective and efficient platform to advance biological and clinical research.
    Keywords:  Acetylation; Data-dependent acquisition (DDA); Data-independent acquisition (DIA); Glycosylation; Mass spectrometry (MS); PTM crosstalk; Phosphorylation; Post-translational modifications (PTMs); Proteomics workflow; Tandem mass tag (TMT); Ubiquitination
    DOI:  https://doi.org/10.1016/j.mcpro.2025.101502
  2. Proteomics. 2025 Dec 26. e70093
    Performance Characteristics of Zeno Trap Scanning DIA for Sensitive and Quantitative Proteomics at High Throughput.
    Ludwig R Sinn, Ziyue Wang, Claudia P Alvarez, Anjali Chelur, Ihor Batruch, Patrick Pribil, Daniela Ludwig, Stephen Tate, Jose Castro-Perez, Christoph B Messner, Vadim Demichev, Markus Ralser.
      Proteomic experiments, particularly those addressing dynamic proteome properties, time series, or genetic diversity, require the analysis of large sample numbers. Despite significant advancements in proteomic technologies in recent years, further improvements are needed to accelerate measurement and enhance proteome coverage and quantitative performance. Previously, we demonstrated that incorporating a scanning MS2 dimension into data-independent acquisition (DIA) methods (Scanning SWATH, or more generally scanning DIA), but also ion trapping, improves analytical depth and quantitative performance, especially in proteomic methods using fast chromatography. Here, we evaluate the scanning DIA approach combined with ion trapping via the Zeno trap in a method termed ZT Scan DIA, using a ZenoTOF 7600+ instrument (SCIEX). Applying this method to established proteome standards across various analytical setups, enabling intermediate to high sample throughput, we observed a 30%-40% increase in identified precursors. This enhancement extended to overall protein identification and precise quantification. Furthermore, ZT Scan DIA effectively eliminated quantitative bias, as demonstrated by its ability to deconvolute proteomes in multi-species mixtures. We propose that ZT Scan DIA can be used for a broad range of applications in proteomics, particularly in studies requiring high quantitative precision with low sample input and high-throughput workflows.
    Keywords:  SWATH; data‐independent acquisition; high‐throughput; mass spectrometry; proteomics
    DOI:  https://doi.org/10.1002/pmic.70093
  3. J Proteome Res. 2025 Dec 21.
    Automated Mag-Net Enrichment Unlocks Deep and Cost-Effective LC-MS Plasma Proteomics.
    Erkka Järvinen, Xiaonan Liu, Markku Varjosalo, Salla Keskitalo.
      Plasma is an ideal material for proteomics due to its diverse protein content reflecting physiological and pathological states and its compatibility with minimally invasive sampling. Deep proteomic profiling of plasma is limited by high-abundant proteins that mask the detection of low-abundant proteins. To overcome this, we compared five plasma protein enrichment methods, Mag-Net, ENRICHplus, ENRICHiST, EasySep, and EXONET, against neat plasma using LC-MS proteomics. All five methods substantially increased protein identifications, with Mag-Net, ENRICHplus, EasySep, and EXONET yielding up to 4200 proteins per sample, over 7-fold more than neat plasma, using a 44 min gradient on the Evosep One and data-independent acquisition on the timsTOF Pro 2. These methods enriched extracellular vesicle-associated proteins while effectively depleting high-abundant proteins. To further enhance performance and scalability, we optimized the Mag-Net protocol by increasing the plasma-to-bead ratio and automated the workflow, including Evotip loading, on the Biomek i5 liquid handler. The automated Mag-Net, combined with the Orbitrap Astral mass spectrometer, yielded up to 4500 proteins per sample with a throughput of 100 samples per day. The workflow demonstrated high reproducibility and a remarkably low total cost of just a few dollars per sample. Newer enrichment methods (Proteonano, P2-iST Plasma, and P2) showed improved plasma proteome coverage compared with Mag-Net but are likely to incur higher costs. The streamlined Mag-Net enrichment strategy enables affordable, scalable, high-throughput LC-MS plasma proteomics, supporting biomarker discovery across large cohorts.
    Keywords:  Biomek; DIA-NN; EXONET; Mag-Net; automation; dia-PASEF; extracellular vesicles (EVs); mass spectrometry; neat plasma; plasma proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00420
  4. Nat Commun. 2025 Dec 22. 16(1): 11377
    Cross-platform clinical proteomics using the Charité open standard for plasma proteomics (OSPP).
    Ziyue Wang, Vadim Farztdinov, Ludwig Roman Sinn, Pinkus Tober-Lau, Daniela Ludwig, Anja Freiwald, Fatma Amari, Kathrin Textoris-Taube, Agathe Niewienda, Anna Sophie Welter, Alan An Jung Wei, Luise Luckau, Florian Kurth, Matthias Selbach, Johannes Hartl, Michael Mülleder, Markus Ralser.
      The role of plasma and serum proteomics in characterizing human disease, identifying biomarkers, and advancing diagnostic technologies is rapidly increasing. However, there is an ongoing need to improve proteomic workflows in terms of accuracy, reproducibility, and cost-effectiveness, and to achieve cross-platform transferability. Based on large serum and plasma proteome studies, we generate the Charité Open Peptide Standard for Plasma Proteomics (OSPP), an open, versatile peptide internal standard for targeted and untargeted mass spectrometry-based proteomic studies. The OSPP includes 211 concentration-matched stable-isotope-labeled peptides selected for consistent quantification across a large number of plasma and serum proteome studies, and synthetic accessibility. We show they are consistently quantified across serum and EDTA, citrate, and heparin plasma using multiple LC-MS platforms. Despite being selected for technical parameters, the OSPP peptides represent proteins that function in a wide range of biological processes, are used in routine clinical tests, or are targets of FDA-approved drugs, making OSPP able to serve as an expandable clinical marker panel. We demonstrate the utility of OSPP in a COVID-19 inpatient cohort study for improving analytical performances, for cross-platform alignment of proteomic data, disease stratification, and biomarker discovery.
    DOI:  https://doi.org/10.1038/s41467-025-67264-9
  5. ACS Appl Bio Mater. 2025 Dec 22.
    A Polymeric Nanocarrier Platform for Rapid and Precise Fatty Acid Tracing in Cells.
    Michael P Vincent, Abigail E Ellis, Lisa M DeCamp, Susan M Kitchen-Goosen, Thomas J Rogers, Anna R Tarach, Christine Isaguirre, Shelby E Compton, Evan C Lien, Kelsey S Williams, Russell G Jones, Ryan D Sheldon.
      Stable isotope tracing provides insights into metabolism by tracking the movement of isotopically labeled precursors through metabolic networks. Fatty acid tracers, such as uniformly labeled 13C-palmitate, are used to study lipid biosynthesis, energy storage, and/or signaling. These tracers are complexed with BSA to improve solubility; yet, this approach is limited by transport bottlenecks, toxicity, and immunogenicity. Here, we developed biodegradable nanocarriers that improve hydrophobic tracer delivery and benchmarked performance against BSA with metabolomics and lipidomics. Nanocarriers accumulated U-13C-palmitate to higher intracellular levels, and more rapidly, than BSA-conjugated controls. Once inside the cell, nanocarrier-delivered tracers exhibited first-order depletion kinetics, ensuring predictable and efficient metabolism. In contrast, BSA produced delayed or biphasic tracer depletion due to transport limitations, which hindered the bioavailability. Entrance of nanocarrier-delivered U-13C-palmitate into the cellular metabolic network manifested through 13C-labeled desaturated and elongated fatty acids and incorporation into complex lipids without material-mediated aberrations. Our results demonstrate that nanocarrier-assisted tracing captures key metabolic trends with enhanced labeling while overcoming limitations of BSA-mediated delivery. This versatile, customizable platform enables opportunities for metabolic tracing in complex systems.
    Keywords:  BSA; lipidomics; metabolism; metabolomics; nanocarrier; nanoparticle; palmitate; tracing
    DOI:  https://doi.org/10.1021/acsabm.5c01770
  6. JACS Au. 2025 Dec 22. 5(12): 5828-5850
    A Perspective on Unintentional Fragments and Their Impact on the Dark Metabolome, Untargeted Profiling, Molecular Networking, Public Data, and Repository Scale Analysis.
    Yasin El Abiead, Ipsita Mohanty, Shipei Xing, Adriano Rutz, Vincent Charron-Lamoureux, Tito Damiani, Wenyun Lu, Gary J Patti, Nicola Zamboni, Oscar Yanes, Pieter C Dorrestein.
      In/postsource fragments (ISFs) arise during electrospray ionization or ion transfer in mass spectrometry when molecular bonds break, generating ions that can complicate data interpretation. Although ISFs have been recognized for decades, their contribution to untargeted metabolomicsparticularly in the context of the so-called "dark matter" (unannotated MS or MS/MS spectra) and the "dark metabolome" (unannotated molecules)remains unsettled. This ongoing debate reflects a central tension: while some caution against overinterpreting unidentified signals lacking biological evidence, others argue that dismissing them too quickly risks overlooking genuine molecular discoveries. These discussions also raise a deeper question: what exactly should be considered part of the metabolome? As metabolomics advances toward large-scale data mining and high-throughput computational analysis, resolving these conceptual and methodological ambiguities has become essential. In this perspective, we propose a refined definition of the "dark metabolome" and present a systematic overview of ISFs and related ion forms, including adducts and multimers. We examine their impact on metabolite annotation, experimental design, statistical analysis, computational workflows, and repository-scale data mining. Finally, we provide practical recommendationsincluding a set of dos and do nots for researchers and reviewersand discuss the broader implications of ISFs for how the field explores unknown molecular space. By embracing a more nuanced understanding of ISFs, metabolomics can achieve greater rigor, reduce misinterpretation, and unlock new opportunities for discovery.
    Keywords:  analytical artifact; dark metabolome; electrospray ionization; in-source fragmentation; mass spectrometry; metabolomics
    DOI:  https://doi.org/10.1021/jacsau.5c01063
  7. Proteomics Clin Appl. 2026 Jan;20(1): e70037
    Simple, Fast, and Reliable Analysis of Label-Free Proteomics Data With the Proteomics Eye (ProtE).
    Theodoros Margelos, Manousos Makridakis, Charis Gonidaki, Foteini Paradeisi, Manos Vossos, Jerome Zoidakis, Antonia Vlahou, Rafael Stroggilos.
      In label-free mass spectrometry experiments, the data output is typically a proteome table that requires further processing, quality testing, and visualization to fully interpret the captured proteomic signals. Currently, post-quantification analysis of these tables often relies on complex programmatic pipelines, which can become challenging to use. Here, we introduce the Proteomics Eye (ProtE), a single-function R package designed to streamline the analysis of proteome tables generated by commonly used software tools (DIA-NN, ProteomeDiscoverer, and MaxQuant). ProtE provides a broad range of options for data processing, preparation, and statistical testing. It also performs gene set enrichment analysis and offers a comprehensive suite of visualization plots to assess data quality and facilitate biological interpretation. Given a categorical variable with two or more groups, ProtE enables group-wide and pairwise statistical comparisons across all group combinations, using both traditional statistical tests and linear models for differential expression analysis. By integrating all these features into a single, user-friendly R function, ProtE simplifies the analysis of large-scale label-free DDA and DIA datasets, making advanced proteomic analysis accessible to both experienced researchers and beginners.
    Keywords:  DIANN; MaxQuant; Proteome Discoverer; bioinformatics; computational proteomics
    DOI:  https://doi.org/10.1002/prca.70037
  8. Mol Cell Proteomics. 2025 Dec 22. pii: S1535-9476(25)00599-7. [Epub ahead of print] 101500
    Assessing the Performance of Mass Spectrometry Search Strategies in Identifying Translational Errors Using PDX Proteomics Data.
    Araf Mahmud, Yingnan Song, Qi Zhou, Chen Huang.
      Translational errors (TEs) result in a mismatch between mRNA codons and the amino acids (AAs) of the corresponding protein. Unlike DNA mutations or RNA editing, where nucleotide sequences can be used to infer AA substitutions, TEs can only be detected at the protein level. Although high-throughput mass spectrometry (MS) proteomics offers the potential to resolve peptide sequences and could theoretically be used to identify TEs, the feasibility of current MS data analysis approaches for this application remains uncertain. Here, we utilize patient-derived xenograft (PDX) proteomics data, which include both human and mouse peptides with identifiable cross-species AA variations, as a ground truth for benchmarking TE identification methods. By using high-confidence mouse peptides as surrogates for 'TE-containing' peptides, we show that current open search approaches can achieve >65% overall sensitivity and >70% overall precision for high-quality samples. The intersection of different search strategies significantly enhances precision, albeit at the expense of reduced sensitivity. Notably, the evaluation metrics vary significantly across individual AA substitutions, suggesting that caution is warranted when detecting or interpreting specific AA substitutions. Moreover, closed searches targeting predefined AA changes exhibit poor precision, with PTM mislocalization identified as a key bottleneck for this application. Overall, our study provides a first-of-its-kind benchmark for MS-based TE discovery and offers guidance for optimizing MS search strategies.
    DOI:  https://doi.org/10.1016/j.mcpro.2025.101500
  9. J Proteome Res. 2025 Dec 23.
    Proteome-Wide Multipoint Internal Calibration Curves for Evaluating Peptide-Level Linearity in Relative Quantitative Proteomics.
    Cristina Chiva, Zahra Elhamraoui, Julia Morales-Sanfrutos, Olga Pastor, Eduard Sabidó.
      Mass spectrometry (MS)-based proteomics is known for its high accuracy in quantifying peptides and proteins using various calibration strategies including internal and external calibration curves. While external multipoint calibration curves are created from serial dilutions, they often fail to account for sample-specific matrix effects. In contrast, internal calibration curves account for the sample matrix but face scalability and cost challenges for whole proteome analyses. In this manuscript, we present a novel TMT-based multipoint internal calibration curve strategy, which enables the generation of internal calibration curves for all peptides identified within a proteome in a single experiment to assess their linearity prior relative quantification. We applied this strategy to human ovarian cancer cells to evaluate the linear quantitative responses of all of the identified peptides and reveal the significant proteome changes associated with cisplatin treatment.
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00179
  10. J Proteome Res. 2025 Dec 22.
    Comparative Analysis of Lysine-Specific Peptidases for Optimizing Proteomics Workflows.
    Leander Roeland van der Hoeven, Maico Lechner, Cristina Hernandez-Rollan, Tanveer Singh Batth, Jesper V Olsen.
      This study presents a comparative analysis of three LysC endopeptidase homologues from Achromobacter lyticus (A. lyticus),Pseudomonas aeruginosa and Lysobacter enzymogenes for mass spectrometry-based proteomics. Utilizing a protein aggregation capture workflow with HeLa cell lysates, we assessed the enzymes' cleavage specificity, digestion efficiency, and performance across various experimental conditions. Results showed that while all three LysC homologues exhibited high cleavage specificity at lysine residues, A. lyticus LysC outperformed the two others with superior peptide identification, digestion efficiency, and protein coverage, especially at shorter digestion times. Our experiments using a combination ofA. lyticusLysC and trypsin demonstrated the importance of employing LysC for significantly minimizing missed cleavage rates in tryptic digests, especially with regard to lysine-containing peptides. This study underscores A. lyticus LysC's potential as an optimal choice for enhancing mass spectrometry-based proteomics.
    Keywords:  LC-MS/MS; bottom-up proteomics; endoproteinase LysC; label-free quantitation; method development; proteolytic enzymes; proteomics workflow
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00872
  11. Bio Protoc. 2025 Dec 20. 15(24): e5542
    Optimized Secretome Sample Preparation From High Volume Cell Culture Media for LC-MS/MS Proteomic Analysis.
    Basil Baby Mattamana, Raju Gajjela, Jyoti K C, Robert A Parish, Peter Allen Faull.
      The cellular secretome is a rich source of biomarkers and extracellular signaling molecules, but proteomic profiling remains challenging, especially when processing culture volumes greater than 5 mL. Low protein abundance, high serum contamination, and sample loss during preparation limit reproducibility and sensitivity in mass spectrometry-based workflows. Here, we present an optimized and scalable protocol that integrates (i) 50 kDa molecular weight cutoff ultrafiltration, (ii) spin column depletion of abundant serum proteins, and (iii) acetone/TCA precipitation for protein recovery. This workflow enables balanced recovery of both low- and high-molecular-weight proteins while reducing background from serum albumin, thereby improving sensitivity, reproducibility, and dynamic range for LC-MS/MS analysis. Validated in human mesenchymal stromal cell cultures, the protocol is broadly applicable across diverse cell types and experimental designs, making it well-suited for biomarker discovery and extracellular proteomics. Key features • Enables efficient concentration and cleanup of ≥5-500 mL of conditioned media, suitable for low-abundance secreted protein recovery. • Combines 50 kDa ultrafiltration, optional HSA/IgG depletion, and acetone/TCA precipitation for robust removal of serum contaminants and improved signal-to-noise. • Adaptable to various mammalian cell types and serum-free or serum-containing media; scalable for adherent and suspension cultures.
    Keywords:  Albumin/IgG depletion; Amicon 50 kDa; Bottom-up proteomics; Conditioned medium; Extracellular proteomics; LC-MS/MS; Protein identification; Secretome; Ultrafiltration
    DOI:  https://doi.org/10.21769/BioProtoc.5542
  12. J Integr Bioinform. 2025 Dec 24.
    Streamlining feature elaboration and statistics analysis in metabolomics: the GetFeatistics R-package.
    Gianfranco Frigerio.
      Metabolomics studies require complex data processing pipelines to ensure data quality and extract meaningful biological insights. GetFeatistics is an R-package developed to streamline the elaboration and statistical analysis of metabolomics data. For targeted analyses, the package enables calibration curve-based quantification with different data weighting options. For untargeted studies, it includes dedicated functions to import feature tables from tools like patRoon and MS-DIAL, assign annotation confidence levels, and filter features based on pooled quality control (QC) criteria, including options for group-specific pooled QCs. The package also provides functions for univariate and multivariate statistical analyses, notably streamlined regression modelling with fixed effects, mixed-effects models for longitudinal data, and Tobit regression for censoring values exceeding the limits of detection. Output tables are concise and informative, facilitating interpretation and reporting, while output visualisations are fully customisable via the ggplot grammar. Additional functionalities include automated retrieval of chemical properties from PubChem, ontology classification via ClassyFire, and pathway enrichment analysis using the FELLA package. GetFeatistics is publicly available on GitHub, with comprehensive documentation and a step-by-step vignette. By integrating key steps of the metabolomics workflow, the package aims to facilitate both exploratory studies and large-scale epidemiological applications in metabolomics research.
    Keywords:  computational metabolomics; data preprocessing; metabolomics workflow; non-targeted metabolomics; open-source software
    DOI:  https://doi.org/10.1515/jib-2025-0047
  13. Anal Bioanal Chem. 2025 Dec 23.
    Untargeted metabolomics and lipidomics to study autophagy induction in mouse embryonic fibroblasts.
    Rani Robeyns, Freke Mertens, Angela Sisto, Maria van de Lavoir, Maria Del Mar Delgado Povedano, Leen Jacobs, Wim Martinet, Adrian Covaci, Sigrid Stroobants, Vincent Timmerman, Alexander L N van Nuijs.
      Autophagy is a complex self-degradative process that recycles cytoplasmic components through lysosomal degradation, enabling cells to maintain homeostasis during stress and nutrient deprivation. Despite major advances in understanding the basic mechanisms of autophagy, important gaps remain in translating them to human diseases. This study investigated the metabolic fingerprints and footprints of two mechanistically different autophagy inducers, Torin1 (mTOR-dependent) and Tat-Beclin1 (mTOR-independent), in mouse embryonic fibroblasts (MEF). Multi-platform untargeted metabolomics and lipidomics analyses were performed at 3 and 18 h exposure to elucidate both intracellular and extracellular metabolic changes using liquid chromatography-high-resolution mass spectrometry coupled to drift tube ion mobility, complemented by [13C]-glucose tracing. Torin1 exposure caused downregulation of TCA cycle intermediates, accumulation of purine degradation products, enhanced phospholipid catabolism, and triglycerides' enrichment. In contrast, Tat-Beclin1 preserved central carbon metabolism, promoted recovery of glutathione levels, and redirected diglycerides toward the biosynthesis of polyunsaturated phosphocholines (PC) and C18-containing phosphoethanolamines (PE). Despite these compound-specific responses, several common alterations were observed, including downregulation of ceramides, upregulation of ether-linked PEs, consistent enrichment of PC O-12:0_16:0, lyso-PE 22:6, PC 16:0_20:4, PC 16:0_22:5, and depletion of PE 32:1, PE 34:2, and PE 38:6, along with secretion of unsaturated fatty acids and uptake of sphingomyelin 35:1;O2 and cytosine from the extracellular compartment. Together, these results show that Torin1 and Tat-Beclin1 trigger distinct yet partly overlapping metabolic programs. The metabolic signatures identified here provide reference profiles for future mechanistic studies and highlight candidate biomarkers that may support early functional evaluation of autophagy modulators in disease-relevant settings.
    Keywords:  Metabolic fingerprinting; Stable-isotope tracing; Tat-Beclin1; Torin1; mTOR signaling
    DOI:  https://doi.org/10.1007/s00216-025-06275-3
  14. Bioinform Adv. 2025 ;5(1): vbaf301
    Perspectives in computational mass spectrometry: recent developments and key challenges.
    Timo Sachsenberg, Lindsay K Pino, Marie Brunet, Isabell Bludau, Oliver Kohlbacher, Juan Antonio Vizcaino, Wout Bittremieux.
      Summary: Mass spectrometry (MS) is a cornerstone technology in modern molecular biology, powering diverse applications across proteomics, metabolomics, lipidomics, glycomics, and beyond. As the field continues to evolve, rapid advancements in instrumentation, acquisition strategies, machine learning, and scalable computing have reshaped the landscape of computational MS. This perspective reviews recent developments and highlights key challenges, including data harmonization, statistical confidence estimation, repository-scale analysis, multi-omics integration, and privacy in clinical MS. We also discuss the increasing importance of machine learning and the need to build corresponding literacy within the community. Finally, we reflect on the role of the Computational Mass Spectrometry (CompMS) Community of Special Interest of the International Society for Computational Biology in supporting collaboration, innovation, and knowledge exchange. With MS-based technologies now central to both basic and translational research, continued investment in robust and reproducible computational methods will be essential to realize their full potential.
    DOI:  https://doi.org/10.1093/bioadv/vbaf301
  15. J Neuropathol Exp Neurol. 2025 Dec 22. pii: nlaf145. [Epub ahead of print]
    Identification of diagnostic biomarkers for relapsing-remitting multiple sclerosis in plasma by mass spectrometry-based proteomics.
    Víctor M García-Silva, Eduardo Chicano-Galvez, María Isabel García-Sánchez, Ángela Peralbo-Molina, María Hernández-Valladares.
      In the absence of molecular biomarkers, the current diagnosis of multiple sclerosis is based on clinical assessment, neuroimaging, and detection of oligoclonal bands in cerebrospinal fluid. Early and rapid diagnosis using patient samples obtained by non-invasive methods would be a major advance in clinical management. We tested 5 different methods for preparation of the plasma proteome for liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis. These were as follows: (1) single-pot, solid-phase-enhanced sample preparation (SP3), (2) iST and (3) ENRICH-iST of raw plasma, (4) SP3 of highly abundant-proteins depleted plasma (DEPL-SP3), and (5) SP3 of plasma extracellular vesicles (EV-SP3). DEPL-SP3 and EV-SP3 sample preparation workflows yielded the highest numbers of quantified plasma proteins. Using both methods, we analyzed the plasma proteome of 15 relapsing-remitting multiple sclerosis (RRMS) patients and 5 healthy controls. We found 54 and 35 regulated plasma proteins with DEPL-SP3 and EV-SP3 workflows, respectively. Among them, von Villebrand factor (VWF) was identified as a potential RRMS diagnostic biomarker. The use of sample preparation workflows for LC-MS/MS analysis that describe both the soluble and EV plasma proteomes might increase the likelihood of identifying new and robust RRMS biomarkers.
    Keywords:  biomarkers; extracellular vesicles; mass spectrometry; plasma; proteomics; relapsing remitting multiple sclerosis; von Willebrand factor
    DOI:  https://doi.org/10.1093/jnen/nlaf145
  16. Mol Cell Proteomics. 2025 Dec 19. pii: S1535-9476(25)00591-2. [Epub ahead of print] 101492
    Benchmarking Software for DDA-PASEF Immunopeptidomics.
    Yannic Chen, Annica Preikschat, Annette Arnold, Riccardo Pecori, David Gomez-Zepeda, Stefan Tenzer.
      Mass spectrometry (MS) is the method of choice for high-throughput identification of immunopeptides, which are generated by intracellular proteases, unlike proteomics peptides that are typically derived from trypsin-digested proteins. Therefore, the searching space for immunopeptides is not limited by proteolytic specificity, requiring more sophisticated software algorithms to handle the increased complexity. Despite the widespread use of MS in immunopeptidomics, there is a lack of systematic evaluation of data processing software, making it challenging to identify the optimal solution. In this study, we provide a comprehensive benchmarking of the most widespread/used data-dependent acquisition (DDA)-based software platforms for immunopeptidomics: MaxQuant, FragPipe, PEAKS and MHCquant. The evaluation was conducted using data obtained from the JY cell line using the Thunder-DDA-PASEF method. We assessed each software's ability to identify immunopeptides and compared their identification confidence. Additionally, we examined potential biases in the results and tested the impact of database size on immunopeptide identification efficiency. Our findings demonstrate that all software platforms successfully identify the most prominent subset of immunopeptides with 1% false discovery rate (FDR) control, achieving medium to high identification confidence correlations. The largest number of immunopeptides were identified using the commercial PEAKS software, which is closely followed by FragPipe, making it a viable non-commercial alternative. However, we observed that larger database sizes negatively impacted the performance of some software platforms more than others. These results provide valuable insights into the strengths and limitations of current MS data processing tools for immunopeptidomics, supporting the immunopeptidomics/MS community in determining the right choice of software.
    DOI:  https://doi.org/10.1016/j.mcpro.2025.101492
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