A common open representation of mass spectrometry data and its application to proteomics research

SANIST: a rapid mass spectrometric SACI/ESI data acquisition and elaboration platform for verifying potential candidate biomarkers

RATIONALE

Surface-Activated Chemical Ionization/Electrospray Ionization mass spectrometry (SACI/ESI-MS) is a technique with high sensitivity and low noise that allows accurate biomarker discovery studies. We developed a dedicated SACI/ESI software, named SANIST, for both biomarker fingerprint data acquisition and as a diagnostic tool, using prostate cancer (PCa) as the disease of interest.

METHODS

Liquid chromatography (LC)/SACI/ESI-MS technology was employed to detect a potential biomarker panel for PCa disease prediction. Serum from patients with histologically confirmed or negative prostate biopsies for PCa was employed. The biomarker data (m/z or Thompson value, retention time and extraction mass chromatogram peak area) were stored in an ascii database. SANIST software allowed identification of potential biomarkers. A Bayesian scoring algorithm developed in house allowed sample separation based on comparison with samples in the database.

RESULTS

Biomarker candidates from the carnitine family were detected at significantly lower levels in patients showing histologically confirmed PCa. Using these biomarkers, the SANIST scoring algorithm allowed separation of patients with PCa from biopsy negative subjects with high accuracy and sensitivity.

CONCLUSIONS

SANIST was able to rapidly identify and perform a preliminary evaluation of the potential diagnostic efficiency of potential biomarkers for PCa.

Mass-Up: an all-in-one open software application for MALDI-TOF mass spectrometry knowledge discovery

BACKGROUND

Mass spectrometry is one of the most important techniques in the field of proteomics. MALDI-TOF mass spectrometry has become popular during the last decade due to its high speed and sensitivity for detecting proteins and peptides. MALDI-TOF-MS can be also used in combination with Machine Learning techniques and statistical methods for knowledge discovery. Although there are many software libraries and tools that can be combined for these kind of analysis, there is still a need for all-in-one solutions with graphical user-friendly interfaces and avoiding the need of programming skills.

RESULTS

Mass-Up, an open software multiplatform application for MALDI-TOF-MS knowledge discovery is herein presented. Mass-Up software allows data preprocessing, as well as subsequent analysis including (i) biomarker discovery, (ii) clustering, (iii) biclustering, (iv) three-dimensional PCA visualization and (v) classification of large sets of spectra data.

CONCLUSIONS

Mass-Up brings knowledge discovery within reach of MALDI-TOF-MS researchers. Mass-Up is distributed under license GPLv3 and it is open and free to all users at http://sing.ei.uvigo.es/mass-up.

Tracing the metabolism of HT-2 toxin and T-2 toxin in barley by isotope-assisted untargeted screening and quantitative LC-HRMS analysis

An extensive study of the metabolism of the type A trichothecene mycotoxins HT-2 toxin and T-2 toxin in barley using liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is reported. A recently developed untargeted approach based on stable isotopic labelling, LC-Orbitrap-MS analysis with fast polarity switching and data processing by MetExtract software was combined with targeted LC-Q-TOF-MS(/MS) analysis for metabolite structure elucidation and quantification. In total, 9 HT-2 toxin and 13 T-2 toxin metabolites plus tentative isomers were detected, which were successfully annotated by calculation of elemental formulas and further LC-HRMS/MS measurements as well as partly identified with authentic standards. As a result, glucosylated forms of the toxins, malonylglucosides, and acetyl and feruloyl conjugates were elucidated. Additionally, time courses of metabolite formation and mass balances were established. For absolute quantification of those compounds for which standards were available, the method was validated by determining apparent recovery, signal suppression, or enhancement and extraction recovery. Most importantly, T-2 toxin was rapidly metabolised to HT-2 toxin and for both parent toxins HT-2 toxin-3-O-β-glucoside was identified (confirmed by authentic standard) as the main metabolite, which reached its maximum already 1 day after toxin treatment. Graphical Abstract Isotope-assisted untargeted screening of HT-2 toxin and T-2 toxin metabolites in barley.

Fibroblast activation protein-α, a stromal cell surface protease, shapes key features of cancer associated fibroblasts through proteome and degradome alterations

Cancer associated fibroblasts (CAFs) constitute an abundant stromal component of most solid tumors. Fibroblast activation protein (FAP) α is a cell surface protease that is expressed by CAFs. We corroborate this expression profile by immunohistochemical analysis of colorectal cancer specimens. To better understand the tumor-contextual role of FAPα, we investigate how FAPα shapes functional and proteomic features of CAFs using loss- and gain-of function cellular model systems. FAPα activity has a strong impact on the secreted CAF proteome ("secretome"), including reduced levels of anti-angiogenic factors, elevated levels of transforming growth factor (TGF) β, and an impact on matrix processing enzymes. Functionally, FAPα mildly induces sprout formation by human umbilical vein endothelial cells. Moreover, loss of FAPα leads to a more epithelial cellular phenotype and this effect was rescued by exogenous application of TGFβ. In collagen contraction assays, FAPα induced a more contractile cellular phenotype. To characterize the proteolytic profile of FAPα, we investigated its specificity with proteome-derived peptide libraries and corroborated its preference for cleavage carboxy-terminal to proline residues. By "terminal amine labeling of substrates" (TAILS) we explored FAPα-dependent cleavage events. Although FAPα acts predominantly as an amino-dipeptidase, putative FAPα cleavage sites in collagens are present throughout the entire protein length. In contrast, putative FAPα cleavage sites in non-collagenous proteins cluster at the amino-terminus. The degradomic study highlights cell-contextual proteolysis by FAPα with distinct positional profiles. Generally, our findings link FAPα to key aspects of CAF biology and attribute an important role in tumor-stroma interaction to FAPα.

Quantitative proteomic analysis of formalin-fixed, paraffin-embedded clear cell renal cell carcinoma tissue using stable isotopic dimethylation of primary amines

Background

Formalin-fixed, paraffin-embedded (FFPE) tissues represent the most abundant resource of archived human specimens in pathology. Such tissue specimens are emerging as a highly valuable resource for translational proteomic studies. In quantitative proteomic analysis, reductive di-methylation of primary amines using stable isotopic formaldehyde variants is increasingly used due to its robustness and cost-effectiveness.

Results

In the present study we show for the first time that isotopic amine dimethylation can be used in a straightforward manner for the quantitative proteomic analysis of FFPE specimens without interference from formalin employed in the FFPE process. Isotopic amine dimethylation of FFPE specimens showed equal labeling efficiency as for cryopreserved specimens. For both FFPE and cryopreserved specimens, differential labeling of identical samples yielded highly similar ratio distributions within the expected range for dimethyl labeling. In an initial application, we profiled proteome changes in clear cell renal cell carcinoma (ccRCC) FFPE tissue specimens compared to adjacent non–malignant renal tissue. Our findings highlight increased levels of glyocolytic enzymes, annexins as well as ribosomal and proteasomal proteins.

Conclusion

Our study establishes isotopic amine dimethylation as a versatile tool for quantitative proteomic analysis of FFPE specimens and underlines proteome alterations in ccRCC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1768-x) contains supplementary material, which is available to authorized users.

The stromal cell-surface protease fibroblast activation protein-α localizes to lipid rafts and is recruited to invadopodia

Fibroblast activation protein alpha (FAPα) is a cell surface protease expressed by cancer-associated fibroblasts in the microenvironment of most solid tumors. As there is increasing evidence for proteases having non-catalytic functions, we determined the FAPα interactome in cancer-associated fibroblasts using the quantitative immunoprecipitation combined with knockdown (QUICK) method. Complex formation with adenosin deaminase, erlin-2, stomatin, prohibitin, Thy-1 membrane glycoprotein, and caveolin-1 was further validated by immunoblotting. Co-immunoprecipitation (co-IP) of the known stoichiometric FAPα binding partner dipeptidyl-peptidase IV (DPPIV) corroborated the proteomic strategy. Reverse co-IPs validated the FAPα interaction with caveolin-1, erlin-2, and stomatin while co-IP upon RNA-interference mediated knock-down of DPPIV excluded adenosin deaminase as a direct FAPα interaction partner. Many newly identified FAPα interaction partners localize to lipid rafts, including caveolin-1, a widely-used marker for lipid raft localization. We hypothesized that this indicates a recruitment of FAPα to lipid raft structures. In density gradient centrifugation, FAPα co-fractionates with caveolin-1. Immunofluorescence optical sectioning microscopy of FAPα and lipid raft markers further corroborates recruitment of FAPα to lipid rafts and invadopodia. FAPα is therefore an integral component of stromal lipid rafts in solid tumors. In essence, we provide one of the first interactome analyses of a cell surface protease and translate these results into novel biological aspects of a marker protein for cancer-associated fibroblasts.

Natural Gas Residual Fluids: Sources, Endpoints, and Organic Chemical Composition after Centralized Waste Treatment in Pennsylvania

Volumes of natural gas extraction-derived wastewaters have increased sharply over the past decade, but the ultimate fate of those waste streams is poorly characterized. Here, we sought to (a) quantify natural gas residual fluid sources and endpoints to bound the scope of potential waste stream impacts and (b) describe the organic pollutants discharged to surface waters following treatment, a route of likely ecological exposure. Our findings indicate that centralized waste treatment facilities (CWTF) received 9.5% (8.5 × 10(8) L) of natural gas residual fluids in 2013, with some facilities discharging all effluent to surface waters. In dry months, discharged water volumes were on the order of the receiving body flows for some plants, indicating that surface waters can become waste-dominated in summer. As disclosed organic compounds used in high volume hydraulic fracturing (HVHF) vary greatly in physicochemical properties, we deployed a suite of analytical techniques to characterize CWTF effluents, covering 90.5% of disclosed compounds. Results revealed that, of nearly 1000 disclosed organic compounds used in HVHF, only petroleum distillates and alcohol polyethoxylates were present. Few analytes targeted by regulatory agencies (e.g., benzene or toluene) were observed, highlighting the need for expanded and improved monitoring efforts at CWTFs.

Large-scale label-free phosphoproteomics: from technology to data interpretation

Protein phosphorylation plays a central role in the dynamic intracellular signaling and the control of biochemical pathways in all living cells. Recent advances in high-performance MS/MS-based technology make the large-scale identification and quantification of phosphorylation sites possible. Here, we review the full data generation pipeline, starting from sample preparation methods and LC-MS detection procedures, through to data processing and analysis software tools that facilitate the systematic comparative profiling of thousands of phosphoproteins in different biological specimens in a single experiment. We emphasize current challenges and promising avenues for the mechanistic interpretation and visualization of global phosphorylation networks and their relevance to human health and disease.

Automating mass spectrometry-based quantitative glycomics using aminoxy tandem mass tag reagents with SimGlycan

Protein glycosylation is a common post-translational modification, which serves critical roles in the biological processes of organisms. Monitoring of changes in the abundance and structure of glycans may be necessary to explain the correlations between protein glycosylation and various diseases. Hence, the growing importance of glycoproteomics necessitates in-depth qualitative and quantitative studies of glycans. One of the emerging trends in glycomics research is the innovation related to accurate mass spectrometry based quantitative analysis of glycans. Recently, we have introduced aminoxyTMT reagents, which enable efficient relative quantitation of carbohydrates, improved glycan ionization efficiency and increased analytical throughput. These reagents can be used for quantitative analysis of N-glycans by direct infusion or liquid chromatography (LC)-coupled to electrospray ionization mass spectrometry (ESI-MS). However, unlike in proteomics, one of the major challenges left unaddressed is the lack of informatics tools to automate the qualitative and quantitative analysis of generated data. This analysis typically includes identification/quantitation of glycans using MS/MS data and differential analysis across biological samples. We have developed software modules to streamline such protocols for quantitative analysis of aminoxyTMT labeled-glycans derived from complex mixtures. This article is part of a Special Issue entitled: Proteomics in India.

Development of data representation standards by the human proteome organization proteomics standards initiative

OBJECTIVE

To describe the goals of the Proteomics Standards Initiative (PSI) of the Human Proteome Organization, the methods that the PSI has employed to create data standards, the resulting output of the PSI, lessons learned from the PSI's evolution, and future directions and synergies for the group.

MATERIALS AND METHODS

The PSI has 5 categories of deliverables that have guided the group. These are minimum information guidelines, data formats, controlled vocabularies, resources and software tools, and dissemination activities. These deliverables are produced via the leadership and working group organization of the initiative, driven by frequent workshops and ongoing communication within the working groups. Official standards are subjected to a rigorous document process that includes several levels of peer review prior to release.

RESULTS

We have produced and published minimum information guidelines describing what information should be provided when making data public, either via public repositories or other means. The PSI has produced a series of standard formats covering mass spectrometer input, mass spectrometer output, results of informatics analysis (both qualitative and quantitative analyses), reports of molecular interaction data, and gel electrophoresis analyses. We have produced controlled vocabularies that ensure that concepts are uniformly annotated in the formats and engaged in extensive software development and dissemination efforts so that the standards can efficiently be used by the community.Conclusion In its first dozen years of operation, the PSI has produced many standards that have accelerated the field of proteomics by facilitating data exchange and deposition to data repositories. We look to the future to continue developing standards for new proteomics technologies and workflows and mechanisms for integration with other omics data types. Our products facilitate the translation of genomics and proteomics findings to clinical and biological phenotypes. The PSI website can be accessed at http://www.psidev.info.

Fast and Efficient XML Data Access for Next-Generation Mass Spectrometry

Motivation

In mass spectrometry-based proteomics, XML formats such as mzML and mzXML provide an open and standardized way to store and exchange the raw data (spectra and chromatograms) of mass spectrometric experiments. These file formats are being used by a multitude of open-source and cross-platform tools which allow the proteomics community to access algorithms in a vendor-independent fashion and perform transparent and reproducible data analysis. Recent improvements in mass spectrometry instrumentation have increased the data size produced in a single LC-MS/MS measurement and put substantial strain on open-source tools, particularly those that are not equipped to deal with XML data files that reach dozens of gigabytes in size.

Results

Here we present a fast and versatile parsing library for mass spectrometric XML formats available in C++ and Python, based on the mature OpenMS software framework. Our library implements an API for obtaining spectra and chromatograms under memory constraints using random access or sequential access functions, allowing users to process datasets that are much larger than system memory. For fast access to the raw data structures, small XML files can also be completely loaded into memory. In addition, we have improved the parsing speed of the core mzML module by over 4-fold (compared to OpenMS 1.11), making our library suitable for a wide variety of algorithms that need fast access to dozens of gigabytes of raw mass spectrometric data.

Availability

Our C++ and Python implementations are available for the Linux, Mac, and Windows operating systems. All proposed modifications to the OpenMS code have been merged into the OpenMS mainline codebase and are available to the community at https://github.com/OpenMS/OpenMS.

Current controlled vocabularies are insufficient to uniquely map molecular entities to mass spectrometry signal

Background

The comparison of analyte mass spectrometry precursor (MS1) signal is central to many proteomic (and other -omic) workflows. Standard vocabularies for mass spectrometry exist and provide good coverage for most experimental applications yet are insufficient for concise and unambiguous description of data concepts spanning the range of signal provenance from a molecular perspective (e.g. from charged peptides down to fine isotopes). Without a standard unambiguous nomenclature, literature searches, algorithm reproducibility and algorithm evaluation for MS-omics data processing are nearly impossible.

Results

We show how terms from current official ontologies are too vague or ambiguous to explicitly map molecular entities to MS signals and we illustrate the inconsistency and ambiguity of current colloquially used terms. We also propose a set of terms for MS1 signal that uniquely, succinctly and intuitively describe data concepts spanning the range of signal provenance from full molecule downs to fine isotopes. We suggest that additional community discussion of these terms should precede any further standardization efforts. We propose a novel nomenclature that spans the range of the required granularity to describe MS data processing from the perspective of the molecular provenance of the MS signal.

Conclusions

The proposed nomenclature provides a chain of succinct and unique terms spanning the signal created by a charged molecule down through each of its constituent subsignals. We suggest that additional community discussion of these terms should precede any further standardization efforts.

Integration of systems glycobiology with bioinformatics toolboxes, glycoinformatics resources, and glycoproteomics data

The glycome constitutes the entire complement of free carbohydrates and glycoconjugates expressed on whole cells or tissues. 'Systems Glycobiology' is an emerging discipline that aims to quantitatively describe and analyse the glycome. Here, instead of developing a detailed understanding of single biochemical processes, a combination of computational and experimental tools are used to seek an integrated or 'systems-level' view. This can explain how multiple biochemical reactions and transport processes interact with each other to control glycome biosynthesis and function. Computational methods in this field commonly build in silico reaction network models to describe experimental data derived from structural studies that measure cell-surface glycan distribution. While considerable progress has been made, several challenges remain due to the complex and heterogeneous nature of this post-translational modification. First, for the in silico models to be standardized and shared among laboratories, it is necessary to integrate glycan structure information and glycosylation-related enzyme definitions into the mathematical models. Second, as glycoinformatics resources grow, it would be attractive to utilize 'Big Data' stored in these repositories for model construction and validation. Third, while the technology for profiling the glycome at the whole-cell level has been standardized, there is a need to integrate mass spectrometry derived site-specific glycosylation data into the models. The current review discusses progress that is being made to resolve the above bottlenecks. The focus is on how computational models can bridge the gap between 'data' generated in wet-laboratory studies with 'knowledge' that can enhance our understanding of the glycome.

Mass spectrometric metabolomic imaging of biofilms on corroding steel surfaces using laser ablation and solvent capture by aspiration

Ambient laser ablation and solvent capture by aspiration (LASCA) mass spectrometric imaging was combined with metabolomics high-performance liquid chromatography (HPLC) mass spectrometry analysis and light profilometry to investigate the correlation between chemical composition of marine bacterial biofilms on surfaces of 1018 carbon steel and corrosion damage of steel underneath the biofilms. Pure cultures of Marinobacter sp. or a wild population of bacteria present in coastal seawater served as sources of biofilms. Profilometry data of biofilm-free surfaces demonstrated heterogeneous distributions of corrosion damage. LASCA data were correlated with areas on the coupons varying in the level of corrosion attack, to reveal differences in chemical composition within biofilm regions associated with corroding and corrosion-free zones. Putative identification of selected compounds was carried out based on HPLC results and subsequent database searches. This is the first report of successful ambient chemical and metabolomic imaging of marine biofilms on corroding metallic materials. The metabolic analysis of such biofilms is challenging due to the presence in the biofilm of large amounts of corrosion products. However, by using the LASCA imaging interface, images of more than 1000 ions (potential metabolites) are generated, revealing striking heterogeneities within the biofilm. In the two model systems studied here, it is found that some of the patterns observed in selected ion images closely correlate with the occurrence and extent of corrosion in the carbon steel substrate as revealed by profilometry, while others do not. This approach toward the study of microbially influenced corrosion (MIC) holds great promise for approaching a fundamental understanding of the mechanisms involved in MIC.

mzDB: a file format using multiple indexing strategies for the efficient analysis of large LC-MS/MS and SWATH-MS data sets

The analysis and management of MS data, especially those generated by data independent MS acquisition, exemplified by SWATH-MS, pose significant challenges for proteomics bioinformatics. The large size and vast amount of information inherent to these data sets need to be properly structured to enable an efficient and straightforward extraction of the signals used to identify specific target peptides. Standard XML based formats are not well suited to large MS data files, for example, those generated by SWATH-MS, and compromise high-throughput data processing and storing. We developed mzDB, an efficient file format for large MS data sets. It relies on the SQLite software library and consists of a standardized and portable server-less single-file database. An optimized 3D indexing approach is adopted, where the LC-MS coordinates (retention time and m/z), along with the precursor m/z for SWATH-MS data, are used to query the database for data extraction. In comparison with XML formats, mzDB saves ∼25% of storage space and improves access times by a factor of twofold up to even 2000-fold, depending on the particular data access. Similarly, mzDB shows also slightly to significantly lower access times in comparison with other formats like mz5. Both C++ and Java implementations, converting raw or XML formats to mzDB and providing access methods, will be released under permissive license. mzDB can be easily accessed by the SQLite C library and its drivers for all major languages, and browsed with existing dedicated GUIs. The mzDB described here can boost existing mass spectrometry data analysis pipelines, offering unprecedented performance in terms of efficiency, portability, compactness, and flexibility.

Development of gel-filter method for high enrichment of low-molecular weight proteins from serum

The human serum proteome has been extensively screened for biomarkers. However, the large dynamic range of protein concentrations in serum and the presence of highly abundant and large molecular weight proteins, make identification and detection changes in the amount of low-molecular weight proteins (LMW, molecular weight ≤ 30kDa) difficult. Here, we developed a gel-filter method including four layers of different concentration of tricine SDS-PAGE-based gels to block high-molecular weight proteins and enrich LMW proteins. By utilizing this method, we identified 1,576 proteins (n = 2) from 10 μL serum. Among them, 559 (n = 2) proteins belonged to LMW proteins. Furthermore, this gel-filter method could identify 67.4% and 39.8% more LMW proteins than that in representative methods of glycine SDS-PAGE and optimized-DS, respectively. By utilizing SILAC-AQUA approach with labeled recombinant protein as internal standard, the recovery rate for GST spiked in serum during the treatment of gel-filter, optimized-DS, and ProteoMiner was 33.1 ± 0.01%, 18.7 ± 0.01% and 9.6 ± 0.03%, respectively. These results demonstrate that the gel-filter method offers a rapid, highly reproducible and efficient approach for screening biomarkers from serum through proteomic analyses.

3DSpectra: A 3-dimensional quantification algorithm for LC-MS labeled profile data

Mass spectrometry-based proteomics can generate highly informative datasets, as profile three-dimensional (3D) LC-MS data: LC-MS separates peptides in two dimensions (time, m/z) minimizing their overlap, and profile acquisition enhances quantification. To exploit both data features, we developed 3DSpectra, a 3D approach embedding a statistical method for peptide border recognition. 3DSpectra efficiently accesses profile data by means of mzRTree, and makes use of a priori metadata, provided by search engines, to quantify the identified peptides. An isotopic distribution model, shaped by a bivariate Gaussian Mixture Model (GMM), which includes a noise component, is fitted to the peptide peaks using the expectation-maximization (EM) approach. The EM starting parameters, i.e., the centers and shapes of the Gaussians, are retrieved from the metadata. The borders of the peaks are delimited by the GMM iso-density curves, and noisy or outlying data are discarded from subsequent analysis. The 3DSpectra program was compared to ASAPRatio for a controlled mixture of Isotope-Coded Protein Labels (ICPL) labeled proteins, which were mixed at predefined ratios and acquired in enhanced profile mode, in triplicate. The 3DSpectra software showed significantly higher linearity, quantification accuracy, and precision than did ASAPRatio in this real use case simulation where the true ratios are known, and it also achieved wider peptide coverage and dynamic range.

BIOLOGICAL SIGNIFICANCE

Quantitative proteomics is pivotal for many systems biology related fields, such as biomarker discovery. The quantification quality provided by the adopted software is crucial for the success of protein differential expression studies. To determine the reliability of a quantitative computational method, we suggest evaluating performance parameters like accuracy and precision of the quantifications, robustness to outliers and proteome coverage. A quantitative comparison of these parameters is highly desirable since it enables to benchmark software performance. We applied this strategy to 3DSpectra, a 3-dimensional approach to spectra analysis for MS1 peptide quantification. It distinguishes peptide peaks from spurious peaks interfering in the survey scan. 3DSpectra was compared to ASAPRatio in terms of quantification quality performance parameters and showed an overall improvement.

Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics

SUMMARY

The complex, rapidly evolving field of computational metabolomics calls for collaborative infrastructures where the large volume of new algorithms for data pre-processing, statistical analysis and annotation can be readily integrated whatever the language, evaluated on reference datasets and chained to build ad hoc workflows for users. We have developed Workflow4Metabolomics (W4M), the first fully open-source and collaborative online platform for computational metabolomics. W4M is a virtual research environment built upon the Galaxy web-based platform technology. It enables ergonomic integration, exchange and running of individual modules and workflows. Alternatively, the whole W4M framework and computational tools can be downloaded as a virtual machine for local installation.

AVAILABILITY AND IMPLEMENTATION

http://workflow4metabolomics.org homepage enables users to open a private account and access the infrastructure. W4M is developed and maintained by the French Bioinformatics Institute (IFB) and the French Metabolomics and Fluxomics Infrastructure (MetaboHUB).

CONTACT

contact@workflow4metabolomics.org.

MetaDB a Data Processing Workflow in Untargeted MS-Based Metabolomics Experiments

Due to their sensitivity and speed, mass-spectrometry based analytical technologies are widely used to in metabolomics to characterize biological phenomena. To address issues like metadata organization, quality assessment, data processing, data storage, and, finally, submission to public repositories, bioinformatic pipelines of a non-interactive nature are often employed, complementing the interactive software used for initial inspection and visualization of the data. These pipelines often are created as open-source software allowing the complete and exhaustive documentation of each step, ensuring the reproducibility of the analysis of extensive and often expensive experiments. In this paper, we will review the major steps which constitute such a data processing pipeline, discussing them in the context of an open-source software for untargeted MS-based metabolomics experiments recently developed at our institute. The software has been developed by integrating our metaMS R package with a user-friendly web-based application written in Grails. MetaMS takes care of data pre-processing and annotation, while the interface deals with the creation of the sample lists, the organization of the data storage, and the generation of survey plots for quality assessment. Experimental and biological metadata are stored in the ISA-Tab format making the proposed pipeline fully integrated with the Metabolights framework.

iPhos: a toolkit to streamline the alkaline phosphatase-assisted comprehensive LC-MS phosphoproteome investigation

Background

Comprehensive characterization of the phosphoproteome in living cells is critical in signal transduction research. But the low abundance of phosphopeptides among the total proteome in cells remains an obstacle in mass spectrometry-based proteomic analysis. To provide a solution, an alternative analytic strategy to confidently identify phosphorylated peptides by using the alkaline phosphatase (AP) treatment combined with high-resolution mass spectrometry was provided. While the process is applicable, the key integration along the pipeline was mostly done by tedious manual work.

Results

We developed a software toolkit, iPhos, to facilitate and streamline the work-flow of AP-assisted phosphoproteome characterization. The iPhos tookit includes one assister and three modules. The iPhos Peak Extraction Assister automates the batch mode peak extraction for multiple liquid chromatography mass spectrometry (LC-MS) runs. iPhos Module-1 can process the peak lists extracted from the LC-MS analyses derived from the original and dephosphorylated samples to mine out potential phosphorylated peptide signals based on mass shift caused by the loss of some multiples of phosphate groups. And iPhos Module-2 provides customized inclusion lists with peak retention time windows for subsequent targeted LC-MS/MS experiments. Finally, iPhos Module-3 facilitates to link the peptide identifications from protein search engines to the quantification results from pattern-based label-free quantification tools. We further demonstrated the utility of the iPhos toolkit on the data of human metastatic lung cancer cells (CL1-5).

Conclusions

In the comparison study of the control group of CL1-5 cell lysates and the treatment group of dasatinib-treated CL1-5 cell lysates, we demonstrated the applicability of the iPhos toolkit and reported the experimental results based on the iPhos-facilitated phosphoproteome investigation. And further, we also compared the strategy with pure DDA-based LC-MS/MS phosphoproteome investigation. The results of iPhos-facilitated targeted LC-MS/MS analysis convey more thorough and confident phosphopeptide identification than the results of pure DDA-based analysis.

Sample preparation strategies for mass spectrometry imaging of 3D cell culture models

Three dimensional cell cultures are attractive models for biological research. They combine the flexibility and cost-effectiveness of cell culture with some of the spatial and molecular complexity of tissue. For example, many cell lines form 3D structures given appropriate in vitro conditions. Colon cancer cell lines form 3D cell culture spheroids, in vitro mimics of avascular tumor nodules. While immunohistochemistry and other classical imaging methods are popular for monitoring the distribution of specific analytes, mass spectrometric imaging examines the distribution of classes of molecules in an unbiased fashion. While MALDI mass spectrometric imaging was originally developed to interrogate samples obtained from humans or animal models, this report describes the analysis of in vitro three dimensional cell cultures, including improvements in sample preparation strategies. Herein is described methods for growth, harvesting, sectioning, washing, and analysis of 3D cell cultures via matrix-assisted laser desorption/ionization-mass spectrometry (MALDI-MS) imaging. Using colon carcinoma 3D cell cultures as a model system, this protocol demonstrates the ability to monitor analytes in an unbiased fashion across the 3D cell culture system with MALDI-MSI.

Processing shotgun proteomics data on the Amazon cloud with the trans-proteomic pipeline

Cloud computing, where scalable, on-demand compute cycles and storage are available as a service, has the potential to accelerate mass spectrometry-based proteomics research by providing simple, expandable, and affordable large-scale computing to all laboratories regardless of location or information technology expertise. We present new cloud computing functionality for the Trans-Proteomic Pipeline, a free and open-source suite of tools for the processing and analysis of tandem mass spectrometry datasets. Enabled with Amazon Web Services cloud computing, the Trans-Proteomic Pipeline now accesses large scale computing resources, limited only by the available Amazon Web Services infrastructure, for all users. The Trans-Proteomic Pipeline runs in an environment fully hosted on Amazon Web Services, where all software and data reside on cloud resources to tackle large search studies. In addition, it can also be run on a local computer with computationally intensive tasks launched onto the Amazon Elastic Compute Cloud service to greatly decrease analysis times. We describe the new Trans-Proteomic Pipeline cloud service components, compare the relative performance and costs of various Elastic Compute Cloud service instance types, and present on-line tutorials that enable users to learn how to deploy cloud computing technology rapidly with the Trans-Proteomic Pipeline. We provide tools for estimating the necessary computing resources and costs given the scale of a job and demonstrate the use of cloud enabled Trans-Proteomic Pipeline by performing over 1100 tandem mass spectrometry files through four proteomic search engines in 9 h and at a very low cost.

Stable isotope-labeling studies in metabolomics: new insights into structure and dynamics of metabolic networks

The rapid emergence of metabolomics has enabled system-wide measurements of metabolites in various organisms. However, advances in the mechanistic understanding of metabolic networks remain limited, as most metabolomics studies cannot routinely provide accurate metabolite identification, absolute quantification and flux measurement. Stable isotope labeling offers opportunities to overcome these limitations. Here we describe some current approaches to stable isotope-labeled metabolomics and provide examples of the significant impact that these studies have had on our understanding of cellular metabolism. Furthermore, we discuss recently developed software solutions for the analysis of stable isotope-labeled metabolomics data and propose the bioinformatics solutions that will pave the way for the broader application and optimal interpretation of system-scale labeling studies in metabolomics.

Automated deconvolution of overlapped ion mobility profiles

Presence of unresolved ion mobility (IM) profiles limits the efficient utilization of IM mass spectrometry (IM-MS) systems for isomer differentiation. Here, we introduce an automated ion mobility deconvolution (AIMD) computer software for streamlined deconvolution of overlapped IM-MS profiles. AIMD is based on a previously reported post-IM/collision-induced dissociation (CID) deconvolution approach [J. Am. Soc. Mass Spectrom. 23, 1873 (2012)] and, unlike the previously reported manual approach, it does not require resampling of post-IM/CID data. A novel data preprocessing approach is utilized to improve the accuracy and efficiency of the deconvolution process. Results from AIMD analysis of overlapped IM profiles of data from (1) Waters Synapt G1 for a binary mixture of isomeric peptides (amino acid sequences: GRGDS and SDGRG) and (2) Waters Synapt G2-S for a binary mixture of isomeric trisaccharides (raffinose and isomaltotriose) are presented.

Mass-spectrometry-based microbial metabolomics: recent developments and applications

Metabolomics is an omics technique aiming at qualitatively and quantitatively describing a metabolome by various analytical platforms. It is an indispensable component of modern systems biology. Microbial metabolomics can be roughly classified as metabolic footprint analysis and metabolic fingerprint analysis depending on the analyte origins. Both of them have been beneficial to microbiological research for different reasons. Mass spectrometry and nuclear magnetic resonance spectroscopy techniques are popular analytical strategies prevailing in the metabolomics field. In this review, chromatography-mass-spectrometry-based microbial metabolomic analysis steps are summarized, including sample collection, metabolite extraction, instrument analysis, and data analysis. Moreover, their applications in some representative fields are discussed as examples. The aim of this review is to present briefly recent technical advances in mass-spectrometry-based analysis, and to highlight the value of modern applications of microbial metabolomics.

Impact of routinely employed procedures for tissue processing on the proteomic analysis of formalin-fixed paraffin-embedded tissue

PURPOSE

FFPE (formalin fixed, paraffin embedded) tissue cohorts represent an enduring archive of clinical specimens. Proteomic analysis of FFPE tissues is gaining interest for the in-depth analysis of aberrant proteome composition. Procedures for FFPE tissue processing are standardized but there is diversity regarding the different processing systems. This work focuses on three different processing methods commonly used in large European pathology institutes.

EXPERIMENTAL DESIGN

Formalin fixed tissue specimens of different tumors were serially sliced and processed with three different processing systems (xylene, ethanol/vacuum or microwave based). After paraffin embedding, they were subjected to MS-based proteomic analysis to investigate the impact of tissue processing techniques on the quality of proteomic analysis. Results were compared with proteomic analysis of corresponding cryopreserved tissue specimens.

RESULTS

All processing techniques achieved very good proteome coverage similar to the cryopreserved counterpart. Gene ontology profiles, relative protein abundances, and peptide modifications such as methionine oxidation or proteolytic truncation were highly similar for all techniques as well as for the cryopreserved samples.

CONCLUSIONS AND CLINICAL RELEVANCE

The results show that different processing procedures do not impede proteomic analysis as a robust and powerful approach for the identification of protein determinants and markers of disease processes and highlights the general robustness of FFPE-tissue based proteomics.

Carbonylated plasma proteins as potential biomarkers of obesity induced type 2 diabetes mellitus

Protein carbonylation is a common nonenzymatic oxidative post-translational modification, which is often considered as biomarker of oxidative stress. Recent evidence links protein carbonylation also to obesity and type 2 diabetes mellitus (T2DM), though the protein targets of carbonylation in human plasma have not been identified. In this study, we profiled carbonylated proteins in plasma samples obtained from lean individuals and obese patients with or without T2DM. The plasma samples were digested with trypsin, carbonyl groups were derivatized with O-(biotinylcarbazoylmethyl)hydroxylamine, enriched by avidin affinity chromatography, and analyzed by RPC-MS/MS. Signals of potentially modified peptides were targeted in a second LC-MS/MS analysis to retrieve the peptide sequence and the modified residues. A total of 158 unique carbonylated proteins were identified, of which 52 were detected in plasma samples of all three groups. Interestingly, 36 carbonylated proteins were detected only in obese patients with T2DM, whereas 18 were detected in both nondiabetic groups. The carbonylated proteins originated mostly from liver, plasma, platelet, and endothelium. Functionally, they were mainly involved in cell adhesion, signaling, angiogenesis, and cytoskeletal remodeling. Among the identified carbonylated proteins were several candidates, such as VEGFR-2, MMP-1, argin, MKK4, and compliment C5, already connected before to diabetes, obesity and metabolic diseases.

Quantitative proteome profiling of CNS-infiltrating autoreactive CD4+ cells reveals selective changes during experimental autoimmune encephalomyelitis

Experimental autoimmune encephalomyelitis (EAE) is a murine model of multiple sclerosis, a chronic neurodegenerative and inflammatory autoimmune condition of the central nervous system (CNS). Pathology is driven by the infiltration of autoreactive CD4(+) lymphocytes into the CNS, where they attack neuronal sheaths causing ascending paralysis. We used an isotope-coded protein labeling approach to investigate the proteome of CD4(+) cells isolated from the spinal cord and brain of mice at various stages of EAE progression in two EAE disease models: PLP139-151-induced relapsing-remitting EAE and MOG35-55-induced chronic EAE, which emulate the two forms of human multiple sclerosis. A total of 1120 proteins were quantified across disease onset, peak-disease, and remission phases of disease, and of these 13 up-regulated proteins of interest were identified with functions relating to the regulation of inflammation, leukocyte adhesion and migration, tissue repair, and the regulation of transcription/translation. Proteins implicated in processes such as inflammation (S100A4 and S100A9) and tissue repair (annexin A1), which represent key events during EAE progression, were validated by quantitative PCR. This is the first targeted analysis of autoreactive cells purified from the CNS during EAE, highlighting fundamental CD4(+) cell-driven processes that occur during the initiation of relapse and remission stages of disease.

The mzTab data exchange format: communicating mass-spectrometry-based proteomics and metabolomics experimental results to a wider audience

The HUPO Proteomics Standards Initiative has developed several standardized data formats to facilitate data sharing in mass spectrometry (MS)-based proteomics. These allow researchers to report their complete results in a unified way. However, at present, there is no format to describe the final qualitative and quantitative results for proteomics and metabolomics experiments in a simple tabular format. Many downstream analysis use cases are only concerned with the final results of an experiment and require an easily accessible format, compatible with tools such as Microsoft Excel or R. We developed the mzTab file format for MS-based proteomics and metabolomics results to meet this need. mzTab is intended as a lightweight supplement to the existing standard XML-based file formats (mzML, mzIdentML, mzQuantML), providing a comprehensive summary, similar in concept to the supplemental material of a scientific publication. mzTab files can contain protein, peptide, and small molecule identifications together with experimental metadata and basic quantitative information. The format is not intended to store the complete experimental evidence but provides mechanisms to report results at different levels of detail. These range from a simple summary of the final results to a representation of the results including the experimental design. This format is ideally suited to make MS-based proteomics and metabolomics results available to a wider biological community outside the field of MS. Several software tools for proteomics and metabolomics have already adapted the format as an output format. The comprehensive mzTab specification document and extensive additional documentation can be found online.

Transcriptomics and proteomics in stem cell research

Stem cells are capable of self-renewal and differentiation, and the processes regulating these events are among the most comprehensively investigated topics in life sciences. In particular, the molecular mechanisms of the self-renewal, proliferation, and differentiation of stem cells have been extensively examined. Multi-omics integrative analysis, such as transcriptomics combined with proteomics, is one of the most promising approaches to the systemic investigation of stem cell biology. We reviewed the available information on stem cells by examining published results using transcriptomic and proteomic characterization of the different stem cell processes. Comprehensive understanding of these important processes can only be achieved using a systemic methodology, and employing such method will strengthen the study on stem cell biology and promote the clinical applications of stem cells.

Automated peptide mapping and protein-topographical annotation of proteomics data

Background

In quantitative proteomics, peptide mapping is a valuable approach to combine positional quantitative information with topographical and domain information of proteins. Quantitative proteomic analysis of cell surface shedding is an exemplary application area of this approach.

Results

We developed ImproViser ( http://www.improviser.uni-freiburg.de) for fully automated peptide mapping of quantitative proteomics data in the protXML data. The tool generates sortable and graphically annotated output, which can be easily shared with further users. As an exemplary application, we show its usage in the proteomic analysis of regulated intramembrane proteolysis.

Conclusion

ImproViser is the first tool to enable automated peptide mapping of the widely-used protXML format.

Employing ProteoWizard to Convert Raw Mass Spectrometry Data

After raw data have been captured by mass spectrometers in biological LC-MS/MS experiments, they must be converted from vendor-specific binary files to open-format files for manipulation by most software. This protocol details the use of ProteoWizard software for this conversion, taking format features, coding options, and vendor particularities into account. This protocol will aid researchers in preparing their data for analysis by database search engines and other bioinformatics tools.

Using PeptideAtlas, SRMAtlas, and PASSEL: Comprehensive Resources for Discovery and Targeted Proteomics

PeptideAtlas, SRMAtlas, and PASSEL are Web-accessible resources to support discovery and targeted proteomics research. PeptideAtlas is a multi-species compendium of shotgun proteomic data provided by the scientific community; SRMAtlas is a resource of high-quality, complete proteome SRM assays generated in a consistent manner for the targeted identification and quantification of proteins; and PASSEL is a repository that compiles and represents selected reaction monitoring data, all in an easy-to-use interface. The databases are generated from native mass spectrometry data files that are analyzed in a standardized manner including statistical validation of the results. Each resource offers search functionalities and can be queried by user-defined constraints; the query results are provided in tables or are graphically displayed. PeptideAtlas, SRMAtlas, and PASSEL are publicly available freely via the Web site http://www.peptideatlas.org. In this protocol, we describe the use of these resources, we highlight how to submit, search, collate and download data.

Comparative performance of four methods for high-throughput glycosylation analysis of immunoglobulin G in genetic and epidemiological research

The biological and clinical relevance of glycosylation is becoming increasingly recognized, leading to a growing interest in large-scale clinical and population-based studies. In the past few years, several methods for high-throughput analysis of glycans have been developed, but thorough validation and standardization of these methods is required before significant resources are invested in large-scale studies. In this study, we compared liquid chromatography, capillary gel electrophoresis, and two MS methods for quantitative profiling of N-glycosylation of IgG in the same data set of 1201 individuals. To evaluate the accuracy of the four methods we then performed analysis of association with genetic polymorphisms and age. Chromatographic methods with either fluorescent or MS-detection yielded slightly stronger associations than MS-only and multiplexed capillary gel electrophoresis, but at the expense of lower levels of throughput. Advantages and disadvantages of each method were identified, which should inform the selection of the most appropriate method in future studies.

Data visualization in environmental proteomics

From raw data to gene expression profiles, from single cultures to complex microbial communities, environmental proteomics works with data of different complexity levels that need to be interpreted in detail or in its entirety. Although data visualization is closely connected with data analysis approaches, this work will solely focus on data visualization. Complementing traditional tools such as bar charts or line graphs, scientists and visualization professionals have been provided sophisticated visualization tools. Many rules and concerns regarding the display of single but also complex data will be reviewed and discussed. Visual approaches such as microcharts, heat maps, stream graphs, and tree maps will be brought to the reader's attention and demonstrated by utilizing real data sets.

MassCascade: Visual Programming for LC-MS Data Processing in Metabolomics

Liquid chromatography coupled to mass spectrometry (LC-MS) is commonly applied to investigate the small molecule complement of organisms. Several software tools are typically joined in custom pipelines to semi-automatically process and analyse the resulting data. General workflow environments like the Konstanz Information Miner (KNIME) offer the potential of an all-in-one solution to process LC-MS data by allowing easy integration of different tools and scripts. We describe MassCascade and its workflow plug-in for processing LC-MS data. The Java library integrates frequently used algorithms in a modular fashion, thus enabling it to serve as back-end for graphical front-ends. The functions available in MassCascade have been encapsulated in a plug-in for the workflow environment KNIME, allowing combined use with e.g. statistical workflow nodes from other providers and making the tool intuitive to use without knowledge of programming. The design of the software guarantees a high level of modularity where processing functions can be quickly replaced or concatenated. MassCascade is an open-source library for LC-MS data processing in metabolomics. It embraces the concept of visual programming through its KNIME plug-in, simplifying the process of building complex workflows. The library was validated using open data.

Numerical compression schemes for proteomics mass spectrometry data

The open XML format mzML, used for representation of MS data, is pivotal for the development of platform-independent MS analysis software. Although conversion from vendor formats to mzML must take place on a platform on which the vendor libraries are available (i.e. Windows), once mzML files have been generated, they can be used on any platform. However, the mzML format has turned out to be less efficient than vendor formats. In many cases, the naïve mzML representation is fourfold or even up to 18-fold larger compared with the original vendor file. In disk I/O limited setups, a larger data file also leads to longer processing times, which is a problem given the data production rates of modern mass spectrometers. In an attempt to reduce this problem, we here present a family of numerical compression algorithms called MS-Numpress, intended for efficient compression of MS data. To facilitate ease of adoption, the algorithms target the binary data in the mzML standard, and support in main proteomics tools is already available. Using a test set of 10 representative MS data files we demonstrate typical file size decreases of 90% when combined with traditional compression, as well as read time decreases of up to 50%. It is envisaged that these improvements will be beneficial for data handling within the MS community.

Double deficiency of cathepsins B and L results in massive secretome alterations and suggests a degradative cathepsin-MMP axis

Endolysosomal cysteine cathepsins functionally cooperate. Cathepsin B (Ctsb) and L (Ctsl) double-knockout mice die 4 weeks after birth accompanied by (autophago-) lysosomal accumulations within neurons. Such accumulations are also observed in mouse embryonic fibroblasts (MEFs) deficient for Ctsb and Ctsl. Previous studies showed a strong impact of Ctsl on the MEF secretome. Here we show that Ctsb alone has only a mild influence on extracellular proteome composition. Protease cleavage sites dependent on Ctsb were identified by terminal amine isotopic labeling of substrates (TAILS), revealing a prominent yet mostly indirect impact on the extracellular proteolytic cleavages. To investigate the cooperation of Ctsb and Ctsl, we performed a quantitative secretome comparison of wild-type MEFs and Ctsb (-/-) Ctsl (-/-) MEFs. Deletion of both cathepsins led to drastic alterations in secretome composition, highlighting cooperative functionality. While many protein levels were decreased, immunodetection corroborated increased levels of matrix metalloproteinase (MMP)-2. Re-expression of Ctsl rescues MMP-2 abundance. Ctsl and to a much lesser extent Ctsb are able to degrade MMP-2 at acidic and neutral pH. Addition of active MMP-2 to the MEF secretome degrades proteins whose levels were also decreased by Ctsb and Ctsl double deficiency. These results suggest a degradative Ctsl-MMP-2 axis, resulting in increased MMP-2 levels upon cathepsin deficiency with subsequent degradation of secreted proteins such as collagen α-1 (I).

Identifying proteins in zebrafish embryos using spectral libraries generated from dissected adult organs and tissues

Spectral libraries provide a sensitive and accurate method for identifying peptides from tandem mass spectra, complementary to searching genome-derived databases or sequencing de novo. Their application requires comprehensive libraries including peptides from low-abundant proteins. Here we describe a method for constructing such libraries using biological differentiation to "fractionate" the proteome by harvesting adult organs and tissues and build comprehensive libraries for identifying proteins in zebrafish (Danio rerio) embryos and larvae (an important and widely used model system). Hierarchical clustering using direct comparison of spectra was used to prioritize organ selection. The resulting and publicly available library covers 14,164 proteins, significantly improved the number of peptide-spectrum matches in zebrafish developmental stages, and can be used on data from different instruments and laboratories. The library contains information on tissue and organ expression of these proteins and is also applicable for adult experiments. The approach itself is not limited to zebrafish but would work for any model system.

What is mzXML good for?

mzXML (extensible markup language) is one of the pioneering data formats for mass spectrometry-based proteomics data collection. It is an open data format that has benefited and evolved as a result of the input of many groups, and it continues to evolve. Due to its dynamic history, its structure, purpose and applicability have all changed with time, meaning that groups that have looked at the standard at different points during its evolution have differing impressions of the usefulness of mzXML. In discussing mzXML, it is important to understand what mzXML is not. First, mzXML does not capture the raw data. Second, mzXML is not sufficient for regulatory submission. Third, mzXML is not optimized for computation and, finally, mzXML does not capture the experiment design. In general, it is the authors' opinion that XML is not a panacea for bioinformatics or a substitute for good data representation, and groups that want to use mzXML (or other XML-based representations) directly for data storage or computation will encounter performance and scalability problems. With these limitations in mind, the authors conclude that mzXML is, nonetheless, an indispensable data exchange format for proteomics.

Loss of Arabidopsis GAUT12/IRX8 causes anther indehiscence and leads to reduced G lignin associated with altered matrix polysaccharide deposition

GAlactUronosylTransferase12 (GAUT12)/IRregular Xylem8 (IRX8) is a putative glycosyltransferase involved in Arabidopsis secondary cell wall biosynthesis. Previous work showed that Arabidopsis irregular xylem8 (irx8) mutants have collapsed xylem due to a reduction in xylan and a lesser reduction in a subfraction of homogalacturonan (HG). We now show that male sterility in the irx8 mutant is due to indehiscent anthers caused by reduced deposition of xylan and lignin in the endothecium cell layer. The reduced lignin content was demonstrated by histochemical lignin staining and pyrolysis Molecular Beam Mass Spectrometry (pyMBMS) and is associated with reduced lignin biosynthesis in irx8 stems. Examination of sequential chemical extracts of stem walls using 2D (13)C-(1)H Heteronuclear Single-Quantum Correlation (HSQC) NMR spectroscopy and antibody-based glycome profiling revealed a reduction in G lignin in the 1 M KOH extract and a concomitant loss of xylan, arabinogalactan and pectin epitopes in the ammonium oxalate, sodium carbonate, and 1 M KOH extracts from the irx8 walls compared with wild-type walls. Immunolabeling of stem sections using the monoclonal antibody CCRC-M138 reactive against an unsubstituted xylopentaose epitope revealed a bi-lamellate pattern in wild-type fiber cells and a collapsed bi-layer in irx8 cells, suggesting that at least in fiber cells, GAUT12 participates in the synthesis of a specific layer or type of xylan or helps to provide an architecture framework required for the native xylan deposition pattern. The results support the hypothesis that GAUT12 functions in the synthesis of a structure required for xylan and lignin deposition during secondary cell wall formation.

A novel stable isotope labelling assisted workflow for improved untargeted LC-HRMS based metabolomics research

Many untargeted LC-ESI-HRMS based metabolomics studies are still hampered by the large proportion of non-biological sample derived signals included in the generated raw data. Here, a novel, powerful stable isotope labelling (SIL)-based metabolomics workflow is presented, which facilitates global metabolome extraction, improved metabolite annotation and metabolome wide internal standardisation (IS). The general concept is exemplified with two different cultivation variants, (1) co-cultivation of the plant pathogenic fungi Fusarium graminearum on non-labelled and highly ¹³C enriched culture medium and (2) experimental cultivation under native conditions and use of globally U-¹³C labelled biological reference samples as exemplified with maize and wheat. Subsequent to LC-HRMS analysis of mixtures of labelled and non-labelled samples, two-dimensional data filtering of SIL specific isotopic patterns is performed to better extract truly biological derived signals together with the corresponding number of carbon atoms of each metabolite ion. Finally, feature pairs are convoluted to feature groups each representing a single metabolite. Moreover, the correction of unequal matrix effects in different sample types and the improvement of relative metabolite quantification with metabolome wide IS are demonstrated for the F. graminearum experiment. Data processing employing the presented workflow revealed about 300 SIL derived feature pairs corresponding to 87-135 metabolites in F. graminearum samples and around 800 feature pairs corresponding to roughly 350 metabolites in wheat samples. SIL assisted IS, by the use of globally U-¹³C labelled biological samples, reduced the median CV value from 7.1 to 3.6 % for technical replicates and from 15.1 to 10.8 % for biological replicates in the respective F. graminearum samples.

Software tools of the Computis European project to process mass spectrometry images

Among the needs usually expressed by teams using mass spectrometry imaging, one that often arises is that for user-friendly software able to manage huge data volumes quickly and to provide efficient assistance for the interpretation of data. To answer this need, the Computis European project developed several complementary software tools to process mass spectrometry imaging data. Data Cube Explorer provides a simple spatial and spectral exploration for matrix-assisted laser desorption/ionisation-time of flight (MALDI-ToF) and time of flight-secondary-ion mass spectrometry (ToF-SIMS) data. SpectViewer offers visualisation functions, assistance to the interpretation of data, classification functionalities, peak list extraction to interrogate biological database and image overlay, and it can process data issued from MALDI-ToF, ToF-SIMS and desorption electrospray ionisation (DESI) equipment. EasyReg2D is able to register two images, in American Standard Code for Information Interchange (ASCII) format, issued from different technologies. The collaboration between the teams was hampered by the multiplicity of equipment and data formats, so the project also developed a common data format (imzML) to facilitate the exchange of experimental data and their interpretation by the different software tools. The BioMap platform for visualisation and exploration of MALDI-ToF and DESI images was adapted to parse imzML files, enabling its access to all project partners and, more globally, to a larger community of users. Considering the huge advantages brought by the imzML standard format, a specific editor (vBrowser) for imzML files and converters from proprietary formats to imzML were developed to enable the use of the imzML format by a broad scientific community. This initiative paves the way toward the development of a large panel of software tools able to process mass spectrometry imaging datasets in the future.

Catch me if you can: challenges and applications of cross-linking approaches

Biomolecular complexes are the groundwork of life and the basis for cell signaling, energy transfer, motion, stability and cellular metabolism. Understanding the underlying complex interactions on the molecular level is an essential step to obtain a comprehensive insight into cellular and systems biology. For the investigation of molecular interactions, various methods, including Förster resonance energy transfer, nuclear magnetic resonance spectroscopy, X-ray crystallography and yeast two-hybrid screening, can be utilized. Nevertheless, the most reliable approach for structural proteomics and the identification of novel protein-binding partners is chemical cross-linking. The rationale is that upon forming a covalent bond between a protein and its interaction partner (protein, lipid, RNA/DNA, carbohydrate) the native complex state is "frozen" and accessible for detailed mass spectrometric analysis. In this review we provide a synopsis on crosslinker design, chemistry, pitfalls, limitations and novel applications in the field, and feature an overview of current software applications.