Label-free LC-MS/MS quantitative proteomics for large-scale biomarker discovery in complex samples

Proteomic platforms that enable researchers to profile a high number of proteins across large sets of complex samples hold a great potential for biomarker discovery. LC-MS/MS-based methods can be used to analyse many samples without the need for protein labelling. As the analysis is a sequential process, the performance of the system has to be consistent throughout the entire experiment. In this study we used a set of spiked serum samples as well as a set of 55 clinical serum samples from schizophrenia patients and healthy volunteers to show that the label-free proteomic approach yields reproducible results across a large number of samples and can be used to accurately measure the relative protein abundance. Using this approach, we identified 1709 serum proteins covering a dynamic range of over three orders of magnitude. We believe that label-free quantitative proteomics is especially suited for biomarker discovery in large sample sets.     

Peptide correlation: a means to identify high quality quantitative information in large-scale proteomic studies

A major challenge of proteomic studies is the accurate quantitation of proteins. LC-MS/MS-based methods are especially suited for profiling proteins in large sample sets. In this setup, the measurement of relative protein abundance relies on the correct quantitation of tryptic peptides. However, peptide intensities often do not unequivocally reflect the abundance of the native proteins in the sample. In this study, we show that peptides that accurately reflect relative protein abundances in large-scale sample sets can be selected based on the correlation to each other. This strategy was tested in a well-controlled experiment using a set of spiked serum samples as well as 55 clinical serum samples from schizophrenia patients and healthy volunteers. The peptide correlation analysis we present here provides an intuitive and simple procedure to obtain a high quality quantitative information from proteomics data.     

Refining comparative proteomics by spectral counting to account for shared peptides and multiple search engines

Spectral counting has become a widely used approach for measuring and comparing protein abundance in label-free shotgun proteomics. However, when analyzing complex samples, the ambiguity of matching between peptides and proteins greatly affects the assessment of peptide and protein inventories, differentiation, and quantification. Meanwhile, the configuration of database searching algorithms that assign peptides to MS/MS spectra may produce different results in comparative proteomic analysis. Here, we present three strategies to improve comparative proteomics through spectral counting. We show that comparing spectral counts for peptide groups rather than for protein groups forestalls problems introduced by shared peptides. We demonstrate the advantage and flexibility of this new method in two datasets. We present four models to combine four popular search engines that lead to significant gains in spectral counting differentiation. Among these models, we demonstrate a powerful vote counting model that scales well for multiple search engines. We also show that semi-tryptic searching outperforms tryptic searching for comparative proteomics. Overall, these techniques considerably improve protein differentiation on the basis of spectral count tables.     

Quantitative analysis of complex peptide mixtures using FTMS and differential mass spectrometry

Label-free LC-MS profiling is a powerful quantitative proteomic method to study relative peptide abundances between two or more biological samples. Here we demonstrate the use of a previously described comparative LC-MS method, differential mass spectrometry (dMS), to analyze high-resolution Fourier transform mass spectrometry (FTMS) data for detection and quantification of known peptide differences between two sets of complex mixtures. Six standard peptides were spiked into a processed plasma background at fixed ratios from 1.25:1 to 4:1 to make two sets of samples. The resulting mixtures were analyzed by microcapillary LC-FTMS and dMS. dMS successfully identified five out of the six peptides as statistically significant differences (p 相似文献   

Analysis of low-abundance proteins using the proteomic reactor with pH fractionation

We developed a new method consisting of the proteomic reactor coupled with step pH fractionation for the analysis of low-abundance proteins from minute amount of sample. These new reactors were implemented using both SAX and SCX materials. The pH fractions from the SAX reactor provided higher peptide and protein identification than SCX reactor and conventional solution digestion. Interestingly, the physical characteristics (pI, molecular weight, missed cleavage site and grand average hydrophobicity (GRAVY) index, and number of acid and basic amino acid) of the peptides obtained from the SAX and SCX proteomic reactors are drastically different. Furthermore, nearly half of the peptides observed from the pH fractionations from the SAX reactor are of low abundance while only 22% low-abundance proteins are observed with conventional in-solution digestion following 2D LC-MS/MS analysis.     

Large-scale muLC-MS/MS for silver- and Coomassie blue-stained polyacrylamide gels

2-DE combined with LC-MS/MS has become a routine, reliable protein separation and identification technology for proteome analysis. The demand for large-scale protein identifications after 2-DE separation requires a sensitive and high-throughput LC-MS/MS method. In this report, a simple, splitless, fully automated capillary LC-MS/MS system was described for the large-scale identification of proteins from gels stained with either silver or CBB. The gel samples were digested and peptides were extracted using an in-gel digestion workstation. The peptides were automatically introduced into a capillary column by an autosampler connected to an HPLC pump. A nanoLC pump was then used to deliver the gradient and elute the peptides from the capillary column directly into an LCQ IT mass spectrometer. Neither a peptide trapping setting nor a flow split is needed in this simple setup. The collected MS/MS spectra were then automatically searched by SEQUEST, and filtered and organized by DTASelect. Hundreds of silver-stained or CBB-stained Shewanella oneidensis, Geobacter sulfurreducens, and Geobacter metallireducens proteins separated by denaturing or nondenaturing 2-DE were digested and routinely analyzed using this fully automated muLC-MS/MS system. High peptide hits and sequence coverage were achieved for most CBB-stained gel spots. About 75% of the spots were found to contain multiple proteins. Although silver staining is not commonly thought to be optimal for MS analysis, protein identifications were successfully obtained from silver-stained 2-DE spots detected using methods with and without formaldehyde for protein fixation.     

Stable isotope-free quantitative shotgun proteomics combined with sample pattern recognition for rapid diagnostics

Mass spectrometry (MS) has become a powerful tool for the quantitative analysis of complex protein samples. A high-throughput strategy for the comparative analysis of multiple protein samples with high complexity becomes more and more important. Two strategies, spectral count and peak intensity, for label-free MS analysis of prefractionated complex mixtures have been described recently to be useful for quantitation. Here we compare both strategies for rapid and quantitative 1-D shotgun LC/MS/MS analyses of highly complex protein mixtures using silica-based monolithic columns. First, we validated linearity and sensitivity of these methods by spiking varying amounts of an internal standard protein in a complex plant protein extract. Secondly, quantitative data of proteins of Medicago truncatula nodules were visualized with independent components analysis using data either obtained from spectral count or peak integration performed with commercial software. Spectral count showed apparent advantages over peak integration because several peptides per protein are automatically averaged, the linear dynamic range of quantitation increases in complex matrices and the number of quantified proteins surpasses the number of proteins using peak integration. Thus, for the need of rapid comparative analysis of highly complex protein samples, spectral count enables sample pattern recognition and identification of biomarkers in nongel based proteomic studies.     

Use of monolithic supports in proteomics technology

An overview on the utilization of monoliths in proteomics technology will be given. Both silica- and polymer-based monoliths have broad use for microseparation of tryptic peptides in reversed-phase (RP) mode before identification by mass spectrometry (MS) or by MS/MS. For two-dimensional (2D) LC separation of peptides before MS or MS/MS analysis, a combination of ion-exchange, usually cation-exchange (CEX) chromatography with RP chromatography on monolithic supports can be employed. Immobilized metal ion affinity chromatography monoliths with immobilized Fe3+-ions are used for the isolation of phosphopeptides. Monoliths with immobilized affinity ligands are usually applied to the rapid separation of proteins and peptides. Miniaturized reactors with immobilized proteolytic enzymes are utilized for rapid on- or offline digestion of isolated proteins or protein mixtures prior to identification by LC-MS/MS. Monoliths also have broad potential for application in sample preparation, prior to further proteomic analyses. Monolithic supports with large pore sizes can be exploited for the isolation of nanoparticles, such as cells, organelles, viruses and protein aggregates. The potential for further adoption of monolithic supports in protein separation and enrichment of low abundance proteins prior to proteolytic digestion and final LC-MS/MS protein identification will be discussed.     

A Novel Two-Stage Tandem Mass Spectrometry Approach and Scoring Scheme for the Identification of O-GlcNAc Modified Peptides

The modification of serine and threonine residues in proteins by a single N-acetylglucosamine (O-GlcNAc) residue is an emerging post-translational modification (PTM) with broad biological implications. However, the systematic or large-scale analysis of this PTM is hampered by several factors, including low stoichiometry and the lability of the O-glycosidic bond during tandem mass spectrometry. Using a library of 72 synthetic glycopeptides, we developed a two-stage tandem MS approach consisting of pulsed Q dissociation (PQD) for O-GlcNAc peptide detection and electron transfer dissociation (ETD) for identification and site localization. Based on a set of O-GlcNAc specific fragment ions, we further developed a score (OScore) that discriminates O-GlcNAc peptide spectra from spectra of unmodified peptides with 95% sensitivity and >99% specificity. Integrating the OScore into the two-stage LC-MS/MS approach detected O-GlcNAc peptides in the low fmol range and at 10-fold better sensitivity than a single data-dependent ETD tandem MS experiment.     

Targeted tandem mass spectrometry for high-throughput comparative proteomics employing NanoLC-FTICR MS with external ion dissociation

Targeted tandem mass spectrometry (MS/MS) is an attractive proteomic approach that allows selective identification of peptides exhibiting abundance differences, e.g., between culture conditions and/or diseased states. Herein, we report on a targeted LC-MS/MS capability realized with a hybrid quadrupole-7 tesla Fourier transform ion cyclotron resonance (FTICR) mass spectrometer that provides data-dependent ion selection, accumulation, and dissociation external to the ICR trap, and a control software that directs intelligent MS/MS target selection based on LC elution time and m/z ratio. We show that the continuous on-the-fly alignment of the LC elution time during the targeted LC-MS/MS experiment, combined with the high mass resolution of FTICR MS, is crucial for accurate selection of targets, whereas high mass measurement accuracy MS/MS data facilitate unambiguous peptide identifications. Identification of a subset of differentially abundant proteins from Shewanella oneidensis grown under suboxic versus aerobic conditions demonstrates the feasibility of such approach.     

Peptidomics biomarker discovery in mouse models of obesity and type 2 diabetes

Type 2 diabetes mellitus (T2DM) is caused by the failure of the pancreatic beta-cell to secrete sufficient insulin to compensate a decreased response of peripheral tissues to insulin action. The pathological events causing beta-cell dysfunctions are only poorly understood and early markers that would predict islet function are missing. In contrast to immunoassays, unbiased proteomic technologies provide the opportunity to screen for novel marker protein and peptides of T2DM. An important subset of the proteome, peptides and peptide hormones secreted by the pancreas are deregulated in T2DM. The mass range of peptides and small proteins (1-20 kDa) is only sufficiently targeted by peptidomics, a combination of liquid chromatographic and mass spectrometric (MS) peptide analysis. Here, we describe the application of isotope label-free quantitative peptidomics to display and quantify relevant changes in the level of pancreatic peptides and peptide hormones in a preclinical model of T2DM, the Lep(ob)/Lep(ob) mouse. The amino acid sequence of statistical relevant top candidates was determined by MS/MS fragmentation or Edman degradation. The comparison of lean versus obese mice revealed increased levels of islet-specific peptides that can be divided into the following categories 1) the major islet peptide hormones insulin, amylin and glucagon; 2) proinsulin and C-peptide and 3) novel processing products of secretogranin, glucagon and amylin. Furthermore, we found increased levels of proteins and peptides implicated in zymogen granule maturation (syncollin) and nutritional digestion. In summary, our findings demonstrate that peptidomics is a valid approach to screen for novel peptide biomarkers.     

Proteomic analysis of Pseudomonas aeruginosa grown under magnesium limitation

In this study, large-scale qualitative and quantitative proteomic technology was applied to the analysis of the opportunistic bacterial pathogen Pseudomonas aeruginosa grown under magnesium limitation, an environmental condition previously shown to induce expression of various virulence factors. For quantitative analysis, whole cell and membrane proteins were differentially labeled with isotope-coded affinity tag (ICAT) reagents and ICAT reagent-labeled peptides were separated by two-dimensional chromatography prior to analysis by electrospray ionization-tandem mass spectrometry (ESI-MS/MS) in an ion trap mass spectrometer (ITMS). To increase the number of protein identifications, gas-phase fractionation (GPF) in the m/z dimension was employed for analysis of ICAT peptides derived from whole cell extracts. The experiments confirmed expression of 1331 P. aeruginosa proteins of which 145 were differentially expressed upon limitation of magnesium. A number of conserved Gram-negative magnesium stress-response proteins involved in bacterial virulence were among the most abundant proteins induced in low magnesium. Comparative ICAT analysis of membrane versus whole cell protein indicated that growth of P. aeruginosa in low magnesium resulted in altered subcellular compartmentalization of large enzyme complexes such as ribosomes. This result was confirmed by 2-D PAGE analysis of P. aeruginosa outer membrane proteins. This study shows that large-scale quantitative proteomic technology can be successfully applied to the analysis of whole bacteria and to the discovery of functionally relevant biologic phenotypes.     

高通量蛋白质组学分析研究进展

基于质谱的蛋白质组学技术已经日趋成熟,可以对细胞和组织中的成千上万种蛋白质进行全面的定性和定量分析,逐步实现“深度覆盖”。随着生物医学日益增长的大队列蛋白质组学分析需求,如何在保持较为理想的覆盖深度下实现短时间、快速的“高通量”蛋白质组学分析已成为当前亟需解决的关键问题之一。常规的蛋白质组学分析流程通常包括样品前处理、色谱分离、质谱检测和数据分析。该文从以上4个方面展开介绍近10年以来高通量蛋白质组学分析技术取得的一系列研究进展,主要包括:(1)基于高通量、自动化移液工作站的蛋白质组样品前处理方法;(2)基于微升流速液相色谱与质谱联用的高通量蛋白质组检测方法;(3)利用灵敏度高、扫描速度快的质谱仪实现短色谱梯度分离下蛋白质组深度覆盖的分析方法;(4)基于人工智能、深度神经网络、机器学习等的蛋白质组学大数据分析方法。此外,对高通量蛋白质组学面临的挑战及其发展进行展望。总而言之,预期在不久的将来高通量蛋白质组学技术将会逐步“落地转化”,成为大队列蛋白质组学分析的利器。     

Evaluation of confidence and reproducibility in quantitative proteomics performed by a capillary isoelectric focusing-based proteomic platform coupled with a spectral counting approach

Multidimensional separations of the peptides resulting from enzymatic digestions of complex protein mixtures prior to MS/MS, namely shotgun proteomics, is increasingly utilized for large-scale identification and quantitation of proteins. Inherent to the performance of proteomic measurements is the resolving power of each of the separations both separately and in combination. By simply raising the number of CIEF fractions, the resulting enhancement in the overall peak capacity of combined CIEF/nano-RPLC separations greatly reduces the complexity of eluted peptides prior to MS detection and sequencing and increases the proteome coverage. The capabilities of the CIEF-based proteome platform coupled with the spectral counting approach to confidently and reproducibly quantify proteins and changes in protein expression levels among samples are evaluated. Analytical reproducibility of relative protein abundance is determined to exhibit a Pearson R(2) value greater than 0.99 and a CV of 14.1%. The platform is demonstrated to be capable of measuring changes in protein expression as low as 1.5-fold, with confidence following multiple testing adjustment.     

MassUntangler: a novel alignment tool for label-free liquid chromatography-mass spectrometry proteomic data

Liquid chromatography-mass spectrometry (LC-MS) has become an important analytical tool for quantitative proteomics and biomarker discovery. In the label-free differential LC-MS approach computational methods are required for an accurate alignment of peaks extrapolated from the experimental raw data accounting for retention time and m/z signals intensity, which are strongly affected by sample matrix and instrumental performance. A novel procedure "MassUntangler" for pairwise alignment has been developed, relying on a pattern-based matching algorithm integrated with filtering algorithms in a multi-step approach. The procedure has been optimized employing a two-step approach. Firstly, low-complexity LC-MS data derived from the enzymatic digestion of two standard proteins have been analyzed. Then, the algorithm's performance has been evaluated by comparing the results with other achieved using state-of-the-art alignment tools. In the second step, our algorithm has been used for the alignment of high-complexity LC-MS data consisting of peptides obtained by an Escherichia coli lysate available from a public repository previously used for the comparison of other alignment tools. MassUntangler gave excellent results in terms of precision scores (from 80% to 93%) and recall scores (from 68% to 89%), showing performances similar and even better than the previous developed tools. Considering the mass spectrometry sensitivity and accuracy, this approach allows the identification and quantification of peptides present in a biological sample at femtomole level with high confidence. The procedure's capability of aligning LC-MS data previously corrected for distortion in retention time has been studied through a hybrid approach, in which MassUntangler was interfaced with the OpenMS TOPP tool MapAligner. The hybrid aligner yielded better results, showing that an integration of different bioinformatic approaches for accurate label-free LC-MS data alignment should be used.     

Development of an integrated approach for evaluation of 2-D gel image analysis: impact of multiple proteins in single spots on comparative proteomics in conventional 2-D gel/MALDI workflow

With 2-D gel mapping, it is often observed that essentially identical proteins migrate to different positions in the gel, while some seemingly well-resolved protein spots consist of multiple proteins. These observations can undermine the validity of gel-based comparative proteomic studies. Through a comparison of protein identifications using direct MALDI-TOF/TOF and LC-ESI-MS/MS analyses of 2-D gel separated proteins from cauliflower florets, we have developed an integrated approach to improve the accuracy and reliability of comparative 2-D electrophoresis. From 46 spots of interest, we identified 51 proteins by MALDI-TOF/TOF analysis and 108 proteins by LC-ESI-MS/MS. The results indicate that 75% of the analyzed spots contained multiple proteins. A comparison of hit rank for protein identifications showed that 37 out of 43 spots identified by MALDI matched the top-ranked hit from the ESI-MS/MS. By using the exponentially modified protein abundance index (emPAI) to determine the abundance of the individual component proteins for the spots containing multiple proteins, we found that the top-hit proteins from 40 out of 43 spots identified by MALDI matched the most abundant proteins determined by LC-MS/MS. Furthermore, our 2-D-GeLC-MS/MS results show that the top-hit proteins in 44 identified spots contributed on average 81% of the spots' staining intensity. This is the first quantitative measurement of the average rate of false assignment for direct MALDI analysis of 2-D gel spots using a new integrated workflow (2-D gel imaging, "2-D GeLC-MS/MS", and emPAI analysis). Here, the new approach is proposed as an alternative to traditional gel-based quantitative proteomics studies.     

NSI and NSMT: usages of MS/MS fragment ion intensity for sensitive differential proteome detection and accurate protein fold change calculation in relative label-free proteome quantification

Although widely applied in the label-free quantification of proteomics, spectral count (SC)-based abundance measurements suffer from the narrow dynamic range of attainable ratios, leading to the serious underestimation of true protein abundance fold changes, especially when studying biological samples that exhibit very large fold changes in protein expression. MS/MS fragment ion intensity, as an alternative to SC, has recently gained acceptance as the abundance feature of protein in label-free proteomic studies. Herein, we implemented two formats of MS/MS fragment ion intensity, Spectral Index (SI) and Summed MS/MS TIC (SMT), to alleviate this particular deficiency arising from SC. Both were in forms of replacing SC in the Normalized Spectral Abundance Factor (NSAF) formula, resulting in two algorithms, abbreviated as NSI and NSMT, respectively. The necessity of the normalization process was validated using a publicly available dataset. Furthermore, when applied to another well characterized benchmark dataset, both NSI and NSMT showed improved overall accuracy over NSAF for the relative quantification of proteomes. Hereinto, NSI enabled the sensitive detection of differentially expressed proteins, while NSMT ensured accurate calculation for protein abundance fold change. Therefore, the selective use of both algorithms might facilitate the screening and quantification of potential biomarkers on the proteome scale.     

Characterization of vestibular schwannoma tissues using liquid chromatography-tandem mass spectrometry analysis of specific peptide fragments separated by in-sample tryptic protein digestion followed by mathematical analysis

Vestibular schwannoma is the most common benign neoplasm of the cerebellopontine angle. Its first symptoms include hearing loss, tinnitus, and vestibular symptoms, followed by cerebellar and brainstem symptoms, along with palsy of the adjacent cranial nerves. However, the clinical picture has unpredictable dynamics and currently, there are no reliable predictors of tumor behavior. Hence, it is desirable to have a fast routine method for analysis of vestibular schwannoma tissues at the molecular level. The major objective of this study was to verify whether a technique using in-sample specific protein digestion with trypsin would have the potential to provide a proteomic characterization of these pathological tissues. The achieved results showed that the use of this approach with subsequent liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis of released peptides allowed a fast identification of a considerable number of proteins in two differential parts of vestibular schwannoma tissue as well as in tissues of control healthy samples. Furthermore, mathematical analysis of MS data was able to discriminate between pathological vestibular schwannoma tissues and healthy tissues. Thus, in-sample protein digestion combined with LC-MS/MS separation and identification of released specific peptides followed by mathematical analysis appears to have the potential for routine characterization of vestibular schwannomas at the molecular level. Data are available via ProteomeXchange with identifier PXD045261.     

In-bone protein digestion followed by LC-MS/MS peptide analysis as a new way towards the routine proteomic characterization of human maxillary and mandibular bone tissue in oral surgery

Proteomic characterization of alveolar bones in oral surgery represents an analytical challenge due to their insoluble character. The implementation of a straightforward technique could lead to the routine use of proteomics in this field. This work thus developed a simple technique for the characterization of bone tissue for human maxillary and mandibular bones. It is based on the direct in-bone tryptic digestion of proteins in both healthy and pathological human maxillary and mandibular bone samples. The released peptides were then identified by the LC-MS/MS. Using this approach, a total of 1120 proteins were identified in the maxillary bone and 1151 proteins in the mandibular bone. The subsequent partial least squares–discrimination analysis (PLS-DA) of protein data made it possible to reach 100% discrimination between the samples of healthy alveolar bones and those of the bone tissue surrounding the inflammatory focus. These results indicate that the in-bone protein digestion followed by the LC-MS/MS and subsequent statistical analysis can provide a deeper insight into the field of oral surgery at the molecular level. Furthermore, it could also have a diagnostic potential in the differentiation between the proteomic patterns of healthy and pathological alveolar bone tissue. Data are available via ProteomeXchange with the identifier PXD026775.     

Discovery of Post-Translational Modifications in Emiliania huxleyi

Emiliania huxleyi is a cosmopolitan coccolithophore that plays an essential role in global carbon and sulfur cycling, and contributes to marine cloud formation and climate regulation. Previously, the proteomic profile of Emiliania huxleyi was investigated using a three-dimensional separation strategy combined with liquid chromatography-tandem mass spectrometry (LC-MS/MS). The current study reuses the MS/MS spectra obtained, for the global discovery of post-translational modifications (PTMs) in this species without specific enrichment methods. Twenty-five different PTM types were examined using Trans-Proteomic Pipeline (Comet and PeptideProphet). Overall, 13,483 PTMs were identified in 7421 proteins. Methylation was the most frequent PTM with more than 2800 modified sites, and lysine was the most frequently modified amino acid with more than 4000 PTMs. The number of proteins identified increased by 22.5% to 18,780 after performing the PTM search. Compared to intact peptides, the intensities of some modified peptides were superior or equivalent. The intensities of some proteins increased dramatically after the PTM search. Gene ontology analysis revealed that protein persulfidation was related to photosynthesis in Emiliania huxleyi. Additionally, various membrane proteins were found to be phosphorylated. Thus, our global PTM discovery platform provides an overview of PTMs in the species and prompts further studies to uncover their biological functions. The combination of a three-dimensional separation method with global PTM search is a promising approach for the identification and discovery of PTMs in other species.