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Summary Estrogen receptor-α (ER) drives tumor development in ER-positive (ER+) breast cancer. The transcription factor GATA3 has been closely linked to ER function, but its precise role in this setting remains unclear. Quantitative proteomics was used to assess changes to the ER complex in response to GATA3 depletion. Unexpectedly, few proteins were lost from the ER complex in the absence of GATA3, with the only major change being depletion of the dioxygenase TET2. TET2 binding constituted a near-total subset of ER binding in multiple breast cancer models, with loss of TET2 associated with reduced activation of proliferative pathways. TET2 knockdown did not appear to change global methylated cytosine (5mC) levels; however, oxidation of 5mC to 5-hydroxymethylcytosine (5hmC) was significantly reduced, and these events occurred at ER enhancers. These findings implicate TET2 in the maintenance of 5hmC at ER sites, providing a potential mechanism for TET2-mediated regulation of ER target genes., Graphical Abstract, Highlights • TET2 is identified as a component of the ER complex through depletion of GATA3 • TET2 global chromatin binding tracks that of ER/GATA3 in multiple breast cancer models • Loss of TET2 is linked to dysregulated expression of ER target genes • Concurrent loss of 5hmC at ER sites provides insights into TET2’s role in ER activity, Broome et al. use quantitative proteomics and chromatin immunoprecipitation to explore the contribution of TET2 to ER/GATA3 transcriptional activity. TET2 tracks ER chromatin binding in breast cancer models and plays an essential role in the maintenance of 5-hydroxymethylcytosine at ER enhancers, revealing a role for TET2 in ER-driven gene expression.

Proteomics monitoring of an elite adventure athlete (age 33 years) was conducted over a 28-week period that culminated in the successful, solo, unassisted, and unsupported two month trek across the Antarctica (1500 km). Training distress was monitored weekly using a 19-item, validated training distress scale (TDS). Weekly dried blood spot (DBS) specimens were collected via fingerprick blood drops onto standard blood spot cards. DBS proteins were measured with nano-electrospray ionization liquid chromatography tandem mass spectrometry (nanoLC-MS/MS) in data-independent acquisition (DIA) mode, and 712 proteins were identified and quantified. The 28-week period was divided into time segments based on TDS scores, and a contrast analysis between weeks five and eight (low TDS) and between weeks 20 and 23 (high TDS, last month of Antarctica trek) showed that 31 proteins (n = 20 immune related) were upregulated and 35 (n = 17 immune related) were downregulated. Protein&ndash, protein interaction (PPI) networks supported a dichotomous immune response. Gene ontology (GO) biological process terms for the upregulated immune proteins showed an increase in regulation of the immune system process, especially inflammation, complement activation, and leukocyte mediated immunity. At the same time, GO terms for the downregulated immune-related proteins indicated a decrease in several aspects of the overall immune system process including neutrophil degranulation and the antimicrobial humoral response. These proteomics data support a dysfunctional immune response in an elite adventure athlete during a sustained period of mental and physical distress while trekking solo across the Antarctica.

[Purpose]: To evaluate relationships of proteomics data, athlete-reported illness, athlete training distress (TDS), and coaches’ ratings of distress and performance over the course of the competitive season., [Methods]: Thirty-five NCAA Division II swimmers were recruited to the study (male n = 19, female n = 16; age 19.1 ± 1.6 years). Athletes provided fingerprick dried blood spot (DBS) samples, illness symptoms, and TDS every Monday for 19 of 25 weeks in their season. Coaches monitored performance and rated visual signs of distress. DBS samples were analyzed for a targeted panel of 12 immune-related proteins using liquid chromatography/mass spectrometry (LC/MS)., [Results]: Thirty-two swimmers completed the protocol. The data were grouped in 2–3 weeks segments to facilitate interpretation and analysis of the data. TDS scores varied between athletes, and were highest during the early fall conditioning ramp up period (8.9 ± 1.6 at baseline to a peak of 22.6 ± 2.0). The percent of athletes reporting illness was high throughout the season (50–78%). Analysis of TDS using Principle Component Analysis (PCA) revealed that 40.5% of the variance (PC1) could be attributed to illness prevalence, and TDS scores for the athletes reporting illness and no illness were different across the season (P < 0.001). The coaches’ ratings of swim performance and swimmer’s distress, sex, and racing distance (sprinters, middle distance, long distance) were not correlated with PC1. Linear Discriminant Analysis (LDA) analysis of the data showed a separation of the baseline weeks from exam weeks with or without competitions, and with competitions alone (p < 0.001). Seven of the 12 proteins monitored over the course of training were upregulated, and the addition of the protein data to LDA analysis enhanced the separation between these groups of weeks., [Conclusion]: TDS and illness were related in this group of 32 collegiate swimmers throughout the competitive season, and expression of immune proteins improved the statistical separation of baseline weeks from the most stressful weeks. TDS data provided by the swimmers did not match their coaches’ ratings of distress and swim performance. The importance of the immune system in the reaction to internal and external stress in athletes should be an area of further research.

MicroRNAs (miRNAs) are 21-24-nucleotide RNAs present in many eukaryotes that regulate gene expression as part of the RNA-induced silencing complex. The sequence identity of the miRNA provides the specificity to guide the silencing effector Argonaute (AGO) protein to target mRNAs via a base-pairing process 1 . The AGO complex promotes translation repression and/or accelerated decay of this target mRNA 2 . There is overwhelming evidence both in vivo and in vitro that translation repression plays a major role 3-7 . However, there has been controversy about which of these three mechanisms is more significant in vivo, especially when effects of miRNA on endogenous genes cannot be faithfully represented by reporter systems in which, at least in metazoans, the observed repression vastly exceeds that typically observed for endogenous mRNAs 8,9 . Here, we provide a comprehensive global analysis of the evolutionarily distant unicellular green alga Chlamydomonas reinhardtii to quantify the effects of miRNA on protein synthesis and RNA abundance. We show that, similar to metazoan steady-state systems, endogenous miRNAs in Chlamydomonas can regulate gene expression both by destabilization of the mRNA and by translational repression. However, unlike metazoan miRNA where target site utilization localizes mainly to 3' UTRs, in Chlamydomonas utilized target sites lie predominantly within coding regions. These results demonstrate the evolutionarily conserved mode of action for miRNAs, but details of the mechanism diverge between the plant and metazoan kingdoms.

In a previous study, proteomics procedures identified blood proteins as potential overreaching and overtraining biomarkers, and a targeted proteomics panel of 21 proteins was developed. Purpose To measure targeted blood protein changes in an ultraendurance cyclist competing in RAAM. Methods The athlete underwent testing 4-week pre-RAAM and 4-day post-RAAM to determine body composition and aerobic capacity. During RAAM training distress score (TDS) and body mass were measured daily. Power output and heart rate (HR) were measured during cycling. Blood sampling for proteomic analysis occurred 4 weeks, 24, and 2 h before the start, twice per day of the race, and after 1 and 4 days recovery. Results The athlete completed the 4941 km race in 10.1 days at a speed of 24.5 km/h with 20 total hours of sleep. TDS was very low, 1, pre-RAAM and increased to very high, 47, at the finish. Post-RAAM maximal aerobic capacity and HR were 6.3 and 5.7% lower (61.6 vs. 57.5 mL.kg-1.min-1 and 192 bpm vs. 181 bpm). Body composition did not change. The change in blood proteins was calculated using pre-race samples and samples collected on days 8, 9, and recovery day 1. The blood proteins with the largest increase were complement component C7 (359%), complement C4-B (231%), serum amyloid A-4 protein (210%), inter-alpha-trypsin inhibitor heavy chain H4 (191%), and alpha-1-antitrypsin (188%). Conclusion The RAAM athlete exhibited non-functional overreaching symptoms including increased training distress and decreased work capacity. Proteomic analysis indicated large increases for immune-related proteins involved with complement activation and the acute phase response, which could be useful biomarkers for non-functional overreaching.

During embryogenesis, organisms undergo considerable cellular remodelling requiring the combined action of thousands of proteins. In case of the well-studied model Drosophila melanogaster, transcriptomic studies, most notably from the modENCODE project, have described in detail changes in gene expression at the mRNA level across development. Although such data are clearly very useful to understand how the genome is regulated during embryogenesis, it is important to understand how changes in gene expression are reflected at the level of the proteome. In this study, we describe a combination of two quantitative label-free approaches, SWATH and data-dependent acquisition, to monitor changes in protein expression across a timecourse of D. melanogaster embryonic development. We demonstrate that both approaches provide robust and reproducible methods for the analysis of proteome changes. In a preliminary analysis of Drosophila embryogenesis, we identified several pathways, including the heat-shock response, nuclear protein import and energy production that are regulated during embryo development. In some cases changes in protein expression mirrored transcript levels across development, whereas other proteins showed signatures of post-transcriptional regulation. Taken together, our pilot study provides a solid platform for a more detailed exploration of the embryonic proteome.

OBJECTIVES: Proteomics when combined with psychological, nutrition, and performance measures may serve as a useful monitoring system for immune dysfunction, training distress, and exercise-induced muscle damage and exhaustion in athletes. Global proteomics monitoring of an elite adventure athlete (age 33 years) was conducted over a 28-week period that culminated in the successful, unassisted 2-month trek across Antarctica (1500 km). METHODS: Training distress was monitored weekly using the 19-item, validated Training Distress Scale (TDS). Weekly dried blood spot (DBS) specimens were collected via fingerprick blood drops onto standard blood spot cards. DBS proteins were measured with nano-electrospray ionization liquid chromatography tandem mass spectrometry (nanoLC-MS/MS) in data-independent acquisition (DIA) mode, and 712 proteins were identified and quantified. RESULTS: The participant experienced a decrease of 11.4 kg in body mass during the Antarctica trek. The 28-week period was divided into time segments based on TDS scores, and a contrast analysis between weeks 5–8 (low TDS) and weeks 20–23 (high TDS, last month of Antarctica trek) showed that 31 proteins (n = 20 immune related, n = 14 nutrition related with n = 8 in dual roles) were upregulated and 35 (n = 17 immune related) were downregulated. Protein-protein interaction (PPI) networks and gene ontology (GO) biological process analysis supported an increase in plasma lipoprotein particle remodeling, regulation of lipid transport, retinoid metabolic process, and vitamin transport due to high energy intake (7048 kcal/d). PPI networks also supported a dichotomous immune response. GO terms for the upregulated immune proteins showed an increase in regulation of the immune system process, especially inflammation, complement activation, and leukocyte mediated immunity. GO terms for the downregulated immune-related proteins indicated a decrease in several aspects of the overall immune system process including neutrophil degranulation and the antimicrobial humoral response. CONCLUSIONS: These proteomics data support a dysfunctional immune response in an elite adventure athlete during a sustained period of mental and physical distress, high energy intake, and significant loss of body mass while trekking solo across Antarctica. FUNDING SOURCES: Standard Process, Inc., Palmyra, WI.

The study of proteomes provides new insights into stimulus-specific responses of protein synthesis and turnover, and the role of post-translational modifications at the systems level. Due to the diverse chemical nature of proteins and shortcomings in the analytical techniques used in their study, only a partial display of the proteome is achieved in any study, and this holds particularly true for plant proteomes. Here we show that different solubilization and separation methods have profound effects on the resulting proteome. In particular, we observed that the type of detergents employed in the solubilization buffer preferentially enriches proteins in different functional categories. These include proteins with a role in signaling, transport, response to temperature stimuli and metabolism. This data may offer a functional bias on comparative analysis studies. In order to obtain a broader coverage, we propose a two-step solubilization protocol with first a detergent-free buffer and then a second step utilizing a combination of two detergents to solubilize proteins.

The second messenger cyclic nucleotide 3′,5′-cyclic guanosine monophosphate (cGMP) is increasingly recognized as a key signaling molecule that mediates many physiological and developmental processes in plants (Supplemental Figure 1A). While cGMP-dependent phosphorylation of Arabidopsis proteins is a known phenomenon (Isner et al., 2012), a quantification of the cGMP-dependent protein phosphorylation at the system level has not been reported previously. Here, we applied a Ti4+-IMAC (immobilized metal-ion affinity chromatography) phosphopeptide enrichment technique combined with tandem mass spectrometry to analyze the cGMP-dependent phosphoproteome of Arabidopsis thaliana cell suspension culture cells that are metabolically labeled with 15N (Supplemental Figure 1B) and show highly specific response signatures.

Estrogen Receptor-β (ERβ) has been implicated in many cancers. In prostate and breast cancer its function is controversial, but genetic studies implicate a role in cancer progression. Much of the confusion around ERβ stems from antibodies that are inadequately validated, yet have become standard tools for deciphering its role. Using an ERβ-inducible cell system we assessed commonly utilized ERβ antibodies and show that one of the most commonly used antibodies, NCL-ER-BETA, is non-specific for ERβ. Other antibodies have limited ERβ specificity or are only specific in one experimental modality. ERβ is commonly studied in MCF-7 (breast) and LNCaP (prostate) cancer cell lines, but we found no ERβ expression in either, using validated antibodies and independent mass spectrometry-based approaches. Our findings question conclusions made about ERβ using the NCL-ER-BETA antibody, or LNCaP and MCF-7 cell lines. We describe robust reagents, which detect ERβ across multiple experimental approaches and in clinical samples., Highlights • ERβ is important in prostate and breast cancer, but its role is controversial. • ERβ antibodies are problematic, with varying specificity. • We tested a panel of ERβ antibodies and show the most commonly used is non-specific. • Two antibodies were validated across multiple experimental approaches. • Using multiple techniques, we show cell lines used to study ERβ lack its expression.

The brassinosteroid receptor brassinosteroid insensitive 1 (BRI1) is a member of the leucine-rich repeat receptor-like kinase family. The intracellular kinase domain of BRI1 is an active kinase and also encapsulates a guanylate cyclase catalytic centre. Using liquid chromatography tandem mass spectrometry, we confirmed that the recombinant cytoplasmic domain of BRI1 generates pmol amounts of cGMP per μg protein with a preference for magnesium over manganese as a co-factor. Importantly, a functional BRI1 kinase is essential for optimal cGMP generation. Therefore, the guanylate cyclase activity of BRI1 is modulated by the kinase while cGMP, the product of the guanylate cyclase, in turn inhibits BRI1 kinase activity. Furthermore, we show using Arabidopsis root cell cultures that cGMP rapidly potentiates phosphorylation of the downstream substrate brassinosteroid signaling kinase 1 (BSK1). Taken together, our results suggest that cGMP acts as a modulator that enhances downstream signaling while dampening signal generation from the receptor.

Knowledge of protein subcellular localization assists in the elucidation of protein function and understanding of different biological mechanisms that occur at discrete subcellular niches. Organelle-centric proteomics enables localization of thousands of proteins simultaneously. Although such techniques have successfully allowed organelle protein catalogues to be achieved, they rely on the purification or significant enrichment of the organelle of interest, which is not achievable for many organelles. Incomplete separation of organelles leads to false discoveries, with erroneous assignments. Proteomics methods that measure the distribution patterns of specific organelle markers along density gradients are able to assign proteins of unknown localization based on comigration with known organelle markers, without the need for organelle purification. These methods are greatly enhanced when coupled to sophisticated computational tools. Here we apply and compare multiple approaches to establish a high-confidence data set of Arabidopsis root tissue trans-Golgi network (TGN) proteins. The method employed involves immunoisolations of the TGN, coupled to probability-based organelle proteomics techniques. Specifically, the technique known as LOPIT (localization of organelle protein by isotope tagging), couples density centrifugation with quantitative mass-spectometry-based proteomics using isobaric labeling and targeted methods with semisupervised machine learning methods. We demonstrate that while the immunoisolation method gives rise to a significant data set, the approach is unable to distinguish cargo proteins and persistent contaminants from full-time residents of the TGN. The LOPIT approach, however, returns information about many subcellular niches simultaneously and the steady-state location of proteins. Importantly, therefore, it is able to dissect proteins present in more than one organelle and cargo proteins en route to other cellular destinations from proteins whose steady-state location favors the TGN. Using this approach, we present a robust list of Arabidopsis TGN proteins.

MicroRNAs regulate gene expression as part of the RNA-induced silencing complex, where the sequence identity of the miRNA provides the specificity to the target messenger RNA, and the result is target repression. The mode of repression can be through target cleavage, RNA destabilization and/or decreased translational efficiency. Here, we provide a comprehensive global analysis of the evolutionarily distant unicellular green alga Chlamydomonas reinhardtii to quantify the effects of miRNA on protein synthesis and RNA abundance. We show that, similar to metazoan systems, miRNAs in Chlamydomonas regulate gene-expression primarily by destabilizing mRNAs. However, unlike metazoan miRNA where target site utilization localizes mainly to 3’UTRs, in Chlamydomonas utilized target sites lie predominantly within coding regions. These results demonstrate that destabilization of mRNA is the main evolutionarily conserved mode of action for miRNAs, but details of the mechanism diverge between plant and metazoan kingdoms.

Prediction of protein sub-cellular localisation by employing quantitative mass spectrometry experiments is an expanding field. Several methods have led to the assignment of proteins to specific subcellular localisations by partial separation of organelles across a fractionation scheme coupled with computational analysis. Methods developed to analyse organelle data have largely employed supervised machine learning algorithms to map unannotated abundance profiles to known protein–organelle associations. Such approaches are likely to make association errors if organelle-related groupings present in experimental output are not included in data used to create a protein–organelle classifier. Currently, there is no automated way to detect organelle-specific clusters within such datasets. In order to address the above issues we adapted a phenotype discovery algorithm, originally created to filter image-based output for RNAi screens, to identify putative subcellular groupings in organelle proteomics experiments. We were able to mine datasets to a deeper level and extract interesting phenotype clusters for more comprehensive evaluation in an unbiased fashion upon application of this approach. Organelle-related protein clusters were identified beyond those sufficiently annotated for use as training data. Furthermore, we propose avenues for the incorporation of observations made into general practice for the classification of protein–organelle membership from quantitative MS experiments. Biological significance Protein sub-cellular localisation plays an important role in molecular interactions, signalling and transport mechanisms. The prediction of protein localisation by quantitative mass-spectrometry (MS) proteomics is a growing field and an important endeavour in improving protein annotation. Several such approaches use gradient-based separation of cellular organelle content to measure relative protein abundance across distinct gradient fractions. The distribution profiles are commonly mapped in silico to known protein–organelle associations via supervised machine learning algorithms, to create classifiers that associate unannotated proteins to specific organelles. These strategies are prone to error, however, if organelle-related groupings present in experimental output are not represented, for example owing to the lack of existing annotation, when creating the protein–organelle mapping. Here, the application of a phenotype discovery approach to LOPIT gradient-based MS data identifies candidate organelle phenotypes for further evaluation in an unbiased fashion. Software implementation and usage guidelines are provided for application to wider protein–organelle association experiments. In the wider context, semi-supervised organelle discovery is discussed as a paradigm with which to generate new protein annotations from MS-based organelle proteomics experiments. This article is part of a Special Issue entitled: New Horizons and Applications for Proteomics [EuPA 2012].

Estrogen Receptor-β (ERβ) has been implicated in many cancers. In prostate and breast cancer its function is controversial, but genetic studies implicate a role in cancer progression. Much of the confusion around ERβ stems from antibodies that are inadequately validated, yet have become standard tools for deciphering its role. Using an ERβ-inducible cell system we assessed commonly utilized ERβ antibodies and show that one of the most commonly used antibodies, NCL-ER-BETA, is non-specific for ERβ. Other antibodies have limited ERβ specificity or are only specific in one experimental modality. ERβ is commonly studied in MCF-7 (breast) and LNCaP (prostate) cancer cell lines, but we found no ERβ expression in either, using validated antibodies and independent mass spectrometry-based approaches. Our findings question conclusions made about ERβ using the NCL-ER-BETA antibody, or LNCaP and MCF-7 cell lines. We describe robust reagents, which detect ERβ across multiple experimental approaches and in clinical samples.

Spatial organisation of proteins according to their function plays an important role in the specificity of their molecular interactions. Emerging proteomics methods seek to assign proteins to sub-cellular locations by partial separation of organelles and computational analysis of protein abundance distributions among partially separated fractions. Such methods permit simultaneous analysis of unpurified organelles and promise proteome-wide localisation in scenarios wherein perturbation may prompt dynamic re-distribution. Resolving organelles that display similar behavior during a protocol designed to provide partial enrichment represents a possible shortcoming. We employ the Localisation of Organelle Proteins by Isotope Tagging (LOPIT) organelle proteomics platform to demonstrate that combining information from distinct separations of the same material can improve organelle resolution and assignment of proteins to sub-cellular locations. Two previously published experiments, whose distinct gradients are alone unable to fully resolve six known protein-organelle groupings, are subjected to a rigorous analysis to assess protein-organelle association via a contemporary pattern recognition algorithm. Upon straightforward combination of single-gradient data, we observe significant improvement in protein-organelle association via both a non-linear support vector machine algorithm and partial least-squares discriminant analysis. The outcome yields suggestions for further improvements to present organelle proteomics platforms, and a robust analytical methodology via which to associate proteins with sub-cellular organelles.

No reliable biomarkers exist to identify athletes in various training states including functional overreaching (FOR), non-functional overreaching (NFOR), and overtraining syndrome (OTS). Participants (N = 10, age 38.3 &plusmn, 3.4 years) served as their own controls and in random, counterbalanced order either ran/cycled 2.5 h (70.0 &plusmn, 3.7% VO2max) three days in a row (FOR) or sat in the lab (rest) (separated by three weeks, 7:00&ndash, 9:30 am, overnight fasted state). Participants provided fingerprick samples for dried blood spot samples (DBS) pre- and post-exercise/rest, and then during two recovery days. DBS proteins were measured with nanoLC-MS in data-independent acquisition (DIA) mode, and 593 proteins were identified and quantified. Proteins were considered for the FOR cluster if they were elevated during one of the two recovery days but not more than one of the exercise days (compared to rest). The generalized estimating equation (GEE) was used to identify proteins linked to FOR. A total of 13 proteins was linked to FOR and most were associated with the acute phase response and innate immune system activation. This study used a system-wide proteomics approach to define a targeted panel of blood proteins related to FOR that could form the basis of future NFOR- and OTS-based studies.

International audience; Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.

Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique [1]. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security.Public policy requires public support, which in turn implies a need to enable the public not just to understand policy but also to be engaged in its development. Where complex science and technology issues are involved in policy making, this takes time, so it is important to identify emerging issues of this type and prepare engagement plans. In our horizon scanning exercise, we used a modified Delphi technique [1]. A wide group of people with interests in the science and policy interface (drawn from policy makers, policy adviser, practitioners, the private sector and academics) elicited a long list of emergent policy issues in which science and technology would feature strongly and which would also necessitate public engagement as policies are developed. This was then refined to a short list of top priorities for policy makers. Thirty issues were identified within broad areas of business and technology; energy and environment; government, politics and education; health, healthcare, population and aging; information, communication, infrastructure and transport; and public safety and national security.

Blood serum is one of the easiest accessible sources of biomarkers and its proteome presents a significant parcel of immune system proteins. These proteins can provide not only biological explanation but also diagnostic and drug response answers independently of the type of disease or condition in question. Shotgun mass spectrometry has profoundly contributed to proteome analysis and is presently considered as an indispensible tool in the field of biomarker discovery. In addition, the multiplexing potential of isotopic labeling techniques such as iTRAQ can increase statistical relevance and accuracy of proteomic data through the simultaneous analysis of different biological samples. Here, we describe a complete protocol using iTRAQ in a shotgun proteomics workflow along with data analysis steps, customized for the challenges associated with the serum proteome.

Phosphoproteomics is a fast-growing field that aims at characterizing phosphorylated proteins in a cell or a tissue at a given time. Phosphorylation of proteins is an important regulatory mechanism in many cellular processes. Gel-free phosphoproteome technique involving enrichment of phosphopeptide coupled with mass spectrometry has proven to be invaluable to detect and characterize phosphorylated proteins. In this chapter, a gel-free quantitative approach involving (15)N metabolic labelling in combination with phosphopeptide enrichment by titanium dioxide (TiO2) and their identification by MS is described. This workflow can be used to gain insights into the role of signalling molecules such as cyclic nucleotides on regulatory networks through the identification and quantification of responsive phospho(proteins).

In eukaryotes, numerous complex sub-cellular structures exist. The majority of these are delineated by membranes. Many proteins are trafficked to these in order to be able to carry out their correct physiological function. Assigning the sub-cellular location of a protein is of paramount importance to biologists in the elucidation of its role and in the refinement of knowledge of cellular processes by tracing certain activities to specific organelles. Membrane proteins are a key set of proteins as these form part of the boundary of the organelles and represent many important functions such as transporters, receptors, and trafficking. They are, however, some of the most challenging proteins to work with due to poor solubility, a wide concentration range within the cell and inaccessibility to many of the tools employed in proteomics studies. This review focuses on membrane proteins with particular emphasis on sub-cellular localization in terms of methodologies that can be used to determine the accurate location of membrane proteins to organelles. We also discuss what is known about the membrane protein cohorts of major organelles.

Membrane trafficking, including that of integral membrane proteins as well as peripherally associated proteins, appears to be a vital process common to all eukaryotes. An important element of membrane trafficking is to determine the protein composition of the various endomembrane compartments. A major issue with such a compositional analysis is the difficulty of having to distinguish between resident components involved in specific tasks and the proteins that are in transit through the endomembrane system. Examples of resident proteins include components of the SNARE complex used to target membrane vesicles to different locations in the cell. In the case of functionally important residents, one would expect such proteins to have a fairly precise subcellular localization. In the case of proteins "passing through" an endosomal compartment en route to a final destination, one would expect to find the proteins colocalizing with many membrane compartments. As is evident from several Update articles in this issue, ambiguity exists when employing cytological techniques to identify specific endomembrane compartments, while markers identified based on homology may behave differently in plant cells. Therefore, a proteomics approach based on proteins that would traffic through various parts of the endomembrane system, such as plasma membrane (PM) receptors, would be a welcome addition to membrane-trafficking studies. PM receptors are highly dependent on correct trafficking for their eventual localization, their biological function, and finally their degradation, while recent evidence suggests that endocytosis of PM receptors is an integral part of their biological function. In this review, first, a short update on endocytosis and endosomal trafficking in Arabidopsis (Arabidopsis thaliana) is provided. In this section, we emphasize trafficking of PM receptors as a proteomics tool by looking at how the PM receptors traffic in a time-dependent fashion in order to determine the relationship between different endosomal compartments. Second, we describe the recent progress in advanced proteomics techniques such as localization of organelle proteins by isotope tagging (LOPIT), by which proteins are assigned to different endosomal compartments.