Quantitative proteomics

In proteomics, it is essential to quantify proteins in absolute terms if we wish to compare results among studies and integrate high-throughput biological data into genome-scale metabolic models. While labeling target peptides with stable isotopes allow protein abundance to be accurately quantified, the utility of this technique is constrained by the low number of quantifiable proteins that it yields. Recently, label-free shotgun proteomics has become the "gold standard" for carrying out global assessments of biological samples containing thousands of proteins. However, this tool must be further improved if we wish to accurately quantify absolute levels of proteins. Here, we used different label-free quantification techniques to estimate absolute protein abundance in the model yeast Saccharomyces cerevisiae. More specifically, we evaluated the performance of seven different quantification methods, based either on spectral counting (SC) or extracted-ion chromatogram (XIC), which were applied to samples from five different proteome backgrounds. We also compared the accuracy and reproducibility of two strategies for transforming relative abundance into absolute abundance: a UPS2-based strategy and the total protein approach (TPA). This study mentions technical challenges related to UPS2 use and proposes ways of addressing them, including utilizing a smaller, more highly optimized amount of UPS2. Overall, three SC-based methods (PAI, SAF, and NSAF) yielded the best results because they struck a good balance between experimental performance and protein quantification.

Subcellular quantitative proteomics identified rapidly depleted proteins, which were enriched for a number of biological processes. These proteins can be depleted by a number of mechanisms. This analysis also showed that proteasome inhibition induced a negative feedback loop that inhibits translation. In the background is a cell stained for the PRR11 protein, a novel cell cycle regulated protein that is rapidly degraded.

Qualitative proteome profiling of formalin-fixed, paraffin-embedded (FFPE) tissue is advancing the field of clinical proteomics. However, quantitative proteome analysis of FFPE tissue is hampered by the lack of an efficient labelling method. The usage of conventional protein labelling on FFPE tissue has turned out to be inefficient. Classical labelling targets lysine residues that are blocked by the formalin treatment. The aim of this study was to establish a quantitative proteomics analysis of FFPE tissue by combining the label-free approach with optimised protein extraction and separation conditions. As a model system we used FFPE heart tissue of control and exposed C57BL/6 mice after total body irradiation using a gamma ray dose of 3 gray. We identified 32 deregulated proteins (p ≤ 0.05) in irradiated hearts 24 h after the exposure. The proteomics data were further evaluated and validated by bioinformatics and immunoblotting investigation. In good agreement with our previous results using fresh-frozen tissue, the analysis indicated radiation-induced alterations in three main biological pathways: respiratory chain, lipid metabolism and pyruvate metabolism. The label-free approach enables the quantitative measurement of radiation-induced alterations in FFPE tissue and facilitates retrospective biomarker identification using clinical archives.

Organelle proteomics is the analysis of the protein contents of a subcellular compartment. Proteins identified in subcellular proteomic studies can only be assigned to an organelle if there are no contaminants present in the sample preparation. As a result, the majority of plant organelle proteomic studies have focused on the chloroplast and mitochondria, which can be isolated relatively easily. However, the isolation of components of the endomembrane system is far more difficult due to their similar sizes and densities. For this reason, quantitative proteomics methods are being developed to enable the assignment of proteins to a specific component of the endomembrane system without the need to obtain pure organelles.

Tandem mass tags (TMTs) enable simple and accurate quantitative proteomics for multiplexed samples by relative quantification of tag reporter ions. Orbitrap quantification of reporter ions has been associated with a characteristic notch region in intensity distribution, within which few reporter intensities are recorded. This has been resolved in version 3 of the instrument acquisition software Tune. However, 47% of Orbitrap Fusion, Lumos, or Eclipse submissions to PRIDE were generated using prior software versions. To quantify the impact of the notch on existing quantitative proteomics data, we generated a mixed species benchmark and acquired quantitative data using Tune versions 2 and 3. Intensities below the notch are predominantly underestimated with Tune version 2, leading to overestimation of the true differences in intensities between samples. However, when summarizing reporter ion intensities to higherlevel features, such as peptides and proteins, few features are significantly affected. Targeted removal of spectra with reporter ion intensities below the notch is not beneficial for differential peptide or protein testing. Overall, we find that the systematic quantification bias associated with the notch is not detrimental for a typical proteomics experiment.

During mammalian preimplantation development, the cells of the blastocyst's inner cell mass differentiate into the epiblast and primitive endoderm lineages, which give rise to the fetus and extra-embryonic tissues, respectively. Extra-embryonic endoderm (XEN) differentiation can be modeled in vitro by induced expression of GATA transcription factors in mouse embryonic stem cells. Here, we use this GATA-inducible system to quantitatively monitor the dynamics of global proteomic changes during the early stages of this differentiation event and also investigate the fully differentiated phenotype, as represented by embryo-derived XEN cells. Using mass spectrometry-based quantitative proteomic profiling with multivariate data analysis tools, we reproducibly quantified 2,336 proteins across three biological replicates and have identified clusters of proteins characterized by distinct, dynamic temporal abundance profiles. We first used this approach to highlight novel marker candidates...

According to the Arg/N-end rule pathway, proteins with basic N-termini are targeted for degradation by the Arabidopsis thaliana E3 ligase, PROTEOLYSIS6 (PRT6). Proteins can also become PRT6 substrates following post-translational arginylation by arginyltransferases ATE1 and 2. Here, we undertook a quantitative proteomics study of Arg/N-end rule mutants, ate1/2 and prt6, to investigate the impact of this pathway on the root proteome. Tandem mass tag labelling identified a small number of proteins with increased abundance in the mutants, some of which represent downstream targets of transcription factors known to be N-end rule substrates. Isolation of N-terminal peptides using terminal amine isotope labelling of samples (TAILS) combined with triple dimethyl labelling identified 1465 unique N-termini. Stabilising residues were over-represented among the free neo-N-termini, but destabilising residues were not markedly enriched in N-end rule mutants. The majority of free neo-N-termini were revealed following cleavage of organellar targeting signals, thus compartmentation may account in part for the presence of destabilising residues in the wild-type N-terminome. Our data suggest that PRT6 does not have a marked impact on the global proteome of Arabidopsis roots and is likely involved in the controlled degradation of relatively few regulatory proteins. All MS data have been deposited in the ProteomeXchange with identifier PXD001719 ().

Background: Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones. Methods: To address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of Indicator Prey Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Prey Proteins account for measures of protein identifiability as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures. Results: We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions, or in combination with other AP-MS scoring methods to significantly improve inferences. Conclusions: Our method addresses important issues encountered in AP-MS datasets, making ROCS a very promising tool for this purpose, either on its own or in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called "ROCS", freely available from the CRAN repository .

The modern paradigm shift toward quantitative proteomics, especially label-free methods, raises the bar of reproducibility and quantitative recovery to a new level, creating new demands for robust sample preparation methods. Protein extraction and digestion are the two fundamental parts of proteomic sample preparation which have been shown to benefit from high hydrostatic pressure. However, most reports to date have not provided sufficient information on mechanistic aspects of pressure effects on the proteolytic digestion. We present preliminary results from a systematic study aiming to deconvolute pressure effects on protease activity from pressure effects on conformation of substrate proteins. In this study several model proteins were digested in controlled conditions using three enzyme-to-substrate ratios with and without high hydrostatic pressure. Resulting digests were separated on nanoflow HPLC and analyzed by high resolution MS/MS on the LTQ-Orbitrap XL. Pressure effects on tryptic digestion were assessed by relative label-free quantitation of resulting mass spectra.

The recent growth of innovating biologics has opened fascinating avenues for the management of patients. In rheumatoid arthritis, many biologics are currently available, the choice of which being mostly determined empirically. Importantly, a given biologic may not be active in a fraction of patients and may even provoke side effects. Here, we conducted a comparative proteomics study in attempt to identify a predictive theranostic signature of non-response in patients with rheumatoid arthritis treated by etanercept/methotrexate combination. Methods: A serum sample was collected prior to treatment exposure from a cohort of 22 patients with active RA. A proteomic "label free" approach was then designed to quantitate protein biomarkers using mass spectrometry. To verify these results, a relative quantification followed by an absolute quantification of interesting protein candidates were performed on a second cohort. The criterion of judgment was the response to etanercept/methotrexate combination according to the EULAR criteria assessed at 6 months of treatment. Results: These investigations led to the identification of a set of 12 biomarkers with capacity to predict treatment response. A targeted quantitative analysis allowed to confirm the potential of 7 proteins from the latter combination on a new cohort of 16 patients. Two highly discriminating proteins, PROS and CO7, were further evaluated by ELISA on this second cohort. By combining the concentration threshold of each protein associated to a right classification (responders vs non-responders), the sensitivity and specificity reached 88.9 % and 100 %, respectively. Conclusion: Prior to methotrexate/etanercept treatment, abundance of several sera proteins, notably PROS and CO7, were associated to response status of RA patients 6 month after treatment initiation.

-like kinase 1 (PLK1) is a key regulator of mitotic progression and cell division, and small molecule inhibitors of PLK1 are undergoing clinical trials to evaluate their utility in cancer therapy. Despite this importance, current knowledge about the identity of PLK1 substrates is limited. Here we present the results of a proteome-wide analysis of PLK1-regulated phosphorylation sites in mitotic human cells. We compared phosphorylation sites in HeLa cells that were or were not treated with the PLK1-inhibitor BI 4834, by labeling peptides via methyl esterification, fractionation of peptides by strong cation exchange chromatography, and phosphopeptide enrichment via immobilized metal affinity chromatography. Analysis by quantitative mass spectrometry identified 4070 unique mitotic phosphorylation sites on 2069 proteins. Of these, 401 proteins contained one or multiple phosphorylation sites whose abundance was decreased by PLK1 inhibition. These include proteins implicated in PLK1-regulated processes such as DNA damage, mitotic spindle formation, spindle assembly checkpoint signaling, and chromosome segregation, but also numerous proteins that were not suspected to be regulated by PLK1. Analysis of amino acid sequence motifs among phosphorylation sites down-regulated under PLK1 inhibition in this data set identified two potential novel variants of the PLK1 consensus motif. Molecular & Cellular

In many studies, particularly in the field of systems biology, it is essential that identical protein sets are precisely quantified in multiple samples such as those representing differentially perturbed cell states. The high degree of reproducibility required for such experiments has not been achieved by classical mass spectrometry-based proteomics methods. In this study we describe the implementation of a targeted quantitative approach by which predetermined protein sets are first identified and subsequently quantified at high sensitivity reliably in multiple samples. This approach consists of three steps. First, the proteome is extensively mapped out by multidimensional fractionation and tandem mass spectrometry, and the data generated are assembled in the PeptideAtlas database. Second, based on this proteome map, peptides uniquely identifying the proteins of interest, proteotypic peptides, are selected, and multiple reaction monitoring (MRM) transitions are established and validated by MS2 spectrum acquisition. This process of peptide selection, transition selection, and validation is supported by a suite of software tools, TIQAM (Targeted Identification for Quantitative Analysis by MRM), described in this study. Third, the selected target protein set is quantified in multiple samples by MRM. Applying this approach we were able to reliably quantify low abundance virulence factors from cultures of the human pathogen Streptococcus pyogenes exposed to increasing amounts of plasma. The resulting quantitative protein patterns enabled us to clearly define the subset of virulence proteins that is regulated upon plasma exposure.

The relative quantification of proteins using liquid chromatography mass spectrometry (LC-MS) has allowed researchers to compile lists of potential disease markers. These complex quantitative workflows often include isobaric labeling of enzymatically-produced peptides to analyze their relative abundances across multiple samples in a single LC-MS run. Recent efforts by our lab have provided scientists with cost-effective alternatives to expensive commercial labels. Although the quantitative performance of these dimethyl leucine (DiLeu) labels has been reported using known ratios of complex protein and peptide standards, their potential in large-scale proteomics studies using a clinically relevant system has never been investigated. Our work rectifies this oversight by implementing 4-plex DiLeu to quantify proteins in the urine of aging human males who suffer from lower urinary tract symptoms (LUTS). Protein abundances in 25 LUTS and 15 control patients were compared, revealing that of the 836 proteins quantified, 50 were found to be differentially expressed (>20% change) and statistically significant (p-value <0.05). Gene ontology (GO) analysis of the differentiated proteins showed that many were involved in inflammatory responses and implicated in fibrosis. While confirmation of individual protein abundance changes would be required to verify protein expression, this study represents the first report using the custom isobaric label, 4-plex DiLeu, to quantify protein abundances in a clinically relevant system.

Rationale-Relative quantification of proteins via their enzymatically digested peptide products determines disease biomarker candidate lists in discovery studies. Isobaric label-based strategies using TMT and iTRAQ allow for up to 10 samples to be multiplexed in one experiment, but their expense limits their use. The demand for cost-effective tagging reagents capable of multiplexing many samples led us to develop an 8-plex version of our isobaric labeling reagent, DiLeu. Methods-The original 4-plex DiLeu reagent was extended to an 8-plex set by coupling isotopic variants of dimethylated leucine to an alanine balance group designed to offset the increasing mass of the label's reporter group. Tryptic peptides from a single protein digest, a protein mixture digest, and Saccharomyces cerevisiae lysate digest were labeled with 8-plex DiLeu and analyzed via nanoLC-MS 2 on a Q-Exactive Orbitrap mass spectrometer. Characteristics of 8-plex DiLeulabeled peptides, including quantitative accuracy and fragmentation, were examined. Results-An 8-plex set of DiLeu reagents with 1 Da-spaced reporters was synthesized at a yield of 36%. The average cost to label eight 100 μg peptide samples was calculated to be approximately $15. Normalized collision energy tests on the Q-Exactive revealed that a higherenergy collisional dissociation value of 27 generated the optimum number of high-quality spectral matches. Relative quantification of DiLeu-labeled peptides yielded normalized median ratios accurate to within 12% of their expected values. Conclusions-Cost-effective 8-plex DiLeu reagents can be synthesized and applied to relative peptide and protein quantification. These labels increase the multiplexing capacity of our previous 4-plex implementation without requiring high-resolution instrumentation to resolve reporter ion signals.

Absolute quantification of protein targets using liquid chromatography-mass spectrometry (LC-MS) is a key component of candidate biomarker validation. One popular method combines multiple reaction monitoring (MRM) using a triple quadrupole instrument with stable isotope-labeled standards (SIS) for absolute quantification (AQUA). LC-MRM AQUA assays are sensitive and specific, but they are also expensive because of the cost of synthesizing stable isotope peptide standards. While the chemical modification approach using mass differential tags for relative and absolute quantification (mTRAQ) represents a more economical approach when quantifying large numbers of peptides, these reagents are costly and still suffer from lower throughput because only two concentration values per peptide can be obtained in a single LC-MS run. Here, we have developed and applied a set of five novel mass difference reagents, isotopic N,N-dimethyl leucine (iDiLeu). These labels contain an amine reactive group...

Clostridioides dif f icile-associated infection (CDI) is a health-careassociated infection caused, as the name suggests, by obligate anaerobic pathogen C. dif f icile and thus mainly transmitted via highly resistant endospores from one person to the other. In vivo, the spores need to germinate into cells prior to establishing an infection. Bile acids and glycine, both available in sufficient amounts inside the human host intestinal tract, serve as efficient germinants for the spores. It is therefore, for better understanding of C. dif f icile virulence, crucial to study both the cell and spore states with respect to their genetic, metabolic, and proteomic composition. In the present study, mass spectrometric relative protein quantification, based on the 14 N/ 15 N peptide isotopic ratios, has led to quantification of over 700 proteins from combined spore and cell samples. The analysis has revealed that the proteome turnover between a vegetative cell and a spore for this organism is moderate. Additionally, specific cell and spore surface proteins, vegetative cell proteins CD1228, CD3301 and spore proteins CD2487, CD2434, and CD0684 are identified as potential protein markers for C. diff icile infection.

Macrophages can be polarized into classically activated macrophages (M1) and alternatively activated macrophages (M2) in the immune system, performing pro-inflammatory and anti-inflammatory functions, respectively. Human THP-1 and mouse RAW264.7 cell line models have been widely used in various macrophage-associated studies, while the similarities and differences in protein expression profiles between the two macrophage models are still largely unclear. In this study, the protein expression profiles of M1 and M2 phenotypes from both THP-1 and RAW264.7 macrophages were systematically investigated using mass spectrometry-based proteomics. By quantitatively analyzing more than 5,000 proteins among different types of macrophages (M0, M1 and M2) from both cell lines, we identified a list of proteins that were uniquely up-regulated in each macrophage type and further confirmed 43 proteins that were commonly up-regulated in M1 macrophages of both cell lines. These results revealed consider...

Carbonic anhydrases (CAs) catalyse reversible interconversion of CO 2 and water into bicarbonate and protons and regulate concentration of CO 2 around photosynthetic enzymes. In higher plants the CAs are divided into three distinct classes α, β and γ, with members off each of them being involved in CO 2 uptake, fixation or recycling. The most abundant group is βCAs. In C4 plants they are localized in the cytosol of mesophyll cells and catalyse first step of carbon concentration pathway. C3 plants contain orthologues genes encoding βCAs's, however their functions are unknown. Given the importance of βCAs in the present study we analysed the effect of carbonic ions, selected orthologues βCAs's gene expression and βCAs enzymatic activity on Arabidopsis photosynthesis, growth and cell death in different light conditions. Plants fertilised with 0.5 -3 mM sodium bicarbonate had a significantly increased number of leaves, improved fresh and dry weight and reduced cell death (cellular ion leakage). This effect was dependent on provided photon flux density and photoperiod. Higher content of carbonic ions also stimulated photoprotective mechanisms such as non-photochemical quenching and foliar content of photoprotective pigments (neoxanthin, violaxanthin and carotenes). Function of various βCAs genes examined in null βcas mutants showed to be complementary and additive, and confirm results of fertilizing experiments. Taken together, regulation of βCAs gene expression and enzymatic activities are important for optimal plant growth and probably can be one of the factor influencing a switch between C3 and C4 photosynthesis mode in variable light conditions. Therefore, biotechnological amelioration of βCAs activity in economically important plants and their fertilisation with carbonic ions may lead to improved photosynthetic efficiency and further crop productivity. CAs, carbonic anhydrases; CO 2 , carbon dioxide; Fv/Fm, photosynthetic efficiency; LHCs, light harvesting complexes; lsd1, lesion simulating disease 1; ME, malic enzyme; NaHCO 3 , sodium bicarbonate; NPQ, nonphotochemical quenching; PCD: plant cell death; PEPC, phosphoenolpyruvate carboxylase; PSI, photosystem I; PSII, photosystem II; ROS, reactive oxygen species; RuBisCO, ribulose-1,5-biphosphate carboxylase oxygenase; RCD, runaway cell death; QY, maximal quantum yield.

Purpose: Detailed understanding of host pathogen interaction in tuberculosis is an important avenue for identifying novel therapeutic targets. Small extracellular vesicles (EVs) like exosomes that are rich in proteins, nucleic acids and lipids, act as messengers and may show altered composition in disease conditions. Experimental design: In this case control study, small EVs are isolated from serum of 58 subjects (all male, 33 (15-70) in years) including drug naïve active tuberculosis (ATB: n = 22), non-tuberculosis (NTB: n = 18), and healthy subjects (n = 18). Serum small EVs proteome analysis is carried out using isobaric tag for relative and absolute quantification (iTRAQ) experiments and an independent sample (n = 36) is used for validation. Results: A set of 132 and 68 proteins are identified in iTRAQ-I (ATB/Healthy) and iTRAQ-II (ATB/NTB) experiments, respectively. Four proteins (KYAT3, SERPINA1, HP, and APOC3) show deregulation (log 2 -fold change > ±0.48, p < 0.05) in ATB with respect to healthy controls and Western blot data corroborated mass spectrometry findings. Conclusions and clinical relevance: These important proteins, involved in neutrophil degranulation, plasma heme scavenging, kynurenine, and lipid metabolism, show deregulation in ATB patients. Identification of such a protein panel in circulating small EVs besides providing novel insights into their role in tuberculosis may prove to be useful targets to develop host-directed therapeutic intervention.

Purpose: Detailed understanding of host pathogen interaction in tuberculosis is an important avenue for identifying novel therapeutic targets. Small extracellular vesicles (EVs) like exosomes that are rich in proteins, nucleic acids and lipids, act as messengers and may show altered composition in disease conditions. Experimental design: In this case control study, small EVs are isolated from serum of 58 subjects (all male, 33 (15-70) in years) including drug naïve active tuberculosis (ATB: n = 22), non-tuberculosis (NTB: n = 18), and healthy subjects (n = 18). Serum small EVs proteome analysis is carried out using isobaric tag for relative and absolute quantification (iTRAQ) experiments and an independent sample (n = 36) is used for validation. Results: A set of 132 and 68 proteins are identified in iTRAQ-I (ATB/Healthy) and iTRAQ-II (ATB/NTB) experiments, respectively. Four proteins (KYAT3, SERPINA1, HP, and APOC3) show deregulation (log 2 -fold change > ±0.48, p < 0.05) in ATB with respect to healthy controls and Western blot data corroborated mass spectrometry findings. Conclusions and clinical relevance: These important proteins, involved in neutrophil degranulation, plasma heme scavenging, kynurenine, and lipid metabolism, show deregulation in ATB patients. Identification of such a protein panel in circulating small EVs besides providing novel insights into their role in tuberculosis may prove to be useful targets to develop host-directed therapeutic intervention.

A number of proteomic techniques have been developed to quantify proteins in biological systems. This review focuses on the quantitative proteomic technique known as “proteolytic 18O‐labeling.” This technique utilizes a protease and H218O to produce labeled peptides, with subsequent chromatographic and mass spectrometric analysis to identify and quantify (relative) the proteins from which the peptides originated. The technique determines the ratio of individual protein&#39;s expression level between two samples relative to each other, and can be used to quantitatively examine protein expression (comparative proteomics) and post‐translational modifications, and to study protein–protein interactions. The present review discusses various aspects of the 18O‐labeling technique, including: its history, the advantages and disadvantages of the proteolytic 18O‐labeling technique compared to other techniques, enzymatic considerations, the problem of variable incorporation of 18O atoms into pe...

Iron Oxide Nanoparticles (IONPs) present unique properties making them one of the most used NPs in the biomedical field. Nevertheless, for many years, growing production and use of IONPs are associated with risks that can affect human and the environment. Thus, it is essential to study the effects of these nanoparticles to better understand their mechanism of action and the molecular perturbations induced in the organism. In the present study, we investigated the toxicological effects of IONPs (γ-Fe2O3) on liver, lung and brain proteomes in Wistar rats. Exposed rats received IONP solution during 7 consecutive days by intranasal instillation at a dose of 10 mg/kg body weight. An iTRAQ-based quantitative proteomics was used to study proteomic variations at the level of the three organs. Using this proteomic approach, we identified 1565; 1135 and 1161 proteins respectively in the brain, liver and lung. Amon them, we quantified 1541; 1125 and 1128 proteins respectively in the brain, liv...

Background: Since it was first reported in 1935, Cucumber green mottle mosaic virus (CGMMV) has become a serious pathogen in a range of cucurbit crops. The virus is generally transmitted by propagation materials, and to date no effective chemical or cultural methods of control have been developed to combat its spread. The current study presents a preliminary analysis of the pathogenic mechanisms from the perspective of protein expression levels in an infected cucumber host, with the objective of elucidating the infection process and potential strategies to reduce both the economic and yield losses associated with CGMMV. Methods: Isobaric tags for relative and absolute quantitation (iTRAQ) technology coupled with liquid chromatography-tandem mass spectrometric (LC-MS/MS) were used to identify the differentially expressed proteins in cucumber plants infected with CGMMV compared with mock-inoculated plants. The functions of the proteins were deduced by functional annotation and their involvement in metabolic processes explored by KEGG pathway analysis to identify their interactions during CGMMV infection, while their in vivo expression was further verified by qPCR. Results: Infection by CGMMV altered both the expression level and absolute quantity of 38 proteins (fold change >0.6) in cucumber hosts. Of these, 23 were found to be up-regulated, while 15 were down-regulated. Gene ontology (GO) analysis revealed that 22 of the proteins had a combined function and were associated with molecular function (MF), biological process (BP) and cellular component (CC). Several other proteins had a dual function with 1, 7, and 2 proteins being associated with BP/CC, BP/MF, CC/MF, respectively. The remaining 3 proteins were only involved in MF. In addition, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis identified 18 proteins that were involved in 13 separate metabolic pathways. These pathways were subsequently merged to generate three network diagrams illustrating the interactions between the different pathways, while qPCR was used to track the changes in expression levels of the proteins identified at 3 time points during CGMMV infection. Taken together these results greatly expand our understanding of the relationships between CGMMV and cucumber hosts. The results of the study indicate that CGMMV infection significantly changes the physiology of cucumbers, affecting the expression levels of individual proteins as well as entire metabolic pathways. The bioinformatic analysis also identified several pathogenesis-related (PR) proteins that could be useful in the development of disease-resistant plants.

Blood is an ideal window for viewing our health and disease status. Because blood circulates throughout the entire body and carries secreted, shed, and excreted signature proteins from every organ and tissue type, it is thus possible to use the blood proteome to achieve a comprehensive assessment of multiple-organ physiology and pathology. To date, the blood proteome has been frequently examined for diseases of individual organs; studies on compound insults impacting multiple organs are, however, elusive. We believe that a characterization of peripheral blood for organ-specific proteins affords a powerful strategy to allow early detection, staging, and monitoring of diseases and their treatments at a whole-body level. In this paper we test this hypothesis by examining a mouse model of acetaminophen (APAP)-induced hepatic and extrahepatic toxicity. We used a glycocapture-assisted global quantitative proteomics (gagQP) approach to study serum proteins and validated our results using Western blot. We discovered in mouse sera both hepatic and extra-hepatic organ-specific proteins. From our validation, it was determined that selected organ-specific proteins had changed their blood concentration during the course of toxicity development and recovery. Interestingly, the peak responding time of proteins specific to different organs varied in a time-course study. The collected molecular information shed light on a complex, dynamic, yet interweaving, multiorgan-enrolled APAP toxicity. The developed technique as well as the identified protein markers is translational to human studies. We hope our work can broaden the utility of blood proteomics in diagnosis and research of the wholebody response to pathogenic cues.

Traumatic brain injury (TBI) is a complex and common problem resulting in the loss of cognitive function. In order to build a comprehensive knowledge base of the proteins that underlie these cognitive deficits, we employed unbiased quantitative mass spectrometry, proteomics, and bioinformatics to identify and quantify dysregulated proteins in the CA3 subregion of the hippocampus in the fluid percussion model of TBI in rats. Using stable isotope 18 O-water differential labeling and multidimensional tandem liquid chromatography (LC)-MS/MS with high stringency statistical analyses and filtering, we identified and quantified 1002 common proteins, with 124 increased and 76 decreased. The Ingenuity Pathway Analysis (IPA) bioinformatics tool identified that TBI had profound effects on downregulating global energy metabolism, including glycolysis, the Krebs cycle, and oxidative phosphorylation, as well as cellular structure and function. Widespread upregulation of actin-related cytoskeletal dynamics was also found. IPA indicated a common integrative signaling node, calcineurin B1 (CANB1, CaNBa, or PPP3R1), which was downregulated by TBI. Western blotting confirmed that the calcineurin regulatory subunit, CANB1, and its catalytic binding partner PP2BA, were decreased without changes in other calcineurin subunits. CANB1 plays a critical role in downregulated networks of calcium signaling and homeostasis through calmodulin and calmodulin-dependent kinase II to highly interconnected structural networks dominated by tubulins. This large-scale knowledge base lays the foundation for the identification of novel therapeutic targets for cognitive rescue in TBI.

Determining global proteome changes is important for advancing a systems biology view of cellular processes and for discovering biomarkers. Liquid chromatography, coupled to mass spectrometry, has been widely used as a proteomics technique for discovering differentially expressed proteins in biological samples. However, although a large number of high-throughput studies have identified differentially regulated proteins, only a small fraction of these results have been reproduced and independently verified. The use of different approaches to data processing and analyses is among the factors which contribute to inconsistent conclusions. This perspective provides a comprehensive and critical overview of bioinformatics methods for commonly used mass spectrometry-based quantitative proteomics, employing both stable isotope labeling and label-free approaches. We evaluate the challenges associated with current quantitative proteomics techniques, placing particular emphasis on data analyses. The complexity of processing and interpreting proteomics datasets has become a central issue as sensitivity, mass resolution, mass accuracy and throughput of mass spectrometers have improved. A number of computer programs are available to address these challenges, and are reviewed here. We focus on approaches for signal processing, noise reduction, and methods for protein abundance estimation.

A mid-infrared free-electron laser (FEL) is a linearly polarized, high-peak powered pulse laser with tunable wavelength within the mid-infrared absorption region. It was recently found that pathogenic amyloid fibrils could be partially dissociated to the monomer form by the irradiation of the FEL targeting the amide I band (C=O stretching vibration), amide II band (N—H bending vibration) and amide III band (C—N stretching vibration). In this study, the irradiation effect of the FEL on keratin aggregate was tested as another model to demonstrate an applicability of the FEL for dissociation of protein aggregates. Synchrotron radiation infrared microscopy analysis showed that the α-helix content in the aggregate structure decreased to almost the same level as that in the monomer state after FEL irradiation tuned to 6.06 µm (amide I band). Both irradiations at 6.51 µm (amide II band) and 8.06 µm (amide III band) also decreased the content of the aggregate but to a lesser extent than for...

Proteomics methods may be used to analyze changes occurring in stored blood products. These data sets can identify processes leading to storage-associated losses of blood component quality such as the platelet (PLT) storage lesion (PSL). The optimal strategy to perform such analyses to obtain the most informative data sets, including which proteomics methods, is undefined. This study addresses relative differences among proteomics approaches to the analysis of the PLT storage lesion. STUDY DESIGN AND METHODS: Changes to the PLT proteome between Days 1 and 7 of storage were analyzed with three complementary proteomic approaches with final mass spectrometry analysis: twodimensional (2D) gel electrophoresis/differential gel electrophoresis (DIGE), isotope tagging for relative and absolute quantitation (iTRAQ), and isotope-coded affinity tagging (ICAT). Observed changes in concentration during storage of selected proteins were confirmed by immunoblotting. RESULTS: In total, 503 individual proteins changed concentration over a 7-day storage period. By method, a total of 93 proteins were identified by 2D gel/DIGE, 355 by iTRAQ, and 139 by ICAT. Less than 16 percent of the 503 proteins, however, were identified by not more than at least two proteomic approaches. Only 5 proteins were identified by all approaches. Membrane protein changes were not reliably detected with 2D gel/DIGE methods. CONCLUSION: Although proteomics analyses identified many storage-associated protein changes, these varied significantly by method suggesting that a combination of protein-centric (2D gel or DIGE) and peptide-centric (iTRAQ or ICAT) approaches are essential to acquire adequate data. The use of one proteomics method to study changes in stored blood products may give insufficient information.

Affinity purification (AP) of protein complexes combined with LC-MS/MS analysis is the current method of choice for identification of protein-protein interactions. Their interpretation with respect to significance, specificity, and selectivity requires quantification methods coping with enrichment factors of more than 1000-fold, variable amounts of total protein, and low abundant, unlabeled samples. We used standardized samples (0.1-1000 fmol) measured on high resolution hybrid linear ion trap instruments (LTQ-FT/Orbitrap) to characterize and improve linearity and dynamic range of label-free approaches. Quantification based on spectral counts was limited by saturation and ion suppression effects with samples exceeding 100 ng of protein, depending on the instrument setup. In contrast, signal intensities of peptides (peak volumes) selected by a novel correlation-based method (TopCorr-PV) were linear over at least 4 orders of magnitude and allowed for accurate relative quantification of standard proteins spiked into a complex protein background. Application of this procedure to APs of the voltage-gated potassium channel Kv1.1 as a model membrane protein complex unambiguously identified the whole set of known interaction partners together with novel candidates. In addition to discriminating these proteins from background, we could determine efficiency, cross-reactivities, and selection biases of the used purification antibodies. The enhanced dynamic range of the developed quantification procedure appears well suited for sensitive identification of specific protein-protein interactions, detection of antibody-related artifacts, and optimization of AP conditions. Molecular & Cellular Pro-

ABSTRACTIn-depth proteome quantitation is of great significance for understanding protein functions, advancing biological, medical, environmental and metabolic engineering research. Herein, benefiting from the high formation efficiencies and intensities of dimethyl-labeled a1 ions for accurate quantitation, we developed an in-depth a1 ion-based proteome quantitation method, named deep-APQ, by a sequential MS/MS acquisition of the high mass range for identification and the low mass range for a1 ion intensity extraction to increase quantitative protein number and sequence coverage. By the analysis of HeLa protein digests, our developed method showed deeper quantitative coverage than our previously reported a1 ion-based quantitation method without mass range segmentation and lower missing values than widely-used label-free quantitation method. It also exhibited excellent accuracy and precision within a 20-fold dynamic range. We further integrated a workflow combining the deep-APQ metho...

The aim of this study was to find serum biomarkers of rheumatoid arthritis (RA) by high-resolution proteomic analysis. Low-abundance proteins from pooling serum sample of early RA patients and healthy controls were enriched using ProteoMiner™ enrichment kits. The enriched proteins were separated on SDS-PAGE, digested by trypsin, labeled with tandem mass tag (TMT) reagents, and desalted by C18 stage tip column. Then, the labeled peptides were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) with nano-LC combined with Orbitrap Q Exactive mass spectrometer, and experiments were carried out three times using different specimens, and differentially expressed proteins were screened by intensity ratios of identified peptides. Enzyme-linked immunosorbent assays (ELISAs) were performed to confirm differentially expressed proteins. Twenty-six proteins were found differentially expressed in RA serum by high-resolution proteomic analysis. Among them, levels of thrombospondin-1, ficolin-2, isoform 10 of fibronectin, and apolipoprotein E were higher in RA patients than in healthy controls (RA/ healthy control (HC)≥1.5, p<0.05), while levels of angiotensinogen, paraoxonase/arylesterase 1, isoform E of proteoglycan 4, and plasminogen were significantly lower (RA/HC≤0.67, p<0.05). Further study by ELISA showed a higher level of ficolin-2 in the serum of RA patients compared to healthy controls; the level of ficolin-2 was found to be positively correlated with swollen joint counts (SJCs), disease activity score (DAS28), rheumatoid factor, and IgM in RA patients and with DAS28 and IgM in early RA patients through statistical analysis. The results of this study suggest that ficolin-2, as a newly screened biomarker by high-resolution quantitative proteomic analysis, offers the potentiality to become a diagnostic or disease evaluation tool in RA.

odern microscopyʼs versatility, resolution and multi-modal nature delivers increasingly detailed images of single-cell heterogeneity and tissue organization 1 . Currently, a predefined subset of proteins is usually targeted, far short of the actual complexity of the proteome. Taking advantage of substantially increased sensitivity in technology based on mass spectrometry (MS), we set out to enable the analysis of proteomes within their native, subcellular context to explore their contribution to health and disease. We combined sub-micron-resolution imaging, image analysis for single-cell phenotyping based on artificial intelligence (AI) and isolation with an ultra-sensitive proteomics workflow 2 (Fig. ). Key challenges turned out to be the accurate definition of single-cell boundaries and cell classes as well as the transfer of the automatically defined features into proteomic samples, ready for analysis. To this end, we introduce the software 'BIAS' (Biology Image Analysis Software), which coordinates scanning and laser microdissection (LMD) microscopes. This seamlessly combines data-rich imaging of cell cultures or archived biobank tissues (formalin-fixed and paraffin-embedded (FFPE)) with deep-learning-based cell segmentation and machine-learning-based identification of cell types and states. Cellular or subcellular objects of interest are selected by the AI alone or after instruction before being subjected to automated LMD and proteomic profiling. Data generated by DVP can be mined to discover protein signatures providing molecular insights into proteome variation at the phenotypic level while retaining complete spatial information. Image-guided single-cell isolation for cell-type-resolved proteomics. The microscopy-related aspects of the DVP workflow build on high-resolution whole-slide imaging, machine learning (ML) and deep learning (DL) for image analysis. First, we used scanning microscopy to obtain high-resolution whole-slide images and developed a software suite for integrative image analysis termed 'BIAS' (Methods). BIAS processes multiple two-dimensional (2D) and three-dimensional (3D) microscopy image file formats, supporting major microscope vendors and data formats. It combines image pre-processing, DL-based image

Introduction: The goal of many MS-based proteomics experiments nowadays is to quantify changes in the abundance of the proteomes across several samples of biological interest. The iTRAQ labeling method is a powerful relative quantitation technique that combined with liquid chromatography coupled to tandem mass spectrometry allows quantify up to eight different samples simultaneously. The transformation of the multiple

A major goal in proteomics is the comprehensive and accurate description of a proteome. This task includes not only the identification of proteins in a sample, but also the accurate quantification of their abundance. Although mass spectrometry typically provides information on peptide identity and abundance in a sample, it does not directly measure the concentration of the corresponding proteins. Specifically, most mass-spectrometry-based approaches (e.g. shotgun proteomics or selected reaction monitoring) allow one to quantify peptides using chromatographic peak intensities or spectral counting information. Ultimately, based on these measurements, one wants to infer the concentrations of the corresponding proteins. Inferring properties of the proteins based on experimental peptide evidence is often a complex problem because of the ambiguity of peptide assignments and different chemical properties of the peptides that affect the observed concentrations. We present SCAMPI, a novel generic and statistically sound framework for computing protein abundance scores based on quantified peptides. In contrast to most previous approaches, our model explicitly includes information from shared peptides to improve protein quantitation, especially in eukaryotes with many homologous sequences. The model accounts for uncertainty in the input data, leading to statistical prediction intervals for the protein scores. Furthermore, peptides with extreme abundances can be reassessed and classified as either regular data points or actual outliers. We used the proposed model with several datasets and compared its performance to that of other, previously used approaches for protein quantification in bottom-up mass spectrometry. Molecular & Cellular Proteomics 13: 10.1074/mcp.M112.

Background: The inhalation of allergens by allergic asthmatics results in the early asthmatic response (EAR), which is characterized by acute airway obstruction beginning within a few minutes. The EAR is the earliest indicator of the pathological progression of allergic asthma. Because the molecular mechanism underlying the EAR is not fully defined, this study will contribute to a better understanding of asthma. Methods: In order to gain insight into the molecular basis of the EAR, we examined changes in protein expression patterns in the lung tissue of asthmatic rats during the EAR using 2-DE/MS-based proteomic techniques. Bioinformatic analysis of the proteomic data was then performed using PPI Spider and KEGG Spider to investigate the underlying molecular mechanism. Results: In total, 44 differentially expressed protein spots were detected in the 2-DE gels. Of these 44 protein spots, 42 corresponded to 36 unique proteins successfully identified using mass spectrometry. During subsequent bioinformatic analysis, the gene ontology classification, the protein-protein interaction networking and the biological pathway exploration demonstrated that the identified proteins were mainly involved in glycolysis, calcium binding and mitochondrial activity. Using western blot and semi-quantitative RT-PCR, we confirmed the changes in expression of five selected proteins, which further supports our proteomic and bioinformatic analyses. Conclusions: Our results reveal that the allergen-induced EAR in asthmatic rats is associated with glycolysis, calcium binding and mitochondrial activity, which could establish a functional network in which calcium binding may play a central role in promoting the progression of asthma.

Infection of type II alveolar epithelial (ATII) cells by influenza A viruses (IAV) correlates with severe respiratory disease in humans and mice. To understand pathogenic mechanisms during IAV infection of ATII cells, murine ATII cells were cultured to maintain a differentiated phenotype, infected with IAV-PR8, which causes severe lung pathology in mice, and proteomics analyses were performed using liquid chromatography-mass spectrometry. PR8 infection increased levels of proteins involved in interferon signaling, antigen presentation, and cytoskeleton regulation. Proteins involved in mitochondrial membrane permeability, energy metabolism, and chromatin formation had reduced levels in PR8-infected cells. Phenotypic markers of ATII cells in vivo were identified, confirming the differentiation status of the cultures. Surfactant protein B had decreased levels in Pisis isiiiR8-infected cells, which was confirmed by immunoblotting and immunofluorescence assays. Analysis of ATII cell prot...

Liquid chromatography coupled with mass spectrometry (LC‐MS) is widely used to identify and quantify peptides in complex biological samples. In particular, label‐free shotgun proteomics is highly effective for the identification of peptides and subsequently obtaining a global protein profile of a sample. As a result, this approach is widely used for discovery studies. Typically, the objective of these discovery studies is to identify proteins that are affected by some condition of interest (e.g. disease, exposure). However, for complex biological samples, label‐free LC‐MS proteomics experiments measure peptides and do not directly yield protein quantities. Thus, protein quantification must be inferred from one or more measured peptides. In recent years, many computational approaches to relative protein quantification of label‐free LC‐MS data have been published. In this review, we examine the most commonly employed quantification approaches to relative protein abundance from peak in...

Proteomics strategies based on nanoflow (nano-) LC-MS/MS allow the identification of hundreds to thousands of proteins in complex mixtures. When combined with protein isotopic labeling, quantitative comparison of the proteome from different samples can be achieved using these approaches. However, bioinformatics analysis of the data remains a bottleneck in large scale quantitative proteomics studies. Here we present a new software named Mascot File Parsing and Quantification (MFPaQ) that easily processes the results of the Mascot search engine and performs protein quantification in the case of isotopic labeling experiments using either the ICAT or SILAC (stable isotope labeling with amino acids in cell culture) method. This new tool provides a convenient interface to retrieve Mascot protein lists; sort them according to Mascot scoring or to user-defined criteria based on the number, the score, and the rank of identified peptides; and to validate the results. Moreover the software extracts quantitative data from raw files obtained by nano-LC-MS/MS, calculates peptide ratios, and generates a non-redundant list of proteins identified in a multisearch experiment with their calculated averaged and normalized ratio. Here we apply this software to the proteomics analysis of membrane proteins from primary human endothelial cells (ECs), a cell type involved in many physiological and pathological processes including chronic inflammatory diseases such as rheumatoid arthritis. We analyzed the EC membrane proteome and set up methods for quantitative analysis of this proteome by ICAT labeling. EC microsomal proteins were fractionated and analyzed by nano-LC-MS/MS, and database searches were per-formed with Mascot. Data validation and clustering of proteins were performed with MFPaQ, which allowed identification of more than 600 unique proteins. The software was also successfully used in a quantitative differential proteomics analysis of the EC membrane proteome after stimulation with a combination of proinflammatory mediators (tumor necrosis factor-␣, interferon-␥, and lymphotoxin ␣/␤) that resulted in the identification of a full spectrum of EC membrane proteins regulated by inflammation.

Here we provide data for SILAC and iTRAQ based hyperplexing combined with BONCAT based click chemistry for selective enrichment of newly synthesized proteins secreted by THP1 macrophages at various time points after infection with four different strains of Mycobacterium tuberculosis. The macrophages were infected with H37Ra, H37Rv, BND433 and JAL2287 strains of M. tuberculosis. Newly-synthesized secreted host proteins were observed, starting from six hours post-infection till 26 h, at 4 h intervals. We have combined BONCAT with hyperplexing (18-plex), which blends SILAC and iTRAQ, for the first time. Two sets of triplex SILAC were used to encode the strains of M. tuberculosis - H37Ra & H37Rv in one and BND433 & JAL2287 in another with a control in each. BONCAT was used to enrich the secretome for newly synthesized proteins while 6-plex iTRAQ labeling was employed to quantify the temporal changes in the captured proteome. Each set of 18-plex was run in 4 MS replicates with two linear...

Accurate quantification of proteins is one of the major tasks in current proteomics research. To address this issue, a wide range of stable isotope labeling techniques have been developed, allowing one to quantitatively study thousands of proteins by means of mass spectrometry. In this article, the FindPairs module of the PeakQuant software suite is detailed. It facilitates the automatic determination of protein abundance ratios based on the automated analysis of stable isotope-coded mass spectrometric data. Furthermore, it implements statistical methods to determine outliers due to biological as well as technical variance of proteome data obtained in replicate experiments. This provides an important means to evaluate the significance in obtained protein expression data. For demonstrating the high applicability of FindPairs, we focused on the quantitative analysis of proteome data acquired in 14 N/ 15 N labeling experiments. We further provide a comprehensive overview of the features of the FindPairs software, and compare these with existing quantification packages. The software presented here supports a wide range of proteomics applications, allowing one to quantitatively assess data derived from different stable isotope labeling approaches, such as 14 N/ 15 N labeling, SILAC, and iTRAQ. The software is publicly available at and free for academic use.

Quantitative proteomic studies, based on two-dimensional gel electrophoresis, are commonly used to find proteins that are differentially expressed between samples or groups of samples. These proteins are of interest as potential diagnostic or prognostic biomarkers, or as proteins associated with a trait. The complexity of proteomic data poses many challenges, so while experiments may reveal proteins that are differentially expressed, these are often not significant when subjected to rigorous statistical analysis. However, this can be addressed through appropriate experimental design. A good experimental design considers the impact of different sources of variation, both analytical and biological, on the statistical importance of the results. The design should address the number of samples that must be analyzed and the number of replicate gels per sample, in the context of a particular minimum difference that one is seeking to achieve. In this study, we explore the ways to improve the quality of protein expression data from 2-DE gels, and describe an approach for defining the number of samples required and the number of gels per sample. It has been developed for the simplest of situations, two groups of samples with variation at two levels: between samples and between gels. This approach will also be useful as a guide for more complex designs involving more than two groups of samples. We describe some Internet-accessible tools that can assist in the design of proteomic studies.