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From phenotype to genotype: whole tissue profiling for plant breeding

Authors
  • Goodacre, Royston1, 2
  • Roberts, Luned3
  • Ellis, David I.1, 2, 4
  • Thorogood, Danny3
  • Reader, Stephen M.5
  • Ougham, Helen3
  • King, Ian3
  • 1 University of Wales, Institute of Biological Sciences, Aberystwyth, Ceredigion, SY23 3DD, UK , Aberystwyth, Ceredigion (United Kingdom)
  • 2 The University of Manchester, School of Chemistry and Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester, M1 7ND, UK , Manchester (United Kingdom)
  • 3 Institute of Grassland & Environmental Research, Plant Genetics & Breeding Department, Plas Gogerddan, Aberystwyth, SY23 3EB, Wales, UK , Aberystwyth (United Kingdom)
  • 4 The University of Manchester, School of Chemistry, Sackville Street, Manchester, M60 1QD, UK , Manchester (United Kingdom)
  • 5 Norwich Research Park, John Innes Centre, Colney, Norwich, NR4 7UH, UK , Colney, Norwich (United Kingdom)
Type
Published Article
Journal
Metabolomics
Publisher
Springer US
Publication Date
Jun 19, 2007
Volume
3
Issue
4
Pages
489–501
Identifiers
DOI: 10.1007/s11306-007-0062-6
Source
Springer Nature
Keywords
License
Yellow

Abstract

Fourier transform infrared spectroscopy (FT-IR) was used to obtain ‘holistic’ metabolic fingerprints from a wide range of plants to differentiate species, population, single plant genotype, and chromosomal constitution differences. Sample preparation simply entailed the maceration of fresh leaves with water, and these samples were then dried and analysed by reflectance FT-IR where spectral acquisition was typically 10 s. All samples gave reproducible, characteristic biological infrared absorption spectra and these were analysed by chemometric methods. FT-IR is not biased to any particular chemical species and thus the whole tissue profiles produced measure the total biochemical makeup of the test sample; that is to say it represents a plant phenotype. We show that by simple cluster analysis these phenotypic measurements can be related to the genotypes of the plants and can reliably differentiate closely related individuals. We believe that this approach provides a valuable new tool for the rapid metabolomic profiling of plants, with applications to plant breeding and the assessment of substantial equivalency for genetically-modified plants.

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