Semi-targeted plasma proteomics discovery workflow utilizing two-stage protein depletion and off-line LC-MALDI MS/MS

A quantitative proteomics workflow was implemented that provides extended plasma protein coverage by extensive protein depletion in combination with the sensitivity and breadth of analysis of two-dimensional LC-MS/MS shotgun analysis. Abundant proteins were depleted by a two-stage process using IgY and Supermix depletion columns in series. Samples are then extensively fractionated by two-dimensional chromatography with fractions directly deposited onto MALDI plates. Decoupling sample fractionation from mass spectrometry facilitates a targeted MS/MS precursor selection strategy that maximizes measurement of a consistent set of peptides across experiments. Multiplexed stable isotope labeling provides quantification relative to a common reference sample and ensures an identical set of peptides measured in the set of samples (set of eight) combined in a single experiment. The more extensive protein depletion provided by the addition of the Supermix column did not compromise overall reproducibility of the measurements or the ability to reliably detect changes in protein levels between samples. The implementation of this workflow is presented for a case study aimed at generating molecular signatures for prediction of first heart attack.     

Quantitative proteome analysis using differential stable isotopic labeling and microbore LC-MALDI MS and MS/MS

We demonstrate an approach for global quantitative analysis of protein mixtures using differential stable isotopic labeling of the enzyme-digested peptides combined with microbore liquid chromatography (LC) matrix-assisted laser desorption ionization (MALDI) mass spectrometry (MS). Microbore LC provides higher sample loading, compared to capillary LC, which facilitates the quantification of low abundance proteins in protein mixtures. In this work, microbore LC is combined with MALDI MS via a heated droplet interface. The compatibilities of two global peptide labeling methods (i.e., esterification to carboxylic groups and dimethylation to amine groups of peptides) with this LC-MALDI technique are evaluated. Using a quadrupole-time-of-flight mass spectrometer, MALDI spectra of the peptides in individual sample spots are obtained to determine the abundance ratio among pairs of differential isotopically labeled peptides. MS/MS spectra are subsequently obtained from the peptide pairs showing significant abundance differences to determine the sequences of selected peptides for protein identification. The peptide sequences determined from MS/MS database search are confirmed by using the overlaid fragment ion spectra generated from a pair of differentially labeled peptides. The effectiveness of this microbore LC-MALDI approach is demonstrated in the quantification and identification of peptides from a mixture of standard proteins as well as E. coli whole cell extract of known relative concentrations. It is shown that this approach provides a facile and economical means of comparing relative protein abundances from two proteome samples.     

Improving peptide relative quantification in MALDI‐TOF MS for biomarker assessment

Proteomic profiling by MALDI‐TOF MS presents various advantages (speed of analysis, ease of use, relatively low cost, sensitivity, tolerance against detergents and contaminants, and possibility of automation) and is being currently used in many applications (e.g. peptide/protein identification and quantification, biomarker discovery, and imaging MS). Earlier studies by many groups indicated that moderate reproducibility in relative peptide quantification is a major limitation of MALDI‐TOF MS. In the present work, we examined and demonstrate a clear effect, in cases apparently random, of sample dilution in complex samples (urine) on the relative quantification of peptides by MALDI‐TOF MS. Results indicate that in urine relative abundance of peptides cannot be assessed with confidence based on a single MALDI‐TOF MS spectrum. To account for this issue, we developed and propose a novel method of determining the relative abundance of peptides, taking into account that peptides have individual linear quantification ranges in relation to sample dilution. We developed an algorithm that calculates the range of dilutions at which each peptide responds in a linear manner and normalizes the received peptide intensity values accordingly. This concept was successfully applied to a set of urine samples from patients diagnosed with diabetes presenting normoalbuminuria (controls) and macroalbuminuria (cases).     

The Effect of Peptide Identification Search Algorithms on MS2-Based Label-Free Protein Quantification

Abstract Several approaches exist for the quantification of proteins in complex samples processed by liquid chromatography-mass spectrometry followed by fragmentation analysis (MS2). One of these approaches is label-free MS2-based quantification, which takes advantage of the information computed from MS2 spectrum observations to estimate the abundance of a protein in a sample. As a first step in this approach, fragmentation spectra are typically matched to the peptides that generated them by a search algorithm. Because different search algorithms identify overlapping but non-identical sets of peptides, here we investigate whether these differences in peptide identification have an impact on the quantification of the proteins in the sample. We therefore evaluated the effect of using different search algorithms by examining the reproducibility of protein quantification in technical repeat measurements of the same sample. From our results, it is clear that a search engine effect does exist for MS2-based label-free protein quantification methods. As a general conclusion, it is recommended to address the overall possibility of search engine-induced bias in the protein quantification results of label-free MS2-based methods by performing the analysis with two or more distinct search engines.     

基于质谱的磷酸化蛋白质组学: 富集、检测、鉴定和定量

磷酸化是一种调控生命活动的重要翻译后修饰,调控生物的生长发育、信号转导、以及疾病的发生发展．从上世纪80年代开始,质谱应用于蛋白质磷酸化的检测中,极大地推动了磷酸化蛋白质组学的发展．质谱检测拥有高灵敏度、高通量的特点,更重要的是具有位点分辨率,因此基于质谱的磷酸化蛋白质组检测方法得到不断的发展和推广．常见的磷酸化蛋白质组研究,首先对磷酸化肽段进行富集,然后进行串联质谱分析,最后通过搜索引擎对修饰位点进行鉴定和定量．本文从这个三个基本方面,对磷酸化蛋白质组研究进行综述,并对未来研究发展方向进行讨论．     

Validation of peptide MS/MS spectra using metabolic isotope labeling for spectral matching-based shotgun proteome analysis

We report an isotope labeling shotgun proteome analysis strategy to validate the spectrum-to-sequence assignments generated by using sequence-database searching for the construction of a more reliable MS/MS spectral library. This strategy is demonstrated in the analysis of the E. coli K12 proteome. In the workflow, E. coli cells were cultured in normal and (15)N-enriched media. The differentially labeled proteins from the cell extracts were subjected to trypsin digestion and two-dimensional liquid chromatography quadrupole time-of-flight tandem mass spectrometry (2D-LC QTOF MS/MS) analysis. The MS/MS spectra of the two samples were individually searched using Mascot against the E. coli proteome database to generate lists of peptide sequence matches. The two data sets were compared by overlaying the spectra of unlabeled and labeled matches of the same peptide sequence for validation. Two cutoff filters, one based on the number of common fragment ions and another one on the similarity of intensity patterns among the common ions, were developed and applied to the overlaid spectral pairs to reject the low quality or incorrectly assigned spectra. By examining 257,907 and 245,156 spectra acquired from the unlabeled and (15)N-labeled samples, respectively, an experimentally validated MS/MS spectral library of tryptic peptides was constructed for E. coli K12 that consisted of 9,302 unique spectra with unique sequence and charge state, representing 7,763 unique peptide sequences. This E. coli spectral library could be readily expanded, and the overall strategy should be applicable to other organisms. Even with this relatively small library, it was shown that more peptides could be identified with higher confidence using the spectral search method than by sequence-database searching.     

Extracting histones for the specific purpose of label‐free MS

Extracting histones from cells is the first step in studies that aim to characterize histones and their post‐translational modifications (hPTMs) with MS. In the last decade, label‐free quantification is more frequently being used for MS‐based histone characterization. However, many histone extraction protocols were not specifically designed for label‐free MS. While label‐free quantification has its advantages, it is also very susceptible to technical variation. Here, we adjust an established histone extraction protocol according to general label‐free MS guidelines with a specific focus on minimizing sample handling. These protocols are first evaluated using SDS‐PAGE. Hereafter, a selection of extraction protocols was used in a complete histone workflow for label‐free MS. All protocols display nearly identical relative quantification of hPTMs. We thus show that, depending on the cell type under investigation and at the cost of some additional contaminating proteins, minimizing sample handling can be done during histone isolation. This allows analyzing bigger sample batches, leads to reduced technical variation and minimizes the chance of in vitro alterations to the hPTM snapshot. Overall, these results allow researchers to determine the best protocol depending on the resources and goal of their specific study. Data are available via ProteomeXchange with identifier PXD002885.     

The importance of peptide detectability for protein identification, quantification, and experiment design in MS/MS proteomics

Peptide detectability is defined as the probability that a peptide is identified in an LC-MS/MS experiment and has been useful in providing solutions to protein inference and label-free quantification. Previously, predictors for peptide detectability trained on standard or complex samples were proposed. Although the models trained on complex samples may benefit from the large training data sets, it is unclear to what extent they are affected by the unequal abundances of identified proteins. To address this challenge and improve detectability prediction, we present a new algorithm for the iterative learning of peptide detectability from complex mixtures. We provide evidence that the new method approximates detectability with useful accuracy and, based on its design, can be used to interpret the outcome of other learning strategies. We studied the properties of peptides from the bacterium Deinococcus radiodurans and found that at standard quantities, its tryptic peptides can be roughly classified as either detectable or undetectable, with a relatively small fraction having medium detectability. We extend the concept of detectability from peptides to proteins and apply the model to predict the behavior of a replicate LC-MS/MS experiment from a single analysis. Finally, our study summarizes a theoretical framework for peptide/protein identification and label-free quantification.     

Platform for establishing interlaboratory reproducibility of selected reaction monitoring-based mass spectrometry peptide assays

Mass spectrometry (MS) is an attractive alternative to quantification of proteins by immunoassays, particularly for protein biomarkers of clinical relevance. Reliable quantification requires that the MS-based assays are robust, selective, and reproducible. Thus, the development of standardized protocols is essential to introduce MS into clinical research laboratories. The aim of this study was to establish a complete workflow for assessing the transferability and reproducibility of selected reaction monitoring (SRM) assays between clinical research laboratories. Four independent laboratories in North America, using identical triple-quadrupole mass spectrometers (Quantum Ultra, Thermo), were provided with standard protocols and instrumentation settings to analyze unknown samples and internal standards in a digested plasma matrix to quantify 51 peptides from 39 human proteins using a multiplexed SRM assay. The interlaboratory coefficient of variation (CV) was less than 10% for 25 of 39 peptides quantified (12 peptides were not quantified based upon hydrophobicity) and exhibited CVs less than 20% for the remaining peptides. In this report, we demonstrate that previously developed research platforms for SRM assays can be improved and optimized for deployment in clinical research environments.     

Index-ion triggered MS2 ion quantification: a novel proteomics approach for reproducible detection and quantification of targeted proteins in complex mixtures

Biomedical research requires protein detection technology that is not only sensitive and quantitative, but that can reproducibly measure any set of proteins in a biological system in a high throughput manner. Here we report the development and application of a targeted proteomics platform termed index-ion triggered MS2 ion quantification (iMSTIQ) that allows reproducible and accurate peptide quantification in complex mixtures. The key feature of iMSTIQ is an approach called index-ion triggered analysis (ITA) that permits the reproducible acquisition of full MS2 spectra of targeted peptides independent of their ion intensities. Accurate quantification is achieved by comparing the relative intensities of multiple pairs of fragment ions derived from isobaric targeted peptides during MS2 analysis. Importantly, the method takes advantage of the favorable performance characteristics of the LTQ-Orbitrap, which include high mass accuracy, resolution, and throughput. As such it provides an attractive targeted proteomics tool to meet the demands of systems biology research and biomarker studies.     

A simple workflow to increase MS2 identification rate by subsequent spectral library search

Searching a spectral library for the identification of protein MS/MS data has proven to be a fast and accurate method, while yielding a high identification rate. We investigated the potential to increase peptide discovery rate, with little increase in computational time, by constructing a workflow based on a sequence search with Phenyx followed by a library search with SpectraST. Searching a consensus library compiled from the search results of the prior Phenyx search increased the number of confidently matched spectra by up to 156%. Additionally matched spectra by SpectraST included noisy spectra, spectra representing missed cleaved peptides as well as spectra from post‐translationally modified peptides.     

Identification and quantification of differentially expressed proteins in E-cadherin deficient SCC9 cells and SCC9 transfectants expressing E-cadherin by dimethyl isotope labeling, LC-MALDI MS and MS/MS

A strategy based on isotope labeling of peptides and liquid chromatography matrix-assisted laser desorption ionization mass spectrometry (LC-MALDI MS) has been employed to accurately quantify and confidently identify differentially expressed proteins between an E-cadherin-deficient human carcinoma cell line (SCC9) and its transfectants expressing E-cadherin (SCC9-E). Proteins extracted from each cell line were tryptically digested and the resultant peptides were labeled individually with either d(0)- or d(2)-formaldehyde. The labeled peptides were combined and the peptide mixture was separated and fractionated by a strong cation exchange (SCX) column. Peptides from each SCX fraction were further separated by a microbore reversed-phase (RP) LC column. The effluents were then directly spotted onto a MALDI target using a heated droplet LC-MALDI interface. After mixing with a MALDI matrix, individual sample spots were analyzed by MALDI quadrupole time-of-flight MS, using an initial MS scan to quantify the dimethyl labeled peptide pairs. MS/MS analysis was then carried out on the peptide pairs having relative peak intensity changes of greater than 2-fold. The MS/MS spectra were subjected to database searching for protein identification. The search results were further confirmed by comparing the MS/MS spectra of the peptide pairs. Using this strategy, we detected and compared relative peak intensity changes of 5480 peptide pairs. Among them, 320 peptide pairs showed changes of greater than 2-fold. MS/MS analysis of these changing pairs led to the identification of 49 differentially expressed proteins between the parental SCC9 cells and SCC9-E transfectants. These proteins were determined to be involved in different pathways regulating cytoskeletal organization, cell adhesion, epithelial polarity, and cell proliferation. The changes in protein expression were consistent with increased cell-cell and cell-matrix adhesion and decreased proliferation in SCC9-E cells, in line with E-cadherin tumor suppressor activity. Finally, the accuracy of the MS quantification and subcellular localization for 6 differentially expressed proteins were validated by immunoblotting and immunofluorescence assays.     

Absolute quantification of proteins by LCMSE: a virtue of parallel MS acquisition

Relative quantification methods have dominated the quantitative proteomics field. There is a need, however, to conduct absolute quantification studies to accurately model and understand the complex molecular biology that results in proteome variability among biological samples. A new method of absolute quantification of proteins is described. This method is based on the discovery of an unexpected relationship between MS signal response and protein concentration: the average MS signal response for the three most intense tryptic peptides per mole of protein is constant within a coefficient of variation of less than +/-10%. Given an internal standard, this relationship is used to calculate a universal signal response factor. The universal signal response factor (counts/mol) was shown to be the same for all proteins tested in this study. A controlled set of six exogenous proteins of varying concentrations was studied in the absence and presence of human serum. The absolute quantity of the standard proteins was determined with a relative error of less than +/-15%. The average MS signal responses of the three most intense peptides from each protein were plotted against their calculated protein concentrations, and this plot resulted in a linear relationship with an R(2) value of 0.9939. The analyses were applied to determine the absolute concentration of 11 common serum proteins, and these concentrations were then compared with known values available in the literature. Additionally within an unfractionated Escherichia coli lysate, a subset of identified proteins known to exist as functional complexes was studied. The calculated absolute quantities were used to accurately determine their stoichiometry.     

Effect of 2MEGA labeling on membrane proteome analysis using LC-ESI QTOF MS

One of the challenges associated with large-scale proteome analysis using tandem mass spectrometry (MS/MS) and automated database searching is to reduce the number of false positive identifications without sacrificing the number of true positives found. In this work, a systematic investigation of the effect of 2MEGA labeling (N-terminal dimethylation after lysine guanidination) on the proteome analysis of a membrane fraction of an Escherichia coli cell extract by 2-dimensional liquid chromatography MS/MS is presented. By a large-scale comparison of MS/MS spectra of native peptides with those from the 2MEGA-labeled peptides, the labeled peptides were found to undergo facile fragmentation with enhanced a1 or a1-related (a(1)-17 and a(1)-45) ions derived from all N-terminal amino acids in the MS/MS spectra; these ions are usually difficult to detect in the MS/MS spectra of nonderivatized peptides. The 2MEGA labeling alleviated the biased detection of arginine-terminated peptides that is often observed in MALDI and ESI MS experiments. 2MEGA labeling was found not only to increase the number of peptides and proteins identified but also to generate enhanced a1 or a1-related ions as a constraint to reduce the number of false positive identifications. In total, 640 proteins were identified from the E. coli membrane fraction, with each protein identified based on peptide mass and sequence match of one or more peptides using MASCOT database search algorithm from the MS/MS spectra generated by a quadrupole time-of-flight mass spectrometer. Among them, the subcellular locations of 336 proteins are presently known, including 258 membrane and membrane-associated proteins (76.8%). Among the classified proteins, there was a dramatic increase in the total number of integral membrane proteins identified in the 2MEGA-labeled sample (153 proteins) versus the unlabeled sample (77 proteins).     

Identification and characterization of disulfide bonds in proteins and peptides from tandem MS data by use of the MassMatrix MS/MS search engine

A new database search algorithm has been developed to identify disulfide-linked peptides in tandem MS data sets. The algorithm is included in the newly developed tandem MS database search program, MassMatrix. The algorithm exploits the probabilistic scoring model in MassMatrix to achieve identification of disulfide bonds in proteins and peptides. Proteins and peptides with disulfide bonds can be identified with high confidence without chemical reduction or other derivatization. The approach was tested on peptide and protein standards with known disulfide bonds. All disulfide bonds in the standard set were identified by MassMatrix. The algorithm was further tested on bovine pancreatic ribonuclease A (RNaseA). The 4 native disulfide bonds in RNaseA were detected by MassMatrix with multiple validated peptide matches for each disulfide bond with high statistical scores. Fifteen nonnative disulfide bonds were also observed in the protein digest under basic conditions (pH = 8.0) due to disulfide bond interchange. After minimizing the disulfide bond interchange (pH = 6.0) during digestion, only one nonnative disulfide bond was observed. The MassMatrix algorithm offers an additional approach for the discovery of disulfide bond from tandem mass spectrometry data.     

A reproducibility-based evaluation procedure for quantifying the differences between MS/MS peak intensity normalization methods

The identification of peptides and proteins from fragmentation mass spectra is a very common approach in the field of proteomics. Contemporary high-throughput peptide identification pipelines can quickly produce large quantities of MS/MS data that contain valuable knowledge about the actual physicochemical processes involved in the peptide fragmentation process, which can be extracted through extensive data mining studies. As these studies attempt to exploit the intensity information contained in the MS/MS spectra, a critical step required for a meaningful comparison of this information between MS/MS spectra is peak intensity normalization. We here describe a procedure for quantifying the efficiency of different published normalization methods in terms of the quartile coefficient of dispersion (qcod) statistic. The quartile coefficient of dispersion is applied to measure the dispersion of the peak intensities between redundant MS/MS spectra, allowing the quantification of the differences in computed peak intensity reproducibility between the different normalization methods. We demonstrate that our results are independent of the data set used in the evaluation procedure, allowing us to provide generic guidance on the choice of normalization method to apply in a certain MS/MS pipeline application.     

An improved workflow for identifying ubiquitin/ubiquitin‐like protein conjugation sites from tandem mass spectra

The identification of ubiquitin (Ub) and Ub‐like protein (Ubl) conjugation sites is important in understanding their roles in biological pathway regulations. However, unambiguously and sensitively identifying Ub/Ubl conjugation sites through high‐throughput MS remains challenging. We introduce an improved workflow for identifying Ub/Ubl conjugation sites based on the ChopNSpice and X!Tandem software. ChopNSpice is modified to generate Ub/Ubl conjugation peptides in the form of a cross‐link. A combinatorial FASTA database can be acquired using the modified ChopNSpice (MchopNSpice). The modified X!Tandem (UblSearch) introduces a new fragmentation model for the Ub/Ubl conjugation peptides to match unambiguously the MS/MS spectra with linear peptides or Ub/Ubl conjugation peptides using the combinatorial FASTA database. The novel workflow exhibited better performance in analyzing an Ub and Ubl spectral library and a large‐scale Trypanosoma cruzi small Ub‐related modifier dataset compared with the original ChopNSpice method. The proposed workflow is more suitable for processing large‐scale MS datasets of Ub/Ubl modification. MchopNSpice and UblSearch are freely available under the GNU General Public License v3.0 at http://sourceforge.net/projects/maublsearch .     

Semi-automated identification of N-Glycopeptides by hydrophilic interaction chromatography, nano-reverse-phase LC-MS/MS, and glycan database search

Glycoproteins fulfill many indispensable biological functions, and changes in protein glycosylation have been observed in various diseases. Improved analytical methods are needed to allow a complete characterization of this complex and common post-translational modification. In this study, we present a workflow for the analysis of the microheterogeneity of N-glycoproteins that couples hydrophilic interaction and nanoreverse-phase C18 chromatography to tandem QTOF mass spectrometric analysis. A glycan database search program, GlycoPeptideSearch, was developed to match N-glycopeptide MS/MS spectra with the glycopeptides comprised of a glycan drawn from the GlycomeDB glycan structure database and a peptide from a user-specified set of potentially glycosylated peptides. Application of the workflow to human haptoglobin and hemopexin, two microheterogeneous N-glycoproteins, identified a total of 57 distinct site-specific glycoforms in the case of haptoglobin and 14 site-specific glycoforms of hemopexin. Using glycan oxonium ions and peptide-characteristic glycopeptide fragment ions and by collapsing topologically redundant glycans, the search software was able to make unique N-glycopeptide assignments for 51% of assigned spectra, with the remaining assignments primarily representing isobaric topological rearrangements. The optimized workflow, coupled with GlycoPeptideSearch, is expected to make high-throughput semiautomated glycopeptide identification feasible for a wide range of users.     

The EIPeptiDi tool: enhancing peptide discovery in ICAT-based LC MS/MS experiments

Background  

Isotope-coded affinity tags (ICAT) is a method for quantitative proteomics based on differential isotopic labeling, sample digestion and mass spectrometry (MS). The method allows the identification and relative quantification of proteins present in two samples and consists of the following phases. First, cysteine residues are either labeled using the ICAT Light or ICAT Heavy reagent (having identical chemical properties but different masses). Then, after whole sample digestion, the labeled peptides are captured selectively using the biotin tag contained in both ICAT reagents. Finally, the simplified peptide mixture is analyzed by nanoscale liquid chromatography-tandem mass spectrometry (LC-MS/MS). Nevertheless, the ICAT LC-MS/MS method still suffers from insufficient sample-to-sample reproducibility on peptide identification. In particular, the number and the type of peptides identified in different experiments can vary considerably and, thus, the statistical (comparative) analysis of sample sets is very challenging. Low information overlap at the peptide and, consequently, at the protein level, is very detrimental in situations where the number of samples to be analyzed is high.