The use of mass spectrometry for the proteomic analysis of glycosylation

Of all protein PTMs, glycosylation is by far the most common, and is a target for proteomic research. Glycosylation plays key roles in controlling various cellular processes and the modifications of the glycan structures in diseases highlight the clinical importance of this PTM. Glycosylation analysis remains a difficult task. MS, in combination with modern separation methodologies, is one of the most powerful and versatile techniques for the structural analysis of glycoconjugates. This review describes methodologies based on MS for detailed characterization of glycoconjugates in complex biological samples at the sensitivity required for proteomic work.     

Data mining in proteomic mass spectrometry

Data mining application to proteomic data from mass spectrometry has gained much interest in recent years. Advances made in proteomics and mass spectrometry have resulted in considerable amount of data that cannot be easily visualized or interpreted. Mass spectral proteomic datasets are typically high dimensional but with small sample size. Consequently, advanced artificial intelligence and machine learning algorithms are increasingly being used for knowledge discovery from such datasets. Their overall goal is to extract useful information that leads to the identification of protein biomarker candidates. Such biomarkers could potentially have diagnostic value as tools for early detection, diagnosis, and prognosis of many diseases. The purpose of this review is to focus on the current trends in mining mass spectral proteomic data. Special emphasis is placed on the critical steps involved in the analysis of surface-enhanced laser desorption/ionization mass spectrometry proteomic data. Examples are drawn from previously published studies and relevant data mining terminology and techniques are exlained.     

Stable isotope-coded proteomic mass spectrometry

Developing the ability to quantify changes in protein abundance between cells subjected to a variety of physiological and environmental conditions is an extremely active area of proteome research. Although advances in chromatography, mass spectrometry instrumentation, and bioinformatics have contributed to producing a viable method for comparative proteome-wide analyses, the highest precision of quantitation is based, in part, upon improved methods for chemical and metabolic stable isotope labeling of proteins and peptides. The ability to quantify differences in protein expression and post-translational modifications using stable isotope labeling has been achieved, but insights into the biochemical mechanisms that will contribute to the development of new biotechnologies have yet to be realized.     

Bayesian analysis of mass spectrometry proteomic data using wavelet-based functional mixed models

Summary .   In this article, we apply the recently developed Bayesian wavelet-based functional mixed model methodology to analyze MALDI-TOF mass spectrometry proteomic data. By modeling mass spectra as functions, this approach avoids reliance on peak detection methods. The flexibility of this framework in modeling nonparametric fixed and random effect functions enables it to model the effects of multiple factors simultaneously, allowing one to perform inference on multiple factors of interest using the same model fit, while adjusting for clinical or experimental covariates that may affect both the intensities and locations of peaks in the spectra. For example, this provides a straightforward way to account for systematic block and batch effects that characterize these data. From the model output, we identify spectral regions that are differentially expressed across experimental conditions, in a way that takes both statistical and clinical significance into account and controls the Bayesian false discovery rate to a prespecified level. We apply this method to two cancer studies.     

Semiquantitative proteomic analysis of rat forebrain postsynaptic density fractions by mass spectrometry

The postsynaptic density (PSD) of central excitatory synapses plays a key role in postsynaptic signal transduction and contains a high concentration of glutamate receptors and associated scaffold and signaling proteins. We report here a comprehensive analysis of purified PSD fractions by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). We identified 374 different proteins that copurified with the PSD structure and discovered thirteen phosphorylated sites from eight proteins. These proteins were classified into numerous functional groups, implying that the signaling pathways in the PSD are complex and diverse. Furthermore, using quantitative mass spectrometry, we measured the molar concentration and relative stoichiometries of a number of glutamate receptor subunits and scaffold proteins in the postsynaptic density. Thus this proteomic study reveals crucial information about molecular abundance as well as molecular diversity in the PSD, and provides a basis for further studies on the molecular mechanisms of synaptic function and plasticity.     

Mass spectrometry in plant proteomic analysis

Abstract

The current revolution in proteomics has been generated by the combination of very sensitive mass spectrometers coupled to microcapillary liquid chromatography, specific proteolysis of protein mixtures and software that is capable of searching vast numbers of mass measurements against predicted peptides from sequenced genomes. The challenges of post‐genomic plant biology include characterization of protein function, post‐translational modifications and composition of protein complexes as well as deciphering protein complements in intracellular compartments – proteomes of cell organelles. In this review we summarize the current mass spectrometry methods currently being used in plant proteomics and discuss the various tagging strategies that are being used for purification and proteomic analysis of plant protein complexes.

Abbreviations: BCCD, biotin carboxyl carrier protein domain; CBP, calmodulin‐binding protein; CID, collision‐induced dissociation; ESI, electrospray ionization; EST, expressed sequence tag; FT‐ICR, Fourier transform ion cyclotron resonance; GFP, green fluorescent protein; GST, glutathione S‐transferase; HA, haemagglutinin; HILEP, hydroponic isotope labelling of entire plants; His, histidine; HPB, HA–PreScission–Biotin; HPLC, high‐performance liquid chromatography; ICAT, isotope‐coded affinity tags; ICPL, isotope‐coded protein label; iTRAQ, isobaric tag for relative and absolute quantification; LC, liquid chromatography; MALDI, matrix‐assisted laser desorption ionization; MBP, maltose‐binding protein; MS, mass spectrometry; SDS‐PAGE, sodium dodecyl sulphate‐polyacrylamide gel electrophoresis; SILAC, stable isotope labelling with amino acids in cell culture; SILIP, stable isotope labelling in planta; Strep, streptavidin; TAP, tandem affinity purification; TBP, TATA‐box‐binding protein; TOF, time‐of‐flight; UPLC, ultraperformance liquid chromatography     

Algorithms for alignment of mass spectrometry proteomic data

MOTIVATION: The analysis of biological samples with high-throughput mass spectrometers has increased greatly in recent years. As larger datasets are processed, it is important that the spectra are aligned to ensure that the same protein intensities are correctly identified in each sample. Without such an alignment procedure it is possible to make errors in identifying the signals from peptides with similar molecular weight. Two algorithms are provided that can improve the alignment among samples. One algorithm is designed to work with SELDI data produced from a Ciphergen instrument, and the other can be used with data in a more general format. RESULTS: The two algorithms were applied to samples drawn from a common pool of reference serum. The results indicate substantial improvement in consistently identifying peptide signals in different samples.     

High-throughput proteomic analysis of formalin-fixed paraffin-embedded tissue microarrays using MALDI imaging mass spectrometry

A novel method for high-throughput proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue microarrays (TMA) is described using on-tissue tryptic digestion followed by MALDI imaging MS. A TMA section containing 112 needle core biopsies from lung-tumor patients was analyzed using MS and the data were correlated to a serial hematoxylin and eosin (H&E)-stained section having various histological regions marked, including cancer, non-cancer, and normal ones. By correlating each mass spectrum to a defined histological region, statistical classification models were generated that can sufficiently distinguish biopsies from adenocarcinoma from squamous cell carcinoma biopsies. These classification models were built using a training set of biopsies in the TMA and were then validated on the remaining biopsies. Peptide markers of interest were identified directly from the TMA section using MALDI MS/MS sequence analysis. The ability to detect and characterize tumor marker proteins for a large cohort of FFPE samples in a high-throughput approach will be of significant benefit not only to investigators studying tumor biology, but also to clinicians for diagnostic and prognostic purposes.     

Enrichment of integral membrane proteins for proteomic analysis using liquid chromatography-tandem mass spectrometry

An increasing number of proteomic strategies rely on liquid chromatography-tandem mass spectrometry (LC-MS/MS) to detect and identify constituent peptides of enzymatically digested proteins obtained from various organisms and cell types. However, sample preparation methods for isolating membrane proteins typically involve the use of detergents and chaotropes that often interfere with chromatographic separation and/or electrospray ionization. To address this problem, a sample preparation method combining carbonate extraction, surfactant-free organic solvent-assisted solubilization, and proteolysis was developed and demonstrated to target the membrane subproteome of Deinococcus radiodurans. Out of 503 proteins identified, 135 were recognized as hydrophobic on the basis of their calculated hydropathy values (GRAVY index), corresponding to coverage of 15% of the predicted hydrophobic proteome. Using the PSORT algorithm, 53 of the proteins identified were classified as integral outer membrane proteins and 215 were classified as integral cytoplasmic membrane proteins. All identified integral cytoplasmic membrane proteins had from 1 to 16 mapped transmembrane domains (TMDs), and 65% of those containing four or more mapped TMDs were identified by at least one hydrophobic membrane spanning peptide. The extensive coverage of the membrane subproteome (24%) by identification of highly hydrophobic proteins containing multiple TMDs validates the efficacy of the described sample preparation technique to isolate and solubilize hydrophobic integral membrane proteins from complex protein mixtures.     

Clinical mass spectrometry proteomic diagnosis by conformal predictors

The paper describes an application of conformal predictors to diagnose breast cancer using proteomic mass spectrometry data provided by Leiden University Medical Center. Unlike many conventional classification systems, this approach allows us not just to classify samples, but add valid measures of confidence in our predictions for individual patients.     

MALDI imaging mass spectrometry for in situ proteomic analysis of preneoplastic lesions in pancreatic cancer

The identification of new biomarkers for preneoplastic pancreatic lesions (PanINs, IPMNs) and early pancreatic ductal adenocarcinoma (PDAC) is crucial due to the diseases high mortality rate upon late detection. To address this task we used the novel technique of matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) on genetically engineered mouse models (GEM) of pancreatic cancer. Various GEM were analyzed with MALDI IMS to investigate the peptide/protein-expression pattern of precursor lesions in comparison to normal pancreas and PDAC with cellular resolution. Statistical analysis revealed several discriminative m/z-species between normal and diseased tissue. Intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasm (IPMN) could be distinguished from normal pancreatic tissue and PDAC by 26 significant m/z-species. Among these m/z-species, we identified Albumin and Thymosin-beta 4 by liquid chromatography and tandem mass spectrometry (LC-MS/MS), which were further validated by immunohistochemistry, western blot, quantitative RT-PCR and ELISA in both murine and human tissue. Thymosin-beta 4 was found significantly increased in sera of mice with PanIN lesions. Upregulated PanIN expression of Albumin was accompanied by increased expression of liver-restricted genes suggesting a hepatic transdifferentiation program of preneoplastic cells. In conclusion we show that GEM of endogenous PDAC are a suitable model system for MALDI-IMS and subsequent LC-MS/MS analysis, allowing in situ analysis of small precursor lesions and identification of differentially expressed peptides and proteins.     

The proteomic future: where mass spectrometry should be taking us

A newcomer to the -omics era, proteomics, is a broad instrument-intensive research area that has advanced rapidly since its inception less than 20 years ago. Although the 'wet-bench' aspects of proteomics have undergone a renaissance with the improvement in protein and peptide separation techniques, including various improvements in two-dimensional gel electrophoresis and gel-free or off-gel protein focusing, it has been the seminal advances in MS that have led to the ascension of this field. Recent improvements in sensitivity, mass accuracy and fragmentation have led to achievements previously only dreamed of, including whole-proteome identification, and quantification and extensive mapping of specific PTMs (post-translational modifications). With such capabilities at present, one might conclude that proteomics has already reached its zenith; however, 'capability' indicates that the envisioned goals have not yet been achieved. In the present review we focus on what we perceive as the areas requiring more attention to achieve the improvements in workflow and instrumentation that will bridge the gap between capability and achievement for at least most proteomes and PTMs. Additionally, it is essential that we extend our ability to understand protein structures, interactions and localizations. Towards these ends, we briefly focus on selected methods and research areas where we anticipate the next wave of proteomic advances.     

MALDI-TOF mass spectrometry proteomic phenotyping of clinically relevant fungi

Proteomics is particularly suitable for characterising human pathogens with high life cycle complexity, such as fungi. Protein content and expression levels may be affected by growth states and life cycle morphs and correlate to species and strain variation. Identification and typing of fungi by conventional methods are often difficult, time-consuming and frequently, for unusual species, inconclusive. Proteomic phenotypes from MALDI-TOF MS were employed as analytical and typing expression profiling of yeast, yeast-like species and strain variants in order to achieve a microbial proteomics population study. Spectra from 303 clinical isolates were generated and processed by standard pattern matching with a MALDI-TOF Biotyper (MT). Identifications (IDs) were compared to a reference biochemical-based system (Vitek-2) and, when discordant, MT IDs were verified with genotyping IDs, obtained by sequencing the 25-28S rRNA hypervariable D2 region. Spectra were converted into virtual gel-like formats, and hierarchical clustering analysis was performed for 274 Candida profiles to investigate species and strain typing correlation. MT provided 257/303 IDs consistent with Vitek-2 ones. However, amongst 26/303 discordant MT IDs, only 5 appeared "true". No MT identification was achieved for 20/303 isolates for incompleteness of database species variants. Candida spectra clustering agreed with identified species and topology of Candida albicans and Candida parapsilosis specific dendrograms. MT IDs show a high analytical performance and profiling heterogeneity which seems to complement or even outclass existing typing tools. This variability reflects the high biological complexity of yeasts and may be properly exploited to provide epidemiological tracing and infection dispersion patterns.     

Fluorescence two-dimensional difference gel electrophoresis and mass spectrometry based proteomic analysis of Escherichia coli

Separation and relative quantitation of complex protein mixtures remain two of the most challenging aspects of proteomics. Here an advanced technique called fluorescence difference 2-D gel electrophoresis technology (2D-DIGE) has been applied to a model system study of the Escherichia coli proteome after benzoic acid treatment. The molecular weight and charge matched cyanine dyes enable pre-electrophoretic labelling of control and treated samples which are then mixed and run in the same gel. Pooled control and treated samples labelled with Cy trade mark 3 were used as an internal standard for both Cy5 labelled control and treated E. coli samples. Together with DeCyder trade mark imaging analysis software, more accurate quantitative analysis than conventional two-dimensional polyacrylamide gel electrophoresis was achieved. Using matrix-assisted laser desorption/ionization-time of flight and quadrupole-time of flight mass spectrometry a total of 179 differentially expressed protein spots were identified. These included enzymes, stress related and substrate (e.g. amino acids, maltose, ribose and TRP repressor) binding proteins. Of the spots analysed, 77% contained only one protein species per spot, hence the change in protein expression measured was solely attributed to the identified protein. Many membrane proteins and protein isoforms were identified indicating both adequate solubilization of E. coli samples and potential post-translational modification. The results indicate that the regulatory mechanisms following benzoic acid treatment of E. coli are far more complicated than hitherto expected.     

Liquid ultraviolet matrix-assisted laser desorption/ionization -- mass spectrometry for automated proteomic analysis

We have combined several key sample preparation steps for the use of a liquid matrix system to provide high analytical sensitivity in automated ultraviolet -- matrix-assisted laser desorption/ionisation -- mass spectrometry (UV-MALDI-MS). This new sample preparation protocol employs a matrix-mixture which is based on the glycerol matrix-mixture described by Sze et al. The low-femtomole sensitivity that is achievable with this new preparation protocol enables proteomic analysis of protein digests comparable to solid-state matrix systems. For automated data acquisition and analysis, the MALDI performance of this liquid matrix surpasses the conventional solid-state MALDI matrices. Besides the inherent general advantages of liquid samples for automated sample preparation and data acquisition the use of the presented liquid matrix significantly reduces the extent of unspecific ion signals in peptide mass fingerprints compared to typically used solid matrices, such as 2,5-dihydroxybenzoic acid (DHB) or alpha-cyano-hydroxycinnamic acid (CHCA). In particular, matrix and low-mass ion signals and ion signals resulting from cation adduct formation are dramatically reduced. Consequently, the confidence level of protein identification by peptide mass mapping of in-solution and in-gel digests is generally higher.     

The proteomic reactor: a microfluidic device for processing minute amounts of protein prior to mass spectrometry analysis

Gel-free proteomics has emerged as a complement to conventional gel-based proteomics. Gel-free approaches focus on peptide or protein fractionation, but they do not address the efficiency of protein processing. We report the development of a microfluidic proteomic reactor that greatly simplifies the processing of complex proteomic samples by combining multiple proteomic steps. Rapid extraction and enrichment of proteins from complex proteomic samples or directly from cells are readily performed on the reactor. Furthermore, chemical and enzymatic treatments of proteins are performed in 50 nL effective volume, which results in an increased number of generated peptides. The products are compatible with mass spectrometry. We demonstrated that the proteomic reactor is at least 10 times more sensitive than current gel-free methodologies with one protein identified per 440 pg of protein lysate injected on the reactor. Furthermore, as little as 300 cells can be directly introduced on the proteomic reactor and analyzed by mass spectrometry.     

Non-invasive proteomic analysis of human skin keratins: screening of methionine oxidation in keratins by mass spectrometry

Keratins are the main constituent of human skin and have been identified as major oxidative target proteins. However, there has been a lack of studies aimed at identifying the oxidation sites of keratins because of the difficulties associated with their insolubility and handling. Here, we introduce a mass spectrometry (MS)-based proteomic methodology to screen oxidative modifications in human skin keratins. Human skin proteins were obtained non-invasively by tape stripping and solubilized in SDS buffer, followed by purification and digestion using the modified filter-aided sample preparation method. The tryptic peptides were then analyzed by MALDI-TOF/MS, LC-ESI/MS, and MS/MS. PMF analyses have identified keratins K1 and K10 as the major proteins of human skin. Met(259), Met(262), Met(296), and Met(469), located in the α-helical rod domain of K1, were the most susceptible sites to oxidation induced by hydrogen peroxide in vitro and in vivo. Our results indicate a potential use of the identified methionine residues as biomarkers of oxidative skin damage. The present methodology is the first MS-based approach to detecting oxidative modifications in keratins obtained directly from human skin and can be easily applied to the monitoring of other keratin modifications in various skin conditions.