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Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions
INTRODUCTION: Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC–MS) is a developing...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Springer US
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5438430/ https://www.ncbi.nlm.nih.gov/pubmed/28596718 http://dx.doi.org/10.1007/s11306-017-1213-z |
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author | Herman, Stephanie Emami Khoonsari, Payam Aftab, Obaid Krishnan, Shibu Strömbom, Emil Larsson, Rolf Hammerling, Ulf Spjuth, Ola Kultima, Kim Gustafsson, Mats |
author_facet | Herman, Stephanie Emami Khoonsari, Payam Aftab, Obaid Krishnan, Shibu Strömbom, Emil Larsson, Rolf Hammerling, Ulf Spjuth, Ola Kultima, Kim Gustafsson, Mats |
author_sort | Herman, Stephanie |
collection | PubMed |
description | INTRODUCTION: Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC–MS) is a developing area that is yet attached to several pitfalls and challenges. To reach a level of high reliability and robustness, these issues need to be tackled by implementation of refined experimental and computational protocols. OBJECTIVES: This study illustrates some key pitfalls in LC–MS based metabolomics and introduces an automated computational procedure to compensate for them. METHOD: Non-cancerous mammary gland derived cells were exposed to 27 chemicals from four pharmacological classes plus a set of six pesticides. Changes in the metabolome of cell lysates were assessed after 24 h using LC–MS. A data processing pipeline was established and evaluated to handle issues including contaminants, carry over effects, intensity decay and inherent methodology variability and biases. A key component in this pipeline is a latent variable method called OOS-DA (optimal orthonormal system for discriminant analysis), being theoretically more easily motivated than PLS-DA in this context, as it is rooted in pattern classification rather than regression modeling. RESULT: The pipeline is shown to reduce experimental variability/biases and is used to confirm that LC–MS spectra hold drug class specific information. CONCLUSION: LC–MS based metabolomics is a promising methodology, but comes with pitfalls and challenges. Key difficulties can be largely overcome by means of a computational procedure of the kind introduced and demonstrated here. The pipeline is freely available on www.github.com/stephanieherman/MS-data-processing. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-017-1213-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5438430 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-54384302017-06-06 Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions Herman, Stephanie Emami Khoonsari, Payam Aftab, Obaid Krishnan, Shibu Strömbom, Emil Larsson, Rolf Hammerling, Ulf Spjuth, Ola Kultima, Kim Gustafsson, Mats Metabolomics Original Article INTRODUCTION: Mass spectrometry based metabolomics has become a promising complement and alternative to transcriptomics and proteomics in many fields including in vitro systems pharmacology. Despite several merits, metabolomics based on liquid chromatography mass spectrometry (LC–MS) is a developing area that is yet attached to several pitfalls and challenges. To reach a level of high reliability and robustness, these issues need to be tackled by implementation of refined experimental and computational protocols. OBJECTIVES: This study illustrates some key pitfalls in LC–MS based metabolomics and introduces an automated computational procedure to compensate for them. METHOD: Non-cancerous mammary gland derived cells were exposed to 27 chemicals from four pharmacological classes plus a set of six pesticides. Changes in the metabolome of cell lysates were assessed after 24 h using LC–MS. A data processing pipeline was established and evaluated to handle issues including contaminants, carry over effects, intensity decay and inherent methodology variability and biases. A key component in this pipeline is a latent variable method called OOS-DA (optimal orthonormal system for discriminant analysis), being theoretically more easily motivated than PLS-DA in this context, as it is rooted in pattern classification rather than regression modeling. RESULT: The pipeline is shown to reduce experimental variability/biases and is used to confirm that LC–MS spectra hold drug class specific information. CONCLUSION: LC–MS based metabolomics is a promising methodology, but comes with pitfalls and challenges. Key difficulties can be largely overcome by means of a computational procedure of the kind introduced and demonstrated here. The pipeline is freely available on www.github.com/stephanieherman/MS-data-processing. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-017-1213-z) contains supplementary material, which is available to authorized users. Springer US 2017-05-19 2017 /pmc/articles/PMC5438430/ /pubmed/28596718 http://dx.doi.org/10.1007/s11306-017-1213-z Text en © The Author(s) 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
spellingShingle | Original Article Herman, Stephanie Emami Khoonsari, Payam Aftab, Obaid Krishnan, Shibu Strömbom, Emil Larsson, Rolf Hammerling, Ulf Spjuth, Ola Kultima, Kim Gustafsson, Mats Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions |
title | Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions |
title_full | Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions |
title_fullStr | Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions |
title_full_unstemmed | Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions |
title_short | Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions |
title_sort | mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5438430/ https://www.ncbi.nlm.nih.gov/pubmed/28596718 http://dx.doi.org/10.1007/s11306-017-1213-z |
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