bims-mascan 2025-03-02 papers

bims-mascan

Biomed News

on Mass spectrometry in cancer research

Issue of 2025–03–02
thirty-two papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz

Enhancing Proteomics Quality Control: Insights from the Visualization Tool QCeltis.
MS-based Solutions for Single Cell Proteomics.
Comprehensive analysis of oxidized arachidonoyl-containing glycerophosphocholines using ion mobility spectrometry-mass spectrometry.
A New, Validated GC-PICI-MS Method for the Quantification of 32 Lipid Fatty Acids via Base-Catalyzed Transmethylation and the Isotope-Coded Derivatization of Internal Standards.
pyOpenMS-viz: Streamlining Mass Spectrometry Data Visualization with pandas.
Methionine cycle inhibition disrupts antioxidant metabolism and reduces glioblastoma cell survival.
Mass-spectrometry-based proteomics: from single cells to clinical applications.
Human Untargeted Metabolomics in High-Throughput Gut Microbiome Research: Ethanol vs Methanol.
Quantification of Glycosaminoglycans in Urine by Isotope Dilution Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometry.
Structural Annotation Method for Locating sn- and C═C Positions of Lipids Using Liquid Chromatography-Electron Impact Excitation of Ions from Organics (EIEIO)-Mass Spectrometry.
Enhancing the Accuracy of Identification and Relative Quantification of Unsaturated Fatty Acids in Serum via a Stable Isotope-Labeled Double Derivatization Strategy.
Reference Intervals of Serum Metabolites and Lipids of a Healthy Chinese Population Determined by Liquid Chromatography-Mass Spectrometry.
Comprehensive Protein Inference Analysis with PyProteinInference Elucidates Biological Understanding of Tandem Mass Spectrometry Data.
MSConnect: Open-Source, End-to-End Platform for Automated Mass Spectrometry Data Management, Analysis, and Visualization.
Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments.
Deep Learning-Based Molecular Fingerprint Prediction for Metabolite Annotation.
A systems-level, semi-quantitative landscape of metabolic flux in C. elegans.
A liquid chromatography-high-resolution mass spectrometry method for separation and identification of hemoglobin variant subunits with mass shifts less than 1 Da.
Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics.
Exploring Single-Probe Single-Cell Mass Spectrometry: Current Trends and Future Directions.
Glyoxalase 2 coordinates de novo serine biosynthesis.
To Impute or Not To Impute in Untargeted Metabolomics─That is the Compositional Question.
Integrating the lactulose-mannitol test for intestinal permeability with untargeted metabolomics for drug monitoring through dual liquid chromatography-mass spectrometry.
Metabolic Regulation by p53: Implications for Cancer Therapy.
Oxidative Stress and Reprogramming of Lipid Metabolism in Cancers.
Multi-Pass Arrival Time Correction in Cyclic Ion Mobility Mass Spectrometry for Imaging and Shotgun Lipidomics.
Dynamic Measurement of Protein Translation in Mycobacteria Using Nontargeted Stable Isotope Labeling in Combination with MALDI-TOF Mass Spectrometry-Based Readout.
A clinical drug candidate that triggers non-apoptotic cancer cell death.
Microflow Liquid Chromatography Coupled to Multinozzle Electrospray Ionization for Improved Lipidomics Coverage of 3D Clear Cell Renal Cell Carcinoma.
Isobaric Labeling Update in MaxQuant.
Chiral supercritical fluid chromatography-mass spectrometry with liquid chromatography fractionation for the characterization of enantiomeric composition of fatty acid esters of hydroxy fatty acids.
Discovery of Key Candidate Protein Biomarkers in Early-Stage Nonsmall Cell Lung Carcinoma through Quantitative Proteomics.

J Proteome Res. 2025 Feb 24.

Enhancing Proteomics Quality Control: Insights from the Visualization Tool QCeltis.

Manasa Vegesna, Niveda Sundararaman, Ajay Bharadwaj, Kirstin Washington, Rakhi Pandey, Ali Haghani, Blandine Chazarin, Aleksandra Binek, Qin Fu, Susan Cheng, David Herrington, Jennifer E Van Eyk.

  Large-scale mass-spectrometry-based proteomics experiments are complex and prone to analytical variability, requiring rigorous quality checks across each step in the workflow: sample preparation, chromatography, mass spectrometry, and the bioinformatics stages. This includes quality control (QC) measures that address biological and technical variation. Most QC approaches involve detecting sample outliers and monitoring parameters related to sample preparation and mass spectrometer performance. Evaluating these parameters regularly is essential for reliable downstream analysis and proteomics research. Here, we introduce "QCeltis", a Python package designed to facilitate automated QC analysis across the proteomics workflow, aiding in the identification of technical biases and consistency verification. QCeltis is a versatile tool for detecting QC issues in large-scale data-independent acquisition proteomics experiments by not only identifying sample preparation and acquisition issues but also aiding in differentiating between QC issues vs batch effects. QCeltis is available for command-line use in Windows and Linux environments. We present three case studies showcasing QCeltis's capabilities across different data sets, including depleted plasma, whole blood vs plasma, and dried blood spot samples, emphasizing its potential impact on large-scale proteomics projects. This package can be used to enhance data reliability and enable nuanced downstream analysis and interpretation for proteomics studies.

Keywords:  DIA; mass spectrometry; proteomics; quality control; visualization tool

DOI:  https://doi.org/10.1021/acs.jproteome.4c00777
Genomics Proteomics Bioinformatics. 2025 Feb 22. pii: qzaf012. [Epub ahead of print]

MS-based Solutions for Single Cell Proteomics.

Siqi Li, Shuwei Li, Siqi Liu, Yan Ren.

  Mass spectrometry-based single cell proteomics (MS-SCP) is attracting tremendous attention because it is now technically feasible to quantify thousands of proteins in minute samples. Since protein amplification is still not possible, technological improvements in MS-SCP focus on minimizing sample loss and increasing throughput, resolution, and sensitivity, as well as achieving the measurement depth, accuracy, and stability as bulk samples. Major advances in MS-SCP have facilitated its use in biological and even medical applications. Here, we review the key advancements in MS-SCP technology and discuss the strategies of the classic proteomics workflow to improve MS-SCP analysis from single cell isolation, sample preparation and liquid chromatography separation to MS data acquisition and analysis. The review will provide an overall understanding of the development and application of MS-SCP and inspire more novel ideas regarding the innovation of MS-SCP technology.

Keywords:  Data acquisition and analysis; Liquid chromatography separation; Mass spectrometry-based single cell proteomics; Sample preparation; Single cell isolation

DOI:  https://doi.org/10.1093/gpbjnl/qzaf012
Talanta. 2025 Feb 08. pii: S0039-9140(25)00198-5. [Epub ahead of print]289 127712

Comprehensive analysis of oxidized arachidonoyl-containing glycerophosphocholines using ion mobility spectrometry-mass spectrometry.

Sandra M Camunas-Alberca, Ameer Y Taha, Ana Gradillas, Coral Barbas.

  The biological significance of oxidized arachidonoyl-containing glycerophosphocholines, exemplified by the oxidation products of 1-palmitoyl-2-arachidonoyl-sn-glycero-3-phosphocholine (oxPAPC), in pathological processes is well-established. However, despite their widespread use in redox lipidomics research, the precise chemical composition of the heterogeneous mixtures of oxPAPC generated in vitro -including the high prevalence of isomers and the oxidation mechanisms involved- remain inadequately understood. To address these knowledge gaps, we developed a multidimensional in-house database from a commercial oxPAPC preparation -employing Liquid Chromatography coupled to Quadrupole Time-of-Flight Mass Spectrometry (LC-QTOF-MS) and Ion Mobility Spectrometry-Mass Spectrometry (IMS-MS). This database includes lipid names, retention times, accurate mass values (m/z), adduct profiles, MS/MS information, as well as collision cross-section (CCS) values. Our investigation elucidated 34 compounds belonging to distinct subsets of oxPAPC products, encompassing truncated, full-length, and cyclized variants. The integration of IMS-MS crucially facilitated: (i) structural insights among regioisomers, exemplified by the 5,6-PEIPC and 11,12-PEIPC epoxy-isoprostane derivatives, (ii) novel Collision Cross Section (CCS) values, and (iii) cleaner MS/MS spectra for elucidating the fragmentation mechanisms involved to yield specific fragment ions. These diagnostic ions were employed to successfully characterize full-length isomers present in human plasma samples from patients with mucormycosis. This comprehensive oxPAPC characterization not only advances the understanding of lipid peroxidation products but also enhances analytical capabilities for in vitro-generated oxidized mixtures. The implementation of this robust database, containing multiple orthogonal (i.e., independent) pieces of information, will serve as a comprehensive resource for the field.

Keywords:  Arachidonic Acid (AA); Ion Mobility Spectrometry (IMS); Mass Spectrometry (MS); Multidimensional LC-MS Database; Oxidized Glycerophosphocholines; Redox Lipidomics; oxPAPC

DOI:  https://doi.org/10.1016/j.talanta.2025.127712
Metabolites. 2025 Feb 07. pii: 104. [Epub ahead of print]15(2):

A New, Validated GC-PICI-MS Method for the Quantification of 32 Lipid Fatty Acids via Base-Catalyzed Transmethylation and the Isotope-Coded Derivatization of Internal Standards.

Petr Vodrážka, Lucie Řimnáčová, Petra Berková, Jan Vojtíšek, Miroslav Verner, Martin Moos, Petr Šimek.

  Background: Fatty acids (FAs) represent a ubiquitous class of nonpolar alkyl carboxylate metabolites with diverse biological functions. Nutrition, metabolism, and endogenous and exogenous stress influence the overall FA metabolic status and transport via the bloodstream. FAs esterified in lipids are of particular interest, as they represent promising biomarkers of pathological diseases and nutritional status. Methods: Here, we report a validated gas chromatographic-mass spectrometric (GC-MS) method for the quantitative analysis of 32 FAs exclusively bound in esterified lipids. The developed sample preparation protocol comprises three steps using only 5 µL of human serum for Folch extraction, sodium methoxide-catalyzed transesterification in tert-butyl methyl ether, and re-extraction in isooctane prior to a quantitative GC-MS analysis with positive ion chemical ionization (PICI) and selected ion monitoring (SIM). Results: The base-catalyzed transmethylation step was studied for 14 lipid classes and was found to be efficient under mild conditions for all major esterified lipids but not for free FAs, lipid amides, or sphingolipids. To minimize matrix effects and instrument bias, internal fatty acid trideuteromethyl esters (D3-FAME) standards were prepared through isotope-coded derivatization with D3-labeled methylchloroformate/methanol medium mixed with each transmethylated serum extract for the assay. The method was validated according to FDA guidelines and evaluated by analyzing NIST SRM 2378 Serum 1 and sera from three healthy donors. Conclusions: The measured quantitative FA values are consistent with the reference data of SRM 2378, and they demonstrate the application potential of the described method for general FA analysis in esterified lipids as a novel complementary tool for lipidomics, as well as for the analysis of membrane FAs in dry blood spots and red blood cells.

Keywords:  GC-MS; NIST SRM 2378; fatty acid analysis; human serum; isotope-coded derivatization; positive ion chemical ionization; quantitative analysis; transmethylation

DOI:  https://doi.org/10.3390/metabo15020104
J Proteome Res. 2025 Feb 28.

pyOpenMS-viz: Streamlining Mass Spectrometry Data Visualization with pandas.

Justin Cyril Sing, Joshua Charkow, Axel Walter, Mingxuan Gao, Tom David Müller, Wout Bittremieux, Timo Sachsenberg, Hannes Luc Röst.

  Mass spectrometry data visualization is essential for a wide range of applications, such as validation of workflows and results, benchmarking new algorithms, and creating comprehensive quality control reports. Python offers a popular and powerful framework for analyzing and visualizing multidimensional data; however, generating commonly used mass spectrometry plots in Python can be cumbersome. Here we present pyOpenMS-viz, a versatile, unified framework for generating mass spectrometry plots. pyOpenMS-viz directly extends pandas DataFrame plotting for generating figures in a single line of code. This implementation enables easy integration across various Python-based mass spectrometry tools that already use pandas DataFrames to store MS data. pyOpenMS-viz is open-source under a BSD 3-Clause license and freely available at https://github.com/OpenMS/pyopenms_viz.

Keywords:  mass-spectrometry; python; quality control; validation; visualization

DOI:  https://doi.org/10.1021/acs.jproteome.4c00873
J Biol Chem. 2025 Feb 25. pii: S0021-9258(25)00198-X. [Epub ahead of print] 108349

Methionine cycle inhibition disrupts antioxidant metabolism and reduces glioblastoma cell survival.

Emma C Rowland, Matthew D'Antuono, Anna Jermakowicz, Nagi G Ayad.

  Glioblastoma (GBM) is a highly aggressive primary malignant adult brain tumor that inevitably recurs with a fatal prognosis. This is due in part to metabolic reprogramming that allows tumors to evade treatment. Therefore, we must uncover the pathways mediating these adaptations to develop novel and effective treatments. We searched for genes that are essential in GBM cells as measured by a whole-genome pan-cancer CRISPR screen available from DepMap and identified the methionine metabolism genes MAT2A and AHCY. We conducted genetic knockdown, evaluated mitochondrial respiration, and performed targeted metabolomics to study the function of these genes in GBM. We demonstrate that MAT2A or AHCY knockdown induces oxidative stress, hinders cellular respiration, and reduces the survival of GBM cells. Furthermore, selective MAT2a or AHCY inhibition reduces GBM cell viability, impairs oxidative metabolism, and shifts the cellular metabolic profile towards oxidative stress and cell death. Mechanistically, MAT2a and AHCY regulate spare respiratory capacity, the redox buffer cystathionine, lipid and amino acid metabolism, and prevent oxidative damage in GBM cells. Our results point to the methionine metabolic pathway as a novel vulnerability point in GBM. Significance We demonstrated that methionine metabolism maintains antioxidant production to facilitate pro-tumorigenic ROS signaling and GBM tumor cell survival. Importantly, targeting this pathway in GBM has the potential to reduce tumor growth and improve survival in patients.

Keywords:  glioblastoma; lipid peroxidation; metabolism; metabolomics; methionine; mitochondria; oxidative stress

DOI:  https://doi.org/10.1016/j.jbc.2025.108349
Nature. 2025 Feb;638(8052): 901-911

Mass-spectrometry-based proteomics: from single cells to clinical applications.

Tiannan Guo, Judith A Steen, Matthias Mann.

Mass-spectrometry (MS)-based proteomics has evolved into a powerful tool for comprehensively analysing biological systems. Recent technological advances have markedly increased sensitivity, enabling single-cell proteomics and spatial profiling of tissues. Simultaneously, improvements in throughput and robustness are facilitating clinical applications. In this Review, we present the latest developments in proteomics technology, including novel sample-preparation methods, advanced instrumentation and innovative data-acquisition strategies. We explore how these advances drive progress in key areas such as protein-protein interactions, post-translational modifications and structural proteomics. Integrating artificial intelligence into the proteomics workflow accelerates data analysis and biological interpretation. We discuss the application of proteomics to single-cell analysis and spatial profiling, which can provide unprecedented insights into cellular heterogeneity and tissue architecture. Finally, we examine the transition of proteomics from basic research to clinical practice, including biomarker discovery in body fluids and the promise and challenges of implementing proteomics-based diagnostics. This Review provides a broad and high-level overview of the current state of proteomics and its potential to revolutionize our understanding of biology and transform medical practice.

DOI: https://doi.org/10.1038/s41586-025-08584-0
Anal Chem. 2025 Feb 27.

Human Untargeted Metabolomics in High-Throughput Gut Microbiome Research: Ethanol vs Methanol.

Simone Zuffa, Vincent Charron-Lamoureux, Caitriona Brennan, Madison Ambre, Rob Knight, Pieter C Dorrestein.

Untargeted metabolomics is frequently performed on human fecal samples in conjunction with sequencing to unravel the gut microbiome functionality. As sample collection efforts are rapidly expanding, with individuals often collecting specimens at home, metabolomics experiments should adapt to accommodate the safety and needs of bulk off-site collections and improve high throughput. Here, we show that a 95% ethanol, safe to be shipped and handled, extraction part of the Matrix Method pipeline recovers comparable amounts of metabolites as a validated 50% methanol extraction, preserving metabolic profile differences between investigated subjects. Additionally, we show that the fecal metabolome remains relatively stable when stored in 95% ethanol for up to 1 week at room temperature. Finally, we suggest a metabolomics data analysis workflow based on robust centered log ratio transformation, which removes the variance introduced by possible different sample weights and concentrations, allowing for reliable and integration-ready untargeted metabolomics experiments in gut microbiome research.

DOI: https://doi.org/10.1021/acs.analchem.4c05142
Curr Protoc. 2025 Feb;5(2): e70110

Quantification of Glycosaminoglycans in Urine by Isotope Dilution Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometry.

Haoyue Zhang, James Beasley, Iskren Menkovic, Ashlee Stiles, David S Millington, Sarah P Young.

  Mucopolysaccharidoses (MPSs) are complex lysosomal diseases that result in the accumulation of glycosaminoglycans (GAGs) in urine, blood, and tissues. Lysosomal enzymes responsible for GAG degradation are defective in MPSs. GAGs including chondroitin sulfate (CS), dermatan sulfate (DS), heparan sulfate (HS), and keratan sulfate (KS) are biomarkers for MPSs. This article describes a stable isotope dilution-tandem mass spectrometric method for quantifying CS, DS, and HS in urine samples. The GAGs are methanolyzed to uronic/iduronic acid-N-acetylhexosamine or uronic/iduronic acid-N-glucosamine dimers and mixed with internal standards derived from deuteriomethanolysis of GAG standards. Specific dimers derived from HS, DS, and CS are separated by ultra-performance liquid chromatography (UPLC) and analyzed by electrospray ionization (ESI) tandem mass spectrometry (MS/MS) using selected reaction monitoring for each targeted GAG product and its corresponding internal standard. This UPLC-MS/MS GAG assay is useful for identifying patients with MPS types I, II, III, VI, and VII. © 2025 Wiley Periodicals LLC. Basic Protocol: Urinary GAG analysis by ESI-MS/MS Support Protocol 1: Prepare calibration samples Support Protocol 2: Preparation of stable-isotope-labeled internal standards Support Protocol 3: Preparation of quality controls for GAG analysis in urine Support Protocol 4: Optimization of methanolysis time Support Protocol 5: Measurement of methanolic HCl concentration Support Protocol 6: Preparation of working methanolic HCl solution (1.1 M) Support Protocol 7: Dilution of prepared urine sample.

Keywords:  LC‐ESI‐MS/MS; dermatan sulfate; glycosaminoglycan; heparan sulfate; isotope dilution; mucopolysaccharidosis

DOI:  https://doi.org/10.1002/cpz1.70110
Anal Chem. 2025 Feb 26.

Structural Annotation Method for Locating sn- and C═C Positions of Lipids Using Liquid Chromatography-Electron Impact Excitation of Ions from Organics (EIEIO)-Mass Spectrometry.

Yao Chen, Jun Yang, Xinxin Wang, Yuqing Zhang, Yaping Shao, Hang Li, Xiaoyan Dong, Fei Jiang, Chunxiu Hu, Guowang Xu.

Definitive structural elucidation of lipids is pivotal for unraveling the functions of lipids in biological systems. Despite advancements in mass spectrometry (MS) for lipid analysis, challenges in annotation scope and efficiency remain, especially in resolving isomers. Herein, we introduce an optimized method using liquid chromatography coupled with electron impact excitation of ions from organic tandem mass spectrometry (LC-EIEIO-MS/MS) for comprehensive analysis and structural annotation of lipids. This approach integrates a six-step analytical protocol for precise lipid annotation, including (1) extracting MS information, (2) classifying lipids, (3) aligning sum composition, (4) determining sn-positions, (5) locating C═C positions, and (6) ascertaining annotation levels. In analyzing 34 lipid standards spiked into serum, our method achieved 100% and 82.4% annotation accuracy at the sn- and C═C isomer levels, respectively, compared to 26.5% and 0% in the CID mode using MS-DIAL. A total of 1312 sn-positions and 1033 C═C locations of lipids were annotated in quality control plasma pooled from healthy individuals and patients with Alzheimer's disease. The isomers of lipids revealed more pronounced differences between the healthy and diseased groups compared to the sum compositions of the lipids. Overall, the LC-EIEIO-MS/MS approach provides a comprehensive profiling and efficient annotation method for lipidomics, promising to shed new light on lipid-related biological pathways and disease mechanisms.

DOI: https://doi.org/10.1021/acs.analchem.4c05560
Anal Chem. 2025 Feb 28.

Enhancing the Accuracy of Identification and Relative Quantification of Unsaturated Fatty Acids in Serum via a Stable Isotope-Labeled Double Derivatization Strategy.

Yunxia Yuan, Bingqing Chi, Jiajia Fan, Ying Wang, Gan Luo, Xiaoyan Gao.

Accurate identification and quantification of fatty acids are critical for investigating their biological function in disease models. Although several derivatization methods have been proposed for identifying the positions of C═C bonds in unsaturated fatty acids, poor ionization efficiency of the carboxyl group leads to lower intensity of molecular ion peaks, making their identification difficult and interfering with the accuracy of quantification based on peak areas of characteristic ion pairs. In this study, a strategy of stable isotope-labeled carboxyl derivatization combined with C═C derivatization was employed for simultaneously the identification and quantification of fatty acids using d0/d9-5-amino-N,N,N-trimethylpentane-1-ammonium iodide (d0/d9-ATPAI) to label the carboxyl group and m-chloroperoxybenzoic acid to label C═C bonds. The stable isotope-labeled quaternary amine groups in the novel carboxyl derivatization reagent d0/d9-ATPAI can enhance the accuracy of the recognition of characteristic ion pairs to facilitate the structural elucidation of various fatty acids. The heavy isotope-labeled fatty acids can be served as internal standards to achieve accurate relative quantification of the C═C position isomers of individual unsaturated fatty acids among samples based on the peak area ratio of the characteristic ion pairs. Unsaturated fatty acid C═C positional isomers were quantified using aldehyde or alkenyl diagnostic ions. In addition, saturated fatty acids were quantified using the m/z 86.09679 cyclamine characteristic ion. This approach enhanced the detection sensitivity of fatty acids by 60,000 times, allowing for the characterization of 70 fatty acids in rat serum, including 26 unsaturated fatty acid C═C positional isomers. Pseudotargeted metabolomics analysis of serum fatty acids revealed alterations in the fatty acid metabolic pathway during diabetic cognitive dysfunction. Overall, the proposed method, with high sensitivity and wide coverage, could provide accurate identification and relative quantification of various fatty acids in complex matrices.

DOI: https://doi.org/10.1021/acs.analchem.4c06375
Metabolites. 2025 Feb 07. pii: 106. [Epub ahead of print]15(2):

Reference Intervals of Serum Metabolites and Lipids of a Healthy Chinese Population Determined by Liquid Chromatography-Mass Spectrometry.

Yuqing Zhang, Jinhui Zhao, Hui Zhao, Xin Lu, Xueni Jia, Xinjie Zhao, Guowang Xu.

  Background: Metabolomics serves as a very useful tool for elucidating disease mechanisms and identifying biomarkers. Establishing reference intervals (RIs) of metabolites in a healthy population is crucial to the application of metabolomics in life sciences and clinics. Methods: We enrolled 615 healthy Chinese adults aged between 21 and 85 years. Their health status was ascertained through clinical examinations, biochemical parameters, and medical history. Targeted metabolomics and lipidomics analyses were applied to quantify 705 metabolites and lipids in the serum, establishing RIs and investigating the effect of sex and age on the metabolome and lipidome. Results: This study is the first large-scale effort in China to establish RIs for metabolites in the apparently healthy population. We found that most of the sex-related metabolites, including amino acids, acyl-carnitines and triacylglycerols, had higher concentrations in males, while the other sex-related lipids showed higher concentrations in females. Most of the age-related metabolites increased with age, including those associated with protein synthesis, nitric oxide synthesis, energy metabolism, and lipid metabolism. Conclusions: This study gives the reference intervals of the healthy Chinese metabolome and lipidome and their relationship with sex and age, which facilitates life sciences and precision medicine, especially for disease research and biomarker discovery.

Keywords:  lipidomics; liquid chromatography; mass spectrometry; metabolomics; quantitative analysis; reference intervals

DOI:  https://doi.org/10.3390/metabo15020106
J Proteome Res. 2025 Feb 28.

Comprehensive Protein Inference Analysis with PyProteinInference Elucidates Biological Understanding of Tandem Mass Spectrometry Data.

Trent B Hinkle, Corey E Bakalarski.

  Selection and application of protein inference algorithms can have a significant impact on the data output from tandem mass spectrometry (MS/MS) experiments. However, this critical step is often taken for granted, with many studies simply utilizing the inference method embedded within the end-to-end software pipeline employed for analysis without consideration of the particular algorithm's suitability for the experiment at hand or its effects on the resulting data. Although many individual inference algorithms have been demonstrated, few unified tools are available that allow the researcher to quickly apply a variety of different inference algorithms to meet the needs of their analysis, are agnostic of other tools in the analysis pipeline, and are easy to use for the bench biologist. PyProteinInference provides a comprehensive suite of tools that enable researchers to apply different inference algorithms and compute protein-level set-based false discovery rates (FDR) from MS/MS data through a unified interface. Here, we describe the software and its application to a traditional protein inference benchmarking data set and to a K562 whole-cell lysate to demonstrate its utility in facilitating conclusions about underlying biological mechanisms in proteomic data.

Keywords:  Mass Spectrometry; Protein Inference; Proteomics; Python

DOI:  https://doi.org/10.1021/acs.jproteome.4c00734
J Proteome Res. 2025 Feb 28.

MSConnect: Open-Source, End-to-End Platform for Automated Mass Spectrometry Data Management, Analysis, and Visualization.

Xiaofeng Xie, Parker S Reyes, Hsien-Jung L Lin, G Alex Mannewitz, Thy Truong, Kei G I Webber, Samuel H Payne, Ryan T Kelly.

  The scale of mass spectrometry-based proteomics data sets continues to increase, and the analysis workflows are becoming more complex as various steps are carried out using a multitude of software programs developed by both commercial providers and the research community. Manually shepherding data across multiple programs and in-house-developed scripts can be error prone and labor intensive. It is also difficult for others to follow the same steps, leading to poor repeatability. We have developed an integrated data management and analysis platform termed MSConnect that enables simple and traceable processing workflows across multiple programs, thus improving repeatability and automating common backup and analysis steps from the point of data collection through summarization and visualization. The open nature of the MSConnect platform enables the diverse omics community to seamlessly integrate third-party tools or develop and automate their own unique workflows. With an open license and design architecture, MSConnect has the potential to become a community-driven platform serving a wide range of MS-based omics researchers.

Keywords:  LIMS; automation; data analysis; open source; real-time quality control

DOI:  https://doi.org/10.1021/acs.jproteome.4c00854
J Vis Exp. 2025 Feb 07.

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments.

The Huong Chau, Naaz Bansal, Anastasia Chernykh, Rebeca Kawahara, Morten Thaysen-Andersen.

Glycosylation is a common and structurally diverse protein modification that impacts a wide range of tumorigenic processes. Mass spectrometry-driven glycomics and glycoproteomics have emerged as powerful approaches to analyze liberated glycans and intact glycopeptides, respectively, offering a deeper understanding of the heterogeneous glycoproteome in the tumor microenvironment (TME). This protocol details the glycomics-guided glycoproteomics method, an integrated omics technology, which firstly employs porous graphitized carbon-LC-MS/MS-based glycomics to elucidate the glycan structures and their quantitative distribution in the glycome of tumor tissues, cell populations, or bodily fluids being investigated. This allows for a comparative glycomics analysis to identify altered glycosylation across patient groups, disease stages, or conditions, and, importantly, serves to enhance the downstream glycoproteomics analysis of the same sample(s) by creating a library of known glycan structures, thus reducing the data search time and the glycoprotein misidentification rate. Focusing on N-glycoproteome profiling, the sample preparation steps for the glycomics-guided glycoproteomics method are detailed in this protocol, and key aspects of the data collection and analysis are discussed. The glycomics-guided glycoproteomics method provides quantitative information on the glycoproteins present in the TME and their glycosylation sites, site occupancy, and site-specific glycan structures. Representative results are presented from formalin-fixed paraffin-embedded tumor tissues from colorectal cancer patients, demonstrating that the method is sensitive and compatible with tissue sections commonly found in biobanks. Glycomics-guided glycoproteomics, therefore, offers a comprehensive view into the heterogeneity and dynamics of the glycoproteome in complex TMEs, generating robust biochemical data required to better understand the glycobiology of cancers.

DOI: https://doi.org/10.3791/67405
Metabolites. 2025 Feb 14. pii: 132. [Epub ahead of print]15(2):

Deep Learning-Based Molecular Fingerprint Prediction for Metabolite Annotation.

Hoi Yan Katharine Chau, Xinran Zhang, Habtom W Ressom.

  Background/Objectives: Liquid chromatography coupled with mass spectrometry (LC-MS) is a commonly used platform for many metabolomics studies. However, metabolite annotation has been a major bottleneck in these studies in part due to the limited publicly available spectral libraries, which consist of tandem mass spectrometry (MS/MS) data acquired from just a fraction of known compounds. Application of deep learning methods is increasingly reported as an alternative to spectral matching due to their ability to map complex relationships between molecular fingerprints and mass spectrometric measurements. The objectives of this study are to investigate deep learning methods for molecular fingerprint based on MS/MS spectra and to rank putative metabolite IDs according to similarity of their known and predicted molecular fingerprints. Methods: We trained three types of deep learning methods to model the relationships between molecular fingerprints and MS/MS spectra. Prior to training, various data processing steps, including scaling, binning, and filtering, were performed on MS/MS spectra obtained from National Institute of Standards and Technology (NIST), MassBank of North America (MoNA), and Human Metabolome Database (HMDB). Furthermore, selection of the most relevant m/z bins and molecular fingerprints was conducted. The trained deep learning models were evaluated on ranking putative metabolite IDs obtained from a compound database for the challenges in Critical Assessment of Small Molecule Identification (CASMI) 2016, CASMI 2017, and CASMI 2022 benchmark datasets. Results: Feature selection methods effectively reduced redundant molecular and spectral features prior to model training. Deep learning methods trained with the truncated features have shown comparable performances against CSI:FingerID on ranking putative metabolite IDs. Conclusion: The results demonstrate a promising potential of deep learning methods for metabolite annotation.

Keywords:  LC-MS/MS; deep learning; metabolite identification; molecular fingerprint prediction

DOI:  https://doi.org/10.3390/metabo15020132
Nature. 2025 Feb 26.

A systems-level, semi-quantitative landscape of metabolic flux in C. elegans.

Hefei Zhang, Xuhang Li, L Tenzin Tseyang, Gabrielle E Giese, Hui Wang, Bo Yao, Jingyan Zhang, Rachel L Neve, Elizabeth A Shank, Jessica B Spinelli, L Safak Yilmaz, Albertha J M Walhout.

Metabolic flux, or the rate of metabolic reactions, is one of the most fundamental metrics describing the status of metabolism in living organisms. However, measuring fluxes across the entire metabolic network remains nearly impossible, especially in multicellular organisms. Computational methods based on flux balance analysis have been used with genome-scale metabolic network models to predict network-level flux wiring1-6. However, such approaches have limited power because of the lack of experimental constraints. Here, we introduce a strategy that infers whole-animal metabolic flux wiring from transcriptional phenotypes in the nematode Caenorhabditis elegans. Using a large-scale Worm Perturb-Seq (WPS) dataset for roughly 900 metabolic genes7, we show that the transcriptional response to metabolic gene perturbations can be integrated with the metabolic network model to infer a highly constrained, semi-quantitative flux distribution. We discover several features of adult C. elegans metabolism, including cyclic flux through the pentose phosphate pathway, lack of de novo purine synthesis flux and the primary use of amino acids and bacterial RNA as a tricarboxylic acid cycle carbon source, all of which we validate by stable isotope tracing. Our strategy for inferring metabolic wiring based on transcriptional phenotypes should be applicable to a variety of systems, including human cells.

DOI: https://doi.org/10.1038/s41586-025-08635-6
J Mass Spectrom Adv Clin Lab. 2025 Jan;35 1-7

A liquid chromatography-high-resolution mass spectrometry method for separation and identification of hemoglobin variant subunits with mass shifts less than 1 Da.

Ainslie Chen, Ryan M Aquino, Hector A Vidal, Carolyn V Wong, Ruben Y Luo.

   Background: Identification of hemoglobin (Hb) variants is valuable in clinical testing. A common issue with conventional methods for identifying Hb variants is their subpar ability to provide structural breakdowns of the variants. Reports have surfaced of high-resolution mass spectrometry (HR-MS) methods that improve on traditional methods; however, ambiguities may arise without separation of Hb subunits prior to HR-MS analysis.
Methods: We report a liquid chromatography-high-resolution mass spectrometry (LC-HR-MS) method to separate several pairs of normal and variant Hb subunits with mass shifts of less than 1 Da and successfully identify them in intact-protein and top-down analyses. LC separation was facilitated by a C4 reversed-phase column.
Results: Seven heterozygous Hb variant samples (Hb C with α-thalassemia trait, Hb E, Hb D-Punjab, Hb G-Accra, Hb G-Siriraj, Hb Tarrant, and Hb G-Waimanalo) were selected to demonstrate the LC separation of Hb variant and normal subunits with mass shifts of less than 1 Da. The analytes could be explicitly observed in the deconvoluted MS1 mass spectra. The top-down analysis matched the amino acid sequences of the correct Hb variant subunits.
Conclusions: The LC-HR-MS method described can effectively separate and identify Hb subunits, especially when the variant subunits have mass deviations of less than 1 Da from their corresponding normal subunits. With further evaluation to prove the clinical utility, the HR-MS methods including CE-HR-MS have the potential to complement or partially replace conventional methods of Hb variant identification in clinical laboratories.

Keywords:  Hb Variants; Intact-Protein Analysis; LC-HR-MS; Top-down Analysis

DOI:  https://doi.org/10.1016/j.jmsacl.2025.01.002
Anal Chem. 2025 Feb 25.

Recent Advances in Mass Spectrometry-Based Bottom-Up Proteomics.

Cameron S Movassaghi, Jie Sun, Yuming Jiang, Natalie Turner, Vincent Chang, Nara Chung, Ryan J Chen, Elizabeth N Browne, Chuwei Lin, Devin K Schweppe, Stacy A Malaker, Jesse G Meyer.

Mass spectrometry-based proteomics is about 35 years old, and recent progress appears to be speeding up across all subfields. In this review, we focus on advances over the last two years in select areas within bottom-up proteomics, including approaches to high-throughput experiments, data analysis using machine learning, drug discovery, glycoproteomics, extracellular vesicle proteomics, and structural proteomics.

DOI: https://doi.org/10.1021/acs.analchem.4c06750
Anal Chem. 2025 Feb 25.

Exploring Single-Probe Single-Cell Mass Spectrometry: Current Trends and Future Directions.

Deepti Bhusal, Shakya Wije Munige, Zongkai Peng, Zhibo Yang.

The Single-probe single-cell mass spectrometry (SCMS) is an innovative analytical technique designed for metabolomic profiling, offering a miniaturized, multifunctional device capable of direct coupling to mass spectrometers. It is an ambient technique leveraging microscale sampling and nanoelectrospray ionization (nanoESI), enabling the analysis of cells in their native environments without the need for extensive sample preparation. Due to its miniaturized design and versatility, this device allows for applications in diverse research areas, including single-cell metabolomics, quantification of target molecules in single cell, MS imaging (MSI) of tissue sections, and investigation of extracellular molecules in live single spheroids. This review explores recent advancements in Single-probe-based techniques and their applications, emphasizing their potential utility in advancing MS methodologies in microscale bioanalysis.

DOI: https://doi.org/10.1021/acs.analchem.4c06824
Chembiochem. 2025 Feb 23. e202401086

Glyoxalase 2 coordinates de novo serine biosynthesis.

Marissa N Trujillo, Erin Q Jennings, Dominique O Farrera, Naoya Kitamura, Colin C Anderson, Sarah Gehrke, Julie A Reisz, Mogens Johannsen, James R Roede, Angelo D'Alessandro, James Galligan.

  Phosphoglycerate dehydrogenase (PHGDH) is the first enzyme in de novo Ser biosynthesis. Numerous metabolic pathways rely on Ser as a precursor, most notably one-carbon metabolism, glutathione biosynthesis, and de novo nucleotide biosynthesis. To facilitate proliferation, many cancer cells shunt glycolytic flux through this pathway, placing PHGDH as a metabolic liability and feasible therapeutic target for the treatment of cancer. Herein, we demonstrate the post-translational modification (PTM) of PHGDH by lactoylLys. These PTMs are generated through a non-enzymatic acyl transfer from the glyoxalase cycle intermediate, lactoylglutathione (LGSH). Knockout of the primary LGSH regulatory enzyme, glyoxalase 2 (GLO2), results in increased LGSH and resulting lactoylLys modification of PHGDH. These PTMs reduce enzymatic activity, resulting in a marked reduction in intracellular Ser. Using stable isotope tracing, we demonstrate reduced flux through the de novo Ser biosynthetic pathway. Collectively, these data identify PHGDH as a target for modification by lactoylLys, resulting in reduced enzymatic activity and reduced intracellular Ser.

Keywords:  3-phosphoglycerate dehydrogenase (PHGDH); cell metabolism; glycolysis; lactoylation

DOI:  https://doi.org/10.1002/cbic.202401086
J Am Soc Mass Spectrom. 2025 Feb 25.

To Impute or Not To Impute in Untargeted Metabolomics─That is the Compositional Question.

Dennis D Krutkin, Sydney Thomas, Simone Zuffa, Prajit Rajkumar, Rob Knight, Pieter C Dorrestein, Scott T Kelley.

Untargeted metabolomics often produce large datasets with missing values. These missing values are derived from biological or technical factors and can undermine statistical analyses and lead to biased biological interpretations. Imputation methods, such as k-Nearest Neighbors (kNN) and Random Forest (RF) regression, are commonly used, but their effects vary depending on the type of missing data, e.g., Missing Completely At Random (MCAR) and Missing Not At Random (MNAR). Here, we determined the impacts of degree and type of missing data on the accuracy of kNN and RF imputation using two datasets: a targeted metabolomic dataset with spiked-in standards and an untargeted metabolomic dataset. We also assessed the effect of compositional data approaches (CoDA), such as the centered log-ratio (CLR) transform, on data interpretation since these methods are increasingly being used in metabolomics. Overall, we found that kNN and RF performed more accurately when the proportion of missing data across samples for a metabolic feature was low. However, these imputations could not handle MNAR data and generated wildly inflated or imputed values where none should exist. Furthermore, we show that the proportion of missing values had a strong impact on the accuracy of imputation, which affected the interpretation of the results. Our results suggest imputation should be used with extreme caution even with modest levels of missing data and especially when the type of missingness is unknown.

DOI: https://doi.org/10.1021/jasms.4c00434
Anal Bioanal Chem. 2025 Feb 27.

Integrating the lactulose-mannitol test for intestinal permeability with untargeted metabolomics for drug monitoring through dual liquid chromatography-mass spectrometry.

Felina Hildebrand, Cemre Cukaci, Harald Schoeny, Christoph Baumgartinger, Bruno Stelzer, Matteo Spedicato, Tobias Frey, Martina Catani, Klaus Schmetterer, Richard Frey, Gunda Koellensperger.

  In this study, we developed a customized high-resolution mass spectrometry metabolomics workflow integrating the dual sugar test employing lactulose and mannitol as test probes for intestinal permeability assessment with untargeted screening of small molecules. Urine samples were collected from patients with major depression and healthy controls as part of a clinical study at the psychiatric department. Using a dual injection/dual chromatography setup, the test probes were quantified by hydrophilic interaction liquid chromatography (HILIC) in a targeted assay, while drugs and their metabolites were profiled in an untargeted manner by reversed-phase separation. Rigorous method development and validation allowed for selective separation of sugar isomers and consequently accurate quantification of lactulose and mannitol in urine. Internal standardization with compound specific stable isotope-labeled standards enabled excellent analytical figures of merit such as high recoveries, precision (< 5%), and working range (5 orders of magnitude). Within one analytical run, intestinal permeability was assessed together with drugs and their metabolites, allowing to screen for confounding drugs and patient compliance to the therapeutic scheme.

Keywords:  Clinical/biomedical analysis; Drug monitoring; Dual LC–MS; Dual sugar test; Intestinal permeability testing

DOI:  https://doi.org/10.1007/s00216-025-05790-7
Mol Cells. 2025 Feb 20. pii: S1016-8478(25)00022-6. [Epub ahead of print] 100198

Metabolic Regulation by p53: Implications for Cancer Therapy.

Ki Yeon Koo, Kwanho Moon, Hwa Seob Song, Min-Sik Lee.

  The tumor suppressor p53, long known for its roles in maintaining genomic integrity and suppressing tumorigenesis, has recently been recognized as a key regulator of cellular metabolism. Here, we review p53's emerging metabolic functions, highlighting its ability to orchestrate glucose, amino acid, and lipid metabolism. By promoting oxidative phosphorylation while inhibiting glycolysis and anabolic pathways, wild-type p53 counters metabolic reprogramming characteristic of cancer cells, such as the Warburg effect, and protects cells from mild cellular stresses. In contrast, mutant p53 disrupts these processes, fostering metabolic adaptations that support tumor progression. These findings pave the way for therapeutic approaches targeting p53-driven metabolic vulnerabilities in cancer.

Keywords:  Cancer metabolism; Metabolic reprogramming; Tumor suppressor; p53 (TP53)

DOI:  https://doi.org/10.1016/j.mocell.2025.100198
Antioxidants (Basel). 2025 Feb 10. pii: 201. [Epub ahead of print]14(2):

Oxidative Stress and Reprogramming of Lipid Metabolism in Cancers.

Siqi Li, Hang Yuan, Liang Li, Qin Li, Ping Lin, Kai Li.

  Oxidative stress is a common event involved in cancer pathophysiology, frequently accompanied by unique lipid metabolic reprogramming phenomena. Oxidative stress is caused mainly by an imbalance between the production of reactive oxygen species (ROS) and the antioxidant system in cancer cells. Emerging evidence has reported that oxidative stress regulates the expression and activity of lipid metabolism-related enzymes, leading to the alteration of cellular lipid metabolism; this involves a significant increase in fatty acid synthesis and a shift in the way in which lipids are taken up and utilized. The dysregulation of lipid metabolism provides abundant intermediates to synthesize biological macromolecules for the rapid proliferation of cancer cells; moreover, it contributes to the maintenance of intracellular redox homeostasis by producing a variety of reducing agents. Moreover, lipid derivatives and metabolites play critical roles in signal transduction within cancer cells and in the tumor microenvironment that evades immune destruction and facilitates tumor invasion and metastasis. These findings suggest a close relationship between oxidative stress and lipid metabolism during the malignant progression of cancers. This review focuses on the crosstalk between the redox system and lipid metabolic reprogramming, which provides an in-depth insight into the modulation of ROS on lipid metabolic reprogramming in cancers and discusses potential strategies for targeting lipid metabolism for cancer therapy.

Keywords:  biomarker; cancer; cancer therapy; lipid metabolism; oxidative stress; redox signaling

DOI:  https://doi.org/10.3390/antiox14020201
ACS Meas Sci Au. 2025 Feb 19. 5(1): 109-119

Multi-Pass Arrival Time Correction in Cyclic Ion Mobility Mass Spectrometry for Imaging and Shotgun Lipidomics.

Pattipong Wisanpitayakorn, Narumol Jariyasopit, Kassaporn Duangkumpha, Jun Xian Goh, Martin E Palmer, Yongyut Sirivatanauksorn, Sakda Khoomrung.

Direct-infusion mass spectrometry (DI-MS) and mass spectrometry imaging (MSI) are powerful techniques for lipidomics research. However, annotating isomeric and isobaric lipids with these methods is challenging due to the absence of chromatographic separation. Recently, cyclic ion mobility mass spectrometry (cIM-MS) has been proposed to overcome this limitation. However, fluctuations in room conditions can affect ion mobility multipass arrival times, potentially reducing annotation confidence. In this study, we developed a multipass arrival time correction method that proved effective across various dates, room temperatures, ion mobility settings, and laboratories using mixtures of reference standards. We observed slight variations in the linear correction lines between lipid and nonlipid molecules, underscoring the importance of choosing appropriate reference molecules. Based on these results, we demonstrated that an accurate multipass arrival time database can be constructed from corrected t 0 and t p for interlaboratory use and can effectively identify isomeric lipids in MSI using only a single measurement. This approach significantly simplifies the identification process compared to determining multipass collision cross-section, which requires multiple measurements that are both sample- and time-intensive for MSI. Additionally, we validated our multipass drift time correction method in shotgun lipidomics analyses of human and mouse serum samples and observed no matrix effect for the analysis. Despite variations in dates, room temperatures, instruments, and ion mobility settings, our approach reduced the mean drift time differences from over 2% to below 0.2%.

DOI: https://doi.org/10.1021/acsmeasuresciau.4c00077
Anal Chem. 2025 Feb 24.

Dynamic Measurement of Protein Translation in Mycobacteria Using Nontargeted Stable Isotope Labeling in Combination with MALDI-TOF Mass Spectrometry-Based Readout.

Christoph Haisch, Anna-Cathrine Neumann-Cip, Axel Imhof, Andreas Schmidt, Ignasi Forne, Michael Hoelscher, Andreas Wieser.

Understanding the metabolic pathways of mycobacteria is essential to identify novel antibiotics and to compose synergistic antibiotic regimens against tuberculosis, one of the world's most deadly infectious diseases with >1.7 Mio yearly deaths. We present a novel proteomics approach for the dynamic measurement of the nascent fractions of specific proteins. We use nontargeted stable isotope incorporation to label the nascent proteins after adding glycerol-1,3-13C2. The analysis is performed using matrix-assisted laser desorption-ionization time-of-flight mass spectrometry (MALDI-TOF-MS) with a self-programmed script, allowing quantitative data. We compared the de novo synthesis of proteins under regular growth conditions and the effect of four antimicrobials, including rifampicin as a first-line drug, linezolid and bedaquiline as second-line drugs, and benzithiazinone-043 as promising drug candidates against tuberculosis. Changes in the synthesis of individual proteins, either due to antimicrobial action or due to regulations in the organism, can be followed in high temporal resolution within the 1/2 doubling cycle of mycobacteria. The analysis of de novo protein synthesis offers a fast screening and testing tool, allowing assessment of the onset and extent of antimycobacterial activity or regulatory phenotypes in different organisms. Due to the untargeted approach, it can be used in model strains and clinical isolates alike and does not require genetic modifications. The dynamic readout and labeling reveal the onset of action of drugs or drug candidates and allow for the prediction of synergistic effects of several substances.

DOI: https://doi.org/10.1021/acs.analchem.4c03931
Res Sq. 2025 Feb 11. pii: rs.3.rs-4138879. [Epub ahead of print]

A clinical drug candidate that triggers non-apoptotic cancer cell death.

Scott Dixon, Logan Leak, Ziwei Wang, Weaverly Colleen Lee, Brianna Johnson, Alec Millner, Pin-Joe Ko, Cassandra Decosto, Leslie Magtanong, Joan Ritho, Rachid Skouta, Ekin Atilla-Gokcumen, Chad Myers, Jason Moffat, Charles Boone, Steven Bensinger, Everett Moding, Alby Joseph, Alyssa Chan, Shweta Chitkara, Jenny Salinas, David Nathanson.

Small molecules that induce non-apoptotic cell death are of fundamental mechanistic interest and may be useful to treat certain cancers. Here, we report that tegavivint, a drug candidate undergoing human clinical trials, can activate a unique mechanism of non-apoptotic cell death in sarcomas and other cancer cells. This lethal mechanism is distinct from ferroptosis, necroptosis and pyroptosis and requires the lipid metabolic enzyme trans -2,3-enoyl-CoA reductase (TECR). TECR is canonically involved in the synthesis of very long chain fatty acids but appears to promote non-apoptotic cell death in response to CIL56 and tegavivint via the synthesis of the saturated long-chain fatty acid palmitate. These findings outline a lipid-dependent non-apoptotic cell death mechanism that can be induced by a drug candidate currently being tested in humans.

DOI: https://doi.org/10.21203/rs.3.rs-4138879/v1
Anal Chem. 2025 Feb 25.

Microflow Liquid Chromatography Coupled to Multinozzle Electrospray Ionization for Improved Lipidomics Coverage of 3D Clear Cell Renal Cell Carcinoma.

Sergey Girel, Mathieu Galmiche, Mathis Fiault, Valentin Mieville, Patrycja Nowak-Sliwinska, Serge Rudaz, Isabel Meister.

In most bioanalytical laboratories, high-resolution mass spectrometry (HRMS) systems with electrospray ionization (ESI) are hyphenated to liquid chromatography platforms. The latter typically operate under analytical flow (AF; 0.2-1 mL/min) regimes. Hence, AF/ESI-HRMS methods prioritize the detection of analytes of higher abundances or ionizability and tend to suffer from matrix effects or ion suppression. A far higher sensitivity can be obtained with electrospray at nanoflow (10-1000 nL/min) thanks to a better ionization efficiency and significant decrease in matrix effects. Both advantages are crucial to reliably accessing low-abundance compounds or weakly ionizable analytes. This work presents a microflow (μF) chromatographic setup coupled to a novel microfabricated multinozzle electrospray (mnESI) emitter with five nozzles spraying at 600 nL/min per nozzle for untargeted HRMS lipidomic profiling. With a runtime of 19 min, similar to our established analytical flow (AF/ESI) lipidomics platform, μF/mnESI produced a 16-fold median increase across 69 deuterated lipid standards. The performance of this new configuration was also evaluated in the context of the profiling of a 3D clear cell renal cell carcinoma (ccRCC) model exposed to a multidrug combination therapy. The processing of the acquired data resulted in 1270 (μF/mnESI) vs 752 (AF/ESI) MS2-annotated lipids. Among those, 762 achieved <10% variation on pooled QC samples for μF/mnESI compared to only 361 for the AF method. In addition, the measurements of ccRCC samples confirmed the improvements in ionization efficiency and adduct patterns observed with standards, enabling to annotate 79 oxidized triglycerides, 38 cholesterol esters (only five and four detected in AF/ESI, respectively), and 12 sitosterol esters, not yet reported in mammalian cell cultures.

DOI: https://doi.org/10.1021/acs.analchem.4c06337
J Proteome Res. 2025 Feb 25.

Isobaric Labeling Update in MaxQuant.

Daniela Ferretti, Pelagia Kyriakidou, Jinqiu Xiao, Shamil Urazbakhtin, Carlo De Nart, Jürgen Cox.

  We present an update of the MaxQuant software for isobaric labeling data and evaluate its performance on benchmark data sets. Impurity correction factors can be applied to labels mixing C- and N-type reporter ions such as TMT Pro. Application to a single-cell multispecies mixture benchmark shows the high accuracy of the impurity-corrected results. TMT data recorded with FAIMS separation can be analyzed directly in MaxQuant without splitting the raw data into separate files per FAIMS voltage. Weighted median normalization is applied to several data sets, including large-scale human body atlas data. In the benchmark data sets, the weighted median normalization either removes or strongly reduces the batch effects between different TMT plexes and results in clustering by biology. In data sets including reference channels, we find that weighted median normalization performs as well or better when the reference channels are ignored and only the sample channel intensities are used, suggesting that the measurement of reference channels is unnecessary when using weighted median normalization in MaxQuant. We demonstrate that MaxQuant including the weighted median normalization performs well on multinotch MS3 data, as well as on phosphorylation data. MaxQuant is freely available for any purpose and can be downloaded from https://www.maxquant.org/.

Keywords:  FAIMS; MaxQuant; TMT; UMAP; batch effects; human body map; isobaric labeling; normalization; single cell proteomics; t-SNE; weighted median normalization

DOI:  https://doi.org/10.1021/acs.jproteome.4c00869
Anal Chim Acta. 2025 Apr 01. pii: S0003-2670(25)00129-1. [Epub ahead of print]1345 343735

Chiral supercritical fluid chromatography-mass spectrometry with liquid chromatography fractionation for the characterization of enantiomeric composition of fatty acid esters of hydroxy fatty acids.

Oleksandr Kozlov, Miroslav Lísa, Martin Riecan, Ondrej Kuda.

   BACKGROUND: Fatty acid esters of hydroxy fatty acids (FAHFAs) are a recently discovered class of endogenous bioactive lipids with promising therapeutic potential for diabetes and inflammation. They represent complex mixtures of different isomers whose biological functions are the subject of investigation. Highly selective methods are required to characterize the composition of enantiomers in biological samples composed of many isobars and regioisomers. We aimed to develop a method for characterizing the enantiomeric composition of FAHFAs in biological samples using supercritical fluid chromatography-mass spectrometry (SFC-MS).
RESULTS: The influence of key chromatographic parameters, such as column chemistry, mobile phase composition, and gradient, on the separation efficiency of 21 commercially available FAHFA regioisomers without stated absolute configuration and 4 FAHFA enantiomers was assessed. The optimized SFC-MS method utilizes a chiral column based on a tris-(3-chloro-5-methylphenylcarbamate) derivative of amylose (Lux i-Amylose-3) and acetonitrile-methanol mobile phase modifier, enabling fast enantioseparation of most FAHFA racemic pairs in 5 min. However, the SFC separation of FAHFA regioisomers was less effective, limiting its applicability to complex biological samples. To address this, we propose an offline two-dimensional separation approach with reversed-phase liquid chromatography for isolating FAHFA regioisomers, followed by chiral SFC-MS analysis of fractions. The suitability of the method was demonstrated by characterizing the enantiomeric composition of FAHFA in white adipose tissue and rice samples. The chiral analysis revealed the presence of both R- and S-FAHFA isomers in the samples, with one enantiomer being predominant.
SIGNIFICANCE: The developed approach represents a proof of concept for the use of SFC-MS with LC prefractionation for the characterization of FAHFA enantiomeric composition in complex biological samples, providing a valuable tool for future research on the biological roles of bioactive lipids in health and disease.

Keywords:  Chiral separation; FAHFA; Isomers; Lipids; Offline two-dimensional chromatography; Supercritical fluid chromatography

DOI:  https://doi.org/10.1016/j.aca.2025.343735
J Proteome Res. 2025 Feb 27.

Discovery of Key Candidate Protein Biomarkers in Early-Stage Nonsmall Cell Lung Carcinoma through Quantitative Proteomics.

Baby Rorielyn T Dimayacyac-Esleta, Ferdinand D Mira, Lorenzo M Zarate, Ben Joshua O Porras, Dave Laurence A Juntilla, Lara Beatrice L Suñga, Venus B Pondevida, Sullian S Naval, Treah May S Sayo, Herdee Gloriane C Luna, Eloise I Prieto.

  Difficulties in early-stage diagnosis are among the factors contributing to the high mortality of nonsmall cell lung carcinoma (NSCLC) patients. Unfortunately, diagnostic biomarkers are currently lacking, limiting options in the clinic. To discover proteins that have potential for biomarker applications, we performed an in-depth quantitative proteomic analysis on a cohort of Filipino early-stage NSCLC lung adenocarcinoma (LUAD) patients. Differentially expressed proteins (DEPs) were obtained by using tandem mass tag (TMT) labeling and mass spectrometry (MS)-based quantitative proteomics. A total of 6240 quantified proteins were identified with 3155 significantly upregulated and 1248 significantly downregulated. Integration of the proteomic result with curated transcriptome data allowed the identification of 33 proteins with biomarker potential. This study also provided insights into relevant pathways in NSCLC LUAD, such as protein translation and metabolic pathways. Interestingly, all of the enzymes in the hexosamine biosynthetic pathway (HBP) are found to be upregulated, suggesting its important role in NSCLC LUAD. It is worthwhile to look at the potential of targeting the metabolic vulnerability of NSCLC LUAD as a new strategy in drug development. All MS data were deposited into ProteomeXchange with the identifier PXD050598.

Keywords:  LUAD; NSCLC; biomarker; hexosamine biosynthetic pathway; proteomics

DOI:  https://doi.org/10.1021/acs.jproteome.4c00764