1
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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.
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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
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2
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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.
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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
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3
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Huang S, Righetti L, Claassen FW, Krishna A, Ma M, van Beek TA, Chen B, Zuilhof H, Salentijn GIJ. Ultrafast, Selective, and Highly Sensitive Nonchromatographic Analysis of Fourteen Cannabinoids in Cannabis Extracts, Δ8-Tetrahydrocannabinol Synthetic Mixtures, and Edibles by Cyclic Ion Mobility Spectrometry-Mass Spectrometry. Anal Chem 2024; 96:10170-10181. [PMID: 38862388 PMCID: PMC11209660 DOI: 10.1021/acs.analchem.3c05879] [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: 12/22/2023] [Revised: 05/28/2024] [Accepted: 05/30/2024] [Indexed: 06/13/2024]
Abstract
The diversity of cannabinoid isomers and complexity of Cannabis products pose significant challenges for analytical methodologies. In this study, we developed a method to analyze 14 different cannabinoid isomers in diverse samples within milliseconds by leveraging the unique adduct-forming behavior of silver ions in advanced cyclic ion mobility spectrometry-mass spectrometry. The developed method achieved the separation of isomers from four groups of cannabinoids: Δ3-tetrahydrocannabinol (THC) (1), Δ8-THC (2), Δ9-THC (3), cannabidiol (CBD) (4), Δ8-iso-THC (5), and Δ(4)8-iso-THC (6) (all MW = 314); 9α-hydroxyhexahydrocannabinol (7), 9β-hydroxyhexahydrocannabinol (8), and 8-hydroxy-iso-THC (9) (all MW = 332); tetrahydrocannabinolic acid (THCA) (10) and cannabidiolic acid (CBDA) (11) (both MW = 358); Δ8-tetrahydrocannabivarin (THCV) (12), Δ8-iso-THCV (13), and Δ9-THCV (14) (all MW = 286). Moreover, experimental and theoretical traveling wave collision cross section values in nitrogen (TWCCSN2) of cannabinoid-Ag(I) species were obtained for the first time with an average error between experimental and theoretical values of 2.6%. Furthermore, a workflow for the identification of cannabinoid isomers in Cannabis and Cannabis-derived samples was established based on three identification steps (m/z and isotope pattern of Ag(I) adducts, TWCCSN2, and MS/MS fragments). Afterward, calibration curves of three major cannabinoids were established with a linear range of 1-250 ng·ml-1 for Δ8-THC (2) (R2 = 0.9999), 0.1-25 ng·ml-1 for Δ9-THC (3) (R2 = 0.9987), and 0.04-10 ng·ml-1 for CBD (4) (R2 = 0.9986) as well as very low limits of detection (0.008-0.2 ng·ml-1). Finally, relative quantification of Δ8-THC (2), Δ9-THC (3), and CBD (4) in eight complex acid-treated CBD mixtures was achieved without chromatographic separation. The results showed good correspondence (R2 = 0.999) with those obtained by gas chromatography-flame ionization detection/mass spectrometry.
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Affiliation(s)
- Si Huang
- Key
Laboratory of Phytochemical R&D of Hunan Province and Key Laboratory
of Chemical Biology & Traditional Chinese Medicine Research of
Ministry of Education, Hunan Normal University, No.36, Lushan Road, Changsha 410081, China
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
| | - Laura Righetti
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
- Wageningen
Food Safety Research (WFSR), Wageningen
University & Research, P.O. Box 230, Wageningen 6700 AE, The Netherlands
| | - Frank W. Claassen
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
| | - Akash Krishna
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
| | - Ming Ma
- Key
Laboratory of Phytochemical R&D of Hunan Province and Key Laboratory
of Chemical Biology & Traditional Chinese Medicine Research of
Ministry of Education, Hunan Normal University, No.36, Lushan Road, Changsha 410081, China
| | - Teris A. van Beek
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
| | - Bo Chen
- Key
Laboratory of Phytochemical R&D of Hunan Province and Key Laboratory
of Chemical Biology & Traditional Chinese Medicine Research of
Ministry of Education, Hunan Normal University, No.36, Lushan Road, Changsha 410081, China
| | - Han Zuilhof
- Key
Laboratory of Phytochemical R&D of Hunan Province and Key Laboratory
of Chemical Biology & Traditional Chinese Medicine Research of
Ministry of Education, Hunan Normal University, No.36, Lushan Road, Changsha 410081, China
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
| | - Gert IJ. Salentijn
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
- Wageningen
Food Safety Research (WFSR), Wageningen
University & Research, P.O. Box 230, Wageningen 6700 AE, The Netherlands
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4
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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.
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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
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5
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Zimnicka MM. Structural studies of supramolecular complexes and assemblies by ion mobility mass spectrometry. MASS SPECTROMETRY REVIEWS 2024; 43:526-559. [PMID: 37260128 DOI: 10.1002/mas.21851] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 04/26/2023] [Accepted: 05/10/2023] [Indexed: 06/02/2023]
Abstract
Recent advances in instrumentation and development of computational strategies for ion mobility mass spectrometry (IM-MS) studies have contributed to an extensive growth in the application of this analytical technique to comprehensive structural description of supramolecular systems. Apart from the benefits of IM-MS for interrogation of intrinsic properties of noncovalent aggregates in the experimental gas-phase environment, its merits for the description of native structural aspects, under the premises of having maintained the noncovalent interactions innate upon the ionization process, have attracted even more attention and gained increasing interest in the scientific community. Thus, various types of supramolecular complexes and assemblies relevant for biological, medical, material, and environmental sciences have been characterized so far by IM-MS supported by computational chemistry. This review covers the state-of-the-art in this field and discusses experimental methods and accompanying computational approaches for assessing the reliable three-dimensional structural elucidation of supramolecular complexes and assemblies by IM-MS.
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Affiliation(s)
- Magdalena M Zimnicka
- Mass Spectrometry Group, Institute of Organic Chemistry, Polish Academy of Sciences, Warsaw, Poland
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6
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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%.
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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.
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7
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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.
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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
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8
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Jaroensuk J, Sutthaphirom C, Phonbuppha J, Chinantuya W, Kesornpun C, Akeratchatapan N, Kittipanukul N, Phatinuwat K, Atichartpongkul S, Fuangthong M, Pongtharangkul T, Hollmann F, Chaiyen P. A versatile in situ cofactor enhancing system for meeting cellular demands for engineered metabolic pathways. J Biol Chem 2024; 300:105598. [PMID: 38159859 PMCID: PMC10850783 DOI: 10.1016/j.jbc.2023.105598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 12/02/2023] [Accepted: 12/18/2023] [Indexed: 01/03/2024] Open
Abstract
Cofactor imbalance obstructs the productivities of metabolically engineered cells. Herein, we employed a minimally perturbing system, xylose reductase and lactose (XR/lactose), to increase the levels of a pool of sugar phosphates which are connected to the biosynthesis of NAD(P)H, FAD, FMN, and ATP in Escherichia coli. The XR/lactose system could increase the amounts of the precursors of these cofactors and was tested with three different metabolically engineered cell systems (fatty alcohol biosynthesis, bioluminescence light generation, and alkane biosynthesis) with different cofactor demands. Productivities of these cells were increased 2-4-fold by the XR/lactose system. Untargeted metabolomic analysis revealed different metabolite patterns among these cells, demonstrating that only metabolites involved in relevant cofactor biosynthesis were altered. The results were also confirmed by transcriptomic analysis. Another sugar reducing system (glucose dehydrogenase) could also be used to increase fatty alcohol production but resulted in less yield enhancement than XR. This work demonstrates that the approach of increasing cellular sugar phosphates can be a generic tool to increase in vivo cofactor generation upon cellular demand for synthetic biology.
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Affiliation(s)
- Juthamas Jaroensuk
- School of Biomolecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand
| | - Chalermroj Sutthaphirom
- School of Biomolecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand
| | - Jittima Phonbuppha
- School of Biomolecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand
| | - Wachirawit Chinantuya
- School of Biomolecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand; Faculty of Science, Department of Biochemistry and Center for Excellence in Protein and Enzyme Technology, Mahidol University, Bangkok, Thailand
| | - Chatchai Kesornpun
- School of Biomolecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand
| | - Nattanon Akeratchatapan
- School of Biomolecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand
| | - Narongyot Kittipanukul
- School of Biomolecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand
| | - Kamonwan Phatinuwat
- Program in Applied Biological Sciences, Chulabhorn Graduate Institute, Bangkok, Thailand
| | | | - Mayuree Fuangthong
- Program in Applied Biological Sciences, Chulabhorn Graduate Institute, Bangkok, Thailand; Laboratory of Biotechnology, Chulabhorn Research Institute, Bangkok, Thailand
| | | | - Frank Hollmann
- Department of Biotechnology, Delft University of Technology, Delft, Netherlands
| | - Pimchai Chaiyen
- School of Biomolecular Science and Engineering, Vidyasirimedhi Institute of Science and Technology (VISTEC), Rayong, Thailand.
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9
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King AM, Wilson ID, Plumb RS, Gethings LA, Trengove R, Maker G. The rapid separation and characterization of sulfates of tyrosine and its metabolites in reaction mixtures and human urine using a cyclic ion mobility device and mass spectrometry. J Chromatogr A 2024; 1715:464597. [PMID: 38183784 DOI: 10.1016/j.chroma.2023.464597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 12/13/2023] [Accepted: 12/19/2023] [Indexed: 01/08/2024]
Abstract
Ion mobility (IM) separations, especially when combined with mass spectrometry, offer the opportunity for the rapid analysis and characterization of mixtures. However, the limited resolution afforded by many IM systems means that in practice applications may be limited. Here we have employed an IM separation on a high-resolution cyclic IM device with MS/MS to separate and characterize mixtures of sulfated isomers of tyrosine and associated metabolites containing multiple sulfated isoforms present in reaction mixtures. The cIMS device allowed ions, not resolved using a single pass, to be subjected to multiple passes, enabling the resolution of those with similar collision cross sections (CCS). Predicted single pass CCS values calculated for the isomers likely to be present in these mixtures showed only small differences between them, ranging between of between 0.1 - 0.7 % depending on structure. These small differences highlight the high degree of mobility resolution required for separating the isomers. Experimentally different isoforms of tyrosine sulfate and sulfated tyrosine metabolites could be sufficiently resolved via multipass separations (3-35 passes). This degree of separation provided resolving powers of up to 384 CCS/ΔCCS for sulfated dopamine which enabled good MS/MS spectra to be generated. In human urine the presence of a single sulfated form of tyrosine was detected and identified as the O-sulfate after 3 passes based on the synthetic standard. Of the other tyrosine-related sulfates for which synthetic standards had been prepared only dopamine sulfate was detected in this sample.
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Affiliation(s)
- Adam M King
- Waters Corporation, Wilmslow, Cheshire, SK9 4AX, UK; Medical and Molecular and Forensic Sciences, Murdoch University, South Street, Murdoch, WA 6150, Australia.
| | - Ian D Wilson
- Computational and Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, Burlington Danes Building, Du Cane Road, London, W12 0NN, UK.
| | - Robert S Plumb
- Medical and Molecular and Forensic Sciences, Murdoch University, South Street, Murdoch, WA 6150, Australia; Waters Corporation, Milford, MA, 01757, USA
| | - Lee A Gethings
- Waters Corporation, Wilmslow, Cheshire, SK9 4AX, UK; School of Biological Sciences, Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK; Faculty of Health & Medical Sciences, University of Surrey, Guildford, UK
| | - Robert Trengove
- CHIRI, Curtin University, Kent St, Bentley, WA, 6102, Australia
| | - Garth Maker
- Medical and Molecular and Forensic Sciences, Murdoch University, South Street, Murdoch, WA 6150, Australia
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10
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Mu H, Yang Z, Chen L, Gu C, Ren H, Wu B. Suspect and nontarget screening of per- and polyfluoroalkyl substances based on ion mobility mass spectrometry and machine learning techniques. JOURNAL OF HAZARDOUS MATERIALS 2024; 461:132669. [PMID: 37797577 DOI: 10.1016/j.jhazmat.2023.132669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 09/13/2023] [Accepted: 09/27/2023] [Indexed: 10/07/2023]
Abstract
High-resolution mass spectrometry (HRMS)-based suspect and nontarget screening techniques are powerful tools for the comprehensive identification of per- and polyfluoroalkyl substances (PFASs), but the interference of complex matrices (especially for wastewater) pose an analytical challenge. This study explored the potential of combining ion mobility spectrometry (IMS) with HRMS and machine learning techniques to achieve the rapid and accurate suspect and nontarget screening of PFAS in wastewater. There were fewer interfering peaks and a clearer spectrum in the data acquired by IMS-HRMS than conventional HRMS. The introduction of collision cross section (CCS) in PFAS homologous series search could filter out 63% of false positive results. Retention time and CCS prediction models were helpful in improving the confidence for PFAS qualitative identification and the random forest algorithm combined with RDKit descriptor performed best for CCS prediction. With the inclusion of extra dimensional information, this study also proposed a comprehensive and concise confidence assignment criterion to better convey the certainty of the qualitative identification of PFAS. Finally, a total of 56 potential PFASs were identified in the wastewater sample using the newly developed method and 45 of them were identified outside reference standards, emphasizing the importance of suspect and nontarget screening for PFAS.
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Affiliation(s)
- Hongxin Mu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Zhongchao Yang
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Ling Chen
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Cheng Gu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Hongqiang Ren
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China
| | - Bing Wu
- State Key Laboratory of Pollution Control and Resource Reuse, School of Environment, Nanjing University, Nanjing 210023, PR China.
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11
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Wooding M, Dodgen T, Rohwer ER, Naudé Y. Advancing the analytical toolkit in the investigation of vector mosquito host biting site selection. JOURNAL OF MASS SPECTROMETRY : JMS 2024; 59:e4992. [PMID: 38108549 DOI: 10.1002/jms.4992] [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: 09/27/2023] [Revised: 11/13/2023] [Accepted: 11/20/2023] [Indexed: 12/19/2023]
Abstract
High-resolution mass spectrometry and ion mobility spectrometry provide additional confidence in biological marker discovery and elucidation by adding additional peak capacity through physiochemical separation orthogonal to chromatography. Sophisticated analytical techniques have proved valuable in the identification of human skin surface chemicals used by vector mosquitoes to find their human host. Polydimethylsiloxane (PDMS) was used as a non-invasive passive wearable sampler to concentrate skin surface non-volatile and semi-volatile compounds prior to solvent desorption directly in an LC vial, thereby simplifying the link between extraction and analysis. Ultra-performance liquid chromatography with ion mobility spectrometry coupled with high-resolution mass spectrometry (UPLC-IMS-HRMS) was used for compound separation and detection. A comparison of the skin chemical profiles between the ankle and wrist skin surface region sampled over a 5-day period for a human volunteer was done. Twenty-three biomarkers were tentatively identified with the aid of a collision cross-section (CCS) prediction tool, seven associated with the ankle skin surface region and 16 closely associated with the wrist skin surface. Ten amino acids were detected and unequivocally identified on the human skin surface for the first time. Furthermore, 22 previously unreported skin surface compounds were tentatively identified on the human skin surface using accurate mass, CCS values and fragmentation patterns. Method limits of detection for the passive skin sampling method ranged from 8.7 (sulfadimethoxine) to 95 ng (taurine). This approach enabled the detection and identification of as-yet unknown human skin surface compounds and provided corresponding CCS values.
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Affiliation(s)
- Madelien Wooding
- Department of Chemistry, University of Pretoria, Pretoria, South Africa
| | - Tyren Dodgen
- Waters Corporation, Rydalmere, New South Wales, Australia
| | - Egmont R Rohwer
- Department of Chemistry, University of Pretoria, Pretoria, South Africa
| | - Yvette Naudé
- Department of Chemistry, University of Pretoria, Pretoria, South Africa
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12
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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.
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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
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13
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Witting M. (Re-)use and (re-)analysis of publicly available metabolomics data. Proteomics 2023; 23:e2300032. [PMID: 37670538 DOI: 10.1002/pmic.202300032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 08/23/2023] [Accepted: 08/24/2023] [Indexed: 09/07/2023]
Abstract
Metabolomics, the systematic measurement of small molecules (<1000 Da) in a given biological sample, is a fast-growing field with many different applications. In contrast to transcriptomics and proteomics, sharing of data is not as widespread in metabolomics, though more scientists are sharing their data nowadays. However, to improve data analysis tools and develop new data analytical approaches and to improve metabolite annotation and identification, sharing of reference data is crucial. Here, different possibilities to share (metabolomics) data are reviewed and some recent approaches and applications regarding the (re-)use and (re-)analysis are highlighted.
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Affiliation(s)
- Michael Witting
- Metabolomics and Proteomics Core, Helmholtz Zentrum München, Neuherberg, Germany
- Chair of Analytical Food Chemistry, TUM School of Life Sciences, Freising-Weihenstephan, Germany
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14
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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/.
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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
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15
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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.
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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
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16
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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.
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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.
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17
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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.
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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
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18
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Habibi SC, Nagy G. General Method to Obtain Collision Cross-Section Values in Multipass High-Resolution Cyclic Ion Mobility Separations. Anal Chem 2023; 95:8028-8035. [PMID: 37163363 DOI: 10.1021/acs.analchem.3c00919] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
In recent years, ion mobility spectrometry-mass spectrometry (IMS-MS) has advanced the field of omics-based research, especially with the development of high-resolution platforms; however, these separations have generally been qualitative in nature. The rotationally averaged ion neutral collision cross section (CCS) is one of the only quantitative metrics available for aiding in characterizing biomolecules in IMS-MS. However, determining the CCS of an ion for multipass IMS systems, such as in cyclic ion mobility-mass spectrometry (cIMS-MS) and structures for lossless ion manipulations, has been challenging due to the lack of methods available for calculating CCS when more than a single pass is required for separation as well as the laborious nature of requiring calibrants and unknown compounds to be subjected to identical number of passes, which may not be possible in certain instances because of peak splitting, high levels of diffusion, etc. Herein, we present a general method that uses average ion velocities for calculating CCS values in cIMS-MS-based separations. Initially, we developed calibration curves using common CCS calibrants [i.e., tetra-alkylammonium salts, polyalanine, and hexakis(fluoroalkoxy)phosphazines] at different traveling wave (TW) conditions and the calculated cIMS CCS values were within ∼1% error or less compared to previously established drift tube IMS CCS measurements. Since it has been established that glycans can split into their α/β anomers, we utilized this method for two glycan species, 2α-mannobiose and melibiose. Both glycans were analyzed at the same TW conditions as the calibrants, and we observed anomer splitting at pathlengths of 20 m for 2α-mannobiose and 40 m for melibiose and thus assigned two unique CCS values for each glycan, which is the first time this has ever been done. We have demonstrated that the use of average ion velocities is a robust approach for obtaining CCS values with good agreement to CCS measurements from the previous literature and anticipate that this methodology can be applied to any IMS-MS platform that utilizes multipass separations. Our future work aims to incorporate this methodology for the development of a high-resolution CCS database to aid in the characterization of human milk oligosaccharides.
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Affiliation(s)
- Sanaz C Habibi
- Department of Chemistry, University of Utah, 315 South 1400 East, Room 2020, Salt Lake City, Utah 84112, United States
| | - Gabe Nagy
- Department of Chemistry, University of Utah, 315 South 1400 East, Room 2020, Salt Lake City, Utah 84112, United States
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19
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Bertola M, Righetti L, Gazza L, Ferrarini A, Fornasier F, Cirlini M, Lolli V, Galaverna G, Visioli G. Perenniality, more than genotypes, shapes biological and chemical rhizosphere composition of perennial wheat lines. FRONTIERS IN PLANT SCIENCE 2023; 14:1172857. [PMID: 37223792 PMCID: PMC10200949 DOI: 10.3389/fpls.2023.1172857] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Accepted: 04/06/2023] [Indexed: 05/25/2023]
Abstract
Perennial grains provide various ecosystem services compared to the annual counterparts thanks to their extensive root system and permanent soil cover. However, little is known about the evolution and diversification of perennial grains rhizosphere and its ecological functions over time. In this study, a suite of -OMICSs - metagenomics, enzymomics, metabolomics and lipidomics - was used to compare the rhizosphere environment of four perennial wheat lines at the first and fourth year of growth in comparison with an annual durum wheat cultivar and the parental species Thinopyrum intermedium. We hypothesized that wheat perenniality has a greater role in shaping the rhizobiome composition, biomass, diversity, and activity than plant genotypes because perenniality affects the quality and quantity of C input - mainly root exudates - hence modulating the plant-microbes crosstalk. In support of this hypothesis, the continuous supply of sugars in the rhizosphere along the years created a favorable environment for microbial growth which is reflected in a higher microbial biomass and enzymatic activity. Moreover, modification in the rhizosphere metabolome and lipidome over the years led to changes in the microbial community composition favoring the coexistence of more diverse microbial taxa, increasing plant tolerance to biotic and abiotic stresses. Despite the dominance of the perenniality effect, our data underlined that the OK72 line rhizobiome distinguished from the others by the increase in abundance of Pseudomonas spp., most of which are known as potential beneficial microorganisms, identifying this line as a suitable candidate for the study and selection of new perennial wheat lines.
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Affiliation(s)
- Marta Bertola
- Department of Food and Drugs, University of Parma, Parma, Italy
| | - Laura Righetti
- Department of Food and Drugs, University of Parma, Parma, Italy
- Wageningen Food Safety Research, Wageningen University and Research, Wageningen, Netherlands
- Laboratory of Organic Chemistry, Wageningen University, Wageningen, Netherlands
| | - Laura Gazza
- Council for Agricultural Research and Economics, Research Centre for Engineering and Agro-Food Processing, Rome, Italy
| | - Andrea Ferrarini
- Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Piacenza, Italy
| | - Flavio Fornasier
- Council for Agricultural Research and Economics (CREA) Research Centre for Viticulture and Enology, Unit of Gorizia, Gorizia, Italy
| | - Martina Cirlini
- Department of Food and Drugs, University of Parma, Parma, Italy
| | - Veronica Lolli
- Department of Food and Drugs, University of Parma, Parma, Italy
| | | | - Giovanna Visioli
- Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, Parma, Italy
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20
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Mafata M, Stander M, Masike K, Buica A. Exploratory data fusion of untargeted multimodal LC-HRMS with annotation by LCMS-TOF-ion mobility: White wine case study. EUROPEAN JOURNAL OF MASS SPECTROMETRY (CHICHESTER, ENGLAND) 2023; 29:111-122. [PMID: 36942424 PMCID: PMC10068406 DOI: 10.1177/14690667231164096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
Applied sciences have increased focus on omics studies which merge data science with analytical tools. These studies often result in large amounts of data produced and the objective is to generate meaningful interpretations from them. This can sometimes mean combining and integrating different datasets through data fusion techniques. The most strategic course of action when dealing with products of unknown profile is to use exploratory approaches. For omics, this means using untargeted analytical methods and exploratory data analysis techniques. The current study aimed to perform data fusion on untargeted multimodal (negative and positive mode) liquid chromatography-high-resolution mass spectrometry data using multiple factor analysis. The data fusion results were interpreted using agglomerative hierarchical clustering on biplot projections. The study reduced the thousands of spectral signals processed to less than a hundred features (a primary parameter combination of retention time and mass-to-charge ratios, RT_m/z). The correlations between cluster members (samples and features from) were calculated and the top 10% highly correlated features were identified for each cluster. These features were then tentatively identified using secondary parameters (drift time, ion mobility constant and collision cross-section values) from the ion mobility spectra. These ion mobility (secondary) parameters can be used for future studies in wine chemical analysis and added to the growing list of annotated chemical signals in applied sciences.
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Affiliation(s)
- Mpho Mafata
- School for Data Science and Computational Thinking,
Stellenbosch
University, Stellenbosch, South
Africa
- Department of Viticulture and Oenology, South African Grape and Wine
Research Institute, Stellenbosch
University, Stellenbosch, South
Africa
| | - Maria Stander
- Central Analytical Facility, Stellenbosch
University, Stellenbosch, South Africa
| | - Keabetswe Masike
- Central Analytical Facility, Stellenbosch
University, Stellenbosch, South Africa
| | - Astrid Buica
- School for Data Science and Computational Thinking,
Stellenbosch
University, Stellenbosch, South
Africa
- Department of Viticulture and Oenology, South African Grape and Wine
Research Institute, Stellenbosch
University, Stellenbosch, South
Africa
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21
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Christofi E, Barran P. Ion Mobility Mass Spectrometry (IM-MS) for Structural Biology: Insights Gained by Measuring Mass, Charge, and Collision Cross Section. Chem Rev 2023; 123:2902-2949. [PMID: 36827511 PMCID: PMC10037255 DOI: 10.1021/acs.chemrev.2c00600] [Citation(s) in RCA: 38] [Impact Index Per Article: 38.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2023]
Abstract
The investigation of macromolecular biomolecules with ion mobility mass spectrometry (IM-MS) techniques has provided substantial insights into the field of structural biology over the past two decades. An IM-MS workflow applied to a given target analyte provides mass, charge, and conformation, and all three of these can be used to discern structural information. While mass and charge are determined in mass spectrometry (MS), it is the addition of ion mobility that enables the separation of isomeric and isobaric ions and the direct elucidation of conformation, which has reaped huge benefits for structural biology. In this review, where we focus on the analysis of proteins and their complexes, we outline the typical features of an IM-MS experiment from the preparation of samples, the creation of ions, and their separation in different mobility and mass spectrometers. We describe the interpretation of ion mobility data in terms of protein conformation and how the data can be compared with data from other sources with the use of computational tools. The benefit of coupling mobility analysis to activation via collisions with gas or surfaces or photons photoactivation is detailed with reference to recent examples. And finally, we focus on insights afforded by IM-MS experiments when applied to the study of conformationally dynamic and intrinsically disordered proteins.
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Affiliation(s)
- Emilia Christofi
- Michael Barber Centre for Collaborative Mass Spectrometry, Manchester Institute of Biotechnology, University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
| | - Perdita Barran
- Michael Barber Centre for Collaborative Mass Spectrometry, Manchester Institute of Biotechnology, University of Manchester, Princess Street, Manchester M1 7DN, United Kingdom
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22
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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.
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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
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23
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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.
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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
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24
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Rainey MA, Watson CA, Asef CK, Foster MR, Baker ES, Fernández FM. CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics. Anal Chem 2022; 94:17456-17466. [PMID: 36473057 PMCID: PMC9772062 DOI: 10.1021/acs.analchem.2c03491] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M - H]-, 155 [M + H]+, and 138 [M + Na]+ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a ΔCCS threshold of 2.8% and a mass error of 10 ppm.
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Affiliation(s)
- Markace A. Rainey
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Chandler A. Watson
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Carter K. Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Makayla R. Foster
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Erin S. Baker
- Department of Chemistry and Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States; Petit Institute of Bioengineering and Biotechnology, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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25
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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.
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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.
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26
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High-end ion mobility mass spectrometry: A current review of analytical capacity in omics applications and structural investigations. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116761] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
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27
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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]
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28
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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.
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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,
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29
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Cao Y, Li W, Chen W, Niu X, Wu N, Wang Y, Li J, Tu P, Zheng J, Song Y. Squared Energy-Resolved Mass Spectrometry Advances Quantitative Bile Acid Submetabolome Characterization. Anal Chem 2022; 94:15395-15404. [DOI: 10.1021/acs.analchem.2c03269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Yan Cao
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, East Road of North 3rd Ring, Chaoyang District, Beijing 100029, China
| | - Wei Li
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, East Road of North 3rd Ring, Chaoyang District, Beijing 100029, China
| | - Wei Chen
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, East Road of North 3rd Ring, Chaoyang District, Beijing 100029, China
| | - Xiaoya Niu
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, East Road of North 3rd Ring, Chaoyang District, Beijing 100029, China
| | - Nian Wu
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, East Road of North 3rd Ring, Chaoyang District, Beijing 100029, China
| | - Yitao Wang
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa 999078, Macao
| | - Jun Li
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, East Road of North 3rd Ring, Chaoyang District, Beijing 100029, China
| | - Pengfei Tu
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, East Road of North 3rd Ring, Chaoyang District, Beijing 100029, China
| | - Jiao Zheng
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, East Road of North 3rd Ring, Chaoyang District, Beijing 100029, China
| | - Yuelin Song
- Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, East Road of North 3rd Ring, Chaoyang District, Beijing 100029, China
- State Key Laboratory of Quality Research in Chinese Medicine, Institute of Chinese Medical Sciences, University of Macau, Avenida da Universidade, Taipa 999078, Macao
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30
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Metabolomics and modelling approaches for systems metabolic engineering. Metab Eng Commun 2022; 15:e00209. [PMID: 36281261 PMCID: PMC9587336 DOI: 10.1016/j.mec.2022.e00209] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/21/2022] Open
Abstract
Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.
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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.
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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.
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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.
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Carbonell-Rozas L, Hernández-Mesa M, Righetti L, Monteau F, Lara FJ, Gámiz-Gracia L, Bizec BL, Dall'Asta C, García-Campaña AM, Dervilly G. Ion mobility-mass spectrometry to extend analytical performance in the determination of ergot alkaloids in cereal samples. J Chromatogr A 2022; 1682:463502. [PMID: 36174373 DOI: 10.1016/j.chroma.2022.463502] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/30/2022] [Accepted: 09/12/2022] [Indexed: 11/28/2022]
Abstract
This work evaluates the potential of ion mobility spectrometry (IMS) to improve the analytical performance of current liquid chromatography-mass spectrometry (LC-MS) workflows applied to the determination of ergot alkaloids (EAs) in cereal samples. Collision cross section (CCS) values for EA epimers are reported for the first time to contribute to their unambiguous identification. Additionally, CCS values have been inter-laboratory cross-validated and compared with CCS values predicted by machine-learning models. Slight differences were observed in terms of CCS values for ergotamine, ergosine and ergocristine and their corresponding epimers (from 3.3 to 4%), being sufficient to achieve a satisfactory peak-to-peak resolution for their unequivocal identification. A LC-travelling wave ion mobility (TWIM)-MS method has been developed for the analysis of EAs in barley and wheat samples. Signal-to-noise ratio (S/N) was improved between 2.5 and 4-fold compared to the analog LC-TOF-MS method. The quality of the extracted ion chromatograms was also improved by using IMS.
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Affiliation(s)
- Laura Carbonell-Rozas
- Oniris, INRAE, LABERCA, 44300 Nantes, France; Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Campus Fuentenueva s/n, 18071 Granada, Spain
| | - Maykel Hernández-Mesa
- Oniris, INRAE, LABERCA, 44300 Nantes, France; Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Campus Fuentenueva s/n, 18071 Granada, Spain.
| | - Laura Righetti
- Department of Food and Drug, University of Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy
| | | | - Francisco J Lara
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Campus Fuentenueva s/n, 18071 Granada, Spain
| | - Laura Gámiz-Gracia
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Campus Fuentenueva s/n, 18071 Granada, Spain
| | | | - Chiara Dall'Asta
- Department of Food and Drug, University of Parma, Parco Area delle Scienze 17/A, 43124 Parma, Italy
| | - Ana M García-Campaña
- Department of Analytical Chemistry, Faculty of Sciences, University of Granada, Campus Fuentenueva s/n, 18071 Granada, Spain
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Applications of ion mobility-mass spectrometry in the chemical analysis in traditional Chinese medicines. Se Pu 2022; 40:782-787. [PMID: 36156624 PMCID: PMC9516353 DOI: 10.3724/sp.j.1123.2022.01028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
离子淌度质谱(IM-MS)是一种将离子淌度分离与质谱分析相结合的新型分析技术。IM-MS的主要优势不仅是在质谱检测前提供了基于气相离子形状、大小、电荷数等因素的多一维分离,而且能够提供碰撞截面积、漂移时间等质谱信息进而辅助化合物鉴定。近年来,随着IM-MS技术的不断发展,该技术在中药化学成分分析中受到越来越多的关注。首先,IM-MS已成功应用于改善中药复杂成分尤其是同分异构体或等量异位素等成分的分离;其次,IM-MS可通过多重碎裂模式辅助高质量中药小分子质谱信息的获取;此外,IM-MS提供的高维质谱数据信息还可促进中药复杂体系多成分的整合分析。该文在对IM-MS分类和基本原理进行概述的基础上,从分离能力及分离策略、多重碎裂模式、多维质谱数据处理策略3个方面,重点综述了IM-MS在中药化学成分分析中的应用,以期为IM-MS在中药化学成分研究提供参考。
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Paglia G, Smith AJ, Astarita G. Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics. MASS SPECTROMETRY REVIEWS 2022; 41:722-765. [PMID: 33522625 DOI: 10.1002/mas.21686] [Citation(s) in RCA: 86] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/17/2021] [Accepted: 01/17/2021] [Indexed: 06/12/2023]
Abstract
Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.
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Affiliation(s)
- Giuseppe Paglia
- School of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro (MB), Italy
| | - Andrew J Smith
- School of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro (MB), Italy
| | - Giuseppe Astarita
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
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Delvaux A, Rathahao-Paris E, Alves S. Different ion mobility-mass spectrometry coupling techniques to promote metabolomics. MASS SPECTROMETRY REVIEWS 2022; 41:695-721. [PMID: 33492707 DOI: 10.1002/mas.21685] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Metabolomics has become increasingly popular in recent years for many applications ranging from clinical diagnosis, human health to biotechnological questioning. Despite technological advances, metabolomic studies are still currently limited by the difficulty of identifying all metabolites, a class of compounds with great chemical diversity. Although lengthy chromatographic analyses are often used to obtain comprehensive data, many isobar and isomer metabolites still remain unresolved, which is a critical point for the compound identification. Currently, ion mobility spectrometry is being explored in metabolomics as a way to improve metabolome coverage, analysis throughput and isomer separation. In this review, all the steps of a typical workflow for untargeted metabolomics are discussed considering the use of an ion mobility instrument. An overview of metabolomics is first presented followed by a brief description of ion mobility instrumentation. The ion mobility potential for complex mixture analysis is discussed regarding its coupling with a mass spectrometer alone, providing gas-phase separation before mass analysis as well as its combination with different separation platforms (conventional hyphenation but also multidimensional ion mobility couplings), offering multidimensional separation. Various instrumental and analytical conditions for improving the ion mobility separation are also described. Finally, data mining, including software packages and visualization approaches, as well as the construction of ion mobility databases for the metabolite identification are examined.
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Affiliation(s)
- Aurélie Delvaux
- Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Sorbonne Université, Paris, 75005, France
| | - Estelle Rathahao-Paris
- Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Sorbonne Université, Paris, 75005, France
- Département Médicaments et Technologies pour la Santé (DMTS), SPI, Université Paris-Saclay, CEA, INRAE, Gif-sur-Yvette, 91191, France
| | - Sandra Alves
- Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Sorbonne Université, Paris, 75005, France
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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.
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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:
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Guo J, Yu H, Xing S, Huan T. Addressing big data challenges in mass spectrometry-based metabolomics. Chem Commun (Camb) 2022; 58:9979-9990. [PMID: 35997016 DOI: 10.1039/d2cc03598g] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Advancements in computer science and software engineering have greatly facilitated mass spectrometry (MS)-based untargeted metabolomics. Nowadays, gigabytes of metabolomics data are routinely generated from MS platforms, containing condensed structural and quantitative information from thousands of metabolites. Manual data processing is almost impossible due to the large data size. Therefore, in the "omics" era, we are faced with new challenges, the big data challenges of how to accurately and efficiently process the raw data, extract the biological information, and visualize the results from the gigantic amount of collected data. Although important, proposing solutions to address these big data challenges requires broad interdisciplinary knowledge, which can be challenging for many metabolomics practitioners. Our laboratory in the Department of Chemistry at the University of British Columbia is committed to combining analytical chemistry, computer science, and statistics to develop bioinformatics tools that address these big data challenges. In this Feature Article, we elaborate on the major big data challenges in metabolomics, including data acquisition, feature extraction, quantitative measurements, statistical analysis, and metabolite annotation. We also introduce our recently developed bioinformatics solutions for these challenges. Notably, all of the bioinformatics tools and source codes are freely available on GitHub (https://www.github.com/HuanLab), along with revised and regularly updated content.
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Affiliation(s)
- Jian Guo
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Huaxu Yu
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Shipei Xing
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
| | - Tao Huan
- Department of Chemistry, University of British Columbia, 2036 Main Mall, Vancouver, BC Canada, V6T 1Z1, Canada.
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Chen C, Lou MM, Sun YM, Luo F, Liu FT, Luo SS, Wang WY, Wang J. Serum metabolomic characterization of PLA2G6-associated dystonia-parkinsonism: A case-control biomarker study. Front Neurosci 2022; 16:879548. [PMID: 36033628 PMCID: PMC9406281 DOI: 10.3389/fnins.2022.879548] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2022] [Accepted: 07/15/2022] [Indexed: 01/09/2023] Open
Abstract
Introduction Phospholipase A2 Group VI (PLA2G6), encoding calcium-independent phospholipase A2, has been isolated as the gene responsible for an autosomal recessive form of early-onset Parkinson's disease (namely, PARK14). Compared to idiopathic Parkinson's disease (iPD), PARK14 has several atypical clinical features. PARK14 has an earlier age at onset and is more likely to develop levodopa-induced dyskinesia. In iPD, serum metabolomics has observed alterations in several metabolic pathways that are related to disease status and clinical manifestations. This study aims to describe the serum metabolomics features of patients with PARK14. Design This case-control biomarker study tested nine patients diagnosed with PARK14. Eight age and sex-matched healthy subjects were recruited as controls. To evaluate the influence of single heterozygous mutation, we enrolled eight healthy one-degree family members of patients with PARK14, two patients diagnosed with early-onset Parkinson's disease (EOPD) who had only a single heterozygous PLA2G6 mutation, and one patient with EOPD without any known pathogenic mutation. Methods The diagnosis of PARK14 was made according to the diagnostic criteria for Parkinson's disease (PD) and confirmed by genetic testing. To study the serum metabolic features, we analyzed participants' serum using UHPLC-QTOF/MS analysis, a well-established technology. Results We quantified 50 compounds of metabolites from the serum of all the study subjects. Metabolites alterations in serum had good predictive accuracy for PARK14 diagnosis (AUC 0.903) and advanced stage in PARK14 (AUC 0.944). Of the 24 metabolites that changed significantly in patients' serum, eight related to lipid metabolism. Oleic acid and xanthine were associated with MMSE scores. Xanthine, L-histidine, and phenol correlated with UPDRS-III scores. Oleic acid and 1-oleoyl-L-alpha-lysophosphatidic acid could also predict the subclass of the more advanced stage in the PLA2G6 Group in ROC models. Conclusion The significantly altered metabolites can be used to differentiate PLA2G6 pathogenic mutations and predict disease severity. Patients with PLA2G6 mutations had elevated lipid compounds in C18:1 and C16:0 groups. The alteration of lipid metabolism might be the key intermediate process in PLA2G6-related disease that needs further investigation.
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Affiliation(s)
- Chen Chen
- State Key Laboratory of Medical Neurobiology, Department of Neurology and National Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China,State Key Laboratory of Medical Neurobiology, National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Min-Min Lou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences – University of Chinese Academy of Sciences, Shanghai, China
| | - Yi-Min Sun
- State Key Laboratory of Medical Neurobiology, Department of Neurology and National Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China,State Key Laboratory of Medical Neurobiology, National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Fang Luo
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences – University of Chinese Academy of Sciences, Shanghai, China
| | - Feng-Tao Liu
- State Key Laboratory of Medical Neurobiology, Department of Neurology and National Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China,State Key Laboratory of Medical Neurobiology, National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Su-Shan Luo
- State Key Laboratory of Medical Neurobiology, Department of Neurology and National Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China,State Key Laboratory of Medical Neurobiology, National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China
| | - Wen-Yuan Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences – University of Chinese Academy of Sciences, Shanghai, China,*Correspondence: Wen-Yuan Wang,
| | - Jian Wang
- State Key Laboratory of Medical Neurobiology, Department of Neurology and National Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China,State Key Laboratory of Medical Neurobiology, National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, China,Jian Wang,
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Song XC, Dreolin N, Canellas E, Goshawk J, Nerin C. Prediction of Collision Cross-Section Values for Extractables and Leachables from Plastic Products. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:9463-9473. [PMID: 35730527 PMCID: PMC9261268 DOI: 10.1021/acs.est.2c02853] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
The use of ion mobility separation (IMS) in conjunction with high-resolution mass spectrometry has proved to be a reliable and useful technique for the characterization of small molecules from plastic products. Collision cross-section (CCS) values derived from IMS can be used as a structural descriptor to aid compound identification. One limitation of the application of IMS to the identification of chemicals from plastics is the lack of published empirical CCS values. As such, machine learning techniques can provide an alternative approach by generating predicted CCS values. Herein, experimental CCS values for over a thousand chemicals associated with plastics were collected from the literature and used to develop an accurate CCS prediction model for extractables and leachables from plastic products. The effect of different molecular descriptors and machine learning algorithms on the model performance were assessed. A support vector machine (SVM) model, based on Chemistry Development Kit (CDK) descriptors, provided the most accurate prediction with 93.3% of CCS values for [M + H]+ adducts and 95.0% of CCS values for [M + Na]+ adducts in testing sets predicted with <5% error. Median relative errors for the CCS values of the [M + H]+ and [M + Na]+ adducts were 1.42 and 1.76%, respectively. Subsequently, CCS values for the compounds in the Chemicals associated with Plastic Packaging Database and the Food Contact Chemicals Database were predicted using the SVM model developed herein. These values were integrated in our structural elucidation workflow and applied to the identification of plastic-related chemicals in river water. False positives were reduced, and the identification confidence level was improved by the incorporation of predicted CCS values in the suspect screening workflow.
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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
| | - Nicola Dreolin
- Waters
Corporation, Altrincham
Road, SK9 4AX Wilmslow, U.K.
| | - Elena Canellas
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Jeff Goshawk
- Waters
Corporation, Altrincham
Road, SK9 4AX Wilmslow, U.K.
| | - Cristina Nerin
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
- .
Phone: +34 976761873
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Wang M, Xu XY, Wang HD, Wang HM, Liu MY, Hu WD, Chen BX, Jiang MT, Qi J, Li XH, Yang WZ, Gao XM. A multi-dimensional liquid chromatography/high-resolution mass spectrometry approach combined with computational data processing for the comprehensive characterization of the multicomponents from Cuscuta chinensis. J Chromatogr A 2022; 1675:463162. [DOI: 10.1016/j.chroma.2022.463162] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/17/2022] [Accepted: 05/18/2022] [Indexed: 02/07/2023]
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42
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Foster M, Rainey M, Watson C, Dodds JN, Kirkwood KI, Fernández FM, Baker ES. Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:9133-9143. [PMID: 35653285 PMCID: PMC9474714 DOI: 10.1021/acs.est.2c00201] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate a xenobiotic structural annotation workflow using ion mobility spectrometry coupled with MS (IMS-MS), mass defect filtering, and machine learning to uncover potential xenobiotic classes and species in large metabolomic feature lists. Xenobiotic classes examined included those of known high toxicities, including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and pesticides. Specifically, when the workflow was applied to identify PFAS in the NIST SRM 1957 and 909c human serum samples, it greatly reduced the hundreds of detected liquid chromatography (LC)-IMS-MS features by utilizing both mass defect filtering and m/z versus IMS collision cross sections relationships. These potential PFAS features were then compared to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns illustrating the importance of nontargeted studies for detecting new molecules with known chemical characteristics. Additionally, this workflow can also be utilized to evaluate other xenobiotics and enable more confident annotations from nontargeted studies.
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Affiliation(s)
- MaKayla Foster
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Markace Rainey
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive NW, Atlanta, Georgia 30332, United States
| | - Chandler Watson
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive NW, Atlanta, Georgia 30332, United States
| | - James N Dodds
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Kaylie I Kirkwood
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive NW, Atlanta, Georgia 30332, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
- Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina 27695, United States
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43
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Ross D, Seguin RP, Krinsky AM, Xu L. High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1061-1072. [PMID: 35548857 PMCID: PMC9165597 DOI: 10.1021/jasms.2c00111] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a rapid (millisecond) time scale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion's gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we assembled a large-scale database of drug and drug metabolite CCS values using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors, achieving high prediction accuracies (0.8-2.2% median relative error on test set data). The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions.
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Affiliation(s)
| | | | | | - Libin Xu
- . Tel: (206) 543-1080. Fax: (206) 685-3252
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Song XC, Canellas E, Dreolin N, Goshawk J, Nerin C. A Collision Cross Section Database for Extractables and Leachables from Food Contact Materials. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:4457-4466. [PMID: 35380813 PMCID: PMC9011387 DOI: 10.1021/acs.jafc.2c00724] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The chemicals in food contact materials (FCMs) can migrate into food and endanger human health. In this study, we developed a database of traveling wave collision cross section in nitrogen (TWCCSN2) values for extractables and leachables from FCMs. The database contains a total of 1038 TWCCSN2 values from 675 standards including those commonly used additives and nonintentionally added substances in FCMs. The TWCCSN2 values in the database were compared to previously published values, and 85.7, 87.7, and 64.9% [M + H]+, [M + Na]+, and [M - H]- adducts showed deviations <2%, with the presence of protomers, post-ion mobility spectrometry dissociation of noncovalent clusters and inconsistent calibration are possible sources of CCS deviations. Our experimental TWCCSN2 values were also compared to CCS values from three prediction tools. Of the three, CCSondemand gave the most accurate predictions. The TWCCSN2 database developed will aid the identification and differentiation of chemicals from FCMs in targeted and untargeted analysis.
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Affiliation(s)
- Xue-Chao Song
- 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, 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, EINA, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
- . Phone: +34 976761873
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45
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Zhong P, Wei X, Li X, Wei X, Wu S, Huang W, Koidis A, Xu Z, Lei H. Untargeted metabolomics by liquid chromatography‐mass spectrometry for food authentication: A review. Compr Rev Food Sci Food Saf 2022; 21:2455-2488. [DOI: 10.1111/1541-4337.12938] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 02/20/2022] [Accepted: 02/21/2022] [Indexed: 12/17/2022]
Affiliation(s)
- Peng Zhong
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiaoqun Wei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiangmei Li
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Xiaoyi Wei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Shaozong Wu
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Weijuan Huang
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Anastasios Koidis
- Institute for Global Food Security Queen's University Belfast Belfast UK
| | - Zhenlin Xu
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
| | - Hongtao Lei
- Guangdong Provincial Key Laboratory of Food Quality and Safety / National–Local Joint Engineering Research Center for Precision Machining and Safety of Livestock and Poultry Products, College of Food Science South China Agricultural University Guangzhou 510642 China
- Guangdong Laboratory for Lingnan Modern Agriculture South China Agricultural University Guangzhou 510642 China
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46
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Qian YX, Zhao DX, Wang HD, Sun H, Xiong Y, Xu XY, Hu WD, Liu MY, Chen BX, Hu Y, Li X, Jiang MT, Yang WZ, Gao XM. An ion mobility-enabled and high-efficiency hybrid scan approach in combination with ultra-high performance liquid chromatography enabling the comprehensive characterization of the multicomponents from Carthamus tinctorius. J Chromatogr A 2022; 1667:462904. [DOI: 10.1016/j.chroma.2022.462904] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Revised: 02/11/2022] [Accepted: 02/12/2022] [Indexed: 01/09/2023]
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47
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Ion Mobility Mass Spectrometry for Structural Elucidation of Petroleum Compounds. Trends Analyt Chem 2022. [DOI: 10.1016/j.trac.2022.116597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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48
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Pičmanová M, Moses T, Cortada-Garcia J, Barrett G, Florance H, Pandor S, Burgess K. Rapid HILIC-Z ion mobility mass spectrometry (RHIMMS) method for untargeted metabolomics of complex biological samples. Metabolomics 2022; 18:16. [PMID: 35229219 PMCID: PMC8885480 DOI: 10.1007/s11306-022-01871-1] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 01/19/2022] [Indexed: 11/03/2022]
Abstract
INTRODUCTION Recent advances in high-throughput methodologies in the 'omics' and synthetic biology fields call for rapid and sensitive workflows in the metabolic phenotyping of complex biological samples. OBJECTIVE The objective of this research was to evaluate a straightforward to implement LC-MS metabolomics method using a commercially available chromatography column that provides increased throughput. Reducing run time can potentially impact chromatography and therefore the effects of ion mobility spectrometry to expand peak capacity were also evaluated. Additional confidence provided via collision cross section measurements for detected features was also explored. METHODS A rapid untargeted metabolomics workflow was developed with broad metabolome coverage, combining zwitterionic-phase hydrophilic interaction chromatography (HILIC-Z) with drift tube ion mobility-quadrupole time-of-flight (DTIM-qTOF) mass spectrometry. The analytical performance of our method was explored using extracts from complex biological samples, including a reproducibility study on chicken serum and a simple comparative study on a bacterial metabolome. RESULTS The method is acronymised RHIMMS for rapid HILIC-Z ion mobility mass spectrometry. We present the RHIMMS workflow starting with data acquisition, followed by data processing and analysis. RHIMMS demonstrates improved chromatographic separation for a selection of metabolites with wide physicochemical properties while maintaining reproducibility at better than 20% over 200 injections at 3.5 min per sample for the selected metabolites, and a mean of 13.9% for the top 50 metabolites by intensity. Additionally, the combination of rapid chromatographic separation with ion mobility allows improved annotation and the ability to distinguish isobaric compounds. CONCLUSION Our results demonstrate RHIMMS to be a rapid, reproducible, sensitive and high-resolution analytical platform that is highly applicable to the untargeted metabolomics analysis of complex samples.
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Affiliation(s)
- Martina Pičmanová
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Max Born Crescent, Edinburgh, EH9 3BF, UK
| | - Tessa Moses
- EdinOmics, University of Edinburgh, Max Born Crescent, Edinburgh, EH9 3BF, UK
| | - Joan Cortada-Garcia
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Max Born Crescent, Edinburgh, EH9 3BF, UK
| | - Georgina Barrett
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Max Born Crescent, Edinburgh, EH9 3BF, UK
| | - Hannah Florance
- Agilent Technologies UK Limited, Cheadle Royal Business Park Stockport, Cheshire, SK8 3GR, UK
| | - Sufyan Pandor
- Agilent Technologies UK Limited, Cheadle Royal Business Park Stockport, Cheshire, SK8 3GR, UK
| | - Karl Burgess
- Institute of Quantitative Biology, Biochemistry and Biotechnology, University of Edinburgh, Max Born Crescent, Edinburgh, EH9 3BF, UK.
- EdinOmics, University of Edinburgh, Max Born Crescent, Edinburgh, EH9 3BF, UK.
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49
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Song XC, Dreolin N, Damiani T, Canellas E, Nerin C. Prediction of Collision Cross Section Values: Application to Non-Intentionally Added Substance Identification in Food Contact Materials. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2022; 70:1272-1281. [PMID: 35041428 PMCID: PMC8815070 DOI: 10.1021/acs.jafc.1c06989] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/31/2021] [Accepted: 01/05/2022] [Indexed: 05/24/2023]
Abstract
The synthetic chemicals in food contact materials can migrate into food and endanger human health. In this study, the traveling wave collision cross section in nitrogen values of more than 400 chemicals in food contact materials were experimentally derived by traveling wave ion mobility spectrometry. A support vector machine-based collision cross section (CCS) prediction model was developed based on CCS values of food contact chemicals and a series of molecular descriptors. More than 92% of protonated and 81% of sodiated adducts showed a relative deviation below 5%. Median relative errors for protonated and sodiated molecules were 1.50 and 1.82%, respectively. The model was then applied to the structural annotation of oligomers migrating from polyamide adhesives. The identification confidence of 11 oligomers was improved by the direct comparison of the experimental data with the predicted CCS values. Finally, the challenges and opportunities of current machine-learning models on CCS prediction were also discussed.
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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
| | - Nicola Dreolin
- Waters
Corporation, Altrincham
Road, SK9 4AX Wilmslow, U.K.
| | - Tito Damiani
- Institute
of Organic Chemistry and Biochemistry, Flemingovo náměstí 542/2, 160 00 Prague, Czech Republic
| | - Elena Canellas
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Cristina Nerin
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, CPS-University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
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50
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Koomen DC, May JC, McLean JA. Insights and prospects for ion mobility-mass spectrometry in clinical chemistry. Expert Rev Proteomics 2022; 19:17-31. [PMID: 34986717 PMCID: PMC8881341 DOI: 10.1080/14789450.2022.2026218] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
INTRODUCTION Ion mobility-mass spectrometry is an emerging technology in the clinical setting for high throughput and high confidence molecular characterization from complex biological samples. Ion mobility spectrometry can provide isomer separations on the basis of molecular structure, the ability of which is increasing through technological developments that afford enhanced resolving power. Integrating multiple separation dimensions, such as liquid chromatography-ion mobility-mass spectrometry (LC-IM-MS) provide dramatic enhancements in the mitigation of molecular interferences for high accuracy clinical measurements. AREAS COVERED Multidimensional separations with LC-IM-MS provide better selectivity and sensitivity in molecular analysis. Mass spectrometry imaging of tissues to inform spatial molecular distribution is improved by complementary ion mobility analyses. Biomarker identification in surgical environments is enhanced by intraoperative biochemical analysis with mass spectrometry and holds promise for integration with ion mobility spectrometry. New prospects in high resolving power ion mobility are enhancing analysis capabilities, such as distinguishing isomeric compounds. EXPERT OPINION Ion mobility-mass spectrometry holds many prospects for the field of isomer identification, molecular imaging, and intraoperative tumor margin delineation in clinical settings. These advantages are afforded while maintaining fast analysis times and subsequently high throughput. High resolving power ion mobility will enhance these advantages further, in particular for analyses requiring high confidence isobaric selectivity and detection.
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Affiliation(s)
- David C. Koomen
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - Jody C. May
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
| | - John A. McLean
- Department of Chemistry, Center for Innovative Technology, Institute of Chemical Biology, Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, USA
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