bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2026–06–14
sixteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Revisiting resonance-excitation collision-induced dissociation for data-independent acquisition.
  2. Tandem Mass Spectrometry Imaging of Low-Abundance Ether Phospholipids Guided by Bulk Lipidomics Analysis.
  3. Comprehensive Metabolomic Analysis of Saliva Using SWATH-DIA Reveals Systemic Metabolic Adaptations to Exercise.
  4. Label-Free Quantification in the Crux Toolkit.
  5. ToxBase: A Multidimensional ToxCast Reference Database for High-Throughput Human Exposome Analysis.
  6. Lipidomics reveals AMI-induced brown adipose tissue lipid remodeling linked to serum lipid changes in mice.
  7. Ground Truth-Based Evaluation of False Discovery Rate and Statistical Power in DIA Proteomics.
  8. The Language of Elution: Autoregressive Prediction of the Next Feature in Untargeted LC-HRMS Lipidomics.
  9. iDeepLC: Chemical Structure Information Yields Improved Retention Time Prediction of Peptides with Unseen Modifications.
  10. MultiMS2: A curated multi-modal, multi-energy spectral library for metabolomics.
  11. Robust analytical methods for bis(monoacylglycero)phosphate profiling in health and disease.
  12. eMZed 3: flexible and interactive development of scalable LC-MS/MS data analysis workflows in python.
  13. High-Throughput In-Depth Single-Cell Lipidomics by Ozone-Induced Dissociation and Mass Cytometry.
  14. Emerging Quantitative Targeted Metabolomics Approaches for Future Inborn Errors of Metabolism Screening.
  15. Untargeted metabolomics suggests a trade-off between phenolamides and flavonoids in the pollen of Petunia hybrida with contrasting flower colours.
  16. Why machine learning fails at mass spectrometry for small molecules.
  1. bioRxiv. 2026 Jun 03. pii: 2026.05.31.729078. [Epub ahead of print]
    Revisiting resonance-excitation collision-induced dissociation for data-independent acquisition.
    Chris Hsu, Lilian R Heil, Bo Wen, Graeme McAlister, Gennifer Merrihew, Philip M Remes, Deanna L Plubell, Rafael Melani, Vlad Zabrouskov, Michael J MacCoss.
      Data-independent acquisition (DIA) proteomics relies almost exclusively on beam-type collision-induced dissociation (HCD) because its short activation time supports fast acquisition rates required for DIA. However, HCD requires charge state calibration and is therefore imperfect for mixed-charge DIA isolation windows. Resonance-excitation collision-induced dissociation (reCID) offers a promising alternative to HCD for DIA because the ion activation is effectively independent of charge state. Historically, reCID's longer activation time has been considered too slow for DIA. Here, we revisit reCID for DIA proteomics using a modified Orbitrap Tribrid Apex MultiOmics mass spectrometer (Apex) that recovers acquisition-matrix overhead as additional ion injection time, enabling reCID acquisition rates comparable to HCD. Using matched acquisition rate settings of tryptic HeLa digests, reCID achieved precursor and protein detections similar to HCD when used with Carafe fine-tuned, fragmentation-matched spectral libraries. Library fine-tuning improved reCID precursor detections more than HCD detections, 24% versus 5%, indicating that HCD-trained prediction models are suboptimal for reCID spectra. ReCID also maintained peptide-level quantitative performance, including ions measured per peptide, precision, and accuracy. Across seven NCI cancer cell lines and pooled mixtures, protein abundance rankings were highly conserved between the Apex reCID and HCD methods, and also across Orbitrap Astral Zoom platforms. These results support reCID as a practical fragmentation mode for DIA proteomics.
    DOI:  https://doi.org/10.64898/2026.05.31.729078
  2. Anal Chem. 2026 Jun 12.
    Tandem Mass Spectrometry Imaging of Low-Abundance Ether Phospholipids Guided by Bulk Lipidomics Analysis.
    Tommy Zhang, Sara Amer, Julia Laskin.
      A central challenge in mass spectrometry imaging (MSI) of lipids is detecting and identifying species overshadowed by higher abundance isobaric lipids. Although tandem mass spectrometry can differentiate many lipid isobars and some isomers, the diversity and heterogeneity of lipids observed in MSI experiments necessitate the development of targeted approaches to differentiate and localize species with an overlapping m/z. To address this challenge, we leverage the fast acquisition speed of multiple reaction monitoring (MRM) to identify and localize hundreds of lipids in a single MSI experiment. We present a workflow to generate comprehensive, system-specific, and information-rich MRM transition lists for MSI and use them to examine the spatial localization of specific lipid classes with nanospray desorption electrospray ionization (nano-DESI) MSI in MRM mode. The lists are generated by integrating information from the LIPID MAPS database with data-dependent acquisition (DDA) and filtering by examining MRM signals of the corresponding tissue lipid extract. Using 165 MRM transitions in a single nano-DESI MSI experiment, we examined the localization of plasmalogen species, their isomers, and corresponding isobars in mouse brain tissue with sn-chain-level annotation. This workflow, which can be readily adapted to other lipid classes and tissue types, establishes MRM-MSI as a powerful strategy for mapping lipid targets in complex tissues.
    DOI:  https://doi.org/10.1021/acs.analchem.6c01209
  3. ACS Omega. 2026 Jun 02. 11(21): 30561-30569
    Comprehensive Metabolomic Analysis of Saliva Using SWATH-DIA Reveals Systemic Metabolic Adaptations to Exercise.
    Maroof Athar Hashmi, Diksha Gogia, Nirpendra Singh, Arvind Ramanathan.
      Saliva is an accessible, noninvasive biofluid for real-time monitoring of physiological and metabolic changes. However, its potential to capture physical-exciton-induced biochemical responses has been limited by the metabolite identification depth and reproducibility of conventional metabolomics tools. In this study, we established a Sequential Window Acquisition of All Theoretical Fragment-Ion Spectra (SWATH-DIA)-based untargeted LC-MS metabolomics workflow for comprehensive profiling and relative quantitation of the salivary metabolome before and after physical exercise. Saliva samples were collected from 27 recreational runners before and immediately after a standardized 5 km run to investigate acute metabolic fluctuations in participants. The Zeno SWATH-DIA method enabled the simultaneous acquisition of precursor and fragment ion spectra across the full m/z range (50-800 Da) in positive and negative mode of electrospray ionization (ESI), resulting in detection and validation metabolites spanning lipids, amino acids, organic acids, carbohydrates, and short-chain carnitines. Compared with traditional data-dependent acquisition (DDA) approaches, Zeno SWATH-DIA provided enhanced metabolite coverage, improved reproducibility, and reduced precursor selection bias (a statement of quantitation of how much more). Multivariate analyses (PCA, OPLS-DA) revealed clear separation between pre- and postexercise samples, highlighting metabolic shifts involving carbohydrate metabolism (lactate, pyruvate), fatty acid oxidation (acylcarnitines, glycerol), amino acid turnover (BCAAs, arginine, ornithine), and nitrogen metabolism (urea, spermidine). Collectively, these findings establish Zeno SWATH-DIA saliva metabolomics as a robust, high-coverage analytical approach for noninvasive assessment of acute metabolic responses to the exercise-induced physiological changes. The workflow provides a methodological foundation for future integrative studies linking saliva-based metabolomics with performance, fatigue, and metabolic health monitoring.
    DOI:  https://doi.org/10.1021/acsomega.5c11847
  4. J Proteome Res. 2026 Jun 11.
    Label-Free Quantification in the Crux Toolkit.
    Frank Lawrence Nii Adoquaye Acquaye, Bo Wen, Charles E Grant, William S Noble, Attila Kertesz-Farkas.
      Ultimately, most tandem mass spectrometry (MS/MS) proteomics experiments aim to not just detect but also quantify the proteins in a given complex sample. Here, we describe an extension to the Crux MS/MS analysis toolkit to enable label-free quantification of peptides. We demonstrate that Crux's new quantification command, which is modeled after the algorithms implemented in the widely used FlashLFQ software, is both efficient and accurate. In particular, we achieve a 1.9-fold speedup while reducing the memory usage by 26%. The new crux-lfq command is available in Crux v5.0.
    Keywords:  label-free quantification; protein quantification; proteomics; tandem mass spectrometry
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00336
  5. Environ Sci Technol. 2026 Jun 09.
    ToxBase: A Multidimensional ToxCast Reference Database for High-Throughput Human Exposome Analysis.
    Ryan Nguyen, Griffin Rangel, Xingzhu Liu, Reuben A Santoso, Amogh Bantwal, Dylan H Ross, Ryan P Seguin, Jennifer Liem, Yvonne S Lin, Brian Pratt, Brendan X MacLean, Michael J MacCoss, Libin Xu.
      High-resolution mass spectrometry (HRMS) is the gold-standard technique for comprehensively profiling chemical exposures in complex human matrices, making it a powerful analytical tool for advancing human exposome research. Yet the scarcity of HRMS reference data, including collision cross-section (CCS) measurements from ion mobility-mass spectrometry (IM-MS) and MS/MS fragmentation spectra, hinders confident structural annotation of chemical exposure agents across laboratories. We therefore developed ToxBase, a multidimensional (m/z, retention time, CCS, MS/MS) reference database for over 2,000 chemicals sourced from the U.S. Environmental Protection Agency's ToxCast chemical library. Built via high-throughput liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS), the ToxBase database comprises 3,598 precursor ions spanning 2,075 unique compounds with excellent precision (98.5% of compounds display interday CCS RSDs < 1%) and strong cross-platform agreement. A high-quality MS/MS reference library of the fragmented precursors was assembled using targeted data-dependent acquisition and DDARawProcessor, a novel data extraction algorithm. When applied to LC-IM-MS/MS data obtained from human plasma, urine, and fecal samples (n = 20 per matrix), ToxBase rapidly enabled 42 high-confidence (Level 1) identifications. The ToxBase database is freely available and compatible with the open-source MS data processing platform Skyline for vendor-agnostic suspect screening workflows, providing a valuable resource for standardized, large-scale exposome analysis.
    Keywords:  MS/MS; ToxCast; collision cross section; exposome; ion mobility-mass spectrometry; suspect screening
    DOI:  https://doi.org/10.1021/acs.est.5c18068
  6. Anal Biochem. 2026 Jun 08. pii: S0003-2697(26)00134-X. [Epub ahead of print]717 116178
    Lipidomics reveals AMI-induced brown adipose tissue lipid remodeling linked to serum lipid changes in mice.
    Fang Wang, Jia Xie, Gaosong Wu.
      Accumulated evidence suggests that brown adipose tissue (BAT) has potential benefits for promoting cardiometabolic health. However, the underlying mechanism by which BAT affects acute myocardial infarction (AMI) remains unclear. In this study, we optimized the lipidomics data acquisition mode and lipid extraction protocol of serum and BAT based on the UPLC-HRMS platform, and applied it to the analysis of AMI mice. The result showed that targeted data-dependent acquisition based on the inclusion list of differential and preidentified ions (dpDDA) mode has broader characteristic ion coverage, better stability, and higher quality MS/MS spectra compared with the data-independent acquisition (DIA) and data-dependent acquisition (DDA). The isopropanol (IPA) extraction protocol obtained more lipids and provided a higher response for BAT and serum lipids. Based on the above verification method, AMI mice were analyzed by the screening criteria of the Student's t-test (p < 0.05, VIP >1), 80 and 100 lipids (the main types were fatty acids and glycerophospholipids) were identified in serum and BAT, respectively. Further correlation analysis found that fatty acids are the most important lipids affected in serum and BAT, these fatty acids have been previously implicated closely associated with the thermogenic function of BAT and vascular function, suggesting a potential mechanism by which BAT may influence cardiac vascular function. In summary, the established lipidomics method can achieve high coverage, high stability, and high-quality collection of BAT and serum lipids, which can provide new insights into the physiological mechanism of BAT that regulates lipid metabolism and affects AMI.
    Keywords:  Acute myocardial infarction; Brown adipose tissue; Fatty acid; Lipidomics; Tandem mass spectrometry
    DOI:  https://doi.org/10.1016/j.ab.2026.116178
  7. bioRxiv. 2026 Jun 02. pii: 2026.05.29.728747. [Epub ahead of print]
    Ground Truth-Based Evaluation of False Discovery Rate and Statistical Power in DIA Proteomics.
    Jay M Yarbro, Ya Huang, Vishwajeeth Pagala, Yingxue Fu, Zhen Wang, Long Wu, Xusheng Wang, Anthony A High, Stephanie Byrum, Junmin Peng, Zuo-Fei Yuan.
      Data-independent acquisition (DIA) mass spectrometry enables rapid proteomic quantification, yet the reliability of statistical inference in DIA-based protein quantification remains incompletely understood. Here, we systematically evaluated missingness, false discovery rate (FDR), and statistical power, defined as true positive rate (i.e. sensitivity or recall), using technical replicates and a spike-in benchmark with known ground truth. Analysis of 18 HeLa replicates revealed persistent, abundance-dependent missingness. In the spike-in experiment with five replicates, human peptides were titrated against a stable yeast background, allowing fold changes (FCs) to be compared with expected values. Across comparisons with log2FCs ranging from 0.2 to 2.5, the nominal BH-FDR substantially underestimated the true FDR. For example, at a BH-FDR threshold of 0.05, the true FDR was ∼0.2. Statistical power was ∼40% for a log2FC of 0.2 and increased to nearly 100% for a log2FC of 2.5. Additional incorporation of FC thresholds improved the true FDR for large-FC comparisons, with slight loss of power, but markedly reduced sensitivity for small-FC comparisons. Together, these results indicate that nominal FDR does not necessarily reflect actual error rates in DIA proteomics and that DIA performance is influenced by protein abundance and expected fold changes. This study provides a framework for experimental design and data interpretation in DIA-based proteomic studies.
    DOI:  https://doi.org/10.64898/2026.05.29.728747
  8. ArXiv. 2026 Jun 02. pii: arXiv:2606.05225v1. [Epub ahead of print]
    The Language of Elution: Autoregressive Prediction of the Next Feature in Untargeted LC-HRMS Lipidomics.
    Dayanjan S Wijesinghe.
      Untargeted liquid chromatography-high-resolution mass spectrometry (LC-HRMS) detects thousands of molecular features per sample, yet only 2-20% receive confident structural annotations. A root cause of this "dark metabolome" is that tandem MS/MS acquisition is reactive: instruments select precursors only after ions appear, blind to what elutes next. We reframe chromatographic elution as an autoregressive sequence prediction task. Because reversed-phase elution order is governed by hydrophobicity, successive features form a physically constrained sequence, like tokens in language. We discretize the mass-to-charge (m/z) axis into 110 bins and train long short-term memory (LSTM) and Transformer models to predict the next eluting m/z bin from five annotation-free per-token features: m/z bin, mass defect, retention-time gap, polarity, and intensity rank. Trained on 15,242 features from four clinical lipidomics cohorts (342 plasma samples; SCIEX TripleTOF 6600+, Waters CSH C18), the LSTM reaches 98.4% top-1 accuracy (99.99% top-5; mean absolute error 3.6 Da) and the Transformer 98.0%. Ablation shows autoregressive context accounts for 55.5 percentage points while no single feature contributes more than 0.2 pp: the sequential pattern, not molecular properties, drives prediction. Models transfer across instruments sharing the method (r=0.999 on an independent Agilent 6530 dataset) but fail under a different column chemistry (5.1% top-1) or polarity mode (2.6%), confirming method- and mode-specificity. Fine-tuning on as few as two to five quality-control injections recovers held-out accuracy from 2.6% to nearly 50%, so cross-condition deployment needs minimal calibration. These results establish that elution sequences are highly predictable and lay the groundwork for predictive MS/MS acquisition to improve annotation coverage in untargeted metabolomics.
  9. Anal Chem. 2026 Jun 08.
    iDeepLC: Chemical Structure Information Yields Improved Retention Time Prediction of Peptides with Unseen Modifications.
    Alireza Nameni, Arthur Declercq, Ralf Gabriels, Robbe Devreese, Sven Degroeve, Amélie De Maesschalck, Maarten Dhaenens, Cristina Chiva, Eduard Sabidó, Lennart Martens, Robbin Bouwmeester.
      Deep learning has notably advanced the field of liquid chromatography-mass spectrometry-based proteomics. Accurate prediction of peptide retention times significantly enhances our ability to match LC-MS data with the correct peptides and proteins, especially for data-independent acquisition data. While numerous models predict peptide LC retention times with high accuracy, few can accurately predict the retention times of chemically modified peptides, particularly those with modifications not encountered during model training. In our previously developed DeepLC model, accurate predictions could be made for unseen modifications by leveraging the chemical compositions of (modified) residues. Here, however, we present a further enhancement of this model based on the chemical structural information. The resulting model, called iDeepLC, shows overall more accurate predictions and better generalization performance for predicting the retention time of modifications structurally defined as SMILES but unseen during training than DeepLC. iDeepLC is freely available as an open-source software under the Apache2 license and can be found at https://github.com/CompOmics/iDeepLC.
    DOI:  https://doi.org/10.1021/acs.analchem.5c08017
  10. Gigascience. 2026 Jun 10. pii: giag069. [Epub ahead of print]
    MultiMS2: A curated multi-modal, multi-energy spectral library for metabolomics.
    Adriano Rutz, Mario S P Correia, Nicola Zamboni.
       BACKGROUND: Spectral libraries are essential for mass spectrometry-based metabolomics, enabling accurate metabolite annotation. Collision-induced dissociation (CID) dominates existing public libraries, but is rarely sufficient for structural elucidation. Electron-activated dissociation (EAD) provides complementary, radical-driven fragmentation, but remains sparsely represented. The lack of datasets spanning multiple dissociation mechanisms, energies, and ionization modes limits both analytical workflows and the development of robust machine learning models.
    FINDINGS: We present MultiMS2, a curated metabolomics spectral library comprising 43,728 MS/MS spectra from 2,899 unique compounds. Spectra were acquired using both CID and EAD at three energies each, in positive and negative ionization modes. The dataset substantially expands publicly available EAD coverage while preserving matched acquisition conditions across energies and dissociation types.
    CONCLUSIONS: By systematically combining CID and EAD across multiple energies and polarities, MultiMS2 provides a unique resource for metabolite annotation, benchmarking, and machine learning. The library supports energy-aware and dissociation-aware analysis, enabling methodological innovation and improved generalization in computational metabolomics.
    Keywords:  Collision-induced dissociation; Electron-activated dissociation; Metabolomics; Spectral library
    DOI:  https://doi.org/10.1093/gigascience/giag069
  11. Nat Protoc. 2026 Jun 10.
    Robust analytical methods for bis(monoacylglycero)phosphate profiling in health and disease.
    Wentao Dong, Kwamina Nyame, Eshaan S Rawat, Uche N Medoh, Jian Xiong, Caio C Bonin, Hisham N Alsohybe, Hunter Y Liu, Sara Gomes, Tammy Shalit, Marianna Arnold, Frank Hsieh, Esther Sammler, Monther Abu-Remaileh.
      Bis(monoacylglycero)phosphates (BMPs), a distinct class of anionic phospholipids predominantly found in late endosomes and lysosomes, plays a pivotal role in supporting lysosomal functions and maintaining metabolic homeostasis. Dysregulation of BMPs is associated with an array of disorders, notably neurodegenerative diseases. However, the identification and quantitation of BMP remains difficult because of its structural similarity to its isomer, phosphatidylglycerol (PG), thus necessitating robust analytical methods for accurate and reliable BMP profiling. In this study, we present comprehensive liquid chromatography (LC)-tandem mass spectrometry (MS2) methodologies for the precise and systematic analysis of BMP species in biological samples. We detail LC/MS methods for both an untargeted Orbitrap mass spectrometer and a targeted triple quadrupole mass spectrometer. We use differences in hydrophobicity and structure to annotate BMPs and PGs on the basis of retention time and positive-mode MS2 fragmentation patterns, respectively. Because genetic ablation of the BMP synthase CLN5 leads to specific depletion of BMPs but not PGs, lipid extracts from CLN5 knockout and wild-type cells can be compared to confidently annotate BMPs when MS2 data are incomplete. Lipid extraction and preparation of samples for LC/MS takes ~4 h, unattended LC/MS instrument time depends on the number of samples and computer-based data analysis takes ~1 d. Altogether, this approach constitutes a robust method for BMP profiling and annotation, furthering research into health and disease.
    DOI:  https://doi.org/10.1038/s41596-026-01379-1
  12. Bioinform Adv. 2026 ;6(1): vbag138
    eMZed 3: flexible and interactive development of scalable LC-MS/MS data analysis workflows in python.
    Uwe Schmitt, Jethro Hemmann, Nicola Zamboni, Julia A Vorholt, Patrick Kiefer.
       Summary: Liquid chromatography-mass spectrometry (LC-MS/MS) data analysis requires adaptable software solutions to meet diverse analytical needs. We present eMZed 3, a modern Python framework for flexible and interactive analysis of LC-MS/MS data. eMZed 3 enables users to develop scalable workflows tailored to their specific requirements while leveraging Python's extensive ecosystem of libraries. Building on its predecessor, eMZed 3 is now Python 3-based and includes substantial enhancements, including support for chromatogram-based LC-MS data, a new SQLite-based backend supporting optional out-of-memory processing, and rich interactive visualization tools. Compared to the previous version, eMZed 3 is now split into three packages: emzed (core functionalities), emzed-gui (interactive data visualization), and emzed-spyder (an integrated development environment). This modular architecture allows straightforward integration of the emzed core library into headless Python environments, including computational notebooks (such as Jupyter) or high-performance computing clusters. eMZed 3 incorporates well-established libraries such as OpenMS, and is suited for both targeted and untargeted metabolomics. Overall, eMZed 3 supports the efficient development of scalable and reproducible LC-MS data analysis and is accessible to both novice and advanced programmers.
    Availability and implementation: eMZed 3 and its documentation are freely available at https://emzed.ethz.ch, the source code is hosted at https://gitlab.com/groups/emzed3.An online-executable example workflow is available on Binder at: https://mybinder.org/v2/gl/emzed3%2Femzed-example-workflow/HEAD?_labpath=example.ipynb.
    DOI:  https://doi.org/10.1093/bioadv/vbag138
  13. Anal Chem. 2026 Jun 09.
    High-Throughput In-Depth Single-Cell Lipidomics by Ozone-Induced Dissociation and Mass Cytometry.
    Yunyun Yang, Haishen Zeng, Yuehua Wu, Jiewei Deng, Chunyan Hao, Tiangang Luan.
      Lipids are essential chemical components of living cells, and performing in-depth lipidomics at the single-cell level provides profound insights into the cellular structure and function. Here, we couple ozone-induced dissociation (OzID) with mass cytometry to develop a high-throughput in-depth single-cell lipidomics platform. By precisely controlling the OzID reaction conditions to cleave approximately half of the C═C bonds in unsaturated lipids, appropriate abundances of diagnostic products are generated while retaining a comparable proportion of unreacted lipids. Subsequently, high-throughput single-cell mass cytometry analysis is performed to obtain in-depth lipidomic information comprising intact lipids and C═C structural information. The in-depth lipidomic platform achieves a throughput of 166 cells/min, enabling clear discrimination among clinically relevant cell types with highly similar intact lipid profiles, e.g., normal lung epithelial cells (Beas-2B) versus non-small-cell lung cancer cells (A549 and H1299). Our platform demonstrates outstanding potential for discriminating cell types and discovering refined lipid biomarkers.
    DOI:  https://doi.org/10.1021/acs.analchem.6c02709
  14. Diagnostics (Basel). 2026 Jun 03. pii: 1717. [Epub ahead of print]16(11):
    Emerging Quantitative Targeted Metabolomics Approaches for Future Inborn Errors of Metabolism Screening.
    Shayma A Osman, Ahmed Malki, Haya Al-Sulaiti, Osama Y Al-Dirbashi, Mohamed A Elrayess.
      Inborn errors of metabolism (IEMs) are a group of inherited genetic conditions that, in general, result from a specific enzyme defect. Clinical consequences caused by abnormal enzyme levels often disrupt affected metabolic pathways and their intermediary metabolites. Because diagnostic outcomes depend on early intervention, a timely and accurate diagnosis is essential. Quantitative targeted metabolomics (QTM) is an analytical approach that quantifies predefined metabolites and generates interpretable biochemical phenotypes. Instead of focusing solely on screening, expanded QTM methods enable higher coverage with multi-analyte profiling that can provide more comprehensive characterization of disease-associated metabolic perturbations, particularly in IEMs with overlapping biochemical profiles. This review summarizes diagnostic techniques for IEMs, outlines the principles and advantages of QTM, and evaluates its established role and emerging opportunities and limitations in advancing method development and deep metabolic phenotyping to support precision medicine.
    Keywords:  biomarkers; clinical metabolomics; inborn errors of metabolism; newborn screening; precision medicine; quantitative targeted metabolomics
    DOI:  https://doi.org/10.3390/diagnostics16111717
  15. BMC Plant Biol. 2026 Jun 11.
    Untargeted metabolomics suggests a trade-off between phenolamides and flavonoids in the pollen of Petunia hybrida with contrasting flower colours.
    Matheus Fernandes Alves, Abigail Moreno-Pedraza, Paula Carolina Pires Bueno, Khabat Vahabi, Nicole M van Dam.
       BACKGROUND: Pollen is vital for reproduction of flowering plants. Several nutritional and biological benefits for humans and pollinators are described and related to its richness and diversity of metabolites. However, the chemical composition of pollen from several horticulturally significant plant species, such as those in the genus Petunia, has not been thoroughly characterised. Here, we present the first comprehensive description of the chemical profile of two distinct P. hybrida lines: V26 (violet flowers) and W115 (white flowers) using untargeted metabolomics. Our workflow started from pollen sampling and isolation, followed by a 3-in-1 liquid phase extraction for wide-range metabolite recovery, and data acquisition by ultra-high-performance liquid chromatography coupled to high-resolution tandem mass spectrometry (UHPLC-HRMS/MS) using three chromatographic columns (C8, C18, and HILIC) with positive and negative ionisation. For data analysis, we implemented a user-friendly and reproducible data processing pipeline based on open-source computational tools.
    RESULTS: The P. hybrida pollen is rich in glycosylated flavonoids, phenolamides, and lipids, which were detected mostly in non-polar phase extracts analysed by reversed-phase chromatography. Simple phenylpropanoids, fatty acids, amino acids, and terpenoids were annotated to a lesser extent. Statistical analyses integrated with molecular networking demonstrated a distinct metabolic dichotomy: phenolamide derivatives are predominantly present in the pollen of the V26 line, while flavonoids are accumulated in the pollen of W115. This finding suggests different regulation of the phenylpropanoid metabolism in pollen of P. hybrida lines with differing flower colours and sheds light on hypotheses of ecological roles of pollen secondary metabolites, for example, in plant-pollinator interactions.
    CONCLUSIONS: Our findings suggest a metabolic trade-off in the phenylpropanoid pathway in the two studied P. hybrida lines. These cultivar-specific chemical signatures may have significant implications for pollen viability and interactions with pollinators. Furthermore, our analytical and computational workflow serves as a robust template for the deep metabolic profiling of other under-characterised and complex natural matrices.
    Keywords:   Petunia hybrida ; Liquid chromatography - mass spectrometry; Metabolic profiling; Phenylpropanoids; Pollen analysis; Pollen metabolomics; Specialised metabolites
    DOI:  https://doi.org/10.1186/s12870-026-08968-y
  16. Nat Metab. 2026 Jun 11.
    Why machine learning fails at mass spectrometry for small molecules.
    Ling Min Serena Khoo, Regina Barzilay.
      
    DOI:  https://doi.org/10.1038/s42255-026-01544-6
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