51
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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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52
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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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53
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Izquierdo-Sandoval D, Fabregat-Safont D, Lacalle-Bergeron L, Sancho JV, Hernández F, Portoles T. Benefits of Ion Mobility Separation in GC-APCI-HRMS Screening: From the Construction of a CCS Library to the Application to Real-World Samples. Anal Chem 2022; 94:9040-9047. [PMID: 35696365 PMCID: PMC9974067 DOI: 10.1021/acs.analchem.2c01118] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The performance of gas chromatography (GC) combined with the improved identification properties of ion mobility separation coupled to high-resolution mass spectrometry (IMS-HRMS) is presented as a promising approach for the monitoring of (semi)volatile compounds in complex matrices. The soft ionization promoted by an atmospheric pressure chemical ionization (APCI) source designed for GC preserves the molecular and/or quasi-molecular ion information enabling a rapid, sensitive, and efficient wide-scope screening. Additionally, ion mobility separation (IMS) separates species of interest from coeluting matrix interferences and/or resolves isomers based on their charge, shape, and size, making IMS-derived collision cross section (CCS) a robust and matrix-independent parameter comparable between instruments. In this way, GC-APCI-IMS-HRMS becomes a powerful approach for both target and suspect screening due to the improvements in (tentative) identifications. In this work, mobility data for 264 relevant multiclass organic pollutants in environmental and food-safety fields were collected by coupling GC-APCI with IMS-HRMS, generating CCS information for molecular ion and/or protonated molecules and some in-source fragments. The identification power of GC-APCI-IMS-HRMS for the studied compounds was assessed in complex-matrix samples, including fish feed extracts, surface waters, and different fruit and vegetable samples.
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54
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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: 4.0] [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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55
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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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56
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te Brinke E, Arrizabalaga-Larrañaga A, Blokland MH. Insights of ion mobility spectrometry and its application on food safety and authenticity: A review. Anal Chim Acta 2022; 1222:340039. [DOI: 10.1016/j.aca.2022.340039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 06/01/2022] [Accepted: 06/03/2022] [Indexed: 11/01/2022]
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57
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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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58
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Li A, Hines KM, Ross DH, MacDonald JW, Xu L. Temporal changes in the brain lipidome during neurodevelopment of Smith-Lemli-Opitz syndrome mice. Analyst 2022; 147:1611-1621. [PMID: 35293916 PMCID: PMC9018458 DOI: 10.1039/d2an00137c] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Neurodevelopment is an intricately orchestrated program of cellular events that occurs with tight temporal and spatial regulation. While it is known that the development and proper functioning of the brain, which is the second most lipid rich organ behind adipose tissue, greatly rely on lipid metabolism and signaling, the temporal lipidomic changes that occur throughout the course of neurodevelopment have not been investigated. Smith-Lemli-Opitz syndrome is a metabolic disorder caused by genetic mutations in the DHCR7 gene, leading to defective 3β-hydroxysterol-Δ7-reductase (DHCR7), the enzyme that catalyzes the last step of the Kandutsch-Russell pathway of cholesterol synthesis. Due to the close regulatory relationship between sterol and lipid homeostasis, we hypothesize that altered or dysregulated lipid metabolism beyond the primary defect of cholesterol biosynthesis is present in the pathophysiology of SLOS. Herein, we applied our HILIC-IM-MS method and LiPydomics Python package to streamline an untargeted lipidomics analysis of developing mouse brains in both wild-type and Dhcr7-KO mice, identifying lipids at Level 3 (lipid species level: lipid class/subclass and fatty acid sum composition). We compared relative lipid abundances throughout development, from embryonic day 12.5 to postnatal day 0 and determined differentially expressed brain lipids between wild-type and Dhcr7-KO mice at specific developmental time points, revealing lipid metabolic pathways that are affected in SLOS beyond the cholesterol biosynthesis pathway, such as glycerolipid, glycerophospholipid, and sphingolipid metabolism. Implications of the altered lipid metabolic pathways in SLOS pathophysiology are discussed.
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Affiliation(s)
- Amy Li
- Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
| | - Kelly M Hines
- Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
| | - Dylan H Ross
- Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
| | - James W MacDonald
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA
| | - Libin Xu
- Department of Medicinal Chemistry, University of Washington, Seattle, WA, USA.
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59
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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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60
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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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61
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Mosekiemang TT, Stander MA, de Villiers A. Ultra-high pressure liquid chromatography coupled to travelling wave ion mobility-time of flight mass spectrometry for the screening of pharmaceutical metabolites in wastewater samples: Application to antiretrovirals. J Chromatogr A 2021; 1660:462650. [PMID: 34788673 DOI: 10.1016/j.chroma.2021.462650] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/23/2021] [Accepted: 10/25/2021] [Indexed: 10/20/2022]
Abstract
The presence of pharmaceutical compounds in the aquatic environment is a significant environmental health concern, which is exacerbated by recent evidence of the contribution of drug metabolites to the overall pharmaceutical load. In light of a recent report of the occurrence of metabolites of antiretroviral drugs (ARVDs) in wastewater, we investigate in the present work the occurrence of further ARVD metabolites in samples obtained from a domestic wastewater treatment plant in the Western Cape, South Africa. Pharmacokinetic data indicate that ARVDs are biotransformed into several positional isomeric metabolites, only two of which have been reported wastewater samples. Given the challenges associated with the separation and identification of isomeric species in complex wastewater samples, a method based on liquid chromatography hyphenated to ion mobility spectrometry-high resolution mass spectrometry (LC-IMS-HR-MS) was implemented. Gradient LC separation was achieved on a sub-2 µm reversed phase column, while the quadrupole-time-of-flight MS was operated in data independent acquisition (DIA) mode to increase spectral coverage of detected features. A mass defect filter (MDF) template was implemented to detect ARVD metabolites with known phase I and phase II mass shifts and fractional mass differences and to filter out potential interferents. IMS proved particularly useful in filtering the MS data for co-eluting species according to arrival time to provide cleaner mass spectra. This approach allowed us to confirm the presence of two known hydroxylated efavirenz and nevirapine metabolites using authentic standards, and to tentatively identify a carboxylate metabolite of abacavir previously reported in literature. Furthermore, three hydroxylated-, two sulphated and one glucuronidated metabolite of efavirenz, two hydroxylated metabolites of nevirapine and one hydroxylated metabolite of ritonavir were tentatively or putatively identified in wastewater samples for the first time. Assignment of the metabolites is discussed in terms of high resolution fragmentation data, while collisional cross section (CCS) values measured for the detected analytes are reported to facilitate further work in this area.
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Affiliation(s)
- Tlou T Mosekiemang
- Department of Chemistry and Polymer Science, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
| | - Maria A Stander
- Central Analytical Facility, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa
| | - André de Villiers
- Department of Chemistry and Polymer Science, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa.
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62
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Guntner AS, Bögl T, Mlynek F, Buchberger W. Large-Scale Evaluation of Collision Cross Sections to Investigate Blood-Brain Barrier Permeation of Drugs. Pharmaceutics 2021; 13:pharmaceutics13122141. [PMID: 34959422 PMCID: PMC8703848 DOI: 10.3390/pharmaceutics13122141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/30/2021] [Accepted: 12/08/2021] [Indexed: 11/16/2022] Open
Abstract
Successful drug administration to the central nervous system requires accurate adjustment of the drugs’ molecular properties. Therefore, structure-derived descriptors of potential brain therapeutic agents are essential for an early evaluation of pharmacokinetics during drug development. The collision cross section (CCS) of molecules was recently introduced as a novel measurable parameter to describe blood-brain barrier (BBB) permeation. This descriptor combines molecular information about mass, structure, volume, branching and flexibility. As these chemical properties are known to influence cerebral pharmacokinetics, CCS determination of new drug candidates may provide important additional spatial information to support existing models of BBB penetration of drugs. Besides measuring CCS, calculation is also possible; but however, the reliability of computed CCS values for an evaluation of BBB permeation has not yet been fully investigated. In this work, prediction tools based on machine learning were used to compute CCS values of a large number of compounds listed in drug libraries as negative or positive with respect to brain penetration (BBB+ and BBB− compounds). Statistical evaluation of computed CCS and several other descriptors could prove the high value of CCS. Further, CCS-deduced maximum molecular size of BBB+ drugs matched the dimensions of BBB pores. A threshold for transcellular penetration and possible permeation through pore-like openings of cellular tight-junctions is suggested. In sum, CCS evaluation with modern in silico tools shows high potential for its use in the drug development process.
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Affiliation(s)
- Armin Sebastian Guntner
- Institute of Analytical and General Chemistry, Johannes Kepler University, 4040 Linz, Austria
| | - Thomas Bögl
- Institute of Analytical and General Chemistry, Johannes Kepler University, 4040 Linz, Austria
| | - Franz Mlynek
- Institute of Analytical and General Chemistry, Johannes Kepler University, 4040 Linz, Austria
| | - Wolfgang Buchberger
- Institute of Analytical and General Chemistry, Johannes Kepler University, 4040 Linz, Austria
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63
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da Silva KM, Iturrospe E, Heyrman J, Koelmel JP, Cuykx M, Vanhaecke T, Covaci A, van Nuijs ALN. Optimization of a liquid chromatography-ion mobility-high resolution mass spectrometry platform for untargeted lipidomics and application to HepaRG cell extracts. Talanta 2021; 235:122808. [PMID: 34517665 DOI: 10.1016/j.talanta.2021.122808] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 12/26/2022]
Abstract
Analytical methods to evaluate the lipidome of biological samples need to provide high data quality to ensure comprehensive profiling and reliable structural elucidation. In this perspective, liquid chromatography-high resolution mass spectrometry (LC-HRMS) is the state-of-the-art technique for lipidomic analysis of biological samples. There are thousands of lipids in most biological samples, and therefore separation methods before introduction to the mass spectrometer is key for relative quantitation and identification. Chromatographic methods differ across laboratories, without any consensus on the best methodologies. Therefore, we designed an experiment to determine the optimal LC methodology, and assessed the value of ion mobility for an additional dimension of separation. To apply an untargeted method for hypothesis generation focused on lipidomics, LC-HRMS parameters were optimized based on the measurement of 50 panel lipids covering key human metabolic pathways. Reversed-phase liquid chromatography columns were compared based on a quality scoring system considering the signal-to-noise ratio, peak shape, and retention factor. Furthermore, drift tube ion mobility spectrometry (DTIMS) was implemented to increase peak capacity and confidence during annotation by providing collision cross section (CCS) values for the analytes under investigation. However, hyphenating DTIMS to LC-HRMS may result in a reduced sensitivity due to impaired duty cycles. To increase the signal intensity, a Box-Behnken design (BBD) was used to optimize four key factors, i.e. drift entrance voltage, drift exit voltage, rear funnel entrance, and rear funnel exit voltages. Application of a maximized desirability function provided voltages for the above-mentioned parameters resulting in higher signal intensity compared to each combination of parameters used during the BBD. In addition, the influence of single pulse and Hadamard 4-bit multiplexed modes on signal intensity was explored and different trap filling and release times of ions were evaluated. The optimized LC-DTIM-HRMS platform was applied to extracts from HepaRG cells and resulted in 3912 high-quality features (<30% median relative standard deviation; n = 6, t = 24 h). From these features, 436 lipid species could be annotated (i.e., matching based on accurate mass <5 ppm, isotopic pattern, in-silico MS/MS fragmentation, and in-silico CCS database matching <3%). The application of LC-DTIM-HRMS for untargeted analysis workflows is growing and the platform optimization, as described here, can be used to guide the method development and CCS database comparison for high confidence lipid annotation.
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Affiliation(s)
| | - Elias Iturrospe
- University of Antwerp, Toxicological Centre, Universiteitsplein 1, 2610, Antwerp, Belgium; Vrije Universiteit Brussel, Department of In Vitro Toxicology and Dermato-cosmetology, Laarbeeklaan 103, 1090, Jette, Belgium
| | - Joris Heyrman
- University of Antwerp, Toxicological Centre, Universiteitsplein 1, 2610, Antwerp, Belgium
| | - Jeremy P Koelmel
- Yale University, School of Public Health, New Haven, CT, 06520, United States
| | - Matthias Cuykx
- Antwerp University Hospital, Laboratory of Clinical Medicine, Drie Eikenstraat 655, 2650, Edegem, Belgium
| | - Tamara Vanhaecke
- Vrije Universiteit Brussel, Department of In Vitro Toxicology and Dermato-cosmetology, Laarbeeklaan 103, 1090, Jette, Belgium
| | - Adrian Covaci
- University of Antwerp, Toxicological Centre, Universiteitsplein 1, 2610, Antwerp, Belgium
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64
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Celma A, Ahrens L, Gago-Ferrero P, Hernández F, López F, Lundqvist J, Pitarch E, Sancho JV, Wiberg K, Bijlsma L. The relevant role of ion mobility separation in LC-HRMS based screening strategies for contaminants of emerging concern in the aquatic environment. CHEMOSPHERE 2021; 280:130799. [PMID: 34162120 DOI: 10.1016/j.chemosphere.2021.130799] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/29/2021] [Accepted: 05/01/2021] [Indexed: 05/24/2023]
Abstract
Ion mobility separation (IMS) coupled to high resolution mass spectrometry (IMS-HRMS) is a promising technique for (non-)target/suspect analysis of micropollutants in complex matrices. IMS separates ionized compounds based on their charge, shape and size facilitating the removal of co-eluting isomeric/isobaric species. Additionally, IMS data can be translated into collision cross-section (CCS) values, which can be used to increase the identification reliability. However, IMS-HRMS for the screening of contaminants of emerging concern (CECs) have been scarcely explored. In this study, the role of IMS-HRMS for the identification of CECs in complex matrices is highlighted, with emphasis on when and with which purpose is of use. The utilization of IMS can result in much cleaner mass spectra, which considerably facilitates data interpretation and the obtaining of reliable identifications. Furthermore, the robustness of IMS measurements across matrices permits the use of CCS as an additional relevant parameter during the identification step even when reference standards are not available. Moreover, an effect on the number of true and false identifications could be demonstrated by including IMS restrictions within the identification workflow. Data shown in this work is of special interest for environmental researchers dealing with the detection of CECs with state-of-the-art IMS-HRMS instruments.
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Affiliation(s)
- Alberto Celma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain
| | - Lutz Ahrens
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, SE-750 07, Uppsala, Sweden
| | - Pablo Gago-Ferrero
- Institute of Environmental Assessment and Water Research (IDAEA) Severo Ochoa Excellence Center, Spanish Council for Scientific Research (CSIC), Jordi Girona 18-26, E-08034, Barcelona, Spain
| | - Félix Hernández
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain
| | - Francisco López
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain
| | - Johan Lundqvist
- Department of Biomedicine and Veterinary Public Health, Swedish University of Agricultural Sciences, Box 7028, SE-750 07, Uppsala, Sweden
| | - Elena Pitarch
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain
| | - Juan Vicente Sancho
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain
| | - Karin Wiberg
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, SE-750 07, Uppsala, Sweden
| | - Lubertus Bijlsma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain.
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65
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Abstract
Native mass spectrometry (MS) is aimed at preserving and determining the native structure, composition, and stoichiometry of biomolecules and their complexes from solution after they are transferred into the gas phase. Major improvements in native MS instrumentation and experimental methods over the past few decades have led to a concomitant increase in the complexity and heterogeneity of samples that can be analyzed, including protein-ligand complexes, protein complexes with multiple coexisting stoichiometries, and membrane protein-lipid assemblies. Heterogeneous features of these biomolecular samples can be important for understanding structure and function. However, sample heterogeneity can make assignment of ion mass, charge, composition, and structure very challenging due to the overlap of tens or even hundreds of peaks in the mass spectrum. In this review, we cover data analysis, experimental, and instrumental advances and strategies aimed at solving this problem, with an in-depth discussion of theoretical and practical aspects of the use of available deconvolution algorithms and tools. We also reflect upon current challenges and provide a view of the future of this exciting field.
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Affiliation(s)
- Amber D Rolland
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97403-1253, United States
| | - James S Prell
- Department of Chemistry and Biochemistry, University of Oregon, Eugene, Oregon 97403-1253, United States.,Materials Science Institute, University of Oregon, Eugene, Oregon 97403-1252, United States
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66
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Connolly JRFB, Munoz-Muriedas J, Lapthorn C, Higton D, Vissers JPC, Webb A, Beaumont C, Dear GJ. Investigation into Small Molecule Isomeric Glucuronide Metabolite Differentiation Using In Silico and Experimental Collision Cross-Section Values. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:1976-1986. [PMID: 34296869 DOI: 10.1021/jasms.0c00427] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Identifying isomeric metabolites remains a challenging and time-consuming process with both sensitivity and unambiguous structural assignment typically only achieved through the combined use of LC-MS and NMR. Ion mobility mass spectrometry (IMMS) has the potential to produce timely and accurate data using a single technique to identify drug metabolites, including isomers, without the requirement for in-depth interpretation (cf. MS/MS data) using an automated computational pipeline by comparison of experimental collision cross-section (CCS) values with predicted CCS values. An ion mobility enabled Q-Tof mass spectrometer was used to determine the CCS values of 28 (14 isomeric pairs of) small molecule glucuronide metabolites, which were then compared to two different in silico models; a quantum mechanics (QM) and a machine learning (ML) approach to test these approaches. The difference between CCS values within isomer pairs was also assessed to evaluate if the difference was large enough for unambiguous structural identification through in silico prediction. A good correlation was found between both the QM- and ML-based models and experimentally determined CCS values. The predicted CCS values were found to be similar between ML and QM in silico methods, with the QM model more accurately describing the difference in CCS values between isomer pairs. Of the 14 isomeric pairs, only one (naringenin glucuronides) gave a sufficient difference in CCS values for the QM model to distinguish between the isomers with some level of confidence, with the ML model unable to confidently distinguish the studied isomer pairs. An evaluation of analyte structures was also undertaken to explore any trends or anomalies within the data set.
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Affiliation(s)
- John R F B Connolly
- RCSI University of Medicine and Health Sciences, 123 St. Stephen's Green, Dublin D02 YN77, Ireland
| | | | - Cris Lapthorn
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, United Kingdom
| | - David Higton
- Waters Corporation, Stamford Ave, Wilmslow SK9 4AX, United Kingdom
| | | | - Alison Webb
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, United Kingdom
| | - Claire Beaumont
- GlaxoSmithKline, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, United Kingdom
| | - Gordon J Dear
- GlaxoSmithKline, Park Road, Ware, Hertfordshire SG12 0DP, United Kingdom
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67
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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: 7.7] [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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68
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Law KP, He W, Tao J, Zhang C. Characterization of the Exometabolome of Nitrosopumilus maritimus SCM1 by Liquid Chromatography-Ion Mobility Mass Spectrometry. Front Microbiol 2021; 12:658781. [PMID: 34276593 PMCID: PMC8281238 DOI: 10.3389/fmicb.2021.658781] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2021] [Accepted: 05/04/2021] [Indexed: 11/13/2022] Open
Abstract
Marine Thaumarchaeota (formerly known as the marine group I archaea) have received much research interest in recent years since these chemolithoautotrophic organisms are abundant in the subsurface ocean and oxidize ammonium to nitrite, which makes them a major contributor to the marine carbon and nitrogen cycles. However, few studies have investigated the chemical composition of their exometabolome and their contributions to the pool of dissolved organic matter (DOM) in seawater. This study exploits the recent advances in ion mobility mass spectrometry (IM-MS) and integrates this instrumental capability with bioinformatics to reassess the exometabolome of a model ammonia-oxidizing archaeon, Nitrosopumilus maritimus strain SCM1. Our method has several advantages over the conventional approach using an Orbitrap or ion cyclotron resonance mass analyzer and allows assignments or annotations of spectral features to known metabolites confidently and indiscriminately, as well as distinction of biological molecules from background organics. Consistent with the results of a previous report, the SPE-extracted exometabolome of N. maritimus is dominated by biologically active nitrogen-containing metabolites, in addition to peptides secreted extracellularly. Cobalamin and associated intermediates, including α-ribazole and α-ribazole 5'-phosphate, are major components of the SPE-extracted exometabolome of N. maritimus. This supports the proposition that Thaumarchaeota have the capacity of de novo biosynthesizing cobalamin. Other biologically significant metabolites, such as agmatidine and medicagenate, predicted by genome screening are also detected, which indicates that Thaumarchaeota have remarkable metabolic potentials, underlining their importance in driving elemental cycles critical to biological processes in the ocean.
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Affiliation(s)
- Kai P. Law
- SUSTech Academy for Advanced Interdisciplinary Studies, Southern University of Science and Technology, Shenzhen, China
- Shenzhen Key Laboratory of Marine Geo-Omics Research, Southern University of Science and Technology, Shenzhen, China
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Wei He
- Shenzhen Key Laboratory of Marine Geo-Omics Research, Southern University of Science and Technology, Shenzhen, China
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Jianchang Tao
- Shenzhen Key Laboratory of Marine Geo-Omics Research, Southern University of Science and Technology, Shenzhen, China
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Chuanlun Zhang
- Shenzhen Key Laboratory of Marine Geo-Omics Research, Southern University of Science and Technology, Shenzhen, China
- Department of Ocean Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China
- Shanghai Sheshan National Geophysical Observatory, Shanghai, China
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69
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Zhang Y, Lei H, Tao J, Yuan W, Zhang W, Ye J. An integrated approach for structural characterization of Gui Ling Ji by traveling wave ion mobility mass spectrometry and molecular network. RSC Adv 2021; 11:15546-15556. [PMID: 35481180 PMCID: PMC9029087 DOI: 10.1039/d1ra01834e] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 04/21/2021] [Indexed: 12/11/2022] Open
Abstract
Gui Ling Ji (GLJ), an ancient reputable traditional Chinese medicine (TCM) formula prescription, has been applied for the treatment of oligospermia and asthenospermia in clinical practice. However, its inherent compounds have not yet been systematically elucidated, which hampers developing standards or guidelines for quality evaluation and even the understanding of pharmacological effects. In this study, an integrated approach has been established for comprehensive structural characterization of GLJ. Mass spectrometry datasets of GLJ and each of the single herb medicines in this prescription have been developed by dynamic exclusion fast data-dependent acquisition and high-definition data-independent acquisition modes on ultra-high-performance liquid chromatography coupled with travelling wave ion mobility quadrupole time-of-flight mass spectrometry (UPLC-TWIMS-QTOF-MS). A global natural product social molecular networking (GNPS) platform was then applied for the visualization of chemical space of GLJ and further for the high throughput identification of the targeted or untargeted compounds due to the support of data-transmitting from each single herbal medicine to the formula GLJ. Moreover, drift time, predicted CCS, and diagnostic fragment ions were induced for annotating isomer compounds. Consequently, based on molecular network and library hits, a total of 257 compounds from GLJ, which were classified into 4 structural types, were positively or tentatively characterized. Among them, 20 potential new compounds were detected and 30 pairs of isomers were comprehensively distinguished. The established strategy was effective for attribution, classification, recognition of various constituents, and also was valuable for integrating large amounts of disordered MS/MS data and mining trace compounds in other complex chemical or biochemical systems. An integrated approach for structural characterization of Gui Ling Ji by traveling wave ion mobility mass spectrometry and molecular network.![]()
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Affiliation(s)
- Yuhao Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244
| | - Huibo Lei
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244
| | - Jianfei Tao
- College of Pharmacy, The Second Military Medical University Shanghai 200433 China +86 021 81871248.,Pharmacy Department, Shanghai Yang Si Hospital Shanghai 200126 China
| | - Wenlin Yuan
- College of Pharmacy, The Second Military Medical University Shanghai 200433 China +86 021 81871248
| | - Weidong Zhang
- Institute of Interdisciplinary Integrative Medicine Research, Shanghai University of Traditional Chinese Medicine Shanghai 201203 China +86 021 81871244.,College of Pharmacy, The Second Military Medical University Shanghai 200433 China +86 021 81871248
| | - Ji Ye
- College of Pharmacy, The Second Military Medical University Shanghai 200433 China +86 021 81871248
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70
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Target, suspect and non-target screening analysis from wastewater treatment plant effluents to drinking water using collision cross section values as additional identification criterion. Anal Bioanal Chem 2021; 414:425-438. [PMID: 33768366 PMCID: PMC8748347 DOI: 10.1007/s00216-021-03263-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 02/18/2021] [Accepted: 03/01/2021] [Indexed: 12/13/2022]
Abstract
The anthropogenic entry of organic micropollutants into the aquatic environment leads to a potential risk for drinking water resources and the drinking water itself. Therefore, sensitive screening analysis methods are needed to monitor the raw and drinking water quality continuously. Non-target screening analysis has been shown to allow for a more comprehensive investigation of drinking water processes compared to target analysis alone. However, non-target screening is challenging due to the many features that can be detected. Thus, data processing techniques to reduce the high number of features are necessary, and prioritization techniques are important to find the features of interest for identification, as identification of unknown substances is challenging as well. In this study, a drinking water production process, where drinking water is supplied by a water reservoir, was investigated. Since the water reservoir provides surface water, which is anthropogenically influenced by wastewater treatment plant (WWTP) effluents, substances originating from WWTP effluents and reaching the drinking water were investigated, because this indicates that they cannot be removed by the drinking water production process. For this purpose, ultra-performance liquid chromatography coupled with an ion-mobility high-resolution mass spectrometer (UPLC-IM-HRMS) was used in a combined approach including target, suspect and non-target screening analysis to identify known and unknown substances. Additionally, the role of ion-mobility-derived collision cross sections (CCS) in identification is discussed. To that end, six samples (two WWTP effluent samples, a surface water sample that received the effluents, a raw water sample from a downstream water reservoir, a process sample and the drinking water) were analyzed. Positive findings for a total of 60 substances in at least one sample were obtained through quantitative screening. Sixty-five percent (15 out of 23) of the identified substances in the drinking water sample were pharmaceuticals and transformation products of pharmaceuticals. Using suspect screening, further 33 substances were tentatively identified in one or more samples, where for 19 of these substances, CCS values could be compared with CCS values from the literature, which supported the tentative identification. Eight substances were identified by reference standards. In the non-target screening, a total of ten features detected in all six samples were prioritized, whereby metoprolol acid/atenolol acid (a transformation product of the two β-blockers metoprolol and atenolol) and 1,3-benzothiazol-2-sulfonic acid (a transformation product of the vulcanization accelerator 2-mercaptobenzothiazole) were identified with reference standards. Overall, this study demonstrates the added value of a comprehensive water monitoring approach based on UPLC-IM-HRMS analysis.
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71
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Abstract
This paper aims to cover the main strategies based on ion mobility spectrometry (IMS) for the analysis of biological samples. The determination of endogenous and exogenous compounds in such samples is important for the understanding of the health status of individuals. For this reason, the development of new approaches that can be complementary to the ones already established (mainly based on liquid chromatography coupled to mass spectrometry) is welcomed. In this regard, ion mobility spectrometry has appeared in the analytical scenario as a powerful technique for the separation and characterization of compounds based on their mobility. IMS has been used in several areas taking advantage of its orthogonality with other analytical separation techniques, such as liquid chromatography, gas chromatography, capillary electrophoresis, or supercritical fluid chromatography. Bioanalysis is not one of the areas where IMS has been more extensively applied. However, over the last years, the interest in using this approach for the analysis of biological samples has clearly increased. This paper introduces the reader to the principles controlling the separation in IMS and reviews recent applications using this technique in the field of bioanalysis.
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72
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Mass spectrometry based untargeted metabolomics for plant systems biology. Emerg Top Life Sci 2021; 5:189-201. [DOI: 10.1042/etls20200271] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2020] [Revised: 02/04/2021] [Accepted: 02/22/2021] [Indexed: 12/12/2022]
Abstract
Untargeted metabolomics enables the identification of key changes to standard pathways, but also aids in revealing other important and possibly novel metabolites or pathways for further analysis. Much progress has been made in this field over the past decade and yet plant metabolomics seems to still be an emerging approach because of the high complexity of plant metabolites and the number one challenge of untargeted metabolomics, metabolite identification. This final and critical stage remains the focus of current research. The intention of this review is to give a brief current state of LC–MS based untargeted metabolomics approaches for plant specific samples and to review the emerging solutions in mass spectrometer hardware and computational tools that can help predict a compound's molecular structure to improve the identification rate.
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73
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Schymanski EL, Kondić T, Neumann S, Thiessen PA, Zhang J, Bolton EE. Empowering large chemical knowledge bases for exposomics: PubChemLite meets MetFrag. J Cheminform 2021; 13:19. [PMID: 33685519 PMCID: PMC7938590 DOI: 10.1186/s13321-021-00489-0] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 01/13/2021] [Accepted: 01/22/2021] [Indexed: 12/31/2022] Open
Abstract
Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of potential (chemical and other) exposures over entire lifetimes. This daunting task, with over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps in these resources, leaves researchers faced with too much-yet not enough-information at the same time to perform comprehensive exposomics research. Furthermore, the improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput "big data" services from the research community present significant challenges for both data hosts and workflow developers. This article explores how to reduce candidate search spaces in non-target small molecule identification workflows, while increasing content usability in the context of environmental and exposomics analyses, so as to profit from the increasing size and information content of large compound databases, while increasing efficiency at the same time. In this article, these methods are explored using PubChem, the NORMAN Network Suspect List Exchange and the in silico fragmentation approach MetFrag. A subset of the PubChem database relevant for exposomics, PubChemLite, is presented as a database resource that can be (and has been) integrated into current workflows for high resolution mass spectrometry. Benchmarking datasets from earlier publications are used to show how experimental knowledge and existing datasets can be used to detect and fill gaps in compound databases to progressively improve large resources such as PubChem, and topic-specific subsets such as PubChemLite. PubChemLite is a living collection, updating as annotation content in PubChem is updated, and exported to allow direct integration into existing workflows such as MetFrag. The source code and files necessary to recreate or adjust this are jointly hosted between the research parties (see data availability statement). This effort shows that enhancing the FAIRness (Findability, Accessibility, Interoperability and Reusability) of open resources can mutually enhance several resources for whole community benefit. The authors explicitly welcome additional community input on ideas for future developments.
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Affiliation(s)
- Emma L Schymanski
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 avenue du Swing, 4367, Belvaux, Luxembourg.
| | - Todor Kondić
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 avenue du Swing, 4367, Belvaux, Luxembourg
| | - Steffen Neumann
- Bioinformatics and Scientific Data, Leibniz Institute of Plant Biochemistry (IPB Halle), 06120, Halle, Germany.,German Centre for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Deutscher Platz 5e, 04103, Leipzig, Germany
| | - Paul A Thiessen
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Jian Zhang
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA
| | - Evan E Bolton
- National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD, 20894, USA.
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74
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Naylor CN, Clowers BH. Reevaluating the Role of Polarizability in Ion Mobility Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:618-627. [PMID: 33533630 DOI: 10.1021/jasms.0c00338] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
With the expanding commercial availability of gas-phase separation systems that incorporate gas-phase mobility, there is a concurrent rise in efforts to cast the gas-phase mobility coefficient in terms of an ion-neutral collision cross-section (CCS). The motivating factors for this trend are varied, but many aim to complement experimental results with computationally generated CCS values from in silico structural approximations. Unfortunately, the current paradigm for relating experimental mobility results to computationally derived structures relies upon empirical approaches, including a myriad of variables that do not realistically bound the comparison. In this Critical Insight, we advocate for the development of a self-consistent experimental and computational framework that uses laboratory results to constrain the scope of the modeling effort. This paper aims to prompt discussion, challenge assumptions, and promote the development of more efficient, accurate computational techniques within the gas-phase ion measurement community. Specifically, we postulate whether experimental deviations from Langevin's polarization limit (Kpol) are suitable to estimate the relative contributions of hard-sphere collisions and long-range interactions within CCS values. Not surprisingly, different molecule classes exhibit different trends in the K/Kpol ratio when normalized for reduced mass, and the most common IMS calibrants (e.g., tune mix, polyalanine, tetraalkylammonium salts) follow different polarizability trends than many of the analytes probed in the literature. Succinctly, if gas-phase ion structure is largely invariant based upon the colliding neutral and newly developed experimental efforts can quantitatively capture ion polarizability, then modeling efforts describing a target analyte must be self-consistent as the collision neutral is changed in silico.
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Affiliation(s)
- Cameron N Naylor
- Department of Chemistry, Washington State University, Pullman, Washington 99164, United States
| | - Brian H Clowers
- Department of Chemistry, Washington State University, Pullman, Washington 99164, United States
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75
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Broeckling CD, Yao L, Isaac G, Gioioso M, Ianchis V, Vissers JPC. Application of Predicted Collisional Cross Section to Metabolome Databases to Probabilistically Describe the Current and Future Ion Mobility Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2021; 32:661-669. [PMID: 33539078 DOI: 10.1021/jasms.0c00375] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Metabolomics is a powerful phenotyping platform with potential for high-throughput analyses. The primary technology for metabolite profiling is mass spectrometry. In recent years, the coupling of mass spectrometry with ion mobility spectrometry (IMS) has offered the promise of faster analysis time and greater resolving power. Our understanding of the potential impact of IMS on the field of metabolomics is limited by availability of comprehensive experimental data. In this analysis, we use a probabilistic approach to enumerate the strengths and limitations, the present and future, of this technology. This is accomplished through use of "model" metabolomes, predicted physicochemical properties, and probabilistic descriptions of resolving power. This analysis advances our understanding of the importance of orthogonality in resolving (separation) dimensions, describes the impact of the metabolome composition on resolution demands, and offers a system resolution landscape that may serve to guide practitioners in the coming years.
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Affiliation(s)
- Corey D Broeckling
- Analytical Resources Core, Bioanalysis and Omics Center, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Linxing Yao
- Analytical Resources Core, Bioanalysis and Omics Center, Colorado State University, Fort Collins, Colorado 80523, United States
| | - Giorgis Isaac
- Waters Corporation, Milford, Massachusetts 01757, United States
| | - Marisa Gioioso
- Waters Corporation, Milford, Massachusetts 01757, United States
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76
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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: 27] [Impact Index Per Article: 9.0] [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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77
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Rampler E, Abiead YE, Schoeny H, Rusz M, Hildebrand F, Fitz V, Koellensperger G. Recurrent Topics in Mass Spectrometry-Based Metabolomics and Lipidomics-Standardization, Coverage, and Throughput. Anal Chem 2021; 93:519-545. [PMID: 33249827 PMCID: PMC7807424 DOI: 10.1021/acs.analchem.0c04698] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Evelyn Rampler
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
| | - Yasin El Abiead
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Harald Schoeny
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Mate Rusz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Institute of Inorganic
Chemistry, University of Vienna, Währinger Straße 42, 1090 Vienna, Austria
| | - Felina Hildebrand
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Veronika Fitz
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
| | - Gunda Koellensperger
- Department of Analytical
Chemistry, Faculty of Chemistry, University of Vienna, Währinger Str. 38, 1090 Vienna, Austria
- Vienna Metabolomics Center (VIME), University of Vienna, Althanstraße 14, 1090 Vienna, Austria
- University of Vienna, Althanstraße 14, 1090 Vienna, Austria
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78
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Mesa Sanchez D, Creger S, Singla V, Kurulugama RT, Fjeldsted J, Laskin J. Ion Mobility-Mass Spectrometry Imaging Workflow. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2020; 31:2437-2442. [PMID: 32841564 DOI: 10.1021/jasms.0c00142] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Mass spectrometry imaging (MSI) is a powerful technique for the label-free spatially resolved analysis of biological tissues. Coupling ion mobility (IM) separation with MSI allows for separation of isobars in the mobility dimension and increases confidence of peak assignments. Recently, imaging experiments have been implemented on several commercially available and custom-designed ion mobility instruments, making IM-MSI experiments more broadly accessible to the MS community. However, the absence of open access data analysis software for IM-MSI systems presents a bottleneck. Herein, we present an imaging workflow to visualize IM-MSI data produced on the Agilent 6560 ion mobility quadrupole time-of-flight system. Specifically, we have developed a Python script, the ion mobility-mass spectrometry image creation script (IM-MSIC), which interfaces Agilent's Mass Hunter Mass Profiler software with the MacCoss lab's Skyline software and generates drift time and mass-to-charge-selected ion images. In the workflow, Mass Profiler is used for an untargeted feature detection. The IM-MSIC script mediates user input of data, extracts ion chronograms utilizing Skyline's command-line interface, and then proceeds toward ion image generation within a single user interface. Ion image postprocessing is subsequently performed using different tools implemented in accompanying scripts. Though the current work only showcases Agilent IM-MSI data, this workflow can be readily adapted for use with most major instrument vendors.
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Affiliation(s)
- Daniela Mesa Sanchez
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47906, United States
| | - Steve Creger
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47906, United States
| | - Veerupaksh Singla
- Department of Chemical Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, Maharashtra 400076, India
| | | | - John Fjeldsted
- Agilent Technologies Inc., Santa Clara, California 95051, United States
| | - Julia Laskin
- Department of Chemistry, Purdue University, West Lafayette, Indiana 47906, United States
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79
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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: 3.0] [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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80
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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: 31] [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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81
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Righetti L, Dreolin N, Celma A, McCullagh M, Barknowitz G, Sancho JV, Dall’Asta C. Travelling Wave Ion Mobility-Derived Collision Cross Section for Mycotoxins: Investigating Interlaboratory and Interplatform Reproducibility. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2020; 68:10937-10943. [PMID: 32870673 PMCID: PMC8154562 DOI: 10.1021/acs.jafc.0c04498] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 08/20/2020] [Accepted: 09/01/2020] [Indexed: 05/24/2023]
Abstract
Parent and modified mycotoxin analysis remains a challenge because of their chemical diversity, the presence of isomeric forms, and the lack of analytical standards. The creation and application of a collision cross section (CCS) database for mycotoxins may bring new opportunities to overcome these analytical challenges. However, it is still an open question whether common CCS databases can be used independently from the instrument type and ion mobility mass spectrometry (IM-MS) technologies, which utilize different methodologies for determining the gas-phase mobility. Here, we demonstrated the reproducibility of CCS measurements for mycotoxins in an interlaboratory study (average RSD 0.14% ± 0.079) and across different traveling wave IM-MS (TWIMS) systems commercially available (ΔCCS% < 2). The separation in the drift time dimension of critical pairs of isomers for modified mycotoxins was also achieved. In addition, the comparison of measured and predicted CCS values, including regulated and emerging mycotoxins, was addressed.
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Affiliation(s)
- Laura Righetti
- Department
of Food and Drug, University of Parma, Viale Delle Scienze 17/A, I-43124 Parma, Italy
| | - Nicola Dreolin
- Waters
Corporation, Altrincham
Road, SK9 4AX Wilmslow, United Kingdom
| | - 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
| | - Mike McCullagh
- Waters
Corporation, Altrincham
Road, SK9 4AX Wilmslow, United Kingdom
| | - Gitte Barknowitz
- Waters
Corporation, Altrincham
Road, SK9 4AX Wilmslow, United Kingdom
| | - 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
| | - Chiara Dall’Asta
- Department
of Food and Drug, University of Parma, Viale Delle Scienze 17/A, I-43124 Parma, Italy
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82
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Zhou Z, Luo M, Chen X, Yin Y, Xiong X, Wang R, Zhu ZJ. Ion mobility collision cross-section atlas for known and unknown metabolite annotation in untargeted metabolomics. Nat Commun 2020; 11:4334. [PMID: 32859911 PMCID: PMC7455731 DOI: 10.1038/s41467-020-18171-8] [Citation(s) in RCA: 157] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Accepted: 08/07/2020] [Indexed: 01/04/2023] Open
Abstract
The metabolome includes not just known but also unknown metabolites; however, metabolite annotation remains the bottleneck in untargeted metabolomics. Ion mobility - mass spectrometry (IM-MS) has emerged as a promising technology by providing multi-dimensional characterizations of metabolites. Here, we curate an ion mobility CCS atlas, namely AllCCS, and develop an integrated strategy for metabolite annotation using known or unknown chemical structures. The AllCCS atlas covers vast chemical structures with >5000 experimental CCS records and ~12 million calculated CCS values for >1.6 million small molecules. We demonstrate the high accuracy and wide applicability of AllCCS with medium relative errors of 0.5-2% for a broad spectrum of small molecules. AllCCS combined with in silico MS/MS spectra facilitates multi-dimensional match and substantially improves the accuracy and coverage of both known and unknown metabolite annotation from biological samples. Together, AllCCS is a versatile resource that enables confident metabolite annotation, revealing comprehensive chemical and metabolic insights towards biological processes.
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Affiliation(s)
- Zhiwei Zhou
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049, Beijing, People's Republic of China
| | - Mingdu Luo
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049, Beijing, People's Republic of China
| | - Xi Chen
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049, Beijing, People's Republic of China
| | - Yandong Yin
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
| | - Xin Xiong
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
| | - Ruohong Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China
- University of Chinese Academy of Sciences, 100049, Beijing, People's Republic of China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 200032, Shanghai, People's Republic of China.
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83
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Picache JA, May JC, McLean JA. Chemical Class Prediction of Unknown Biomolecules Using Ion Mobility-Mass Spectrometry and Machine Learning: Supervised Inference of Feature Taxonomy from Ensemble Randomization. Anal Chem 2020; 92:10759-10767. [DOI: 10.1021/acs.analchem.0c02137] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Jaqueline A. Picache
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - Jody C. May
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
| | - John A. McLean
- Department of Chemistry, Center for Innovative Technology, Vanderbilt Institute of Chemical Biology, Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt-Ingram Cancer Center, Vanderbilt University, Nashville, Tennessee 37235, United States
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