bims-mascan Biomed News
on Mass spectrometry in cancer research
Issue of 2024–12–22
sixteen papers selected by
Giovanny Rodríguez Blanco, Uniklinikum Graz
  1. Electrophoresis-Correlative Ion Mobility Deepens Single-cell Proteomics in Capillary Electrophoresis Mass Spectrometry.
  2. DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data.
  3. Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification.
  4. Diazonium-based derivatization for enhanced detection of phosphorylated metabolites by LC-MS in cells.
  5. Compartment-Resolved Proteomics with Deep Extracellular Matrix Coverage.
  6. Development of an Optimized LC-MS Workflow for Host Cell Protein Characterization to Support Upstream Process Development.
  7. Establishment of MS LOC platform and its pilot application in clinical lipidomics.
  8. Protocol for quantifying xenografted human cancer cells in zebrafish larvae using Cellpose.
  9. Fatty Acid Synthase-Derived Lipid Stores Support Breast Cancer Metastasis.
  10. A hormetic response model for glutamine stress in cancer.
  11. Improving Glycoproteomic Analysis Workflow by Systematic Evaluation of Glycopeptide Enrichment, Quantification, Mass Spectrometry Approach, and Data Analysis Strategies.
  12. Challenges of MS-based small extracellular vesicles proteomics.
  13. Mass Spectrometry-Based Workflow for the Identification and Quantification of Alternative and Canonical Proteins in Pancreatic Cancer Cells.
  14. High-Recovery Desalting Tip Columns for a Wide Variety of Peptides in Mass Spectrometry-Based Proteomics.
  15. Bromine signature coded derivatization LC-MS for specific profiling of carboxyl or carbonyl-containing metabolites in Mycoplasma pneumoniae infection.
  16. Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies.
  1. Mol Cell Proteomics. 2024 Dec 12. pii: S1535-9476(24)00182-8. [Epub ahead of print] 100892
    Electrophoresis-Correlative Ion Mobility Deepens Single-cell Proteomics in Capillary Electrophoresis Mass Spectrometry.
    Bowen Shen, Fei Zhou, Peter Nemes.
      Detection of trace-sensitive signals is a current challenge in single-cell mass spectrometry (MS) proteomics. Separation prior to detection improves the fidelity and depth of proteome identification and quantification. We recently recognized capillary electrophoresis (CE) electrospray ionization (ESI) for ordering peptides into mass-to-charge (m/z)-dependent series, introducing electrophoresis-correlative (Eco) data-independent acquisition. Here, we demonstrate that these correlations based on electrophoretic mobility (μef) in the liquid phase are transferred into the gas phase, essentially temporally ordering the peptide ions into charge-dependent ion mobility (IM, 1/K0) trends (ρ > 0.97). Rather than sampling the entire IM region broadly, we pursued these predictable correlations to schedule narrower frames. Compared to classical ddaPASEF, Eco-framing significantly enhanced the resolution of IM MS (IMS) on a trapped ion mobility mass spectrometer (timsTOF PRO). This approach returned ∼50% more proteins from HeLa proteome digests approximating to one-to-two cells, identifying ∼962 proteins from ∼200 pg in <20 min of effective electrophoresis, without match-between-runs. As a proof of principle, we deployed Eco-IMS on 1,157 proteins by analyzing <4% of the total proteome in single, yolk-laden embryonic stem cells (∼80-μm) that were isolated from the animal cap of the South African clawed frog (Xenopus laevis). Quantitative profiling of 9 different blastomeres revealed detectable differences among these cells, which are normally fated to form the ectoderm but retain pluripotentiality. Eco-framing effectively deepens the proteome sensitivity in IMS using ddaPASEF, facilitating the proteome-driven classification of embryonic cell differentiation, as demonstrated in this report.
    Keywords:  Single cell; Xenopus laevis; capillary electrophoresis; mass spectrometry; proteomics
    DOI:  https://doi.org/10.1016/j.mcpro.2024.100892
  2. BMC Bioinformatics. 2024 Dec 18. 25(1): 383
    DNEA: an R package for fast and versatile data-driven network analysis of metabolomics data.
    Christopher Patsalis, Gayatri Iyer, Marci Brandenburg, Alla Karnovsky, George Michailidis.
       BACKGROUND: Metabolomics is a high-throughput technology that measures small molecule metabolites in cells, tissues or biofluids. Analysis of metabolomics data is a multi-step process that involves data processing, quality control and normalization, followed by statistical and bioinformatics analysis. The latter step often involves pathway analysis to aid biological interpretation of the data. This approach is limited to endogenous metabolites that can be readily mapped to metabolic pathways. An alternative to pathway analysis that can be used for any classes of metabolites, including unknown compounds that are ubiquitous in untargeted metabolomics data, involves defining metabolite-metabolite interactions using experimental data. Our group has developed several network-based methods that use partial correlations of experimentally determined metabolite measurements. These were implemented in CorrelationCalculator and Filigree, two software tools for the analysis of metabolomics data we developed previously. The latter tool implements the Differential Network Enrichment Analysis (DNEA) algorithm. This analysis is useful for building differential networks from metabolomics data containing two experimental groups and identifying differentially enriched metabolic modules. While Filigree is a user-friendly tool, it has certain limitations when used for the analysis of large-scale metabolomics datasets.
    RESULTS: We developed the DNEA R package for the data-driven network analysis of metabolomics data. We present the DNEA workflow and functionality, algorithm enhancements implemented with respect to the package's predecessor, Filigree, and discuss best practices for analyses. We tested the performance of the DNEA R package and illustrated its features using publicly available metabolomics data from the environmental determinants of diabetes in the young. To our knowledge, this package is the only publicly available tool designed for the construction of biological networks and subsequent enrichment testing for datasets containing exogenous, secondary, and unknown compounds. This greatly expands the scope of traditional enrichment analysis tools that can be used to analyze a relatively small set of well-annotated metabolites.
    CONCLUSIONS: The DNEA R package is a more flexible and powerful implementation of our previously published software tool, Filigree. The modular structure of the package, along with the parallel processing framework built into the most computationally extensive steps of the algorithm, make it a powerful tool for the analysis of large and complex metabolomics datasets.
    Keywords:  Enrichment analysis; Metabolomics; Network analysis; Network visualization; Partial correlation; Pathway analysis
    DOI:  https://doi.org/10.1186/s12859-024-05994-1
  3. Proteomics. 2024 Dec 18. e202400087
    Advancements in Global Phosphoproteomics Profiling: Overcoming Challenges in Sensitivity and Quantification.
    Gul Muneer, Ciao-Syuan Chen, Yu-Ju Chen.
      Protein phosphorylation introduces post-genomic diversity to proteins, which plays a crucial role in various cellular activities. Elucidation of system-wide signaling cascades requires high-performance tools for precise identification and quantification of dynamics of site-specific phosphorylation events. Recent advances in phosphoproteomic technologies have enabled the comprehensive mapping of the dynamic phosphoproteomic landscape, which has opened new avenues for exploring cell type-specific functional networks underlying cellular functions and clinical phenotypes. Here, we provide an overview of the basics and challenges of phosphoproteomics, as well as the technological evolution and current state-of-the-art global and quantitative phosphoproteomics methodologies. With a specific focus on highly sensitive platforms, we summarize recent trends and innovations in miniaturized sample preparation strategies for micro-to-nanoscale and single-cell profiling, data-independent acquisition mass spectrometry (DIA-MS) for enhanced coverage, and quantitative phosphoproteomic pipelines for deep mapping of cell and disease biology. Each aspect of phosphoproteomic analysis presents unique challenges and opportunities for improvement and innovation. We specifically highlight evolving phosphoproteomic technologies that enable deep profiling from low-input samples. Finally, we discuss the persistent challenges in phosphoproteomic technologies, including the feasibility of nanoscale and single-cell phosphoproteomics, as well as future outlooks for biomedical applications.
    Keywords:  data‐independent acquisition; mass spectrometry; phosphoproteomics; protein phosphorylation
    DOI:  https://doi.org/10.1002/pmic.202400087
  4. J Pharm Biomed Anal. 2024 Dec 16. pii: S0731-7085(24)00684-8. [Epub ahead of print]255 116642
    Diazonium-based derivatization for enhanced detection of phosphorylated metabolites by LC-MS in cells.
    Yikang Wang, Feifei Lin, Guozheng Zhu, Xiaoxue Zhou, Youhong Hu, Jia Liu.
      Phosphorylated small molecule metabolites play crucial roles in physiological processes such as glycogen metabolism and inflammation regulation. However, their high polarity, structural similarity, poor chromatographic separation, and weak mass spectrometric signals make their accurate quantification challenging, thereby hindering the study of related metabolic mechanisms and diseases. To address these challenges, we developed a novel derivatization reagent, DMQX (5-diazomethane quinoxaline), and combined it with liquid chromatography-mass spectrometry (LC-MS). This approach achieved baseline separation of five groups of isomers and enabled the quantification of 24 phosphorylated metabolites, providing comprehensive coverage of these metabolites in biological pathways. We applied this method to quantify 21 endogenous phosphorylated metabolites in HepG2 cells with and without vesicular stomatitis virus infection, demonstrating the potential of this analytical approach for advancing the study of metabolic mechanisms through quantitative analysis of phosphorylated metabolites in biological samples.
    Keywords:  Chromatographic separation; Derivatization; LC-MS; Phosphorylated metabolite; Quantitative analysis
    DOI:  https://doi.org/10.1016/j.jpba.2024.116642
  5. Bio Protoc. 2024 Dec 05. 14(23): e5123
    Compartment-Resolved Proteomics with Deep Extracellular Matrix Coverage.
    Maxwell C McCabe, Anthony J Saviola, Kirk C Hansen.
      The extracellular matrix (ECM) is a complex network of proteins that provides structural support and biochemical cues to cells within tissues. Characterizing ECM composition is critical for understanding this tissue component's roles in development, homeostasis, and disease processes. This protocol describes an integrated pipeline for profiling both cellular and ECM proteins across varied tissue types using mass spectrometry-based proteomics. The workflow covers stepwise extraction of cellular and extracellular proteins, enzymatic digestion into peptides, peptide cleanup, mass spectrometry analysis, and bioinformatic data processing. The key advantages include unbiased coverage of cellular, ECM-associated, and core-ECM proteins, including the fraction of ECM that cannot be solubilized using strong chaotropic agents such as urea or guanidine hydrochloride. Additionally, the method has been optimized for reproducible ECM enrichment and quantification across diverse tissue samples. This protocol enables systematic mapping of the ECM at a proteome-wide scale. Key features • Improved profiling of core extracellular matrix and matrisome-associated proteins through multi-step decellularization and chemical extraction of insoluble ECM • Extraction buffers optimized for effectiveness across a broad range of tissue types and compatibility with varied MS platforms • Measurement of protein solubility via resistance to detergent and chaotrope extraction • Integrated LC-MS/MS analysis and data processing pipeline for ECM-focused analysis.
    Keywords:  collagen; extracellular matrix; mass spectrometry; matrisome; protein extraction; proteomics
    DOI:  https://doi.org/10.21769/BioProtoc.5123
  6. J Proteome Res. 2024 Dec 19.
    Development of an Optimized LC-MS Workflow for Host Cell Protein Characterization to Support Upstream Process Development.
    Janik D Seidel, Mark R Condina, Manuela Klingler-Hoffmann, Clifford Young, Leigh Donnellan, Craig Kyngdon, Peter Hoffmann.
      Host cell proteins (HCPs) coexpressed during the production of biotherapeutics can affect the safety, efficacy, and stability of the final product. As such, monitoring HCP populations and amounts throughout the production and purification process is an essential part of the overall quality control framework. Mass spectrometry (MS) is used as an orthogonal method to enzyme-linked immunosorbent assays (ELISA) for the simultaneous identification and quantification of HCPs, particularly for the analysis of downstream processes. In this study, we present an MS-based analytical protocol with improvements in both speed and identification performance that can be implemented for routine analysis to support upstream process development. The protocol adopts a streamlined sample preparation strategy, combined with a high-throughput MS analysis pipeline. The developed method identifies and quantifies over 1000 HCPs, including 20 proteins listed as high risk in the literature, in a clarified cell culture sample with repeatability and precision shown for digest replicates. In addition, we explore the effects of varying standard spike-ins and changes to the data processing pipeline on absolute quantification estimates of the HCPs, which highlight the importance of standardization for wider use in the industry. Data are available via ProteomeXchange with the identifier PXD053035.
    Keywords:  Chinese hamster ovary; LC-MS; absolute quantification; bioprocessing; clarified cell culture fluid; data-independent acquisition; hi3 quantification; host cell proteins; process analytical technologies
    DOI:  https://doi.org/10.1021/acs.jproteome.4c00637
  7. Talanta. 2024 Dec 07. pii: S0039-9140(24)01696-5. [Epub ahead of print]285 127314
    Establishment of MS LOC platform and its pilot application in clinical lipidomics.
    Xiaoming Chen, Shuo Cao, Liye Tao, Runlan Yan, Sheng Cao, Jingwen Hao, Yuelin Yi, Chunyan Luan, Jianmin Wu, Yue Gao, Xiao Liang.
      Lipidomics has demonstrated significant potential for disease diagnosis and prediction. The development and optimization of a robust mass spectrometry (MS) platform for lipidome analysis is critically important, as it can facilitate biomarker discovery, cohort testing, and performance evaluation in clinical lipidomics studies. In this work, we developed a high-throughput and reliable platform, termed MS Lab on a Chip (MS LOC), which integrates the MetArray chip, an automated lipidomics pretreatment protocol, and the reflectron matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) instrument. The MetArray chip, produced through a mass production process, exhibited exceptional stability as an MS substrate. The integration of automated lipid pretreatment and MS detection processes ensures high throughput, stability and efficiency during sample preparation. The analysis of various lipid standards and different types of biological samples enabled comprehensive investigation of lipid features and annotation using the MS LOC. Furthermore, a small cohort study, consisting of hepatocellular carcinoma (HCC) and non-HCC groups, was conducted on this platform, providing preliminary validation of its performance and suggesting that this platform offers a comprehensive protocol for clinical lipidomics testing.
    Keywords:  Clinical lipidomics; MS LOC; Mass spectrometry; MetArray chip
    DOI:  https://doi.org/10.1016/j.talanta.2024.127314
  8. STAR Protoc. 2024 Dec 09. pii: S2666-1667(24)00644-0. [Epub ahead of print]5(4): 103479
    Protocol for quantifying xenografted human cancer cells in zebrafish larvae using Cellpose.
    Karina Köpke, Carla-Sophie Lembke, Nynke Oosterhof, Emma S C Dijkstra, Judith T M L Paridaen.
      Here, we present a protocol for quantifying xenografted human glioblastoma stem cells (GSCs) in zebrafish larvae. We first describe steps for orthotopic xenotransplantation of GSCs into the midbrain of zebrafish larvae, immunofluorescent labeling, and confocal imaging. We then detail procedures for GSC quantification using the Cellpose algorithm. This protocol provides a technique for the semi-automatic segmentation and quantification of human cancer cells in xenograft experiments. With this approach, cancer cell survival and proliferation can be determined in an unbiased manner.
    Keywords:  Cancer; Computer sciences; Model Organisms
    DOI:  https://doi.org/10.1016/j.xpro.2024.103479
  9. Res Sq. 2024 Dec 05. pii: rs.3.rs-5510550. [Epub ahead of print]
    Fatty Acid Synthase-Derived Lipid Stores Support Breast Cancer Metastasis.
    Chaylen Andolino, Eylem Kulkoyluoglu Cotul, Zilin Xianyu, Yun Li, Divya Bhat, Mitchell Ayers, Kimberly K Buhman, Stephen D Hursting, Michael K Wendt, Dorothy Teegarden.
      Lipid accumulation is associated with breast cancer metastasis. However, the mechanisms underlying how breast cancer cells increase lipid stores and their functional role in disease progression remain incompletely understood. Herein we quantified changes in lipid metabolism and characterized cytoplasmic lipid droplets in metastatic versus non-metastatic breast cancer cells. 14 C-labeled palmitate was used to determine differences in fatty acid (FA) uptake and oxidation. Despite similar levels of palmitate uptake, metastatic cells increase lipid accumulation and oxidation of endogenous FAs compared to non-metastatic cells. Isotope tracing also demonstrated that metastatic cells support increased de novo lipogenesis by converting higher levels of glutamine and glucose into the FA precursor, citrate. Consistent with this, metastatic cells displayed increased levels of fatty acid synthase (FASN) and de novo lipogenesis. Genetic depletion or pharmacologic inhibition of FASN reduced cell migration, survival in anoikis assays, and in vivo metastasis. Finally, global proteomic analysis indicated that proteins involved in proteasome function, mitotic cell cycle, and intracellular protein transport were reduced following FASN inhibition of metastatic cells. Overall, these studies demonstrate that breast cancer metastases accumulate FAs by increasing de novo lipogenesis, storing TAG as cytoplasmic lipid droplets, and catabolizing these stores to drive several FAO-dependent steps in metastasis.
    DOI:  https://doi.org/10.21203/rs.3.rs-5510550/v1
  10. Trends Cancer. 2024 Dec 16. pii: S2405-8033(24)00274-7. [Epub ahead of print]
    A hormetic response model for glutamine stress in cancer.
    Shea F Grenier, Cosimo Commisso.
      Glutamine metabolism supports the development and progression of many cancers and is considered a therapeutic target. Attempts to inhibit glutamine metabolism have resulted in limited success and have not translated into clinical benefit. The outcomes of these clinical studies, along with preclinical investigations, suggest that cellular stress responses to glutamine deprivation or targeting may be modeled as a biphasic hormetic response. By recognizing the multifaceted aspects of glutamine metabolism inhibition within a more comprehensive biological framework, the adoption of this model may guide future fundamental and translational studies. To achieve clinical efficacy, we posit that as a field we will need to anticipate the hormetic effects of glutamine stress and consider how best to co-target cancer cell adaptive mechanisms.
    Keywords:  cancer; glutamine stress; hormesis; metabolism; targeted therapies; therapeutics
    DOI:  https://doi.org/10.1016/j.trecan.2024.11.008
  11. Anal Chem. 2024 Dec 16.
    Improving Glycoproteomic Analysis Workflow by Systematic Evaluation of Glycopeptide Enrichment, Quantification, Mass Spectrometry Approach, and Data Analysis Strategies.
    Zhenyu Sun, T Mamie Lih, Jongmin Woo, Liyuan Jiao, Yingwei Hu, Yuefan Wang, Hongyi Liu, Hui Zhang.
      Glycosylation is one of the most prevalent and crucial protein modifications. Quantitative site-specific characterization of glycosylation usually requires sophisticated intact glycopeptide analysis using glycoproteomics. Recent efforts have focused on the interrogation of intact glycopeptide analyses using tandem mass spectrometry. However, a systematic evaluation of the quantitative glycoproteomic workflow is still lacking. This study compared different strategies for glycopeptide enrichment alongside glycopeptide quantitation, as well as mass spectrometry and data analysis strategies, providing a comprehensive assessment of their efficacy. The ZIC-HILIC enrichment method demonstrated superior performance, representing a 26% improvement in identified glycopeptiudes compared to the MAX enrichment method. Quantification using TMT provided high precision and throughput with an average CV of 8%. Through systematic evaluation, this study established that the ZIC-HILIC enrichment method, quantification with TMT, and collision energies of 25, 35, and 45 using tandem mass spectrometry are the optimal workflow for higher-energy collisional dissociation (HCD) fragmentation, significantly enhancing the analysis of intact glycopeptides. Precise energy adjustment is crucial for the identification of certain glycans. Intact glycopeptides were analyzed using different software tools to investigate the identification and quantification of glycopeptides. By applying optimal settings, 5514 unique intact glycopeptides were in luminal and basal patient-derived xenograft (PDX) characterized models, highlighting distinct glycosylation profiles that may influence tumor behavior. This study offers a systematic approach to evaluate glycoproteomic analysis workflow.
    DOI:  https://doi.org/10.1021/acs.analchem.4c04466
  12. J Extracell Vesicles. 2024 Dec;13(12): e70020
    Challenges of MS-based small extracellular vesicles proteomics.
    Daniel Fochtman, Lukasz Marczak, Monika Pietrowska, Anna Wojakowska.
      Proteomic profiling of small extracellular vesicles (sEV) is a powerful tool for discovering biomarkers of various diseases. This process most often assisted by mass spectrometry (MS) usually lacks standardization and recognition of challenges which may lead to unreliable results. General recommendations for sEV MS analyses have been briefly given in the MISEV2023 guidelines. The present work goes into detail for every step of sEV protein profiling with an overview of factors influencing such analyses. This includes reporting and defining the sEV source and vesicle isolation, protein solubilization and digestion, 'offline' and 'online' sample complexity reduction, the analysis type itself, and subsequent data analysis. Every stage in this process affects the others, which could result in different outcomes. Although characterization and comparisons of different sEV isolation methods are known and accessible and MS-based profiling details are provided for cell or tissue samples, no consensus work has been ever published to describe the whole process of sEV proteomic analysis. Reliable results can be obtained from sEV profiling provided that the analysis is well planned, prepared for, and backed by pilot studies or appropriate research.
    Keywords:  extracellular vesicles; mass spectrometry; proteomics; reliability; sEV isolation
    DOI:  https://doi.org/10.1002/jev2.70020
  13. Cells. 2024 Nov 28. pii: 1966. [Epub ahead of print]13(23):
    Mass Spectrometry-Based Workflow for the Identification and Quantification of Alternative and Canonical Proteins in Pancreatic Cancer Cells.
    Clémence Guillon, Carole Pichereaux, Ikrame Lazar, Karima Chaoui, Emmanuelle Mouton-Barbosa, Mehdi Liauzun, Edith Gourbeyre, Pinar Altiner, David Bouyssié, Alexandre Stella, Odile Burlet-Schiltz, Serge Plaza, Yvan Martineau, Bertrand Fabre.
      The identification of small proteins and proteins produced from unannotated open reading frames (called alternative proteins or AltProts) has changed our vision of the proteome and has attracted more and more attention from the scientific community. Despite several studies investigating particular AltProts in diseases and demonstrating their importance in such context, we are still missing data on their expression and functions in many pathologies. Among these, pancreatic ductal adenocarcinoma (PDAC) is a particularly relevant case to study alternative proteins. Indeed, late detection of this disease, notably due to the lack of reliable biomarkers of early-stage PDAC, and the fact that tumors rapidly develop resistance to most of the treatments used in the clinics warrant the exploration of new repertoires of molecules. In the present article, we aim to investigate the alternative proteome of pancreatic cancer cell lines as a first attempt to decipher the expression of AltProts in PDAC. Thanks to a combined data-dependent and data-independent acquisition mass spectrometry workflow, we were able to identify tryptic peptides matching 113 AltProts in a panel of 6 cell lines. In addition, we identified AltProts differentially expressed between pancreatic cancer cell lines and other cells (HeLa and HEK293T). Finally, mining the TCGA and Gtex databases showed that the corresponding transcripts encoding several AltProts we identified are differentially expressed between PDAC tumors and normal tissues and are correlated with the patient's survival.
    Keywords:  alternative proteins; data independent acquisition; microproteins; pancreatic ductal adenocarcinoma; proteomics; short open reading frame-encoded peptides
    DOI:  https://doi.org/10.3390/cells13231966
  14. Anal Chem. 2024 Dec 16.
    High-Recovery Desalting Tip Columns for a Wide Variety of Peptides in Mass Spectrometry-Based Proteomics.
    Eisuke Kanao, Shunsuke Tanaka, Ayana Tomioka, Kosuke Ogata, Tetsuya Tanigawa, Takuya Kubo, Yasushi Ishihama.
      In mass spectrometry-based proteomics, loss-minimized peptide purification techniques play a key role in improving sensitivity and coverage. We have developed a desalting tip column packed with thermoplastic polymer-coated chromatographic particles, named ChocoTip, to achieve high recoveries in peptide purification by pipet-tip-based LC with centrifugation (tipLC). ChocoTip identified more than twice as many peptides from 20 ng of tryptic peptides from Hela cell lysate compared to a typical StageTip packed with chromatographic particles entangled in a Teflon mesh in tipLC. The high recovery of ChocoTip in tipLC was maintained for peptides with a wide variety of physical properties over the entire retention time range of the LC-MS/MS analysis, and was especially noteworthy for peptides with long retention times. These excellent properties are attributable to the unique morphology of ChocoTip, in which the thermoplastic polymer covers the pores, thereby inhibiting irreversible adsorption of peptides into mesopores of the chromatographic particles. ChocoTip is expected to find applications, especially in clinical proteomics and single-cell proteomics, where sample amounts are limited.
    DOI:  https://doi.org/10.1021/acs.analchem.4c03753
  15. Talanta. 2024 Dec 06. pii: S0039-9140(24)01727-2. [Epub ahead of print]285 127345
    Bromine signature coded derivatization LC-MS for specific profiling of carboxyl or carbonyl-containing metabolites in Mycoplasma pneumoniae infection.
    Jie Han, Qinyan Yang, Zheng Zhi, Na Li, Jian-Lin Wu.
      Carboxyl or carbonyl-containing metabolites (CoCCMs) are widely distributed in biological samples. Global profiling of CoCCMs is essential for ascertaining specific functions of metabolites and their potential physiological roles in biogenic activities. However, simultaneous determination of these compounds is hampered by poor ionization efficiency, vast polarity differences, wide discrepancy of concentration ranges. Herein, a novel bromine isotope derivatization reagent 5-bromo-2- hydrazinopyridine was employed for CoCCMs profiling by liquid chromatography-mass spectrometry (LC-MS). This method enabled rapid derivatization of 44 CoCCMs under mild conditions. Enhanced separation efficiencies, detection sensitivities, and distinctive MS fragmentation characteristics were observed. Furthermore, this method was demonstrated to be efficient in revealing metabolic alternations, and abnormal serum levels of 6-keto-PGF1α, 12(S)-HHTrE, 15(S)-HEPE and N-acetyl tryptophan were disclosed for the first time in Mycoplasma pneumoniae (MP) infectious patients. Finally, based on the distinctive 2 Da differences of molecular ion peak pairs with almost 1:1 intensity ratio originated from 79Br and 81Br isotopes, an MS-DIAL and Python assisted MS1 isotope screening-MS2 fragments characterization combination strategy was developed for rapid screening, classification, and identification of detected CoCCMs. A total of 1069 CoCCMs were detected, of which 198 CoCCMs were identified in untargeted analysis. Statistical analysis revealed altered metabolic pathways, while glutamic acid, oxoglutaric acid, succinic acid, pyruvic acid, glyceric acid, and glycine were selected as potential biomarkers of MP infection. This bromine signature coded derivatization-LC-MS approach was proved to be a valuable tool for global probing of CoCCMs in biological samples with high sensitivity and broad coverage.
    Keywords:  Bromine coded derivatization; Carboxyl or carbonyl-containing metabolites; Liquid chromatography-mass spectrometry; Mycoplasma pneumoniae infection
    DOI:  https://doi.org/10.1016/j.talanta.2024.127345
  16. Anal Chem. 2024 Dec 19.
    Introducing "Identification Probability" for Automated and Transferable Assessment of Metabolite Identification Confidence in Metabolomics and Related Studies.
    Thomas O Metz, Christine H Chang, Vasuk Gautam, Afia Anjum, Siyang Tian, Fei Wang, Sean M Colby, Jamie R Nunez, Madison R Blumer, Arthur S Edison, Oliver Fiehn, Dean P Jones, Shuzhao Li, Edward T Morgan, Gary J Patti, Dylan H Ross, Madelyn R Shapiro, Antony J Williams, David S Wishart.
      Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence─the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in the context of the chemical space being considered. Neither are they easily automated nor transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a database that matches an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multiproperty reference libraries constructed from a subset of the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.
    DOI:  https://doi.org/10.1021/acs.analchem.4c04060
This page is being maintained by Thomas Krichel. It was last updated on 2024‒12‒22 at 7:08.