MudPIT: multidimensional protein identification technology

Large-scale biology emerged out of the efforts to sequence genomes of important organisms. Based on resources created by whole genome sequencing, large-scale analyses of messenger RNA (mRNA) and protein expression are now possible. With the availability of large amounts of genomic sequence information, a convenient method for the identification and analysis of proteins based on proteolytic digestion into peptides emerged. Processes to fragment peptides using collision-activated dissociation (CAD) in tandem mass spectrometers and computer algorithms to match the tandem mass spectra of peptides to sequences in databases enable rapid identification of amino acid sequences, and hence proteins, present in mixtures. The inherent complexity of the peptide mixtures has necessitated improvements in methodology for mass spectrometry (MS) analysis of peptides.     

Large scale protein profiling by combination of protein fractionation and multidimensional protein identification technology (MudPIT)

In the past decade, shotgun proteomic analysis has been utilized extensively to answer complex biological questions. New challenges arise in large scale proteomic profiling when dealing with complex biological mixtures such as the mammalian cell lysate. In this study, we explored the approach of protein separation prior to the shotgun multidimensional protein identification technology (MudPIT) analysis. We fractionated the mammalian cancer cell lysate using the PF 2D ProteomeLab system and analyzed the distribution of molecular weight, isoelectric point, and cellular localization of the eluted proteins. As a result, we were able to reduce sample complexity by protein fractionation and increase the possibility of detecting proteins with lower abundance in the complex protein mixture.     

Principles and applications of multidimensional protein identification technology

Multidimensional chromatography coupled to tandem mass spectrometry is an emerging technique for the analysis of proteomes and is rapidly being implemented by many researchers for proteomic analysis. In this technology profile, a particular proteomic approach known as multidimensional protein identification technology (MudPIT) is discussed. In MudPIT, a biphasic microcapillary column is packed with high-performance liquid chromatography grade reversed phase and strong cation exchange packing materials, loaded with a complex peptide mixture and placed in line with quaternary high-performance liquid chromatography and a tandem mass spectrometer. MudPIT has the capability to analyze highly complex proteomic mixtures such as whole proteomes, organelles and protein complexes.     

Arabidopsis cell wall proteome defined using multidimensional protein identification technology

With the completion of the sequencing of the Arabidopsis genome and the recent advances in proteomic technology, the identification of proteins from highly complex mixtures is now possible. Rather than using gel electrophoresis and peptide mass fingerprinting, we have used multidimensional protein identification technology (MudPIT) to analyse the "tightly-bound" proteome for purified cell walls from Arabidopsis cell suspension cultures. Using bioinformatics for the prediction of signal peptides for targeting to the secretory pathway and for the absence of ER retention signal, 89 proteins were selected as potential extracellular proteins. Only 33% of these were identified in previous proteomic analyses of Arabidopsis cell walls. A functional classification revealed that a large proportion of the proteins were enzymes, notably carbohydrate active enzymes, peroxidases and proteases. Comparison of all the published proteomic analyses for the Arabidopsis cell wall identified 268 non-redundant genes encoding wall proteins. Sixty of these (22%) were derived from our analysis of tightly-bound wall proteins.     

Rapid enrichment of bioactive milk proteins and iterative, consolidated protein identification by multidimensional protein identification technology

Direct injection of complex protein mixtures, e.g. those derived from crude biological fluids, is often incompatible with conventional LC supports, because of column clogging and rapid deterioration of chromatographic performance. In this paper, we report the use of restricted access media to rapidly enrich and fractionate human breast milk. This resin, combining size exclusion and anion exchange functionalities, yielded a fraction enriched in soluble CD14 and showing specific sCD14-dependant activity. This fraction was split into five aliquots, which were individually characterized using multidimensional protein identification technology. Reproducibility of the results was addressed by analysing and comparing five datasets using different protein identification tools available within the Sequest software. Furthermore, a comparison of three major releases of the Ensembl human protein database was performed to examine the effect of database updates on our results. We report here the benefit of repeated analysis of aliquots of the same fraction: first to increase the confidence in peptide identification by repeated confirmation in several aliquots; and second to assess experimental reproducibility. We demonstrate furthermore the effect of database modifications on the results and the importance of constantly re-analysing data with new releases to keep them consistent and up to date with the latest protein identities and predictions available.     

Proteomic analysis of urine exosomes by multidimensional protein identification technology (MudPIT)

Exosomes are membrane vesicles that are secreted by cells upon fusion of multivesicular bodies with the plasma membrane. Exosomal proteomics has emerged as a powerful approach to understand the molecular composition of exosomes and has potential to accelerate biomarker discovery. Different proteomic analysis methods have been previously employed to establish several exosome protein databases. In this study, TFE solution-phase digestion was compared with in-gel digestion and found to yield similar results. Proteomic analysis of urinary exosomes was performed by multidimensional protein identification technology (MudPIT) after TFE digestion. Nearly, 3280 proteins were identified from nine human urine samples with 31% overlap among nine samples. Gene ontology (GO) analysis, coupled with detection of all of the members of ESCRT machinery complex, supports the multivesicular origin of these particles. These results significantly expand the existing database of urinary exosome proteins. Our results also indicate that more than 1000 proteins can be detected from exosomes prepared from as little as 25 mL of urine. This study provides the largest set of proteins present in human urinary exosome proteomes, provides a valuable reference for future studies, and provides methods that can be applied to exosomal proteomic analysis from other tissue sources.     

Identification of pathways associated with invasive behavior by ovarian cancer cells using multidimensional protein identification technology (MudPIT)

Proteomic profiling has emerged as a useful tool for identifying tissue alterations in disease states including malignant transformation. The aim of this study was to reveal expression profiles associated with the highly motile/invasive ovarian cancer cell phenotype. Six ovarian cancer cell lines were subjected to proteomic characterization using multidimensional protein identification technology (MudPIT), and evaluated for their motile/invasive behavior, so that these parameters could be compared. Within whole cell extracts of the ovarian cancer cells, MudPIT identified proteins that mapped to 2245 unique genes. Western blot analysis for selected proteins confirmed the expression profiles revealed by MudPIT, demonstrating the fidelity of this high-throughput analysis. Unsupervised cluster analysis partitioned the cell lines in a manner that reflected their motile/invasive capacity. A comparison of protein expression profiles between cell lines of high (group 1) versus low (group 2) motile/invasive capacity revealed 300 proteins that were differentially expressed, of which 196 proteins were significantly upregulated in group 1. Protein network and KEGG pathway analysis indicated a functional interplay between proteins up-regulated in group 1 cells, with increased expression of several key members of the actin cytoskeleton, extracellular matrix (ECM) and focal adhesion pathways. These proteomic expression profiles can be utilized to distinguish highly motile, aggressive ovarian cancer cells from lesser invasive ones, and could prove to be essential in the development of more effective strategies that target pivotal cell signaling pathways used by cancer cells during local invasion and distant metastasis.     

Ligand profiling and identification technology for searching bioactive ligands

We introduce a new methodology named ligand profiling and identification for effective discovery of bioactive ligands such as peptide hormones. This technology was developed from a new concept of parallel column chromatography and active fraction profiling by nano-LC MS. Traditional methods use sequential column chromatography, and thus are inevitably limited by the low abundance of the peptide of interest and by a low yield due to the many column steps. Using this new technology, insulin was successfully identified and diarginylinsulin, a minor intermediate form of insulin, was unexpectedly also identified simultaneously from 100 mg of porcine pancreatic tissue. This integrative technology could be used to search for various low-abundance peptides (or bioactive molecules) rapidly and simultaneously, by applying this to the later stages of traditional sequential purification.     

Comprehensive proteome analysis of malignant pleural effusion for lung cancer biomarker discovery by using multidimensional protein identification technology

Malignant pleural effusion (MPE) obtained from lung adenocarcinoma may contain potentially useful biomarkers for detection of lung cancer. In this study, we used a removal system for high-abundance proteins followed by one-dimensional SDS-PAGE combined with nano-LC-MS/MS to generate a comprehensive MPE proteome data set with 482 nonredundant proteins. Next, we integrated the MPE proteome and secretome data sets from three adenocarcinoma cell lines, with a view to identifying potential PE biomarkers originating from malignant cells. Four potential candidates, alpha-2-HS-glycoprotein (AHSG), angiogenin, cystatin-C, and insulin-like growth factor-binding protein 2, (IGFBP2), were isolated for preclinical validation using ELISA. Both AHSG and IGFBP2 levels were increased in lung patients with MPE (n = 68), compared to those with nonmalignant pleural effusion (n = 119). Notably, the IGFBP2 level was higher in MPE, compared with that in benign diseases (bacteria pneumonia and tuberculosis pleuritis), and significantly associated with malignancy, regardless of the cancer type. Our data additionally support an extracellular function of IGFBP2 in migration in lung cancer cells. These findings collectively suggest that the adenocarcinoma MPE proteome provides a useful data set for malignancy biomarker research.     

Evaluation of reproducibility of protein identification results after multidimensional human serum protein separation

We developed a gel- and label-free proteomics platform for comparative studies of human serum. The method involves the depletion of the six most abundant proteins, protein fractionation by Off-Gel IEF and RP-HPLC, followed by tryptic digestion, LC-MS/MS, protein identification, and relative quantification using probabilistic peptide match score summation (PMSS). We evaluated performance and reproducibility of the complete platform and the individual dimensions, by using chromatograms of the RP-HPLC runs, PMSS based abundance scores and abundance distributions as objective endpoints. We were interested if a relationship exists between the quantity ratio and the PMSS score ratio. The complete analysis was performed four times with two sets of serum samples containing different concentrations of spiked bovine beta-lactoglobulin (0.1 and 0.3%, w/w). The two concentrations resulted in significantly differing PMSS scores when compared to the variability in PMSS scores of all other protein identifications. We identified 196 proteins, of which 116 were identified four times in corresponding fractions whereof 73 qualified for relative quantification. Finally, we characterized the PMSS based protein abundance distributions with respect to the two dimensions of fractionation and discussed some interesting patterns representing discrete isoforms. We conclude that combination of Off-Gel electrophoresis (OGE) and HPLC is a reproducible protein fractionation technique, that PMSS is applicable for relative quantification, that the number of quantifiable proteins is always smaller than the number of identified proteins and that reproducibility of protein identifications should supplement probabilistic acceptance criteria.     

Characterization of the human submandibular/sublingual saliva glycoproteome using lectin affinity chromatography coupled to multidimensional protein identification technology

In-depth analysis of the salivary proteome is fundamental to understanding the functions of salivary proteins in the oral cavity and to reveal disease biomarkers involved in different pathophysiological conditions, with the ultimate goal of improving patient diagnosis and prognosis. Submandibular and sublingual glands contribute saliva rich in glycoproteins to the total saliva output, making them valuable sources for glycoproteomic analysis. Lectin-affinity chromatography coupled to mass spectrometry-based shotgun proteomics was used to explore the submandibular/sublingual (SM/SL) saliva glycoproteome. A total of 262 N- and O-linked glycoproteins were identified by multidimensional protein identification technology (MudPIT). Only 38 were previously described in SM and SL salivas from the human salivary N-linked glycoproteome, while 224 were unique. Further comparison analysis with SM/SL saliva of the human saliva proteome, revealed 125 glycoproteins not formerly reported in this secretion. KEGG pathway analyses demonstrated that many of these glycoproteins are involved in processes such as complement and coagulation cascades, cell communication, glycosphingolipid biosynthesis neo-lactoseries, O-glycan biosynthesis, glycan structures-biosynthesis 2, starch and sucrose metabolism, peptidoglycan biosynthesis or others pathways. In summary, lectin-affinity chromatography coupled to MudPIT mass spectrometry identified many novel glycoproteins in SM/SL saliva. These new additions to the salivary proteome may prove to be a critical step for providing reliable biomarkers in the diagnosis of a myriad of oral and systemic diseases.     

Serological protein profiling of neuroblastoma by ProteinChip SELDI-TOF technology

Serological proteins of neuroblastoma were profiled and analyzed by ProteinChip-SELDI-TOF MS technology with five types of protein chips. By comparing with normal control, a number of protein or polypeptide signals were found significantly and consistently different in their intensities (expression levels) in tumor sera. Interestingly, nine polypeptide peaks in these proteomic features can be simultaneously detected with consistent variations by more than one type of protein chips. None of the expression differences of these nine polypeptides was found in similar comparisons between healthy controls and hepatomas. Preliminary protein identification showed hints for that some of these proteomic alterations may be closely related to the tumorigenesis of neuroblastoma. These results demonstrated the potential of serological biomarker identification for neuroblastoma by ProteinChip-SELDI technology.     

Recent advances in protein profiling of tissues and tissue fluids

Creating protein profiles of tissues and tissue fluids, which contain secreted proteins and peptides released from various cells, is critical for biomarker discovery as well as drug and vaccine target selection. It is extremely difficult to obtain pure samples from tissues or tissue fluids, however, and identification of complex protein mixtures is still a challenge for mass spectrometry analysis. Here, we summarize recent advances in techniques for extracting proteins from tissues for mass spectrometry profiling and imaging. We also introduce a novel technique using a capillary ultrafiltration (CUF) probe to enable in vivo collection of proteins from the tissue microenvironment. The CUF probe technique is compared with existing sampling techniques, including perfusion, saline wash, fine-needle aspiration and microdialysis. In this review, we also highlight quantitative mass spectrometric proteomic approaches with, and without, stable-isotope labels. Advances in quantitative proteomics will significantly improve protein profiling of tissue and tissue fluid samples collected by CUF probes.     

Human adipose tissue hormone-sensitive lipase: identification and comparison with other species

The mRNA for human hormone-sensitive lipase (HSL) was identified using Northern blot analysis and a cDNA-probe for rat HSL. As in the rat, human adipose tissue expresses a single mRNA species of 3.3 kb. Using Western blotting with a polyclonal rabbit antibody towards rat adipose tissue HSL, the corresponding enzyme in human adipose tissue was identified with an apparent 88 kDa polypeptide, thus slightly larger than the rat and bovine 84 kDa, and the mouse and guinea-pig 82 kDa species. Additional evidence for the identification was provided by the inhibition of HSL diacylglycerol lipase activity by the anti-rat HSL antibody, and by NaF, DFP and Hg2+, known inhibitors of HSL. The concentration of the enzyme, as reflected by its activity per g tissue and the specific activity was about two thirds of that in the rat adipose tissue (200 g rats). The identification of the human enzyme protein made it possible to directly demonstrate its phosphorylation by cAMP-dependent protein kinase, thus extending the previous report regarding activation of the lipase with this kinase and ATP-Mg2+ in human adipose tissue extracts (Khoo, J.C., Aquino, A.A. and Steinberg, D. (1974) J. Clin. Invest. 53, 1124-1131).     

Rapid identification of dinoflagellates using protein profiling with matrix-assisted laser desorption/ionization mass spectrometry

The occurrence of harmful algal blooms (HABs) or red tides is an important and expanding threat to human health, fishery resources, and the tourism industries. Toxic species post an additional treat of intoxication when consumed either in seafood or directly swallowed. Rapid and accurate identification of the HAB species is critical for minimizing or controlling the damage. We report the use of protein/peptide mass fingerprint profiles obtained with matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) for the identification of dinoflagellates, common causative agents of HABs. The method is simple, fast and reproducible. The peptide mass fingerprint spectral patterns are unique for different dinoflagellate species and are easily distinguishable by visual inspection. In addition to the whole mass spectra, several specific biomarkers were identified from the mass spectra of different species. These biomarker ions and the mass spectral patterns form an unambiguous basis for species discrimination.     

Development of an all-in-one technology for glycan profiling targeting formalin-embedded tissue sections

An ultra-sensitive method for glycan analysis targeting small tissue sections (1.5 mm in diameter) is described as an application of a recently-established lectin microarray technology. The developed system achieved a high level of detection of a tissue section consisting of approximately 500 cells for differential profiling, where both N- and O-glycans attached to a pool of glycoproteins are subjected to multiplex analysis with 43 lectins. By using an optimized protocol for differential glycan analysis, sections of adenocarcinoma (n = 28) and normal epithelia (n = 12) of the colon were analyzed in an all-in-one manner. As a result, Wisteria floribunda agglutinin (WFA) was found to clearly differentiate cancerous from normal epithelia with P < 0.0001. The obtained results correlated well with the subsequent histochemical study using biotinylated WFA. Thus, the developed technology proved to be valid for expanding the lectin microarray applications to tissue-based glycomics, and hence, should accelerate a discovery phase of glycan-related biomarkers.     

Biomarker discovery using protein microarray technology platforms: antibody-antigen complex profiling

Protein microarrays represent an important new tool in proteomic systems biology. This review focuses on the contributions of protein microarrays to the discovery of novel disease biomarkers through antibody-based assays. Of particular interest is the use of protein microarrays for immune response profiling, through which a disease-specific antibody repertoire may be defined. The antigens and antibodies revealed by these studies are useful for clinical assay development, with enormous potential to aid in diagnosis, prognosis, disease staging and treatment selection. The discovery and characterization of novel biomarkers specifically tailored to disease type and stage are expected to enable personalized medicine by facilitating preventative medicine, predictive diagnostics and individualized curative therapies.