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Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification

Annotation and interpretation of full scan electrospray mass spectra of metabolites is complicated by the presence of a wide variety of ions. Not only protonated, deprotonated, and neutral loss ions but also sodium, potassium, and ammonium adducts as well as oligomers are frequently observed. This d...

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Autores principales: Stricker, Thomas, Bonner, Ron, Lisacek, Frédérique, Hopfgartner, Gérard
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806579/
https://www.ncbi.nlm.nih.gov/pubmed/33123762
http://dx.doi.org/10.1007/s00216-020-03019-3
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author Stricker, Thomas
Bonner, Ron
Lisacek, Frédérique
Hopfgartner, Gérard
author_facet Stricker, Thomas
Bonner, Ron
Lisacek, Frédérique
Hopfgartner, Gérard
author_sort Stricker, Thomas
collection PubMed
description Annotation and interpretation of full scan electrospray mass spectra of metabolites is complicated by the presence of a wide variety of ions. Not only protonated, deprotonated, and neutral loss ions but also sodium, potassium, and ammonium adducts as well as oligomers are frequently observed. This diversity challenges automatic annotation and is often poorly addressed by current annotation tools. In many cases, annotation is integrated in metabolomics workflows and is based on specific chromatographic peak-picking tools. We introduce mzAdan, a nonchromatography-based multipurpose standalone application that was developed for the annotation and exploration of convolved high-resolution ESI-MS spectra. The tool annotates single or multiple accurate mass spectra using a customizable adduct annotation list and outputs a list of [M+H](+) candidates. MzAdan was first tested with a collection of 408 analytes acquired with flow injection analysis. This resulted in 402 correct [M+H](+) identifications and, with combinations of sodium, ammonium, and potassium adducts and water and ammonia losses within a tolerance of 10 mmu, explained close to 50% of the total ion current. False positives were monitored with mass accuracy and bias as well as chromatographic behavior which led to the identification of adducts with calcium instead of the expected potassium. MzAdan was then integrated in a workflow with XCMS for the untargeted LC-MS data analysis of a 52 metabolite standard mix and a human urine sample. The results were benchmarked against three other annotation tools, CAMERA, findMAIN, and CliqueMS: findMAIN and mzAdan consistently produced higher numbers of [M+H](+) candidates compared with CliqueMS and CAMERA, especially with co-eluting metabolites. Detection of low-intensity ions and correct grouping were found to be essential for annotation performance. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version contains supplementary material available at 10.1007/s00216-020-03019-3.
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spelling pubmed-78065792021-01-21 Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification Stricker, Thomas Bonner, Ron Lisacek, Frédérique Hopfgartner, Gérard Anal Bioanal Chem Research Paper Annotation and interpretation of full scan electrospray mass spectra of metabolites is complicated by the presence of a wide variety of ions. Not only protonated, deprotonated, and neutral loss ions but also sodium, potassium, and ammonium adducts as well as oligomers are frequently observed. This diversity challenges automatic annotation and is often poorly addressed by current annotation tools. In many cases, annotation is integrated in metabolomics workflows and is based on specific chromatographic peak-picking tools. We introduce mzAdan, a nonchromatography-based multipurpose standalone application that was developed for the annotation and exploration of convolved high-resolution ESI-MS spectra. The tool annotates single or multiple accurate mass spectra using a customizable adduct annotation list and outputs a list of [M+H](+) candidates. MzAdan was first tested with a collection of 408 analytes acquired with flow injection analysis. This resulted in 402 correct [M+H](+) identifications and, with combinations of sodium, ammonium, and potassium adducts and water and ammonia losses within a tolerance of 10 mmu, explained close to 50% of the total ion current. False positives were monitored with mass accuracy and bias as well as chromatographic behavior which led to the identification of adducts with calcium instead of the expected potassium. MzAdan was then integrated in a workflow with XCMS for the untargeted LC-MS data analysis of a 52 metabolite standard mix and a human urine sample. The results were benchmarked against three other annotation tools, CAMERA, findMAIN, and CliqueMS: findMAIN and mzAdan consistently produced higher numbers of [M+H](+) candidates compared with CliqueMS and CAMERA, especially with co-eluting metabolites. Detection of low-intensity ions and correct grouping were found to be essential for annotation performance. [Figure: see text] ELECTRONIC SUPPLEMENTARY MATERIAL: The online version contains supplementary material available at 10.1007/s00216-020-03019-3. Springer Berlin Heidelberg 2020-10-29 2021 /pmc/articles/PMC7806579/ /pubmed/33123762 http://dx.doi.org/10.1007/s00216-020-03019-3 Text en © The Author(s) 2020, corrected publication 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Research Paper
Stricker, Thomas
Bonner, Ron
Lisacek, Frédérique
Hopfgartner, Gérard
Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification
title Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification
title_full Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification
title_fullStr Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification
title_full_unstemmed Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification
title_short Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification
title_sort adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7806579/
https://www.ncbi.nlm.nih.gov/pubmed/33123762
http://dx.doi.org/10.1007/s00216-020-03019-3
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