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.     

Large-scale identification of Caenorhabditis elegans proteins by multidimensional liquid chromatography-tandem mass spectrometry

A proteome of a model organism, Caenorhabditis elegans, was analyzed by an integrated liquid chromatography (LC)-based protein identification system, which was constructed by microscale two-dimensional liquid chromatography (2DLC) coupled with electrospray ionization (ESI) tandem mass spectrometry (MS/MS) on a high-resolution hybrid mass spectrometer with an automated data analysis system. Soluble and insoluble protein fractions were prepared from a mixed growth phase culture of the worm C. elegans, digested with trypsin, and fractionated separately on the 2DLC system. The separated peptides were directly analyzed by on-line ESI-MS/MS in a data-dependent mode, and the resultant spectral data were automatically processed to search a genome sequence database, wormpep 66, for protein identification. The total number of proteins of the composite proteome identified in this method was 1,616, including 110 secreted/targeted proteins and 242 transmembrane proteins. The codon adaptation indices of the identified proteins suggested that the system could identify proteins of relatively low abundance, which are difficult to identify by conventional 2D-gel electrophoresis (GE) followed by an offline mass spectrometric analysis such as peptide mass fingerprinting. Among the approximately 5,400 peptides assigned in this study, many peptides with post-translational modifications, such as N-terminal acetylation and phosphorylation, were detected. This expression profile of C. elegans, containing 571 hypothetical gene products, will serve as the basic data of a major proteome set expressed in the worm.     

Protein identification assisted by the prediction of retention time in liquid chromatography/tandem mass spectrometry

Two-dimensional liquid chromatography (2D-LC) coupled on-line with electrospray ionization tandem mass spectrometry (2D-LC-ESI-MS/MS) is a new platform for analysis and identification of proteome. Peptides are separated by 2D-LC and then performed MS/MS analysis by tandem MS/MS. The MS/MS data are searched against database for protein identification. In one 2D-LC-ESI-MS/MS run, we obtained not only the structural information of peptides directly from MS/MS, but also the retention time of peptides eluted from LC. Information on the chromatographic behavior of peptides can assist protein identification in the new platform for proteomics. The retention time of the matching peptides of the identified protein was predicted by the hydrophobic contribute of each amino acid on reversed-phase liquid chromatography (RPLC). By using this strategy proteins were identified by four types of information: peptide mass fingerprinting (PMF), sequence query, and MS/MS ions searched and the predicted retention time. This additional information obtained from LC could assist protein identification with no extra experimental cost.     

Large-scale identification of proteins in human salivary proteome by liquid chromatography/mass spectrometry and two-dimensional gel electrophoresis-mass spectrometry

Human saliva contains a large number of proteins and peptides (salivary proteome) that help maintain homeostasis in the oral cavity. Global analysis of human salivary proteome is important for understanding oral health and disease pathogenesis. In this study, large-scale identification of salivary proteins was demonstrated by using shotgun proteomics and two-dimensinal gel electrophoresis-mass spectrometry (2-DE-MS). For the shotgun approach, whole saliva proteins were prefractionated according to molecular weight. The smallest fraction, presumably containing salivary peptides, was directly separated by capillary liquid chromatography (LC). However, the large protein fractions were digested into peptides for subsequent LC separation. Separated peptides were analyzed by on-line electrospray tandem mass spectrometry (MS/MS) using a quadrupole-time of flight mass spectrometer, and the obtained spectra were automatically processed to search human protein sequence database for protein identification. Additionally, 2-DE was used to map out the proteins in whole saliva. Protein spots 105 in number were excised and in-gel digested; and the resulting peptide fragments were measured by matrix-assisted laser desorption/ionization-mass spectrometry and sequenced by LC-MS/MS for protein identification. In total, we cataloged 309 proteins from human whole saliva by using these two proteomic approaches.     

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.     

Application of displacement chromatography for the analysis of a lipid raft proteome

Defining membrane proteomes is fundamental to understand the role of membrane proteins in biological processes and to find new targets for drug development. Usually multidimensional chromatography using step or gradient elution is applied for the separation of tryptic peptides of membrane proteins prior to their mass spectrometric analysis. Displacement chromatography (DC) offers several advantages that are helpful for proteome analysis. However, DC has so far been applied for proteomic investigations only in few cases. In this study we therefore applied DC in a multidimensional LC–MS approach for the separation and identification of membrane proteins located in cholesterol-enriched membrane microdomains (lipid rafts) obtained from rat kidney by density gradient centrifugation. The tryptic peptides were separated on a cation-exchange column in the displacement mode with spermine used as displacer. Fractions obtained from DC were analyzed using an HPLC-chip system coupled to an electrospray-ionization ion-trap mass spectrometer. This procedure yielded more than 400 highly significant peptide spectrum matches and led to the identification of more than 140 reliable protein hits within an established rat kidney lipid raft proteome. The majority of identified proteins were membrane proteins. In sum, our results demonstrate that DC is a suitable alternative to gradient elution separations for the identification of proteins via a multidimensional LC–MS approach.     

Protein identification from product ion spectra of peptides validated by correlation between measured and predicted elution times in liquid chromatography/mass spectrometry

Reversed-phase liquid chromatography (LC) directly coupled with electrospray-tandem mass spectrometry (MS/MS) is a successful choice to obtain a large number of product ion spectra from a complex peptide mixture. We describe a search validation program, ScoreRidge, developed for analysis of LC-MS/MS data. The program validates peptide assignments to product ion spectra resulting from usual probability-based searches against primary structure databases. The validation is based only on correlation between the measured LC elution time of each peptide and the deduced elution time from the amino acid sequence assigned to product ion spectra obtained from the MS/MS analysis of the peptide. Sufficient numbers of probable assignments gave a highly correlative curve. Any peptide assignments within a certain tolerance from the correlation curve were accepted for the following arrangement step to list identified proteins. Using this data validation program, host protein candidates responsible for interaction with human hepatitis B virus core protein were identified from a partially purified protein mixture. The present simple and practical program complements protein identification from usual product ion search algorithms and reduces manual interpretation of the search result data. It will lead to more explicit protein identification from complex peptide mixtures such as whole proteome digests from tissue samples.     

Analysis of the adenovirus type 5 proteome by liquid chromatography and tandem mass spectrometry methods

We compared detection sensitivity and protein sequence coverage of the adenovirus type 5 proteome achievable by liquid chromatography and tandem mass spectroscopy (LC/MS/MS) using three sample preparation and clean up methods. Tryptic digestion was performed on either purified viral proteins or whole virus, and followed by shotgun sequencing using tandem mass spectrometry for peptide identification. We used a recombinant adenovirus type 5 as a test system. The methods included separation of adenoviral proteins by reversed-phase high-performance liquid chromatography followed by tryptic digestion and analysis by LC/MS/MS. Alternatively, the purified whole virus was digested with trypsin and the peptides separated either by one-dimensional (reversed-phase) or by two-dimensional (cation exchange and reversed-phase) chromatography and analyzed by tandem mass spectrometry. A total of 11 protein species were identified from 154 peptides. All of the major viral proteins were found. In addition, two minor proteins, the 23 kDa viral protease and the late L1 protein, were identified for the first time by chromatography based assays. The 23 kDa viral protease, present at only 10 copies per virus, and representing 0.2% of the protein content of the virus, was detected by the 2D LC/MS/MS analysis of the whole virus digest from a sample containing only 70 fmols of the protein. This demonstrates the high sensitivity and selectivity of the method. The 2D LC/MS/MS analysis of the whole virus digest was also able to detect all viral proteins with copy numbers at or above 10/virus particle, with broad coverage of the amino acid sequences. Coverage ranged from 2 to 54%, a majority between 20 and 35%, suggesting the possibility of using this analysis to assess the purity of the virus preparations. This broad coverage may also provide a useful approach to identify posttranslational modifications on the structural proteins of the adenovirus.     

Analysis of the human serum proteome

Changes in serum proteins that signal histopathological states, such as cancer, are useful diagnostic and prognostic biomarkers. Unfortunately, the large dynamic concentration range of proteins in serum makes it a challenging proteome to effectively characterize. Typically, methods to deplete highly abundant proteins to decrease this dynamic protein concentration range are employed, yet such depletion results in removal of important low abundant proteins. A multi-dimensional peptide separation strategy utilizing conventional separation techniques combined with tandem mass spectrometry (MS/MS) was employed for a proteome analysis of human serum. Serum proteins were digested with trypsin and resolved into 20 fractions by ampholyte-free liquid phase isoelectric focusing. These 20 peptide fractions were further fractionated by strong cation-exchange chromatography, each of which was analyzed by microcapillary reversed-phase liquid chromatography coupled online with MS/MS analysis. This investigation resulted in the identification of 1444 unique proteins in serum. Proteins from all functional classes, cellular localization, and abundance levels were identified. This study illustrates that a majority of lower abundance proteins identified in serum are present as secreted or shed species by cells as a result of signalling, necrosis, apoptosis, and hemolysis. These findings show that the protein content of serum is quite reflective of the overall profile of the human organism and a conventional multidimensional fractionation strategy combined with MS/MS is entirely capable of characterizing a significant fraction of the serum proteome. We have constructed a publicly available human serum proteomic database (http://bpp.nci.nih.gov) to provide a reference resource to facilitate future investigations of the vast archive of pathophysiological content in serum. These authors contributed equally to this work.     

Standardization of a sample preparation and analytical workflow for proteomics of archival endometrial cancer tissue

The goal of the present study was to establish a standard operating procedure for mass spectrometry (MS)-based proteomic analysis of laser microdissected (LMD) formalin-fixed, paraffin-embedded (FFPE) uterine tissue. High resolution bioimage analysis of a large endometrial cancer tissue microarray immunostained for the breast cancer type 1 susceptibility protein enabled precise counting of cells to establish that there is an average of 600 cells/nL of endometrial cancer tissue. We sought to characterize the peptide recovery from various volumes of tissue gathered by LMD and processed/digested using the present methodology. We observed a nearly linear increase in peptide recovery amount with increasing tissue volume dissected. There was little discernible difference in the peptide recovery from stromal versus malignant epithelium, and there was no apparent difference in the day-to-day recovery. This methodology reproducibly results in 100 ng of digested peptides per nL of endometrial tissue, or ～25 pg peptides/endometrial cancer cell. Results from liquid chromatography (LC)-MS/MS experiments to assess the impact of total peptide load on column on the total number of peptides and proteins identified from FFPE tissue digests prepared with the present methodology indicate a demonstrable increase in the total number of peptides identified up to 1000 ng, beyond which diminishing returns were observed. Furthermore, we observed no impact on the peptide identification rates from analyses of equivalent peptide amounts derived from lower volume LMD samples. These results show that this single-tube collection-to-injection proteomics (CTIP) workflow represents a straightforward, scalable, and highly reliable methodology for sample preparation to enable high throughput LMD-MS analysis of tissues derived from biopsy or surgery.     

The human plasma proteome: a nonredundant list developed by combination of four separate sources

We have merged four different views of the human plasma proteome, based on different methodologies, into a single nonredundant list of 1175 distinct gene products. The methodologies used were 1) literature search for proteins reported to occur in plasma or serum; 2) multidimensional chromatography of proteins followed by two-dimensional electrophoresis and mass spectroscopy (MS) identification of resolved proteins; 3) tryptic digestion and multidimensional chromatography of peptides followed by MS identification; and 4) tryptic digestion and multidimensional chromatography of peptides from low-molecular-mass plasma components followed by MS identification. Of 1,175 nonredundant gene products, 195 were included in more than one of the four input datasets. Only 46 appeared in all four. Predictions of signal sequence and transmembrane domain occurrence, as well as Genome Ontology annotation assignments, allowed characterization of the nonredundant list and comparison of the data sources. The "nonproteomic" literature (468 input proteins) is strongly biased toward signal sequence-containing extracellular proteins, while the three proteomics methods showed a much higher representation of cellular proteins, including nuclear, cytoplasmic, and kinesin complex proteins. Cytokines and protein hormones were almost completely absent from the proteomics data (presumably due to low abundance), while categories like DNA-binding proteins were almost entirely absent from the literature data (perhaps unexpected and therefore not sought). Most major categories of proteins in the human proteome are represented in plasma, with the distribution at successively deeper layers shifting from mostly extracellular to a distribution more like the whole (primarily cellular) proteome. The resulting nonredundant list confirms the presence of a number of interesting candidate marker proteins in plasma and serum.     

mTRAQ-based quantitative analysis combined with peptide fractionation based on cysteinyl peptide enrichment

In the present study, the fractionation scheme for cysteinyl peptide enrichment (CPE) was combined with the mass differential tags for relative and absolute quantification (mTRAQ) method to reduce sample complexity and increase proteome coverage. Cysteine residues of the proteins were first alkylated using iodoacetyl PEG₂–biotin instead of other conventional alkylating agents such as iodoacetamide. After trypsin digestion, amine groups were labeled with mTRAQ, and these labeled peptides were fractionated according to the presence or absence of cysteine residues using avidin–biotin affinity chromatography. With these approaches, we were able to divide the peptides into the two fractions with more than 90% fractionation efficiency for standard protein and MCF7 cell lysate. When the fractionation strategy was applied to colorectal cancer tissue samples, we were able to obtain quantitative information that was consistent with the previous study based on mTRAQ quantification, implying that the cysteine-based fractionation method does not affect mTRAQ quantification. We expect that the mTRAQ-based quantitative analysis combined with peptide fractionation through the CPE strategy would allow for deep-down analysis of proteome samples and ultimately for increasing proteome coverage with simultaneous quantification for biomarker discovery.     

Utility of electrophoretically derived protein mass estimates as additional constraints in proteome analysis of human serum based on MS/MS analysis

The proteome of a HUPO human serum reference sample was analyzed using multidimensional separation techniques at both the protein and the peptide levels. To eliminate false-positive identifications from the search results, we employed a data filtering method using molecular weight (MW) correlations derived from denaturing 1-DE. First, the six most abundant serum proteins were removed from the sample using immunoaffinity chromatography. 1-DE was then used to fractionate the remaining serum proteins according to the MW. Gel bands were isolated and in-gel digested with trypsin, and the resulting peptides were analyzed by 2-D LC/ESI-MS/MS. A SEQUEST search using the MS/MS results identified 494 proteins. Of these, 202 were excluded formally using protein data filtering as they were single-assignment proteins and their theoretical and electrophoretically-derived MWs did not correlate at high confidence. To evaluate this method, the results were compared with those of 1-D LC/MALDI-TOF/TOF and HUPO Plasma Proteome Project analyses. Our data filtering approach proved valuable in analysis of complex, large-scale proteomes such as human serum.     

The turnover kinetics of major histocompatibility complex peptides of human cancer cells

Peptides presented by the major histocompatibility complex (MHC) are derived from the degradation of cellular proteins. Thus, the repertoire of these peptides (the MHC peptidome) should correlate better with the cellular protein degradation scheme (the degradome) than with the cellular proteome. To test the validity of this statement and to determine whether the majority of MHC peptides are derived from short lived proteins, from defective ribosome products, or from regular long lived cellular proteins we analyzed in parallel the turnover kinetics of both MHC peptides and cellular proteins in the same cancer cells. The analysis was performed by pulse-chase experiments based on stable isotope labeling in tissue culture followed by capillary chromatography and tandem mass spectrometry. Indeed only a limited correlation was observed between the proteome and the MHC peptidome observed in the same cells. Moreover a detailed analysis of the turnover kinetics of the MHC peptides helped to assign their origin to normal, to short lived or long lived proteins, or to the defective ribosome products. Furthermore the analysis of the MHC peptides turnover kinetics helped to direct attention to abnormalities in the degradation schemes of their source proteins. These observations can be extended to search for cancer-related abnormalities in protein degradation, including those that lead to loss of tumor suppressors and cell cycle regulatory proteins.     

A Heuristic method for assigning a false-discovery rate for protein identifications from Mascot database search results

MS/MS and database searching has emerged as a valuable technology for rapidly analyzing protein expression, localization, and post-translational modifications. The probability-based search engine Mascot has found widespread use as a tool to correlate tandem mass spectra with peptides in a sequence database. Although the Mascot scoring algorithm provides a probability-based model for peptide identification, the independent peptide scores do not correlate with the significance of the proteins to which they match. Herein, we describe a heuristic method for organizing proteins identified at a specified false-discovery rate using Mascot-matched peptides. We call this method PROVALT, and it uses peptide matches from a random database to calculate false-discovery rates for protein identifications and reduces a complex list of peptide matches to a nonredundant list of homologous protein groups. This method was evaluated using Mascot-identified peptides from a Trypanosoma cruzi epimastigote whole-cell lysate, which was separated by multidimensional LC and analyzed by MS/MS. PROVALT was then compared with the two traditional methods of protein identification when using Mascot, the single peptide score and cumulative protein score methods, and was shown to be superior to both in regards to the number of proteins identified and the inclusion of lower scoring nonrandom peptide matches.     

Automated in-solution protein digestion using a commonly available high-performance liquid chromatography autosampler

A completely automated peptide mapping liquid chromatography/mass spectrometry (LC/MS) system for characterization of therapeutic proteins in which a common high-performance liquid chromatography (HPLC) autosampler is used for automated sample preparation, including protein denaturation, reduction, alkylation, and enzymatic digestion, is described. The digested protein samples are then automatically subjected to LC/MS analysis using the same HPLC system. The system was used for peptide mapping of monoclonal antibodies (mAbs), known as a challenging group of therapeutic proteins for achieving complete coverage and quantitative representation of all peptides. Detailed sample preparation protocols, using an Agilent HPLC system, are described for Lys-C digestion of mAbs with intact disulfide bonds and tryptic digestion of mAbs after reduction and alkylation. The automated procedure of Lys-C digestion of nonreduced antibody, followed by postdigestion disulfide reduction, produces both the nonreduced and reduced digests that facilitate disulfide linkage analysis. The automated peptide mapping LC/MS system has great utility in preparing and analyzing multiple samples for protein characterization, identification, and quantification of posttranslational modifications during process and formulation development as well as for protein identity and quality control.     

Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 16O/18O labeling and the accurate mass and time tag approach

Identification of novel diagnostic or therapeutic biomarkers from human blood plasma would benefit significantly from quantitative measurements of the proteome constituents over a range of physiological conditions. Herein we describe an initial demonstration of proteome-wide quantitative analysis of human plasma. The approach utilizes postdigestion trypsin-catalyzed 16O/18O peptide labeling, two-dimensional LC-FTICR mass spectrometry, and the accurate mass and time (AMT) tag strategy to identify and quantify peptides/proteins from complex samples. A peptide accurate mass and LC elution time AMT tag data base was initially generated using MS/MS following extensive multidimensional LC separations to provide the basis for subsequent peptide identifications. The AMT tag data base contains >8,000 putative identified peptides, providing 938 confident plasma protein identifications. The quantitative approach was applied without depletion of high abundance proteins for comparative analyses of plasma samples from an individual prior to and 9 h after lipopolysaccharide (LPS) administration. Accurate quantification of changes in protein abundance was demonstrated by both 1:1 labeling of control plasma and the comparison between the plasma samples following LPS administration. A total of 429 distinct plasma proteins were quantified from the comparative analyses, and the protein abundances for 25 proteins, including several known inflammatory response mediators, were observed to change significantly following LPS administration.     

Quantitative proteome analysis of breast cancer cell lines using 18O-labeling and an accurate mass and time tag strategy

Proteome comparison of cell lines derived from cancer and normal breast epithelium provide opportunities to identify differentially expressed proteins and pathways associated with specific phenotypes. We employed 16O/18O peptide labeling, FT-ICR MS, and an accurate mass and time (AMT) tag strategy to simultaneously compare the relative abundance of hundreds of proteins in non-cancer and cancer cell lines derived from breast tissue. A cell line reference panel allowed relative protein abundance comparisons among multiple cell lines and across multiple experiments. A peptide database generated from multidimensional LC separations and MS/MS analysis was used for subsequent AMT tag-based peptide identifications. This peptide database represented a total of 2299 proteins, including 514 that were quantified in five cell lines using the AMT tag and 16O/18O strategies. Eighty-six proteins showed at least a threefold protein abundance change between cancer and non-cancer cell lines. Hierarchical clustering of protein abundance ratios revealed that several groups of proteins were differentially expressed between the cancer cell lines.     

Mass spectrometry in the analysis of grape and wine proteins

Wine proteins play an important role in the quality of wine, because they affect taste, clarity and stability of product. The majority of wine proteins are in the range of 20-30 kDa. Different mass spectrometry (MS) techniques have been successfully applied to study the grape and wine proteins. By liquid chromatography (LC) electrospray ionization (ESI) MS and nano-LC/MS, nine dipeptides and 80 peptides were unambiguously identified in Champagne and Sauvignon Blanc wines, respectively. Using matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) and surface-enhanced laser desorption/ionization TOF, the protein and peptide fingerprints in Chardonnay, Sauvignon Blanc and Muscat of Alexandria wines were determined. MALDI-TOF identified the mesocarp proteome of six Vitis grape varieties. Proteins in different grape tissue extracts were also studied. The major grape pathogenic-related proteins are chitinases and thaumatin-like proteins, which both persist through the vinification process and cause hazes and sediments in bottled wines. ESI-MS, LC/ESI-MS and MALDI-TOF analysis of these proteins in grape and wine were also used to characterize different grape varieties.