Proteome analyses using accurate mass and elution time peptide tags with capillary LC time-of-flight mass spectrometry

Optimizing Spectronaut Search Parameters to Improve Data Quality with Minimal Proteome Coverage Reductions in DIA Analyses of Heterogeneous Samples

Data-independent acquisition has seen breakthroughs that enable comprehensive proteome profiling using short gradients. As the proteome coverage continues to increase, the quality of the data generated becomes much more relevant. Using Spectronaut, we show that the default search parameters can be easily optimized to minimize the occurrence of false positives across different samples. Using an immunological infection model system to demonstrate the impact of adjusting search settings, we analyzed Mus musculus macrophages and compared their proteome to macrophages spiked withCandida albicans. This experimental system enabled the identification of "false positives" as Candida albicans peptides and proteins should not be present in the Mus musculus-only samples. We show that adjusting the search parameters reduced "false positive" identifications by 89% at the peptide and protein level, thereby considerably increasing the quality of the data. We also show that these optimized parameters incurred a moderate cost, only reducing the overall number of "true positive" identifications across each biological replicate by <6.7% at both the peptide and protein level. We believe the value of our updated search parameters extends beyond a two-organism analysis and would be of great value to any DIA experiment analyzing heterogeneous populations of cell types or tissues.

Analysing Complex Oral Protein Samples: Complete Workflow and Case Analysis of Salivary Pellicles

Studies on small quantity, highly complex protein samples, such as salivary pellicle, have been enabled by recent major technological and analytical breakthroughs. Advances in mass spectrometry-based computational proteomics such as Multidimensional Protein Identification Technology have allowed precise identification and quantification of complex protein samples on a proteome-wide scale, which has enabled the determination of corresponding genes and cellular functions at the protein level. The latter was achieved via protein-protein interaction mapping with Gene Ontology annotation. In recent years, the application of these technologies has broken various barriers in small-quantity-complex-protein research such as salivary pellicle. This review provides a concise summary of contemporary proteomic techniques contributing to (1) increased complex protein (up to hundreds) identification using minute sample sizes (µg level), (2) precise protein quantification by advanced stable isotope labelling or label-free approaches and (3) the emerging concepts and techniques regarding computational integration, such as the Gene Ontology Consortium and protein-protein interaction mapping. The latter integrates the structural, genomic, and biological context of proteins and genes to predict protein interactions and functional connections in a given biological context. The same technological breakthroughs and computational integration concepts can also be applied to other low-volume oral protein complexes such as gingival crevicular or peri-implant sulcular fluids.

Exploring sample preparation and data evaluation strategies for enhanced identification of host cell proteins in drug products of therapeutic antibodies and Fc-fusion proteins

Manufacturing of biopharmaceuticals involves recombinant protein expression in host cells followed by extensive purification of the target protein. Yet, host cell proteins (HCPs) may persist in the final drug product, potentially reducing its quality with respect to safety and efficacy. Consequently, residual HCPs are closely monitored during downstream processing by techniques such as enzyme-linked immunosorbent assay (ELISA) or high-performance liquid chromatography combined with tandem mass spectrometry (HPLC-MS/MS). The latter is especially attractive as it provides information with respect to protein identities. Although the applied HPLC-MS/MS methodologies are frequently optimized with respect to HCP identification, acquired data is typically analyzed using standard settings. Here, we describe an improved strategy for evaluating HPLC-MS/MS data of HCP-derived peptides, involving probabilistic protein inference and peptide detection in the absence of fragment ion spectra. This data analysis workflow was applied to data obtained for drug products of various biotherapeutics upon protein A affinity depletion. The presented data evaluation strategy enabled in-depth comparative analysis of the HCP repertoires identified in drug products of the monoclonal antibodies rituximab and bevacizumab, as well as the fusion protein etanercept. In contrast to commonly applied ELISA strategies, the here presented workflow is process-independent and may be implemented into existing HPLC-MS/MS setups for drug product characterization and process development.

Cancer neoantigen prioritization through sensitive and reliable proteogenomics analysis

Genomics-based neoantigen discovery can be enhanced by proteomic evidence, but there remains a lack of consensus on the performance of different quality control methods for variant peptide identification in proteogenomics. We propose to use the difference between accurately predicted and observed retention times for each peptide as a metric to evaluate different quality control methods. To this end, we develop AutoRT, a deep learning algorithm with high accuracy in retention time prediction. Analysis of three cancer data sets with a total of 287 tumor samples using different quality control strategies results in substantially different numbers of identified variant peptides and putative neoantigens. Our systematic evaluation, using the proposed retention time metric, provides insights and practical guidance on the selection of quality control strategies. We implement the recommended strategy in a computational workflow named NeoFlow to support proteogenomics-based neoantigen prioritization, enabling more sensitive discovery of putative neoantigens.

Identifying mutation-derived neoantigens by proteogenomics requires robust strategies for quality control. Here, the authors propose peptide retention time as an evaluation metric for proteogenomics quality control methods, and develop a deep learning algorithm for accurate retention time prediction.

Retention Time Prediction and Protein Identification

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in bottom-up proteomics.

Visualization and application of amino acid retention coefficients obtained from modeling of peptide retention

We introduce a method for data inspection in liquid separations of peptides using amino acid retention coefficients and their relative change across experiments. Our method allows for the direct comparison between actual experimental conditions, regardless of sample content and without the use of internal standards. The modeling uses linear regression of peptide retention time as a function of amino acid composition. We demonstrate the pH dependency of the model in a control experiment where the pH of the mobile phase was changed in controlled way. We introduce a score to identify the false discovery rate on peptide spectrum match level that corresponds to the set of most robust models, i.e. to maximize the shared agreement between experiments. We demonstrate the method utility in reversed-phase liquid chromatography using 24 datasets with minimal peptide overlap. We apply our method on datasets obtained from a public repository representing various separation designs, including one-dimensional reversed-phase liquid chromatography followed by tandem mass spectrometry, and two-dimensional online strong cation exchange coupled to reversed-phase liquid chromatography followed by tandem mass spectrometry, and highlight new insights. Our method provides a simple yet powerful way to inspect data quality, in particular for multidimensional separations, improving comparability of data at no additional experimental cost.

PROCAL: A Set of 40 Peptide Standards for Retention Time Indexing, Column Performance Monitoring, and Collision Energy Calibration

Beyond specific applications, such as the relative or absolute quantification of peptides in targeted proteomic experiments, synthetic spike-in peptides are not yet systematically used as internal standards in bottom-up proteomics. A number of retention time standards have been reported that enable chromatographic aligning of multiple LC-MS/MS experiments. However, only few peptides are typically included in such sets limiting the analytical parameters that can be monitored. Here, we describe PROCAL (ProteomeTools Calibration Standard), a set of 40 synthetic peptides that span the entire hydrophobicity range of tryptic digests, enabling not only accurate determination of retention time indices but also monitoring of chromatographic separation performance over time. The fragmentation characteristics of the peptides can also be used to calibrate and compare collision energies between mass spectrometers. The sequences of all selected peptides do not occur in any natural protein, thus eliminating the need for stable isotope labeling. We anticipate that this set of peptides will be useful for multiple purposes in individual laboratories but also aiding the transfer of data acquisition and analysis methods between laboratories, notably the use of spectral libraries.

Phosphoproteomics-Based Profiling of Kinase Activities in Cancer Cells

Cellular signaling, predominantly mediated by phosphorylation through protein kinases, is found to be deregulated in most cancers. Accordingly, protein kinases have been subject to intense investigations in cancer research, to understand their role in oncogenesis and to discover new therapeutic targets. Despite great advances, an understanding of kinase dysfunction in cancer is far from complete.A powerful tool to investigate phosphorylation is mass-spectrometry (MS)-based phosphoproteomics, which enables the identification of thousands of phosphorylated peptides in a single experiment. Since every phosphorylation event results from the activity of a protein kinase, high-coverage phosphoproteomics data should indirectly contain comprehensive information about the activity of protein kinases.In this chapter, we discuss the use of computational methods to predict kinase activity scores from MS-based phosphoproteomics data. We start with a short explanation of the fundamental features of the phosphoproteomics data acquisition process from the perspective of the computational analysis. Next, we briefly review the existing databases with experimentally verified kinase-substrate relationships and present a set of bioinformatic tools to discover novel kinase targets. We then introduce different methods to infer kinase activities from phosphoproteomics data and these kinase-substrate relationships. We illustrate their application with a detailed protocol of one of the methods, KSEA (Kinase Substrate Enrichment Analysis). This method is implemented in Python within the framework of the open-source Kinase Activity Toolbox (kinact), which is freely available at http://github.com/saezlab/kinact/ .

Progress in mass spectrometry-based proteomic research of tumor hypoxia (Review)

A hypoxic microenvironment effects various signaling pathways in the human body, including those that are critical for normal physiology and those that support tumorigenesis or cancer progression. A hypoxic tumor microenvironment, in particular, modulates cell migration, invasion and resistance to radiotherapy and chemotherapy. Development of the mass spectrometry (MS) technique has allowed for expansion of proteomic study to a wide variety of fields, with the study of tumor hypoxia being among the latest to enjoy its benefits. In such studies, changes in the proteome of tumor tissue or cells induced by the hypoxic conditions are analyzed. A multitude of hypoxic regulatory proteins have already been identified, increasing our understanding of the mechanisms underlying tumor occurrence and development and representing candidate reference markers for tumor diagnosis and therapy. The present review provides the first summary of the collective studies on tumor microenvironment hypoxia that have been completed using MS-based proteomic techniques, providing a systematic discussion of the benefits and current challenges of the various applications.

Transformative Impact of Proteomics on Cardiovascular Health and Disease: A Scientific Statement From the American Heart Association

The year 2014 marked the 20th anniversary of the coining of the term proteomics. The purpose of this scientific statement is to summarize advances over this period that have catalyzed our capacity to address the experimental, translational, and clinical implications of proteomics as applied to cardiovascular health and disease and to evaluate the current status of the field. Key successes that have energized the field are delineated; opportunities for proteomics to drive basic science research, facilitate clinical translation, and establish diagnostic and therapeutic healthcare algorithms are discussed; and challenges that remain to be solved before proteomic technologies can be readily translated from scientific discoveries to meaningful advances in cardiovascular care are addressed. Proteomics is the result of disruptive technologies, namely, mass spectrometry and database searching, which drove protein analysis from 1 protein at a time to protein mixture analyses that enable large-scale analysis of proteins and facilitate paradigm shifts in biological concepts that address important clinical questions. Over the past 20 years, the field of proteomics has matured, yet it is still developing rapidly. The scope of this statement will extend beyond the reaches of a typical review article and offer guidance on the use of next-generation proteomics for future scientific discovery in the basic research laboratory and clinical settings.

Hybrid data acquisition and processing strategies with increased throughput and selectivity: pSMART analysis for global qualitative and quantitative analysis

Data-dependent acquisition (DDA) and data-independent acquisition strategies (DIA) have both resulted in improved understanding of proteomics samples. Both strategies have advantages and disadvantages that are well-published, where DDA is typically applied for deep discovery and DIA may be used to create sample records. In this paper, we present a hybrid data acquisition and processing strategy (pSMART) that combines the strengths of both techniques and provides significant benefits for qualitative and quantitative peptide analysis. The performance of pSMART is compared to published DIA strategies in an experiment that allows the objective assessment of DIA performance with respect to interrogation of previously acquired MS data. The results of this experiment demonstrate that pSMART creates fewer decoy hits than a standard DIA strategy. Moreover, we show that pSMART is more selective, sensitive, and reproducible than either standard DIA or DDA strategies alone.

Comparative performance of four methods for high-throughput glycosylation analysis of immunoglobulin G in genetic and epidemiological research

The biological and clinical relevance of glycosylation is becoming increasingly recognized, leading to a growing interest in large-scale clinical and population-based studies. In the past few years, several methods for high-throughput analysis of glycans have been developed, but thorough validation and standardization of these methods is required before significant resources are invested in large-scale studies. In this study, we compared liquid chromatography, capillary gel electrophoresis, and two MS methods for quantitative profiling of N-glycosylation of IgG in the same data set of 1201 individuals. To evaluate the accuracy of the four methods we then performed analysis of association with genetic polymorphisms and age. Chromatographic methods with either fluorescent or MS-detection yielded slightly stronger associations than MS-only and multiplexed capillary gel electrophoresis, but at the expense of lower levels of throughput. Advantages and disadvantages of each method were identified, which should inform the selection of the most appropriate method in future studies.

Peptide profiling of cerebrospinal fluid by mass spectrometry

The search for biomarkers is driven by the increasing clinical importance of early diagnosis. Reliable biomarkers can also assist in directing therapy, monitoring disease activity and the efficacy of treatment. In addition, the discovery of novel biomarkers might provide clues to the pathogenesis of a disease. The dynamic range of protein concentrations in body fluids exceeds 10 orders of magnitude. These huge differences in concentrations complicate the detection of proteins with low expression levels. Since all classical biomarkers have low expression levels (e.g., prostate-specific antigen: 2-4 microg/l; and CA125: 20-35 U/ml), new developments with respect to identification and validation techniques of the low-abundance proteins are required. This review will discuss the current status of profiling cerebrospinal fluid using mass spectrometry-based techniques, and new developments in this area.

Advanced nanoscale separations and mass spectrometry for sensitive high-throughput proteomics

Recent developments in combined separations with mass spectrometry for sensitive and high-throughput proteomic analyses are reviewed herein. These developments primarily involve high-efficiency (separation peak capacities of approximately 10(3)) nanoscale liquid chromatography (flow rates extending down to approximately 20 nl/min at optimal liquid mobile-phase separation linear velocities through narrow packed capillaries) in combination with advanced mass spectrometry and in particular, high-sensitivity and high-resolution Fourier transform ion cyclotron resonance mass spectrometry. Such approaches enable analysis of low nanogram level proteomic samples (i.e., nanoscale proteomics) with individual protein identification sensitivity at the low zeptomole level. The resultant protein measurement dynamic range can approach 10(6) for nanogram-sized proteomic samples, while more abundant proteins can be detected from subpicogram-sized (total) proteome samples. These qualities provide the foundation for proteomics studies of single or small populations of cells. The instrumental robustness required for automation and providing high-quality routine performance nanoscale proteomic analyses is also discussed.

Mass spectrometry for proteomics-based investigation

Within the past years, we have witnessed a great improvement in mass spectrometry (MS) and proteomics approaches in terms of instrumentation, protein fractionation, and bioinformatics. With the current technology, protein identification alone is no longer sufficient. Both scientists and clinicians want not only to identify proteins but also to identify the protein's posttranslational modifications (PTMs), protein isoforms, protein truncation, protein-protein interaction (PPI), and protein quantitation. Here, we describe the principle of MS and proteomics and strategies to identify proteins, protein's PTMs, protein isoforms, protein truncation, PPIs, and protein quantitation. We also discuss the strengths and weaknesses within this field. Finally, in our concluding remarks we assess the role of mass spectrometry and proteomics in scientific and clinical settings in the near future. This chapter provides an introduction and overview for subsequent chapters that will discuss specific MS proteomic methodologies and their application to specific medical conditions. Other chapters will also touch upon areas that expand beyond proteomics, such as lipidomics and metabolomics.

Improving qualitative and quantitative performance for MS(E)-based label-free proteomics

Label-free quantitation by data independent methods (for instance MS(E)) is growing in popularity due to the high technical reproducibility of mass spectrometry analysis. The recent introduction of Synapt hybrid instruments capable of incorporating ion mobility separation within mass spectrometry analysis now allows acquisition of high definition MS(E) data (HDMS(E)). HDMS(E) enables deeper proteome coverage and more confident peptide identifications when compared to MS(E), while the latter offers a higher dynamic range for quantitation. We have developed synapter as, a versatile tool to better evaluate the results of data independent acquisitions on Waters instruments. We demonstrate that synapter can be used to combine HDMS(E) and MS(E) data to achieve deeper proteome coverage delivered by HDMS(E) and more accurate quantitation for high intensity peptides, delivered by MS(E). For users who prefer to run samples exclusively in one mode, synapter allows other useful functionality like false discovery rate estimation, filtering on peptide match type and mass error, and filling missing values. Our software integrates with existing tools, thus permitting us to easily combine peptide quantitation information into protein quantitation by a range of different approaches.

Data processing methods and quality control strategies for label-free LC-MS protein quantification

Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Performance comparisons of nano-LC systems, electrospray sources and LC-MS-MS platforms

Selecting a suitable nano-liquid chromatography system (LC), ionization source and mass spectrometer for LC-tandem mass spectrometry (MS-MS) studies is complicated by numerous competing technologies. This study compares four popular nano-LC systems, four ionization sources and three MS facilities that use completely different LC-MS-MS systems. Statistically significant differences in LC performance were identified with similarly performing Proxeon, Waters and Eksigent nanoLC-Ultra systems [retention time routinely at 0.7-0.9% relative standard deviation (RSD)], and all outperformed the Eksigent nanoLC-2D (RSD ∼2%). In addition, compatibility issues were identified between the Bruker HCT ion trap mass spectrometer and both the Eksigent nanoLC-2D and the Bruker nanoelectrospray source. The electrospray source itself had an unexpected and striking effect on chromatographic reproducibility on the Bruker HCT ion trap. The New Objective nanospray source significantly outperformed the Bruker nanospray source in retention time RSD (1% RSD versus 14% RSD, respectively); and the Bruker nebulized nanospray source outperformed both of these traditional, non-nebulized sources (0.5% RSD in retention time). Finally, to provide useful benchmarks for overall proteomics sensitivity, different LC-MS-MS platforms were compared by analyzing a range of concentrations of tryptic digests of bovine serum albumin at three MS facilities. The results indicate that similar sensitivity can be realized with a Bruker HCT-Ultra ion trap, a Thermo LTQ-Velos Linear ion trap and a Thermo LTQ-Orbitrap XL-ETD.

Applications of Mass Spectrometry in Proteomics

Characterisation of proteins and whole proteomes can provide a foundation to our understanding of physiological and pathological states and biological diseases or disorders. Constant development of more reliable and accurate mass spectrometry (MS) instruments and techniques has allowed for better identification and quantification of the thousands of proteins involved in basic physiological processes. Therefore, MS-based proteomics has been widely applied to the analysis of biological samples and has greatly contributed to our understanding of protein functions, interactions, and dynamics, advancing our knowledge of cellular processes as well as the physiology and pathology of the human body. This review will discuss current proteomic approaches for protein identification and characterisation, including post-translational modification (PTM) analysis and quantitative proteomics as well as investigation of protein–protein interactions (PPIs).

Identification of deacetylase substrates with the biotin switch approach

The identification of lysine-acetylated proteins and deacetylase substrates has primarily relied on protein immune-affinity techniques with antibodies that recognize acetylated lysine residues (Kac antibodies). While these antibody-based techniques are continuously improving, they can be limited by the narrow and many times unknown epitope specificity of Kac antibodies. An alternative approach is the biotin switch capture of deacetylated proteins. Similar in part to other biotin switch methodologies, this technique relies on the blocking of native lysine residues and removal of the modification of interest in vitro, after which the newly deacetylated proteins can be captured and identified by mass spectrometry (MS). In this chapter, we cover the essential steps of the procedure, highlight key points in the assay to reduce false positive protein identification, and discuss the quantitative MS methods useful for identifying the captured deacetylase substrates. We also discuss potential strategies and future improvements to overcome current limitations of the assay.

Retention time prediction and protein identification

In bottom-up proteomics, proteins are typically identified by enzymatic digestion into peptides, tandem mass spectrometry and comparison of the tandem mass spectra with those predicted from a sequence database for peptides within measurement uncertainty from the experimentally obtained mass. Although now decreasingly common, isolated proteins or simple protein mixtures can also be identified by measuring only the masses of the peptides resulting from the enzymatic digest, without any further fragmentation. Separation methods such as liquid chromatography and electrophoresis are often used to fractionate complex protein or peptide mixtures prior to analysis by mass spectrometry. Although the primary reason for this is to avoid ion suppression and improve data quality, these separations are based on physical and chemical properties of the peptides or proteins and therefore also provide information about them. Depending on the separation method, this could be protein molecular weight (SDS-PAGE), isoelectric point (IEF), charge at a known pH (ion exchange chromatography), or hydrophobicity (reversed phase chromatography). These separations produce approximate measurements on properties that to some extent can be predicted from amino acid sequences. In the case of molecular weight of proteins without posttranslational modifications this is straightforward: simply add the molecular weights of the amino acid residues in the protein. For IEF, charge and hydrophobicity, the order of the amino acids, and folding state of the peptide or protein also matter, but it is nevertheless possible to predict the behavior of peptides and proteins in these separation methods to a degree which renders such predictions useful. This chapter reviews the topic of using data from separation methods for identification and validation in proteomics, with special emphasis on predicting retention times of tryptic peptides in reversed-phase chromatography under acidic conditions, as this is one of the most commonly used separation methods in proteomics.

De novo correction of mass measurement error in low resolution tandem MS spectra for shotgun proteomics

We report an algorithm designed for the calibration of low resolution peptide mass spectra. Our algorithm is implemented in a program called FineTune, which corrects systematic mass measurement error in 1 min, with no input required besides the mass spectra themselves. The mass measurement accuracy for a set of spectra collected on an LTQ-Velos improved 20-fold from -0.1776 ± 0.0010 m/z to 0.0078 ± 0.0006 m/z after calibration (avg ± 95 % confidence interval). The precision in mass measurement was improved due to the correction of non-linear variation in mass measurement accuracy across the m/z range.

Solid-phase capture for the detection and relative quantification of S-nitrosoproteins by mass spectrometry

The proteomic analysis of S-nitrosylated protein (SNO-proteins) has long depended on the biotin switch technique (BST), which requires blocking of free thiols, ascorbate-based denitrosylation of SNO-Cys, biotinylation of nascent thiol and avidin-based affinity isolation. A more recent development is resin assisted-capture of SNO-proteins (SNO-RAC), which substitutes thiopropyl Sepharose (TPS) for biotin-avidin, thus reducing the number of steps required for enrichment of S-nitrosylated proteins. In addition, SNO-RAC facilitates on-resin proteolytic digestion following SNO-protein capture, greatly simplifying the purification of peptides containing sites of S-nitrosylation ("SNO-sites"). This resin-based approach has also now been applied to detection of alternative Cys-based modifications, including S-palmitoylation (Acyl-RAC) and S-oxidation (Ox-RAC). Here, we review the important steps to minimize false-positive identification of SNO-proteins, give detailed methods for processing of protein-bound TPS for mass spectrometry (MS) based analysis, and discuss the various quantitative MS methods that are compatible with SNO-RAC. We also discuss strategies to overcome the current limitations surrounding MS-based SNO-site localization in peptides containing more than one potential target Cys residue. This article therefore serves as a starting point and guide for the MS-focused exploration of SNO-proteomes by SNO-RAC.

Using iRT, a normalized retention time for more targeted measurement of peptides

Multiple reaction monitoring (MRM) has recently become the method of choice for targeted quantitative measurement of proteins using mass spectrometry. The method, however, is limited in the number of peptides that can be measured in one run. This number can be markedly increased by scheduling the acquisition if the accurate retention time (RT) of each peptide is known. Here we present iRT, an empirically derived dimensionless peptide-specific value that allows for highly accurate RT prediction. The iRT of a peptide is a fixed number relative to a standard set of reference iRT-peptides that can be transferred across laboratories and chromatographic systems. We show that iRT facilitates the setup of multiplexed experiments with acquisition windows more than four times smaller compared to in silico RT predictions resulting in improved quantification accuracy. iRTs can be determined by any laboratory and shared transparently. The iRT concept has been implemented in Skyline, the most widely used software for MRM experiments.

An improved calibration method for the matrix-assisted laser desorption/ionization-Fourier transform ion cyclotron resononance analysis of 15N-metabolically- labeled proteome digests using a mass difference approach

High mass measurement accuracy of peptides in enzymatic digests is critical for confident protein identification and characterization in proteomics research. Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) can provide low or sub-ppm mass accuracy and ultrahigh resolving power. While for ESI-FT-ICR-MS, the mass accuracy is generally 1 ppm or better, with matrix-assisted laser desorption/ionization (MALDI)-FT-ICR-MS, the mass errors can vary from sub-ppm with internal calibration to over 100 ppm with conventional external calibration. A novel calibration method for (15)N-metabolically labeled peptides from a batch digest of a proteome is described which corrects for space charge induced frequency shifts in FT-ICR spectra without using an internal calibrant. This strategy utilizes the information from the mass difference between the (14)N/(15)N peptide peak pairs to correct for space charge induced mass shifts after data collection. A procedure for performing the mass correction has been written into a computer program and has been successfully applied to high-performance liquid chromatography-MALDI-FT- ICR-MS measurement of (15)N-metabolic labeled proteomes. We have achieved an average measured mass error of 1.0 ppm and a standard deviation of 3.5 ppm for 900 peptides from 68 MALDI-FT-ICR mass spectra of the proteolytic digest of a proteome from Methanococcus maripaludis.

Extensive characterization of Tupaia belangeri neuropeptidome using an integrated mass spectrometric approach

Neuropeptidomics is used to characterize endogenous peptides in the brain of tree shrews (Tupaia belangeri). Tree shrews are small animals similar to rodents in size but close relatives of primates, and are excellent models for brain research. Currently, tree shrews have no complete proteome information available on which direct database search can be allowed for neuropeptide identification. To increase the capability in the identification of neuropeptides in tree shrews, we developed an integrated mass spectrometry (MS)-based approach that combines methods including data-dependent, directed, and targeted liquid chromatography (LC)-Fourier transform (FT)-tandem MS (MS/MS) analysis, database construction, de novo sequencing, precursor protein search, and homology analysis. Using this integrated approach, we identified 107 endogenous peptides that have sequences identical or similar to those from other mammalian species. High accuracy MS and tandem MS information, with BLAST analysis and chromatographic characteristics were used to confirm the sequences of all the identified peptides. Interestingly, further sequence homology analysis demonstrated that tree shrew peptides have a significantly higher degree of homology to equivalent sequences in humans than those in mice or rats, consistent with the close phylogenetic relationship between tree shrews and primates. Our results provide the first extensive characterization of the peptidome in tree shrews, which now permits characterization of their function in nervous and endocrine system. As the approach developed fully used the conservative properties of neuropeptides in evolution and the advantage of high accuracy MS, it can be portable for identification of neuropeptides in other species for which the fully sequenced genomes or proteomes are not available.

SILACtor: software to enable dynamic SILAC studies

Stable isotope labeling by amino acids in cell culture (SILAC) is a versatile tool in proteomics that has been used to explore protein turnover on a large scale. However, these studies pose a significant undertaking that can be greatly simplified through the use of computational tools that automate the data analysis. While SILAC technology has enjoyed rapid adoption through the availability of several software tools, algorithms do not exist for the automated analysis of protein turnover data generated using SILAC technology. Presented here is a software tool, SILACtor, designed to trace and compare SILAC-labeled peptides across multiple time points. SILACtor is used to profile protein turnover rates for more than 500 HeLa cell proteins using a SILAC label-chase approach. Additionally, SILACtor contains a method for the automated generation of accurate mass and retention time inclusion lists that target peptides of interest showing fast or slow turnover rates relative to the other peptides observed in the samples. SILACtor enables improved protein turnover studies using SILAC technology and also provides a framework for features extensible to comparative SILAC analyses and targeted methods.

Integrative metabolome and transcriptome profiling reveals discordant energetic stress between mouse strains with differential sensitivity to acrolein-induced acute lung injury

SCOPE

This investigation sought to better understand the metabolic role of the lung and to generate insights into the pathogenesis of acrolein-induced acute lung injury. A respiratory irritant, acrolein is generated by overheating cooking oils or by domestic cooking using biomass fuels, and is in environmental tobacco smoke, a health hazard in the restaurant workplace.

METHODS AND RESULTS

Using SM/J (sensitive) and 129X1/SvJ (resistant) inbred mouse strains, the lung metabolome was integrated with the transcriptome profile before and after acrolein exposure. A total of 280 small molecules were identified and mean values (log 2 >0.58 or <-0.58, p<0.05) were considered different for between-strain comparisons or within-strain responses to acrolein treatment. At baseline, 24 small molecules increased and 33 small molecules decreased in the SM/J mouse lung as compared to 129X1/SvJ mouse lung. Notable among the increased compounds was malonylcarnitine. Following acrolein exposure, several molecules indicative of glycolysis and branched chain amino acid metabolism increased similarly in both strains, whereas SM/J mice were less effective in generating metabolites related to fatty acid β-oxidation.

CONCLUSION

These findings suggest management of energetic stress varies between these strains, and that the ability to evoke auxiliary energy generating pathways rapidly and effectively may be critical in enhancing survival during acute lung injury in mice.

A statistical method for assessing peptide identification confidence in accurate mass and time tag proteomics

Current algorithms for quantifying peptide identification confidence in the accurate mass and time (AMT) tag approach assume that the AMT tags themselves have been correctly identified. However, there is uncertainty in the identification of AMT tags, because this is based on matching LC-MS/MS fragmentation spectra to peptide sequences. In this paper, we incorporate confidence measures for the AMT tag identifications into the calculation of probabilities for correct matches to an AMT tag database, resulting in a more accurate overall measure of identification confidence for the AMT tag approach. The method is referenced as Statistical Tools for AMT Tag Confidence (STAC). STAC additionally provides a uniqueness probability (UP) to help distinguish between multiple matches to an AMT tag and a method to calculate an overall false discovery rate (FDR). STAC is freely available for download, as both a command line and a Windows graphical application.

Liquid chromatography-mass spectrometry-based quantitative proteomics

LC-MS-based quantitative proteomics has become increasingly applied to a wide range of biological applications due to growing capabilities for broad proteome coverage and good accuracy and precision in quantification. Herein, we review the current LC-MS-based quantification methods with respect to their advantages and limitations and highlight their potential applications.

Proteomic profiling of a layered tissue reveals unique glycolytic specializations of photoreceptor cells

The retina is a highly ordered tissue whose outermost layers are formed by subcellular compartments of photoreceptors generating light-evoked electrical responses. We studied protein distributions among individual photoreceptor compartments by separating the entire photoreceptor layer of a flat-mounted frozen retina into a series of thin tangential cryosections and analyzing protein compositions of each section by label-free quantitative mass spectrometry. Based on 5038 confidently identified peptides assigned to 896 protein database entries, we generated a quantitative proteomic database (a "map") correlating the distribution profiles of identified proteins with the profiles of marker proteins representing individual compartments of photoreceptors and adjacent cells. We evaluated the applicability of several common peptide-to-protein quantification algorithms in the context of our database and found that the highest reliability was obtained by summing the intensities of all peptides representing a given protein, using at least the 5-6 most intense peptides when applicable. We used this proteome map to investigate the distribution of glycolytic enzymes, critical in fulfilling the extremely high metabolic demands of photoreceptor cells, and obtained two major findings. First, unlike the majority of neurons rich in hexokinase I, but similar to other highly metabolically active cells, photoreceptors express hexokinase II. Hexokinase II has a very high catalytic activity when associated with mitochondria, and indeed we found it colocalized with mitochondria in photoreceptors. Second, photoreceptors contain very little triosephosphate isomerase, an enzyme converting dihydroxyacetone phosphate into glyceraldehyde-3-phosphate. This may serve as a functional adaptation because dihydroxyacetone phosphate is a major precursor in phospholipid biosynthesis, a process particularly active in photoreceptors because of the constant renewal of their light-sensitive membrane disc stacks. Overall, our approach for proteomic profiling of very small tissue amounts at a resolution of a few microns, combining cryosectioning and liquid chromatography-tandem MS, can be applied for quantitative investigation of proteomes where spatial resolution is paramount.

Assigning significance in label-free quantitative proteomics to include single-peptide-hit proteins with low replicates

Selecting differentially regulated proteins with an assignment of statistical significance remains difficult for proteins with a single-peptide hit or a small fold-change when sample replicates are limited. This study presents a label-free quantitative proteomics scheme that was used to select differentially regulated proteins with single-peptide hits and with <2-fold change at a 5% false discovery rate. The scheme incorporated a labeled internal control into two unlabeled samples to facilitate error modeling when there were no replicates for the unlabeled samples. The results showed that, while both a power law global error model with a signal-to-noise ratio statistic (PLGEM-STN) and a constant fold-change threshold could be used, neither of them alone was stringent enough to select differentially regulated proteins at a 5% false discovery rate. Thus, the rule of minimum number of permuted significant pairings (MPSP) was introduced to reduce false discovery rates in combination with PLGEM-STN or a fold-change threshold. MPSP played a critical role in extending the selection of differentially regulated proteins to those with single-peptide hits or with lower fold-changes. Although the approaches were demonstrated for limited sample replicates, they should also be applicable to the situation where more sample replicates are available.

[Application of peptide retention time in proteome research]

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) has been one of the most popular approaches in proteome analysis. As an independent parameter to mass spectrometry information, peptide retention time has been utilized to facilitate protein identification and quantification. In the field of peptide identification, the prediction of the retention time combined with routine tandem mass spectrometry database searching methods could help improve the confidence of identification. The sensitivity of identification could also be improved by matching peaks with both the accurate mass and retention time in multiple aligned LC-MS runs. Meanwhile, because small changes of liquid chromatography conditions lead to variability in retention times unavoidably, retention time alignment is crucial to label-free quantification. Additionally, post-translational modifications (PTM) could be identified by combining retention time shifts and mass deviation information.

A novel mass spectrometry cluster for high-throughput quantitative proteomics

We have developed and implemented a novel mass spectrometry (MS) platform combining the advantages of high mass accuracy and resolving power of Fourier transform ion cyclotron resonance (FTICR) with the economy and speed of multiple ion traps for tandem mass spectrometry. The instruments are integrated using novel algorithms and software and work in concert as one system. Using chromatographic time compression, a single expensive FTICR mass spectrometer can match the throughput of multiple relatively inexpensive ion trap instruments. Liquid chromatography (LC)-mass spectrometry data from the two types of spectrometers are aligned and combined to hybrid datasets, from which peptides are identified using accurate mass from the FTICR data and tandem mass spectra from the ion trap data. In addition, the high resolving power and dynamic range of a 12 tesla FTICR also allows precise label-free quantitation. Using two ion traps in parallel with one LC allows simultaneous MS/MS experiments and optimal application of collision induced dissociation and electron-transfer dissociation throughout the chromatographic separation for increased proteome coverage, characterization of post-translational modifications and/or simultaneous measurement in positive and negative ionization mode. An FTICR-ion trap cluster can achieve similar performance and sample throughput as multiple hybrid ion trap-FTICR instruments, but at a lower cost. We here describe the first such FTICR-ion trap cluster, its performance and the idea of chromatographic compression.

A review of current applications of mass spectrometry for neuroproteomics in epilepsy

The brain is unquestionably the most fascinating organ, and the hippocampus is crucial in memory storage and retrieval and plays an important role in stress response. In temporal lobe epilepsy (TLE), the seizure origin typically involves the hippocampal formation. Despite tremendous progress, current knowledge falls short of being able to explain its function. An emerging approach toward an improved understanding of the complex molecular mechanisms that underlie functions of the brain and hippocampus is neuroproteomics. Mass spectrometry has been widely used to analyze biological samples, and has evolved into an indispensable tool for proteomics research. In this review, we present a general overview of the application of mass spectrometry in proteomics, summarize neuroproteomics and systems biology-based discovery of protein biomarkers for epilepsy, discuss the methodology needed to explore the epileptic hippocampus proteome, and also focus on applications of ingenuity pathway analysis (IPA) in disease research. This neuroproteomics survey presents a framework for large-scale protein research in epilepsy that can be applied for immediate epileptic biomarker discovery and the far-reaching systems biology understanding of the protein regulatory networks. Ultimately, knowledge attained through neuroproteomics could lead to clinical diagnostics and therapeutics to lessen the burden of epilepsy on society.

Comparison of database search strategies for high precursor mass accuracy MS/MS data

In shotgun proteomics, the analysis of tandem mass spectrometry data from peptides can benefit greatly from high mass accuracy measurements. In this study, we have evaluated two database search strategies which use high mass accuracy measurements of the peptide precursor ion. Our results indicate that peptide identifications are improved when spectra are searched with a wide mass tolerance window and precursor mass is used as a filter to discard incorrect matches. Database searches with a peptide data set constrained to peptides within a narrow mass window resulted in fewer peptide identifications but a significantly faster database search time.

Comparative proteomics of erythrocyte aging in vivo and in vitro

During aging in vivo and in vitro, erythrocytes display removal signals. Phagocytosis is triggered by binding of autologous IgG to a senescent cell antigen originating on band 3. Erythrocytes generate vesicles as an integral part of the aging process in vivo and in vitro, i.e. during storage. These vesicles display senescent cell antigens as well as phosphatidylserine, that is recognized by scavenger receptors. Recent comparative proteomic analyses of erythrocytes and their vesicles support the hypothesis that aging is accompanied by increased binding of modified hemoglobins to band 3, disruption of the band 3-mediated anchorage of the cytoskeleton to the lipid bilayer, vesicle formation, and antigenic changes in band 3 conformation. Proteomic data also suggest an, until then unknown, involvement of chaperones, stress proteins, and proteasomes. Thus, the presently available comparative proteomic analyses not only confirm previous immunochemical and functional data, but also (1) provide new clues to the mechanisms that maintain erythrocyte homeostasis; (2) open new roads to elucidate the processes that regulate physiological erythrocyte aging and removal, and thereby; (3) provide the foundation for rational interventions to prevent untimely erythrocyte removal, and unwanted interactions between the erythrocyte and the immune system, especially after transfusion.

Instrumentation for LC-MS/MS in proteomics

Mass spectrometers now have sufficient sensitivity and acquisition rates to allow analysis of complex proteomic samples on a chromatographic timescale. In this chapter the different instrument options for protein and peptide analysis will be presented, along with their relative strengths and weaknesses for producing different types of information, such as protein identification, modification characterization, or reporting quantitative measurements.

Overview of quantitative LC-MS techniques for proteomics and activitomics

LC-MS is a useful technique for protein and peptide quantification. In addition, as a powerful tool for systems biology research, LC-MS can also be used to quantify post-translational modifications and metabolites that reflect biochemical pathway activity. This review discusses the different analytical techniques that use LC-MS for the quantification of proteins, their modifications and activities in a multiplex manner.