1
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Liu Z, Foley JP, Shackman JG, Wang Q, Zhou Y. A simulation-guided approach to the selection of RPLC columns for platform small molecule separations. J Chromatogr A 2024; 1730:465131. [PMID: 39002508 DOI: 10.1016/j.chroma.2024.465131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 06/27/2024] [Accepted: 06/28/2024] [Indexed: 07/15/2024]
Abstract
Simulations were conducted to evaluate the potential of several hundred reversed-phase columns to separate small molecules. By calculating the retention factor of compounds in randomly generated virtual mixtures via the HSM (hydrophobic subtraction model) and applying basic chromatography theory, the simulation can estimate the retention time and peak width of every virtual compound and calculate the resolution between every adjacent pair of compounds. A preferred column set based on the number of successful separations of randomly generated virtual mixtures was developed. The tandem-column liquid chromatography (TC-LC) approach can separate 53.2 % of the 16-compound samples using 20 tandem-column pairs, while a single-column approach can only separate 42.6 % of the 16-compound samples with 20 single columns. The preferred set of columns obtained from the simulation was almost the same as the empirical set of columns previously obtained. In screening applications, TC-LC can achieve a comparably successful separation factor (selectivity) with a smaller column inventory (nine 50-mm columns) compared to the larger inventory needed by single-column LC (twenty-one 100-mm columns).
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Affiliation(s)
- Zhiyang Liu
- Department of Chemistry, Drexel University, 32 South 32nd St., Philadelphia, PA 19104, USA
| | - Joe P Foley
- Bristol Myers Squibb, 1 Squibb Dr, New Brunswick, NJ 08901, USA.
| | | | - Qinggang Wang
- Department of Chemistry, Drexel University, 32 South 32nd St., Philadelphia, PA 19104, USA
| | - Yiyang Zhou
- Department of Chemistry, Drexel University, 32 South 32nd St., Philadelphia, PA 19104, USA
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2
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Zhang Y, Liu F, Li XQ, Gao Y, Li KC, Zhang QH. Generic and accurate prediction of retention times in liquid chromatography by post-projection calibration. Commun Chem 2024; 7:54. [PMID: 38459241 PMCID: PMC10923921 DOI: 10.1038/s42004-024-01135-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 02/21/2024] [Indexed: 03/10/2024] Open
Abstract
Retention time predictions from molecule structures in liquid chromatography (LC) are increasingly used in MS-based targeted and untargeted analyses, providing supplementary evidence for molecule annotation and reducing experimental measurements. Nevertheless, different LC setups (e.g., differences in gradient, column, and/or mobile phase) give rise to many prediction models that can only accurately predict retention times for a specific chromatographic method (CM). Here, a generic and accurate method is present to predict retention times across different CMs, by introducing the concept of post-projection calibration. This concept builds on the direct projections of retention times between different CMs and uses 35 external calibrants to eliminate the impact of LC setups on projection accuracy. Results showed that post-projection calibration consistently achieved a median projection error below 3.2% of the elution time. The ranking results of putative candidates reached similar levels among different CMs. This work opens up broad possibilities for coordinating retention times between different laboratories and developing extensive retention databases.
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Affiliation(s)
- Yan Zhang
- Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences, Beijing, 100083, People's Republic of China
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Fei Liu
- Key Laboratory of Groundwater Conservation of MWR, China University of Geosciences, Beijing, 100083, People's Republic of China.
| | - Xiu Qin Li
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Yan Gao
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Kang Cong Li
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China
| | - Qing He Zhang
- Division of Chemical Metrology and Analytical Science, National Institute of Metrology, Beijing, 100029, People's Republic of China.
- Key Laboratory of Chemical Metrology and Applications on Nutrition and Health for State Market Regulation, Beijing, 100029, China.
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3
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Parinet J, Makni Y, Diallo T, Guérin T. Liquid chromatographic retention time prediction models to secure and improve the feature annotation process in high-resolution mass spectrometry. Talanta 2024; 267:125214. [PMID: 37734288 DOI: 10.1016/j.talanta.2023.125214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 09/07/2023] [Accepted: 09/14/2023] [Indexed: 09/23/2023]
Abstract
The development of quantitative structure-retention relationship (QSRR) models has, until recently, required an adequate selection of molecular descriptors necessarily obtained based on a known chemical structure. However, these complex descriptors are not always available nor calculable when the high-resolution mass spectrometry (HRMS) annotation process is underway. Depending on the level of annotation, many structures or even various molecular formulas could be candidates. To secure and improve the annotation process and to save time, a QSRR model (using only 0D molecular descriptors) to predict retention times in reverse-phase liquid chromatography (RPLC) based on the molecular formula was developed, and a general QSRR annotation-based methodology was also proposed.
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Affiliation(s)
- Julien Parinet
- ANSES, Laboratory for Food Safety, 94701, Maisons-Alfort, France.
| | - Yassine Makni
- ANSES, Laboratory for Food Safety, 94701, Maisons-Alfort, France
| | - Thierno Diallo
- ANSES, Laboratory for Food Safety, 94701, Maisons-Alfort, France
| | - Thierry Guérin
- ANSES, Strategy and Programmes Department, 94701, Maisons-Alfort, France
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4
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Kajtazi A, Russo G, Wicht K, Eghbali H, Lynen F. Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction. CHEMOSPHERE 2023; 337:139361. [PMID: 37392796 DOI: 10.1016/j.chemosphere.2023.139361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/06/2023] [Accepted: 06/26/2023] [Indexed: 07/03/2023]
Abstract
Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol-1. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.
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Affiliation(s)
- Ardiana Kajtazi
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium
| | - Giacomo Russo
- School of Applied Sciences, Sighthill Campus, Edinburgh Napier University, 9 Sighthill Ct, EH11 4BN, Edinburgh, United Kingdom
| | - Kristina Wicht
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium
| | - Hamed Eghbali
- Packaging and Specialty Plastics R&D, Dow Benelux B.V., Terneuzen, 4530 AA, the Netherlands
| | - Frédéric Lynen
- Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium.
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5
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Wei Y, Sun Y, Jia S, Yan P, Xiong C, Qi M, Wang C, Du Z, Jiang H. Identification of endogenous carbonyl steroids in human serum by chemical derivatization, hydrogen/deuterium exchange mass spectrometry and the quantitative structure-retention relationship. J Chromatogr B Analyt Technol Biomed Life Sci 2023; 1226:123776. [PMID: 37311272 DOI: 10.1016/j.jchromb.2023.123776] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/09/2023] [Accepted: 05/30/2023] [Indexed: 06/15/2023]
Abstract
Steroids are tetracyclic aliphatic compounds, and most of them contain carbonyl groups. The disordered homeostasis of steroids is closely related to the occurrence and progression of various diseases. Due to high structural similarity, low concentrations in vivo, poor ionization efficiency, and interference from endogenous substances, it is very challenging to comprehensively and unambiguously identify endogenous steroids in biological matrix. Herein, an integrated strategy was developed for the characterization of endogenous steroids in serum based on chemical derivatization, ultra-performance liquid chromatography quadrupole Exactive mass spectrometry (UPLC-Q-Exactive-MS/MS), hydrogen/deuterium (H/D) exchange, and a quantitative structure-retention relationship (QSRR) model. To enhance the mass spectrometry (MS) response of carbonyl steroids, the ketonic carbonyl group was derivatized by Girard T (GT). Firstly, the fragmentation rules of derivatized carbonyl steroid standards by GT were summarized. Then, carbonyl steroids in serum were derivatized by GT and identified based on the fragmentation rules or by comparing retention time and MS/MS spectra with those of standards. H/D exchange MS was utilized to distinguish derivatized steroid isomers for the first time. Finally, a QSRR model was constructed to predict the retention time of the unknown steroid derivatives. With this strategy, 93 carbonyl steroids were identified from human serum, and 30 of them were determined to be dicarbonyl steroids by the charge number of characteristic ions and the number of exchangeable hrdrogen or comparing with standards. The QSRR model built by the machine learning algorithms has an excellent regression correlation, thus the accurate structures of 14 carbonyl steroids were determined, among which three steroids were reported for the first time in human serum. This study provides a new analytical method for the comprehensive and reliable identification of carbonyl steroids in biological matrix.
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Affiliation(s)
- Yinyu Wei
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Yi Sun
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Shuailong Jia
- Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 430030 Wuhan, China
| | - Pan Yan
- Department of Pharmacy, The Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China, Changsha 410028, China
| | - Chaomei Xiong
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Meiling Qi
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Chenxi Wang
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Zhifeng Du
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.
| | - Hongliang Jiang
- Tongji School of Pharmacy, Huazhong University of Science and Technology, Wuhan 430030, China.
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6
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Jäger MC, Patt M, González-Ruiz V, Boccard J, Wey T, Winter DV, Rudaz S, Odermatt A. Extended steroid profiling in H295R cells provides deeper insight into chemical-induced disturbances of steroidogenesis: Exemplified by prochloraz and anabolic steroids. Mol Cell Endocrinol 2023; 570:111929. [PMID: 37037411 DOI: 10.1016/j.mce.2023.111929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2020] [Revised: 03/29/2023] [Accepted: 04/04/2023] [Indexed: 04/12/2023]
Abstract
Human adrenocortical H295R cells have been validated by the OECD Test Guideline 456 to detect chemicals disrupting testosterone and 17β-estradiol (estradiol) biosynthesis. This study evaluated a novel approach to detect disturbances of steroidogenesis in H295R cells, exemplified by prochloraz and five anabolic steroids. Steroid profiles were assessed by an untargeted LC-MS-based method, providing a relative quantification of 57 steroids annotated according to their accurate masses and retention times. Such a panel of steroids included several mineralocorticoids, glucocorticoids, progestins and adrenal androgens. The coverage of a high number of metabolites in this extended steroid profiling facilitated grouping of chemicals with similar effects and detecting subtler differences between chemicals. It allowed, for example, distinguishing between the effects of turinabol and oxymetholone, supposed to act similarly in a previous characterization including only nine adrenal steroids. Furthermore, the results revealed that product/substrate ratios can provide superior information on altered enzyme activities compared to individual metabolite levels. For example, the 17α-hydroxypregnenolone/pregnenolone ratio was found to be a more sensitive marker for detecting 17α-hydroxylase inhibition by prochloraz than the corresponding individual steroids. These results illustrate that chemical grouping and calculation of product/substrate ratios can provide valuable information on mode-of-action and help prioritizing further experimental work.
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Affiliation(s)
- Marie-Christin Jäger
- Swiss Centre for Applied Human Toxicology (SCAHT), University of Basel, Missionsstrasse 64, 4055, Basel, Switzerland; Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056, Basel, Switzerland.
| | - Melanie Patt
- Swiss Centre for Applied Human Toxicology (SCAHT), University of Basel, Missionsstrasse 64, 4055, Basel, Switzerland; Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056, Basel, Switzerland.
| | - Víctor González-Ruiz
- Swiss Centre for Applied Human Toxicology (SCAHT), University of Basel, Missionsstrasse 64, 4055, Basel, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva 4, Switzerland.
| | - Julien Boccard
- Swiss Centre for Applied Human Toxicology (SCAHT), University of Basel, Missionsstrasse 64, 4055, Basel, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva 4, Switzerland.
| | - Tim Wey
- Swiss Centre for Applied Human Toxicology (SCAHT), University of Basel, Missionsstrasse 64, 4055, Basel, Switzerland; Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056, Basel, Switzerland.
| | - Denise V Winter
- Swiss Centre for Applied Human Toxicology (SCAHT), University of Basel, Missionsstrasse 64, 4055, Basel, Switzerland; Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056, Basel, Switzerland.
| | - Serge Rudaz
- Swiss Centre for Applied Human Toxicology (SCAHT), University of Basel, Missionsstrasse 64, 4055, Basel, Switzerland; Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva 4, Switzerland.
| | - Alex Odermatt
- Swiss Centre for Applied Human Toxicology (SCAHT), University of Basel, Missionsstrasse 64, 4055, Basel, Switzerland; Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, Klingelbergstrasse 50, 4056, Basel, Switzerland.
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7
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Xu Z, Chughtai H, Tian L, Liu L, Roy JF, Bayen S. Development of quantitative structure-retention relationship models to improve the identification of leachables in food packaging using non-targeted analysis. Talanta 2023; 253:123861. [PMID: 36095943 DOI: 10.1016/j.talanta.2022.123861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 08/15/2022] [Accepted: 08/17/2022] [Indexed: 12/13/2022]
Abstract
Quantitative structure-retention relationship (QSRR) models can be used to predict the chromatographic retention time of chemicals and facilitate the identification of unknown compounds, notably with non-targeted analysis. In this study, QSRR models were developed from the data obtained for 178 pure chemical standards and four types of analytical columns (C18, phenylhexyl, pentafluorophenyl, cyano) in liquid chromatography quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS). First, different data partitioning ratios and feature selection methods [random forest (RF) and support vector machine (SVM)] were tested to build models to predict chromatographic retention times based on 2D molecular descriptors. The internal and external performances of the non-linear (RF) and corresponding linear predictive models were systematically compared, and RF models resulted in better predictive capacities [p < 0.05, with an average PVE (proportion of variance explained) value of 0.89 ± 0.02] than linear models (0.79 ± 0.03). For each column, the resulting model was applied to identify leachables from actual plastic packaging samples. An in-depth investigation of the top 20 most intense molecular features revealed that all false-positives could be identified as outliers in the QSRR models (outside of the 95% prediction bands). Furthermore, analyzing a sample on multiple chromatographic columns and applying the associated QSRR models increased the capacity to filter false positives. Such an approach will contribute to a more effective identification of unknown or unexpected leachables in plastics (e.g. non-intended added substances), therefore refining our understanding of the chemical risks associated with food contact materials.
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Affiliation(s)
- Ziyun Xu
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Hamza Chughtai
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lei Tian
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | - Lan Liu
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada
| | | | - Stéphane Bayen
- Department of Food Science and Agricultural Chemistry, McGill University, Ste-Anne-de-Bellevue, QC, Canada.
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8
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Van Laethem T, Kumari P, Boulanger B, Hubert P, Fillet M, Sacré PY, Hubert C. User-Driven Strategy for In Silico Screening of Reversed-Phase Liquid Chromatography Conditions for Known Pharmaceutical-Related Small Molecules. MOLECULES (BASEL, SWITZERLAND) 2022; 27:molecules27238306. [PMID: 36500399 PMCID: PMC9735675 DOI: 10.3390/molecules27238306] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 11/21/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022]
Abstract
In the pharmaceutical field, and more precisely in quality control laboratories, robust liquid chromatographic methods are needed to separate and analyze mixtures of compounds. The development of such chromatographic methods for new mixtures can result in a long and tedious process even while using the design of experiments methodology. However, developments could be accelerated with the help of in silico screening. In this work, the usefulness of a strategy combining response surface methodology (RSM) followed by multicriteria decision analysis (MCDA) applied to predictions from a quantitative structure-retention relationship (QSRR) model is demonstrated. The developed strategy shows that selecting equations for the retention time prediction models based on the pKa of the compound allows flexibility in the models. The MCDA developed is shown to help to make decisions on different criteria while being robust to the user's decision on the weights for each criterion. This strategy is proposed for the screening phase of the method lifecycle. The strategy offers the possibility to the user to select chromatographic conditions based on multiple criteria without being too sensitive to the importance given to them. The conditions with the highest desirability are defined as the starting point for further optimization steps.
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Affiliation(s)
- Thomas Van Laethem
- Laboratory for the Analysis of Medicines, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
- Correspondence: (T.V.L.); (C.H.)
| | - Priyanka Kumari
- Laboratory for the Analysis of Medicines, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
| | | | - Philippe Hubert
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
| | - Marianne Fillet
- Laboratory for the Analysis of Medicines, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
| | - Pierre-Yves Sacré
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
| | - Cédric Hubert
- Laboratory of Pharmaceutical Analytical Chemistry, University of Liège (ULiège), CIRM, 4000 Liège, Belgium
- Correspondence: (T.V.L.); (C.H.)
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9
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Peets P, Wang WC, MacLeod M, Breitholtz M, Martin JW, Kruve A. MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:15508-15517. [PMID: 36269851 PMCID: PMC9670854 DOI: 10.1021/acs.est.2c02536] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 10/07/2022] [Accepted: 10/07/2022] [Indexed: 06/16/2023]
Abstract
To achieve water quality objectives of the zero pollution action plan in Europe, rapid methods are needed to identify the presence of toxic substances in complex water samples. However, only a small fraction of chemicals detected with nontarget high-resolution mass spectrometry can be identified, and fewer have ecotoxicological data available. We hypothesized that ecotoxicological data could be predicted for unknown molecular features in data-rich high-resolution mass spectrometry (HRMS) spectra, thereby circumventing time-consuming steps of molecular identification and rapidly flagging molecules of potentially high toxicity in complex samples. Here, we present MS2Tox, a machine learning method, to predict the toxicity of unidentified chemicals based on high-resolution accurate mass tandem mass spectra (MS2). The MS2Tox model for fish toxicity was trained and tested on 647 lethal concentration (LC50) values from the CompTox database and validated for 219 chemicals and 420 MS2 spectra from MassBank. The root mean square error (RMSE) of MS2Tox predictions was below 0.89 log-mM, while the experimental repeatability of LC50 values in CompTox was 0.44 log-mM. MS2Tox allowed accurate prediction of fish LC50 values for 22 chemicals detected in water samples, and empirical evidence suggested the right directionality for another 68 chemicals. Moreover, by incorporating structural information, e.g., the presence of carbonyl-benzene, amide moieties, or hydroxyl groups, MS2Tox outperforms baseline models that use only the exact mass or log KOW.
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Affiliation(s)
- Pilleriin Peets
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, SE-106
91 Stockholm, Sweden
| | - Wei-Chieh Wang
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, SE-106
91 Stockholm, Sweden
| | - Matthew MacLeod
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 16, SE-106 91 Stockholm, Sweden
| | - Magnus Breitholtz
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 16, SE-106 91 Stockholm, Sweden
| | - Jonathan W. Martin
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 16, SE-106 91 Stockholm, Sweden
| | - Anneli Kruve
- Department
of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius Väg 16, SE-106
91 Stockholm, Sweden
- Department
of Environmental Science, Stockholm University, Svante Arrhenius Väg 16, SE-106 91 Stockholm, Sweden
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10
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Tian Z, Liu F, Li D, Fernie AR, Chen W. Strategies for structure elucidation of small molecules based on LC–MS/MS data from complex biological samples. Comput Struct Biotechnol J 2022; 20:5085-5097. [PMID: 36187931 PMCID: PMC9489805 DOI: 10.1016/j.csbj.2022.09.004] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 11/06/2022] Open
Abstract
LC–MS/MS is a major analytical platform for metabolomics, which has become a recent hotspot in the research fields of life and environmental sciences. By contrast, structure elucidation of small molecules based on LC–MS/MS data remains a major challenge in the chemical and biological interpretation of untargeted metabolomics datasets. In recent years, several strategies for structure elucidation using LC–MS/MS data from complex biological samples have been proposed, these strategies can be simply categorized into two types, one based on structure annotation of mass spectra and for the other on retention time prediction. These strategies have helped many scientists conduct research in metabolite-related fields and are indispensable for the development of future tools. Here, we summarized the characteristics of the current tools and strategies for structure elucidation of small molecules based on LC–MS/MS data, and further discussed the directions and perspectives to improve the power of the tools or strategies for structure elucidation.
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11
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Parinet J. Predicting reversed-phase liquid chromatographic retention times of pesticides by deep neural networks. Heliyon 2021; 7:e08563. [PMID: 34950792 PMCID: PMC8671870 DOI: 10.1016/j.heliyon.2021.e08563] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 08/26/2021] [Accepted: 12/03/2021] [Indexed: 11/29/2022] Open
Abstract
To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues. The development of quantitative structure-retention relationship models (QSRR) requires selection of the best molecular descriptors and machine-learning algorithms. In the present work, two main approaches have been tested and compared, one based on an extensive literature review to select the best set of molecular descriptors (16), and a second with diverse strategies in order to select among 1545 molecular descriptors (MD), 16 MD. In both cases, a deep neural network (DNN) were optimized through a gridsearch.
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Affiliation(s)
- Julien Parinet
- Université de Paris-Est, ANSES, Laboratory for Food Safety, 94700, Maisons-Alfort, France
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12
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Kawai T, Matsumori N, Otsuka K. Recent advances in microscale separation techniques for lipidome analysis. Analyst 2021; 146:7418-7430. [PMID: 34787600 DOI: 10.1039/d1an00967b] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This review paper highlights the recent research on liquid-phase microscale separation techniques for lipidome analysis over the last 10 years, mainly focusing on capillary liquid chromatography (LC) and capillary electrophoresis (CE) coupled with mass spectrometry (MS). Lipids are one of the most important classes of biomolecules which are involved in the cell membrane, energy storage, signal transduction, and so on. Since lipids include a variety of hydrophobic compounds including numerous structural isomers, lipidomes are a challenging target in bioanalytical chemistry. MS is the key technology that comprehensively identifies lipids; however, separation techniques like LC and CE are necessary prior to MS detection in order to avoid ionization suppression and resolve structural isomers. Separation techniques using μm-scale columns, such as a fused silica capillary and microfluidic device, are effective at realizing high-resolution separation. Microscale separation usually employs a nL-scale flow, which is also compatible with nanoelectrospray ionization-MS that achieves high sensitivity. Owing to such analytical advantages, microscale separation techniques like capillary/microchip LC and CE have been employed for more than 100 lipidome studies. Such techniques are still being evolved and achieving further higher resolution and wider coverage of lipidomes. Therefore, microscale separation techniques are promising as the fundamental technology in next-generation lipidome analysis.
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Affiliation(s)
- Takayuki Kawai
- Department of Chemistry, Faculty of Science, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.
| | - Nobuaki Matsumori
- Department of Chemistry, Faculty of Science, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395, Japan.
| | - Koji Otsuka
- Department of Material Chemistry, Graduate School of Engineering, Kyoto University, Katsura, Nishikyo-ku, Kyoto 615-8510, Japan.
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Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification. Metabolites 2021; 11:metabo11110772. [PMID: 34822429 PMCID: PMC8620857 DOI: 10.3390/metabo11110772] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 10/28/2021] [Accepted: 11/08/2021] [Indexed: 11/16/2022] Open
Abstract
A key unmet need in metabolomics continues to be the specific, selective, accurate detection of traditionally difficult to retain molecules including simple sugars, sugar phosphates, carboxylic acids, and related amino acids. Designed to retain the metabolites of central carbon metabolism, this Mixed Mode (MM) chromatography applies varied pH, salt concentration and organic content to a positively charged quaternary amine polyvinyl alcohol stationary phase. This MM method is capable of separating glucose from fructose, and four hexose monophosphates a single chromatographic run. Coupled to a QExactive Orbitrap Mass Spectrometer with negative ESI, linearity, LLOD, %CV, and mass accuracy were assessed using 33 metabolite standards. The standards were linear on average >3 orders of magnitude (R2 > 0.98 for 30/33) with LLOD < 1 pmole (26/33), median CV of 12% over two weeks, and median mass accuracy of 0.49 ppm. To assess the breadth of metabolome coverage and better define the structural elements dictating elution, we injected 607 unique metabolites and determined that 398 are well retained. We then split the dataset of 398 documented RTs into training and test sets and trained a message-passing neural network (MPNN) to predict RT from a featurized heavy atom connectivity graph. Unlike traditional QSAR methods that utilize hand-crafted descriptors or pre-defined structural keys, the MPNN aggregates atomic features across the molecular graph and learns to identify molecular subgraphs that are correlated with variations in RTs. For sugars, sugar phosphates, carboxylic acids, and isomers, the model achieves a predictive RT error of <2 min on 91%, 50%, 77%, and 72% of held-out compounds from these subsets, with overall root mean square errors of 0.11, 0.34, 0.18, and 0.53 min, respectively. The model was then applied to rank order metabolite IDs for molecular features altered by GLS2 knockout in mouse primary hepatocytes.
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Parinet J. Prediction of pesticide retention time in reversed-phase liquid chromatography using quantitative-structure retention relationship models: A comparative study of seven molecular descriptors datasets. CHEMOSPHERE 2021; 275:130036. [PMID: 33676277 DOI: 10.1016/j.chemosphere.2021.130036] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 06/12/2023]
Abstract
Predicting chromatographic retention times of pesticides has become more and more important for suspect and non-target screening. Indeed, high-resolution mass spectrometry hyphenated (HRMS) to liquid chromatography (LC) are of growing interest for research and monitoring of pesticides, their metabolites and transformation products. The development of quantitative structure-retention relationship models require selecting the most adequate and best set of molecular descriptors and the best machine-learning algorithm. Here, we used seven molecular descriptor sets extracted from four well-known studies and applied them to roughly 800 pesticides and their chromatographic reversed-phase retention times. We used and optimized five different machine-learning algorithms with these descriptor sets to carry out predictions. Our results show that a support-vector machine regression algorithm with only eight molecular descriptors gave the best compromise between the number of molecular descriptors, processing time and model complexity to optimize prediction performance for this specific gradient LC method.
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Affiliation(s)
- Julien Parinet
- Université de Paris-Est, ANSES, Laboratory for Food Safety, 94700, Maisons-Alfort, France.
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Bride E, Heinisch S, Bonnefille B, Guillemain C, Margoum C. Suspect screening of environmental contaminants by UHPLC-HRMS and transposable Quantitative Structure-Retention Relationship modelling. JOURNAL OF HAZARDOUS MATERIALS 2021; 409:124652. [PMID: 33277075 DOI: 10.1016/j.jhazmat.2020.124652] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/26/2020] [Revised: 10/02/2020] [Accepted: 11/20/2020] [Indexed: 06/12/2023]
Abstract
A Quantitative Structure-Retention Relationship (QSRR) model is proposed and aims at increasing the confidence level associated to the identification of organic contaminants by Ultra-High Performance Liquid Chromatography hyphenated to High Resolution Mass Spectrometry (UHPLC-HRMS) in environmental samples under a suspect screening approach. The model was built from a selection of 8 easily accessible physicochemical descriptors, and was validated from a set of 274 organic compounds commonly found in environmental samples. The proposed predictive figure approach is based on the mobile phase composition at solute elution (expressed as % acetonitrile), that has the major advantage of making the model reusable by other laboratories, since the elution composition is independent of both the column geometry and the UHPLC-system. The model quality was assessed and was altered neither by the columns from different lots, nor by the complex matrices of environmental water samples. Then, the solute retention of any organic compound present in water samples is expected to be predicted within ± 14.3% acetonitrile by our model. Solute retention can therefore be used as a supplementary tool for the identification of environmental contaminants by UHPLC-HRMS, in addition to mass spectrometry data already used in the suspect screening approach.
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Affiliation(s)
- Eloi Bride
- INRAE, UR RiverLy, F-69625 Villeurbanne, France
| | - Sabine Heinisch
- Université de Lyon, Institut des Sciences Analytiques, UMR 5280, CNRS, F-69100 Villeurbanne, France
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Vaškevičius M, Kapočiūtė-Dzikienė J, Šlepikas L. Prediction of Chromatography Conditions for Purification in Organic Synthesis Using Deep Learning. Molecules 2021; 26:2474. [PMID: 33922736 PMCID: PMC8123027 DOI: 10.3390/molecules26092474] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 04/15/2021] [Accepted: 04/22/2021] [Indexed: 01/27/2023] Open
Abstract
In this research, a process for developing normal-phase liquid chromatography solvent systems has been proposed. In contrast to the development of conditions via thin-layer chromatography (TLC), this process is based on the architecture of two hierarchically connected neural network-based components. Using a large database of reaction procedures allows those two components to perform an essential role in the machine-learning-based prediction of chromatographic purification conditions, i.e., solvents and the ratio between solvents. In our paper, we build two datasets and test various molecular vectorization approaches, such as extended-connectivity fingerprints, learned embedding, and auto-encoders along with different types of deep neural networks to demonstrate a novel method for modeling chromatographic solvent systems employing two neural networks in sequence. Afterward, we present our findings and provide insights on the most effective methods for solving prediction tasks. Our approach results in a system of two neural networks with long short-term memory (LSTM)-based auto-encoders, where the first predicts solvent labels (by reaching the classification accuracy of 0.950 ± 0.001) and in the case of two solvents, the second one predicts the ratio between two solvents (R2 metric equal to 0.982 ± 0.001). Our approach can be used as a guidance instrument in laboratories to accelerate scouting for suitable chromatography conditions.
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Affiliation(s)
- Mantas Vaškevičius
- Department of Applied Informatics, Vytautas Magnus University, LT-44404 Kaunas, Lithuania;
- JSC Synhet, Biržų Str. 6, LT-44139 Kaunas, Lithuania;
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Atanasov AG, Zotchev SB, Dirsch VM, Supuran CT. Natural products in drug discovery: advances and opportunities. Nat Rev Drug Discov 2021; 20:200-216. [PMID: 33510482 PMCID: PMC7841765 DOI: 10.1038/s41573-020-00114-z] [Citation(s) in RCA: 1742] [Impact Index Per Article: 580.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/12/2020] [Indexed: 02/07/2023]
Abstract
Natural products and their structural analogues have historically made a major contribution to pharmacotherapy, especially for cancer and infectious diseases. Nevertheless, natural products also present challenges for drug discovery, such as technical barriers to screening, isolation, characterization and optimization, which contributed to a decline in their pursuit by the pharmaceutical industry from the 1990s onwards. In recent years, several technological and scientific developments - including improved analytical tools, genome mining and engineering strategies, and microbial culturing advances - are addressing such challenges and opening up new opportunities. Consequently, interest in natural products as drug leads is being revitalized, particularly for tackling antimicrobial resistance. Here, we summarize recent technological developments that are enabling natural product-based drug discovery, highlight selected applications and discuss key opportunities.
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Affiliation(s)
- Atanas G Atanasov
- Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences, Jastrzebiec, Poland.
- Department of Pharmacognosy, University of Vienna, Vienna, Austria.
- Institute of Neurobiology, Bulgarian Academy of Sciences, Sofia, Bulgaria.
- Ludwig Boltzmann Institute for Digital Health and Patient Safety, Medical University of Vienna, Vienna, Austria.
| | - Sergey B Zotchev
- Department of Pharmacognosy, University of Vienna, Vienna, Austria
| | - Verena M Dirsch
- Department of Pharmacognosy, University of Vienna, Vienna, Austria
| | - Claudiu T Supuran
- Università degli Studi di Firenze, NEUROFARBA Dept, Sezione di Scienze Farmaceutiche, Florence, Italy.
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Olesti E, Boccard J, Visconti G, González-Ruiz V, Rudaz S. From a single steroid to the steroidome: Trends and analytical challenges. J Steroid Biochem Mol Biol 2021; 206:105797. [PMID: 33259940 DOI: 10.1016/j.jsbmb.2020.105797] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 11/02/2020] [Accepted: 11/24/2020] [Indexed: 12/23/2022]
Abstract
For several decades now, the analysis of steroids has been a key tool in the diagnosis and monitoring of numerous endocrine pathologies. Thus, the available methods used to analyze steroids in biological samples have dramatically evolved over time following the rapid pace of technology and scientific knowledge. This review aims to synthetize the advances in steroids' analysis, from classical approaches considering only a few steroids or a limited number of steroid ratios, up to the new steroid profiling strategies (steroidomics) monitoring large sets of steroids in biological matrices. In this context, the use of liquid chromatography coupled to mass spectrometry has emerged as the technique of choice for the simultaneous determination of a high number of steroids, including phase II metabolites, due to its sensitivity and robustness. However, the large dynamic range to be covered, the low natural abundance of some key steroids, the selectivity of the analytical methods, the extraction protocols, and the steroid ionization remain some of the current challenges in steroid analysis. This review provides an overview of the different analytical workflows available depending on the number of steroids under study. Special emphasis is given to sample treatment, acquisition strategy, data processing, steroid identification and quantification using LC-MS approaches. This work also outlines how the availability of steroid standards, the need for complementary analytical strategies and the improvement of calibration approaches are crucial for achieving complete steroidome quantification.
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Affiliation(s)
- Eulalia Olesti
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Switzerland; School of Pharmaceutical Sciences, University of Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Julien Boccard
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Switzerland; School of Pharmaceutical Sciences, University of Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Gioele Visconti
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Switzerland; School of Pharmaceutical Sciences, University of Geneva, Switzerland
| | - Víctor González-Ruiz
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Switzerland; School of Pharmaceutical Sciences, University of Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Switzerland
| | - Serge Rudaz
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Switzerland.
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Pezzatti J, González-Ruiz V, Boccard J, Guillarme D, Rudaz S. Evaluation of Different Tandem MS Acquisition Modes to Support Metabolite Annotation in Human Plasma Using Ultra High-Performance Liquid Chromatography High-Resolution Mass Spectrometry for Untargeted Metabolomics. Metabolites 2020; 10:metabo10110464. [PMID: 33203160 PMCID: PMC7697060 DOI: 10.3390/metabo10110464] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 10/23/2020] [Accepted: 11/09/2020] [Indexed: 12/18/2022] Open
Abstract
Ultra-high performance liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) is a powerful and essential technique for metabolite annotation in untargeted metabolomic applications. The aim of this study was to evaluate the performance of diverse tandem MS (MS/MS) acquisition modes, i.e., all ion fragmentation (AIF) and data-dependent analysis (DDA), with and without ion mobility spectrometry (IM), to annotate metabolites in human plasma. The influence of the LC separation was also evaluated by comparing the performance of MS/MS acquisition in combination with three complementary chromatographic separation modes: reversed-phase chromatography (RPLC) and hydrophilic interaction chromatography (HILIC) with either an amide (aHILIC) or a zwitterionic (zHILIC) stationary phase. RPLC conditions were first chosen to investigate all the tandem MS modes, and we found out that DDA did not provide a significant additional amount of chemical coverage and that cleaner MS/MS spectra can be obtained by performing AIF acquisitions in combination with IM. Finally, we were able to annotate 338 unique metabolites and demonstrated that zHILIC was a powerful complementary approach to both the RPLC and aHILIC chromatographic modes. Moreover, a better analytical throughput was reached for an almost negligible loss of metabolite coverage when IM-AIF and AIF using ramped instead of fixed collision energies were used.
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Affiliation(s)
- Julian Pezzatti
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland; (J.P.); (V.G.-R.); (J.B.); (D.G.)
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1211 Geneva 4, Switzerland
| | - Víctor González-Ruiz
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland; (J.P.); (V.G.-R.); (J.B.); (D.G.)
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1211 Geneva 4, Switzerland
- Swiss Centre for Applied Human Toxicology (SCATH), 4055 Basel, Switzerland
| | - Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland; (J.P.); (V.G.-R.); (J.B.); (D.G.)
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1211 Geneva 4, Switzerland
- Swiss Centre for Applied Human Toxicology (SCATH), 4055 Basel, Switzerland
| | - Davy Guillarme
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland; (J.P.); (V.G.-R.); (J.B.); (D.G.)
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1211 Geneva 4, Switzerland
| | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland; (J.P.); (V.G.-R.); (J.B.); (D.G.)
- Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, 1211 Geneva 4, Switzerland
- Swiss Centre for Applied Human Toxicology (SCATH), 4055 Basel, Switzerland
- Correspondence: ; Tel.: +41-2‐2379-6572
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Prediction of liquid chromatographic retention time using quantitative structure-retention relationships to assist non-targeted identification of unknown metabolites of phthalates in human urine with high-resolution mass spectrometry. J Chromatogr A 2020; 1634:461691. [PMID: 33221657 DOI: 10.1016/j.chroma.2020.461691] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 11/03/2020] [Accepted: 11/05/2020] [Indexed: 11/22/2022]
Abstract
The non-targeted analysis and identification of contaminant metabolites such as metabolites of phthalates and their alternatives in human biofluid samples constitutes a growing research field in human biomonitoring because of their importance as biomarkers of human exposure to the parent compounds. High-resolution mass spectrometry (HRMS) combined with high-performance liquid chromatography (HPLC) can provide fast separation and sensitive analysis using this application. However, the diversity of potential metabolites, especially isomers, in human samples, makes mass spectrometry-based structural identification very challenging, even with high-resolution and accurate mass. In this study, we present a retention time (tR) prediction model based on quantitative structure-retention relationship (QSRR). This model can predict the retention time of a given structure of phthalates including isomers. Twenty-three molecular descriptors were used in the development of the multivariate linear regression QSRR model. The regression coefficient (R2) between predicted and experimental retention times of 26 training set compounds was 0.9912. The combination of the retention time prediction model with identification via accurate mass search and target MS/MS spectrum interpretation can enhance the identification confidence in the lack of reference standards. Two previously unreported phthalate metabolites were identified in human urine, using this model. The results of this study showed that the developed QSRR model could be a useful tool to predict the retention times of unknown metabolites of phthalates and their alternatives in future non-targeted screening analysis. The concentration of these two unknown compounds was also estimated using a quantitative structure-ion intensity relationship (QSIIR) model.
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21
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Guo Z, Huang S, Wang J, Feng YL. Recent advances in non-targeted screening analysis using liquid chromatography - high resolution mass spectrometry to explore new biomarkers for human exposure. Talanta 2020; 219:121339. [DOI: 10.1016/j.talanta.2020.121339] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 05/16/2020] [Accepted: 06/09/2020] [Indexed: 12/29/2022]
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Haddad PR, Taraji M, Szücs R. Prediction of Analyte Retention Time in Liquid Chromatography. Anal Chem 2020; 93:228-256. [DOI: 10.1021/acs.analchem.0c04190] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- Paul R. Haddad
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
| | - Maryam Taraji
- Australian Centre for Research on Separation Science, School of Natural Sciences, University of Tasmania, Private Bag 75, Hobart, Tasmania, Australia 7001
- The Australian Wine Research Institute, P.O. Box 197, Adelaide, South Australia 5064, Australia
- Metabolomics Australia, P.O. Box 197, Adelaide, South Australia 5064, Australia
| | - Roman Szücs
- Pfizer R&D UK Limited, Ramsgate Road, Sandwich CT13 9NJ, U.K
- Department of Analytical Chemistry, Faculty of Natural Sciences, Comenius University in Bratislava, Mlynská Dolina CH2, Ilkovičova 6, SK-84215 Bratislava, Slovakia
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Machine learning to predict retention time of small molecules in nano-HPLC. Anal Bioanal Chem 2020; 412:7767-7776. [PMID: 32860519 DOI: 10.1007/s00216-020-02905-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 07/29/2020] [Accepted: 08/20/2020] [Indexed: 01/22/2023]
Abstract
Retention time is an important parameter for identification in untargeted LC-MS screening. Precise retention time prediction facilitates the annotation process and is well known for proteomics. However, the lack of available experimental information for a long time has limited the prediction accuracy for small molecules. Recently introduced large databases for small-molecule retention times make possible reliable machine learning-based predictions for the whole diversity of compounds. Applying simple projections may expand these predictions on various LC systems and conditions. In our work, we describe a complex approach to predict retention times for nano-HPLC that includes the consequent deployment of binary and regression gradient boosting models trained on the METLIN small-molecule dataset and simple projection of the results with a small number of easily available compounds onto nano-HPLC separations. The proposed model outperforms previous attempts to use machine learning for predictions with a 46-s mean absolute error. The overall performance after transfer to nano-LC conditions is less than 155 s (10.8%) in terms of the median absolute (relative) error. To illustrate the applicability of the described approach, we successfully managed to eliminate averagely 25 to 42% of false-positives with a filter threshold derived from ROC curves. Thus, the proposed approach should be used in addition to other well-established in silico methods and their integration may broaden the range of correctly identified molecules.
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Magny R, Regazzetti A, Kessal K, Genta-Jouve G, Baudouin C, Mélik-Parsadaniantz S, Brignole-Baudouin F, Laprévote O, Auzeil N. Lipid Annotation by Combination of UHPLC-HRMS (MS), Molecular Networking, and Retention Time Prediction: Application to a Lipidomic Study of In Vitro Models of Dry Eye Disease. Metabolites 2020; 10:metabo10060225. [PMID: 32486009 PMCID: PMC7345884 DOI: 10.3390/metabo10060225] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/07/2020] [Accepted: 05/25/2020] [Indexed: 12/28/2022] Open
Abstract
Annotation of lipids in untargeted lipidomic analysis remains challenging and a systematic approach needs to be developed to organize important datasets with the help of bioinformatic tools. For this purpose, we combined tandem mass spectrometry-based molecular networking with retention time (tR) prediction to annotate phospholipid and sphingolipid species. Sixty-five standard compounds were used to establish the fragmentation rules of each lipid class studied and to define the parameters governing their chromatographic behavior. Molecular networks (MNs) were generated through the GNPS platform using a lipid standards mixture and applied to lipidomic study of an in vitro model of dry eye disease, i.e., human corneal epithelial (HCE) cells exposed to hyperosmolarity (HO). These MNs led to the annotation of more than 150 unique phospholipid and sphingolipid species in the HCE cells. This annotation was reinforced by comparing theoretical to experimental tR values. This lipidomic study highlighted changes in 54 lipids following HO exposure of corneal cells, some of them being involved in inflammatory responses. The MN approach coupled to tR prediction thus appears as a suitable and robust tool for the discovery of lipids involved in relevant biological processes.
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Affiliation(s)
- Romain Magny
- Sorbonne Université UM80, INSERM UMR 968, CNRS UMR 7210, Institut de la Vision, IHU ForeSight, 75006 Paris, France; (R.M.); (K.K.); (C.B.); (S.M.-P.); (F.B.-B.)
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
| | - Anne Regazzetti
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
| | - Karima Kessal
- Sorbonne Université UM80, INSERM UMR 968, CNRS UMR 7210, Institut de la Vision, IHU ForeSight, 75006 Paris, France; (R.M.); (K.K.); (C.B.); (S.M.-P.); (F.B.-B.)
- Centre Hospitalier National d’Ophtalmologie des Quinze-Vingts, IHU ForeSight, 75006 Paris, France
| | - Gregory Genta-Jouve
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
- Laboratoire Ecologie, Evolution, Interactions des Systèmes Amazoniens (LEEISA), USR 3456, Université De Guyane, CNRS Guyane, 97300 Cayenne, French Guiana, France
| | - Christophe Baudouin
- Sorbonne Université UM80, INSERM UMR 968, CNRS UMR 7210, Institut de la Vision, IHU ForeSight, 75006 Paris, France; (R.M.); (K.K.); (C.B.); (S.M.-P.); (F.B.-B.)
- Centre Hospitalier National d’Ophtalmologie des Quinze-Vingts, IHU ForeSight, 75006 Paris, France
- Hôpital Ambroise Paré, AP-HP, Université Versailles Saint-Quentin-en-Yvelines, 92100 Boulogne-Billancourt, France
| | - Stéphane Mélik-Parsadaniantz
- Sorbonne Université UM80, INSERM UMR 968, CNRS UMR 7210, Institut de la Vision, IHU ForeSight, 75006 Paris, France; (R.M.); (K.K.); (C.B.); (S.M.-P.); (F.B.-B.)
| | - Françoise Brignole-Baudouin
- Sorbonne Université UM80, INSERM UMR 968, CNRS UMR 7210, Institut de la Vision, IHU ForeSight, 75006 Paris, France; (R.M.); (K.K.); (C.B.); (S.M.-P.); (F.B.-B.)
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
- Centre Hospitalier National d’Ophtalmologie des Quinze-Vingts, IHU ForeSight, 75006 Paris, France
| | - Olivier Laprévote
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
- Hôpital Européen Georges Pompidou, AP-HP, Service de Biochimie, 75006 Paris, France
| | - Nicolas Auzeil
- UMR CNRS 8038 CiTCoM, Chimie Toxicologie Analytique et Cellulaire, Université de Paris, Faculté de Pharmacie, 75006 Paris, France; (A.R.); (G.G.-J.); (O.L.)
- Correspondence:
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Witting M, Böcker S. Current status of retention time prediction in metabolite identification. J Sep Sci 2020; 43:1746-1754. [DOI: 10.1002/jssc.202000060] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2020] [Revised: 03/04/2020] [Accepted: 03/04/2020] [Indexed: 12/23/2022]
Affiliation(s)
- Michael Witting
- Research Unit Analytical BioGeoChemistryHelmholtz Zentrum München Neuherberg Germany
- Chair of Analytical Food ChemistryTUM School of Life Sciences, Technische Universität München Freising Germany
| | - Sebastian Böcker
- Chair of BioinformaticsFriedrich‐Schiller‐Universität Jena Jena Germany
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26
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Steroid identification via deep learning retention time predictions and two-dimensional gas chromatography-high resolution mass spectrometry. J Chromatogr A 2020; 1612:460661. [DOI: 10.1016/j.chroma.2019.460661] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2019] [Revised: 10/08/2019] [Accepted: 10/27/2019] [Indexed: 12/23/2022]
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27
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Olesti E, Garcia A, Rahban R, Rossier MF, Boccard J, Nef S, González-Ruiz V, Rudaz S. Steroid profile analysis by LC-HRMS in human seminal fluid. J Chromatogr B Analyt Technol Biomed Life Sci 2020; 1136:121929. [DOI: 10.1016/j.jchromb.2019.121929] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 11/05/2019] [Accepted: 12/04/2019] [Indexed: 12/15/2022]
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28
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Ni Z, Goracci L, Cruciani G, Fedorova M. Computational solutions in redox lipidomics - Current strategies and future perspectives. Free Radic Biol Med 2019; 144:110-123. [PMID: 31035005 DOI: 10.1016/j.freeradbiomed.2019.04.027] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 04/15/2019] [Accepted: 04/23/2019] [Indexed: 12/31/2022]
Abstract
The high chemical diversity of lipids allows them to perform multiple biological functions ranging from serving as structural building blocks of biological membranes to regulation of metabolism and signal transduction. In addition to the native lipidome, lipid species derived from enzymatic and non-enzymatic modifications (the epilipidome) make the overall picture even more complex, as their functions are still largely unknown. Oxidized lipids represent the fraction of epilipidome which has attracted high scientific attention due to their apparent involvement in the onset and development of numerous human disorders. Development of high-throughput analytical methods such as liquid chromatography coupled on-line to mass spectrometry provides the possibility to address epilipidome diversity in complex biological samples. However, the main bottleneck of redox lipidomics, the branch of lipidomics dealing with the characterization of oxidized lipids, remains the lack of optimal computational tools for robust, accurate and specific identification of already discovered and yet unknown modified lipids. Here we discuss the main principles of high-throughput identification of lipids and their modified forms and review the main software tools currently available in redox lipidomics. Different levels of confidence for software assisted identification of redox lipidome are defined and necessary steps toward optimal computational solutions are proposed.
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Affiliation(s)
- Zhixu Ni
- Institute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, University of Leipzig, Germany; Center for Biotechnology and Biomedicine, University of Leipzig, Deutscher Platz 5, Leipzig, Germany
| | - Laura Goracci
- Department of Chemistry, Biology and Biotechnology, University of Perugia, via Elce di Sotto 8, 06123 Perugia, Italy; Consortium for Computational Molecular and Materials Sciences (CMS), via Elce di Sotto 8, 06123 Perugia, Italy
| | - Gabriele Cruciani
- Department of Chemistry, Biology and Biotechnology, University of Perugia, via Elce di Sotto 8, 06123 Perugia, Italy; Consortium for Computational Molecular and Materials Sciences (CMS), via Elce di Sotto 8, 06123 Perugia, Italy
| | - Maria Fedorova
- Institute of Bioanalytical Chemistry, Faculty of Chemistry and Mineralogy, University of Leipzig, Germany; Center for Biotechnology and Biomedicine, University of Leipzig, Deutscher Platz 5, Leipzig, Germany.
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Xue J, Lai Y, Liu CW, Ru H. Towards Mass Spectrometry-Based Chemical Exposome: Current Approaches, Challenges, and Future Directions. TOXICS 2019; 7:toxics7030041. [PMID: 31426576 PMCID: PMC6789759 DOI: 10.3390/toxics7030041] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2019] [Revised: 08/12/2019] [Accepted: 08/14/2019] [Indexed: 12/13/2022]
Abstract
The proposal of the “exposome” concept represents a shift of the research paradigm in studying exposure-disease relationships from an isolated and partial way to a systematic and agnostic approach. Nevertheless, exposome implementation is facing a variety of challenges including measurement techniques and data analysis. Here we focus on the chemical exposome, which refers to the mixtures of chemical pollutants people are exposed to from embryo onwards. We review the current chemical exposome measurement approaches with a focus on those based on the mass spectrometry. We further explore the strategies in implementing the concept of chemical exposome and discuss the available chemical exposome studies. Early progresses in the chemical exposome research are outlined, and major challenges are highlighted. In conclusion, efforts towards chemical exposome have only uncovered the tip of the iceberg, and further advancement in measurement techniques, computational tools, high-throughput data analysis, and standardization may allow more exciting discoveries concerning the role of exposome in human health and disease.
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Affiliation(s)
- Jingchuan Xue
- Center for Environmental Health and Susceptibility, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yunjia Lai
- Center for Environmental Health and Susceptibility, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Chih-Wei Liu
- Center for Environmental Health and Susceptibility, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongyu Ru
- Department of Population Health and Pathobiology, North Carolina State University, Raleigh, NC 27607, USA.
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30
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Steroidomics for highlighting novel serum biomarkers of testosterone doping. Bioanalysis 2019; 11:1171-1187. [DOI: 10.4155/bio-2019-0079] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Aim: Quantification of testosterone (T) and 5α-dihydrotestosterone serum concentrations proved to be an efficient alternative to urinary steroid profiling for the detection of T doping. In this context, additional serum markers could be discovered by exploratory untargeted steroidomics studies. Results: Endogenous steroid metabolites were monitored by ultra high-performance liquid chromatography coupled to high-resolution mass spectrometry in serum samples collected during a T administration clinical trial. A three-step workflow for accurate review of annotation was used and multifactorial data analysis allowed highlighting promising serum biomarkers. Longitudinal monitoring of selected compounds was performed to assess T abuse detection capabilities. Conclusion: Application of serum steroidomics showed high potential for biomarker discovery of T doping, suggesting longitudinal monitoring of steroid hormones in serum as a significant improvement in detection of endogenous steroids abuse.
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31
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Codesido S, Randazzo GM, Lehmann F, González-Ruiz V, García A, Xenarios I, Liechti R, Bridge A, Boccard J, Rudaz S. DynaStI: A Dynamic Retention Time Database for Steroidomics. Metabolites 2019; 9:E85. [PMID: 31052310 PMCID: PMC6572260 DOI: 10.3390/metabo9050085] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 04/12/2019] [Accepted: 04/24/2019] [Indexed: 12/11/2022] Open
Abstract
: Steroidomics studies face the challenge of separating analytical compounds with very similar structures (i.e., isomers). Liquid chromatography (LC) is commonly used to this end, but the shared core structure of this family of compounds compromises effective separations among the numerous chemical analytes with comparable physico-chemical properties. Careful tuning of the mobile phase gradient and an appropriate choice of the stationary phase can be used to overcome this problem, in turn modifying the retention times in different ways for each compound. In the usual workflow, this approach is suboptimal for the annotation of features based on retention times since it requires characterizing a library of known compounds for every fine-tuned configuration. We introduce a software solution, DynaStI, that is capable of annotating liquid chromatography-mass spectrometry (LC-MS) features by dynamically generating the retention times from a database containing intrinsic properties of a library of metabolites. DynaStI uses the well-established linear solvent strength (LSS) model for reversed-phase LC. Given a list of LC-MS features and some characteristics of the LC setup, this software computes the corresponding retention times for the internal database and then annotates the features using the exact masses with predicted retention times at the working conditions. DynaStI (https://dynasti.vital-it.ch) is able to automatically calibrate its predictions to compensate for deviations in the input parameters. The database also includes identification and structural information for each annotation, such as IUPAC name, CAS number, SMILES string, metabolic pathways, and links to external metabolomic or lipidomic databases.
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Affiliation(s)
- Santiago Codesido
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
| | - Giuseppe Marco Randazzo
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
- Dalle Molle Institute for Artificial Intelligence, 6928 Manno, Switzerland.
| | - Fabio Lehmann
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| | - Víctor González-Ruiz
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
- Swiss Center for Applied Human Toxicology, 4055 Basel, Switzerland.
| | - Arnaud García
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
| | - Ioannis Xenarios
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
- Center for Integrative Genomics, University of Lausanne, 1015 Lausanne, Switzerland.
| | - Robin Liechti
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| | - Alan Bridge
- Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland.
| | - Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
- Swiss Center for Applied Human Toxicology, 4055 Basel, Switzerland.
| | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva, University of Lausanne, 1206 Geneva, Switzerland.
- Swiss Center for Applied Human Toxicology, 4055 Basel, Switzerland.
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32
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Wen Y, Amos RIJ, Talebi M, Szucs R, Dolan JW, Pohl CA, Haddad PR. Retention prediction using quantitative structure‐retention relationships combined with the hydrophobic subtraction model in reversed‐phase liquid chromatography. Electrophoresis 2019; 40:2415-2419. [DOI: 10.1002/elps.201900022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Revised: 03/19/2019] [Accepted: 03/20/2019] [Indexed: 11/07/2022]
Affiliation(s)
- Yabin Wen
- Australian Centre for Research on Separation Science (ACROSS)School of Physical Sciences‐ChemistryUniversity of Tasmania Hobart Australia
| | - Ruth I. J. Amos
- Australian Centre for Research on Separation Science (ACROSS)School of Physical Sciences‐ChemistryUniversity of Tasmania Hobart Australia
| | - Mohammad Talebi
- Australian Centre for Research on Separation Science (ACROSS)School of Physical Sciences‐ChemistryUniversity of Tasmania Hobart Australia
| | - Roman Szucs
- Pfizer Global Research and Development Sandwich UK
| | | | | | - Paul R. Haddad
- Australian Centre for Research on Separation Science (ACROSS)School of Physical Sciences‐ChemistryUniversity of Tasmania Hobart Australia
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33
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Ren X, Hu C, Gao D, Fu Q, Zhang K, Zu F, Zeng J, Wang L, Xia Z. Preparation of a poly(ethyleneimine) embedded phenyl stationary phase for mixed-mode liquid chromatography. Anal Chim Acta 2018; 1042:165-173. [DOI: 10.1016/j.aca.2018.09.049] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2018] [Revised: 09/13/2018] [Accepted: 09/20/2018] [Indexed: 11/17/2022]
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34
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Wolfender JL, Nuzillard JM, van der Hooft JJJ, Renault JH, Bertrand S. Accelerating Metabolite Identification in Natural Product Research: Toward an Ideal Combination of Liquid Chromatography–High-Resolution Tandem Mass Spectrometry and NMR Profiling, in Silico Databases, and Chemometrics. Anal Chem 2018; 91:704-742. [DOI: 10.1021/acs.analchem.8b05112] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Affiliation(s)
- Jean-Luc Wolfender
- School of Pharmaceutical Sciences, EPGL, University of Geneva, University of Lausanne, CMU, 1 Rue Michel Servet, 1211 Geneva 4, Switzerland
| | - Jean-Marc Nuzillard
- Institut de Chimie Moléculaire de Reims, UMR CNRS 7312, Université de Reims Champagne Ardenne, 51687 Reims Cedex 2, France
| | | | - Jean-Hugues Renault
- Institut de Chimie Moléculaire de Reims, UMR CNRS 7312, Université de Reims Champagne Ardenne, 51687 Reims Cedex 2, France
| | - Samuel Bertrand
- Groupe Mer, Molécules, Santé-EA 2160, UFR des Sciences Pharmaceutiques et Biologiques, Université de Nantes, 44035 Nantes, France
- ThalassOMICS Metabolomics Facility, Plateforme Corsaire, Biogenouest, 44035 Nantes, France
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35
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Drouin N, Pezzatti J, Gagnebin Y, González-Ruiz V, Schappler J, Rudaz S. Effective mobility as a robust criterion for compound annotation and identification in metabolomics: Toward a mobility-based library. Anal Chim Acta 2018; 1032:178-187. [DOI: 10.1016/j.aca.2018.05.063] [Citation(s) in RCA: 33] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 04/04/2018] [Accepted: 05/23/2018] [Indexed: 12/20/2022]
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36
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Honour JW, Conway E, Hodkinson R, Lam F. The evolution of methods for urinary steroid metabolomics in clinical investigations particularly in childhood. J Steroid Biochem Mol Biol 2018; 181:28-51. [PMID: 29481855 DOI: 10.1016/j.jsbmb.2018.02.013] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 02/21/2018] [Accepted: 02/21/2018] [Indexed: 12/15/2022]
Abstract
The metabolites of cortisol, and the intermediates in the pathways from cholesterol to cortisol and the adrenal sex steroids can be analysed in a single separation of steroids by gas chromatography (GC) coupled to MS to give a urinary steroid profile (USP). Steroids individually and in profile are now commonly measured in plasma by liquid chromatography (LC) coupled with MS/MS. The steroid conjugates in urine can be determined after hydrolysis and derivative formation and for the first time without hydrolysis using GC-MS, GC-MS/MS and liquid chromatography with mass spectrometry (LC-MS/MS). The evolution of the technology, practicalities and clinical applications are examined in this review. The patterns and quantities of steroids changes through childhood. Information can be obtained on production rates, from which children with steroid excess and deficiency states can be recognised when presenting with obesity, adrenarche, adrenal suppression, hypertension, adrenal tumours, intersex condition and early puberty, as examples. Genetic defects in steroid production and action can be detected by abnormalities from the GC-MS of steroids in urine. New mechanisms of steroid synthesis and metabolism have been recognised through steroid profiling. GC with tandem mass spectrometry (GC-MS/MS) has been used for the tentative identification of unknown steroids in urine from newborn infants with congenital adrenal hyperplasia. Suggestions are made as to areas for future research and for future applications of steroid profiling. As routine hospital laboratories become more familiar with the problems of chromatographic and MS analysis they can consider steroid profiling in their test repertoire although with LC-MS/MS of urinary steroids this is unlikely to become a routine test because of the availability, cost and purity of the internal standards and the complexity of data interpretation. Steroid profiling with quantitative analysis by mass spectrometry (MS) after chromatography now provides the most versatile of tests of adrenal function in childhood.
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Affiliation(s)
- John W Honour
- Institute for Women's Health, University College London, 74 Huntley Street, London, WC1E 6AU, UK.
| | - E Conway
- Clinical Biochemistry, HSL Analytics LLP, Floor 2, 1 Mabledon Place, London, WC1H 9AX, UK
| | - R Hodkinson
- Clinical Biochemistry, HSL Analytics LLP, Floor 2, 1 Mabledon Place, London, WC1H 9AX, UK
| | - F Lam
- Clinical Biochemistry, HSL Analytics LLP, Floor 2, 1 Mabledon Place, London, WC1H 9AX, UK
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37
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Zhang Q, Huo M, Zhang Y, Qiao Y, Gao X. A strategy to improve the identification reliability of the chemical constituents by high-resolution mass spectrometry-based isomer structure prediction combined with a quantitative structure retention relationship analysis: Phthalide compounds in Chuanxiong as a test case. J Chromatogr A 2018; 1552:17-28. [DOI: 10.1016/j.chroma.2018.03.055] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 03/23/2018] [Accepted: 03/27/2018] [Indexed: 11/27/2022]
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38
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Retention prediction in reversed phase high performance liquid chromatography using quantitative structure-retention relationships applied to the Hydrophobic Subtraction Model. J Chromatogr A 2018; 1541:1-11. [PMID: 29454529 DOI: 10.1016/j.chroma.2018.01.053] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 01/31/2018] [Accepted: 01/31/2018] [Indexed: 11/22/2022]
Abstract
Quantitative Structure-Retention Relationships (QSRR) methodology combined with the Hydrophobic Subtraction Model (HSM) have been utilized to accurately predict retention times for a selection of analytes on several different reversed phase liquid chromatography (RPLC) columns. This approach is designed to facilitate early prediction of co-elution of analytes, for example in pharmaceutical drug discovery applications where it is advantageous to predict whether impurities might be co-eluted with the active drug component. The QSRR model utilized VolSurf+ descriptors and a Partial Least Squares regression combined with a Genetic Algorithm (GA-PLS) to predict the solute coefficients in the HSM. It was found that only the hydrophobicity (η'H) term in the HSM was required to give the accuracy necessary to predict potential co-elution of analytes. Global QSRR models derived from all 148 compounds in the dataset were compared to QSRR models derived using a range of local modelling techniques based on clustering of compounds in the dataset by the structural similarity of compounds (as represented by the Tanimoto similarity index), physico-chemical similarity of compounds (represented by log D), the neutral, acidic, or basic nature of the compound, and the second dominant interaction between analyte and stationary phase after hydrophobicity. The global model showed reasonable prediction accuracy for retention time with errors of 30 s and less for up to 50% of modeled compounds. The local models for Tanimoto, nature of the compound and second dominant interaction approaches all exhibited prediction errors less than 30 s in retention time for nearly 70% of compounds for which models could be derived. Predicted retention times of five representative compounds on nine reversed-phase columns were compared with known experimental retention data for these columns and this comparison showed that the accuracy of the proposed modelling approach is sufficient to reliably predict the retention times of analytes based only on their chemical structures.
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39
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Godzien J, Gil de la Fuente A, Otero A, Barbas C. Metabolite Annotation and Identification. COMPREHENSIVE ANALYTICAL CHEMISTRY 2018. [DOI: 10.1016/bs.coac.2018.07.004] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
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40
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Parr MK, Schmidt AH. Life cycle management of analytical methods. J Pharm Biomed Anal 2018; 147:506-517. [DOI: 10.1016/j.jpba.2017.06.020] [Citation(s) in RCA: 53] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2017] [Revised: 06/10/2017] [Accepted: 06/12/2017] [Indexed: 11/30/2022]
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41
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Bruderer T, Varesio E, Hopfgartner G. The use of LC predicted retention times to extend metabolites identification with SWATH data acquisition. J Chromatogr B Analyt Technol Biomed Life Sci 2017; 1071:3-10. [DOI: 10.1016/j.jchromb.2017.07.016] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 06/30/2017] [Accepted: 07/07/2017] [Indexed: 01/15/2023]
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42
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Navarro-Reig M, Ortiz-Villanueva E, Tauler R, Jaumot J. Modelling of Hydrophilic Interaction Liquid Chromatography Stationary Phases Using Chemometric Approaches. Metabolites 2017; 7:E54. [PMID: 29064436 PMCID: PMC5746734 DOI: 10.3390/metabo7040054] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2017] [Revised: 10/11/2017] [Accepted: 10/21/2017] [Indexed: 11/16/2022] Open
Abstract
Metabolomics is a powerful and widely used approach that aims to screen endogenous small molecules (metabolites) of different families present in biological samples. The large variety of compounds to be determined and their wide diversity of physical and chemical properties have promoted the development of different types of hydrophilic interaction liquid chromatography (HILIC) stationary phases. However, the selection of the most suitable HILIC stationary phase is not straightforward. In this work, four different HILIC stationary phases have been compared to evaluate their potential application for the analysis of a complex mixture of metabolites, a situation similar to that found in non-targeted metabolomics studies. The obtained chromatographic data were analyzed by different chemometric methods to explore the behavior of the considered stationary phases. ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA) and partial least squares regression (PLS) were used to explore the experimental factors affecting the stationary phase performance, the main similarities and differences among chromatographic conditions used (stationary phase and pH) and the molecular descriptors most useful to understand the behavior of each stationary phase.
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Affiliation(s)
- Meritxell Navarro-Reig
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, 08034 Barcelona, Spain.
| | - Elena Ortiz-Villanueva
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, 08034 Barcelona, Spain.
| | - Romà Tauler
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, 08034 Barcelona, Spain.
| | - Joaquim Jaumot
- Department of Environmental Chemistry, IDAEA-CSIC, Jordi Girona 18-26, 08034 Barcelona, Spain.
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43
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Taraji M, Haddad PR, Amos RIJ, Talebi M, Szucs R, Dolan JW, Pohl CA. Chemometric-assisted method development in hydrophilic interaction liquid chromatography: A review. Anal Chim Acta 2017; 1000:20-40. [PMID: 29289311 DOI: 10.1016/j.aca.2017.09.041] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2017] [Revised: 09/22/2017] [Accepted: 09/24/2017] [Indexed: 02/09/2023]
Abstract
With an enormous growth in the application of hydrophilic interaction liquid chromatography (HILIC), there has also been significant progress in HILIC method development. HILIC is a chromatographic method that utilises hydro-organic mobile phases with a high organic content, and a hydrophilic stationary phase. It has been applied predominantly in the determination of small polar compounds. Theoretical studies in computer-aided modelling tools, most importantly the predictive, quantitative structure retention relationship (QSRR) modelling methods, have attracted the attention of researchers and these approaches greatly assist the method development process. This review focuses on the application of computer-aided modelling tools in understanding the retention mechanism, the classification of HILIC stationary phases, prediction of retention times in HILIC systems, optimisation of chromatographic conditions, and description of the interaction effects of the chromatographic factors in HILIC separations. Additionally, what has been achieved in the potential application of QSRR methodology in combination with experimental design philosophy in the optimisation of chromatographic separation conditions in the HILIC method development process is communicated. Developing robust predictive QSRR models will undoubtedly facilitate more application of this chromatographic mode in a broader variety of research areas, significantly minimising cost and time of the experimental work.
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Affiliation(s)
- Maryam Taraji
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia
| | - Paul R Haddad
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia.
| | - Ruth I J Amos
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia
| | - Mohammad Talebi
- Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart 7001, Australia
| | - Roman Szucs
- Pfizer Global Research and Development, CT13 9NJ, Sandwich, UK
| | - John W Dolan
- LC Resources, 1795 NW Wallace Rd., McMinnville, OR 97128, USA
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44
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Use of dual-filtering to create training sets leading to improved accuracy in quantitative structure-retention relationships modelling for hydrophilic interaction liquid chromatographic systems. J Chromatogr A 2017; 1507:53-62. [DOI: 10.1016/j.chroma.2017.05.044] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 05/17/2017] [Accepted: 05/18/2017] [Indexed: 01/31/2023]
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45
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Randazzo GM, Tonoli D, Strajhar P, Xenarios I, Odermatt A, Boccard J, Rudaz S. Enhanced metabolite annotation via dynamic retention time prediction: Steroidogenesis alterations as a case study. J Chromatogr B Analyt Technol Biomed Life Sci 2017; 1071:11-18. [PMID: 28479067 DOI: 10.1016/j.jchromb.2017.04.032] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2016] [Revised: 03/07/2017] [Accepted: 04/20/2017] [Indexed: 10/19/2022]
Abstract
The development of metabolomics based on ultra-high pressure liquid chromatography coupled to high-resolution mass spectrometry (UHPLC-HRMS) now allows hundreds to thousands of metabolites to be simultaneously monitored in biological matrices. In that context, bioinformatics and multivariate data analysis (MVA) play a crucial role in the detection of relevant alteration patterns. However, sound biological interpretations must necessarily be supported by metabolite identifications to be definitive or at least have a high degree of confidence. Each compound, should be characterised by unique molecular properties. Among them, the exact mass and the chromatographic retention time are recognised as major and complementary criteria for compound identification. While the former is easily derived from the molecular structure, building generic and accurate retention time open databases still constitutes a critical issue because of the vast diversity of instruments, stationary phases and operating conditions in UHPLC-HRMS. Because several hits matching a molecular formula obtained from an exact mass and an isotopic pattern are often generated for each analyte, this methodology rarely allows a unique and unambiguous molecular identity to be gained. This work aims to provide a flexible solution to facilitate reliable compound annotation based on retention time in reversed-phase liquid chromatography (RPLC). It proposes an innovative approach based on the chromatographic linear solvent strength (LSS) theory, allowing retention times under any gradient conditions at fixed temperature, stationary phase and mobile phase type to be predicted. Starting from a subset of the Human Metabolite Database (HMDB), a new dynamic database involving LSS parameters was developed. A real case study involving steroidogenesis alterations due to forskolin exposure was conducted using the adrenal H295R OECD reference cell model for endocrine disruptor screening. The prediction of retention times was successfully achieved, facilitating steroid identification. An automated procedure which implements the compound annotation levels encouraged by the Metabolite Standard Initiative (MSI) and the Coordination of Standards in Metabolomics (COSMOS) was also developed to speed up the process and enhance the data reusability.
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Affiliation(s)
- Giuseppe Marco Randazzo
- School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, 1211 Geneva, Switzerland
| | - David Tonoli
- School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, 1211 Geneva, Switzerland; Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, 4055 Basel, Switzerland; Human Protein Sciences Department, University of Geneva, 1211 Geneva, Switzerland
| | - Petra Strajhar
- Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, 4055 Basel, Switzerland; Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland
| | - Ioannis Xenarios
- Vital-IT/Swiss-Prot Groups, SIB Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
| | - Alex Odermatt
- Swiss Centre for Applied Human Toxicology (SCAHT), Universities of Basel and Geneva, 4055 Basel, Switzerland; Division of Molecular and Systems Toxicology, Department of Pharmaceutical Sciences, University of Basel, 4056 Basel, Switzerland
| | - Julien Boccard
- School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, 1211 Geneva, Switzerland
| | - Serge Rudaz
- School of Pharmaceutical Sciences, University of Geneva and University of Lausanne, 1211 Geneva, Switzerland.
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Nekrasova NA, Kurbatova SV. Quantitative structure–chromatographic retention correlations of quinoline derivatives. J Chromatogr A 2017; 1492:55-60. [DOI: 10.1016/j.chroma.2017.02.063] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 02/07/2017] [Accepted: 02/26/2017] [Indexed: 12/19/2022]
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Zisi C, Sampsonidis I, Fasoula S, Papachristos K, Witting M, Gika HG, Nikitas P, Pappa-Louisi A. QSRR Modeling for Metabolite Standards Analyzed by Two Different Chromatographic Columns Using Multiple Linear Regression. Metabolites 2017; 7:metabo7010007. [PMID: 28208794 PMCID: PMC5372210 DOI: 10.3390/metabo7010007] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2016] [Accepted: 02/05/2017] [Indexed: 01/07/2023] Open
Abstract
Modified quantitative structure retention relationships (QSRRs) are proposed and applied to describe two retention data sets: A set of 94 metabolites studied by a hydrophilic interaction chromatography system under organic content gradient conditions and a set of tryptophan and its major metabolites analyzed by a reversed-phase chromatographic system under isocratic as well as pH and/or simultaneous pH and organic content gradient conditions. According to the proposed modification, an additional descriptor is added to a conventional QSRR expression, which is the analyte retention time, tR(R), measured under the same elution conditions, but in a second chromatographic column considered as a reference one. The 94 metabolites were studied on an Amide column using a Bare Silica column as a reference. For the second dataset, a Kinetex EVO C18 and a Gemini-NX column were used, where each of them was served as a reference column of the other. We found in all cases a significant improvement of the performance of the QSRR models when the descriptor tR(R) was considered.
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Affiliation(s)
- Chrysostomi Zisi
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (S.F.); (K.P.); (P.N.); (A.P.-L.)
- Correspondence: ; Tel.: +30-231-099-7765
| | - Ioannis Sampsonidis
- Infrastructure and Environment Research Division, School of Engineering, University of Glasgow, Rankine Building, Oakfield Avenue, Glasgow G12 8LT, UK;
| | - Stella Fasoula
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (S.F.); (K.P.); (P.N.); (A.P.-L.)
| | - Konstantinos Papachristos
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (S.F.); (K.P.); (P.N.); (A.P.-L.)
| | - Michael Witting
- Helmholtz Zentrum München, Research Unit Analytical BioGeoChemistry, Ingolstaedter Landstrasse 1, D-85764 Neuherberg, Germany;
| | - Helen G. Gika
- Department of Medicine, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
| | - Panagiotis Nikitas
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (S.F.); (K.P.); (P.N.); (A.P.-L.)
| | - Adriani Pappa-Louisi
- Department of Chemistry, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; (S.F.); (K.P.); (P.N.); (A.P.-L.)
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48
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Tyteca E, Talebi M, Amos R, Park SH, Taraji M, Wen Y, Szucs R, Pohl CA, Dolan JW, Haddad PR. Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio. J Chromatogr A 2017; 1486:50-58. [DOI: 10.1016/j.chroma.2016.09.062] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2016] [Revised: 09/22/2016] [Accepted: 09/25/2016] [Indexed: 11/29/2022]
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49
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Allard PM, Genta-Jouve G, Wolfender JL. Deep metabolome annotation in natural products research: towards a virtuous cycle in metabolite identification. Curr Opin Chem Biol 2017; 36:40-49. [DOI: 10.1016/j.cbpa.2016.12.022] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Revised: 12/20/2016] [Accepted: 12/22/2016] [Indexed: 12/20/2022]
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50
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Song Q, Zhao Y, Chen X, Li J, Li P, Jiang Y, Wang Y, Song Y, Tu P. New instrumentation for large-scale quantitative analysis of components spanning a wide polarity range by column-switching hydrophilic interaction chromatography-turbulent flow chromatography-reversed phase liquid chromatography-tandem mass spectrometry. RSC Adv 2017. [DOI: 10.1039/c7ra03788k] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
New instrumentation, namely column-switching HILIC-TFC-RPLC-MS/MS, was configured for large-scale quantitative analysis of components spanning a wide polarity range in a mimic complicated sample.
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Affiliation(s)
- Qingqing Song
- Modern Research Center for Traditional Chinese Medicine
- School of Chinese Materia Medica
- Beijing University of Chinese Medicine
- Beijing 100029
- China
| | - Yunfang Zhao
- Modern Research Center for Traditional Chinese Medicine
- School of Chinese Materia Medica
- Beijing University of Chinese Medicine
- Beijing 100029
- China
| | - Xiaojia Chen
- State Key Laboratory of Quality Research in Chinese Medicine
- Institute of Chinese Medical Sciences
- University of Macau
- Taipa 999078
- Macao
| | - Jun Li
- Modern Research Center for Traditional Chinese Medicine
- School of Chinese Materia Medica
- Beijing University of Chinese Medicine
- Beijing 100029
- China
| | - Peng Li
- State Key Laboratory of Quality Research in Chinese Medicine
- Institute of Chinese Medical Sciences
- University of Macau
- Taipa 999078
- Macao
| | - Yong Jiang
- State Key Laboratory of Natural and Biomimetic Drugs
- School of Pharmaceutical Sciences
- Peking University
- Beijing 100191
- China
| | - Yitao Wang
- State Key Laboratory of Quality Research in Chinese Medicine
- Institute of Chinese Medical Sciences
- University of Macau
- Taipa 999078
- Macao
| | - Yuelin Song
- Modern Research Center for Traditional Chinese Medicine
- School of Chinese Materia Medica
- Beijing University of Chinese Medicine
- Beijing 100029
- China
| | - Pengfei Tu
- Modern Research Center for Traditional Chinese Medicine
- School of Chinese Materia Medica
- Beijing University of Chinese Medicine
- Beijing 100029
- China
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