bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2026–09–06
forty-nine papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz



  1. Methods Mol Biol. 2026 ;3063 189-204
      MetDNA ( http://metdna.zhulab.cn/ ) is a network-based computational platform for large-scale metabolite annotation in untargeted metabolomics using liquid chromatography-mass spectrometry (LC-MS). By using a metabolic reaction network (MRN) to guide recursive MS2 spectral similarity matching, MetDNA can accurately annotate both known and unknown metabolites, going beyond the limits of conventional spectral libraries. Since 2019, the platform has evolved from MetDNA to MetDNA2 and now to MetDNA3, with improvements in efficiency, coverage, and confidence in metabolite annotation for untargeted metabolomics. In this protocol, we outline best practices for data preparation, parameter configuration, and result interpretation, offering users a practical workflow to maximize the utility of MetDNA for high-confidence metabolite annotation.
    Keywords:  LC–MS; MetDNA; Metabolite annotation; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_11
  2. Methods Mol Biol. 2026 ;3063 19-50
      Liquid chromatography-mass spectrometry (LC-MS) is a key technology in metabolomics, enabling high-throughput detection of small molecules across diverse biological samples. However, raw LC-MS data are complex, requiring careful preprocessing to ensure accurate and reproducible feature detection. This chapter introduces a step-by-step protocol for LC-MS data preprocessing using the open-source xcms package in R. Designed for users ranging from beginners to experienced analysts, the chapter outlines critical stages including data inspection, peak detection, retention time alignment, correspondence, and result export. Special attention is given to parameter optimization and diagnostic visualization to guide users in making informed decisions tailored to their specific datasets. We demonstrate the approach on human serum samples, using real-life example compounds such as proline to showcase retention time alignment and peak correspondence. By combining practical code snippets with conceptual insights, this chapter empowers researchers to harness xcms for robust, reproducible LC-MS workflows in untargeted metabolomics. Whether you are setting up your first analysis or refining an established pipeline, this chapter serves as both a tutorial and a reference for high-quality LC-MS data processing.
    Keywords:  Data processing; Liquid chromatography; Mass spectrometry; Metabolomics; Peak detection; Preprocessing; Quality control; Retention time correction; xcms
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_2
  3. Methods Mol Biol. 2026 ;3063 67-84
      Liquid chromatography-mass spectrometry (LC-MS) is a cornerstone of metabolomics, enabling detailed analysis of complex biological samples. Untargeted approaches, however, face challenges in handling complex data and ensuring scalability. UmetaFlow, built on the OpenMS platform, addresses these challenges through a unique re-quantification step for low-abundance metabolites and a suite of complementary annotation tools. Available in four formats, from command-line tools to an interactive WebApp, UmetaFlow supports both high-throughput automation and manual data exploration. This chapter provides a step-by-step guide to UmetaFlow and its applications.
    Keywords:  OpenMS; UmetaFlow; Untargeted metabolomics; Workflow managers; Workflows
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_4
  4. Anal Chem. 2026 Aug 25. 98(33): 23989-23997
      Stable isotope dilution mass spectrometry (IDMS) has become a cornerstone of quantitative metabolomics, enabling accurate intracellular metabolite quantification across a range of biological systems. However, the broader adoption of IDMS in high-throughput studies remains limited by the high costs of commercially available 13C-labeled internal standards (ISs), labor-intensive in-house IS production, and the narrow applicability of existing methods to different organisms. Here, we present a robust and scalable IDMS-based LC-MS/MS workflow for the high-throughput profiling of primary metabolism in diverse bacteria. The analytical method couples ion-pairing liquid chromatography with multiple reaction monitoring (MRM) to quantify 96 intracellular metabolites in under 16 min, with an average RSD of 21%. We developed a protocol for large-scale production of high-quality 13C-labeled IS, considerably lowering the costs and labor necessary to perform high-throughput IDMS studies. We then demonstrated the applicability of the same workflow by performing relative quantification of 5 diverse bacterial species in different cultivation conditions. This work provides a versatile platform for microbial metabolomics, supporting systems biology and data-driven metabolic engineering at scale.
    DOI:  https://doi.org/10.1021/acs.analchem.5c04931
  5. Methods Mol Biol. 2026 ;3063 293-334
      MetaboAnalyst is a comprehensive, user-friendly web-based platform for metabolomics data analysis and interpretation. It offers a unified analytical workflow with an intuitive interface designed to streamline data analysis for both targeted and untargeted metabolomics, while supporting integration with other omics datasets. MetaboAnalyst 6.0 represents a significant advancement toward this vision by supporting LC-MS raw spectral processing and MS2 spectral annotation. This chapter builds on our 2019 protocols, which presented 12 step-by-step procedures for statistical and functional analysis using MetaboAnalyst 4.0, with emphasis on targeted metabolomics. Here, we extend that foundation with eight new or substantially revised protocols for MetaboAnalyst 6.0, centered mainly on LC-MS untargeted metabolomics, advanced statistics, and functional analysis. The main topics covered include: Basic Protocol 1: LC-MS raw spectral processing with or without MS2 spectra Basic Protocol 2: MS2 spectral annotation Basic Protocol 3: Functional analysis of untargeted LC-MS metabolomics data Basic Protocol 4: Advanced statistical analysis for studies with multiple factors Basic Protocol 5: Dose-response analysis Basic Protocol 6: Pathway and joint-pathway analysis Basic Protocol 7: Causal analysis Basic Protocol 8: Using MetaboAnalystR for batch processing.
    Keywords:  Causal analysis; Compound identification; Covariate adjustment; Dose response analysis; LC–MS; MS/MS; Mendelian randomization; Pathway analysis; Spectral processing
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_16
  6. J Proteome Res. 2026 Sep 04. 25(9): 4987-4999
      Nanoflow liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) underpins modern quantitative proteomics, yet the column-to-mass spectrometer interface remains an important yet often underappreciated determinant of analytical depth, sensitivity, and reproducibility. Here, we benchmark an integrated workflow comprising the newly developed OptiSpray ion source and a micropillar array column (μPAC) cartridge against a conventional Nanospray Flex Source with an Accucore resin-packed capillary column. We performed a TMTpro 18-plex experiment across nine human cell lines on a FAIMS Pro-equipped Orbitrap Exploris 480. Following basic-pH reversed-phase fractionation, 12 fractions were analyzed on both workflow configurations under matched chromatographic gradient and acquisition conditions. Across both configurations, we quantified >9000 protein groups with highly comparable quantitative reproducibility and principal component clustering. Direct comparison of protein abundance ratios across cell lines showed agreement (Pearson R2 ≈ 0.7-0.8) without systematic bias. These results were achieved without workflow-specific optimization of the OptiSpray-μPAC platform, enabling direct transfer of established acquisition methods. Despite differences in column architecture, both configurations delivered comparable proteome coverage and quantitative fidelity. These findings establish the OptiSpray-μPAC workflow as a standardized alternative to conventional capillary-based interfaces, offering simplified operation while preserving quantitative performance.
    Keywords:  FAIMS; micropillar array column (μPAC); multiplexed proteomics; nanoliquid chromatography; quantitative proteomics; tandem mass tags (TMT)
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00473
  7. J Proteome Res. 2026 Sep 04. 25(9): 4919-4931
      Modern DIA proteomics increasingly emphasizes throughput and depth for large-cohort studies, but methods are often optimized using proxy metrics that can mask losses in quantifiable signal and statistical power. Here, we evaluate how data points per peak and other chromatographic features jointly contribute to quantification and downstream biological discovery. Using a matrix-matched calibration curve dataset, we checked how the number of data points per peak (DPPP) affects the limits of detection and quantification (LOD/LOQ). Reduced DPPP minimally affected LOD but substantially degraded LOQ. Feature modeling and nonparametric association analyses identified precursor peak area as the strongest feature-level predictor of LOQ, whereas DPPP showed weaker and context-dependent effects. Simulations of chromatographic peak integration recapitulated these trends, showing that increased sampling primarily improves integration precision, while quantitative accuracy is strongly governed by peak height and peak shape. Finally, when comparing 20 cancer vs 20 control plasma samples processed with Seer Proteograph, the decrease in DPPP led to a loss of statistical significance for proteins with low-abundance precursors. These findings argue that DIA optimization should prioritize LOQ and statistical power metrics─not identifications alone─by balancing sampling density with chromatographic peak height and quality to maximize useful biological signal.
    Keywords:  Accuracy; Biological Resolution; Data Points Per Peak; Data-Independent Acquisition; Population Proteomics; Quantitative Proteomics; Statistical Power
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00415
  8. Methods Mol Biol. 2026 ;3063 205-225
      The Lipid Data Analyzer (LDA) is a platform-independent software tool for the automated identification and quantification of lipid species and other metabolites in both untargeted and targeted mass spectrometry (MS) data. LDA mirrors the decision-making process of trained MS experts, enabling automated, high-throughput analyses across any instrumental setup through customizable, rule-based logic. These decision rule sets represent chemical and structural logic, yielding annotation results of high reliability. Furthermore, decision rules can be easily extended to accommodate new lipid classes, adduct forms, or fragmentation mechanisms. This makes LDA particularly effective for identifying novel lipid species or structural isomers that are often missed by traditional approaches, and enables users without a bioinformatics background to easily adapt to evolving analytical protocols. Moreover, LDA supports the identification of double bond positions and other modifications, such as oxidations. Here, we present a step-by-step guide and practical tips for efficient operation of LDA, and provide an introductory discussion of common pitfalls in lipidomics data analysis.
    Keywords:  Chromatography; LDA; Lipid identification; Lipidomics; Mass spectrometry; Metabolomics; Oxidized lipids; Untargeted; double bond localization
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_12
  9. Methods Mol Biol. 2026 ;3063 51-66
      Modern mass spectrometry (MS) experiments generate increasingly complex, multidimensional datasets across diverse analytical modalities, including liquid chromatography (LC)-MS, gas chromatography (GC)-MS, ion mobility spectrometry, and mass spectrometry imaging. Efficient analysis of such heterogeneous data has traditionally required multiple vendor-specific software tools, limiting reproducibility and integration. mzmine 4 addresses this challenge by providing a unified, vendor-neutral, and extensible software platform for comprehensive MS data processing and interpretation. Building on nearly two decades of community-driven development, mzmine 4 introduces substantial improvements in performance, scalability, and usability, enabling the routine analysis of large-scale and multimodal datasets on standard consumer hardware. The software integrates workflows for feature detection, alignment, spectral library matching, molecular networking, small molecule annotation, and advanced MS2 interpretation within a single graphical environment. New capabilities, including interactive molecular networking, guided workflow automation, enhanced visualization, and machine learning-based spectral similarity scoring, further support exploratory and reproducible data analysis. The transition to an enterprise-supported yet open innovation model ensures long-term sustainability while preserving open-source principles. Together, these advances position mzmine 4 as a comprehensive, future-ready platform for untargeted metabolomics and mass spectrometry-based research.
    Keywords:  Interactive molecular networking; Mass spectrometry data processing; Multimodal mass spectrometry; Universal mass spectrometry platform; Vendor-neutral data integration
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_3
  10. Methods Mol Biol. 2026 ;3063 101-123
      Mass spectrometry (MS) has become the cornerstone of metabolomics and lipidomics because of its high sensitivity, specificity, and ability to analyze complex biological samples. However, the data complexity necessitates the use of robust computational tools. MS-DIAL is a versatile and user-friendly software for untargeted metabolomics and lipidomic workflows. It was launched as a universal program for untargeted metabolomics and supports multiple instruments (GC/MS, GC/MS/MS, LC/MS, and LC/MS/MS) and MS vendors (Agilent, Bruker, LECO, Sciex, Shimadzu, Thermo, and Waters). Common data formats such as netCDF (AIA) and mzML can also be managed. In addition, several MSP files, including EI and MS/MS spectra, were included as a "start-up kit." Moreover, MS-DIAL has an internal version of the Fiehn lab's GC/MS database (oriented by the FAME RI index), in silico retention time, and MS/MS database for LC/MS/MS-based lipidomics. Isotope-labeled tracking can be executed using an LC/MS project. This chapter provides two detailed protocols using LC-MS/MS for hydrophilic metabolome and lipidome analyses using MS-DIAL. Each protocol covers the key steps, including raw data import, peak detection, alignment, annotation, and export. Practical tips, troubleshooting strategies, and case studies are included to enhance the reproducibility and interpretation of the results.
    Keywords:  Annotations; Curations; LC–MS/MS; Lipidomics; MS-DIAL
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_6
  11. Methods Mol Biol. 2026 ;3063 85-100
      Asari is a software tool for metabolomics data processing, which is designed from the ground up to address issues in reproducibility, performance and interoperability by introducing new algorithms and data structures. It is significantly faster than other tools and offers qualities that are suitable for large-scale metabolomic and exposomic analyses. The reusable data structures and modular libraries enabled the rapid development of a full-scale pipeline that includes QA/QC, MS/MS integration, annotation, and extension to untargeted stable isotope tracing data. This chapter provides an overview of the key concepts and designs in Asari, step-by-step applications to metabolomics data processing and annotation, and resources related to both LC-MS and GC-MS data.
    Keywords:  Asari; Data processing; Metabolomics; Pipeline
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_5
  12. Methods Mol Biol. 2026 ;3016 93-103
      Conventional non-targeted approaches using data-dependent acquisition (DDA) with isotopic labeling mass spectrometry (MS) have demonstrated limited effectiveness in characterizing site-specific disulfide bond redox states, primarily due to suboptimal coverage and inconsistent reproducibility. Here, we introduce a targeted approach employing differential cysteine alkylation coupled with parallel reaction monitoring (PRM)-MS to identify the redox states of specific disulfide bond sites in proteins, which significantly improves the coverage and reproducibility of mass spectrometry data.
    Keywords:  Cysteine alkylation; Disulfide bond; Mass spectrometry (MS); Parallel reaction monitoring (PRM); Redox state
    DOI:  https://doi.org/10.1007/978-1-0716-5158-2_8
  13. J Proteome Res. 2026 Sep 04. 25(9): 4393-4406
      Dynamic range, repeatability, and reproducibility remain the central limitations of data-independent acquisition (DIA) proteomics. Current workflows emphasize protein group identification counts and throughput, but these metrics mask the fundamental measurement challenge: generating a repeatable, reproducible, high-fidelity, and relatively complete digital representation of complex proteomes. In particular, plasma proteomics spans more than 10 orders of magnitude in protein abundance, far exceeding the capacity and dynamic range of any single mass spectrometer. Incremental advances have not closed this gap. In this Perspectives article, I introduce the Super Mass Spectrometry framework and then highlight the Delayed Electrospray Ionization (Delayed-ESI) technique, as a practical approach to address these limitations. By producing compositionally identical but temporally staggered ion beams, the Delayed-ESI technique enables deterministic remeasurement of the same analyte profile, supporting various novel strategies to improve analytical figures of merit. While recent implementations of the Delayed-ESI technique have emphasized throughput, I argue that the broader value of the Delayed-ESI technique lies in extending dynamic range and improving repeatability and reproducibility─objectives that should take precedence if proteomics is to evolve into a robust measurement science capable of supporting population-scale proteomics studies.
    Keywords:  Analytical Figures of Merit; Data-Independent Acquisition; Delayed Electrospray Ionization; Deterministic Measurement; Extended Dynamic Range; LC-MS Workflows; Measurement Fidelity; Proteomics; Repeatability and Reproducibility; Super Mass Spectrometry
    DOI:  https://doi.org/10.1021/acs.jproteome.5c00971
  14. Methods Mol Biol. 2026 ;3063 159-176
      Liquid chromatography-mass spectrometry (LC-MS)-based non-targeted metabolomics produces intricate datasets that need advanced tools for identifying metabolites (MetID). Metabolite annotation and identification in non-targeted metabolomics require high-quality fragmentation data from biological samples and reference libraries. Public and commercial databases, such as NIST, MassBank, MoNA, GNPS, HMDB, mzCloud, and METLIN, are vital resources for both spectral matching and providing training data for machine-learning-driven MetID tools. These libraries differ in their coverage, curation, and access models, and they are often supplemented by in-house databases that cater to specific laboratory conditions, ensuring the highest level of confidence in identifications. To maximize the benefits of MS2 libraries and ensure they work seamlessly together, we rely heavily on standardized file formats. Standard formats, such as mzML, MGF, MSP, JSON, and the MassBank format, each come with different levels of metadata richness and compatibility with various software tools. This chapter gives an overview of key MS2 libraries, discusses the strengths and weaknesses of standard data formats, and introduces R-based solutions for better integration.
    Keywords:  Data formats; MS2 libraries; Mass spectrometry; Metabolite identification; Metabolomics; R; Spectra package
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_9
  15. Methods Mol Biol. 2026 ;3063 145-157
      This chapter provides resources and step-by-step processing guidelines for analyzing liquid chromatography-ion mobility spectrometry-mass spectrometry (LC-IMS-MS) data in metabolomics. The methods described here are based on open-source software and freely available executables developed at Pacific Northwest National Laboratory (PNNL), including PNNL-PreProcessor, MZA, mzapy, LipidOz, PeakQC, and IonToolPack. Importantly, the same software ecosystem is broadly applicable to IMS-MS workflows both with and without LC and includes algorithms that support other modalities such as proteomics, making it suitable for a wide range of experimental designs. The chapter is written for scientists seeking to establish reproducible workflows to analyze multidimensional metabolomics data, regardless of prior experience with IMS. Demonstrations for both Python-based programmatic data processing and graphical user interface (GUI) workflows are provided to facilitate implementation by users with different levels of computational expertise. Following these procedures, researchers can successfully process, visualize, and interpret LC-IMS-MS data using freely available software and data resources.
    Keywords:  AI; Ion mobility spectrometry; Liquid chromatography; MZA; Mass spectrometry; Metabolomics; Software; mzapy
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_8
  16. Anal Chem. 2026 Sep 01. 98(34): 24719-24731
      Metabolomics is widely applied to dissect metabolic pathways and their correlations with biological phenotypes. Unlike genomics and proteomics, metabolites exhibit substantial heterogeneity in chemical structure, physicochemical properties, and biological origin. Accordingly, pathway enrichment and annotation relying merely on alterations in metabolite abundance are prone to incomplete coverage, ionization bias, and ambiguous annotation, which inevitably impair the accuracy of pathway interpretation. Metabolic flux analysis (MFA) coupled with stable isotope-resolved metabolomics (SIRM) offers a powerful quantitative framework for tracing in vivo carbon flow and estimating reaction fluxes across key metabolic nodes. Glucose metabolism lies at the core of systemic energy homeostasis; however, most current investigations are confined to cell lines or in vitro systems, and a simple, easy-to-implement computational pipeline for in vivo glucose flux analysis in animal models is still lacking. Herein, we established an in vivo 13C-labeling-based MFA workflow to trace and resolve the systemic metabolic fate of glucose in rats. The pipeline covers tracer administration, sample preparation, LC-MS detection, isotopologue data acquisition and correction, construction of a glucose-metabolism-related metabolite database, MFA model establishment, and metabolic flux quantification. By infusing rats with [U-13C6]-glucose and [U-13C3]-sodium L-lactate, we precisely characterized the in vivo metabolic fates of circulating glucose and lactate and quantified their respective contributions to glycolytic flux and tricarboxylic acid (TCA) cycle flux. We further applied this workflow to profile energy metabolic reprogramming in depression. The results revealed a systemic shift toward aerobic glycolysis in rats exposed to chronic unpredictable mild stress (CUMS). Overall, the expanded application of this MFA strategy can provide mechanistic and quantitative insights into the regulation of metabolic pathways.
    DOI:  https://doi.org/10.1021/acs.analchem.6c00087
  17. Methods Mol Biol. 2026 ;3063 227-242
      The epilipidome, a subset of the natural lipidome arising from enzymatic and non-enzymatic lipid modifications, remains largely unexplored. Within this emerging class, oxidized complex lipids have raised considerable interest due to their diverse biological functions, including the modulation of inflammation, cell fate decisions, and the execution of programmed cell death. However, the discovery and annotation of these typically low-abundant yet structurally diverse lipid species present significant analytical challenges, often necessitating advanced bioinformatics tools. Here, we present a computational pipeline powered by LPPtiger2 software, designed for the comprehensive discovery, detection, and annotation of complex oxidized lipids within the context of a defined lipidome. The LPPtiger2 hybrid workflow offers a robust solution for high-quality epilipid profiling by integrating a predictive algorithm with a semi-targeted experimental protocol. Using a knowledge-based in silico epilipidome prediction algorithm, it generates a highly customized, sample-specific search space prior to data acquisition. This approach transforms the conventional untargeted lipidomics pipeline into a semi-targeted workflow that selectively focuses on predicted epilipid precursors. Such specificity enhances LPPtiger2-supported annotation of modified epilipids through improved sensitivity, superior MS/MS spectral quality, and a tailored lipid search space.
    Keywords:  Epilipidomics; Lipid annotations; Lipidomics; Oxidized lipids; Software
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_13
  18. Methods Mol Biol. 2026 ;3016 125-134
      Post-translational modifications (PTMs) can play vital roles in the protein's structure, function, localization, and interactions, enabling dynamic control of cellular processes. As such, understanding these modifications is essential to elucidate key biological mechanisms. This chapter discusses an unbiased, mass spectrometry-based approach using wildcard (open) searches in the Byonic platform to detect and identify nondisulfide cysteine PTMs. By targeting individual cysteines and scanning for mass shifts across a broad range, this strategy enables discovery of novel or unexpected modifications. The method is applicable to both new and existing datasets and can be extended to any amino acid, offering a versatile tool for redox and PTM-focused proteomics.
    Keywords:  Byonic; Cysteine; Mass spectrometry; Post-translational modifications; Prothrombin
    DOI:  https://doi.org/10.1007/978-1-0716-5158-2_11
  19. Methods Mol Biol. 2026 ;3070 331-347
      Localization of proteins to different cell compartments is a major posttranslational regulatory mechanism that eukaryotic cells have evolved to coordinate protein homeostasis and responses to stimuli. Subcellular fractionation allows the separation of distinct protein populations from cellular compartments such as the cytosol, cytoskeleton, membrane, endoplasmic reticulum, or nucleolus. This fractionation can be followed by mass spectrometry-based proteomics to provide insights into spatiotemporal regulation or cellular protein networks. Here, we describe a high-throughput workflow based on sequential cell lysis, which enables the profiling of subcellular proteome architecture and the detection of changes in protein localization at global and posttranslational levels.
    Keywords:  Cell signalling; Mass spectrometry; Posttranslational regulation; Proteomics; Subcellular fractionation
    DOI:  https://doi.org/10.1007/978-1-0716-5515-3_19
  20. Methods Mol Biol. 2026 ;3063 243-272
      Liquid chromatography-mass spectrometry (LC-MS) is widely used in metabolomics. Raw LC-MS data is relatively complex, consisting of molecular features originating not only from unique metabolites but also from redundant adducts, in-source fragments, artifacts, and impurities. It is also prone to signal intensity drift during long sequences, missing values, and false positives in statistical tests. To tackle these instrument-related and data-dependent challenges with robust pre-processing and quality evaluation tools, we have developed the notame R package bundle, which recently became available as a R/Bioconductor release and now supports SummarizedExperiment data format. It pre-processes LC-MS metabolomics data by correcting signal intensity drift, flagging potential low-quality and contaminant features, imputing missing values, and clustering features likely originating from the same metabolite. Most of the functions include default recommended values that can be modified by the user. Univariate and multivariate statistics with parametric and non-parametric alternatives and false discovery rate can be performed with notameStats package. Results and data visualizations, such as quality control figures, PCA, heatmaps, volcano plots, and feature-wise graphs, are available in notameViz package. Together, these packages contribute to a complete metabolomics data analysis workflow, connecting the phases between signal detection/alignment and metabolite identification while producing publication-ready illustrations.
    Keywords:  Preprocessing, Statistics, Quality assurance, Data visualization, Programming
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_14
  21. Anal Chem. 2026 Sep 01. 98(34): 24964-24975
      Untargeted LC-MS metabolomics offers a broad view of the microbial metabolism. However, its application is hindered by two intertwined challenges: distinguishing true biological signals from chemical artifacts and quantifying nutrient partitioning under nutrient-competitive conditions. Here, we present TRACE, an integrated experimental and computational framework that dynamically calibrates mass and retention time tolerances from the data itself to construct isotope-informed peak networks, enabling rigorous discrimination of biological metabolites from artifacts. Across four LC-MS platforms, TRACE reveals that the proportion of high-confidence annotations fell from 2.94 to 1.48%, while the total features increased by 331% from lower- to higher-sensitivity instruments. TRACE also maps nutrient fates into metabolic pathways by detecting isotopic dilution in Saccharomyces cerevisiae cultured with 13C-glucose, 15N-ammonium, and other unlabeled nutrients. Specifically, labeling of glutathione, a linear assembly of three amino acids, accurately reflect direct incorporation from its constituent amino acids; NAD+, whose biosynthesis proceeds through concurrent salvage and de novo pathways, revealed how adenine, tryptophan, and glutamine shaped its final isotopologue pattern. By converting untargeted LC-MS data into functional maps of nutrient flow, TRACE establishes a system-level approach to interrogate microbial metabolism under physiologically relevant competitive conditions.
    DOI:  https://doi.org/10.1021/acs.analchem.6c02292
  22. Methods Mol Biol. 2026 ;3063 125-144
      Liquid chromatography-tandem mass spectrometry (LC-MS/MS) is widely used in the field of metabolomics because it enables the simultaneous detection of hundreds to thousands of metabolites in a single analysis. MS-DIAL 5 is a software platform that facilitates the analysis of mass spectrometry data, including those obtained from LC-MS/MS, and provides a function for creating molecular spectral networks. In the analysis of hydrophilic metabolites, the availability of standard compounds is limited compared with that of lipids, and the utilization of MS/MS libraries is often restricted. To address this challenge, molecular spectral networking serves as an effective strategy for extending annotations to unknown ions. In this chapter, we introduce an analytical strategy for hydrophilic metabolite analysis using MS-DIAL 5, which utilizes retention time prediction and molecular spectral networking to enhance the reliability and comprehensiveness of metabolite annotation.
    Keywords:  Hydrophilic metabolomics; LC–MS/MS; MS-DIAL; Molecular spectral network; Retention time prediction
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_7
  23. J Proteome Res. 2026 Sep 04. 25(9): 4676-4687
      Peptide-spectrum match (PSM) rescoring has become standard in proteomics workflows, improving peptide identification accuracy across diverse search engines. Despite the availability of multiple rescoring strategies, systematic comparisons spanning several search engines, data sets, and database configurations remain limited. Here, we benchmarked seven publicly available search engines, evaluating standard target-decoy-based false discovery rate (FDR) estimation alongside Percolator, MS2Rescore, and Oktoberfest across four data sets acquired on different mass spectrometry platforms in data-dependent mode and searched against protein databases of varying size and composition. Rescoring substantially increased identification consensus and reduced variability between search engines, with prediction-based approaches yielding the largest gains. While database size had limited impact for human data sets, it significantly affected identification rates on a metaproteomic data set. Entrapment-based evaluation indicated generally adequate FDR control across methods, although prediction-based rescoring exhibited a higher tendency toward FDR underestimation in specific configurations. Overall, advanced rescoring strategies harmonize peptide identification outcomes across search engines, thereby enhancing robustness and comparability in proteomics analyses. However, careful feature selection and appropriate database choice remain essential to ensure reliable FDR control and optimal performance across diverse experimental settings.
    Keywords:  machine learning in proteomics; peptide identification; proteomics workflows; rescoring algorithms; target-decoy approach
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00226
  24. Methods Mol Biol. 2026 ;3070 349-395
      Mass spectrometry-based quantitative proteomics usually produces large datasets that require exhaustive analysis to extract underlying biological information. This chapter presents a step-by-step pipeline for the statistical and computational analysis of such data, oriented and generalizable to any mass spectrometry-derived proteomic dataset. These steps include: (i) data preprocessing and quality control, (ii) identification of differentially abundant proteins through statistical modeling, (iii) functional enrichment analyses, including Over Representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA) to integrate proteomic changes in the context of biological processes and pathways, and, finally, (iv) interactome (protein-protein interaction) construction and visualization to situate proteomic alterations within signaling networks. Throughout, reproducible off-the-shelf R code and practical guidance for each step are provided, facilitating an end-to-end analysis from raw proteomic data to the biological interpretation, illustrated with visualization examples and best-practice recommendations. The complete script and necessary files are freely available at https://github.com/UMBB-IIS-Princesa/Quantitative-Proteomics-Pipeline.
    Keywords:  Computational; Proteomics; Proteomics-analysis
    DOI:  https://doi.org/10.1007/978-1-0716-5515-3_20
  25. Anal Bioanal Chem. 2026 Sep 01.
      Untargeted metabolomics of clinical toxicology samples is often constrained by limited sample volume, incomplete metabolome coverage, and technical variability introduced by multiple LC-MS injections. Here, we applied and evaluated a valve-assisted 4-in-1 polarity-partitioned LC-QTOF-MS workflow for single-injection plasma metabolomic profiling. The term "4-in-1" refers to four complementary LC-ionization data channels acquired from a single injection: HILIC-ESI(+), HILIC-ESI(-), C8-ESI(+), and C8-ESI(-). The workflow combines valve-controlled collection and transfer of weakly retained HILIC effluent with sequential HILIC and C8 analyses and dual-polarity MS acquisition. Quality-control analyses demonstrated stable retention behavior and reproducible feature detection, and the single-injection design reduced the need for multiple separate LC-MS injections. As a clinical toxicology application, the workflow was applied to plasma samples from patients with chlorfenapyr poisoning and healthy controls, with poisoned patients further stratified according to plasma tralopyril concentration. PCA and OPLS-DA were used as exploratory tools to visualize global metabolic differences. Differential LC-MS features were screened using multivariate and univariate statistical criteria, followed by metabolite annotation and pathway enrichment analysis based on annotated differential metabolites. Prominent perturbations were observed in amino acid metabolism, the urea cycle, and energy-related pathways. Several annotated amino acids, including glutamine, asparagine, alanine, and threonine, differed among the exposure groups. These exploratory findings support the feasibility of the workflow for limited-volume clinical plasma metabolomics and identify candidate metabolic alterations consistent with mitochondrial metabolic stress in chlorfenapyr poisoning.
    Keywords:  Amino acid metabolism; Chlorfenapyr poisoning; Polarity-partitioned workflow; Untargeted metabolomics; Valve-assisted LC-QTOF-MS
    DOI:  https://doi.org/10.1007/s00216-026-06784-9
  26. Methods Mol Biol. 2026 ;3063 363-379
      The Human Metabolome Database (HMDB) is a web-based resource that supports metabolite identification and interpretation for human studies. This chapter outlines five protocols: (1) identifying metabolites using mass spectrometry with retention time or collision cross-section data (MS-RT/CCS); (2) identifying metabolites using tandem mass spectrometry (MS/MS); (3) identifying metabolites using nuclear magnetic resonance (NMR); (4) comparing measured concentrations to HMDB reference values; and (5) interpreting metabolomics results via HMDB's MetaboCards and PathBank links.
    Keywords:  Data analysis; Database; Disease; Human; Metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_18
  27. Methods Mol Biol. 2026 ;3063 335-362
      Metabolomics, the study of small molecules in biological systems, is a powerful tool for understanding biochemical pathways, discovering biomarkers, and elucidating disease mechanisms. This chapter provides a guide to performing metabolomics data analysis in R, focusing on enrichment analysis and network-based approaches. It covers essential steps in data processing, quality control (QC), differential expression analysis, integration with proteomics using multi-omics factor analysis (MOFA), and statistical network analysis, as well as enrichment analysis using prior knowledge. The methods outlined provide a framework for biomarker discovery and advancing systems-level understanding of disease processes using metabolomics data in combination with prior knowledge and proteomics data.
    Keywords:  Knowledge graphs; Metabolomics; Network analysis; Pathway; Processing; Proteomics; Quality control
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_17
  28. J Am Soc Mass Spectrom. 2026 Sep 02. 37(9): 2179-2191
      High-field asymmetric waveform ion mobility spectrometry (FAIMS) provides a gas-phase separation dimension for LC-MS/MS proteomics, yet the selection of optimal compensation voltages (CV) remains largely empirical. To address this, we evaluated label-free and TMTpro-derivatized peptides from whole-cell lysates, systematically characterizing the transmission of over 141,000 unique tryptic peptides across a broad CV range (-10 V to -100 V). We demonstrate that FAIMS transmission is highly charge-state-dependent and modulated by mass and discrete amino acid compositions. Because single-CV methods capture less than half of the detectable proteome, we applied combinatorial modeling to evaluate multiplexed strategies. We determined that optimized 3-CV methods successfully captured ∼90% of the cumulative peptide pool identified across all tested voltages, providing evidence-based guidelines for maximizing proteome coverage in single-shot analyses.
    Keywords:  FAIMS; TMTpro; compensation voltage; ion mobility; mass spectrometry
    DOI:  https://doi.org/10.1021/jasms.6c00208
  29. Methods Mol Biol. 2026 ;3045 113-125
      With the latest advances in analytical techniques based on liquid chromatography (LC) coupled with mass spectrometry (MS), knowledge of plant metabolomics has risen exponentially in recent years. The study of metabolomic changes associated with mycorrhizal symbiosis interacting with different environmental situations exemplifies the expansion of knowledge in this field. In the present chapter, we aim to provide a standard procedure for the analysis of shoot metabolites using liquid chromatography coupled with high-resolution mass spectrometry. The provided information includes an extraction buffer of compromised polarity, as well as LC and MS conditions suitable for a general characterization of secondary metabolites from mycorrhizal plants. These conditions may require further adaptation in case a lipidomic or highly polar compound analysis is required or when a different instrumentation is used. In addition, we provide a protocol for a preliminary bioinformatic analysis of the identified features using public non-proprietary software, which, combined with the construction of pure standard libraries, can yield a powerful tool for the identification and semi-quantitative analysis of hundreds of secondary metabolites from mycorrhizal plants.
    Keywords:  Arbuscular mycorrhiza; LC-MS; Libraries; Untargeted metabolomics; qTOF
    DOI:  https://doi.org/10.1007/978-1-0716-5324-1_9
  30. J Chem Inf Model. 2026 Aug 24. 66(16): 10412-10425
      Feature annotation in liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics remains challenging. Retention time (RT) prediction can support candidate prioritization and improve annotation confidence. Here, we present FastRet, an R package predicting RTs using Least Absolute Shrinkage and Selection Operator (LASSO) and Boosted Regression Trees (BRT) on molecular descriptors. FastRet provides a flexible framework combining from-scratch model training, selective measuring to prioritize metabolites for remeasurement, and model adjustment to adapt existing models to changed chromatographic conditions. Model training and prediction are completed within seconds on a single CPU core, and FastRet is accessible both from the R console and through a web interface. We validated FastRet on three in-house data sets covering reversed-phase chromatography (RP; N = 458), RP-anion-exchange mixed-mode chromatography (RP-AXMM; N = 436), and hydrophilic interaction chromatography (HILIC; N = 388), plus one external HILIC data set from the Retip package (N = 970). Using a 2:1 training/test split, BRT models trained from scratch achieved a test-set coefficient of determination (R2) of 0.86, 0.66, and 0.81 for the three in-house data sets. FastRet can also adjust a model to new chromatographic conditions from a few remeasured metabolites: using 25 RP metabolites measured under six modified conditions, adjustment reached R2 of 0.74 to 0.84 on unseen metabolites, a mean 0.22 gain over from-scratch models. Compared with published methods on identical splits, FastRet showed competitive performance for de novo prediction and superior performance in low-data transfer scenarios, while generalizing to 14 external data sets (median held-out R2 0.59). FastRet is available on CRAN with the web interface hosted at https://fastret.spang-lab.de.
    DOI:  https://doi.org/10.1021/acs.jcim.6c01344
  31. Anal Chem. 2026 Sep 01. 98(34): 24677-24689
      Mass spectrometry-based untargeted metabolomics analyzes complex biological matrices containing thousands of individual features. Linearity is a key analytical parameter in quantitative mass spectrometry, reflecting proportionality between signal intensity and analyte concentration within a defined range. In untargeted metabolomics, linearity cannot be directly assessed due to the absence of reference concentrations. Instead, the range in which features exhibit approximately linear dilution-dependent behavior (ALB) can be evaluated as a practical proxy. Feature selection based on this response enables the early removal of noise and unreliable features, thereby reducing the risk of false-positive findings and improving analytical robustness, while the reduced number of retained features lowers the multiple-testing burden in downstream statistical analyses. We present MSlineaR, an open-source R-based software tool implementing a six-step process to assess dilution-dependent response behavior in metabolomic data sets. MSlineaR evaluates dilution curves to identify nonclassical response patterns, detect outliers, and iteratively trim boundary regions to remove plateau effects. It then reassesses the remaining data to retain features exhibiting ALB. Importantly, it defines boundaries of the approximately linear range (ALR) and applies them for data curation, enabling exclusion of unreliable signals while preserving robust features. Application to three independent data sets demonstrated a ∼10% improvement in the number of features classified as exhibiting ALB compared to classical linear regression-based approaches. MSlineaR was complementary to relative standard deviation (RSD) filtering, improved median RSD values and enhanced the robustness of statistical modeling.
    DOI:  https://doi.org/10.1021/acs.analchem.5c04480
  32. Methods Mol Biol. 2026 ;3022 135-147
      Collagen-rich musculoskeletal connective tissues are in constant turnover, reflecting a balance between synthesis and breakdown, which is crucial for tissue remodeling and regeneration. Stable isotopes, particularly 15N-proline and 2H2O, offer a precise method for tracing collagen turnover. This chapter details the use of these isotopes to study collagen dynamics in response to various interventions. The protocol includes the administration of stable isotope tracers, tissue sampling, collagen isolation, and derivatization of amino acids for analysis by mass spectrometry.
    Keywords:  Collagen turnover; Human tissues; Intramuscular connective tissue; Stable isotope tracers; Tendon
    DOI:  https://doi.org/10.1007/978-1-0716-5194-0_8
  33. Methods Mol Biol. 2026 ;3022 123-133
      Mass spectrometery (MS)-based proteomics analysis is a powerful tool that allows for an in-depth understanding of the protein composition, including protein post-translational modifications, of a sample. Classic bulk MS methods on tissues usually results in a loss of resolution, particularly on the protein composition that defines morphological features. Further, many patient-derived samples are preserved using the formalin-fixed paraffin-embedded (FFPE) method due to ease of storage. These methods present a barrier to MS, leading to low yield. Here, we describe a method using laser-capture microdissection on FFPE tissue sections to isolate specific regions of interest, coupled with modified MS proteomics sample preparation to maximize protein yield, allowing us to spatially identify and quantify protein changes in tissues.
    Keywords:  Formalin-fixed paraffin-embedded; Laser-capture microdissection; Mass spectrometry; Patient-derived samples
    DOI:  https://doi.org/10.1007/978-1-0716-5194-0_7
  34. J Proteome Res. 2026 Sep 04. 25(9): 4734-4743
      Colon cancer (CC) is one of the malignant tumors with high incidence and mortality rates worldwide, necessitating innovative diagnostic tools to improve early detection and management. In this study, we developed a large-scale metabolome relative quantitative method workflow using ultrahigh-performance liquid chromatography coupled with Q-TRAP mass spectrometry to identify novel biomarkers in the serum of CC patients. This method enables the detection of 776 metabolic features, spanning 16 chemical classes and 63 metabolic pathways. All detected features were evaluated by multiple identification criteria and assigned confidence scores to ensure annotation reliability. Through validation and application of this method to clinical samples, we compared the serum metabolic profiles of CC patients with those of healthy controls and screened 72 significantly altered metabolites. Pathway enrichment analysis revealed perturbations in tryptophan metabolism, arginine biosynthesis, and the TCA cycle. Notably, three differential metabolites (indole, tryptophan, and xanthurenic acid) were identified that could potentially serve as diagnostic biomarkers with area under the curve values exceeding 0.9. Our large-scale metabolome relative quantitative method demonstrates applicability in identifying potential metabolite biomarkers for CC and provides a promising tool for both basic and clinical metabolomics research.
    Keywords:  LC-MS; colon cancer; metabolite biomarkers; serum metabolomics
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00263
  35. Anal Chem. 2026 Sep 01. 98(34): 24669-24676
      Metabolomics enables the simultaneous monitoring of hundreds to thousands of metabolites in complex biological matrices; however, confident identification of low-abundance and unknown compounds remains challenging. Gas chromatography-mass spectrometry (GC-MS) workflows based on electron ionization (EI) often provide limited molecular-ion information because of extensive fragmentation. Here, we present a novel rapid screening workflow combining 1 min heptafluorobutyl chloroformate (HFBCF) derivatization of protic metabolites and concurrent liquid-liquid microextraction with gas chromatography-atmospheric pressure chemical ionization mass spectrometry (GC-APCI-MS). The proven HFBCF-mediated reaction produces stable heptafluorobutyl derivatives that yield abundant protonated molecular ions in the APCI mass spectra, predictable class-specific fragmentation, and characteristic fluorine-specific mass signatures. Compared with conventional full-scan GC-EI-MS, full-scan GC-APCI-MS provided up to 10-1000-fold higher sensitivity, enabling metabolite screening from extremely limited sample amounts. In low-input HeLa cell extracts, at least 100 metabolites were consistently detected from as few as 15,000 cells. Specific heptafluorobutyl-derived mass shifts and fluorine-based elemental constraints facilitated determination of the number and, in selected cases, the type of derivatized functional groups and supported elemental-composition assignment of unknown metabolites, even at mass accuracies of several tens of ppm. To aid compound annotation, we established an openly accessible database comprising more than 620 derivatizable metabolites and experimentally characterized retention and mass-spectral data for 240 reference standards. Together, these results establish HFBCF-GC-APCI-MS as a sensitive molecular-ion-centric workflow for exploratory metabolomics, enabling annotation of unknown metabolites across defined confidence levels, from standard-confirmed identifications to database-supported candidate assignments, suitable for limited biological samples.
    DOI:  https://doi.org/10.1021/acs.analchem.6c04134
  36. Anal Chem. 2026 Sep 01. 98(34): 24843-24854
      Recent instrumental and computational innovations in mass-spectrometry-based proteomics offer new promise in biomarker discovery, thanks to unprecedented proteome coverage and depth. Data-independent acquisition (DIA) methods are very promising in this context as they allow improved proteome coverage, reduced missing value rates, and enhanced quantification precision. However, DIA methods also suffer from their own challenges, such as increased data complexity, cycle times, and background noise. In this work, we propose a sample-aware diaPASEF method optimization strategy for a timsTOF platform. Thorough method optimizations have first been conducted on standard HeLa lysates. Then, a ground-truth calibrated sample series, consisting of a range of UPS amounts spiked into a complex Arabidopsis background, was used to mimic differential analyses under controlled conditions. These benchmark experiments demonstrate clear benefits of using narrowPASEF for differential protein discovery. Finally, our strategy was applied to real use case biological samples to conduct a differential analysis of purified mouse astrocyte cells across two different conditions. narrowPASEF improved the proteome depth by 13%, considering proteins quantified with a coefficient of variation (CV) of <20%, and led to a 68% (435 vs 729) increase in differentially expressed proteins. These results provide an opportunity for a more precise and comprehensive analysis of the biological functions of biomarkers, offering a more profound understanding of the disease mechanisms. The benefits of our sample-aware narrowPASEF strategy demonstrated the most substantial impact on low-abundance proteins. Overall, these results show promise for more valuable and robust biomarker discoveries in the future.
    DOI:  https://doi.org/10.1021/acs.analchem.6c01740
  37. Mass Spectrom Rev. 2026 Sep 04.
      The proteome is a dynamic landscape of proteoforms arising from genetic mutations, alternative splicing, and post-translational modifications (PTMs), which collectively drive biological function and disease phenotypes. Mass spectrometry (MS)-based proteomics has emerged as an essential technique for elucidating this molecular complexity. Although bottom-up proteomics enables deep protein identification and quantification through peptide-level analysis, it disrupts molecular connectivity and introduces a peptide-to-protein inference problem, which is suboptimal for proteoform analysis. Top-down proteomics (TDP) offers a complementary approach by analyzing intact proteins, preserving molecular connectivity, and enabling direct characterization and quantification of proteoforms. This capability is increasingly vital for understanding heterogeneous human diseases. Here, we review the evolving role of TDP in biomedical research, highlighting studies that revealed proteoform-level alterations, identified candidate biomarkers, and advanced our understanding of the roles of proteoforms in human diseases.
    Keywords:  biomedical applications; disease; mass spectrometry; proteoforms; top‐down proteomics
    DOI:  https://doi.org/10.1002/mas.70042
  38. Anal Chem. 2026 Sep 01. 98(34): 24817-24830
      Microplate-based assays are indispensable in life sciences and drug discovery; however, conventional optical readouts provide limited chemical information. Here, we report a microplate-based mass spectrometry imaging (MP-MSI) workflow based on air flow-assisted desorption electrospray ionization (AFADESI) that enables high-throughput molecular analysis of complex biological samples with minimal sample consumption (down to 1 μL). In contrast to existing MSI-based high-throughput strategies that primarily perform single-mode screening, this platform integrates targeted quantitation and untargeted metabolomics from the same sample spot. Using formaldehyde (FA) as a model analyte, we developed a rapid spermidine derivatization method and performed full method validation in blank mouse plasma. The targeted assay achieved an interbatch precision of 6.18% RSD for the analyte-to-internal standard ratio across 0.01-0.8 mmol/L, with a minimum detectable concentration change of 1.12-fold. For untargeted metabolomics, the median within-run RSD evaluated from 180 repeated spottings of blank mouse plasma was 19.1%, corresponding to a minimum detectable fold change of 1.38, at a throughput of 2.2 min per sample. To demonstrate dual-mode integration, we applied the validated FA method to plasma from an Alzheimer's disease mouse model (APP/PS1 and wild-type, 10 and 12 months of age), simultaneously quantifying FA and profiling the global metabolome from the same acquisition. Age- and genotype-dependent metabolic alterations were revealed. The platform was further extended to cell coculture models and to drug quantitation (e.g., irinotecan in plasma), demonstrating versatility across sample types and analytes. This integrated strategy offers a versatile platform for high-content screening in biomedical and pharmacological research.
    DOI:  https://doi.org/10.1021/acs.analchem.6c01560
  39. Talanta. 2026 Aug 24. pii: S0039-9140(26)01163-X. [Epub ahead of print]312(Pt C): 130507
      Quantification of key effectors in the circulating renin-angiotensin system (RAS), including angiotensin (Ang) I, Ang (1-9), Ang II, Ang (1-7), and Ang A, is essential for understanding RAS-mediated blood pressure regulation. Herein, we present a highly sensitive and selective analytical method based on methoxyacetylation coupled with liquid chromatography-trapped ion mobility spectrometry-quadrupole time-of-flight mass spectrometry (LC-TIMS-qTOF/MS) operated in parallel reaction monitoring-parallel accumulation serial fragmentation (prm-PASEF) mode. Methoxyacetylation using an NHS ester reagent modulated protonation and fragmentation behavior, resulting in enhanced signal intensity and more concentrated product-ion formation. The TIMS parameters were optimized to achieve maximal sensitivity, with an inverse reduced mobility (1/k0) range of 0.7-1.1 cm2 V-1 s-1 and accumulation and ramp times of 150 ms. Under these conditions, the method exhibited excellent analytical performance, with limits of detection ranging from 0.6 to 3.4 fmol/mL, precision below 9.3% (CV), and accuracy within 95.1-105.8%. Using [13C5,15N]-labeled Ang II as an internal standard, all five target angiotensin peptides were successfully quantified from 100 μL of Sprague-Dawley rat plasma. The developed method provides a robust and sensitive platform for multiplexed quantification of angiotensin metabolites and enables detailed investigation of RAS dynamics in biological systems.
    DOI:  https://doi.org/10.1016/j.talanta.2026.130507
  40. Methods Mol Biol. 2026 ;3063 177-188
      Metabolite annotation and identification represent one of the major bottlenecks in non-targeted metabolomics. Different levels of confidence have been defined to report results of this task, but tools often treat annotation results as equal. Here, we present example analyses based on the MetaboAnnotation R/Bioconductor package to perform annotation conforming to the Metabolomics Standard Initiative or the Schymanski scale.
    Keywords:  MetaboAnnotation; Metabolite identification; Non-targeted metabolomics; R; RforMassSpectrometry
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_10
  41. Methods Mol Biol. 2026 ;3050 435-446
      Ustilago maydis, the causative agent of corn smut in maize, is a well-established model organism for fundamental biological research. In addition, this fungus has growing biotechnological importance, as it proliferates in a haploid, non-filamentous form and efficiently converts renewable substrates into a range of industrially valuable products. To complement the extensive molecular and genetic tools already available, metabolomics provides essential insights into cellular physiology and supports the exploitation of its production potential. Here, a GC-MS-based workflow for the absolute quantification of intracellular metabolites in U. maydis is presented. This protocol enables systematic analysis of metabolic network operation and can be readily extended to metabolomics studies in related fungi in the Ustilaginaceae family.
    Keywords:   Ustilago maydis; Central carbon metabolism; GC–MS; Metabolic engineering; Metabolomics; Sample preparation; Ustilaginaceae
    DOI:  https://doi.org/10.1007/978-1-0716-5364-7_37
  42. Anal Bioanal Chem. 2026 Sep 02.
      The evolution of metabolomics, a field aimed at comprehensively measuring the small organic molecule composition of a biological matrix, fundamentally reshaped the landscape of analytical measurement reliability. The 2011 release of Standard Reference Material (SRM) 1950 Metabolites in Frozen Human Plasma by the National Institute of Standards and Technology (NIST) marked the first reference material providing certification for 45 analytes to address the measurement precision of complex biological matrices. Assigning high-order values to many compounds is highly resource-intensive for a National Metrology Institute. Furthermore, certified values for individual metabolites cannot resolve the broad challenges associated with sample processing, analytical measurement, and data processing in untargeted workflows. The metabolomics community recognized the potential for SRM 1950 to serve as a benchmark material for method development and technical quality control (QC) rather than strictly for quantification. Recognizing this consumer-driven shift, NIST launched a novel reference material (RM) development strategy to provide accessible, fit-for-purpose materials evaluated by a new statistical framework for production homogeneity, the Coefficient of Disagreement. Here, we introduce the transition toward matrix-specific QC Suites featuring phenotypically distinct metabolite profiles, the first generation of which includes a human plasma suite, urine suite, liver suite, and fecal material. By prioritizing efficient production and embracing a community engagement model for consensus-based deep characterization, these suites offer a reliable tool for laboratory comparisons, instrument assessment, software development, and training. This new class of RMs underpins the next phase of measurement reliability, complementing traditional measurand certification while supporting the diverse needs of comprehensive metabolic profiling.
    Keywords:  Coefficient of disagreement; Metabolomics; Metrology; Quality control; Reference materials
    DOI:  https://doi.org/10.1007/s00216-026-06769-8
  43. J Proteome Res. 2026 Sep 04. 25(9): 4367-4379
      Blood-based proteomics is approaching a translational inflection point. Driven by advances in measurement technologies, rapid expansion of analytical capabilities, and growing adoption across research and medical communities, there is increasing demand for clinically actionable biomarkers. As the field transitions away from purely large-scale discovery-oriented studies toward more informed, targeted, application-driven analyses, the generation of proteomic data is no longer the bottleneck. Instead, the central challenge is to translate these measurements into robust, reproducible, and clinically meaningful insights. In this Review, we assess recent technological and methodological developments, evaluate persistent preanalytical and interpretative limitations, and outline the key steps required for clinical translation. We focus on three deeply interconnected dimensions: the capabilities and constraints of current measurement platforms, the role of computational and machine learning approaches in extracting biological and clinical signals, and the emergence of large-scale population studies that create new opportunities for validation and generalization. Finally, we discuss a forward-looking vision in which proteomics plays a central role in dynamic, multilayered omics frameworks, where integration with genomics, temporal profiling, and imaging can deepen our understanding of health, disease, and therapeutic response.
    Keywords:  Biomarkers; Blood-based proteomics; Causal inference; Clinical proteomics; Early disease detection; Machine learning; Multiomics integration; Patient stratification; Population-scale proteomics; Therapeutic monitoring
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00277
  44. Methods Mol Biol. 2026 ;3063 1-18
      The Jupyter Notebook is a platform for interactive computing that displays code and results in the same browser, making it valuable for teaching, prototyping, data analysis, and collaboration. Its explicit and transparent structure greatly reproducibility while its backend server supports flexible deployment. In the past few years, Jupyter notebooks and similar tools have become increasingly popular. In this chapter, we will review key aspects of data analysis in a cloud environment and demonstrate common tasks for analyzing metabolomics data using template notebooks. This is an accompaniment to the basic bioinformatics tools and essential data science toolkit introduced in the first edition.
    Keywords:  Data analysis; Data science; Jupyter; Metabolomics; Notebooks; Python; R
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_1
  45. Pharmacol Ther. 2026 Aug 29. pii: S0163-7258(26)00136-1. [Epub ahead of print]288 109109
      Cancer cells undergo profound metabolic reprogramming to sustain uncontrolled proliferation within a nutrient-limited and often hypoxic tumor microenvironment (TME). Metabolic rewiring is an active driver of oncogenesis, immune evasion, epigenetic remodeling, and therapy resistance. Over the past century, our understanding of tumor metabolism has grown from Warburg's seminal description of aerobic glycolysis to a comprehensive adaptive network. Cancer cells coordinate glucose catabolism, mitochondrial oxidative metabolism, fatty acid synthesis and oxidation, amino acid catabolism, nucleotide biosynthesis, and one‑carbon metabolism into an integrated metabolic framework. These pathways form a deeply interconnected web in which metabolic intermediates serve as biosynthetic building blocks, bioenergetic substrates, redox buffers, signaling molecules, and epigenetic cofactors. Within the TME, metabolic competition between tumor cells and immune cells, together with the accumulation of immunosuppressive metabolites such as lactate, kynurenine, and adenosine, creates a profoundly immune-hostile landscape. Recent work has further revealed that key post-translational modifications, directly driven by metabolic flux, reshape the chromatin and proteome of both cancer cells and tumor-infiltrating immune cells, linking metabolism to gene regulation in previously unanticipated ways. Therapeutically, the FDA approval of IDH1/IDH2 inhibitors for acute myeloid leukemia demonstrated that metabolic enzymes are tractable oncology drug targets. Yet the broader effort to translate metabolic insights into robust clinical benefit has encountered formidable obstacles, including metabolic plasticity, intratumoral heterogeneity, overlap with normal tissue function, and inadequate biomarkers. This review traces the evolution of our understanding of cancer metabolism from its origins to therapeutic targeting. It further examines how anabolic and catabolic pathways, energy production, redox balance, and metabolic crosstalk across intracellular, intercellular, and systemic domains shape tumor biology and therapeutic response. It also critically analyzes approved and investigational metabolic therapies and charts a course for the emerging era of precision metabolic oncology.
    Keywords:  Cancer metabolism; Epigenetics; Ferroptosis; Glycolysis; Immunometabolism; Metabolic reprogramming; Oncometabolites; Oxidative phosphorylation; Tumor microenvironment; Warburg effect
    DOI:  https://doi.org/10.1016/j.pharmthera.2026.109109
  46. Methods Mol Biol. 2026 ;3063 273-291
      TIGER, a non-parametric method, was developed to address technical variations (e.g., plate and batch effects) in targeted and non-targeted metabolomics datasets. It integrates the random forest (RF) algorithm into a flexible ensemble learning framework, combining multiple base models with a meta-model. These base models are trained using diverse RF hyperparameter combinations, eliminating the need for manual hyperparameter tuning. This chapter highlights practical considerations for using TIGER effectively, including incorporating quality control (QC) samples into study design. When QCs are unavailable, randomly selected samples can be remeasured to facilitate cross-kit corrections. To optimize processing time, highly correlated metabolites from QC samples are selected to train the base models, with weights assigned via an exponential decay function. TIGER employs relative standard deviation (RSD) and mean absolute percentage error (MAPE) as key metrics to ensure models generalize well to unseen data while avoiding overfitting. Additionally, the developed dynamic website has been demonstrated with raw and TIGER-normalized data, enabling performance evaluation and visualization of longitudinal patterns of metabolites or metabolite ratios. This platform demonstrates TIGER's ability to accurately normalize data and uncover trends across three time points spanning a decade. With its proper application, TIGER stands to be a powerful tool for metabolomics studies.
    Keywords:  Batch correction; Cross-kit correction; Data-preprocessing; ML algorithms; Metabolomics; Non-targeted metabolomics; Targeted metabolomics
    DOI:  https://doi.org/10.1007/978-1-0716-5452-1_15
  47. Methods Mol Biol. 2026 ;3062 21-51
      Proteomic studies are highly informative for identifying biological signatures. The data generated from such experiments are highly complex and require substantial computational expertise to analyze appropriately. Rigorous analysis is further confounded by the presence of missing values and multiple sources of technical variance. Here, we present a methodical walkthrough of a workflow for the analysis of Data Independent Acquisition (DIA) proteomics data. The workflow is derived from the MultiScholaR framework, which aims to make best-in-class tools and practices available to researchers of all skill levels. Users will analyze a publicly available neuroproteomics dataset, searching data using DIA-NN, filtering and normalizing resultant data, before imputing missing values and removing technical variance using relevant tools in the field. Users will be able to generate easy to understand results on the individual protein and functional annotation level, with results and plots automatically formatted to be publication-ready. Designed for researchers of all skill levels, MultiScholaR emphasizes modularity, transparency, and learning through tunable parameters, extensive documentation, and automated reporting. Upon completion, users should feel equipped to analyze their own datasets confidently, bridging the gap between data generation and biological interpretation.
    Keywords:  Data independent acquisition; Neuroscience; Proteomics; R
    DOI:  https://doi.org/10.1007/978-1-0716-5440-8_2
  48. Talanta. 2026 Aug 31. pii: S0039-9140(26)01184-7. [Epub ahead of print]312(Pt C): 130528
      Chemical derivatization technology significantly expands the detection scope and enhances the sensitivity of untargeted metabolomics. However, the complex adduct ion formations and diverse ionization patterns of derivatized products present substantial challenges for compound screening and data interpretation. Additionally, understanding the correlations between chiral metabolites and biological systems is essential for uncovering molecular mechanisms underlying physiological processes. To address these challenges, this study developed the (S), (R)-(5-(2-(((1-((N,4-dimethylphenyl) sulfonamido) vinyl) oxy) carbonyl) pyrrolidin-1-yl)-5-oxopentyl) triphenylphosphonium ((S), (R)-TPP-BSA) and d15-(S)-TPP-BSA probes, establishing a relative quantitative non-targeted metabolomics strategy for chiral amine-containing metabolites (RQ-NMCA). RQ-NMCA facilitates the formation of stable [M]+ adduct ions of amine derivatives in mass spectrometry, enabling effective identification and analysis of chiral amine metabolites. By leveraging isotope structures, relative quantitative analysis of amine-containing metabolites between groups is achievable. In serum samples from healthy volunteers (HV) and colorectal cancer (CRC) patients, 38 significant differential amine-containing metabolites were identified, including 15 chiral amines. RQ-NMCA introduces the fixed addition ion concept into chemical labeling non-targeted metabolomics, significantly enhancing ionization efficiency while simplifying the screening and identification processes. Furthermore, the integration of chiral recognition and relative quantification approaches is pivotal for understanding the relationship between chiral amine-containing metabolites and physiological or pathological states. This technology provides a robust platform for chiral non-targeted metabolomics research and will facilitate the identification of novel biomarkers for disease diagnosis.
    Keywords:  Chiral recognition; Fixed adduct ions; Nontargeted analysis; Relative quantification; TPP-BSA
    DOI:  https://doi.org/10.1016/j.talanta.2026.130528
  49. Talanta. 2026 Sep 01. pii: S0039-9140(26)01208-7. [Epub ahead of print]312(Pt C): 130552
      Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the preferred method for vitamin K quantification, yet conventional workflows are labor-intensive and scale poorly. We developed and validated a simple, high-throughput automated magnetic bead-assisted LC-MS/MS method for simultaneous quantification of vitamin K1 (VK1), menaquinone-4 (MK-4), and menaquinone-7 (MK-7) in human serum according to Clinical and Laboratory Standards Institute (CLSI) C62 (2nd ed.) guidelines. This method used MSi050/PLS magnetic beads (5 mg/mL) for extraction via methanol loading and acetonitrile elution, requiring only 100 μL of serum to complete sample pretreatment within 17 min. All three calibration curves were linear from 0.20 to 10.00 ng/mL (r > 0.99), with a lower limit of quantification of 0.10 ng/mL. Total coefficients of variation for VK1, MK-4, and MK-7 ranged from 1.98% to 3.54%, 2.53% to 3.73%, and 2.77% to 4.59%, respectively. The recoveries of VK1, MK-4, and MK-7 were 94.8% to 112%, 98.0% to 113%, and 90.3% to 106%, respectively. Both matrix effects and carryover met CLSI acceptance criteria. Because vitamin K2 is photosensitive, all samples were processed under light-protected conditions. Unextracted samples were stable for 14 days at -20 °C, and extracted samples were stable for 48 h at 4 °C. Additionally, our method was applied to 245 healthy volunteers to characterize serum VK distribution profiles. By integrating automated magnetic bead extraction with rapid LC-MS/MS detection, this robust strategy markedly improves analytical throughput and reproducibility while maintaining high accuracy, offering a practical solution for large-scale clinical vitamin K assessment.
    Keywords:  Liquid chromatography-tandem mass spectrometry; MK-4; MK-7; Magnetic bead extraction method; Vitamin K1
    DOI:  https://doi.org/10.1016/j.talanta.2026.130552