Metabolomics最新文献

{"title":"A diagnostic algorithm for inherited metabolic disorders using untargeted metabolomics.","authors":"Qian Gao, Adnan Khan, Mette Christensen, Xiaomin Zhou, Allan Lund, Sabine Weller Grønborg, Flemming Wibrand, Elsebet Østergaard, Thomas Moritz","doi":"10.1007/s11306-025-02302-7","DOIUrl":"10.1007/s11306-025-02302-7","url":null,"abstract":"<p><strong>Introduction: </strong>Untargeted metabolomics is a powerful tool for detecting perturbations in biological systems, offering significant potential for screening for rare inherited metabolic disorders (IMDs). However, the rarity and vast diversity of these diseases, results in limited availability of samples and incomplete metabolic pathway knowledge for each condition. Current diagnostic procedures rely heavily on manual interpretation, which is time-consuming, and data driven approaches are insufficient for small sample sizes.</p><p><strong>Objectives: </strong>To develop a diagnostic algorithm for IMDs addressing the challenges posed by small sample sizes and continuously evolving datasets.</p><p><strong>Methods: </strong>77 IMD patients (35 different IMDs) and 136 control samples were collected from Copenhagen University Hospital, Rigshospitalet. The metabolome was analyzed using liquid chromatography-mass spectrometry. An algorithm partially based on sparse hierarchical clustering was designed to generate IMD-specific metabolic signatures from metabolomics data, enabling comparison with undiagnosed patient samples to provide diagnostic predictions. An iterative improvement strategy was employed, where new data are continuously integrated to refine the IMD-specific signatures. The algorithm's performance was evaluated through both the current study and a case study using literature-derived data.</p><p><strong>Results: </strong>The algorithm demonstrated iterative improvement with each training round, correctly identifying the diagnosis within top 3 potential IMDs in 60% of samples (top 1 in 42%). The case study applied the method to literature-based data comprising 95 IMD samples (11 different IMDs) and 68 controls, yielding a correct diagnosis in 73.5% of cases.</p><p><strong>Conclusion: </strong>These results demonstrate that the algorithm provides a flexible, data-driven framework for continuous improvement in IMD diagnosis, even with limited number of samples.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 4","pages":"101"},"PeriodicalIF":3.3,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12301266/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144732172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feasibility of amino acid profiling in long-term stored formalin-fixed paraffin-embedded colorectal neoplasia tissue.","authors":"Roza C M Opperman, Puck E Bruchner, Sofie Bosch, Tim G J de Meij, Evelien Dekker, Nanne K H de Boer, Eduard A Struys","doi":"10.1007/s11306-025-02301-8","DOIUrl":"10.1007/s11306-025-02301-8","url":null,"abstract":"<p><strong>Introduction: </strong>Metabolic processes play a role in cancer development, with faecal amino acids emerging as potential biomarkers for colorectal neoplasia. While fresh frozen tissue is preferred for metabolomic analysis, formalin-fixed paraffin-embedded (FFPE) tissue is more widely available.</p><p><strong>Objectives: </strong>We aimed to evaluate amino acid profiles in FFPE tissue across different stages of the adenoma-carcinoma sequence.</p><p><strong>Method: </strong>A panel of 20 amino acids was measured using liquid chromatography-tandem mass spectrometry.</p><p><strong>Results: </strong>Fourteen amino acids were detected, with proline elevated in colorectal carcinoma versus advanced (FC 2.33, p = 0.04) and non-advanced adenomas (FC 2.42, p = 0.02).</p><p><strong>Conclusion: </strong>Despite analytical challenges, amino acid profiling in FFPE tissue is feasible.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 4","pages":"102"},"PeriodicalIF":3.3,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12301268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144718115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of popular enrichment methods for untargeted in vitro metabolomics.","authors":"Yannik Schermer, Frederic Wagner, Simone Stegmüller, Elke Richling","doi":"10.1007/s11306-025-02309-0","DOIUrl":"10.1007/s11306-025-02309-0","url":null,"abstract":"<p><strong>Introduction: </strong>Untargeted metabolomics is a popular method by which researchers measure a large portion of the metabolites present in a biological system at once. This approach usually results in complex data sets containing tens to hundreds of thousands of observations which require sophisticated data analysis workflows. To help with the functional interpretation of the data, researchers often rely on enrichment analysis. However, little advice is available on what method to use, and, to the best of our knowledge, there is no comparison of popular approaches available for in vitro data with a focus on toxicological and pharmacological testing.</p><p><strong>Objectives: </strong>In this study, we compared three popular enrichment analysis approaches-Metabolite Set Enrichment Analysis (MSEA), Mummichog and Over Representation Analysis (ORA)-with data obtained by treating Hep-G2 cells with 11 compounds with five different mechanisms of action. We compared the results and assessed the consistency of the individual methods as well as their correctness.</p><p><strong>Methods: </strong>Hep-G2 cells were treated with subtoxic concentrations of 11 test compounds. After preparation, samples were measured on an Elute UHPLC coupled to a timsTOF Pro (both Bruker). Spectra were processed in MetaboScape (Bruker) and annotated using spectral library search. Datasets were further processed using R and enrichment analysis was performed in MetaboAnalyst.</p><p><strong>Results: </strong>Overall, we observed a low to moderate similarity between different enrichment methods with the highest similarity between MSEA and Mummichog. Further, Mummichog outperformed both MSEA and ORA in terms of consistency and correctness.</p><p><strong>Conclusion: </strong>In our comparison, Mummichog showed the best performance for in vitro untargeted metabolomics data.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 4","pages":"103"},"PeriodicalIF":3.3,"publicationDate":"2025-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12301278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144732173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Metabolite classification through novel metabolomics framework reveals mechanism underlying the therapeutic effects of PSD95-nNOS blockade for post-stroke depression.","authors":"Yudie Hu, Ran Xiao, Cong Wang, Huihui Meng, Benxing Yao, Qian Xu, Xi Xu, Junsong Wang","doi":"10.1007/s11306-025-02306-3","DOIUrl":"https://doi.org/10.1007/s11306-025-02306-3","url":null,"abstract":"<p><strong>Introduction: </strong>Post-stroke depression (PSD) pathophysiology involves glutamate excitotoxicity mediated through 'postsynaptic density protein95-neuronal nitric oxide synthase' (PSD95-nNOS) coupling. However, the therapeutic mechanisms of targeting this complex remain incompletely understood.</p><p><strong>Objective: </strong>To elucidate the antidepressant mechanisms of the PSD95-nNOS decoupler ZL006 using an innovative integrated metabolomics approach.</p><p><strong>Methods: </strong>We developed an innovative integrated metabolomics approach to investigate the antidepressant mechanisms of ZL006, a selective PSD95-nNOS decoupler. Using a rat model of PSD, we employed untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics combined with a novel analytical framework that distinguished treatment efficacy-associated metabolites from drug bias-associated ones. This classification enabled identification of primary drug mechanisms versus secondary responses. Pathway analyses focused on proteins interacting with drug-specific metabolites, with key findings validated through quantitative polymerase chain reaction (qPCR).</p><p><strong>Results: </strong>ZL006 demonstrated dose-dependent antidepressant effects while modulating multiple neurotransmitter pathways, including tryptophan, tyrosine, and arginine metabolism, along with steroid hormone synthesis. Our integrated metabolomics approach revealed vascular endothelial growth factor (VEGF) signaling, hypoxia-inducible factor (HIF) pathway, and tight junction regulation as primary mechanisms of action.</p><p><strong>Conclusion: </strong>This novel metabolomics strategy, by discriminating between treatment-associated and compound-intrinsic pathways, provided unprecedented mechanistic insights into ZL006's therapeutic effects. The findings suggest that ZL006 alleviates PSD through coordinated modulation of neuroplasticity, angiogenesis, and stress responses via PSD95-nNOS targeting. This integrated analytical approach presents a valuable framework for mechanistic investigation of therapeutic compounds.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 4","pages":"100"},"PeriodicalIF":3.5,"publicationDate":"2025-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144608744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma and ovarian metabolomic responses to chronic stress in female mice.","authors":"Nan Lin, Tianyi Huang, Chirag J Patel, Elizabeth M Poole, Clary B Clish, Guillermo N Armaiz-Pena, Archana S Nagaraja, A Heather Eliassen, Katherine H Shutta, Raji Balasubramanian, Laura D Kubzansky, Susan E Hankinson, Oana A Zeleznik, Anil K Sood, Shelley S Tworoger","doi":"10.1007/s11306-025-02287-3","DOIUrl":"10.1007/s11306-025-02287-3","url":null,"abstract":"<p><strong>Introduction: </strong>Chronic stress has been linked with higher risk of ovarian cancer and one posited pathway is through altered metabolism of amino acids, lipids, and other small molecule metabolites. However, the types of alterations that occur may not be uniform across tissue types.</p><p><strong>Objectives: </strong>We aim to examine and compare the impacts of chronic stress on metabolomic changes in circulation and ovarian tissue.</p><p><strong>Methods: </strong>Twelve-week-old, healthy, female, C57 black mice were randomly assigned to three-week of chronic stress using daily restraint (2-hours/day; n = 9) or normal care (n = 10). Metabolomic profiling was conducted on plasma and ovarian tissues via mass spectrometry. We utilized Wilcoxon Rank Tests, Metabolite Set Enrichment Analysis, Differential Network Analysis and a previously derived metabolite-based distress score to identify metabolomic alterations under restraint stress. We used the false discovery rate to account for testing multiple correlated comparisons.</p><p><strong>Results: </strong>In plasma, individual lysophosphatidylcholines and the metabolite class carnitines were positively associated while diacylglycerols and triacylglycerols were inversely associated with restraint stress (adjusted-p < 0.2). In contrast, in ovarian tissue, diacylglycerols and triacylglycerols were positively associated while carnitines were inversely associated with restraint stress (adjusted-p < 0.2). Other metabolites (cholesteryl esters, phosphatidylcholines/ phosphatidylethanolamines plasmalogens and multiple amino acids) were inversely associated with restraint stress in both plasma and ovarian tissue (adjusted-p < 0.2). A previously developed human metabolite-based distress score was higher in restraint stress mice compared to controls, with a larger difference observed in ovarian tissue than in plasma.</p><p><strong>Conclusion: </strong>These findings suggest research to understand the metabolic impact of chronic stress needs to consider both systemic and tissue-specific alterations.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 4","pages":"99"},"PeriodicalIF":3.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of normalization strategies for mass spectrometry-based multi-omics datasets.","authors":"Chi Yen Tseng, Jessica A Salguero, Joshua D Breidenbach, Emilia Solomon, Claire K Sanders, Tara Harvey, M Grace Thornhill, Salvator J Palmisano, Zachary J Sasiene, Brett R Blackwell, Ethan M McBride, Kes A Luchini, Erick S LeBrun, Marc Alvarez, Phillip M Mach, Emilio S Rivera, Trevor G Glaros","doi":"10.1007/s11306-025-02297-1","DOIUrl":"10.1007/s11306-025-02297-1","url":null,"abstract":"<p><strong>Introduction: </strong>Data normalization is crucial for multi-omics integration, reducing systematic errors and maximizing the likelihood of discovering true biological variation. Most studies assess normalization for a single omics type or use datasets from separate experiments. Few address time-course data, where normalization might bias temporal differentiation. In this study, we compared common normalization methods and a machine learning approach, Systematical Error Removal using Random Forest (SERRF), using multi-omics datasets generated from the same experiment-even from the same cell lysate.</p><p><strong>Objectives: </strong>To develop a straightforward process to assess normalization effects and identify the most robust methods across multi-omics datasets.</p><p><strong>Methods: </strong>We analyzed metabolomics, lipidomics, and proteomics datasets from primary human cardiomyocytes and motor neurons exposed to acetylcholine-active compounds over time. Normalization effectiveness was evaluated based on improvement in QC features consistency and observing the change in treatment and time-related variance.</p><p><strong>Results: </strong>Probabilistic Quotient Normalization (PQN) and Locally Estimated Scatterplot Smoothing (LOESS) QC were identified as optimal for metabolomics and lipidomics, while PQN, Median, and LOESS normalization excelled for proteomics. These methods consistently enhanced QC feature consistency in metabolomics and lipidomics, and preserved time-related variance or treatment-related variance in proteomics, demonstrating their effectiveness and robustness. SERRF normalization, applied only to metabolomics in this study, outperformed other methods in some datasets but inadvertently masked treatment-related variance in others.</p><p><strong>Conclusion: </strong>Our evaluation identified PQN and LoessQC as the top methods for metabolomics and lipidomics, and PQN, Median, and Loess normalization for proteomics, in multi-omics integration in a temporal study.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 4","pages":"98"},"PeriodicalIF":3.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12214035/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Oxygen attachment dissociation (OAD) MS/MS in the identification of positional isomers of dysregulated lipids detected in an ethanol exposure metabolomics study in mice.","authors":"Emily G Armitage, Alan Barnes, Olga Deda, Christina Virgiliou, Neil J Loftus, Helen Gika, Ian D Wilson","doi":"10.1007/s11306-025-02282-8","DOIUrl":"10.1007/s11306-025-02282-8","url":null,"abstract":"<p><strong>Introduction: </strong>In metabolic profiling studies the structural characterisation of lipids requires the identification of the head group, carbon number and the position(s) of carbon-carbon double bonds (C = C). Locating the position of double bonds is vital since minor structural differences between positional isomers can alter a lipid's biochemical function.</p><p><strong>Objectives: </strong>Oxygen Attachment Dissociation (OAD) is a novel fragmentation technology that enables the localisation of C = C double bonds in lipids. To evaluate its use in the structural characterisation of lipids, OAD has been applied in a discovery-based untargeted analysis of the metabolic impact of acute ethanol exposure in a mouse model.</p><p><strong>Methods: </strong>UHPLC-OAD-MS/MS was used to enhance the identification of lipids found to be significantly altered by acute ethanol exposure in the gut, liver and pancreas tissues of male C57BL/6 mice receiving a Lieber-DeCarli liquid diet either containing 5% ethanol or an isocaloric control diet. Tissue extracts were analysed using untargeted UHPLC-DIA-MS/MS; UHPLC-OAD-MS/MS analysis was performed to further annotate lipids that were significantly increased or diminished in the animals exposed to ethanol.</p><p><strong>Results: </strong>UHPLC-DIA-MS/MS analysis of gut, liver and pancreas tissue revealed 101 lipids that were significantly increased or diminished in ethanol treated mice. Of the included 83 unsaturated lipids detected, UHPLC-OAD-MS/MS enabled the localisation of C = C double bonds in 61, including isomers indistinguishable by MS/MS with collision induced dissociation.</p><p><strong>Conclusions: </strong>The results demonstrate the value of OAD-MS/MS in enhancing lipid identification. The resulting improvement may enable better understanding of the underlying biochemistry in the response of mice to exposure to ethanol.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 4","pages":"95"},"PeriodicalIF":3.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213981/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540893","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bioactive lipid profiles as non-invasive biomarkers of advanced fibrosis in people with HIV/HCV co-infection.","authors":"Tzu-Hao Lee, Kara Wegermann, Rachel Safeek, Meredith Mock, Sam Lusk, Lisa St John-Williams, Will J Thompson, Joseph E Lucas, M Arthur Moseley, Keyur Patel, Susanna Naggie","doi":"10.1007/s11306-025-02293-5","DOIUrl":"https://doi.org/10.1007/s11306-025-02293-5","url":null,"abstract":"<p><p>Non-invasive assessments for advanced liver fibrosis have limited accuracy in persons with human immunodeficiency virus (HIV) (PWH) who have hepatitis C virus (HCV) co-infection, and new tools are needed. Our aim was to discover oxylipin profiles associated with advanced liver fibrosis in treatment-naïve patients with HIV/HCV co-infection. Serum samples from 40 PWH with HCV were subjected to targeted oxylipin analysis. A model with AST and seven metabolites, including 5(S)-HEPE, 8-HETE, 14,15-DiHETrE, 4-HDoHE, 14- HDoHE, 7-HDoHE, and 9,10-DiHOME yielded an area under the receiver operating characteristic curve of 0.93, with optimal sensitivity and specificity of 86% and 88%, respectively.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 4","pages":"96"},"PeriodicalIF":3.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Untargeted metabolomic profiling of serum and urine in kidney cancer: a non-invasive approach for biomarker discovery.","authors":"Anna Ossolińska, Aneta Płaza-Altamer, Krzysztof Ossoliński, Tadeusz Ossoliński, Tomasz Ruman, Joanna Nizioł","doi":"10.1007/s11306-025-02294-4","DOIUrl":"10.1007/s11306-025-02294-4","url":null,"abstract":"<p><strong>Introduction: </strong>Kidney cancer (KC) is a significant global health burden. Early diagnosis remains challenging due to the limited sensitivity and specificity of existing biomarkers. Metabolomics enables the detection of disease-specific metabolic alterations, offering potential for improved non-invasive biomarker discovery.</p><p><strong>Objectives: </strong>This study aims to characterize metabolic signatures distinguishing KC patients from non-cancer controls and evaluate the diagnostic potential of annotated metabolites in serum and urine.</p><p><strong>Methods: </strong>An untargeted metabolomic analysis was performed on serum and urine samples from 56 KC patients and 200 controls using ultra-high-resolution mass spectrometry coupled with ultra-high-performance liquid chromatography (UHPLC-UHRMS in both positive and negative ionization modes with vacuum insulated probe heated electrospray ionization (VIP-HESI)). Samples were collected from the same individuals, which helped minimize inter-individual variability and enabled cross-biofluid comparison of metabolic profiles. Multivariate statistical techniques were applied to detect metabolic differences, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). An external validation strategy using training and validation subsets was employed to assess the robustness of candidate metabolite biomarkers matched in the discovery dataset.</p><p><strong>Results: </strong>Distinct metabolic signatures were observed between KC patients and controls, with key metabolic pathways involving lipid metabolism, amino acid biosynthesis, and glycerophospholipid metabolism. 19 serum and 12 urine metabolites showed high diagnostic potential (AUC > 0.90), demonstrating strong sensitivity and specificity.</p><p><strong>Conclusion: </strong>These findings support the application of metabolomics for RCC detection and highlight the metabolic alterations associated with kidney cancer. Further validation in larger cohorts is necessary to confirm the clinical utility of these potential biomarkers.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 4","pages":"97"},"PeriodicalIF":3.5,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12213972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144540895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic screening of COVID-19 based on multiomics data by high-resolution mass spectrometry (MALDI (+)-TOF MS and ESI(±)-Orbitrap MS).","authors":"Camila Medeiros de Almeida, Larissa Campos Motta, Gabriely Silveira Folli, Juliana de Mello do Carmo, Andréa Rodrigues Chaves, José Brango-Vanegas, Rosiane Andrade da Costa, Octavio Luiz Franco, Frederico Garcia Pinto, Denise Coutinho Endringer, Paulo Roberto Filgueiras, Valério Garrone Barauna, José Geraldo Mill, Wanderson Romão","doi":"10.1007/s11306-025-02299-z","DOIUrl":"https://doi.org/10.1007/s11306-025-02299-z","url":null,"abstract":"<p><strong>Introduction: </strong>The urgency for rapid diagnostics during the COVID-19 pandemic in 2020 highlighted the importance of effective methods such as RT-PCR, however multiomics analyses offer a more comprehensive approach, going beyond simple viral detection to the biological understanding of the disease.</p><p><strong>Objective: </strong>this study aimed to devise an effective multiomic method for differentiating SARS-CoV-2-infected patients, leveraging serum lipid and proteomic profiles.</p><p><strong>Method: </strong>Electrospray ionization mass spectrometry (ESI-MS) with an Orbitrap analyzer was used to investigate the lipid profile of 239 serum samples (119 positive and 120 negative for test ELISA for COVID-19). Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) was used to analyze the proteomic profile of 300 serum samples (150 positive and 150 negative for test ELISA for SARS-CoV-2). After processing MS data and selecting variables, statistical analyses such as the Volcano plot, Heatmap, Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Support Vector Machine (SVM) were performed to distinguish the most relevant variables to classify samples that presented or did not present antibodies for SARS-CoV-2.</p><p><strong>Results: </strong>Lipidomics analysis using ESI(±)-Orbitrap MS and SVM models, showed sensitivities of 96.67% and 100%, specificities of 82.14% and 96.88%, and accuracies of 89.66% and 98.44% for positive and negative ion mode analyses, respectively. For Proteomics analyses using MALDI(+)-TOF MS, the linear PLS-DA model demonstrated an accuracy of 99.10%.</p><p><strong>Conclusion: </strong>both ESI-Orbitrap MS and MALDI-TOF MS techniques, combined with chemometrics, demonstrated promising alternatives with high sensitivity and specificity for distinguishing the immune response. However, the investigation of the lipid profile by direct infusion ESI MS represents a valuable and efficient approach that reinforces the application of mass spectrometry in clinical diagnostics, particularly when aiming for high-throughput and minimally invasive analysis.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"21 4","pages":"94"},"PeriodicalIF":3.5,"publicationDate":"2025-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144528724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}