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Accurate Sphingolipid Quantification Reducing Fragmentation Bias by Nonlinear Models

[Image: see text] Quantitative sphingolipid analysis is crucial for understanding the roles of these bioactive molecules in various physiological and pathological contexts. Molecular sphingolipid species are typically quantified using sphingoid base-derived fragments relative to a class-specific int...

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Autores principales: Troppmair, Nina, Kopczynski, Dominik, Assinger, Alice, Lehmann, Rainer, Coman, Cristina, Ahrends, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Chemical Society 2023
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585660/
https://www.ncbi.nlm.nih.gov/pubmed/37782305
http://dx.doi.org/10.1021/acs.analchem.3c02445
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author Troppmair, Nina
Kopczynski, Dominik
Assinger, Alice
Lehmann, Rainer
Coman, Cristina
Ahrends, Robert
author_facet Troppmair, Nina
Kopczynski, Dominik
Assinger, Alice
Lehmann, Rainer
Coman, Cristina
Ahrends, Robert
author_sort Troppmair, Nina
collection PubMed
description [Image: see text] Quantitative sphingolipid analysis is crucial for understanding the roles of these bioactive molecules in various physiological and pathological contexts. Molecular sphingolipid species are typically quantified using sphingoid base-derived fragments relative to a class-specific internal standard. However, the commonly employed “one standard per class” strategy fails to account for fragmentation differences presented by the structural diversity of sphingolipids. To address this limitation, we developed a novel approach for quantitative sphingolipid analysis. This approach utilizes fragmentation models to correct for structural differences and thus overcomes the limitations associated with using a limited number of standards for quantification. Importantly, our method is independent of the internal standard, instrumental setup, and collision energy. Furthermore, we integrated this method into a user-friendly KNIME workflow. The validation results illustrate the effectiveness of our approach in accurately quantifying ceramide subclasses from various biological matrices. This breakthrough opens up new avenues for exploring sphingolipid metabolism and gaining insights into its implications.
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spelling pubmed-105856602023-10-20 Accurate Sphingolipid Quantification Reducing Fragmentation Bias by Nonlinear Models Troppmair, Nina Kopczynski, Dominik Assinger, Alice Lehmann, Rainer Coman, Cristina Ahrends, Robert Anal Chem [Image: see text] Quantitative sphingolipid analysis is crucial for understanding the roles of these bioactive molecules in various physiological and pathological contexts. Molecular sphingolipid species are typically quantified using sphingoid base-derived fragments relative to a class-specific internal standard. However, the commonly employed “one standard per class” strategy fails to account for fragmentation differences presented by the structural diversity of sphingolipids. To address this limitation, we developed a novel approach for quantitative sphingolipid analysis. This approach utilizes fragmentation models to correct for structural differences and thus overcomes the limitations associated with using a limited number of standards for quantification. Importantly, our method is independent of the internal standard, instrumental setup, and collision energy. Furthermore, we integrated this method into a user-friendly KNIME workflow. The validation results illustrate the effectiveness of our approach in accurately quantifying ceramide subclasses from various biological matrices. This breakthrough opens up new avenues for exploring sphingolipid metabolism and gaining insights into its implications. American Chemical Society 2023-10-02 /pmc/articles/PMC10585660/ /pubmed/37782305 http://dx.doi.org/10.1021/acs.analchem.3c02445 Text en © 2023 The Authors. Published by American Chemical Society https://creativecommons.org/licenses/by/4.0/Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Troppmair, Nina
Kopczynski, Dominik
Assinger, Alice
Lehmann, Rainer
Coman, Cristina
Ahrends, Robert
Accurate Sphingolipid Quantification Reducing Fragmentation Bias by Nonlinear Models
title Accurate Sphingolipid Quantification Reducing Fragmentation Bias by Nonlinear Models
title_full Accurate Sphingolipid Quantification Reducing Fragmentation Bias by Nonlinear Models
title_fullStr Accurate Sphingolipid Quantification Reducing Fragmentation Bias by Nonlinear Models
title_full_unstemmed Accurate Sphingolipid Quantification Reducing Fragmentation Bias by Nonlinear Models
title_short Accurate Sphingolipid Quantification Reducing Fragmentation Bias by Nonlinear Models
title_sort accurate sphingolipid quantification reducing fragmentation bias by nonlinear models
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585660/
https://www.ncbi.nlm.nih.gov/pubmed/37782305
http://dx.doi.org/10.1021/acs.analchem.3c02445
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