Monitoring of changes in the membrane proteome during stationary phase adaptation of Bacillus subtilis using in vivo labeling techniques

Bacillus subtilis has been developed as a model system for physiological proteomics. However, thus far these studies have mainly been limited to cytoplasmic, extracellular, and cell-wall attached proteins. Although being certainly important for cell physiology, the membrane protein fraction has not been studied in comparable depth due to inaccessibility by traditional 2-DE-based workflows and limitations in reliable quantification. In this study, we now compare the potential of stable isotope labeling with amino acids (SILAC) and (14)N/(15)N-labeling for the analysis of bacterial membrane fractions in physiology-driven proteomic studies. Using adaptation of B. subtilis to amino acid (lysine) and glucose starvation as proof of principle scenarios, we show that both approaches provide similarly valuable data for the quantification of bacterial membrane proteins. Even if labeling with stable amino acids allows a more straightforward analysis of data, the (14)N/(15)N-labeling has some advantages in general such as labeling of all amino acids and thereby increasing the number of peptides for quantification. Both, SILAC as well as (14)N/(15)N-labeling are compatible with 2-DE, 2-D LC-MS/MS, and GeLC-MS/MS and thus will allow comprehensive simultaneous interrogation of cytoplasmic and enriched membrane proteomes.     

Mass Spectrometry-Based Approaches Toward Absolute Quantitative Proteomics

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.Key Words: Quantitative proteomics, mass spectrometry, absolute quantification, stable isotope labeling, label-free.     

定量蛋白质组学中的同位素标记技术

定量蛋白质组学的目的是对复杂的混合体系中所有的蛋白质进行鉴定,并对蛋白质的量及量的变化进行准确的测定,是当前系统生物科学研究的重要内容。近年来,由于质谱技术和生物信息学的进步,定量蛋白质组学在分析蛋白质组或亚蛋白质组方面已取得了令人瞩目的成就,但其最显著的成就应该归功于稳定同位素标记技术的应用。该技术使用针对某一类蛋白具有特异性的化学探针来标记目的蛋白质或肽段,同时化学探针要求含有用以精确定量的稳定同位素信号。在此基础上,实现了对表达的蛋白质差异和翻译后修饰的蛋白质差异进行精确定量分析。综述了在定量蛋白质组学中使用的各种同位素标记技术及其应用。     

Mascot file parsing and quantification (MFPaQ), a new software to parse, validate, and quantify proteomics data generated by ICAT and SILAC mass spectrometric analyses: application to the proteomics study of membrane proteins from primary human endothelial cells

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 performed 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-alpha, interferon-gamma, and lymphotoxin alpha/beta) that resulted in the identification of a full spectrum of EC membrane proteins regulated by inflammation.     

SWATH enables precise label‐free quantification on proteome scale

MS‐based proteomics has emerged as a powerful tool in biological studies. The shotgun proteomics strategy, in which proteolytic peptides are analyzed in data‐dependent mode, enables a detection of the most comprehensive proteome (>10 000 proteins from whole‐cell lysate). The quantitative proteomics uses stable isotopes or label‐free method to measure relative protein abundance. The isotope labeling strategies are more precise and accurate compared to label‐free methods, but labeling procedures are complicated and expensive, and the sample number and types are also limited. Sequential window acquisition of all theoretical mass spectra (SWATH) is a recently developed technique, in which data‐independent acquisition is coupled with peptide spectral library match. In principle SWATH method is able to do label‐free quantification in an MRM‐like manner, which has higher quantification accuracy and precision. Previous data have demonstrated that SWATH can be used to quantify less complex systems, such as spiked‐in peptide mixture or protein complex. Our study first time assessed the quantification performance of SWATH method on proteome scale using a complex mouse‐cell lysate sample. In total 3600 proteins got identified and quantified without sample prefractionation. The SWATH method shows outstanding quantification precision, whereas the quantification accuracy becomes less perfect when protein abundances differ greatly. However, this inaccuracy does not prevent discovering biological correlates, because the measured signal intensities had linear relationship to the sample loading amounts; thus the SWATH method can predict precisely the significance of a protein. Our results prove that SWATH can provide precise label‐free quantification on proteome scale.     

Metadegradomics: toward in vivo quantitative degradomics of proteolytic post-translational modifications of the cancer proteome

Post-translational modifications enable extra layers of control of the proteome, and perhaps the most important is proteolysis, a major irreversible modification affecting every protein. The intersection of the protease web with a proteome sculpts that proteome, dynamically modifying its state and function. Protease expression is distorted in cancer, so perturbing signaling pathways and the secretome of the tumor and reactive stromal cells. Indeed many cancer biomarkers are stable proteolytic fragments. It is crucial to determine which proteases contribute to the pathology versus their roles in homeostasis and in mitigating cancer. Thus the full substrate repertoire of a protease, termed the substrate degradome, must be deciphered to define protease function and to identify drug targets. Degradomics has been used to identify many substrates of matrix metalloproteinases that are important proteases in cancer. Here we review recent degradomics technologies that allow for the broadly applicable identification and quantification of proteases (the protease degradome) and their activity state, substrates, and interactors. Quantitative proteomics using stable isotope labeling, such as ICAT, isobaric tags for relative and absolute quantification (iTRAQ), and stable isotope labeling by amino acids in cell culture (SILAC), can reveal protease substrates by taking advantage of the natural compartmentalization of membrane proteins that are shed into the extracellular space. Identifying the actual cleavage sites in a complex proteome relies on positional proteomics and utilizes selection strategies to enrich for protease-generated neo-N termini of proteins. In so doing, important functional information is generated. Finally protease substrates and interactors can be identified by interactomics based on affinity purification of protease complexes using exosite scanning and inactive catalytic domain capture strategies followed by mass spectrometry analysis. At the global level, the N terminome analysis of whole communities of proteases in tissues and organs in vivo provides a full scale understanding of the protease web and the web-sculpted proteome, so defining metadegradomics.     

Expression profiling of more than 3500 proteins of MSS-type colorectal cancer by stable isotope labeling and mass spectrometry

An efficient means of identifying protein biomarkers is essential to proper cancer management. A well-characterized proteome resource holds special promise for the discovery of novel biomarkers. However, quantification of the differences between physiological conditions together with deep down profiling has become increasingly challenging in proteomics. Here, we perform expression profiling of the colorectal cancer (CRC) proteome by stable isotope labeling and mass spectrometry. Quantitative analysis included performing mTRAQ and cICAT labeling in a pooled sample of three microsatellite stable (MSS) type CRC tissues and a pooled sample of their matched normal tissues. We identified and quantified a total of 3688 proteins. Among them, 1487 proteins were expressed differentially between normal and cancer tissues by higher than 2-fold; 1009 proteins showed increased expression in cancer tissue, whereas 478 proteins showed decreased expression. Bioinformatic analysis revealed that our data were largely consistent with known CRC relevant signaling pathways, such as the Wnt/β-catenin, caveolar-mediated endocytosis, and RAN signaling pathways. Mitochondrial dysfunction, known as the Waburg hypothesis, was also confirmed. Therefore, our data showing alterations in the proteomic profile of CRC constitutes a useful resource that may provide insights into tumor progression with later goal of identifying biologically and clinically relevant marker proteins. This article is part of a Special Issue entitled: Proteomics: The clinical link.     

Combination of online enzyme digestion with stable isotope labeling for high‐throughput quantitative proteome analysis

Various enzyme reactors and online enzyme digestion strategies have been developed in recent years. These reactors greatly enhanced the detection sensitivity and proteome coverage in qualitative proteomics. However, these devices have higher rates of miscleavage in protein digestion. Therefore, we investigated the effect of online enzyme digestion on the quantification accuracy of quantitative proteomics using chemical or metabolic isotope labeling approaches. The incomplete digestion would introduce some unexpected variations in comparative quantification when the samples are digested and then chemically isotope labeled in different aliquots. Even when identical protein aliquots are processed on these devices using post‐digestion chemical isotope labeling and the CVs of the ratios controlled to less than 50% in replicate analyses, about 10% of the quantified proteins have a ratio greater than two‐fold, whereas in theory the ratio is 1:1. Interestingly, the incomplete digestion with enzyme reactor is not a problem when metabolic isotope labeling samples were processed because the proteins are isotopically labeled in vivo prior to their simultaneous digestion within the reactor. Our results also demonstrated that both high quantification accuracy and high proteome coverage can be achieved in comparative proteome quantification using online enzyme digestion even when a limited amount of metabolic isotope labeling samples is used (1683 proteins comparatively quantified from 10⁵ Hela cells).     

Advances in quantitative proteomics via stable isotope tagging and mass spectrometry

The high-throughput identification and accurate quantification of proteins are essential components of proteomic strategies for studying cellular functions and processes. Techniques that are largely based on stable isotope protein or peptide labeling and automated tandem mass spectrometry are increasingly being applied in quantitative proteomic studies. Over the past year, significant progress has been made toward improving and diversifying these technologies with respect to the methods for stable isotope labeling, process automation and data processing and analysis. Advances in stable isotope protein labeling and recent biological studies that used stable isotope based quantitative proteomics techniques are reviewed.     

Enhanced analysis of metastatic prostate cancer using stable isotopes and high mass accuracy instrumentation

The primary goal of proteomics is to gain a better understanding of biological function at the protein expression level. As the field matures, numerous technologies are being developed to aid in the identification, quantification and characterization of protein expression and post-translational modifications on a near-global scale. Stable isotope labeling by amino acids in cell culture is one such technique that has shown broad biological applications. While we have recently shown the application of this technology to a model of metastatic prostate cancer, we now report a substantial improvement in quantitative analysis using a linear ion-trap Fourier transform ion cyclotron resonance mass spectrometer (LTQ FT) and novel quantification software. This resulted in the quantification of nearly 1400 proteins, a greater than 3-fold increase in comparison to our earlier study. This dramatic increase in proteome coverage can be attributed to (1) use of a double-labeling strategy, (2) greater sensitivity, speed and mass accuracy provided by the LTQ FT mass spectrometer, and (3) more robust quantification software. Finally, by using a concatenated target/decoy protein database for our peptide searches, we now report these data in the context of an estimated false-positive rate of one percent.     

A label-free quantification method by MS/MS TIC compared to SILAC and spectral counting in a proteomics screen

In order to assess the biological function of proteins and their modifications for understanding signaling mechanisms within cells as well as specific biomarkers to disease, it is important that quantitative information be obtained under different experimental conditions. Stable isotope labeling is a powerful method for accurately determining changes in the levels of proteins and PTMs; however, isotope labeling experiments suffer from limited dynamic range resulting in signal change ratios of less than approximately 20:1 using most commercial mass spectrometers. Label-free approaches to relative quantification in proteomics such as spectral counting have gained popularity since no additional chemistries are needed. Here, we show a label-free method for relative quantification based on the TIC from peptide MS/MS spectra collected from data-dependent runs can be used effectively as a quantitative measure and expands the dynamic range over isotope labeling experiments allowing for abundance differences up to approximately 60:1 in a screen for proteins that bind to phosphotyrosine residues.     

The 15N isotope effect in Escherichia coli: A neutron can make the difference

Several techniques based on stable isotope labeling are used for quantitative MS. These include stable isotope metabolic labeling methods for cells in culture as well as live organisms with the assumption that the stable isotope has no effect on the proteome. Here, we investigate the ¹⁵N isotope effect on Escherichia coli cultures that were grown in either unlabeled (¹⁴N) or ¹⁵N‐labeled media by LC‐ESI‐MS/MS‐based relative protein quantification. Consistent protein expression level differences and altered growth rates were observed between ¹⁴N and ¹⁵N‐labeled cultures. Furthermore, targeted metabolite analyses revealed altered metabolite levels between ¹⁴N and ¹⁵N‐labeled bacteria. Our data demonstrate for the first time that the introduction of the ¹⁵N isotope affects protein and metabolite levels in E. coli and underline the importance of implementing controls for unbiased protein quantification using stable isotope labeling techniques.     

Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments

Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.     

Application of Mass Spectrometry in Proteomics

Mass spectrometry has arguably become the core technology in proteomics. The application of mass spectrometry based techniques for the qualitative and quantitative analysis of global proteome samples derived from complex mixtures has had a big impact in the understanding of cellular function. Here, we give a brief introduction to principles of mass spectrometry and instrumentation currently used in proteomics experiments. In addition, recent developments in the application of mass spectrometry in proteomics are summarised. Strategies allowing high-throughput identification of proteins from highly complex mixtures include accurate mass measurement of peptides derived from total proteome digests and multidimensional peptide separations coupled with mass spectrometry. Mass spectrometric analysis of intact proteins permits the characterisation of protein isoforms. Recent developments in stable isotope labelling techniques and chemical tagging allow the mass spectrometry based differential display and quantitation of proteins, and newly established affinity procedures enable the targeted characterisation of post-translationally modified proteins. Finally, advances in mass spectrometric imaging allow the gathering of specific information on the local molecular composition, relative abundance and spatial distribution of peptides and proteins in thin tissue sections.     

SIMSI-Transfer: Software-Assisted Reduction of Missing Values in Phosphoproteomic and Proteomic Isobaric Labeling Data Using Tandem Mass Spectrum Clustering

Isobaric stable isotope labeling techniques such as tandem mass tags (TMTs) have become popular in proteomics because they enable the relative quantification of proteins with high precision from up to 18 samples in a single experiment. While missing values in peptide quantification are rare in a single TMT experiment, they rapidly increase when combining multiple TMT experiments. As the field moves toward analyzing ever higher numbers of samples, tools that reduce missing values also become more important for analyzing TMT datasets. To this end, we developed SIMSI-Transfer (Similarity-based Isobaric Mass Spectra 2 [MS2] Identification Transfer), a software tool that extends our previously developed software MaRaCluster (© Matthew The) by clustering similar tandem MS2 from multiple TMT experiments. SIMSI-Transfer is based on the assumption that similarity-clustered MS2 spectra represent the same peptide. Therefore, peptide identifications made by database searching in one TMT batch can be transferred to another TMT batch in which the same peptide was fragmented but not identified. To assess the validity of this approach, we tested SIMSI-Transfer on masked search engine identification results and recovered >80% of the masked identifications while controlling errors in the transfer procedure to below 1% false discovery rate. Applying SIMSI-Transfer to six published full proteome and phosphoproteome datasets from the Clinical Proteomic Tumor Analysis Consortium led to an increase of 26 to 45% of identified MS2 spectra with TMT quantifications. This significantly decreased the number of missing values across batches and, in turn, increased the number of peptides and proteins identified in all TMT batches by 43 to 56% and 13 to 16%, respectively.     

Identification of Protein Interaction Partners in Mammalian Cells Using SILAC-immunoprecipitation Quantitative Proteomics

Quantitative proteomics combined with immuno-affinity purification, SILAC immunoprecipitation, represent a powerful means for the discovery of novel protein:protein interactions. By allowing the accurate relative quantification of protein abundance in both control and test samples, true interactions may be easily distinguished from experimental contaminants. Low affinity interactions can be preserved through the use of less-stringent buffer conditions and remain readily identifiable. This protocol discusses the labeling of tissue culture cells with stable isotope labeled amino acids, transfection and immunoprecipitation of an affinity tagged protein of interest, followed by the preparation for submission to a mass spectrometry facility. This protocol then discusses how to analyze and interpret the data returned from the mass spectrometer in order to identify cellular partners interacting with a protein of interest. As an example this technique is applied to identify proteins binding to the eukaryotic translation initiation factors: eIF4AI and eIF4AII.     

Tissue proteomics by one-dimensional gel electrophoresis combined with label-free protein quantification

Label-free methods streamline quantitative proteomics of tissues by alleviating the need for metabolic labeling of proteins with stable isotopes. Here we detail and implement solutions to common problems in label-free data processing geared toward tissue proteomics by one-dimensional gel electrophoresis followed by liquid chromatography tandem mass spectrometry (geLC MS/MS). Our quantification pipeline showed high levels of performance in terms of duplicate reproducibility, linear dynamic range, and number of proteins identified and quantified. When applied to the liver of an adenomatous polyposis coli (APC) knockout mouse, we demonstrated an 8-fold increase in the number of statistically significant changing proteins compared to alternative approaches, including many more previously unidentified hydrophobic proteins. Better proteome coverage and quantification accuracy revealed molecular details of the perturbed energy metabolism.     

Amino acid-coded tagging approaches in quantitative proteomics

To improve the efficiency, accuracy, reproducibility, throughput and proteome coverage of mass spectrometry-based quantitative approaches, both in vitro and in vivo tagging of particular amino acid residues of cellular proteins have been introduced to assist mass spectrometry for global-scale comparative studies of differentially expressed proteins/modifications between different biologically relevant cell states or cells at different pathological states. The basic features of these methods introduce pair-wise isotope signals of each individual peptide containing a particular type of tagged amino acid (amino acid-coded mass tagging) that originated from different cell states. In this review, the applications of major amino acid-coded mass tagging-based quantitative proteomics approaches, including isotope-coded affinity tag, isobaric tags for relative and absolute quantification (iTRAQ) and stable isotope labeling by amino acids in cell culture are summarized in the context of their respective strengths/weakness in identifying those differentially expressed or post-translational modified proteins regulated by particular cellular stress on a genomic scale in a high-throughput manner. Importantly, these gel-free, in-spectra quantitative mechanisms have been further explored to identify/characterize large-scale protein-protein interactions involving various functional pathways. Taken together, the information about quantitative proteome changes, including multiple regulated proteins and their interconnected relationships, will provide an important insight into the molecular mechanisms, where novel targets for diagnosis and therapeutic intervention will be identified.     

A MS data search method for improved 15N‐labeled protein identification

Quantitative proteomics using stable isotope labeling strategies combined with MS is an important tool for biomarker discovery. Methods involving stable isotope metabolic labeling result in optimal quantitative accuracy, since they allow the immediate combination of two or more samples. Unfortunately, stable isotope incorporation rates in metabolic labeling experiments using mammalian organisms usually do not reach 100%. As a consequence, protein identifications in ¹⁵N database searches have poor success rates. We report on a strategy that significantly improves the number of ¹⁵N‐labeled protein identifications and results in a more comprehensive and accurate relative peptide quantification workflow.