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Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions.
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2017 (English)In: Metabolomics, ISSN 1573-3882, E-ISSN 1573-3890, Vol. 13, no 7, 79Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2017. Vol. 13, no 7, 79
Keyword [en]
Batch effects, Data handling, Drug metabolism, Mass spectrometry, Metabolomics
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:uu:diva-323946DOI: 10.1007/s11306-017-1213-zPubMedID: 28596718OAI: oai:DiVA.org:uu-323946DiVA: diva2:1107942
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
Available from: 2017-06-11 Created: 2017-06-11 Last updated: 2017-06-11

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Spjuth, Ola
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CiteExportLink to record
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Citation style
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