Mass spectrometry has served as a major tool for the discipline of proteomics to catalogue proteins in an unprecedented scale. With chemical and metabolic techniques for stable isotope labeling developed over the past decade, it is now routinely used as a method for relative quantification to provide valuable information on alteration of protein abundance in a proteome-wide scale. More recently, absolute or stoichiometric quantification of proteome is becoming feasible, in particular, with the development of strategies with isotope-labeled standards composed of concatenated peptides. On the other hand, remarkable progress has been also made in label-free quantification methods based on the number of identified peptides. Here we review these mass spectrometry-based approaches for absolute quantification of proteome and discuss their implications.
Quantitative proteomics; mass spectrometry; absolute quantification; stable isotope labeling; label-free.
Mass spectrometry based methods for relative proteome quantification have broadly impacted life science research. However, important research directions, particularly those involving mathematical modeling and simulation of biological processes, also critically depend on absolutely quantitative data, i.e. knowledge of the concentration of the expressed proteins as a function of cellular state. Until now, absolute protein concentration measurements of a significant fraction of the proteome (73%) have only been derived from genetically altered S. cerevisiae cells 1, a technique that is not directly portable from yeast to other species. In this study we developed and applied a mass spectrometry based strategy to determine the absolute quantity i.e. the average number of protein copies per cell in a cell population, for a significant fraction of the proteome in genetically unperturbed cells. Applying the technology to the human pathogen Leptospira interrogans, a spirochete responsible for Leptospirosis 4, we generated an absolute protein abundance scale for 83% of the mass spectrometry detectable proteome, from cells at different states. Taking advantage of the unique cellular dimensions of L. interrogans, we used cryo electron tomography (cryoET) morphological measurements to verify at the single cell level the average absolute abundance values of selected proteins determined by mass spectrometry on a population of cells. As the strategy is relatively fast and applicable to any cell type we expect that it will become a cornerstone of quantitative biology and systems biology.
Mass spectrometry based quantification of peptides can be performed using the iTRAQ™ reagent in conjunction with mass spectrometry. This technology yields information about the relative abundance of single peptides. A method for the calculation of reliable quantification information is required in order to obtain biologically relevant data at the protein expression level.
A method comprising sound error estimation and statistical methods is presented that allows precise abundance analysis plus error calculation at the peptide as well as at the protein level. This yields the relevant information that is required for quantitative proteomics. Comparing the performance of our method named Quant with existing approaches the error estimation is reliable and offers information for precise bioinformatic models. Quant is shown to generate results that are consistent with those produced by ProQuant™, thus validating both systems. Moreover, the results are consistent with that of Mascot™ 2.2. The MATLAB® scripts of Quant are freely available via and , each under the GNU Lesser General Public License.
The software Quant demonstrates improvements in protein quantification using iTRAQ™. Precise quantification data can be obtained at the protein level when using error propagation and adequate visualization. Quant integrates both and additionally provides the possibility to obtain more reliable results by calculation of wise quality measures. Peak area integration has been replaced by sum of intensities, yielding more reliable quantification results. Additionally, Quant allows the combination of quantitative information obtained by iTRAQ™ with peptide and protein identifications from popular tandem MS identification tools. Hence Quant is a useful tool for the proteomics community and may help improving analysis of proteomic experimental data. In addition, we have shown that a lognormal distribution fits the data of mass spectrometry based relative peptide quantification.
Comparative global proteome analyses were performed on Leptospira interrogans serovar Copenhageni grown under conventional in vitro conditions and those mimicking in vivo conditions (iron limitation and serum presence). Proteomic analyses were conducted using iTRAQ and LC-ESI-tandem mass spectrometry complemented with two-dimensional gel electrophoresis and MALDI-TOF mass spectrometry. A total of 563 proteins were identified in this study. Altered expression of 65 proteins, including upregulation of the L. interrogans virulence factor Loa22 and 5 novel proteins with homology to virulence factors found in other pathogens, was observed between the comparative conditions. Immunoblot analyses confirmed upregulation of 5 of the known or putative virulence factors in L. interrogans exposed to the in vivo-like environmental conditions. Further, ELISA analyses using serum from patients with leptospirosis and immunofluorescence studies performed on liver sections derived from L. interrogans-infected hamsters verified expression of all but one of the identified proteins during infection. These studies, which represent the first documented comparative global proteome analysis of Leptospira, demonstrated proteome alterations under conditions that mimic in vivo infection and allowed for the identification of novel putative L. interrogans virulence factors.
The L. interrogans proteome was analyzed using iTRAQ and 2DGE. These analyses identified 563 proteins and altered expression of 65 proteins upon growth of L. interrogans under in vivo-like conditions, including upregulation of the L. interrogans virulence factor Loa22, a putative lipoprotein with primary amino acid sequence similarity to the outer surface protein ErpY of B. burgdorferi, and 4 additional proteins with homology to virulence factors found in other pathogens.
comparative proteomics; iTRAQ; two-dimensional gel electrophoresis; mass spectrometry; Leptospira; virulence factors; pathogenesis
Systems biology conceptualizes biological systems as dynamic networks of interacting elements, whereby functionally important properties are thought to emerge from the structure of such networks. Due to the ubiquitous role of complexes of interacting proteins in biological systems, their subunit composition and temporal and spatial arrangement within the cell are of particular interest. ‘Visual proteomics’ attempts to localize individual macromolecular complexes inside of intact cells by template matching reference structures into cryo electron tomograms. Here we have combined quantitative mass spectrometry and cryo electron tomography to detect, count and localize specific protein complexes within the cytoplasm of the human pathogen Leptospira interrogans. We describe a novel scoring function for visual proteomics and assess its performance and accuracy under realistic conditions. We discuss current and general limitations of the approach, as well as expected improvements in the future.
Isobaric multiplexed quantitative proteomics can complement high-resolution sample isolation techniques. Here, we report a simple workflow: exponentially modified protein abundance index (emPAI)-MW deconvolution (EMMOL) for normalizing isobaric reporter ratios within and between experiments, where small or unknown amounts of protein are used. EMMOL deconvolutes the isobaric tags for relative and absolute quantification (iTRAQ) data to yield the quantity of each protein of each sample in the pool, a new approach that enables the comparison of many samples without including a channel of reference standard. Moreover, EMMOL allows using a sufficient quantity of control sample to facilitate the peptide fractionation (isoelectricfocusing was used in this report), and mass spectrometry MS/MS sequencing yet relies on the broad dynamic range of iTRAQ quantification to compare relative protein abundance. We demonstrated EMMOL by comparing four pooled samples with 20-fold range differences in protein abundance and performed data normalization without using prior knowledge of the amounts of proteins in each sample, simulating an iTRAQ experiment without protein quantification prior to labeling. We used emPAI, the target protein MW, and the iTRAQ reporter ratios to calculate the amount of each protein in each of the four channels. Importantly, the EMMOL-delineated proteomes from separate iTRAQ experiments can be assorted for comparison without using a reference sample. We observed no compression of expression in iTRAQ ratios over a 20-fold range for all protein abundances. To complement this ability to analyze minute samples, we report an optimized iTRAQ labeling protocol for using 5 μg of protein as the starting material.
Isobaric multiplexed quantitative proteomics can complement high-resolution sample isolation techniques. Here, we report a simple workflow exponentially modified protein abundance index (emPAI)-MW deconvolution (EMMOL) for normalizing isobaric reporter ratios within and between experiments, where small or unknown amounts of protein are used. EMMOL deconvolutes the isobaric tags for relative and absolute quantification (iTRAQ) data to yield the quantity of each protein of each sample in the pool, a new approach that enables the comparison of many samples without including a channel of reference standard. Moreover, EMMOL allows using a sufficient quantity of control sample to facilitate the peptide fractionation (isoelectric-focusing was used in this report), and mass spectrometry MS/MS sequencing yet relies on the broad dynamic range of iTRAQ quantitation to compare relative protein abundance. We demonstrated EMMOL by comparing four pooled samples with 20-fold range differences in protein abundance and performed data normalization without using prior knowledge of the amounts of proteins in each sample, simulating an iTRAQ experiment without protein quantitation prior to labeling. We used emPAI,1 the target protein MW, and the iTRAQ reporter ratios to calculate the amount of each protein in each of the four channels. Importantly, the EMMOL-delineated proteomes from separate iTRAQ experiments can be assorted for comparison without using a reference sample. We observed no compression of expression in iTRAQ ratios over a 20-fold range for all protein abundances. To complement this ability to analyze minute samples, we report an optimized iTRAQ labeling protocol for using 5 μg protein as the starting material.
proteome; iTRAQ; optimization; EMMOL; LCM
Voxelation creates expression atlases by high-throughput analysis of spatially registered cubes or voxels harvested from the brain. The modality independence of voxelation allows a variety of bioanalytical techniques to be used to map abundance. Protein expression patterns in the brain can be obtained using liquid chromatography (LC) combined with mass spectrometry (MS). Here we describe the methodology of voxelation as it pertains particularly to LC-MS proteomic analysis: sample preparation, instrumental set up and analysis, peptide identification and protein relative abundance quantitation. We also briefly describe some of the advantages, limitations and insights into the brain that can be obtained using combined proteomic and transcriptomic maps.
Brain atlas; Brain mapping; Mass spectrometry; Proteomics; Transcriptomics; Voxelation
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.
Aspergillus niger; Trichoderma; Cell-organelle proteomics; Protein secretion; Metabolites; Antibiotics
For many research questions in modern molecular and systems biology, information about absolute protein quantities is imperative. This information includes, for example, kinetic modeling of processes, protein turnover determinations, stoichiometric investigations of protein complexes, or quantitative comparisons of different proteins within one sample or across samples. To date, the vast majority of proteomic studies are limited to providing relative quantitative comparisons of protein levels between limited numbers of samples. Here we describe and demonstrate the utility of a targeting MS technique for the estimation of absolute protein abundance in unlabeled and nonfractionated cell lysates. The method is based on selected reaction monitoring (SRM) mass spectrometry and the “best flyer” hypothesis, which assumes that the specific MS signal intensity of the most intense tryptic peptides per protein is approximately constant throughout a whole proteome. SRM-targeted best flyer peptides were selected for each protein from the peptide precursor ion signal intensities from directed MS data. The most intense transitions per peptide were selected from full MS/MS scans of crude synthetic analogs. We used Monte Carlo cross-validation to systematically investigate the accuracy of the technique as a function of the number of measured best flyer peptides and the number of SRM transitions per peptide. We found that a linear model based on the two most intense transitions of the three best flying peptides per proteins (TopPep3/TopTra2) generated optimal results with a cross-correlated mean fold error of 1.8 and a squared Pearson coefficient R2 of 0.88. Applying the optimized model to lysates of the microbe Leptospira interrogans, we detected significant protein abundance changes of 39 target proteins upon antibiotic treatment, which correlate well with literature values. The described method is generally applicable and exploits the inherent performance advantages of SRM, such as high sensitivity, selectivity, reproducibility, and dynamic range, and estimates absolute protein concentrations of selected proteins at minimized costs.
Proteomics experiments are designed to provide useful information about the biological system under study. As mass spectrometry technology and methodology have improved, many or most of these studies now focus on relative or absolute quantitation of proteins and their posttranslational modifications under more than one experimental condition in order to do this. The goal of this workshop is to provide an overview of possible proteomic strategies for relative or absolute quantitation of proteins that are suitable for a given purpose. We also hope to provide practical advice for experimental design and sample preparation for quantitative studies and characterization of posttranslational modifications. Issues to be covered include requirements for protein sequence coverage, discovery vs. directed (verification) approaches, strategies for chemical and metabolic labeling as well as label free methods, introduction to relevant informatics approaches for quantitation studies, and sample preparation and enrichment for posttranslational modification studies.
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 14N/15N 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 14N/15N labeling, SILAC, and iTRAQ. The software is publicly available at http://www.medizinisches-proteom-center.de/software and free for academic use.
Mass spectrometry-based proteomics increasingly relies on relative or absolute quantification. In relative quantification, stable isotope based methods often allow mixing at early stages of sample preparation, whereas for absolute quantification this has generally required recombinant expression of full length, labeled protein standards. Here we make use of a very large library of Protein Epitope Signature Tags (PrESTs) that has been developed in the course of the Human Protein Atlas Project. These PrESTs are expressed recombinantly in E. coli and they consist of a short and unique region of the protein of interest as well as purification and solubility tags. We first quantify a highly purified, stable isotope labeling of amino acids in cell culture (SILAC)-labeled version of the solubility tag and use it determine the precise amount of each PrEST by its SILAC ratios. The PrESTs are then spiked into cell lysates and the SILAC ratios of PrEST peptides to peptides from endogenous target proteins yield their cellular quantities. The procedure can readily be multiplexed, as we demonstrate by simultaneously determining the copy number of 40 proteins in HeLa cells. Among the proteins analyzed, the cytoskeletal protein vimentin was found to be most abundant with 20 million copies per cell, while the transcription factor and oncogene FOS only had 6000 copies. Direct quantification of the absolute amount of single proteins is possible via a SILAC experiment in which labeled cell lysate is mixed both with the heavy labeled solubility tag and with the corresponding PrEST. The SILAC-PrEST combination allows accurate and streamlined quantification of the absolute or relative amount of proteins of interest in a wide variety of applications.
A semi-quantitative gel-based analysis identifies distinct proteomic profiles associated with specific growth points for the nosocomial pathogen Ochrobactrum anthropi.
The α-Proteobacteria are capable of interaction with eukaryotic cells, with some members, such as Ochrobactrum anthropi, capable of acting as human pathogens. O. anthropi has been the cause of a growing number of hospital-acquired infections; however, little is known about its growth, physiology and metabolism. We used proteomics to investigate how protein expression of this organism changes with time during growth.
This first gel-based liquid chromatography-mass spectrometry (GeLC-MS) temporal proteomic analysis of O. anthropi led to the positive identification of 131 proteins. These were functionally classified and physiochemically characterized. Utilizing the emPAI protocol to estimate protein abundance, we assigned molar concentrations to all proteins, and thus were able to identify 19 with significant changes in their expression. Pathway reconstruction led to the identification of a variety of central metabolic pathways, including nucleotide biosynthesis, fatty acid anabolism, glycolysis, TCA cycle and amino acid metabolism. In late phase growth we identified a number of gene products under the control of the oxyR regulon, which is induced in response to oxidative stress and whose protein products have been linked with pathogen survival in response to host immunity reactions.
This study identified distinct proteomic profiles associated with specific growth points for O. anthropi, while the use of emPAI allowed semi-quantitative analyses of protein expression. It was possible to reconstruct central metabolic pathways and infer unique functional and adaptive processes associated with specific growth phases, thereby resulting in a deeper understanding of the physiology and metabolism of this emerging pathogenic bacterium.
Quantitative proteome analysis of microbial systems generates large datasets that can be difficult and time consuming to interpret. Fortunately, many of the data display and gene clustering tools developed to analyze large transcriptome microarray datasets are also applicable to proteomes. Plots of abundance ratio versus total signal or spectral counts can highlight regions of random error and putative change. Displaying data in the physical order of the genes in the genome sequence can highlight potential operons. At a basic level of transcriptional organization, identifying operons can give insights into regulatory pathways as well as provide corroborating evidence for proteomic results. Classification and clustering algorithms can group proteins together by their abundance changes under different conditions, helping to identify interesting expression patterns, but often work poorly with noisy data like that typically generated in a large-scale proteome analysis. Biological interpretation can be aided more directly by overlaying differential protein abundance data onto metabolic pathways, indicating pathways with altered activities. More broadly, ontology tools detect altered levels of protein abundance for different metabolic pathways, molecular functions and cellular localizations. In practice, pathway analysis and ontology are limited by the level of database curation associated with the organism of interest.
DAVID; Gene Ontology; GoMiner; bioinformatics; proteomics; Porphyromonas gingivalis; Methanococcus maripaludis; protein profiling; protein expression
While several techniques are available in proteomics, LC-MS based analysis of complex protein/peptide mixtures has turned out to be a mainstream analytical technique for quantitative proteomics. Significant technical advances at both sample preparation/separation and mass spectrometry levels have revolutionized comprehensive proteome analysis. Moreover, automation and robotics for sample handling process permit multiple sampling with high throughput.
For LC-MS based quantitative proteomics, sample preparation turns out to be critical step, as it can significantly influence sensitivity of downstream analysis. Several sample preparation strategies exist, including depletion of high abundant proteins or enrichment steps that facilitate protein quantification but with a compromise of focusing on a smaller subset of a proteome. While several experimental strategies have emerged, certain limitations such as physiochemical properties of a peptide/protein, protein turnover in a sample, analytical platform used for sample analysis and data processing, still imply challenges to quantitative proteomics. Other aspects that make analysis of a proteome a challenging task include dynamic nature of a proteome, need for efficient and fast analysis of protein due to its constant modifications inside a cell, concentration range of proteins that exceed dynamic range of a single analytical method, and absence of appropriate bioinformatics tools for analysis of large volume and high dimensional data.
This paper gives an overview of various LC-MS methods currently used in quantitative proteomics and their potential for detecting differential protein expression. Fundamental steps such as sample preparation, LC separation, mass spectrometry, quantitative assessment and protein identification are discussed.
For quantitative assessment of protein expression, both label and label free approaches are evaluated for their set of merits and demerits. While most of these methods edge on providing “relative abundance” information, absolute quantification is achieved with limitation as it caters to fewer proteins. Isotope labeling is extensively used for quantifying differentially expressed proteins, but is severely limited by successful incorporation of its heavy label. Lengthy labeling protocols restrict the number of samples that can be labeled and processed. Alternatively, label free approach appears promising as it can process many samples with any number of comparisons possible but entails reproducible experimental data for its application.
Liquid chromatography-mass spectrometry (LC-MS); Quantitative proteomics; Labeling; Label-free; Tandem mass spectrometry (MS/MS)
The commercial cultivation of genetically engineered (GE) crops in Europe has met with considerable consumer resistance, which has led to vigorous safety assessments including the measurement of substantial equivalence between the GE and parent lines. This necessitates the identification and quantification of significant changes to the metabolome and proteome in the GE crop. In this study, the quantitative proteomic analysis of tomato fruit from lines that have been transformed with the carotenogenic gene phytoene synthase-1 (Psy-1), in the sense and antisense orientations, in comparison with a non-transformed, parental line is described. Multidimensional protein identification technology (MudPIT), with tandem mass spectrometry, has been used to identify proteins, while quantification has been carried out with isobaric tags for relative and absolute quantification (iTRAQ). Fruit from the GE plants showed significant alterations to their proteomes compared with the parental line, especially those from the Psy-1 sense transformants. These results demonstrate that MudPIT and iTRAQ are suitable techniques for the verification of substantial equivalence of the proteome in GE crops.
Genetic modification; mass spectrometry; multidimensional liquid chromatography; phytoene synthase; proteomics; Solanum lycopersicum
Mass spectrometry, an analytical technique that measures the mass-to-charge ratio of ionized atoms or molecules, dates back more than 100 years, and has both qualitative and quantitative uses for determining chemical and structural information. Quantitative proteomic mass spectrometry on biological samples focuses on identifying the proteins present in the samples, and establishing the relative abundances of those proteins. Such protein inventories create the opportunity to discover novel biomarkers and disease targets. We have previously introduced a normalized, label-free method for quantification of protein abundances under a shotgun proteomics platform (Griffin et al., 2010). The introduction of this method for quantifying and comparing protein levels leads naturally to the issue of modeling protein abundances in individual samples. We here report that protein abundance levels from two recent proteomics experiments conducted by the authors can be adequately represented by Sichel distributions. Mathematically, Sichel distributions are mixtures of Poisson distributions with a rather complex mixing distribution, and have been previously and successfully applied to linguistics and species abundance data. The Sichel model can provide a direct measure of the heterogeneity of protein abundances, and can reveal protein abundance differences that simpler models fail to show.
Two-dimensional gel electrophoresis (2-DE) is widely applied and remains the method of choice in proteomics; however, pervasive 2-DE-related concerns undermine its prospects as a dominant separation technique in proteome research. Consequently, the state-of-the-art shotgun techniques are slowly taking over and utilising the rapid expansion and advancement of mass spectrometry (MS) to provide a new toolbox of gel-free quantitative techniques. When coupled to MS, the shotgun proteomic pipeline can fuel new routes in sensitive and high-throughput profiling of proteins, leading to a high accuracy in quantification. Although label-based approaches, either chemical or metabolic, gained popularity in quantitative proteomics because of the multiplexing capacity, these approaches are not without drawbacks. The burgeoning label-free methods are tag independent and suitable for all kinds of samples. The challenges in quantitative proteomics are more prominent in plants due to difficulties in protein extraction, some protein abundance in green tissue, and the absence of well-annotated and completed genome sequences. The goal of this perspective assay is to present the balance between the strengths and weaknesses of the available gel-based and -free methods and their application to plants. The latest trends in peptide fractionation amenable to MS analysis are as well discussed.
The accurate quantitation of proteins and peptides in complex biological systems is one of the most challenging areas of proteomics. Mass spectrometry-based approaches have forged significant in-roads allowing accurate and sensitive quantitation and the ability to multiplex vastly complex samples through the application of robust bioinformatic tools. These relative and absolute quantitative measures using label-free, tags, or stable isotope labelling have their own strengths and limitations. The continuous development of these methods is vital for increasing reproducibility in the rapidly expanding application of quantitative proteomics in biomarker discovery and validation. This paper provides a critical overview of the primary mass spectrometry-based quantitative approaches and the current status of quantitative proteomics in biomedical research.
Mass spectrometry (MS)-based shotgun proteomics allows protein identifications even in complex biological samples. Protein abundances can then be estimated from the counts of MS/MS spectra attributable to each protein, provided that one corrects for differential MS-detectability of the contributing peptides. We describe the use of a method, APEX, which calculates Absolute Protein EXpression levels based on learned correction factors, MS/MS spectral counts, and each protein's probability of correct identification.
The APEX-based calculations consist of three parts: (1) Using training data, peptide sequences and their sequence properties, a model is built that can be used to estimate MS-detectability (Oi) for any given protein. (2) Absolute abundances of proteins measured in an MS/MS experiment are calculated with information from spectral counts, identification probabilities and the learned Oi -values. (3) Simple statistics allow for significance analysis of differential expression in two distinct biological samples, i.e., measuring relative protein abundances. APEX-based protein abundances span more than four orders of magnitude and are applicable to mixtures of hundreds to thousands of proteins from any type of organism.
Quantitative proteomics; Protein expression; Label-free mass spectrometry; Spectral counting
Endodontic infections are very prevalent and have a polymicrobial etiology characterized by complex interrelationships between endodontic microorganisms and the host defenses. Proteomic analysis of endodontic infections can provide global insights into the invasion, pathogenicity mechanisms, and multifactorial interactions existing between root canal bacteria and the host in the initiation and progression of apical periodontitis. The purpose of this study was to apply proteomic techniques such as liquid chromatography–tandem mass spectrometry (LC–MS/MS) for the identification of proteins of bacterial origin present in endodontic infections.
Endodontic specimens were aseptically obtained from seven patients with root canal infections. Protein mixtures were subjected to tryptic in-solution digestion and analysed by reverse-phase nano-LC–MS/MS followed by a database search.
Proteins, mainly of cell wall or membrane origin, from endodontic bacteria especially Enterococcus faecalis, Enterococcus faecium, Porphyromonas gingivalis, Fusobacterium nucleatum, and Treponema denticola were identified from all the samples tested. Identified proteins included adhesins, autolysins, proteases, virulence factors, and antibiotic-resistance proteins.
LC–MS/MS offers a sensitive analytical platform to study the disease processes in the root canal environment. The array of proteins expressed in endodontic infections reflects the complex microbial presence and highlights the bacterial species involved in the inflammatory process.
bacteria; endodontics; liquid chromatography/mass spectroscopy; proteomics; virulence
The rise of systems biology implied a growing demand for highly sensitive techniques for the fast and consistent detection and quantification of target sets of proteins across multiple samples. This is only partly achieved by classical mass spectrometry or affinity-based methods. We applied a targeted proteomics approach based on selected reaction monitoring (SRM) to detect and quantify proteins expressed to a concentration below 50 copies/cell in total S. cerevisiae digests. The detection range can be extended to single-digit copies/cell and to proteins which were undetected by classical methods. We illustrate the power of the technique by the consistent and fast measurement of a network of proteins spanning the entire abundance range over a growth time-course of S. cerevisiae transiting through a series of metabolic phases. We therefore demonstrate the potential of SRM-based proteomics to provide assays for the measurement of any set of proteins of interest in yeast at high-throughput and quantitative accuracy.
targeted proteomics; S. cerevisiae; selected / multiple reaction monitoring; MRM/SRM; dynamic range
Although the clinical effects of infliximab therapy in rheumatoid arthritis have been documented extensively, the biological effects of this intervention continue to be defined. We sought to examine the impact of infliximab therapy on the serum proteome of rheumatoid arthritis patients by means of a mass spectrometry-based approach.
Sera from 10 patients with rheumatoid arthritis were obtained prior to and following 12 weeks of infliximab therapy using a standard clinical protocol. The sera were immunodepleted of the 12 highest abundance proteins, labeled by the iTRAQ (isobaric tagging for relative and absolute protein quantification) technique, and analyzed by mass spectrometry to identify proteomic changes associated with treatment.
An average of 373 distinct proteins were identified per patient with greater than 95% confidence. In the 3 patients demonstrating the most robust clinical responses, changes of greater than 20% in the serum levels were observed in 39 proteins following treatment. The majority of these proteins were regulated directly or indirectly by tumour necrosis factor-alpha (TNF-α) and nuclear factor-kappa-B, with acute-phase proteins being uniformly down-regulated. A number of proteins, including members of the SERPIN family and S100A8, were down-regulated irrespective of clinical response.
The present study demonstrates that a robust clinical response to infliximab is associated with the down-regulation of a spectrum of serum proteins regulated by TNF-α, and provides a possible basis for defining the broader biological effects of the treatment in vivo.
Proteomics and mass spectrometry have provided unprecedented tools for fast, accurate, high throughput biomolecular separation and characterization, which are indispensable towards understanding the biological and medical systems. Studying at the protein level allows researchers to investigate how proteins, their dynamics and modifications affect cellular processes and how cellular processes and the environment affect proteins. The mission of our facility is to provide excellent service and training in proteomics and mass spectrometry to UF scientists and students. Here we present our capabilities in proteomics and other analytical services. The tools include a gel-based 2D-DIGE (Two Dimentional Difference Gel Electrophoresis) and gelfree iTRAQ (Isobaric Tags for Relative and Absolute Quantitation). Along with our capacity of separating thousands of proteins and characterizing differential protein expression, we have a suite of state-of-the-art mass spectrometers available for biomedical sciences and advanced technology research, including a tandem time-of-flight (4700 Proteomics Analyzer, AB), quadrupole/time-of-flight (QSTAR XL, AB), and hybrid quadrupole-linear ion-trap (4000 QTRAP, AB). These instruments are mainly used for protein identification, posttranslational modification characterization and protein expression analysis (e.g., Mass Western). Our facility is also set up to provide Edman de novo N-terminal protein sequence analysis and Biacore biomolecule interaction analysis. We are fully set up to synthesize and purify peptides and have a good track record with this service as well. Proteomics and mass spectrometry are useful in large-scale survey of proteome for hypothesis generation as well as in detailed analysis of target proteins for hypothesis testing. Our services also include accurate molecular weight analysis, MRM-based protein screening and targeted metabolite profiling. To ensure success and maximize productivity, the facility offers education, consultation, data processing and reporting, and support of grant application.