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Proteomics techniques generate an avalanche of data and promise to satisfy biologists' long-held desire to measure absolute protein abundances on a genome-wide scale. However, can this knowledge be translated into a clearer picture of how cells invest their protein resources? This article aims to give a broad perspective on the composition of proteomes as gleaned from recent quantitative proteomics studies. We describe proteomaps, an approach for visualizing the composition of proteomes with a focus on protein abundances and functions. In proteomaps, each protein is shown as a polygon-shaped tile, with an area representing protein abundance. Functionally related proteins appear in adjacent regions. General trends in proteomes, such as the dominance of metabolism and protein production, become easily visible. We make interactive visualizations of published proteome datasets accessible at www.proteomaps.net . We suggest that evaluating the way protein resources are allocated by various organisms and cell types in different conditions will sharpen our understanding of how and why cells regulate the composition of their proteomes.

Protein glycosylation is a highly important, yet poorly understood protein post-translational modification. Thousands of possible glycan structures and compositions create potential for tremendous site heterogeneity. A lack of suitable analytical methods for large-scale analyses of intact glycopeptides has limited our abilities both to address the degree of heterogeneity across the glycoproteome and to understand how this contributes biologically to complex systems. Here we show that N-glycoproteome site-specific microheterogeneity can be captured via large-scale glycopeptide profiling methods enabled by activated ion electron transfer dissociation (AI-ETD), ultimately characterizing 1,545 N-glycosites (>5,600 unique N-glycopeptides) from mouse brain tissue. Our data reveal that N-glycosylation profiles can differ between subcellular regions and structural domains and that N-glycosite heterogeneity manifests in several different forms, including dramatic differences in glycosites on the same protein. Moreover, we use this large-scale glycoproteomic dataset to develop several visualizations that will prove useful for analyzing intact glycopeptides in future studies. Mass spectrometry facilitates large-scale glycosylation profiling but in-depth analysis of intact glycopeptides is still challenging. Here, the authors show that activated ion electron transfer dissociation is suitable for glycopeptide fragmentation and improves glycoproteome coverage.

Many biological systems are composed of diverse single cells. This diversity necessitates functional and molecular single-cell analysis. Single-cell protein analysis has long relied on affinity reagents, but emerging mass-spectrometry methods (either label-free or multiplexed) have enabled quantifying >1,000 proteins per cell while simultaneously increasing the specificity of protein quantification. Here we describe the Single Cell ProtEomics (SCoPE2) protocol, which uses an isobaric carrier to enhance peptide sequence identification. Single cells are isolated by FACS or CellenONE into multiwell plates and lysed by Minimal ProteOmic sample Preparation (mPOP), and their peptides labeled by isobaric mass tags (TMT or TMTpro) for multiplexed analysis. SCoPE2 affords a cost-effective single-cell protein quantification that can be fully automated using widely available equipment and scaled to thousands of single cells. SCoPE2 uses inexpensive reagents and is applicable to any sample that can be processed to a single-cell suspension. The SCoPE2 workflow allows analyzing ~200 single cells per 24 h using only standard commercial equipment. We emphasize experimental steps and benchmarks required for achieving quantitative protein analysis. Biological systems can now be studied at the single-cell level using mass spectrometry. In Single Cell ProtEomics (SCoPE2), a carrier sample is used to enhance peptide sequence identification with multiplexed analysis using isobaric mass tags.

Novel platelet and megakaryocyte transcriptome analysis allows prediction of the full or theoretical proteome of a representative human platelet. Here, we integrated the established platelet proteomes from six cohorts of healthy subjects, encompassing 5.2 k proteins, with two novel genome-wide transcriptomes (57.8 k mRNAs). For 14.8 k protein-coding transcripts, we assigned the proteins to 21 UniProt-based classes, based on their preferential intracellular localization and presumed function. This classified transcriptome-proteome profile of platelets revealed: (i) Absence of 37.2 k genome-wide transcripts. (ii) High quantitative similarity of platelet and megakaryocyte transcriptomes (R = 0.75) for 14.8 k protein-coding genes, but not for 3.8 k RNA genes or 1.9 k pseudogenes (R = 0.43–0.54), suggesting redistribution of mRNAs upon platelet shedding from megakaryocytes. (iii) Copy numbers of 3.5 k proteins that were restricted in size by the corresponding transcript levels (iv) Near complete coverage of identified proteins in the relevant transcriptome (log2fpkm > 0.20) except for plasma-derived secretory proteins, pointing to adhesion and uptake of such proteins. (v) Underrepresentation in the identified proteome of nuclear-related, membrane and signaling proteins, as well proteins with low-level transcripts. We then constructed a prediction model, based on protein function, transcript level and (peri)nuclear localization, and calculated the achievable proteome at ~ 10 k proteins. Model validation identified 1.0 k additional proteins in the predicted classes. Network and database analysis revealed the presence of 2.4 k proteins with a possible role in thrombosis and hemostasis, and 138 proteins linked to platelet-related disorders. This genome-wide platelet transcriptome and (non)identified proteome database thus provides a scaffold for discovering the roles of unknown platelet proteins in health and disease.

Treatment of plant seeds with electromagnetic fields or non-thermal plasmas aims to take advantage of plant functional plasticity towards stimulation of plant agricultural performance. In this study, the effects of pre-sowing seed treatment using 200 Pa vacuum (7 min), 5.28 MHz radio-frequency cold plasma (CP −2, 5, and 7 min) and electromagnetic field (EMF −5, 10, 15 min) on seed germination kinetics, content of phytohormones, morphometric parameters of seedlings and leaf proteome were assessed. CP 7 min and EMF 15 min treatments caused 19–24% faster germination in vitro ; germination in the substrate was accelerated by vacuum (9%) and EMF 15 min (17%). The stressors did not change the seed germination percentage, with exception of EMF 5 min treatment that caused a decrease by 7.5%. Meanwhile both CP 7 min and EMF 15 min treatments stimulated germination, but the EMF treatment resulted in higher weight of leaves. Stressor-specific changes in phytohormone balance were detected in seeds: vacuum treatment decreased zeatin amount by 39%; CP treatments substantially increased gibberellin content, but other effects strongly varied with the treatment duration; the abscisic acid content was reduced by 55–60% after the EMF treatment. Analysis of the proteome showed that short exposure of seeds to the EMF or CP induced a similar long-term effect on gene expression in leaves, mostly stimulating expression of proteins involved in photosynthetic processes and their regulation.

The costs and benefits of protein expression are balanced through evolution. Expression of un-utilized protein (that have no benefits in the current environment) incurs a quantifiable fitness costs on cellular growth rates; however, the magnitude and variability of un-utilized protein expression in natural settings is unknown, largely due to the challenge in determining environment-specific proteome utilization. We address this challenge using absolute and global proteomics data combined with a recently developed genome-scale model of Escherichia coli that computes the environment-specific cost and utility of the proteome on a per gene basis. We show that nearly half of the proteome mass is unused in certain environments and accounting for the cost of this unused protein expression explains >95% of the variance in growth rates of Escherichia coli across 16 distinct environments. Furthermore, reduction in unused protein expression is shown to be a common mechanism to increase cellular growth rates in adaptive evolution experiments. Classification of the unused protein reveals that the unused protein encodes several nutrient- and stress- preparedness functions, which may convey fitness benefits in varying environments. Thus, unused protein expression is the source of large and pervasive fitness costs that may provide the benefit of hedging against environmental change.

Proteome-wide measurements of protein turnover have largely ignored the impact of post-translational modifications (PTMs). To address this gap, we employ stable isotope labeling and mass spectrometry to measure the turnover of >120,000 peptidoforms including >33,000 phosphorylated, acetylated, and ubiquitinated peptides for >9,000 native proteins. This site-resolved protein turnover (SPOT) profiling discloses global and site-specific differences in turnover associated with the presence or absence of PTMs. While causal relationships may not always be immediately apparent, we speculate that PTMs with diverging turnover may distinguish states of differential protein stability, structure, localization, enzymatic activity, or protein-protein interactions. We show examples of how the turnover data may give insights into unknown functions of PTMs and provide a freely accessible online tool that allows interrogation and visualisation of all turnover data. The SPOT methodology is applicable to many cell types and modifications, offering the potential to prioritize PTMs for future functional investigations. Post-translational modifications (PTMs) can regulate cellular protein function but their global impact on protein turnover is largely unknown. Here, the authors develop proteomic workflows to profile PTM-resolved protein turnover and analyze the effects of phosphorylation, acetylation and ubiquitination.

In corals, biocalcification is a major function that may be drastically affected by ocean acidification (OA). Scleractinian corals grow by building up aragonitic exoskeletons that provide support and protection for soft tissues. Although this process has been extensively studied, the molecular basis of biocalcification is poorly understood. Notably lacking is a comprehensive catalog of the skeleton-occluded proteins—the skeletal organic matrix proteins (SOMPs) that are thought to regulate the mineral deposition. Using a combination of proteomics and transcriptomics, we report the first survey of such proteins in the staghorn coral Acropora millepora. The organic matrix (OM) extracted from the coral skeleton was analyzed by mass spectrometry and bioinformatics, enabling the identification of 36 SOMPs. These results provide novel insights into the molecular basis of coral calcification and the macroevolution of metazoan calcifying systems, whereas establishing a platform for studying the impact of OA at molecular level. Besides secreted proteins, extracellular regions of transmembrane proteins are also present, suggesting a close control of aragonite deposition by the calicoblastic epithelium. In addition to the expected SOMPs (Asp/Glu-rich, galaxins), the skeletal repertoire included several proteins containing known extracellular matrix domains. From an evolutionary perspective, the number of coral-specific proteins is low, many SOMPs having counterparts in the noncalcifying cnidarians. Extending the comparison with the skeletal OM proteomes of other metazoans allowed the identification of a pool of functional domains shared between phyla. These data suggest that co-option and domain shuffling may be general mechanisms by which the trait of calcification has evolved.

During oviposition, ectoparasitoid wasps not only inject their eggs but also a complex mixture of proteins and peptides (venom) in order to regulate the host physiology to benefit their progeny. Although several endoparasitoid venom proteins have been identified, little is known about the components of ectoparasitoid venom. To characterize the protein composition of Torymus sinensis Kamijo (Hymenoptera: Torymidae) venom, we used an integrated transcriptomic and proteomic approach and identified 143 venom proteins. Moreover, focusing on venom gland transcriptome, we selected additional 52 transcripts encoding putative venom proteins. As in other parasitoid venoms, hydrolases, including proteases, phosphatases, esterases, and nucleases, constitute the most abundant families in T. sinensis venom , followed by protease inhibitors. These proteins are potentially involved in the complex parasitic syndrome, with different effects on the immune system, physiological processes and development of the host, and contribute to provide nutrients to the parasitoid progeny. Although additional in vivo studies are needed, initial findings offer important information about venom factors and their putative host effects, which are essential to ensure the success of parasitism.