Skip to main content
SpringerLink
Log in

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

  • Protocol
  • First Online:
Plant Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1778))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 139.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 219.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Dixon RA, Strack D (2003) Phytochemistry meets genome analysis, and beyond. Phytochemistry 62:815–816

    Article  CAS  PubMed  Google Scholar 

  2. Bijlsma S, Bobeldijk I, Verheij ER et al (2006) Large-scale human metabolomics studies: a strategy for data (pre-)processing and validation. Anal Chem 78:567–574

    Article  CAS  Google Scholar 

  3. Schauer N, Semel Y, Roessner U et al (2006) Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nat Biotechnol 24:447–454

    Article  CAS  PubMed  Google Scholar 

  4. Fiehn O (2002) Metabolomics - the link between genotypes and phenotypes. Plant Mol Biol 48:155–171

    Article  CAS  PubMed  Google Scholar 

  5. Fu J, Keurentjes JJ, Bouwmeester H et al (2009) System-wide molecular evidence for phenotypic buffering in Arabidopsis. Nat Genetics 41:166–167

    Article  CAS  PubMed  Google Scholar 

  6. Rowe HC, Hansen BG, Halkier BA et al (2008) Biochemical networks and epistasis shape the Arabidopsis thaliana metabolome. Plant Cell 20:1199–1216

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wentzell AM, Rowe HC, Hansen BG et al (2007) Linking metabolic QTLs with network and cis-eQTLs controlling biosynthetic pathways. PLoS Genet 3:1687–1701

    Article  CAS  PubMed  Google Scholar 

  8. Biais B, Bernillon S, Deborde C et al (2012) Precautions for harvest, sampling, storage, and transport of crop plant metabolomics samples. In: Hardy N, Hall R (eds) Plant Metabolomics, Methods in Molecular Biology (Methods and Protocols), vol 860. Humana Press, New York, pp 51–63

    Chapter  Google Scholar 

  9. Gibon Y, Rolin D (2012) Aspects of experimental design for plant metabolomics experiments and guidelines for growth of plant material. Methods Mol Biol 860:13–30

    Article  CAS  PubMed  Google Scholar 

  10. Sysi-Aho M, Katajamaa M, Yetukuri L, Oresic M (2007) Normalization method for metabolomics data using optimal selection of multiple internal standards. BMC Bioinformatics 15:8–93

    Google Scholar 

  11. van der Kloet FM, Bobeldijk I, Verheij ER, Jellema RH (2009) Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. J Proteome Res 8:5132–5141

    Article  CAS  PubMed  Google Scholar 

  12. van der Greef J, Martin S, Juhasz P et al (2007) The art and practice of systems biology in medicine: Mapping patterns of relationships. J Proteome Res 6:1540–1559

    Article  CAS  PubMed  Google Scholar 

  13. Dunn WB, Broadhurst D, Brown M et al (2008) Metabolic profiling of serum using ultra performance liquid chromatography and the LTQ-orbitrap mass spectrometry system. J Chromatogr B Anal Technol Biomed Life Sci 871:288–298

    Article  CAS  Google Scholar 

  14. Chen MJ, Rao RP, Zhang Y et al (2014) A modified data normalization method for GC-MS-based metabolomics to minimize batch variation. Spring 3:439

    Article  CAS  Google Scholar 

  15. Fiehn O, Kopka J, Dörmann P et al (2001) Metabolite profiling for plant functional genomics. Nat Biotechnol 18:1157–1161

    Article  CAS  Google Scholar 

  16. Lai Z, Fiehn O (2016) Mass spectral fragmentation of trimethylsilylated small molecules. Mass Spectrom Rev 9999:1–13

    Google Scholar 

  17. Lisec J, Schauer N, Kopka J et al (2006) Gas chromatography mass spectrometry-based metabolite profiling in plants. Nat Protocols 1:387–396

    Article  CAS  PubMed  Google Scholar 

  18. Joseph B, Corwin JA, Kliebenstein DJ (2015) Genetic variation in the nuclear and organellar genomes modulates stochastic variation in the metabolome, growth, and defense. PLoS Genet 11:e1004779

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Boyes DC, Zayed AM, Ascenzi R et al (2001) Growth stage-based phenotypic analysis of arabidopsis: a model for high throughput functional genomics in plants. Plant Cell 13:1499–1510

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Brukhin V, Hernould M, Gonzalez N et al (2003) Flower development schedule in tomato Lycopersicon esculentum cv. sweet cherry. Sex Plant Reprod 15:311–320

    Google Scholar 

  21. Markert B (1995) Sample preparation (cleaning, drying, homogenization) for trace element analysis in plant matrices. Science Total Environ 176:45–61

    Article  CAS  Google Scholar 

  22. Osorio S, Do PT, Fernie AR (2012) Profiling primary metabolites of tomato fruit with gas chromatography-mass spectrometry. In: Hardy N, Hall R (eds) Plant Metabolomics, Methods in Molecular Biology (Methods and Protocols), vol 860. Humana Press, New York, pp 101–109

    Chapter  Google Scholar 

  23. Kopka J, Fernie A, Weckwerth W et al (2004) Metabolite profiling in plant biology: platforms and destinations. Genome Biol 5:109

    Article  PubMed  PubMed Central  Google Scholar 

  24. Allwood JW, De Vos RC, Moing A et al (2011) Plant metabolomics and its potential for systems biology research background concepts, technology, and methodology. In: Jameson D, Verma M, Westerhoff HV (eds) Methods in Enzymology, vol 500. Academic Press, Amsterdam, pp 299–33623

    Google Scholar 

  25. Allwood JW, Clarke A, Goodacre R, Mur LA (2010) Dual metabolomics: a novel approach to understanding plant-pathogen interactions. Phytochemistry 71:590–597

    Article  CAS  PubMed  Google Scholar 

  26. Karpievitch YV, Nikolic SB, Wilson R et al (2014) Metabolomics data normalization with EigenMS. PLoS One 9:e116221

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sehgal D, Singh R, Rajpal VR (2016) Quantitative trait loci mapping in plants: concepts and approaches. In: Rajpal V, Rao S, Raina S (eds) Molecular breeding for sustainable crop improvement. sustainable development and biodiversity, vol 11. Springer, Cham

    Google Scholar 

  28. Strehmel N, Hummel J, Erban A et al (2008) Retention index thresholds for compound matching in GC-MS metabolite profiling. J Chromatogr B AnalTechnol Biomed Life Sci 871:182–190

    Article  CAS  Google Scholar 

  29. Broman KW (2001) Review of statistical methods for QTL mapping in experimental crosses. Lab Anim 30:44–52

    CAS  Google Scholar 

  30. Tanksley SD (1993) Mapping polygenes. Ann Rev Genetics 27:205–233

    Article  CAS  Google Scholar 

  31. Collard BCY, Pang ECK, Taylor PWJ (2003) Selection of wild Cicer accessions for the generation of mapping populations segregating for resistance to ascochyta blight. Euphytica 130:1–9

    Article  CAS  Google Scholar 

  32. Soltis NE, Kliebenstein DJ (2015) Natural variation of plant metabolism: genetic mechanisms, interpretive caveats, and evolutionary and mechanistic insights. Plant Physiol 169:1456–1468

    PubMed  PubMed Central  CAS  Google Scholar 

  33. Han F, Ullrich SE, Kleinhofs A et al (1997) Fine structure mapping of the barley chromosome-1 centromere region containing malting-quality QTLs. Theoretical Applied Genetics 95:903–910

    Article  CAS  Google Scholar 

  34. Rae AM, Howell EC, Kearsey MJ (1999) More QTL for flowering time revealed by substitution lines in Brassica oleracea. Heredity 83:586–596

    Article  PubMed  Google Scholar 

  35. von Korff M, WJ LK, Pillen K (2004) Development of candidate introgression lines using an exotic barley accession (Hordeum vulgare ssp spontaneum) as donor. Theoretical Applied Genetics 109:1736–1745

    Article  CAS  Google Scholar 

  36. Balding DJ, Bishop M, Cannings C, Jansen RC (2004) Quantitative Trait Loci in Inbred Lines. In: Balding DJ, Bishop M, Cannings C (eds) Handbook of Statistical Genetics, 3rd edn. John Wiley & Sons Ltd, Chichester, UK

    Chapter  Google Scholar 

  37. Monforte AJ, Tanksley SD (2000) Development of a set of near isogenic and backcross recombinant inbred lines containing most of the Lycopersicon hirsutum genome in a L-esculentum genetic background: A tool for gene mapping and gene discovery. Genome 43:803–813

    Article  CAS  PubMed  Google Scholar 

  38. Jeuken MJW, Lindhout P (2004) The development of lettuce backcross inbred lines (BILs) for exploitation of the Lactuca saligna (wild lettuce) germplasm. Theoretical Applied Genetics 109:394–401

    Article  CAS  PubMed  Google Scholar 

  39. Blanco A, Simeone R, Gadaleta A (2006) Detection of QTLs for grain protein content in durum wheat. Theoretical Applied Genetics 113:563–565

    Article  CAS  Google Scholar 

  40. Jamann TM, Balint-Kurti PJ, Holland JB (2015) QTL mapping using high-throughput sequencing. In: Alonso J, Stepanova A (eds) Plant functional genomics, Methods in molecular biology, vol 1284. Humana Press, New York

    Google Scholar 

  41. Platt A, Vilhjalmsson BJ, Nordborg M (2010) Conditions under which genome-wide association studies will be positively misleading. Genetics 186:1045–1052

    Article  PubMed  PubMed Central  Google Scholar 

  42. Larsson SJ, Lipka AE, Buckler ES (2013) Lessons from Dwarf8 on the strengths and weaknesses of structured association mapping. PLoS Genet 9:e1003246

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

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).

Author information

Authors and Affiliations

  1. Max Planck Institute of Molecular Plant Physiology, Potsdam-Golm, Germany

    Saleh Alseekh, Si Wu, Yariv Brotman & Alisdair R. Fernie

  2. Center of Plant System Biology and Biotechnology, Plovdiv, Bulgaria

    Saleh Alseekh

  3. Department of Life Sciences, Ben-Gurion University of the Negev, Beersheba, Israel

    Si Wu & Yariv Brotman

Authors
  1. Saleh Alseekh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Si Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yariv Brotman
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Alisdair R. Fernie
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saleh Alseekh .

Editor information

Editors and Affiliations

  1. Plant Metabolomics Laboratory, Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa (ITQB NOVA), Oeiras, Portugal

    Carla António

Rights and permissions

Reprints and permissions

Copyright information

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

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Alseekh, S., Wu, S., Brotman, Y., Fernie, A.R. (2018). Guidelines for Sample Normalization to Minimize Batch Variation for Large-Scale Metabolic Profiling of Plant Natural Genetic Variance. In: António, C. (eds) Plant Metabolomics. Methods in Molecular Biology, vol 1778. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7819-9_3

Download citation

  • DOI: https://doi.org/10.1007/978-1-4939-7819-9_3

  • Published:

  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-7818-2

  • Online ISBN: 978-1-4939-7819-9

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation