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Anderson BG, Popov P, Cicali AR, Nwamba A, Evans CR, Kennedy RT. In-Depth Chemical Analysis of the Brain Extracellular Space Using In Vivo Microdialysis with Liquid Chromatography-Tandem Mass Spectrometry. Anal Chem 2024. [PMID: 39360623 DOI: 10.1021/acs.analchem.4c03806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2024]
Abstract
Metabolomic analysis of samples acquired in vivo from the brain extracellular space by microdialysis sampling can provide insights into chemical underpinnings of a given brain state and how it changes over time. Small sample volumes and low physiological concentrations have limited the identification of compounds from this compartment, so at present, we have scant knowledge of its composition. As a result, most in vivo measurements have limited depth of analysis. Here, we describe an approach to (1) identify hundreds of compounds in brain dialysate and (2) routinely detect many of these compounds in 5 μL microdialysis samples to enable deep monitoring of brain chemistry in time-resolved studies. Dialysate samples collected over 12 h were concentrated 10-fold and then analyzed using liquid chromatography with iterative tandem mass spectrometry (LC-MS/MS). Using this approach on dialysate from the rat striatum with both reversed-phase and hydrophilic interaction liquid chromatography yielded 479 unique compound identifications. 60% of the identified compounds could be detected in 5 μL of dialysate without further concentration using a single 20 min LC-MS analysis, showing that once identified, most compounds can be detected using small sample volumes and shorter analysis times compatible with routine in vivo monitoring. To detect more neurochemicals, LC-MS analysis of dialysate derivatized with light and isotopically labeled benzoyl chloride was employed. 872 nondegenerate benzoylated features were detected with this approach, including most small-molecule neurotransmitters and several metabolites involved in dopamine metabolism. This strategy allows deeper annotation of the brain extracellular space than previously possible and provides a launching point for defining the chemistry underlying brain states.
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Affiliation(s)
- Brady G Anderson
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Pavlo Popov
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Psychology, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Amanda R Cicali
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Adanna Nwamba
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Charles R Evans
- Biomedical Research Core Facilities Metabolomics Core, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Robert T Kennedy
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
- Department of Pharmacology, University of Michigan, Ann Arbor, Michigan 48109, United States
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2
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Zhao M, Chen Z, Ye D, Yu R, Yang Q. Comprehensive lipidomic profiling of human milk from lactating women across varying lactation stages and gestational ages. Food Chem 2024; 463:141242. [PMID: 39278081 DOI: 10.1016/j.foodchem.2024.141242] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Revised: 08/28/2024] [Accepted: 09/09/2024] [Indexed: 09/17/2024]
Abstract
An untargeted lipidomic analysis was conducted to investigate the lipid composition of human milk across different lactation stages and gestational ages systematically. A total of 25 lipid subclasses and 934 lipid species as well as 90 free fatty acids were identified. Dynamic changes of the lipids throughout lactation and gestational phases were highlighted. In general, lactation stages introduced more variations in the lipid composition of human milk than gestational ages. Most lipids decreased as the milk progressed from the colostral stage to the mature stage, with some reaching a peak at the transitional stage. Significant variations in lipid composition across gestational ages were predominantly evident during early lactation period. In mature milks, most of the lipids exhibited no discernible statistical differences among gestational ages. This elucidation offers valuable insights and guidance for tailoring precise nutritional strategies for infants with diverse health needs.
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Affiliation(s)
- Min Zhao
- Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China
| | - Zhenying Chen
- Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China
| | - Danni Ye
- Department of Neonatology, Affiliated Women's Hospital of Jiangnan University, Wuxi 214002, China
| | - Renqiang Yu
- Department of Neonatology, Affiliated Women's Hospital of Jiangnan University, Wuxi 214002, China.
| | - Qin Yang
- Wuxi School of Medicine, Jiangnan University, Wuxi 214122, China; Wuxi Translational Medicine Research Center and School of Translational Medicine, Jiangnan University, Wuxi 214122, China.
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3
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Rakusanova S, Cajka T. Metabolomics and Lipidomics for Studying Metabolic Syndrome: Insights into Cardiovascular Diseases, Type 1 & 2 Diabetes, and Metabolic Dysfunction-Associated Steatotic Liver Disease. Physiol Res 2024; 73:S165-S183. [PMID: 39212142 PMCID: PMC11412346 DOI: 10.33549/physiolres.935443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/04/2024] Open
Abstract
Metabolomics and lipidomics have emerged as tools in understanding the connections of metabolic syndrome (MetS) with cardiovascular diseases (CVD), type 1 and type 2 diabetes (T1D, T2D), and metabolic dysfunction-associated steatotic liver disease (MASLD). This review highlights the applications of these omics approaches in large-scale cohort studies, emphasizing their role in biomarker discovery and disease prediction. Integrating metabolomics and lipidomics has significantly advanced our understanding of MetS pathology by identifying unique metabolic signatures associated with disease progression. However, challenges such as standardizing analytical workflows, data interpretation, and biomarker validation remain critical for translating research findings into clinical practice. Future research should focus on optimizing these methodologies to enhance their clinical utility and address the global burden of MetS-related diseases.
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Affiliation(s)
- S Rakusanova
- Laboratory of Translational Metabolism, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic.
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4
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Metz TO, Chang CH, Gautam V, Anjum A, Tian S, Wang F, Colby SM, Nunez JR, Blumer MR, Edison AS, Fiehn O, Jones DP, Li S, Morgan ET, Patti GJ, Ross DH, Shapiro MR, Williams AJ, Wishart DS. Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.30.605945. [PMID: 39131324 PMCID: PMC11312557 DOI: 10.1101/2024.07.30.605945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
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 context of the chemical space being considered, are easily automated, or are 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 reference library or chemical space that match to 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 multi-property reference libraries constructed from 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.
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Affiliation(s)
- Thomas O. Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Christine H. Chang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Afia Anjum
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Siyang Tian
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Fei Wang
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Sean M. Colby
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Jamie R. Nunez
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Madison R. Blumer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Arthur S. Edison
- Department of Biochemistry & Molecular Biology, Complex Carbohydrate Research Center and Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis, Davis, CA, USA
| | - Dean P. Jones
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia, USA
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Edward T. Morgan
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Gary J. Patti
- Center for Mass Spectrometry and Metabolic Tracing, Department of Chemistry, Department of Medicine, Washington University, Saint Louis, Missouri, USA
| | - Dylan H. Ross
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Madelyn R. Shapiro
- Artificial Intelligence & Data Analytics Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Antony J. Williams
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), Research Triangle Park, NC USA
| | - David S. Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
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Di Francesco G, Vincenti F, Montesano C, Bracaglia I, Croce M, Napoletano S, Lombardozzi A, Sergi M. Target and suspect screening of psychoactive substances in seizures and oral fluid exploiting retention time prediction and LC-MS/MS analysis. Anal Chim Acta 2024; 1303:342529. [PMID: 38609268 DOI: 10.1016/j.aca.2024.342529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 03/08/2024] [Accepted: 03/25/2024] [Indexed: 04/14/2024]
Abstract
BACKGROUND Novel psychoactive substances (NPS) are a group of substances, mainly of synthetic origin, characterized by toxicological properties extremely dangerous. The main difficulty in recognizing NPS in seizures and biological samples lies in their dynamic nature, related to the continuous synthesis and introduction on the market of new drugs, often with very similar structures to existing ones. The aim of this study was the creation of a robust and versatile method for the analysis of traditional drugs and NPS in different matrices. RESULTS Both target analysis and suspect screening methodologies were developed. The strategy used for suspect screening allowed to collect data through a scheduled multi reaction monitoring (sMRM) survey which triggered the collection of enhanced product ion (EPI) spectra when a compound met information dependent acquisition (IDA) criteria. The retention time of the different drugs, which was crucial to define the sMRM survey scan parameters, was predicted with a Quantitative Structure Retention (Chromatographic) Relationship (QSRR) model by Multiple Linear Regression. The model was validated through the evaluation of training set predictions, an external validation set and a leave-one out strategy; the results showed that the method fit for its purpose. The target method was validated in oral fluid as a testing matrix, with excellent results in term of recovery, accuracy, precision and matrix effect. Finally, the performances of the suspect method were evaluated by analysing a mixture containing 8 reference standards not included in the initial dataset, as well as seizures and real oral fluid samples. Four NPS were putatively identified in the analysed samples. SIGNIFICANCE The advantage of the proposed approach is the possibility of quantifying 65 classical drugs of abuse and NPS and, at the same time, detect and putatively identify 146 additional drugs in one single LC-MS/MS run. This is an innovative strategy for multi analyte detection and enables detection of low concentrations of drugs in complex biological matrices such as oral fluid. Considering the highly dynamic drug market, a strength of this strategy is that the analytical method can be kept up to date through the addition of new compounds based on the last drug monitoring bodies alerts without the need of authentic standards.
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Affiliation(s)
| | | | - Camilla Montesano
- Department of Chemistry, University La Sapienza, 00185, Rome, Italy.
| | - Ilenia Bracaglia
- Department of Chemistry, University La Sapienza, 00185, Rome, Italy
| | - Martina Croce
- Department of Chemistry, University La Sapienza, 00185, Rome, Italy; Department of Public Health and Infectious Disease, Sapienza University of Rome, 00185, Rome, Italy
| | - Sabino Napoletano
- Department of Public Security, Central Anticrime Directorate of Italian National Police, Forensic Science Police Service (DAC-SPS), Rome, Italy
| | - Antonietta Lombardozzi
- Department of Public Security, Central Anticrime Directorate of Italian National Police, Forensic Science Police Service (DAC-SPS), Rome, Italy
| | - Manuel Sergi
- Department of Chemistry, University La Sapienza, 00185, Rome, Italy
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Mazraedoost S, Žuvela P, Ulenberg S, Bączek T, Liu JJ. Cross-column density functional theory-based quantitative structure-retention relationship model development powered by machine learning. Anal Bioanal Chem 2024:10.1007/s00216-024-05243-7. [PMID: 38507043 DOI: 10.1007/s00216-024-05243-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2023] [Revised: 03/03/2024] [Accepted: 03/06/2024] [Indexed: 03/22/2024]
Abstract
Quantitative structure-retention relationship (QSRR) modeling has emerged as an efficient alternative to predict analyte retention times using molecular descriptors. However, most reported QSRR models are column-specific, requiring separate models for each high-performance liquid chromatography (HPLC) system. This study evaluates the potential of machine learning (ML) algorithms and quantum mechanical (QM) descriptors to develop QSRR models that can predict retention times across three different reversed-phase HPLC columns under varying conditions. Four machine learning methods-partial least squares (PLS) regression, ridge regression (RR), random forest (RF), and gradient boosting (GB)-were compared on a dataset of 360 retention times for 15 aromatic analytes. Molecular descriptors were calculated using density functional theory (DFT). Column characteristics like particle size and pore size and experimental conditions like temperature and gradient time were additionally used as descriptors. Results showed that the GB-QSRR model demonstrated the best predictive performance, with Q2 of 0.989 and root mean square error of prediction (RMSEP) of 0.749 min on the test set. Feature analysis revealed that solvation energy (SE), HOMO-LUMO energy gap (∆E HOMO-LUMO), total dipole moment (Mtot), and global hardness (η) are among the most influential predictors for retention time prediction, indicating the significance of electrostatic interactions and hydrophobicity. Our findings underscore the efficiency of ensemble methods, GB and RF models employing non-linear learners, in capturing local variations in retention times across diverse experimental setups. This study emphasizes the potential of cross-column QSRR modeling and highlights the utility of ML models in optimizing chromatographic analysis.
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Affiliation(s)
- Sargol Mazraedoost
- Intelligent Systems Laboratory, Department of Chemical Engineering, Pukyong National University, Busan, 48513, Republic of Korea
| | - Petar Žuvela
- Intelligent Systems Laboratory, Department of Chemical Engineering, Pukyong National University, Busan, 48513, Republic of Korea
| | - Szymon Ulenberg
- Department of Pharmaceutical Chemistry, Medical University of Gdańsk, Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - Tomasz Bączek
- Department of Pharmaceutical Chemistry, Medical University of Gdańsk, Gen. J. Hallera 107, 80-416, Gdańsk, Poland
| | - J Jay Liu
- Intelligent Systems Laboratory, Department of Chemical Engineering, Pukyong National University, Busan, 48513, Republic of Korea.
- Institute of Cleaner Production Technology, Pukyong National University, (48513) 45, Yongso-Ro, Nam-Gu, Busan, South Korea.
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7
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Kretschmer F, Harrieder EM, Hoffmann MA, Böcker S, Witting M. RepoRT: a comprehensive repository for small molecule retention times. Nat Methods 2024; 21:153-155. [PMID: 38191934 DOI: 10.1038/s41592-023-02143-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2024]
Affiliation(s)
- Fleming Kretschmer
- Chair for Bioinformatics, Institute for Computer Science, Friedrich Schiller University Jena, Jena, Germany
| | - Eva-Maria Harrieder
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Neuherberg, Germany
| | - Martin A Hoffmann
- Chair for Bioinformatics, Institute for Computer Science, Friedrich Schiller University Jena, Jena, Germany
- Bright Giant GmbH, Jena, Germany
| | - Sebastian Böcker
- Chair for Bioinformatics, Institute for Computer Science, Friedrich Schiller University Jena, Jena, Germany.
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Neuherberg, Germany.
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany.
- Chair of Analytical Food Chemistry, TU München, Freising, Germany.
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8
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Sandström H, Rissanen M, Rousu J, Rinke P. Data-Driven Compound Identification in Atmospheric Mass Spectrometry. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306235. [PMID: 38095508 PMCID: PMC10885664 DOI: 10.1002/advs.202306235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 11/04/2023] [Indexed: 02/24/2024]
Abstract
Aerosol particles found in the atmosphere affect the climate and worsen air quality. To mitigate these adverse impacts, aerosol particle formation and aerosol chemistry in the atmosphere need to be better mapped out and understood. Currently, mass spectrometry is the single most important analytical technique in atmospheric chemistry and is used to track and identify compounds and processes. Large amounts of data are collected in each measurement of current time-of-flight and orbitrap mass spectrometers using modern rapid data acquisition practices. However, compound identification remains a major bottleneck during data analysis due to lacking reference libraries and analysis tools. Data-driven compound identification approaches could alleviate the problem, yet remain rare to non-existent in atmospheric science. In this perspective, the authors review the current state of data-driven compound identification with mass spectrometry in atmospheric science and discuss current challenges and possible future steps toward a digital era for atmospheric mass spectrometry.
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Affiliation(s)
- Hilda Sandström
- Department of Applied Physics, Aalto University, P.O. Box 11000, FI-00076, Aalto, Espoo, Finland
| | - Matti Rissanen
- Aerosol Physics Laboratory, Tampere University, FI-33720, Tampere, Finland
- Department of Chemistry, University of Helsinki, P.O. Box 55, A.I. Virtasen aukio 1, FI-00560, Helsinki, Finland
| | - Juho Rousu
- Department of Computer Science, Aalto University, P.O. Box 11000, FI-00076, Aalto, Espoo, Finland
| | - Patrick Rinke
- Department of Applied Physics, Aalto University, P.O. Box 11000, FI-00076, Aalto, Espoo, Finland
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Torigoe T, Takahashi M, Heravizadeh O, Ikeda K, Nakatani K, Bamba T, Izumi Y. Predicting Retention Time in Unified-Hydrophilic-Interaction/Anion-Exchange Liquid Chromatography High-Resolution Tandem Mass Spectrometry (Unified-HILIC/AEX/HRMS/MS) for Comprehensive Structural Annotation of Polar Metabolome. Anal Chem 2024; 96:1275-1283. [PMID: 38186224 DOI: 10.1021/acs.analchem.3c04618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2024]
Abstract
The accuracy of the structural annotation of unidentified peaks obtained in metabolomic analysis using liquid chromatography/tandem mass spectrometry (LC/MS/MS) can be enhanced using retention time (RT) information as well as precursor and product ions. Unified-hydrophilic-interaction/anion-exchange liquid chromatography high-resolution tandem mass spectrometry (unified-HILIC/AEX/HRMS/MS) has been recently developed as an innovative method ideal for nontargeted polar metabolomics. However, the RT prediction for unified-HILIC/AEX has not been developed because of the complex separation mechanism characterized by the continuous transition of the separation modes from HILIC to AEX. In this study, we propose an RT prediction model of unified-HILIC/AEX/HRMS/MS, which enables the comprehensive structural annotation of polar metabolites. With training data for 203 polar metabolites, we ranked the feature importance using a random forest among 12,420 molecular descriptors (MDs) and constructed an RT prediction model with 26 selected MDs. The accuracy of the RT model was evaluated using test data for 51 polar metabolites, and 86.3% of the ΔRTs (difference between measured and predicted RTs) were within ±1.50 min, with a mean absolute error of 0.80 min, indicating high RT prediction accuracy. Nontargeted metabolomic data from the NIST SRM 1950-Metabolites in frozen human plasma were analyzed using the developed RT model and in silico MS/MS prediction, resulting in a successful structural estimation of 216 polar metabolites, in addition to the 62 identified based on standards. The proposed model can help accelerate the structural annotation of unknown hydrophilic metabolites, which is a key issue in metabolomic research.
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Affiliation(s)
- Taihei Torigoe
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Masatomo Takahashi
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Omidreza Heravizadeh
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Kazuki Ikeda
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Kohta Nakatani
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Takeshi Bamba
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
| | - Yoshihiro Izumi
- Department of Systems Life Sciences, Graduate School of Systems Life Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
- Division of Metabolomics/Mass Spectrometry Center, Medical Research Center for High Depth Omics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
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10
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Basov NV, Rogachev AD, Aleshkova MA, Gaisler EV, Sotnikova YS, Patrushev YV, Tolstikova TG, Yarovaya OI, Pokrovsky AG, Salakhutdinov NF. Global LC-MS/MS targeted metabolomics using a combination of HILIC and RP LC separation modes on an organic monolithic column based on 1-vinyl-1,2,4-triazole. Talanta 2024; 267:125168. [PMID: 37708770 DOI: 10.1016/j.talanta.2023.125168] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 08/28/2023] [Accepted: 09/04/2023] [Indexed: 09/16/2023]
Abstract
The paper presents an LC-MS/MS-based approach to targeted screening of both polar and non-polar metabolites using a synthesized monolithic column which is a copolymer of styrene, divinylbenzene, and 1-vinyl-1,2,4-triazole. It was shown that this column in combination with eluents 20 mM (NH4)2CO3 + NH3 (pH = 9.8, eluent A) and ACN (eluent B) allows for separation of metabolites of different nature in two modes, HILIC and RP LC, and these methods are mutually complementary. A combination of analyses based on these two modes was proposed, allowing detection of about 400 metabolites in a total time of less than 30 min. Comparison of the developed method with those utilizing commercially available columns with sorbents of various types showed that it could provide a broader metabolite coverage. Using the developed approach, metabolomic screening of dried blood spots samples of mice exposed with X-ray was performed, and metabolites that could be considered as possible markers of irradiation exposure and organ tissue damage were detected. Analysis of marker metabolites revealed metabolic pathways that were altered by radiation exposure. Comparison of the results with literature data showed the effectiveness of the developed metabolomic screening approach.
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Affiliation(s)
- Nikita V Basov
- N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Acad. Lavrentiev Ave., 9, 630090, Novosibirsk, Russia; Novosibirsk State University, Pirogov Str., 2, 630090, Novosibirsk, Russia
| | - Artem D Rogachev
- N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Acad. Lavrentiev Ave., 9, 630090, Novosibirsk, Russia; Novosibirsk State University, Pirogov Str., 2, 630090, Novosibirsk, Russia.
| | - Maria A Aleshkova
- N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Acad. Lavrentiev Ave., 9, 630090, Novosibirsk, Russia; Novosibirsk State University, Pirogov Str., 2, 630090, Novosibirsk, Russia
| | - Evgeny V Gaisler
- N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Acad. Lavrentiev Ave., 9, 630090, Novosibirsk, Russia; Novosibirsk State University, Pirogov Str., 2, 630090, Novosibirsk, Russia
| | - Yulia S Sotnikova
- N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Acad. Lavrentiev Ave., 9, 630090, Novosibirsk, Russia; Novosibirsk State University, Pirogov Str., 2, 630090, Novosibirsk, Russia; Boreskov Institute of Catalysis, Acad. Lavrentiev Ave., 5, 630090, Novosibirsk, Russia
| | - Yuri V Patrushev
- Novosibirsk State University, Pirogov Str., 2, 630090, Novosibirsk, Russia; Boreskov Institute of Catalysis, Acad. Lavrentiev Ave., 5, 630090, Novosibirsk, Russia
| | - Tatiana G Tolstikova
- N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Acad. Lavrentiev Ave., 9, 630090, Novosibirsk, Russia; Novosibirsk State University, Pirogov Str., 2, 630090, Novosibirsk, Russia
| | - Olga I Yarovaya
- N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Acad. Lavrentiev Ave., 9, 630090, Novosibirsk, Russia; Novosibirsk State University, Pirogov Str., 2, 630090, Novosibirsk, Russia
| | - Andrey G Pokrovsky
- Novosibirsk State University, Pirogov Str., 2, 630090, Novosibirsk, Russia
| | - Nariman F Salakhutdinov
- N. N. Vorozhtsov Novosibirsk Institute of Organic Chemistry, Acad. Lavrentiev Ave., 9, 630090, Novosibirsk, Russia; Novosibirsk State University, Pirogov Str., 2, 630090, Novosibirsk, Russia
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11
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Witting M. (Re-)use and (re-)analysis of publicly available metabolomics data. Proteomics 2023; 23:e2300032. [PMID: 37670538 DOI: 10.1002/pmic.202300032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 09/07/2023]
Abstract
Metabolomics, the systematic measurement of small molecules (<1000 Da) in a given biological sample, is a fast-growing field with many different applications. In contrast to transcriptomics and proteomics, sharing of data is not as widespread in metabolomics, though more scientists are sharing their data nowadays. However, to improve data analysis tools and develop new data analytical approaches and to improve metabolite annotation and identification, sharing of reference data is crucial. Here, different possibilities to share (metabolomics) data are reviewed and some recent approaches and applications regarding the (re-)use and (re-)analysis are highlighted.
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Affiliation(s)
- Michael Witting
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Analytical Food Chemistry, TUM School of Life Sciences, Freising-Weihenstephan, Germany
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12
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Kang Q, Fang P, Zhang S, Qiu H, Lan Z. Deep graph convolutional network for small-molecule retention time prediction. J Chromatogr A 2023; 1711:464439. [PMID: 37865024 DOI: 10.1016/j.chroma.2023.464439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 10/04/2023] [Accepted: 10/06/2023] [Indexed: 10/23/2023]
Abstract
The retention time (RT) is a crucial source of data for liquid chromatography-mass spectrometry (LCMS). A model that can accurately predict the RT for each molecule would empower filtering candidates with similar spectra but differing RT in LCMS-based molecule identification. Recent research shows that graph neural networks (GNNs) outperform traditional machine learning algorithms in RT prediction. However, all of these models use relatively shallow GNNs. This study for the first time investigates how depth affects GNNs' performance on RT prediction. The results demonstrate that a notable improvement can be achieved by pushing the depth of GNNs to 16 layers by the adoption of residual connection. Additionally, we also find that graph convolutional network (GCN) model benefits from the edge information. The developed deep graph convolutional network, DeepGCN-RT, significantly outperforms the previous state-of-the-art method and achieves the lowest mean absolute percentage error (MAPE) of 3.3% and the lowest mean absolute error (MAE) of 26.55 s on the SMRT test set. We also finetune DeepGCN-RT on seven datasets with various chromatographic conditions. The mean MAE of the seven datasets largely decreases 30% compared to previous state-of-the-art method. On the RIKEN-PlaSMA dataset, we also test the effectiveness of DeepGCN-RT in assisting molecular structure identification. By 30% lessening the number of potential structures, DeepGCN-RT is able to improve top-1 accuracy by about 11%.
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Affiliation(s)
- Qiyue Kang
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310024, China.
| | - Pengfei Fang
- School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, 210096, China
| | - Shuai Zhang
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310024, China
| | - Huachuan Qiu
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310024, China
| | - Zhenzhong Lan
- School of Engineering, Westlake University, Hangzhou, Zhejiang, 310024, China.
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13
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Kajtazi A, Russo G, Wicht K, Eghbali H, Lynen F. Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction. CHEMOSPHERE 2023; 337:139361. [PMID: 37392796 DOI: 10.1016/j.chemosphere.2023.139361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/06/2023] [Accepted: 06/26/2023] [Indexed: 07/03/2023]
Abstract
Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol-1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.
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Affiliation(s)
- Ardiana Kajtazi
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium
| | - Giacomo Russo
- School of Applied Sciences, Sighthill Campus, Edinburgh Napier University, 9 Sighthill Ct, EH11 4BN, Edinburgh, United Kingdom
| | - Kristina Wicht
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium
| | - Hamed Eghbali
- Packaging and Specialty Plastics R&D, Dow Benelux B.V., Terneuzen, 4530 AA, the Netherlands
| | - Frédéric Lynen
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium.
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14
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Tang S, Zhang P, Gao M, Xiao Q, Li Z, Dong H, Tian Y, Xu F, Zhang Y. A chemical derivatization-based pseudotargeted LC-MS/MS method for high coverage determination of dipeptides. Anal Chim Acta 2023; 1274:341570. [PMID: 37455081 DOI: 10.1016/j.aca.2023.341570] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Revised: 06/04/2023] [Accepted: 06/27/2023] [Indexed: 07/18/2023]
Abstract
Dipeptides (DPs) have attracted more and more attention in many research fields due to their important biological functions and promising roles as disease biomarkers. However, the determination of DPs in biological samples is very challenging owing to the limited availability of commercial standards, high structure diversity, distinct physical and chemical characteristics, wide concentration range, and the extensive existence of isomers. In this study, a pseudotargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) method coupled with chemical derivatization for the simultaneous analysis of 400 DPs and their constructing amino acids (AAs) in biospecimens is established. Dansyl chloride (Dns-Cl) chemical derivatization was introduced to provide characteristic MS fragments for annotation and improve the chromatographic separation of DP isomers. A retention time (RT) prediction model was constructed using 83 standards (63 DPs and 20 AAs) based on their quantitative structural retention relationship (QSRR) after the Dns-Cl labeling, which largely facilitated the annotation of the DPs without standards. Finally, we applied this method to investigate the profile change of DPs in a cisplatin-induced acute kidney injury (AKI) rat model. The established workflow provides a platform to profile DPs and expand our understanding of these little-studied metabolites.
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Affiliation(s)
- Shaoran Tang
- China Pharmaceutical University Nanjing Drum Tower Hospital, Nanjing, 210009, PR China; Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Pei Zhang
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Meiyu Gao
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Qinwen Xiao
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Zhaoqian Li
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Haijuan Dong
- The Public Laboratory Platform, China Pharmaceutical University, Nanjing, 210009, PR China
| | - Yuan Tian
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China
| | - Fengguo Xu
- Key Laboratory of Drug Quality Control and Pharmacovigilance (Ministry of Education), China Pharmaceutical University, Nanjing, 210009, PR China.
| | - Yuxin Zhang
- China Pharmaceutical University Nanjing Drum Tower Hospital, Nanjing, 210009, PR China.
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15
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van de Lavoir M, da Silva KM, Iturrospe E, Robeyns R, van Nuijs ALN, Covaci A. Untargeted hair lipidomics: comprehensive evaluation of the hair-specific lipid signature and considerations for retrospective analysis. Anal Bioanal Chem 2023; 415:5589-5604. [PMID: 37468753 DOI: 10.1007/s00216-023-04851-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 06/30/2023] [Accepted: 07/06/2023] [Indexed: 07/21/2023]
Abstract
Lipidomics investigates the composition and function of lipids, typically employing blood or tissue samples as the primary study matrices. Hair has recently emerged as a potential complementary sample type to identify biomarkers in early disease stages and retrospectively document an individual's metabolic status due to its long detection window of up to several months prior to the time of sampling. However, the limited coverage of lipid profiling presented in previous studies has hindered its exploitation. This study aimed to evaluate the lipid coverage of hair using an untargeted liquid chromatography-high-resolution mass spectrometry lipidomics platform. Two distinct three-step exhaustive extraction experiments were performed using a hair metabolomics one-phase extraction technique that has been recently optimized, and the two-phase Folch extraction method which is recognized as the gold standard for lipid extraction in biological matrices. The applied lipidomics workflow improved hair lipid coverage, as only 99 species could be annotated using the one-phase extraction method, while 297 lipid species across six categories were annotated with the Folch method. Several lipids in hair were reported for the first time, including N-acyl amino acids, diradylglycerols, and coenzyme Q10. The study suggests that hair lipids are not solely derived from de novo synthesis in hair, but are also incorporated from sebum and blood, making hair a valuable matrix for clinical, forensic, and dermatological research. The improved understanding of the lipid composition and analytical considerations for retrospective analysis offers valuable insights to contextualize untargeted hair lipidomic analysis and facilitate the use of hair in translational studies.
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Affiliation(s)
- Maria van de Lavoir
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium.
| | - Katyeny Manuela da Silva
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium
| | - Elias Iturrospe
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Brussels, Belgium
| | - Rani Robeyns
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium
| | - Alexander L N van Nuijs
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium
| | - Adrian Covaci
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Antwerp, Belgium.
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16
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Akhlaqi M, Wang WC, Möckel C, Kruve A. Complementary methods for structural assignment of isomeric candidate structures in non-target liquid chromatography ion mobility high-resolution mass spectrometric analysis. Anal Bioanal Chem 2023; 415:5247-5259. [PMID: 37452839 PMCID: PMC10404200 DOI: 10.1007/s00216-023-04852-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/03/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
Non-target screening with LC/IMS/HRMS is increasingly employed for detecting and identifying the structure of potentially hazardous chemicals in the environment and food. Structural assignment relies on a combination of multidimensional instrumental methods and computational methods. The candidate structures are often isomeric, and unfortunately, assigning the correct structure among a number of isomeric candidate structures still is a key challenge both instrumentally and computationally. While practicing non-target screening, it is usually impossible to evaluate separately the limitations arising from (1) the inability of LC/IMS/HRMS to resolve the isomeric candidate structures and (2) the uncertainty of in silico methods in predicting the analytical information of isomeric candidate structures due to the lack of analytical standards for all candidate structures. Here we evaluate the feasibility of structural assignment of isomeric candidate structures based on in silico-predicted retention time and database collision cross-section (CCS) values as well as based on matching the empirical analytical properties of the detected feature with those of the analytical standards. For this, we investigated 14 candidate structures corresponding to five features detected with LC/HRMS in a spiked surface water sample. Considering the predicted retention times and database CCS values with the accompanying uncertainty, only one of the isomeric candidate structures could be deemed as unlikely; therefore, the annotation of the LC/IMS/HRMS features remained ambiguous. To further investigate if unequivocal annotation is possible via analytical standards, the reversed-phase LC retention times and low- and high-resolution ion mobility spectrometry separation, as well as high-resolution MS2 spectra of analytical standards were studied. Reversed-phase LC separated the highest number of candidate structures while low-resolution ion mobility and high-resolution MS2 spectra provided little means for pinpointing the correct structure among the isomeric candidate structures even if analytical standards were available for comparison. Furthermore, the question arises which prediction accuracy is required from the in silico methods to par the analytical separation. Based on the experimental data of the isomeric candidate structures studied here and previously published in the literature (516 retention time and 569 CCS values), we estimate that to reduce the candidate list by 95% of the structures, the confidence interval of the predicted retention times would need to decrease to below 0.05 min for a 15-min gradient while that of CCS values would need to decrease to 0.15%. Hereby, we set a clear goal to the in silico methods for retention time and CCS prediction.
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Affiliation(s)
- Masoumeh Akhlaqi
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden
| | - Wei-Chieh Wang
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden
| | - Claudia Möckel
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden
| | - Anneli Kruve
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden.
- Department of Environmental Science, Svante Arrhenius väg 8, 114 18, Stockholm, Sweden.
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17
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Brookhart A, Arora M, McCullagh M, Wilson ID, Plumb RS, Vissers JP, Tanna N. Understanding mobile phase buffer composition and chemical structure effects on electrospray ionization mass spectrometry response. J Chromatogr A 2023; 1696:463966. [PMID: 37054638 DOI: 10.1016/j.chroma.2023.463966] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/01/2023] [Accepted: 04/03/2023] [Indexed: 04/15/2023]
Abstract
Mobile phase selection is of critical importance in liquid chromatography - mass spectrometry (LC-MS) based studies, since it affects retention, chromatographic selectivity, ionization, limits of detection and quantification, and linear dynamic range. Generalized LC-MS mobile phase selection criteria, suitable for a broad class of chemical compounds, do not exist thus far. Here we have performed a large-scale qualitative assessment of the effect of solvent composition used for reversed-phase LC separations on electrospray ionization (ESI) response for 240 small molecular weight drugs, representing various chemical compound classes. Of these 240 analytes 224 were detectable using ESI. The main chemical structural features affecting ESI response were found to all be surface area or surface charge-related. Mobile phase composition was found to be less differentiating, although for some compounds a pH effect was noted. Unsurprisingly, chemical structure was found to be the dominant factor for ESI response for the majority of the investigated analytes, representing about 85% of the replicating detectable complement of the sample data set. A weak correlation between ESI response and structure complexity was observed. Solvents based on isopropanol, and those containing phosphoric or di- and trifluoracetic acids, performed relatively poorly in terms of chromatographic or ESI response, whilst the best performing 'generic' LC solvents were based on methanol, acetonitrile using formic acid and ammonium acetate as buffer components, consistent with current practice in many laboratories.
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Affiliation(s)
- Allison Brookhart
- Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, MA
| | - Mahika Arora
- Manning College of Information and Computer Sciences, University of Massachusetts Amherst, MA
| | | | - Ian D Wilson
- Computational & Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, United Kingdom
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18
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Wang X, Zheng F, Sheng M, Xu G, Lin X. Retention time prediction for small samples based on integrating molecular representations and adaptive network. J Chromatogr B Analyt Technol Biomed Life Sci 2023; 1217:123624. [PMID: 36780745 DOI: 10.1016/j.jchromb.2023.123624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Revised: 01/13/2023] [Accepted: 01/27/2023] [Indexed: 02/07/2023]
Abstract
Retention time (RT) can provide orthogonal information different from that of mass spectrometry and contribute to identifying compounds. Many machine learning methods have been developed and applied to RT prediction. In application, the training data size is usually small in most chromatography systems. To enhance the performance of RT prediction, this study proposes a RT prediction method based on multi-data combinations and adaptive neural network (MDC-ANN). MDC-ANN establishes the RT prediction model for the target chromatographic system through transfer learning and a base deep learning model trained on a big dataset. It selects the optimal molecular representation combination from the multiple input candidates and automatically determines the neural network structure according to the determined input combination. MDC-ANN was compared with two new efficient deep learning methods, three transferring methods and four popular machine learning methods on 14 small datasets and showed advantages in MAE, MedAE, MRE and R2 in most cases. The experiment results illustrated that integrating multiple molecular representations can provide more information, improve the performance of RT prediction and contribute to compound annotation, different chromatographic systems may use different molecular representation combinations to obtain good RT prediction performance. Hence, MDC-ANN which automatically determines the best combination of molecular representations for a specific system is promising for predicting RTs accurately in real applications.
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Affiliation(s)
- Xiaoxiao Wang
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Fujian Zheng
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China.
| | - Meizhen Sheng
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, Liaoning, China
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, Liaoning, China.
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19
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Xu Z, Chughtai H, Tian L, Liu L, Roy JF, Bayen S. Development of quantitative structure-retention relationship models to improve the identification of leachables in food packaging using non-targeted analysis. Talanta 2023; 253:123861. [PMID: 36095943 DOI: 10.1016/j.talanta.2022.123861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 08/15/2022] [Accepted: 08/17/2022] [Indexed: 12/13/2022]
Abstract
Quantitative structure-retention relationship (QSRR) models can be used to predict the chromatographic retention time of chemicals and facilitate the identification of unknown compounds, notably with non-targeted analysis. In this study, QSRR models were developed from the data obtained for 178 pure chemical standards and four types of analytical columns (C18, phenylhexyl, pentafluorophenyl, cyano) in liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). First, different data partitioning ratios and feature selection methods [random forest (RF) and support vector machine (SVM)] were tested to build models to predict chromatographic retention times based on 2D molecular descriptors. The internal and external performances of the non-linear (RF) and corresponding linear predictive models were systematically compared, and RF models resulted in better predictive capacities [p < 0.05, with an average PVE (proportion of variance explained) value of 0.89 ± 0.02] than linear models (0.79 ± 0.03). For each column, the resulting model was applied to identify leachables from actual plastic packaging samples. An in-depth investigation of the top 20 most intense molecular features revealed that all false-positives could be identified as outliers in the QSRR models (outside of the 95% prediction bands). Furthermore, analyzing a sample on multiple chromatographic columns and applying the associated QSRR models increased the capacity to filter false positives. Such an approach will contribute to a more effective identification of unknown or unexpected leachables in plastics (e.g. non-intended added substances), therefore refining our understanding of the chemical risks associated with food contact materials.
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Affiliation(s)
- Ziyun Xu
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Hamza Chughtai
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lei Tian
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lan Liu
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | | | - Stéphane Bayen
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
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20
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Damiani T, Bonciarelli S, Thallinger GG, Koehler N, Krettler CA, Salihoğlu AK, Korf A, Pauling JK, Pluskal T, Ni Z, Goracci L. Software and Computational Tools for LC-MS-Based Epilipidomics: Challenges and Solutions. Anal Chem 2023; 95:287-303. [PMID: 36625108 PMCID: PMC9835057 DOI: 10.1021/acs.analchem.2c04406] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Tito Damiani
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Praha 6, Czech Republic
| | - Stefano Bonciarelli
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy
| | - Gerhard G. Thallinger
- Institute
of Biomedical Informatics, Graz University
of Technology, 8010 Graz, Austria,
| | - Nikolai Koehler
- LipiTUM,
Chair of Experimental Bioinformatics, Technical
University of Munich, Maximus-von-Imhof Forum 3, 85354 Freising, Germany
| | | | - Arif K. Salihoğlu
- Department
of Physiology, Faculty of Medicine and Institute of Health Sciences, Karadeniz Technical University, 61080 Trabzon, Turkey
| | - Ansgar Korf
- Bruker Daltonics
GmbH & Co. KG, Fahrenheitstraße 4, 28359 Bremen, Germany
| | - Josch K. Pauling
- LipiTUM,
Chair of Experimental Bioinformatics, Technical
University of Munich, Maximus-von-Imhof Forum 3, 85354 Freising, Germany
| | - Tomáš Pluskal
- Institute
of Organic Chemistry and Biochemistry of the Czech Academy of Sciences, Flemingovo nám. 2, 160 00 Praha 6, Czech Republic
| | - Zhixu Ni
- Center of
Membrane Biochemistry and Lipid Research, University Hospital and Faculty of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy,
| | - Laura Goracci
- Department
of Chemistry, Biology and Biotechnology, University of Perugia, Via Elce di Sotto 8, 06123 Perugia, Italy,
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21
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Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00577-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
AbstractStructural annotation of small molecules in biological samples remains a key bottleneck in untargeted metabolomics, despite rapid progress in predictive methods and tools during the past decade. Liquid chromatography–tandem mass spectrometry, one of the most widely used analysis platforms, can detect thousands of molecules in a sample, the vast majority of which remain unidentified even with best-of-class methods. Here we present LC-MS2Struct, a machine learning framework for structural annotation of small-molecule data arising from liquid chromatography–tandem mass spectrometry (LC-MS2) measurements. LC-MS2Struct jointly predicts the annotations for a set of mass spectrometry features in a sample, using a novel structured prediction model trained to optimally combine the output of state-of-the-art MS2 scorers and observed retention orders. We evaluate our method on a dataset covering all publicly available reversed-phase LC-MS2 data in the MassBank reference database, including 4,327 molecules measured using 18 different LC conditions from 16 contributors, greatly expanding the chemical analytical space covered in previous multi-MS2 scorer evaluations. LC-MS2Struct obtains significantly higher annotation accuracy than earlier methods and improves the annotation accuracy of state-of-the-art MS2 scorers by up to 106%. The use of stereochemistry-aware molecular fingerprints improves prediction performance, which highlights limitations in existing approaches and has strong implications for future computational LC-MS2 developments.
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22
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de Jonge NF, Mildau K, Meijer D, Louwen JJR, Bueschl C, Huber F, van der Hooft JJJ. Good practices and recommendations for using and benchmarking computational metabolomics metabolite annotation tools. Metabolomics 2022; 18:103. [PMID: 36469190 PMCID: PMC9722809 DOI: 10.1007/s11306-022-01963-y] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 11/18/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Untargeted metabolomics approaches based on mass spectrometry obtain comprehensive profiles of complex biological samples. However, on average only 10% of the molecules can be annotated. This low annotation rate hampers biochemical interpretation and effective comparison of metabolomics studies. Furthermore, de novo structural characterization of mass spectral data remains a complicated and time-intensive process. Recently, the field of computational metabolomics has gained traction and novel methods have started to enable large-scale and reliable metabolite annotation. Molecular networking and machine learning-based in-silico annotation tools have been shown to greatly assist metabolite characterization in diverse fields such as clinical metabolomics and natural product discovery. AIM OF REVIEW We highlight recent advances in computational metabolite annotation workflows with a special focus on their evaluation and comparison with other tools. Whilst the progress is substantial and promising, we also argue that inconsistencies in benchmarking different tools hamper users from selecting the most appropriate and promising method for their research. We summarize benchmarking strategies of the different tools and outline several recommendations for benchmarking and comparing novel tools. KEY SCIENTIFIC CONCEPTS OF REVIEW This review focuses on recent advances in mass spectral library-based and machine learning-supported metabolite annotation workflows. We discuss large-scale library matching and analogue search, the current bloom of mass spectral similarity scores, and how molecular networking has changed the field. In addition, the potentials and challenges of machine learning-supported metabolite annotation workflows are highlighted. Overall, recent developments in computational metabolomics have started to fundamentally change metabolomics workflows, and we expect that as a community we will be able to overcome current method performance ambiguities and annotation bottlenecks.
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Affiliation(s)
- Niek F. de Jonge
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
| | - Kevin Mildau
- Department of Analytical Chemistry, Biochemical Network Analysis Lab, University of Vienna, Vienna, Austria
| | - David Meijer
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
| | - Joris J. R. Louwen
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
| | - Christoph Bueschl
- Department of Analytical Chemistry, Biochemical Network Analysis Lab, University of Vienna, Vienna, Austria
| | - Florian Huber
- Centre for Digitalization and Digitality (ZDD), University of Applied Sciences Düsseldorf, Düsseldorf, Germany
| | - Justin J. J. van der Hooft
- Bioinformatics Group, Wageningen University, Wageningen, the Netherlands
- Department of Biochemistry, University of Johannesburg, Johannesburg, South Africa
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23
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Ding J, Feng YQ. Mass spectrometry-based metabolomics for clinical study: Recent progresses and applications. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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24
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Harrieder EM, Kretschmer F, Dunn W, Böcker S, Witting M. Critical assessment of chromatographic metadata in publicly available metabolomics data repositories. Metabolomics 2022; 18:97. [PMID: 36436113 PMCID: PMC9701651 DOI: 10.1007/s11306-022-01956-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Accepted: 11/11/2022] [Indexed: 11/28/2022]
Abstract
INTRODUCTION The structural identification of metabolites represents one of the current bottlenecks in non-targeted liquid chromatography-mass spectrometry (LC-MS) based metabolomics. The Metabolomics Standard Initiative has developed a multilevel system to report confidence in metabolite identification, which involves the use of MS, MS/MS and orthogonal data. Limitations due to similar or same fragmentation pattern (e.g. isomeric compounds) can be overcome by the additional orthogonal information of the retention time (RT), since it is a system property that is different for each chromatographic setup. OBJECTIVES In contrast to MS data, sharing of RT data is not as widespread. The quality of data and its (re-)useability depend very much on the quality of the metadata. We aimed to evaluate the coverage and quality of this metadata from public metabolomics repositories. METHODS We acquired an overview on the current reporting of chromatographic separation conditions. For this purpose, we defined the following information as important details that have to be provided: column name and dimension, flow rate, temperature, composition of eluents and gradient. RESULTS We found that 70% of descriptions of the chromatographic setups are incomplete (according to our definition) and an additional 10% of the descriptions contained ambiguous and/or incorrect information. Accordingly, only about 20% of the descriptions allow further (re-)use of the data, e.g. for RT prediction. Therefore, we have started to develop a unified and standardized notation for chromatographic metadata with detailed and specific description of eluents, columns and gradients. CONCLUSION Reporting of chromatographic metadata is currently not unified. Our recommended suggestions for metadata reporting will enable more standardization and automatization in future reporting.
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Affiliation(s)
- Eva-Maria Harrieder
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Fleming Kretschmer
- Chair of Bioinformatics, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Warwick Dunn
- Department of Biochemistry and Systems Biology, Institute of Systems, Molecular, and Integrative Biology, University of Liverpool, Liverpool, L69 7ZB, UK
| | - Sebastian Böcker
- Chair of Bioinformatics, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, 07743, Jena, Germany
| | - Michael Witting
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany.
- Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Maximus-Von-Imhof-Forum 2, 85354, Freising, Germany.
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25
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Toward building mass spectrometry-based metabolomics and lipidomics atlases for biological and clinical research. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116825] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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26
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Prediction of surface excess adsorption and retention factors in reversed-phase liquid chromatography from molecular dynamics simulations. J Chromatogr A 2022; 1685:463627. [DOI: 10.1016/j.chroma.2022.463627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Revised: 10/27/2022] [Accepted: 10/29/2022] [Indexed: 11/06/2022]
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27
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Retention Time Prediction with Message-Passing Neural Networks. SEPARATIONS 2022. [DOI: 10.3390/separations9100291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
Retention time prediction, facilitated by advances in machine learning, has become a useful tool in untargeted LC-MS applications. State-of-the-art approaches include graph neural networks and 1D-convolutional neural networks that are trained on the METLIN small molecule retention time dataset (SMRT). These approaches demonstrate accurate predictions comparable with the experimental error for the training set. The weak point of retention time prediction approaches is the transfer of predictions to various systems. The accuracy of this step depends both on the method of mapping and on the accuracy of the general model trained on SMRT. Therefore, improvements to both parts of prediction workflows may lead to improved compound annotations. Here, we evaluate capabilities of message-passing neural networks (MPNN) that have demonstrated outstanding performance on many chemical tasks to accurately predict retention times. The model was initially trained on SMRT, providing mean and median absolute cross-validation errors of 32 and 16 s, respectively. The pretrained MPNN was further fine-tuned on five publicly available small reversed-phase retention sets in a transfer learning mode and demonstrated up to 30% improvement of prediction accuracy for these sets compared with the state-of-the-art methods. We demonstrated that filtering isomeric candidates by predicted retention with the thresholds obtained from ROC curves eliminates up to 50% of false identities.
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28
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Petrick LM, Shomron N. AI/ML-driven advances in untargeted metabolomics and exposomics for biomedical applications. CELL REPORTS. PHYSICAL SCIENCE 2022; 3:100978. [PMID: 35936554 PMCID: PMC9354369 DOI: 10.1016/j.xcrp.2022.100978] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Metabolomics describes a high-throughput approach for measuring a repertoire of metabolites and small molecules in biological samples. One utility of untargeted metabolomics, unbiased global analysis of the metabolome, is to detect key metabolites as contributors to, or readouts of, human health and disease. In this perspective, we discuss how artificial intelligence (AI) and machine learning (ML) have promoted major advances in untargeted metabolomics workflows and facilitated pivotal findings in the areas of disease screening and diagnosis. We contextualize applications of AI and ML to the emerging field of high-resolution mass spectrometry (HRMS) exposomics, which unbiasedly detects endogenous metabolites and exogenous chemicals in human tissue to characterize exposure linked with disease outcomes. We discuss the state of the science and suggest potential opportunities for using AI and ML to improve data quality, rigor, detection, and chemical identification in untargeted metabolomics and exposomics studies.
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Affiliation(s)
- Lauren M. Petrick
- The Bert Strassburger Metabolic Center, Sheba Medical Center, Tel-Hashomer, Israel
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Exposomics Research, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Noam Shomron
- Faculty of Medicine, Edmond J. Safra Center for Bioinformatics, Sagol School of Neuroscience, Center for Nanoscience and Nanotechnology, Center for Innovation Laboratories (TILabs), Tel Aviv University, Tel Aviv, Israel
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29
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Rose B, May JC, Reardon AR, McLean JA. Collision Cross-Section Calibration Strategy for Lipid Measurements in SLIM-Based High-Resolution Ion Mobility. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1229-1237. [PMID: 35653638 PMCID: PMC9516683 DOI: 10.1021/jasms.2c00067] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Structures for lossless ion manipulation-based high-resolution ion mobility (HRIM) interfaced with mass spectrometry has emerged as a powerful tool for the separation and analysis of many isomeric systems. IM-derived collision cross section (CCS) is increasingly used as a molecular descriptor for structural analysis and feature annotation, but there are few studies on the calibration of CCS from HRIM measurements. Here, we examine the accuracy, reproducibility, and practical applicability of CCS calibration strategies for a broad range of lipid subclasses and develop a straightforward and generalizable framework for obtaining high-resolution CCS values. We explore the utility of using structurally similar custom calibrant sets as well as lipid subclass-specific empirically derived correction factors. While the lipid calibrant sets lowered overall bias of reference CCS values from ∼2-3% to ∼0.5%, application of the subclass-specific correction to values calibrated with a broadly available general calibrant set resulted in biases <0.4%. Using this method, we generated a high-resolution CCS database containing over 90 lipid values with HRIM. To test the applicability of this method to a broader class range typical of lipidomics experiments, a standard lipid mix was analyzed. The results highlight the importance of both class and arrival time range when correcting or scaling CCS values and provide guidance for implementation of the method for more general applications.
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30
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da Silva KM, Iturrospe E, van den Boom R, van de Lavoir M, Robeyns R, Vergauwen L, Knapen D, Cuykx M, Covaci A, van Nuijs ALN. Lipidomics profiling of zebrafish liver through untargeted liquid chromatography-high resolution mass spectrometry. J Sep Sci 2022; 45:2935-2945. [PMID: 35716100 DOI: 10.1002/jssc.202200214] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 06/13/2022] [Accepted: 06/13/2022] [Indexed: 11/10/2022]
Abstract
Lipidomics analysis of zebrafish tissues has shown promising results to understand disease-related outcomes of exposure to toxic substances at molecular level. However, knowledge about their lipidome is limited, as most untargeted studies only identify the lipids that are statistically significant in their setup. In this work, liquid chromatography-high resolution mass spectrometry was used to study different aspects of the analytical workflow, i.e., extraction solvents (methanol/chloroform/water (3/2/2, v/v/v), methanol/dichloromethane/water (2/3/2, v/v/v) and methanol/methyl-tert-butyl ether/water (3/10/2.5, v/v/v), instrumental response, and strategies used for lipid annotation. The number of high-quality features (relative standard deviation of the intensity values ≤ 10% in the range 103 -107 counts) was affected by the dilution of lipid extracts, indicating that it is an important parameter for developing untargeted methods. The workflows used allowed the selection of a dilution factor to annotate 712 lipid species (507 bulk lipids) in zebrafish liver using four software (LipidMatch, LipidHunter, MS-DIAL and Lipostar). Retention time mapping was a valuable tool to filter lipid annotations obtained from automatic software annotations. The lipid profiling of zebrafish livers will help in a better understanding of the true constitution of their lipidome at the species level, as well as in the use of zebrafish in toxicological studies. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Katyeny Manuela da Silva
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Elias Iturrospe
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium.,Department of In Vitro Toxicology and Dermato-Cosmetology, Faculty of Medicine and Pharmacy, Campus Jette, Vrije Universiteit Brussels, Laarbeeklaan 103, Brussels, 1090, Belgium
| | - Rik van den Boom
- Zebrafishlab, Veterinary Physiology and Biochemistry, Department of Veterinary Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Maria van de Lavoir
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Rani Robeyns
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Lucia Vergauwen
- Zebrafishlab, Veterinary Physiology and Biochemistry, Department of Veterinary Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Dries Knapen
- Zebrafishlab, Veterinary Physiology and Biochemistry, Department of Veterinary Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Matthias Cuykx
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium.,Department of Laboratory Medicine AZ Turnhout, Rubenslaan 166, Turnhout, 2300, Belgium
| | - Adrian Covaci
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
| | - Alexander L N van Nuijs
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Universiteitsplein 1, Antwerp, 2610, Belgium
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31
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García CA, Gil-de-la-Fuente A, Barbas C, Otero A. Probabilistic metabolite annotation using retention time prediction and meta-learned projections. J Cheminform 2022; 14:33. [PMID: 35672784 PMCID: PMC9172150 DOI: 10.1186/s13321-022-00613-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 05/20/2022] [Indexed: 12/31/2022] Open
Abstract
Retention time information is used for metabolite annotation in metabolomic experiments. But its usefulness is hindered by the availability of experimental retention time data in metabolomic databases, and by the lack of reproducibility between different chromatographic methods. Accurate prediction of retention time for a given chromatographic method would be a valuable support for metabolite annotation. We have trained state-of-the-art machine learning regressors using the 80, 038 experimental retention times from the METLIN Small Molecule Retention Tim (SMRT) dataset. The models included deep neural networks, deep kernel learning, several gradient boosting models, and a blending approach. 5, 666 molecular descriptors and 2, 214 fingerprints (MACCS166, Extended Connectivity, and Path Fingerprints fingerprints) were generated with the alvaDesc software. The models were trained using only the descriptors, only the fingerprints, and both types of features simultaneously. Bayesian hyperparameter search was used for parameter tuning. To avoid data-leakage when reporting the performance metrics, nested cross-validation was employed. The best results were obtained by a heavily regularized deep neural network trained with cosine annealing warm restarts and stochastic weight averaging, achieving a mean and median absolute errors of \documentclass[12pt]{minimal}
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\begin{document}$$17.2 \pm 0.9\;s$$\end{document}17.2±0.9s, respectively. To the best of our knowledge, these are the most accurate predictions published up to date over the SMRT dataset. To project retention times between chromatographic methods, a novel Bayesian meta-learning approach that can learn from just a few molecules is proposed. By applying this projection between the deep neural network retention time predictions and a given chromatographic method, our approach can be integrated into a metabolite annotation workflow to obtain z-scores for the candidate annotations. To this end, it is enough that just as few as 10 molecules of a given experiment have been identified (probably by using pure metabolite standards). The use of z-scores permits considering the uncertainty in the projection when ranking candidates, and not only the accuracy. In this scenario, our results show that in 68% of the cases the correct molecule was among the top three candidates filtered by mass and ranked according to z-scores. This shows the usefulness of this information to support metabolite annotation. Python code is available on GitHub at https://github.com/constantino-garcia/cmmrt.
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32
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Milman BL, Zhurkovich IK. Present-Day Practice of Non-Target Chemical Analysis. JOURNAL OF ANALYTICAL CHEMISTRY 2022. [DOI: 10.1134/s1061934822050070] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Abstract
We review the main techniques, procedures, and information products used in non-target analysis (NTA) to reveal the composition of substances. Sampling and sample preparation methods are preferable that ensure the extraction of analytes from test samples in a wide range of analyte properties with the most negligible loss. The necessary techniques of analysis are versions of chromatography–high-resolution tandem mass spectrometry (HRMS), yielding individual characteristics of analytes (mass spectra, retention properties) to accurately identify them. The prioritization of the analytical strategy discards unnecessary measurements and thereby increases the performance of the NTA. Chemical databases, collections of reference mass spectra and retention characteristics, algorithms, and software for processing HRMS data are indispensable in NTA.
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33
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Chen Z, Yan D, Zhang M, Han W, Wang Y, Xu S, Tang K, Gao J, Cao Z. MetNC: Predicting Metabolites in vivo for Natural Compounds. Front Chem 2022; 10:881975. [PMID: 35646826 PMCID: PMC9135178 DOI: 10.3389/fchem.2022.881975] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 04/11/2022] [Indexed: 12/02/2022] Open
Abstract
Natural compounds (NCs) undergo complicated biotransformation in vivo to produce diverse forms of metabolites dynamically, many of which are of high medicinal value. Predicting the profiles of chemical products may help to narrow down possible candidates, yet current computational methods for predicting biotransformation largely focus on synthetic compounds. Here, we proposed a method of MetNC, a tailor-made method for NC biotransformation prediction, after exploring the overall patterns of NC in vivo metabolism. Based on 850 pairs of the biotransformation dataset validated by comprehensive in vivo experiments with sourcing compounds from medicinal plants, MetNC was designed to produce a list of potential metabolites through simulating in vivo biotransformation and then prioritize true metabolites into the top list according to the functional groups in compound structures and steric hindrance around the reaction sites. Among the well-known peers of GLORYx and BioTransformer, MetNC gave the highest performance in both the metabolite coverage and the ability to short-list true products. More importantly, MetNC seemed to display an extra advantage in recommending the microbiota-transformed metabolites, suggesting its potential usefulness in the overall metabolism estimation. In summary, complemented to those techniques focusing on synthetic compounds, MetNC may help to fill the gap of natural compound metabolism and narrow down those products likely to be identified in vivo.
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Affiliation(s)
- Zikun Chen
- Dept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Deyu Yan
- Dept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Mou Zhang
- Dept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Wenhao Han
- Dept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Yuan Wang
- Dept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Shudi Xu
- Dept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Kailin Tang
- Dept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
| | - Jian Gao
- International Human Phenome Institutes, Shanghai, China
- Department of Thoracic Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
- *Correspondence: Zhiwei Cao, ; Jian Gao,
| | - Zhiwei Cao
- Dept. of Gastroenterology, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China
- School of Life Sciences, Fudan University, Shanghai, China
- *Correspondence: Zhiwei Cao, ; Jian Gao,
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34
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Kohler I, Verhoeven M, Haselberg R, Gargano AF. Hydrophilic interaction chromatography – mass spectrometry for metabolomics and proteomics: state-of-the-art and current trends. Microchem J 2022. [DOI: 10.1016/j.microc.2021.106986] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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35
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Zhong P, Wei X, Li X, Wei X, Wu S, Huang W, Koidis A, Xu Z, Lei H. Untargeted metabolomics by liquid chromatography‐mass spectrometry for food authentication: A review. Compr Rev Food Sci Food Saf 2022; 21:2455-2488. [DOI: 10.1111/1541-4337.12938] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 02/20/2022] [Accepted: 02/21/2022] [Indexed: 12/17/2022]
Affiliation(s)
- Peng Zhong
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiaoqun Wei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiangmei Li
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiaoyi Wei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Shaozong Wu
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Weijuan Huang
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Anastasios Koidis
- Institute for Global Food Security Queen's University Belfast Belfast UK
| | - Zhenlin Xu
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Hongtao Lei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
- Guangdong Laboratory for Lingnan Modern Agriculture South China Agricultural University Guangzhou 510642 China
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36
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Saldívar-González FI, Aldas-Bulos VD, Medina-Franco JL, Plisson F. Natural product drug discovery in the artificial intelligence era. Chem Sci 2022; 13:1526-1546. [PMID: 35282622 PMCID: PMC8827052 DOI: 10.1039/d1sc04471k] [Citation(s) in RCA: 50] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 12/10/2021] [Indexed: 12/19/2022] Open
Abstract
Natural products (NPs) are primarily recognized as privileged structures to interact with protein drug targets. Their unique characteristics and structural diversity continue to marvel scientists for developing NP-inspired medicines, even though the pharmaceutical industry has largely given up. High-performance computer hardware, extensive storage, accessible software and affordable online education have democratized the use of artificial intelligence (AI) in many sectors and research areas. The last decades have introduced natural language processing and machine learning algorithms, two subfields of AI, to tackle NP drug discovery challenges and open up opportunities. In this article, we review and discuss the rational applications of AI approaches developed to assist in discovering bioactive NPs and capturing the molecular "patterns" of these privileged structures for combinatorial design or target selectivity.
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Affiliation(s)
- F I Saldívar-González
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - V D Aldas-Bulos
- Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
| | - J L Medina-Franco
- DIFACQUIM Research Group, School of Chemistry, Department of Pharmacy, Universidad Nacional Autónoma de México Avenida Universidad 3000 04510 Mexico Mexico
| | - F Plisson
- CONACYT - Unidad de Genómica Avanzada, Laboratorio Nacional de Genómica para la Biodiversidad (Langebio), Centro de Investigación y de Estudios Avanzados del IPN Irapuato Guanajuato Mexico
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37
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Souihi A, Mohai MP, Palm E, Malm L, Kruve A. MultiConditionRT: Predicting liquid chromatography retention time for emerging contaminants for a wide range of eluent compositions and stationary phases. J Chromatogr A 2022; 1666:462867. [DOI: 10.1016/j.chroma.2022.462867] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 01/29/2022] [Accepted: 01/29/2022] [Indexed: 12/25/2022]
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38
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Caldeweyher E, Bauer C, Tehrani AS. An open-source framework for fast-yet-accurate calculation of quantum mechanical features. Phys Chem Chem Phys 2022; 24:10599-10610. [DOI: 10.1039/d2cp01165d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
We present the open-source framework kallisto that enables the efficient and robust calculation of quantum mechanical features for atoms and molecules. For a benchmark set of 49 experimental molecular polarizabilities,...
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39
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Zarrouk E, Lenski M, Bruno C, Thibert V, Contreras P, Privat K, Ameline A, Fabresse N. High-resolution mass spectrometry: Theoretical and technological aspects. TOXICOLOGIE ANALYTIQUE ET CLINIQUE 2022. [DOI: 10.1016/j.toxac.2021.11.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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40
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Tian Z, Liu F, Li D, Fernie AR, Chen W. Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples. Comput Struct Biotechnol J 2022; 20:5085-5097. [PMID: 36187931 PMCID: PMC9489805 DOI: 10.1016/j.csbj.2022.09.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.
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Stoffel R, Quilliam MA, Hardt N, Fridstrom A, Witting M. N-Alkylpyridinium sulfonates for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry-based metabolomics. Anal Bioanal Chem 2021; 414:7387-7398. [PMID: 34907452 PMCID: PMC9482907 DOI: 10.1007/s00216-021-03828-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Revised: 11/26/2021] [Accepted: 12/02/2021] [Indexed: 11/28/2022]
Abstract
Chromatographic retention time information is valuable, orthogonal information to MS and MS/MS data that can be used in metabolite identification. However, while comparison of MS data between different instruments is possible to a certain degree, retention times (RTs) can vary extensively, even when nominally the same phase system is used. Different factors such as column dead volumes, system extra column volume, and gradient dwell volume can influence absolute retention times. Retention time indexing (RTI), routinely employed in gas chromatography (e.g., Kovats index), allows compensation for deviations in experimental conditions. Different systems have been reported for RTI in liquid chromatography, but none of them have been applied to metabolomics to the same extent as they have with GC. Recently, a more universal RTI system has been reported based on a homologous series of N-alkylpyridinium sulfonates (NAPS). These reference standards ionize in both positive and negative ionization modes and are UV-active. We demonstrate the NAPS can be used for retention time indexing in reversed-phase-liquid chromatography-mass spectrometry (RP-LC–MS)–based metabolomics. Having measured >500 metabolite standards and varying flow rate and column dimension, we show that conversion of RT to retention indices (RI) substantially improves comparability of retention information and enables to use of RI for metabolite annotation and identification. Graphical Abstract ![]()
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Affiliation(s)
- Rainer Stoffel
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Michael A Quilliam
- National Research Council Canada, Biotoxin Metrology, 1411 Oxford Street, Halifax, N.S, B3H 3Z1, Canada
| | - Normand Hardt
- Merck, Frankfurter Straße 250, 64293, Darmstadt, Germany
| | | | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany. .,Metabolomics and Proteomics Core, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764, Neuherberg, Germany. .,Chair of Analytical Food Chemistry, TUM School of Life Sciences, Technical University of Munich, Maximus-von-Imhof-Forum 2, 85354, Freising, Germany.
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Anderson BG, Raskind A, Habra H, Kennedy RT, Evans CR. Modifying Chromatography Conditions for Improved Unknown Feature Identification in Untargeted Metabolomics. Anal Chem 2021; 93:15840-15849. [PMID: 34794310 PMCID: PMC10634695 DOI: 10.1021/acs.analchem.1c02149] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Untargeted metabolomics is an essential component of systems biology research, but it is plagued by a high proportion of detectable features not identified with a chemical structure. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) experiments produce spectra that can be searched against databases to help identify or classify these unknowns, but many features do not generate spectra of sufficient quality to enable successful annotation. Here, we explore alterations to gradient length, mass loading, and rolling precursor ion exclusion parameters for reversed phase liquid chromatography (RPLC) and hydrophilic interaction liquid chromatography (HILIC) that improve compound identification performance for human plasma samples. A manual review of spectral matches from the HILIC data set was used to determine reasonable thresholds for search score and other metrics to enable semi-automated MS/MS data analysis. Compared to typical LC-MS/MS conditions, methods adapted for compound identification increased the total number of unique metabolites that could be matched to a spectral database from 214 to 2052. Following data alignment, 68.0% of newly identified features from the modified conditions could be detected and quantitated using a routine 20-min LC-MS run. Finally, a localized machine learning model was developed to classify the remaining unknowns and select a subset that shared spectral characteristics with successfully identified features. A total of 576 and 749 unidentified features in the HILIC and RPLC data sets were classified by the model as high-priority unknowns or higher-importance targets for follow-up analysis. Overall, our study presents a simple strategy to more deeply annotate untargeted metabolomics data for a modest additional investment of time and sample.
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Affiliation(s)
- Brady G. Anderson
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109
- Biomedical Research Core Facilities Metabolomics Core, University of Michigan, Ann Arbor MI 48109
| | - Alexander Raskind
- Biomedical Research Core Facilities Metabolomics Core, University of Michigan, Ann Arbor MI 48109
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109
| | - Hani Habra
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109
| | - Robert T. Kennedy
- Department of Chemistry, University of Michigan, Ann Arbor, MI 48109
- Biomedical Research Core Facilities Metabolomics Core, University of Michigan, Ann Arbor MI 48109
- Department of Pharmacology, University of Michigan, Ann Arbor, MI 48109
| | - Charles R. Evans
- Biomedical Research Core Facilities Metabolomics Core, University of Michigan, Ann Arbor MI 48109
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI 48109
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Ju R, Liu X, Zheng F, Lu X, Xu G, Lin X. Deep Neural Network Pretrained by Weighted Autoencoders and Transfer Learning for Retention Time Prediction of Small Molecules. Anal Chem 2021; 93:15651-15658. [PMID: 34780148 DOI: 10.1021/acs.analchem.1c03250] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Retention time (RT) prediction contributes to identification of small molecules measured by high-performance liquid chromatography coupled with high-resolution mass spectrometry. Deep learning algorithms based on big data can enhance the accuracy of RT prediction. But at different chromatographic conditions, RTs of compounds are different, and the number of compounds with known RTs is small in most cases. Therefore, the transfer of big data is necessary. In this work, a strategy using a deep neural network (DNN) pretrained by weighed autoencoders and transfer learning (DNNpwa-TL) was proposed to efficiently predict RTs of compounds. The loss function in the autoencoders was calculated with features weighted by mutual information. Then, a DNN pretrained by weighted autoencoders (DNNpwa) was produced. For other specific chromatographic methods, the transfer learning model DNNpwa-TLs were built through fine-tuning the DNNpwa with the help of some compounds with known RTs to conduct the RT prediction. With the above strategy, a DNNpwa was first built with the METLIN small molecule retention time data set containing 80 038 small molecule compounds. A median relative error of 3.1% and a mean relative error of 4.9% were achieved. Then, 17 data sets from different chromatographic methods were studied, and the results showed that the performance of DNNpwa-TL was better than those of other deep learning models. Besides, DNNpwa-TL outperformed random forest, gradient boost, least absolute shrinkage and selection operator regression, and DNN for most of the 17 data sets. Therefore, DNNpwa-TL can provide an efficient method to perform RT prediction of small molecule compounds for different chromatographic methods and conditions.
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Affiliation(s)
- Ran Ju
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
| | - Xinyu Liu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Fujian Zheng
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Xin Lu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Guowang Xu
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China
| | - Xiaohui Lin
- School of Computer Science and Technology, Dalian University of Technology, Dalian 116024, China
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Harrieder EM, Kretschmer F, Böcker S, Witting M. Current state-of-the-art of separation methods used in LC-MS based metabolomics and lipidomics. J Chromatogr B Analyt Technol Biomed Life Sci 2021; 1188:123069. [PMID: 34879285 DOI: 10.1016/j.jchromb.2021.123069] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 11/10/2021] [Accepted: 11/24/2021] [Indexed: 12/23/2022]
Abstract
Metabolomics deals with the large-scale analysis of metabolites, belonging to numerous compound classes and showing an extremely high chemical diversity and complexity. Lipidomics, being a subcategory of metabolomics, analyzes the cellular lipid species. Both require state-of-the-art analytical methods capable of accessing the underlying chemical complexity. One of the major techniques used for the analysis of metabolites and lipids is Liquid Chromatography-Mass Spectrometry (LC-MS), offering both different selectivities in LC separation and high sensitivity in MS detection. Chromatography can be divided into different modes, based on the properties of the employed separation system. The most popular ones are Reversed-Phase (RP) separation for non- to mid-polar molecules and Hydrophilic Interaction Liquid Chromatography (HILIC) for polar molecules. So far, no single analysis method exists that can cover the entire range of metabolites or lipids, due to the huge chemical diversity. Consequently, different separation methods have been used for different applications and research questions. In this review, we explore the current use of LC-MS in metabolomics and lipidomics. As a proxy, we examined the use of chromatographic methods in the public repositories EBI MetaboLights and NIH Metabolomics Workbench. We extracted 1484 method descriptions, collected separation metadata and generated an overview on the current use of columns, eluents, etc. Based on this overview, we reviewed current practices and identified potential future trends as well as required improvements that may allow us to increase metabolite coverage, throughput or both simultaneously.
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Affiliation(s)
- Eva-Maria Harrieder
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany
| | - Fleming Kretschmer
- Chair of Bioinformatics, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany
| | - Sebastian Böcker
- Chair of Bioinformatics, Friedrich-Schiller-Universität Jena, Ernst-Abbe-Platz 2, 07743 Jena, Germany
| | - Michael Witting
- Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; Metabolomics and Proteomics Core, Helmholtz Zentrum München, Ingolstädter Landstraße 1, 85764 Neuherberg, Germany; Chair of Analytical Food Chemistry, Technical University of Munich, Maximus-von-Imhof-Forum 2, 85354 Freising, Germany.
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Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification. Metabolites 2021; 11:metabo11110772. [PMID: 34822429 PMCID: PMC8620857 DOI: 10.3390/metabo11110772] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/28/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022] Open
Abstract
A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixed Mode (MM) chromatography applies varied pH, salt concentration and organic content to a positively charged quaternary amine polyvinyl alcohol stationary phase. This MM method is capable of separating glucose from fructose, and four hexose monophosphates a single chromatographic run. Coupled to a QExactive Orbitrap Mass Spectrometer with negative ESI, linearity, LLOD, %CV, and mass accuracy were assessed using 33 metabolite standards. The standards were linear on average >3 orders of magnitude (R2 > 0.98 for 30/33) with LLOD < 1 pmole (26/33), median CV of 12% over two weeks, and median mass accuracy of 0.49 ppm. To assess the breadth of metabolome coverage and better define the structural elements dictating elution, we injected 607 unique metabolites and determined that 398 are well retained. We then split the dataset of 398 documented RTs into training and test sets and trained a message-passing neural network (MPNN) to predict RT from a featurized heavy atom connectivity graph. Unlike traditional QSAR methods that utilize hand-crafted descriptors or pre-defined structural keys, the MPNN aggregates atomic features across the molecular graph and learns to identify molecular subgraphs that are correlated with variations in RTs. For sugars, sugar phosphates, carboxylic acids, and isomers, the model achieves a predictive RT error of <2 min on 91%, 50%, 77%, and 72% of held-out compounds from these subsets, with overall root mean square errors of 0.11, 0.34, 0.18, and 0.53 min, respectively. The model was then applied to rank order metabolite IDs for molecular features altered by GLS2 knockout in mouse primary hepatocytes.
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Pasin D, Mollerup CB, Rasmussen BS, Linnet K, Dalsgaard PW. Development of a single retention time prediction model integrating multiple liquid chromatography systems: Application to new psychoactive substances. Anal Chim Acta 2021; 1184:339035. [PMID: 34625246 DOI: 10.1016/j.aca.2021.339035] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 10/20/2022]
Abstract
Database-driven suspect screening has proven to be a useful tool to detect new psychoactive substances (NPS) outside the scope of targeted screening; however, the lack of retention times specific to a liquid chromatography (LC) system can result in a large number of false positives. A singular stream-lined, quantitative structure-retention relationship (QSRR)-based retention time prediction model integrating multiple LC systems with different elution conditions is presented using retention time data (n = 1281) from the online crowd-sourced database, HighResNPS. Modelling was performed using an artificial neural network (ANN), specifically a multi-layer perceptron (MLP), using four molecular descriptors and one-hot encoding of categorical labels. Evaluation of test set predictions (n = 193) yielded coefficient of determination (R2) and mean absolute error (MAE) values of 0.942 and 0.583 min, respectively. The model successfully differentiated between LC systems, predicting 54%, 81% and 97% of the test set within ±0.5, ±1 and ±2 min, respectively. Additionally, retention times for an analyte not previously observed by the model were predicted within ±1 min for each LC system. The developed model can be used to predict retention times for all analytes on HighResNPS for each participating laboratory's LC system to further support suspect screening.
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Affiliation(s)
- Daniel Pasin
- Section of Forensic Chemistry, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
| | - Christian Brinch Mollerup
- Section of Forensic Chemistry, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Brian Schou Rasmussen
- Section of Forensic Chemistry, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Kristian Linnet
- Section of Forensic Chemistry, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Petur Weihe Dalsgaard
- Section of Forensic Chemistry, Department of Forensic Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
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47
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Yang Q, Ji H, Fan X, Zhang Z, Lu H. Retention time prediction in hydrophilic interaction liquid chromatography with graph neural network and transfer learning. J Chromatogr A 2021; 1656:462536. [PMID: 34563892 DOI: 10.1016/j.chroma.2021.462536] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2021] [Revised: 09/02/2021] [Accepted: 09/03/2021] [Indexed: 01/04/2023]
Abstract
The combination of retention time (RT), accurate mass and tandem mass spectra can improve the structural annotation in untargeted metabolomics. However, the incorporation of RT for metabolite identification has received less attention because of the limitation of available RT data, especially for hydrophilic interaction liquid chromatography (HILIC). Here, the Graph Neural Network-based Transfer Learning (GNN-TL) is proposed to train a model for HILIC RTs prediction. The graph neural network was pre-trained using an in silico HILIC RT dataset (pseudo-labeling dataset) with ∼306 K molecules. Then, the weights of dense layers in the pre-trained GNN (pre-GNN) model were fine-tuned by transfer learning using a small number of experimental HILIC RTs from the target chromatographic system. The GNN-TL outperformed the methods in Retip, including the Random Forest (RF), Bayesian-regularized neural network (BRNN), XGBoost, light gradient-boosting machine (LightGBM), and Keras. It achieved the lowest mean absolute error (MAE) of 38.6 s on the test set and 33.4 s on an additional test set. It has the best ability to generalize with a small performance difference between training, test, and additional test sets. Furthermore, the predicted RTs can filter out nearly 60% false positive candidates on average, which is valuable for the identification of compounds complementary to mass spectrometry.
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Affiliation(s)
- Qiong Yang
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China
| | - Hongchao Ji
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China
| | - Xiaqiong Fan
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China
| | - Zhimin Zhang
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China.
| | - Hongmei Lu
- College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, PR China.
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48
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Ding J, Ji J, Rabow Z, Shen T, Folz J, Brydges CR, Fan S, Lu X, Mehta S, Showalter MR, Zhang Y, Araiza R, Bower LR, Lloyd KCK, Fiehn O. A metabolome atlas of the aging mouse brain. Nat Commun 2021; 12:6021. [PMID: 34654818 PMCID: PMC8519999 DOI: 10.1038/s41467-021-26310-y] [Citation(s) in RCA: 94] [Impact Index Per Article: 31.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 09/24/2021] [Indexed: 12/30/2022] Open
Abstract
The mammalian brain relies on neurochemistry to fulfill its functions. Yet, the complexity of the brain metabolome and its changes during diseases or aging remain poorly understood. Here, we generate a metabolome atlas of the aging wildtype mouse brain from 10 anatomical regions spanning from adolescence to old age. We combine data from three assays and structurally annotate 1,547 metabolites. Almost all metabolites significantly differ between brain regions or age groups, but not by sex. A shift in sphingolipid patterns during aging related to myelin remodeling is accompanied by large changes in other metabolic pathways. Functionally related brain regions (brain stem, cerebrum and cerebellum) are also metabolically similar. In cerebrum, metabolic correlations markedly weaken between adolescence and adulthood, whereas at old age, cross-region correlation patterns reflect decreased brain segregation. We show that metabolic changes can be mapped to existing gene and protein brain atlases. The brain metabolome atlas is publicly available ( https://mouse.atlas.metabolomics.us/ ) and serves as a foundation dataset for future metabolomic studies.
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Affiliation(s)
- Jun Ding
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
- Department of Chemistry, Wuhan University, 430072, Wuhan, Hubei, P.R. China
| | - Jian Ji
- School of Food Science, State Key Laboratory of Food Science and Technology, National Engineering Research Center for Functional Foods, Synergetic Innovation Center of Food Safety and Nutrition, Jiangnan University, 214122, Wuxi, Jiangsu, P.R. China
| | - Zachary Rabow
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
| | - Tong Shen
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
| | - Jacob Folz
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
| | - Christopher R Brydges
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
| | - Sili Fan
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
| | - Xinchen Lu
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
| | - Sajjan Mehta
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
| | - Megan R Showalter
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
| | - Ying Zhang
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA
| | - Renee Araiza
- Mouse Biology Program, and Department of Surgery, School of Medicine, University of California, Davis, Davis, CA, 95618, USA
| | - Lynette R Bower
- Mouse Biology Program, and Department of Surgery, School of Medicine, University of California, Davis, Davis, CA, 95618, USA
| | - K C Kent Lloyd
- Mouse Biology Program, and Department of Surgery, School of Medicine, University of California, Davis, Davis, CA, 95618, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, 451 Health Sciences Drive, Davis, CA, 95616, USA.
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Drug-Exposome Interactions: The Next Frontier in Precision Medicine. Trends Pharmacol Sci 2021; 41:994-1005. [PMID: 33186555 DOI: 10.1016/j.tips.2020.09.012] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 09/30/2020] [Accepted: 09/30/2020] [Indexed: 12/21/2022]
Abstract
Drug-drug interactions are a known concern during medical treatment. However, in addition to therapeutic drugs, humans are exposed to thousands of environment- and food-related chemicals on a daily basis. The exposome (i.e.,the total measure of environmental factors on the human body) is an emerging concept in the field of environmental health. Many chemicals have the potential to interact with drugs and subsequently influence health outcomes. To date, this concept has not been systematicallyinvestigated. Nevertheless, adverse effects have been observed betweenenvironmental, dietary, and microbiome-derived xenobiotics and a number of drugs, including chemotherapeutics. Recent technological advances in mass spectrometry-based metabolomics and the establishment of omic-scale exposure assessment will enable a broader and systemic investigation of these interactions. As a complement to pharmacogenomics and pharmacometabolomics, research ondrug-exposome interactions holds immense potential to elevate precision medicineto an unprecedented level.
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50
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Bach E, Rogers S, Williamson J, Rousu J. Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification. Bioinformatics 2021; 37:1724-1731. [PMID: 33244585 PMCID: PMC8289373 DOI: 10.1093/bioinformatics/btaa998] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 10/27/2020] [Accepted: 11/17/2020] [Indexed: 11/14/2022] Open
Abstract
Motivation Identification of small molecules in a biological sample remains a major bottleneck in molecular biology, despite a decade of rapid development of computational approaches for predicting molecular structures using mass spectrometry (MS) data. Recently, there has been increasing interest in utilizing other information sources, such as liquid chromatography (LC) retention time (RT), to improve identifications solely based on MS information, such as precursor mass-per-charge and tandem mass spectrometry (MS2). Results We put forward a probabilistic modelling framework to integrate MS and RT data of multiple features in an LC-MS experiment. We model the MS measurements and all pairwise retention order information as a Markov random field and use efficient approximate inference for scoring and ranking potential molecular structures. Our experiments show improved identification accuracy by combining MS2 data and retention orders using our approach, thereby outperforming state-of-the-art methods. Furthermore, we demonstrate the benefit of our model when only a subset of LC-MS features has MS2 measurements available besides MS1. Availability and implementation Software and data are freely available at https://github.com/aalto-ics-kepaco/msms_rt_score_integration. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Eric Bach
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
| | - Simon Rogers
- School of Computing Science, University of Glasgow, Glasgow, UK
| | - John Williamson
- School of Computing Science, University of Glasgow, Glasgow, UK
| | - Juho Rousu
- Department of Computer Science, School of Science, Aalto University, Espoo, Finland
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