Abstract
Experiments involve the deliberate variation of one or more factors in order to provoke responses, the identification of which then provides the first step towards functional knowledge. Because environmental, biological, and/or technical noise is unavoidable, biological experiments usually need to be designed. Thus, once the major sources of experimental noise have been identified, individual samples can be grouped, randomised, and/or pooled. Like other ‘omics approaches, metabolomics is characterised by the numbers of analytes largely exceeding sample number. While this unprecedented singularity in biology dramatically increases false discovery, experimental error can nevertheless be decreased in plant metabolomics experiments. For this, each step from plant cultivation to data acquisition needs to be evaluated in order to identify the major sources of error and then an appropriate design can be produced, as with any other experimental approach. The choice of technology, the time at which tissues are harvested, and the way metabolism is quenched also need to be taken into consideration, as they decide which metabolites can be studied. A further recommendation is to document data and metadata in a machine readable way. The latter should also describe every aspect of the experiment. This should provide valuable hints for future experimental design and ultimately give metabolomic data a second life. To facilitate the identification of critical steps, a list of items to be considered before embarking on time-consuming and costly metabolomic experiments is proposed.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Joyce, A.R. and Palsson, B.O. (2006) The model organism as a system: integrating ‘omics’ data sets. Nature Review Molecular Cell Biology 7, 198–210.
Ge, H., Walhout, A.J.M., and Vidal, M. (2003) Integrating ‘omic’ information: a bridge between genomics and systems biology. Trends in Genetics 19, 551–60.
Van Dien, S. and Schilling, C.H. (2006) Bringing metabolomics data into the forefront of systems biology. Molecular Systems Biology 2, 1–2.
Liu, E.T. (2005) Systems Biology, Integrative Biology, Predictive Biology. Cell 121, 505–6.
Bacon, F. (1620) The new organon or true directions concerning the interpretation of nature, in The Works Vol. VIII (Spedding J., Ellis R.L., and D.D. Heath, eds.): Taggard and Thompson, Boston, USA; 1863.
Anderson, M.J. and Whitcomb, P.J. (2007) DOE simplified practical tools for effective experimentation. 2nd edition Productivity Press (New York).
Fernandez, L., Romieu, C., Moing, A., Bouquet, A., Maucourt, M., Thomas, M.R., and Torregrosa, L. (2006) The Grapevine fleshless berry mutation. A unique genotype to investigate differences between fleshy and non fleshy fruits. Plant Physiology 140, 537–47.
Fisher, R. (1926) The arrangement of field experiments. Journal of the Ministry of Agriculture of Great Britain 33, 503–13.
Peric-Concha, N. and Long, P.F. (2003) Mining the microbial metabolome: a new frontier for natural product lead discovery. Drug Discovery Today 8, 1078–84.
Rocke, D.M. (2004) Design and analysis of experiments with high throughput biological assay data. Seminars in Cell & Developmental Biology 15, 703–13.
Usadel, B., Nagel, A., Steinhauser, D., Gibon, Y., Bläsing, O.E., Redestig, H., et al. (2006) PageMan: An interactive ontology tool to generate, display, and annotate overview graphs for profiling experiments. BMC Bioinformatics 7, 535.
Lay, J.O., Liyanagea, R., Borgmannb, S., and Wilkins, C.L. (2006) Problems with the “omics”. Trends in Analytical Chemistry 25, 1046–56.
Pan, W. (2002) A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics 18, 546–54.
Benjamini, Y. and Hochberg, Y. (1995) Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 57, 289–300.
Festing, M. (1994) Reduction of animal experimental design and quality of experiments. Laboratory Animals 28, 212–21.
Sumner, L.W., Mendes, P., and Dixon, R.A. (2003) Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry 62, 817–36.
Ap Rees, T. and Hill, S.A. (1994) Metabolic control analysis of plant metabolism. Plant, Cell and Environment 17, 587–99.
Fiehn, O. (2002) Metabolomics: the link between genotypes and phenotypes. Plant Molecular Biology 48, 155–71.
Giavalisco, P., Hummel, J., Lisec, J., Inostroza, A., C, Catchpole, G., and Willmitzer, L. (2008) High-Resolution Direct Infusion-Based Mass Spectrometry in Combination with Whole C-13 Metabolome Isotope Labeling Allows Unambiguous Assignment of Chemical Sum Formulas. Analytical Chemistry 80, 9417–25.
Kopka, J., Fernie, A.R., Weckwerth, W., Gibon, Y., and Stitt, M. (2004) Metabolite profiling in plant biology: platforms and destinations. Genome Biology 5, 109.
Lommen, A., Weseman, J.M., Smith, G.O., and Noteborn, H.P.J.M. (1998) On the detection of environmental effects on complex matrices combining off-line liquid chromatography and 1H-NMR. Biodegradation 9, 513–25.
Bailey, N.J.C., Oven, M., Holmes, E., Nicholson, J.K., and Zenk, M.H. (2003) Metabolomic analysis of the consequences of cadmium exposure in Silene cucubalus cell cultures via 1H NMR spectroscopy and chemometrics. Phytochemistry 62, 851–8.
Ott, K.-H., AranÌbar, N., Singh, B., and Stockton, G.W. (2003) Metabonomics classifies pathways affected by bioactive compounds. Artificial neural network classification of NMR spectra of plant extracts. Phytochemistry 62, 971–85.
Noteborn, H.P.J.M., Lommen, A., van der Jagt, R.C., and Weseman, J.M. (2000) Chemical fingerprinting for the evaluation of unintended secondary metabolic changes in transgenic food crops. Journal of Biotechnology 77, 103–14.
Le Gall, G., DuPont, M.S., Mellon, F.A., Davis, A.L., Collins, G.J., Verhoeyen, M.E., and Colquhoun, I.J. (2003) Characterization and Content of Flavonoid Glycosides in Genetically Modified Tomato (Lycopersicon esculentum) Fruits. Journal of Agricultural and Food Chemistry 51, 2438–46.
Saito, K., Dixon, R.A., and Willmitzer, L. (2006) Plant Metabolomics. Springer (Berlin Heidelberg).
Gullberg, J., Jonsson, P., Nordstrom, A., Sjostrom, M., and Moritz, T. (2004) Design of experiments: an efficient strategy to identify factors influencing extraction and derivatization of Arabidopsis thaliana samples in metabolomic studies with gas chromatography/mass spectrometry. Analytical Biochemistry 331, 283–95.
Lunn, J.E., Feil, R, Hendriks, J.H.M., Gibon, Y., Morcuende, R., Osuna, D., et al. (2006) Sugar-induced increases in trehalose 6-phosphate are correlated with redox activation of ADPglucose pyrophosphorylase and higher rates of starch synthesis in Arabidopsis thaliana. Biochemical Journal 397, 139–48.
Bonferroni, C.E. (1935) Il calcolo delle assicurazioni su gruppi di teste, in Studi in Onore del Professore Salvatore Ortu Carboni. Rome Italy; pp. 13–60.
Yang, M.C.K., Yang, J.J., McIndoe, R.A., and She, J.X. (2003) Microarray experimental design: power and sample size considerations. Physiological Genomics 16, 24–8.
Pawitan, Y., Michiels, S., Koscielny, S., Gusnanto, A., and Ploner, A. (2005) False discovery rate, sensitivity and sample size for microarray studies. Bioinformatics 21, 3017–24.
Jørstad, T.S., Langaas, M., and Bones, A.M. (2007) Understanding sample size: what determines the required number of microarrays for an experiment? Trends in Plant Science 12, 46–50.
Broadhurst, D.I. and Kell, D.B. (2006) Statistical strategies for avoiding false discoveries in metabolomics and related experiments. Metabolomics 2, 171–96.
Gibon, Y., Vigeolas, H., Tiessen, A., Geigenberger, P., and Stitt, M. (2002) Sensitive and high throughput metabolite assays for inorganic pyrophosphate, ADPGlc, nucleotide phosphates, and glycolytic intermediates based on a novel enzymic cycling system. Plant Journal 30, 221–35.
Mashego M.R., Wu L., Van Dam J.C., Ras C., Vinke J.L., Van Winden W.A., et al. (2004) MIRACLE: mass isotopomer ratio analysis of U-C-13-labeled extracts. A new method for accurate quantification of changes in concentrations of intracellular metabolites. Biotechnology and Bioengineering 85 620–8.
Huang, X. and Regnier, F.E. (2008) Differential Metabolomics Using Stable Isotope Labeling and Two-Dimensional Gas Chromatography with Time-of-Flight Mass Spectrometry. Analytical Chemistry 80, 107–14.
Gibon, Y., Usadel, B., Blaesing, O.E., Kamlage, B., Hoehne, M., Trethewey, R., and Stitt, M. (2006) Integration of metabolite with transcript and enzyme activity profiling during diurnal cycles in Arabidopsis rosettes. Genome Biolology 7, R76.
Scholz, M., Gatzek, S., Sterling, A., Fiehn, O., and Selbig, J. (2004) Metabolite fingerprinting: detecting biological features by independent component analysis. Bioinformatics 20, 2447–54.
Schauer, N., Semel, Y., Roessner, U., Gur, A., Balbo, I., Carrari, F., et al. (2006) Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nature Biotechnology 24, 447–54.
Keurentjes, J.J., Fu, J., de Vos, C.H., Lommen, A., Hall, R.D., Bino, R.J., et al. (2006) The genetics of plant metabolism. Nature Genetics 38, 842–9.
Rowe, H.C., Hansen, B.G., Halkier, B.A., and Kliebenstein, D.J. (2008) Biochemical Networks and Epistasis Shape the Arabidopsis thaliana Metabolome. The Plant Cell 20, 1199–216.
Fernie, A.R. and Schauer, N. (2009) Metabolomics-assisted breeding: a viable option for crop improvement? Trends in Genetics 25 39–48.
Yu, J., Holland, J.B., McMullen, M.D., and Buckler, E.S. (2008) Genetic Design and Statistical Power of Nested Association Mapping in Maize. Genetics 178, 539–51.
Thimm, O., Bläsing, O.E., Usadel, B., and Gibon, Y. (2006) Evaluation of the transcriptome and genome to inform the study of metabolic control, in Control of Primary Metabolism in Plants. (Plaxton B, McManus M, eds.) Blackwell Publishing Oxford (UK). pp. 1–23.
Stitt, M., Gibon, Y., Lunn, J.E., and Piques, M. (2006) Multilevel genomics analysis of carbon signalling during low carbon availability: coordinating the supply and utilisation of carbon in a fluctuating environment. Functional Plant Biology 34, 526–49.
Hannemann, J., Poorter, H., Usadel, B., Bläsing, O.E., Finck, A., Tardieu, F., et al. (2009) Xeml Lab: a software suite for a standardised description of the growth environment of plants. Plant, Cell and Environment 32, 1185–200.
Sultan, S.E. (2000) Phenotypic plasticity for plant development, function and life history. Trends in Plant Science 5, 537–42.
Allan, W.L. and Shelp, B.J. (2006) Fluctuations of gamma-aminobutyrate, gamma-hydroxybutyrate and related amino acids in Arabidopsis leaves as a function of the light–dark cycle, leaf age, and N stress. Canadian Journal of Botany 84, 1339–46.
Geiger, D.R. and Servaites, J.C. (1994) Diurnal regulation of photosynthetic carbon metabolism in C3 plants. Annual Review of Plant Physiology 45, 235–56.
Winter, H., Lohaus, G., and Heldt, H.W. (1992) Phloem transport of amino-acids in relation to their cytosolic levels in barley leaves. Plant Physiology 99, 996–1004.
Fahnenstich, H., Saigo, M., Niessen, M., Drincovich, M., F, Flügge, U.-I., and Maurino, V.G. (2008) Malate and fumarate emerge as key players in primary metabolism: Arabidopsis thaliana overexpressing C4-NADP-ME offer a way to manipulate the levels of malate and to analyse the physiological consequences, in Photosynthesis. Energy from the Sun (J.F. Allen, E. Gantt, J.H. Golbeck and B. Osmond eds.) Springer-Verlag, Heidelberg, Germany pp. 971–5.
Ma, F. and Cheng, L. (2003) The sun-exposed peel of apple fruit has higher xanthophyll cycle dependent thermal dissipation and antioxidants of the ascorbate/glutathione pathway than the shaded peel. Plant Science 165, 819–27.
Sharkey, T.D., Stitt, M., Heineke, D., Gerhardt, R., Raschke, K., and Heldt, H.W. (1986) Limitation of Photosynthesis by Carbon Metabolism: II. O2-Insensitive CO2 Uptake Results from Limitation Of Triose Phosphate Utilization. Plant Physiology 81, 1123–9.
Ap Rees, T., Fuller, W.A., and Wright, B.W. (1977) Measurements of glycolytic intermediates during the onset of thermogenesis in the spadix of Arum maculatum. Biochimica Biophysica Acta 461, 274–82.
Verdonk, J.C., de Vos, C.H.R., Verhoeven, H.A., Haring, M.A., van Tunen, A.J., and Schuurink, R.C. (2003) Regulation of floral scent production in petunia revealed by targeted metabolomics. Phytochemistry 62, 997–1008.
Tikunov Y.M., Verstappen F.W., and Hall R.D. (2007) Metabolomic profiling of natural volatiles: headspace trapping: GC-MS. Methods in Molecular Biology 358 39–53.
Tikunov, Y., Lommen, A., de Vos, C.H., Verhoeven, H.A., Bino, R.J., Hall, R.D., and Bovy, A.G. (2005) A Novel Approach for Nontargeted Data Analysis for Metabolomics. Large-Scale Profiling of Tomato Fruit Volatiles. Plant Physiology 139, 1125–37.
Brazma, A., Hingamp, P., Quackenbush, J., Sherlock, G., Spellman, P., Stoeckert, C., et al. (2001) Minimum information about a microarray experiment (MIAME) – toward standards for microarray data. Nature Genetics 29, 365–71.
Jenkins, H., Hardy, N., Beckmann, M., Draper, J., Smith, A.R., Taylor, J., et al. (2004) A proposed framework for the description of plant metabolomics experiments and their results. Nature Biotechnology 22, 1601–6.
Fiehn, O., Wohlgemuth, G., Scholz, M., Kind, T., Lee Do, Y., Lu, Y., Moon, S., and Nikolau, B. (2008) Quality control for plant metabolomics: reporting MSI-compliant studies. The Plant Journal 53 691–704.
Gruber, T.R. (1995) Toward principles for the design of ontologies used for knowledge sharing? International Journal of Human-Computer Studies 43, 907–28.
Larsson, O. and Sandberg, R. (2006) Lack of correct data format and comparability limits future integrative microarray research. Nature Biotechnology 24, 1322–3.
Scholz, M. and Fiehn, O. (2007) Setup X – A public study design database for metabolomic projects. Pacific Symposium on Biocomputing 12, 169–80.
Acknowledgements
This work was supported by the EU META-PHOR Project (FOOD-CT-2006-036220).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer Science+Business Media, LLC
About this protocol
Cite this protocol
Gibon, Y., Rolin, D. (2011). Aspects of Experimental Design for Plant Metabolomics Experiments and Guidelines for Growth of Plant Material. 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_2
Download citation
DOI: https://doi.org/10.1007/978-1-61779-594-7_2
Published:
Publisher Name: Humana Press
Print ISBN: 978-1-61779-593-0
Online ISBN: 978-1-61779-594-7
eBook Packages: Springer Protocols