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Fourier Transform Ion Cyclotron Resonance Mass Spectrometry for Plant Metabolite Profiling and Metabolite Identification

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Plant Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 860))

Abstract

Mass spectrometry (MS) is usually the technique of choice for metabolomic studies where the volume of sample material is too limited for applications employing nuclear magnetic resonance (NMR) spectroscopy. With the advent of ultra-high accuracy mass spectrometers such as the Orbitrap (resolution ∼ 105) and the Fourier Transform Ion Cyclotron Resonance (FT-ICR) analysers (resolution potentially in excess of 106) there is the opportunity to generate an accurate mass fingerprint (often referred to as a profile since the variables are considered as effectively discrete) of an infused sample extract. In such data representations mass “peaks” are detected in the raw data and the centroid mass intensity calculated. The resolving power and sensitivity of these ultra-high accuracy mass analysers is such that metabolite signals from molecules containing naturally abundant elemental isotopes (e.g. 13C, 41K, 15N, 17O, 34S, and 37Cl) are visible in the data. Such is the instruments precision that it allows for the calculation of highly accurate elemental compositions for the unknown signals, thus aiding greatly in the selection of potential metabolite candidates for the annotation of unknowns prior to their confirmation by comparisons to analytical standards. The application of FT-ICR-MS to plant metabolomics has thus far been limited to a few studies and clear step-by-step methodologies are as yet unavailable. This chapter presents a rigorous method for the extraction and FT-ICR-MS analysis of plant leaf tissues as well as downstream data processing.

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Abbreviations

DI:

Direct infusion

FI:

Flow infusion

FT:

Fourier transform

ICR:

Ion cyclotron resonance

MS:

Mass spectrometry

ESI:

Electrospray ionisation

LTQ:

Linear trap quadrupole

CID:

Collision-induced dissociation

QC:

Quality control

PCA:

Principal components analysis

LDA:

Linear discriminant analysis

RF:

Random forest

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Acknowledgements

JWA and RG would like to acknowledge the EU Frame work VI initiative for research funding and support as part of the plant metabolomics project META-PHOR (FOOD-CT-2006-036220). RG is also grateful to the UK BBSRC for financial support of the MCISB (Manchester Centre for Integrative Systems Biology). DP, JD, and MB would like to acknowledge research support received from Aberystwyth University and UK BBSRC grant BB/D006953/1; MB is further supported by a Research Councils UK Fellowship.

Author information

Authors and Affiliations

  1. IBERS – Institute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, UK

    J. William Allwood, David Parker, Manfred Beckmann & John Draper

  2. School of Chemistry, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK

    J. William Allwood & Royston Goodacre

  3. Manchester Centre for Integrative Systems Biology, Manchester Interdisciplinary Biocentre, The University of Manchester, Manchester, UK

    Royston Goodacre

Authors
  1. J. William Allwood
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  2. David Parker
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  3. Manfred Beckmann
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  4. John Draper
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  5. Royston Goodacre
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Corresponding author

Correspondence to J. William Allwood .

Editor information

Editors and Affiliations

  1. , Dept. Computer Sciences, Aberystwyth University, King Street, Aberystwyth, SY23 3DB, United Kingdom

    Nigel W. Hardy

  2. Plant Research International, Wageningen, Netherlands

    Robert D. Hall

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Allwood, J.W., Parker, D., Beckmann, M., Draper, J., Goodacre, R. (2011). Fourier Transform Ion Cyclotron Resonance Mass Spectrometry for Plant Metabolite Profiling and Metabolite Identification. In: Hardy, N., Hall, R. (eds) Plant Metabolomics. Methods in Molecular Biology, vol 860. Humana Press. https://doi.org/10.1007/978-1-61779-594-7_11

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  • DOI: https://doi.org/10.1007/978-1-61779-594-7_11

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  • Publisher Name: Humana Press

  • Print ISBN: 978-1-61779-593-0

  • Online ISBN: 978-1-61779-594-7

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