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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
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 (t(R))...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7345884/ https://www.ncbi.nlm.nih.gov/pubmed/32486009 http://dx.doi.org/10.3390/metabo10060225 |
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author | Magny, Romain Regazzetti, Anne Kessal, Karima Genta-Jouve, Gregory Baudouin, Christophe Mélik-Parsadaniantz, Stéphane Brignole-Baudouin, Françoise Laprévote, Olivier Auzeil, Nicolas |
author_facet | Magny, Romain Regazzetti, Anne Kessal, Karima Genta-Jouve, Gregory Baudouin, Christophe Mélik-Parsadaniantz, Stéphane Brignole-Baudouin, Françoise Laprévote, Olivier Auzeil, Nicolas |
author_sort | Magny, Romain |
collection | PubMed |
description | 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 (t(R)) 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 t(R) 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 t(R) prediction thus appears as a suitable and robust tool for the discovery of lipids involved in relevant biological processes. |
format | Online Article Text |
id | pubmed-7345884 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-73458842020-07-09 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 Magny, Romain Regazzetti, Anne Kessal, Karima Genta-Jouve, Gregory Baudouin, Christophe Mélik-Parsadaniantz, Stéphane Brignole-Baudouin, Françoise Laprévote, Olivier Auzeil, Nicolas Metabolites Article 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 (t(R)) 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 t(R) 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 t(R) prediction thus appears as a suitable and robust tool for the discovery of lipids involved in relevant biological processes. MDPI 2020-05-29 /pmc/articles/PMC7345884/ /pubmed/32486009 http://dx.doi.org/10.3390/metabo10060225 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Magny, Romain Regazzetti, Anne Kessal, Karima Genta-Jouve, Gregory Baudouin, Christophe Mélik-Parsadaniantz, Stéphane Brignole-Baudouin, Françoise Laprévote, Olivier Auzeil, Nicolas 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7345884/ https://www.ncbi.nlm.nih.gov/pubmed/32486009 http://dx.doi.org/10.3390/metabo10060225 |
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