bims-mascan 2026-05-31 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2026–05–31
twenty-two papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Low-Temperature HILIC Provides Enhanced Separations and Stability for LC-MS-Based Metabolomics.
Consensus statement on mass spectrometry-based proteomic analysis of cardiac tissue.
MS-MINT: An Open-Source Data Analysis Software for Large-Scale Metabolomics Studies.
Collection and Lipidomic Analysis of Murine Knee Synovium and Infrapatellar Fat Pad.
Tissue Heterogeneity-Driven Parallel Acquisition for High-Coverage MS/MS Imaging of Lipids.
Accurate quantification in proteomics with QuantUMS.
Predicting Discrete Structural Transformations in Small Molecules from Tandem Mass Spectrometry.
Deciphering cytokine-driven ADP-ribosylation signaling networks via Af1521-based mass spectrometry analysis of labile Glu/Asp-linkages.
Automated Microvolume Secretome Proteomics Enables Sensitive and Deep Profiling and Noninvasive Single-Embryo Analysis.
Mass spectrometry-based proteomics delivers in-depth proteome profiling of FFPE lung cancer biopsies from single glass slides.
Assessing the metabolomics "dark matter" by a detectable khipu model.
PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries.
An LC-MS untargeted metabolomic comparison between three blood microsampling devices, whole blood, and plasma.
Peptidomics and Beyond: Development of a Novel Platform Based on Capillary Electrophoresis-Mass Spectrometry for Metabolomics of Medium-Molecular-Weight Compounds.
A decision-driven framework for the mass spectrometry analysis of previously uncharacterized protein modifications.
Circulating Dipeptides in Cancer: Degradation Fragments or Functional Metabolites?
A Structured Computational Roadmap for Lipidomics in R: Reproducible Workflows from Raw Data to Functional Insight.
Proteomics at scale: Bottlenecks and opportunities for early-career researchers in a fast developing field.
Iodoacetamine-Alkyne Derivatization-Based Liquid Chromatography-Mass Spectrometry Method for Quantification of Thiol Metabolites in Serum Samples of Hepatocellular Carcinoma Patients.
Ultrafast Postcolumn Microdroplet Derivatization of Nucleobases for Enhanced Online Detection, Characterization, and Quantification of Nucleic Acid Modifications Using LC-MS2.
Alkamines reveal a hidden layer of steroid and drug metabolism.
Dispersive μSPE in a 96-well filtration plate for high-throughput LC-MS/MS quantification of bile acid sulfates in human urine.

J Proteome Res. 2026 May 27.

Low-Temperature HILIC Provides Enhanced Separations and Stability for LC-MS-Based Metabolomics.

Yifan Liu, Madison L Jastrab, Michael Xiao, Miriam Lisci, Bindu Y Srinivasu, Taysir K Bader, Alexis A Jourdain, Thomas E Wales, Owen S Skinner.

  Liquid chromatography-mass spectrometry is a potent and robust tool for studying metabolism. However, conventional workflows can suffer from poor peak shapes, limited pressure tolerance, coelution of polar metabolites, and unstable retention times. Here, we describe the development of a more stable HILIC method for LC-MS metabolomics of human plasma and cell extracts, optimizing a zwitterionic HILIC (Z-HILIC) column for improved untargeted performance. We found that using high-pH ammonium bicarbonate with 90% acetonitrile in mobile phase B (ABC B) can greatly improve peak shapes of select metabolites when compared to 100% acetonitrile (ACN B), but at the cost of poor retention time stability. We therefore focused on optimizing chromatography for the ACN B method and observed that cooling the column to 5 °C substantially enhanced peak shape for the BEH-bound Z-HILIC and amide columns but had little effect on the polymeric ZIC-pHILIC column. The low-temperature method with the Z-HILIC column (LT-ZHILIC) enables high-resolution separation of 471 metabolite library standards and from both cellular extracts and human plasma and demonstrates robust stability over 100 consecutive injections and multiple days. Application of the untargeted LT-ZHILIC method to characterize the metabolic consequences of glutamine and pyruvate deficiency in human cells revealed a striking change in nucleotide phosphates─a perturbation that was not observed in the ZIC-pHILIC analysis of the same samples likely due to inadequate elution profiles. In sum, the LT-ZHILIC workflow offers a robust platform to advance untargeted metabolomics by improving metabolite coverage, resolution, and retention time stability, making it a promising technique for providing novel insights into cellular metabolic rewiring and the human plasma metabolome.

Keywords:  HILIC; metabolomics; nucleotides; separation

DOI:  https://doi.org/10.1021/acs.jproteome.5c01216
Nat Cardiovasc Res. 2026 May 25.

Consensus statement on mass spectrometry-based proteomic analysis of cardiac tissue.

Alicia Lundby, Jennifer E Van Eyk, Manuel Mayr, Melanie Y White, Jonathan A Kirk, Jonathan S Achter, Justyna Fert-Bober, Michael Wierer, Philipp Mertins, Maggie P Y Lam, Sean J Humphrey, Edward Lau, Anthony O Gramolini, Ying Ge, Rebekah L Gundry.

Mass spectrometry-based cardiac proteomics provides direct molecular insight into cardiac physiology and disease. While plasma proteomics has advanced biomarker discovery, the analysis of cardiac tissue is essential for mechanistic understanding and therapeutic target identification; however, proteomic investigation of cardiac tissue faces unique challenges, including limited sample availability, regional heterogeneity, variability in collection and processing, and inconsistent reporting practices that hinder reproducibility and data integration. Here, we provide a practical framework for designing and conducting mass spectrometry-based proteomic studies of cardiac tissue and primary cardiac cells. We outline best practices and key considerations for sample handling, experimental design, data acquisition, quality control and statistical analysis. This guideline aims to support cardiac researchers in generating robust and reproducible proteomics datasets that advance our understanding of cardiac biology in both physiological and pathological contexts.

DOI: https://doi.org/10.1038/s44161-026-00824-4
Anal Chem. 2026 May 26.

MS-MINT: An Open-Source Data Analysis Software for Large-Scale Metabolomics Studies.

Mario E Valdés-Tresanco, Mario S Valdés-Tresanco, Soren Wacker, Nicholas I Brodie, Luis F Ponce, Raied Aburashed, Alikhan Mansuri, Ryan A Groves, Annegret Ulke-Lemée, Ian A Lewis.

Metabolomics has emerged as a mainstream approach for investigating the complex metabolic underpinnings of living systems, and over recent years, it has increasingly been applied to large cohort studies that tax the limits of existing computational tools. Most existing metabolomics software tools are effective at analyzing small data sets but exhibit a number of shortcomings that limit their utility when applied to large studies: they store entire data sets in memory, they use batch-dependent fitting algorithms, and they do not use concrete metrics for peak fitting, which not only results in inconsistent peak-picking results across samples but also complicates the documentation of data analyses. To address this, we developed the mass-spectrometry metabolomics integrator (MS-MINT), a Python application for processing, analyzing, and visualizing large liquid chromatography-mass spectrometry (LC-MS) data sets. To enable reproducible large-scale data processing, MS-MINT uses a region of interest (ROI)-based approach to extract data. We illustrate the function of this new tool by analyzing metabolites present in the media of a large data set (3334 files) of Staphylococcus aureus cultures. We show that MS-MINT accurately reproduces data generated from other software tools in a fraction of the time. In summary, MS-MINT offers a purpose-built software platform to support large-scale metabolomics data analyses. MS-MINT software is freely available at https://www.lewisresearchgroup.org/software.

DOI: https://doi.org/10.1021/acs.analchem.6c01083
Methods Protoc. 2026 May 02. pii: 70. [Epub ahead of print]9(3):

Collection and Lipidomic Analysis of Murine Knee Synovium and Infrapatellar Fat Pad.

Tong Yang, Luke Stasikelis, Alexander J Knights.

  Intra-articular soft connective tissues such as synovium and adipose tissue play a crucial role in governing joint homeostasis and disease progression in various forms of arthritis. In the knee, like many synovial joints, adipose tissue forms an integrated anatomic and functional unit with the joint-lining synovium, and the most prominent adipose depot is the infrapatellar fat pad (IFP). With growing evidence that lipid profiles in the synovium-IFP unit shift during progression of joint diseases like osteoarthritis (OA), there is strong impetus for consistent tissue collection approaches and reproducible subsequent lipid characterization. Here, we present a standardized dissection and low-input untargeted lipidomics workflow optimized for mouse knee synovium and IFP, to enable comprehensive lipid profiling. Synovium/IFP from multiple joints are pooled to increase input mass and guarantee robust lipid yield, followed by lipid extraction and high-resolution liquid chromatography-mass spectrometry (LC-MS) acquisition for global, untargeted lipidomic profiling. The analysis workflow encompasses robust feature detection, accurate lipid annotation, data transformation and normalization. These steps enhance comparability across samples, particularly those with low input amounts, while minimizing technical variance and batch effects. Using this approach, we detect a broad spectrum of lipid species spanning the major lipid categories. As expected for untargeted discovery, a subset of non-lipid species is also observed. This protocol provides a practical framework for robust, reproducible lipidomics in murine intra-articular soft tissues to support future disease-specific biomarker and drug target discovery in OA and other joint diseases.

Keywords:  infrapatellar fat pad; lipidomics; mass spectrometry; osteoarthritis; synovium

DOI:  https://doi.org/10.3390/mps9030070
Angew Chem Int Ed Engl. 2026 May 25. e4721868

Tissue Heterogeneity-Driven Parallel Acquisition for High-Coverage MS/MS Imaging of Lipids.

Dan Li, Yao Qian, Aolei Tan, Shenghui Ye, Zhuoning Xie, Zheng Ouyang, Xiaoxiao Ma.

  Elucidating tissue architecture and function necessitates lipid analysis that is both spatially comprehensive and achieved at high resolution while maintaining spatial context. Conventional mass spectrometry imaging (MSI) techniques typically perform MS/MS analysis sequentially for individual lipids, creating a significant trade-off between spatial resolution and analytical depth. To circumvent this limitation, we introduce a deconvolution-based structure-specific spatial lipidomics (DeconS2L) method for coupling with per-pixel and broadband MS/MS sampling. DeconS2L capitalizes on the spatial and compositional heterogeneity inherent to all biological tissues for large-scale lipid annotation and lipidome imaging. To facilitate rapid imaging throughput and improved sample usage, lipids per pixel are co-fragmented to generate convolved MS/MS spectra. DeconS2L analysis of ∼4000 pixels in a mouse cerebellum tissue yielded 100 annotated lipids and allowed multiplexed MS/MS imaging of the tissue lipidome. Furthermore, applying DeconS2L to human hepatocellular carcinoma (HCC) tissues effectively resolved isobaric interferences that obscured tumor margins in conventional imaging. It successfully revealed the tumor-specific enrichment of odd-chain lipid PC 33:1 and distinct spatial heterogeneity in triglyceride saturation, correlating with metabolic reprogramming in HCC. DeconS2L represents a versatile methodology that effectively integrates molecular annotation with spatial lipidomics, demonstrating significant potential for in-depth biomarker discovery in clinical pathology.

Keywords:  mass spectrometry imaging; spatial lipidomics; spectrum deconvolution; tissue heterogeneity

DOI:  https://doi.org/10.1002/anie.4721868
Nat Biotechnol. 2026 May 27.

Accurate quantification in proteomics with QuantUMS.

Justus L Grossmann, Franziska Kistner, Ludwig R Sinn, Lukasz Szyrwiel, Juri Rappsilber, Vadim Demichev.

In mass-spectrometry-based proteomics it remains challenging to ensure the accuracy of protein quantities. Here we introduce QuantUMS (quantification using an uncertainty-minimizing solution), a machine learning-based method that dynamically tunes the quantification algorithm to minimize quantitative errors. When applied to data-independent acquisition proteomics, QuantUMS increases accuracy and precision, ameliorates ratio compression bias and enhances differential expression analysis. It further reports an uncertainty measure enabling quality control of individual quantities.

DOI: https://doi.org/10.1038/s41587-026-03131-2
bioRxiv. 2026 May 11. pii: 2026.05.06.723373. [Epub ahead of print]

Predicting Discrete Structural Transformations in Small Molecules from Tandem Mass Spectrometry.

Xianghu Wang, Gwendolyn Kiler, Daniela Herrera-Rosero, Mohammed Reza Shahneh, Michael Strobel, Christian Geibel, Yasin El Abiead, Vanessa V Phelan, Daniel Petras, Mingxun Wang.

Tandem mass spectrometry (MS/MS) fragments molecules into smaller pieces, generating spectra composed of m/z values and intensities that encode structural information for molecular annotation. With increasing mass spectrometry data acquisition speeds, manual annotation from MS/MS lags far behind data generation and remains a bottleneck in metabolite annotation. Current computational methods, such as molecular networking, address this challenge by organizing similar structures into families of related compounds. However, they generally provide only similarity scores, offering weak actionable insights for structural annotation. To address this limitation, we present the Molecular Transformation Graph Edit Measure (MT-GEM), a distance metric that quantifies discrete structural transformations between molecules through graph edge removals that approximate structural modifications. Building on this metric, we developed an ensemble machine learning architecture, the Spectrum Transformation Edit Predictor (STEP), that builds upon TransExION and DREAMS to predict MT-GEM distances from MS/MS spectra. STEP achieves an average precision of 48.4% for identifying single structural transformations between MS/MS pairs, representing more than a tenfold improvement over state-of-the-art similarity metrics, including spectral entropy similarity (3.8%) and modified cosine (2.5%). On experimental human gut microbial community data, STEP identifies 3 times more single-transformation metabolite pairs than feature-based molecular networking at equivalent precision. In a discovery application, STEP highlights one drug metabolite and two new natural product analogs missed by modified cosine in feature-based molecular networking. By providing discrete transformation predictions rather than continuous similarity scores, MT-GEM and STEP enable hypothesis-driven metabolite annotation with testable structural modifications, which we envision will accelerate discovery of new molecules from MS/MS metabolomics datasets.

DOI: https://doi.org/10.64898/2026.05.06.723373
Nat Commun. 2026 May 27.

Deciphering cytokine-driven ADP-ribosylation signaling networks via Af1521-based mass spectrometry analysis of labile Glu/Asp-linkages.

Sara C Buch-Larsen, Ivo A Hendriks, Kyuto Tashiro, Jonas D Elsborg, Sergey Y Vakhrushev, Jesper V Olsen, Bernhard Lüscher, Glen Liszczak, Ivan Ahel, Michael L Nielsen.

ADP-ribosylation (ADPr) is a regulatory post-translational modification targeting nine amino acid residues, but glutamate/aspartate-linked ADPr (Glu/Asp-ADPr) is labile and remains challenging to detect using conventional mass spectrometry (MS)-based workflows. Using synthetic peptides, we show that ester-linked Glu/Asp-ADPr is lost under alkaline conditions, elevated temperatures, and by hydrolysis via wildtype Af1521. We developed an acidic enrichment workflow incorporating an Af1521 mutant that preserves Glu/Asp-ADPr, enabling site-specific, system-wide MS analysis. In cytokine-stimulated A549 and HeLa cells, we identified >600 Glu/Asp- and >200 Cys-ADPr sites. Glu/Asp-ADPr marks cytoplasmic, immune-related protein networks, contrasting with nuclear Ser-ADPr. Quantitative profiling revealed reproducible, cell type- and treatment-specific patterns. PARP10-mediated Glu/Asp ADPr of ubiquitin indicates direct crosstalk with ubiquitin signaling pathways. Interferon treatments revealed conserved antiviral PARP networks extensively modified on Glu/Asp residues. Together, our work establishes a robust MS-based workflow and provides a resource of site-specific ADPr events, revealing residue-specific ADPr in innate immune signaling.

DOI: https://doi.org/10.1038/s41467-026-73677-x
J Proteome Res. 2026 May 27.

Automated Microvolume Secretome Proteomics Enables Sensitive and Deep Profiling and Noninvasive Single-Embryo Analysis.

Jingsheng Xie, Jiwei Wang, Yuhe Ge, Zilu Ye, Hong Zhang.

  Cells continuously deliver proteins into the extracellular space, forming the secretome, which provides a dynamic, biologically informative, and noninvasive readout of cellular functional states. However, conventional secretome proteomics typically requires large sample volumes greater than 2 mL, delivers limited proteome depth, and supports low processing throughput of less than 30 samples a day. Here, we developed an automated microvolume secretome profiling workflow that integrates optimized sample pretreatment and magnetic bead-based proteome sample preparation. This workflow enables efficient secretome profiling from less than 20 μL of conditioned medium, achieving deep proteome coverage of over 3000 proteins with a sample processing throughput exceeding 96 samples per day. Using this workflow, we achieved high-depth, time-resolved secretome profiling from microscale culture medium, capturing temporal changes of secretome. We further applied the method to single mouse embryo culture medium and consistently identified more than 200 secretome proteins per embryo. Notably, Sdc4 and Ooep were consistently observed across developmental stages, highlighting the potential for noninvasive secretome profiling at the single-embryo level. Together, this work establishes a robust and scalable framework for high-depth secretome profiling from ultralow-input samples, broadening the scope of LC-MS-based analysis to microscale and longitudinal biological applications.

Keywords:  automation; non-invasive biopsy; proteomics; secretome; single-embryo

DOI:  https://doi.org/10.1021/acs.jproteome.6c00284
NPJ Precis Oncol. 2026 May 29. pii: 196. [Epub ahead of print]10(1):

Mass spectrometry-based proteomics delivers in-depth proteome profiling of FFPE lung cancer biopsies from single glass slides.

Olena Berkovska, Igor Schliemann, Georgios Mermelekas, Nazlı Ezgi Özkan, Mahnaz Nikpour, Vilde Drageset Haakensen, Åslaug Helland, Janne Lehtiö, Lukas M Orre.

Clinical proteomics has the potential to add a valuable data layer to genomic and histopathological analyses in precision oncology, but its application to limited clinical material remains challenging. Here, we demonstrate that state-of-the-art mass spectrometry-based proteomics enables in-depth proteomic profiling of formalin-fixed paraffin-embedded (FFPE) clinical samples, including small diagnostic biopsies and single tissue sections mounted on glass slides. Despite minimal input material, single-slide analyses of clinical lung tumor specimens yielded biologically and clinically informative data, supporting detection of actionable proteins, immune-related signatures, and multivariate biomarkers. These results establish the feasibility of proteomics for retrospective FFPE studies and routine clinical practice, expanding opportunities for biomarker discovery and precision medicine from scarce tissue material.

DOI: https://doi.org/10.1038/s41698-026-01517-8
Metabolomics. 2026 May 24. pii: 79. [Epub ahead of print]22(3):

Assessing the metabolomics "dark matter" by a detectable khipu model.

Yuanye Chi, Joshua M Mitchell, Shujian Zheng, Maheshwor Thapa, Shuzhao Li.

Background There is no consensus on how to interpret the large number of unknown features in untargeted metabolomics, which are sometimes referred as the "dark matter". Are these features real compounds or artifacts? Understanding this problem is critical to the annotation and interpretation of metabolomics data and future development of the field. Methods We propose a "detectable khipu" model here, to show that compounds exhibit ion group patterns that depend on their abundance. We apply this model to a systematic analysis of 61 representative public datasets from blood LC-MS metabolomics, the most common data type in biomedical studies. Results The results indicate that majority of abundant features have identifiable ion patterns, and in-source fragments contribute to less than 10% of features. Each dataset detects 1 ~ 2,000 high confidence compounds, over half of which are unknown. Conclusion The major knowledge gap in LC-MS metabolomics is therefore not the methods of grouping ions or counting fragments, but the identification of unknown compounds.

DOI: https://doi.org/10.1007/s11306-026-02456-y
Adv Sci (Weinh). 2026 May 27. e75856

PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries.

Ben Wang, Zhiyuan Cheng, Chengying She, Hongwei Zhao, Jiahui Zhang, Lin Lv, Zhihao Yan, Hongwen Zhu, Lizhuang Liu, Yan Fu, Xinpei Yi.

  Protein phosphorylation is a key regulator of signaling, with mass spectrometry (MS) based phosphoproteomics serving as the premier technology for its analysis. However, phosphorylation profiling is hindered by acquisition biases: Data-Dependent Acquisition (DDA) suffers from stochastic undersampling and missing values, while Data-Independent Acquisition (DIA) faces computational bottlenecks and inefficiencies from vast spectral libraries. We present PhosSight, a unified deep learning framework designed to augment identification depth and accelerate search efficiency. PhosSight features PhosDetect, a model that explicitly encodes phosphorylation-specific physicochemical features to accurately predict peptide detectability. For DDA, PhosSight leverages predicted retention time, fragment intensity, and detectability to refine site localization and rescoring, recovering marginal, low-abundance spectra. For DIA, PhosSight utilizes detectability-guided library pruning to remove non-detectable noise, accelerating search speeds without compromising sensitivity. Benchmarking on synthetic and real-world datasets confirms PhosSight's superior performance in both modes. Applying PhosSight to a large-scale Uterine Corpus Endometrial Carcinoma (UCEC) cohort improved data completeness and expanded the quantifiable phosphoproteome. This enhanced completeness enabled the discovery of novel prognosis-associated kinase targets, such as MARK2, underscoring PhosSight as a powerful tool for biological discovery in precision oncology. Trial Registration: Not applicable. This study did not prospectively assign human participants to any health-related intervention and therefore does not constitute a clinical trial requiring registration.

Keywords:  cancer; computer science; deep learning; kinase; mass spectrometry; phosphoproteomics; phosphorylation

DOI:  https://doi.org/10.1002/advs.75856
Metabolomics. 2026 May 29. pii: 89. [Epub ahead of print]22(3):

An LC-MS untargeted metabolomic comparison between three blood microsampling devices, whole blood, and plasma.

Dennisse Avella, Soile Turunen, Glykeria Avgerinou, Anatoli Petridou, Vassilis Mougios, Iman Zarei, Seppo Auriola, Kati Hanhineva, Olli Kärkkäinen.

   INTRODUCTION: Blood microsampling (BµS) devices collect less than 100 µL of blood, offering a less invasive and more cost-effective alternative to venipuncture. However, its metabolomic comparability to conventional samples remains unclear, and standardized BµS metabolomic workflows are lacking.
OBJECTIVES: This study evaluated the impact of using three BµS devices (Mitra®, Capitainer®, and Whatman™ 903) on the metabolomic interpretation of human biomonitoring samples. We compared them to conventional samples (plasma and whole blood) and evaluated the interplay of different analytical conditions.
METHODS: Venous blood from 10 adults (5 males, 5 females) was sampled onto the three devices. First, three agitation conditions (ultrasound, shaker, and homogenizer) were evaluated at three blood concentrations (1.5%, 5.5%, and 11%). The optimized method was then used to compare the metabolite profiles between BµS devices, whole blood, and plasma. Reverse-phase and hydrophilic-interaction chromatography, in positive and negative ionization modes, were combined for liquid chromatography-mass spectrometry (LC-MS) analysis.
RESULTS: All agitation conditions and concentrations proved suitable for BµS untargeted metabolomics. Combining different analytical modes and fragmentation ranges proved helpful for maximizing metabolite coverage. BµS-derived metabolite profiles aligned more closely with whole blood than plasma. Some metabolites were more characteristic of a sample type, whereas others were common across sample types. All sample types enabled sex-based differentiation, with metabolites such as amino acids, lipids, and acylcarnitines driving the separation.
CONCLUSIONS: These findings enhance our understanding of BµS metabolite coverage and highlight its potential in human biomonitoring. The choice of device depends on the application and the metabolites of interest, offering flexibility for clinical use and research.

Keywords:  Blood microsampling (BµS); Capitainer® ; Dried blood spots (DBS); Liquid chromatography–mass spectrometry (LC-MS); Metabolomics; Volumetric absorptive microsampling (VAMS)

DOI:  https://doi.org/10.1007/s11306-026-02424-6
J Proteome Res. 2026 May 27.

Peptidomics and Beyond: Development of a Novel Platform Based on Capillary Electrophoresis-Mass Spectrometry for Metabolomics of Medium-Molecular-Weight Compounds.

Tomoaki Nitta, Hiroyuki Yamamoto, Hajime Tomatsu, Rira Matsuta, Kenjiro Kami, Kazunori Sasaki.

  Proteomics targets proteins typically >10 000 Da, while metabolomics focuses on small molecules <1000 Da. In contrast, omics approaches targeting medium-molecular-weight compounds (MMWCs) remain underdeveloped. Although peptidomics targets these molecules, progress has been limited by several factors, including the low abundance of bioactive peptides, their instability during sample preparation, and the lack of analytical platforms capable of comprehensively analyzing diverse modified peptides and other as-yet-uncharacterized MMWCs. Here, we developed a novel high-sensitivity platform based on capillary electrophoresis coupled with high-resolution mass spectrometry (CE-HRMS) for the comprehensive analysis of peptides and other MMWCs. To achieve this, we implemented large-volume dual preconcentration by isotachophoresis and stacking (LDIS), which increased the injection volume by 60-fold over the standard volume and enabled detection limits of 10 pg/mL for peptides such as bradykinin and ANP. We then applied this novel platform to plasma samples from hyperlipidemia patients and found significantly elevated levels of a series of peptides, including bradykinin, CLIP, and schizophrenia-related peptides, along with 63 additional protein-derived peptide fragments and two unknown MMWCs. In conclusion, this platform enables systematic exploration of previously uncharacterized molecules, facilitating the discovery of novel functional peptides and other MMWC biomarkers.

Keywords:  biomarker; capillary electrophoresis; functional peptides; hyperlipidemia; medium-molecular-weight compounds; metabolomics; orbitrap; peptidomics; plasma; preconcentration

DOI:  https://doi.org/10.1021/acs.jproteome.5c00880
STAR Protoc. 2026 May 28. pii: S2666-1667(26)00220-0. [Epub ahead of print]7(2): 104567

A decision-driven framework for the mass spectrometry analysis of previously uncharacterized protein modifications.

Yiying Zhu.

  Identification of unknown protein modifications remains challenging when the modification chemistry or site is not defined in advance. Conventional workflows often rely on predefined modification lists or enrichment strategies that assume prior knowledge of modification type and may therefore bias discovery toward annotated post-translational modifications (PTMs). This primer outlines a decision-driven analytical framework for investigating previously uncharacterized modifications using bottom-up (liquid chromatography-tandem mass spectrometry) LC-MS/MS that emphasizes chemistry-informed hypothesis generation, iterative refinement of candidate modification search space, integration of experimental controls, and targeted data interpretation. Rather than presenting a single prescriptive workflow, the guide highlights key decision points in experimental design, acquisition strategy, and database search configuration that influence confident identification and residue-level localization. The framework is broadly applicable to drug-induced covalent adducts, chemically introduced modifications, as well as endogenous modifications arising across diverse experimental and biological contexts.

Keywords:  Chemistry; Mass Spectrometry; Proteomics

DOI:  https://doi.org/10.1016/j.xpro.2026.104567
Int J Mol Sci. 2026 May 15. pii: 4438. [Epub ahead of print]27(10):

Circulating Dipeptides in Cancer: Degradation Fragments or Functional Metabolites?

Kyung-Hee Kim, Byong Chul Yoo.

  Advances in mass spectrometry-based metabolomics have enabled the detection of numerous small molecules in biological systems, revealing complex metabolic alterations associated with cancer. Among these, dipeptides are consistently detected in plasma, serum, and tumor tissue metabolomic profiles, yet their biological significance is not fully understood. In most studies, circulating dipeptides are interpreted as nonspecific byproducts of protein degradation generated during increased proteolysis. However, accumulating evidence suggests that at least some endogenous dipeptides may have biological activities, including antioxidant effects, metabolic modulation, and potential signaling functions. In this review, we examine the possible origins, transport mechanisms, and biological implications of circulating dipeptides in cancer metabolomics. We discuss multiple sources of dipeptide generation, including intracellular proteolysis, autophagy, extracellular matrix remodeling, tumor cell death, host tissue catabolism, and microbiome metabolism. We also summarize current knowledge regarding peptide transport systems and intracellular dipeptide metabolism that may regulate the fate of these molecules within mammalian systems. In addition, evidence supporting the biological activities of certain endogenous dipeptides is reviewed to evaluate the possibility that some circulating dipeptides may function as bioactive metabolites. Finally, we propose conceptual frameworks for interpreting circulating dipeptides in cancer, including their potential roles as indicators of protein turnover, intermediates in amino acid recycling, stress-buffering molecules, metabolic signals, or components of tumor-host metabolic communication. A better understanding of circulating dipeptides may provide new insights into cancer metabolism and reveal previously overlooked metabolite classes with potential biomarker or functional significance.

Keywords:  bioactive peptides; cancer metabolomics; circulating dipeptides; metabolic communication; peptide metabolism; proteolysis; tumor metabolism

DOI:  https://doi.org/10.3390/ijms27104438
Metabolites. 2026 Apr 22. pii: 288. [Epub ahead of print]16(5):

A Structured Computational Roadmap for Lipidomics in R: Reproducible Workflows from Raw Data to Functional Insight.

Maria-Christina P Papatheodorou, Panagiotis Vlamos, Marios G Krokidis.

  Lipidomics has emerged as a transformative discipline in biomedical research, providing high-resolution insights into metabolic signaling and disease pathophysiology. The R programming language provides a widely adopted framework for extensible analysis of complex lipidomic datasets due to its robust biostatistical infrastructure. Herein, we present a comprehensive roadmap for lipidomics in R, structured around a standardized analytical lifecycle: from raw data acquisition and preprocessing to structural annotation, statistical modeling and functional interpretation. We critically contextualize and integrate a curated suite of widely adopted R packages (version 4.3.0), including xcms and MSnbase for feature extraction, LipidMS 3.0 for fragmentation-based identification, and lipidr for quality control and normalization. Furthermore, we demonstrate how advanced tools such as mixOmics and clusterProfiler can be integrated to bridge the gap between differential lipid abundance and systems-level biological insights. Particular emphasis is placed on reproducibility, nomenclature standardization and the emerging role of machine learning in biomarker discovery. By synthesizing these resources into a coherent pipeline, this guide provides a structured reference for researchers. Further discussion addresses methodological pitfalls, statistical assumptions and reproducibility constraints that frequently compromise lipidomics studies. Ultimately, this structured approach facilitates systematic tool selection, accelerating the translation of complex lipidomic signatures into reproducible and clinically meaningful discoveries.

Keywords:  R libraries; data processing; functional analysis; lipid ontology; lipidomics; multi-omics integration

DOI:  https://doi.org/10.3390/metabo16050288
J Proteomics. 2026 May 24. pii: S1874-3919(26)00082-5. [Epub ahead of print] 105679

Proteomics at scale: Bottlenecks and opportunities for early-career researchers in a fast developing field.

Young Proteomics Investigators Club. Electronic address: ypic@eupa.org

  The field of proteomics has rapidly evolved over the last five years enabled by rapid advances in instrumentation and computation. At the same time, the proteomics community is also growing. This is reflected by the increasing participation in international conferences such as those organized by the European Proteomics Association and the Human Proteome Organization. These events provide early-career researchers with unique opportunities to exchange ideas, develop collaborations, and build networks that support professional development. One such network is the Young Proteomics Investigators Club, a European initiative supported by European Proteomics Association and led by early-career researchers. In this Community-Driven project, we investigate recent trends in proteomics by screening conference abstracts and evaluating the session attendance at Human Proteome Organization Congresses and European Proteomics Association conferences. Based on these analyses, we identified five areas that, from our perspective, are shaping the current trends in proteomics: clinical proteomics, proteomics of post-translational modifications, single-cell proteomics, systems biology and multi-omics, and computational proteomics. For each area, we highlight both unique challenges and identify a common theme: a shift from exploratory studies with manageable sample numbers toward large screenings and cohorts and the generation of big data, which often comes with the lack of computational support, organizational networks, and infrastructure. In this light, we describe the unique challenges and opportunities faced by early-career researchers. We point to actionable directions for enabling reproducible and transparent proteomics as well as community-driven projects and initiatives, which are often providing training and support. SIGNIFICANCE: In this perspective, the Young Proteomics Investigators Club (YPIC) discusses advances in analytical developments and computational approaches in proteomics research. Based on empirical analysis of recent European Proteomics Association conference and Human Proteome Organization congresses contributions, we identify clinical, single-cell, post-translational and systems-level proteomics as the research areas that have gained most momentum in the last three to five years. What makes this work distinctive is that it is written by and for early-career researchers, thereby uniquely identifying where momentum, challenges, and unmet needs converge for the newest generation of proteomics researchers. Rather than cataloguing advances, we examine the widening gap between what modern proteomics can generate and what individual researchers can realistically process, validate, and interpret. We describe specific structural barriers including access to high performance computing, limited formal training in scalable data analysis, the need for unified benchmarking standards and navigating clinical collaboration frameworks. We then highlight opportunities for the field, such as community-curated benchmarks, interdisciplinary mentorship models, and shared computational infrastructure. By making these challenges explicit from an early-career researchers standpoint, we aim to inform how training, funding, and community initiatives can be shaped to support the next generation of proteomics researchers.

Keywords:  Computational proteomics; Early career researchers; Proteomics; Systems biology; YPIC

DOI:  https://doi.org/10.1016/j.jprot.2026.105679
Metabolites. 2026 May 20. pii: 345. [Epub ahead of print]16(5):

Iodoacetamine-Alkyne Derivatization-Based Liquid Chromatography-Mass Spectrometry Method for Quantification of Thiol Metabolites in Serum Samples of Hepatocellular Carcinoma Patients.

Chun Mei, Xin-Ze Wu, Hua-Ming Xiao, Azamat Temerdashev, Na An, Quan-Fei Zhu, Yu-Qi Feng.

  Background/Objectives: The dysregulation of thiol metabolites is strongly linked to hepatocellular carcinoma (HCC) pathogenesis. However, quantifying these highly polar and oxidation-prone thiols in clinical serum samples via conventional liquid chromatography-mass spectrometry (LC-MS) remains challenging due to their poor sensitivity and reproducibility. Methods: We developed a sensitive and robust iodoacetamine-alkyne (IAM) derivatization-based LC-MS method for quantification of seven trans-sulfuration pathway thiols in human serum. Results: IAM derivatization markedly improved the method's specificity due to enhanced chromatographic retention and diagnostic MS/MS fragments containing both the alkyne tag and analyte backbone. Sensitivity increased 33-to-160-fold versus underivatized analytes, with limits of detection of 0.02-0.1 nM. All analytes exhibited good linearity, acceptable precision with intra-day and inter-day relative standard deviations in the range of 1.2-13.8%, and high recovery from 88.6% to 102.9%. Conclusions: From the thiol quantification in human serum from 40 HCC patients and 40 healthy controls, it was found that levels of cysteine, homocysteine, glutathione, and cysteinylglycine were significantly lower in HCC patients (p < 0.05). A two-variable logistic regression model using cysteine and cysteinylglycine achieved 90.0% specificity and 80.0% sensitivity for robust HCC discrimination between HCC patients and healthy controls to some extent, with an area under the receiver operating characteristic curve of 0.88 (95% confidence interval: 0.792-0.968).

Keywords:  chemical derivatization; hepatocellular carcinoma; iodoacetamine-alkyne; liquid chromatography-mass spectrometry; thiol metabolite

DOI:  https://doi.org/10.3390/metabo16050345
Anal Chem. 2026 May 28.

Ultrafast Postcolumn Microdroplet Derivatization of Nucleobases for Enhanced Online Detection, Characterization, and Quantification of Nucleic Acid Modifications Using LC-MS2.

Quynh-Trang Do, Husam Kafeenah, Ching-Hua Huang, Shu-Hui Chen.

Modified DNAs and RNAs often exist in trace amounts, which makes detection challenging. Adding ionizable groups to nucleobases (NBs) can enhance sensitivity in liquid chromatography-tandem mass spectrometry (LC-MS2). Traditional derivatization methods for modified nucleosides require complex preparation under harsh conditions and cover only limited modifications. We present an ultrafast (microseconds) derivatization technique using ionizable imidazole, facilitated by microdroplets through supersonic electrospray-induced aldehyde condensation for direct LC-MS2 analysis. This technique universally tags the exocyclic amine (-NH2) or pyrrolic nitrogen (-NH) of five NBs without base damage, particularly guanosine, which is prone to oxidation. Sensitive modifications located on ring carbon, like oxidative lesions and epigenetic marks, or nontagging sites of ring nitrogen, like N-alkylation on guanosine, and depurinating adducts, remain intact. The rapid derivatization avoids artifact generation and allows online postcolumn in situ detection, maintaining chromatographic separation. This method achieves over 90% derivatization ratio and improves quantification sensitivity by more than 10-fold. It also enables a quick test for tagging site-blocked modifications. We demonstrate this method by locating stable adduction sites of 4-hydroxy-estradiol (4OHE2) in chromatin DNA and quantifying 4OHE2-induced oxidation and released adducts in MCF-7 cells, achieving 13-43-fold higher sensitivity with significantly reduced sample amounts. Key parameters influencing detection efficiency were systematically explored for routine application.

DOI: https://doi.org/10.1021/acs.analchem.6c00004
bioRxiv. 2026 May 15. pii: 2026.05.13.724743. [Epub ahead of print]

Alkamines reveal a hidden layer of steroid and drug metabolism.

Julius Agongo, Subhaskar R Panga, Shipei Xing, Vincent Charron-Lamoureux, Harsha Gouda, Yasin El Abiead, Megan R Nelson, Abubaker Patan, Marvic Carrillo Terrazas, Kine Eide Kvitne, Jeong In Seo, Prajit Rajkumar, Sadie Giddings, Helena Mannochio-Russo, Jasmine Zemlin, Ipsita Mohanty, Marta Sala-Climent, Zhewen Hu, Victoria Delaray, Simpa K Yeboah, Haoqi Nina Zhao, Andrés M Caraballo-Rodríguez, Claire E Williams, Candace L Williams, Wilhan D Gonçalves Nunes, Kathleen Dorrestein, Jennifer Cao, Isabelle Shepherd, Rachel Bock, Nathaniel Roethler, Adrian Jinich, Lindsey A Burnett, Jeremy Carver, Robert N Devine, Christopher K Arnatt, Iain A Murray, Rob Knight, Monica Guma, Lee R Hagey, Gary Perdew, Nuno Bandeira, Mingxun Wang, Hiutung Chu, Dionicio Siegel, Pieter C Dorrestein.

Biomedical research overlooks most genes in favor of a well-studied minority, yet whether analogous blind spots exist in metabolomics remains unknown. We show that reductive amination, forming secondary amines from aldehydes or ketones and amines, generates a previously hidden class of metabolites we term alkamines. Multiplexed synthesis of 8,475 alkamines combined with MS/MS searches across 1.7 billion spectra identified 1,626 candidates across multiple species and organs. Of these, 56 were confirmed in biological samples, including 27 steroid- and 12 drug-derived alkamines matching prescription patterns. Notably, 77% of synthesized alkamines are absent from PubChem. This combinatorial logic likely explains why alkamines have evaded detection and suggests drug metabolism frameworks substantially underestimate drug-derived metabolite diversity. Reductive amination is an overlooked route modifying steroids, bile acids, and xenobiotics.

DOI: https://doi.org/10.64898/2026.05.13.724743
Talanta. 2026 May 22. pii: S0039-9140(26)00684-3. [Epub ahead of print]309 130028

Dispersive μSPE in a 96-well filtration plate for high-throughput LC-MS/MS quantification of bile acid sulfates in human urine.

Miloš Hroch, Marian Kacerovský, Linda Petráčková, Stanislav Mičuda.

  We report a chemometrically optimized high-throughput analytical workflow integrating filtration-assisted dispersive micro-solid phase extraction (FA-D-μSPE) with LC-MS/MS for quantitative profiling of ten major human bile acid-3-sulfates (BA-S) in urine. BA-S are important urinary metabolites of bile acid detoxification and promising non-invasive biomarkers of hepatobiliary disorders. The proposed workflow implements dispersive μSPE in a 96-well filtration format, enabling in-plate sorbent extraction without centrifugation, magnetic separation, or manual phase separation. Method development was guided by sequential experimental designs (Plackett-Burman, central composite, and Box-Behnken), allowing statistically guided optimization of chromatographic separation, mass spectrometer settings, and extraction conditions. A custom 3D-printed dispenser enabled fast and reproducible in-house loading of extraction sorbent into filtration plates and facilitated flexible customization of sorbent chemistry. Compared with conventional cartridge-based or well plate SPE, FA-D-μSPE reduces solvent consumption, processing time, and consumable costs while maintaining robust analytical performance. Due to the endogenous presence of BA-S in human urine, method validation was performed using rat urine as a surrogate matrix. The method was validated according to ICH M10 guidelines, demonstrating linearity over 10-4000 nmol L-1, high accuracy and precision, and controlled matrix effects. The proposed FA-D-μSPE LC-MS/MS method provides a robust and cost-efficient strategy for rapid quantitative bioanalysis of urinary BA-S and may serve as a framework for clinical and epidemiological studies.

Keywords:  Bile acid sulfates; Dispersive micro-SPE; Filtration-assisted extraction; High-throughput bioanalysis; LC–MS/MS; Surrogate matrix

DOI:  https://doi.org/10.1016/j.talanta.2026.130028