1
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Lai Y, Koelmel JP, Walker DI, Price EJ, Papazian S, Manz KE, Castilla-Fernández D, Bowden JA, Nikiforov V, David A, Bessonneau V, Amer B, Seethapathy S, Hu X, Lin EZ, Jbebli A, McNeil BR, Barupal D, Cerasa M, Xie H, Kalia V, Nandakumar R, Singh R, Tian Z, Gao P, Zhao Y, Froment J, Rostkowski P, Dubey S, Coufalíková K, Seličová H, Hecht H, Liu S, Udhani HH, Restituito S, Tchou-Wong KM, Lu K, Martin JW, Warth B, Godri Pollitt KJ, Klánová J, Fiehn O, Metz TO, Pennell KD, Jones DP, Miller GW. High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:12784-12822. [PMID: 38984754 PMCID: PMC11271014 DOI: 10.1021/acs.est.4c01156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024]
Abstract
In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.
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Affiliation(s)
- Yunjia Lai
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Jeremy P. Koelmel
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Douglas I. Walker
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Elliott J. Price
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Stefano Papazian
- Department
of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
- National
Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - Katherine E. Manz
- Department
of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Delia Castilla-Fernández
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, 1010 Vienna, Austria
| | - John A. Bowden
- Center for
Environmental and Human Toxicology, Department of Physiological Sciences,
College of Veterinary Medicine, University
of Florida, Gainesville, Florida 32611, United States
| | | | - Arthur David
- Univ Rennes,
Inserm, EHESP, Irset (Institut de recherche en santé, environnement
et travail) − UMR_S, 1085 Rennes, France
| | - Vincent Bessonneau
- Univ Rennes,
Inserm, EHESP, Irset (Institut de recherche en santé, environnement
et travail) − UMR_S, 1085 Rennes, France
| | - Bashar Amer
- Thermo
Fisher Scientific, San Jose, California 95134, United States
| | | | - Xin Hu
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Elizabeth Z. Lin
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Akrem Jbebli
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Brooklynn R. McNeil
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Dinesh Barupal
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Marina Cerasa
- Institute
of Atmospheric Pollution Research, Italian National Research Council, 00015 Monterotondo, Rome, Italy
| | - Hongyu Xie
- Department
of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Vrinda Kalia
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Renu Nandakumar
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Randolph Singh
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Zhenyu Tian
- Department
of Chemistry and Chemical Biology, Northeastern
University, Boston, Massachusetts 02115, United States
| | - Peng Gao
- Department
of Environmental and Occupational Health, and Department of Civil
and Environmental Engineering, University
of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- UPMC Hillman
Cancer Center, Pittsburgh, Pennsylvania 15232, United States
| | - Yujia Zhao
- Institute
for Risk Assessment Sciences, Utrecht University, Utrecht 3584CM, The Netherlands
| | | | | | - Saurabh Dubey
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Kateřina Coufalíková
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Hana Seličová
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Helge Hecht
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Sheng Liu
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Hanisha H. Udhani
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Sophie Restituito
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Kam-Meng Tchou-Wong
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Kun Lu
- Department
of Environmental Sciences and Engineering, Gillings School of Global
Public Health, The University of North Carolina
at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Jonathan W. Martin
- Department
of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
- National
Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - Benedikt Warth
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, 1010 Vienna, Austria
| | - Krystal J. Godri Pollitt
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Jana Klánová
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Oliver Fiehn
- West Coast
Metabolomics Center, University of California−Davis, Davis, California 95616, United States
| | - Thomas O. Metz
- Biological
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99354, United States
| | - Kurt D. Pennell
- School
of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Dean P. Jones
- Department
of Medicine, School of Medicine, Emory University, Atlanta, Georgia 30322, United States
| | - Gary W. Miller
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
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2
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Li H, Zhang K, Chen W, Zhou Y, Li J, Zhao Y, Song Y. Metabolite identification of salvianolic acid A in rat using post collision-induced dissociation energy-resolved mass spectrometry. Chin Med 2024; 19:64. [PMID: 38671484 PMCID: PMC11046765 DOI: 10.1186/s13020-024-00931-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2024] [Accepted: 03/30/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND As one of the most famous natural products, salvianolic acid A (SAA) is undergoing clinical trials for the treatments of angina pectoris and coronary heart disorders. However, the in vivo metabolites of SAA have only been tentatively identified, leading to a barrier for precise therapeutical drug monitoring. METHODS Ultra-high performance liquid chromatography coupled with quadrupole time of flight tandem mass spectrometry (UPLC-Qtof-MS/MS) was firstly employed to acquire high-resolution MS1 and MS2 spectra for all metabolites. Through paying special attention onto the features of ester bond dissociation, metabolism sites were restricted at certain regions. To further determine the metabolism site, such as the monomethylated products (M23, M25, and M26), post collision-induced dissociation energy-resolved mass spectrometry (post-CID ER-MS) was proposed through programming progressive exciting energies to the second collision chamber of hybrid triple quadrupole-linear ion trap mass spectrometry (Qtrap-MS) device. RESULTS After SAA oral administration, 29 metabolites (M1-M29), including five, thirteen, and sixteen ones in rat plasma, urine, and feces, respectively, were detected in rats. The metabolism route was initially determined by applying well-defined mass fragmentation pathways to those HR-m/z values of precursor and fragment ions. Metabolism site was limited to SAF- or DSS-unit based on the fragmentation patterns of ester functional group. Through matching the dissociation trajectories of concerned 1st-generation fragment ions with expected decomposition product anions using post-CID ER-MS strategy, M23 and M25 were unequivocally assigned as 3'-methyl-SAA and 3''-methyl-SAA, and M26 was identified as 2-methyl-SAA or 3-methyl-SAA. Hydrolysis, methylation, glucuronidation, sulfation, and oxidation were the primary metabolism channels being responsible for the metabolites' generation. CONCLUSION Together, the metabolism regions and sites of SAA metabolites were sequentially identified based on the ester bond dissociation features and post-CID ER-MS strategy. Importantly, the present study provided a promising way to elevate the structural identification confidence of natural products and metabolites.
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Affiliation(s)
- Han Li
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, China
| | - Ke Zhang
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, China
| | - Wei Chen
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, China
| | - Yuxuan Zhou
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, China
| | - Jun Li
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China
- School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 102488, China
| | - Yunfang Zhao
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China.
| | - Yuelin Song
- Modern Research Center for Traditional Chinese Medicine, Beijing Research Institute of Chinese Medicine, Beijing University of Chinese Medicine, Beijing, 102488, China.
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Alves MF, Katchborian-Neto A, Bueno PCP, Carnevale-Neto F, Casoti R, Ferreira MS, Murgu M, de Paula ACC, Dias DF, Soares MG, Chagas-Paula DA. LC-MS/DIA-based strategy for comprehensive flavonoid profiling: an Ocotea spp. applicability case. RSC Adv 2024; 14:10481-10498. [PMID: 38567345 PMCID: PMC10985591 DOI: 10.1039/d4ra01384k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 03/22/2024] [Indexed: 04/04/2024] Open
Abstract
We introduce a liquid chromatography - mass spectrometry with data-independent acquisition (LC-MS/DIA)-based strategy, specifically tailored to achieve comprehensive and reliable glycosylated flavonoid profiling. This approach facilitates in-depth and simultaneous exploration of all detected precursors and fragments during data processing, employing the widely-used open-source MZmine 3 software. It was applied to a dataset of six Ocotea plant species. This framework suggested 49 flavonoids potentially newly described for these plant species, alongside 45 known features within the genus. Flavonols kaempferol and quercetin, both exhibiting O-glycosylation patterns, were particularly prevalent. Gas-phase fragmentation reactions further supported these findings. For the first time, the apigenin flavone backbone was also annotated in most of the examined Ocotea species. Apigenin derivatives were found mainly in the C-glycoside form, with O. porosa displaying the highest flavone : flavonol ratio. The approach also allowed an unprecedented detection of kaempferol and quercetin in O. porosa species, and it has underscored the untapped potential of LC-MS/DIA data for broad and reliable flavonoid profiling. Our study annotated more than 50 flavonoid backbones in each species, surpassing the current literature.
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Affiliation(s)
- Matheus Fernandes Alves
- Institute of Chemistry, Federal University of Alfenas-MG 37130-001 Alfenas Minas Gerais Brazil
| | - Albert Katchborian-Neto
- Institute of Chemistry, Federal University of Alfenas-MG 37130-001 Alfenas Minas Gerais Brazil
| | - Paula Carolina Pires Bueno
- Leibniz Institute of Vegetable and Ornamental Crops (IGZ) Theodor-Echtermeyer-Weg 1 14979 Großbeeren Germany
| | - Fausto Carnevale-Neto
- Northwest Metabolomics Research Center, Department of Anesthesiology and Pain Medicine, University of Washington 850 Republican Street Seattle Washington 98109 USA
| | - Rosana Casoti
- Antibiotics Department, Federal University of Pernambuco 50670-901 Recife Pernambuco Brazil
| | - Miller Santos Ferreira
- Institute of Chemistry, Federal University of Alfenas-MG 37130-001 Alfenas Minas Gerais Brazil
| | - Michael Murgu
- Waters Corporation Alameda Tocantins 125, Alphaville 06455-020 São Paulo Brazil
| | | | - Danielle Ferreira Dias
- Institute of Chemistry, Federal University of Alfenas-MG 37130-001 Alfenas Minas Gerais Brazil
| | - Marisi Gomes Soares
- Institute of Chemistry, Federal University of Alfenas-MG 37130-001 Alfenas Minas Gerais Brazil
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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350 10.1002/mrc.5350] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/23/2024]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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5
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Jeppesen MJ, Powers R. Multiplatform untargeted metabolomics. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2023; 61:628-653. [PMID: 37005774 PMCID: PMC10948111 DOI: 10.1002/mrc.5350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
Metabolomics samples like human urine or serum contain upwards of a few thousand metabolites, but individual analytical techniques can only characterize a few hundred metabolites at best. The uncertainty in metabolite identification commonly encountered in untargeted metabolomics adds to this low coverage problem. A multiplatform (multiple analytical techniques) approach can improve upon the number of metabolites reliably detected and correctly assigned. This can be further improved by applying synergistic sample preparation along with the use of combinatorial or sequential non-destructive and destructive techniques. Similarly, peak detection and metabolite identification strategies that employ multiple probabilistic approaches have led to better annotation decisions. Applying these techniques also addresses the issues of reproducibility found in single platform methods. Nevertheless, the analysis of large data sets from disparate analytical techniques presents unique challenges. While the general data processing workflow is similar across multiple platforms, many software packages are only fully capable of processing data types from a single analytical instrument. Traditional statistical methods such as principal component analysis were not designed to handle multiple, distinct data sets. Instead, multivariate analysis requires multiblock or other model types for understanding the contribution from multiple instruments. This review summarizes the advantages, limitations, and recent achievements of a multiplatform approach to untargeted metabolomics.
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Affiliation(s)
- Micah J. Jeppesen
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
| | - Robert Powers
- Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
- Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE 68588-0304, United States
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Salas-Espejo E, Terrón-Camero LC, Ruiz JL, Molina NM, Andrés-León E. Exploring the Microbiome in Human Reproductive Tract: High-Throughput Methods for the Taxonomic Characterization of Microorganisms. Semin Reprod Med 2023; 41:125-143. [PMID: 38320576 DOI: 10.1055/s-0044-1779025] [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: 02/08/2024]
Abstract
Microorganisms are important due to their widespread presence and multifaceted roles across various domains of life, ecology, and industries. In humans, they underlie the proper functioning of multiple systems crucial to well-being, including immunological and metabolic functions. Emerging research addressing the presence and roles of microorganisms within human reproduction is increasingly relevant. Studies implementing new methodologies (e.g., to investigate vaginal, uterine, and semen microenvironments) can now provide relevant insights into fertility, reproductive health, or pregnancy outcomes. In that sense, cutting-edge sequencing techniques, as well as others such as meta-metabolomics, culturomics, and meta-proteomics, are becoming more popular and accessible worldwide, allowing the characterization of microbiomes at unprecedented resolution. However, they frequently involve rather complex laboratory protocols and bioinformatics analyses, for which researchers may lack the required expertise. A suitable pipeline would successfully enable both taxonomic classification and functional profiling of the microbiome, providing easy-to-understand biological interpretations. However, the selection of an appropriate methodology would be crucial, as it directly impacts the reproducibility, accuracy, and quality of the results and observations. This review focuses on the different current microbiome-related techniques in the context of human reproduction, encompassing niches like vagina, endometrium, and seminal fluid. The most standard and reliable methods are 16S rRNA gene sequencing, metagenomics, and meta-transcriptomics, together with complementary approaches including meta-proteomics, meta-metabolomics, and culturomics. Finally, we also offer case examples and general recommendations about the most appropriate methods and workflows and discuss strengths and shortcomings for each technique.
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Affiliation(s)
- Eduardo Salas-Espejo
- Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of Granada, Granada, Spain
| | - Laura C Terrón-Camero
- Bioinformatics Unit, Institute of Parasitology and Biomedicine "López-Neyra" (IPBLN), CSIC, Granada, Spain
| | - José L Ruiz
- Bioinformatics Unit, Institute of Parasitology and Biomedicine "López-Neyra" (IPBLN), CSIC, Granada, Spain
| | - Nerea M Molina
- Department of Biochemistry and Molecular Biology, Faculty of Sciences, University of Granada, Granada, Spain
| | - Eduardo Andrés-León
- Bioinformatics Unit, Institute of Parasitology and Biomedicine "López-Neyra" (IPBLN), CSIC, Granada, Spain
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Li Y, Li H, Luo T, Lin G, Li L. Intensity-dependent mass search for improving metabolite database matches in chemical isotope labeling LC-QTOF-MS-based metabolomics. Anal Chim Acta 2023; 1272:341467. [PMID: 37355326 DOI: 10.1016/j.aca.2023.341467] [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: 03/26/2023] [Revised: 05/01/2023] [Accepted: 05/31/2023] [Indexed: 06/26/2023]
Abstract
Liquid chromatography mass spectrometry (LC-MS) has been increasingly used for metabolome analysis. One of the critical steps in the LC-MS metabolome analysis workflow is related to metabolite identification. Among the measured parameters, peak mass is commonly used to search against a database for potential metabolite matches. Higher accuracy mass measurement allows the use of a narrower mass tolerance window for mass search. While various types of mass analyzers can routinely measure a peak mass with an error of less than a few ppm, mass measurement accuracy is not uniform for peaks with different intensities, particularly for quadrupole time-of-flight (QTOF) MS. Herein we present a simple and convenient method to determine the relation between peak intensity and mass error in LC-QTOF-MS-based metabolome analysis, followed by intensity-dependent mass search (IDMS) of a database for metabolite matches. This method is based on running a series of sodium formate mass calibrants, as part of the standard operating procedure (SOP) in LC-MS data acquisition, and then curve-fitting the measured mass errors and peak intensities. We show that, in two different quadrupole time-of-flight (QTOF) mass analyzers, mass accuracy is generally reduced as peak intensity decreases, which is independent of m/z values in the range commonly used for metabolite detection (e.g., m/z < 1000). We demonstrate the improvement in metabolite matches using IDMS in the analyses of dansyl labeled standards and human urine samples. We have implemented the IDMS method in the freely available MCID database at www.mycompoundid.org, which is composed of 8021 known human endogenous metabolites and their predicted metabolic products (375,809 compounds from one metabolic reaction and 10,583,901 compounds from two reactions).
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Affiliation(s)
- Yunong Li
- Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada
| | - Hao Li
- Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada
| | - Taibo Luo
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Guohui Lin
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Liang Li
- Department of Chemistry, University of Alberta, Edmonton, Alberta, Canada.
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8
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Bremer PL, Fiehn O. SMetaS: A Sample Metadata Standardizer for Metabolomics. Metabolites 2023; 13:941. [PMID: 37623884 PMCID: PMC10456726 DOI: 10.3390/metabo13080941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2023] [Revised: 07/26/2023] [Accepted: 08/10/2023] [Indexed: 08/26/2023] Open
Abstract
Metabolomics has advanced to an extent where it is desired to standardize and compare data across individual studies. While past work in standardization has focused on data acquisition, data processing, and data storage aspects, metabolomics databases are useless without ontology-based descriptions of biological samples and study designs. We introduce here a user-centric tool to automatically standardize sample metadata. Using such a tool in frontends for metabolomic databases will dramatically increase the FAIRness (Findability, Accessibility, Interoperability, and Reusability) of data, specifically for data reuse and for finding datasets that share comparable sets of metadata, e.g., study meta-analyses, cross-species analyses or large scale metabolomic atlases. SMetaS (Sample Metadata Standardizer) combines a classic database with an API and frontend and is provided in a containerized environment. The tool has two user-centric components. In the first component, the user designs a sample metadata matrix and fills the cells using natural language terminology. In the second component, the tool transforms the completed matrix by replacing freetext terms with terms from fixed vocabularies. This transformation process is designed to maximize simplicity and is guided by, among other strategies, synonym matching and typographical fixing in an n-grams/nearest neighbors model approach. The tool enables downstream analysis of submitted studies and samples via string equality for FAIR retrospective use.
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Affiliation(s)
- Parker Ladd Bremer
- Department of Chemistry, University of California, Davis, CA 95616, USA;
| | - Oliver Fiehn
- West Coast Metabolomics Center for Compound Identification, UC Davis Genome Center, University of California, Davis, CA 95616, USA
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9
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Reveglia P, Agudo-Jurado FJ, Barilli E, Masi M, Evidente A, Rubiales D. Uncovering Phytotoxic Compounds Produced by Colletotrichum spp. Involved in Legume Diseases Using an OSMAC-Metabolomics Approach. J Fungi (Basel) 2023; 9:610. [PMID: 37367546 DOI: 10.3390/jof9060610] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 06/28/2023] Open
Abstract
Different fungal species belonging to the Colletotrichum genus cause anthracnose disease in a range of major crops, resulting in huge economic losses worldwide. Typical symptoms include dark, sunken lesions on leaves, stems, or fruits. Colletotrichum spp. have synthesized, in vitro, a number of biologically active and structurally unusual metabolites that are involved in their host's infection process. In this study, we applied a one strain many compounds (OSMAC) approach, integrated with targeted and non-targeted metabolomics profiling, to shed light on the secondary phytotoxic metabolite panels produced by pathogenic isolates of Colletotrichum truncatum and Colletotrichum trifolii. The phytotoxicity of the fungal crude extracts was also assessed on their primary hosts and related legumes, and the results correlated with the metabolite profile that arose from the different cultural conditions. To the best of our knowledge, this is the first time that the OSMAC strategy integrated with metabolomics approaches has been applied to Colletotrichum species involved in legume diseases.
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Affiliation(s)
| | | | - Eleonora Barilli
- Institute for Sustainable Agriculture, CSIC, 14004 Cordoba, Spain
| | - Marco Masi
- Department of Chemical Sciences, University of Naples Federico II (UNINA), 80126 Naples, Italy
| | - Antonio Evidente
- Department of Chemical Sciences, University of Naples Federico II (UNINA), 80126 Naples, Italy
- Institute of Sciences of Food Production, National Research Council (ISPA-CNR), 70125 Bari, Italy
| | - Diego Rubiales
- Institute for Sustainable Agriculture, CSIC, 14004 Cordoba, Spain
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Guo J, Huan T. Mechanistic Understanding of the Discrepancies between Common Peak Picking Algorithms in Liquid Chromatography–Mass Spectrometry-Based Metabolomics. Anal Chem 2023; 95:5894-5902. [PMID: 36972195 DOI: 10.1021/acs.analchem.2c04887] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
Abstract
Inconsistent peak picking outcomes are a critical concern in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. This work systematically studied the mechanisms behind the discrepancies among five commonly used peak picking algorithms, including CentWave in XCMS, linear-weighted moving average in MS-DIAL, automated data analysis pipeline (ADAP) in MZmine 2, Savitzky-Golay in El-MAVEN, and FeatureFinderMetabo in OpenMS. We first collected 10 public metabolomics datasets representing various LC-MS analytical conditions. We then incorporated several novel strategies to (i) acquire the optimal peak picking parameters of each algorithm for a fair comparison, (ii) automatically recognize false metabolic features with poor chromatographic peak shapes, and (iii) evaluate the real metabolic features that are missed by the algorithms. By applying these strategies, we compared the true, false, and undetected metabolic features in each data processing outcome. Our results show that linear-weighted moving average consistently outperforms the other peak picking algorithms. To facilitate a mechanistic understanding of the differences, we proposed six peak attributes: ideal slope, sharpness, peak height, mass deviation, peak width, and scan number. We also developed an R program to automatically measure these attributes for detected and undetected true metabolic features. From the results of the 10 datasets, we concluded that four peak attributes, including ideal slope, scan number, peak width, and mass deviation, are critical for the detectability of a peak. For instance, the focus on ideal slope critically hinders the extraction of true metabolic features with low ideal slope scores in linear-weighted moving average, Savitzky-Golay, and ADAP. The relationships between peak picking algorithms and peak attributes were also visualized in a principal component analysis biplot. Overall, the clear comparison and explanation of the differences between peak picking algorithms can lead to the design of better peak picking strategies in the future.
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