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.     

Accurate mass spectrometry based protein quantification via shared peptides

In mass spectrometry-based protein quantification, peptides that are shared across different protein sequences are often discarded as being uninformative with respect to each of the parent proteins. We investigate the use of shared peptides which are ubiquitous (~50% of peptides) in mass spectrometric data-sets for accurate protein identification and quantification. Different from existing approaches, we show how shared peptides can help compute the relative amounts of the proteins that contain them. Also, proteins with no unique peptide in the sample can still be analyzed for relative abundance. Our article uses shared peptides in protein quantification and makes use of combinatorial optimization to reduce the error in relative abundance measurements. We describe the topological and numerical properties required for robust estimates, and use them to improve our estimates for ill-conditioned systems. Extensive simulations validate our approach even in the presence of experimental error. We apply our method to a model of Arabidopsis thaliana root knot nematode infection, and investigate the differential role of several protein family members in mediating host response to the pathogen.     

Definitive screening design enables optimization of LC–ESI–MS/MS parameters in proteomics

In proteomics, more than 100,000 peptides are generated from the digestion of human cell lysates. Proteome samples have a broad dynamic range in protein abundance; therefore, it is critical to optimize various parameters of LC–ESI–MS/MS to comprehensively identify these peptides. However, there are many parameters for LC–ESI–MS/MS analysis. In this study, we applied definitive screening design to simultaneously optimize 14 parameters in the operation of monolithic capillary LC–ESI–MS/MS to increase the number of identified proteins and/or the average peak area of MS1. The simultaneous optimization enabled the determination of two-factor interactions between LC and MS. Finally, we found two parameter sets of monolithic capillary LC–ESI–MS/MS that increased the number of identified proteins by 8.1% or the average peak area of MS1 by 67%. The definitive screening design would be highly useful for high-throughput analysis of the best parameter set in LC–ESI–MS/MS systems.     

Including shared peptides for estimating protein abundances: A significant improvement for quantitative proteomics

Inferring protein abundances from peptide intensities is the key step in quantitative proteomics. The inference is necessarily more accurate when many peptides are taken into account for a given protein. Yet, the information brought by the peptides shared by different proteins is commonly discarded. We propose a statistical framework based on a hierarchical modeling to include that information. Our methodology, based on a simultaneous analysis of all the quantified peptides, handles the biological and technical errors as well as the peptide effect. In addition, we propose a practical implementation suitable for analyzing large data sets. Compared to a method based on the analysis of one protein at a time (that does not include shared peptides), our methodology proved to be far more reliable for estimating protein abundances and testing abundance changes. The source codes are available at http://pappso.inra.fr/bioinfo/all_p/.     

Improvements in proteomic metrics of low abundance proteins through proteome equalization using ProteoMiner prior to MudPIT

Ideally, shotgun proteomics would facilitate the identification of an entire proteome with 100% protein sequence coverage. In reality, the large dynamic range and complexity of cellular proteomes results in oversampling of abundant proteins, while peptides from low abundance proteins are undersampled or remain undetected. We tested the proteome equalization technology, ProteoMiner, in conjunction with Multidimensional Protein Identification Technology (MudPIT) to determine how the equalization of protein dynamic range could improve shotgun proteomics methods for the analysis of cellular proteomes. Our results suggest low abundance protein identifications were improved by two mechanisms: (1) depletion of high abundance proteins freed ion trap sampling space usually occupied by high abundance peptides and (2) enrichment of low abundance proteins increased the probability of sampling their corresponding more abundant peptides. Both mechanisms also contributed to dramatic increases in the quantity of peptides identified and the quality of MS/MS spectra acquired due to increases in precursor intensity of peptides from low abundance proteins. From our large data set of identified proteins, we categorized the dominant physicochemical factors that facilitate proteome equalization with a hexapeptide library. These results illustrate that equalization of the dynamic range of the cellular proteome is a promising methodology to improve low abundance protein identification confidence, reproducibility, and sequence coverage in shotgun proteomics experiments, opening a new avenue of research for improving proteome coverage.     

Mitochondrial phosphoproteome revealed by an improved IMAC method and MS/MS/MS

IMAC in combination with mass spectrometry is a promising approach for global analysis of protein phosphorylation. Nevertheless this approach suffers from two shortcomings: inadequate efficiency of IMAC and poor fragmentation of phosphopeptides in the mass spectrometer. Here we report optimization of the IMAC procedure using (32)P-labeled tryptic peptides and development of MS/MS/MS (MS3) for identifying phosphopeptide sequences and phosphorylation sites. The improved IMAC method allowed recovery of phosphorylated tryptic peptides up to approximately 77% with only minor retention of unphosphorylated peptides. MS3 led to efficient fragmentation of the peptide backbone in phosphopeptides for sequence assignment. Proteomics of mitochondrial phosphoproteins using the resulting IMAC protocol and MS3 revealed 84 phosphorylation sites in 62 proteins, most of which have not been reported before. These results revealed diverse phosphorylation pathways involved in the regulation of mitochondrial functions. Integration of the optimized batchwise IMAC protocol with MS3 offers a relatively simple and more efficient approach for proteomics of protein phosphorylation.     

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.     

Analysis of membrane proteins from human chronic myelogenous leukemia cells: comparison of extraction methods for multidimensional LC-MS/MS

An important strategy for "shotgun proteomics" profiling involves solution proteolysis of proteins, followed by peptide separation using multidimensional liquid chromatography and automated sequencing by mass spectrometry (LC-MS/MS). Several protocols for extracting and handling membrane proteins for shotgun proteomics experiments have been reported, but few direct comparisons of different protocols have been reported. We compare four methods for preparing membrane proteins from human cells, using acid labile surfactants (ALS), urea, and mixed organic-aqueous solvents. These methods were compared with respect to their efficiency of protein solubilization and proteolysis, peptide and protein recovery, membrane protein enrichment, and peptide coverage of transmembrane proteins. Overall, approximately 50-60% of proteins recovered were membrane-associated, identified from Gene Ontology annotations and transmembrane prediction software. Samples extracted with ALS, extracted with urea followed by dilution, or extracted with urea followed by desalting yielded comparable peptide recoveries and sequence coverage of transmembrane proteins. In contrast, suboptimal proteolysis was observed with organic solvent. Urea extraction followed by desalting may be a particularly useful approach, as it is less costly than ALS and yields satisfactory protein denaturation and proteolysis under conditions that minimize reactivity with urea-derived cyanate. Spectral counting was used to compare datasets of proteins from membrane samples with those of soluble proteins from K562 cells, and to estimate fold differences in protein abundances. Proteins most highly abundant in the membrane samples showed enrichment of integral membrane protein identifications, consistent with their isolation by differential centrifugation.     

Validation of an LC‐MS based approach for profiling histones in chronic lymphocytic leukemia

The in vitro evaluation of histones and their PTMs has drawn substantial interest in the development of epigenetic therapies. The differential expression of histone isoforms may serve as a potential marker in the classification of diseases affected by chromatin abnormalities. In this study, protein profiling by LC and MS was used to explore differences in histone composition in primary chronic lymphocytic leukemia (CLL) cells. Extensive method validations were performed to determine the experimental variances that would impact histone relative abundance. The resulting data demonstrated that the proposed methodology was suitable for the analysis of histone profiles. In 4 normal individuals and 40 CLL patients, a significant decrease in the relative abundance of histone H2A variants (H2AFL and H2AFA/M*) was observed in primary CLL cells as compared to normal B cells. Protein identities were determined using high mass accuracy MS and shotgun proteomics.     

Effect of iTRAQ Labeling on the Relative Abundance of Peptide Fragment Ions Produced by MALDI-MS/MS

The identification of proteins in proteomics experiments is usually based on mass information derived from tandem mass spectrometry data. To improve the performance of the identification algorithms, additional information available in the fragment peak intensity patterns has been shown to be useful. In this study, we consider the effect of iTRAQ labeling on the fragment peak intensity patterns of singly charged peptides from MALDI tandem MS data. The presence of an iTRAQ-modified basic group on the N-terminus leads to a more pronounced set of b-ion peaks and distinct changes in the abundance of specific peptide types. We performed a simple intensity prediction by using a decision-tree machine learning approach and were able to show that the relative ion abundance in a spectrum can be correctly predicted and distinguished from closely related sequences. This information will be useful for the development of improved method-specific intensity-based protein identification algorithms.     

Proteome profile of human breast cancer tissue generated by LC-ESI-MS/MS combined with sequential protein precipitation and solubilization

Comprehensive proteome profiling of breast cancer tissue samples is challenging, as the tissue samples contain many proteins with varying concentrations and modifications. We report an effective sample preparation strategy combined with liquid chromatography (LC) electrospray ionization (ESI) quadrupole time-of-flight (QTOF) MS/MS for proteome analysis of human breast cancer tissue. The complexity of the breast cancer tissue proteome was reduced by using protein precipitation from a tissue extract, followed by sequential protein solubilization in solvents of different solubilizing strength. The individual fractions of protein mixtures or subproteomes were subjected to trypsin digestion and the resultant peptides were separated by strong cation exchange (SCX) chromatography, followed by reversed-phase capillary LC combined with high resolution and high accuracy ESI-QTOF MS/MS. This approach identified 14407 unique peptides from 3749 different proteins based on peptide matches with scores above the threshold scores at the 95% confidence level in MASCOT database search of the acquired MS/MS spectra. The false positive rate of peptide matches was determined to be 0.95% by using the target-decoy sequence search strategy. On the basis of gene ontology categorization, the identified proteins represented a wide variety of biological functions, cellular processes, and cellular locations.     

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.     

Spectral index for assessment of differential protein expression in shotgun proteomics

Detecting differentially expressed proteins is a key goal of proteomics. We describe a label-free method, the spectral index, for analyzing relative protein abundance in large-scale data sets derived from biological samples by shotgun proteomics. The spectral index is comprised of two biochemically plausible features: relative protein abundance (assessed by spectral counts) and the number of samples within a group with detectable peptides. We combined the spectral index with permutation analysis to establish confidence intervals for assessing differential protein expression in bronchoalveolar lavage fluid from cystic fibrosis and control subjects. Significant differences in protein abundance determined by the spectral index agreed well with independent biochemical measurements. When used to analyze simulated data sets, the spectral index outperformed four other statistical tests (Student's t-test, G-test, Bayesian t-test, and Significance Analysis of Microarrays) by correctly identifying the largest number of differentially expressed proteins. Correspondence analysis and functional annotation analysis indicated that the spectral index improves the identification of enriched proteins corresponding to clinical phenotypes. The spectral index is easily implemented and statistically robust, and its results are readily interpreted graphically. Therefore, it should be useful for biomarker discovery and comparisons of protein expression between normal and disease states.     

Salt-induced changes in the plasma membrane proteome of the halotolerant alga Dunaliella salina as revealed by blue native gel electrophoresis and nano-LC-MS/MS analysis

The halotolerant alga Dunaliella salina is a recognized model photosynthetic organism for studying plant adaptation to high salinity. The adaptation mechanisms involve major changes in the proteome composition associated with energy metabolism and carbon and iron acquisition. To clarify the molecular basis for the remarkable resistance to high salt, we performed a comprehensive proteomics analysis of the plasma membrane. Plasma membrane proteins were recognized by tagging intact cells with a membrane-impermeable biotin derivative. Proteins were resolved by two-dimensional blue native/SDS-PAGE and identified by nano-LC-MS/MS. Of 55 identified proteins, about 60% were integral membrane or membrane-associated proteins. We identified novel surface coat proteins, lipid-metabolizing enzymes, a new family of membrane proteins of unknown function, ion transporters, small GTP-binding proteins, and heat shock proteins. The abundance of 20 protein spots increased and that of two protein spots decreased under high salt. The major salt-regulated proteins were implicated in protein and membrane structure stabilization and within signal transduction pathways. The migration profiles of native protein complexes on blue native gels revealed oligomerization or co-migration of major surface-exposed proteins, which may indicate mechanisms of stabilization at high salinity.     

Impact of protein stability, cellular localization, and abundance on proteomic detection of tumor-derived proteins in plasma

Tumor-derived, circulating proteins are potentially useful as biomarkers for detection of cancer, for monitoring of disease progression, regression and recurrence, and for assessment of therapeutic response. Here we interrogated how a protein's stability, cellular localization, and abundance affect its observability in blood by mass-spectrometry-based proteomics techniques. We performed proteomic profiling on tumors and plasma from two different xenograft mouse models. A statistical analysis of this data revealed protein properties indicative of the detection level in plasma. Though 20% of the proteins identified in plasma were tumor-derived, only 5% of the proteins observed in the tumor tissue were found in plasma. Both intracellular and extracellular tumor proteins were observed in plasma; however, after normalizing for tumor abundance, extracellular proteins were seven times more likely to be detected. Although proteins that were more abundant in the tumor were also more likely to be observed in plasma, the relationship was nonlinear: Doubling the spectral count increased detection rate by only 50%. Many secreted proteins, even those with relatively low spectral count, were observed in plasma, but few low abundance intracellular proteins were observed. Proteins predicted to be stable by dipeptide composition were significantly more likely to be identified in plasma than less stable proteins. The number of tryptic peptides in a protein was not significantly related to the chance of a protein being observed in plasma. Quantitative comparison of large versus small tumors revealed that the abundance of proteins in plasma as measured by spectral count was associated with the tumor size, but the relationship was not one-to-one; a 3-fold decrease in tumor size resulted in a 16-fold decrease in protein abundance in plasma. This study provides quantitative support for a tumor-derived marker prioritization strategy that favors secreted and stable proteins over all but the most abundant intracellular proteins.     

Liquid chromatography MALDI MS/MS for membrane proteome analysis

Membrane proteins play critical roles in many biological functions and are often the molecular targets for drug discovery. However, their analysis presents a special challenge largely due to their highly hydrophobic nature. We present a surfactant-aided shotgun proteomics approach for membrane proteome analysis. In this approach, membrane proteins were solubilized and digested in the presence of SDS followed by newly developed auto-offline liquid chromatography/matrix-assisted laser desorption ionization (LC/MALDI) tandem MS analysis. Because of high tolerance of MALDI to SDS, one-dimensional (1D) LC separation can be combined with MALDI for direct analysis of protein digests containing SDS, without the need for extensive sample cleanup. In addition, the heated droplet interface used in LC/MALDI can work with high flow LC separations, allowing a relatively large amount of protein digest to be used for 1D LC/MALDI which facilitates the detection of low abundance proteins. The proteome identification results obtained by LC/MALDI are compared to the gel electrophoresis/MS method as well as the shotgun proteomics method using 2D LC/electrospray ionization MS. It is demonstrated that, while LC/MALDI provides more extensive proteome coverage compared to the other two methods, these three methods are complementary to each other and a combination of these methods should provide a more comprehensive membrane proteome analysis.