bims-mascan 2025-07-06 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–07–06
fifteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Software Design and Analytical Challenges for Confident Glycopeptide Identification With Data-Independent Acquisition.
Enhancing Tandem MS Sensitivity and Peptide Identification via Ion Preaccumulation in an Orbitrap Mass Spectrometer.
Comprehensive evaluation of dual-mode LC-MS conditions for enhanced metabolomic profiling: Application to microplastic exposure studies.
Evaluation of separation performance and quantification accuracy in lipidomics methods.
MSCI: an open-source Python package for information content assessment of peptide fragmentation spectra.
Longitudinal Fragment Profiles Based on Multi-Collision Energy Tandem Mass Spectra Improve the Accuracy of Metabolite Identification in Untargeted Metabolomics.
A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data.
Top-Down Proteomic Analysis of Limited Samples Using Porous Layer Open Tubular Columns and High-Field Asymmetric Ion Mobility Spectrometry Coupled to Mass Spectrometry.
De Novo Glycan Annotation of Mass Spectrometry Data.
Will it be a turning point?
MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers.
Chemical Derivatization-Based Liquid Chromatography-Mass Spectrometry Method for Fatty Acid Profiling in Biological Samples.
Beyond glucose and Warburg: finding the sweet spot in cancer metabolism models.
Enrichment of extracellular vesicles using Mag-Net for the analysis of the plasma proteome.
Skeletal muscle proteomics: considerations and opportunities.

Mass Spectrom Rev. 2025 Jul 01.

Software Design and Analytical Challenges for Confident Glycopeptide Identification With Data-Independent Acquisition.

Mary Rachel Nalehua, Joseph Zaia.

  Glycosylation is an abundant post-translational modification that impacts a wide variety of functions, including protein regulation, cell adhesion, and structural integrity. The application of proteomics methods to glycopeptide assignment faces unique challenges due to high heterogeneity, which results in complex populations with low overall abundance per glycopeptide. In addition, glycans dissociate at a lower collision energy compared to their attached peptide component. The resulting mass spectral data require specialized assignment software, which has caused glycoproteomics to lag traditional proteomics. Existing software primarily focuses on data-dependent acquisition (DDA), but manual validation is frequently required, and experiments are necessarily limited by the stochastic nature of DDA ion-selection. Data-independent acquisition (DIA) allows for a more complete and robust analysis of glycopeptide samples, but analysis software is still sparse. In this review, we discuss the current state of DDA analysis software, the limitations, and how it can inform our forays into DIA glycoproteomics.

Keywords:  bioinformatics; data‐independent acquisition; glycoproteomics; mass spectrometry

DOI:  https://doi.org/10.1002/mas.70000
J Proteome Res. 2025 Jun 30.

Enhancing Tandem MS Sensitivity and Peptide Identification via Ion Preaccumulation in an Orbitrap Mass Spectrometer.

Florian Harking, Ulises H Guzman, Julia Kraegenbring, Hamish Stewart, Konstantin Aizikov, Heiner Koch, Kyle L Fort, Alexander Harder, Jesper V Olsen.

  High-throughput mass spectrometry-based proteomics has gained increasing interest for both academic and industrial applications. As implementation of faster gradients has facilitated higher sample throughput, mass spectrometers must adapt to shorter analysis times by enhancing the scanning speed and sensitivity. For Orbitrap mass spectrometers, faster scan rates are constrained by the need for sufficient ion accumulation time, particularly given the limitations on the duty cycle at high repetition rates, and transient length, which determines analyzer sensitivity and resolving power. In this context, implementing alternative ion scheduling and better ion signal-processing strategies is needed to unleash the speed of these instruments. Here, we introduce a new scanning strategy termed preaccumulation, which enables the storage of ions in the bent flatapole in parallel to the operation of the C-trap/IRM, leading to a significant improvement in ion beam utilization and enabling for the first time scanning speeds of ∼70 Hz on hybrid Orbitrap instruments. The combination of preaccumulation and increased scan speeds notably enhances peptide and protein group identifications for short LC gradients and improves sensitivity for high-throughput applications. These benefits were further amplified when coupled with the full mass range phase-constrained spectrum deconvolution method (ΦSDM), especially for fast, lower-resolution Orbitrap measurements with short LC gradients. Overall, we demonstrate that preaccumulation of ions in the bent flatapole offers distinct advantages, particularly for conditions with reduced signal input. Since no hardware changes are required, this approach is highly attractive for Orbitrap mass spectrometers operated with fast MS/MS acquisition methods.

Keywords:  Orbitrap; bent flatapole; preaccumulation; scanning speed; ΦSDM

DOI:  https://doi.org/10.1021/acs.jproteome.5c00186
Anal Chim Acta. 2025 Sep 15. pii: S0003-2670(25)00707-X. [Epub ahead of print]1367 344313

Comprehensive evaluation of dual-mode LC-MS conditions for enhanced metabolomic profiling: Application to microplastic exposure studies.

Kuan-Lu Wu, Pei-Chen Lin, Wei-Chen Lin, Sung-Fang Chen.

  Polystyrene microplastics (PS-MPs) are emerging contaminants of concern due to their potential health impacts and widespread presence in the environment. Metabolomics offers a powerful approach to investigate biological responses to such exposures. However, current LC-MS methods are often limited by the suitability of chromatographic conditions for metabolites with diverse physicochemical properties, leading to suboptimal coverage and analytical redundancy. This study addresses these limitations by establishing a robust, broadly applicable dual-mode LC-MS strategy to improve coverage and analytical efficiency in microplastic exposure studies. This study evaluated 18 chromatographic conditions using six commercial columns including amide, silica, Obelisc N, C18, pentafluoophenyl (F5), and cyanopropyl (CN), to optimize metabolite separation in both positive and negative electrospray ionization (ESI) modes. Mouse large intestine extracts exposed to PS-MPs showed broad metabolome coverage under optimized conditions. In positive mode, the amide column with ammonium acetate/acetic acid (AmAc/AcA) effectively captured diverse polar metabolites. In negative mode, the F5 column with ammonium formate/formic acid (AmF/FA) excelled in phospholipid detection and lipid separation. Combining these conditions enabled complementary profiling with minimal overlap. Additionally, 42 differential metabolites affected by PS-MPs were associated with key metabolic pathways, including amino acid, taurine, hypotaurine, and glutathione metabolism. This optimized, high-coverage LC-MS strategy provides a novel analytical framework that maximizes metabolome profiling efficiency and minimizes sample input. It improves detection of diverse metabolite classes and supports robust biological interpretation, offering broad applicability for future studies on environmental exposures and complex biological challenges.

Keywords:  Liquid chromatography; Mass spectrometry; Metabolomics

DOI:  https://doi.org/10.1016/j.aca.2025.344313
J Chromatogr A. 2025 Jun 22. pii: S0021-9673(25)00511-4. [Epub ahead of print]1758 466165

Evaluation of separation performance and quantification accuracy in lipidomics methods.

Noriyuki Tomiyasu, Yoshihiro Izumi, Omidreza Heraviadeh, Masatomo Takahashi, Takeshi Bamba.

  In mass-spectrometry-based lipidomics, diverse analytical methods are employed in different institutions, resulting in variations in isomer identification, the number of detected compounds, and quantitation accuracy. The primary analytical methods include flow injection (FI), reversed-phase liquid chromatography (RP-LC), and hydrophilic interaction liquid chromatography (HILIC). Recently, supercritical fluid chromatography (SFC) has gained attention for its enhanced separation of hydrophobic and structural isomers. However, the most suitable analytical method for lipidomics remains in debate because of the scarcity of comparative performance studies. In this study, we evaluated the quantitative performance of four methods (FI, RP-LC, HILIC, and SFC) each connected to a triple quadrupole mass spectrometer (MS/MS) using the NIST SRM1950 plasma reference under identical conditions, focusing purely on methodological differences. Quantification was performed for 355 lipid species across 14 lipid classes using one deuterated standard per class as an internal standard. The results revealed no significant differences in quantification across six lipid classes, whereas other classes showed notable method-specific variations. Additionally, chromatographic performance, including analysis time, pressure drop, theoretical plate height, and isomer separation, was compared between HILIC-MS/MS and SFC-MS/MS. SFC-MS/MS outperformed HILIC-MS/MS in all parameters, including height equivalent to a theoretical plate, resolution, peak height, and structural isomer separation performance. We conclude that although all methods are applicable in lipidomics, method selection and optimization should be consistent with specific analytical requirements, including target lipid class, sample consumption, and analysis time.

Keywords:  Flow injection; Hydrophilic interaction liquid chromatography; Lipidomics; Mass spectrometry; Quantification accuracy; Reverse-phase liquid chromatography; Supercritical fluid chromatography

DOI:  https://doi.org/10.1016/j.chroma.2025.466165
Bioinform Adv. 2025 ;5(1): vbaf125

MSCI: an open-source Python package for information content assessment of peptide fragmentation spectra.

Zahra Elhamraoui, Eva Borràs, Mathias Wilhelm, Eduard Sabidó.

Motivation: In mass spectrometry-based proteomics, the availability of peptide prior knowledge has improved our ability to assign fragmentation spectra to specific peptide sequences. However, some peptides exhibit similar analytical values and fragmentation patterns, which makes them nearly indistinguishable with current data analysis tools.
Results: Here we developed the Mass Spectrometry Content Information (MSCI) Python package to tackle the challenges of peptide identification in mass spectrometry-based proteomics, particularly regarding indistinguishable peptides. MSCI provides a comprehensive toolset that streamlines the workflow from data import to spectral analysis, enabling researchers to effectively evaluate fragmentation similarity scores among peptide sequences and pinpoint indistinguishable peptide pairs in a given proteome.
Availability and implementation: MSCI is implemented in Python and it is released under a permissive MIT license. The source code and the installers are available on GitHub at https://github.com/proteomicsunitcrg/MSCI.

DOI: https://doi.org/10.1093/bioadv/vbaf125
Anal Chem. 2025 Jul 01.

Longitudinal Fragment Profiles Based on Multi-Collision Energy Tandem Mass Spectra Improve the Accuracy of Metabolite Identification in Untargeted Metabolomics.

Xian Fu, Qiang Li, Hou-Hua Yin, Ya-Nan Liu, Wenlin Wu, Bruce D Hammock, Jianbo Pan, Jun-Yan Liu.

Metabolite identification in untargeted metabolomics via tandem mass spectrometry (MS/MS) spectral matching is commonly performed by comparing experimental and reference MS/MS spectra acquired at one or a few collision energies, which generates a similarity score based on the relative intensities of fragment ions within each spectrum, referred to as cross-sectional profiling. Here, we introduced a novel method that significantly improved identification accuracy by comparing longitudinal fragment profiles, which consisted of the intensities of individual MS/MS fragments across multiple collision energies. This approach, termed longitudinal profiling, highlighted low-abundance fragments that were often overlooked by conventional cross-sectional methods, emphasizing predominant ions. We optimized the Jaccard similarity algorithm for longitudinal profiling and established identification criteria using an in-house spectral database comprising approximately 1,80,000 MS/MS spectra. The robustness of the method was validated using inter-instrument datasets, spiked standards, and human plasma samples. Compared with cross-sectional profiling using the optimal entropy algorithm, the longitudinal profiling method improved annotation accuracy by 8.7-25.9% and reduced the false discovery rate by 28.6-41.7%, resulting in a fair increase in the number of confidently annotated metabolites. This method enhances the probability of discovering true diagnostic markers while reducing the likelihood of false diagnostic markers. Our results demonstrate that longitudinal profiling provides a promising new avenue for more accurate metabolite identification in untargeted metabolomics.

DOI: https://doi.org/10.1021/acs.analchem.5c01414
Nat Methods. 2025 Jul 01.

A transformer model for de novo sequencing of data-independent acquisition mass spectrometry data.

Justin Sanders, Bo Wen, Paul A Rudnick, Richard S Johnson, Christine C Wu, Michael Riffle, Sewoong Oh, Michael J MacCoss, William Stafford Noble.

A core computational challenge in the analysis of mass spectrometry data is the de novo sequencing problem, in which the generating amino acid sequence is inferred directly from an observed fragmentation spectrum without the use of a sequence database. Recently, deep learning models have made substantial advances in de novo sequencing by learning from massive datasets of high-confidence labeled mass spectra. However, these methods are designed primarily for data-dependent acquisition experiments. Over the past decade, the field of mass spectrometry has been moving toward using data-independent acquisition (DIA) protocols for the analysis of complex proteomic samples owing to their superior specificity and reproducibility. Hence, we present a de novo sequencing model called Cascadia, which uses a transformer architecture to handle the more complex data generated by DIA protocols. In comparisons with existing approaches for de novo sequencing of DIA data, Cascadia achieves substantially improved performance across a range of instruments and experimental protocols.

DOI: https://doi.org/10.1038/s41592-025-02718-y
Anal Chem. 2025 Jun 30.

Top-Down Proteomic Analysis of Limited Samples Using Porous Layer Open Tubular Columns and High-Field Asymmetric Ion Mobility Spectrometry Coupled to Mass Spectrometry.

Michal Greguš, Yunfan Gao, Kendall R Johnson, Somak Ray, Anne-Lise Marie, Alexander R Ivanov.

Despite significant recent progress in the field of mass spectrometry (MS)-based top-down proteomics (TDP), the analysis of limited samples is still a major challenge. Here, we explored the potential of ultralow flow (ULF) liquid chromatography (LC) porous layer open tubular (PLOT) columns interfaced with MS via high-field asymmetric waveform ion mobility spectrometry (FAIMS) to enable high-sensitivity TDP analysis of small populations of mammalian cells. The developed robust and easy-to-use platform delivered high reproducibility of retention times (RSD < 0.4%) and high separation performance for intact proteins (∼14-s peak full width at half-maximum and peak capacity of >125 for a 60 min effective gradient). The FAIMS-based experiments resulted in a ∼2-fold increase in identifications compared to the control experiments for ∼200 HeLa cell aliquots, i.e., 819 vs 454 proteins and 2645 vs 1305 proteoforms, respectively. The pilot ULF LC-MS analysis of six HeLa cells yielded 29 ± 3 proteins and 38 ± 2 proteoforms, on average, and a total of 44 proteins and 68 proteoforms. Data revealed a high degree of acetylation, methylation, phosphorylation, glycosylation, lactylation, and other relevant post-translational modifications. Notably, the presented protein identification results for limited samples are comparable to those of recent large-scale TDP studies of bulk samples, demonstrating the potential to enable informative single-cell TDP profiling.

DOI: https://doi.org/10.1021/acs.analchem.4c06727
J Am Soc Mass Spectrom. 2025 Jul 02.

De Novo Glycan Annotation of Mass Spectrometry Data.

Margot Bligh, Sebastian Silva-Solar, Linda Biehler, Christopher C J Fitzgerald, Conor J Crawford, Mikkel Schultz-Johansen, Sofie Niggemeier, Peter H Seeberger, Manuel Liebeke, Jan-Hendrik Hehemann.

  Carbohydrates are fundamental molecules of life that are involved in virtually all biological processes. The chemical diversity of glycans─carbohydrate chains─enables diverse functions but also challenges analytics. Annotation of glycans in mass spectrometry (MS) data relies heavily on experimental databases or manual calculations, hindering the discovery of novel glycan compositions and structures. Here, we introduce GlycoAnnotateR─a package in the open-source programming language R─for de novo annotation of glycan compositions in MS data. GlycoAnnotateR calculates all possible monomer and modification combinations, which are then filtered against a defined set of chemical rules to provide biologically relevant compositions. The "glycoPredict" function can return compositions for oligosaccharides ranging from 1 to 22 monomers in length while accounting for four different modifications in under 10 min with less than 4 GB of random-access memory (RAM). Here, three case studies demonstrate the efficacy and versatility of GlycoAnnotateR: (1) accurate identification of mono- and oligosaccharide standards, (2) characterization of sulfated fucan oligosaccharides obtained by enzymatic digestion of fucoidan, a complex algal glycan, and (3) reproduction and expansion of glycan annotations for a published mouse lung MALDI-MS imaging data set previously annotated by NGlycDB. GlycoAnnotateR rapidly provides accurate annotations and complements existing R packages for MS data processing, enabling metabolomic and glycomic data integration. This combinatorial, rule-based approach enhances glycan annotation capabilities and supports hypothesis generation in glycoscience, expanding our ability to explore the chemical space of glycan diversity.

Keywords:  R, annotation tool; carbohydrates; glycans; mass spectrometry

DOI:  https://doi.org/10.1021/jasms.5c00093
Nat Cancer. 2025 Jul 02.

Will it be a turning point?

Sarah-Maria Fendt.

DOI: https://doi.org/10.1038/s43018-025-01011-3
Nat Commun. 2025 Jul 01. 16(1): 5487

MassCube improves accuracy for metabolomics data processing from raw files to phenotype classifiers.

Huaxu Yu, Jun Ding, Tong Shen, Min Liu, Yuanyue Li, Oliver Fiehn.

Nontargeted peak detection in LC-MS-based metabolomics must become robust and benchmarked. We present MassCube, a Python-based open-source framework for MS data processing that we systematically benchmark against other algorithms and different types of input data. From raw data, peaks are detected by constructing mass traces through signal clustering and Gaussian-filter assisted edge detection. Peaks are then grouped for adduct and in-source fragment detection, and compounds are annotated by both identity- and fuzzy searches. Final data tables undergo quality controls and can be used for metabolome-informed phenotype prediction. Peak detection in MassCube achieves 100% signal coverage with comprehensive reporting of chromatographic metadata for quality assurance. MassCube outperforms MS-DIAL, MZmine3 or XCMS for speed, isomer detection, and accuracy. It supports diverse numerical routines for MS data analysis while maintaining efficiency, capable for handling 105 GB of Astral MS data on a laptop within 64 min, while other programs took 8-24 times longer. MassCube automatically detected age, sex and regional differences when applied to the Metabolome Atlas of the Aging Mouse Brain data despite batch effects. MassCube is available at https://github.com/huaxuyu/masscube for direct use or implementation into larger applications in omics or biomedical research.

DOI: https://doi.org/10.1038/s41467-025-60640-5
J Sep Sci. 2025 Jul;48(7): e70211

Chemical Derivatization-Based Liquid Chromatography-Mass Spectrometry Method for Fatty Acid Profiling in Biological Samples.

Jingxuan Yang, Yi Wu, Liang Zhao, Meng Li, Wenjun Guo, Yajuan Xu, Yang Wang.

  Fatty acids (FAs) are important metabolites for various biochemical functions in living organisms, including energy storage, cell structure, and cell signaling. Changes in FAs composition were significantly related to abnormal metabolic levels and body status. In this study, the derivatization-based liquid chromatography-quadrupole-orbitrap mass spectrometry method was established for FA analysis under parallel reaction monitoring mode. The 4-amino-1-benzylpiperidine (4A1BP) was used as a derivatization reagent to amide with FAs. The results showed that protonated molecules of FA+4A1BP-H2O were observed, and characteristic fragment ions at m/z 174.1280 and 91.0548 were detected in the tandem mass spectrum. The derivatization method was optimized in terms of sample preparation, chromatographic separation, and mass spectrometric conditions. The linearity, stability, and repeatability of the method were also investigated with good results. Finally, we applied the established method to analyze the serum and liver tissue samples, successfully evaluating the efficacy of Qizha Shuangye granules in the treatment of hyperlipidemia rats. The established method has broad utility and great potential in the detection of FAs, which can provide technical support for disease mechanism research, biomarker discovery, as well as food quality assurance, and nutritional value evaluation.

Keywords:  LC‐MS; biological sample; chemical derivatization; fatty acid; parallel reaction monitoring

DOI:  https://doi.org/10.1002/jssc.70211
npj metabolic health and disease... 2024 Sep 02. 2(1): 11

Beyond glucose and Warburg: finding the sweet spot in cancer metabolism models.

Nia G Hammond, Robert B Cameron, Brandon Faubert.

Advances in cancer biology have highlighted metabolic reprogramming as an essential aspect of tumorigenesis and progression. However, recent efforts to study tumor metabolism in vivo have identified some disconnects between in vitro and in vivo biology. This is due, at least in part, to the simplified nature of cell culture models and highlights a growing need to utilize more physiologically relevant approaches to more accurately assess tumor metabolism. In this review, we outline the evolution of our understanding of cancer metabolism and discuss some discrepancies between in vitro and in vivo conditions. We describe how the development of physiological media, in combination with advanced culturing methods, can bridge the gap between in vitro and in vivo metabolism.

DOI: https://doi.org/10.1038/s44324-024-00017-2
Nat Commun. 2025 Jul 01. 16(1): 5447

Enrichment of extracellular vesicles using Mag-Net for the analysis of the plasma proteome.

Christine C Wu, Kristine A Tsantilas, Jea Park, Deanna Plubell, Justin A Sanders, Previn Naicker, Ireshyn Govender, Sindisiwe Buthelezi, Stoyan Stoychev, Justin Jordaan, Gennifer Merrihew, Eric Huang, Edward D Parker, Michael Riffle, Andrew N Hoofnagle, William S Noble, Kathleen L Poston, Thomas J Montine, Michael J MacCoss.

Extracellular vesicles (EVs) in plasma are composed of exosomes, microvesicles, and apoptotic bodies. We report a plasma EV enrichment strategy using magnetic beads called Mag-Net. Proteomic interrogation of this plasma EV fraction enables the detection of proteins that are beyond the dynamic range of liquid chromatography-mass spectrometry of unfractionated plasma. Mag-Net is robust, reproducible, inexpensive, and requires <100 μL plasma input. Coupled to data-independent mass spectrometry, we demonstrate the measurement of >37,000 peptides from >4,000 proteins. Using Mag-Net on a pilot cohort of patients with neurodegenerative disease and healthy controls, we find 204 proteins that differentiate (q-value < 0.05) patients with Alzheimer's disease dementia (ADD) from those without ADD. There are also 310 proteins that differ between individuals with Parkinson's disease and without. Using machine learning we distinguish between individuals with ADD and not ADD with an area under the receiver operating characteristic curve (AUROC) = 0.98 ± 0.06.

DOI: https://doi.org/10.1038/s41467-025-60595-7
npj metabolic health and disease... 2025 Jul 02. 3(1): 30

Skeletal muscle proteomics: considerations and opportunities.

Julian P H Wong, Yaan-Kit Ng, Jeppe Kjærgaard, Ronnie Blazev, Atul S Deshmukh, Benjamin L Parker.

Skeletal muscle accounts for 30-40% of body weight and plays an indispensable role in maintaining movement and is also a central regulator of whole-body metabolism. As such, understanding the molecular mechanisms of skeletal muscle health and disease is vital. Proteomics has been revolutionized in recent years and provided new insights into skeletal muscle. In this review, we first highlight important considerations unique to the field which make skeletal muscle one of the most challenging tissues to analyse by mass spectrometry. We then highlight recent advances using the latest case studies and how this has allowed coverage of the skeletal muscle temporal, fibre type and stem cells proteome. We also discuss how exercise and metabolic dysfunction can remodel the muscle proteome. Finally, we discuss the future directions of the field and how they can be best leveraged to increase understanding of human biology.

DOI: https://doi.org/10.1038/s44324-025-00073-2