Comparison of Mascot and X!Tandem performance for low and high accuracy mass spectrometry and the development of an adjusted Mascot threshold

Comparison of Database Searching Programs for the Analysis of Single-Cell Proteomics Data

Single-cell proteomics is emerging as an important subfield in the proteomics and mass spectrometry communities, with potential to reshape our understanding of cell development, cell differentiation, disease diagnosis, and the development of new therapies. Compared with significant advancements in the "hardware" that is used in single-cell proteomics, there has been little work comparing the effects of using different "software" packages to analyze single-cell proteomics datasets. To this end, seven popular proteomics programs were compared here, applying them to search three single-cell proteomics datasets generated by three different platforms. The results suggest that MSGF+, MSFragger, and Proteome Discoverer are generally more efficient in maximizing protein identifications, that MaxQuant is better suited for the identification of low-abundance proteins, that MSFragger is superior in elucidating peptide modifications, and that Mascot and X!Tandem are better for analyzing long peptides. Furthermore, an experiment with different loading amounts was carried out to investigate changes in identification results and to explore areas in which single-cell proteomics data analysis may be improved in the future. We propose that this comparative study may provide insight for experts and beginners alike operating in the emerging subfield of single-cell proteomics.

Peptidomic Approaches and Observations in Neurodegenerative Diseases

Mass spectrometry (MS), with its immense technological developments over the last two decades, has emerged as an unavoidable technique in analyzing biomolecules such as proteins and peptides. Its multiplexing capability and explorative approach make it a valuable tool for analyzing complex clinical samples concerning biomarker research and investigating pathophysiological mechanisms. Peptides regulate various biological processes, and several of them play a critical role in many disease-related pathological conditions. One important example in neurodegenerative diseases is the accumulation of amyloid-beta peptides (Aβ) in the brain of Alzheimer’s disease (AD) patients. When investigating brain function and brain-related pathologies, such as neurodegenerative diseases, cerebrospinal fluid (CSF) represents the most suitable sample because of its direct contact with the brain. In this review, we evaluate publications applying peptidomics analysis to CSF samples, focusing on neurodegenerative diseases. We describe the methodology of peptidomics analysis and give an overview of the achievements of CSF peptidomics over the years. Finally, publications reporting peptides regulated in AD are discussed.

MS-Decipher: a user-friendly proteome database search software with an emphasis on deciphering the spectra of O-linked glycopeptides

MOTIVATION

The interpretation of mass spectrometry (MS) data is a crucial step in proteomics analysis, and the identification of post-translational modifications (PTMs) is vital for the understanding of the regulation mechanism of the living system. Among various PTMs, glycosylation is one of the most diverse ones. Though many search engines have been developed to decipher proteomic data, some of them are difficult to operate and have poor performance on glycoproteomic datasets compared to advanced glycoproteomic software.

RESULTS

To simplify the analysis of proteomic datasets, especially O-glycoproteomic datasets, here, we present a user-friendly proteomic database search platform, MS-Decipher, for the identification of peptides from MS data. Two scoring schemes can be chosen for peptide-spectra matching. It was found that MS-Decipher had the same sensitivity and confidence in peptide identification compared to traditional database searching software. In addition, a special search mode, O-Search, is integrated into MS-Decipher to identify O-glycopeptides for O-glycoproteomic analysis. Compared with Mascot, MetaMorpheus and MSFragger, MS-Decipher can obtain about 139.9%, 48.8% and 6.9% more O-glycopeptide-spectrum matches. A useful tool is provided in MS-Decipher for the visualization of O-glycopeptide-spectra matches. MS-Decipher has a user-friendly graphical user interface, making it easier to operate. Several file formats are available in the searching and validation steps. MS-Decipher is implemented with Java, and can be used cross-platform.

AVAILABILITY AND IMPLEMENTATION

MS-Decipher is freely available at https://github.com/DICP-1809/MS-Decipher for academic use. For detailed implementation steps, please see the user guide.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A Systematic Evaluation of Semispecific Peptide Search Parameter Enables Identification of Previously Undescribed N-Terminal Peptides and Conserved Proteolytic Processing in Cancer Cell Lines

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become the most commonly used technique in explorative proteomic research. A variety of open-source tools for peptide-spectrum matching have become available. Most analyses of explorative MS data are performed using conventional settings, such as fully specific enzymatic constraints. Here we evaluated the impact of the fragment mass tolerance in combination with the enzymatic constraints on the performance of three search engines. Three open-source search engines (Myrimatch, X! Tandem, and MSGF+) were evaluated concerning the suitability in semi- and unspecific searches as well as the importance of accurate fragment mass spectra in non-specific peptide searches. We then performed a semispecific reanalysis of the published NCI-60 deep proteome data applying the most suited parameters. Semi- and unspecific LC-MS/MS data analyses particularly benefit from accurate fragment mass spectra while this effect is less pronounced for conventional, fully specific peptide-spectrum matching. Search speed differed notably between the three search engines for semi- and non-specific peptide-spectrum matching. Semispecific reanalysis of NCI-60 proteome data revealed hundreds of previously undescribed N-terminal peptides, including cases of proteolytic processing or likely alternative translation start sites, some of which were ubiquitously present in all cell lines of the reanalyzed panel. Highly accurate MS2 fragment data in combination with modern open-source search algorithms enable the confident identification of semispecific peptides from large proteomic datasets. The identification of previously undescribed N-terminal peptides in published studies highlights the potential of future reanalysis and data mining in proteomic datasets.

Isoginkgetin derivative IP2 enhances the adaptive immune response against tumor antigens

The success of cancer immunotherapy relies on the induction of an immunoprotective response targeting tumor antigens (TAs) presented on MHC-I molecules. We demonstrated that the splicing inhibitor isoginkgetin and its water-soluble and non-toxic derivative IP2 act at the production stage of the pioneer translation products (PTPs). We showed that IP2 increases PTP-derived antigen presentation in cancer cells in vitro and impairs tumor growth in vivo. IP2 action is long-lasting and dependent on the CD8+ T cell response against TAs. We observed that the antigen repertoire displayed on MHC-I molecules at the surface of MCA205 fibrosarcoma is modified upon treatment with IP2. In particular, IP2 enhances the presentation of an exon-derived epitope from the tumor suppressor nischarin. The combination of IP2 with a peptide vaccine targeting the nischarin-derived epitope showed a synergistic antitumor effect in vivo. These findings identify the spliceosome as a druggable target for the development of epitope-based immunotherapies.

Automation of mass spectrometric detection of analytes and related workflows: A review

The developments in mass spectrometry (MS) in the past few decades reveal the power and versatility of this technology. MS methods are utilized in routine analyses as well as research activities involving a broad range of analytes (elements and molecules) and countless matrices. However, manual MS analysis is gradually becoming a thing of the past. In this article, the available MS automation strategies are critically evaluated. Automation of analytical workflows culminating with MS detection encompasses involvement of automated operations in any of the steps related to sample handling/treatment before MS detection, sample introduction, MS data acquisition, and MS data processing. Automated MS workflows help to overcome the intrinsic limitations of MS methodology regarding reproducibility, throughput, and the expertise required to operate MS instruments. Such workflows often comprise automated off-line and on-line steps such as sampling, extraction, derivatization, and separation. The most common instrumental tools include autosamplers, multi-axis robots, flow injection systems, and lab-on-a-chip. Prototyping customized automated MS systems is a way to introduce non-standard automated features to MS workflows. The review highlights the enabling role of automated MS procedures in various sectors of academic research and industry. Examples include applications of automated MS workflows in bioscience, environmental studies, and exploration of the outer space.

Novel interconnections of HOG signaling revealed by combined use of two proteomic software packages

Modern quantitative mass spectrometry (MS)-based proteomics enables researchers to unravel signaling networks by monitoring proteome-wide cellular responses to different stimuli. MS-based analysis of signaling systems usually requires an integration of multiple quantitative MS experiments, which remains challenging, given that the overlap between these datasets is not necessarily comprehensive. In a previous study we analyzed the impact of the yeast mitogen-activated protein kinase (MAPK) Hog1 on the hyperosmotic stress-affected phosphorylome. Using a combination of a series of hyperosmotic stress and kinase inhibition experiments, we identified a broad range of direct and indirect substrates of the MAPK. Here we re-evaluate this extensive MS dataset and demonstrate that a combined analysis based on two software packages, MaxQuant and Proteome Discoverer, increases the coverage of Hog1-target proteins by 30%. Using protein-protein proximity assays we show that the majority of new targets gained by this analysis are indeed Hog1-interactors. Additionally, kinetic profiles indicate differential trends of Hog1-dependent versus Hog1-independent phosphorylation sites. Our findings highlight a previously unrecognized interconnection between Hog1 signaling and the RAM signaling network, as well as sphingolipid homeostasis.

Proteomic Analysis of Cerebral Cortex Extracts from Sus scrofa with Induced Hemorrhagic Stroke

Cerebrovascular diseases, including stroke and micro stroke, are the main causes of death in contemporary society. Hemorrhagic stroke is the fast emerging defficiency in the brain function resulting from disturbance of blood supply to the brain caused by the rapture of blood vessels (Lopez et al. in Proteomics Clin Appl 6:190-200, 2012). The influence of a model hemorrhagic stroke on white pigs with the change in the protein profile of their cortical samples 24 h and 2 months after the stroke was examined using mass-spectrometric analysis. Different proteins (n = 30) were identified, and their content was elevated. These proteins are involved in the mechanisms of neuroprotection, including compensation of oxidative stress (TXN, SNCA, PRDX6, ENO1), prevention of unwanted protein aggregation and apoptosis (PTMA, SNCA, SNCB), release of neurotransmitters (GAPDH, PEBP1) and assembly of the cytoskeleton (ACTA2, PTMA, TUBA4A, TUBA1D), etc. Also, a group of seven Ras family proteins involved in the regulation of cell proliferation and differentiation was found in the samples taken 24 h following the stroke. The relative concentrations of most of the proteins in the samples taken 2 months after the stroke demonstrate intermediate values between the control sample and the sample taken in 24 h, indicating the extinction of change in the protein profile with time. During the first 24 h after the stroke, there is an increase in protein fractions participating in exocytosis, synaptic plasticity/signaling, and support of neurotransmitter transport. Such shift in the weight of protein functional clusters can be attributed to activation of compensatory mechanisms in the body focused on neuroprotection.

Single-platform ‘multi-omic’ profiling: unified mass spectrometry and computational workflows for integrative proteomics–metabolomics analysis

Advances in instrumentation and analysis tools are permitting evermore comprehensive interrogation of diverse biomolecules and allowing investigators to move from linear signaling cascades to network models, which more accurately reflect the molecular basis of biological systems and processes.

Random and independent sampling of endogenous tryptic peptides from normal human EDTA plasma by liquid chromatography micro electrospray ionization and tandem mass spectrometry

Background

Normal human EDTA plasma samples were collected on ice, processed ice cold, and stored in a freezer at – 80 °C prior to experiments. Plasma test samples from the – 80 °C freezer were thawed on ice or intentionally warmed to room temperature.

Methods

Protein content was measured by CBBR binding and the release of alcohol soluble amines by the Cd ninhydrin assay. Plasma peptides released over time were collected over C18 for random and independent sampling by liquid chromatography micro electrospray ionization and tandem mass spectrometry (LC–ESI–MS/MS) and correlated with X!TANDEM.

Results

Fully tryptic peptides by X!TANDEM returned a similar set of proteins, but was more computationally efficient, than “no enzyme” correlations. Plasma samples maintained on ice, or ice with a cocktail of protease inhibitors, showed lower background amounts of plasma peptides compared to samples incubated at room temperature. Regression analysis indicated that warming plasma to room temperature, versus ice cold, resulted in a ~ twofold increase in the frequency of peptide identification over hours–days of incubation at room temperature. The type I error rate of the protein identification from the X!TANDEM algorithm combined was estimated to be low compared to a null model of computer generated random MS/MS spectra.

Conclusion

The peptides of human plasma were identified and quantified with low error rates by random and independent sampling that revealed 1000s of peptides from hundreds of human plasma proteins from endogenous tryptic peptides.

Electronic supplementary material

The online version of this article (10.1186/s12014-017-9176-7) contains supplementary material, which is available to authorized users.

Proteomic Cinderella: Customized analysis of bulky MS/MS data in one night

Proteomic challenges, stirred up by the advent of high-throughput technologies, produce large amount of MS data. Nowadays, the routine manual search does not satisfy the "speed" of modern science any longer. In our work, the necessity of single-thread analysis of bulky data emerged during interpretation of HepG2 proteome profiling results for proteoforms searching. We compared the contribution of each of the eight search engines (X!Tandem, MS-GF[Formula: see text], MS Amanda, MyriMatch, Comet, Tide, Andromeda, and OMSSA) integrated in an open-source graphical user interface SearchGUI ( http://searchgui.googlecode.com ) into total result of proteoforms identification and optimized set of engines working simultaneously. We also compared the results of our search combination with Mascot results using protein kit UPS2, containing 48 human proteins. We selected combination of X!Tandem, MS-GF[Formula: see text] and OMMSA as the most time-efficient and productive combination of search. We added homemade java-script to automatize pipeline from file picking to report generation. These settings resulted in rise of the efficiency of our customized pipeline unobtainable by manual scouting: the analysis of 192 files searched against human proteome (42153 entries) downloaded from UniProt took 11[Formula: see text]h.

Expanding the cerebrospinal fluid endopeptidome

Biomarkers of neurodegenerative disorders are needed to assist in diagnosis, to monitor disease progression and therapeutic interventions, and to provide insight into disease mechanisms. One route to identify such biomarkers is by proteomic and peptidomic analysis of cerebrospinal fluid (CSF). In the current study, we performed an in-depth analysis of the human CSF endopeptidome to establish an inventory that may serve as a basis for future targeted biomarker studies. High-pH RP HPLC was employed for off-line sample prefractionation followed by low-pH nano-LC-MS analysis. Different software programs and scoring algorithms for peptide identification were employed and compared. A total of 18 031 endogenous peptides were identified at a FDR of 1%, increasing the number of known endogenous CSF peptides 10-fold compared to previous studies. The peptides were derived from 2 053 proteins of which more than 60 have been linked to neurodegeneration. Notably, among the findings were six peptides derived from microtubule-associated protein tau, three of which span the diagnostically interesting threonine-181 (Tau-F isoform). Also, 213 peptides from amyloid precursor protein were identified, 58 of which were partially or completely within the sequence of amyloid β 1-40/42, as well as 109 peptides from apolipoprotein E, spanning sequences that discriminate between the E2/E3/E4 isoforms of the protein.

Analysis of the Cerebrospinal Fluid Proteome in Alzheimer's Disease

Alzheimer’s disease is a neurodegenerative disorder accounting for more than 50% of cases of dementia. Diagnosis of Alzheimer’s disease relies on cognitive tests and analysis of amyloid beta, protein tau, and hyperphosphorylated tau in cerebrospinal fluid. Although these markers provide relatively high sensitivity and specificity for early disease detection, they are not suitable for monitor of disease progression. In the present study, we used label-free shotgun mass spectrometry to analyse the cerebrospinal fluid proteome of Alzheimer’s disease patients and non-demented controls to identify potential biomarkers for Alzheimer’s disease. We processed the data using five programs (DecyderMS, Maxquant, OpenMS, PEAKS, and Sieve) and compared their results by means of reproducibility and peptide identification, including three different normalization methods. After depletion of high abundant proteins we found that Alzheimer’s disease patients had lower fraction of low-abundance proteins in cerebrospinal fluid compared to healthy controls (p<0.05). Consequently, global normalization was found to be less accurate compared to using spiked-in chicken ovalbumin for normalization. In addition, we determined that Sieve and OpenMS resulted in the highest reproducibility and PEAKS was the programs with the highest identification performance. Finally, we successfully verified significantly lower levels (p<0.05) of eight proteins (A2GL, APOM, C1QB, C1QC, C1S, FBLN3, PTPRZ, and SEZ6) in Alzheimer’s disease compared to controls using an antibody-based detection method. These proteins are involved in different biological roles spanning from cell adhesion and migration, to regulation of the synapse and the immune system.

Revisiting peptide identification by high-accuracy mass spectrometry: problems associated with the use of narrow mass precursor windows

Peptide identification is increasingly achieved through database searches in which mass precursor tolerance is set in the ppm range. This trend is driven by the high resolution and accuracy of modern mass spectrometers and the belief that the quality of peptide identification is fully controlled by estimating the false discovery rate (FDR) using the decoy-target approach. However, narrowing mass tolerance decreases the number of sequence candidates, and several authors have raised concerns that these search conditions can introduce inaccuracies. Here, we demonstrate that when scores that only depend on one sequence candidate are used, decoy-based estimates of the number of false positive identifications are accurate even with an average number of candidates of just 200, to the point that remarkably accurate FDR predictions can be made in completely different search conditions. However, when scores that are constructed taking information from additional sequence candidates are used together with low precursor mass tolerances, the proportion of peptides incorrectly identified may become significantly higher than the FDR estimated by the target-decoy approach. Our results suggest that with this kind of score the high mass accuracy of modern mass spectrometers should be exploited by using wide mass windows followed by postscoring mass filtering algorithms.

Identification of related peptides through the analysis of fragment ion mass shifts

Mass spectrometry (MS) has become the method of choice to identify and quantify proteins, typically by fragmenting peptides and inferring protein identification by reference to sequence databases. Well-established programs have largely solved the problem of identifying peptides in complex mixtures. However, to prevent the search space from becoming prohibitively large, most search engines need a list of expected modifications. Therefore, unexpected modifications limit both the identification of proteins and peptide-based quantification. We developed mass spectrometry-peak shift analysis (MS-PSA) to rapidly identify related spectra in large data sets without reference to databases or specified modifications. Peptide identifications from established tools, such as MASCOT or SEQUEST, may be propagated onto MS-PSA results. Modification of a peptide alters the mass of the precursor ion and some of the fragmentation ions. MS-PSA identifies characteristic fragmentation masses from MS/MS spectra. Related spectra are identified by pattern matching of unchanged and mass-shifted fragment ions. We illustrate the use of MS-PSA with simple and complex mixtures with both high and low mass accuracy data sets. MS-PSA is not limited to the analysis of peptides but can be used for the identification of related groups of spectra in any set of fragmentation patterns.

Evaluation and optimization of mass spectrometric settings during data-dependent acquisition mode: focus on LTQ-Orbitrap mass analyzers

Mass-spectrometry-based proteomics has evolved as the preferred method for the analysis of complex proteomes. Undoubtedly, recent advances in mass spectrometry instrumentation have greatly enhanced proteomic analysis. A popular instrument platform in proteomics research is the LTQ-Orbitrap mass analyzer. In this tutorial, we discuss the significance of evaluating and optimizing mass spectrometric settings on the LTQ-Orbitrap during CID data-dependent acquisition (DDA) mode to improve protein and peptide identification rates. We focus on those MS and MS/MS parameters that have been systematically examined and evaluated by several researchers and are commonly used during DDA. More specifically, we discuss the effect of mass resolving power, preview mode for FTMS scan, monoisotopic precursor selection, signal threshold for triggering MS/MS events, number of microscans per MS/MS scan, number of MS/MS events, automatic gain control target value (ion population) for MS and MS/MS, maximum ion injection time for MS/MS, rapid and normal scan rate, and prediction of ion injection time. We furthermore present data from the latest generation LTQ-Orbitrap system, the Orbitrap Elite, along with recommended MS and MS/MS parameters. The Orbitrap Elite outperforms the Orbitrap Classic in terms of scan speed, sensitivity, dynamic range, and resolving power and results in higher identification rates. Several of the optimized MS parameters determined on the LTQ-Orbitrap Classic and XL were easily transferable to the Orbitrap Elite, whereas others needed to be reevaluated. Finally, the Q Exactive and HCD are briefly discussed, as well as sample preparation, LC-optimization, and bioinformatics analysis. We hope this tutorial will serve as guidance for researchers new to the field of proteomics and assist in achieving optimal results.

Improved proteomic analysis pipeline for LC-ETD-MS/MS using charge enhancing methods

Electron transfer dissociation (ETD) is a useful and complementary activation method for peptide fragmentation in mass spectrometry. However, ETD spectra typically receive a relatively low score in the identifications of 2+ ions. To overcome this challenge, we, for the first time, systematically interrogated the benefits of combining ion charge enhancing methods (dimethylation, guanidination, m-nitrobenzyl alcohol (m-NBA) or Lys-C digestion) and differential search algorithms (Mascot, Sequest, OMSSA, pFind and X!Tandem). A simple sample (BSA) and a complex sample (AMJ2 cell lysate) were selected in benchmark tests. Clearly distinct outcomes were observed through different experimental protocol. In the analysis of AMJ2 cell lines, X!Tandem and pFind revealed 92.65% of identified spectra; m-NBA adduction led to a 5-10% increase in average charge state and the most significant increase in the number of successful identifications, and Lys-C treatment generated peptides carrying mostly triple charges. Based on the complementary identification results, we suggest that a combination of m-NBA and Lys-C strategies accompanied by X!Tandem and pFind can greatly improve ETD identification.

Phytoplasma proteomic analysis

Proteome analysis is becoming a powerful tool in the functional characterization of organisms, and takes a broad, comprehensive, systematic approach to understanding biology. Following the sequencing of the phytoplasma genomes, the next step is to characterize the expressed proteome of phytoplasmas to acquire the verification and functional annotation of all predicted genes and their protein products. Here, we describe the protocol of mulberry dwarf phytoplasma purification, phytoplasma protein extraction and separation by SDS-PAGE, in-gel tryptic digestion of the proteins, separation of the digested peptides by liquid chromatography, and identification of the peptides by mass spectrometry. The protocol described here is also applicable to the analysis of other phytoplasma proteomes.

Learning from decoys to improve the sensitivity and specificity of proteomics database search results

The statistical validation of database search results is a complex issue in bottom-up proteomics. The correct and incorrect peptide spectrum match (PSM) scores overlap significantly, making an accurate assessment of true peptide matches challenging. Since the complete separation between the true and false hits is practically never achieved, there is need for better methods and rescoring algorithms to improve upon the primary database search results. Here we describe the calibration and False Discovery Rate (FDR) estimation of database search scores through a dynamic FDR calculation method, FlexiFDR, which increases both the sensitivity and specificity of search results. Modelling a simple linear regression on the decoy hits for different charge states, the method maximized the number of true positives and reduced the number of false negatives in several standard datasets of varying complexity (18-mix, 49-mix, 200-mix) and few complex datasets (E. coli and Yeast) obtained from a wide variety of MS platforms. The net positive gain for correct spectral and peptide identifications was up to 14.81% and 6.2% respectively. The approach is applicable to different search methodologies- separate as well as concatenated database search, high mass accuracy, and semi-tryptic and modification searches. FlexiFDR was also applied to Mascot results and showed better performance than before. We have shown that appropriate threshold learnt from decoys, can be very effective in improving the database search results. FlexiFDR adapts itself to different instruments, data types and MS platforms. It learns from the decoy hits and sets a flexible threshold that automatically aligns itself to the underlying variables of data quality and size.

Microbial proteomics using mass spectrometry

Proteomic analyses involve a series of intricate, interdependent steps involving approaches and technical issues that must be fully coordinated to obtain the optimal amount of required information about the test subject. Fortunately, many of these steps are common to most test subjects, requiring only modifications to or, in some cases, substitution of some of the steps to ensure they are relevant to the desired objective of a study. This fortunate occurrence creates an essential core of proteomic approaches and techniques that are consistently available for most studies, regardless of test subject. In this chapter, an overview of some of these core approaches, techniques, and mass spectrometric instrumentation is given, while indicating how such steps are useful for and applied to bacterial investigations. To exemplify how such proteomic concepts and techniques are applicable to bacterial investigations, a practical, quantitative method useful for bacterial proteomic analysis is presented with a discussion of possibilities, pitfalls, and some emerging technology to provide a compilation of information from the diverse literature that is intermingled with experimental experience.

Mass spectrometric analysis of novel phosphorylation sites in the TRPC4β channel

RATIONALE

The transient receptor potential canonical (TRPC) channel 4β is a non-selective cation channel that is regulated by intracellular Ca(2+) and G protein-coupled receptors. Tyrosine phosphorylation of TRPC4β is important in mediating the activity and membrane expression of this channel protein. However, studies of TRPC4β Ser/Thr phosphorylation are lacking.

METHODS

To investigate the phosphorylation sites involved in regulating the diverse functions of TRPC4β in mammalian cells, we used nano-liquid chromatography/tandem mass spectrometry to identify key phosphorylation sites in TRPC4β that was immunopurified from HEK293 cells with monoclonal anti-TRPC4β antibody.

RESULTS

We identified four phosphorylation sites in the C-terminus of TRPC4β, none of which had been previously reported. Our data show that TRPC4β in mammalian cells is highly phosphorylated under basal conditions at multiple sites, and that a mass spectrometric proteomic technique combined with antibody-based affinity purification is an effective approach to define the phosphorylation sites of TRPC4β channels in mammalian cells.

CONCLUSIONS

These novel phosphorylation sites on TRPC4β may play a potential role in the phosphorylation-mediated regulation of TRPC4β channel activity and function in mammalian cells.

Data extraction from proteomics raw data: an evaluation of nine tandem MS tools using a large Orbitrap data set

In shot-gun proteomics raw tandem MS data are processed with extraction tools to produce condensed peak lists that can be uploaded to database search engines. Many extraction tools are available but to our knowledge, a systematic comparison of such tools has not yet been carried out. Using raw data containing more than 400,000 tandem MS spectra acquired using an Orbitrap Velos we compared 9 tandem MS extraction tools, freely available as well as commercial. We compared the tools with respect to number of extracted MS/MS events, fragment ion information, number of matches, precursor mass accuracies and agreement in-between tools. Processing a primary data set with 9 different tandem MS extraction tools resulted in a low overlap of identified peptides. The tools differ by assigned charge states of precursors, precursor and fragment ion masses, and we show that peptides identified very confidently using one extraction tool might not be matched when using another tool. We also found a bias towards peptides of lower charge state when extracting fragment ion data from higher resolution raw data without deconvolution. Collecting and comparing the extracted data from the same raw data allow adjusting parameters and expectations and selecting the right tool for extraction of tandem MS data.

Effect of mass spectrometric parameters on peptide and protein identification rates for shotgun proteomic experiments on an LTQ-orbitrap mass analyzer

The success of a shotgun proteomic experiment relies heavily on the performance and optimization of both the LC and the MS systems. Despite this, little consideration has, so far, been given to the importance of evaluating and optimizing the MS instrument settings during data-dependent acquisition mode. Moreover, during data-dependent acquisition, the users have to decide and choose among various MS parameters and settings, making a successful analysis even more challenging. We have systematically investigated and evaluated the effect of enabling and disabling the preview mode for FTMS scan, the number of microscans per MS/MS scan, the number of MS/MS events, the maximum ion injection time for MS/MS, and the automatic gain control target value for MS and MS/MS events on protein and peptide identification rates on an LTQ-Orbitrap using the Saccharomyces cerevisiae proteome. Our investigations aimed to assess the significance of each MS parameter to improve proteome analysis and coverage. We observed that higher identification rates were obtained at lower ion injection times i.e. 50-150 ms, by performing one microscan and 12-15 MS/MS events. In terms of ion population, optimal automatic gain control target values were at 5×10(5) -1×10(6) ions for MS and 3×10(3) -1×10(4) ions for MS/MS. The preview mode scan had a minimal effect on identification rates. Using optimized MS settings, we identified 1038 (±2.3%) protein groups with a minimum of two peptide identifications and an estimated false discovery rate of ∼1% at both peptide and protein level in a 160-min LC-MS/MS analysis.

A face in the crowd: recognizing peptides through database search

Peptide identification via tandem mass spectrometry sequence database searching is a key method in the array of tools available to the proteomics researcher. The ability to rapidly and sensitively acquire tandem mass spectrometry data and perform peptide and protein identifications has become a commonly used proteomics analysis technique because of advances in both instrumentation and software. Although many different tandem mass spectrometry database search tools are currently available from both academic and commercial sources, these algorithms share similar core elements while maintaining distinctive features. This review revisits the mechanism of sequence database searching and discusses how various parameter settings impact the underlying search.

Data analysis strategy for maximizing high-confidence protein identifications in complex proteomes such as human tumor secretomes and human serum

Detection of biologically interesting, low-abundance proteins in complex proteomes such as serum typically requires extensive fractionation and high-performance mass spectrometers. Processing of the resulting large data sets involves trade-offs between confidence of identification and depth of protein coverage; that is, higher stringency filters preferentially reduce the number of low-abundance proteins identified. In the current study, an alternative database search and results filtering strategies were evaluated using test samples ranging from purified proteins to ovarian tumor secretomes and human serum to maximize peptide and protein coverage. Full and partial tryptic searches were compared because substantial numbers of partial tryptic peptides were observed in all samples, and the proportion of partial tryptic peptides was particularly high for serum. When data filters that yielded similar false discovery rates (FDR) were used, full tryptic searches detected far fewer peptides than partial tryptic searches. In contrast to the common practice of using full tryptic specificity and a narrow precursor mass tolerance, more proteins and peptides could be confidently identified using a partial tryptic database search with a 100 ppm precursor mass tolerance followed by filtering of results using 10 ppm mass error and full tryptic boundaries.

Shotgun proteomics aids discovery of novel protein-coding genes, alternative splicing, and "resurrected" pseudogenes in the mouse genome

Recent advances in proteomic mass spectrometry (MS) offer the chance to marry high-throughput peptide sequencing to transcript models, allowing the validation, refinement, and identification of new protein-coding loci. We present a novel pipeline that integrates highly sensitive and statistically robust peptide spectrum matching with genome-wide protein-coding predictions to perform large-scale gene validation and discovery in the mouse genome for the first time. In searching an excess of 10 million spectra, we have been able to validate 32%, 17%, and 7% of all protein-coding genes, exons, and splice boundaries, respectively. Moreover, we present strong evidence for the identification of multiple alternatively spliced translations from 53 genes and have uncovered 10 entirely novel protein-coding genes, which are not covered in any mouse annotation data sources. One such novel protein-coding gene is a fusion protein that spans the Ins2 and Igf2 loci to produce a transcript encoding the insulin II and the insulin-like growth factor 2-derived peptides. We also report nine processed pseudogenes that have unique peptide hits, demonstrating, for the first time, that they are not just transcribed but are translated and are therefore resurrected into new coding loci. This work not only highlights an important utility for MS data in genome annotation but also provides unique insights into the gene structure and propagation in the mouse genome. All these data have been subsequently used to improve the publicly available mouse annotation available in both the Vega and Ensembl genome browsers (http://vega.sanger.ac.uk).

Coherent pipeline for biomarker discovery using mass spectrometry and bioinformatics

BACKGROUND

Robust biomarkers are needed to improve microbial identification and diagnostics. Proteomics methods based on mass spectrometry can be used for the discovery of novel biomarkers through their high sensitivity and specificity. However, there has been a lack of a coherent pipeline connecting biomarker discovery with established approaches for evaluation and validation. We propose such a pipeline that uses in silico methods for refined biomarker discovery and confirmation.

RESULTS

The pipeline has four main stages: Sample preparation, mass spectrometry analysis, database searching and biomarker validation. Using the pathogen Clostridium botulinum as a model, we show that the robustness of candidate biomarkers increases with each stage of the pipeline. This is enhanced by the concordance shown between various database search algorithms for peptide identification. Further validation was done by focusing on the peptides that are unique to C. botulinum strains and absent in phylogenetically related Clostridium species. From a list of 143 peptides, 8 candidate biomarkers were reliably identified as conserved across C. botulinum strains. To avoid discarding other unique peptides, a confidence scale has been implemented in the pipeline giving priority to unique peptides that are identified by a union of algorithms.

CONCLUSIONS

This study demonstrates that implementing a coherent pipeline which includes intensive bioinformatics validation steps is vital for discovery of robust biomarkers. It also emphasises the importance of proteomics based methods in biomarker discovery.

Sialic acid-focused quantitative mouse serum glycoproteomics by multiple reaction monitoring assay

Despite increasing importance of protein glycosylation, most of the large-scale glycoproteomics have been limited to profiling the sites of N-glycosylation. However, in-depth knowledge of protein glycosylation to uncover functions and their clinical applications requires quantitative glycoproteomics eliciting both peptide and glycan sequences concurrently. Here we describe a novel strategy for the multiplexed quantitative mouse serum glycoproteomics based on a specific chemical ligation, namely, reverse glycoblotting technique, focusing sialic acids and multiple reaction monitoring (MRM). LC-MS/MS analysis of de-glycosylated peptides identified 270 mouse serum peptides (95 glycoproteins) as sialylated glycopeptides, of which 67 glycopeptides were fully characterized by MS/MS analyses in a straightforward manner. We revealed the importance of a fragment ion containing innermost N-acetylglucosamine (GlcNAc) residue as MRM transitions regardless the sequence of the peptides. Versatility of the reverse glycoblotting-assisted MRM assays was demonstrated by quantitative comparison of 25 targeted glycopeptides from 16 proteins between mice with homo and hetero types of diabetes disease model.

Target-Decoy with Mass Binning: a simple and effective validation method for shotgun proteomics using high resolution mass spectrometry

Shotgun proteomics using mass spectrometry (MS) has become the choice for large-scale peptide and protein identification. The recent development of high-resolution mass spectrometers such as FT-ICR or Orbitrap makes it possible to identify peptides within only a few parts per million (ppm), and it is expected to dramatically improve performance of peptide identification, as compared to low-resolution instruments. To fully exploit such significantly higher mass accuracy, however, appropriate data analysis methods are required. Here, we present a new target-decoy strategy, called Target-Decoy with Mass Binning, utilizing high mass accuracy for peptide identification validation, which remains a challenging problem in MS-based proteomics. When tested on various high-resolution MS data, our method was very effective and yet simple and showed comparable or better performance when compared with other validation methods.

Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology

Over the past 2 decades, there have been revolutionary developments in life science technologies characterized by high throughput, high efficiency, and rapid computation. Nutritionists now have the advanced methodologies for the analysis of DNA, RNA, protein, low-molecular-weight metabolites, as well as access to bioinformatics databases. Statistics, which can be defined as the process of making scientific inferences from data that contain variability, has historically played an integral role in advancing nutritional sciences. Currently, in the era of systems biology, statistics has become an increasingly important tool to quantitatively analyze information about biological macromolecules. This article describes general terms used in statistical analysis of large, complex experimental data. These terms include experimental design, power analysis, sample size calculation, and experimental errors (Type I and II errors) for nutritional studies at population, tissue, cellular, and molecular levels. In addition, we highlighted various sources of experimental variations in studies involving microarray gene expression, real-time polymerase chain reaction, proteomics, and other bioinformatics technologies. Moreover, we provided guidelines for nutritionists and other biomedical scientists to plan and conduct studies and to analyze the complex data. Appropriate statistical analyses are expected to make an important contribution to solving major nutrition-associated problems in humans and animals (including obesity, diabetes, cardiovascular disease, cancer, ageing, and intrauterine growth retardation).

Database search strategies for proteomic data sets generated by electron capture dissociation mass spectrometry

Large data sets of electron capture dissociation (ECD) mass spectra from proteomic experiments are rich in information; however, extracting that information in an optimal manner is not straightforward. Protein database search engines currently available are designed for low resolution CID data, from which Fourier transform ion cyclotron resonance (FT-ICR) ECD data differs significantly. ECD mass spectra contain both z-prime and z-dot fragment ions (and c-prime and c-dot); ECD mass spectra contain abundant peaks derived from neutral losses from charge-reduced precursor ions; FT-ICR ECD spectra are acquired with a larger precursor m/z isolation window than their low-resolution CID counterparts. Here, we consider three distinct stages of postacquisition analysis: (1) processing of ECD mass spectra prior to the database search; (2) the database search step itself and (3) postsearch processing of results. We demonstrate that each of these steps has an effect on the number of peptides identified, with the postsearch processing of results having the largest effect. We compare two commonly used search engines: Mascot and OMSSA. Using an ECD data set of modest size (3341 mass spectra) from a complex sample (mouse whole cell lysate), we demonstrate that search results can be improved from 630 identifications (19% identification success rate) to 1643 identifications (49% identification success rate). We focus in particular on improving identification rates for doubly charged precursors, which are typically low for ECD fragmentation. We compare our presearch processing algorithm with a similar algorithm recently developed for electron transfer dissociation (ETD) data.

Strategies for improved protein database searching of electron capture dissociation (ECD) mass spectrometry data sets are presented. We show that identification rates can be significantly increased (19−49%) by a combination of presearch processing of ECD mass spectra, optimizing search parameters, and postprocessing of the results.

Evaluation of the Consensus of Four Peptide Identification Algorithms for Tandem Mass Spectrometry Based Proteomics

The availability of different scoring schemes and filter settings of protein database search algorithms has greatly expanded the number of search methods for identifying candidate peptides from MS/MS spectra. We have previously shown that consensus-based methods that combine three search algorithms yield higher sensitivity and specificity compared to the use of a single search engine (individual method). We hypothesized that union of four search engines (Sequest, Mascot, X!Tandem and Phenyx) can further enhance sensitivity and specificity. ROC plots were generated to measure the sensitivity and specificity of 5460 consensus methods derived from the same dataset. We found that Mascot outperformed individual methods for sensitivity and specificity, while Phenyx performed the worst. The union consensus methods generally produced much higher sensitivity, while the intersection consensus methods gave much higher specificity. The union methods from four search algorithms modestly improved sensitivity, but not specificity, compared to union methods that used three search engines. This suggests that a strategy based on specific combination of search algorithms, instead of merely 'as many search engines as possible', may be key strategy for success with peptide identification. Lastly, we provide strategies for optimizing sensitivity or specificity of peptide identification in MS/MS spectra for different user-specific conditions.

Scoring and validation of tandem MS peptide identification methods

A variety of methods are described in the literature to assign peptide sequences to observed tandem MS data. Typically, the identified peptides are associated only with an arbitrary score that reflects the quality of the peptide-spectrum match but not with a statistically meaningful significance measure. In this chapter, we discuss why statistical significance measures can simplify and unify the interpretation of MS-based proteomic experiments. In addition, we also present available software solutions that convert scores into sound statistical measures.

When less can yield more - Computational preprocessing of MS/MS spectra for peptide identification

The effectiveness of database search algorithms, such as Mascot, Sequest and ProteinPilot is limited by the quality of the input spectra: spurious peaks in MS/MS spectra can jeopardize the correct identification of peptides or reduce their score significantly. Consequently, an efficient preprocessing of MS/MS spectra can increase the sensitivity of peptide identification at reduced file sizes and run time without compromising its specificity. We investigate the performance of 25 MS/MS preprocessing methods on various data sets and make software for improved preprocessing of mgf/dta-files freely available from http://hci.iwr.uni-heidelberg.de/mip/proteomics or http://www.childrenshospital.org/research/steenlab.

Proteomic analysis of laser-microdissected paraffin-embedded tissues: (1) Stage-related protein candidates upon non-metastatic lung adenocarcinoma

We used formalin-fixed paraffin-embedded (FFPE) materials for biomarker discovery in cases of lung cancer using proteomic analysis. We conducted a retrospective global proteomic study in order to characterize protein expression reflecting clinical stages of individual patients with stage I lung adenocarcinoma without lymph node involvement (n=7). In addition, we studied more advanced stage IIIA with spread to lymph nodes (n=6), because the degree of lymph node involvement is the most important factor for staging. FFPE sections of cancerous lesions resected surgically from patients with well-characterized clinical history were subjected to laser microdissection (LMD) followed by Liquid Tissue solubilization and digestion trypsin. Spectral counting was used to measure the amounts of proteins identified by shotgun liquid chromatography (LC)/tandem mass spectrometry (MS/MS). More than 500 proteins were identified from IA and IIIA cases, and non-parametric statistics showed that 81 proteins correlated significantly with stage IA or IIIA. A subset of those proteins were verified by multiple-reaction monitoring mass spectrometric quantitation (MRM assay), described in other paper in this issue. These results demonstrated the technical feasibility of a global proteomic study using clinically well documented FFPE sections, and its possible utility for detailed retrospective disease analyses in order to improve therapeutic strategy.

2-D DIGE analysis implicates cytoskeletal abnormalities in psychiatric disease

The mechanisms underlying white matter changes in psychiatric disease are not known. We aimed to characterise the differential protein expression in deep white matter from the dorsolateral prefrontal cortex from 35 schizophrenia, 35 bipolar disorder, and 35 control subjects, from the Stanley Array Collection. We used 2-D DIGE to profile for protein expression changes in the brain. We found 70 protein spots to be significantly differentially expressed between disease and control subjects (ANCOVA, p<0.05), 46 of which were subsequently identified by LC-MS/MS. The proteins identified included novel disease candidates as well as proteins that have previously been reported as abnormal in schizophrenia, thus reinforcing their association with the disease. Furthermore, we confirmed the direction of change for three proteins using ELISA, namely neurofilament-light, amphiphysin II, and Rab-GDP-alpha, in a subset of the Stanley Array Collection. In addition, altered expression of neurofilament-light, amphiphysin II, and Rab-GDP-alpha was not observed in the cortex of mice chronically treated with haloperidol, making it less likely that these alterations are a consequence of neuroleptic medication. The data presented here strongly suggest disruption of the cytoskeleton and its associated signal transduction proteins in schizophrenia, and to a lesser extent in bipolar disorder.

Accurate and sensitive peptide identification with Mascot Percolator

Sound scoring methods for sequence database search algorithms such as Mascot and Sequest are essential for sensitive and accurate peptide and protein identifications from proteomic tandem mass spectrometry data. In this paper, we present a software package that interfaces Mascot with Percolator, a well performing machine learning method for rescoring database search results, and demonstrate it to be amenable for both low and high accuracy mass spectrometry data, outperforming all available Mascot scoring schemes as well as providing reliable significance measures. Mascot Percolator can be readily used as a stand alone tool or integrated into existing data analysis pipelines.

Snapshot peptidomics of the regulated secretory pathway

Neurons and endocrine cells have the regulated secretory pathway (RSP) in which precursor proteins undergo proteolytic processing by prohormone convertase (PC) 1/3 or 2 to generate bioactive peptides. Although motifs for PC-mediated processing have been described ((R/K)X(n)(R/K) where n = 0, 2, 4, or 6), actual processing sites cannot be predicted from amino acid sequences alone. We hypothesized that discovery of bioactive peptides would be facilitated by experimentally identifying signal peptide cleavage sites and processing sites. However, in vivo and in vitro peptide degradation, which is widely recognized in peptidomics, often hampers processing site determination. To obtain sequence information about peptides generated in the RSP on a large scale, we applied a brief exocytotic stimulus (2 min) to cultured endocrine cells and analyzed peptides released into supernatant using LC-MSMS. Of note, 387 of the 400 identified peptides arose from 19 precursor proteins known to be processed in the RSP, including nine peptide hormone and neuropeptide precursors, seven granin-like proteins, and three processing enzymes (PC1/3, PC2, and peptidyl-glycine alpha-amidating monooxygenase). In total, 373 peptides were informative enough to predict processing sites in that they have signal sequence cleavage sites, PC consensus sites, or monobasic cleavage sites. Several monobasic cleavage sites identified here were previously proved to be generated by PCs. Thus, our approach helps to predict processing sites of RSP precursor proteins and will expedite the identification of unknown bioactive peptides hidden in precursor sequences.

Bayesian nonparametric model for the validation of peptide identification in shotgun proteomics

Tandem mass spectrometry combined with database searching allows high throughput identification of peptides in shotgun proteomics. However, validating database search results, a problem with a lot of solutions proposed, is still advancing in some aspects, such as the sensitivity, specificity, and generalizability of the validation algorithms. Here a Bayesian nonparametric (BNP) model for the validation of database search results was developed that incorporates several popular techniques in statistical learning, including the compression of feature space with a linear discriminant function, the flexible nonparametric probability density function estimation for the variable probability structure in complex problem, and the Bayesian method to calculate the posterior probability. Importantly the BNP model is compatible with the popular target-decoy database search strategy naturally. We tested the BNP model on standard proteins and real, complex sample data sets from multiple MS platforms and compared it with Peptide-Prophet, the cutoff-based method, and a simple nonparametric method (proposed by us previously). The performance of the BNP model was shown to be superior for all data sets searched on sensitivity and generalizability. Some high quality matches that had been filtered out by other methods were detected and assigned with high probability by the BNP model. Thus, the BNP model could be able to validate the database search results effectively and extract more information from MS/MS data.

Adaptive discriminant function analysis and reranking of MS/MS database search results for improved peptide identification in shotgun proteomics

Robust statistical validation of peptide identifications obtained by tandem mass spectrometry and sequence database searching is an important task in shotgun proteomics. PeptideProphet is a commonly used computational tool that computes confidence measures for peptide identifications. In this paper, we investigate several limitations of the PeptideProphet modeling approach, including the use of fixed coefficients in computing the discriminant search score and selection of the top scoring peptide assignment per spectrum only. To address these limitations, we describe an adaptive method in which a new discriminant function is learned from the data in an iterative fashion. We extend the modeling framework to go beyond the top scoring peptide assignment per spectrum. We also investigate the effect of clustering the spectra according to their spectrum quality score followed by cluster-specific mixture modeling. The analysis is carried out using data acquired from a mixture of purified proteins on four different types of mass spectrometers, as well as using a complex human serum data set. A special emphasis is placed on the analysis of data generated on high mass accuracy instruments.