Sources of Experimental Variation in 2-D Maps: The Importance of Experimental Design in Gel-Based Proteomics

  • Cristina-Maria Valcu
  • Mihai Valcu
Part of the Methods in Molecular Biology book series (MIMB, volume 1384)


The success of proteomic studies employing 2-D maps largely depends on the way surveys and experiments have been organized and performed. Planning gel-based proteomic experiments involves the selection of equipment, methodology, treatments, types and number of samples, experimental layout, and methods for data analysis. A good experimental design will maximize the output of the experiment while taking into account the biological and technical resources available. In this chapter we provide guidelines to assist proteomics researchers in all these choices and help them to design quantitative 2-DE experiments.

Key words

Biological variation Optimal sample size Power analysis Replication Sampling Sample pools Technical variation 


  1. 1.
    R Development Core Team (2014) R: A language and environment for statistical computing. R Foundation for Statistical Computing Vienna, AustriaGoogle Scholar
  2. 2.
    Martin J (2012) pamm: Power analysis for random effects in mixed models. R package version 0.7 Google Scholar
  3. 3.
    Tibshirani R, Chu G, Narasimhan B et al. (2011) samr: Significance analysis of microarrays. R package version 2.0 Google Scholar
  4. 4.
    W GR (2009) ssize: Compute and plot power, required sample-size, or detectable effect size for gene expression experiment. R package version 1.28.0 Google Scholar
  5. 5.
    Nyangoma S (2014) clippda package: A package for clinical proteomic profiling data analysis. R package version 1.14.0 Google Scholar
  6. 6.
    Lenth R (2001) Some practical guidelines for effective sample size determination. Am Statistician 55:187–193CrossRefGoogle Scholar
  7. 7.
    Alge CS, Hauck SM, Priglinger SG et al. (2006) Differential protein profiling of primary versus immortalized human RPE cells identifies expression patterns associated with cytoskeletal remodeling and cell survival. J Proteome Res 5:862–878CrossRefPubMedGoogle Scholar
  8. 8.
    Karp NA, Lilley KS (2007) Design and analysis issues in quantitative proteomics studies. Proteomics 7:42–50CrossRefPubMedGoogle Scholar
  9. 9.
    Jorge I, Navarro RM, Lenz C et al. (2005) The holm Oak leaf proteome: analytical and biological variability in the protein expression level assessed by 2‐DE and protein identification tandem mass spectrometry de novo sequencing and sequence similarity searching. Proteomics 5:222–234CrossRefPubMedGoogle Scholar
  10. 10.
    Richter SH, Garner JP, Auer C et al. (2010) Systematic variation improves reproducibility of animal experiments. Nat Methods 7:167–168CrossRefPubMedGoogle Scholar
  11. 11.
    Karp NA, Lilley KS (2005) Maximising sensitivity for detecting changes in protein expression: experimental design using minimal CyDyes. Proteomics 5:3105–3115CrossRefPubMedGoogle Scholar
  12. 12.
    Corzett TH, Fodor IK, Choi MW et al. (2006) Statistical analysis of the experimental variation in the proteomic characterization of human plasma by two-dimensional difference gel electrophoresis. J Proteome Res 5:2611–2619CrossRefPubMedGoogle Scholar
  13. 13.
    Marouga R, David S, Hawkins E (2005) The development of the DIGE system: 2D fluorescence difference gel analysis technology. Anal Bioanal Chem 382:669–678CrossRefPubMedGoogle Scholar
  14. 14.
    Wheelock ÅM, Morin D, Bartosiewicz M et al. (2006) Use of a fluorescent internal protein standard to achieve quantitative two‐dimensional gel electrophoresis. Proteomics 6:1385–1398CrossRefPubMedGoogle Scholar
  15. 15.
    Lee JK (2011) Statistical bioinformatics: for biomedical and life science researchers. John Wiley & Sons, Hoboken, NJGoogle Scholar
  16. 16.
    Karp NA, Spencer M, Lindsay H et al. (2005) Impact of replicate types on proteomic expression analysis. J Proteome Res 4:1867–1871CrossRefPubMedGoogle Scholar
  17. 17.
    Hunt SM, Thomas MR, Sebastian LT et al. (2005) Optimal replication and the importance of experimental design for gel-based quantitative proteomics. J Proteome Res 4:809–819CrossRefPubMedGoogle Scholar
  18. 18.
    Chich J-F, David O, Villers F et al. (2007) Statistics for proteomics: experimental design and 2-DE differential analysis. J Chromatogr B 849:261–272CrossRefGoogle Scholar
  19. 19.
    Karp NA, Lilley KS (2009) Investigating sample pooling strategies for DIGE experiments to address biological variability. Proteomics 9:388–397CrossRefPubMedGoogle Scholar
  20. 20.
    Valcu C-M, Valcu M (2007) Reproducibility of two-dimensional gel electrophoresis at different replication levels. J Proteome Res 6:4677–4683CrossRefPubMedGoogle Scholar
  21. 21.
    Ruxton G, Colegrave N (2011) Experimental design for the life sciences. Oxford University Press, OxfordGoogle Scholar
  22. 22.
    Krzywinski M, Altman N, Blainey P (2014) Points of Significance: Nested designs. Nat Methods 11:977–978CrossRefPubMedGoogle Scholar
  23. 23.
    Kowalski SM, Potcner KJ (2003) How to recognize a split-plot experiment. Quality Progress 36:60–66Google Scholar
  24. 24.
    Valcu C-M, Reger K, Ebner J et al. (2012) Accounting for biological variation in differential display two-dimensional electrophoresis experiments. J Proteomics 75:3585–3591CrossRefPubMedGoogle Scholar
  25. 25.
    Horgan GW (2007) Sample size and replication in 2D gel electrophoresis studies. J Proteome Res 6:2884–2887CrossRefPubMedGoogle Scholar
  26. 26.
    Aberson CL (2011) Applied power analysis for the behavioral sciences. Routledge, New York, NYGoogle Scholar
  27. 27.
    Zhang S-D, Gant TW (2005) Effect of pooling samples on the efficiency of comparative studies using microarrays. Bioinformatics 21:4378–4383CrossRefPubMedGoogle Scholar
  28. 28.
    Bahrman N, Zivy M, Damerval C et al. (1994) Organisation of the variability of abundant proteins in seven geographical origins of maritime pine (Pinus pinaster Ait.). Theor Appl Genet 88:407–411PubMedGoogle Scholar
  29. 29.
    Westermeier R, Marouga R (2005) Protein detection methods in proteomics research. Biosci Rep 25:19–32CrossRefPubMedGoogle Scholar
  30. 30.
    Ferguson RE, Hochstrasser DF, Banks RE (2007) Impact of preanalytical variables on the analysis of biological fluids in proteomic studies. Proteomics-Clin Appl 1:739–746CrossRefPubMedGoogle Scholar
  31. 31.
    Pasella S, Baralla A, Canu E et al. (2013) Pre-analytical stability of the plasma proteomes based on the storage temperature. Proteome Sci 11:10PubMedCentralCrossRefPubMedGoogle Scholar
  32. 32.
    Hulmes JD, Bethea D, Ho K et al. (2004) An investigation of plasma collection, stabilization, and storage procedures for proteomic analysis of clinical samples. Clin Proteomics 1:17–31CrossRefGoogle Scholar
  33. 33.
    Shaw J, Rowlinson R, Nickson J et al. (2003) Evaluation of saturation labelling two‐dimensional difference gel electrophoresis fluorescent dyes. Proteomics 3:1181–1195CrossRefPubMedGoogle Scholar
  34. 34.
    Valcu C-M, Junqueira M, Shevchenko A et al. (2009) Comparative proteomic analysis of responses to pathogen infection and wounding in Fagus sylvatica. J Proteome Res 8:4077–4091CrossRefPubMedGoogle Scholar
  35. 35.
    Khoudoli GA, Porter IM, Blow JJ et al. (2004) Optimisation of the two-dimensional gel electrophoresis protocol using the Taguchi approach. Proteome Sci 2:6PubMedCentralCrossRefPubMedGoogle Scholar
  36. 36.
    Lansky D (2001) Strip-plot designs, mixed models, and comparisons between linear and non-linear models for microtitre plate bioassays. Dev Biol 107:11–23Google Scholar
  37. 37.
    Castelloe JM (2000) Sample size computations and power analysis with the SAS system. In: Proceedings of the Twenty-Fifth Annual SAS User’s Group International Conference. Citeseer, p 265–225Google Scholar
  38. 38.
    Valcu M, Valcu C-M (2011) Data transformation practices in biomedical sciences. Nat Methods 8:104–105CrossRefPubMedGoogle Scholar
  39. 39.
    Karp NA, Mccormick PS, Russell MR et al. (2007) Experimental and statistical considerations to avoid false conclusions in proteomics studies using differential in-gel electrophoresis. Mol Cell Proteomics 6:1354–1364CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Cristina-Maria Valcu
    • 1
  • Mihai Valcu
    • 1
  1. 1.Department of Behavioural Ecology & Evolutionary GeneticsMax Planck Institute for OrnithologySeewiesenGermany

Personalised recommendations