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.     

The utility of ETD mass spectrometry in proteomic analysis

Mass spectrometry has played an integral role in the identification of proteins and their post-translational modifications (PTM). However, analysis of some PTMs, such as phosphorylation, sulfonation, and glycosylation, is difficult with collision-activated dissociation (CAD) since the modification is labile and preferentially lost over peptide backbone fragmentation, resulting in little to no peptide sequence information. The presence of multiple basic residues also makes peptides exceptionally difficult to sequence by conventional CAD mass spectrometry. Here we review the utility of electron transfer dissociation (ETD) mass spectrometry for sequence analysis of post-translationally modified and/or highly basic peptides. Phosphorylated, sulfonated, glycosylated, nitrosylated, disulfide bonded, methylated, acetylated, and highly basic peptides have been analyzed by CAD and ETD mass spectrometry. CAD fragmentation typically produced spectra showing limited peptide backbone fragmentation. However, when these peptides were fragmented using ETD, peptide backbone fragmentation produced a complete or almost complete series of ions and thus extensive peptide sequence information. In addition, labile PTMs remained intact. These examples illustrate the utility of ETD as an advantageous tool in proteomic research by readily identifying peptides resistant to analysis by CAD. A further benefit is the ability to analyze larger, non-tryptic peptides, allowing for the detection of multiple PTMs within the context of one another.     

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.     

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-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.     

Data-dependent electron capture dissociation FT-ICR mass spectrometry for proteomic analyses

Electron capture dissociation (ECD) offers many benefits for the analysis of peptides and proteins, and consequently shows great potential for the field of proteomics. Recent developments have reduced the time scale required for ECD to milliseconds resulting in the technique's compatibility with on-line separation techniques, e.g., HPLC. Here, we demonstrate incorporation of ECD into a high-throughput data-dependent LC-MS/MS approach for the analysis of proteomic samples. The approach is applied to analysis of the protein Fc-ROR2 isolated from chondrocytes and is the first example of LC-ECD-MS/MS of such a sample. Protein sequence coverage was 29%. Within that coverage, fifteen peptides were isolated and subjected to ECD. In most cases, the sequence tag generated by ECD was over 70% (in terms of the number of peptide backbone cleavages). The ECD data were searched against the nonredundant human NCBI database using the SEQUEST algorithm. Protein ROR2 was assigned, as was IgG (Fc domain). The results demonstrate the suitability of ECD as an integral technique in high-throughput proteomic strategies.     

Integrated multi-level quality control for proteomic profiling studies using mass spectrometry

Background

Evolutionary conservation of RNA secondary structure is a typical feature of many functional non-coding RNAs. Since almost all of the available methods used for prediction and annotation of non-coding RNA genes rely on this evolutionary signature, accurate measures for structural conservation are essential.

Results

We systematically assessed the ability of various measures to detect conserved RNA structures in multiple sequence alignments. We tested three existing and eight novel strategies that are based on metrics of folding energies, metrics of single optimal structure predictions, and metrics of structure ensembles. We find that the folding energy based SCI score used in the RNAz program and a simple base-pair distance metric are by far the most accurate. The use of more complex metrics like for example tree editing does not improve performance. A variant of the SCI performed particularly well on highly conserved alignments and is thus a viable alternative when only little evolutionary information is available. Surprisingly, ensemble based methods that, in principle, could benefit from the additional information contained in sub-optimal structures, perform particularly poorly. As a general trend, we observed that methods that include a consensus structure prediction outperformed equivalent methods that only consider pairwise comparisons.

Conclusion

Structural conservation can be measured accurately with relatively simple and intuitive metrics. They have the potential to form the basis of future RNA gene finders, that face new challenges like finding lineage specific structures or detecting mis-aligned sequences.     

Analysis and validation of proteomic data generated by tandem mass spectrometry

The analysis of the large amount of data generated in mass spectrometry-based proteomics experiments represents a significant challenge and is currently a bottleneck in many proteomics projects. In this review we discuss critical issues related to data processing and analysis in proteomics and describe available methods and tools. We place special emphasis on the elaboration of results that are supported by sound statistical arguments.     

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.     

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.     

Sample size calculations for designing clinical proteomic profiling studies using mass spectrometry

In cancer clinical proteomics, MALDI and SELDI profiling are used to search for biomarkers of potentially curable early-stage disease. A given number of samples must be analysed in order to detect clinically relevant differences between cancers and controls, with adequate statistical power. From clinical proteomic profiling studies, expression data for each peak (protein or peptide) from two or more clinically defined groups of subjects are typically available. Typically, both exposure and confounder information on each subject are also available, and usually the samples are not from randomized subjects. Moreover, the data is usually available in replicate. At the design stage, however, covariates are not typically available and are often ignored in sample size calculations. This leads to the use of insufficient numbers of samples and reduced power when there are imbalances in the numbers of subjects between different phenotypic groups. A method is proposed for accommodating information on covariates, data imbalances and design-characteristics, such as the technical replication and the observational nature of these studies, in sample size calculations. It assumes knowledge of a joint distribution for the protein expression values and the covariates. When discretized covariates are considered, the effect of the covariates enters the calculations as a function of the proportions of subjects with specific attributes. This makes it relatively straightforward (even when pilot data on subject covariates is unavailable) to specify and to adjust for the effect of the expected heterogeneities. The new method suggests certain experimental designs which lead to the use of a smaller number of samples when planning a study. Analysis of data from the proteomic profiling of colorectal cancer reveals that fewer samples are needed when a study is balanced than when it is unbalanced, and when the IMAC30 chip-type is used. The method is implemented in the clippda package and is available in R at: http://www.bioconductor.org/help/bioc-views/release/bioc/html/clippda.html.     

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.     

Monolithic capillary columns for liquid chromatography-electrospray ionization mass spectrometry in proteomic and genomic research

Peptides, proteins, single-stranded oligonucleotides, and double-stranded DNA fragments were separated with high resolution in micropellicular, monolithic capillary columns prepared by in situ radical copolymerization of styrene and divinylbenzene. Miniaturized chromatography both in the reversed-phase and the ion-pair reversed-phase mode could be realized in the same capillary column because of the nonpolar character of the poly-(styrene/divinylbenzene) stationary phase. The high chromatographic performance of the monolithic stationary phase facilitated the generation of peak capacities for the biopolymers in the range of 50-140 within 10 min under gradient elution conditions. Employing volatile mobile phase components, separations in the two chromatographic separation modes were on-line hyphenated to electrospray ionization (tandem) mass spectrometry, which yielded intact accurate molecular masses as well as sequence information derived from collision-induced fragmentation. The inaccuracy of mass determination in a quadrupole ion trap mass spectrometer was in the range of 0.01-0.02% for proteins up to a molecular mass of 20000, and 0.02-0.12% for DNA fragments up to a molecular mass of 310000. High-performance liquid chromatography-electrospray ionization mass spectrometry utilizing monolithic capillary columns was applied to the identification of proteins by peptide mass fingerprinting, tandem mass spectrometric sequencing, or intact molecular mass determination, as well as to the accurate sizing of double-stranded DNA fragments ranging in size from 50 to 500 base pairs, and to the detection of sequence variations in DNA fragments amplified by the polymerase chain reaction.     

Sample size determination in clinical proteomic profiling experiments using mass spectrometry for class comparison

Mass spectrometric profiling approaches such as MALDI‐TOF and SELDI‐TOF are increasingly being used in disease marker discovery, particularly in the lower molecular weight proteome. However, little consideration has been given to the issue of sample size in experimental design. The aim of this study was to develop a protocol for the use of sample size calculations in proteomic profiling studies using MS. These sample size calculations can be based on a simple linear mixed model which allows the inclusion of estimates of biological and technical variation inherent in the experiment. The use of a pilot experiment to estimate these components of variance is investigated and is shown to work well when compared with larger studies. Examination of data from a number of studies using different sample types and different chromatographic surfaces shows the need for sample‐ and preparation‐specific sample size calculations.     

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.     

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.