bims-mascan 2026-08-30 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–08–30
sixteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Quantitative Profiling of Metabolites, Sugars, and Lipids in Pollen Tubes by Mass Spectrometry.
Chemical chemical derivatization-assisted LC-MS for food analysis: Analytical principles, performance enhancement, applications, and perspectives.
Next-generation multiplexed targeted proteomics quantifies post-translational modifications in disease and compound-protein interactions with high throughput.
Quantitative MALDI-MSI Workflow for Lipids with a LC-MS-Based Quality Control Pipeline.
FERMO: A Dashboard for Biochemometric Prioritization of Molecular Features from Mass Spectral Data.
Comprehensive non-targeted LC×LC-HRMS lipidomics workflow with active solvent modulation for the characterization of endocrine disruptor effects in zebrafish eleutheroembryos.
PyCompound: An Open-Source Python Package for Spectral-Library Matching in Mass Spectrometry-Based Metabolomics.
Advances in Single-Cell Metabolomics and Their Applications in Disease Biology.
Protocol for preparing small, fragile samples from Schistosoma mansoni and Drosophila melanogaster brains for MALDI mass spectrometry imaging.
Exercise and Sports Metabolomics: Analytical Platforms, Metabolite Annotation, Pathway-Level Interpretation, and Biomarker-Panel Readiness.
A porcine spectral assay library to quantify brain proteome by DIA-MS.
An integrated workflow for combined organic and inorganic chemical exposome analysis.
Isomer-Resolved Mass Spectrometry Imaging Reveals Regional and Age-Dependent Glycolipid Metabolism in Rat Brain.
Balancing speed and separation: A high-throughput native mass spectrometry platform for screening complex bispecific antibody variants.
Re-evaluating wavelet normalization in chromatographic peak detection: An amplitude-preserving approach for high-precision LC-MS.
LipidAnalyst: A Comprehensive Tool for Lipidomic Data Visualization and Analysis.

Methods Mol Biol. 2026 ;3042 93-109

Quantitative Profiling of Metabolites, Sugars, and Lipids in Pollen Tubes by Mass Spectrometry.

Maneesh Lingwan, Neeta Lohani, Erin Cullen.

  Pollen tube germination and elongation are energy-demanding, metabolically active processes essential for plant reproduction. Recent research combining metabolomics, imaging, and molecular genetics has highlighted the crucial roles of sucrose synthase activity, starch breakdown, and lipid signaling in maintaining pollen tube health and promoting precise growth, particularly under stress conditions. This chapter presents a mass spectrometry-based workflow for high-resolution profiling of metabolites, sugars, and lipids in pollen and pollen tubes. The workflow includes metabolite extraction, chemical derivatization, and chromatographic separation steps compatible with both gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). These techniques enable simultaneous quantification of polar metabolites, lipids, and fatty acids, capturing the metabolic complexity that supports pollen tube germination, elongation, and fertilization. It also covers the application of internal standards and isotopic normalization to ensure precise quantification and reproducibility across experiments. These analytical approaches collectively provide a foundation for comparative metabolite profiling in reproductive biology and can be adapted to various plant species, lipid signaling, and membrane biogenesis, thereby offering a mechanistic understanding of how pollen tubes sustain polarized growth under both developmental and stress conditions.

Keywords:  Chromatography; Lipid; Mass spectrometry; Metabolites; Pollen tube; Sugar

DOI:  https://doi.org/10.1007/978-1-0716-5308-1_7
J Chromatogr A. 2026 Aug 22. pii: S0021-9673(26)00709-0. [Epub ahead of print]1786 467382

Chemical chemical derivatization-assisted LC-MS for food analysis: Analytical principles, performance enhancement, applications, and perspectives.

Xiaoyu Xie, Chunling Zhao, Ruonan Zhang, Shuhui Wang, Chao Li, Zhihong Yan.

  The increasing complexity of food matrices and the demand for comprehensive foodomics have exposed the limitations of conventional LC-MS workflows in chemical coverage, structural annotation, and quantitative analysis. Chemical derivatization provides an effective strategy to address these challenges through selective functional-group modification and integration with advanced analytical approaches, enabling improved chromatographic performance, ionization efficiency, and structural characterization. Recent advances integrating derivatization with high-resolution mass spectrometry, stable isotope encoding, retention time prediction, and computational annotation have further expanded its role from sensitivity enhancement to information-rich analysis. This review summarizes recent developments in derivatization-assisted LC-MS strategies for food analysis from 2013 to 2026. Derivatization approaches targeting amino, hydroxyl, carbonyl, carboxyl, and other functional groups are systematically discussed, with emphasis on reaction principles, analytical advantages, representative applications, and methodological limitations. Particular attention is given to the role of derivatization in improving molecular coverage, structural confidence, and quantitative reliability in complex food matrices. Remaining challenges include balancing reaction selectivity and applicability, improving workflow standardization, and enhancing automation compatibility. Future advances will rely on integrating selective derivatization chemistry with advanced MS technologies and computational approaches to achieve more comprehensive, reliable, and accurate characterization of chemically diverse compounds in complex food systems.

Keywords:  Chemical derivatization; Food analysis; Liquid chromatography-mass spectrometry

DOI:  https://doi.org/10.1016/j.chroma.2026.467382
Nat Commun. 2026 08 25. pii: 9048. [Epub ahead of print]17(1):

Next-generation multiplexed targeted proteomics quantifies post-translational modifications in disease and compound-protein interactions with high throughput.

Steven R Shuken, Geordon A Frere, Charlotte R Beard, Christopher D McGann, Jesse D Canterbury, Nathan R Zuniga, Brandon M Gassaway, Shane L Dawson, Kean Hean Ooi, João A Paulo, M Windy McNerney, Steven P Gygi, Qing Yu.

The GoDig platform enables sensitive, multiplexed targeted pathway proteomics without manual scheduling or synthetic standards. Here we present GoDig 2.0, which increases sample multiplexing to 35-fold, improves time efficiency and reduces scan delays for higher success rates, and allows flexible spectral and elution library generation from different mass spectrometry data types. GoDig 2.0 measures 2.4× more targets than GoDig 1.0, quantifying >99% of 800 peptides in a single run. We compile a library of 23,989 human phosphorylation sites from a phosphoproteomic dataset and use it to profile kinase signaling differences across cell lines. In human brain tissue, we establish a hyperphosphorylated tau assay including pTau127, revealing potential biomarkers for Alzheimer's disease. We also quantify diglycyl-lysine peptides to assess polyubiquitin branching. Finally, we build a library of 20,946 reactive cysteines and profile covalent compound-protein interactions spanning diverse pathways. GoDig 2.0 enables high-throughput analyses of site-specific protein modifications across many biological contexts.

DOI: https://doi.org/10.1038/s41467-026-76929-y
ACS Meas Sci Au. 2026 Aug 19. 6(4): 1194-1205

Quantitative MALDI-MSI Workflow for Lipids with a LC-MS-Based Quality Control Pipeline.

Felix-Levin Hormann, Sven Heiles.

  Mass spectrometry imaging (MSI) has established itself as a major analytical method in the spatial analysis of biological tissue. Besides qualitative mapping of analyte distributions, recent developments aim to achieve quantification of target analytes. After successful spatial quantification of pharmaceuticals, the field is now shifting toward endogenous biomolecules such as lipids and metabolites. Here, we present a quantitative MSI (qMSI) workflow for endogenous lipids employing matrix-assisted laser desorption/ionization (MALDI) and stable isotope labeled standards (SILS) integrated into a quality control pipeline utilizing quantitative reversed-phase liquid chromatography tandem mass spectrometry (RP-LC-MS/MS). We thoroughly characterized the standard deposition of 13 lipid SILS with a focus on homogeneity and detection errors on mouse heart septa with intraday coefficient of variations (CVs) of ≤15%. Unlike RP-LC-MS/MS for which CV values increase mainly for lowly abundant lipids, variations of lipids in MALDI-MSI are affected by concentration as well as lipid class. Additionally, we compared lipid annotations based on MALDI-MSI and RP-LC-MS/MS and correlated these annotations between the methods based on quantification results. This method comparison revealed that most polar lipids such as PCs or SMs are captured in both MALDI-MSI and RP-LC-MS/MS, whereas less polar compounds such as TGs exhibit limited overlap. These results highlight that a determination of the SILS amounts and lipid annotations via RP-LC-MS/MS is necessary to counter the increase variability during quantitative MALDI-MSI. To demonstrate the analytical capabilities of the quantitative MALDI-MSI pipeline combined with RP-LC-MS/MS quality control steps, we analyzed mouse kidney from a two-hit heart failure with preserved ejection fraction model and achieved good comparability between RP-LC-MS/MS and MALDI-MSI quantitation results. Our data suggest increased ether lipid concentrations in obese mice kidney, likely to cope with increased oxidative stress induced by the progressing disease state of the mice.

Keywords:  MALDI; lipid metabolism; lipidomics; mass spectrometry imaging; quantification

DOI:  https://doi.org/10.1021/acsmeasuresciau.6c00083
ACS Meas Sci Au. 2026 Aug 19. 6(4): 1011-1022

FERMO: A Dashboard for Biochemometric Prioritization of Molecular Features from Mass Spectral Data.

Mitja M Zdouc, Hannah E Augustijn, Nataliia V Machushynets, Lina M Bayona, Sylvia Soldatou, Niek F de Jonge, Sarah Casu, Marcel Jaspars, Gilles P van Wezel, Marnix H Medema, Justin J J van der Hooft.

  Many natural products can selectively modulate biological processes, making them prime candidates for drug discovery. However, the complexity of biological samples makes clear attribution of activity to molecules challenging, thereby hampering hypothesis-driven prioritization, with liquid chromatography-tandem mass spectrometry routinely detecting hundreds of molecules per sample. Existing biochemometric tools typically focus on facilitating data-driven exploration to support manual interpretation, rather than more objective, data-driven prioritization and hypothesis generation. Here, we introduce FERMO, a free online dashboard interface for biochemometrics-based prioritization of molecular features and samples. FERMO accepts qualitative and quantitative bioactivity assay data and further integrates group metadata and results from genome mining. FERMO performs automated data processing, organization, and annotation, supporting prioritization with the calculation of custom scores. FERMO supports both exploratory and targeted analysis through efficient interactive visualization, reproducible prioritization, and data filtering. We demonstrate FERMO's utility in benchmarking studies prioritizing bioactive natural products from complex biological matrices. FERMO is freely available at https://fermo.bioinformatics.nl/.

Keywords:  Antibiotics; Computational Metabolomics; Data Integration; Mass Spectrometry; Natural Products; Secondary/Specialized Metabolites

DOI:  https://doi.org/10.1021/acsmeasuresciau.6c00022
Anal Chim Acta. 2026 10 22. pii: S0003-2670(26)00911-6. [Epub ahead of print]1420 345961

Comprehensive non-targeted LC×LC-HRMS lipidomics workflow with active solvent modulation for the characterization of endocrine disruptor effects in zebrafish eleutheroembryos.

Miriam Pérez-Cova, Laia Navarro-Martin, Gabriel Mazzi Leme, Romà Tauler, Benjamin Piña, Joaquim Jaumot, Dwight R Stoll.

  Lipidomics is a powerful approach for investigating lipid alterations in complex biological systems. However, conventional liquid chromatography coupled to mass spectrometry (LC-MS) may be limited in non-targeted studies due to the high structural diversity and wide concentration range of lipid species. In this work, an optimized comprehensive two-dimensional liquid chromatography coupled to high-resolution mass spectrometry (LC×LC-HRMS) workflow was developed for non-targeted lipidomics. The method combines reversed-phase (RP) separation in the first chromatographic dimension with hydrophilic interaction liquid chromatography (HILIC) separation in the second dimension (RP×HILIC) and incorporates active solvent modulation (ASM) as an interface between the two chromatographic dimensions to improve analytical sensitivity and solvent compatibility across both dimensions. This approach improves sensitivity, solvent compatibility and lipidome coverage compared to conventional LC-MS workflows. The proposed workflow was applied to investigate lipidomic alterations in zebrafish (Danio rerio) eleutheroembryos exposed to the endocrine-disrupting chemical bisphenol A (BPA), using 17β-estradiol (E2) as an estrogenic control. The LC×LC-HRMS approach enabled enhanced lipidome coverage, resulting in the detection of 567 lipid features, of which 134 showed significant alterations associated with endocrine-disruptor exposure. Comparative analysis revealed lipidomic changes consistent with estrogenic responses for both BPA and E2 treatments, while additional lipid alterations suggested potential obesogenic effects under E2 exposure. In summary, the proposed LC×LC-HRMS workflow with ASM provides enhanced separation performance. The obtained results demonstrate the potential of comprehensive multidimensional chromatography combined with multivariate analysis tools for in-depth lipidomics studies in complex biological samples.

Keywords:  Bisphenol A; Chemometrics; Endocrine disrupting chemicals; LC × LC-HRMS; Lipidomics; Zebrafish

DOI:  https://doi.org/10.1016/j.aca.2026.345961
Metabolites. 2026 Aug 18. pii: 585. [Epub ahead of print]16(8):

PyCompound: An Open-Source Python Package for Spectral-Library Matching in Mass Spectrometry-Based Metabolomics.

Hunter Dlugas, Xiang Zhang, Xun Bao, Jing Li, Ikuko Kato, Seongho Kim.

  Spectral-library matching is a widely used approach for compound annotation in mass spectrometry (MS)-based metabolomics, yet annotation performance is influenced by spectrum preprocessing, parameter selection, and similarity-measure choice. We present PyCompound, an open-source Python package for spectral-library matching with flexible preprocessing, parameter optimization, and diverse similarity measures for both nominal-resolution and high-resolution mass spectrometry data. PyCompound implements six preprocessing procedures, nineteen similarity measures, including the newly developed Rényi entropy similarity in this work for spectral-library matching, user-defined composite similarity scores, and automated parameter optimization using grid search and differential evolution. The software supports common spectral formats and is accessible through a Python API, command-line interface, and interactive Shiny application. The software was validated using public GNPS LC-MS/MS and WebNIST GC-MS datasets, where the newly developed Rényi entropy similarity demonstrated annotation performance comparable to that of the established Shannon and Tsallis entropy similarity measures. PyCompound provides a flexible platform for practical compound annotation through configurable preprocessing workflows, diverse similarity measures, and automated parameter optimization.

Keywords:  chemoinformatics; compound annotation; entropy; mass spectrometry; metabolomics; similarity measures; spectral library matching

DOI:  https://doi.org/10.3390/metabo16080585
OMICS. 2026 Aug 26. 15578100261480571

Advances in Single-Cell Metabolomics and Their Applications in Disease Biology.

Jie Du, Huihui Tao, Yujie Zhang, Mengyao Wu, Erjiao Hao, Tianyuan Li, Min Feng, Feng Zhu, Ruijie Yang, Ning Ni, Yong Dai.

  Single-cell metabolomics (SCM) provides a functional view of cellular heterogeneity by measuring metabolic states at cellular or subcellular resolution. Unlike bulk metabolomics, SCM can reveal rare metabolic cell states, spatially restricted metabolic niches, dynamic pathway activity, and treatment-associated metabolic adaptations. This review summarizes major SCM strategies, including spatial mass spectrometry imaging (MSI), isolated single-cell mass spectrometry (MS), isotope-assisted approaches, fluorogenic probes, and vibrational spectroscopy-based methods. Rather than treating these platforms as interchangeable technologies, we organize the review around a question-driven framework linking biological questions to platform selection, data structures, computational workflows, and interpretation boundaries. We also discuss major analytical challenges, including limited sample amount, missing values, ion suppression, batch effects, metabolite annotation uncertainty, and incomplete standardization. Disease-related studies suggest that SCM can identify recurrent metabolic programs, candidate biomarkers, and intervention-relevant metabolic nodes, but most applications remain at the discovery or early translational stage. Future progress will require standardized workflows, improved quantitative confidence, multimodal integration, and validation in clinically relevant models and cohorts.

Keywords:  biomarker discovery; mass spectrometry; metabolic heterogeneity; single-cell metabolomics; standardized workflows

DOI:  https://doi.org/10.1177/15578100261480571
STAR Protoc. 2026 Aug 24. pii: S2666-1667(26)00442-9. [Epub ahead of print]7(3): 104789

Protocol for preparing small, fragile samples from Schistosoma mansoni and Drosophila melanogaster brains for MALDI mass spectrometry imaging.

Annika Sabine Mokosch, Max A Müller, Laura Garmendia Sanchez, Stefanie Gerbig, Bernhard Spengler.

  Mass spectrometry imaging is a routine tool for investigating biological samples with high spatial and mass resolution. However, sample preparation, especially when dealing with very small samples, can be challenging. Here, we describe a sample preparation protocol for small and fragile samples based on examples of Schistosoma mansoni parasites and dissected Drosophila melanogaster brains. We describe steps for cryosectioning, matrix application, measurement, and data analysis to enable the acquisition of high-quality spatial lipidomics and metabolomics data from small samples. For complete details on the use and execution of this protocol, please refer to Mokosch et al. and Rorsman et al., respectively.1,2.

Keywords:  Chemistry; Organoids; Protocols in Metabolomics and Lipidomics

DOI:  https://doi.org/10.1016/j.xpro.2026.104789
Metabolites. 2026 Jul 27. pii: 530. [Epub ahead of print]16(8):

Exercise and Sports Metabolomics: Analytical Platforms, Metabolite Annotation, Pathway-Level Interpretation, and Biomarker-Panel Readiness.

Donghai Lin, Yifen Chen, Caihua Huang.

  Exercise and sports metabolomics provide a systems-level approach to characterizing how acute exercise, training adaptation, nutrition, recovery, and environmental stress reshape human metabolism. By profiling metabolites related to substrate utilization, mitochondrial function, redox balance, inflammation, muscle stress, and recovery kinetics, these approaches can reveal pathway-level responses that conventional single biomarkers cannot capture. However, many exercise-responsive features remain difficult to interpret because of incomplete chemical identification, uncertain annotation confidence, limited quantitative reproducibility, variable pre-analytical control, inconsistent data processing, and insufficient biological validation. This narrative review examines recent advances in exercise and sports metabolomics, with emphasis on LC-MS, GC-MS, NMR spectroscopy, IMS-MS, and CE-MS workflows; platform selection; metabolite annotation and identification; pathway-level interpretation; and evidence requirements for candidate-panel development. Exercise-responsive metabolites should be interpreted as context-dependent pathway signals rather than isolated indicators of fatigue, recovery, adaptation, or performance. The review consolidates requirements for sampling, quality control, metadata capture, repeated-measures analysis, and external validation within an evidence-readiness roadmap. Wearable biochemical monitoring, AI-assisted analysis, and multi-omics integration may support future applications, but their value depends on analytical robustness, external validation, and physiological interpretability. Exercise and sports metabolomics should therefore progress from descriptive feature discovery toward reproducible, quantitatively reliable, and biologically validated pathway-level interpretation.

Keywords:  biomarker-panel readiness; exercise metabolomics; metabolite annotation; metabolite identification; pathway validation; quality control; sports metabolomics

DOI:  https://doi.org/10.3390/metabo16080530
Sci Data. 2026 Aug 24. pii: 1218. [Epub ahead of print]13(1):

A porcine spectral assay library to quantify brain proteome by DIA-MS.

Jakub Cervenka, Rita Sucha, Jirina Tyleckova, Ievgeniia Poliakh, Jaromir Novak, Eva Kotrcova, Karel Harant, Pavel Rehulka, Petr Vodicka.

Neurological disorders are the leading cause of health loss worldwide. The growing number of patients suffering from such conditions calls for improved strategies for their prevention, diagnosis, and therapy. To better understand human pathologies, relevant models and methodologies must be made available. In this study, we focused on a biomedical model capable of recapitulating the complexity of human pathology, the pig (Sus scrofa). Brain tissue and cerebrospinal fluid samples from a transgenic minipig model of Huntington's disease were subjected to multiple extraction and fractionation steps. A proteomic mass spectrometry (MS) methodology then allowed the generation of a porcine spectral library for 8,321 proteins. Using data-independent acquisition (DIA), we demonstrated that our porcine spectral library substantially enhanced the quantitative potential of this untargeted MS approach, generating reproducible proteome-wide data. The porcine library also provides a comprehensive resource for the development of targeted MS assays, enabling the quantification of selected proteins with a key role not only in neuroscience.

DOI: https://doi.org/10.1038/s41597-026-07785-0
Talanta. 2026 Aug 22. pii: S0039-9140(26)01155-0. [Epub ahead of print]312(Pt B): 130499

An integrated workflow for combined organic and inorganic chemical exposome analysis.

S Braeuer, M L Feuerstein, T Buerki-Thurnherr, B Warth, G Koellensperger.

  Humans are exposed to a multitude of non-genetic factors throughout their lives, all of which are encompassed in the concept of the exposome. To assess its chemical component, combinations of complementary analytical techniques are needed for broad chemical space coverage. However, approaches for combined organic and inorganic analysis remain scarce and are further constrained by limited sample availability (e.g., in historical biobanks or preterm infants), as individual techniques require different sample preparation workflows. Therefore, we developed and evaluated an approach to combine sample preparation and analysis of organic and inorganic constituents of the exposome from a single low-volume sample aliquot (50 μL of blood or 50 mg of tissue). Liquid chromatography (LC) tandem mass spectrometry (MS) and inductively coupled plasma mass spectrometry (ICP-MS) were employed to target 94 organic and 55 inorganic analytes, including food and environmental contaminants (e.g., PFAS, mycotoxins, phytoestrogens, and heavy metals) as well as hormones, trace elements, metalloids and mineral elements. In a proof-of-principle study, the workflow was applied to human placental tissue and matched umbilical cord blood plasma samples from twelve individuals. In total, the workflow enables quantitative assessment of 66 to 71 targeted organic analytes, of which 24 to 35 were detected in the cord blood, cord plasma, or placental tissue samples studied. Moreover, it allowed for the quantification of 14 to 20 inorganic analytes, with another 16 to 29 analytes below the limit of detection in the biological samples. Moreover, non-targeted analysis (NTA) using high resolution MS further expanded the analyte coverage of the workflow. This first-of-its-kind approach enables (semi-)quantitative analysis of trace levels of a broad range of chemical exposures from a single sample aliquot, which is particularly advantageous when sample availability is limited.

Keywords:  Exposomics; Mass spectrometry; Metabolome; Metallomics; Sample preparation techniques; Suspect screening; Untargeted metabolomics

DOI:  https://doi.org/10.1016/j.talanta.2026.130499
ACS Cent Sci. 2026 Aug 26. 12(8): 1146-1157

Isomer-Resolved Mass Spectrometry Imaging Reveals Regional and Age-Dependent Glycolipid Metabolism in Rat Brain.

Hsi-Chun Chao, Marisa Asadian, Stanislav S Rubakhin, Fan Lam, Jonathan V Sweedler.

Glycolipids are essential for myelin integrity, but their extensive isomeric complexity, where isomers like galactosylceramide (GalCer) and glucosylceramide (GlcCer) have unique functions, has hindered our ability to map their metabolism in the brain. Existing mass spectrometry methods fail to resolve this complexity. We fundamentally overcome this barrier with a powerful new isomer-resolved mass spectrometry imaging workflow. By coupling ion mobility and mass spectrometry imaging with targeted enzymatic pretreatment, our method provides direct visualization of distinct de novo metabolic pathways for GalCer and GlcCer in situ. When applied to rat brains across the lifespan (2, 6, and 12 months), this technology revealed a previously unrecognized age-related decline that selectively affected the GalCer pathway, while the isomeric GlcCer pathway remained comparatively stable. We confirmed these metabolic alterations colocalize with myelin-associated proteins specifically in oligodendrocyte-rich regions, providing a functional link to altered oligodendrocyte-related lipid metabolism. This integrated approach provides a new paradigm for dissecting isomer-specific glycolipid metabolism, offering insight into lipid alterations in brain aging and opening new avenues for biomarker discovery in neurodegeneration.

DOI: https://doi.org/10.1021/acscentsci.6c00340
Talanta. 2026 Aug 26. pii: S0039-9140(26)01179-3. [Epub ahead of print]312(Pt B): 130523

Balancing speed and separation: A high-throughput native mass spectrometry platform for screening complex bispecific antibody variants.

Kyle Spitler, Kateryna Stepurska, Y Diana Liu, Kevin Legg, Shannon Hayes, Steffen Lippold, Delia Li, Tara Griffitts, Feng Yang.

  High-throughput liquid chromatography coupled to mass spectrometry (LC-MS) workflows are increasingly required to support the structural characterization of complex antibody formats such as multispecific (e.g. 2 + 1 and 1 + 1+1) and bispecific antibodies (bsAb) where misassembled product related variants pose significant challenges to analytical process development and patient safety. Conventional size-exclusion chromatography coupled to native mass spectrometry (SEC-MS) remains limited by long run times, manual sample preparation, and susceptibility to operator-dependent variability. Here, we report a semi-automated, 96 well plate-based SEC-MS platform that integrates liquid-handler assisted sample preparation, with two rapid chromatography strategies coupled to native mass spectrometry to enable high-throughput assessment of light-chain mispairing and related product variants. Automated liquid-handler sample preparation demonstrated comparable analytical reproducibility to manual workflows, minimal intraplate positional and concentration effects, and consistent performance across independent automated runs. Rapid chromatography approaches showed distinct performance characteristics, with high-throughput size exclusion chromatography coupled to native mass spectrometry (HT-SEC-MS) closely matching benchmark SEC-MS for quantitative agreement, while online buffer-exchange coupled to native mass spectrometry (OBE-MS) enabled rapid elimination of low-quality clones with high levels of LC-mispairs. Integration of an automated data-processing pipeline further standardized peak detection, quantitation, and reporting. Collectively, this native-state LC-MS platform reduces end-to-end processing timelines four fold - from multiple days to just 24 h per 96-sample batch - and decreases analyst hands-on time by more than 50%, providing a flexible and analytically rigorous framework for the high-throughput screening of complex bispecific antibody formats.

Keywords:  Automated sample preparation; Bispecific antibody (bsAb); Half-antibody; High-throughput SEC-MS; Light-chain mispair; Native mass spectrometry; Stacked homodimer

DOI:  https://doi.org/10.1016/j.talanta.2026.130523
Anal Chim Acta. 2026 Oct 22. pii: S0003-2670(26)00932-3. [Epub ahead of print]1420 345982

Re-evaluating wavelet normalization in chromatographic peak detection: An amplitude-preserving approach for high-precision LC-MS.

Yong Deng, Yun Sun.

   BACKGROUND: Untargeted LC-MS metabolomics converts chromatographic ion signals into feature tables used for downstream comparison, annotation, and biomarker discovery. However, widely used preprocessing workflows often return discordant feature lists and missingness patterns from the same raw data, limiting reproducible quantitative interpretation. Continuous wavelet transform (CWT)-based peak detection is central to several workflows, yet its scale-normalization convention was inherited from energy-preserving signal analysis rather than area-oriented chromatographic integration. The problem addressed here is whether CWT normalization itself creates scale-selection and integration-boundary bias in LC-MS feature-table construction.
RESULTS: Under a Gaussian reference peak model with a Mexican-hat wavelet, amplitude-preserving 1/a normalization produced a defined optimum at a = √2σ and model-derived integration boundaries at ±3σ, supporting area-oriented peak integration. We implemented this correction in MetaboQuality with shape-driven grouping and anchor-guided recovery, then evaluated it using authentic standards, pooled-QC replicates, public QC data, component ablation, comparator sensitivity, decoy controls, non-Gaussian simulations, and chemical-reference validation. In the primary pooled-QC benchmark before post-detection filling, MetaboQuality produced 4029 complete groups with RSD <30%, compared with 2461 for XCMS and 1672 for MZmine 4. At the stricter RSD <10% threshold, MetaboQuality yielded 931 complete groups before filling versus 526 for the XCMS reference branch; after filling, MetaboQuality yielded 973 groups, compared with 678 for XCMS and 748 in the XCMS fitgauss sensitivity run.
SIGNIFICANCE AND NOVELTY: The novelty lies in defining CWT normalization as an LC-MS-specific peak-integration determinant rather than a generic signal-processing detail. MetaboQuality links amplitude-preserving integration, shape-driven grouping, and anchor-guided recovery into a coordinated workflow, providing a principled route to more complete and reproducible feature tables without relying on unconstrained statistical imputation. This clarifies where algorithmic design can improve measurement consistency.

Keywords:  Continuous wavelet transform; Feature grouping; LC-MS; Peak detection; Signal recovery; Untargeted metabolomics

DOI:  https://doi.org/10.1016/j.aca.2026.345982
Metabolites. 2026 Jul 25. pii: 526. [Epub ahead of print]16(8):

LipidAnalyst: A Comprehensive Tool for Lipidomic Data Visualization and Analysis.

Xinyi Liu, Alla Karnovsky, Subramaniam Pennathur, Farsad Afshinnia.

  Introduction: Proper analysis of high-throughput lipidomic data requires specialized tools for data processing, normalization, visualization, and statistical and bioinformatic analysis. However, limitations in lipid parsing, data processing, and visualization capabilities in existing software packages create challenges for comprehensive lipidomic data analysis. To address these limitations, we developed LipidAnalyst (v 1.0.3), a user-friendly tool designed to facilitate efficient lipid parsing and processing, visualization, and analysis of lipidomic datasets. Methods: LipidAnalyst was developed using the R Shiny framework. It is hosted on MiServer for online work but can also be downloaded from GitHub. Results: LipidAnalyst provides functionalities in three major areas: data processing, visualization, and statistical analysis. Data processing features include quality control filtering, normalization, internal standard-based quantification, and unique capabilities for missing-value imputation, lipid parsing, and aggregation. Visualization tools include box and violin plots for data distribution assessment, principal component analysis (PCA) plots, hierarchical clustering and differential abundance heatmaps, volcano plots, correlation plots, and Debiased Sparse Partial Correlation (DSPC) clustering plots. Statistical analysis modules include t-test, analysis of variance (ANOVA), Partial Least Squares Differential Analysis (PLS-DA), Orthogonal Partial Least Squares Differential Analysis (OPLS-DA), and Random Forest (RF) modeling. Conclusions: LipidAnalyst is a comprehensive platform for optimal processing, visualization, and analysis of lipidomic data. By integrating advanced data processing workflows with extensive visualization and statistical analysis capabilities, LipidAnalyst enables researchers to explore lipidomic datasets more effectively and develop informed analytical strategies.

Keywords:  analysis; data processing; lipidomics; software; visualization

DOI:  https://doi.org/10.3390/metabo16080526