bims-mascan 2025-06-15 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–06–15
33 papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Evaluation of a prototype Orbitrap Astral Zoom mass spectrometer for quantitative proteomics - Beyond identification lists.
Nontargeted Metabolomics Using the Sciex ZenoTOF 7600.
Untargeted Metabolomic Analysis in the Short-Lived Killifish Using HILIC Ultra Performance Liquid Chromatography Method Coupled to a Q Exactive Orbitrap Mass Spectrometer.
Enhancing Sensitivity in Low-Load Proteomics Orbitrap Workflows via SLIM Integration.
Revealing the Metabolome: Metabolite Workflow for Retinal Ganglion Cells.
Increasing mass spectrometry throughput using time-encoded sample multiplexing.
Comparison of Untargeted LC-MS/MS Metabolomics Data Processing Through Compound Discoverer and MS-DIAL.
Isotopic Ratio Outlier Analysis (IROA) for Quantitative Analysis.
Data-Independent Acquisition Parallel Accumulation-Serial Fragmentation (diaPASEF) Analysis of the Separated Zebrafish Lens Improves Identifications.
The microbiome diversifies long- to short-chain fatty acid-derived N-acyl lipids.
Chromatographic Choice in Cerebrospinal Fluid Metabolomics Identification Outcome : Metabolomics for Cerebrospinal Fluid.
Metabolic Pathway Tracing for NAD+ Synthesis and Consumption.
Untargeted Metabolomics of Human Embryonic Kidney (HEK) Cell Line.
Improved quantitative accuracy in data-independent acquisition proteomics via retention time boundary imputation.
Correlative Analysis of Amino Acids Sites for Structure Elucidation with Covalent Protein Painting.
Cytokine profiling in COVID-19 patients by using ion mobility-mass spectrometry-based parallel reaction monitoring.
In-cell Proteomics Enables High-Resolution Spatial and Temporal Mapping of Early Xenopus tropicalis Embryos.
Enrichment analysis for spatial and single-cell metabolomics accounting for molecular ambiguity.
Integrating Alternative Fragmentation Techniques into Standard LC-MS Workflows Using a Single Deep Learning Model Enhances Proteome Coverage.
A fully automated, high-throughput electro-extraction and analysis workflow for acylcarnitines in human plasma and mouse muscle tissues.
Analytical quality control in targeted lipidomics: Evaluating the performance of commercial plasma as a surrogate for pooled study samples.
Polyamine Metabolism in Optic Nerve: Spatial Metabolomics with AP/MALDI Mass Spectrometry Imaging.
Spatial Atmospheric Pressure MALDI HRMS Analysis of Histological Sections of FFPE Samples Reveals a Unique Subset of the Metabolome.
DANGO: An MS data annotation tool for glycolipidomics.
Epigenetic roles of lysine-specific demethylase 1 (LSD1) in cancer metabolism.
Glycosaminoglycan-driven lipoprotein uptake protects tumours from ferroptosis.
NMR Metabolomics in Livestock: An Update of Current Methods and Applications.
Federated deep learning enables cancer subtyping by proteomics.
Quantification of galacturonic acid in pectin-containing samples using a stable isotope dilution approach and LC-MS.
Reproducible Sectioning and Sample Preparation for Mass Spectrometry Imaging of Cancer Metabolism in Frozen Lung Tissue.
Multi-Omic Profiling Identifies Conserved Metabolic Pathways Critical for SARS-CoV-2 Variants Infection.
Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform.
Direct Colony Atmospheric Pressure Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Assay for Targeted Operon Metabolite Analysis.

bioRxiv. 2025 Jun 02. pii: 2025.05.30.657132. [Epub ahead of print]

Evaluation of a prototype Orbitrap Astral Zoom mass spectrometer for quantitative proteomics - Beyond identification lists.

Chris Hsu, Nicholas Shulman, Hamish Stewart, Johannes Petzoldt, Anna Pashkova, Deanna Plubell, Eduard Denisov, Bernd Hagedorn, Eugen Damoc, Brendan X MacLean, Philip Remes, Jesse Canterbury, Alexander Makarov, Christian Hock, Vlad Zabrouskov, Christine C Wu, Michael J MacCoss.

Mass spectrometry instrumentation continues to evolve rapidly, yet quantifying these advances beyond conventional peptide and protein detections remains challenging. Here, we evaluate a modified Orbitrap Astral Zoom mass spectrometer (MS) prototype and compare its performance to the standard Orbitrap Astral MS. Across a range of acquisition methods and sample inputs, the prototype instrument outperformed the standard Orbitrap Astral MS in precursor and protein identifications, ion accumulation efficiency, and reproducibility of measurements. To enable meaningful cross-platform comparisons, we implemented an ion calibration framework that converts signal intensity from arbitrary units to absolute ion counts (ions/sec). This benchmarking strategy showed that the prototype sampled 30% more ions per peptide than the original Orbitrap Astral MS. This increase in the ion beam utilization resulted in improved sensitivity and quantitative precision. To make these metrics broadly accessible, we added new metrics to the Skyline document grid to report ion counts directly from data-independent acquisition (DIA) data. Taken together, our results demonstrate the Orbitrap Astral Zoom prototype as a high-performance platform for DIA proteomics and establish a generalizable framework for evaluation of mass spectrometer performance based on the number of ions detected for each analyte.

DOI: https://doi.org/10.1101/2025.05.30.657132
Methods Mol Biol. 2025 ;2925 1-23

Nontargeted Metabolomics Using the Sciex ZenoTOF 7600.

Anna Artati, Pauline Couacault, Michael Witting.

  Nontargeted metabolomics aims to capture as many metabolites as possible present in a biological system under a certain condition. Despite several analytical techniques commonly applied for nontargeted metabolomics, liquid chromatography coupled to high-resolution tandem mass spectrometry (LC-HRMS/MS) is one of the most employed techniques. To cover broad metabolic classes, different chromatographic separation techniques are implemented, i.e., reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC) techniques. Each method offers a complementary polarity range. While RP covers semi- to nonpolar metabolites, such as fatty acids, acylcarnitines, steroids, bile acids, etc., HILIC allows the analysis of polar metabolites, such as amino acids, amines, and organic acids.Nontargeted metabolomics platforms at Helmholtz Munich have been validated for various biological matrices, including biological liquids (plasma, serum, urine), tissues (liver, heart), and cell samples of human and rodent. Analysis is based on the Sciex ZenoTOF 7600 and three chromatographic separation methods, which can be used individually or in combination.

Keywords:  Hydrophilic interaction liquid chromatography (HILIC); LC-HRMS/MS; Metabolomics; Non-targeted; Reversed phase (RP)

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_1
Methods Mol Biol. 2025 ;2925 397-405

Untargeted Metabolomic Analysis in the Short-Lived Killifish Using HILIC Ultra Performance Liquid Chromatography Method Coupled to a Q Exactive Orbitrap Mass Spectrometer.

Isabella G Moceri, Selene Cabrera, Jackson Watson, Pieter-Jan Serneels, Julie De Schutter, Lieve Moons, Sanjoy K Bhattacharya.

  Over the past decade, advancements in liquid chromatography-mass spectrometry (LC-MS) have transformed untargeted metabolomic analysis. These improvements have led to biomarker discoveries, enabling scientists to deepen their understanding of biological systems in disease contexts. Achieving reproducibility, robustness, and reliable quantification is essential for confident metabolite identification. In this chapter, we present an untargeted metabolomic approach to analyze metabolites in the optic nerve tissue of adult Nothobranchius furzeri, an established biogerontology model that is increasingly used in tissue repair studies. The analysis is conducted using HILIC chromatography coupled with a Q Exactive Orbitrap mass spectrometer.

Keywords:  HILIC; Killifish; Q Exactive Orbitrap; Ultra-performance liquid chromatography-mass spectrometry; Untargeted metabolomics

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_26
Anal Chem. 2025 Jun 11.

Enhancing Sensitivity in Low-Load Proteomics Orbitrap Workflows via SLIM Integration.

Liulin Deng, Brian Adamson, Alan McKenzie-Coe, Kyle L Fort, Tobias P Wörner, Oliver Bernhardt, Roland Bruderer, Tejas Gandhi, Lukas Reiter, Eloy R Wouters, Jean-Jacques Dunyach, Alexander Makarov, Daniel DeBord.

A Structures for Lossless Ion Manipulation-Orbitrap Exploris 480 (SLIM-OE) ion mobility mass spectrometry (IM-MS) platform was developed, integrating SLIM IM separation with Orbitrap MS analysis. A "staggered IMS" mode was designed to acquire IM-m/z two-dimensional heatmap data via direct infusion for method development, and an LC compatible SLIM-enabled data-independent acquisition (SLIM-DIA) workflow was implemented for low sample load proteomics analysis. The study evaluated IM separation across a broad m/z range of ions and demonstrated up to a 190× sensitivity enhancement in both IM-MS and IM-MS/MS modes of operation relative to operation in MS and MS/MS modes, respectively. The increased sensitivity improved protein coverage for the Qual/Quant QC Mix proteins. Compared to standard DIA, SLIM-DIA achieved a 2.3× increase in protein group identification from 2 ng of HeLa on the modified instrument, while maintaining quantitative capabilities. This research highlights the potential of the SLIM-OE IM-MS system to enhance proteomics analysis, providing a foundation for future high-performance SLIM-Orbitrap instrumentation development.

DOI: https://doi.org/10.1021/acs.analchem.5c00897
Methods Mol Biol. 2025 ;2925 271-288

Revealing the Metabolome: Metabolite Workflow for Retinal Ganglion Cells.

Alejandra Hernandez, Sofia Yarosh, Amr Almobayed, Sanjoy K Bhattacharya.

  In this chapter, we outline the preparation and isolation of retinal ganglion cells (RGCs) from postnatal mice. This includes a metabolite extraction method using 1:1 methanol/water from 6 or 12-well plate dishes. We provide liquid chromatography-mass spectrometry (LC-MS) parameters using a QExactive High Resolution Orbitrap Mass Spectrometer and HILIC column. The software used for analysis includes Compound Discoverer and MetaboAnalyst. Through this metabolic workflow, this protocol provides a biomarker discovery, allowing for future experimentation to treat and cure ocular neuropathies.

Keywords:  Axon regeneration; Compound discoverer; Extraction; Mass spectrometry; MetaboAnalyst; Metabolomic profiling; Metabolomics; Retinal ganglion cells

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_20
bioRxiv. 2025 May 27. pii: 2025.05.22.655515. [Epub ahead of print]

Increasing mass spectrometry throughput using time-encoded sample multiplexing.

Jason Derks, Kevin McDonnell, Nathan Wamsley, Peyton Stewart, Maddy Yeh, Harrison Specht, Nikolai Slavov.

Liquid chromatography-mass spectrometry (LC-MS) can enable precise and accurate quantification of analytes at high-sensitivity, but the rate at which samples can be analyzed remains limiting. Throughput can be increased by multiplexing samples in the mass domain with plexDIA, yet multiplexing along one dimension will only linearly scale throughput with plex. To enable combinatorial-scaling of proteomics throughput we developed a complementary multiplexing strategy in the time domain, termed `timePlex'. timePlex staggers and overlaps the separation periods of individual samples. This strategy is orthogonal to isotopic multiplexing, which enables combinatorial multiplexing in mass and time domains when paired together and thus multiplicatively increased throughput. We demonstrate this with 3-timePlex and 3-plexDIA, enabling the multiplexing of 9 samples per LC-MS run, and 3-timePlex and 9-plexDIA exceeding 500 samples / day with a combinatorial 27-plex. Crucially, timePlex supports sensitive analyses, including of single cells. These results establish timePlex as a methodology for label-free multiplexing and for combinatorially scaling the throughput of LC-MS proteomics. We project this combined approach will eventually enable an increase in throughput exceeding 1,000 samples / day.

DOI: https://doi.org/10.1101/2025.05.22.655515
Methods Mol Biol. 2025 ;2925 113-120

Comparison of Untargeted LC-MS/MS Metabolomics Data Processing Through Compound Discoverer and MS-DIAL.

Michael Coronado, Anh Pham, Sanjoy K Bhattacharya.

  Untargeted metabolomics is a technique used to detect the presence of all metabolites, or small-molecular-weight molecules, within a given sample. This technique is often accomplished through the use of liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). LC-MS/MS provides raw data that require the use of bioinformatics software to extract significant features. Many bioinformatics software options are available, such as Compound Discoverer and MS-DIAL. This protocol details the processing of raw data through both software for functional comparison and optimization.

Keywords:  Compound discoverer; Liquid chromatography; MS-DIAL; Mass spectrometry; Metabolomics

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_7
Methods Mol Biol. 2025 ;2925 69-89

Isotopic Ratio Outlier Analysis (IROA) for Quantitative Analysis.

Chris Beecher, Felice A de Jong.

  Metabolomics is an omics science that tries to measure the metabolic composition of a sample rapidly, but the current state of the art cannot provide the reproducibility or accuracy needed for clinical use or even daily reproducibility for larger experiments. The IROA TruQuant Workflow uses a daily long-term reference standard (LTRS) and a chemically identical Internal Standard (IS) to provide validated chemical identity, accurate reproducible quantitation, and daily QA/QC (Quality Assurance / Quality Control) on instrument and sample preparation. It does this for hundreds of compounds, generating comparable measurements across days, instruments, and chromatographic methods. All the compounds in the LTRS are isotopically signed with formula-indicating IROA patterns, ensuring they cannot be mistaken as artifacts. These patterns allow software-driven analysis to determine daily instrument performance in terms of sensitivity, in-source fragmentation, chromatographic and injection stability, and to provide reproducible quantitation.

Keywords:  Clinical metabolomics; Dual-MSTUS Normalization; Error-corrected quantitation; IROA TruQuant Workflow; Isotopic ratio outlier analysis; MS error correction; Metabolic profiling; Metabolomics Internal Standard; Metabolomics normalization; Suppression correction

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_4
J Am Soc Mass Spectrom. 2025 Jun 09.

Data-Independent Acquisition Parallel Accumulation-Serial Fragmentation (diaPASEF) Analysis of the Separated Zebrafish Lens Improves Identifications.

Sarah R Zelle, W Hayes McDonald, Kristie L Rose, Hassane S Mchaourab, Kevin L Schey.

  Ocular lens fiber cells degrade their organelles during differentiation to prevent light scattering. Organelle degradation occurs continuously throughout an individual's lifespan, creating a spatial gradient of young cortical fiber cells in the lens periphery to older nuclear fiber cells in the center of the lens. Therefore, separation of cortical and nuclear regions enables examination of protein aging. Previously, the human lens cortex and nucleus have been studied using data-independent acquisition (DIA) proteomics, allowing for the identification of low-abundance protein groups. In this study, we employed data-independent acquisition parallel accumulation-serial fragmentation (diaPASEF) proteomics on a timsTOF HT instrument to study the zebrafish lens proteome and compared results to a standard DIA method employed on an Orbitrap Exploris 480 mass spectrometer. Using the additional ion mobility gas phase separation of diaPASEF, peptide and protein group identifications increased by over 200% relative to an Orbitrap DIA method in the zebrafish lens. With diaPASEF, we identified 13,721 and 11,996 unique peptides in the cortex and nucleus of the zebrafish lens, respectively, which correspond to 1,537 and 1,389 protein groups. Thus, separation of the zebrafish lens into cortical and nuclear regions followed by diaPASEF analysis produced the most comprehensive zebrafish lens proteomic data set to date.

Keywords:  DIA; aging; diaPASEF; lens; proteomics; zebrafish

DOI:  https://doi.org/10.1021/jasms.5c00087
Cell. 2025 Jun 09. pii: S0092-8674(25)00565-3. [Epub ahead of print]

The microbiome diversifies long- to short-chain fatty acid-derived N-acyl lipids.

Helena Mannochio-Russo, Vincent Charron-Lamoureux, Martijn van Faassen, Santosh Lamichhane, Wilhan D Gonçalves Nunes, Victoria Deleray, Adriana V Ayala, Yuichiro Tanaka, Abubaker Patan, Kyle Vittali, Prajit Rajkumar, Yasin El Abiead, Haoqi Nina Zhao, Paulo Wender Portal Gomes, Ipsita Mohanty, Carlynda Lee, Aidan Sund, Meera Sharma, Yuanhao Liu, David Pattynama, Gregory T Walker, Grant J Norton, Lora Khatib, Mohammadsobhan S Andalibi, Crystal X Wang, Ronald J Ellis, David J Moore, Jennifer E Iudicello, Donald Franklin, Scott Letendre, Loryn Chin, Corinn Walker, Simone Renwick, Jasmine Zemlin, Michael J Meehan, Xinyang Song, Dennis Kasper, Zachary Burcham, Jane J Kim, Sejal Kadakia, Manuela Raffatellu, Lars Bode, Hiutung Chu, Karsten Zengler, Mingxun Wang, Dionicio Siegel, Rob Knight, Pieter C Dorrestein.

  N-Acyl lipids are important mediators of several biological processes including immune function and stress response. To enhance the detection of N-acyl lipids with untargeted mass spectrometry-based metabolomics, we created a reference spectral library retrieving N-acyl lipid patterns from 2,700 public datasets, identifying 851 N-acyl lipids that were detected 356,542 times. 777 are not documented in lipid structural databases, with 18% of these derived from short-chain fatty acids and found in the digestive tract and other organs. Their levels varied with diet and microbial colonization and in people living with diabetes. We used the library to link microbial N-acyl lipids, including histamine and polyamine conjugates, to HIV status and cognitive impairment. This resource will enhance the annotation of these compounds in future studies to further the understanding of their roles in health and disease and to highlight the value of large-scale untargeted metabolomics data for metabolite discovery.

Keywords:  HIV; MASST; MassQL; N-acyl lipids; SCFA; metabolomics data mining; microbial; neurocognitive impairment; repository-scale analysis; short-chain fatty acids

DOI:  https://doi.org/10.1016/j.cell.2025.05.015
Methods Mol Biol. 2025 ;2925 91-101

Chromatographic Choice in Cerebrospinal Fluid Metabolomics Identification Outcome : Metabolomics for Cerebrospinal Fluid.

Sofía C Ayala Rodríguez, Alyssa M Suarez, Victor M Lu, Sanjoy K Bhattacharya, Ashish H Shah.

  Limited literature on the optimal chromatographic method for cerebrospinal fluid (CSF) extraction and identification is available. Ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) is an extensively used tool in metabolic profiling because of its high resolving power, analysis sensitivity, and selectivity. This technique allows for reliable structural elucidation for metabolite identification and quantification at low micromolar to nanomolar concentration levels. In this book chapter, we discuss two complementary UPLC-MS methods, reversed-phase (RP) and chromatography hydrophilic interaction liquid chromatography (HILIC), to characterize metabolomic analysis of CSF samples.

Keywords:  Cerebrospinal fluid; Hydrophilic interaction liquid chromatography; Metabolomics; Reversed-phase C18; Ultraperformance liquid chromatography-mass spectrometry

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_5
Methods Mol Biol. 2025 ;2925 203-222

Metabolic Pathway Tracing for NAD+ Synthesis and Consumption.

Tumpa Dutta, Stephen J Gardell.

  NAD+ is an abundant cellular metabolite which plays vital roles in central metabolism while serving as a cofactor for oxidoreductases and cosubstrate for sirtuins and poly(ADP-ribose)polymerases (PARPs). Decreased tissue NAD+ levels have been linked to aging-associated metabolic decline and a host of chronic diseases. Cellular steady-state NAD+ levels are governed by contemporaneous synthetic and consumptive processes. Hence, lower NAD+ levels in aged tissues can arise from decreased synthesis or increased consumption. A static snapshot of the tissue levels of NAD+ is inadequate for assessing the highly dynamic pathway network which mediates NAD+ synthesis and consumption. Metabolic pathway tracing with stable isotope-labeled NAD+ precursors (e.g., nicotinamide (NAM), nicotinic acid (NA), tryptophan) and high-resolution mass spectrometry (HRMS) can unveil the individual contributions of synthesis and consumption to the steady-state NAD+ concentration. The metabolic fate of the NAD+ precursor can also be traced to metabolic products of NAD+ including NADH, NADP, and NADPH as well as intermediates in the various NAD+ biosynthetic pathways. Metabolic tracing of NAD+ synthesis and degradation as well as conversion of NAD+ to its downstream products is a highly versatile technique. It can be used to interrogate isolated cells, tissues slices, or specimens collected from preclinical or clinical in vivo studies (e.g., blood, urine, tissues). Bold claims about the pivotal role of NAD+ in human health and disease are typically fraught with uncertainty due to an incomplete understanding of NAD+ metabolism. Insight gleaned from metabolic pathway tracing can shed important new light on NAD+ metabolism and help to critically evaluate the intriguing link between cellular NAD+ levels and healthy aging.

Keywords:  Mass isotopomer distribution profiling; Mass spectrometry; NAD+ consumption; NAD+ flux; NAD+ metabolism; NAD+ synthesis; Stable isotope tracing

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_14
Methods Mol Biol. 2025 ;2925 297-308

Untargeted Metabolomics of Human Embryonic Kidney (HEK) Cell Line.

Xing Du, Claudia Gomez, Susanna Li, Emily Dorairaj, Samyak Jain, Sanjoy K Bhattacharya.

  Human embryonic kidney (HEK) cells' low maintenance and quick proliferation make it an ideal target to begin cellular research. Current literature on the metabolomic changes in the fatty acids synthesis (FAS) process is sparse. Thus, inhibition of the pathway in extensively researched HEK cells would provide further insight into upregulation and downregulation of metabolites. Liquid chromatography-mass spectrometry allows for highly sensitive quantification and qualification to provide a comprehensive understanding of the metabolomic changes that can be applied to specialized cell types.

Keywords:  Fatty acids synthesis; HEK cells; Liquid chromatography

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_22
bioRxiv. 2025 May 31. pii: 2025.05.27.656394. [Epub ahead of print]

Improved quantitative accuracy in data-independent acquisition proteomics via retention time boundary imputation.

Lincoln Harris, Michael Riffle, William Stafford Noble, Michael J MacCoss.

The traditional approaches to handling missing values in DIA proteomics are to either remove high-missingness proteins or impute them with statistical procedures. Both have their disadvantages-removal can limit statistical power, while imputation can introduce spurious correlations or dilute signal. We present an alternative approach based on imputing peptide retention times (RTs) rather than quantitations. For each missing value, we impute the RT boundaries, then obtain a quantitation by integrating the chromatographic signal within the imputed boundaries. Our method yields more accurate quantitations than existing proteomics imputation methods. RT boundary imputation also identifies differentially abundant peptides from key Alzheimer's genes that were not identified with library search alone. RT boundary imputation improves the ability to estimate radiation exposure in biological tissues. RT boundary imputation significantly increases the number of peptides with quantitations, leading to increases in statistical power. Finally, RT boundary imputation better quantifies low abundance peptides than library search alone. Our RT boundary imputation method, called Nettle, is available as a standalone tool.

DOI: https://doi.org/10.1101/2025.05.27.656394
J Proteome Res. 2025 Jun 13.

Correlative Analysis of Amino Acids Sites for Structure Elucidation with Covalent Protein Painting.

Casimir Bamberger, Titus Jung, Patrick Garrett, John R Yates.

  Modern, quantitative proteome biology relies on bottom-up mass spectrometry-based quantification techniques. Current proteomic methods quantify proteins and compare sample conditions with either isotope-defined metabolic or chemical labels that modify select amino acids in the proteome. Covalent modification of proteins with isotope-defined reagents enables protein footprinting techniques to quantify site-specific conformational information, such as the solvent exposure of amino acids on the surface of proteins. However, the current analysis of chemical protein footprinting experiments like covalent protein painting (CPP) misses quantifying a large proportion of peptides because of ambiguities in the position of the label in case more than one amino acid is modified in the peptide. Here, we developed a mass spectrometry-based approach to deconvolute and quantify the relative mass modifications of two lysine sites in the same peptide. We determined at which lysine site the modification is located on the basis of the fragment ion quantification of the isobaric isotopologues. The quantification approach retained the correlative information on the solvent accessibility between the two lysine sites. The new approach increased the overall quantification efficiency by 15% in a large data set comprising 60 different cancer cell lines. This gain in structural information indicated that one conformational state of the protein nucleolin was present in 10 out of 60 cancer cell lines. In summary, deconvoluting the chemical protein footprinting information on peptides with two modified amino acids afforded higher proteome coverage and finer-grained insights into protein structural information.

Keywords:  3D proteomics; MS/MS-based quantification; bottom-up proteomics; cellular protein conformation; protein conformation; protein-biomolecule interaction; protein−protein interaction; quantitative proteomics; structural proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.5c00072
Anal Chim Acta. 2025 Aug 22. pii: S0003-2670(25)00621-X. [Epub ahead of print]1364 344227

Cytokine profiling in COVID-19 patients by using ion mobility-mass spectrometry-based parallel reaction monitoring.

Ling Yan, Chenrui Yuan, Runhou Zhou, Li Zhong, Kelvin Kai-Wang To, Na Liu, Xin Diao, Guangshan Xie, Hongzhi Zhao, Haijiang Wu, Lin Zhu, Zhiwei Chen, Zongwei Cai.

  Cytokine therapy, a non-antigen-specific strategy, has led to several FDA-approved drugs. Given the role of dysregulated cytokine expression in diseases such as COVID-19, accurate quantification is critical in both clinical and research settings. While antibody-based assays offer high sensitivity, their reliance on specific antibodies limits multiplexing and increases analytical complexity. Conversely, mass spectrometry methods like multiplexed reaction monitoring provide higher throughput but lack the sensitivity to detect physiological cytokine levels and the resolution to distinguish structural isomers. Thus, a new MS-based approach is needed that integrates high sensitivity with the ability to resolve structurally similar cytokines. We developed an ion mobility-mass spectrometry (IM-MS)-based parallel reaction monitoring (PRM) method to establish the first Cytokine Ion Mobility Peptide (CIMP) databank and enable high-throughput cytokine profiling in serum samples from COVID-19 patients. By introducing ion mobility as an additional gas-phase separation dimension alongside liquid chromatography, the method enhances analyte resolution based on structural differences, facilitating the separation of isomers within the ion mobility trap. The incorporation of ion mobility as a complementary separation parameter enables the distinction of homologous cytokines and structural isomers (e.g., IFNA1/IFNA2, IFNL1/IFNL3, and peptide isomers), which remains challenging for conventional antibody-based assays. The method achieved a limit of detection of 62.9 fmol/L and a limit of quantification of 210 fmol/L across 31 cytokines, demonstrating greater sensitivity than traditional multiple reaction monitoring (MRM) approaches and enabling quantification at physiological concentration levels, assuming comparable background signal across platforms. The IM-MS-PRM method offers a multiplexed, high-throughput, and adaptable platform that eliminates the need for multiple assays while delivering excellent reproducibility. It enables accurate and sensitive cytokine quantification from minimal volumes of COVID-19 patient serum. Combined with the CIMP databank, this approach allows precise differentiation between early and late severe COVID-19 cases, supporting improved diagnostic and therapeutic decision-making.

Keywords:  COVID-19; Cytokine profiling; Ion mobility; Label-free; Parallel reaction monitoring

DOI:  https://doi.org/10.1016/j.aca.2025.344227
bioRxiv. 2025 May 28. pii: 2025.05.23.655823. [Epub ahead of print]

In-cell Proteomics Enables High-Resolution Spatial and Temporal Mapping of Early Xenopus tropicalis Embryos.

Jian Sun, Xiaolu Xu, Shuo Wei, Yanbao Yu.

Early embryonic development requires tightly regulated molecular programs to coordinate cell division, fate specification, and spatial patterning. While transcriptomic profiling is widely performed, proteomic analyses of early vertebrate embryos remain limited due to technical challenges in embryonic sample preparation. Here, we propose an "in-cell proteomics" approach, which bypasses cell lysis and yolk depletion, processes individual embryos directly in functionalized filter devices, and generates liquid chromatography-mass spectrometry (LC-MS)-friendly samples in an extremely robust and streamlined manner. Combined with a single-shot data-independent acquisition (DIA) MS workflow, this approach enabled us to consistently quantify ∼6,200 proteins from a single Xenopus tropicalis embryo, representing the deepest proteomic coverage of early X. tropicalis development reported to date. Investigation of the temporal proteomes across five cleavage stages (1- to 16-cell) revealed a drastic proteomic shift between 2- and 4-cell stages, followed by more gradual transitions thereafter. Spatial analysis of dissected 8-cell blastomeres uncovered pronounced molecular asymmetry along the animal-vegetal axis, while dorsal-ventral differences were minimal. This study establishes a novel in-cell proteomics technology in conjunction with DIA-MS as a robust platform for high-resolution, low-input developmental proteomics analysis, and provides a comprehensive spatiotemporal protein atlas for early X. tropicalis embryos.

DOI: https://doi.org/10.1101/2025.05.23.655823
Bioinform Adv. 2025 ;5(1): vbaf100

Enrichment analysis for spatial and single-cell metabolomics accounting for molecular ambiguity.

Bishoy Wadie, Martijn R Molenaar, Lucas M Vieira, Theodore Alexandrov.

Summary: Imaging mass spectrometry (imaging MS) has advanced spatial and single-cell metabolomics, but the reliance on MS1 data complicates the accurate identification of molecular structures, not being able to resolve isomeric and isobar molecules. This prevents application of conventional methods for overrepresentation analysis (ORA) and metabolite set enrichment analysis (MSEA). To address this, we introduce S2IsoMEr R package and a web app for METASPACE, which uses bootstrapping to propagate isomeric/isobaric ambiguities into the enrichment analysis. We demonstrate S2IsoMEr for single-cell metabolomics and the METASPACE web app for spatial metabolomics.
Availability and implementation: METASPACE web app can be used on existing and new datasets submitted to METASPACE (https://metaspace2020.org). The source code for the S2IsoMEr R package is available on GitHub (https://github.com/alexandrovteam/S2IsoMEr).

DOI: https://doi.org/10.1093/bioadv/vbaf100
bioRxiv. 2025 Jun 01. pii: 2025.05.28.656555. [Epub ahead of print]

Integrating Alternative Fragmentation Techniques into Standard LC-MS Workflows Using a Single Deep Learning Model Enhances Proteome Coverage.

Nikita Levin, Cemil Can Saylan, Joel Lapin, Yana Demyanenko, Kevin L Yang, John Sidda, Alexey I Nesvizhskii, Mathias Wilhelm, Shabaz Mohammed.

We built and characterised a mass spectrometer capable of performing CID (both beam type and resonant type), UVPD, EID and ECD in an automated fashion during an LCMS type experiment. We exploited this ability to generate large datasets through multienzyme deep proteomics experiments for characterisation of these activation techniques. As a further step, motivated by the complexity generated by these dissociation techniques, we developed a single Prosit deep learning model for fragment ion intensity prediction covering all of these techniques. The generated model has been made publicly available and has been utilised in FragPipe within its MSBooster module. Rescoring allowed both data-dependent acquisition (DDA) and data-independent acquisition (DIA) to achieve on average more than 10% increase in protein identifications across all dissociation techniques and enzymatic digestions. We demonstrate that these alternative fragmentation approaches can now be used within standard data analysis pipelines and can produce data competitive to CID in terms of eficiency, but in the cases of EID and UVPD with far richer and more comprehensive spectra.

DOI: https://doi.org/10.1101/2025.05.28.656555
Anal Chim Acta. 2025 Aug 22. pii: S0003-2670(25)00618-X. [Epub ahead of print]1364 344224

A fully automated, high-throughput electro-extraction and analysis workflow for acylcarnitines in human plasma and mouse muscle tissues.

Yupeng He, Paul Miggiels, Amy Harms, Yvonne Rijksen, Renata M C Brandt, Wilbert P Vermeij, Bert Wouters, Thomas Hankemeier.

   BACKGROUND: The labor-intensive and time-consuming nature of sample preparation poses significant challenges for bioanalysis, especially for large-scale samples characterized by limited volumes/mass, and low analyte abundance. Additionally, manual sample processing can compromise reproducibility. To overcome these limitations, automation and high-throughput methodologies are essential, highlighting the need for an automated, high-throughput sample preparation and analysis workflow.
RESULTS: This study presents a fully automated, high-throughput electro-extraction (EE) platform integrated with a CTC PAL3 autosampler and liquid chromatography-mass spectrometry analyzer. The integrated platform underwent qualification, followed by optimization of EE parameters using a Design of Experiment approach. Ten acylcarnitines were selected as model analytes. The optimization models exhibited strong fits (p < 0.006, R2 > 0.91). The optimized platform achieved an enrichment factor of up to 400 (an extraction recovery of up to 99 %) in designed academic samples, and was effectively implemented and evaluated using 20 μL of spiked human plasma samples. To test clinically relevant materials, the platform was utilized to study the effects of muscle tissue isolation speed on acylcarnitine stability, and to examine acylcarnitine abundance across muscle types in progeria (sarcopenia) mouse muscle. We found that the speed of muscle isolation does not affect measured levels of acylcarnitines, and detected higher acylcarnitine abundances are consistent with literature.
SIGNIFICANCE: This study provides an automated, high-throughput, and cost-effective workflow enabling extraction and analysis of 120 samples per day, with a cost of <0.1 Euro per sample. It presents a significant stride towards the creation of fully-automated, high-throughput bioanalysis workflows for large-scale studies involving biomass limited samples in the foreseeable future.

Keywords:  Automation; Bioanalysis; Electro-extraction; High-throughput; Sample-preparation; Sarcopenia

DOI:  https://doi.org/10.1016/j.aca.2025.344224
Anal Chim Acta. 2025 Sep 01. pii: S0003-2670(25)00619-1. [Epub ahead of print]1365 344225

Analytical quality control in targeted lipidomics: Evaluating the performance of commercial plasma as a surrogate for pooled study samples.

Alanah Grant-St James, Aude-Claire Lee, Alex J Lee, Julien Wist, Ferdous Sohel, Kok Wai Wong, Bu B Yeap, Ruey Leng Loo, Amanda Henry, Daniella Susic, Emad El-Omar, Jeremy K Nicholson, Elaine Holmes, Luke Whiley, Nicola Gray.

   BACKGROUND: Pooled quality control (PQC) samples are the gold standard for data quality monitoring in metabolic phenotyping studies. Typically composed of equal parts from all study samples, PQCs can be challenging to generate in large cohorts or when sample volumes are low. As an alternative, externally sourced matrix-matched surrogate QCs (sQC) have been proposed. This study evaluates the performance of sQCs against PQCs for assessing analytical variation, data pre-processing, and downstream data analysis in a targeted lipidomics workflow.
RESULTS: Plasma samples (n = 701) from the Microbiome Understanding in Maternity Study, along with PQC (n = 80) and sQC (n = 80) samples, were analyzed using a lipidomics assay targeting 1162 lipids. QC samples were injected throughout acquisition, and data pre-processing was performed using each strategy. For simplicity, a subset (n = 381) of the study samples was used to assess differences in downstream statistical analyses. Both QC approaches demonstrated high analytical repeatability. While PQC and sQC compositions differed, use of PQCs retained less than 4 % more lipid species during pre-processing. Univariate analysis identified more statistically significant lipids with PQC-based pre-processing, but multivariate model performance was similar between datasets.
SIGNIFICANCE: This study provides a comprehensive comparison of QC strategies and emphasizes the importance of careful QC workflow selection. While PQCs offer advantages, sQCs serve as a suitable alternative for quality assessment and pre-processing. Their commercial availability also supports use as intra- and inter-laboratory long-term references, aiding data harmonization across studies and laboratories.

Keywords:  Data pre-processing; Long-term reference (LTR); Pooled quality control (PQC); Surrogate quality control (sQC); Targeted lipidomics; Ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS); analytical variation

DOI:  https://doi.org/10.1016/j.aca.2025.344225
Methods Mol Biol. 2025 ;2925 161-172

Polyamine Metabolism in Optic Nerve: Spatial Metabolomics with AP/MALDI Mass Spectrometry Imaging.

Sean Meehan, Caleigh O'Connor, Eugene Moskovets, Konstantin Novoselov, Nivedita Bhattacharya, Sanjoy K Bhattacharya.

  Matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) is widely recognized as a leading method for studying spatial omics. Spatial multiomics platforms are most effective when they integrate multimodal datasets to provide a comprehensive understanding of biological contexts. By combining approaches like metabolomics and lipidomics, the throughput of spatial analysis methods can be significantly enhanced. In this particular protocol, polyamine pathway-related metabolites and lipids from a mouse optic nerve are imaged using atmospheric pressure MALDI.

Keywords:  AP/MALDI; Lipids; Mass spectrometry imaging; OCT-embedded tissue; Ocular neurodegenerative disorders; Polyamines; amino acid

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_11
Methods Mol Biol. 2025 ;2925 145-160

Spatial Atmospheric Pressure MALDI HRMS Analysis of Histological Sections of FFPE Samples Reveals a Unique Subset of the Metabolome.

Gilles Frache, David C Muddiman, Simona Ospitalieri, Caleigh R O'Connor, Dietmar R Thal, Peter D E M Verhaert.

  We describe a method that allows high-resolution mass spectrometry (HRMS) imaging of metabolites in tissue sections from formaldehyde-fixed, paraffin-embedded (FFPE) biobanks. This top-down variant of MS imaging expands the molecular scope of mass spectrometry histochemistry (MSHC) from peptidomics to metabolomics. The method makes the vast archives of FFPE biobanks accessible for MSHC-based biomarker discovery research of not only small endogenous peptides but also (a subset of) metabolites. FFPE biobank tissues include well-documented clinical samples representing diseases with a high medical need and often presently not clinically diagnosable and/or curable.Our protocol starts with FFPE tissue sections prepared from samples procured from biobanks. We describe how to remove paraffin and coat the section with MALDI matrix while maximally reducing analyte delocalization or washout. We detail appropriate programming of the MSHC data acquisition and illustrate a way to process MSHC data (including conversion to the generic imzML format) and browse MSHC datasets. Finally, we show options to present the data in the form of annotated MSHC images.

Keywords:  Atmospheric pressure MALDI; Formaldehyde-fixed paraffin-embedded tissues; Mass spectrometry histochemistry; Orbitrap HRMS; Top-down mass spectrometry imaging

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_10
BBA Adv. 2025 ;7 100161

DANGO: An MS data annotation tool for glycolipidomics.

Masaaki Matsubara, Mayumi Ishihara, Michael Tiemeyer, Kazuhiro Aoki, René Ranzinger.

  Glycosphingolipids are essential components of all eukaryotic cells and play a major role in a broad range of cellular and biological processes, including growth, cell signaling, survival, differentiation, and disease. Glycosphingolipid structural diversity arises from heterogeneity in both the glycan and lipid moieties. Most currently available computational tools for annotating mass spectrometry data for glycosphingolipids primarily focus on glycan structure analysis, although a tool for annotating intact glycosphingolipids has recently been reported. Developing tools that integrate both glycan-centric analytical approaches and dynamic lipid composition changes, which influence functional membrane characteristics, would be highly beneficial. We have developed a glycosphingolipid computational tool, named DANGO (Data ANnotation system for GlycolipidOmics), for the automated annotation of glycolipidomic datasets. DANGO supports processing and annotation of mass spectrometry data to characterize both the glycan and lipid (ceramide) moieties (http://www.ms-dango.org/). DANGO annotates MS datasets using a glycosphingolipid database, which is created from a curated or user-defined glycan and ceramide collection, and proposes candidate structures to the user that match the experimental data. DANGO is implemented as an extension of GRITS Toolbox (http://www.grits-toolbox.org), employing functionalities such as display routines for post-processing and organized annotation of data and relevant metadata. Implementation of a novel filter algorithm in DANGO reduces false-positive identifications, resulting in enhanced reliability and shortened computational time for acquiring glycosphingolipid structural annotation. The labor-intensive manual annotation of mass spectrometry datasets has been the only approach to confident assignment of glycosphingolipid structural identity. DANGO provides intuitive workflows for enhancing the annotation of glycosphingolipidomic data.

Keywords:  DANGO; Free software; Glycolipidomics; Glycosphingolipid; Mass spectrometry; Mass spectrometry data processing

DOI:  https://doi.org/10.1016/j.bbadva.2025.100161
Drug Discov Today. 2025 Jun 08. pii: S1359-6446(25)00110-2. [Epub ahead of print] 104397

Epigenetic roles of lysine-specific demethylase 1 (LSD1) in cancer metabolism.

Bing Cheng, Xiangyu Xu, Boyi Fan, Ting Ma, Bin Yu.

  Lysine-specific demethylase 1 (LSD1) is a histone demethylase with a crucial role in cancer initiation and progression. Several LSD1 inhibitors and degraders are undergoing clinical trials. Metabolic reprogramming, a key hallmark of cancer cells, allows them to survive in harsh environments. Studies have highlighted the roles of epigenetic regulators, particularly LSD1, in modulating metabolic pathways to promote cancer cell growth, metastasis, and invasion. In this review, we highlight the roles of glucose, glutamine (Gln), and fatty acid (FA) metabolism in tumor cells, as well as the signaling pathways through which LSD1 regulates these metabolic processes, aiming to provide new insights and strategies for cancer treatment.

Keywords:  LSD1; cancer metabolism; cancer therapy; epigenetics; metabolic reprogramming

DOI:  https://doi.org/10.1016/j.drudis.2025.104397
Nature. 2025 Jun 11.

Glycosaminoglycan-driven lipoprotein uptake protects tumours from ferroptosis.

Dylan Calhoon, Lingjie Sang, Fubo Ji, Divya Bezwada, Sheng-Chieh Hsu, Feng Cai, Nathaniel Kim, Amrita Basu, Renfei Wu, Anastasia Pimentel, Bailey Brooks, Konnor La, Ana Paulina Serrano, Daniel L Cassidy, Ling Cai, Vanina Toffessi-Tcheuyap, Maryam E Moussa, Winnie Uritboonthai, Linh Truc Hoang, Meghana Kolli, Brooklyn Jackson, Vitaly Margulis, Gary Siuzdak, James Brugarolas, Ian Corbin, Derek A Pratt, Ryan J Weiss, Ralph J DeBerardinis, Kıvanç Birsoy, Javier Garcia-Bermudez.

Lipids are essential components of cancer cells due to their structural and signalling roles1. To meet metabolic demands, many cancers take up extracellular lipids2-5; however, how these lipids contribute to cancer growth and progression remains poorly understood. Here, using functional genetic screens, we identify uptake of lipoproteins-the primary mechanism for lipid transport in circulation-as a key determinant of ferroptosis sensitivity in cancer. Lipoprotein supplementation robustly inhibits ferroptosis across diverse cancer types, primarily through the delivery of α-tocopherol (α-toc), the most abundant form of vitamin E in human lipoproteins. Mechanistically, cancer cells take up lipoproteins through a pathway dependent on sulfated glycosaminoglycans (GAGs) linked to cell-surface proteoglycans. Disrupting GAG biosynthesis or acutely degrading surface GAGs reduces lipoprotein uptake, sensitizes cancer cells to ferroptosis and impairs tumour growth in mice. Notably, human clear cell renal cell carcinomas-a lipid-rich malignancy-exhibit elevated levels of chondroitin sulfate and increased lipoprotein-derived α-toc compared with normal kidney tissue. Together, our study establishes lipoprotein uptake as a critical anti-ferroptotic mechanism in cancer and implicates GAG biosynthesis as a therapeutic target.

DOI: https://doi.org/10.1038/s41586-025-09162-0
Methods Mol Biol. 2025 ;2925 383-396

NMR Metabolomics in Livestock: An Update of Current Methods and Applications.

Claudia Torres-Calzada, Brian Lee, Mark Berjanskii, David S Wishart.

  As the global population increases, the livestock sector will face immense challenges to meet the demand for animal products. A more comprehensive, molecular-level understanding of livestock metabolism and physiology is essential to provide the key insights needed to enhance livestock productivity and health. In this regard, metabolomics, especially Nuclear Magnetic Resonance (NMR)-based metabolomics, offers a superb way to gain molecular-level understanding. NMR allows non-destructive analysis of biological samples without derivatization or chromatographic separation. Its high level of automation facilitates high-throughput analysis with minimal manual effort and very modest costs. Despite some limitations in sensitivity and sample volume requirements, NMR's exceptional reproducibility ensures consistent results across various experiments and laboratories. This chapter provides a comprehensive guide on preparing livestock samples from diverse biofluids and tissues for NMR analysis. It details the procedures for obtaining high-quality NMR spectra and describes spectral profiling techniques to identify and quantify water-soluble metabolites, leveraging metabolomics to enhance livestock productivity and health.

Keywords:  Biofluids; High-throughput analysis; Livestock; Metabolomics; Nuclear Magnetic Resonance; Tissue; Water-soluble metabolites

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_25
Cancer Discov. 2025 Jun 09.

Federated deep learning enables cancer subtyping by proteomics.

Zhaoxiang Cai, Emma L Boys, Zainab Noor, Adel T Aref, Dylan Xavier, Natasha Lucas, Steven G Williams, Jennifer Ms Koh, Rebecca C Poulos, Yangxiu Wu, Michael Dausmann, Karen L MacKenzie, Adriana Aguilar-Mahecha, Carolina Armengol, Maria M Barranco, Mark Basik, Elise D Bowman, Roderick Clifton-Bligh, Elizabeth A Connolly, Wendy A Cooper, Bhavik Dalal, Anna DeFazio, Martin Filipits, Peter J Flynn, J Dinny Graham, Jacob George, Anthony J Gill, Michael Gnant, Rosemary Habib, Curtis C Harris, Kate Harvey, Lisa G Horvath, Christopher Jackson, Maija R J Kohonen-Corish, Elgene Lim, Jia Jenny Liu, Georgina V Long, Reginald V Lord, Graham J Mann, Geoffrey W McCaughan, Lucy Morgan, Leigh Murphy, Sumanth Nagabushan, Adnan Nagrial, Jordi Navinés, Benedict J Panizza, Jaswinder S Samra, Richard A Scolyer, John Souglakos, Alexander Swarbrick, David Thomas, Rosemary L Balleine, Peter G Hains, Phillip J Robinson, Qing Zhong, Roger R Reddel.

Artificial intelligence applications in biomedicine face major challenges from data privacy requirements. To address this issue for clinically annotated tissue proteomic data, we developed a Federated Deep Learning (FDL) approach (ProCanFDL), training local models on simulated sites containing data from a pan-cancer cohort (n=1,260) and 29 cohorts held behind private firewalls (n=6,265), representing 19,930 replicate data-independent acquisition mass spectrometry (DIA-MS) runs. Local parameter updates were aggregated to build the global model, achieving a 43% performance gain on the hold-out test set (n=625) in 14 cancer subtyping tasks compared to local models, and matching centralized model performance. The approach's generalizability was demonstrated by retraining the global model with data from two external DIA-MS cohorts (n=55) and eight acquired by tandem mass tag (TMT) proteomics (n=832). ProCanFDL presents a solution for internationally collaborative machine learning initiatives using proteomic data, e.g., for discovering predictive biomarkers or treatment targets, while maintaining data privacy.

DOI: https://doi.org/10.1158/2159-8290.CD-24-1488
Carbohydr Polym. 2025 Oct 01. pii: S0144-8617(25)00599-5. [Epub ahead of print]365 123816

Quantification of galacturonic acid in pectin-containing samples using a stable isotope dilution approach and LC-MS.

Johanna Braun, Mirko Bunzel.

  Reliable quantification of galacturonic acids (GalA) is essential for understanding the structural and functional properties of pectin-containing materials. However, conventional photometric methods often suffer from low reproducibility, limited sensitivity, and poorly understood reactions involved during analysis. Here, an LC-MS-based method for the sensitive and precise determination of total GalA contents in soluble and insoluble dietary fiber fractions of pectin containing samples is presented. The method is based on the degradation of GalA to the characteristic product 5-formyl-2-furancarboxylic acid (5FFA) in concentrated sulfuric acid under optimized conditions. The degradation product is extracted and quantified by UHPLC-ESI-MS. To compensate for degradation and extraction variability, the internal standard 13C6-galacturonic acid is used. Quantification is achieved by single ion monitoring (SIM) of 5FFA and the equivalent 13C-labeled degradation product. The validated method was successfully applied to various sample materials, including isolated galacturonic acid oligosaccharide standards with defined degrees of polymerization and plant-derived dietary fiber samples such as carrot, apple, and citrus pulp. Comparison to a widely used colorimetric assay demonstrated that the results of the two methods differ if applied to soluble fiber samples. Thus, the LC-MS approach represents a robust alternative to photometric assays, offering enhanced sensitivity, precision, and applicability for pectin analysis.

Keywords:  5-Formyl-2-furancarboxylic acid; Chromatography-mass spectrometry; Colorimetric assays; Galacturonic acid; Pectins; Stable isotope dilution approach; Uronic acids

DOI:  https://doi.org/10.1016/j.carbpol.2025.123816
Methods Mol Biol. 2025 ;2925 309-328

Reproducible Sectioning and Sample Preparation for Mass Spectrometry Imaging of Cancer Metabolism in Frozen Lung Tissue.

Vanessa Y Rubio, Hayley D Ackerman, Nicole Hackel, Christina L Carr, Jaden Baldwin, John H Lockhart, Claudenir Ferrari, Jodi Balasi, Joseph O Johnson, Brooke Smedley, Eric Welsh, Oscar E Ospina, Paul A Stewart, Elsa R Flores, John M Koomen.

  Mass Spectrometry Imaging (MSI) for small molecules has emerged as the optimal technique to ascertain meaningful biology from spatial distributions of metabolites, lipids, and other classes of molecules. The success or failure of this approach rests on the sample preparation. Each tissue can have its unique challenges. In addition, the established histological processes for embedding frozen tissue, such as optimal cutting temperature medium, produce large polyethylene glycol clusters that suppress and interfere with the signal of biological molecules. To address these challenges, we provide a reproducible protocol for sectioning lung cancer tissue using a commercially available histological embedding matrix using tissues from genetically engineered mouse models of lung adenocarcinoma with a fluorescent reporter cassette to highlight additional microscopy methods used in parallel with mass spectrometry imaging to select regions of interest to compare tumor and adjacent lung tissue. These improvements to existing techniques produce high-quality sections of frozen tissue for histology and mass spectrometry imaging.

Keywords:  Frozen Tissue; Lipids; Lung Cancer; Mass Spectrometry Imaging; Matrix-Assisted Laser Desorption Ionization (MALDI); Metabolomics; Tissue Sectioning

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_23
bioRxiv. 2025 Jun 03. pii: 2025.06.02.657371. [Epub ahead of print]

Multi-Omic Profiling Identifies Conserved Metabolic Pathways Critical for SARS-CoV-2 Variants Infection.

Scotland E Farley, Jennifer E Kyle, Helene Jahn, Lisa Bramer, Paul Piehowski, Athena Shepmoes, Brooke Ld Kaiser, Sarai M Williams, Josie G Eder, Carsten Schultz, Fikadu Tafesse.

The rapid evolution of SARS-CoV-2 has led to the emergence of numerous variants with en-hanced transmissibility and immune evasion. Despite widespread vaccination, infections persist, and the mechanisms by which SARS-CoV-2 reprograms host metabolism remain in-completely understood. Here, we investigated whether virus-induced lipid remodeling is con-served across variants and whether changes in lipid abundance correlate with alterations in lipid biosynthetic enzymes. Using global untargeted lipidomics and quantitative proteomics, we analyzed A549-ACE2 cells infected with the Delta (B.1.617.2) or Omicron (B.1.1.529) var-iants and compared them to cells infected with the ancestral WA1 strain. In parallel, we conducted quantitative proteomics to assess virus-induced changes in the host proteome. Our results reveal that SARS-CoV-2 drives a remarkably consistent pattern of metabolic re-wiring at both the lipidomic and proteomic levels across all three variants. We mapped changes in the expression of host metabolic enzymes and compared these to corresponding shifts in lipid abundance. This integrative analysis identified key host proteins involved in virus-mediated lipid remodeling, including fatty acid synthase (FASN), lysosomal acid lipase (LIPA), and ORM1-like protein 2 (ORMDL2). Together, these findings highlight conserved metabolic dependencies of SARS-CoV-2 variants and underscore host lipid metabolism as a potential target for broad-spectrum antiviral strategies.

DOI: https://doi.org/10.1101/2025.06.02.657371
bioRxiv. 2025 May 31. pii: 2025.05.27.656447. [Epub ahead of print]

Analysis of isobaric quantitative proteomic data using TMT-Integrator and FragPipe computational platform.

Hui-Yin Chang, Yamei Deng, Ruohong Li, Dmitry Avtonomov, Bo Wen, Sarah E Haynes, Felipe da Veiga Leprevost, Bing Zhang, Fengchao Yu, Alexey I Nesvizhskii.

Isobaric mass tags, such as iTRAQ and TMT, are widely utilized for peptide and protein quantification in multiplex quantitative proteomics. We present TMT-Integrator, a bioinformatics tool for processing quantitation results from TMT and iTRAQ experiments, offering integrative reports at the gene, protein, peptide, and post-translational modification site levels. We demonstrate the versatility of TMT-Integrator using five publicly available TMT datasets: an E. coli dataset with 13 spike-in proteins, the clear cell renal cell carcinoma (ccRCC) whole proteome and phosphopeptide-enriched datasets from the Clinical Proteomic Tumor Analysis Consortium, and two human cell lysate datasets showcasing the latest advances with the Astral instrument and TMT 35-plex reagents. Integrated into the widely used FragPipe computational platform ( https://fragpipe.nesvilab.org/ ), TMT-Integrator is a core component of TMT and iTRAQ data analysis workflows. We evaluate the FragPipe/TMT-Integrator analysis pipeline's performance against MaxQuant and Proteome Discoverer with multiple benchmarks, facilitated by the bioinformatics tool OmicsEV. Our results show that FragPipe/TMT-Integrator quantifies more proteins in the E. coli and ccRCC whole proteome datasets, identifies more phosphorylated sites in the ccRCC phosphoproteome dataset, and delivers overall more robust quantification performance compared to other tools.

DOI: https://doi.org/10.1101/2025.05.27.656447
Methods Mol Biol. 2025 ;2925 173-184

Direct Colony Atmospheric Pressure Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Assay for Targeted Operon Metabolite Analysis.

Nivedita Bhattacharya, Deepanwita Banerjee, Caleigh O'Connor, Dharmeshkumar Parmar, Eugene Moskovets, Peter D E M Verhaert, Anu Raghunathan, Venkateswarlu Panchagnula.

  Atmospheric pressure matrix-assisted laser desorption/ionization (AP/MALDI) mass spectrometry (MS) allows for highly sensitive and specific detection and relative quantification of metabolites of interest, with the key benefit of minimal sample preparation requirements for complex biological samples such as bacteria. Violacein is a violet-colored pigment produced by the bacterium Chromobacterium violaceum. In this chapter, we detail a high-throughput, high-resolution (HR) MS-based, targeted metabolite profiling protocol for analyzing violacein and its intermediates directly from bacterial colonies. This technique applies to a wide range of metabolites regulated by operon control and is suitable for a molecular phenotyping platform.

Keywords:  AP/MALDI MS; Adaptive laboratory evolution (ALE); Antibiotic resistance; Chromobacterium violaceum; Metabolomics; Microbial analysis; Pathway metabolites; Vio operon

DOI:  https://doi.org/10.1007/978-1-0716-4534-5_12