1
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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: 7] [Impact Index Per Article: 3.5] [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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2
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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: 9] [Impact Index Per Article: 4.5] [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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3
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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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4
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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: 103] [Impact Index Per Article: 34.3] [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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5
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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: 44] [Impact Index Per Article: 14.7] [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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6
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Luo YS, Chen Z, Hsieh NH, Lin TE. Chemical and biological assessments of environmental mixtures: A review of current trends, advances, and future perspectives. JOURNAL OF HAZARDOUS MATERIALS 2022; 432:128658. [PMID: 35290896 DOI: 10.1016/j.jhazmat.2022.128658] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/21/2022] [Accepted: 03/07/2022] [Indexed: 05/28/2023]
Abstract
Considering the chemical complexity and toxicity data gaps of environmental mixtures, most studies evaluate the chemical risk individually. However, humans are usually exposed to a cocktail of chemicals in real life. Mixture health assessment remains to be a research area having significant knowledge gaps. Characterization of chemical composition and bioactivity/toxicity are the two critical aspects of mixture health assessments. This review seeks to introduce the recent progress and tools for the chemical and biological characterization of environmental mixtures. The state-of-the-art techniques include the sampling, extraction, rapid detection methods, and the in vitro, in vivo, and in silico approaches to generate the toxicity data of an environmental mixture. Application of these novel methods, or new approach methodologies (NAMs), has increased the throughput of generating chemical and toxicity data for mixtures and thus refined the mixture health assessment. Combined with computational methods, the chemical and biological information would shed light on identifying the bioactive/toxic components in an environmental mixture.
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Affiliation(s)
- Yu-Syuan Luo
- Institute of Food Safety and Health, College of Public Health, National Taiwan University, Taipei City, Taiwan.
| | - Zunwei Chen
- Program in Molecular and Integrative Physiological Sciences, Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Nan-Hung Hsieh
- Interdisciplinary Faculty of Toxicology and Department of Veterinary Integrative Biosciences, College of Veterinary Medicine and Biomedical Sciences, Texas A&M University, College Station, TX 77843, USA
| | - Tzu-En Lin
- Institute of Biomedical Engineering, College of Electrical and Computer Engineering, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
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7
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Moran-Garrido M, Camunas-Alberca SM, Gil-de-la Fuente A, Mariscal A, Gradillas A, Barbas C, Sáiz J. Recent developments in data acquisition, treatment and analysis with ion mobility-mass spectrometry for lipidomics. Proteomics 2022; 22:e2100328. [PMID: 35653360 DOI: 10.1002/pmic.202100328] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/29/2022] [Accepted: 05/30/2022] [Indexed: 11/08/2022]
Abstract
Lipids are involved in many biological processes and their study is constantly increasing. To identify a lipid among thousand requires of reliable methods and techniques. Ion Mobility (IM) can be coupled with Mass Spectrometry (MS) to increase analytical selectivity in lipid analysis of lipids. IM-MS has experienced an enormous development in several aspects, including instrumentation, sensitivity, amount of information collected and lipid identification capabilities. This review summarizes the latest developments in IM-MS analyses for lipidomics and focusses on the current acquisition modes in IM-MS, the approaches for the pre-treatment of the acquired data and the subsequent data analysis. Methods and tools for the calculation of Collision Cross Section (CCS) values of analytes are also reviewed. CCS values are commonly studied to support the identification of lipids, providing a quasi-orthogonal property that increases the confidence level in the annotation of compounds and can be matched in CCS databases. The information contained in this review might be of help to new users of IM-MS to decide the adequate instrumentation and software to perform IM-MS experiments for lipid analyses, but also for other experienced researchers that can reconsider their routines and protocols. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- María Moran-Garrido
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain
| | - Sandra M Camunas-Alberca
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain
| | - Alberto Gil-de-la Fuente
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain.,Departamento de Tecnologías de la Información, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain
| | - Antonio Mariscal
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain.,Departamento de Tecnologías de la Información, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain
| | - Ana Gradillas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain
| | - Jorge Sáiz
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain
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8
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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: 16] [Impact Index Per Article: 5.3] [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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9
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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: 11] [Impact Index Per Article: 3.7] [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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10
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Wang JY, Yin YH, Zheng JY, Liu LF, Yao ZP, Xin GZ. Least absolute shrinkage and selection operator-based prediction of collision cross section values for ion mobility mass spectrometric analysis of lipids. Analyst 2022; 147:1236-1244. [DOI: 10.1039/d1an02161c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A least absolute shrinkage and selection operator (LASSO)-based prediction method was developed for the prediction of lipids’ CCS values.
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Affiliation(s)
- Jian-Ying Wang
- State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, China
- State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation) and Shenzhen Key Laboratory of Food Biological Safety Control, Shenzhen Research Institute of Hong Kong Polytechnic University, Shenzhen 518057, China
- State Key Laboratory of Chemical Biology and Drug Discovery, Food Safety and Technology Research Centre and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Ying-Hao Yin
- State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, China
| | - Jia-Yi Zheng
- State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, China
| | - Li-Fang Liu
- State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, China
| | - Zhong-Ping Yao
- State Key Laboratory of Chinese Medicine and Molecular Pharmacology (Incubation) and Shenzhen Key Laboratory of Food Biological Safety Control, Shenzhen Research Institute of Hong Kong Polytechnic University, Shenzhen 518057, China
- State Key Laboratory of Chemical Biology and Drug Discovery, Food Safety and Technology Research Centre and Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong SAR, China
| | - Gui-Zhong Xin
- State Key Laboratory of Natural Medicines, Department of Chinese Medicines Analysis, School of Traditional Chinese Pharmacy, China Pharmaceutical University, No. 24 Tongjia Lane, Nanjing, China
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11
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Tsai YT, Huang CW, Yu SS. The effect of temperature on the kinetics of enhanced amide bond formation from lactic acid and valine driven by deep eutectic solvents. Phys Chem Chem Phys 2021; 23:27498-27507. [PMID: 34874376 DOI: 10.1039/d1cp03243g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Deep eutectic solvents have been found to facilitate the copolymerization of hydroxy acids and amino acids through an ester-amide exchange reaction, and to drive the formation of amino acid-enriched oligomers with peptide backbones. The complexity of oligomer distribution is significantly reduced in deep eutectic solvents and amide-linked oligomers can be selectively produced. In the present study, we investigated the kinetics of amide bond formation in deep eutectic solvents to understand how the solvents regulate the pathways of complex copolymerization. A mathematical model successfully simulated the reaction of a lactic acid/valine mixture in deep eutectic solvents at different temperatures and provided insight into the activation energy of each step. Our findings indicated that the esterification and the evaporation of hydroxy acids were greatly suppressed in deep eutectic solvents because of the strong interaction between the quaternary ammonium salts and the hydroxy acids. In contrast, the ester-amide exchange reaction in deep eutectic solvents was significantly enhanced by lowering the activation entropies. The synergic effect of reduced esterification and increased exchange leads to amino acid-enriched oligomers with high yield and high selectivity. Furthermore, the reduced evaporation of hydroxy acids in deep eutectic solvents may preserve limited reactants in the prebiotic scenario. These results reveal deep eutectic solvents as sustainable media for the simple synthesis of amide bonds.
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Affiliation(s)
- Yi-Ting Tsai
- Department of Chemical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan.
| | - Cong-Wei Huang
- Department of Chemical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan.
| | - Sheng-Sheng Yu
- Department of Chemical Engineering, National Cheng Kung University, Tainan, 70101, Taiwan. .,Core Facility Center, National Cheng Kung University, Tainan, 70101, Taiwan
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12
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Tötsch K, Fjeldsted JC, Stow SM, Schmitz OJ, Meckelmann SW. Effect of Sampling Rate and Data Pretreatment for Targeted and Nontargeted Analysis by Means of Liquid Chromatography Coupled to Drift Time Ion Mobility Quadruple Time-of-Flight Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:2592-2603. [PMID: 34515480 DOI: 10.1021/jasms.1c00217] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Ion mobility as an additional separation dimension can help to resolve and annotate metabolite and lipid biomarkers and provides important information about the components in a sample. Identifying relevant information in the resulting data is challenging because of the complexity of the data and data evaluation strategies for both targeted or nontargeted workflows. Frequently, feature analysis is used as a first step to search for differences between samples in discovery workflows. However, follow-up experimentation often leads to more targeted data extraction methods. In both cases, optimizing data sets for data extraction can make an important contribution to the overall results. In this work, we evaluate the effect of experimental conditions including acquisition sampling rate and data pretreatment on lipid standards and lipid extracts as examples of complex biological samples analyzed by liquid chromatography coupled to drift time ion mobility quadrupole time-of-flight mass spectrometry. The results show that a reduction of both peak variation and background noise can be achieved by optimizing the sampling rate. The use of data pretreatment including data smoothing, intensity thresholding, and spike removal also play an important role in improving detection and annotation of analytes from complex biological samples, whereas nonoptimal data sampling rates and preprocessing can lead to adverse effects including the loss or alternation of small, or closely eluting, low-abundant peaks.
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Affiliation(s)
- Kristina Tötsch
- Applied Analytical Chemistry, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
- Teaching and Research Center for Separation, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
| | - John C Fjeldsted
- Agilent Technologies, Santa Clara, California 95051, United States
| | - Sarah M Stow
- Agilent Technologies, Santa Clara, California 95051, United States
| | - Oliver J Schmitz
- Applied Analytical Chemistry, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
- Teaching and Research Center for Separation, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
| | - Sven W Meckelmann
- Applied Analytical Chemistry, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
- Teaching and Research Center for Separation, University of Duisburg-Essen, Universitätsstrasse 5, 45141 Essen, Germany
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13
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Zhang L, Xu T, Zhang J, Wong SCC, Ritchie M, Hou HW, Wang Y. Single Cell Metabolite Detection Using Inertial Microfluidics-Assisted Ion Mobility Mass Spectrometry. Anal Chem 2021; 93:10462-10468. [PMID: 34289696 DOI: 10.1021/acs.analchem.1c00106] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Single-cell metabolite measurement remains highly challenging due to difficulties related to single cell isolation, metabolite detection, and identification of low levels of metabolites. Here, as a first step of the technological development, we propose a novel strategy integrating spiral inertial microfluidics and ion mobility mass spectrometry (IM-MS) for single-cell metabolite detection and identification. Cells in methanol suspension are inertially focused into a single stream in the spiral microchannel. This stream of separated cells is delivered to the nanoelectrospray needle to be lysed and ionized and subsequently analyzed in real time by IM-MS. This analytical system enables six to eight single-cell metabolic fingerprints to be collected per minute, including gas-phase collisional cross section (CCS) measurements as an additional molecular descriptor, giving increased confidence in metabolite identification. As a proof of concept, the metabolic profiles of three types of cancer cells (U2OS, HepG2, and HepG2.215) were successfully screened, and 19 distinct lipids species were identified with CCS value filtering. Furthermore, principal component analysis (PCA) showed differentiation of the three cancer cell lines, mainly due to cellular surface phospholipids. Taken together, our technology platform offers a simple and efficient method for single-cell lipid profiling, with additional ion mobility separation of lipids significantly improving the confidence toward identification of metabolites.
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Affiliation(s)
- Leicheng Zhang
- Singapore Phenome Center, Lee Kong Chian School of Medicine, Nanyang Technological University, 639798 Singapore
| | - Tengfei Xu
- School of Civil and Environmental Engineering, Nanyang Technological University, 639798 Singapore
| | - Jingtao Zhang
- Singapore Phenome Center, Lee Kong Chian School of Medicine, Nanyang Technological University, 639798 Singapore
| | | | - Mark Ritchie
- Waters Pacific Pte Ltd, Science Park 2, 117528 Singapore
| | - Han Wei Hou
- Singapore Phenome Center, Lee Kong Chian School of Medicine, Nanyang Technological University, 639798 Singapore.,School of Mechanical & Aerospace Engineering, Nanyang Technological University, 639798 Singapore
| | - Yulan Wang
- Singapore Phenome Center, Lee Kong Chian School of Medicine, Nanyang Technological University, 639798 Singapore
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14
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Osipenko S, Botashev K, Nikolaev E, Kostyukevich Y. Transfer learning for small molecule retention predictions. J Chromatogr A 2021; 1644:462119. [PMID: 33845426 DOI: 10.1016/j.chroma.2021.462119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2021] [Revised: 03/23/2021] [Accepted: 03/25/2021] [Indexed: 10/21/2022]
Abstract
Small molecule retention time prediction is a sophisticated task because of the wide variety of separation techniques resulting in fragmented data available for training machine learning models. Predictions are typically made with traditional machine learning methods such as support vector machine, random forest, or gradient boosting. Another approach is to use large data sets for training with a consequent projection of predictions. Here we evaluate the applicability of transfer learning for small molecule retention prediction as a new approach to deal with small retention data sets. Transfer learning is a state-of-the-art technique for natural language processing (NLP) tasks. We propose using text-based molecular representations (SMILES) widely used in cheminformatics for NLP-like modeling on molecules. We suggest using self-supervised pre-training to capture relevant features from a large corpus of one million molecules followed by fine-tuning on task-specific data. Mean absolute error (MAE) of predictions was in range of 88-248 s for tested reversed-phase data sets and 66 s for HILIC data set, which is comparable with MAE reported for traditional machine learning models based on descriptors or projection approaches on the same data.
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Affiliation(s)
- Sergey Osipenko
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Nobel Str., 3, 121205 Moscow, Russia
| | - Kazii Botashev
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Nobel Str., 3, 121205 Moscow, Russia
| | - Eugene Nikolaev
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Nobel Str., 3, 121205 Moscow, Russia.
| | - Yury Kostyukevich
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Nobel Str., 3, 121205 Moscow, Russia.
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15
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Ross DH, Xu L. Determination of drugs and drug metabolites by ion mobility-mass spectrometry: A review. Anal Chim Acta 2021; 1154:338270. [PMID: 33736803 DOI: 10.1016/j.aca.2021.338270] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 01/25/2021] [Accepted: 01/27/2021] [Indexed: 01/04/2023]
Abstract
Ion mobility-mass spectrometry (IM-MS) has gained increased applications in the characterization and identification of drugs and drug metabolites, largely owning to the complementary separation of analyte ions based on their gas-phase size and shape in the IM dimension in addition to their mass-to-charge ratios. In this review, we discuss recent advances in such applications. We first introduce various types of IM techniques, focusing on those that allow the measurement of collision cross section (CCS), the physical property of an ion that reflects its gas-phase size and shape. Next, we discuss the IM-MS landscape of the large chemical space of drugs and multimodal distributions of certain drugs in IM separation due to the presence of protomers. We then review drug metabolism reactions and discuss the application of IM-MS in separation and identification of isomeric drug metabolites. Subsequently, we discuss various approaches to generate theoretical and predicted CCS data, including theory-based calculation methods and data-driven prediction models, and currently available resources on these approaches. Finally, current limitations and future directions of application of IM-MS are discussed.
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Affiliation(s)
- Dylan H Ross
- Department of Medicinal Chemistry, University of Washington, 1959, NE Pacific Street, HSB H-172, Seattle, WA, USA
| | - Libin Xu
- Department of Medicinal Chemistry, University of Washington, 1959, NE Pacific Street, HSB H-172, Seattle, WA, USA.
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16
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Chen X, Yin Y, Zhou Z, Li T, Zhu ZJ. Development of a combined strategy for accurate lipid structural identification and quantification in ion-mobility mass spectrometry based untargeted lipidomics. Anal Chim Acta 2020; 1136:115-124. [DOI: 10.1016/j.aca.2020.08.048] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2020] [Revised: 08/13/2020] [Accepted: 08/24/2020] [Indexed: 10/23/2022]
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17
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Ross DH, Cho JH, Zhang R, Hines KM, Xu L. LiPydomics: A Python Package for Comprehensive Prediction of Lipid Collision Cross Sections and Retention Times and Analysis of Ion Mobility-Mass Spectrometry-Based Lipidomics Data. Anal Chem 2020; 92:14967-14975. [PMID: 33119270 PMCID: PMC7816765 DOI: 10.1021/acs.analchem.0c02560] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Comprehensive profiling of lipid species in a biological sample, or lipidomics, is a valuable approach to elucidating disease pathogenesis and identifying biomarkers. Currently, a typical lipidomics experiment may track hundreds to thousands of individual lipid species. However, drawing biological conclusions requires multiple steps of data processing to enrich significantly altered features and confident identification of these features. Existing solutions for these data analysis challenges (i.e., multivariate statistics and lipid identification) involve performing various steps using different software applications, which imposes a practical limitation and potentially a negative impact on reproducibility. Hydrophilic interaction liquid chromatography-ion mobility-mass spectrometry (HILIC-IM-MS) has shown advantages in separating lipids through orthogonal dimensions. However, there are still gaps in the coverage of lipid classes in the literature. To enable reproducible and efficient analysis of HILIC-IM-MS lipidomics data, we developed an open-source Python package, LiPydomics, which enables performing statistical and multivariate analyses ("stats" module), generating informative plots ("plotting" module), identifying lipid species at different confidence levels ("identification" module), and carrying out all functions using a user-friendly text-based interface ("interactive" module). To support lipid identification, we assembled a comprehensive experimental database of m/z and CCS of 45 lipid classes with 23 classes containing HILIC retention times. Prediction models for CCS and HILIC retention time for 22 and 23 lipid classes, respectively, were trained using the large experimental data set, which enabled the generation of a large predicted lipid database with 145,388 entries. Finally, we demonstrated the utility of the Python package using Staphylococcus aureus strains that are resistant to various antimicrobials.
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Affiliation(s)
- Dylan H Ross
- Department of Medicinal Chemistry, University of Washington, 1959 NE Pacific Street, HSB H-172, Seattle, Washington 98195, United States
| | - Jang Ho Cho
- Department of Medicinal Chemistry, University of Washington, 1959 NE Pacific Street, HSB H-172, Seattle, Washington 98195, United States
| | - Rutan Zhang
- Department of Medicinal Chemistry, University of Washington, 1959 NE Pacific Street, HSB H-172, Seattle, Washington 98195, United States
| | - Kelly M Hines
- Department of Medicinal Chemistry, University of Washington, 1959 NE Pacific Street, HSB H-172, Seattle, Washington 98195, United States
| | - Libin Xu
- Department of Medicinal Chemistry, University of Washington, 1959 NE Pacific Street, HSB H-172, Seattle, Washington 98195, United States
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18
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Zheng H, Zhen XT, Chen Y, Zhu SC, Ye LH, Yang SW, Wang QY, Cao J. In situ antioxidation-assisted matrix solid-phase dispersion microextraction and discrimination of chiral flavonoids from citrus fruit via ion mobility quadrupole time-of-flight high-resolution mass spectrometry. Food Chem 2020; 343:128422. [PMID: 33143965 DOI: 10.1016/j.foodchem.2020.128422] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 10/11/2020] [Accepted: 10/14/2020] [Indexed: 02/07/2023]
Abstract
A simple and sensitive in situ antioxidation process assisted with a matrix solid-phase dispersion method for extracting chiral flavonoids in citrus fruit was established, and samples were further analyzed using ion mobility quadrupole time-of-flight high-resolution mass spectrometry. The collision cross-sections of the target compounds were studied using single-field and stepped-field methods. The optimal conditions were obtained using 30 mg of C18 as a dispersant, methanol as an elution solvent and 0.6 mM 1,1-diphenyl-2-picrylhydrazyl (DPPH) as a radical solution. Additionally, the method showed satisfactory limits of detection (3.70-6.52 ng/mL) and good recoveries (96.78-104.67%) for four flavonoids in citrus fruit. The IC50 values of DPPH radical-scavenging activities ranged from 817.8 to 981.55 μg/mL for tested samples. The method was a good alternative for the microextraction and determination of antioxidant capacity and chiral differentiation of narirutin, naringin, hesperidin and neohesperidin in citrus fruit.
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Affiliation(s)
- Hui Zheng
- College of Material Chemistry and Chemical Engineering, Hangzhou Normal University, Hangzhou 311121, China.
| | - Xiao-Ting Zhen
- College of Material Chemistry and Chemical Engineering, Hangzhou Normal University, Hangzhou 311121, China
| | - Yan Chen
- College of Material Chemistry and Chemical Engineering, Hangzhou Normal University, Hangzhou 311121, China
| | - Si-Chen Zhu
- College of Material Chemistry and Chemical Engineering, Hangzhou Normal University, Hangzhou 311121, China
| | - Li-Hong Ye
- Department of Traditional Chinese Medicine, Hangzhou Red Cross Hospital, Hangzhou 310003, China.
| | - Si-Wei Yang
- College of Material Chemistry and Chemical Engineering, Hangzhou Normal University, Hangzhou 311121, China
| | - Qiu-Yan Wang
- College of Material Chemistry and Chemical Engineering, Hangzhou Normal University, Hangzhou 311121, China
| | - Jun Cao
- College of Material Chemistry and Chemical Engineering, Hangzhou Normal University, Hangzhou 311121, China.
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19
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Soper-Hopper MT, Vandegrift J, Baker ES, Fernández FM. Metabolite collision cross section prediction without energy-minimized structures. Analyst 2020; 145:5414-5418. [PMID: 32583823 DOI: 10.1039/d0an00198h] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Matching experimental ion mobility-mass spectrometry data to computationally-generated collision cross section (CCS) values enables more confident metabolite identifications. Here, we show for the first time that accurately predicting CCS values with simple models for the largest library of metabolite cross sections is indeed possible, achieving a root mean square error of 7.0 Å2 (median error of ∼2%) using linear methods accesible to most researchers. A comparison on the performance of 2D vs. 3D molecular descriptors for the purposes of CCS prediction is also presented for the first time, enabling CCS prediction without a priori knowledge of the metabolite's energy-minimized structure.
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Affiliation(s)
- M T Soper-Hopper
- Northern Kentucky University, Department of Chemistry and Biochemistry, 1 Nunn Drive, Highland Heights, KY 41099, USA
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20
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Ross DH, Cho JH, Xu L. Breaking Down Structural Diversity for Comprehensive Prediction of Ion-Neutral Collision Cross Sections. Anal Chem 2020; 92:4548-4557. [PMID: 32096630 DOI: 10.1021/acs.analchem.9b05772] [Citation(s) in RCA: 94] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Identification of unknowns is a bottleneck for large-scale untargeted analyses like metabolomics or drug metabolite identification. Ion mobility-mass spectrometry (IM-MS) provides rapid two-dimensional separation of ions based on their mobility through a neutral buffer gas. The mobility of an ion is related to its collision cross section (CCS) with the buffer gas, a physical property that is determined by the size and shape of the ion. This structural dependency makes CCS a promising characteristic for compound identification, but this utility is limited by the availability of high-quality reference CCS values. CCS prediction using machine learning (ML) has recently shown promise in the field, but accurate and broadly applicable models are still lacking. Here we present a novel ML approach that employs a comprehensive collection of CCS values covering a wide range of chemical space. Using this diverse database, we identified the structural characteristics, represented by molecular quantum numbers (MQNs), that contribute to variance in CCS and assessed the performance of a variety of ML algorithms in predicting CCS. We found that by breaking down the chemical structural diversity using unsupervised clustering based on the MQNs, specific and accurate prediction models for each cluster can be trained, which showed superior performance than a single model trained with all data. Using this approach, we have robustly trained and characterized a CCS prediction model with high accuracy on diverse chemical structures. An all-in-one web interface (https://CCSbase.net) was built for querying the CCS database and accessing the predictive model to support unknown compound identifications.
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Affiliation(s)
- Dylan H Ross
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Jang Ho Cho
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
| | - Libin Xu
- Department of Medicinal Chemistry, University of Washington, Seattle, Washington 98195, United States
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21
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McBride M, Liu A, Reichmanis E, Grover MA. Toward data-enabled process optimization of deformable electronic polymer-based devices. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2019.11.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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22
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Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts. Nat Commun 2020; 11:331. [PMID: 31949144 PMCID: PMC6965134 DOI: 10.1038/s41467-019-14044-x] [Citation(s) in RCA: 126] [Impact Index Per Article: 25.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Accepted: 12/12/2019] [Indexed: 01/08/2023] Open
Abstract
A comprehensive characterization of the lipidome from limited starting material remains very challenging. Here we report a high-sensitivity lipidomics workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS). Taking advantage of parallel accumulation–serial fragmentation (PASEF), we fragment on average 15 precursors in each of 100 ms TIMS scans, while maintaining the full mobility resolution of co-eluting isomers. The acquisition speed of over 100 Hz allows us to obtain MS/MS spectra of the vast majority of isotope patterns. Analyzing 1 µL of human plasma, PASEF increases the number of identified lipids more than three times over standard TIMS-MS/MS, achieving attomole sensitivity. Building on high intra- and inter-laboratory precision and accuracy of TIMS collisional cross sections (CCS), we compile 1856 lipid CCS values from plasma, liver and cancer cells. Our study establishes PASEF in lipid analysis and paves the way for sensitive, ion mobility-enhanced lipidomics in four dimensions. Trapped ion mobility (TIMS)-mass spectrometry with parallel accumulation-serial fragmentation (PASEF) facilitates high-sensitivity proteomics experiments. Here, the authors expand TIMS and PASEF to small molecules and demonstrate fast and comprehensive lipidomics of low biological sample amounts.
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23
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Odenkirk MT, Baker ES. Utilizing Drift Tube Ion Mobility Spectrometry for the Evaluation of Metabolites and Xenobiotics. Methods Mol Biol 2020; 2084:35-54. [PMID: 31729652 DOI: 10.1007/978-1-0716-0030-6_2] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Metabolites and xenobiotics are small molecules with a molecular weight that often falls below 600 Da. Over the last few decades, multiple small molecule databases have been curated listing structures, masses, and fragmentation spectra possible in metabolomic and exposomic measurements. To date only a small portion of the spectra in these databases are experimentally derived due to the high expense of obtaining, synthesizing, and analyzing standards. A vast majority of spectra have thus been created using theoretical programs to fit the available experimental data. The errors associated with theoretical data have however caused problems with current small molecule identifications, and accurate quantitation as searching the databases using just one or two analysis dimensions (i.e., chromatography retention times and mass spectrometry (MS) m/z values) results in numerous annotations for each experimental feature. Additional analysis dimensions are therefore needed to better annotate and identify small molecules. Drift tube ion mobility spectrometry coupled with MS (DTIMS-MS) is a promising technique to address this challenge as it is able to perform rapid structural evaluations of small molecules in complex matrices by assessing the collision cross section values for each in addition to their m/z values. The use of IMS in conjunction with other separation techniques such as gas or liquid chromatography and MS has therefore enabled more accurate identifications for the small molecules present in complex biological and environmental samples. Here, we present a review of relevant parameter considerations for DTIMS application with emphasis on xenobiotics and metabolomics isomer separations.
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Affiliation(s)
- Melanie T Odenkirk
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, NC, USA.
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24
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Dodds JN, Baker ES. Ion Mobility Spectrometry: Fundamental Concepts, Instrumentation, Applications, and the Road Ahead. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2019; 30:2185-2195. [PMID: 31493234 PMCID: PMC6832852 DOI: 10.1007/s13361-019-02288-2] [Citation(s) in RCA: 309] [Impact Index Per Article: 51.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2019] [Revised: 07/08/2019] [Accepted: 07/15/2019] [Indexed: 05/07/2023]
Abstract
Ion mobility spectrometry (IMS) is a rapid separation technique that has experienced exponential growth as a field of study. Interfacing IMS with mass spectrometry (IMS-MS) provides additional analytical power as complementary separations from each technique enable multidimensional characterization of detected analytes. IMS separations occur on a millisecond timescale, and therefore can be readily nested into traditional GC and LC/MS workflows. However, the continual development of novel IMS methods has generated some level of confusion regarding the advantages and disadvantages of each. In this critical insight, we aim to clarify some common misconceptions for new users in the community pertaining to the fundamental concepts of the various IMS instrumental platforms (i.e., DTIMS, TWIMS, TIMS, FAIMS, and DMA), while addressing the strengths and shortcomings associated with each. Common IMS-MS applications are also discussed in this review, such as separating isomeric species, performing signal filtering for MS, and incorporating collision cross-section (CCS) values into both targeted and untargeted omics-based workflows as additional ion descriptors for chemical annotation. Although many challenges must be addressed by the IMS community before mobility information is collected in a routine fashion, the future is bright with possibilities.
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Affiliation(s)
- James N Dodds
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA.
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25
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Tu J, Zhou Z, Li T, Zhu ZJ. The emerging role of ion mobility-mass spectrometry in lipidomics to facilitate lipid separation and identification. Trends Analyt Chem 2019. [DOI: 10.1016/j.trac.2019.03.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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26
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Monge ME, Dodds JN, Baker ES, Edison AS, Fernández FM. Challenges in Identifying the Dark Molecules of Life. ANNUAL REVIEW OF ANALYTICAL CHEMISTRY (PALO ALTO, CALIF.) 2019; 12:177-199. [PMID: 30883183 PMCID: PMC6716371 DOI: 10.1146/annurev-anchem-061318-114959] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Metabolomics is the study of the metabolome, the collection of small molecules in living organisms, cells, tissues, and biofluids. Technological advances in mass spectrometry, liquid- and gas-phase separations, nuclear magnetic resonance spectroscopy, and big data analytics have now made it possible to study metabolism at an omics or systems level. The significance of this burgeoning scientific field cannot be overstated: It impacts disciplines ranging from biomedicine to plant science. Despite these advances, the central bottleneck in metabolomics remains the identification of key metabolites that play a class-discriminant role. Because metabolites do not follow a molecular alphabet as proteins and nucleic acids do, their identification is much more time consuming, with a high failure rate. In this review, we critically discuss the state-of-the-art in metabolite identification with specific applications in metabolomics and how technologies such as mass spectrometry, ion mobility, chromatography, and nuclear magnetic resonance currently contribute to this challenging task.
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Affiliation(s)
- María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), C1425FQD, Ciudad de Buenos Aires, Argentina
| | - James N Dodds
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, USA
| | - Arthur S Edison
- Department of Genetics, Department of Biochemistry and Molecular Biology, and Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, USA
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology and Petit Institute for Biochemistry and Bioscience, Atlanta, Georgia 30332, USA;
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27
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Burnum-Johnson KE, Zheng X, Dodds JN, Ash J, Fourches D, Nicora CD, Wendler JP, Metz TO, Waters KM, Jansson JK, Smith RD, Baker ES. Ion Mobility Spectrometry and the Omics: Distinguishing Isomers, Molecular Classes and Contaminant Ions in Complex Samples. Trends Analyt Chem 2019; 116:292-299. [PMID: 31798197 DOI: 10.1016/j.trac.2019.04.022] [Citation(s) in RCA: 80] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Ion mobility spectrometry (IMS) is a widely used analytical technique providing rapid gas phase separations. IMS alone is useful, but its coupling with mass spectrometry (IMS-MS) and various front-end separation techniques has greatly increased the molecular information achievable from different omic analyses. IMS-MS analyses are specifically gaining attention for improving metabolomic, lipidomic, glycomic, proteomic and exposomic analyses by increasing measurement sensitivity (e.g. S/N ratio), reducing the detection limit, and amplifying peak capacity. Numerous studies including national security-related analyses, disease screenings and environmental evaluations are illustrating that IMS-MS is able to extract information not possible with MS alone. Furthermore, IMS-MS has shown great utility in salvaging molecular information for low abundance molecules of interest when high concentration contaminant ions are present in the sample by reducing detector suppression. This review highlights how IMS-MS is currently being used in omic analyses to distinguish structurally similar molecules, isomers, molecular classes and contaminant ions.
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Affiliation(s)
| | - Xueyun Zheng
- Department of Chemistry, Texas A &M University, College Station, TX
| | - James N Dodds
- Department of Chemistry, NC State University, Raleigh, NC
| | - Jeremy Ash
- Department of Chemistry, NC State University, Raleigh, NC
| | - Denis Fourches
- Department of Chemistry, NC State University, Raleigh, NC
| | - Carrie D Nicora
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Jason P Wendler
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Katrina M Waters
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Janet K Jansson
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Richard D Smith
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA
| | - Erin S Baker
- Department of Chemistry, NC State University, Raleigh, NC
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New Frontiers in Lipidomics Analyses using Structurally Selective Ion Mobility-Mass Spectrometry. Trends Analyt Chem 2019; 116:316-323. [PMID: 31983792 DOI: 10.1016/j.trac.2019.03.031] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The growth of lipidomics and the high isomeric complexity of the lipidome has revealed a need for analytical techniques capable of structurally characterizing lipids with a high degree of specificity. Lipids are morphologically diverse molecules that can exist as any one of a large number of isomeric species, and as such are often indistinguishable by mass spectrometry without a complementary separation method. Recent developments in the field of lipidomics aim to address these challenges by utilizing a combination of multiple analytical techniques which are selective to lipid primary structure. This review summarizes two emerging strategies for lipidomic analysis, namely, ion mobility-mass spectrometry and ion fragmentation via ozonolysis.
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Leaptrot KL, May JC, Dodds JN, McLean JA. Ion mobility conformational lipid atlas for high confidence lipidomics. Nat Commun 2019; 10:985. [PMID: 30816114 PMCID: PMC6395675 DOI: 10.1038/s41467-019-08897-5] [Citation(s) in RCA: 132] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2018] [Accepted: 02/06/2019] [Indexed: 02/05/2023] Open
Abstract
Lipids are highly structurally diverse molecules involved in a wide variety of biological processes. Here, we use high precision ion mobility-mass spectrometry to compile a structural database of 456 mass-resolved collision cross sections (CCS) of sphingolipid and glycerophospholipid species. Our CCS database comprises sphingomyelin, cerebroside, ceramide, phosphatidylethanolamine, phosphatidylcholine, phosphatidylserine, and phosphatidic acid classes. Primary differences observed are between lipid categories, with sphingolipids exhibiting 2–6% larger CCSs than glycerophospholipids of similar mass, likely a result of the sphingosine backbone’s restriction of the sn1 tail length, limiting gas-phase packing efficiency. Acyl tail length and degree of unsaturation are found to be the primary structural descriptors determining CCS magnitude, with degree of unsaturation being four times as influential per mass unit. The empirical CCS values and previously unmapped quantitative structural trends detailed in this work are expected to facilitate prediction of CCS in broadscale lipidomics research. The biological functions of lipids critically depend on their highly diverse molecular structures. Here, the authors determine the mass-resolved collision cross sections of 456 sphingolipid and glycerophospholipid species, providing a reference for future structural lipidomics studies.
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Affiliation(s)
- Katrina L Leaptrot
- Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, 37235, USA
| | - Jody C May
- Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, 37235, USA
| | - James N Dodds
- Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, 37235, USA
| | - John A McLean
- Center for Innovative Technology, Department of Chemistry, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, TN, 37235, USA.
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30
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Couvillion SP, Zhu Y, Nagy G, Adkins JN, Ansong C, Renslow RS, Piehowski PD, Ibrahim YM, Kelly RT, Metz TO. New mass spectrometry technologies contributing towards comprehensive and high throughput omics analyses of single cells. Analyst 2019; 144:794-807. [PMID: 30507980 PMCID: PMC6349538 DOI: 10.1039/c8an01574k] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Mass-spectrometry based omics technologies - namely proteomics, metabolomics and lipidomics - have enabled the molecular level systems biology investigation of organisms in unprecedented detail. There has been increasing interest for gaining a thorough, functional understanding of the biological consequences associated with cellular heterogeneity in a wide variety of research areas such as developmental biology, precision medicine, cancer research and microbiome science. Recent advances in mass spectrometry (MS) instrumentation and sample handling strategies are quickly making comprehensive omics analyses of single cells feasible, but key breakthroughs are still required to push through remaining bottlenecks. In this review, we discuss the challenges faced by single cell MS-based omics analyses and highlight recent technological advances that collectively can contribute to comprehensive and high throughput omics analyses in single cells. We provide a vision of the potential of integrating pioneering technologies such as Structures for Lossless Ion Manipulations (SLIM) for improved sensitivity and resolution, novel peptide identification tactics and standards free metabolomics approaches for future applications in single cell analysis.
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Affiliation(s)
- Sneha P Couvillion
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
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31
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Chouinard CD, Nagy G, Smith RD, Baker ES. Ion Mobility-Mass Spectrometry in Metabolomic, Lipidomic, and Proteomic Analyses. ADVANCES IN ION MOBILITY-MASS SPECTROMETRY: FUNDAMENTALS, INSTRUMENTATION AND APPLICATIONS 2019. [DOI: 10.1016/bs.coac.2018.11.001] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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32
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Karanji AK, Khakinejad M, Kondalaji SG, Majuta SN, Attanayake K, Valentine SJ. Comparison of Peptide Ion Conformers Arising from Non-Helical and Helical Peptides Using Ion Mobility Spectrometry and Gas-Phase Hydrogen/Deuterium Exchange. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2018; 29:2402-2412. [PMID: 30324261 PMCID: PMC6553874 DOI: 10.1007/s13361-018-2053-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2018] [Revised: 07/17/2018] [Accepted: 08/03/2018] [Indexed: 05/06/2023]
Abstract
The dominant gas-phase conformer of [M+3H]3+ ions of the model peptide acetyl-PSSSSKSSSSKSSSSKSSSSK has been examined with ion mobility spectrometry (IMS), gas-phase hydrogen deuterium exchange (HDX), and mass spectrometry (MS) techniques. The [M+3H]3+ peptide ions are observed predominantly as a relatively compact conformer type. Upon subjecting these ions to electron transfer dissociation (ETD), the level of protection for each amino acid residue in the peptide sequence is assessed. The overall per-residue deuterium uptake is observed to be relatively more efficient for the neutral residues than for the model peptide acetyl-PAAAAKAAAAKAAAAKAAAAK. In comparison, the N-terminal and C-terminal regions of the serine peptide show greater relative protection compared with interior residues. Molecular dynamics (MD) simulations have been used to generate candidate structures for collision cross section and HDX reactivity matching. Hydrogen accessibility scoring (HAS) for select structural candidates from MD simulations has been used to suggest conformer types that could contribute to the observed HDX patterns. The results are discussed with respect to recent studies employing extensive MD simulations of gas-phase structure establishment of a peptide system. Graphical Abstract ᅟ.
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Affiliation(s)
- Ahmad Kiani Karanji
- Department of Chemistry, West Virginia University, Morgantown, WV, 26506, USA
| | - Mahdiar Khakinejad
- Department of Biophysics, Johns Hopkins University, Baltimore, MD, 21218, USA
| | | | - Sandra N Majuta
- Department of Chemistry, West Virginia University, Morgantown, WV, 26506, USA
| | - Kushani Attanayake
- Department of Chemistry, West Virginia University, Morgantown, WV, 26506, USA
| | - Stephen J Valentine
- Department of Chemistry, West Virginia University, Morgantown, WV, 26506, USA.
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33
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Zang X, Monge ME, Gaul DA, Fernández FM. Flow Injection–Traveling-Wave Ion Mobility–Mass Spectrometry for Prostate-Cancer Metabolomics. Anal Chem 2018; 90:13767-13774. [DOI: 10.1021/acs.analchem.8b04259] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Affiliation(s)
- Xiaoling Zang
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - María Eugenia Monge
- Centro de Investigaciones en Bionanociencias (CIBION), Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2390, Ciudad de Buenos Aires C1425FQD, Argentina
| | - David A. Gaul
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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34
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A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network. APPLIED SCIENCES-BASEL 2018. [DOI: 10.3390/app8071121] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
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35
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Mollerup CB, Mardal M, Dalsgaard PW, Linnet K, Barron LP. Prediction of collision cross section and retention time for broad scope screening in gradient reversed-phase liquid chromatography-ion mobility-high resolution accurate mass spectrometry. J Chromatogr A 2018; 1542:82-88. [DOI: 10.1016/j.chroma.2018.02.025] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 02/06/2018] [Accepted: 02/14/2018] [Indexed: 12/15/2022]
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36
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The potential of Ion Mobility Mass Spectrometry for high-throughput and high-resolution lipidomics. Curr Opin Chem Biol 2017; 42:42-50. [PMID: 29145156 DOI: 10.1016/j.cbpa.2017.10.018] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2017] [Revised: 10/12/2017] [Accepted: 10/13/2017] [Indexed: 11/23/2022]
Abstract
Lipids are a large and highly diverse family of biomolecules, which play essential structural, storage and signalling roles in cells and tissues. Although traditional mass spectrometry (MS) approaches used in lipidomics are highly sensitive and selective, lipid analysis remains challenging due to the chemical diversity of lipid structures, multiple isobaric species and incomplete separation using many forms of chromatography. Ion mobility (IM) separates ions in the gas phase based on their physicochemical properties. Addition of IM to the traditional lipidomic workflow both enhances separation of complex lipid mixtures, beneficial for lipid identification, and improves isomer resolution. Herein, we discuss the recent developments in IM-MS for lipidomics.
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37
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Zhou Z, Tu J, Zhu ZJ. Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era. Curr Opin Chem Biol 2017; 42:34-41. [PMID: 29136580 DOI: 10.1016/j.cbpa.2017.10.033] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 10/28/2017] [Accepted: 10/30/2017] [Indexed: 01/02/2023]
Abstract
Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics.
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Affiliation(s)
- Zhiwei Zhou
- Interdisciplinary Research Center on Biology and Chemistry, and Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Jia Tu
- Interdisciplinary Research Center on Biology and Chemistry, and Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, PR China; University of Chinese Academy of Sciences, Beijing 100049, PR China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, and Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, PR China.
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