bims-mascan 2025-08-10 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–08–10
twenty-two papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

An Improved Metabolomics Workflow Enables Untargeted Data Acquisition and Targeted Data Analysis Using Liquid Chromatography-Mass Spectrometry.
Batchwise data analysis with inter-batch feature alignment in large scale platelet lipidomics study using UHPLC-ESI-QTOF-MS/MS by data-independent SWATH acquisition.
The $10 proteome: low-cost, deep and quantitative proteome profiling of limited sample amounts using the Orbitrap Astral and timsTOF Ultra 2 mass spectrometers.
Off-site processing of data-dependent and data-independent acquisition shotgun proteomics data with MASSyPupX.
scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data.
Dynamic Quadrupole Selection to Associate Precursor Masses with MS/MS Products in Data-Independent Acquisition.
Expanding Targeted Instrumentation for Discovery Applications: Complement Reporter Ion Quantification with a Quadrupole-Ion Trap Instrument.
Plasma-Derived Extracellular Vesicle Proteomics.
Quantitative HILIC-Q-TOF-MS Analysis of Glycosaminoglycans and Non-reducing End Carbohydrate Biomarkers via Glycan Reductive Isotopic Labeling.
One-Step Labeling and Purification: Accelerating FAHFA Detection in Vegetable Oils via Chemoselective Probe-Assisted LC-MS.
Spatial quantitative metabolomics enables identification of remote and sustained ipsilateral cortical metabolic reprogramming after stroke.
Deep and Quantitative Proteomic Profiling of Low Volume Mouse Serum Across the Lifespan.
Refining nanoflow LC and orbitrap MS data acquisition parameters for pico- and nanogram scale proteomics.
Clinical validation of a liquid chromatography single quadrupole mass spectrometry (LC-MS) method using Waters Kairos™ Amino Acid Kit reagents.
Leveraging open cheminformatics tools for non-targeted metabolomics analysis of C. elegans: a workflow comparison and application to strains related to xenobiotic metabolism and neurodegeneration.
Integrating Electrostatic Adsorption-Laser Desorption/Ionization-MS with a Derivative Full-Spectrum Internal Standard Approach for Acylcarnitine Quantification in Clinical Samples.
Quantitative profiling of lipid transport between organelles enabled by subcellular photocatalytic labelling.
mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Data Analysis.
Insect Lipidomics: Advances, Applications, and Physiological Insights.
Integral Synthesis and Clearance Analysis via DIA (ISDia) Reveals Coordinated Protein Dynamics Regulation during Endoplasmic Reticulum Stress.
Nitrogen metabolism profiling reveals cell state-specific pyrimidine synthesis pathway choice.
Determination of Double Bond Positions in Unsaturated Fatty Acids using Permanganate Oxidation and Tandem Mass Spectrometry of CuIICl+ Adducted Ions.

J Proteome Res. 2025 Aug 07.

An Improved Metabolomics Workflow Enables Untargeted Data Acquisition and Targeted Data Analysis Using Liquid Chromatography-Mass Spectrometry.

Yi Wu, Yang Wang.

  In this study, we present an improved metabolomics methodological framework that synergistically integrates untargeted data acquisition with targeted data analysis using chemical derivatization combined with ultrahigh performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF MS) analysis. Data-independent acquisition (DIA)-based mass spectrometry (MS1) data were used to conduct conventional untargeted analysis for biomarker discovery. The data-dependent acquisition (DDA) method was applied to obtain high-quality tandem mass spectrometry (MS2) information for quantitative data analysis. 1-Aminopiperidine (1AP) served as the derivatization reagent for sample preparation, which selectively reacts with carboxyl-containing compounds. Fatty acid (FA) standards were used to examine the derivatization reaction, and the results of LC-MS analysis showed that protonated FA + 1AP-H2O was the precursor. The ion at m/z 84.08, along with the product ion from a neutral loss of 45.02 Da, emerged as characteristic fragments, facilitating compound annotation and quantitative analysis. Method validation results demonstrated the proposed method with excellent repeatability, stability, and linearity. The lung tissue samples were successfully analyzed using this method, which was further employed to evaluate the therapeutic efficacy of Zhuye Shigao Decoction (ZSD) against lipopolysaccharide (LPS)-induced acute pneumonia in mice.

Keywords:  1-Aminopiperidine; liquid chromatography−mass spectrometry; metabolomics; targeted data analysis; untargeted data acquisition

DOI:  https://doi.org/10.1021/acs.jproteome.5c00427
J Pharm Biomed Anal. 2025 Aug 05. pii: S0731-7085(25)00429-7. [Epub ahead of print]266 117088

Batchwise data analysis with inter-batch feature alignment in large scale platelet lipidomics study using UHPLC-ESI-QTOF-MS/MS by data-independent SWATH acquisition.

Kristina Dittrich, Xiaoqing Fu, Adrian Brun, Madhumita Chatterjee, Meinrad Gawaz, Michael Lämmerhofer.

  Untargeted lipidomics by ultra-high-performance liquid chromatography (UHPLC) hyphenated with tandem mass spectrometry using data-independent acquisition (DIA) is a technique with increasing popularity for generating new hypotheses in support of clinical research. Its strength is its data comprehensiveness on both MS and MS/MS level. However, especially when applying SWATH acquisition for large-scale analysis, e.g. clinical studies with over 1000 s to 10,000 s of samples, simultaneous processing of acquired data in multiple batches over longer period of time may be challenging due to retention time and mass shifts as well as huge bulk of data, particularly when computer power is limited. This problem can be alleviated by a batchwise data processing strategy by inter-batch feature alignment of separately processed sample batches. After batchwise automated data processing in MS-DIAL, feature lists can be combined by aligning identical features from different batches attributed to similarity in precursor m/z and retention time, with the intention to generate a representative reference peak list for targeted data extraction. The workflow was established with detected features from three batches of platelet lipid extracts of coronary artery disease (CAD) patients (n = 120) and then applied on a clinical cohort with 1057 CAD patients measured in 22 batches. As a result, the lipidome coverage was significantly increased when several batches were used to create the target feature list compared to a single batch and the increase of annotated features levelled off with 7-8 batches. Further, the lipid identification was improved in terms of number of structurally annotated features.

Keywords:  Coronary artery disease; Data processing; Lipidomics; Mass spectrometry; Platelet

DOI:  https://doi.org/10.1016/j.jpba.2025.117088
bioRxiv. 2025 Jul 31. pii: 2025.07.29.667408. [Epub ahead of print]

The $10 proteome: low-cost, deep and quantitative proteome profiling of limited sample amounts using the Orbitrap Astral and timsTOF Ultra 2 mass spectrometers.

Siqi Huang, Chao Wang, Hsien-Jung L Lin, Ryan T Kelly.

Mass spectrometry (MS)-based proteomics remains technically demanding and prohibitively expensive for many large-scale or routine applications, with per-sample costs of hundreds of dollars or more. To democratize access to proteomics and facilitate its integration into more high-throughput multi-omic studies, we present a robust analytical framework for achieving in-depth, quantitative proteome profiling at a cost of approximately $10 per sample, termed the "$10 proteome." Using the Thermo Fisher Orbitrap Astral and Bruker timsTOF Ultra 2 mass spectrometers, we evaluated performance across sample inputs ranging from 200 pg to 100 ng and active gradient lengths from 5 to 60 minutes. Proteome coverage saturated within the low-nanogram input range, with ∼8000 protein groups quantified from as little as 10 ng of input and nearly 6000 protein groups from 200 pg. With already demonstrated low-cost one-pot sample preparation workflows that are appropriate for this sample input range, standardized MS acquisition settings, and high-throughput nanoLC operated at ∼10 min per sample, the $10 proteome becomes feasible. This study establishes a practical, scalable, and cost-effective foundation for global proteome profiling, paving the way for routine, large-scale applications in systems biology, clinical research and beyond.

DOI: https://doi.org/10.1101/2025.07.29.667408
J Proteomics. 2025 Aug 01. pii: S1874-3919(25)00121-6. [Epub ahead of print] 105494

Off-site processing of data-dependent and data-independent acquisition shotgun proteomics data with MASSyPupX.

Jorge Noé García-Chávez, Robert Winkler.

  The rapid pace of shotgun proteomics data generation presents challenges for timely data analysis. In parallel, the scientific community is creating novel data interpretation tools, such as artificial intelligence, that have not yet been integrated into commercial software. Off-site data processing with free and open-source software (FOSS) enables the decentralization and scaling of informatics workflows. FOSS platforms also lower the costs of education and research. MASSyPupX is a FOSS mass spectrometry (MS) software collection that runs directly from a USB drive. Alternatively, setting up a MASSyPupX workstation or server provides a ready-to-use and reproducible MS analysis platform. Installed programming languages and libraries support the development of custom MS software and workflows. This paper demonstrates using MASSyPupX to convert and process raw shotgun proteomics data. Raw Thermo files were downloaded from ProteomeXchange and converted to the HUPO community format mzML. Data-dependent acquisition (DDA) data were evaluated with Comet, PeptideProphet, ProteinProphet, ProtyQuant, and the Trans-Proteomic Pipeline. Data-independent acquisition (DIA) shotgun proteomics data were analyzed with DIA-NN. Custom Bash, Python, and R scripts were used to post-process and visualize the results. The MASSyPupX project is hosted at https://codeberg.org/LabABI/MASSyPupX, and the current ISO can be downloaded from https://doi.org/10.5281/zenodo.14618430. The MASSyPupX platform significantly advances shotgun proteomics data processing by offering a free and open-source software (FOSS) solution that is portable, scalable, and accessible. Operating directly from a USB drive or server, this Debian-based Linux distribution enables researchers to analyze data-dependent (DDA) and data- independent (DIA) acquisition proteomics data without installation, decentralizing workflows, reducing costs, and fostering collaboration and mass spectrometry data processing training. With pre-installed programming languages, libraries, and support for tools like Comet, PeptideProphet, DIA-NN, and ProtyQuant, MASSyPupX facilitates reproducible analyses, integrates cutting-edge computational techniques, and provides a user-friendly environment for education, research, and custom workflow development. MASSyPupX democratizes access to advanced proteomics analysis, serving as a helpful tool for advancing biological and medical research through decentralized and cost-effective workflows.

Keywords:  Data-dependent acquisition (DDA); Data-independent acquisition (DIA); Free and open source software (FOSS); Shotgun proteomics

DOI:  https://doi.org/10.1016/j.jprot.2025.105494
Genome Biol. 2025 Aug 07. 26(1): 237

scplainer: using linear models to understand mass spectrometry-based single-cell proteomics data.

Christophe Vanderaa, Laurent Gatto.

  Analyzing mass spectrometry (MS)-based single-cell proteomics (SCP) data faces important challenges inherent to MS-based technologies and single-cell experiments. We present scplainer, a principled and standardized approach for extracting meaningful insights from SCP data using minimal data processing and linear modeling. scplainer performs variance analysis, differential abundance analysis, and component analysis while streamlining result visualization. scplainer effectively corrects for technical variability, enabling the integration of data sets from different SCP experiments. In conclusion, this work reshapes the analysis of SCP data by moving efforts from dealing with the technical aspects of data analysis to focusing on answering biologically relevant questions.

Keywords:  Batch correction; Data analysis; Data interpretation; Linear modeling; Mass spectrometry; Missing values; Proteomics; Reproducible research; Single-cell

DOI:  https://doi.org/10.1186/s13059-025-03713-4
J Am Soc Mass Spectrom. 2025 Aug 08.

Dynamic Quadrupole Selection to Associate Precursor Masses with MS/MS Products in Data-Independent Acquisition.

Keaton L Mertz, Lia R Serrano, Pavel Sinitcyn, Joshua J Coon.

Data-independent acquisition (DIA) mass spectrometry facilitates high-throughput, reproducible bottom-up proteomic analyses. Typically, DIA methods coselect multiple precursor ions within a wide selection window. These precursors are simultaneously fragmented, superimposing the product ion signals into a complex chimeric spectrum. A method for varying the quadrupole selection width over the ion accumulation period is described. This method couples the intensity of a product ion to the mass of its precursor ion. By overlapping consecutive selection windows, scan-to-scan product ion intensity profiles can be used to infer precursor mass. We assess the method's sensitivity to quadrupole width, accumulation time, and mass-to-charge range using internal fluoranthene calibrant and FlexMix calibration solution with Q-Orbitrap configured mass analyzers. Additionally, we explore usability of the described technique on a tryptic-digest monoclonal antibody sample, including both direct infusion and liquid chromatography of the sample. With direct infusion, product ions from two precursors separated by 1 thomson (Th) are resolved with this method using 10 Th windows with 5 Th overlap. The product ions are associated within 0.3 Th of their respective precursor ion's m/z. Therefore, product ion spectra have a precursor ion m/z resolving power of ∼33.

DOI: https://doi.org/10.1021/jasms.5c00110
J Proteome Res. 2025 Aug 06.

Expanding Targeted Instrumentation for Discovery Applications: Complement Reporter Ion Quantification with a Quadrupole-Ion Trap Instrument.

Edward R Cruz, Alex N T Johnson, Vyas Pujari, Qi Zhang, Thao Nguyen, Michael Stadlmeier, Jessica Wang, Cristina C Jacob, Graeme C McAlister, Philip M Remes, Martin Wühr.

  Proteomic workflows have traditionally been divided into discovery-based and targeted approaches with instrumentation optimized specifically for each. Discovery experiments typically utilize high-resolution analyzers, while targeted workflows rely on the sensitivity and specificity of triple quadrupole systems. Recently, a quadrupole-ion trap instrument (Stellar MS) has demonstrated superior performance for targeted applications compared to conventional triple quadrupoles. In this study, we expand the capabilities of this platform to multiplexed shotgun proteomics using complement reporter ion quantification in an ion trap (iTMTproC). Benchmarking experiments with defined standards show that iTMTproC achieves quantification accuracy and interference reduction comparable to MultiNotch MS3 on the Orbitrap Fusion Lumos, a dedicated quadrupole-ion trap-Orbitrap tribrid instrument optimized for this purpose. Notably, iTMTproC quantifies slightly more proteins than does MultiNotch MS3. We further validate this approach through a developmental time-series analysis of frog embryos, obtaining proteomic data nearly indistinguishable from those from MultiNotch MS3, with slightly increased protein quantification depth. These findings significantly extend the functionality of targeted instrumentation, underscoring the versatility of quadrupole-ion trap systems and providing cost-effective access to highly accurate, multiplexed quantitative shotgun proteomics.

Keywords:  Stellar; TMTpro; complement reporter ions; multiplexing; quadrupole–ion trap; shotgun proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.5c00356
J Proteome Res. 2025 Aug 08.

Plasma-Derived Extracellular Vesicle Proteomics.

Yanqi Tan, Wei-Chun Kao, Marni Boppart, Jonathan V Sweedler.

  Extracellular vesicles (EVs) are nanometer-scale lipid bilayer-enclosed particles released by cells under physiological and pathological conditions. Their molecular cargos, including proteins, can reflect the chemical composition and physiological state of the parent cells, carrying signatures of health and disease. As such, EVs are valuable tools for biomarker discovery and mechanistic studies. Among them, plasma-derived EVs (pEVs) are particularly promising, as sampling plasma allows capture of EVs from virtually all of the tissues and organs. The minimally invasive nature of plasma collection further enhances the diagnostic and therapeutic potential of the pEVs. Proteomic profiling of pEVs enables the identification of disease-specific EV-biomarkers. However, the complexity of plasma, with high levels of abundant proteins and large EV heterogeneity, presents challenges for pEV proteomics. Mass spectrometry (MS) has emerged as the preferred state-of-the-art analytical tool for pEV studies due to its nonbiased ability to characterize thousands of proteins in an experiment and its ability to identify low-abundance EV proteins. Here, a comprehensive overview of the advancements in MS-based pEV proteomics in the recent 5 years is presented with a focus on three key areas: sample preparation methodologies, MS-based approaches for protein identification and quantification, and description of pEV studies for basic and disease research. Technical advancements enable greater proteomic details from pEVs, enhancing biomarker discovery, elucidating disease mechanisms, and advancing an understanding of EVs' biological roles.

Keywords:  biomarker discovery; bottom-up proteomics; extracellular vesicles; mass spectrometry

DOI:  https://doi.org/10.1021/acs.jproteome.5c00316
Anal Chem. 2025 Aug 06.

Quantitative HILIC-Q-TOF-MS Analysis of Glycosaminoglycans and Non-reducing End Carbohydrate Biomarkers via Glycan Reductive Isotopic Labeling.

Amrita Basu, Stephanie Archer-Hartmann, Pradeep Chopra, Mehrnoush Taherzadeh Ghahfarrokhi, Xiaolin Dong, Neil G Patel, Yiwen Zhang, Biswa Choudhury, Kosuke Funato, Dhananjay Yellajoshyula, Geert-Jan Boons, Parastoo Azadi, Ryan J Weiss.

Glycosaminoglycans (GAGs) are linear, heterogeneous polysaccharides expressed on all animal cells. Sulfated GAGs, including heparan sulfate (HS) and chondroitin/dermatan sulfate (CS/DS), are involved in numerous physiological and pathological processes; therefore, precise and robust analytical methods for their characterization are essential to correlate structure with function. In this study, we developed a method utilizing hydrophilic interaction liquid chromatography coupled with time-of-flight mass spectrometry (HILIC-Q-TOF-MS) and glycan reductive isotopic reducing end labeling (GRIL) for the quantitative compositional analysis of HS and CS/DS polysaccharides. Lyase-generated disaccharides and commercial standards were chemically tagged on the reducing end with aniline stable isotopes, thus enabling the absolute quantification of HS and CS/DS disaccharides in complex biological samples. In addition, we adapted this workflow, in conjunction with new synthetic carbohydrate standards, for the quantification of disease-specific non-reducing end (NRE) carbohydrate biomarkers that accumulate in patients with mucopolysaccharidoses (MPS), a subclass of lysosomal storage disorders. As a proof of concept, we applied this method to measure NRE biomarkers in patient-derived MPS IIIA and MPS IIID fibroblasts, as well as in cortex tissue from a murine model of MPS VII. Overall, this method demonstrates improved sensitivity compared to previous GRIL-LC/MS techniques and, importantly, avoids the use of ion-pairing reagents, which are undesirable in certain mass spectrometry instrumentation and contexts. By combining the benefits of HILIC separation with isotopic labeling, our approach offers a robust and accessible tool for the analysis of GAGs, paving the way for advancements in understanding GAG structure and function.

DOI: https://doi.org/10.1021/acs.analchem.5c02338
J Agric Food Chem. 2025 Aug 06. 73(31): 19813-19822

One-Step Labeling and Purification: Accelerating FAHFA Detection in Vegetable Oils via Chemoselective Probe-Assisted LC-MS.

Xin-Ze Wu, Kai-Wen Tan, Xu Zhang, Jun-Jie Tan, Na An, Fang Cong, Wenbin Wu, Azamat Temerdashev, Quan-Fei Zhu, Hong Wang, Yu-Qi Feng.

  Fatty acid esters of hydroxy fatty acids (FAHFAs) are emerging bioactive lipids in edible oils, but their trace-level presence and matrix complexity hinder accurate analysis. Here, we designed a novel chemoselective probe piperazine-modified silica (SiO2-PP) that specifically captures FAHFAs while removing almost totally triglycerides in a one-step labeling reaction. The probe enabled single-batch sample pretreatment within 30 min, integrating capture, purification, and signal amplification. Coupled with liquid chromatography-mass spectrometry, the method achieved high sensitivity (LOD: 0.0039-0.033 ng/mL) and enabled quantification of 47 FAHFA isomers in 47 vegetable oil samples, with concentrations ranging from 0.56 to 1.76 × 104 ng/g. Five dominant isomers (e.g., 10-OAHSA) were identified, with olive and peanut oils exhibiting the highest FAHFA concentrations. This approach provides a promising platform for high-throughput functional lipid profiling, effectively overcoming the inefficiencies of traditional methods while significantly enhancing the assessment of vegetable oil quality.

Keywords:  chemoselective probe; fatty acid esters of hydroxy fatty acids; high-throughput quantification; liquid chromatography−mass spectrometry; vegetable oils

DOI:  https://doi.org/10.1021/acs.jafc.5c06401
Nat Metab. 2025 Aug 05.

Spatial quantitative metabolomics enables identification of remote and sustained ipsilateral cortical metabolic reprogramming after stroke.

Gangqi Wang, Bernard M van den Berg, Sarantos Kostidis, Kelsey Pinkham, Marleen E Jacobs, Arthur Liesz, Martin Giera, Ton J Rabelink.

Mass spectrometry imaging (MSI) has become a cornerstone of spatial biology research. However, various factors that are intrinsic to the technology limit the quantitative capacity of MSI-based spatial metabolomics and thus reliable interpretation. Here we developed an improved quantitative MSI workflow, based on isotopically 13C-labelled yeast extract as internal standards, to overcome these pitfalls. Using brain and kidney tissue, we demonstrate that this approach allows for quantification of more than 200 metabolic features. Applying our workflow to a stroke model allowed us to not only map metabolic remodelling of the infarct and peri-infarct area over time, but also discover hitherto unnoted remote metabolic remodelling in the histologically unaffected ipsilateral sensorimotor cortex. At day 7 post-stroke, increased levels of neuroprotective lysine and reduced excitatory glutamate levels were found when compared with the contralateral cortex. By day 28 post-stroke, lysine and glutamate levels appeared normal, while decreased precursor pools of uridine diphosphate N-acetylglucosamine and linoleate persisted that were previously linked to vulnerability. Importantly, traditional normalization strategies not using internal standards were unable to visualize these differences. Using 13C-labelled yeast extracts as a normalization strategy establishes a paradigm in quantitative MSI-based spatial metabolomics that greatly enhances reliability and interpretive strength.

DOI: https://doi.org/10.1038/s42255-025-01340-8
Res Sq. 2025 Jul 31. pii: rs.3.rs-7179817. [Epub ahead of print]

Deep and Quantitative Proteomic Profiling of Low Volume Mouse Serum Across the Lifespan.

Nathan Basisty, Amit Dey, Bradley Olinger, Mozhgan Boroumand, Maria Fernandez, Slam Investigators, Simonetta Camandola, Nathan Price, Rafael de Cabo.

Assessing and validating circulating biomarkers is essential for the development of pre-clinical biomarkers that predict biological aging and aging-phenotypes in mice. However, comprehensive proteomics of serum, especially in longitudinal mouse studies, are limited by low volumes of samples. In this study, we develop a workflow for comprehensive and quantitative proteomic analysis of low volume mouse serum and demonstrate its utility and performance in identifying and evaluating key associations with aging phenotypes and cellular senescence. Notably, a nanoparticle (NP)-based serum processing workflow coupled to mass spectrometry (MS) increases proteomic coverage by 3 to 6-fold across a range of volumes and provides a quantitative and reproducible (CV < 10%) pipeline for NP-based studies. In a study of 30 mice (aged 12, 24, and 30 months), we uncovered 3992 protein groups across all samples (2235 on average) in 20 µL of serum and highlight novel insights into aging-associated changes in serum and associations with glucose and body composition. With 1 µL additional serum, a 48-cytokine assay quantified 39 additional proteins not identified by MS. This study establishes a powerful workflow that enables deep quantitative proteomics of biologically relevant proteins, including hundreds of senescence-associated proteins, in volumes feasibly obtained from mice (21 µL of serum) and presents fundamental insights into the aging serum proteome.

DOI: https://doi.org/10.21203/rs.3.rs-7179817/v1
Anal Methods. 2025 Aug 06.

Refining nanoflow LC and orbitrap MS data acquisition parameters for pico- and nanogram scale proteomics.

Theodore R Keppel, Yu Zhou, John R Barr, Dongxia Wang.

Researchers need enhanced analytical techniques to profile and characterize tissue and cellular proteomes in studying nanogram scale peptide samples. To meet this demand, nanoflow liquid chromatography (nLC) and mass spectrometry (MS) work has focused on method development, while improvements are made in new cell sorting and isolation instrumentation. In this article, we describe improvements in peptide and protein identifications using simple, cost-effective changes to common mass spectrometry procedures. We focused on procedures that used an Orbitrap instrument to analyze 1 nanogram of peptide material or less. We found protein identifications increased over 40% when applying lowered precursor intensity thresholds in data-dependent selection. We also demonstrate improvements in identifying late-eluting peptides using sample diluents containing n-dodecyl-β-D-maltoside (DDM). We also show lower nLC flow rates can enhance protein identifications over 20%. Finally, we report improvements of 18% in peptide identifications when multiple high-field asymmetric waveform ion mobility spectrometry (FAIMS) compensation voltages (CV) are applied within a single method. These simple modifications provide researchers with options to improve peptide detection in very limited or low concentration samples.

DOI: https://doi.org/10.1039/d5ay00923e
Clin Chem Lab Med. 2025 Aug 08.

Clinical validation of a liquid chromatography single quadrupole mass spectrometry (LC-MS) method using Waters Kairos™ Amino Acid Kit reagents.

Rowan Hellier, Luke Griffiths, Chandra Sundas, Annabel Rodham, Stuart J Moat.

   OBJECTIVES: Quantitation of plasma amino acids (AA) is critical for the diagnosis and monitoring of inherited disorders of AA metabolism. AA analysis using ion-exchange chromatography (IEC) with post-column ninhydrin derivatization is time consuming, with run times of ∼2 h, limiting sample throughput. Liquid chromatography mass-spectrometry can potentially address some of the current challenges.
METHODS: Performance of components of the Waters Kairos Amino Acid Kit using liquid chromatography single quadrupole mass-spectrometry (LC-MS) following derivatization of samples with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AccQ•Tag™ Ultra Derivatization Reagent) was evaluated. Results were compared with the Biochrom-IEC method using patient specimens (n=115), ClinChek® control and external quality assessment (EQA) material.
RESULTS: The kit reagents and our developed method had a 19-min analysis time, demonstrated acceptable inter-assay imprecision (CV<10 %) and bias vs. IEC-method (overall mean bias <2 %). Excellent correlation (concordance correlation coefficient (CCC) >0.99) with IEC was demonstrated for 10/23 analytes, good correlation (CCC >0.95) for 10/23, with the remaining three amino acids (aspartate, histidine and tryptophan) demonstrating moderate concordance (CCC ≥0.90 but <0.95). 1/23 AAs had a mean bias >10 % using EQA material. The method demonstrated a lower limit of quantitation of ≤2.5 μmol/L for all AA, making this assay suitable for CSF analysis. Calibration stability bias was <5 % over 12-weeks. Derivatized AAs were stable for ≤17 days. The analytical column supplied demonstrated good retention time stability (<0.4 %) and was capable of >2000 injections.
CONCLUSIONS: The tested methodology demonstrated good analytical performance and correlation with IEC. This approach confers practical advantages over IEC, including analytical selectivity and workflow time efficiency.

Keywords:  amino acids; derivatization; liquid chromatography; mass spectrometry

DOI:  https://doi.org/10.1515/cclm-2025-0424
Anal Bioanal Chem. 2025 Aug 08.

Leveraging open cheminformatics tools for non-targeted metabolomics analysis of C. elegans: a workflow comparison and application to strains related to xenobiotic metabolism and neurodegeneration.

Gianfranco Frigerio, Yunjia Lai, Emma L Schymanski, Gary W Miller.

  Caenorhabditis elegans (C. elegans) is a well-established nematode model for studying metabolism and neurodegenerative disorders, such as Alzheimer's (AD) and Parkinson's disease (PD). Non-targeted metabolomics via liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has proven useful for uncovering metabolic changes in biological systems. Here, we present workflows for C. elegans metabolomics, leveraging advanced open science tools. We compared two metabolite extraction methods: a monophasic extraction, which provided broader metabolite coverage in analyses conducted in hydrophilic interaction with positive polarity (HILIC POS), and a biphasic extraction, which yielded more features in reverse-phase C18 chromatography with negative polarity (RPLC NEG) analyses. Data were processed using patRoon, integrating IPO, XCMS, CAMERA, and MetFrag, which incorporated PubChemLite compounds and C. elegans-specific metabolites from an expanded WormJam database enhanced with PubChem and literature sources. MS-DIAL was also employed for data processing, allowing for expanded annotations with predicted spectra for the expanded WormJam metabolites calculated using CFM-ID. Significant metabolite differences were identified when comparing the Bristol (N2) wild-type strain with two knockout strains of xenobiotic-metabolizing enzymes and two transgenic strains related to neurodegenerative pathways. Pooled quality control (QC) samples for each strain ensured robust data quality and the detection of strain-related metabolites. Our study demonstrates the potential of non-targeted metabolomics for metabolite discovery employing open science tools in model organisms.

Keywords:  CYP enzyme mutant; Exposomics; FMO enzyme mutant; SV2C expression; Tau aggregation; Untargeted metabolomics

DOI:  https://doi.org/10.1007/s00216-025-06048-y
J Am Soc Mass Spectrom. 2025 Aug 06.

Integrating Electrostatic Adsorption-Laser Desorption/Ionization-MS with a Derivative Full-Spectrum Internal Standard Approach for Acylcarnitine Quantification in Clinical Samples.

Shuo Cao, Xinrong Jiang, Xiaoming Chen, Xuetong Qu, Jianmin Wu.

Acylcarnitines (ACs), essential intermediates in fatty acid β-oxidation, are increasingly recognized as biomarkers for inborn errors of metabolism (IEM), insulin resistance, heart failure, and neurodegenerative diseases. However, conventional liquid chromatography-mass spectrometry (LC-MS) and flow injection-mass spectrometry (FI-MS) methods are hindered by complex sample preparation, limited throughput, and matrix interferences. Meanwhile, matrix-assisted laser desorption/ionization-MS (MALDI-MS) suffers from low-mass background noise and poor reproducibility. Here, we present a dual-innovation mass spectrometry platform combining a substrate composed of sulfonic acid-gold nanoparticle-decorated silicon nanowires (Sulfo-Au-SiNWs) for electrostatic adsorption-laser desorption/ionization (EALDI) and a full-spectrum internal standard (FS IS) strategy based on d6-ethanol derivatization. The optimized Sulfo-Au-SiNWs offer uniform crystallinity, in situ electrostatic enrichment, efficient thermal desorption, and gold-mediated charge-hole separation, resulting in a ∼300% increase in AC signal intensity. This enabled the selective detection of over ten medium- and long-chain ACs that were previously undetectable using unmodified SiNWs. The FS IS method incorporates over 30 derivatized AC species, enabling simultaneous quantification with detection limits below 0.01 μmol/L, surpassing the sensitivity of traditional FI-MS. The platform delivers spike recovery rates of 96-112% and supports high-throughput analysis, processing each sample within seconds. Validation against FI-ESI-MS/MS in both urine and dried blood spot samples demonstrated excellent agreement (ρ > 0.95) and effectively identified IEM patient profiles via statistically significant AC elevations. With strong intra- and interbatch precision (median RSD < 12.5%) and substrate stability exceeding one month, this EALDI-MS/FS IS workflow provides a high-throughput, high-coverage, and quantitatively robust solution for clinical AC profiling and newborn screening.

DOI: https://doi.org/10.1021/jasms.5c00136
Nat Chem. 2025 Aug 06.

Quantitative profiling of lipid transport between organelles enabled by subcellular photocatalytic labelling.

Xi Chen, Ru He, Haolin Xiong, Ruohong Wang, Yandong Yin, Yiyun Chen, Zheng-Jiang Zhu.

Subcellular lipid composition and transport substantially influence the physiological and pathological functions of both cells and organelles. However, lipid transport and turnover between organelles remain poorly understood due to a lack of methods for selectively labelling lipids in organelles. Here we develop a subcellular photocatalytic labelling strategy that enables organelle-selective lipid analysis by mass spectrometry and the quantitative profiling of lipid transport between organelles. We use this approach to quantitatively characterize fatty-acyl-dependent transport of phosphatidylethanolamine and phosphatidylserine lipids between the endoplasmic reticulum and mitochondria, the nucleus or lysosomes. Further experiments revealed the relative contributions of various biosynthesis pathways to the phosphatidylethanolamine and phosphatidylserine lipid compositions in the mitochondria, nucleus and lysosomes. Lysosome-specific photocatalytic labelling revealed the impact of the mTOR kinase pathway on lysosomal lipid metabolism. Together, this subcellularly localized photocatalytic labelling of lipids quantitatively deciphers the subcellular lipid composition and transport, enhancing our understanding of lipid metabolism in living organisms.

DOI: https://doi.org/10.1038/s41557-025-01886-w
Anal Chem. 2025 Aug 05.

mzrtsim: Raw Data Simulation for Reproducible Gas/Liquid Chromatography-Mass Spectrometry-Based Nontargeted Metabolomics Data Analysis.

Miao Yu, Vivek Philip.

Reproducibility of data analysis is pivotal in the context of nontargeted metabolomics based on mass spectrometry coupled with chromatography. While various algorithms have been proposed for feature or peak extraction, their validation often revolves around a limited set of known compounds or standards. While data simulation is widely used in other omics studies, simulations are focused on the feature level, neglecting uncertainties inherent in the feature or peak extraction process for metabolomics mass spectrometry data. In this technique note, we introduce an R package called "mzrtsim"' to simulate gas/liquid chromatography full scan raw data in the mzML format. Unlike simulations solely based on virtual features, our approach leverages experimental spectral data from MassBank of North America (MoNA) and the human metabolome database (HMDB). We developed algorithms to simulate chromatographic peaks, accounting for the tailing factor. The results of our study demonstrate the potential of this tool for comparing established metabolomics software (e.g., XCMS, mzMine, and OpenMS) against ground truth. We found that the investigated software introduced false positive peaks and/or loss of compounds with fewer peaks. They also showed different sensitivity to the tailing and leading peaks. This R package is free and available online (https://github.com/yufree/mzrtsim).

DOI: https://doi.org/10.1021/acs.analchem.5c01213
Adv Exp Med Biol. 2025 Aug 09.

Insect Lipidomics: Advances, Applications, and Physiological Insights.

Laura Palanker Musselman, Doga Cedden, Gözde Güney, Umut Toprak.

  Lipidomics, a specialized branch of metabolomics, investigates the diversity and functionality of lipids in biological systems. Lipids serve crucial roles in energy storage, membrane composition, and environmental acclimation in insects, underpinning processes such as development and stress responses. Advances in analytical technologies, such as liquid chromatography-mass spectrometry (LC-MS), have enabled precise identification and quantification of lipid species, providing unprecedented insights into lipid metabolism and dynamics. Key lipid classes, including triacylglycerols and phospholipids, exhibit structural and functional versatility, adapting to environmental pressures through mechanisms like homeoviscous adaptation. These dynamic lipid responses are essential for maintaining cellular and cuticular integrity and functionality under stress. By exploring lipid diversity and adaptations, lipidomics offers valuable perspectives on insect physiology, survival strategies, and evolutionary ecology. This chapter summarizes methods used to study insect lipidomes and highlights comparative lipidomic studies that have advanced our understanding of insect biology.

Keywords:  Cuticular hydrocarbons; Diapause; Ecdysteroids; Insect lipidomics; Mass spectrometry

DOI:  https://doi.org/10.1007/5584_2025_878
bioRxiv. 2025 Jul 30. pii: 2025.07.23.666381. [Epub ahead of print]

Integral Synthesis and Clearance Analysis via DIA (ISDia) Reveals Coordinated Protein Dynamics Regulation during Endoplasmic Reticulum Stress.

Yue Dou, Danny Qiu, Vivien Li, Maja E Wierzbińska, Gregory R Keele, Wenpeng Liu, Jie Yang, Joao A Paulo, Ling Qi, Tian Zhang.

Endoplasmic Reticulum (ER) stress disrupts protein homeostasis and impacts protein dynamics, driving cellular responses critical for survival, development and disease. However, no current proteome-wide technology enables simultaneous identification of proteins undergoing altered synthesis and clearance and distinguish their relative contribution during ER stress. To fill this gap, we developed Integral Synthesis and clearance analysis via DIA (ISDia), a robust mass spectrometry-based platform that integrates pulsed-SILAC labeling with data-independent acquisition (DIA) to quantify heavy and light peptide changes and determine the drivers of protein dynamics with high proteome coverage under non-steady-state conditions. Using ISDia, we uncover diverse regulatory mechanisms by which protein synthesis and clearance are modulated to control protein abundances during ER stress, revealing PERK dependent and independent regulatory mechanisms across subcellular compartments, complexes and isoforms. These findings highlight the potential of ISDia as a powerful and widely applicable platform for elucidating protein dynamic regulatory mechanisms.

DOI: https://doi.org/10.1101/2025.07.23.666381
bioRxiv. 2025 Jul 24. pii: 2025.07.23.666448. [Epub ahead of print]

Nitrogen metabolism profiling reveals cell state-specific pyrimidine synthesis pathway choice.

Milan R Savani, Bailey C Smith, Wen Gu, Yi Xiao, Gerard Baquer, Bingbing Li, Skyler S Oken, Namya Manoj, Lauren G Zacharias, Vinesh T Puliyappadamba, Sylwia A Stopka, Michael S Regan, Michael M Levitt, Charles K Edgar, William H Hicks, Soummitra Anand, Tracey Shipman, Misty S Martin-Sandoval, Rainah Winston, João S Patrício, Xandria Johnson, Trevor S Tippetts, Diana D Shi, Andrew Lemoff, Timothy E Richardson, Pascal O Zinn, Ashley Solmonson, Thomas P Mathews, Nathalie Y R Agar, Ralph J DeBerardinis, Kalil G Abdullah, Samuel K McBrayer.

Conventional stable isotope tracing assays track one or several metabolites. However, cells use an array of nutrients to sustain nitrogen metabolic pathways. This incongruency hampers a system level understanding of cellular nitrogen metabolism. Therefore, we created a platform to simultaneously trace 30 nitrogen isotope-labeled metabolites. This platform revealed that while primitive cells engage both de novo and salvage pyrimidine synthesis pathways, differentiated cells nearly exclusively salvage uridine despite expressing de novo pathway enzymes. This link between cell state and pyrimidine synthesis routes persisted in physiological contexts, including primary murine and human tissues and tumor xenografts. Mechanistically, we found that Ser1900 phosphorylation of CAD, the first enzyme of the de novo pathway, was enriched in primitive cells and that mimicking this modification in differentiated cells abrogated their preference for pyrimidine salvage. Collectively, we establish a method for nitrogen metabolism profiling and define a mechanism of cell state-specific pyrimidine synthesis pathway choice.

DOI: https://doi.org/10.1101/2025.07.23.666448
Anal Chem. 2025 Aug 05.

Determination of Double Bond Positions in Unsaturated Fatty Acids using Permanganate Oxidation and Tandem Mass Spectrometry of CuIICl+ Adducted Ions.

Danna Hu, T-Y Lui, Xiangfeng Chen, T-W Dominic Chan.

Positional characterization of C═C bonds is crucial for elucidating the structure of unsaturated fatty acids (UFAs). Chemical derivatization of C═C bonds prior to tandem mass spectrometry (MS/MS) has proven to be an effective approach for producing diagnostic fragments that enable precise determination of C═C locations. This study employed neutral potassium permanganate to convert C═C bonds of unsaturated fatty acids into α-hydroxy ketones, which were subsequently analyzed by low-energy collision-induced dissociation (CID). Negative-ion mode CID of deprotonated mono-oxidized unsaturated fatty acids generated diagnostic fragment ions alongside abundant false-positive peaks. Various metal ions (including alkali and transition metals) were then evaluated as charge carriers for mono-oxidized unsaturated fatty acids. Notably, positive-ion mode CID of CuIICl+ adducted ions, [(M+2O)+CuIICl]+, produced abundant diagnostic fragments for identifying C═C bond positions in all tested fatty acids with varying degrees of unsaturation (conjugated/unconjugated C═C bonds), without any interference from false-positive fragments. This method was also successfully applied to the analysis of total UFAs in human plasma, providing information on the number and position(s) of carbon-carbon double bond(s).

DOI: https://doi.org/10.1021/acs.analchem.5c01992