bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2026–05–10
24 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz



  1. bioRxiv. 2026 May 01. pii: 2026.04.28.720212. [Epub ahead of print]
      Incomplete quantification remains a persistent challenge in data-independent acquisition (DIA) mass spectrometry (MS), particularly in low-input and single-cell analyses. In identification-driven workflows, missing protein quantities often arise not from true absence of the corresponding peptides, but from failure to retain low-abundance signals from precursor or product ions for quantification. Here we present JUMPlion (local inference of ion-level missingness), a DIA quantification framework that re-examines MS raw files to recover missing values at the ion level before protein quantification. JUMPlion re-extracts precursor- and product-ion signals directly from raw data, infers ion-level measurements within precursor-specific local quantitative neighborhoods, and combines complementary precursor- and product-ion signals into downstream quantification. Using benchmark datasets acquired on multiple DIA platforms, JUMPlion increased protein-level completeness, improved fold-change accuracy, and enhanced detection of differentially abundant proteins while maintaining low differential-abundance false discovery rates. These gains were most evident in low-input and single-cell DIA datasets. Together, these results show that addressing missingness at the ion level before protein-level summarization can improve DIA quantification in diverse acquisition settings.
    DOI:  https://doi.org/10.64898/2026.04.28.720212
  2. J Chromatogr A. 2026 Apr 21. pii: S0021-9673(26)00353-5. [Epub ahead of print]1779 467023
      Lipids exhibit extensive molecular diversity and structural complexity, which poses major analytical challenges for comprehensive lipidomic profiling. Phospholipids, in particular, display extensive structural diversity and isomerism. Given the limited lipidomic data available for lymphoma cells, this work focuses on comprehensive phospholipid screening, which inherently requires the characterization of isomeric species, including plasmalogens that have been implicated in oxidative stress and ferroptosis-related cell death. Therefore, we present an efficient isomer-selective workflow based on reversed-phase liquid chromatography (RPLC) coupled to trapped ion mobility spectrometry (TIMS) and high-resolution tandem mass spectrometry (HR-MS/MS). High-confidence structural lipid annotation is achieved through the integrated evaluation of chromatographic retention time (tR), exact mass-to-charge ratio (m/z), collision cross section (CCS) and mobility-resolved MS/MS data. Applied to human lymphoma cell lipid extracts, the workflow enabled confident identification of 263 individual lipid species spanning 10 phospholipid and 2 sphingolipid subclasses, including the resolution of 63 isomeric species at the fatty-acyl compositional level. The multidimensional approach allowed partial discrimination of fatty-acyl compositional, sn- and double bond positional isomers. Notably, characteristic deviations in both retention time and ion mobility were observed for plasmalogens relative to alkyl-ether linked phospholipids, reflecting the unique physicochemical properties of the vinyl-ether linkage. These systematic offsets enabled confident plasmalogen assignment in representative cases, supported by authentic standards, co-chromatograms and mobility-resolved fragmentation data. Collectively, this streamlined analytical platform markedly expands phospholipidome coverage and provides enhanced structural resolution of complex lipid mixtures.
    Keywords:  Ether-lipids; Ion mobility; Isomers; LC-MS/MS; Lymphoma cells; Phospholipids; Plasmalogens
    DOI:  https://doi.org/10.1016/j.chroma.2026.467023
  3. Rapid Commun Mass Spectrom. 2026 Aug 15. 40(15): e70097
       RATIONALE: Biological systems are regulated through strongly interconnected molecular layers that cannot be accurately resolved using single-omics approaches. Although genomics and transcriptomics provide essential regulatory information, they often face obstacles to reflect functional molecular outcomes. Mass spectrometry (MS)-based multi-omics integration is currently recognized as a central analytical strategy to overcome this limitation by enabling direct, high-resolution measurement of proteins, metabolites, and lipids, thereby supporting systems-level biological interpretation and translational discovery.
    METHODS: This review critically examines mass spectrometry-based multi-omics approaches through analysis of published literature, with a focus on integrating proteomic, metabolomic, lipidomic, and spatial omics data. Computational frameworks and translational applications relevant to biomarker discovery and precision medicine are highlighted.
    RESULTS: MS-centered multi-omics integration significantly enhances molecular coverage, quantitative accuracy, and pathway-level interpretation by combining various analytical layers. Applications across cancer biology, metabolic disorders, neurodegenerative diseases, and environmental research have exhibited improved biomarker robustness and mechanistic resolution contrast with single-omics studies. Recent developments in spatial and single-cell MS address cellular heterogeneity, while integrative computational approaches minimise challenges associated with data complexity, normalization, and cross platform variability.
    CONCLUSIONS: Mass spectrometry-based multi-omics integration represents a rapidly evolving analytical approach for systems biology and translational research. Continued advances in MS instrumentation, acquisition strategies, and computational integration are expected to further improve biological interpretability that fastens the discovery of clinically and biologically relevant molecular signatures.
    Keywords:  lipidomics; mass spectrometry; metabolomics; multi‐omics integration; proteomics; spatial MS
    DOI:  https://doi.org/10.1002/rcm.70097
  4. J Proteome Res. 2026 May 02.
      Plasma proteomics based on mass spectrometry has great potential for biomarker discovery. Plasma is challenging for mass spectrometry due to the high dynamic range in protein abundance. Several workflows have been developed to overcome this, and in this study, we compare prominent enrichment and depletion workflows using platelet-poor plasma (PPP), platelet-rich plasma (PRP), and serum (SER). Our results show that depletion workflows including Top14 depletion and acid precipitation allow quantification of very different proteomes than methods based on enrichments of extracellular vesicles such as bead-based enrichment or ultracentrifugation. Enrichment methods are superior in terms of proteome depth and quantitative performance but may be less robust in large cohorts. There is a very high correlation between PPP and PRP samples for all methods and less to SER samples, especially with enrichment workflows. The correlation of 10 protein measurements, performed by clinical routine processes on a Cobas system, showed heterogeneous results. Low-abundant proteins with biological dynamics within a healthy cohort, including C-reactive protein and lipoprotein(a), correlated very well to proteomics-based workflows, while others, including albumin and transferrin, correlated poorly. In conclusion, the workflow for plasma proteomics should be aligned with the aim of the analysis and setup of the sample collection.
    Keywords:  Cobas; Orbitrap Astral; biomarker discovery; clinical proteomics; extracellular vesicles; plasma proteomics; platelet-poor plasma; platelet-rich plasma; workflow assessment
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00078
  5. Adv Exp Med Biol. 2026 ;1504 247-269
      Mass spectrometry (MS)-based metabolomics is a powerful tool for understanding the complexity of biochemical processes and to identify biomarkers across diverse biological systems. The vast amount of data generated by extreme resolution mass spectrometers poses significant data processing challenges, requiring robust computational approaches and workflows for meaningful data interpretation. This chapter provides a comprehensive overview of current methodologies in MS-based metabolomics data analysis, with a focus on data preprocessing and pretreatment, m/z extraction and annotation, univariate and multivariate statistical approaches, as well as data visualization. We discuss key considerations for ensuring data quality and the growing role of bioinformatics in pathway analysis and metabolite identification. We highlight the transforming role of extreme resolution and mass accuracy enabled by FT-ICR mass spectrometers, and finally, we explore emerging trends, including artificial intelligence-driven insights and real-time data processing, to guide future developments in this rapidly evolving field.
    Keywords:  Compound identification; Data processing and treatment; FT-ICR-MS; Untargeted metabolomics
    DOI:  https://doi.org/10.1007/978-3-032-18966-0_12
  6. Trends Biochem Sci. 2026 May 07. pii: S0968-0004(26)00111-8. [Epub ahead of print]
      Single-cell proteomics (SCP) has emerged as a transformative approach for characterizing cellular heterogeneity at the protein level. Recent advances in mass spectrometry workflows, with improvements spanning sample preparation, peptide separation, data acquisition, and data interpretation, have enabled unprecedented proteome depth and throughput at single-cell resolution. Beyond technological innovations, SCP is now addressing complex biological questions in oncology, developmental biology, and neuroscience, revealing dynamic cellular states and regulatory mechanisms. Integration with other single-cell omics is bridging the gap between genotype-phenotype relationships and uncovering multilayered regulation. In this review, we summarize recent progress in SCP technologies and highlight emerging applications and integrative strategies that mark a transition from technological development to broad biological understanding.
    Keywords:  cellular heterogeneity; mass spectrometry; multiomics; single-cell proteomics
    DOI:  https://doi.org/10.1016/j.tibs.2026.04.011
  7. Biomedicines. 2026 Apr 10. pii: 872. [Epub ahead of print]14(4):
      Background: Untargeted metabolomics enables comprehensive profiling of biological systems, but accurate metabolite annotation remains a critical bottleneck due to incomplete spectral libraries and structural isomerism. The use of in silico annotation tools can increase the coverage of annotated compounds, but it remains unclear whether these tools, in the absence of reference standards, can reliably annotate real-world experimental LC-HRMS data and whether they are sufficient for this task. Methods: This study assesses the performance and limitations of four widely used in silico structure prediction tools (MassFrontier, MetFrag, MS-FINDER, and SIRIUS/CSI:FingerID) when applied to an experimentally acquired feature set previously used to differentiate patients with depressive disorders from healthy controls. To ensure uniform evaluation across tools under realistic but optimized conditions, the quality of MS/MS data was improved using a parallel reaction monitoring method, allowing acquisition of interpretable fragmentation spectra for 26 of the 28 detected features. Results: For most features, all tools were able to suggest structure candidates. However, none of the tools proved sufficient as a standalone solution for reliable metabolite annotation. Due to their different algorithms, each tool had strengths and weaknesses in fragmentation interpretation, candidate generation, and ranking, resulting in incomplete or inconsistent annotations. While the combined application of all four tools provided a substantial improvement in putative annotation over conventional spectral library matching, the in silico structure prediction tools often prioritized chemically implausible, biologically irrelevant, or artifactual candidates. Consequently, manual expert evaluation was required to assess the chemical plausibility and biological relevance of the proposed structures. This ultimately reduced the number of biologically plausible metabolites putatively associated with disease to ten. Conclusions: Overall, these results demonstrate that existing in silico annotation tools can substantially support the annotation of experimental metabolomics data, but are insufficient on their own. Reliable identification of metabolites in complex biological matrices still depends on high-quality MS/MS data acquisition, the combined use of complementary tools, and mandatory post-annotation expert curation.
    Keywords:  MS-FINDER; MassFrontier; MetFrag; PRM; SIRIUS; biomarker discovery; in silico annotation; mass spectrometry; untargeted metabolomics
    DOI:  https://doi.org/10.3390/biomedicines14040872
  8. bioRxiv. 2026 Apr 21. pii: 2025.04.26.649581. [Epub ahead of print]
      This protocol describes a computational approach for constructing correlation-based molecular networks from untargeted metabolomics data using MetVAE, a variational autoencoder-based framework. Complementing spectral similarity networks, it captures functional relationships re-flected in cross-sample correlations. The workflow imports metabolomics features and sample metadata, adjusts for compositionality, missingness, confounding, and high-dimensionality, esti-mates sparse metabolite correlations, and exports GraphML files for network visualization. In a hepatocellular carcinoma mouse model, it links lipid classes in high-fat-diet animals, suggesting an endogenous "auto-brewery" route to lipotoxic metabolites.
    DOI:  https://doi.org/10.1101/2025.04.26.649581
  9. Adv Exp Med Biol. 2026 ;1504 69-93
      Metabolomics is one of the most recent OMICs sciences and considered one of the most complex. It aims to provide a comprehensive study of small molecules (metabolites) in a biological system to obtain insights of cellular processes and metabolic networks. For such purpose, several advanced analytical techniques such as mass spectrometry (MS) are employed to detect, identify, and even quantify metabolites.In this book chapter, we present the two main approaches (targeted and untargeted) used in metabolomics, as well as their individual advantages and limitations and their complementary strengths. As far as MS analytical techniques are concerned, both the various mass spectrometers and ionization sources, currently used for studying metabolites, are discussed, as well as the field of application linked to their performance.Lastly, recent advances made in exploring the different organisms through single-cell metabolomics as well as metabolomics data integration with other OMIC sciences are also discussed.
    Keywords:  Ionization sources; Mass analyzers; Mass spectrometry imaging; Metabolomics; Targeted and untargeted analysis
    DOI:  https://doi.org/10.1007/978-3-032-18966-0_4
  10. Anal Chem. 2026 May 02.
      The electrospray regime is a key determinant of ion formation during electrospray ionization (ESI), but it is often unreported and rarely verified in separation-coupled mass spectrometry (MS) workflows. In capillary electrophoresis (CE)-mass spectrometry (CE-ESI-MS), sheath-flow microflow ESI interfaces are widely used for metabolite analysis, whereas nanoflow ESI interfaces dominate peptide and protein measurements. Here, we benchmarked CE-ESI-MS performance in the cone-jet (CJ) and pulsating (P) regimes for trace-level metabolites, peptides, and proteome digests across microflow (μESI) and nanoflow (nanoESI) operation. In CE-μESI on a time-of-flight mass spectrometer, CJ increased metabolite sensitivity by up to ∼2-fold, improved signal stability, and shifted peptide ion generation to higher charge states relative to P. In CE-nanoESI proteomics on a timsTOF platform, CJ increased HeLa peptide and protein identifications by ∼50% and extended coverage to lower-abundance proteins while improving quantification completeness across technical replicates (i.e., a higher fraction of proteins quantified in all runs). For Xenopus laevis embryonic samples at single-cell-scale input, CJ significantly increased the number of detected metabolite features and improved recovery of functionally annotated protein groups. These results establish ESI regime as a controllable driver of sensitivity and quantitative performance in CE-ESI-MS and provide practical guidance for selecting ionization conditions for trace-limited analyses.
    DOI:  https://doi.org/10.1021/acs.analchem.6c01323
  11. J Proteome Res. 2026 May 07.
      Proteomics provides a systematic and high-throughput approach to comprehensively characterize protein networks, enabling insights into cellular functions and disease mechanisms. Carbamidomethylation using iodoacetamide (IAA), a common method for cysteine alkylation, is known to cause nonspecific modifications that increase spectral complexity in mass spectrometry and reduce quantitative accuracy. Here, we established a reproducibility-focused 2-mercaptoethanol (2-ME)/dimethyl sulfoxide (DMSO) workflow and systematically evaluated its quantitative performance at the proteome-wide level. Mouse liver proteomes were processed using either 2-ME/DMSO or conventional IAA treatment, followed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. The optimized 2-ME treatment increased the number of cysteine-modified peptides by 1.6- to 1.9-fold. Although total protein identifications were comparable, 77% of proteins exhibited improved sequence coverage with the optimized 2-ME treatment. Quantitative reproducibility was also enhanced, with the peptide quantified CV ≤ 20% increasing from 61.4% with IAA treatment to 86.1% with 2-ME treatment, and protein quantified CV ≤ 20% increasing from 80.6% with IAA treatment to 93.5% with 2-ME treatment. Application of this new workflow to ovarian clear cell carcinoma reliably detected cisplatin-induced alterations. The 2-ME/DMSO workflow offers a simple and highly reproducible proteomics strategy for accurate quantitative proteomics.
    Keywords:  2-mercaptoethanol; DMSO; cysteine modification
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00010
  12. Adv Exp Med Biol. 2026 ;1504 95-118
      Advanced analytical techniques are required to decipher metabolome complexity. The previous chapter ("Latest Developments in Mass Spectrometry-Based Techniques for Metabolomics Analysis") shows general aspect of mass spectrometry techniques for the detection, identification, and quantification of metabolites by target and non-targeted approaches. It also points out that, in some instances, the sole direct analysis by mass spectrometry is a limitation for deciphering the high molecular complexity of the metabolome. Therefore, upstream separative methods such as chromatography, capillary electrophoresis, or ion mobility can be an effective solution. Indeed, these techniques reduce the ion suppression effects, separate some isomers, and increase the capacity measurement for metabolite identification and quantification. This chapter focuses on the separative methods most frequently used in metabolomic studies. Their principles and some examples are addressed along with their coupling with MS.Lastly, analytical improvements and recent advances in MS hyphenated approaches for metabolomic study are also discussed with multidimensional analysis.
    Keywords:  Capillary electrophoresis; Chromatography; Ion mobility; Metabolomics; Separative techniques
    DOI:  https://doi.org/10.1007/978-3-032-18966-0_5
  13. Talanta. 2026 Apr 27. pii: S0039-9140(26)00521-7. [Epub ahead of print]307 129865
      The transition from untargeted discovery to targeted validation in metabolomics is a major challenge, often hindered by poor method transferability and retention time (RT) variation between liquid chromatography-mass spectrometry (LC-MS) platforms. To address this, "Scout-MRM Builder," an R-based package for the automated creation of highly multiplexed targeted methods from untargeted high-resolution MS2 data, is presented here. A Scout-Triggered Multiple Reaction Monitoring (StMRM) strategy is employed, using N-Alkylpyridinium-3-Sulfonate (NAPS) standards as dynamic RT markers ("scouts"). A specific list of transitions is triggered by the detection of each scout, ensuring robustness against RT shifts. Ion pairs are automatically extracted, scouts are identified, and ready-to-use StMRM methods, including pseudo-MRM transitions for features lacking fragmentation spectra, are generated. From an untargeted analysis of porcine liver extracts, a single StMRM method monitoring 1312 transitions was generated from 558 features. High reproducibility was demonstrated, with 89.9% of detected transitions exhibiting a relative standard deviation (RSD) below 20%. When applied to a model of liver ischemia-reperfusion injury, results highly comparable to the initial untargeted analysis were obtained. A common core of potential biomarkers was identified, with slightly improved statistical performance. In conclusion, the Scout-MRM Builder provides a powerful framework to bridge the gap between discovery and validation, enabling robust targeted analysis at an untargeted scale through enhanced method transferability and reliability.
    Keywords:  Liquid chromatography-mass spectrometry; Liver ischemia-reperfusion; Method development; Scout-triggered MRM; Targeted metabolomics; Untargeted metabolomics
    DOI:  https://doi.org/10.1016/j.talanta.2026.129865
  14. Anal Chem. 2026 May 04.
      Normalization is a critical step in metabolomics studies to ensure the quality of metabolomics data, reduce quantitative variability, and enable confident and robust statistical analyses. From an analytical perspective, metabolomics normalization encompasses multiple distinct processes. Broadly, normalization can refer to (1) sample normalization, which mitigates variation due to differences in total metabolite amounts; (2) signal correction, which reduces batch effects, instrumental fluctuations, and retention time drifts during data collection; and (3) statistical transformation and scaling, which prepare data for statistical analyses. Each of these normalization processes addresses unique analytical and bioinformatic needs, but the term "normalization" is often used broadly, leading to confusion in method development, selection, and implementation. Moreover, many well-established normalization algorithms in genomics and proteomics are not always transferable to metabolomics due to differences in analytical workflows and data characteristics. To address these issues, we believe it is crucial to gain a clear understanding of the purpose of each normalization type, its appropriate implementation, and the evaluation criteria. This perspective outlines the key normalization tasks in metabolomics, reviews existing tools, and provides recommendations for their appropriate applications. We also highlight two critical considerations: (1) selecting an appropriate missing value imputation method and (2) establishing strategies to evaluate and compare normalization outcomes. The goal of this work is to provide recommendations for the rigorous development and implementation of normalization techniques in metabolomics, thereby enhancing analytical accuracy and precision, improving data interpretability, and ultimately advancing the biological insights gained from metabolomics studies.
    DOI:  https://doi.org/10.1021/acs.analchem.6c00292
  15. J Proteome Res. 2026 May 05.
      Group 3 medulloblastoma (G3MB) is an aggressive pediatric brain tumor subtype accounting for 20-25% of cases and characterized by poor prognosis and frequent metastasis. This subgroup frequently exhibits MYC amplification, driving oncogenic transcription, biosynthesis, and metabolic reprogramming, which is crucial for the rapid growth of tumor cells. Prior proteomic analysis of G3MB samples showed disrupted glucose and pyruvate metabolism, with notable overexpression of mitochondrial phosphoenolpyruvate carboxykinase (PCK2). This links the TCA cycle and pyruvate metabolism, aiding metabolic flexibility under nutrient stress. Consistent with this observation, we observed PCK2 overexpression in two independent patient data sets. To investigate its functional role, we performed shRNA-mediated knockdown of PCK2 in HD-MB03 and G3MB cells. Quantitative proteomics using Evosep-Tims TOF revealed dysregulation of metabolic interactors along with enrichment of ribosomal and RNA processing pathways. Complementary metabolomic profiling showed alterations in phosphocholine, carnitine, and metabolites related to redox imbalance upon PCK2 loss. Together, these findings provide insights into PCK2's role in Group 3 MB cells and expose vulnerabilities for therapeutic targeting.
    Keywords:  PCK2; RNA processing; evosep-tims TOF; group 3 medulloblastoma; metabolic vulnerabilities; metabolomics; proteomics; ribonucleoprotein biogenesis; shRNA knockdown
    DOI:  https://doi.org/10.1021/acs.jproteome.5c01150
  16. Anal Chem. 2026 May 04.
      Stable isotope labeling is widely used to study metabolic fluxes. Mass spectrometry is the primary tool for measuring isotope ratios in biomolecules, but there are often trade-offs between mass resolution, sensitivity, analytic speed, and, most importantly for flux measurements, accuracy and precision of relative isotope abundances. Orbitrap mass spectrometers have been found to exhibit high isotope ratio measurement accuracy and precision in targeted measurements on a narrow m/z range but are biased in untargeted measurements of multiple biomolecules concurrently across a wide m/z range. This measurement bias is known to be caused by multiple factors, including the ion signal intensity. Here, we developed a scan-by-scan, machine-learning-based correction method to address the bias and predict bias-free mass isotopomer ratios. The fully trained random forest model reduces the mean absolute percentage error of isotopic measurements in detected metabolites for the M1 mass isotopomer from 21.3 to 3.5% and for M2 from 25.8 to 3.1%. The model also provides a ranking of factors by their impact on the measurement bias, and it was found that the ion signal intensity/TIC ratio of individual scans is the most dominant and is a previously neglected bias-causing factor. Use of this correction model improves the accuracy of metabolic flux measurements from heavy water (2H2O) labeling studies. Our correction method overcomes the limitation that current Orbitrap mass spectrometers cannot achieve isotope measurement accuracy in methods covering a wide m/z range. The approach presented here enables stable isotope labeling experiments to be high-throughput and may advance stable isotope labeling toward untargeted "fluxomics".
    DOI:  https://doi.org/10.1021/acs.analchem.5c06638
  17. Adv Exp Med Biol. 2026 ;1504 119-144
      Metabolomics has emerged as a powerful discipline for characterizing the small molecules that define cellular physiology, environmental responses, and disease states. As technologies advance, researchers face an expanding landscape of analytical platforms, data-processing strategies, and integrative approaches that require clear guidance for effective application. This chapter was written to provide a comprehensive and accessible resource for students, clinicians, and researchers entering or advancing in the field. We outline the fundamentals of metabolomics, describe major analytical methodologies-including MS, NMR, chromatography, and imaging-and summarize key considerations for experimental design, data preprocessing, statistical analysis, and functional interpretation. We also address current challenges related to metabolite identification, reproducibility, and multi-omic integration, and highlight emerging innovations such as stable-isotope tracing, spatial metabolomics, and AI-driven analytics. Together, these elements offer a detailed roadmap for conducting robust, reproducible, and insightful metabolomic studies.
    Keywords:  Capillary electrophoresis; Chemoinformatic; Chromatography; Cross-Omics; Heteroscedasticity; Mass analyzing; Metabolite imaging; Metabolome; Microbiome; Network modeling; Small molecules
    DOI:  https://doi.org/10.1007/978-3-032-18966-0_6
  18. J Proteome Res. 2026 May 06.
      Human plasma proteomics is critical for biomedical research but challenged by an extraordinary dynamic range. We evaluated six mass spectrometry workflows, including bead-based enrichment (Proteograph XT, P2, Mag-Net), depletion of the 14 most abundant proteins, and neat plasma analysis. Enrichment methods achieved superior proteome coverage, identifying up to 4600 proteins, but exhibited acute susceptibility to preanalytical bias from cellular contaminants and vesicles in plasma. Increased centrifugation stringency before proteomics analysis drastically decreased protein identifications. Crucially, the abundance of the vast majority of secreted proteins was not significantly affected by centrifugation for all tested workflows, suggesting that their quantification is little influenced by contaminating cells or vesicles, whereas depleted proteins are enriched for cellular components like cytoskeleton organization and platelet activation. Our results underscore that nanoparticle-based methods allow robust and deep measurement of the secreted plasma proteome, providing additional insights by reporting the contributions of proteins of cellular origin when applying the common practice of 2000 × g centrifugation prior to analysis.
    Keywords:  biofluids; blood analysis; clinical proteomics; extracellular vesicles; mass spectrometry; method comparison; nanoparticles; plasma proteomics; preanalytical bias; protein corona
    DOI:  https://doi.org/10.1021/acs.jproteome.6c00048
  19. Anal Chem. 2026 May 05.
      Detecting thiol metabolites at the single-cell level is crucial for unraveling their roles in redox regulation and disease pathogenesis, but their detection is hindered by weak mass spectrometry (MS) signals and severe ion suppression due to their low abundance. Herein, we develop a live-cell derivatization strategy coupled with label-free mass cytometry (CyESI-MS) for sensitive and high-throughput analysis of thiols in single cells. Tetramethylrhodamine-5-maleimide (TAMRA-5-maleimide) was employed as the derivatization reagent, which can react specifically with thiols via its maleimide group, boost MS signal response through the rhodamine moiety, and exhibit good biocompatibility. Using this strategy combined with CyESI-MS, we successfully detected five thiol metabolites (GSH, Cys, Cys-Gly, hCys, and NCys) in single cells. The reliability of this method was further validated in multiple-cell application scenarios. By expanding single-cell coverage of low-abundance metabolites, this strategy holds promise for advancing redox biology and cancer research.
    DOI:  https://doi.org/10.1021/acs.analchem.5c07995
  20. J Am Soc Mass Spectrom. 2026 May 06.
      Direct infusion-based single-cell metabolomics analysis has the potential to isolate the causes of drug resistance and cancer progression; however, processing and analyzing the data generated remains a challenge. While many packages exist for metabolomics analysis, they are not optimized for direct infusion-based single-cell measurements, which do not rely on chromatographic separation and are typically noisier than traditional population-level methods. To address this gap, the MeDUSA (Metabolomics of direct-infusion untargeted single-cell analysis) R package was developed. MeDUSA was built especially for direct infusion-based single-cell metabolomics, with modularity, noise filtering, and user-customization in mind. In this work, we introduce the package, how to use it, and implement it in a single-cell metabolomics experiment to identify the differences between two cell lines. MeDUSA compromises several functions that deal with file import, peak picking, spectral processing, statistical analysis, and feature annotation. Each function is defined with the purpose, usage, parameters, default values, and output of an example data set. MeDUSA was built to be a modular platform that aims to be a foundation to be built upon with additional modules for the single-cell metabolomics field.
    Keywords:  data analysis; direct infusion; metabolomics; single cell
    DOI:  https://doi.org/10.1021/jasms.5c00297
  21. J Invest Dermatol. 2026 May 07. pii: S0022-202X(26)01028-6. [Epub ahead of print]
      Kinases are central regulators of multiple signaling cascades, controlling processes such as cellular growth, proliferation, and differentiation. Given their vital role within the cell, dysregulated kinase activity contributes to several skin diseases, including melanoma and dermatitis. Poor disease response or resistance to targeted inhibitors can be driven by adaptive kinase responses. Genomic assays are highly informative but do not accurately capture kinase abundance and activity at the protein level. In this paper, we review 2 complementary mass spectrometry-based proteomics methods for functional kinome analysis that are readily applicable to dermatology research. Multiplexed inhibitor beads coupled with mass spectrometry (MIB-MS) uses broad-spectrum, immobilized kinase inhibitors to enrich for kinases in active conformation, providing an unbiased, pathway-level readout of kinase network dynamics, adaptive rewiring, and drug specificity. Internal standard triggered-parallel reaction monitoring (IS-PRM)-targeted proteomics, including the Thermo SureQuant acquisition method, leverages heavy peptide triggers to deliver sensitive, consistent quantification of predefined kinase peptides from limited input clinical specimens, including formalin fixed, paraffin embedded. We summarize optimized workflows, instrument set-up, sample requirements, technical considerations, and limitations. Together, MIB-MS and IS-PRM SureQuant offer orthogonal, scalable strategies to profile kinase networks in skin biology and to inform target discovery, biomarker development, and rational therapeutic strategies.
    Keywords:  Adaptive resistance; Chemical proteomics; Kinase inhibitor; Kinome profiling; Targeted proteomics
    DOI:  https://doi.org/10.1016/j.jid.2026.03.032
  22. Front Med (Lausanne). 2026 ;13 1791030
       Introduction: Current multiple myeloma (MM) risk stratification, anchored on the Revised International Staging System (R-ISS), provides a static snapshot of disease but fails to capture its dynamic biological evolution, functional tumor-microenvironment crosstalk, and real-time treatment response. Mass spectrometry (MS)-based proteomic and metabolomic profiling has emerged as a high-sensitivity tool for both novel biomarker discovery and minimal residual disease (MRD) monitoring. This systematic review evaluates the independent prognostic value and clinical utility of quantitative MS-based proteomics and metabolomics compared to standard-of-care risk models and traditional disease monitoring techniques.
    Methods: Following PRISMA 2020 guidelines, a systematic search of PubMed, Embase, and Web of Science was conducted. Inclusion required quantitative MS-based proteomics or metabolomics in MM cohorts with outcomes compared to ISS/R-ISS or traditional MRD detection methods. Data analysis was performed with a focus on overall survival (OS), progression-free survival (PFS), hazard ratios (HR), and MRD sensitivity thresholds.
    Results: From 1,077 records, 19 studies met the inclusion criteria. Eleven discovery-focused studies identified specific MS-derived signatures, such as microenvironmental proteins (e.g., MTA2, CD44) and dysregulated lipid metabolites (e.g., acylcarnitines, LysoPE) that were consistently associated with PFS and OS. Crucially, MS-based biomarkers retained independent prognostic significance in multivariate models adjusted for R-ISS. Furthermore, 8 studies tackling blood-based MS-MRD detection demonstrated up to 1,000-fold higher sensitivity than traditional immunofixation electrophoresis, identified biochemical relapse 2-11 months earlier, and achieved high concordance with bone marrow-based assays (NGS/NGF).
    Conclusion: In conclusion, quantitative MS profiling provides a high-resolution molecular lens that significantly refines MM risk stratification beyond static staging. By enabling non-invasive, longitudinal MRD monitoring with superior lead times, MS integration facilitates a shift from reactive to proactive intervention. Standardization of bioinformatics pipelines and MS methodologies is now the final barrier to implementing MS-guided treatment adjustments in routine clinical practice.
    Keywords:  biomarkers; mass spectrometry (MS); metabolomics; minimal residual disease (MRD); multiple myeloma; prognosis; proteomics; risk stratification
    DOI:  https://doi.org/10.3389/fmed.2026.1791030
  23. Anal Chem. 2026 May 08.
      Reliable comparison of collision cross section (CCS) measurements across ion mobility platforms remains a key challenge for standardized molecular identification. We present a hierarchical, data-driven postcalibration correction framework that harmonizes CCS values across drift tube (DTIMS), trapped ion mobility (TIMS), and traveling wave (TWIMS) instruments, enabling quantitative cross-technology comparison despite differing calibration methods. Using a multilaboratory dataset of 840 measurements for 347 compounds, the framework reduced intertechnology CCS variability by approximately 95%, bringing residual differences within instrumental precision limits. Leave-one-compound-out validation demonstrated robust cross-technology transfer, lowering the median absolute percentage error from 8.9% to 3.2% and achieving 94.6% empirical coverage of 95% prediction intervals. The approach requires as few as three reference compounds, reducing recalibration workload by more than half, and provides full uncertainty estimates for probability-based database matching. This statistically grounded yet practical strategy enables harmonized, uncertainty-aware CCS databases and supports reproducible compound identification across laboratories and ion mobility technologies.
    DOI:  https://doi.org/10.1021/acs.analchem.5c06667
  24. J Proteome Res. 2026 May 05.
      Targeted protein degradation (TPD) is a therapeutic strategy that utilizes small molecules to induce the proximity-driven degradation of disease-causing proteins. Because the efficacy and selectivity of TPD compounds must be validated across thousands of proteins, high-throughput proteomics is essential for the rapid screening and characterization of these novel degraders. Here, we developed a 300 samples per day (SPD) LC-MS/MS method using the Orbitrap Astral mass spectrometer for ultrahigh-throughput TPD compound screening. We identified close to 8000 protein groups from a single cell line with a coefficient of variation (CV) of less than 10%, highlighting the deep proteome coverage and method reproducibility even at 300 SPD. This high degree of precision provides the statistical confidence to detect subtle, yet significant, changes in protein abundance that were previously challenging to quantify in high-throughput workflows. To evaluate the quantitation accuracy of this method, we further mixed the digests from two or three species at different ratios. Our three-proteome mixture results demonstrated highly accurate quantitation for proteins with both small and large fold changes. Moreover, our two-proteome mixture experiment, where 20 to 160 ng of yeast digest was spiked into 200 ng of HeLa digest, showed an R2 of 0.999 for the yeast proteome, underscoring the quantitation accuracy of the method. Utilizing this workflow, we studied dose-dependent protein degradation patterns induced by pomalidomide, iberdomide, and mezigdomide. Our results indicate that mezigdomide may possess enhanced efficacy in T cells by degrading additional proteins such as IKZF2, thereby boosting anticancer immunity. Together, we developed an ultrahigh-throughput LC-MS/MS method with excellent proteome coverage and quantitation accuracy that is highly suitable for chemoproteomics screening of drug libraries.
    Keywords:  Orbitrap Astral; chemoproteomics; high-throughput screening; molecular glue; quantitative proteomics; targeted protein degradation
    DOI:  https://doi.org/10.1021/acs.jproteome.5c01023