Robert L. Moritz

A Cross-platform Toolkit for Mass Spectrometry and Proteomics

Mass-spectrometry-based proteomics has become an important component of biological research. Numerous proteomics methods have been developed to identify and quantify the proteins in biological and clinical samples1, identify pathways affected by endogenous and exogenous perturbations2, and characterize protein complexes3. Despite successes, the interpretation of vast proteomics datasets remains a challenge. There have been several calls for improvements and standardization of proteomics data analysis frameworks, as well as for an application-programming interface for proteomics data access4,5. In response, we have developed the ProteoWizard Toolkit, a robust set of open-source, software libraries and applications designed to facilitate proteomics research. The libraries implement the first-ever, non-commercial, unified data access interface for proteomics, bridging field-standard open formats and all common vendor formats. In addition, diverse software classes enable rapid development of vendor-agnostic proteomics software. Additionally, ProteoWizard projects and applications, building upon the core libraries, are becoming standard tools for enabling significant proteomics inquiries.

Exosomes: proteomic insights and diagnostic potential.

Exosomes are 40–100-nm diameter membrane vesicles of endocytic origin that are released by most cell types upon fusion of multivesicular bodies with the plasma membrane, presumably as a vehicle for cell-free intercellular communication. While early studies focused on their secretion from diverse cell types in vitro, exosomes have now been identified in body fluids such as urine, amniotic fluid, malignant ascites, bronchoalveolar lavage fluid, synovial fluid, breast milk, saliva and blood. Exosomes have pleiotropic biological functions, including immune response, antigen presentation, intracellular communication and the transfer of RNA and proteins. While they have also been implicated in the transport and propagation of infectious cargo, such as prions, and retroviruses, including HIV, suggesting a role in pathological situations, recent studies suggest that the presence of such infectious cargo may be artefacts of exosome-purification strategies. Improvements in mass spectrometry-based proteomic tools, both hardware and software, coupled with improved purification schemes for exosomes, has allowed more in-depth proteome analyses, contributing immensely to our understanding of the molecular composition of exosomes. Proteomic cataloguing of exosomes from diverse cell types has revealed a common set of membrane and cytosolic proteins, suggesting the evolutionary importance of these membrane particles. Additionally, exosomes express an array of proteins that reflect the originating host cell. Recent findings that exosomes contain inactive forms of both mRNA and microRNA that can be transferred to another cell and be functional in that new environment, have initiated many microRNA profiling studies of exosomes circulating in blood. These studies highlight the potential of exosomal microRNA profiles for use as diagnostic biomarkers of disease through a noninvasive blood test. The exacerbated release of exosomes in tumor cells, as evidenced by their increased levels in blood during the late stage of a disease and their overexpression of certain tumor cell biomarkers, suggests an important role of exosomes in diagnosis and biomarker studies. The aim of this article is to provide a brief overview of exosomes, including methods used to isolate and characterize exosomes. New advances in proteomic methods, and both mass spectrometry hardware and informatics tools will be covered briefly.

A High-Confidence Human Plasma Proteome Reference Set with Estimated Concentrations in PeptideAtlas

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.

iProphet: Multi-level Integrative Analysis of Shotgun Proteomic Data Improves Peptide and Protein Identification Rates and Error Estimates

The combination of tandem mass spectrometry and sequence database searching is the method of choice for the identification of peptides and the mapping of proteomes. Over the last several years, the volume of data generated in proteomic studies has increased dramatically, which challenges the computational approaches previously developed for these data. Furthermore, a multitude of search engines have been developed that identify different, overlapping subsets of the sample peptides from a particular set of tandem mass spectrometry spectra. We present iProphet, the new addition to the widely used open-source suite of proteomic data analysis tools Trans-Proteomics Pipeline. Applied in tandem with PeptideProphet, it provides more accurate representation of the multilevel nature of shotgun proteomic data. iProphet combines the evidence from multiple identifications of the same peptide sequences across different spectra, experiments, precursor ion charge states, and modified states. It also allows accurate and effective integration of the results from multiple database search engines applied to the same data. The use of iProphet in the Trans-Proteomics Pipeline increases the number of correctly identified peptides at a constant false discovery rate as compared with both PeptideProphet and another state-of-the-art tool Percolator. As the main outcome, iProphet permits the calculation of accurate posterior probabilities and false discovery rate estimates at the level of sequence identical peptide identifications, which in turn leads to more accurate probability estimates at the protein level. Fully integrated with the Trans-Proteomics Pipeline, it supports all commonly used MS instruments, search engines, and computer platforms. The performance of iProphet is demonstrated on two publicly available data sets: data from a human whole cell lysate proteome profiling experiment representative of typical proteomic data sets, and from a set of Streptococcus pyogenes experiments more representative of organism-specific composite data sets.

Targeted Peptide Measurements in Biology and Medicine: Best Practices for Mass Spectrometry-based Assay Development Using a Fit-for-Purpose Approach

Adoption of targeted mass spectrometry (MS) approaches such as multiple reaction monitoring (MRM) to study biological and biomedical questions is well underway in the proteomics community. Successful application depends on the ability to generate reliable assays that uniquely and confidently identify target peptides in a sample. Unfortunately, there is a wide range of criteria being applied to say that an assay has been successfully developed. There is no consensus on what criteria are acceptable and little understanding of the impact of variable criteria on the quality of the results generated. Publications describing targeted MS assays for peptides frequently do not contain sufficient information for readers to establish confidence that the tests work as intended or to be able to apply the tests described in their own labs. Guidance must be developed so that targeted MS assays with established performance can be made widely distributed and applied by many labs worldwide. To begin to address the problems and their solutions, a workshop was held at the National Institutes of Health with representatives from the multiple communities developing and employing targeted MS assays. Participants discussed the analytical goals of their experiments and the experimental evidence needed to establish that the assays they develop work as intended and are achieving the required levels of performance. Using this “fit-for-purpose” approach, the group defined three tiers of assays distinguished by their performance and extent of analytical characterization. Computational and statistical tools useful for the analysis of targeted MS results were described. Participants also detailed the information that authors need to provide in their manuscripts to enable reviewers and readers to clearly understand what procedures were performed and to evaluate the reliability of the peptide or protein quantification measurements reported. This paper presents a summary of the meeting and recommendations.

A complete mass-spectrometric map of the yeast proteome applied to quantitative trait analysis

Experience from different fields of life sciences suggests that accessible, complete reference maps of the components of the system under study are highly beneficial research tools. Examples of such maps include libraries of the spectroscopic properties of molecules, or databases of drug structures in analytical or forensic chemistry. Such maps, and methods to navigate them, constitute reliable assays to probe any sample for the presence and amount of molecules contained in the map. So far, attempts to generate such maps for any proteome have failed to reach complete proteome coverage. Here we use a strategy based on high-throughput peptide synthesis and mass spectrometry to generate an almost complete reference map (97% of the genome-predicted proteins) of the Saccharomyces cerevisiae proteome. We generated two versions of this mass-spectrometric map, one supporting discovery-driven (shotgun) and the other supporting hypothesis-driven (targeted) proteomic measurements. Together, the two versions of the map constitute a complete set of proteomic assays to support most studies performed with contemporary proteomic technologies. To show the utility of the maps, we applied them to a protein quantitative trait locus (QTL) analysis, which requires precise measurement of the same set of peptides over a large number of samples. Protein measurements over 78 S. cerevisiae strains revealed a complex relationship between independent genetic loci, influencing the levels of related proteins. Our results suggest that selective pressure favours the acquisition of sets of polymorphisms that adapt protein levels but also maintain the stoichiometry of functionally related pathway members.

Reproducible quantification of cancer-associated proteins in body fluids using targeted proteomics

A compendium of SRM assays for cancer-associated proteins provides a resource for accelerating and planning biomarker verification studies. Dealing with Data Overload With the exponential blossoming of information sources—from 24-hour news to blogs to Twitter feeds—it can be difficult to differentiate fact from fiction. Translational medicine is experiencing a parallel mushrooming of data in research on disease biomarkers. Whereas marker validation once occurred on a case by case basis, the literature now bulges with potential biomarkers at diverse stages of validation, and researchers and clinicians are hard-pressed to make sense of it all. Now, Hüttenhain et al. provide a modern validation method that can keep up with the current pace of biomarker-candidate generation. The authors report on a high-throughput method for developing selected reaction-monitoring (SRM) assays (targeted mass spectrometry) for human proteins. SRM assays can be run in parallel and have low limits of detection and high accuracy. They then used these assays—for more than 1000 cancer-associated proteins—to determine the detectability of the target proteins in plasma and urine from cancer patients and healthy controls. They detected 182 proteins in plasma and 408 in urine, and reproducibly quantified 34 biomarker candidates across 83 patient plasma samples. These SRM assays can be broadly applied for cancer-associated biomarker validation and should help provide a filter to stem information overload. The rigorous testing of hypotheses on suitable sample cohorts is a major limitation in translational research. This is particularly the case for the validation of protein biomarkers; the lack of accurate, reproducible, and sensitive assays for most proteins has precluded the systematic assessment of hundreds of potential marker proteins described in the literature. Here, we describe a high-throughput method for the development and refinement of selected reaction monitoring (SRM) assays for human proteins. The method was applied to generate such assays for more than 1000 cancer-associated proteins, which are functionally related to candidate cancer driver mutations. We used the assays to determine the detectability of the target proteins in two clinically relevant samples: plasma and urine. One hundred eighty-two proteins were detected in depleted plasma, spanning five orders of magnitude in abundance and reaching below a concentration of 10 ng/ml. The narrower concentration range of proteins in urine allowed the detection of 408 proteins. Moreover, we demonstrate that these SRM assays allow reproducible quantification by monitoring 34 biomarker candidates across 83 patient plasma samples. Through public access to the entire assay library, researchers will be able to target their cancer-associated proteins of interest in any sample type using the detectability information in plasma and urine as a guide. The generated expandable reference map of SRM assays for cancer-associated proteins will be a valuable resource for accelerating and planning biomarker verification studies.

Plasmin activates the lymphangiogenic growth factors VEGF-C and VEGF-D.

Vascular endothelial growth factor (VEGF) C and VEGF-D stimulate lymphangiogenesis and angiogenesis in tissues and tumors by activating the endothelial cell surface receptor tyrosine kinases VEGF receptor (VEGFR) 2 and VEGFR-3. These growth factors are secreted as full-length inactive forms consisting of NH2- and COOH-terminal propeptides and a central VEGF homology domain (VHD) containing receptor binding sites. Proteolytic cleavage removes the propeptides to generate mature forms, consisting of dimers of the VEGF homology domain, that bind receptors with much greater affinity than the full-length forms. Therefore, proteolytic processing activates VEGF-C and VEGF-D, although the proteases involved were unknown. Here, we report that the serine protease plasmin cleaved both propeptides from the VEGF homology domain of human VEGF-D and thereby generated a mature form exhibiting greatly enhanced binding and cross-linking of VEGFR-2 and VEGFR-3 in comparison to full-length material. Plasmin also activated VEGF-C. As lymphangiogenic growth factors promote the metastatic spread of cancer via the lymphatics, the proteolytic activation of these molecules represents a potential target for antimetastatic agents. Identification of an enzyme that activates the lymphangiogenic growth factors will facilitate development of inhibitors of metastasis.

Identification and Stoichiometry of Glycosylphosphatidylinositol-anchored Membrane Proteins of the Human Malaria Parasite Plasmodium falciparum

Most proteins that coat the surface of the extracellular forms of the human malaria parasite Plasmodium falciparum are attached to the plasma membrane via glycosylphosphatidylinositol (GPI) anchors. These proteins are exposed to neutralizing antibodies, and several are advanced vaccine candidates. To identify the GPI-anchored proteome of P. falciparum we used a combination of proteomic and computational approaches. Focusing on the clinically relevant blood stage of the life cycle, proteomic analysis of proteins labeled with radioactive glucosamine identified GPI anchoring on 11 proteins (merozoite surface protein (MSP)-1, -2, -4, -5, -10, rhoptry-associated membrane antigen, apical sushi protein, Pf92, Pf38, Pf12, and Pf34). These proteins represent ∼94% of the GPI-anchored schizont/merozoite proteome and constitute by far the largest validated set of GPI-anchored proteins in this organism. Moreover MSP-1 and MSP-2 were present in similar copy number, and we estimated that together these proteins comprise approximately two-thirds of the total membrane-associated surface coat. This is the first time the stoichiometry of MSPs has been examined. We observed that available software performed poorly in predicting GPI anchoring on P. falciparum proteins where such modification had been validated by proteomics. Therefore, we developed a hidden Markov model (GPI-HMM) trained on P. falciparum sequences and used this to rank all proteins encoded in the completed P. falciparum genome according to their likelihood of being GPI-anchored. GPI-HMM predicted GPI modification on all validated proteins, on several known membrane proteins, and on a number of novel, presumably surface, proteins expressed in the blood, insect, and/or pre-erythrocytic stages of the life cycle. Together this work identified 11 and predicted a further 19 GPI-anchored proteins in P. falciparum.

PASSEL: The PeptideAtlas SRMexperiment library

Public repositories for proteomics data have accelerated proteomics research by enabling more efficient cross‐analyses of datasets, supporting the creation of protein and peptide compendia of experimental results, supporting the development and testing of new software tools, and facilitating the manuscript review process. The repositories available to date have been designed to accommodate either shotgun experiments or generic proteomic data files. Here, we describe a new kind of proteomic data repository for the collection and representation of data from selected reaction monitoring (SRM) measurements. The PeptideAtlas SRM Experiment Library (PASSEL) allows researchers to easily submit proteomic data sets generated by SRM. The raw data are automatically processed in a uniform manner and the results are stored in a database, where they may be downloaded or browsed via a web interface that includes a chromatogram viewer. PASSELenables cross‐analysis of SRMdata, supports optimization of SRMdata collection, and facilitates the review process of SRMdata. Further, PASSELwill help in the assessment of proteotypic peptide performance in a wide array of samples containing the same peptide, as well as across multiple experimental protocols.