Guidelines for Sample Normalization to Minimize Batch Variation for Large-Scale Metabolic Profiling of Plant Natural Genetic Variance

  • Saleh AlseekhEmail author
  • Si Wu
  • Yariv Brotman
  • Alisdair R. Fernie
Part of the Methods in Molecular Biology book series (MIMB, volume 1778)


Recent methodological advances in both liquid chromatography–mass spectrometry (LC-MS) and gas chromatography–mass spectrometry (GC-MS) have facilitated the profiling highly complex mixtures of primary and secondary metabolites in order to investigate a diverse range of biological questions. These techniques usually face a large number of potential sources of technical and biological variation. In this chapter we describe guidelines and normalization procedures to reduce the analytical variation, which are essential for the high-throughput evaluation of metabolic variance used in broad genetic populations which commonly entail the evaluation of hundreds or thousands of samples. This chapter specifically deals with handling of large-scale plant samples for metabolomics analysis of quantitative trait loci (mQTL) in order to reduce analytical error as well as batch-to-batch variation.

Key words

Large-scale metabolomics Batch normalization Variation Natural genetic variation QTL mapping LC-MS GC-MS 



This wok was in part supported by the PlantaSYST project by the European Union’s Horizon 2020 Research and Innovation Programme (SGA-CSA Number 664621 and Number 739582 under FPA Number 664620).


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Saleh Alseekh
    • 1
    • 2
    Email author
  • Si Wu
    • 1
    • 3
  • Yariv Brotman
    • 1
    • 3
  • Alisdair R. Fernie
    • 1
  1. 1.Max Planck Institute of Molecular Plant PhysiologyPotsdam-GolmGermany
  2. 2.Center of Plant System Biology and BiotechnologyPlovdivBulgaria
  3. 3.Department of Life SciencesBen-Gurion University of the NegevBeershebaIsrael

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