1
|
Sarkar S, Zheng X, Clair GC, Kwon YM, You Y, Swensen AC, Webb-Robertson BJM, Nakayasu ES, Qian WJ, Metz TO. Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements. Trends Mol Med 2024:S1471-4914(24)00195-3. [PMID: 39152082 DOI: 10.1016/j.molmed.2024.07.009] [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: 05/23/2024] [Revised: 07/17/2024] [Accepted: 07/22/2024] [Indexed: 08/19/2024]
Abstract
Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D.
Collapse
Affiliation(s)
- Soumyadeep Sarkar
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Xueyun Zheng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Geremy C Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Yu Mi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Youngki You
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Adam C Swensen
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | | | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
| |
Collapse
|
2
|
Hupatz H, Rahu I, Wang WC, Peets P, Palm EH, Kruve A. Critical review on in silico methods for structural annotation of chemicals detected with LC/HRMS non-targeted screening. Anal Bioanal Chem 2024:10.1007/s00216-024-05471-x. [PMID: 39138659 DOI: 10.1007/s00216-024-05471-x] [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: 04/30/2024] [Revised: 07/22/2024] [Accepted: 07/24/2024] [Indexed: 08/15/2024]
Abstract
Non-targeted screening with liquid chromatography coupled to high-resolution mass spectrometry (LC/HRMS) is increasingly leveraging in silico methods, including machine learning, to obtain candidate structures for structural annotation of LC/HRMS features and their further prioritization. Candidate structures are commonly retrieved based on the tandem mass spectral information either from spectral or structural databases; however, the vast majority of the detected LC/HRMS features remain unannotated, constituting what we refer to as a part of the unknown chemical space. Recently, the exploration of this chemical space has become accessible through generative models. Furthermore, the evaluation of the candidate structures benefits from the complementary empirical analytical information such as retention time, collision cross section values, and ionization type. In this critical review, we provide an overview of the current approaches for retrieving and prioritizing candidate structures. These approaches come with their own set of advantages and limitations, as we showcase in the example of structural annotation of ten known and ten unknown LC/HRMS features. We emphasize that these limitations stem from both experimental and computational considerations. Finally, we highlight three key considerations for the future development of in silico methods.
Collapse
Affiliation(s)
- Henrik Hupatz
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18, Stockholm, Sweden
- Stockholm University Center for Circular and Sustainable Systems (SUCCeSS), Stockholm University, 106 91, Stockholm, Sweden
| | - Ida Rahu
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18, Stockholm, Sweden.
| | - Wei-Chieh Wang
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18, Stockholm, Sweden
| | - Pilleriin Peets
- Institute of Biodiversity, Faculty of Biological Science, Cluster of Excellence Balance of the Microverse, Friedrich Schiller University Jena, 07743, Jena, Germany
| | - Emma H Palm
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367, Belvaux, Luxembourg
| | - Anneli Kruve
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, 114 18, Stockholm, Sweden.
- Stockholm University Center for Circular and Sustainable Systems (SUCCeSS), Stockholm University, 106 91, Stockholm, Sweden.
- Department of Environmental Science, Stockholm University, Svante Arrhenius Väg 8, 114 18, Stockholm, Sweden.
| |
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Varona M, Dobson DP, Napolitano JG, Thomas R, Ochoa JL, Russell DJ, Crittenden CM. High Resolution Ion Mobility Enables the Structural Characterization of Atropisomers of GDC-6036, a KRAS G12C Covalent Inhibitor. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024. [PMID: 39051157 DOI: 10.1021/jasms.4c00103] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/27/2024]
Abstract
GDC-6036 is a covalent KRAS G12C inhibitor that demonstrates high potency and selectivity. Structurally, GDC-6036 consists of several motifs that make the analytical characterization of this molecule challenging, including a highly basic pyrrolidine motif bonded to a quinazoline ring via an ether bond and an atropisomeric carbon-carbon bond between functionalized pyridine and quinazoline groups. Structurally, the desired atropisomer was synthesized via an atroposelective Negishi coupling with very high yield. However, having a direct way to analyze and confirm the presence of the atropisomeric species remained challenging in routine analytical workflows. In this study, both variable temperature nuclear magnetic resonance (VT-NMR) and two different approaches of in-line ion mobility coupled to liquid chromatography mass spectrometry (LC-MS) workflows were evaluated for the characterization of GDC-6036 and its undesired atropisomer (Compound B) to support synthetic route development. Briefly, both VT-NMR and traveling wave ion mobility spectrometry (TWIMS) enabled by structures for lossless ion manipulation (SLIM) technology coupled to high resolution MS (HRMS) are able to elucidate the structures of the atropisomers in a complex mixture. Drift tube IMS (DTIMS) was also evaluated, but lacked the resolving power to demonstrate separation between the two species in a mixture, but did show slight differences in their arrival times when multiplexed and injected separately. The determined resolving power (Rp) by multiplexing the ions via DTIMS was 67.3 and 60.5 for GDC-6036 and Compound B, respectively, while the two peak resolving power (Rpp) was determined to be 0.41, indicating inadequate resolution between the two species. Alternatively, the SLIM-IM studies showed Rp of 103.8 and 99.4, with a Rpp of 2.64, indicating good separation between the atropisomers. Furthermore, the CCS/z for GDC-6036 and Compound B was determined to be 231.2 Å2/z and 235.0 Å2/z, respectively. Quantitative experiments demonstrate linearity (R2 >0.99) for both GDC-6036 and Compound B while maintaining separation via SLIM-IM. Spike recoveries of one atropisomer relative to the other yielded strong recoveries (98.7% to 102.5%) while maintaining reproducibility (<7% RSD). The study herein describes the analytical process for evaluating new technologies and strategies for implementation in routine biopharmaceutical characterization workflows.
Collapse
Affiliation(s)
- Marcelino Varona
- Synthetic Molecule Analytical Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Daniel P Dobson
- Synthetic Molecule Analytical Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - José G Napolitano
- Synthetic Molecule Analytical Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Rekha Thomas
- Synthetic Molecule Analytical Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Jessica L Ochoa
- Synthetic Molecule Analytical Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - David J Russell
- Synthetic Molecule Analytical Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| | - Christopher M Crittenden
- Synthetic Molecule Analytical Chemistry, Genentech Inc., 1 DNA Way, South San Francisco, California 94080, United States
| |
Collapse
|
5
|
Wang C, Yuan C, Wang Y, Shi Y, Zhang T, Patti GJ. Predicting Collision Cross-Section Values for Small Molecules through Chemical Class-Based Multimodal Graph Attention Network. J Chem Inf Model 2024. [PMID: 38959055 DOI: 10.1021/acs.jcim.3c01934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
Libraries of collision cross-section (CCS) values have the potential to facilitate compound identification in metabolomics. Although computational methods provide an opportunity to increase library size rapidly, accurate prediction of CCS values remains challenging due to the structural diversity of small molecules. Here, we developed a machine learning (ML) model that integrates graph attention networks and multimodal molecular representations to predict CCS values on the basis of chemical class. Our approach, referred to as MGAT-CCS, had superior performance in comparison to other ML models in CCS prediction. MGAT-CCS achieved a median relative error of 0.47%/1.14% (positive/negative mode) and 1.40%/1.63% (positive/negative mode) for lipids and metabolites, respectively. When MGAT-CCS was applied to real-world metabolomics data, it reduced the number of false metabolite candidates by roughly 25% across multiple sample types ranging from plasma and urine to cells. To facilitate its application, we developed a user-friendly stand-alone web server for MGAT-CCS that is freely available at https://mgat-ccs-web.onrender.com. This work represents a step forward in predicting CCS values and can potentially facilitate the identification of small molecules when using ion mobility spectrometry coupled with mass spectrometry.
Collapse
Affiliation(s)
- Cheng Wang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- National Institute of Health Data Science of China, Shandong University, Jinan 250000, China
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130 United States
| | - Chuang Yuan
- School of Life Sciences, and Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing 100871, China
- Department of Biochemistry and Biophysics, School of Basic Medical Sciences, Peking University, Beijing 100191, China
| | - Yahui Wang
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130 United States
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| | - Yuying Shi
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- National Institute of Health Data Science of China, Shandong University, Jinan 250000, China
| | - Tao Zhang
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan 250012, China
- National Institute of Health Data Science of China, Shandong University, Jinan 250000, China
| | - Gary J Patti
- Department of Chemistry, Washington University in St. Louis, St. Louis, Missouri 63130 United States
- Department of Medicine, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Siteman Cancer Center, Washington University in St. Louis, St. Louis, Missouri 63130, United States
- Center for Metabolomics and Isotope Tracing, Washington University in St. Louis, St. Louis, Missouri 63130, United States
| |
Collapse
|
6
|
He X, Guo X, Deng F, Zeng P, Wu B, Sun H, Zhao Z, Duan Y. A study of the transient gas flow affected ion transmission in atmospheric pressure interfaces based on large eddy simulation for electrospray ionization mass spectrometry. Talanta 2024; 274:125980. [PMID: 38579418 DOI: 10.1016/j.talanta.2024.125980] [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: 01/25/2024] [Revised: 03/05/2024] [Accepted: 03/20/2024] [Indexed: 04/07/2024]
Abstract
Modern atmosphere pressure interface (API) enables high-efficiency coupling between mass analyzers in high vacuum and atmosphere ionization sources such as electrospray ionization (ESI) source. The transient gas flow entering API possesses strong compressibility and turbulent characteristics, which exerts a huge impact on ion transmission. However, the instantaneous nature and vortical morphology of the turbulence in API and its affection in ion transmission were hardly covered in the reported research. Here we conduct a transient turbulent flow-affected ion transmission evaluation for two typical APIs, the ion funnel and the S-lens, based on scale-resolving large eddy simulation and electro-hydrodynamical ion tracing simulation. In our simulation, the transient properties of the gas flow in the two APIs are illustrated and analyzed in-depth. After experimentally validated on a homemade ESI-TOF-MS platform, the results suggest that the ion funnel can achieve a higher droplet desolvation rate by introducing a unique droplet recirculation mechanism. Meanwhile, the less-dispersed gas flow in S-lens is beneficial in actuating ions axially. In conclusion, the application of the scale-resolving turbulence model helps us to understand the complicated fluid-ion interaction mechanism in APIs and is promising in the development of mass spectrometry instruments of higher performance.
Collapse
Affiliation(s)
- Xingliang He
- Research Center of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, China
| | - Xing Guo
- Research Center of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, China
| | - Fulong Deng
- Research Center of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, China
| | - Pengyu Zeng
- Research Center of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, China
| | - Bin Wu
- Research Center of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, China
| | - Hong'en Sun
- Research Center of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, China
| | - Zhongjun Zhao
- Research Center of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, China; Aliben Science & Technology, China.
| | - Yixiang Duan
- Research Center of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, China; Aliben Science & Technology, China.
| |
Collapse
|
7
|
Bouwmeester R, Richardson K, Denny R, Wilson ID, Degroeve S, Martens L, Vissers JPC. Predicting ion mobility collision cross sections and assessing prediction variation by combining conventional and data driven modeling. Talanta 2024; 274:125970. [PMID: 38621320 DOI: 10.1016/j.talanta.2024.125970] [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: 11/02/2023] [Revised: 03/01/2024] [Accepted: 03/20/2024] [Indexed: 04/17/2024]
Abstract
The use of collision cross section (CCS) values derived from ion mobility studies is proving to be an increasingly important tool in the characterization and identification of molecules detected in complex mixtures. Here, a novel machine learning (ML) based method for predicting CCS integrating both molecular modeling (MM) and ML methodologies has been devised and shown to be able to accurately predict CCS values for singly charged small molecular weight molecules from a broad range of chemical classes. The model performed favorably compared to existing models, improving compound identifications for isobaric analytes in terms of ranking and assigning identification probability values to the annotation. Furthermore, charge localization was seen to be correlated with CCS prediction accuracy and with gas-phase proton affinity demonstrating the potential to provide a proxy for prediction error based on chemical structural properties. The presented approach and findings represent a further step towards accurate prediction and application of computationally generated CCS values.
Collapse
Affiliation(s)
- Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
| | | | | | - Ian D Wilson
- Computational & Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, United Kingdom
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | | |
Collapse
|
8
|
de Cripan SM, Arora T, Olomí A, Canela N, Siuzdak G, Domingo-Almenara X. Predicting the Predicted: A Comparison of Machine Learning-Based Collision Cross-Section Prediction Models for Small Molecules. Anal Chem 2024; 96:9088-9096. [PMID: 38783786 PMCID: PMC11154685 DOI: 10.1021/acs.analchem.4c00630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 05/09/2024] [Accepted: 05/10/2024] [Indexed: 05/25/2024]
Abstract
The application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.
Collapse
Affiliation(s)
- Sara M. de Cripan
- Computational
Metabolomics for Systems Biology Lab, Eurecat—Technology
Centre of Catalonia, Barcelona 08005, Catalonia, Spain
- Centre
for Omics Sciences (COS), Unique Scientific and Technical Infrastructures
(ICTS), Eurecat—Technology Centre
of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain
- Department
of Electrical, Electronic and Control Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain
| | - Trisha Arora
- Computational
Metabolomics for Systems Biology Lab, Eurecat—Technology
Centre of Catalonia, Barcelona 08005, Catalonia, Spain
- Centre
for Omics Sciences (COS), Unique Scientific and Technical Infrastructures
(ICTS), Eurecat—Technology Centre
of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain
- Department
of Electrical, Electronic and Control Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain
| | - Adrià Olomí
- Computational
Metabolomics for Systems Biology Lab, Eurecat—Technology
Centre of Catalonia, Barcelona 08005, Catalonia, Spain
- Centre
for Omics Sciences (COS), Unique Scientific and Technical Infrastructures
(ICTS), Eurecat—Technology Centre
of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain
| | - Núria Canela
- Centre
for Omics Sciences (COS), Unique Scientific and Technical Infrastructures
(ICTS), Eurecat—Technology Centre
of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain
| | - Gary Siuzdak
- Scripps
Center of Metabolomics and Mass Spectrometry, Department of Chemistry,
Molecular and Computational Biology, Scripps
Research Institute, La Jolla, California 92037, United States
| | - Xavier Domingo-Almenara
- Computational
Metabolomics for Systems Biology Lab, Eurecat—Technology
Centre of Catalonia, Barcelona 08005, Catalonia, Spain
- Centre
for Omics Sciences (COS), Unique Scientific and Technical Infrastructures
(ICTS), Eurecat—Technology Centre
of Catalonia & Rovira i Virgili University Joint Unit, Reus 43204, Catalonia, Spain
- Department
of Electrical, Electronic and Control Engineering (DEEEA), Universitat Rovira i Virgili, Tarragona 43007, Catalonia, Spain
| |
Collapse
|
9
|
Ross DH, Bhotika H, Zheng X, Smith RD, Burnum-Johnson KE, Bilbao A. Computational tools and algorithms for ion mobility spectrometry-mass spectrometry. Proteomics 2024; 24:e2200436. [PMID: 38438732 DOI: 10.1002/pmic.202200436] [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: 11/03/2023] [Revised: 02/12/2024] [Accepted: 02/14/2024] [Indexed: 03/06/2024]
Abstract
Ion mobility spectrometry-mass spectrometry (IMS-MS or IM-MS) is a powerful analytical technique that combines the gas-phase separation capabilities of IM with the identification and quantification capabilities of MS. IM-MS can differentiate molecules with indistinguishable masses but different structures (e.g., isomers, isobars, molecular classes, and contaminant ions). The importance of this analytical technique is reflected by a staged increase in the number of applications for molecular characterization across a variety of fields, from different MS-based omics (proteomics, metabolomics, lipidomics, etc.) to the structural characterization of glycans, organic matter, proteins, and macromolecular complexes. With the increasing application of IM-MS there is a pressing need for effective and accessible computational tools. This article presents an overview of the most recent free and open-source software tools specifically tailored for the analysis and interpretation of data derived from IM-MS instrumentation. This review enumerates these tools and outlines their main algorithmic approaches, while highlighting representative applications across different fields. Finally, a discussion of current limitations and expectable improvements is presented.
Collapse
Affiliation(s)
- Dylan H Ross
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Harsh Bhotika
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Xueyun Zheng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Kristin E Burnum-Johnson
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| | - Aivett Bilbao
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington, USA
| |
Collapse
|
10
|
Wang H, Zhang L, Li X, Sun M, Jiang M, Shi X, Xu X, Ding M, Chen B, Yu H, Li Z, Guo D, Yang W. Machine learning prediction for constructing a universal multidimensional information library of Panax saponins (ginsenosides). Food Chem 2024; 439:138106. [PMID: 38056336 DOI: 10.1016/j.foodchem.2023.138106] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 11/22/2023] [Accepted: 11/26/2023] [Indexed: 12/08/2023]
Abstract
Accurate characterization of Panax herb ginsenosides is challenging because of the isomers and lack of sufficient reference compounds. More structural information could help differentiate ginsenosides and their isomers, enabling more accurate identification. Based on the VionTM ion-mobility high-resolution LC-MS platform, a multidimensional information library for ginsenosides, namely GinMIL, was established by predicting retention time (tR) and collision cross section (CCS) through machine learning. Robustness validation experiments proved tR and CCS were suitable for database construction. Among three machine learning models we attempted, gradient boosting machine (GBM) exhibited the best prediction performance. GinMIL included the multidimensional information (m/z, molecular formula, tR, CCS, and some MS/MS fragments) for 579 known ginsenosides. Accuracy in identifying ginsenosides from diverse ginseng products was greatly improved by a unique LC-MS approach and searching GinMIL, demonstrating a universal Panax saponins library constructed based on hierarchical design. GinMIL could improve the accuracy of isomers identification by approximately 88%.
Collapse
Affiliation(s)
- Hongda Wang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Lin Zhang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Xiaohang Li
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Mengxiao Sun
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Meiting Jiang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Xiaojian Shi
- Cellular & Molecular Physiology, Yale School of Medicine, 850 Yale West Campus, West Haven CT 06516, USA
| | - Xiaoyan Xu
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Mengxiang Ding
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Boxue Chen
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Heshui Yu
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Zheng Li
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Dean Guo
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China.
| | - Wenzhi Yang
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China; Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China.
| |
Collapse
|
11
|
Kurilung A, Limjiasahapong S, Kaewnarin K, Wisanpitayakorn P, Jariyasopit N, Wanichthanarak K, Sartyoungkul S, Wong SCC, Sathirapongsasuti N, Kitiyakara C, Sirivatanauksorn Y, Khoomrung S. Measurement of very low-molecular weight metabolites by traveling wave ion mobility and its use in human urine samples. J Pharm Anal 2024; 14:100921. [PMID: 38799238 PMCID: PMC11127212 DOI: 10.1016/j.jpha.2023.12.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 11/17/2023] [Accepted: 12/13/2023] [Indexed: 05/29/2024] Open
Abstract
The collision cross-sections (CCS) measurement using ion mobility spectrometry (IMS) in combination with mass spectrometry (MS) offers a great opportunity to increase confidence in metabolite identification. However, owing to the lack of sensitivity and resolution, IMS has an analytical challenge in studying the CCS values of very low-molecular-weight metabolites (VLMs ≤ 250 Da). Here, we describe an analytical method using ultrahigh-performance liquid chromatography (UPLC) coupled to a traveling wave ion mobility-quadrupole-time-of-flight mass spectrometer optimized for the measurement of VLMs in human urine samples. The experimental CCS values, along with mass spectral properties, were reported for the 174 metabolites. The experimental data included the mass-to-charge ratio (m/z), retention time (RT), tandem MS (MS/MS) spectra, and CCS values. Among the studied metabolites, 263 traveling wave ion mobility spectrometry (TWIMS)-derived CCS values (TWCCSN2) were reported for the first time, and more than 70% of these were CCS values of VLMs. The TWCCSN2 values were highly repeatable, with inter-day variations of <1% relative standard deviation (RSD). The developed method revealed excellent TWCCSN2 accuracy with a CCS difference (ΔCCS) within ±2% of the reported drift tube IMS (DTIMS) and TWIMS CCS values. The complexity of the urine matrix did not affect the precision of the method, as evidenced by ΔCCS within ±1.92%. According to the Metabolomics Standards Initiative, 55 urinary metabolites were identified with a confidence level of 1. Among these 55 metabolites, 53 (96%) were VLMs. The larger number of confirmed compounds found in this study was a result of the addition of TWCCSN2 values, which clearly increased metabolite identification confidence.
Collapse
Affiliation(s)
- Alongkorn Kurilung
- Siriraj Center of Research Excellent in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Department of Biomedical Informatics, University of Arkansas for Medical Sciences, Little Rock, AR, 72205, USA
| | - Suphitcha Limjiasahapong
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Khwanta Kaewnarin
- Siriraj Center of Research Excellent in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- SingHealth Duke-NUS Institute of Biodiversity Medicine, National Cancer Centre Singapore, 168583, Singapore
| | - Pattipong Wisanpitayakorn
- Siriraj Center of Research Excellent in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Narumol Jariyasopit
- Siriraj Center of Research Excellent in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Kwanjeera Wanichthanarak
- Siriraj Center of Research Excellent in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Sitanan Sartyoungkul
- Siriraj Center of Research Excellent in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | | | - Nuankanya Sathirapongsasuti
- Program in Translational Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan, 10540, Thailand
| | - Chagriya Kitiyakara
- Department of Medicine, Ramathibodi Hospital, Mahidol University, Bangkok, 10400, Thailand
| | - Yongyut Sirivatanauksorn
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
| | - Sakda Khoomrung
- Siriraj Center of Research Excellent in Metabolomics and Systems Biology (SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Siriraj Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Center of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok, 10400, Thailand
| |
Collapse
|
12
|
Nguyen R, Seguin RP, Ross DH, Chen P, Richardson S, Liem J, Lin YS, Xu L. Development and Application of a Multidimensional Database for the Detection of Quaternary Ammonium Compounds and Their Phase I Hepatic Metabolites in Humans. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:6236-6249. [PMID: 38534032 PMCID: PMC11008582 DOI: 10.1021/acs.est.3c10845] [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: 12/22/2023] [Revised: 03/06/2024] [Accepted: 03/08/2024] [Indexed: 03/28/2024]
Abstract
The COVID-19 pandemic has led to significantly increased human exposure to the widely used disinfectants quaternary ammonium compounds (QACs). Xenobiotic metabolism serves a critical role in the clearance of environmental molecules, yet limited data are available on the routes of QAC metabolism or metabolite levels in humans. To address this gap and to advance QAC biomonitoring capabilities, we analyzed 19 commonly used QACs and their phase I metabolites by liquid chromatography-ion mobility-tandem mass spectrometry (LC-IM-MS/MS). In vitro generation of QAC metabolites by human liver microsomes produced a series of oxidized metabolites, with metabolism generally occurring on the alkyl chain group, as supported by MS/MS fragmentation. Discernible trends were observed in the gas-phase IM behavior of QAC metabolites, which, despite their increased mass, displayed smaller collision cross-section (CCS) values than those of their respective parent compounds. We then constructed a multidimensional reference SQLite database consisting of m/z, CCS, retention time (rt), and MS/MS spectra for 19 parent QACs and 81 QAC metabolites. Using this database, we confidently identified 13 parent QACs and 35 metabolites in de-identified human fecal samples. This is the first study to integrate in vitro metabolite biosynthesis with LC-IM-MS/MS for the simultaneous monitoring of parent QACs and their metabolites in humans.
Collapse
Affiliation(s)
- Ryan Nguyen
- Department
of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Ryan P. Seguin
- Department
of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Dylan H. Ross
- Department
of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Pengyu Chen
- Department
of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Sean Richardson
- Department
of Mathematics, University of Washington, Seattle, Washington 98195, United States
| | - Jennifer Liem
- Department
of Pharmaceutics, University of Washington, Seattle, Washington 98195, United States
| | - Yvonne S. Lin
- Department
of Pharmaceutics, University of Washington, Seattle, Washington 98195, United States
| | - Libin Xu
- Department
of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| |
Collapse
|
13
|
Wisanpitayakorn P, Sartyoungkul S, Kurilung A, Sirivatanauksorn Y, Visessanguan W, Sathirapongsasuti N, Khoomrung S. Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion's Polarizability and Molecular Mass with Limited Data. J Chem Inf Model 2024; 64:1533-1542. [PMID: 38393779 PMCID: PMC10934814 DOI: 10.1021/acs.jcim.3c01491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/26/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024]
Abstract
The rotationally averaged collision cross-section (CCS) determined by ion mobility-mass spectrometry (IM-MS) facilitates the identification of various biomolecules. Although machine learning (ML) models have recently emerged as a highly accurate approach for predicting CCS values, they rely on large data sets from various instruments, calibrants, and setups, which can introduce additional errors. In this study, we identified and validated that ion's polarizability and mass-to-charge ratio (m/z) have the most significant predictive power for traveling-wave IM CCS values in relation to other physicochemical properties of ions. Constructed solely based on these two physicochemical properties, our CCS prediction approach demonstrated high accuracy (mean relative error of <3.0%) even when trained with limited data (15 CCS values). Given its ability to excel with limited data, our approach harbors immense potential for constructing a precisely predicted CCS database tailored to each distinct experimental setup. A Python script for CCS prediction using our approach is freely available at https://github.com/MSBSiriraj/SVR_CCSPrediction under the GNU General Public License (GPL) version 3.
Collapse
Affiliation(s)
- Pattipong Wisanpitayakorn
- Siriraj
Center of Research Excellence in Metabolomics and Systems Biology
(SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Sitanan Sartyoungkul
- Siriraj
Center of Research Excellence in Metabolomics and Systems Biology
(SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Alongkorn Kurilung
- Siriraj
Center of Research Excellence in Metabolomics and Systems Biology
(SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Yongyut Sirivatanauksorn
- Siriraj
Center of Research Excellence in Metabolomics and Systems Biology
(SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Wonnop Visessanguan
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), Pathumthani 12120, Thailand
| | - Nuankanya Sathirapongsasuti
- Section
of Translational Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
- Research
Network of NANOTEC - MU Ramathibodi on Nanomedicine, Bangkok 12120, Thailand
| | - Sakda Khoomrung
- Siriraj
Center of Research Excellence in Metabolomics and Systems Biology
(SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Department
of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Center
of Excellence for Innovation in Chemistry (PERCH−CIC), Faculty of Science Mahidol University, Bangkok 10400, Thailand
| |
Collapse
|
14
|
Lou J, Xu XY, Xu B, Wang HD, Li X, Sun H, Zheng XY, Zhou J, Zou YD, Wu HH, Wang YF, Yang WZ. Comprehensive metabolome characterization and comparison between two sources of Dragon's blood by integrating liquid chromatography/mass spectrometry and chemometrics. Anal Bioanal Chem 2024; 416:1571-1587. [PMID: 38279012 DOI: 10.1007/s00216-024-05159-2] [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: 11/16/2023] [Revised: 01/14/2024] [Accepted: 01/15/2024] [Indexed: 01/28/2024]
Abstract
Dragon's Blood (DB) serves as a precious Chinese medicine facilitating blood circulation and stasis dispersion. Daemonorops draco (D. draco; Qi-Lin-Jie) and Dracaena cochinchinensis (D. cochinchinenesis; Long-Xue-Jie) are two reputable plant sources for preparing DB. This work was designed to comprehensively characterize and compare the metabolome differences between D. draco and D. cochinchinenesis, by integrating liquid chromatography/mass spectrometry and untargeted metabolomics analysis. Offline two-dimensional liquid chromatography/ion mobility-quadrupole time-of-flight mass spectrometry (2D-LC/IM-QTOF-MS), by utilizing a powerful hybrid scan approach, was elaborated for multicomponent characterization. Configuration of an XBridge Amide column and an HSS T3 column in offline mode exhibited high orthogonality (A0 0.80) in separating the complex components in DB. Particularly, the hybrid high-definition MSE-high definition data-dependent acquisition (HDMSE-HDDDA) in both positive and negative ion modes was applied for data acquisition. Streamlined intelligent data processing facilitated by the UNIFI™ (Waters) bioinformatics platform and searching against an in-house chemical library (recording 223 known compounds) enabled efficient structural elucidation. We could characterize 285 components, including 143 from D. draco and 174 from D. cochinchinensis. Holistic comparison of the metabolomes among 21 batches of DB samples by the untargeted metabolomics workflows unveiled 43 significantly differential components. Separately, four and three components were considered as the marker compounds for identifying D. draco and D. cochinchinenesis, respectively. Conclusively, the chemical composition and metabolomic differences of two DB resources were investigated by a dimension-enhanced analytical approach, with the results being beneficial to quality control and the differentiated clinical application of DB.
Collapse
Affiliation(s)
- Jia Lou
- Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
| | - Xiao-Yan Xu
- Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
| | - Bei Xu
- Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
| | - Hong-da Wang
- Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
| | - Xue Li
- Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
| | - He Sun
- Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
| | - Xin-Yuan Zheng
- Tianjin Institute for Drug Control, 98 Guizhou Road, Tianjin, 300070, China
| | - Jun Zhou
- Tianjin Institute for Drug Control, 98 Guizhou Road, Tianjin, 300070, China
| | - Ya-Dan Zou
- Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
| | - Hong-Hua Wu
- Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
| | - Yue-Fei Wang
- Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China
| | - Wen-Zhi Yang
- Haihe Laboratory of Modern Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China.
- National Key Laboratory of Chinese Medicine Modernization, State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin, 301617, China.
| |
Collapse
|
15
|
Carpenter JM, Hynds HM, Bimpeh K, Hines KM. HILIC-IM-MS for Simultaneous Lipid and Metabolite Profiling of Bacteria. ACS MEASUREMENT SCIENCE AU 2024; 4:104-116. [PMID: 38404491 PMCID: PMC10885331 DOI: 10.1021/acsmeasuresciau.3c00051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/11/2023] [Revised: 11/08/2023] [Accepted: 11/09/2023] [Indexed: 02/27/2024]
Abstract
Although MALDI-ToF platforms for microbial identifications have found great success in clinical microbiology, the sole use of protein fingerprints for the discrimination of closely related species, strain-level identifications, and detection of antimicrobial resistance remains a challenge for the technology. Several alternative mass spectrometry-based methods have been proposed to address the shortcomings of the protein-centric approach, including MALDI-ToF methods for fatty acid/lipid profiling and LC-MS profiling of metabolites. However, the molecular diversity of microbial pathogens suggests that no single "ome" will be sufficient for the accurate and sensitive identification of strain- and susceptibility-level profiling of bacteria. Here, we describe the development of an alternative approach to microorganism profiling that relies upon both metabolites and lipids rather than a single class of biomolecule. Single-phase extractions based on butanol, acetonitrile, and water (the BAW method) were evaluated for the recovery of lipids and metabolites from Gram-positive and -negative microorganisms. We found that BAW extraction solutions containing 45% butanol provided optimal recovery of both molecular classes in a single extraction. The single-phase extraction method was coupled to hydrophilic interaction liquid chromatography (HILIC) and ion mobility-mass spectrometry (IM-MS) to resolve similar-mass metabolites and lipids in three dimensions and provide multiple points of evidence for feature annotation in the absence of tandem mass spectrometry. We demonstrate that the combined use of metabolites and lipids can be used to differentiate microorganisms to the species- and strain-level for four of the ESKAPE pathogens (Enterococcus faecium, Staphylococcus aureus, Acinetobacter baumannii, and Pseudomonas aeruginosa) using data from a single ionization mode. These results present promising, early stage evidence for the use of multiomic signatures for the identification of microorganisms by liquid chromatography, ion mobility, and mass spectrometry that, upon further development, may improve upon the level of identification provided by current methods.
Collapse
Affiliation(s)
- Jana M. Carpenter
- Department of Chemistry, University of Georgia, Athens, Georgia 30602, United States
| | - Hannah M. Hynds
- Department of Chemistry, University of Georgia, Athens, Georgia 30602, United States
| | - Kingsley Bimpeh
- Department of Chemistry, University of Georgia, Athens, Georgia 30602, United States
| | - Kelly M. Hines
- Department of Chemistry, University of Georgia, Athens, Georgia 30602, United States
| |
Collapse
|
16
|
Hynds H, Hines KM. MOCCal: A Multiomic CCS Calibrator for Traveling Wave Ion Mobility Mass Spectrometry. Anal Chem 2024; 96:1185-1194. [PMID: 38194410 PMCID: PMC10809277 DOI: 10.1021/acs.analchem.3c04290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 12/09/2023] [Accepted: 12/14/2023] [Indexed: 01/11/2024]
Abstract
Ion mobility mass spectrometry (IM-MS) is a rapid, gas-phase separation technology that can resolve ions on the basis of their size-to-charge and mass-to-charge ratios. Since each class of biomolecule has a unique relationship between size and mass, IM-MS spectra of complex biological samples are organized into trendlines that each contain one type of biomolecule (i.e., lipid, peptide, metabolite). These trendlines can aid in the identification of unknown ions by providing a general classification, while more specific identifications require the conversion of IM arrival times to collision cross section (CCS) values to minimize instrument-to-instrument variability. However, the process of converting IM arrival times to CCS values varies between the different IM devices. Arrival times from traveling wave ion mobility (TWIM) devices must undergo a calibration process to obtain CCS values, which can impart biases if the calibrants are not structurally similar to the analytes. For multiomic mixtures, several different types of calibrants must be used to obtain the most accurate CCS values from TWIM platforms. Here we describe the development of a multiomic CCS calibration tool, MOCCal, to automate the assignment of unknown features to the power law calibration that provides the most accurate CCS value. MOCCal calibrates every experimental arrival time with up to three class-specific calibration curves and uses the difference (in Å2) between the calibrated TWCCSN2 value and DTCCSN2 vs m/z regression lines to determine the best calibration curve. Using real and simulated multiomic samples, we demonstrate that MOCCal provides accurately calibrated TWCCSN2 values for small molecules, lipids, and peptides.
Collapse
Affiliation(s)
- Hannah
M. Hynds
- Department of Chemistry, University of Georgia, 302 East Campus Road, Athens, Georgia 30602, United States
| | - Kelly M. Hines
- Department of Chemistry, University of Georgia, 302 East Campus Road, Athens, Georgia 30602, United States
| |
Collapse
|
17
|
Pérez-Victoria I. Natural Products Dereplication: Databases and Analytical Methods. PROGRESS IN THE CHEMISTRY OF ORGANIC NATURAL PRODUCTS 2024; 124:1-56. [PMID: 39101983 DOI: 10.1007/978-3-031-59567-7_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/06/2024]
Abstract
The development of efficient methods for dereplication has been critical in the re-emergence of the research in natural products as a source of drug leads. Current dereplication workflows rapidly identify already known bioactive secondary metabolites in the early stages of any drug discovery screening campaign based on natural extracts or enriched fractions. Two main factors have driven the evolution of natural products dereplication over the last decades. First, the availability of both commercial and public large databases of natural products containing the key annotations against which the biological and chemical data derived from the studied sample are searched for. Second, the considerable improvement achieved in analytical technologies (including instrumentation and software tools) employed to obtain robust and precise chemical information (particularly spectroscopic signatures) on the compounds present in the bioactive natural product samples. This chapter describes the main methods of dereplication, which rely on the combined use of large natural product databases and spectral libraries, alongside the information obtained from chromatographic, UV-Vis, MS, and NMR spectroscopic analyses of the samples of interest.
Collapse
Affiliation(s)
- Ignacio Pérez-Victoria
- Fundación MEDINA, Centro de Excelencia en Investigación de Medicamentos Innovadores en Andalucía, Parque Tecnológico de Ciencias de La Salud, Avda. del Conocimiento 34, 18016, Armilla, Granada, Spain.
| |
Collapse
|
18
|
Song XC, Canellas E, Dreolin N, Goshawk J, Lv M, Qu G, Nerin C, Jiang G. Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21485-21502. [PMID: 38091506 PMCID: PMC10753811 DOI: 10.1021/acs.est.3c03686] [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: 05/16/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 12/27/2023]
Abstract
Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.
Collapse
Affiliation(s)
- Xue-Chao Song
- School
of the Environment, Hangzhou Institute for Advanced Study, University of the Chinese Academy of Sciences, Hangzhou 310024, China
- State
Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese
Academy of Sciences, Beijing 100085, China
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, EINA, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Elena Canellas
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, EINA, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Nicola Dreolin
- Waters
Corporation, Stamford
Avenue, Altrincham Road, SK9 4AX Wilmslow, United Kingdom
| | - Jeff Goshawk
- Waters
Corporation, Stamford
Avenue, Altrincham Road, SK9 4AX Wilmslow, United Kingdom
| | - Meilin Lv
- State
Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese
Academy of Sciences, Beijing 100085, China
- Research
Center for Analytical Sciences, Department of Chemistry, College of
Sciences, Northeastern University, 110819 Shenyang, China
| | - Guangbo Qu
- School
of the Environment, Hangzhou Institute for Advanced Study, University of the Chinese Academy of Sciences, Hangzhou 310024, China
- State
Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese
Academy of Sciences, Beijing 100085, China
- Institute
of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Cristina Nerin
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, EINA, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Guibin Jiang
- School
of the Environment, Hangzhou Institute for Advanced Study, University of the Chinese Academy of Sciences, Hangzhou 310024, China
- State
Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese
Academy of Sciences, Beijing 100085, China
- Institute
of Environment and Health, Jianghan University, Wuhan 430056, China
| |
Collapse
|
19
|
Wen JH, Guo AQ, Li MN, Yang H. A structural similarity networking assisted collision cross-section prediction interval filtering strategy for multi-compound identification of complex matrix by ion-mobility mass spectrometry. Anal Chim Acta 2023; 1278:341720. [PMID: 37709461 DOI: 10.1016/j.aca.2023.341720] [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: 03/20/2023] [Revised: 07/28/2023] [Accepted: 08/14/2023] [Indexed: 09/16/2023]
Abstract
Ion mobility coupled with mass spectrometry (IM-MS), an emerging technology for analysis of complex matrix, has been facing challenges due to the complexities of chemical structures and original data, as well as low-efficiency and error-proneness of manual operations. In this study, we developed a structural similarity networking assisted collision cross-section prediction interval filtering (SSN-CCSPIF) strategy. We first carried out a structural similarity networking (SSN) based on Tanimoto similarities among Morgan fingerprints to classify the authentic compounds potentially existing in complex matrix. By performing automatic regressive prediction statistics on mass-to-charge ratios (m/z) and collision cross-sections (CCS) with a self-built Python software, we explored the IM-MS feature trendlines, established filtering intervals and filtered potential compounds for each SSN classification. Chemical structures of all filtered compounds were further characterized by interpreting their multidimensional IM-MS data. To evaluate the applicability of SSN-CCSPIF, we selected Ginkgo biloba extract and dripping pills. The SSN-CCSPIF subtracted more background interferences (43.24%∼43.92%) than other similar strategies with conventional ClassyFire criteria (10.71%∼12.13%) or without compound classification (35.73%∼36.63%). Totally, 229 compounds, including eight potential new compounds, were characterized. Among them, seven isomeric pairs were discriminated with the integration of IM-separation. Using SSN-CCSPIF, we can achieve high-efficient analysis of complex IM-MS data and comprehensive chemical profiling of complex matrix to reveal their material basis.
Collapse
Affiliation(s)
- Jia-Hui Wen
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China
| | - An-Qi Guo
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China
| | - Meng-Ning Li
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China.
| | - Hua Yang
- State Key Laboratory of Natural Medicines, China Pharmaceutical University, No. 24 Tongjia Xiang, Nanjing, 210009, China.
| |
Collapse
|
20
|
Li A, Xu L. MALDI-IM-MS Imaging of Brain Sterols and Lipids in a Mouse Model of Smith-Lemli-Opitz Syndrome. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.02.560415. [PMID: 37873113 PMCID: PMC10592934 DOI: 10.1101/2023.10.02.560415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Smith-Lemli-Opitz syndrome (SLOS) is a neurodevelopmental disorder caused by genetic mutations in the DHCR7 gene, encoding the enzyme 3β-hydroxysterol-Δ7-reductase (DHCR7) that catalyzes the last step of cholesterol synthesis. The resulting deficiency in cholesterol and accumulation of its precursor, 7-dehydrocholesterol (7-DHC), have a profound impact on brain development, which manifests as developmental delay, cognitive impairment, and behavioral deficits. To understand how the brain regions are differentially affected by the defective Dhcr7, we aim to map the regional distribution of sterols and other lipids in neonatal brains from a Dhcr7-KO mouse model of SLOS, using mass spectrometry imaging (MSI). MSI enables spatial localization of biomolecules in situ on the surface of a tissue section, which is particularly useful for mapping the changes that occur within a metabolic disorder such as SLOS, and in an anatomically complex organ such as the brain. In this work, using MALDI-ion mobility (IM)-MSI, we successfully determined the regional distribution of features that correspond to cholesterol, 7-DHC/desmosterol, and the precursor of desmosterol, 7-dehydrodesmosterol, in WT and Dhcr7-KO mice. Interestingly, we also observed m/z values that match the major oxysterol metabolites of 7-DHC (DHCEO and hydroxy-7-DHC), which displayed similar patterns as 7-DHC. We then identified brain lipids using m/z and CCS at the Lipid Species-level and curated a database of MALDIIM-MS-derived lipid CCS values. Subsequent statistical analysis of regions-of-interest allowed us to identify differentially expressed lipids between Dhcr7-KO and WT brains, which could contribute to defects in myelination, neurogenesis, neuroinflammation, and learning and memory in SLOS.
Collapse
Affiliation(s)
- Amy Li
- Department of Medicinal Chemistry, School of Pharmacy, University of Washington, Seattle, WA 98195
| | - Libin Xu
- Department of Medicinal Chemistry, School of Pharmacy, University of Washington, Seattle, WA 98195
| |
Collapse
|
21
|
Miller A, York EM, Stopka SA, Martínez-François JR, Hossain MA, Baquer G, Regan MS, Agar NYR, Yellen G. Spatially resolved metabolomics and isotope tracing reveal dynamic metabolic responses of dentate granule neurons with acute stimulation. Nat Metab 2023; 5:1820-1835. [PMID: 37798473 PMCID: PMC10626993 DOI: 10.1038/s42255-023-00890-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Accepted: 08/09/2023] [Indexed: 10/07/2023]
Abstract
Neuronal activity creates an intense energy demand that must be met by rapid metabolic responses. To investigate metabolic adaptations in the neuron-enriched dentate granule cell (DGC) layer within its native tissue environment, we employed murine acute hippocampal brain slices, coupled with fast metabolite preservation and followed by mass spectrometry (MS) imaging, to generate spatially resolved metabolomics and isotope-tracing data. Here we show that membrane depolarization induces broad metabolic changes, including increased glycolytic activity in DGCs. Increased glucose metabolism in response to stimulation is accompanied by mobilization of endogenous inosine into pentose phosphates via the action of purine nucleotide phosphorylase (PNP). The PNP reaction is an integral part of the neuronal response to stimulation, because inhibition of PNP leaves DGCs energetically impaired during recovery from strong activation. Performing MS imaging on brain slices bridges the gap between live-cell physiology and the deep chemical analysis enabled by MS.
Collapse
Affiliation(s)
- Anne Miller
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Center for Pathobiochemistry and Genetics, Medical University of Vienna, Vienna, Austria
| | - Elisa M York
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Sylwia A Stopka
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Md Amin Hossain
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Gerard Baquer
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael S Regan
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nathalie Y R Agar
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Cancer Biology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
| | - Gary Yellen
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
22
|
Zhang R, Ashford NK, Li A, Ross DH, Werth BJ, Xu L. High-throughput analysis of lipidomic phenotypes of methicillin-resistant Staphylococcus aureus by coupling in situ 96-well cultivation and HILIC-ion mobility-mass spectrometry. Anal Bioanal Chem 2023; 415:6191-6199. [PMID: 37535099 PMCID: PMC11059195 DOI: 10.1007/s00216-023-04890-6] [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: 07/21/2022] [Revised: 07/23/2023] [Accepted: 07/25/2023] [Indexed: 08/04/2023]
Abstract
Antimicrobial resistance is a major threat to human health as resistant pathogens spread globally, and the development of new antimicrobials is slow. Since many antimicrobials function by targeting cell wall and membrane components, high-throughput lipidomics for bacterial phenotyping is of high interest for researchers to unveil lipid-mediated pathways when dealing with a large number of lab-selected or clinical strains. However, current practice for lipidomic analysis requires the cultivation of bacteria on a large scale, which does not replicate the growth conditions for high-throughput bioassays that are normally carried out in 96-well plates, such as susceptibility tests, growth curve measurements, and biofilm quantitation. Analysis of bacteria grown under the same condition as other bioassays would better inform the differences in susceptibility and other biological metrics. In this work, a high-throughput method for cultivation and lipidomic analysis of antimicrobial-resistant bacteria was developed for standard 96-well plates exemplified by methicillin-resistant Staphylococcus aureus (MRSA). By combining a 30-mm liquid chromatography (LC) column with ion mobility (IM) separation, elution time could be dramatically shortened to 3.6 min for a single LC run without losing major lipid features. Peak capacity was largely rescued by the addition of the IM dimension. Through multi-linear calibration, the deviation of retention time can be limited to within 5%, making database-based automatic lipid identification feasible. This high-throughput method was further validated by characterizing the lipidomic phenotypes of antimicrobial-resistant mutants derived from the MRSA strain, W308, grown in a 96-well plate.
Collapse
Affiliation(s)
- Rutan Zhang
- Department of Medicinal Chemistry, University of Washington, Seattle, WA, 98195, USA
| | - Nate K Ashford
- Department of Pharmacy, University of Washington, Seattle, WA, 98195, USA
| | - Amy Li
- Department of Medicinal Chemistry, University of Washington, Seattle, WA, 98195, USA
| | - Dylan H Ross
- Department of Medicinal Chemistry, University of Washington, Seattle, WA, 98195, USA
- Biological Sciences Division, Pacific Northwest National Laboratory, WA, 99352, Richland, USA
| | - Brian J Werth
- Department of Pharmacy, University of Washington, Seattle, WA, 98195, USA
| | - Libin Xu
- Department of Medicinal Chemistry, University of Washington, Seattle, WA, 98195, USA.
| |
Collapse
|
23
|
Zhang H, Luo M, Wang H, Ren F, Yin Y, Zhu ZJ. AllCCS2: Curation of Ion Mobility Collision Cross-Section Atlas for Small Molecules Using Comprehensive Molecular Representations. Anal Chem 2023; 95:13913-13921. [PMID: 37664900 DOI: 10.1021/acs.analchem.3c02267] [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: 09/05/2023]
Abstract
The development of ion mobility-mass spectrometry (IM-MS) has revolutionized the analysis of small molecules, such as metabolomics, lipidomics, and exposome studies. The curation of comprehensive reference collision cross-section (CCS) databases plays a pivotal role in the successful application of IM-MS for small-molecule analysis. In this study, we presented AllCCS2, an enhanced version of AllCCS, designed for the universal prediction of the ion mobility CCS values of small molecules. AllCCS2 incorporated newly available experimental CCS data, including 10,384 records and 7713 unified values, as training data. By leveraging a neural network trained on diverse molecular representations encompassing mass spectrometry features, molecular descriptors, and graph features extracted using a graph convolutional network, AllCCS2 achieved exceptional prediction accuracy. AllCCS2 achieved median relative error (MedRE) values of 0.31, 0.72, and 1.64% in the training, validation, and testing sets, respectively, surpassing existing CCS prediction tools in terms of accuracy and coverage. Furthermore, AllCCS2 exhibited excellent compatibility with different instrument platforms (DTIMS, TWIMS, and TIMS). The prediction uncertainties in AllCCS2 from the training data and the prediction model were comprehensively investigated by using representative structure similarity and model prediction variation. Notably, small molecules with high structural similarities to the training set and lower model prediction variation exhibited improved accuracy and lower relative errors. In summary, AllCCS2 serves as a valuable resource to support applications of IM-MS technologies. The AllCCS2 database and tools are freely accessible at http://allccs.zhulab.cn/.
Collapse
Affiliation(s)
- Haosong Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingdu Luo
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongmiao Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fandong Ren
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| | - Yandong Yin
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- Shanghai Key Laboratory of Aging Studies, Shanghai 201210, China
| |
Collapse
|
24
|
Ramtanon I, Berlioz-Barbier A, Remy S, Renault JH, Masle AL. A combined liquid chromatography - trapped ion mobility - tandem high-resolution mass spectrometry and multivariate analysis approach for the determination of enzymatic reactivity descriptors in biomass hydrolysates. J Chromatogr A 2023; 1706:464277. [PMID: 37573756 DOI: 10.1016/j.chroma.2023.464277] [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: 03/15/2023] [Revised: 07/17/2023] [Accepted: 08/02/2023] [Indexed: 08/15/2023]
Abstract
Intermediate products such as oxygenated compounds may interfere with bioconversion kinetics of lignocellulosic biomass into bioethanol. This work presents a multidimensional approach, based on liquid chromatography (LC), trapped ion mobility spectrometry (TIMS), tandem high-resolution mass spectrometry (HRMS/MS), and multivariate analysis, for the identification of enzymatic reactivity descriptors in 22 industrial biomass samples, called hydrolysates. The first part of the study is dedicated to the improvement of the chemical diversity assessment of the hydrolysates through an original three-dimensional Van Krevelen diagram displaying the double bond equivalent (DBE) as third dimension. In a second part, the evaluation of data by multivariate data analysis allowed the discrimination of sample according to the biomass type and the level of enzymatic reactivity. In the last part, a potential descriptor of low enzymatic reactivity was selected and used in a case study. An in-depth structural analysis was performed on the feature annotated as carbohydrate derivative. Considering the intricate fragmentation spectrum exhibited by the selected feature, trapped ion mobility was employed to enhance separation prior to the HRMS/MS experiments. This final step improved data interpretation and increased the identification confidence level leading to the characterization of xylotriose, 3,5-dimethoxy-4-hydroxybenzaldehyde and 4-hydroxy-3-methoxy-cinnamaldehyde. This is the first study to present an untargeted multidimensional approach for the identification of enzymatic hydrolysis inhibitors in industrial hydrolysate samples.
Collapse
Affiliation(s)
- Ian Ramtanon
- IFP Energies nouvelles, rond-point de l'échangeur de Solaize, BP 3, 69360 Solaize, France
| | | | - Simon Remy
- Université de Reims Champagne-Ardenne, CNRS, ICMR 7312, 51097, Reims, France
| | - Jean-Hugues Renault
- Université de Reims Champagne-Ardenne, CNRS, ICMR 7312, 51097, Reims, France
| | - Agnès Le Masle
- IFP Energies nouvelles, rond-point de l'échangeur de Solaize, BP 3, 69360 Solaize, France.
| |
Collapse
|
25
|
Reimers N, Do Q, Zhang R, Guo A, Ostrander R, Shoji A, Vuong C, Xu L. Tracking the Metabolic Fate of Exogenous Arachidonic Acid in Ferroptosis Using Dual-Isotope Labeling Lipidomics. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023; 34:2016-2024. [PMID: 37523294 PMCID: PMC10487598 DOI: 10.1021/jasms.3c00181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/14/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 08/02/2023]
Abstract
Lipid metabolism is implicated in a variety of diseases, including cancer, cell death, and inflammation, but lipidomics has proven to be challenging due to the vast structural diversity over a narrow range of mass and polarity of lipids. Isotope labeling is often used in metabolomics studies to follow the metabolism of exogenously added labeled compounds because they can be differentiated from endogenous compounds by the mass shift associated with the label. The application of isotope labeling to lipidomics has also been explored as a method to track the metabolism of lipids in various disease states. However, it can be difficult to differentiate a single isotopically labeled lipid from the rest of the lipidome due to the variety of endogenous lipids present over the same mass range. Here we report the development of a dual-isotope deuterium labeling method to track the metabolic fate of exogenous polyunsaturated fatty acids, e.g., arachidonic acid, in the context of ferroptosis using hydrophilic interaction-ion mobility-mass spectrometry (HILIC-IM-MS). Ferroptosis is a type of cell death that is dependent on lipid peroxidation. The use of two isotope labels rather than one enables the identification of labeled species by a signature doublet peak in the resulting mass spectra. A Python-based software, D-Tracer, was developed to efficiently extract metabolites with dual-isotope labels. The labeled species were then identified with LiPydomics based on their retention times, collision cross section, and m/z values. Changes in exogenous AA incorporation in the absence and presence of a ferroptosis inducer were elucidated.
Collapse
Affiliation(s)
- Noelle Reimers
- Department
of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Quynh Do
- Department
of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Rutan Zhang
- Department
of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Angela Guo
- Department
of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Ryan Ostrander
- Department
of Mechanical Engineering, University of
Washington, Seattle Washington 98195, United States
| | - Alyson Shoji
- Department
of Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Chau Vuong
- Department
of Biochemistry, University of Washington, Seattle, Washington 98195, United States
| | - Libin Xu
- Department
of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| |
Collapse
|
26
|
Kartowikromo KY, Olajide OE, Hamid AM. Collision cross section measurement and prediction methods in omics. JOURNAL OF MASS SPECTROMETRY : JMS 2023; 58:e4973. [PMID: 37620034 PMCID: PMC10530098 DOI: 10.1002/jms.4973] [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: 04/19/2023] [Revised: 06/26/2023] [Accepted: 07/20/2023] [Indexed: 08/26/2023]
Abstract
Omics studies such as metabolomics, lipidomics, and proteomics have become important for understanding the mechanisms in living organisms. However, the compounds detected are structurally different and contain isomers, with each structure or isomer leading to a different result in terms of the role they play in the cell or tissue in the organism. Therefore, it is important to detect, characterize, and elucidate the structures of these compounds. Liquid chromatography and mass spectrometry have been utilized for decades in the structure elucidation of key compounds. While prediction models of parameters (such as retention time and fragmentation pattern) have also been developed for these separation techniques, they have some limitations. Moreover, ion mobility has become one of the most promising techniques to give a fingerprint to these compounds by determining their collision cross section (CCS) values, which reflect their shape and size. Obtaining accurate CCS enables its use as a filter for potential analyte structures. These CCS values can be measured experimentally using calibrant-independent and calibrant-dependent approaches. Identification of compounds based on experimental CCS values in untargeted analysis typically requires CCS references from standards, which are currently limited and, if available, would require a large amount of time for experimental measurements. Therefore, researchers use theoretical tools to predict CCS values for untargeted and targeted analysis. In this review, an overview of the different methods for the experimental and theoretical estimation of CCS values is given where theoretical prediction tools include computational and machine modeling type approaches. Moreover, the limitations of the current experimental and theoretical approaches and their potential mitigation methods were discussed.
Collapse
Affiliation(s)
| | - Orobola E Olajide
- Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama, USA
| | - Ahmed M Hamid
- Department of Chemistry and Biochemistry, Auburn University, Auburn, Alabama, USA
| |
Collapse
|
27
|
Singh RR, Aminot Y, Héas-Moisan K, Preud'homme H, Munschy C. Cracked and shucked: GC-APCI-IMS-HRMS facilitates identification of unknown halogenated organic chemicals in French marine bivalves. ENVIRONMENT INTERNATIONAL 2023; 178:108094. [PMID: 37478678 DOI: 10.1016/j.envint.2023.108094] [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: 05/24/2023] [Revised: 07/06/2023] [Accepted: 07/10/2023] [Indexed: 07/23/2023]
Abstract
High resolution mass spectrometry (HRMS)-based non-target analysis coupled with ion mobility spectrometry (IMS) is gaining momentum due to its ability to provide complementary information which can be useful in the identification of unknown organic chemicals in support of efforts in unraveling the complexity of the chemical exposome. The chemical exposome in the marine environment, though not as well studied as its freshwater counterparts, is not foreign to chemical diversity specially when it comes to potentially bioaccumulative and bioactive polyhalogenated organic contaminants and natural products. In this work we present in detail how we utilized IMS-HRMS coupled with gas chromatographic separation and atmospheric pressure chemical ionization (APCI) to annotate polyhalogenated organic chemicals in French bivalves collected from 25 sites along the French coasts. We describe how we used open cheminformatic tools to exploit isotopologue patterns, isotope ratios, Kendrick mass defect (Cl scale), and collisional cross section (CCS), in order to annotate 157 halogenated features (level 1: 54, level 2: 47, level 3: 50, and level 4: 6). Grouping the features into 11 compound classes was facilitated by a KMD vs CCS plot which showed co-clustering of potentially structurally-related compounds. The features were semi-quantified to gain insight into the distribution of these halogenated features along the French coast, ultimately allowing us to differentiate between sites that are more anthropologically impacted versus sites that are potentially biodiverse.
Collapse
Affiliation(s)
- Randolph R Singh
- Ifremer, CCEM Contamination Chimique des Ecosystèmes Marins, F-44000, Nantes, France.
| | - Yann Aminot
- Ifremer, CCEM Contamination Chimique des Ecosystèmes Marins, F-44000, Nantes, France
| | - Karine Héas-Moisan
- Ifremer, CCEM Contamination Chimique des Ecosystèmes Marins, F-44000, Nantes, France
| | - Hugues Preud'homme
- IPREM-UMR5254, E2S UPPA, CNRS, Technopôle Helioparc, 2 Avenue P. Angot, 64053 Pau Cedex 9, France
| | - Catherine Munschy
- Ifremer, CCEM Contamination Chimique des Ecosystèmes Marins, F-44000, Nantes, France
| |
Collapse
|
28
|
Miller A, York E, Stopka S, Martínez-François J, Hossain MA, Baquer G, Regan M, Agar N, Yellen G. Spatially resolved metabolomics and isotope tracing reveal dynamic metabolic responses of dentate granule neurons with acute stimulation. RESEARCH SQUARE 2023:rs.3.rs-2276903. [PMID: 37546759 PMCID: PMC10402263 DOI: 10.21203/rs.3.rs-2276903/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Neuronal activity creates an intense energy demand that must be met by rapid metabolic responses. To investigate metabolic adaptations in the neuron-enriched dentate granule cell (DGC) layer within its native tissue environment, we employed murine acute hippocampal brain slices coupled with fast metabolite preservation, followed by mass spectrometry imaging (MALDI-MSI) to generate spatially resolved metabolomics and isotope tracing data. Here we show that membrane depolarization induces broad metabolic changes, including increased glycolytic activity in DGCs. Increased glucose metabolism in response to stimulation is accompanied by mobilization of endogenous inosine into pentose phosphates, via the action of purine nucleotide phosphorylase (PNP). The PNP reaction is an integral part of the neuronal response to stimulation, as inhibiting PNP leaves DGCs energetically impaired during recovery from strong activation. Performing MSI on brain slices bridges the gap between live cell physiology and the deep chemical analysis enabled by mass spectrometry.
Collapse
|
29
|
Guo R, Zhang Y, Liao Y, Yang Q, Xie T, Fan X, Lin Z, Chen Y, Lu H, Zhang Z. Highly accurate and large-scale collision cross sections prediction with graph neural networks. Commun Chem 2023; 6:139. [PMID: 37402835 DOI: 10.1038/s42004-023-00939-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 06/23/2023] [Indexed: 07/06/2023] Open
Abstract
The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.
Collapse
Affiliation(s)
- Renfeng Guo
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China
| | - Youjia Zhang
- School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China
| | - Yuxuan Liao
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China
| | - Qiong Yang
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China
| | - Ting Xie
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China
| | - Xiaqiong Fan
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China
| | - Zhonglong Lin
- Yunnan Academy of Tobacco Agricultural Sciences, 650021, Kunming, Yunnan, China
| | - Yi Chen
- Yunnan Academy of Tobacco Agricultural Sciences, 650021, Kunming, Yunnan, China.
| | - Hongmei Lu
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China.
| | - Zhimin Zhang
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China.
| |
Collapse
|
30
|
Morel Y, Jones JW. Utilization of LC-MS/MS and Drift Tube Ion Mobility for Characterizing Intact Oxidized Arachidonate-Containing Glycerophosphatidylethanolamine. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023. [PMID: 37369083 DOI: 10.1021/jasms.3c00083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/29/2023]
Abstract
Lipid peroxidation is a key component in the pathogenesis of numerous disease states, where the oxidative damage of lipids frequently leads to membrane dysfunction and subsequent cellular death. Glycerophosphoethanolamine (PE) is the second most abundant phospholipid found in cellular membranes and, when oxidized, has been identified as an executor of ferroptotic cell death. PE commonly exists in the plasmalogen form, where the presence of the vinyl ether bond and its enrichment in polyunsaturated fatty acids make it especially susceptible to oxidative degradation. This results in a multitude of oxidized products complicating identification and often requiring several analytical techniques for interpretation. In the present study, we outline an analytical approach for the structural characterization of intact oxidized products of arachidonate-containing diacyl and plasmalogen PE. Intact oxidized PE structures, including structural and positional isomers, were identified using complementary liquid chromatography techniques, drift tube ion mobility, and high-resolution tandem mass spectrometry. This work establishes a comprehensive method for the analysis of intact lipid peroxidation products and provides an important pathway to investigate how lipid peroxidation initially impacts glycerophospholipids and their role in redox biology.
Collapse
Affiliation(s)
- Yulemni Morel
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland 21201, United States
| | - Jace W Jones
- Department of Pharmaceutical Sciences, School of Pharmacy, University of Maryland, Baltimore, Maryland 21201, United States
| |
Collapse
|
31
|
Ross D, Bilbao A, Lee JY, Zheng X. mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format. Anal Chem 2023. [PMID: 37307589 DOI: 10.1021/acs.analchem.3c01653] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Analysis of ion mobility spectrometry (IMS) data has been challenging and limited the full utility of these measurements. Unlike liquid chromatography-mass spectrometry, where a plethora of tools with well-established algorithms exist, the incorporation of the additional IMS dimension requires upgrading existing computational pipelines and developing new algorithms to fully exploit the advantages of the technology. We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of core libraries in popular programming languages with standard mass spectrometry utilities will facilitate fast software development and broader adoption of the format. To this end, we present a Python package, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format, especially for complex data containing ion mobility spectrometry dimension. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python and having minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multiomics domain. The mzapy package is free and open-source, includes comprehensive documentation, and is structured to support future extension to meet the evolving needs of the MS community. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.
Collapse
Affiliation(s)
- Dylan Ross
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Aivett Bilbao
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Joon-Yong Lee
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Xueyun Zheng
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| |
Collapse
|
32
|
Cajahuaringa S, Caetano DLZ, Zanotto LN, Araujo G, Skaf MS. MassCCS: A High-Performance Collision Cross-Section Software for Large Macromolecular Assemblies. J Chem Inf Model 2023; 63:3557-3566. [PMID: 37184925 PMCID: PMC10269586 DOI: 10.1021/acs.jcim.3c00405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Indexed: 05/16/2023]
Abstract
Ion mobility mass spectrometry (IM-MS) techniques have become highly valued as a tool for structural characterization of biomolecular systems since they yield accurate measurements of the rotationally averaged collision cross-section (CCS) against a buffer gas. Despite its enormous potential, IM-MS data interpretation is often challenging due to the conformational isomerism of metabolites, lipids, proteins, and other biomolecules in the gas phase. Therefore, reliable and fast CCS calculations are needed to help interpret IM-MS data. In this work, we present MassCCS, a parallelized open-source code for computing CCS of molecules ranging from small organic compounds to massive protein assemblies at the trajectory method level of description using atomic and molecular buffer gas particles. The performance of the code is comparable to other available software for small molecules and proteins but is significantly faster for larger macromolecular assemblies. We performed extensive tests regarding accuracy, performance, and scalability with system size and number of CPU cores. MassCCS has proven highly accurate and efficient, with execution times under a few minutes, even for large (84.87 MDa) virus capsid assemblies with very modest computational resources. MassCCS is freely available at https://github.com/cces-cepid/massccs.
Collapse
Affiliation(s)
- Samuel Cajahuaringa
- Institute
of Computing, University of Campinas, Campinas, São Paulo 13083-852, Brazil
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
| | - Daniel L. Z. Caetano
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
- Institute
of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Leandro N. Zanotto
- Institute
of Computing, University of Campinas, Campinas, São Paulo 13083-852, Brazil
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
| | - Guido Araujo
- Institute
of Computing, University of Campinas, Campinas, São Paulo 13083-852, Brazil
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
| | - Munir S. Skaf
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
- Institute
of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| |
Collapse
|
33
|
Li X, Wang H, Jiang M, Ding M, Xu X, Xu B, Zou Y, Yu Y, Yang W. Collision Cross Section Prediction Based on Machine Learning. Molecules 2023; 28:molecules28104050. [PMID: 37241791 DOI: 10.3390/molecules28104050] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.
Collapse
Affiliation(s)
- Xiaohang Li
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Hongda Wang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Meiting Jiang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Mengxiang Ding
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Xiaoyan Xu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Bei Xu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Yadan Zou
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Yuetong Yu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Wenzhi Yang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| |
Collapse
|
34
|
Luo M, Yin Y, Zhou Z, Zhang H, Chen X, Wang H, Zhu ZJ. A mass spectrum-oriented computational method for ion mobility-resolved untargeted metabolomics. Nat Commun 2023; 14:1813. [PMID: 37002244 PMCID: PMC10066191 DOI: 10.1038/s41467-023-37539-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Accepted: 03/17/2023] [Indexed: 04/03/2023] Open
Abstract
Ion mobility (IM) adds a new dimension to liquid chromatography-mass spectrometry-based untargeted metabolomics which significantly enhances coverage, sensitivity, and resolving power for analyzing the metabolome, particularly metabolite isomers. However, the high dimensionality of IM-resolved metabolomics data presents a great challenge to data processing, restricting its widespread applications. Here, we develop a mass spectrum-oriented bottom-up assembly algorithm for IM-resolved metabolomics that utilizes mass spectra to assemble four-dimensional peaks in a reverse order of multidimensional separation. We further develop the end-to-end computational framework Met4DX for peak detection, quantification and identification of metabolites in IM-resolved metabolomics. Benchmarking and validation of Met4DX demonstrates superior performance compared to existing tools with regard to coverage, sensitivity, peak fidelity and quantification precision. Importantly, Met4DX successfully detects and differentiates co-eluted metabolite isomers with small differences in the chromatographic and IM dimensions. Together, Met4DX advances metabolite discovery in biological organisms by deciphering the complex 4D metabolomics data.
Collapse
Affiliation(s)
- Mingdu Luo
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Yandong Yin
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
| | - Zhiwei Zhou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
| | - Haosong Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Xi Chen
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Hongmiao Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China
- University of Chinese Academy of Sciences, Beijing, 100049, P. R. China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 200032, P. R. China.
- Shanghai Key Laboratory of Aging Studies, Shanghai, 201210, P. R. China.
| |
Collapse
|
35
|
Iturrospe E, Robeyns R, da Silva KM, van de Lavoir M, Boeckmans J, Vanhaecke T, van Nuijs ALN, Covaci A. Metabolic signature of HepaRG cells exposed to ethanol and tumor necrosis factor alpha to study alcoholic steatohepatitis by LC-MS-based untargeted metabolomics. Arch Toxicol 2023; 97:1335-1353. [PMID: 36826472 DOI: 10.1007/s00204-023-03470-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/16/2023] [Indexed: 02/25/2023]
Abstract
Despite the high prevalence of alcoholic liver disease, its identification and characterization remain poor, especially in early stages such as alcoholic fatty liver disease and alcoholic steatohepatitis. This latter implies diagnostic difficulties, few therapeutic options and unclear mechanisms of action. To elucidate the metabolic alterations and pinpoint affected biochemical pathways, alcoholic steatohepatitis was simulated in vitro by exposing HepaRG cells to ethanol (IC10, 368 mM) and tumor necrosis factor alpha (TNF-α, 50 ng/mL) for 24 h. This combined exposure was compared to solely ethanol-exposed as well as -nonexposed cells. Four different metabolomics platforms were used combining liquid chromatography, high-resolution mass spectrometry and drift tube ion mobility to elucidate both intracellular and extracellular metabolic alterations. Some of the key findings include the influence of TNF-α in the upregulation of hepatic triglycerides and the downregulation of hepatic phosphatidylethanolamines and phosphatidylcholines. S-Adenosylmethionine showed to play a central role in the progression of alcoholic steatohepatitis. In addition, fatty acyl esters of hydroxy fatty acid (FAHFA)-containing triglycerides were detected for the first time in human hepatocytes and their alterations showed a potentially important role during the progression of alcoholic steatohepatitis. Ethoxylated phosphorylcholine was identified as a potential new biomarker of ethanol exposure.
Collapse
Affiliation(s)
- Elias Iturrospe
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium.
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Jette, Belgium.
| | - Rani Robeyns
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | | | - Maria van de Lavoir
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Joost Boeckmans
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Jette, Belgium
| | - Tamara Vanhaecke
- Department of In Vitro Toxicology and Dermato-Cosmetology, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Jette, Belgium
| | | | - Adrian Covaci
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium.
| |
Collapse
|
36
|
Lenski M, Maallem S, Zarcone G, Garçon G, Lo-Guidice JM, Anthérieu S, Allorge D. Prediction of a Large-Scale Database of Collision Cross-Section and Retention Time Using Machine Learning to Reduce False Positive Annotations in Untargeted Metabolomics. Metabolites 2023; 13:metabo13020282. [PMID: 36837901 PMCID: PMC9962007 DOI: 10.3390/metabo13020282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/07/2023] [Accepted: 02/12/2023] [Indexed: 02/18/2023] Open
Abstract
Metabolite identification in untargeted metabolomics is complex, with the risk of false positive annotations. This work aims to use machine learning to successively predict the retention time (Rt) and the collision cross-section (CCS) of an open-access database to accelerate the interpretation of metabolomic results. Standards of metabolites were tested using liquid chromatography coupled with high-resolution mass spectrometry. In CCSBase and QSRR predictor machine learning models, experimental results were used to generate predicted CCS and Rt of the Human Metabolome Database. From 542 standards, 266 and 301 compounds were detected in positive and negative electrospray ionization mode, respectively, corresponding to 380 different metabolites. CCS and Rt were then predicted using machine learning tools for almost 114,000 metabolites. R2 score of the linear regression between predicted and measured data achieved 0.938 and 0.898 for CCS and Rt, respectively, demonstrating the models' reliability. A CCS and Rt index filter of mean error ± 2 standard deviations could remove most misidentifications. Its application to data generated from a toxicology study on tobacco cigarettes reduced hits by 76%. Regarding the volume of data produced by metabolomics, the practical workflow provided allows for the implementation of valuable large-scale databases to improve the biological interpretation of metabolomics data.
Collapse
Affiliation(s)
- Marie Lenski
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
- CHU Lille, Unité Fonctionnelle de Toxicologie, F-59037 Lille, France
- Correspondence:
| | - Saïd Maallem
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
| | - Gianni Zarcone
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
| | - Guillaume Garçon
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
| | - Jean-Marc Lo-Guidice
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
| | - Sébastien Anthérieu
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
| | - Delphine Allorge
- ULR 4483, IMPECS—IMPact de l’Environnement Chimique sur la Santé humaine, CHU Lille, Institut Pasteur de Lille, Université de Lille, F-59000 Lille, France
- CHU Lille, Unité Fonctionnelle de Toxicologie, F-59037 Lille, France
| |
Collapse
|
37
|
Asef CK, Rainey MA, Garcia BM, Gouveia GJ, Shaver AO, Leach FE, Morse AM, Edison AS, McIntyre LM, Fernández FM. Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry. Anal Chem 2023; 95:1047-1056. [PMID: 36595469 PMCID: PMC10440795 DOI: 10.1021/acs.analchem.2c03749] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insights into areas requiring further improvement.
Collapse
Affiliation(s)
- Carter K Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia30332, United States
| | - Markace A Rainey
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia30332, United States
| | - Brianna M Garcia
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia30602, United States
- Department of Chemistry, University of Georgia, Athens, Georgia30602, United States
| | - Goncalo J Gouveia
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia30602, United States
- Department of Biochemistry, University of Georgia, Athens, Georgia30602, United States
| | - Amanda O Shaver
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia30602, United States
- Department of Genetics, University of Georgia, Athens, Georgia30602, United States
| | - Franklin E Leach
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia30602, United States
- Department of Environment Health Science, University of Georgia, Athens, Georgia30602, United States
| | - Alison M Morse
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida32611, United States
| | - Arthur S Edison
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia30602, United States
- Department of Chemistry, University of Georgia, Athens, Georgia30602, United States
- Department of Biochemistry, University of Georgia, Athens, Georgia30602, United States
| | - Lauren M McIntyre
- Department of Molecular Genetics and Microbiology, University of Florida, Gainesville, Florida32611, United States
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia30332, United States
- Petit Institute of Bioengineering and Biotechnology, Georgia Institute of Technology, Atlanta, Georgia30332, United States
| |
Collapse
|
38
|
Liu L, Wang Z, Zhang Q, Mei Y, Li L, Liu H, Wang Z, Yang L. Ion Mobility Mass Spectrometry for the Separation and Characterization of Small Molecules. Anal Chem 2023; 95:134-151. [PMID: 36625109 DOI: 10.1021/acs.analchem.2c02866] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Affiliation(s)
- Longchan Liu
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai201203, China
| | - Ziying Wang
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai201203, China
| | - Qian Zhang
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai201203, China
| | - Yuqi Mei
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai201203, China
| | - Linnan Li
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai201203, China
| | - Huwei Liu
- Beijing National Laboratory for Molecular Sciences, College of Chemistry and Molecular Engineering, Peking University, Beijing100871, China
| | - Zhengtao Wang
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai201203, China
| | - Li Yang
- The MOE Key Laboratory of Standardization of Chinese Medicines, The SATCM Key Laboratory of New Resources and Quality Evaluation of Chinese Medicines, The Shanghai Key Laboratory for Compound Chinese Medicines, Institute of Chinese Materia Medica, Shanghai University of Traditional Chinese Medicine, Shanghai201203, China.,Shanghai Frontiers Science Center of TCM Chemical Biology, Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine, Shanghai201203, China
| |
Collapse
|
39
|
Towards a harmonized identification scoring system in LC-HRMS/MS based non-target screening (NTS) of emerging contaminants. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116944] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
|
40
|
da Silva KM, van de Lavoir M, Robeyns R, Iturrospe E, Verheggen L, Covaci A, van Nuijs ALN. Guidelines and considerations for building multidimensional libraries for untargeted MS-based metabolomics. Metabolomics 2022; 19:4. [PMID: 36576608 DOI: 10.1007/s11306-022-01965-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Accepted: 12/05/2022] [Indexed: 12/29/2022]
Abstract
INTRODUCTION Feature annotation is crucial in untargeted metabolomics but remains a major challenge. The large pool of metabolites collected under various instrumental conditions is underrepresented in publicly available databases. Retention time (RT) and collision cross section (CCS) measurements from liquid chromatography ion mobility high-resolution mass spectrometers can be employed in addition to MS/MS spectra to improve the confidence of metabolite annotation. Recent advancements in machine learning focus on improving the accuracy of predictions for CCS and RT values. Therefore, high-quality experimental data are crucial to be used either as training datasets or as a reference for high-confidence matching. METHODS This manuscript provides an easy-to-use workflow for the creation of an in-house metabolite library, offers an overview of alternative solutions, and discusses the challenges and advantages of using open-source software. A total of 100 metabolite standards from various classes were analyzed and subjected to the described workflow for library generation. RESULTS AND DISCUSSION The outcome was an open-access available NIST format metabolite library (.msp) with multidimensional information. The library was used to evaluate CCS prediction tools, MS/MS spectra heterogeneities (e.g., multiple adducts, in-source fragmentation, radical fragment ions using collision-induced dissociation), and the reporting of RT.
Collapse
Affiliation(s)
- Katyeny Manuela da Silva
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Maria van de Lavoir
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Rani Robeyns
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Elias Iturrospe
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
- Department of In Vitro Toxicology and Dermato-Cosmetology, Faculty of Medicine and Pharmacy, Campus Jette, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090, Brussels, Belgium
| | - Lisa Verheggen
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Adrian Covaci
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Alexander L N van Nuijs
- Department of Pharmaceutical Sciences, Faculty of Pharmaceutical, Toxicological Centre, Biomedical and Veterinary Sciences, Campus Drie Eiken, University of Antwerp, Universiteitsplein 1, 2610, Antwerp, Belgium.
| |
Collapse
|
41
|
Menger F, Celma A, Schymanski EL, Lai FY, Bijlsma L, Wiberg K, Hernández F, Sancho JV, Ahrens L. Enhancing spectral quality in complex environmental matrices: Supporting suspect and non-target screening in zebra mussels with ion mobility. ENVIRONMENT INTERNATIONAL 2022; 170:107585. [PMID: 36265356 DOI: 10.1016/j.envint.2022.107585] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 10/11/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
Identification of bioaccumulating contaminants of emerging concern (CECs) via suspect and non-target screening remains a challenging task. In this study, ion mobility separation with high-resolution mass spectrometry (IM-HRMS) was used to investigate the effects of drift time (DT) alignment on spectrum quality and peak annotation for screening of CECs in complex sample matrices using data independent acquisition (DIA). Data treatment approaches (Binary Sample Comparison) and prioritisation strategies (Halogen Match, co-occurrence of features in biota and the water phase) were explored in a case study on zebra mussel (Dreissena polymorpha) in Lake Mälaren, Sweden's largest drinking water reservoir. DT alignment evidently improved the fragment spectrum quality by increasing the similarity score to reference spectra from on average (±standard deviation) 0.33 ± 0.31 to 0.64 ± 0.30 points, thus positively influencing structure elucidation efforts. Thirty-two features were tentatively identified at confidence level 3 or higher using MetFrag coupled with the new PubChemLite database, which included predicted collision cross-section values from CCSbase. The implementation of predicted mobility data was found to support compound annotation. This study illustrates a quantitative assessment of the benefits of IM-HRMS on spectral quality, which will enhance the performance of future screening studies of CECs in complex environmental matrices.
Collapse
Affiliation(s)
- Frank Menger
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), SE-75007 Uppsala, Sweden.
| | - Alberto Celma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, 6, Avenue du Swing, L-4367 Belvaux, Luxembourg
| | - Foon Yin Lai
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), SE-75007 Uppsala, Sweden
| | - Lubertus Bijlsma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Karin Wiberg
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), SE-75007 Uppsala, Sweden
| | - Félix Hernández
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Juan V Sancho
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avda. Sos Baynat s/n, E-12071 Castellón, Spain
| | - Lutz Ahrens
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), SE-75007 Uppsala, Sweden.
| |
Collapse
|
42
|
Cai Y, Zhou Z, Zhu ZJ. Advanced analytical and informatic strategies for metabolite annotation in untargeted metabolomics. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116903] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
|
43
|
Celma A, Bade R, Sancho JV, Hernandez F, Humphries M, Bijlsma L. Prediction of Retention Time and Collision Cross Section (CCS H+, CCS H-, and CCS Na+) of Emerging Contaminants Using Multiple Adaptive Regression Splines. J Chem Inf Model 2022; 62:5425-5434. [PMID: 36280383 PMCID: PMC9709913 DOI: 10.1021/acs.jcim.2c00847] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R2 = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCSH model (R2 = 0.966) was ±4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2 = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.
Collapse
Affiliation(s)
- Alberto Celma
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain,Department
of Aquatic Sciences and Assessment, Swedish
University of Agricultural Sciences (SLU), SE-750 07Uppsala, Sweden
| | - Richard Bade
- University
of South Australia, Adelaide, UniSA: Clinical and Health Sciences,
Health and Biomedical Innovation, AdelaideSA-5000, South
Australia, Australia,Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall Street, WoolloongabbaAUS-4102, Queensland, Australia
| | - Juan Vicente Sancho
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain
| | - Félix Hernandez
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain
| | - Melissa Humphries
- School
of Mathematical Sciences, University of
Adelaide, Ingkarni Wardli Building, North Terrace Campus, SA-5005Adelaide, Australia,
| | - Lubertus Bijlsma
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain,
| |
Collapse
|
44
|
Mohammed Taha H, Aalizadeh R, Alygizakis N, Antignac JP, Arp HPH, Bade R, Baker N, Belova L, Bijlsma L, Bolton EE, Brack W, Celma A, Chen WL, Cheng T, Chirsir P, Čirka Ľ, D’Agostino LA, Djoumbou Feunang Y, Dulio V, Fischer S, Gago-Ferrero P, Galani A, Geueke B, Głowacka N, Glüge J, Groh K, Grosse S, Haglund P, Hakkinen PJ, Hale SE, Hernandez F, Janssen EML, Jonkers T, Kiefer K, Kirchner M, Koschorreck J, Krauss M, Krier J, Lamoree MH, Letzel M, Letzel T, Li Q, Little J, Liu Y, Lunderberg DM, Martin JW, McEachran AD, McLean JA, Meier C, Meijer J, Menger F, Merino C, Muncke J, Muschket M, Neumann M, Neveu V, Ng K, Oberacher H, O’Brien J, Oswald P, Oswaldova M, Picache JA, Postigo C, Ramirez N, Reemtsma T, Renaud J, Rostkowski P, Rüdel H, Salek RM, Samanipour S, Scheringer M, Schliebner I, Schulz W, Schulze T, Sengl M, Shoemaker BA, Sims K, Singer H, Singh RR, Sumarah M, Thiessen PA, Thomas KV, Torres S, Trier X, van Wezel AP, Vermeulen RCH, Vlaanderen JJ, von der Ohe PC, Wang Z, Williams AJ, Willighagen EL, Wishart DS, Zhang J, Thomaidis NS, Hollender J, Slobodnik J, Schymanski EL. The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry. ENVIRONMENTAL SCIENCES EUROPE 2022; 34:104. [PMID: 36284750 PMCID: PMC9587084 DOI: 10.1186/s12302-022-00680-6] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/24/2022] [Indexed: 06/16/2023]
Abstract
Background The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide. Results The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101). Conclusions The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/). Supplementary Information The online version contains supplementary material available at 10.1186/s12302-022-00680-6.
Collapse
Affiliation(s)
- Hiba Mohammed Taha
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg
| | - Reza Aalizadeh
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Nikiforos Alygizakis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
| | | | - Hans Peter H. Arp
- Norwegian Geotechnical Institute (NGI), Ullevål Stadion, P.O. Box 3930, 0806 Oslo, Norway
- Department of Chemistry, Norwegian University of Science and Technology (NTNU), 7491 Trondheim, Norway
| | - Richard Bade
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, QLD 4102 Australia
| | | | - Lidia Belova
- Toxicological Centre, University of Antwerp, Antwerp, Belgium
| | - Lubertus Bijlsma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, Spain
| | - Evan E. Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Werner Brack
- UFZ, Helmholtz Centre for Environmental Research, Leipzig, Germany
- Institute of Ecology, Evolution and Diversity, Goethe University, Frankfurt Am Main, Germany
| | - Alberto Celma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, Spain
- Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
| | - Wen-Ling Chen
- Institute of Food Safety and Health, College of Public Health, National Taiwan University, 17 Xuzhou Rd., Zhongzheng Dist., Taipei, Taiwan
| | - Tiejun Cheng
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Parviel Chirsir
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg
| | - Ľuboš Čirka
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
- Faculty of Chemical and Food Technology, Institute of Information Engineering, Automation, and Mathematics, Slovak University of Technology in Bratislava (STU), Radlinského 9, 812 37 Bratislava, Slovak Republic
| | - Lisa A. D’Agostino
- Science for Life Laboratory, Department of Environmental Science, Stockholm University, 10691 Stockholm, Sweden
| | | | - Valeria Dulio
- INERIS, National Institute for Environment and Industrial Risks, Verneuil en Halatte, France
| | - Stellan Fischer
- Swedish Chemicals Agency (KEMI), P.O. Box 2, 172 13 Sundbyberg, Sweden
| | - Pablo Gago-Ferrero
- Institute of Environmental Assessment and Water Research-Severo Ochoa Excellence Center (IDAEA), Spanish Council of Scientific Research (CSIC), Barcelona, Spain
| | - Aikaterini Galani
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Birgit Geueke
- Food Packaging Forum Foundation, Staffelstrasse 10, 8045 Zurich, Switzerland
| | - Natalia Głowacka
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
| | - Juliane Glüge
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland
| | - Ksenia Groh
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Sylvia Grosse
- Thermo Fisher Scientific, Dornierstrasse 4, 82110 Germering, Germany
| | - Peter Haglund
- Department of Chemistry, Chemical Biological Centre (KBC), Umeå University, Linnaeus Väg 6, 901 87 Umeå, Sweden
| | - Pertti J. Hakkinen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Sarah E. Hale
- Norwegian Geotechnical Institute (NGI), Ullevål Stadion, P.O. Box 3930, 0806 Oslo, Norway
| | - Felix Hernandez
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, Spain
| | - Elisabeth M.-L. Janssen
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Tim Jonkers
- Department Environment and Health, Amsterdam Institute for Life and Environment, Vrije Universiteit, Amsterdam, The Netherlands
| | - Karin Kiefer
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Michal Kirchner
- Water Research Institute (WRI), Nábr. Arm. Gen. L. Svobodu 5, 81249 Bratislava, Slovak Republic
| | - Jan Koschorreck
- German Environment Agency (UBA), Wörlitzer Platz 1, Dessau-Roßlau, Germany
| | - Martin Krauss
- UFZ, Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Jessy Krier
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg
| | - Marja H. Lamoree
- Department Environment and Health, Amsterdam Institute for Life and Environment, Vrije Universiteit, Amsterdam, The Netherlands
| | - Marion Letzel
- Bavarian Environment Agency, 86179 Augsburg, Germany
| | - Thomas Letzel
- Analytisches Forschungsinstitut Für Non-Target Screening GmbH (AFIN-TS), Am Mittleren Moos 48, 86167 Augsburg, Germany
| | - Qingliang Li
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - James Little
- Mass Spec Interpretation Services, 3612 Hemlock Park Drive, Kingsport, TN 37663 USA
| | - Yanna Liu
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences (SKLECE, RCEES, CAS), No. 18 Shuangqing Road, Haidian District, Beijing, 100086 China
| | - David M. Lunderberg
- Hope College, Holland, MI 49422 USA
- University of California, Berkeley, CA USA
| | - Jonathan W. Martin
- Science for Life Laboratory, Department of Environmental Science, Stockholm University, 10691 Stockholm, Sweden
| | - Andrew D. McEachran
- Agilent Technologies, Inc., 5301 Stevens Creek Blvd, Santa Clara, CA 95051 USA
| | - John A. McLean
- Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235 USA
| | - Christiane Meier
- German Environment Agency (UBA), Wörlitzer Platz 1, Dessau-Roßlau, Germany
| | - Jeroen Meijer
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
| | - Frank Menger
- Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
| | - Carla Merino
- University Rovira i Virgili, Tarragona, Spain
- Biosfer Teslab, Reus, Spain
| | - Jane Muncke
- Food Packaging Forum Foundation, Staffelstrasse 10, 8045 Zurich, Switzerland
| | | | - Michael Neumann
- German Environment Agency (UBA), Wörlitzer Platz 1, Dessau-Roßlau, Germany
| | - Vanessa Neveu
- Nutrition and Metabolism Branch, International Agency for Research On Cancer (IARC), 150 Cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Kelsey Ng
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, Brno, Czech Republic
| | - Herbert Oberacher
- Institute of Legal Medicine and Core Facility Metabolomics, Medical University of Innsbruck, Muellerstrasse 44, Innsbruck, Austria
| | - Jake O’Brien
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, QLD 4102 Australia
| | - Peter Oswald
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
| | - Martina Oswaldova
- Environmental Institute, Okružná 784/42, 972 41 Koš, Slovak Republic
| | - Jaqueline A. Picache
- Department of Chemistry, Center for Innovative Technology, Vanderbilt-Ingram Cancer Center, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN 37235 USA
| | - Cristina Postigo
- Swedish University of Agricultural Sciences (SLU), Uppsala, Sweden
- Technologies for Water Management and Treatment Research Group, Department of Civil Engineering, University of Granada, Campus de Fuentenueva S/N, 18071 Granada, Spain
| | - Noelia Ramirez
- University Rovira i Virgili, Tarragona, Spain
- Institute of Health Research Pere Virgili, Tarragona, Spain
| | | | - Justin Renaud
- Agriculture and Agri-Food Canada/Agriculture et Agroalimentaire Canada, 1391 Sandford Street, London, ON N5V 4T3 Canada
| | | | - Heinz Rüdel
- Fraunhofer Institute for Molecular Biology and Applied Ecology (Fraunhofer IME), Schmallenberg, Germany
| | - Reza M. Salek
- Nutrition and Metabolism Branch, International Agency for Research On Cancer (IARC), 150 Cours Albert Thomas, 69372 Lyon Cedex 08, France
| | - Saer Samanipour
- Van’t Hoff Institute for Molecular Sciences, University of Amsterdam, P.O. Box 94157, Amsterdam, 1090 GD The Netherlands
| | - Martin Scheringer
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland
- RECETOX, Faculty of Science, Masaryk University, Kotlářská 2, Brno, Czech Republic
| | - Ivo Schliebner
- German Environment Agency (UBA), Wörlitzer Platz 1, Dessau-Roßlau, Germany
| | - Wolfgang Schulz
- Laboratory for Operation Control and Research, Zweckverband Landeswasserversorgung, Am Spitzigen Berg 1, 89129 Langenau, Germany
| | - Tobias Schulze
- UFZ, Helmholtz Centre for Environmental Research, Leipzig, Germany
| | - Manfred Sengl
- Bavarian Environment Agency, 86179 Augsburg, Germany
| | - Benjamin A. Shoemaker
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Kerry Sims
- Environment Agency, Horizon House, Deanery Road, Bristol, BS1 5AH UK
| | - Heinz Singer
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | - Randolph R. Singh
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg
- Chemical Contamination of Marine Ecosystems (CCEM) Unit, Institut Français de Recherche pour l’Exploitation de la Mer (IFREMER), Rue de l’Ile d’Yeu, BP 21105, 44311 Cedex 3, Nantes France
| | - Mark Sumarah
- Agriculture and Agri-Food Canada/Agriculture et Agroalimentaire Canada, 1391 Sandford Street, London, ON N5V 4T3 Canada
| | - Paul A. Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Kevin V. Thomas
- Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, Woolloongabba, QLD 4102 Australia
| | | | - Xenia Trier
- Section for Environmental Chemistry and Physics, Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
| | - Annemarie P. van Wezel
- Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, Amsterdam, The Netherlands
| | - Roel C. H. Vermeulen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
| | - Jelle J. Vlaanderen
- Institute for Risk Assessment Sciences (IRAS), Utrecht University, Utrecht, The Netherlands
| | | | - Zhanyun Wang
- Technology and Society Laboratory, Empa-Swiss Federal Laboratories for Materials Science and Technology, Lerchenfeldstrasse 5, 9014 St. Gallen, Switzerland
| | - Antony J. Williams
- Computational Chemistry and Cheminformatics Branch (CCCB), Chemical Characterization and Exposure Division (CCED), Center for Computational Toxicology and Exposure (CCTE), United States Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711 USA
| | - Egon L. Willighagen
- Department of Bioinformatics-BiGCaT, NUTRIM, Maastricht University, Maastricht, The Netherlands
| | | | - Jian Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894 USA
| | - Nikolaos S. Thomaidis
- Laboratory of Analytical Chemistry, Department of Chemistry, National and Kapodistrian University of Athens, Panepistimiopolis Zografou, 15771 Athens, Greece
| | - Juliane Hollender
- Institute of Biogeochemistry and Pollutant Dynamics, ETH Zurich, 8092 Zurich, Switzerland
- Eawag, Swiss Federal Institute for Aquatic Science and Technology, Überlandstrasse 133, 8600 Dübendorf, Switzerland
| | | | - Emma L. Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 Avenue du Swing, 4367 Belvaux, Luxembourg
| |
Collapse
|
45
|
Belova L, Celma A, Van Haesendonck G, Lemière F, Sancho JV, Covaci A, van Nuijs ALN, Bijlsma L. Revealing the differences in collision cross section values of small organic molecules acquired by different instrumental designs and prediction models. Anal Chim Acta 2022; 1229:340361. [PMID: 36156233 DOI: 10.1016/j.aca.2022.340361] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 11/17/2022]
Abstract
The number of open access databases containing experimental and predicted collision cross section (CCS) values is rising and leads to their increased use for compound identification. However, the reproducibility of reference values with different instrumental designs and the comparison between predicted and experimental CCS values is still under evaluation. This study compared experimental CCS values of 56 small molecules (Contaminants of Emerging Concern) acquired by both drift tube (DT) and travelling wave (TW) ion mobility mass spectrometry (IM-MS). The TWIM-MS included two instrumental designs (Synapt G2 and VION). The experimental TWCCSN2 values obtained by the TWIM-MS systems showed absolute percent errors (APEs) < 2% in comparison to experimental DTIMS data, indicating a good correlation between the datasets. Furthermore, TWCCSN2 values of [M - H]- ions presented the lowest APEs. An influence of the compound class on APEs was observed. The applicability of prediction models based on artificial neural networks (ANN) and multivariate adaptive regression splines (MARS), both built using TWIM-MS data, was investigated for the first time for the prediction of DTCCSN2 values. For [M+H]+ and [M - H]- ions, the 95th percentile confidence intervals of observed APEs were comparable to values reported for both models indicating a good applicability for DTIMS predictions. For the prediction of DTCCSN2 values of [M+Na]+ ions, the MARS based model provided the best results with 73.9% of the ions showing APEs below the threshold reported for [M+Na]+. Finally, recommendations for database transfer and applications of prediction models for future DTIMS studies are made.
Collapse
Affiliation(s)
- Lidia Belova
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium.
| | - Alberto Celma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avinguda de Vicent Sos Baynat, 12006, Castelló, Spain
| | - Glenn Van Haesendonck
- Biomolecular & Analytical Mass Spectrometry (BAMS) Group, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Filip Lemière
- Biomolecular & Analytical Mass Spectrometry (BAMS) Group, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Juan Vicente Sancho
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avinguda de Vicent Sos Baynat, 12006, Castelló, Spain
| | - Adrian Covaci
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | | | - Lubertus Bijlsma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avinguda de Vicent Sos Baynat, 12006, Castelló, Spain.
| |
Collapse
|
46
|
Jariyasopit N, Limjiasahapong S, Kurilung A, Sartyoungkul S, Wisanpitayakorn P, Nuntasaen N, Kuhakarn C, Reutrakul V, Kittakoop P, Sirivatanauksorn Y, Khoomrung S. Traveling Wave Ion Mobility-Derived Collision Cross Section Database for Plant Specialized Metabolites: An Application to Ventilago harmandiana Pierre. J Proteome Res 2022; 21:2481-2492. [PMID: 36154058 PMCID: PMC9552781 DOI: 10.1021/acs.jproteome.2c00413] [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: 07/12/2022] [Indexed: 11/29/2022]
Abstract
The combination of ion mobility mass spectrometry (IM-MS) and chromatography is a valuable tool for identifying compounds in natural products. In this study, using an ultra-performance liquid chromatography system coupled to a high-resolution quadrupole/traveling wave ion mobility spectrometry/time-of-flight MS (UPLC-TWIMS-QTOF), we have established and validated a comprehensive TWCCSN2 and MS database for 112 plant specialized metabolites. The database included 15 compounds that were isolated and purified in-house and are not commercially available. We obtained accurate m/z, retention times, fragment ions, and TWIMS-derived CCS (TWCCSN2) values for 207 adducts (ESI+ and ESI-). The database included novel 158 TWCCSN2 values from 79 specialized metabolites. In the presence of plant matrix, the CCS measurement was reproducible and robust. Finally, we demonstrated the application of the database to extend the metabolite coverage of Ventilago harmandiana Pierre. In addition to pyranonaphthoquinones, a group of known specialized metabolites in V. harmandiana, we identified flavonoids, xanthone, naphthofuran, and protocatechuic acid for the first time through targeted analysis. Interestingly, further investigation using IM-MS of unknown features suggested the presence of organonitrogen compounds and lipid and lipid-like molecules, which is also reported for the first time. Data are available on the MassIVE (https://massive.ucsd.edu, data set identifier MSV000090213).
Collapse
Affiliation(s)
- Narumol Jariyasopit
- Metabolomics
and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty
of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Suphitcha Limjiasahapong
- Siriraj
Metabolomics and Phenomics Center, Faculty
of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Alongkorn Kurilung
- Metabolomics
and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Sitanan Sartyoungkul
- Metabolomics
and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Pattipong Wisanpitayakorn
- Metabolomics
and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty
of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Narong Nuntasaen
- Center
of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok 10400 Thailand
| | - Chutima Kuhakarn
- Center
of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok 10400 Thailand
| | - Vichai Reutrakul
- Center
of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok 10400 Thailand
| | - Prasat Kittakoop
- Chulabhorn
Graduate Institute, Program in Chemical Sciences, Chulabhorn Royal Academy, Laksi,
Bangkok 10210, Thailand
- Chulabhorn
Research Institute, Kamphaeng Phet 6 Road, Laksi, Bangkok 10210, Thailand
| | - Yongyut Sirivatanauksorn
- Siriraj
Metabolomics and Phenomics Center, Faculty
of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Sakda Khoomrung
- Metabolomics
and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty
of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Center
of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok 10400 Thailand
| |
Collapse
|
47
|
Collision Cross Section Prediction with Molecular Fingerprint Using Machine Learning. Molecules 2022; 27:molecules27196424. [PMID: 36234961 PMCID: PMC9572128 DOI: 10.3390/molecules27196424] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 09/16/2022] [Accepted: 09/19/2022] [Indexed: 11/16/2022] Open
Abstract
High-resolution mass spectrometry is a promising technique in non-target screening (NTS) to monitor contaminants of emerging concern in complex samples. Current chemical identification strategies in NTS experiments typically depend on spectral libraries, chemical databases, and in silico fragmentation tools. However, small molecule identification remains challenging due to the lack of orthogonal sources of information (e.g., unique fragments). Collision cross section (CCS) values measured by ion mobility spectrometry (IMS) offer an additional identification dimension to increase the confidence level. Thanks to the advances in analytical instrumentation, an increasing application of IMS hybrid with high-resolution mass spectrometry (HRMS) in NTS has been reported in the recent decades. Several CCS prediction tools have been developed. However, limited CCS prediction methods were based on a large scale of chemical classes and cross-platform CCS measurements. We successfully developed two prediction models using a random forest machine learning algorithm. One of the approaches was based on chemicals’ super classes; the other model was direct CCS prediction using molecular fingerprint. Over 13,324 CCS values from six different laboratories and PubChem using a variety of ion-mobility separation techniques were used for training and testing the models. The test accuracy for all the prediction models was over 0.85, and the median of relative residual was around 2.2%. The models can be applied to different IMS platforms to eliminate false positives in small molecule identification.
Collapse
|
48
|
Xia J, Xiao W, Lin X, Zhou Y, Qiu P, Si H, Wu X, Niu S, Luo Z, Yang X. Ion Mobility-Derived Collision Cross-Sections Add Extra Capability in Distinguishing Isomers and Compounds with Similar Retention Times: The Case of Aphidicolanes. Mar Drugs 2022; 20:md20090541. [PMID: 36135730 PMCID: PMC9503386 DOI: 10.3390/md20090541] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/11/2022] [Accepted: 08/19/2022] [Indexed: 11/20/2022] Open
Abstract
The hyphenation of ion mobility spectrometry with high-resolution mass spectrometry has been widely used in the characterization of various metabolites. Nevertheless, such a powerful tool remains largely unexplored in natural products research, possibly mainly due to the lack of available compounds. To evaluate the ability of collision cross-sections (CCSs) in characterizing compounds, especially isomeric natural products, here we measured and compared the traveling-wave IMS-derived nitrogen CCS values for 75 marine-derived aphidicolanes. We established a CCS database for these compounds which contained 227 CCS values of different adducts. When comparing the CCS differences, 36 of 57 pairs (over 60%) of chromatographically neighboring compounds showed a ΔCCS over 2%. What is more, 64 of 104 isomeric pairs (over 60%) of aphidicolanes can be distinguished by their CCS values, and 13 of 18 pairs (over 70%) of chromatographically indistinguishable isomers can be differentiated from the mobility dimension. Our results strongly supported CCS as an important parameter with good orthogonality and complementarity with retention time. CCS is expected to play an important role in distinguishing complex and diverse marine natural products.
Collapse
Affiliation(s)
- Jinmei Xia
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Wenhai Xiao
- Key Laboratory of Systems Bioengineering (Ministry of Education), Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Xihuang Lin
- Analyzing and Testing Center, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Yiduo Zhou
- Institute of Food Science and Technology, Hebei Agricultural University, Baoding 071001, China
| | - Peng Qiu
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Hongkun Si
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Xiaorong Wu
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Siwen Niu
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Zhuhua Luo
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Xianwen Yang
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
- Correspondence:
| |
Collapse
|
49
|
Song XC, Canellas E, Dreolin N, Goshawk J, Nerin C. Identification of Nonvolatile Migrates from Food Contact Materials Using Ion Mobility-High-Resolution Mass Spectrometry and in Silico Prediction Tools. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:9499-9508. [PMID: 35856243 PMCID: PMC9354260 DOI: 10.1021/acs.jafc.2c03615] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 07/01/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
The identification of migrates from food contact materials (FCMs) is challenging due to the complex matrices and limited availability of commercial standards. The use of machine-learning-based prediction tools can help in the identification of such compounds. This study presents a workflow to identify nonvolatile migrates from FCMs based on liquid chromatography-ion mobility-high-resolution mass spectrometry together with in silico retention time (RT) and collision cross section (CCS) prediction tools. The applicability of this workflow was evaluated by screening the chemicals that migrated from polyamide (PA) spatulas. The number of candidate compounds was reduced by approximately 75% and 29% on applying RT and CCS prediction filters, respectively. A total of 95 compounds were identified in the PA spatulas of which 54 compounds were confirmed using reference standards. The development of a database containing predicted RT and CCS values of compounds related to FCMs can aid in the identification of chemicals in FCMs.
Collapse
Affiliation(s)
- Xue-Chao Song
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Elena Canellas
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Nicola Dreolin
- Waters
Corporation, Altrincham
Road, SK9 4AX Wilmslow, United Kingdom
| | - Jeff Goshawk
- Waters
Corporation, Altrincham
Road, SK9 4AX Wilmslow, United Kingdom
| | - Cristina Nerin
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| |
Collapse
|
50
|
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: 20] [Impact Index Per Article: 10.0] [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.
Collapse
|