bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2026–10–25
forty-two papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Evaluation of Deep and Dynamic Proteomic Screening Strategies at Sub-50 Hz Scan Speed and without Robotic Sample Preparation.
  2. High-throughput label-free single-cell proteomics enabled by multicolumn NanoLC.
  3. Cross-Platform Evaluation Reveals Sample Preparation-Driven Biological Biases despite Advances in Mass Spectrometry Performance in Serum Proteomics.
  4. A Novel Approach for Post-Acquisition Mass Calibration in Metabolomic Profiling by High-Resolution Mass Spectrometry.
  5. Isobaric Intramolecular Paternò-Büchi Reactions Enable Multiplexed Quantitation of Fatty Acids down to C═C Locations.
  6. Analytical Methods for Mass Spectrometry-Based Metabolomics Studies.
  7. Prioritizing Peptides for Targeted Mass Spectrometry Experiments Using Deep Learning.
  8. Advances in Multiple Reaction Monitoring (MRM) for Targeted Proteomics in Biomedical Research.
  9. A Robust DIA-Based Platform for Large-Scale Plasma Glycoproteomics and Biomarker Discovery.
  10. Application of Ion Mobility Mass Spectrometry in Lipidomics.
  11. Mass Spectrometry-Based Exposomics Is Not Metabolomics by Another Name.
  12. Rapid Eco-AI CE-MS Enables Sub-7 Min Low-Input Proteome Profiling with Quantitative Validation.
  13. Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) for Quantitative Proteomics.
  14. Drug Metabolism and Drug-Drug Interactions Studied Using High-Throughput Ambient Mass Spectrometry.
  15. A Novel Protein Corona-Based Secretome Profiling Assisting Cell Multiomics Analysis.
  16. Cross-Platform Consistency of FAIMS Compensation Voltage Optimization in Modern Orbitrap Mass Spectrometers.
  17. Extending single-cell proteomics from HeLa to small primary human immune cells.
  18. Proteomics for cultivated meat: Evaluation of sample preparation methods and analytical workflows for label-free quantitative profiling of cultivated meat proteome.
  19. A Liquid Chromatography-Mass Spectrometry Method to Quantify Total Coenzyme A Concentration and Isotopic Labeling.
  20. Same-Sample Profiling of Renin-Angiotensin Peptides and Enzymes in Mouse Brain Biopsies by Serial Extraction and High-Resolution Mass Spectrometry.
  21. Analysis of Metabolomics Response to Tick Saliva in Human Cells.
  22. High-throughput mass spectrometry-based metabolomics for large-scale population studies: technical barriers and recent innovations.
  23. Top-Down Proteomics Analysis of pg-Level Cell Lysate Using Spray-Capillary CE-FAIMS-MS.
  24. Untargeted UHPLC-HRMS Lipidomic Profiling of Ticks for Alpha-Gal Syndrome Research.
  25. Pulse SILAC Approaches to the Measurement of Cellular Dynamics.
  26. Cyclic Ion Mobility Separations of Free Oxylipins.
  27. Bottlenecks in Proteomics: An Addendum.
  28. From Stains to Spectra: Multiplexed Immunohistochemistry and Mass Spectrometry Imaging for Spatial Proteomics in Biomedicine.
  29. Bridging Speed and Confidence in Metabolomics: A Retention Time and MS2-Guided HILIC-Orbitrap Workflow.
  30. Mass Spectrometry for Proteomics-Based Investigation.
  31. Hydraplexing: Design Principles for Fragment-Level Signal Amplification Using a Multi-Amine Model Analyte.
  32. Extensive Quantitative Profiling of Plasma Steroidome and Prostaglandin Metabolites for Studies of Female Reproductive Physiology.
  33. An N,N-Dimethylethylenediamine-Derivatized In Silico Mass Spectral Library for Comprehensive Profiling of Isothiocyanates in Brassicaceae Plants.
  34. Performance evaluation of polarity switching for plasma untargeted metabolomics using Orbitrap mass spectrometer.
  35. Ion mobility mass spectrometry for suspect screening of environmental toxicants: Collision cross sections, structural annotation, and fit-for-purpose evidence evaluation.
  36. Metabolomics workbench: data-driven classification and correlation analysis between metabolite classes, biomarkers, and drugs.
  37. Advancing Spatial Lipidomics with MALDI-MS Imaging.
  38. Chromatograms: A Modular Infrastructure for Scalable Chromatographic Data Handling in R.
  39. In Cellulo Observation of De Novo-Synthesized Nucleotide by Spectral Tracing of Glutamine-Derived Nitrogen Metabolism (QN-STIM) in Cancer Cells.
  40. RNabel─A Standalone Software Tool for Annotating Tandem Mass Spectra of Modified Ribonucleic Acids.
  41. In Silico Top-Down Fourier Transform Mass Spectra to Advance Proteoform Analysis Methods.
  42. MetaboT: an LLM-based multi-agent framework for interactive analysis of mass spectrometry metabolomics knowledge graphs.
  1. J Proteome Res. 2026 Oct 02. 25(10): 5255-5266
    Evaluation of Deep and Dynamic Proteomic Screening Strategies at Sub-50 Hz Scan Speed and without Robotic Sample Preparation.
    Bhavesh S Parmar, Yuanyuan Liu, Parviz Ghezellou, Christian Münch.
      Advances in ultrafast mass analyzer technology and procedural automation have enabled proteomic screening at the throughput of hundreds of proteomes per day. However, these approaches often require expensive instrumentation upgrades and robotic automation that remain inaccessible to many research laboratories and core facilities. In this study, we address the feasibility of scaling up proteomic screening capabilities with minimal upgrade cost by focusing on (a) strategies for nonautomated high-throughput sample preparation from 96-well cell culture, (b) data acquisition on sub-50 Hz scan speed hybrid and tribrid Orbitrap instruments, and (c) data analysis strategies for label-free and labeled proteomic screening. We find that the 96-well format STrap, in combination with C18 plates, provides the most robust throughput for a nonautomated sample preparation workflow. Furthermore, we show that for static proteomes, an isobaric tandem mass tag (TMT)-based multiplexing approach provides deeper and more precise proteome coverage, whereas data-independent acquisition (DIA) is more accurate, albeit with a reduced dynamic range and more missing values. Finally, we extend the optimized workflow to proteome turnover studies using pulsed stable isotope labeling by amino acids in cell culture (pSILAC), highlighting the key advantages and trade-offs of DIA and TMT data-dependent acquisition strategies for capturing protein translation.
    Keywords:  DIA; Q Exactive HF; TMT; orbitrap; pSILAC; protein degradation; protein translation; tribrid; unfolded protein response
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00312
  2. Nat Commun. 2026 Sep 24. pii: 11066. [Epub ahead of print]17(1):
    High-throughput label-free single-cell proteomics enabled by multicolumn NanoLC.
    Chao Wang, Hsien-Jung L Lin, Siqi Huang, Garrett D Haynie, Kenneth S Triggs, Ella E Norton, R J S Miercort, Yen-Jou Chang, David V Hansen, Thy Truong, Xiaofeng Xie, Ryan T Kelly.
      Mass spectrometry (MS)-based single-cell proteomics (SCP) enables proteome-wide analysis at single-cell resolution, offering insights into cellular heterogeneity, biological processes, and disease mechanisms. However, conventional nanoLC-MS workflows are constrained by long gradients and low throughput, limiting their application to small cohorts. Here, we present a multicolumn nanoLC-MS platform that achieves 5-minute separation windows with 100% duty cycles at ~100 nL/min, enabling the analysis of up to 288 single cells per day. The system provides stable peptide separation, negligible carryover, and robust retention-time reproducibility across nearly 1200 consecutive injections. We successfully analyze more than 4000 samples at nearly 288 samples per day (SPD) throughput. The platform identifies ~5000 proteins per 250 pg digest injection and an average of ~4000 proteins per single HeLa cell with maxima exceeding 5100, which is comparable to state-of-the-art longer-gradient workflows. Quantitative benchmarking with mixed-species standards confirm the quantitative accuracy is largely maintained during extended acquisition. Applying the workflow to 783 LPS-treated RAW264.7 macrophages reveals substantial cellular heterogeneity under a uniform inflammatory stimulus. Together, these results establish a robust and scalable platform for high-throughput SCP, enabling thousands of single-cell analyses within a single study while maintaining deep proteome coverage and biological interpretability.
    DOI:  https://doi.org/10.1038/s41467-026-78059-x
  3. J Proteome Res. 2026 Oct 02. 25(10): 5480-5492
    Cross-Platform Evaluation Reveals Sample Preparation-Driven Biological Biases despite Advances in Mass Spectrometry Performance in Serum Proteomics.
    Drosos Katsavelis, Marieke G C van der Hart, Justina C Wolters, Hjalmar P Permentier, Peter L Horvatovich, Thomas I F H Cremers.
      Advances in mass spectrometry (MS) instrumentation and protein extraction technologies are reshaping proteomics; however, whether improved analytical performance alters the biological information accessible from different sample preparation strategies remains unclear. Here, we performed a cross-platform evaluation using identical benchmark serum samples previously analyzed on an Orbitrap Exploris 480 platform, including the newly introduced Seer Proteograph ONE workflow, to determine how MS advances and sample preparation influence analytical performance and biological interpretation. Across all preparation strategies, the timsTOF HT improved analytical performance by increasing protein and peptide identifications, reducing missing values, and improving linearity in spike-in experiments, particularly for low-abundance proteins. Despite these improvements, sample preparation remained the major determinant of the accessible serum proteome. Functional enrichment analysis revealed reproducible workflow-specific biases across MS platforms: nanoparticle-based enrichment expanded access to a broader range of serum proteins and improved detection of clinically relevant signaling-associated proteins, including cytokine- and chemokine-related marker classes, whereas the depletion-based strategy showed a distinct functional profile enriched for receptor-related activities. Together, these findings demonstrate that MS advances enhance analytical performance but do not eliminate sample preparation-driven differences in biological accessibility. This study provides a framework for selecting serum proteomics workflows according to specific biomarker and pathway objectives.
    Keywords:  Orbitrap Exploris 480; biological pathways; depth of proteome; quantitative performance; reproducibility; serum proteomics; timsTOF HT
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00493
  4. Adv Exp Med Biol. 2026 ;1359 597-602
    A Novel Approach for Post-Acquisition Mass Calibration in Metabolomic Profiling by High-Resolution Mass Spectrometry.
    Newman Zhang, Guoan Zhang.
      High-resolution mass spectrometry (HRMS) is essential for metabolomics due to its ability to deliver high mass accuracy for confident metabolite identification. However, maintaining this accuracy during large-scale studies is challenging because of instrumental drift, which can compromise data quality. Existing correction strategies, such as frequent recalibration, QC-based adjustments, or internal standard spiking, are often disruptive, costly, or limited in robustness. We present a novel post-acquisition mass calibration approach that uses endogenous metabolites as internal references, eliminating the need for additional experimental steps. Using hydrophilic interaction liquid chromatography coupled with HRMS (HILIC-HRMS) and a predefined metabolite panel, we demonstrate that a preliminary mass matching using a wide mass tolerance window provides sufficient reference points for estimating drift and correcting mass errors. The method effectively removes m/z-dependent deviations and restores accurate mass measurements across diverse sample types. This strategy is robust, easy to implement, and broadly applicable to targeted metabolomics workflows, offering a practical solution for improving mass accuracy in large-scale studies without added cost or complexity.
    DOI:  https://doi.org/10.1007/978-3-032-29437-1_37
  5. J Am Chem Soc. 2026 Oct 21. 148(41): 44629-44638
    Isobaric Intramolecular Paternò-Büchi Reactions Enable Multiplexed Quantitation of Fatty Acids down to C═C Locations.
    Lipeng Qiao, Jing Zhao, Yu Xia.
      Fatty acids (FAs), as key lipids in energy storage and signaling, are dynamically regulated to maintain cellular homeostasis. FA profiling has become a valuable approach for investigating lipid reprogramming in disease. Although isomer-resolved mass spectrometry (MS) methods facilitate differentiation of FA isomers, quantitation is limited by the scarcity of isomer standards. Here, leveraging isobaric intramolecular Paternò-Büchi (i2PB) reactions, we develop an isomer-resolved multiplexed quantitation method for FA profiling. The i2PB reagents, synthesized via amidation of benzoyl-phenylalanine with the 6-plex TMT tags at near-quantitative conversion, overcome the low-yield limitations of conventional PB reactions for low-abundance lipids. Rapid i2PB reactions (4 s) enable online integration with liquid chromatography, while subsequent MS2 and MS3 analyses generate reporter ions for 6-channel FA quantitation at the sum composition and C═C location levels, respectively. For FA quantitation, this approach offers improved throughput, amol sensitivity, and a linear dynamic range of 2 orders of magnitude. Using i2PB, we achieve parallel quantitation of over 100 FAs with defined C═C locations in normal and breast cancer cells, providing new insights into metabolic reprogramming of FAs in breast cancer cells.
    DOI:  https://doi.org/10.1021/jacs.6c13601
  6. Adv Exp Med Biol. 2026 ;1359 581-595
    Analytical Methods for Mass Spectrometry-Based Metabolomics Studies.
    Siyu Wang, Ian A Blair, Clementina Mesaros.
      The advancement of mass spectrometry-based analytical platforms largely facilitates small-molecule metabolomics studies, which allows analysis of a large number of metabolites from bio-samples simultaneously and gives a general picture of metabolic changes related to diseases or environmental alteration. Due to the large diversity of cellular metabolites, globally and precisely examining metabolic profile remains the most challenging part in metabolomic experiment. Mass spectrometry coupled with liquid chromatography enhances the sensitivity and resolving power of metabolites identification and quantification, as well as the versatility of analyzing a wide array of metabolites. In this chapter, we discussed the technical aspects of each step in the workflow of metabolomics studies we aimed to give technical guidelines for metabolomics investigation design and approach.
    DOI:  https://doi.org/10.1007/978-3-032-29437-1_36
  7. J Proteome Res. 2026 Oct 02. 25(10): 5469-5479
    Prioritizing Peptides for Targeted Mass Spectrometry Experiments Using Deep Learning.
    Shreyash Sonthalia, Bo Wen, Priank Dasgupta, Chris Hsu, Michael J MacCoss, William Stafford Noble.
      One critical step in any targeted mass spectrometry experiment is selecting, from each protein of interest, a small number of peptides that respond well in the mass spectrometer and can serve as reliable proxies for protein quantification. Existing methods select target peptides either by relying on prior empirical measurements, limiting their applicability to previously observed peptides, or using machine learning to predict peptide behavior from sequence alone. However, current machine learning tools suffer from various limitations, including using detectability as an indirect proxy for intensity, relying on small training sets, or ignoring the precursor charge state. In this study, we introduce Bromo, a transformer-based deep learning model that ranks peptide precursors from a given protein by their relative response, taking charge state into account. Trained on millions of annotated peptide pairs derived from large-scale, publicly available data-independent acquisition mass spectrometry data, Bromo consistently outperforms existing sequence-based methods across diverse, independent data sets. Furthermore, we show that fine-tuning Bromo on experiment-specific data can account for differences in sample preparation, sample matrix, and instrument platform, all of which influence which peptides serve as optimal targets. This adaptability makes Bromo a practical tool for selecting target peptides for selected reaction monitoring and parallel reaction monitoring assay development across a wide range of experimental conditions.
    Keywords:  deep learning; mass spectrometry; peptide ranking; proteomics; targeted proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00457
  8. Adv Exp Med Biol. 2026 ;1359 555-574
    Advances in Multiple Reaction Monitoring (MRM) for Targeted Proteomics in Biomedical Research.
    Elyssa N Baker, Timon Geib, Christoph H Borchers.
      Targeted proteomics has emerged as a cornerstone of quantitative biomedical research, enabling precise, reproducible measurement of predefined proteins across complex biological matrices. Among targeted mass spectrometry approaches, multiple reaction monitoring (MRM), typically performed on triple quadrupole (QQQ) instruments, remains the gold standard for sensitive and accurate protein quantitation. Over the past decade, substantial advances in instrumentation, experimental workflows, and computational tools have significantly expanded the analytical depth, throughput, and accessibility of MRM-based assays. Concurrently, innovative strategies for proteome-wide quantitation are addressing longstanding challenges related to standardization, cost, and scalability. This chapter provides a comprehensive overview of the principles underlying MRM, recent technological and methodological advancements, and emerging applications in biomedical research, with an emphasis on biomarker validation, clinical proteomics, and absolute proteome profiling.
    Keywords:  Absolute quantitation; Clinical proteomics; Multiple reaction monitoring; Stable isotope-labeled standards; Targeted proteomics; Triple quadrupole mass spectrometry
    DOI:  https://doi.org/10.1007/978-3-032-29437-1_34
  9. J Proteome Res. 2026 Oct 02. 25(10): 5523-5533
    A Robust DIA-Based Platform for Large-Scale Plasma Glycoproteomics and Biomarker Discovery.
    Chi-Hung Lin, Sayee Sawale, Mark Marispini, Adam Poltorak, Joon-Yong Lee, Natalie Smith, Wan-Fang Chou, Hao Qian, Philip Ma, Bruce Wilcox.
      Structural changes in protein glycosylation are recognized as phenotypes in numerous diseases, including cancer. Despite the promise of glycoproteomics for diagnostic biomarker discovery, large-scale studies remain limited by challenges in reproducibility, throughput, and quantitative precision. Here, we present a robust data-independent acquisition (DIA)-based plasma glycoproteomics platform that enables high-throughput and reproducible glycopeptide quantification suitable for large-cohort studies. The workflow integrates automated in-solution digestion and enrichment, optimized DIA-LC-MS acquisition, and a customized bioinformatics pipeline to achieve reproducible glycopeptide identification and quantitation. Across 560 replicates of a pooled human plasma sample processed in seven batches, the platform achieved high reproducibility, with intra-batch coefficients of variation (CVs) below 15% (n = 80 per batch) and an inter-batch CV of 22.9%. The method accurately captured expected fold changes from spiked-in glycoprotein standards. In the setting of a proof-of-concept pilot study to demonstrate the platform's ability to detect disease-associated glycosylation differences, the platform successfully identified glycoform-specific changes and distinguished between noncancer individuals (n = 39) and those with stage III/IV lung cancer (n = 39). Together, these results establish a high-throughput DIA-based glycoproteomics workflow suitable for large-cohort studies to discover glycopeptide biomarker candidates.
    Keywords:  DIA; biomarker; glycoproteomics; plasma
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00522
  10. Adv Exp Med Biol. 2026 ;1359 679-692
    Application of Ion Mobility Mass Spectrometry in Lipidomics.
    Fereshteh Zandkarimi, Lewis M Brown.
      Lipids play significant roles in biological systems, and the study of lipid metabolism may provide a new insight into the diagnosis and pathophysiology of diseases. Recent developments in high-resolution mass spectrometry techniques combined with high-performance chromatographic methods provide deep insight into lipid analysis. Addition of ion mobility mass spectrometry orthogonal to LC-MS analysis workflow enhances the separation of complex lipids, improves isomer resolution, and increases confidence in lipid identification and characterization. In this chapter, we describe the principle of traveling wave ion mobility mass spectrometry (TWIMS) and its applications in untargeted LC-MS analysis for characterizing the structural diversity and complexity of lipid species in biological samples.
    DOI:  https://doi.org/10.1007/978-3-032-29437-1_41
  11. Environ Sci Technol. 2026 Sep 22. 60(37): 25953-25956
    Mass Spectrometry-Based Exposomics Is Not Metabolomics by Another Name.
    Peng Gao.
      
    Keywords:  exposomics; mass spectrometry; metabolomics
    DOI:  https://doi.org/10.1021/acs.est.6c10462
  12. Anal Chem. 2026 Sep 29. 98(38): 28097-28105
    Rapid Eco-AI CE-MS Enables Sub-7 Min Low-Input Proteome Profiling with Quantitative Validation.
    Fei Zhou, Bowen Shen, Isabelle S Luz, Laura G Rodriguez, Steven J Prior, Wagner Fontes, Peter Nemes.
      Low-input and single-cell proteomics by mass spectrometry (MS) have advanced rapidly, but balancing analytical speed and sensitivity remains challenging, especially for small, protein-scarce primary cells such as neutrophils. Here, we report Rapid Eco-AI, an optimized electrophoresis-correlative (Eco) capillary electrophoresis-mass spectrometry (CE-MS) workflow with artificial intelligence (AI)-assisted data processing for fast, high-efficiency, low-input proteome profiling. Relative to our 15 min Eco-AI method, Rapid Eco-AI compresses the effective separation window to 5-7 min while maintaining substantial peptide and proteome coverage, efficient precursor ion sampling, and quantitative precision. Real-time tracking of charge-dependent m/z and migration-time correlations, together with an optimized MS2 strategy, sustained high peptide-spectrum-match conversion under rapid CE, while AI-assisted interpretation of chimeric MS2 spectra (CHIMERYS) supported robust identification and label-free quantification. Implemented on a legacy Orbitrap platform (Q Exactive Plus), Rapid Eco-AI cumulatively identified 1,634 and 1,347 proteins from 300 pg of single-cell-equivalent HeLa digest across technical triplicates using 7 and 5 min effective separations, corresponding to <15 and <11 min injection-to-injection runtimes, respectively. Using the 7 min separation, 835 proteins were cumulatively identified from 75 pg of HeLa digest. Hybrid HeLa-yeast proteome mixtures validated quantitative performance, with median coefficients of variation <5% and measured mean B/A ratios of 0.57 and 1.60 for expected values of 0.60 and 1.80, respectively. As a proof-of-principle application, we analyzed fractional digest loads from 13 freshly isolated single human neutrophils, corresponding to ∼2 pg of digest per run on a manual CE-MS platform, and identified 135 proteins after conservative background filtering. Collectively, these results establish Rapid Eco-AI as a fast CE-MS workflow for low-input proteome profiling with quantitative validation and proof-of-principle transfer to neutrophil-derived digest analysis.
    DOI:  https://doi.org/10.1021/acs.analchem.6c03046
  13. Adv Exp Med Biol. 2026 ;1359 521-532
    Stable Isotope Labeling by Amino Acids in Cell Culture (SILAC) for Quantitative Proteomics.
    Esthelle Hoedt, Guoan Zhang, Thomas A Neubert.
      Stable isotope labeling by amino acids in cell culture (SILAC) is a powerful approach for high-throughput quantitative proteomics. SILAC allows highly accurate protein quantitation through metabolic encoding of whole cell proteomes using stable isotope-labeled amino acids. Since its introduction in 2002, SILAC has become increasingly popular. In this chapter, we review the methodology and application of SILAC, with an emphasis on three research areas: dynamics of posttranslational modifications, protein-protein interactions, and protein turnover.
    DOI:  https://doi.org/10.1007/978-3-032-29437-1_31
  14. Anal Chem. 2026 Oct 20. 98(41): 29604-29615
    Drug Metabolism and Drug-Drug Interactions Studied Using High-Throughput Ambient Mass Spectrometry.
    Mousumi Saha, Nari Talaty, Nicolás M Morato, Mahdiyeh Shahi, Christopher J Welch, Yunfei Feng, Christina R Ferreira, David Wagner, Andreas Kaerner, Robert Graham Cooks.
      Rapid characterization of drug metabolism and drug-drug interactions (DDIs) with quantitative rigor remains challenging due to complex samples and labor-intensive workflows. We present a high-throughput (HT) ambient mass spectrometry (MS) approach using desorption electrospray ionization (DESI) that unites three complementary capabilities available using two different mass spectrometers: (A) untargeted screening to survey the metabolic space, (B) product ion scan tandem MS to characterize novel metabolites, and (C) multiple reaction monitoring (MRM) to deliver precise quantitation of substrates and metabolites. Applying the methodology to diverse in vitro systems, we track substrate depletion, metabolite formation, and DDIs with quantitative accuracy (error <9% for MRM quantitation) validated against liquid chromatography (LC) MS. The method operates two to three orders of magnitude faster (1 s/sample) than traditional LC-MS methods. This allows hundreds of substrate-enzyme-DDI combinations to be followed in time-resolved fashion using ∼4 × 104 individual DESI-MS measurements, acquired at a rate of >3,000 measurements/hour. By combining HT screening and targeted monitoring, the DESI-MS approach delivers metabolic discovery with robust quantitation, overcoming a central bottleneck in large-scale drug metabolism studies. This investigation aims to provide a high-throughput analytical approach with broad utility in drug discovery, toxicology, and precision medicine workflows.
    DOI:  https://doi.org/10.1021/acs.analchem.6c04039
  15. Anal Chem. 2026 Sep 15. 98(36): 26339-26352
    A Novel Protein Corona-Based Secretome Profiling Assisting Cell Multiomics Analysis.
    Caiwei Hu, Jialiang Zhao, Qian Li, Hongwei Hou, Yu Bai.
      Multiomics analysis integrates complementary molecular layers to decode biological complexity; however, the secretome, representing the extracellular and dynamic response of cellular function, remains largely absent from existing multiomics frameworks. Secretome profiling has been constrained by high background interference, poor sensitivity for low-abundance proteins, and perturbations caused by conventional enrichment strategies. Here, we develop a protein corona-based secretome profiling strategy and incorporate it into a multiomics analytical framework. By exploiting the selective adsorption of secreted proteins onto multiple functionalized magnetic nanoparticles under serum-containing and label-free conditions, this method enables the enrichment of secreted proteins under near-physiological conditions. Combining this method with data-independent acquisition (DIA) mass spectrometry enhances identification depth, quantitative dynamic range, and reproducibility. In total, 4,081 protein groups were identified from Michigan Cancer Foundation-7 (MCF-7) cells, including 858 annotated secreted proteins, spanning nearly 5 orders of magnitude in abundance, with >94% consistency across replicates. By integrating secretome profiling with intracellular proteomics and metabolomics, we conducted a multiomics analysis of Tohoku Hospital Pediatrics-1 (THP-1) macrophages across distinct polarization states, revealing differences between M1 and M2 phenotypes in immune activity and metabolic regulation. Notably, polarization-associated variations were more prominent at the secretome level than in intracellular proteomes, highlighting the unique contribution of extracellular protein dynamics to functional state definition. We further applied this strategy to patient-derived organoids (PDOs), enabling secretome profiling in a model that better preserves tumor heterogeneity and microenvironmental context. Collectively, this study establishes a robust and scalable strategy for incorporating the secretome into multiomics analyses.
    DOI:  https://doi.org/10.1021/acs.analchem.6c00743
  16. J Am Soc Mass Spectrom. 2026 Oct 07. 37(10): 2577-2584
    Cross-Platform Consistency of FAIMS Compensation Voltage Optimization in Modern Orbitrap Mass Spectrometers.
    Siwen Zhang, Joao A Paulo.
      High-Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS) enhances peptide identification in LC-MS proteomics, but whether optimal compensation voltages (CVs) are transferable between mass spectrometry platforms remains unclear. To address this gap, we used the Pierce Peptide Retention Time Calibration (PRTC) mixture comprised of 15 synthetic peptides and analyzed the CV transmission profiles across a wide voltage range (-85 to -25 V). We systematically evaluated FAIMS CV profiles across three modern Orbitrap mass spectrometers (Astral, Ascend, and Exploris 480), each equipped with different ionization sources and chromatographic systems. Across the three platforms, optimal CVs were conserved within a narrow tolerance (typically ≤5 V), supporting CV portability for routine method transfer under the conditions evaluated. Additionally, we observed that interplatform CV variability was greatest for peptides with strongly negative optimal CVs, consistent with the greater conformational sensitivity of higher charge-density ions under high-field FAIMS conditions. Together, these results suggest that FAIMS CV requirements are predominantly peptide-intrinsic properties under typical bottom-up proteomics conditions and support the use of the PRTC mixture as a practical standard for routine FAIMS calibration and cross-platform quality control in proteomics laboratories.
    Keywords:  FAIMS; compensation voltage; ion mobility; peptide analysis; proteomics
    DOI:  https://doi.org/10.1021/jasms.6c00307
  17. Nat Commun. 2026 09 19. pii: 10985. [Epub ahead of print]17(1):
    Extending single-cell proteomics from HeLa to small primary human immune cells.
    Ivo A Hendriks, Sara C Buch-Larsen, Martin Rykær, Maico Y Lechner, Anders H Kverneland, Tabiwang N Arrey, Daniel Hermanson, Eugen Damoc, Jesper V Olsen.
      Single-cell proteomics (SCP) holds the promise of decoding cellular heterogeneity at the functional level, yet achieving deep and reproducible proteome coverage from individual cells has remained a formidable challenge. Here, we establish a high-sensitivity, label-free SCP platform that extends current depth and reproducibility. By integrating optimized low-input sample processing, refined liquid chromatography, and the Orbitrap Astral Zoom mass spectrometer, our approach routinely quantifies up to 7000 proteins per individual HeLa cell at over 100 samples-per-day (SPD), capturing thousands of low-abundance proteins that eluded prior SCP studies. Applied to very small human peripheral blood mononuclear cells (PBMCs), we identify up to 4000 proteins per cell, including key markers that distinguish monocytes, T cells, and activated lymphocytes within heterogeneous populations, underscoring that single-cell proteomics can now directly elucidate clinically relevant primary samples with both depth and precision.
    DOI:  https://doi.org/10.1038/s41467-026-77675-x
  18. Food Res Int. 2026 Dec 01. pii: S0963-9969(26)02246-5. [Epub ahead of print]245(Pt 1): 120559
    Proteomics for cultivated meat: Evaluation of sample preparation methods and analytical workflows for label-free quantitative profiling of cultivated meat proteome.
    João Palma, Chloe Colchero Leblanc, Remy Kusters, Franks Kamgang Nzekoue.
      Cultivated meat production requires robust and validated analytical methods for comprehensive characterization. While transcriptomics-based approaches establish the foundational profile of molecular analysis, proteomics provides a direct estimate of the presence and abundance of proteins in cells and tissues, enhancing the scientific certainty in both product development and safety characterization. However, the industrial adoption of proteomics is currently hindered by technical complexity and a critical lack of analytical standardization, which leads to significant workflow-dependent variations in proteome coverage. To address this gap, we investigated the influence of key workflow steps (digestion, cleanup, LC-MS conditions) on the proteome profile of cultivated duck biomass. We compared five bottom-up sample preparation protocols - two traditional in-solution options (urea and SDC-based protocols), two device-based approaches (PreOmics iST and EasyPep kits), and an innovative protocol (SPEED), and demonstrated that device-based protocols offered the highest peptide yield and proteome coverage. However, optimization allowed cost-effective in-solution methods (SDC and SPEED) to achieve comparable performance. Specifically, an optimal digestion time of 3 h at 37 °C and the use of polymer-based desalting columns significantly enhanced protein identification (∼4500-5000 IDs). Moreover, data independent acquisition (DIA) provided deeper proteome coverage than data dependent acquisition (DDA) with higher precision (∼6500 vs 5000 IDs). The optimized and cost-effective sample prep protocols presented here establish a standardized framework for bulk bottom-up proteomics in cultivated meat.
    Keywords:  Bottom-up workflows; Cultivated meat; DDA vs DIA; Method optimization; New approach methodologies (NAMs); Proteome coverage; Proteomics
    DOI:  https://doi.org/10.1016/j.foodres.2026.120559
  19. J Am Soc Mass Spectrom. 2026 Oct 07. 37(10): 2453-2462
    A Liquid Chromatography-Mass Spectrometry Method to Quantify Total Coenzyme A Concentration and Isotopic Labeling.
    Amelia L Taylor, Nathaniel W Snyder, Caroline R Bartman.
      Coenzyme A is an essential cofactor synthesized from pantothenate, which carries activated acyl groups in cellular metabolic reactions. Coenzyme A participates in catabolism of carbohydrates, fats, and amino acids; biosynthesis of fatty acids, cholesterol, and heme; and protein modification, including acetylation and 4-phosphopantetheinylation. Despite CoA's critical functions, the regulation of CoA levels and the rate of CoA synthesis in different cell types and disease states are not well understood. Acyl-CoA species are challenging to measure due to instability, poor ionization, and the wide range of biochemical properties conferred by different acyl chain lengths; moreover, most current methods cannot measure CoA isotopic labeling, required to quantify the CoA synthesis rate or to measure concentration using isotope-labeled internal standards. Here, we describe a method to quantify the concentration and isotopic labeling of total CoA, defined as the sum of CoASH plus all acyl-CoA species. Acyl-CoA species are hydrolyzed using sodium hydroxide to remove acyl chains; then, CoA is derivatized with N-ethylmaleimide (NEM). Following protein precipitation and solid-phase extraction, samples are analyzed by liquid chromatography-mass spectrometry. This method is linear in a wide range that captures mouse tissue CoA levels, with accuracy within 15% error and precision below 15% relative standard deviation for standards and tissue samples. We applied this method to measure total CoA concentration in five mouse tissues and the total CoA synthesis rate in mouse liver via infusion of 13C-15N-pantothenate. Overall, this method offers a tractable approach to measure total CoA concentration and synthesis rate in health and disease.
    DOI:  https://doi.org/10.1021/jasms.6c00206
  20. Anal Chem. 2026 Sep 22. 98(37): 27371-27380
    Same-Sample Profiling of Renin-Angiotensin Peptides and Enzymes in Mouse Brain Biopsies by Serial Extraction and High-Resolution Mass Spectrometry.
    Ailing Li, Ramesh Kumar, Zhe Yu, Paul J Marvar, Peter Nemes.
      The brain renin-angiotensin system (RAS) comprises biologically active peptides generated through enzymatic cascades that regulate cardiovascular and neurobiological processes and are implicated in cardiometabolic and neurodegenerative diseases. However, molecular characterization of brain RAS components remains challenging due to their low abundance and limitations of existing analytical approaches. Conventional mass spectrometry (MS) workflows typically require separate samples for peptidomic and proteomic analyses, increasing sample consumption and biological variability. Here, we developed a same-sample serial extraction approach for paired measurement of angiotensin peptides and RAS enzymes from limited biopsies of mouse cortex and brainstem, regions associated with memory and cardiometabolic regulation. Peptides were first extracted with acidified acetonitrile, and proteins were then recovered from the remaining pellet for bottom-up proteomic analysis. Angiotensin peptides were quantified by capillary electrophoresis (CE)-electrospray ionization (ESI)-parallel reaction monitoring (PRM)-MS, while RAS enzymes were measured by nano-flow liquid chromatography-ESI-PRM-MS with complementary discovery proteomic analysis. The first extraction recovered more than 94% of measurable endogenous angiotensin peptide signal, while protein identification and quantification remained comparable to the conventional separate-sample workflow. This strategy halved animal use. CE-ESI-PRM-MS achieved low-attomole sensitivity, with a lower limit of detection estimated at ∼7 amol for angiotensin III. Applied to mouse cortex and brainstem, the workflow profiled 7 angiotensin peptides and 7 RAS enzymes, revealing a more prominent angiotensin-processing peptide-enzyme profile in brainstem than cortex. This sample-sparing analytical strategy enables matched peptide and protein measurements from the same tissue sample and is broadly applicable to pathway-focused studies in scarce biological material.
    DOI:  https://doi.org/10.1021/acs.analchem.6c02943
  21. Methods Mol Biol. 2027 ;3092 83-96
    Analysis of Metabolomics Response to Tick Saliva in Human Cells.
    Sara Samino, Rita Vaz-Rodrigues, Almudena González-García, José de la Fuente, Núria Amigó.
      This chapter describes a workflow for the analysis of the metabolomic response of the human basophilic cell line KU812 exposed to Ixodes ricinus tick saliva using ¹H-nuclear magnetic resonance (NMR) spectroscopy. The protocol includes procedures for cell culture, harvesting, washing, and metabolite extraction, followed by the separation of aqueous and lipid fractions. Detailed instructions for sample preparation and NMR data acquisition are provided for both fractions. Spectral processing and metabolite quantification are performed using targeted profiling approaches based on constrained line-shape fitting, enabling accurate analysis of complex biological samples. Metabolite identification is supported by spectral databases and literature references, and absolute concentrations are obtained using internal standard-based quantification methods. Finally, data normalization based on cell number ensures comparability across samples. Overall, this protocol provides a robust and reproducible strategy for investigating metabolic alterations induced by tick saliva in human cells.
    Keywords:  Allergy; KU812 cells; Lipidomics; Metabolic response; Metabolomics; Spectral deconvolution; Targeted profiling; Tick saliva; ¹H-NMR spectroscopy
    DOI:  https://doi.org/10.1007/978-1-0716-5635-8_8
  22. Curr Opin Biotechnol. 2026 Oct 22. pii: S0958-1669(26)00154-0. [Epub ahead of print]102 103589
    High-throughput mass spectrometry-based metabolomics for large-scale population studies: technical barriers and recent innovations.
    Mariana Buitrago-Oliveros, Zaineb Hamoodi, Philip Britz-Mckibbin.
      Large-scale metabolomic studies are needed to identify and validate biomarkers for their successful translation in emerging population health and precision medicine initiatives. Yet, untargeted mass spectrometry (MS)-based metabolomics remains hampered by low sample throughput, limited coverage, complicated data processing, poor standardization, and high operating costs. These challenges also contribute to a reproducibility crisis, with most reported biomarkers not independently replicated, given variations in study designs, sample workup protocols, instrumental methods, and data pre-processing workflows. This review will focus on new advances in high-throughput MS platforms and computational software with emerging artificial intelligence/machine learning tools that are scalable, faster, and less prone to error. Recent progress to improve data harmonization in population-level metabolomic, lipidomic, and exposomic studies (n > 1000) will also be discussed. This will include strategies to correct for long-term batch effects, improve metabolite identification, and derive biochemical insights into disease mechanisms when integrated with multi-omic studies.
    DOI:  https://doi.org/10.1016/j.copbio.2026.103589
  23. J Proteome Res. 2026 Oct 02. 25(10): 5430-5439
    Top-Down Proteomics Analysis of pg-Level Cell Lysate Using Spray-Capillary CE-FAIMS-MS.
    Samin Anjum, Zhitao Zhao, Kellye A Cupp-Sutton, Si Wu.
      Capillary electrophoresis-mass spectrometry (CE-MS) is a promising technique for ultrasensitive top-down proteomics due to its high resolution and sensitivity compared with traditional liquid chromatography (LC)-based methods. However, CE-MS-based top-down proteomics of mass-limited samples remains challenging because of the need to handle ultralow sample amounts. Recently, we developed a spray-capillary CE-MS platform for ultrasensitive, low-volume top-down proteomics analysis, allowing for the detection of hundreds of intact proteoforms from pg-level samples. Here, we integrated this platform with high-field asymmetric waveform ion mobility spectrometry (FAIMS) to enhance proteoform coverage in mass-limited samples (i.e., samples with limited input mass). Our results suggested that FAIMS effectively enhanced the signal-to-noise ratio for pg-level samples by filtering background noise. At low injection masses (e.g., 1 pg), experiments performed with FAIMS yielded higher relative abundance than those conducted without FAIMS. Interestingly, in all cases, we did not observe the substantial loss in intensity commonly reported for bottom-up proteomics with FAIMS. Additionally, we implemented 2D CE-FAIMS-MS by applying five different FAIMS compensation voltages (CVs) for gas-phase intact proteoform fractionation. This platform detected >3000 yeast proteoforms using 100 pg per CV. Overall, our results suggested that the spray-capillary CE-FAIMS-MS platform enables deep proteoform characterization of pg-level samples and holds potential for analyzing intact proteoforms in mass-limited biological samples.
    Keywords:  CE-MS; FAIMS; Top-down proteomics; gas-phase ion mobility separation; intact protein analysis; mass-limited samples; proteoforms; single-cell proteomics; spray-capillary; ultrasensitive proteomics
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00353
  24. Methods Mol Biol. 2027 ;3092 133-139
    Untargeted UHPLC-HRMS Lipidomic Profiling of Ticks for Alpha-Gal Syndrome Research.
    Hanna Nikolaichuk, Anna Kozub-Pędrak, Joanna Kulisz, Zbigniew Zając, Aneta Woźniak, Alejandro Cabezas-Cruz, Emilia Fornal.
      Lipids are a fundamental group of biomolecules that play diverse roles in biological systems, including energy storage, membrane architecture, and intra- and extracellular signaling. They are classified into eight major categories, and more than 50 000 distinct lipid structures are currently reported in the Lipid Maps Structure Database (LMSD). Given their importance in cellular metabolism and physiology, accurate identification and quantification of lipids are essential. In ticks, lipid metabolism is closely associated with physiological processes related to survival, pathogen transmission, and host interaction. Tick blood-feeding has also been linked to the development of alpha-gal syndrome (AGS), a delayed allergic reaction to mammalian-derived products, suggesting that characterization of tick lipid and glycolipid composition may provide insights into mechanisms of host sensitization. This chapter describes a protocol for untargeted lipidomic analysis of tick samples using ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS). The workflow includes rapid sample freezing, tissue homogenization, solvent-based lipid extraction, protein precipitation, and sample reconstitution prior to analysis. Lipid separation is achieved using reverse-phase chromatography on a C18 column, followed by detection with a quadrupole time-of-flight (Q-TOF) mass spectrometer operating under optimized ionization conditions. The use of blanks and pooled quality control (QC) samples ensures analytical stability and reproducibility. The presented method enables comprehensive profiling of lipids in tick tissues and provides a reproducible platform for studies of tick biology, host-tick interactions, and tick-induced sensitization processes, supporting the mechanistic studies and the identification of potential lipid-based biomarkers.
    Keywords:  Arthropod metabolomics; High-resolution mass spectrometry; Tick lipidomics; Ultra-high pressure lipid chromatography
    DOI:  https://doi.org/10.1007/978-1-0716-5635-8_12
  25. Adv Exp Med Biol. 2026 ;1359 533-543
    Pulse SILAC Approaches to the Measurement of Cellular Dynamics.
    Justin Snider, Daifeng Wang, Daniel F Bogenhagen, John D Haley.
      The global measurement of assembly and turnover of protein-containing complexes within cells has advanced with the development of pulse stable isotope labelled amino acid approaches. Stable isotope labeling with amino acids in cell culture (SILAC) allows the incorporation of "light" 12-carbon amino acids or "heavy" 13-carbon amino acids into cells or organisms and the quantitation of proteins and peptides containing these amino acid tags using mass spectrometry. The use of these labels in pulse or pulse-chase scenarios has enabled measurements of macromolecular dynamics in cells, on time scales of several hours. Here, we review advances with this approach and alternative or parallel strategies. We also examine the statistical considerations impacting datasets detailing mitochondrial assembly, to highlight key parameters in establishing significance and reproducibility.
    DOI:  https://doi.org/10.1007/978-3-032-29437-1_32
  26. Anal Chem. 2026 Sep 29. 98(38): 28218-28228
    Cyclic Ion Mobility Separations of Free Oxylipins.
    Eric C Gier, Facundo M Fernández.
      Oxylipins are oxygenated lipid metabolites derived from polyunsaturated fatty acids (PUFAs). They play interconnected roles in physiological processes such as inflammation and vascular function, and their dysregulation has been linked to diseases, including cardiovascular, metabolic, and immunological disorders. Liquid chromatography (LC) has a limited ability to resolve the large number of isomers and isobars within this diverse lipid class. Ion mobility spectrometry (IMS) offers an orthogonal gas-phase separation technique and has been shown to improve peak capacity for isomers that differ by more than 1-2% in collision cross section (CCS). High-resolution IMS platforms have progressively enabled the separation of isomers with increasingly similar gas-phase structures and offer the potential to separate complex mixtures in experiments such as mass spectrometry imaging where LC is not available. Here, we demonstrate the application of high-resolution cyclic ion mobility spectrometry-mass spectrometry (cIMS-MS) to separate and characterize six complex mixtures of authentic oxylipin standards. Results show that deprotonated oxylipin species often exhibit multiple mobility features that emerge only after extended separations, whereas metal-adducted species, especially sodium adducts, yield clearer single peaks. Optimized traveling wave parameters enabled the separation of mixtures containing three to five oxylipins within 3-10 m of effective path length before ion wrap-around occurred, resolving features with ΔDTCCS% of approximately 0.5% without front-end chromatographic separation. Multipass cIMSCCSN2 measurements under optimized conditions were highly reproducible and closely matched the DTCCS reference values from Moran-Garrido et al. and da Silva et al., with average deviations of 0.33-0.76%. These findings support the use of multipass cIMS experiments and cIMSCCS measurements as valuable tools for oxylipin separation and annotation, particularly in the absence of chromatographic retention time data.
    DOI:  https://doi.org/10.1021/acs.analchem.6c03450
  27. Adv Exp Med Biol. 2026 ;1359 717-740
    Bottlenecks in Proteomics: An Addendum.
    Devika Channaveerappa, Pathea S Bruno, Ulfet Erdogan, Angiolina Hukovic, Charlotte Talbot, Sarah Abbott, Costel C Darie.
      Mass spectrometry (MS) is the core for advanced methods in proteomic experiments. When effectively used, proteomics may provide extensive information about proteins and their post-translational modifications, as well as their interaction partners. However, there are also many problems that one can encounter during a proteomic experiment, including, but not limited to, sample preparation, sample fractionation, sample analysis, data analysis and interpretation, and biological significance. Here, we discuss some of the problems that researchers should be aware of when performing a proteomic experiment.
    DOI:  https://doi.org/10.1007/978-3-032-29437-1_43
  28. Adv Exp Med Biol. 2026 ;1359 69-88
    From Stains to Spectra: Multiplexed Immunohistochemistry and Mass Spectrometry Imaging for Spatial Proteomics in Biomedicine.
    Anca-Narcisa Neagu, Irina-Draga Caruntu.
      Advances in spatial proteomics have fundamentally transformed the analysis of complex tissues by enabling the simultaneous interrogation of protein expression and spatial organization within intact biological samples. Early immunohistochemical approaches were limited by low multiplexing capacity, restricting the ability to capture cellular heterogeneity and microenvironmental interactions. Recent technological developments have markedly expanded multiplexing power, allowing the parallel detection of dozens of protein markers while preserving tissue architecture and spatial context. Multiplexed immunohistochemistry and immunofluorescence techniques have substantially increased the number of detectable targets in a single tissue section and enabled high-resolution mapping of cellular phenotypes. In parallel, mass cytometry-based imaging and mass spectrometry imaging platforms have introduced label-free or metal-tagged strategies that overcome spectral overlap, further extending multiplexing capabilities and quantitative robustness. These approaches enable comprehensive spatial profiling of tumor ecosystems and tissue architecture in both normal and diseased states. The integration of immunohistochemistry with mass spectrometry-based spatial proteomics has led to hybrid workflows that combine targeted molecular specificity with unbiased proteome-wide analysis. This convergence has expanded the analytical depth of spatial biology, supporting applications ranging from biomarker discovery and drug distribution studies to mechanistic insights into cancer progression, immune regulation, and neurodegenerative disease. Collectively, the continuous increase in multiplexing capacity across imaging and mass spectrometry platforms has positioned spatial proteomics as a powerful and versatile framework for biomedical research, with growing translational and clinical relevance.
    Keywords:  Biomarkers; Biomedicine; Immunofluorescence (IF); Immunohistochemistry (IHC); Mass Spectrometry Imaging (MSI); Proteomics
    DOI:  https://doi.org/10.1007/978-3-032-29437-1_4
  29. J Am Soc Mass Spectrom. 2026 Oct 07. 37(10): 2442-2452
    Bridging Speed and Confidence in Metabolomics: A Retention Time and MS2-Guided HILIC-Orbitrap Workflow.
    Jonas Rösler, Constantin P Krempe, Feyza Cansiz, Jaqueline Leddin, Friederike Jahr, Jost Guinand, Sven W Meckelmann, Alpaslan Tasdogan, Oliver J Schmitz.
      Personalized treatment strategies are rising within modern medicine approaches to dynamic and heterogeneous diseases such as cancer. Human metabolism is important for the understanding of these diseases and their mechanisms, but metabolomics remains often unused in multiomics approaches due to practical challenges in sample analysis and, more importantly, data analysis. Automated feature analysis with many false-positive annotations and a rather long analysis time is effectively hindering rapid metabolite screening, as it would be needed in medical research. Here, we report a novel and efficient workflow for rapid metabolite screening with the capability of yielding high-quality metabolomics data, including data analysis. Therefore, a HILIC (superficially porous sulfobetaine) method has been optimized to separate polar metabolites in complex samples in less than 9 min, and an open-source compatible retention time database has been implemented for this rapid screening method, including about 400 metabolites in central metabolic pathways. The method operates on a Q-Exactive Plus Orbitrap in DDA full-scan acquisition based on an inclusion list and has been shown to be highly repeatable and sensitive. Moreover, false-positive annotations could be severely reduced, enabling a simplified and slim data analysis approach. Using the supplied database and the code for data filtering, a rapid metabolite screening can be conducted, suitable for the application as a rapid metabolic profiling strategy in biological and medical research.
    Keywords:  HILIC; cancer; metabolomics; suspected target screening; system medicine
    DOI:  https://doi.org/10.1021/jasms.6c00203
  30. Adv Exp Med Biol. 2026 ;1359 1-30
    Mass Spectrometry for Proteomics-Based Investigation.
    Pathea S Bruno, Izabela Sokolowska, Armand G Ngounou Wetie, Roshanak Aslebagh, Kelly L Wormwood, Devika Channaveerappa, Emmalyn J Dupree, Madhuri Jayathirtha, Danielle Whitham, Lawrence Tucker, Celeste A Darie, Alisa G Woods.
      Within the past years, we have witnessed a great improvement in mass spectrometry (MS) and proteomics approaches in terms of instrumentation, protein fractionation, and bioinformatics. With the current technology, protein identification alone is no longer sufficient. Both scientists and clinicians want not only to identify the proteins but also to identify the proteins' post-translational modifications (PTMs), protein isoforms, protein truncation, protein-protein interactions (PPI), and protein quantitation. Here, we describe the principles of MS and proteomics and strategies to identify proteins, proteins' PTMs, protein isoforms, protein truncation, PPIs, and protein quantitation. We also discuss the strengths and weaknesses within this field. Finally, in our concluding remarks, we assess the role of mass spectrometry and proteomics in the scientific and clinical settings in the near future. This chapter provides an introduction and overview for subsequent chapters that will discuss specific MS proteomic methodologies and their application to specific medical conditions. Other chapters will also touch upon areas that expand beyond proteomics, such as lipidomics and metabolomics.
    Keywords:  LC-MS/MS; MALDI-MS; Mass spectrometry; Post-translational modifications; Proteomics
    DOI:  https://doi.org/10.1007/978-3-032-29437-1_1
  31. Anal Chem. 2026 Sep 29. 98(38): 27918-27926
    Hydraplexing: Design Principles for Fragment-Level Signal Amplification Using a Multi-Amine Model Analyte.
    Ailing Li, Navid J Ayon, Peter Nemes.
      Fragment-ion signals are often too weak to support confident quantification at trace levels. This challenge is particularly acute for limited-input measurements, including single cells. We introduce hydraplexing, a fragment-readout strategy that installs multiple isobaric barcodes on a single analyte so that barcode-derived fragment ions add in tandem mass spectrometry (MS2). Unlike conventional isobaric labeling that increases sample plexity, hydraplexing multiplexes barcode-release pathways within a single precursor to amplify signal quantitation. Here, hydraplexing is established using a standalone multiamine model analyte, 1,2,4,5-tetraaminobenzene (TAB), bearing 2-4 tandem mass tags as TAB-TMT2, TAB-TMT3, and TAB-TMT4 derivatives, which isolates barcode multiplicity without coupling the scaffold to target analytes. Using this controlled system, we mapped barcode-release dissociation networks and defined collision-energy regimes that maximize reporter-ion (RI) output while maintaining precursor transmission during higher-energy collisional dissociation. RI output increased with tag number across direct-infusion electrospray as well as liquid chromatography and capillary electrophoresis separations using parallel-reaction monitoring. Complementary fragment ions (TMTc) peaked at lower collision energies and were more condition-sensitive, identifying RIs as the more robust quantitative channel under the conditions tested. This amplification persisted in a peptide-rich single-cell Xenopus laevis digest matrix, where TAB-TMT4 produced an ∼3.5-fold higher RI/precursor response than TAB-TMT2. Hydraplexing was further transferred to 3,4,5-triaminobenzoic acid (TABA), a carboxylate-functionalized scaffold forming 1×-3× TMT derivatives, for which the 2× and 3× derivatives yielded 1.4-fold and 2.7-fold higher RI output than the 1× derivative. Hydraplexing establishes fragment-level signal summation as a tunable design principle for engineering additive tag-derived fragment-ion outputs.
    DOI:  https://doi.org/10.1021/acs.analchem.6c01384
  32. Anal Chem. 2026 Sep 15. 98(36): 26818-26830
    Extensive Quantitative Profiling of Plasma Steroidome and Prostaglandin Metabolites for Studies of Female Reproductive Physiology.
    Haolin Chen, Luca Bürgi, Anna-Katharina Hankele, Reto Giacometti, Susanne E Ulbrich, Laurent Bigler.
      Steroid hormones and their metabolites orchestrate female reproductive physiology. Key analytes such as 17β-estradiol and progesterone are integral to standard clinical practice worldwide. For more advanced endocrine monitoring of female reproduction, current analytical approaches lack automated throughput, broad metabolite coverage, and sufficient sensitivity. Herein, we present an ultrahigh-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS) workflow that targets 56 selected steroids (estrogens, progestogens, androgens, glucocorticoids, and steroid conjugates) and 5 prostaglandin metabolites for quantitative profiling using 250 μL of plasma within a single pipeline. The workflow integrates optimized, automated protein precipitation-enhanced solid-phase extraction and two complementary chromatographic separations with tailored eluent additives and ionization modes to accommodate the chemical diversity of the broad target panel. A full method validation demonstrated excellent performance, with low limits of quantification (5-200 pg/mL) determined in charcoal-stripped plasma as a surrogate matrix. The workflow was applied to authentic cattle (n = 5) and human (n = 18) plasma samples from males and, particularly, females across the female reproductive cycle and selected pregnancy stages. Both well-established endocrine hormones and their metabolites, as well as analytes whose roles in reproduction remain insufficiently characterized, were among the 42 and 48 targets detected in cattle and human samples, respectively. Compound-specific, valley-split, and composite quantification were reported separately. This demonstrates extensive target coverage and robust quantitative performance across biological matrices with distinct physiological contexts. The workflow thus offers a robust tool for steroidomic studies and enables broad-spectrum monitoring of endocrine dynamics for basic, translational, and clinical research.
    DOI:  https://doi.org/10.1021/acs.analchem.6c03626
  33. Anal Chem. 2026 Oct 06. 98(39): 29052-29062
    An N,N-Dimethylethylenediamine-Derivatized In Silico Mass Spectral Library for Comprehensive Profiling of Isothiocyanates in Brassicaceae Plants.
    Huidan Li, Ye Liu, Jun Ding.
      Isothiocyanates (ITCs) are bioactive hydrolysis products of glucosinolates in Brassicaceae plants with diverse health-promoting properties. However, comprehensive profiling of ITCs remains challenging due to the limited availability of authentic standards, the underexplored structural diversity of naturally occurring ITCs, and poor mass spectrometry (MS) detection sensitivity. Here, we established a N,N-dimethylethylenediamine (DMEDA) derivatization strategy and constructed an in silico MS/MS library for systematic ITC annotation. DMEDA derivatization markedly improved LC-MS detection sensitivity, enabling the reliable detection of low-abundance ITCs with limits of detection at the nanomolar level. Detailed characterization of the MS/MS fragmentation behaviors of derivatized ITC standards revealed subclass-dependent fragmentation patterns, which were subsequently translated into spectral prediction rules for the generation of an in silico mass spectral library with 2209 simulated ITC structures. Application of the developed workflow to Brassicaceae plant samples enabled the annotation of 50 endogenous ITCs, including 4 low-abundance compounds that were previously not reported. Our work provides a valuable resource for the systematic exploration of ITC structural diversity and facilitates functional studies of glucosinolate-derived bioactive metabolites.
    DOI:  https://doi.org/10.1021/acs.analchem.6c04198
  34. Anal Bioanal Chem. 2026 Oct 21.
    Performance evaluation of polarity switching for plasma untargeted metabolomics using Orbitrap mass spectrometer.
    Hailemariam Abrha Assress, Ahmad Mani-Varnosfaderani, Bashar Amer, Brittany R Allman-Tucker, Elisabet Børsheim, Colin D Kay, Renny S Lan.
      Untargeted metabolomics often requires separate positive and negative ionization mode injections to maximize metabolome coverage, increasing analytical time, analysis cost, and complexity. Polarity switching (PSW) represents a promising alternative, yet its adoption in untargeted metabolomics remains limited, likely due to the lack of systematic performance evaluations as well as lack of polarity-switching capability on some mass spectrometers. In this study, we compared the performance of the PSW method to that of separate polarity injections by assessing the total number of detected features, the total number of metabolite annotations, Level 1 and 2 annotations, statistical analysis outcomes, and functional enrichment analysis results. Overall, separate polarity acquisition yielded the highest number of total detected features and annotations. Despite these differences, a strong overlap was observed at higher annotation confidence levels, with 70-76% overlap in Level 1 and Level 2 identified metabolites between the two methods. Notably, metabolites exhibiting significant fold changes showed consistent directional changes across both acquisition approaches, indicating that both methods captured similar biological trends. Statistical analyses using approaches such as t-tests and linear mixed models (LMM) showed that 50-60% of significant metabolites overlapped between methods, with similarity increasing when adjusted p-values were applied. Functional analysis further demonstrated strong agreement between the two approaches, with the most significant pathways consistently identified by both approaches. Overall, while separate polarity acquisition detects a larger number of features and annotations, PSW acquisition captures largely comparable biological information. These findings support PSW for untargeted metabolomics, particularly in exploratory, high-throughput studies prioritizing global metabolic trends with limited resources, providing practical guidance for strategy selection based on study goals and analytical priorities.
    Keywords:  Annotation; Differential analysis; Functional enrichment; Polarity switching; Separate polarity; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/s00216-026-06868-6
  35. Talanta. 2026 Oct 21. pii: S0039-9140(26)01420-7. [Epub ahead of print]313(Pt B): 130764
    Ion mobility mass spectrometry for suspect screening of environmental toxicants: Collision cross sections, structural annotation, and fit-for-purpose evidence evaluation.
    Meiqing Jin, Qingwei Zhou, Weihong Wu.
      Ion mobility mass spectrometry (IM-MS) is increasingly used in suspect screening of environmental toxicants because it adds an orthogonal gas-phase separation dimension to LC-HRMS workflows. This review evaluates how ion mobility measurements and derived collision cross section (CCS) values support candidate rejection, structural annotation, and fit-for-purpose evidence evaluation for environmental chemicals. The discussion covers drift tube, traveling-wave, trapped ion mobility, and differential mobility approaches; experimentally derived and predicted CCS resources; and applications to PFAS, pesticides, pharmaceuticals, mycotoxins, petroleum contaminants, industrial chemicals, and exposure-related xenobiotics. Three quantitative tables compare CCS resources, application studies, and prediction or complementary annotation tools, with emphasis on compound coverage, tolerances, matrix-dependent ionization and mobility behavior, and evidence criteria. Experimentally derived CCS values can reduce candidate lists, resolve coeluting features, support homologous-series interpretation, and improve transfer of screening evidence across campaigns. Predicted CCS is useful for prioritization but requires class-aware applicability checks, especially for fluorinated, halogenated, surfactant-like, and polymer-associated chemicals that are sparse in training data. CCS is most defensible when reported with ion identity, adduct, drift gas, calibration or derivation method, replicate uncertainty, matrix behavior, and a stated confidence level. IM-MS should therefore be integrated as a supporting evidence layer and not treated as a stand-alone identifier.
    Keywords:  Analytical toxicology; Collision cross section; Environmental toxicants; Ion mobility mass spectrometry; PFAS; Suspect screening
    DOI:  https://doi.org/10.1016/j.talanta.2026.130764
  36. Brief Bioinform. 2026 Sep 01. pii: bbag576. [Epub ahead of print]27(5):
    Metabolomics workbench: data-driven classification and correlation analysis between metabolite classes, biomarkers, and drugs.
    Mikhail M Khandokhin, Aleksandr Sokolov, Nikolay Oskolkov, Robert Fredriksson, Olga E Titova, Helgi B Schiöth.
      Public repositories now accumulate an unprecedented amount of metabolomics data. Despite the opportunities for large-scale meta-analyses opened by these repositories, the deposited data rarely becomes systematically re-analyzed. We conducted a systematic metadata-based re-analysis of all human blood metabolomics studies in the Metabolomics Workbench performed using Orbitrap or QTOF mass spectrometry, encompassing 44 165 samples. After harmonizing metabolites with PubChem identifiers and embedding them using SMILES representations, we generated a map of 6752 blood metabolites forming 25 structural clusters, and subsequently performed correlation analysis between these clusters, biomarkers, and drugs. Analysis of 337 120 metabolite pairs detected in at least 10 studies showed that structurally related clusters display correlated abundances (ρ up to 0.6). Correlation patterns among established clinical biomarkers revealed candidate metabolic correlates, several with ρ > 0.7. We also developed open-source software enabling similar analyses for any metabolite across the Workbench data explored. Additionally, curating drug-containing datasets enabled meta-analyses for compounds such as acetaminophen, metoprolol, and atenolol, identifying both known and novel metabolic signatures. Besides the biological insights, we have found that Orbitrap and QTOF tend to detect different metabolites, but they give quantitatively coherent results on overlapping compounds. Overall, the present study provides the methodology and software to perform large-scale mining of public metabolomics data enabling its re-analysis to obtain novel biomedical insights.
    Keywords:  biomarkers; blood; drugs; human; meta-analysis; metabolomics
    DOI:  https://doi.org/10.1093/bib/bbag576
  37. Adv Exp Med Biol. 2026 ;1359 145-158
    Advancing Spatial Lipidomics with MALDI-MS Imaging.
    Estefania Garcia-Calvo, Gabriel A Peñalver, Jose Luis Luque-Garcia.
      Spatial lipidomics has emerged as a powerful approach for investigating the molecular organization of biological tissues by combining lipid analysis with spatially resolved information. Among the available techniques, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) has become a key platform for mapping lipid distributions directly in tissue sections without the need for labeling. This chapter provides a comprehensive overview of recent advances in spatial lipidomics using MALDI-MSI, with particular emphasis on sample preparation strategies, matrix selection and deposition methods, and analytical approaches for lipid identification. Critical steps influencing data quality, including tissue preservation, sectioning, matrix application, and mitigation of ion suppression effects, are discussed in detail. Furthermore, the identification of major lipid classes, such as phospholipids, sphingolipids, neutral lipids, glycolipids, fatty acids, and cholesterol, is addressed through characteristic ionization behaviors and diagnostic fragmentation patterns. Finally, the integration of MALDI-MSI with thin-layer chromatography is presented as a complementary strategy to enhance lipid coverage and reduce ion suppression. Together, these approaches highlight the versatility of MALDI-MSI as a robust tool for advancing spatial lipidomics in biomedical research.
    Keywords:  Ion suppression; Lipid imaging; MALDI-MSI; Matrix selection; Sample preparation; Spatial lipidomics; Tandem mass spectrometry; Thin-layer chromatography
    DOI:  https://doi.org/10.1007/978-3-032-29437-1_9
  38. Anal Chem. 2026 Sep 29. 98(38): 28337-28344
    Chromatograms: A Modular Infrastructure for Scalable Chromatographic Data Handling in R.
    Philippine Louail, Laurent Gatto, Sebastian Gibb, Johannes Rainer.
      Chromatographic traces are central to LC-MS analysis, yet most software treats them as transient intermediates inside a fixed pipeline rather than as data objects users can inspect and manipulate directly. In R, xcms provides the established infrastructure for large-scale LC-MS analysis, but the way it represents chromatograms limits what can be done with them once extracted. We present chromatograms, an open-source R/Bioconductor package and a core component of the R for mass spectrometry ecosystem, built on two design principles: interchangeable storage backends, which keep the analysis code independent of where the data reside (in memory, on disk, etc.), and delayed evaluation, which applies queued operations only to the chromatograms actually requested and processed at a time. Chromatograms was fully integrated into xcms hence enabling the efficient extraction of EICs for identified features, exemplified on public data, for which extraction of 4980 features across 4000 samples took only a few hours, against a full day with the legacy infrastructure. For targeted data analysis or inspection, a peak table from any external software, or published with a study, is enough to recover the corresponding chromatograms from the raw files, with annotation columns preserved; four published biomarkers were recovered from all 4063 samples of a cohort even without parallel processing in just under an hour. Chromatograms does not perform peak detection: it provides the infrastructure for working with chromatographic data once peaks are defined, giving a scalable foundation for targeted and untargeted LC-MS analysis in R.
    DOI:  https://doi.org/10.1021/acs.analchem.6c03926
  39. Anal Chem. 2026 Sep 29. 98(38): 28330-28336
    In Cellulo Observation of De Novo-Synthesized Nucleotide by Spectral Tracing of Glutamine-Derived Nitrogen Metabolism (QN-STIM) in Cancer Cells.
    Jiro Karlo, Surya Pratap Singh.
      De novo nucleotide biosynthesis is directly linked to glutamine-derived nitrogen metabolism in mammalian cells. It is a fundamental process tightly tied to cellular growth, proliferation, and metabolic reprogramming, particularly in cancer cells. Direct detection and imaging of glutamine-derived nitrogen metabolism at single-cell resolution remains a challenge. The conventional liquid chromatography-mass spectrometry-based approach for detecting de novo-synthesized nucleotide studies is mostly performed on metabolites extracted from a group of cells, which makes it inherently destructive. Herein, we introduce a vibrational spectroscopy-based single-cell metabolic detection and imaging framework, "glutamine (Q)-derived nitrogen spectral tracing and imaging (QN-STIM)". Using this strategy, we detected and mapped nitrogen flow from glutamine to de novo-synthesized nascent nucleotides at the single-cell level. First, a proof-of-concept analysis was conducted in human embryonic kidney HEK293T cells and demonstrated the feasibility of this approach in cancer cells, namely human lung carcinoma A549, human glioblastoma U87 MG, and mouse hepatoma Hepa1-6. QN-STIM technique holds immense potential to be an adjunct tool to advance our understanding of nucleotide dynamics during differentiation, stress adaptation, and immune activation, and to evaluate responses to metabolic drugs in cellulo.
    DOI:  https://doi.org/10.1021/acs.analchem.6c03877
  40. Anal Chem. 2026 Oct 13. 98(40): 29188-29201
    RNabel─A Standalone Software Tool for Annotating Tandem Mass Spectra of Modified Ribonucleic Acids.
    Ge Song, Yi-Jie Nathan Du, Rui-Xiang Sun, Meng-Qiu Dong.
      Ribonucleic acid (RNA) modifications, with over 170 identified types, play diverse roles in cellular processes. The past decade has witnessed a surging demand for accurate identification and localization of RNA modifications in both endogenous and synthetic therapeutic RNAs. With accurate spectral annotation for RNA, tandem mass spectrometry (MS/MS) can meet this demand. Here, we present RNabel, a user-friendly software tool for in-depth annotation of MS/MS spectra of RNA oligonucleotides. RNabel considers a full set of backbone-cleavage ions (a, b, c, d, a-B, w, x, y, z) in which the ribonucleotide unit could be A, U, C, G, Y (pseudouridine), or I (Inosine). Additionally, RNabel considers 196 modifications on the base, the phosphoribose linkage, the 5' or the 3' terminus, or the detachment of a subnucleotide fragment as a neutral or charged group. Users can create new components if needed, including ribonucleotides, modifications, and neutral or charged groups that could detach from a ribonucleotide. RNabel efficiently processes large data sets in four acceptable formats including .mgf, .raw, .txt from msConvert, and RNabel batch files. Multiple statistical metrics are provided for quality assessment of spectral annotation. To accelerate RNA modification analysis, RNabel is made freely available for Mac and Windows users at https://github.com/songge1111/RNabel/releases.
    DOI:  https://doi.org/10.1021/acs.analchem.6c02176
  41. J Am Soc Mass Spectrom. 2026 Oct 07. 37(10): 2422-2431
    In Silico Top-Down Fourier Transform Mass Spectra to Advance Proteoform Analysis Methods.
    Nina A Khristenko, Konstantin O Nagornov, Anton N Kozhinov, David P A Kilgour, Yury O Tsybin.
      Top-down (TD) Fourier transform mass spectrometry (FTMS) of proteins generates highly information-rich mass spectra. However, the resulting spectral complexity can hinder data interpretation and method applicability. Here, we apply transient-mediated, instrument-specific simulations of protein TD mass spectra with user-defined product ion identities. The workflow integrates sequence-based product ion generation, empirically informed fragmentation statistics, charge location modeling, and user-specified FT processing approach. The simulations capture key FTMS characteristics, including m/z-dependent resolution, peak interference, and realistic peak shapes. As a proof of concept, we simulated carbonic anhydrase II TDMS data sets under conditions reported for corresponding 21 T ion cyclotron resonance (ICR) FTMS electron transfer dissociation experiments. The simulated mass spectra reproduced key experimental descriptors of spectral complexity, including peak density, spectral dynamic range, and isotopic structures. Using data sets with increasing complexity, we performed an initial evaluation of TDMS annotation performance and observed an expected decrease in product ion annotation rates from c-ion-only mass spectra to the most complex data sets containing a-, c-, y-, and z-type product ions, reflecting the impact of spectral congestion on confident assignment. These results demonstrate that realistic in silico TDMS data sets can support systematic investigation of spectral properties in TDMS and facilitate the development and evaluation of proteoform-specific data analysis approaches. The initial data set of simulated mass spectra and transients is available through ProteomeXchange under identifier PXD075270.
    DOI:  https://doi.org/10.1021/jasms.6c00199
  42. J Cheminform. 2026 Jul 24. pii: 146. [Epub ahead of print]18(1):
    MetaboT: an LLM-based multi-agent framework for interactive analysis of mass spectrometry metabolomics knowledge graphs.
    Madina Bekbergenova, Lucas Pradi, Benjamin Navet, Emma Tysinger, Franck Michel, Matthieu Feraud, Yousouf Taghzouti, Yan Zhou Chen, Olivier Kirchhoffer, Florence Mehl, Martin Legrand, Tao Jiang, Marco Pagni, Soha Hassoun, Jean-Luc Wolfender, Wout Bittremieux, Fabien Gandon, Louis-Félix Nothias.
      Mass spectrometry-based metabolomics generates complex, high-dimensional data that holds vast potential for biological discovery but remains difficult to integrate and interpret. Knowledge graphs (KGs) unify this heterogeneous information by representing spectra, annotations, taxa, chemical classes, and biological activities as a single interoperable network; however, their practical use is limited by the steep learning curve of corresponding specialized representation and query languages. Here we introduce MetaboT, an open-source multi-agent Large Language Model (LLM) framework that translates natural-language questions into executable SPARQL queries over metabolomics knowledge graphs. MetaboT is designed to mitigate the hallucination and schema-compliance limitations of single-model approaches through a modular architecture in which specialised agents handle scope validation, entity resolution against authoritative resources, schema-aware query generation, iterative refinement, and result interpretation. We validated MetaboT on the Experimental Natural Products Knowledge Graph (ENPKG), using an expert-authored benchmark of natural-language questions paired with reference SPARQL queries, and demonstrate its ability to answer complex questions about plant-metabolite relationships and biological activities, achieving 83.67% accuracy versus 8.16% for a single-shot baseline and reducing schema-mismatch errors to a single residual case in our benchmark. MetaboT lowers the technical barrier for metabolomics researchers and enables semantic data mining without specialised programming expertise.Scientific contributionMetaboT introduces a multi-agent LLM architecture that grounds each step of natural-language-to-SPARQL translation in authoritative identifier resolution and schema-aware validation, substantially reducing the hallucinations observed with single-model baselines. The framework couples orchestrated agents with domain-specific tools for chemical, taxonomic, and biological target resolution, and with an iterative refinement loop that distinguishes query-construction errors from genuine data absence. Released as open-source software with an archived evaluated version, a reusable benchmark, and a public web demonstrator, MetaboT provides a reproducible reference implementation for knowledge-graph-driven exploration of mass spectrometry metabolomics data.
    Keywords:  Cheminformatics software; ENPKG; Entity resolution; Knowledge graph; Large language models; Mass spectrometry; Metabolomics; Multi-agent LLM; Natural products; SPARQL
    DOI:  https://doi.org/10.1186/s13321-026-01257-8
This page is being maintained by Thomas Krichel. It was last updated on 2026‒10‒26 at 19:48.