Barley (Hordeum vulgare) is one of the earliest domesticated cereals and it is the fourth most important crop world-wide in terms of total dry production, only exceeded by maize, rice, and wheat. Barley is mainly used in the brewing industry and as animal feed, but in certain areas of the world it is an important food source for humans (Schulte et al., 2009). The increasing demand for food due to the growing world population has propelled the implementation of plant breeding programs and biomolecular plant research to improve sustainable crop production. Prioritized areas include research in plant resistance to abiotic stress such as soil salinity, temperature, drought, nutrient uptake (Saeed et al., 2012), and biotic stress caused by other living organisms and pathogens (Dreher and Callis, 2007). Barley is by nature diploid, has a low chromosome number (2n = 14) and a large genome size (5.1 Gb), is easy to cross-breed and is able to grow under various climatic conditions. These abilities and the fact that barley is an extremely important crop makes it desirable to identify genes responsible for specific beneficial traits in order to improve crop production and sustainability (Saisho and Takeda, 2011). The recently completed whole-genome sequencing of the barley genome (Mayer et al., 2012), gave rise to several interesting observations. A total of 26,159 high confidence genes with gene-family similarity to other plant genomes, and 53,220 genes with lack of homology denoted low confidence genes were identified. By comparison to Arabidopsis thaliana, the barley genome was estimated to encompass 30,400 genes. RNA sequencing data indicated extensive alternative splicing of the coding regions of the high confidence genes (Mayer et al., 2012), this adds to protein diversity and may play a role in protein regulation and gene expression (Syed et al., 2012). These data opens new opportunities for pursuing in-depth studies of barley biology by using genomics, transcriptomics, metabolomics, and proteomics approaches.

The chloroplast is one of the specialized plastids in the plant cell and it conducts important processes such as photosynthesis and biosynthesis of amino acids, starch, and vitamins. The chloroplast contains its own genome, but most of the estimated 2000–3000 chloroplast proteins are encoded by the nuclear genome. Targeting of proteins to the chloroplast often requires N-terminal pre-sequences called chloroplast transit peptides (cTPs), which to some extend can be predicted from the genome by using computational methods such as chloroP, targeP, WoLF PSORT, iPSORT, predotar, or Protein Prowler (Emanuelsson et al., 1999; Bannai et al., 2002; Small et al., 2004; Boden and Hawkins, 2005; Horton et al., 2007).

Functional proteomics is a rapidly evolving scientific discipline that is driven by advancements in a series of bioanalytical and computational technologies to enable increasingly detailed studies of complex protein mixtures derived from cells, tissues, and organisms (Aebersold and Mann, 2003; Cravatt et al., 2007; Bensimon et al., 2012). The main methods used in proteomics are: (1) protein and peptide separation techniques; (2) mass spectrometry; (3) biological sequence databases and computational query tools (summarized in Boxes 1 and 2).

Proteomics technologies are now extensively used in plant biology, particularly in studies of the model plants and the most important food crops (Jorrin et al., 2007). Proteomics, i.e., the systematic study and characterization of proteins in a cell type, tissue, or a whole organism, encompasses the mapping of protein composition and abundance, protein interactions and protein localization, as well as dynamic events in protein regulatory networks, including signaling mechanisms, metabolism, and transcription (de Hoog and Mann, 2004). A majority of such studies in plants were carried out in A. thaliana and rice where completely sequenced genomes are available (Kaul et al., 2000; Goff et al., 2002). Proteome analysis of plant organelles, including chloroplasts, have been reported (Kleffmann et al., 2004). For example, proteomics strategies were used to elucidate the influence of various biotic and abiotic stresses on chloroplasts proteins.

The recently completed sequencing of the barley genome now provides a foundation for more detailed functional proteomics studies of barley biology. We therefore foresee an increased effort in barley proteomics using state-of-the-art mass spectrometry based strategies for qualitative and quantitative characterization of barley proteins, organelles and regulatory networks. Proteomics will likely play a major role in further improvements of barley cultivars, e.g., by identifying the underlying mechanisms of biotic and abiotic stress. In the following sections we provide an overview of proteomics strategies and techniques and the current state of barley chloroplast proteomics.

Several factors affect the outcome of a proteomics experiment, and need to be included in the experimental planning phase, like for example proteome complexity and protein concentration (summarized in Box 3). This section covers two classical proteomics strategies and highlights things to consider before starting a chloroplast-targeted proteomics experiment.

Purification: The first step toward success in organelle or sub proteomic experiment is the quality and purity of the sample. Contaminating proteins or unwanted cellular debris can obscure the results with respect to assignment of organelle specific proteins and their quantification (Agrawal et al., 2011). Highly purified chloroplasts or mitochondria can be obtained using a Percoll gradient centrifugation step (Neuburger et al., 1982; Aronsson and Jarvis, 2002; van Wijk, 2004; Millar et al., 2005). Endomembrane organelles such as Golgi apparatus, endoplasmic reticulum, vacuoles, and vesicles are more difficult to purify without cross-contamination from other organelles. Gentle rupture of the intact chloroplasts enables further purification of four sub-compartments (1) the inner and outer envelope membranes, (2) the stroma, (3) the thylakoid membrane, (4) the thylakoid lumen (Kieselbach et al., 1998, 2000; Peltier et al., 2000, 2006; Schubert et al., 2002; Ferro et al., 2003). The above mentioned extractions method were used for diverse plant species, and it is important to have in mind that protocols developed for a specific plant species, not necessarily works for other species. Typically intact chloroplasts are obtained using Percoll gradient centrifugation. This is by far the best way to obtain pure chloroplasts, but the yield is rather low. Less pure chloroplast can be obtained in high yields using low speed centrifugation. It is possible to obtain thylakoid, stroma, and envelope fractions using a sucrose gradient of osmotic shocked intact chloroplasts. Soluble luminal thylakoid proteins can be isolated from the thylakoid preparation using yeda press rupture of the membranes (Hall et al., 2011).

How much material is needed? It is possible to make quantitative proteomics experiments with less than 20μg of extracted protein. The number of identified proteins from such an experiment depends not only on the complexity and dynamics of the proteome but also the in-house instrumentation (Eriksson and Fenyo, 2010). In sub proteomic work the amount of starting material might exceed several grams to extract a few micro grams of a desired proteome. As an example, from 100 g of soil grown A. thaliana plants it is possible to extract approximately 1000 mg leaf protein, 100 mg thylakoid proteins, and only 0.4 mg envelope membrane protein (Froehlich et al., 2003).

What buffers should I use? There is no universal buffer composition to be used in proteomics experiments. Depending on the targeted tissue or sub-cellular compartment different protein extraction and sample preparation buffers are used (Fido et al., 2004; Mano et al., 2008). However, there are few universal rules that should be taken into consideration. Always add protease inhibitors, but be aware of the lifetime of the inhibitors, it might be short under certain conditions, or use strong denaturing buffers [e.g., 8 M urea or sodium dodecyl sulfate (SDS)] to inactivate potential proteolytic activity of enzymes present in the sample. Use metal chelating agents, e.g., ethylenediaminetetraacetic acid (EDTA) to trap free metal ions from the sample to prevent unwanted spontaneous protein oxidation – this is particularly important when working with organelles such as chloroplasts and mitochondria. Most buffers used in biological experiments contains components which are not compatible with liquid chromatography-mass spectrometry (LC-MS) but if the proteins are separated by polyacrylamide gel electrophoresis (PAGE) all buffers are allowed, due to the excellent washing ability of gel plugs. For non-gel based strategies some compounds such as detergents (SDS, Triton X-100, etc.) or ampholytes compromise nanoliter-flow LC or mass spectrometry and they need to be avoided or removed prior to analysis (Xiao et al., 2004; Yeung and Stanley, 2010).

Can high abundant proteins be removed? In photosynthetic tissue the predominant protein is the carbon fixation protein ribulose-1,5-bisphosphate carboxylase/oxygenase (Rubisco). In some cases more than 50% of the total leaf protein content consist of Rubisco (Metodiev and Demirevskakepova, 1992). Such highly abundant protein will hamper both gel and non-gel based proteome analysis because this highly abundant protein will obscure other proteins and suppress their detection. In gel based studies it will dominate the gel pattern eclipsing low abundant proteins with similar physico-chemical properties. In non-gel based peptides generated from this abundant protein will saturate high performance liquid chromatography (HPLC) columns and suppress the signal from lower abundant proteins. This problem can be partially solved by removing the highly abundant protein by fractionation, antibody based spin columns, or using the relative newly developed ProteoMiner beads (Boschetti and Righetti, 2008; Frohlich et al., 2012). Removal of highly abundant proteins can also result in removal of the associated low abundant proteins (Cellar et al., 2008; Krishnan and Natarajan, 2009). Another way to reduce the complexity and the dynamics of protein sample is to perform organelle or sub-organelle fractionation. Isolation of mitochondria or a thylakoid preparation from chloroplast will exclude the majority of Rubisco protein from the analysis.

How to proceed after proteome extraction? Proteins, both for gel and non-gel based strategies (see below) need to be digested into peptides prior to mass spectrometry analysis. The aim is to generate ionizable peptides in the mass range 700–2500 Da, which is the optimal range for most biological mass spectrometers. Disulfide bridges (Cys-Cys) in proteins are typical reduced and alkylated using dithiothreitol (DTT) and iodacetamide (IAA) prior to digestion. Denaturation of the proteins improves digestion efficiency, thus contributing to the overall protein identification rate. Proteins separated by SDS-PAGE are inherently denatured and are typically cut out of the gel, reduced, S-alkylated and digested by trypsin. This is a well-established “in-gel digestion” technique routinely used by most proteomics laboratories (Shevchenko et al., 1996, 2006). In solution based digestion is a more delicate procedure. Keeping the proteins in solution, denatured and available for trypsin digestion can be facilitated by buffers containing the commercially available surfactant RapiGest, urea buffers or detergents such as sodium deoxycholate (SDC) that, in contrast to SDS can relatively easy be removed from the sample prior to the mass spectrometry analysis (Speers and Wu, 2007; Norrgran et al., 2009; Lin et al., 2012). In-solution digestion protocols where the digestion is performed within a spin filter device has become popular and is highly recommended for the digestion of protein amounts exceeding 100 μg. The filter enables washing of the sample and retention of large unwanted structures on the filter (Manza et al., 2005; Wisniewski et al., 2009).

How do I evaluate the quality of the experiment? Proteomics experiments often aim to detect differential regulated proteins between groups. This can be accomplished using a statistical test based on hypotheses about characteristics of both the biological samples that represent the population, and the variability of the technical measurements (Podwojski et al., 2012).

If possible, evaluate the protein extract by electrophoresis; this gives an overall picture of the extract. Non-gel based approaches can benefit using an internal spike-in protein standard. The protein standard is digested together with the extract, and by comparing sequence coverage and peptide intensities of the spiked-in standard among samples, the digest efficiency can be evaluated. This can be archived using selected reaction monitoring (SRM) or other label-free quantification methods. Absolute quantification can be archived using spiked-in peptides that act as internal standards (Gerber et al., 2003; Silva et al., 2006).

The areas where barley proteomics has been used can be divided into (a) industry driven biotechnology, including seed germination and maturation, beer proteomes, and malting proteomes and (b) biology driven proteomics covering plant adaptation to abiotic stress and organelle function including the chloroplast that is the focus of this review.

Biotechnology driven proteomics: Understanding the mechanisms involved in seed germination and maturation processes are important aspects in the malting industry where, e.g., enzyme amount such as amylase in different cultivars influences the conversion of starch into fermentable sugars. The work with proteome analysis of different barley seed cultivars and proteomes from different developmental stages of germinating barley started in year 2002 (Finnie et al., 2002; Ostergaard et al., 2002). 2D gels were used as a protein profiling tool. The proteins were extracted using a low salt buffer, favoring the extraction of water soluble seed proteins such as amylases and chitinases, and minimized extraction of high abundant storage proteins such as hordeins that otherwise would dominate the protein profile in the 2D gel. The TrEMBL database at that time only contained 546 barley protein sequences, so therefore most of the protein identifications were based on cross-species protein annotation using other cereals, such as rice, maize, and wheat. The strength of 2D gel electrophoresis was also pointed out in these studies, since the same protein was identified in multiple protein spots, maybe as a consequence of post-translational modifications (PTM) or multiple alleles with almost identical protein sequences.

Hynek et al. (2009) reported the enrichment of hydrophobic membrane proteins from the barley plasma membrane fraction, which may play a key role in the germination process, by using two-phase partitioning and RP chromatography. The enrichment of the membrane fraction was validated using western blotting against H⁺-ATPase, a protein located in the membrane. Sixty-one barley proteins were identified after SDS-PAGE by using electrospray tandem mass spectrometry (ESI-MS/MS).

Protein profiles of different beers are diverse due to differences in the barley cultivar, the malting process and the brewing yeast. 2D gel maps of different beer proteomes representing different cultivars and malting types have been created. The maps can be used as quality control step in the brewing industry and as a tool to detect and identify beer type specific proteins or protein isoforms that might represent taste, flavor, or texture. In the long term this will potentially enable manipulation of, e.g., flavor proteins (Fasoli et al., 2010; Iimure et al., 2010). The industrial induced protein modification called Maillard reactions has also been monitored and characterized and is important for color, taste, and flavor and include thermal stability of proteins and the non-enzymatic glycation of proteins (Perrocheau et al., 2005; Okada et al., 2008; Petry-Podgorska et al., 2010)

Biology driven proteomics: 2D gel electrophoresis was the preferred method to study the proteome of barley plants exposed to salinity stress and adaptation (Fatehi et al., 2012). Barley plants, a tolerant and a salt-sensitive genotype, were exposed to 0 (control) or 300 mM NaCl. More than 500 reproducible protein spots were detected of which 44 appeared to be regulated. The regulated proteins were involved in several biological processes such as reactive oxygen species scavenging, signal transduction, and protein processing. The advantage of this 2D gel strategy for studying a non-sequenced organism was pointed out – only the regulated proteins needed to be analyzed and identified by mass spectrometry. A similar procedure was used in a nitrogen use efficiency study of barley, where proteomes from barley shoots and roots were analyzed using 2D gels. Comparative proteome analysis of plants grown with a nitrogen source and plants grown under nitrogen deficiency revealed 67 and 49 differentially regulated protein spots in roots and shoots, respectively (Moller et al., 2011a). Proteins associated with drought have also been analyzed using 2D gel proteomics (Wendelboe-Nelson and Morris, 2012). In a comparative study of barley, extracted leave and root proteomes from boron tolerant and boron intolerant barley plants were studied using an iTRAQ based method and peptide fractionation by 2D LC prior to mass spectrometry analysis. A total of 138 proteins were identified from leaf tissue and 341 were identified from root tissues. Only 11 out of 1038 peptides from the root tissue were regulated in the boron tolerant barley plant. Interestingly seven of these peptides identified three proteins involved in iron deficiency response (Patterson et al., 2007).

Protein modifications such as acetylation, glycosylation, and phosphorylation are important regulators of a wide range of biological processes in plants (Ytterberg and Jensen, 2010). In barley only a handful of proteomics studies deal with protein modifications. These include protein characterization in seeds during maturation using 2D gels (Finnie et al., 2006; Laugesen et al., 2007), where spot “trains” of the same proteins appeared during maturation as a consequence of small amino acids sequence differences, processing and differences in the degree of protein glycosylation. Phosphoprotein studies in tonoplasts revealed a total of 65 phosphopeptides, and provide a first view into the regulation of several metabolic pathways in tonoplast (Endler et al., 2009). Phosphoproteomics in plants were recently reviewed (Kline-Jonakin et al., 2011).

Proteomics work in barley has to date been hampered by the lack of complete genomic sequence. But by the complete sequencing of the barley genome the goal to identify all of the predicted 2000–3000 chloroplast protein is within reach. The shift in analytical methods in proteomics from 2D gels toward 2D LC-MS/MS based strategies, due to completely sequenced genomes, improved nano-LC systems and faster and more sensitive tandem mass spectrometers has over the years increased the output of proteomics data. We foresee that new robust in-solution digestion protocol coupled with fast online 2D LC-MS/MS systems will enable the next major step in barley proteomics by decreasing workload and increasing the throughput, identification rate and accuracy of quantitation of the proteomics technologies.

In the near future we expect to see more quantitative proteomics studies of barley, e.g., for molecular analysis of abiotic stress, where sensitive versus non-sensitive barley genotypes are compared, with the aim of identifying protein biomarker involved in a certain genotypic trait. Ultimately this would couple proteomics and other technologies into the multidisciplinary systems biology platform in the pursuit of sustainable crop production.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.