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Two-dimensional gel electrophoresis remains the most widely used technique for protein separation in plant proteomics experiments. Despite the continuous technical advances and improvements in current 2-DE protocols, an adequate and correct experimental design and statistical analysis of the data tend to be ignored or not properly documented in current literature. Both proper experimental design and appropriate statistical analysis are requested in order to confidently discuss our results and to conclude from experimental data.In this chapter, we describe a model procedure for a correct experimental design and a complete statistical analysis of proteomic dataset. Our model procedure covers all of the steps in data mining and processing, starting with the data preprocessing (transformation, missing value imputation, definition of outliers) and univariate statistics (parametric and nonparametric tests), and finishing with multivariate statistics (clustering, heat-mapping, PCA, ICA, PLS-DA).

In this first, introductory chapter, it is intended to summarize from a methodological point of view the state of the art in plant proteomics, focusing on mass spectrometry-based strategies. Thus, this chapter is mainly directed at beginners or at those trying to get into the field, rather than at those with real experience or a long trajectory in plant proteomics research. The different alternative workflows, methods, techniques, and protocols from the experimental design to the data analysis will be briefly commented, with cross references to previous monographs and reviews, as well as to the rest of the book chapters. The difficulty of working with proteins, together with the power, limitations, and challenges of the approach will also be briefly discussed.Proteins, as molecular entities, and the cell proteome, as a whole, are much more complex than what we thought in the past and can be studied in a single experiment. Because of that, fractionation and complementary strategies are required for its study. The MS analysis of complex samples may result in up to 100,000-peptide spectra that cannot be easily analyzed with standard procedures. Therefore, proteomics, more than other -omics, needs a dry lab, time, and an effort in data mining.As main conclusion, it can be stated that proteomics is in its beginnings. It is starting to make important contributions to a proper gene annotation, identification, and characterization of gene products or protein species and to the knowledge of living organisms, having also an enormous application potential to translational research. However, and despite its great potential, and as in any other experimental approach, it is far from being a Pandora's Box. In the case of plant research, the full potential of proteomics is quite far from being totally exploited, and second-, third-, and fourth-generation proteomics techniques are still of very limited use. Most of the plant proteomics papers so far published belong to the descriptive, subcellular, and comparative proteomics subgroup, mainly using a few experimental model systems-those whose genome has been sequenced-and being from a biological point of view quite descriptive and speculative. From now on we should put more emphasis on the study of posttranslational proteomics and interactomics, and move to targeted, hypothesis-driven approaches. Furthermore, and even more important, we should move to data validation through other -omics or classical biochemical strategies, in an attempt to get a deeper, real, and more accurate view and understanding of cell biology. In the modern Systems Biology concept, proteomics must be considered as a part of a global, multidisciplinary approach. Making biological sense of a proteomics experiment requires a proper experimental design, data validation, interpretation, and publication policy.

It is impossible to capture in just one experiment all or most of the total set of protein species that constitute the cell's proteome. Thus, according to our results, and even considering that they depend on the experimental system carried out (plant, yeast, fungi, or bacteria), the best protein extraction protocol yielded less than 20 % of the total amount of proteins, as determined by the Kjeldahl method. For this reason, protein cataloguing and the whole proteome characterization require the use of firstly, fractionation techniques at the cellular, subcellular, protein, or peptide level, and secondly, the use of complementary approaches.Within our current research on Holm oak (Quercus ilex subsp. ballota), we aim to characterize its seed proteome. For that we have optimized an experimental workflow in which the Osborne sequential protein extraction (Osborne, Science 28:417-427, 1908) is combined with downstream electrophoretic protein separation or shotgun MS analysis. In general, it can be used to study any plant seed, as well as to investigate on seed maturation and germination, genotype characterization, allergens identification, food traceability, and substantial equivalence, among others.

Proteomic technologies have become a successful tool to provide relevant information on fungal biology. In the case of plant pathogenic fungi, this approach would allow a deeper knowledge of the interaction and the biological cycle of the pathogen, as well as the identification of pathogenicity and virulence factors. These two elements open up new possibilities for crop disease diagnosis and environment-friendly crop protection. Phytopathogenic fungi, due to its particular cellular characteristics, can be considered as a recalcitrant biological material, which makes it difficult to obtain quality protein samples for proteomic analysis. This chapter focuses on protein extraction for gel- and LC-based proteomics with specific protocols of our current research with Botrytis cinerea.