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Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data

The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identific...

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Autores principales: Ross, Dylan H., Guo, Jian, Bilbao, Aivett, Huan, Tao, Smith, Richard D., Zheng, Xueyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142755/
https://www.ncbi.nlm.nih.gov/pubmed/37110719
http://dx.doi.org/10.3390/molecules28083483
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author Ross, Dylan H.
Guo, Jian
Bilbao, Aivett
Huan, Tao
Smith, Richard D.
Zheng, Xueyun
author_facet Ross, Dylan H.
Guo, Jian
Bilbao, Aivett
Huan, Tao
Smith, Richard D.
Zheng, Xueyun
author_sort Ross, Dylan H.
collection PubMed
description The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature.
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spelling pubmed-101427552023-04-29 Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data Ross, Dylan H. Guo, Jian Bilbao, Aivett Huan, Tao Smith, Richard D. Zheng, Xueyun Molecules Review The unambiguous identification of lipids is a critical component of lipidomics studies and greatly impacts the interpretation and significance of analyses as well as the ultimate biological understandings derived from measurements. The level of structural detail that is available for lipid identifications is largely determined by the analytical platform being used. Mass spectrometry (MS) coupled with liquid chromatography (LC) is the predominant combination of analytical techniques used for lipidomics studies, and these methods can provide fairly detailed lipid identification. More recently, ion mobility spectrometry (IMS) has begun to see greater adoption in lipidomics studies thanks to the additional dimension of separation that it provides and the added structural information that can support lipid identification. At present, relatively few software tools are available for IMS-MS lipidomics data analysis, which reflects the still limited adoption of IMS as well as the limited software support. This fact is even more pronounced for isomer identifications, such as the determination of double bond positions or integration with MS-based imaging. In this review, we survey the landscape of software tools that are available for the analysis of IMS-MS-based lipidomics data and we evaluate lipid identifications produced by these tools using open-access data sourced from the peer-reviewed lipidomics literature. MDPI 2023-04-14 /pmc/articles/PMC10142755/ /pubmed/37110719 http://dx.doi.org/10.3390/molecules28083483 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Ross, Dylan H.
Guo, Jian
Bilbao, Aivett
Huan, Tao
Smith, Richard D.
Zheng, Xueyun
Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data
title Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data
title_full Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data
title_fullStr Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data
title_full_unstemmed Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data
title_short Evaluating Software Tools for Lipid Identification from Ion Mobility Spectrometry–Mass Spectrometry Lipidomics Data
title_sort evaluating software tools for lipid identification from ion mobility spectrometry–mass spectrometry lipidomics data
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10142755/
https://www.ncbi.nlm.nih.gov/pubmed/37110719
http://dx.doi.org/10.3390/molecules28083483
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