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Background: Quantitative proteomics is an indispensable tool in life science research. However, there is a lack of reference materials for evaluating the reproducibility of label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based measurements among different instruments and laboratories., Results: Here, we develop the Quartet standard as a proteome reference material with built-in truths, and distribute the same aliquots to 15 laboratories with nine conventional LC-MS/MS platforms across six cities in China. Relative abundance of over 12,000 proteins on 816 mass spectrometry files are obtained and compared for reproducibility among the instruments and laboratories to ultimately generate proteomics benchmark datasets. There is a wide dynamic range of proteomes spanning about 7 orders of magnitude, and the injection order has marked effects on quantitative instead of qualitative characteristics., Conclusion: Overall, the Quartet offers valuable standard materials and data resources for improving the quality control of proteomic analyses as well as the reproducibility and reliability of research findings., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species., (© 2021 The Authors. Mass Spectrometry Reviews published by John Wiley & Sons Ltd.)

Proteomics analysis is often troubled by high-abundance proteins in samples such as plasma. However, many surgical tissue samples inevitably have got contaminated with blood before cryopreservation. Selection of an appropriate method to minimize the effect of high-abundance proteins is important for proteomics analysis of blood contaminated tissues. Here, we investigated and compared the abilities of data-independent acquisition (DIA) and data-dependent acquisition (DDA) strategies for the proteomics analysis of blood contaminated clinical tissue samples. Twelve pairs of carcinoma and para-carcinoma tissue samples from lung cancer patients were used for proteomics assays separately by DIA and DDA, and the blood contamination level in samples was evaluated by contamination index (CI). Compared with the DDA strategy, DIA in whole exhibited much better analytical capabilities in proteomics analysis of these samples with more identified protein groups and a higher discovery of differential proteins. With CI value increasing, whether DIA or DDA showed decreasing analysis ability. However, for samples with high CI values, the DIA strategy still shows acceptable analytical capability and indicates better blood pollution resistance than the DDA strategy. Our results implied that for clinical tissue samples, particularly for those contaminated with blood, DIA strategy should be a preferred method in proteomics studies., (© 2021 Wiley-VCH GmbH.)

Cadmium (Cd) is a heavy metal element with a wide range of hazards and severe biotoxicity. Since Cd can be easily accumulated in the edible parts of plants, the exposure of humans to Cd is mainly through the intake of Cd-contaminated food. However, the intestinal responses to Cd exposure are not completely characterized. Herein, we simulated laboratory and environmental Cd exposure by feeding the piglets with CdCl2-added rice and Cd-contaminated rice (Cdcr) contained diet, as piglets show anatomical and physiological similarities to humans. Subsequent analysis of the metal element concentrations showed that exposure to the two types of Cd significantly increased Cd levels in piglets. After verifying the expression of major Cd transporters by Western blots, multi-omics further expanded the possible transporters of Cd and found Cd exposure causes wide alterations in the metabolism of piglets. Of significance, CdCl2 and Cdcr exhibited different body distribution and metabolic rewiring, and Cdcr had stronger carcinogenic and diabetes-inducing potential. Together, our results indicate that CdCl2 had a significant difference compared with Cdcr, which has important implications for a more intense study of Cd toxicity. [ABSTRACT FROM AUTHOR]

Proteomic studies using mass spectrometry (MS)-based quantification are a main approach to the discovery of new biomarkers. However, a number of analytical conditions in front and during MS data acquisition can affect the accuracy of the obtained outcome. Therefore, comprehensive quality assessment of the acquired data plays a central role in quantitative proteomics, though, due to the immense complexity of MS data, it is often neglected. Here, we address practically the quality assessment of quantitative MS data, describing key steps for the evaluation, including the levels of raw data, identification and quantification. With this, four independent datasets from cerebrospinal fluid, an important biofluid for neurodegenerative disease biomarker studies, were assessed, demonstrating that sample processing-based differences are already reflected at all three levels but with varying impacts on the quality of the quantitative data. Specifically, we provide guidance to critically interpret the quality of MS data for quantitative proteomics. Moreover, we provide the free and open source quality control tool MaCProQC, enabling systematic, rapid and uncomplicated data comparison of raw data, identification and feature detection levels through defined quality metrics and a step-by-step quality control workflow. [ABSTRACT FROM AUTHOR]

The Chinese formula Pien Tze Huang (PZH) has been used to treat hepatocellular carcinoma (HCC) and showed positive clinical effects. However, the antitumor mechanism of PZH in HCC remains unclear. In this study, HCC xenograft Balb/c mice were treated with PZH; then, proteomics detection and Ingenuity Pathway Analysis (IPA) were used to analyze the differentiated phosphorylated proteins in tumor tissues. The results indicated that PZH could inhibit tumor weight by 50.76%. Eighty-four upregulated and 11 downregulated phosphorylated proteins were identified in PZH-treated mice. Twenty signaling pathways were associated with inflammation (including the IL-6 and TNFR1/2 pathways), cancer growth (including the p53 and FAK pathways), and the cell cycle (including the G2/M and G1/S checkpoint regulation pathways). Moreover, TNF-α, IL-6, and several typical differentially expressed phosphorylated proteins (such as p-CCNB1, p-FOXO3, and p-STAT3) in tumor tissues, tumor cell viability, and cell cycle arrest assay in vitro further verify the results of IPA. These results revealed that PZH achieved antitumor activity in HCC; the underlying mechanisms of which were mainly through regulating the inflammation-associated cytokine secretion, cancer growth pathways, and induction of G2/M arrest. These data provided the potential molecular basis for PZH to act as a therapeutic drug or a supplement to chemotherapy drugs for human HCC in the future. [ABSTRACT FROM AUTHOR]

Complex carbohydrates serve a wide range of biological functions in cells and tissues, and their biosynthesis involves more than 200 distinct glycosyltransferases (GTfs) in human cells. The kinetic properties, cellular expression patterns and subcellular topology of the GTfs direct the glycosylation capacity of a cell. Most GTfs are ER or Golgi resident enzymes, and their specific subcellular localization is believed to be distributed in the secretory pathway according to their sequential role in the glycosylation process, although detailed knowledge for individual enzymes is still highly fragmented. Progress in quantitative transcriptome and proteome analyses has greatly advanced our understanding of the cellular expression of this class of enzymes, but availability of appropriate antibodies for in situ monitoring of expression and subcellular topology have generally been limited. We have previously used catalytically active GTfs produced as recombinant truncated secreted proteins in insect cells for generation of mouse monoclonal antibodies (mAbs) to human enzymes primarily involved in mucin-type O-glycosylation. These mAbs can be used to probe subcellular topology of active GTfs in cells and tissues as well as their presence in body fluids. Here, we present several new mAbs to human GTfs and provide a summary of our entire collection of mAbs, available to the community. Moreover, we present validation of specificity for many of our mAbs using human cell lines with CRISPR/Cas9 or zinc finger nuclease (ZFN) knockout and knockin of relevant GTfs. [ABSTRACT FROM AUTHOR]

Despite the identification of many susceptibility genes our knowledge of the underlying mechanisms responsible for complex disease remains limited. Here, we identified a type 2 diabetes disease module in endosomes, and validate it for functional relevance on selected nodes. Using hepatic Golgi/endosomes fractions, we established a proteome of insulin receptor-containing endosomes that allowed the study of physical protein interaction networks on a type 2 diabetes background. The resulting collated network is formed by 313 nodes and 1147 edges with a topology organized around a few major hubs with Cdk2 displaying the highest collective influence. Overall, 88% of the nodes are associated with the type 2 diabetes genetic risk, including 101 new candidates. The Type 2 diabetes module is enriched with cytoskeleton and luminal acidification–dependent processes that are shared with secretion-related mechanisms. We identified new signaling pathways driven by Cdk2 and PTPLAD1 whose expression affects the association of the insulin receptor with TUBA, TUBB, the actin component ACTB and the endosomal sorting markers Rab5c and Rab11a. Therefore, the interactome of internalized insulin receptors reveals the presence of a type 2 diabetes disease module enriched in new layers of feedback loops required for insulin signaling, clearance and islet biology. [ABSTRACT FROM AUTHOR]

Some bacterial cultures are capable of growth on caprolactam as sole carbon and nitrogen source, but the enzymes of the catabolic pathway have not been described. We isolated a caprolactam-degrading strain of Pseudomonas jessenii from soil and identified proteins and genes putatively involved in caprolactam metabolism using quantitative mass spectrometry-based proteomics. This led to the discovery of a caprolactamase and an aminotransferase that are involved in the initial steps of caprolactam conversion. Additionally, various proteins were identified that likely are involved in later steps of the pathway. The caprolactamase consists of two subunits and demonstrated high sequence identity to the 5-oxoprolinases. Escherichia coli cells expressing this caprolactamase did not convert 5-oxoproline but were able to hydrolyze caprolactam to form 6-aminocaproic acid in an ATP-dependent manner. Characterization of the aminotransferase revealed that the enzyme deaminates 6-aminocaproic acid to produce 6-oxohexanoate with pyruvate as amino acceptor. The amino acid sequence of the aminotransferase showed high similarity to subgroup II ω-aminotransferases of the PLP-fold type I proteins. Finally, analyses of the genome sequence revealed the presence of a caprolactam catabolism gene cluster comprising a set of genes involved in the conversion of caprolactam to adipate. [ABSTRACT FROM AUTHOR]

Glycosylation is the most abundant and diverse posttranslational modification of proteins. While several types of glycosylation can be predicted by the protein sequence context, and substantial knowledge of these glycoproteomes is available, our knowledge of the GalNAc-type O-glycosylation is highly limited. This type of glycosylation is unique in being regulated by 20 polypeptide GalNAc-transferases attaching the initiating GalNAc monosaccharides to Ser and Thr (and likely some Tyr) residues. We have developed a genetic engineering approach using human cell lines to simplify O-glycosylation (SimpleCells) that enables proteome-wide discovery of O-glycan sites using 'bottom-up' ETD-based mass spectrometric analysis. We implemented this on 12 human cell lines from different organs, and present a first map of the human O-glycoproteome with almost 3000 glycosites in over 600 O-glycoproteins as well as an improved NetOGlyc4.0 model for prediction of O-glycosylation. The finding of unique subsets of O-glycoproteins in each cell line provides evidence that the O-glycoproteome is differentially regulated and dynamic. The greatly expanded view of the O-glycoproteome should facilitate the exploration of how site-specific O-glycosylation regulates protein function. [ABSTRACT FROM AUTHOR]

Background The goal of many proteomics experiments is to determine the abundance of proteins in biological samples, and the variation thereof in various physiological conditions. High-throughput quantitative proteomics, specifically label-free LC-MS/MS, allows rapid measurement of thousands of proteins, enabling large-scale studies of various biological systems. Prior to analyzing these informationrich datasets, raw data must undergo several computational processing steps. We present a method to address one of the essential steps in proteomics data processing - the matching of peptide measurements across samples. Results We describe a novel method for label-free proteomics data alignment with the ability to incorporate previously unused aspects of the data, particularly ion mobility drift times and product ion information. We compare the results of our alignment method to PEPPeR and OpenMS, and compare alignment accuracy achieved by different versions of our method utilizing various data characteristics. Our method results in increased match recall rates and similar or improved mismatch rates compared to PEPPeR and OpenMS feature-based alignment. We also show that the inclusion of drift time and product ion information results in higher recall rates and more confident matches, without increases in error rates. Conclusions Based on the results presented here, we argue that the incorporation of ion mobility drift time and product ion information are worthy pursuits. Alignment methods should be flexible enough to utilize all available data, particularly with recent advancements in experimental separation methods. [ABSTRACT FROM AUTHOR]

In the modern process of drug discovery, clinical, functional and chemical proteomics can converge and integrate synergies. Functional proteomics explores and elucidates the components of pathways and their interactions which, when deregulated, lead to a disease condition. This knowledge allows the design of strategies to target multiple pathways with combinations of pathway-specific drugs, which might increase chances of success and reduce the occurrence of drug resistance. Chemical proteomics, by analyzing the drug interactome, strongly contributes to accelerate the process of new druggable targets discovery. In the research area of clinical proteomics, proteome and peptidome mass spectrometry-profiling of human bodily fluid (plasma, serum, urine and so on), as well as of tissue and of cells, represents a promising tool for novel biomarker and eventually new druggable targets discovery. In the present review we provide a survey of current strategies of functional, chemical and clinical proteomics. Major issues will be presented for proteomic technologies used for the discovery of biomarkers for early disease diagnosis and identification of new drug targets. [ABSTRACT FROM AUTHOR]

Proteomic technologies have matured to a level enabling accurate and reproducible quantitation of peptides and proteins from complex biological matrices. Analysis of samples as diverse as assembled protein complexes, whole cell lysates or sub-cellular proteomes from cell cultures, and direct analysis of animal and human tissues and fluids demonstrate the incredible versatility of the fundamental nature of the technique that forms the basis of most proteomic applications today (mass spectrometry). Determining the mass of biomolecules and their fragments or related products with high accuracy can convey a highly specific assay for detection and identification. Importantly, ion currents representative of these specifically identified analytes can be accurately quantified with the correct application of smart isobaric tagging chemistries, heavy and light isotopically derivatised samples or standards, or by careful application of workflows to compare unlabelled samples in so-called 'label-free' and targeted selected reaction monitoring experiments. In terms of exploring biology, a myriad of protein changes and modifications are being increasingly probed and quantified, including diverse chemical changes from relatively decisive modifications such as protein splicing and truncation, to more transient dynamic modifications such as phosphorylation, acetylation and ubiquitination. Proteomic workflows can be complex beasts and several key considerations to ensure effective applications have been outlined in the recent literature. The past year has witnessed the publication of several excellent reviews that thoroughly describe the fundamental principles underlying the state of the art. This review further elaborates on specific critical issues introduced by these publications and raises other important unaddressed considerations and new developments that directly impact on the effectiveness of proteomic technologies, in particular for, but not necessarily exclusive to peptide-centric experiments. These factors are discussed both in terms of qualitative analyses, including dynamic range and sampling issues, and developments to improve the translation of peptide fragmentation data into peptide and protein identities, as well as quantitative analyses, including data normalisation and the utility of ontology or functional annotation, the effects of modified peptides, and considered experimental design to facilitate the use of robust statistical methods. [ABSTRACT FROM AUTHOR]

A two-dimensional (2- D) liquid chromatography ( LC) separation of complex peptide mixtures that combines a normal phase utilizing hydrophilic interactions and a reversed phase offers reportedly the highest level of 2- D LC orthogonality by providing an even spread of peptides across multiple LC fractions. Matching experimental peptide retention times to those predicted by empirical models describing chromatographic separation in each LC dimension leads to a significant reduction in a database search space. In this work, we calculated the retention times of tryptic peptides separated in the C18 reversed phase at different separation conditions (pH 2 and pH 10) and in TSK gel Amide-80 normal phase. We show that retention times calculated for different 2- D LC separation schemes utilizing these phases start to correlate once the mass range of peptides under analysis becomes progressively narrow. This effect is explained by high degree of correlation between retention coefficients in the considered phases. [ABSTRACT FROM AUTHOR]

Proteomics is a powerful technique for protein identification at large scales. A number of proteomics approaches have been developed to study the steady state composition of intracellular compartments. Here, we report a novel vectorial proteomics strategy to identify plasma membrane proteins that undergo retrograde transport to the trans-Golgi network (TGN). This strategy is based on the covalent modification of the plasma membrane proteome with a membrane impermeable benzylguanine derivative. Benzylguanine-tagged plasma membrane proteins that are subsequently targeted to the retrograde route are covalently captured by a TGN-localized SNAP-tagged fusion protein, which allows for their identification. The approach was validated step-by-step using a well explored retrograde cargo protein, the B-subunit of Shiga toxin. It was then extended to the proteomics format. Among other hits we found one of the historically first identified cargo proteins that undergo retrograde transport, which further validated our approach. Most of the other hits were kinases, receptors or transporters. In conclusion, we have pioneered a vectorial proteomics approach that complements traditional methods for the study of retrograde protein trafficking. This approach is of generic nature and could in principle be extended to other endocytic pathways. [ABSTRACT FROM AUTHOR]

The knowledge about the interaction between plasma proteins and nanocarriers employed for in vivo delivery is fundamental to understand their biodistribution. Protein adsorption onto nanoparticle surface (protein corona) is strongly affected by vector surface characteristics. In general, the primary interaction is thought to be electrostatic, thus surface charge of carrier is supposed to play a central role in protein adsorption. Because protein corona composition can be critical in modifying the interactive surface that is recognized by cells, characterizing its formation onto lipid particles may serve as a fundamental predictive model for the in vivo efficiency of a lipidic vector. In the present work, protein coronas adsorbed onto three differently charged cationic liposome formulations were compared by a shotgun proteomic approach based on nano-liquid chromatography-high-resolution mass spectrometry. About 130 proteins were identified in each corona, with only small differences between the different cationic liposome formulations. However, this study could be useful for the future controlled design of colloidal drug carriers and possibly in the controlled creation of biocompatible surfaces of other devices that come into contact with proteins into body fluids. [ABSTRACT FROM AUTHOR]

The conventional protocols for in solution or in gel protein digestion require many steps and long reaction times. The use of trypsin immobilized onto solid supports has recently captured the attention of many research groups, because these systems can speed-up protein digestion significantly. The utilization of new materials such as mesoporous silica as supports, in which enzyme and substrate are dramatically concentrated and confined in the nanospace, offers new opportunities to reduce the complexity of proteomics workflows. An overview of the procedures for in situ proteolysis of single proteins or complex protein mixtures is reported, with a special focus on porous materials used as catalysts. The challenging efforts for designing such systems aimed at mimicking the biochemistry of living cells are reviewed. Potentials, limitations and challenges of this branch of enzyme catalysis, which we indicate as in mesopore digestion, are discussed, in relation to its suitability for high-speed and high-throughput proteomics. [ABSTRACT FROM AUTHOR]

Mechanisms underlying neural stem cell proliferation, differentiation and maturation play a critical role in the formation and wiring of neuronal connections. This process involves the activation of multiple serial events, which guide the undifferentiated cells to different lineages via distinctive developmental programs, forming neuronal circuits and thus shaping the adult nervous system. Furthermore, alterations within these strictly regulated pathways can lead to severe neurological and psychiatric diseases. In this framework, the investigation of the high dynamic protein expression changes and other factors affecting protein functions, for example post-translational modifications, the alterations of protein interaction networks, is of pivotal importance for the understanding of the molecular mechanisms responsible for cell differentiation. More recently, proteomic studies in neuroscience ('neuroproteomics') are receiving increased interest for the primary understanding of the regulatory networks underlying neuronal differentiation processes. Besides the classical two-dimensional-based proteomic strategies, the emerging platforms for LC-MS shotgun proteomic analysis hold great promise in unraveling the molecular basis of neural stem cell differentiation. In this review, recent advancements in label-free LC-MS quantitative neuroproteomics are highlighted as a new tool for the study of neural differentiation and functions, in comparison to mass spectrometry-based labeling approaches. The more commonly used protein profiling strategies and model systems for the analysis of neural differentiation are also discussed, along with the challenging proteomic approaches aimed to analyze the nervous system-specific organelles, the neural cells secretome and the specific protein interaction networks. [ABSTRACT FROM AUTHOR]

Filamentous fungi are widely known for their industrial applications, namely, the production of food-processing enzymes and metabolites such as antibiotics and organic acids. In the past decade, the full genome sequencing of filamentous fungi increased the potential to predict encoded proteins enormously, namely, hydrolytic enzymes or proteins involved in the biosynthesis of metabolites of interest. The integration of genome sequence information with possible phenotypes requires, however, the knowledge of all the proteins in the cell in a system-wise manner, given by proteomics. This review summarises the progress of proteomics and its importance for the study of biotechnological processes in filamentous fungi. A major step forward in proteomics was to couple protein separation with high-resolution mass spectrometry, allowing accurate protein quantification. Despite the fact that most fungal proteomic studies have been focused on proteins from mycelial extracts, many proteins are related to processes which are compartmentalised in the fungal cell, e.g. β-lactam antibiotic production in the microbody. For the study of such processes, a targeted approach is required, e.g. by organelle proteomics. Typical workflows for sample preparation in fungal organelle proteomics are discussed, including homogenisation and sub-cellular fractionation. Finally, examples are presented of fungal organelle proteomic studies, which have enlarged the knowledge on areas of interest to biotechnology, such as protein secretion, energy production or antibiotic biosynthesis. [ABSTRACT FROM AUTHOR]

Background: Variability of plasma sample collection and of proteomics technology platforms has been detrimental to generation of large proteomic profile datasets from human biospecimens.Methods: We carried out a clinical trial-like protocol to standardize collection of plasma from 204 healthy and 216 breast cancer patient volunteers. The breast cancer patients provided follow up samples at 3 month intervals. We generated proteomics profiles from these samples with a stable and reproducible platform for differential proteomics that employs a highly consistent nanofabricated ChipCube™ chromatography system for peptide detection and quantification with fast, single dimension mass spectrometry (LC-MS). Protein identification is achieved with subsequent LC-MS/MS analysis employing the same ChipCube™ chromatography system.Results: With this consistent platform, over 800 LC-MS plasma proteomic profiles from prospectively collected samples of 420 individuals were obtained. Using a web-based data analysis pipeline for LC-MS profiling data, analyses of all peptide peaks from these plasma LC-MS profiles reveals an average coefficient of variability of less than 15%. Protein identification of peptide peaks of interest has been achieved with subsequent LC-MS/MS analyses and by referring to a spectral library created from about 150 discrete LC-MS/MS runs. Verification of peptide quantity and identity is demonstrated with several Multiple Reaction Monitoring analyses. These plasma proteomic profiles are publicly available through ProteomeCommons.Conclusion: From a large prospective cohort of healthy and breast cancer patient volunteers and using a nano-fabricated chromatography system, a consistent LC-MS proteomics dataset has been generated that includes more than 800 discrete human plasma profiles. This large proteomics dataset provides an important resource in support of breast cancer biomarker discovery and validation efforts. [ABSTRACT FROM AUTHOR]

Current available data show that about 5 to 40% of coronary patients treated with conventional doses of antithrombotic drugs do not display adequate antiplatelet response. Nowadays, aspirin remains the main antiplatelet therapy. However, a significant number of patients show platelet resistance to aspirin therapy, and recurrent thrombotic events occur. Combined antithrombotic therapies with thienopyridines, such as clopidogrel have been used to resolve this problem. However, clopidogrel treatment has been also associated with wide response variability, and non-responsiveness to clopidogrel also occurs in some patients. Therefore, the main question arising about the antithrombotic therapy is why particular patients do not benefit from the therapy and how they might be identified to improve their treatment. Different hypotheses have been suggested, including genetic factors, platelet heterogeneity, non-compliance and others. However, it is probably that many molecular mechanisms involved in platelet resistance to antithrombotic therapies still remains unknown. New technologies, such as proteomics and genetic, are beginning to show new unknown biological biomarkers and molecular mechanisms which may be associated with platelet antithrombotic drug resistance. [ABSTRACT FROM AUTHOR]

ICP–MS techniques based on isotope dilution analysis can be regarded as an emerging tool in quantitative protein analysis. Well-known concepts, for example species-specific and unspecific isotope dilution analysis, which promoted accurate and precise quantification in elemental speciation studies, have nowadays been transferred to the analysis of large biomolecules, e.g. proteins. Besides detection of heteroatom-containing proteins, the artificial introduction of metal-containing labels has attracted much attention and, as a consequence, ICP–MS-based isotope dilution techniques can serve as a valuable quantification tool. In particular, because isotope dilution ICP–MS techniques can enable absolute protein quantification, they can be regarded as an attractive technique in current and prospective proteomics. In this review, recent developments and applications will be highlighted and critically assessed. [ABSTRACT FROM AUTHOR]

Background: Proteomic-based discovery of biomarkers for disease has recently come under scrutiny for a variety of issues; one prominent issue is the lack of orthogonal validation for biomarkers following discovery. Validation by ELISA or Western blot requires the use of antibodies, which for many potential biomarkers are under-characterized and may lead to misleading or inconclusive results. Gelsolin is one such biomarker candidate in HIV-associated neurocognitive disorders. Methods: Samples from human (plasma and CSF), monkey (plasma), monocyte-derived macrophage (supernatants), and commercial gelsolin (recombinant and purified) were quantitated using Western blot assay and a variety of anti-gelsolin antibodies. Plasma and CSF was used for immunoaffinity purification of gelsolin which was identified in eight bands by tandem mass spectrometry. Results: Immunoreactivity of gelsolin within samples and between antibodies varied greatly. In several instances, multiple bands were identified (corresponding to different gelsolin forms) by one antibody, but not identified by another. Moreover, in some instances immunoreactivity depended on the source of gelsolin, e.g. plasma or CSF. Additionally, some smaller forms of gelsolin were identified by mass spectrometry but not by any antibody. Recombinant gelsolin was used as reference sample. Conclusions: Orthogonal validation using specific monoclonal or polyclonal antibodies may reject biomarker candidates from further studies based on misleading or even false quantitation of those proteins, which circulate in various forms in body fluids. [ABSTRACT FROM AUTHOR]

Amoeba use phagocytosis to internalize bacteria as a source of nutrients, whereas multicellular organisms utilize this process as a defense mechanism to kill microbes and, in vertebrates, initiate a sustained immune response. By using a large‐scale approach to identify and compare the proteome and phosphoproteome of phagosomes isolated from distant organisms, and by comparative analysis over 39 taxa, we identified an ‘ancient’ core of phagosomal proteins around which the immune functions of this organelle have likely organized. Our data indicate that a larger proportion of the phagosome proteome, compared with the whole cell proteome, has been acquired through gene duplication at a period coinciding with the emergence of innate and adaptive immunity. Our study also characterizes in detail the acquisition of novel proteins and the significant remodeling of the phagosome phosphoproteome that contributed to modify the core constituents of this organelle in evolution. Our work thus provides the first thorough analysis of the changes that enabled the transformation of the phagosome from a phagotrophic compartment into an organelle fully competent for antigen presentation. [ABSTRACT FROM AUTHOR]

Flow cytometry was originally established as an automated method for measuring optical or fluorescence characteristics of cells or particles in suspension. With the enormous increase in development of reliable electronics, lasers, micro-fluidics, as well as many advances in immunology and other fields, flow cytometers have become user-friendlier, less-expensive instruments with an increasing importance for both basic research and clinical applications. Conventional uses of flow cytometry include immunophenotyping of blood cells and the analysis of the cell cycle. Importantly, methods for labeling microbeads with unique combinations of fluorescent spectral signatures have made multiplex analysis of soluble analytes (i.e. the ability to detect multiple targets in a single test sample) feasible by flow cytometry. The result is a rapid, high-throughput, sensitive, and reproducible detection technology for a wide range of biomedical applications requiring detection of proteins (in cells and biofluids) and nucleic acids. Thus, novel methods of flow cytometry are becoming important for diagnostic purposes (e.g. identifying multiple clinical biomarkers for a wide range of diseases) as well as for developing novel therapies (e.g. elucidating drug mechanisms and potential toxicities). In addition, flow cytometry for multiplex analysis, coupled with automated sample handling devices, has the potential to significantly enhance proteomics research, particularly analysis of post-translational modifications of proteins, on a large scale. Inherently, flow cytometry methods are strongly rooted in the laws of the physics of optics, fluidics, and electromagnetism. This review article describes principles and early sources of flow cytometry, provides an introduction to the multiplex microbead technology, and discusses its applications and advantages in comparison to other methods. Anticipated future directions, particularly for translational research in medicine, are also discussed. [ABSTRACT FROM AUTHOR]

The endoplasmic reticulum (ER) is a key organelle of the secretion pathway involved in the synthesis of both proteins and lipids destined for multiple sites within and without the cell. The ER functions to both co- and post-translationally modify newly synthesized proteins and lipids and sort them for housekeeping within the ER and for transport to their sites of function away from the ER. In addition, the ER is involved in the metabolism and degradation of specific xenobiotics and endogenous biosynthetic products. A variety of proteomics studies have been reported on different subcompartments of the ER providing an ER protein dictionary with new data being made available on many protein complexes of relevance to the biology of the ER including the ribosome, the translocon, coatomer proteins, cytoskeletal proteins, folding proteins, the antigen-processing machinery, signaling proteins and proteins involved in membrane traffic. This review examines proteomics and cytological data in support of the presence of specific molecular machines at specific sites or subcompartments of the ER. [ABSTRACT FROM AUTHOR]

In recent years proteomic techniques have started to become very useful tools in a variety of model systems of developmental biology. Applications cover many different aspects of development, including the characterization of changes in the proteome during early embryonic stages. During early animal development the embryo becomes patterned through the temporally and spatially controlled activation of distinct sets of genes. Patterning information is then translated, from gastrulation onwards, into regional specific morphogenetic cell and tissue movements that give the embryo its characteristic shape. On the molecular level, patterning is the outcome of intercellular communication via signaling molecules and the local activation or repression of transcription factors. Genetic approaches have been used very successfully to elucidate the processes behind these events. Morphogenetic movements, on the other hand, have to be orchestrated through regional changes in the mechanical properties of cells. The molecular mechanisms that govern these changes have remained much more elusive, at least in part due to the fact that they are more under translational/posttranslational control than patterning events. However, recent studies indicate that proteomic approaches can provide the means to finally unravel the mechanisms that link patterning to the generation of embryonic form. To intensify research in this direction will require close collaboration between proteome scientists and developmental researchers. It is with this aim in mind that we first give an outline of the classical questions of patterning and morphogenesis. We then summarize the proteomic approaches that have been applied in developmental model systems and describe the pioneering studies that have been done to study morphogenesis. Finally we discuss current and future strategies that will allow characterizing the changes in the embryonic proteome and ultimately lead to a deeper understanding of the cellular mechanisms that govern the generation of embryonic form. [ABSTRACT FROM AUTHOR]

The quantification of differences between two or more physiological states of a biological system is among the most important but also most challenging technical tasks in proteomics. In addition to the classical methods of differential protein gel or blot staining by dyes and fluorophores, mass-spectrometry-based quantification methods have gained increasing popularity over the past five years. Most of these methods employ differential stable isotope labeling to create a specific mass tag that can be recognized by a mass spectrometer and at the same time provide the basis for quantification. These mass tags can be introduced into proteins or peptides (i) metabolically, (ii) by chemical means, (iii) enzymatically, or (iv) provided by spiked synthetic peptide standards. In contrast, label-free quantification approaches aim to correlate the mass spectrometric signal of intact proteolytic peptides or the number of peptide sequencing events with the relative or absolute protein quantity directly. In this review, we critically examine the more commonly used quantitative mass spectrometry methods for their individual merits and discuss challenges in arriving at meaningful interpretations of quantitative proteomic data. [Figure not available: see fulltext.] [ABSTRACT FROM AUTHOR]

Background: Honey bee (Apis mellifera) workers are characterized by complex social behavior. Their life-history is dominated by a period of within-nest activity followed by a phase of long-distance flights and foraging. General insights into insect metabolism imply that foraging onset is associated with fundamental metabolic changes, and theory on social evolution suggests metabolic adaptations that are advantageous for the colony as a whole. Results: Here we address the life-history characteristics of workers with LC-MS/MS based relative quantification of major proteins. Our approach includes: i. Calculation of a false positive rate for the identifications, ii. Support of relative protein quantification results obtained from spectral count by non-parametric statistics, and iii. Correction for Type 1 error inflation using a bootstrap iteration analysis. Our data are consistent with the use of glucose as the main fuel for honey bee flight. Moreover, the data delivers information on the expression of ATPsynthases/ATPases, and provide new insights into nurse- and forager-specific patterns of protection against oxidative stress. Conclusion: The results show the suitability of this approach to investigate fundamental biochemical changes in an insect, and provide new evidence for metabolic specializations that occur during the social ontogeny of worker honey bees. [ABSTRACT FROM AUTHOR]

Soil is a complex matrix where biotic and abiotic components establish a still unclear network involving bacteria, fungi, archaea, protists, protozoa, and roots that are in constant communication with each other. Understanding these interactions has recently focused on metagenomics, metatranscriptomics and less on metaproteomics studies. Metaproteomic allows total extraction of intracellular and extracellular proteins from soil samples, providing a complete picture of the physiological and functional state of the "soil community". The advancement of high-performance mass spectrometry technologies was more rapid than the development of ad hoc extraction techniques for soil proteins. The protein extraction from environmental samples is biased due to interfering substances and the lower amount of proteins in comparison to cell cultures. Soil sample preparation and extraction methodology are crucial steps to obtain high-quality resolution and yields of proteins. This review focuses on the several soil protein extraction protocols to date to highlight the methodological challenges and critical issues for the application of proteomics to soil samples. This review concludes that improvements in soil protein extraction, together with the employment of ad hoc metagenome database, may enhance the identification of proteins with low abundance or from non-dominant populations and increase our capacity to predict functional changes in soil. [ABSTRACT FROM AUTHOR]

Decades of biochemical research have identified most of the enzymes that catalyze metabolic reactions in the yeast Saccharomyces cerevisiae. The adaptation of metabolism to changing nutritional conditions, in contrast, is much less well understood. As an important stepping stone toward such understanding, we exploit the power of proteomics assays based on selected reaction monitoring (SRM) mass spectrometry to quantify abundance changes of the 228 proteins that constitute the central carbon and amino-acid metabolic network in the yeast Saccharomyces cerevisiae, at five different metabolic steady states. Overall, 90% of the targeted proteins, including families of isoenzymes, were consistently detected and quantified in each sample, generating a proteomic data set that represents a nutritionally perturbed biological system at high reproducibility. The data set is near comprehensive because we detect 95-99% of all proteins that are required under a given condition. Interpreted through flux balance modeling, the data indicate that S. cerevisiae retains proteins not necessarily used in a particular environment. Further, the data suggest differential functionality for several metabolic isoenzymes. [ABSTRACT FROM AUTHOR]