Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions View Full Text


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Article Info

DATE

2017-07

AUTHORS

Stephanie Herman, Payam Emami Khoonsari, Obaid Aftab, Shibu Krishnan, Emil Strömbom, Rolf Larsson, Ulf Hammerling, Ola Spjuth, Kim Kultima, Mats Gustafsson

ABSTRACT

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. More... »

PAGES

79

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s11306-017-1213-z

DOI

http://dx.doi.org/10.1007/s11306-017-1213-z

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https://app.dimensions.ai/details/publication/pub.1085451125

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/28596718


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