OpenMS - A platform for reproducible analysis of mass spectrometry data.

BACKGROUND: In recent years, several mass spectrometry-based omics technologies emerged to investigate qualitative and quantitative changes within thousands of biologically active components such as proteins, lipids and metabolites. The research enabled through these methods potentially contributes to the diagnosis and pathophysiology of human diseases as well as to the clarification of structures and interactions between biomolecules. Simultaneously, technological advances in the field of mass spectrometry leading to an ever increasing amount of data, demand high standards in efficiency, accuracy and reproducibility of potential analysis software. RESULTS: This article presents the current state and ongoing developments in OpenMS, a versatile open-source framework aimed at enabling reproducible analyses of high-throughput mass spectrometry data. It provides implementations of frequently occurring processing operations on MS data through a clean application programming interface in C++ and Python. A collection of 185 tools and ready-made workflows for typical MS-based experiments enable convenient analyses for non-developers and facilitate reproducible research without losing flexibility. CONCLUSIONS: OpenMS will continue to increase its ease of use for developers as well as users with improved continuous integration/deployment strategies, regular trainings with updated training materials and multiple sources of support. The active developer community ensures the incorporation of new features to support state of the art research.

OpenMS: a flexible open-source software platform for mass spectrometry data analysis.

High-resolution mass spectrometry (MS) has become an important tool in the life sciences, contributing to the diagnosis and understanding of human diseases, elucidating biomolecular structural information and characterizing cellular signaling networks. However, the rapid growth in the volume and complexity of MS data makes transparent, accurate and reproducible analysis difficult. We present OpenMS 2.0 (http://www.openms.de), a robust, open-source, cross-platform software specifically designed for the flexible and reproducible analysis of high-throughput MS data. The extensible OpenMS software implements common mass spectrometric data processing tasks through a well-defined application programming interface in C++ and Python and through standardized open data formats. OpenMS additionally provides a set of 185 tools and ready-made workflows for common mass spectrometric data processing tasks, which enable users to perform complex quantitative mass spectrometric analyses with ease.

Yeast membrane proteomics using leucine metabolic labelling: Bioinformatic data processing and exemplary application to the ER-intramembrane protease Ypf1.

We describe in detail the usage of leucine metabolic labelling in yeast in order to monitor quantitative proteome alterations, e.g. upon removal of a protease. Since laboratory yeast strains are typically leucine auxotroph, metabolic labelling with trideuterated leucine (d3-leucine) is a straightforward, cost-effective, and ubiquitously applicable strategy for quantitative proteomic studies, similar to the widely used arginine/lysine metabolic labelling method for mammalian cells. We showcase the usage of advanced peptide quantification using the FeatureFinderMultiplex algorithm (part of the OpenMS software package) for robust and reliable quantification. Furthermore, we present an OpenMS bioinformatics data analysis workflow that combines accurate quantification with high proteome coverage. In order to enable visualization, peptide-mapping, and sharing of quantitative proteomic data, especially for membrane-spanning and cell-surface proteins, we further developed the web-application Proteator (http://proteator.appspot.com). Due to its simplicity and robustness, we expect metabolic leucine labelling in yeast to be of great interest to the research community. As an exemplary application, we show the identification of the copper transporter Ctr1 as a putative substrate of the ER-intramembrane protease Ypf1 by yeast membrane proteomics using d3-leucine isotopic labelling.

Formalin-Fixed, Paraffin-Embedded Tissues (FFPE) as a Robust Source for the Profiling of Native and Protease-Generated Protein Amino Termini.

Dysregulated proteolysis represents a hallmark of numerous diseases. In recent years, increasing number of studies has begun looking at the protein termini in hope to unveil the physiological and pathological functions of proteases in clinical research. However, the availability of cryopreserved tissue specimens is often limited. Alternatively, formalin-fixed, paraffin-embedded (FFPE) tissues offer an invaluable resource for clinical research. Pathologically relevant tissues are often stored as FFPE, which represent the most abundant resource of archived human specimens. In this study, we established a robust workflow to investigate native and protease-generated protein N termini from FFPE specimens. We demonstrate comparable N-terminomes of cryopreserved and formalin-fixed tissue, thereby showing that formalin fixation/paraffin embedment does not proteolytically damage proteins. Accordingly, FFPE specimens are fully amenable to N-terminal analysis. Moreover, we demonstrate feasibility of FFPE-degradomics in a quantitative N-terminomic study of FFPE liver specimens from cathepsin L deficient or wild-type mice. Using a machine learning approach in combination with the previously determined cathepsin L specificity, we successfully identify a number of potential cathepsin L cleavage sites. Our study establishes FFPE specimens as a valuable alternative to cryopreserved tissues for degradomic studies.

Disseminated Tumor Cells Persist in the Bone Marrow of Breast Cancer Patients through Sustained Activation of the Unfolded Protein Response.

Disseminated tumor cells (DTC), which share mesenchymal and epithelial properties, are considered to be metastasis-initiating cells in breast cancer. However, the mechanisms supporting DTC survival are poorly understood. DTC extravasation into the bone marrow may be encouraged by low oxygen concentrations that trigger metabolic and molecular alterations contributing to DTC survival. Here, we investigated how the unfolded protein response (UPR), an important cytoprotective program induced by hypoxia, affects the behavior of stressed cancer cells. DTC cell lines established from the bone marrow of patients with breast cancer (BC-M1), lung cancer, (LC-M1), and prostate cancer (PC-E1) were subjected to hypoxic and hypoglycemic conditions. BC-M1 and LC-M1 exhibiting mesenchymal and epithelial properties adapted readily to hypoxia and glucose starvation. Upregulation of UPR proteins, such as the glucose-regulated protein Grp78, induced the formation of filamentous networks, resulting in proliferative advantages and sustained survival under total glucose deprivation. High Grp78 expression correlated with mesenchymal attributes of breast and lung cancer cells and with poor differentiation in clinical samples of primary breast and lung carcinomas. In DTCs isolated from bone marrow specimens from breast cancer patients, Grp78-positive stress granules were observed, consistent with the likelihood these cells were exposed to acute cell stress. Overall, our findings provide the first evidence that the UPR is activated in DTC in the bone marrow from cancer patients, warranting further study of this cell stress pathway as a predictive biomarker for recurrent metastatic disease.

Toward improved peptide feature detection in quantitative proteomics using stable isotope labeling.

Reliable detection of peptides in LC-MS data is a key algorithmic step in the analysis of quantitative proteomics experiments. While highly abundant peptides can be detected reliably by most modern software tools, there is much less agreement on medium and low-intensity peptides in a sample. The choice of software tools can have a big impact on the quantification of proteins, especially for proteins that appear in lower concentrations. However, in many experiments, it is precisely this region of less abundant but substantially regulated proteins that holds the biggest potential for discoveries. This is particularly true for discovery proteomics in the pharmacological sector with a specific interest in key regulatory proteins. In this viewpoint article, we discuss how the development of novel software algorithms allows us to study this region of the proteome with increased confidence. Reliable results are one of many aspects to be considered when deciding on a bioinformatics software platform. Deployment into existing IT infrastructures, compatibility with other software packages, scalability, automation, flexibility, and support need to be considered and are briefly addressed in this viewpoint article.

An automated pipeline for high-throughput label-free quantitative proteomics.

We present a computational pipeline for the quantification of peptides and proteins in label-free LC-MS/MS data sets. The pipeline is composed of tools from the OpenMS software framework and is applicable to the processing of large experiments (50+ samples). We describe several enhancements that we have introduced to OpenMS to realize the implementation of this pipeline. They include new algorithms for centroiding of raw data, for feature detection, for the alignment of multiple related measurements, and a new tool for the calculation of peptide and protein abundances. Where possible, we compare the performance of the new algorithms to that of their established counterparts in OpenMS. We validate the pipeline on the basis of two small data sets that provide ground truths for the quantification. There, we also compare our results to those of MaxQuant and Progenesis LC-MS, two popular alternatives for the analysis of label-free data. We then show how our software can be applied to a large heterogeneous data set of 58 LC-MS/MS runs.