Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions
Published: 2017-05-19
Formatted citation
Herman S, Khoonsari PE, Aftab O, Krishnan S, Strömbom E, Larsson R, Hammerling U, Spjuth O, Kultima M, Gustafsson M.
Mass spectrometry based metabolomics for in vitro systems pharmacology: pitfalls, challenges, and computational solutions.
Metabolomics.
13, 79 (2017).
DOI: 10.1007/s11306-017-1213-z
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.