Publishing large proteome datasets: scientific policy meets emerging technologies

Currently, there are various approaches to proteomic analyses based on either 2D gel or HPLC separation platforms, generating data of different formats, structures and types. Identification of these separated proteins or peptide fragments is typically achieved by mass spectrometry (MS) measurements that use either accurate mass measurements or fragmentation (MS–MS) information. Integrating the information generated from these different platforms is essential if proteomics is to succeed. A further challenge lies in generating standards that can accept the hundreds-of-thousands of mass spectra produced per analysis based on threshold or probability measurements. Finally, peer review and electronic publication processes will be crucial to the dissemination and use of proteomic information. Merging the policy requirements of data-intensive research with information technology will enable scientists to gain real value from global proteomics information.     

Genetic Influences on Metabolite Levels: A Comparison across Metabolomic Platforms

Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.     

Characterisation of fungal lanostane-type triterpene acids by electrospray ionisation mass spectrometry

Lanostane-triterpene acids obtained from the culture of the fungus Coriolellus malicola were studied by electrospray mass spectrometry in the negative ion mode using quadrupole time-of-flight and quadrupole ion trap analysers. Despite the differences observed in the mass spectra recorded with these instruments, a set of fragment ions was found to be characteristic of the family, depending on the Delta(7,9(11)) or Delta(8) skeleton and the particular functional group at C-3.     

Fourier transform mass spectrometry

This article provides an introduction to Fourier transform-based mass spectrometry. The key performance characteristics of Fourier transform-based mass spectrometry, mass accuracy and resolution, are presented in the view of how they impact the interpretation of measurements in proteomic applications. The theory and principles of operation of two types of mass analyzer, Fourier transform ion cyclotron resonance and Orbitrap, are described. Major benefits as well as limitations of Fourier transform-based mass spectrometry technology are discussed in the context of practical sample analysis, and illustrated with examples included as figures in this text and in the accompanying slide set. Comparisons highlighting the performance differences between the two mass analyzers are made where deemed useful in assisting the user with choosing the most appropriate technology for an application. Recent developments of these high-performing mass spectrometers are mentioned to provide a future outlook.     

Volatile compound fingerprinting of mixed-culture fermentations

With the advent of the -omics era, classical technology platforms, such as hyphenated mass spectrometry, are currently undergoing a transformation toward high-throughput application. These novel platforms yield highly detailed metabolite profiles in large numbers of samples. Such profiles can be used as fingerprints for the accurate identification and classification of samples as well as for the study of effects of experimental conditions on the concentrations of specific metabolites. Challenges for the application of these methods lie in the acquisition of high-quality data, data normalization, and data mining. Here, a high-throughput fingerprinting approach based on analysis of headspace volatiles using ultrafast gas chromatography coupled to time of flight mass spectrometry (ultrafast GC/TOF-MS) was developed and evaluated for classification and screening purposes in food fermentation. GC-MS mass spectra of headspace samples of milk fermented by different mixed cultures of lactic acid bacteria (LAB) were collected and preprocessed in MetAlign, a dedicated software package for the preprocessing and comparison of liquid chromatography (LC)-MS and GC-MS data. The Random Forest algorithm was used to detect mass peaks that discriminated combinations of species or strains used in fermentations. Many of these mass peaks originated from key flavor compounds, indicating that the presence or absence of individual strains or combinations of strains significantly influenced the concentrations of these components. We demonstrate that the approach can be used for purposes like the selection of strains from collections based on flavor characteristics and the screening of (mixed) cultures for the presence or absence of strains. In addition, we show that strain-specific flavor characteristics can be traced back to genetic markers when comparative genome hybridization (CGH) data are available.     

Peptide and protein imaging mass spectrometry in cancer research

MALDI mass spectrometry is able to acquire protein profiles directly from tissue that can describe the levels of hundreds of distinct proteins. MALDI imaging MS can simultaneously reveal how each of these proteins varies in heterogeneous tissues. Numerous studies have now demonstrated how MALDI imaging MS can generate different protein profiles from the different cell types in a tumor, which can act as biomarker profiles or enable specific candidate protein biomarkers to be identified.     

Clinical Chemistry Laboratory Automation in the 21st Century - Amat Victoria curam (Victory loves careful preparation)

The era of automation arrived with the introduction of the AutoAnalyzer using continuous flow analysis and the Robot Chemist that automated the traditional manual analytical steps. Successive generations of stand-alone analysers increased analytical speed, offered the ability to test high volumes of patient specimens, and provided large assay menus. A dichotomy developed, with a group of analysers devoted to performing routine clinical chemistry tests and another group dedicated to performing immunoassays using a variety of methodologies. Development of integrated systems greatly improved the analytical phase of clinical laboratory testing and further automation was developed for pre-analytical procedures, such as sample identification, sorting, and centrifugation, and post-analytical procedures, such as specimen storage and archiving. All phases of testing were ultimately combined in total laboratory automation (TLA) through which all modules involved are physically linked by some kind of track system, moving samples through the process from beginning-to-end. A newer and very powerful, analytical methodology is liquid chromatography-mass spectrometry/mass spectrometry (LC-MS/MS). LC-MS/MS has been automated but a future automation challenge will be to incorporate LC-MS/MS into TLA configurations. Another important facet of automation is informatics, including middleware, which interfaces the analyser software to a laboratory information systems (LIS) and/or hospital information systems (HIS). This software includes control of the overall operation of a TLA configuration and combines analytical results with patient demographic information to provide additional clinically useful information. This review describes automation relevant to clinical chemistry, but it must be recognised that automation applies to other specialties in the laboratory, e.g. haematology, urinalysis, microbiology. It is a given that automation will continue to evolve in the clinical laboratory, limited only by the imagination and ingenuity of laboratory scientists.     

Integrative computational biology for cancer research

Over the past two decades, high-throughput (HTP) technologies such as microarrays and mass spectrometry have fundamentally changed clinical cancer research. They have revealed novel molecular markers of cancer subtypes, metastasis, and drug sensitivity and resistance. Some have been translated into the clinic as tools for early disease diagnosis, prognosis, and individualized treatment and response monitoring. Despite these successes, many challenges remain: HTP platforms are often noisy and suffer from false positives and false negatives; optimal analysis and successful validation require complex workflows; and great volumes of data are accumulating at a rapid pace. Here we discuss these challenges, and show how integrative computational biology can help diminish them by creating new software tools, analytical methods, and data standards.     

Proteomics in prostate cancer research

The incidence of early prostate cancer (PCa) among middle-aged men has increased rapidly. For many of these men, curatively intended treatment does more harm than good. Established prognostic factors are tumor stage and grade. As a result of earlier detection a majority of patients now have nonpalpable tumors (T1c) of intermediate grade (Gleason score 6). Prostate specific antigen in serum in such cases is generally at a low level and not a reliable predictor of prognosis. Altogether there is an urgent need for adjunctive prognostic indicators. In the search for relevant tumor markers for improved patient selection an exploration of the proteome (the human proteins) could be fruitful. This paper critically reviews the use of 2-dimensional gel electrophoresis (2-DE) for proteome research. Additional steps such as image analysis and mass spectrometry are described. Techniques based on non-2-DE platforms: surface-enhanced laser desorption/ionization (SELDI), isotope coded affinity tags (ICAT) and array-based technologies are also summarized. Although labor-intensive and time-consuming, 2-DE is presently the most powerful method for analysis of cellular protein phenotype and may potentially reveal gene regulations that cannot be detected on a genetic level.     

Synthesis of polymerized thin films for immobilized ligand display in proteomic analysis

We describe a new material for the display of biomolecular ligands for use in proteomic analysis. We report here on the construction of the first functionalized polymerized diacetylene thin films (PDTFs) for use in displaying immobilized ligands and their application in mass spectral proteomic analysis. Functionalized polymerized thin film surfaces were constructed with diacetylene-containing biotin lipid monomers designed for the capture of proteins (streptavidin) from a complex cellular lysate and detection with mass spectrometry (MS). These materials serve as a prototype for ligand-based spotted arrays amenable to high throughput screening. Functionalized PDTFs can be easily manufactured for customized microarrays and demonstrate high protein specificity and low nonspecific protein adsorption, and the resulting microarrays constructed from these materials are compatible with several different protein analysis platforms. Our results suggest that these materials have broad potential applications for use in mass spectral-based proteomic analysis.     

High-Throughput,Amplicon-Based Sequencing of the CREBBP Gene as a Tool to Develop a Universal Platform-Independent Assay

High-throughput sequencing technologies are widely used to analyse genomic variants or rare mutational events in different fields of genomic research, with a fast development of new or adapted platforms and technologies, enabling amplicon-based analysis of single target genes or even whole genome sequencing within a short period of time. Each sequencing platform is characterized by well-defined types of errors, resulting from different steps in the sequencing workflow. Here we describe a universal method to prepare amplicon libraries that can be used for sequencing on different high-throughput sequencing platforms. We have sequenced distinct exons of the CREB binding protein (CREBBP) gene and analysed the output resulting from three major deep-sequencing platforms. platform-specific errors were adjusted according to the result of sequence analysis from the remaining platforms. Additionally, bioinformatic methods are described to determine platform dependent errors. Summarizing the results we present a platform-independent cost-efficient and timesaving method that can be used as an alternative to commercially available sample-preparation kits.     

Process analytical technology (PAT) tools for the cultivation step in biopharmaceutical production

The process analytical technology (PAT) initiative is now 10 years old. This has resulted in the development of many tools and software packages dedicated to PAT application on pharmaceutical processes. However, most applications are restricted to small molecule drugs, mainly for the relatively simple process steps like drying or tableting where only a limited number of parameters need to be controlled. A big challenge for PAT still lies in applications for biopharmaceuticals and then especially in the cultivation process step, where the quality of a biopharmaceutical product is largely determined. This review gives an overview of the currently available tools for monitoring and controlling the biopharmaceutical cultivation step and of the main challenges for the most common cell platforms (i.e. Escherichia coli, yeast, and mammalian cells) used in biopharmaceutical manufacturing. The real challenge is to understand how intracellular mechanisms (from synthesis to excretion) influence the quality of biopharmaceuticals and how these mechanisms can be monitored and controlled to yield the desired end product quality. Modern “omics” tools and advanced process analyzers have opened up the way for PAT applications for the biopharmaceutical cultivation process step.     

Trend analysis of metabonomics and systematic review of metabonomics-derived cancer marker metabolites

In this paper, trend analyses were performed to compare the different ‘omic’ technologies and the different analytical platforms and biological matrices exploited in metabonomic studies. While common and differential marker metabolites had been identified using various analytical platforms in metabonomics, little research was directed to review and consolidate marker metabolites in each disease state. A systematic review of metabonomics-derived marker metabolites in different cancers was performed to understand the significance of metabonomics in elucidating cancer biochemistry. The biological pathways associated with the cancer marker metabolites were further correlated to the pathology of cancers. Our trend analyses indicated that metabonomic publications increased exponentially in recent years, with nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography/mass spectrometry (LC/MS) being the most popular analytical platforms while blood, urine and tissue are the most commonly profiled biological matrices. Based on the consolidated cancer marker metabolites, it is reinforced that different cancers possess some common and yet distinct metabolic phenotypes, exhibiting numerous perturbed biochemical pathways related to their needs to support cell growth and proliferation and facilitate cancer cell survival.     

The standard protein mix database: a diverse data set to assist in the production of improved Peptide and protein identification software tools

Tandem mass spectrometry (MS/MS) is frequently used in the identification of peptides and proteins. Typical proteomic experiments rely on algorithms such as SEQUEST and MASCOT to compare thousands of tandem mass spectra against the theoretical fragment ion spectra of peptides in a database. The probabilities that these spectrum-to-sequence assignments are correct can be determined by statistical software such as PeptideProphet or through estimations based on reverse or decoy databases. However, many of the software applications that assign probabilities for MS/MS spectra to sequence matches were developed using training data sets from 3D ion-trap mass spectrometers. Given the variety of types of mass spectrometers that have become commercially available over the last 5 years, we sought to generate a data set of reference data covering multiple instrumentation platforms to facilitate both the refinement of existing computational approaches and the development of novel software tools. We analyzed the proteolytic peptides in a mixture of tryptic digests of 18 proteins, named the "ISB standard protein mix", using 8 different mass spectrometers. These include linear and 3D ion traps, two quadrupole time-of-flight platforms (qq-TOF), and two MALDI-TOF-TOF platforms. The resulting data set, which has been named the Standard Protein Mix Database, consists of over 1.1 million spectra in 150+ replicate runs on the mass spectrometers. The data were inspected for quality of separation and searched using SEQUEST. All data, including the native raw instrument and mzXML formats and the PeptideProphet validated peptide assignments, are available at http://regis-web.systemsbiology.net/PublicDatasets/.     

Analysis of recombinat glycoproteins by mass spectrometry

The advent of new technologies for analysis of biopolymers by mass spectrometry has revolutionised strategies for recombinant protein characterization. The principal recent developments have been matrix-assisted laser desorption/ionization and electrospray ionization mass spectrometry. Using these tools, accurate molecular mass determinations can now be obtained routinely-often using minute (picomole-femtomole) quantities of protein or protein fragments. These techniques have proved indispensible for detailed characterization of the post-translational modifications of recombinant proteins produced by eukaryotic systems. Glycosylation is arguably the most important and complex of these modifications and has prompted widespread use of these new techniques. In this mini-review article I describe recent advances in the use of mass spectrometry for analysis of recombinant glycoproteins.     

HDX-analyzer: a novel package for statistical analysis of protein structure dynamics

BACKGROUND: HDX mass spectrometry is a powerful platform to probe protein structure dynamics during ligand binding, protein folding, enzyme catalysis, and such. HDX mass spectrometry analysis derives the protein structure dynamics based on the mass increase of a protein of which the backbone protons exchanged with solvent deuterium. Coupled with enzyme digestion and MS/MS analysis, HDX mass spectrometry can be used to study the regional dynamics of protein based on the m/z value or percentage of deuterium incorporation for the digested peptides in the HDX experiments. Various software packages have been developed to analyze HDX mass spectrometry data. Despite the progresses, proper and explicit statistical treatment is still lacking in most of the current HDX mass spectrometry software. In order to address this issue, we have developed the HDXanalyzer for the statistical analysis of HDX mass spectrometry data using R, Python, and RPY2. IMPLEMENTATION AND RESULTS: HDXanalyzer package contains three major modules, the data processing module, the statistical analysis module, and the user interface. RPY2 is employed to enable the connection of these three components, where the data processing module is implemented using Python and the statistical analysis module is implemented with R. RPY2 creates a low-level interface for R and allows the effective integration of statistical module for data processing. The data processing module generates the centroid for the peptides in form of m/z value, and the differences of centroids between the peptides derived from apo and ligand-bound protein allow us to evaluate whether the regions have significant changes in structure dynamics or not. Another option of the software is to calculate the deuterium incorporation rate for the comparison. The two types of statistical analyses are Paired Student's t-test and the linear combination of the intercept for multiple regression and ANCOVA model. The user interface is implemented with wxpython to facilitate the data visualization in graphs and the statistical analysis output presentation. In order to evaluate the software, a previously published xylanase HDX mass spectrometry analysis dataset is processed and presented. The results from the different statistical analysis methods are compared and shown to be similar. The statistical analysis results are overlaid with the three dimensional structure of the protein to highlight the regional structure dynamics changes in the xylanase enzyme. CONCLUSION: Statistical analysis provides crucial evaluation of whether a protein region is significantly protected or unprotected during the HDX mass spectrometry studies. Although there are several other available software programs to process HDX experimental data, HDXanalyzer is the first software program to offer multiple statistical methods to evaluate the changes in protein structure dynamics based on HDX mass spectrometry analysis. Moreover, the statistical analysis can be carried out for both m/z value and deuterium incorporation rate. In addition, the software package can be used for the data generated from a wide range of mass spectrometry instruments.