Quantitative proteogenomic profiling of epidermal barrier formation in vitro.

BACKGROUND: The barrier function of the epidermis is integral to personal well-being, and defects in the skin barrier are associated with several widespread diseases. Currently there is a limited understanding of system-level proteomic changes during epidermal stratification and barrier establishment. OBJECTIVE: Here we report the quantitative proteogenomic profile of an in vitro reconstituted epidermis at three time points of development in order to characterize protein changes during stratification. METHODS: The proteome was measured using data-dependent "shotgun" mass spectrometry and quantified with statistically validated label-free proteomic methods for 20 replicates at each of three time points during the course of epidermal development. RESULTS: Over 3600 proteins were identified in the reconstituted epidermis, with more than 1200 of these changing in abundance over the time course. We also collected and discuss matched transcriptomic data for the three time points, allowing alignment of this new dataset with previously published characterization of the reconstituted epidermis system. CONCLUSION: These results represent the most comprehensive epidermal-specific proteome to date, and therefore reveal several aspects of barrier formation and skin composition. The limited correlation between transcript and protein abundance underscores the importance of proteomic analysis in developing a full understanding of epidermal maturation.

State of the human proteome in 2013 as viewed through PeptideAtlas: comparing the kidney, urine, and plasma proteomes for the biology- and disease-driven Human Proteome Project.

The kidney, urine, and plasma proteomes are intimately related: proteins and metabolic waste products are filtered from the plasma by the kidney and excreted via the urine, while kidney proteins may be secreted into the circulation or released into the urine. Shotgun proteomics data sets derived from human kidney, urine, and plasma samples were collated and processed using a uniform software pipeline, and relative protein abundances were estimated by spectral counting. The resulting PeptideAtlas builds yielded 4005, 2491, and 3553 nonredundant proteins at 1% FDR for the kidney, urine, and plasma proteomes, respectively - for kidney and plasma, the largest high-confidence protein sets to date. The same pipeline applied to all available human data yielded a 2013 Human PeptideAtlas build containing 12,644 nonredundant proteins and at least one peptide for each of ∼14,000 Swiss-Prot entries, an increase over 2012 of ∼7.5% of the predicted human proteome. We demonstrate that abundances are correlated between plasma and urine, examine the most abundant urine proteins not derived from either plasma or kidney, and consider the biomarker potential of proteins associated with renal decline. This analysis forms part of the Biology and Disease-driven Human Proteome Project (B/D-HPP) and is a contribution to the Chromosome-centric Human Proteome Project (C-HPP) special issue.

An assessment of current bioinformatic solutions for analyzing LC-MS data acquired by selected reaction monitoring technology.

Selected reaction monitoring (SRM) is an accurate quantitative technique, typically used for small-molecule mass spectrometry (MS). SRM has emerged as an important technique for targeted and hypothesis-driven proteomic research, and is becoming the reference method for protein quantification in complex biological samples. SRM offers high selectivity, a lower limit of detection and improved reproducibility, compared to conventional shot-gun-based tandem MS (LC-MS/MS) methods. Unlike LC-MS/MS, which requires computationally intensive informatic postanalysis, SRM requires preacquisition bioinformatic analysis to determine proteotypic peptides and optimal transitions to uniquely identify and to accurately quantitate proteins of interest. Extensive arrays of bioinformatics software tools, both web-based and stand-alone, have been published to assist researchers to determine optimal peptides and transition sets. The transitions are oftentimes selected based on preferred precursor charge state, peptide molecular weight, hydrophobicity, fragmentation pattern at a given collision energy (CE), and instrumentation chosen. Validation of the selected transitions for each peptide is critical since peptide performance varies depending on the mass spectrometer used. In this review, we provide an overview of open source and commercial bioinformatic tools for analyzing LC-MS data acquired by SRM.

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.

Halogenated peptides as internal standards (H-PINS): introduction of an MS-based internal standard set for liquid chromatography-mass spectrometry.

As the application for quantitative proteomics in the life sciences has grown in recent years, so has the need for more robust and generally applicable methods for quality control and calibration. The reliability of quantitative proteomics is tightly linked to the reproducibility and stability of the analytical platforms, which are typically multicomponent (e.g. sample preparation, multistep separations, and mass spectrometry) with individual components contributing unequally to the overall system reproducibility. Variations in quantitative accuracy are thus inevitable, and quality control and calibration become essential for the assessment of the quality of the analyses themselves. Toward this end, the use of internal standards cannot only assist in the detection and removal of outlier data acquired by an irreproducible system (quality control) but can also be used for detection of changes in instruments for their subsequent performance and calibration. Here we introduce a set of halogenated peptides as internal standards. The peptides are custom designed to have properties suitable for various quality control assessments, data calibration, and normalization processes. The unique isotope distribution of halogenated peptides makes their mass spectral detection easy and unambiguous when spiked into complex peptide mixtures. In addition, they were designed to elute sequentially over an entire aqueous to organic LC gradient and to have m/z values within the commonly scanned mass range (300-1800 Da). In a series of experiments in which these peptides were spiked into an enriched N-glycosite peptide fraction (i.e. from formerly N-glycosylated intact proteins in their deglycosylated form) isolated from human plasma, we show the utility and performance of these halogenated peptides for sample preparation and LC injection quality control as well as for retention time and mass calibration. Further use of the peptides for signal intensity normalization and retention time synchronization for selected reaction monitoring experiments is also demonstrated.

Mass-spectrometric identification and relative quantification of N-linked cell surface glycoproteins.

Although the classification of cell types often relies on the identification of cell surface proteins as differentiation markers, flow cytometry requires suitable antibodies and currently permits detection of only up to a dozen differentiation markers in a single measurement. We use multiplexed mass-spectrometric identification of several hundred N-linked glycosylation sites specifically from cell surface-exposed glycoproteins to phenotype cells without antibodies in an unbiased fashion and without a priori knowledge. We apply our cell surface-capturing (CSC) technology, which covalently labels extracellular glycan moieties on live cells, to the detection and relative quantitative comparison of the cell surface N-glycoproteomes of T and B cells, as well as to monitor changes in the abundance of cell surface N-glycoprotein markers during T-cell activation and the controlled differentiation of embryonic stem cells into the neural lineage. A snapshot view of the cell surface N-glycoproteins will enable detection of panels of N-glycoproteins as potential differentiation markers that are currently not accessible by other means.

Corra: Computational framework and tools for LC-MS discovery and targeted mass spectrometry-based proteomics.

BACKGROUND: Quantitative proteomics holds great promise for identifying proteins that are differentially abundant between populations representing different physiological or disease states. A range of computational tools is now available for both isotopically labeled and label-free liquid chromatography mass spectrometry (LC-MS) based quantitative proteomics. However, they are generally not comparable to each other in terms of functionality, user interfaces, information input/output, and do not readily facilitate appropriate statistical data analysis. These limitations, along with the array of choices, present a daunting prospect for biologists, and other researchers not trained in bioinformatics, who wish to use LC-MS-based quantitative proteomics. RESULTS: We have developed Corra, a computational framework and tools for discovery-based LC-MS proteomics. Corra extends and adapts existing algorithms used for LC-MS-based proteomics, and statistical algorithms, originally developed for microarray data analyses, appropriate for LC-MS data analysis. Corra also adapts software engineering technologies (e.g. Google Web Toolkit, distributed processing) so that computationally intense data processing and statistical analyses can run on a remote server, while the user controls and manages the process from their own computer via a simple web interface. Corra also allows the user to output significantly differentially abundant LC-MS-detected peptide features in a form compatible with subsequent sequence identification via tandem mass spectrometry (MS/MS). We present two case studies to illustrate the application of Corra to commonly performed LC-MS-based biological workflows: a pilot biomarker discovery study of glycoproteins isolated from human plasma samples relevant to type 2 diabetes, and a study in yeast to identify in vivo targets of the protein kinase Ark1 via phosphopeptide profiling. CONCLUSION: The Corra computational framework leverages computational innovation to enable biologists or other researchers to process, analyze and visualize LC-MS data with what would otherwise be a complex and not user-friendly suite of tools. Corra enables appropriate statistical analyses, with controlled false-discovery rates, ultimately to inform subsequent targeted identification of differentially abundant peptides by MS/MS. For the user not trained in bioinformatics, Corra represents a complete, customizable, free and open source computational platform enabling LC-MS-based proteomic workflows, and as such, addresses an unmet need in the LC-MS proteomics field.

Differential Plasma Glycoproteome of p19 Skin Cancer Mouse Model Using the Corra Label-Free LC-MS Proteomics Platform.

A proof-of-concept demonstration of the use of label-free quantitative glycoproteomics for biomarker discovery workflow is presented here, using a mouse model for skin cancer as an example. Blood plasma was collected from 10 control mice, and 10 mice having a mutation in the p19(ARF) gene, conferring them high propensity to develop skin cancer after carcinogen exposure. We enriched for N-glycosylated plasma proteins, ultimately generating deglycosylated forms of the modified tryptic peptides for liquid chromatography mass spectrometry (LC-MS) analyses. LC-MS runs for each sample were then performed with a view to identifying proteins that were differentially abundant between the two mouse populations. We then used a recently developed computational framework, Corra, to perform peak picking and alignment, and to compute the statistical significance of any observed changes in individual peptide abundances. Once determined, the most discriminating peptide features were then fragmented and identified by tandem mass spectrometry with the use of inclusion lists. We next assessed the identified proteins to see if there were sets of proteins indicative of specific biological processes that correlate with the presence of disease, and specifically cancer, according to their functional annotations. As expected for such sick animals, many of the proteins identified were related to host immune response. However, a significant number of proteins also directly associated with processes linked to cancer development, including proteins related to the cell cycle, localisation, trasport, and cell death. Additional analysis of the same samples in profiling mode, and in triplicate, confirmed that replicate MS analysis of the same plasma sample generated less variation than that observed between plasma samples from different individuals, demonstrating that the reproducibility of the LC-MS platform was sufficient for this application. These results thus show that an LC-MS-based workflow can be a useful tool for the generation of candidate proteins of interest as part of a disease biomarker discovery effort.

Mass spectrometric detection of tissue proteins in plasma.

It has long been thought that blood plasma could serve as a window into the state of one's organs in health and disease because tissue-derived proteins represent a significant fraction of the plasma proteome. Although substantial technical progress has been made toward the goal of comprehensively analyzing the blood plasma proteome, the basic assumption that proteins derived from a variety of tissues could indeed be detectable in plasma using current proteomics technologies has not been rigorously tested. Here we provide evidence that such tissue-derived proteins are both present and detectable in plasma via direct mass spectrometric analysis of captured glycopeptides and thus provide a conceptual basis for plasma protein biomarker discovery and analysis.

Quantitative proteomic analysis of B cell lipid rafts reveals that ezrin regulates antigen receptor-mediated lipid raft dynamics.

Ligation of the B cell antigen receptor (BCR) with antigen induces lipid raft coalescence, a process that occurs after crosslinking of a variety of signaling receptors and is thought to potentiate cellular activation. To investigate lipid raft dynamics during BCR signaling, we quantitatively analyzed the B cell lipid raft proteome. BCR engagement induced dissociation of the adaptor protein ezrin from lipid rafts as well as threonine dephosphorylation of ezrin and its concomitant detachment from actin, indicating a transient uncoupling of lipid rafts from the actin cytoskeleton. Expression of constitutively active ezrin chimeras inhibited the BCR-induced coalescence of lipid rafts. Our data demonstrate that the release of ezrin from lipid rafts acts as a critical trigger that regulates lipid raft dynamics during BCR signaling.

Quantitative phosphoproteome analysis using a dendrimer conjugation chemistry and tandem mass spectrometry.

We present a robust and general method for the identification and relative quantification of phosphorylation sites in complex protein mixtures. It is based on a new chemical derivatization strategy using a dendrimer as a soluble polymer support and tandem mass spectrometry (MS/MS). In a single step, phosphorylated peptides are covalently conjugated to a dendrimer in a reaction catalyzed by carbodiimide and imidazole. Modified phosphopeptides are released from the dendrimer via acid hydrolysis and analyzed by MS/MS. When coupled with an initial antiphosphotyrosine protein immunoprecipitation step and stable-isotope labeling, in a single experiment, we identified all known tyrosine phosphorylation sites within the immunoreceptor tyrosine-based activation motifs (ITAM) of the T-cell receptor (TCR) CD3 chains, and previously unknown phosphorylation sites on total 97 tyrosine phosphoproteins and their interacting partners in human T cells. The dynamic changes in phosphorylation were quantified in these proteins.

Integration with the human genome of peptide sequences obtained by high-throughput mass spectrometry.

A crucial aim upon the completion of the human genome is the verification and functional annotation of all predicted genes and their protein products. Here we describe the mapping of peptides derived from accurate interpretations of protein tandem mass spectrometry (MS) data to eukaryotic genomes and the generation of an expandable resource for integration of data from many diverse proteomics experiments. Furthermore, we demonstrate that peptide identifications obtained from high-throughput proteomics can be integrated on a large scale with the human genome. This resource could serve as an expandable repository for MS-derived proteome information.

Lipid raft proteins and their identification in T lymphocytes.

This review focuses on how membrane lipid rafts have been detected and isolated, mostly from lymphocytes, and their associated proteins identified. These proteins include transmembrane antigens/receptors, GPI-anchored proteins, cytoskeletal proteins, Src-family protein kinases, G-proteins, and other proteins involved in signal transduction. To further understand the biology of lipid rafts, new methodological approaches are needed to help characterize the raft protein component, and changes that occur in this component as a result of cell perturbation. We describe the application of new proteomic approaches to the identification and quantification of raft proteins in T-lymphocytes. Similar approaches, applied to other model cell systems, will provide valuable new insights into both cellular signal transduction and lipid raft biology.

Proteomics/genomics and signaling in lymphocytes.

Recent technological advances in genomics, proteomics and bioinformatics have offered new insights into the molecular mechanisms that underlie lymphocyte signaling and function, and the development of new tools in these areas has opened up new avenues for biological investigation. By adding a quantitative dimension to lymphocyte proteome profiling, molecular machines and spatiotemporal regulatory processes can now be analyzed using such discovery-driven approaches. Biologists employing genomic and proteomic tools are gathering data at increasing speed and their struggle to extract maximal biological information is helped by new software tools that enable the detailed comparison of multiple datasets.

PTPH1 is a predominant protein-tyrosine phosphatase capable of interacting with and dephosphorylating the T cell receptor zeta subunit.

Protein-tyrosine phosphatases (PTPases) play key roles in regulating tyrosine phosphorylation levels in cells, yet the identity of their substrates remains limited. We report here on the identification of PTPases capable of dephosphorylating the phosphorylated immune tyrosine-based activation motifs present in the T cell receptor zeta subunit. To characterize these PTPases, we purified enzyme activities directed against the phosphorylated T cell receptor zeta subunit by a combination of anion and cation chromatography procedures. A novel ELISA-based PTPase assay was developed to rapidly screen protein fractions for enzyme activity following the various chromatography steps. We present data that SHP-1 and PTPH1 are present in highly enriched protein fractions that exhibit PTPase activities toward a tyrosine-phosphorylated TCR zeta substrate (specific activity ranging from 0.23 to 40 pmol/min/microg). We also used a protein-tyrosine phosphatase substrate-trapping library comprising the catalytic domains of 47 distinct protein-tyrosine phosphatases, representing almost all the tyrosine phosphatases identified in the human genome. PTPH1 was the predominant phosphatase capable of complexing phospho-zeta. Subsequent transfection assays indicated that SHP-1 and PTPH1 are the two principal PTPases capable of regulating the phosphorylation state of the TCR zeta ITAMs, with PTPH1 directly dephosphorylating zeta. This is the first reported demonstration that PTPH1 is a candidate PTPase capable of interacting with and dephosphorylating TCR zeta.

The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: I. Statistically annotated datasets for peptide sequences and proteins identified via the application of ICAT and tandem mass spectrometry to proteins copurifying with T cell lipid rafts.

Lipid rafts were prepared according to standard protocols from Jurkat T cells stimulated via T cell receptor/CD28 cross-linking and from control (unstimulated) cells. Co-isolating proteins from the control and stimulated cell preparations were labeled with isotopically normal (d0) and heavy (d8) versions of the same isotope-coded affinity tag (ICAT) reagent, respectively. Samples were combined, proteolyzed, and resultant peptides fractionated via cation exchange chromatography. Cysteine-containing (ICAT-labeled) peptides were recovered via the biotin tag component of the ICAT reagents by avidin-affinity chromatography. On-line micro-capillary liquid chromatography tandem mass spectrometry was performed on both avidin-affinity (ICAT-labeled) and flow-through (unlabeled) fractions. Initial peptide sequence identification was by searching recorded tandem mass spectrometry spectra against a human sequence data base using SEQUEST software. New statistical data modeling algorithms were then applied to the SEQUEST search results. These allowed for discrimination between likely "correct" and "incorrect" peptide assignments, and from these the inferred proteins that they collectively represented, by calculating estimated probabilities that each peptide assignment and subsequent protein identification was a member of the "correct" population. For convenience, the resultant lists of peptide sequences assigned and the proteins to which they corresponded were filtered at an arbitrarily set cut-off of 0.5 (i.e. 50% likely to be "correct") and above and compiled into two separate datasets. In total, these data sets contained 7667 individual peptide identifications, which represented 2669 unique peptide sequences, corresponding to 685 proteins and related protein groups.

The application of new software tools to quantitative protein profiling via isotope-coded affinity tag (ICAT) and tandem mass spectrometry: II. Evaluation of tandem mass spectrometry methodologies for large-scale protein analysis, and the application of statistical tools for data analysis and interpretation.

Proteomic approaches to biological research that will prove the most useful and productive require robust, sensitive, and reproducible technologies for both the qualitative and quantitative analysis of complex protein mixtures. Here we applied the isotope-coded affinity tag (ICAT) approach to quantitative protein profiling, in this case proteins that copurified with lipid raft plasma membrane domains isolated from control and stimulated Jurkat human T cells. With the ICAT approach, cysteine residues of the two related protein isolates were covalently labeled with isotopically normal and heavy versions of the same reagent, respectively. Following proteolytic cleavage of combined labeled proteins, peptides were fractionated by multidimensional chromatography and subsequently analyzed via automated tandem mass spectrometry. Individual tandem mass spectrometry spectra were searched against a human sequence database, and a variety of recently developed, publicly available software applications were used to sort, filter, analyze, and compare the results of two repetitions of the same experiment. In particular, robust statistical modeling algorithms were used to assign measures of confidence to both peptide sequences and the proteins from which they were likely derived, identified via the database searches. We show that by applying such statistical tools to the identification of T cell lipid raft-associated proteins, we were able to estimate the accuracy of peptide and protein identifications made. These tools also allow for determination of the false positive rate as a function of user-defined data filtering parameters, thus giving the user significant control over and information about the final output of large-scale proteomic experiments. With the ability to assign probabilities to all identifications, the need for manual verification of results is substantially reduced, thus making the rapid evaluation of large proteomic datasets possible. Finally, by repeating the experiment, information relating to the general reproducibility and validity of this approach to large-scale proteomic analyses was also obtained.

Quantitative proteome analysis by solid-phase isotope tagging and mass spectrometry.

The adaptation of sequences of chemical reactions to a solid-phase format has been essential to the automation, reproducibility, and efficiency of a number of biotechnological processes including peptide and oligonucleotide synthesis and sequencing. Here we describe a method for the site-specific, stable isotopic labeling of cysteinyl peptides in complex peptide mixtures through a solid-phase capture and release process, and the concomitant isolation of the labeled peptides. The recovered peptides were analyzed by microcapillary liquid chromatography and tandem mass spectrometry (microLC-MS/MS) to determine their sequences and relative quantities. The method was used to detect galactose-induced changes in protein abundance in the yeast Saccharomyces cerevisiae. A side-by-side comparison with the isotope-coded affinity tag (ICAT) method demonstrated that the solid-phase method for stable isotope tagging of peptides is comparatively simpler, more efficient, and more sensitive.