Frode S. Berven

A consensus protocol for the standardization of cerebrospinal fluid collection and biobanking

There is a long history of research into body fluid biomarkers in neurodegenerative and neuroinflammatory diseases. However, only a few biomarkers in CSF are being used in clinical practice. One of the most critical factors in CSF biomarker research is the inadequate powering of studies because of the lack of sufficient samples that can be obtained in single-center studies. Therefore, collaboration between investigators is needed to establish large biobanks of well-defined samples. Standardized protocols for biobanking are a prerequisite to ensure that the statistical power gained by increasing the numbers of CSF samples is not compromised by preanalytical factors. Here, a consensus report on recommendations for CSF collection and biobanking is presented, formed by the BioMS-eu network for CSF biomarker research in multiple sclerosis. We focus on CSF collection procedures, preanalytical factors, and high-quality clinical and paraclinical information. The biobanking protocols are applicable for CSF biobanks for research targeting any neurologic disease.

PeptideShaker enables reanalysis of MS-derived proteomics data sets

VOLUME 33 NUMBER 1 JANUARY 2015 NATURE BIOTECHNOLOGY methods (Supplementary Note 1). The reliability of all statistical metrics has been validated in detail using the complex Pyrococcus furiosus standard with an entrapment database for FDR accuracy verification14 (Supplementary Note 1). PeptideShaker’s user-oriented interface is divided into nine linked tabs, such that selections in any one tab are automatically propagated to the other tabs. The initial display is the ‘Overview’ tab (Fig. 2), which shows a single interactive view that includes identified proteins, peptides and spectra. Additional tabs feature specific aspects of a typical proteomics analysis pipeline, including spectrum identification details (with an emphasis on multiple search engine comparison), protein fractionation analysis, modification site localization analysis, protein three-dimensional structures (with mapped modifications), link to functional annotation resources, gene ontology analysis, identification validation and quality control (Supplementary Note 1). Numerous visualizations are provided in PeptideShaker to help the user understand the significance of the underlying data. For example, the software takes advantage of the identification multiplicity typical of proteomics experiments by visualizing multiple recorded PSMs15 for a given peptide or by displaying posttranslational modification localization both within and across spectra (Supplementary Note 1). Moreover, PeptideShaker provides chromosome and gene mapping, modification analysis and intuitive coverage annotation on the sequence of every identified protein, meeting the goals of the Human Proteome Project16. Results from PeptideShaker can be readily submitted to PRIDE and ProteomeXchange using the built-in PRIDE and mzIdentML17 exports; at the time of writing, this has already resulted in 52 publicly available PeptideShaker-derived ProteomeXchange assays. Other export options include spreadsheet-compatible text files, a descriptive certificate of analysis, highresolution image formats for all displayed graphics, exports to common graph database formats including Cytoscape (http:// cytoscape.org), and export to the widely used Nonlinear (http://www.nonlinear.com) Progenesis liquid chromatography (LC)-MS package for label-free quantification. To the Editor: Mass spectrometry (MS)-based proteomics is commonly used to identify and quantify the hundreds to thousands of proteins that are present in complex biological samples. Widespread data sharing via publicly accessible repositories, such as PRIDE1, has now become standard practice, aided by robust user-oriented tools for data submission2,3 and inspection4 and bolstered by the advent of the ProteomeXchange initiative5. Importantly, repository data sharing has enabled the first high-profile studies in which the repurposing of publicly available proteomics data has revealed new biological insights6,7. However, proteomics data processing and (re-)analysis currently remain far from routine practice. There is a pressing need for a user-friendly, open source tool that empowers users to carry out state-of-the-art proteomics data analysis at any stage in the data life cycle8,9. To maximize the value of public proteomics data, reuse and repurposing must become straightforward, allowing the completion of the proteomics data cycle. Here we describe PeptideShaker (http://peptideshaker.googlecode.com), a proteomics informatics software that can be used at any stage in the proteomics data cycle for the analysis and interpretation of primary data, enabling data sharing and dissemination and re-analysis of publicly available proteomics data. Importantly, PeptideShaker can work with the combined output of multiple identification algorithms (Fig. 1). To identify peptides and proteins PeptideShaker uses the target-decoy search strategy10 to estimate posterior error probabilities and uses these to unify the peptide-to-spectrum match (PSM) lists of different search engines, thus increasing the confidence and sensitivity of hits compared with single-search-engine processing11,12. PeptideShaker provides statistical confidence estimates for each peptide and protein, taking into account protein inference issues13. Furthermore, as well as providing false discovery rates (FDRs) at the PSM, peptide and protein levels, PeptideShaker calculates reliable false negative rates (FNRs), providing the user with a novel and highly useful interface to filter results according to an FDR-versus-FNR cost-benefit rationale that has so far been absent from proteomics. This filter for specificity and sensitivity includes interactive graphs providing immediate feedback on the values of FDR and FNR for PSMs, peptides and proteins at any chosen threshold. In addition to these identification reliability measures, PeptideShaker also provides confident modification site inference using the latest localization PeptideShaker enables reanalysis of MS-derived proteomics data sets

SearchGUI: An open-source graphical user interface for simultaneous OMSSA and X!Tandem searches

The identification of proteins by mass spectrometry is a standard technique in the field of proteomics, relying on search engines to perform the identifications of the acquired spectra. Here, we present a user‐friendly, lightweight and open‐source graphical user interface called SearchGUI (http://searchgui.googlecode.com), for configuring and running the freely available OMSSA (open mass spectrometry search algorithm) and X!Tandem search engines simultaneously. Freely available under the permissible Apache2 license, SearchGUI is supported on Windows, Linux and OSX.

BOMP: a program to predict integral β-barrel outer membrane proteins encoded within genomes of Gram-negative bacteria

This work describes the development of a program that predicts whether or not a polypeptide sequence from a Gram-negative bacterium is an integral beta-barrel outer membrane protein. The program, called the beta-barrel Outer Membrane protein Predictor (BOMP), is based on two separate components to recognize integral beta-barrel proteins. The first component is a C-terminal pattern typical of many integral beta-barrel proteins. The second component calculates an integral beta-barrel score of the sequence based on the extent to which the sequence contains stretches of amino acids typical of transmembrane beta-strands. The precision of the predictions was found to be 80% with a recall of 88% when tested on the proteins with SwissProt annotated subcellular localization in Escherichia coli K 12 (788 sequences) and Salmonella typhimurium (366 sequences). When tested on the predicted proteome of E.coli, BOMP found 103 of a total of 4346 polypeptide sequences to be possible integral beta-barrel proteins. Of these, 36 were found by BLAST to lack similarity (E-value score < 1e-10) to proteins with annotated subcellular localization in SwissProt. BOMP predicted the content of integral beta-barrels per predicted proteome of 10 different bacteria to range from 1.8 to 3%. BOMP is available at http://www.bioinfo.no/tools/bomp.

Proteomics of human cerebrospinal fluid: Discovery and verification of biomarker candidates in neurodegenerative diseases using quantitative proteomics

There is an urgent need for novel biomarkers that can be used to improve the diagnosis, predict the disease progression, improve our understanding of the pathology or serve as therapeutic targets for neurodegenerative diseases. Cerebrospinal fluid (CSF) is in direct contact with the CNS and reflects the biochemical state of the CNS under different physiological and pathological settings. Because of this, CSF is regarded as an excellent source for identifying biomarkers for neurological diseases and other diseases affecting the CNS. Quantitative proteomics and sophisticated computational software applied to analyze the protein content of CSF has been fronted as an attractive approach to find novel biomarkers for neurological diseases. This review will focus on some of the potential pitfalls in biomarker studies using CSF, summarize the status of the field of CSF proteomics in general, and discuss some of the most promising proteomics biomarker study approaches. A brief status of the biomarker discovery efforts in multiple sclerosis, Alzheimers disease, and Parkinsons disease is also given.

Consensus definitions and application guidelines for control groups in cerebrospinal fluid biomarker studies in multiple sclerosis.

The choice of appropriate control group(s) is critical in cerebrospinal fluid (CSF) biomarker research in multiple sclerosis (MS). There is a lack of definitions and nomenclature of different control groups and a rationalized application of different control groups. We here propose consensus definitions and nomenclature for the following groups: healthy controls (HCs), spinal anesthesia subjects (SASs), inflammatory neurological disease controls (INDCs), peripheral inflammatory neurological disease controls (PINDCs), non-inflammatory neurological controls (NINDCs), symptomatic controls (SCs). Furthermore, we discuss the application of these control groups in specific study designs, such as for diagnostic biomarker studies, prognostic biomarker studies and therapeutic response studies. Application of these uniform definitions will lead to better comparability of biomarker studies and optimal use of available resources. This will lead to improved quality of CSF biomarker research in MS and related disorders.

compomics-utilities: an open-source Java library for computational proteomics

BackgroundThe growing interest in the field of proteomics has increased the demand for software tools and applications that process and analyze the resulting data. And even though the purpose of these tools can vary significantly, they usually share a basic set of features, including the handling of protein and peptide sequences, the visualization of (and interaction with) spectra and chromatograms, and the parsing of results from various proteomics search engines. Developers typically spend considerable time and effort implementing these support structures, which detracts from working on the novel aspects of their tool.ResultsIn order to simplify the development of proteomics tools, we have implemented an open-source support library for computational proteomics, called compomics-utilities. The library contains a broad set of features required for reading, parsing, and analyzing proteomics data. compomics-utilities is already used by a long list of existing software, ensuring library stability and continued support and development.ConclusionsAs a user-friendly, well-documented and open-source library, compomics-utilities greatly simplifies the implementation of the basic features needed in most proteomics tools. Implemented in 100% Java, compomics-utilities is fully portable across platforms and architectures. Our library thus allows the developers to focus on the novel aspects of their tools, rather than on the basic functions, which can contribute substantially to faster development, and better tools for proteomics.

In-depth Characterization of the Cerebrospinal Fluid (CSF) Proteome Displayed Through the CSF Proteome Resource (CSF-PR)

In this study, the human cerebrospinal fluid (CSF) proteome was mapped using three different strategies prior to Orbitrap LC-MS/MS analysis: SDS-PAGE and mixed mode reversed phase-anion exchange for mapping the global CSF proteome, and hydrazide-based glycopeptide capture for mapping glycopeptides. A maximal protein set of 3081 proteins (28,811 peptide sequences) was identified, of which 520 were identified as glycoproteins from the glycopeptide enrichment strategy, including 1121 glycopeptides and their glycosylation sites. To our knowledge, this is the largest number of identified proteins and glycopeptides reported for CSF, including 417 glycosylation sites not previously reported. From parallel plasma samples, we identified 1050 proteins (9739 peptide sequences). An overlap of 877 proteins was found between the two body fluids, whereas 2204 proteins were identified only in CSF and 173 only in plasma. All mapping results are freely available via the new CSF Proteome Resource (http://probe.uib.no/csf-pr), which can be used to navigate the CSF proteome and help guide the selection of signature peptides in targeted quantitative proteomics.

IsobariQ: software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT.

Isobaric peptide labeling plays an important role in relative quantitative comparisons of proteomes. Isobaric labeling techniques utilize MS/MS spectra for relative quantification, which can be either based on the relative intensities of reporter ions in the low mass region (iTRAQ and TMT) or on the relative intensities of quantification signatures throughout the spectrum due to isobaric peptide termini labeling (IPTL). Due to the increased quantitative information found in MS/MS fragment spectra generated by the recently developed IPTL approach, new software was required to extract the quantitative information. IsobariQ was specifically developed for this purpose; however, support for the reporter ion techniques iTRAQ and TMT is also included. In addition, to address recently emphasized issues about heterogeneity of variance in proteomics data sets, IsobariQ employs the statistical software package R and variance stabilizing normalization (VSN) algorithms available therein. Finally, the functionality of IsobariQ is validated with data sets of experiments using 6-plex TMT and IPTL. Notably, protein substrates resulting from cleavage by proteases can be identified as shown for caspase targets in apoptosis.

Analysing the outer membrane subproteome of Methylococcus capsulatus (Bath) using proteomics and novel biocomputing tools

High-resolution two-dimensional gel electrophoresis and mass spectrometry has been used to identify the outer membrane (OM) subproteome of the Gram-negative bacterium Methylococcus capsulatus (Bath). Twenty-eight unique polypeptide sequences were identified from protein samples enriched in OMs. Only six of these polypeptides had previously been identified. The predictions from novel bioinformatic methods predicting β-barrel outer membrane proteins (OMPs) and OM lipoproteins were compared to proteins identified experimentally. BOMP (http://www.bioinfo.no/tools/bomp) predicted 43 β-barrel OMPs (1.45%) from the 2,959 annotated open reading frames. This was a lower percentage than predicted from other Gram-negative proteomes (1.8–3%). More than half of the predicted BOMPs in M. capsulatus were annotated as (conserved) hypothetical proteins with significant similarity to very few sequences in Swiss-Prot or TrEMBL. The experimental data and the computer predictions indicated that the protein composition of the M. capsulatus OM subproteome was different from that of other Gram-negative bacteria studied in a similar manner. A new program, Lipo, was developed that can analyse entire predicted proteomes and give a list of recognised lipoproteins categorised according to their lipo-box similarity to known Gram-negative lipoproteins (http://www.bioinfo.no/tools/lipo). This report is the first using a proteomics and bioinformatics approach to identify the OM subproteome of an obligate methanotroph.