PeptideShaker Online: A User-Friendly Web-Based Framework for the Identification of Mass Spectrometry-Based Proteomics Data.

Mass spectrometry-based proteomics is a high-throughput technology generating ever-larger amounts of data per project. However, storing, processing, and interpreting these data can be a challenge. A key element in simplifying this process is the development of interactive frameworks focusing on visualization that can greatly simplify both the interpretation of data and the generation of new knowledge. Here we present PeptideShaker Online, a user-friendly web-based framework for the identification of mass spectrometry-based proteomics data, from raw file conversion to interactive visualization of the resulting data. Storage and processing of the data are performed via the versatile Galaxy platform (through SearchGUI, PeptideShaker, and moFF), while the interaction with the results happens via a locally installed web server, thus enabling researchers to process and interpret their own data without requiring advanced bioinformatics skills or direct access to compute-intensive infrastructures. The source code, additional documentation, and a fully functional demo is available at https://github.com/barsnes-group/peptide-shaker-online.

CSF-PR 2.0: An Interactive Literature Guide to Quantitative Cerebrospinal Fluid Mass Spectrometry Data from Neurodegenerative Disorders.

The rapidly growing number of biomedical studies supported by mass spectrometry based quantitative proteomics data has made it increasingly difficult to obtain an overview of the current status of the research field. A better way of organizing the biomedical proteomics information from these studies and making it available to the research community is therefore called for. In the presented work, we have investigated scientific publications describing the analysis of the cerebrospinal fluid proteome in relation to multiple sclerosis, Parkinson's disease and Alzheimer's disease. Based on a detailed set of filtering criteria we extracted 85 data sets containing quantitative information for close to 2000 proteins. This information was made available in CSF-PR 2.0 (http://probe.uib.no/csf-pr-2.0), which includes novel approaches for filtering, visualizing and comparing quantitative proteomics information in an interactive and user-friendly environment. CSF-PR 2.0 will be an invaluable resource for anyone interested in quantitative proteomics on cerebrospinal fluid.

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.

Essential Features and Use Cases of the Cerebrospinal Fluid Proteome Resource (CSF-PR).

Every year, a large number of published studies present biomarkers for various neurological disorders. Many of these studies are based on mass spectrometry proteomics data and describe comparison of the abundance of proteins in cerebrospinal fluid between two or more disease groups. As the number of such studies is growing, it is no longer straightforward to obtain an overview of which specific proteins are increased or decreased between the numerous relevant diseases and their many subcategories, or to see the larger picture or trends between related diseases. To alleviate this situation, we therefore mined the literature for mass spectrometry-based proteomics studies including quantitative protein data from cerebrospinal fluid of patients with multiple sclerosis, Alzheimer's disease, and Parkinson's disease and organized the extracted data in the Cerebrospinal Fluid Proteome Resource (CSF-PR). CSF-PR is freely available online at http://probe.uib.no/csf-pr , is highly interactive, and allows for easy navigation, visualization, and export of the published scientific data. This chapter will guide the user through some of the most important features of the tool and show examples of the suggested use cases.

Freezing effects on the acute myeloid leukemia cell proteome and phosphoproteome revealed using optimal quantitative workflows.

UNLABELLED: MS-based proteomic studies aiming for the discovery of acute myeloid leukemia (AML) biomarkers require sample processing that can assure an optimal proteome coverage and identification of PTMs. We evaluated different in-solution and filter-aided sample preparation (FASP) proteomic workflows and different enrichment strategies of phosphorylated peptides. The FASP protocols in the label-free and SILAC (stable isotope labelling with amino acids in cell culture) approaches were selected for producing the highest number of quantified proteins with reduced number of missed cleavages. The IMAC method was selected for producing the highest number of quantified phosphopeptides from SILAC-labelled peptides prepared with FASP. Using these selected workflows, we studied the effect of liquid nitrogen storage on the proteome and phosphoproteome of four AML patients. Our results showed that although there was not a major global proteome and phosphoproteome change when compared to their freshly processed counterparts, the freezing appeared to influence the abundance of mitochondrial proteins involved in the respiratory chain transport and affect the phosphorylation of apoptosis related proteins, cell surface interactors, ERK/MAPK targets and proteins involved in thrombin signalling. Our results encourage the assessment of current procedures of AML sample collection and preservation that could be used in future AML biomarker discovery studies. BIOLOGICAL SIGNIFICANCE: Proteomic studies aiming to identify potential cancer biomarkers need to utilize the best sample preparation workflows on the samples of interest to achieve maximal proteome coverage. We have tested the most popular and recent proteomic and phosphoproteomic methods on cell lysates from patients with AML and systematically evaluated their performance. Our study shows the relevance of selecting the patient sample procedure giving the highest protein and PTM coverage. Moreover, we assessed how the proteome and phosphoproteome were affected by the conventional liquid nitrogen storage compared to cell lysis of fresh material, using the methods that worked best in our hands. For potential biomarkers that could be used for AML diagnostic and prognostic, it is of great importance to study the behaviour during sample conservation in order to avoid artefactual findings. Our results recommend caution in data interpretation when using different protocols of sample collection and conservation for proteomic and phosphosproteomic research.

Distributed and interactive visual analysis of omics data.

The amount of publicly shared proteomics data has grown exponentially over the last decade as the solutions for sharing and storing the data have improved. However, the use of the data is often limited by the manner of which it is made available. There are two main approaches: download and inspect the proteomics data locally, or interact with the data via one or more web pages. The first is limited by having to download the data and thus requires local computational skills and resources, while the latter most often is limited in terms of interactivity and the analysis options available. A solution is to develop web-based systems supporting distributed and fully interactive visual analysis of proteomics data. The use of a distributed architecture makes it possible to perform the computational analysis at the server, while the results of the analysis can be displayed via a web browser without the need to download the whole dataset. Here the challenges related to developing such systems for omics data will be discussed. Especially how this allows for multiple connected interactive visual displays of omics dataset in a web-based setting, and the benefits this provide for computational analysis of proteomics data.This article is part of a Special Issue entitled: Computational Proteomics.