PeptidePicker: A scientific workflow with web interface for selecting appropriate peptides for targeted proteomics experiments

Simultaneous LC/MS/MS quantification of eight apolipoproteins in normal and hypercholesterolemic mouse plasma

Apolipoproteins are major structural and functional constituents of lipoprotein particles. As modulators of lipid metabolism, adipose tissue biology, and energy homeostasis, apolipoproteins may serve as biomarkers or potential therapeutic targets for cardiometabolic diseases. Mice are the preferred model to study metabolic disease and CVD, but a comprehensive method to quantify circulating apolipoproteins in mice is lacking. We developed and validated a targeted proteomics assay to quantify eight apolipoproteins in mice via proteotypic signature peptides and corresponding stable isotope-labeled analogs. The LC/MS/MS method requires only a 3 µl sample volume to simultaneously determine mouse apoA-I, apoA-II, apoA-IV, apoB-100, total apoB, apoC-I, apoE, and apoJ concentrations. ApoB-48 concentrations can be calculated by subtracting apoB-100 from total apoB. After we established the analytic performance (sensitivity, linearity, and imprecision) and compared results for selected apolipoproteins against immunoassays, we applied the method to profile apolipoprotein levels in plasma and isolated HDL from normocholesterolemic C57BL/6 mice and from hypercholesterolemic Ldl-receptor- and Apoe-deficient mice. In conclusion, we present a robust, quantitative LC/MS/MS method for the multiplexed analysis of eight apolipoproteins in mice. This assay can be applied to investigate the effects of genetic manipulation or dietary interventions on apolipoprotein levels in plasma and isolated lipoprotein fractions.

Detecting post-translational modification signatures as potential biomarkers in clinical mass spectrometry

INTRODUCTION

Numerous diseases are caused by changes in post-translational modifications (PTMs). Therefore, the number of clinical proteomics studies that include the analysis of PTMs is increasing. Combining complementary information-for example changes in protein abundance, PTM levels, with the genome and transcriptome (proteogenomics)-holds great promise for discovering important drivers and markers of disease, as variations in copy number, expression levels, or mutations without spatial/functional/isoform information is often insufficient or even misleading. Areas covered: We discuss general considerations, requirements, pitfalls, and future perspectives in applying PTM-centric proteomics to clinical samples. This includes samples obtained from a human subject, for instance (i) bodily fluids such as plasma, urine, or cerebrospinal fluid, (ii) primary cells such as reproductive cells, blood cells, and (iii) tissue samples/biopsies. Expert commentary: PTM-centric discovery proteomics can substantially contribute to the understanding of disease mechanisms by identifying signatures with potential diagnostic or even therapeutic relevance but may require coordinated efforts of interdisciplinary and eventually multi-national consortia, such as initiated in the cancer moonshot program. Additionally, robust and standardized mass spectrometry (MS) assays-particularly targeted MS, MALDI imaging, and immuno-MALDI-may be transferred to the clinic to improve patient stratification for precision medicine, and guide therapies.

A rapid methods development workflow for high-throughput quantitative proteomic applications

Recent improvements in the speed and sensitivity of liquid chromatography-mass spectrometry systems have driven significant progress toward system-wide characterization of the proteome of many species. These efforts create large proteomic datasets that provide insight into biological processes and identify diagnostic proteins whose abundance changes significantly under different experimental conditions. Yet, these system-wide experiments are typically the starting point for hypothesis-driven, follow-up experiments to elucidate the extent of the phenomenon or the utility of the diagnostic marker, wherein many samples must be analyzed. Transitioning from a few discovery experiments to quantitative analyses on hundreds of samples requires significant resources both to develop sensitive and specific methods as well as analyze them in a high-throughput manner. To aid these efforts, we developed a workflow using data acquired from discovery proteomic experiments, retention time prediction, and standard-flow chromatography to rapidly develop targeted proteomic assays. We demonstrated this workflow by developing MRM assays to quantify proteins of multiple metabolic pathways from multiple microbes under different experimental conditions. With this workflow, one can also target peptides in scheduled/dynamic acquisition methods from a shotgun proteomic dataset downloaded from online repositories, validate with appropriate control samples or standard peptides, and begin analyzing hundreds of samples in only a few minutes.

Development and Validation of Multiple Reaction Monitoring (MRM) Assays for Clinical Applications

Selected/multiple reaction monitoring-mass spectrometry (SRM/MRM) is an analytical method that is frequently combined to the use of stable isotope-labeled standard (SIS) peptides for absolute protein quantification. The application of SRM/MRM is a relatively recent development in the proteomics field for analysis of biological samples (plasma, urine, cell/tissue lysates) targeting to a large extent biomarker validation. Although MRM generally by-passes the use of antibodies (being linked to sub-optimal assay specificity in many cases), bioanalytical validation of MRM protocols has not been robustly applied because of sensitivity issues, in contrary to antibody-based methods. In this chapter, we will discuss the points that should be addressed for MRM method development in clinical proteomics applications.

Rapidly Assessing the Quality of Targeted Proteomics Experiments through Monitoring Stable-Isotope Labeled Standards

Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in the use of clinical biomarkers, cellular modeling, and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards that are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved mass spectrometry (MS) instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy-labeled peptides and flags those that fail quality-control metrics. Using four metrics, the tool detects problems with both individual SRM transitions and the collective group of transitions that monitor a single peptide. The program's speed and simplicity enable its use at the point of data acquisition and can be ideally run immediately upon the completion of a liquid chromatography-SRM-MS analysis.

Artificial Intelligence Understands Peptide Observability and Assists With Absolute Protein Quantification

Targeted mass spectrometry has become the method of choice to gain absolute quantification information of high quality, which is essential for a quantitative understanding of biological systems. However, the design of absolute protein quantification assays remains challenging due to variations in peptide observability and incomplete knowledge about factors influencing peptide detectability. Here, we present a deep learning algorithm for peptide detectability prediction, d::pPop, which allows the informed selection of synthetic proteotypic peptides for the successful design of targeted proteomics quantification assays. The deep neural network is able to learn a regression model that relates the physicochemical properties of a peptide to its ion intensity detected by mass spectrometry. The approach makes use of experimentally detected deviations from the assumed equimolar abundance of all peptides derived from a given protein. Trained on extensive proteomics datasets, d::pPop's plant and non-plant specific models can predict the quality of proteotypic peptides for not yet experimentally identified proteins. Interrogating the deep neural network after learning from ~76,000 peptides per model organism allows to investigate the impact of different physicochemical properties on the observability of a peptide, thus providing insights into peptide observability as a multifaceted process. Empirical evaluation with rank accuracy metrics showed that our prediction approach outperforms existing algorithms. We circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the need for selecting the top most promising peptides for targeting a protein of interest. Further, we used an artificial QconCAT protein to experimentally validate the observability prediction. Our proteotypic peptide prediction approach not only facilitates the design of absolute protein quantification assays via a user-friendly web interface but also enables the selection of proteotypic peptides for not yet observed proteins, hence rendering the tool especially useful for plant research.

MRMAssayDB: an integrated resource for validated targeted proteomics assays

Motivation

Multiple Reaction Monitoring (MRM)-based targeted proteomics is increasingly being used to study the molecular basis of disease. When combined with an internal standard, MRM allows absolute quantification of proteins in virtually any type of sample but the development and validation of an MRM assay for a specific protein is laborious. Therefore, several public repositories now host targeted proteomics MRM assays, including NCI's Clinical Proteomic Tumor Analysis Consortium assay portals, PeptideAtlas SRM Experiment Library, SRMAtlas, PanoramaWeb and PeptideTracker, with all of which contain different levels of information.

Results

Here we present MRMAssayDB, a web-based application that integrates these repositories into a single resource. MRMAssayDB maps and links the targeted assays, annotates the proteins with information from UniProtKB, KEGG pathways and Gene Ontologies, and provides several visualization options on the peptide and protein level. Currently MRMAssayDB contains >168K assays covering more than 34K proteins from 63 organisms; >13.5K of these proteins are present in >2.3K KEGG biological pathways corresponding to >300 master pathways, and mapping to >13K GO biological processes. MRMAssayDB allows comprehensive searches for a targeted-proteomics assay depending on the user's interests, by using target-protein name or accession number, or using annotations such as subcellular localization, biological pathway, or disease or drug associations. The user can see how many data repositories include a specific peptide assay, and the commonly used transitions for each peptide in all empirical data from the repositories.

Availability and implementation

http://mrmassaydb.proteincentre.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

Co-regulatory networks of human serum proteins link genetics to disease

Proteins circulating in the blood are critical for age-related disease processes; however, the serum proteome has remained largely unexplored. To this end, 4137 proteins covering most predicted extracellular proteins were measured in the serum of 5457 Icelanders over 65 years of age. Pairwise correlation between proteins as they varied across individuals revealed 27 different network modules of serum proteins, many of which were associated with cardiovascular and metabolic disease states, as well as overall survival. The protein modules were controlled by cis- and trans-acting genetic variants, which in many cases were also associated with complex disease. This revealed co-regulated groups of circulating proteins that incorporated regulatory control between tissues and demonstrated close relationships to past, current, and future disease states.

Development of an automated, interference-free, 2D-LC–MS/MS assay for quantification of a therapeutic mAb in human sera

Aim: Hybrid LC–MS/MS assays are increasingly used to quantitate proteins in biological matrices. These assays involve analyte enrichment at the protein level. Although suitability has been demonstrated, they are limited by the lack of appropriate affinity reagents and may suffer from interferences caused by binding proteins or antibodies. Results: An online stable isotope standards and capture by anti-peptide antibodies assay was developed, which involves tryptic digestion of a therapeutic monoclonal antibody in human serum to destroy interfering proteins followed by enrichment using high affinity peptide antibodies. The assay was validated and compared with a standard ligand-binding assay currently used for quantification. Conclusion: The data show that the stable isotope standards and capture by anti-peptide antibodies-2D-LC–MS/MS assay can be used as an alternative method for measurement of monoclonal antibodies in clinical samples.

Molecular phenotyping of laboratory mouse strains using 500 multiple reaction monitoring mass spectrometry plasma assays

Mouse is the predominant experimental model for the study of human disease due, in part, to phylogenetic relationship, ease of breeding, and the availability of molecular tools for genetic manipulation. Advances in genome-editing methodologies, such as CRISPR-Cas9, enable the rapid production of new transgenic mouse strains, necessitating complementary high-throughput and systematic phenotyping technologies. In contrast to traditional protein phenotyping techniques, multiple reaction monitoring (MRM) mass spectrometry can be highly multiplexed without forgoing specificity or quantitative precision. Here we present MRM assays for the quantitation of 500 proteins and subsequently determine reference concentration values for plasma proteins across five laboratory mouse strains that are typically used in biomedical research, revealing inter-strain and intra-strain phenotypic differences. These 500 MRM assays will have a broad range of research applications including high-throughput phenotypic validation of novel transgenic mice, identification of candidate biomarkers, and general research applications requiring multiplexed and precise protein quantification.

ExSTA: External Standard Addition Method for Accurate High-Throughput Quantitation in Targeted Proteomics Experiments

PURPOSE

Targeted proteomics using MRM with stable-isotope-labeled internal-standard (SIS) peptides is the current method of choice for protein quantitation in complex biological matrices. Better quantitation can be achieved with the internal standard-addition method, where successive increments of synthesized natural form (NAT) of the endogenous analyte are added to each sample, a response curve is generated, and the endogenous concentration is determined at the x-intercept. Internal NAT-addition, however, requires multiple analyses of each sample, resulting in increased sample consumption and analysis time.

EXPERIMENTAL DESIGN

To compare the following three methods, an MRM assay for 34 high-to-moderate abundance human plasma proteins is used: classical internal SIS-addition, internal NAT-addition, and external NAT-addition-generated in buffer using NAT and SIS peptides. Using endogenous-free chicken plasma, the accuracy is also evaluated.

RESULTS

The internal NAT-addition outperforms the other two in precision and accuracy. However, the curves derived by internal vs. external NAT-addition differ by only ≈3.8% in slope, providing comparable accuracies and precision with good CV values.

CONCLUSIONS AND CLINICAL RELEVANCE

While the internal NAT-addition method may be "ideal", this new external NAT-addition can be used to determine the concentration of high-to-moderate abundance endogenous plasma proteins, providing a robust and cost-effective alternative for clinical analyses or other high-throughput applications.

Application of targeted mass spectrometry in bottom-up proteomics for systems biology research

The enormous diversity of proteoforms produces tremendous complexity within cellular proteomes, facilitates intricate networks of molecular interactions, and constitutes a formidable analytical challenge for biomedical researchers. Currently, quantitative whole-proteome profiling often relies on non-targeted liquid chromatography-mass spectrometry (LC-MS), which samples proteoforms broadly, but can suffer from lower accuracy, sensitivity, and reproducibility compared with targeted LC-MS. Recent advances in bottom-up proteomics using targeted LC-MS have enabled previously unachievable identification and quantification of target proteins and posttranslational modifications within complex samples. Consequently, targeted LC-MS is rapidly advancing biomedical research, especially systems biology research in diverse areas that include proteogenomics, interactomics, kinomics, and biological pathway modeling. With the recent development of targeted LC-MS assays for nearly the entire human proteome, targeted LC-MS is positioned to enable quantitative proteomic profiling of unprecedented quality and accessibility to support fundamental and clinical research. Here we review recent applications of bottom-up proteomics using targeted LC-MS for systems biology research. SIGNIFICANCE: Advances in targeted proteomics are rapidly advancing systems biology research. Recent applications include systems-level investigations focused on posttranslational modifications (such as phosphoproteomics), protein conformation, protein-protein interaction, kinomics, proteogenomics, and metabolic and signaling pathways. Notably, absolute quantification of metabolic and signaling pathway proteins has enabled accurate pathway modeling and engineering. Integration of targeted proteomics with other technologies, such as RNA-seq, has facilitated diverse research such as the identification of hundreds of "missing" human proteins (genes and transcripts that appear to encode proteins but direct experimental evidence was lacking).

A fully validated liquid chromatography-mass spectrometry method for the quantification of the soluble receptor of advanced glycation end-products (sRAGE) in serum using immunopurification in a 96-well plate format

The study of proteins is central to unraveling (patho)physiological processes and has contributed greatly to our understanding of biological systems. Corresponding studies often employ procedures to enrich proteins from their biological matrix using antibodies or other affinity binders coupled to beads with a large surface area and a correspondingly high binding capacity. Striving for maximal binding capacity may, however, not always be required or desirable, for example for proteins of low abundance. Here we describe a simplified immunoprecipitation in 96-well ELISA format (IPE) approach for fast and easy enrichment of proteins. The applicability of this approach for enriching low-abundant proteins was demonstrated by an IPE-based quantitative workflow using liquid chromatography-mass spectrometry (LC-MS) for the soluble Receptor of Advanced Glycation End-products (sRAGE), a promising biomarker in chronic obstructive pulmonary disease (COPD). The method was validated according to U.S. Food and Drug Administration (FDA) and European Medicines Agency (EMA) guidelines and enabled accurate quantitation of sRAGE between 0.1 and 10 ng/mL in 50 µL serum. The assay showed substantial correlation with the two most commonly-used sRAGE immunoassays (ELISAs) (R²-values between 0.7 and 0.8). However, the LC-MS method reported 2-4 times higher sRAGE levels compared to the ELISAs, which is largely due to a suboptimal amount of capturing antibody and/or calibration strategy used by the immunoassays. In conclusion, our simplified IPE approach proved to be an efficient strategy for enriching the low-abundant protein sRAGE from serum and may provide an easy to use platform for enriching other (low-abundant) proteins from complex, biological matrices.

Targeted On-line SPE-LC-MS/MS Assay for the Quantitation of 12 Apolipoproteins from Human Blood

Laborious sample pretreatment of biological samples represents the most limiting factor for the translation of targeted proteomics assays from research to clinical routine. An optimized method for the simultaneous quantitation of 12 major apolipoproteins (apos) combining on-line SPE and fast LC-MS/MS analysis in 6.5 min total run time was developed, reducing the manual sample pretreatment time of 3 μL serum or plasma by 60%. Within-run and between-day imprecisions below 10 and 15% (n = 10) and high recovery rates (94-131%) were obtained applying the high-throughput setup. High-quality porcine trypsin was used, which outperformed cost-effective bovine trypsin regarding digestion efficiency. Comparisons with immunoassays and another LC-MS/MS assay demonstrated good correlation (Pearson's R: 0.81-0.98). Further, requirements on sample quality concerning sampling, processing, and long-term storage up to 1 year were investigated revealing significant influences of the applied sampling material and coagulant on quantitation results. Apo profiles of 1339 subjects of the LIFE-Adult-Study were associated with lifestyle and physiological parameters as well as establish parameters of lipid metabolism (e.g., triglycerides, cholesterol). Besides gender effects, most significant impact was seen regarding lipid-lowering medication. In conclusion, this novel highly standardized, high-throughput targeted proteomics assay utilizes a fast, simultaneous analysis of 12 apos from least sample amounts.

Absolute two-point quantification of proteins using dimethylated proteotypic peptides

For absolute quantification of target proteins by LC-MS, adding two versions of spike-in peptides can be used as a quality control against each other.

Manipulating trypsin digestion conditions to accelerate proteolysis and simplify digestion workflows in development of protein mass spectrometric assays for the clinical laboratory

For ease of measurement and accurate identification of proteins by mass spectrometry, protein targets are commonly cleaved into peptides. Protein digestion is a critical step in sample preparation, yielding peptides amenable to both chromatographic separation and mass spectrometric analysis. Trypsin is the most extensively used protease due to its high cleavage specificity; however, it can yield highly variable digestion profiles and is dependent on several factors including digestion buffer, denaturant, trypsin quality selected, and composition/complexity of the sample matrix. Historically, trypsin digestion protocols have relied on lengthy digestion times-which are unsuitable for many clinical applications-to ensure effective proteolysis. Here, we performed an iterative and comprehensive evaluation of digestion conditions for five structurally diverse proteins in plasma and serum: apolipoprotein A-1, retinol-binding protein 4, transthyretin, complement component 9 and C-reactive protein. Conditions were monitored for improvements in signal intensity, reproducibility of digestion profile, and rate of release of proteolytic peptides. This approach yielded an optimized digestion protocol for detection of all five proteins in a single workflow requiring a brief 20 min digestion, without the use of chemical denaturants or reduction/alkylation steps, and only 1 μl of plasma. It is our hope that this data can accelerate the development phase of targeted mass spectrometric protein assays by identifying practical approaches to accelerate and simplify digestion protocols for clinical applications and assist with the selection of tryptic peptides for protein quantitation.

Absolute Quantification of Middle- to High-Abundant Plasma Proteins via Targeted Proteomics

The increasing number of peptide and protein biomarker candidates requires expeditious and reliable quantification strategies. The utilization of liquid chromatography coupled to quadrupole tandem mass spectrometry (LC-MS/MS) for the absolute quantitation of plasma proteins and peptides facilitates the multiplexed verification of tens to hundreds of biomarkers from smallest sample quantities. Targeted proteomics assays derived from bottom-up proteomics principles rely on the identification and analysis of proteotypic peptides formed in an enzymatic digestion of the target protein. This protocol proposes a procedure for the establishment of a targeted absolute quantitation method for middle- to high-abundant plasma proteins waiving depletion or enrichment steps. Essential topics as proteotypic peptide identification and LC-MS/MS method development as well as sample preparation and calibration strategies are described in detail.

Multiplexed targeted proteomic assay to assess coagulation factor concentrations and thrombosis-associated cancer

The plasma levels of pro- and anticoagulant proteins are important markers for venous thrombosis (VT) risk and can be affected by both genetic and acquired factors, including cancer. Generally, these markers are measured using activity- or antibody-based assays. Targeted proteomics with stable-isotope-labeled internal standards has proven adept at the rapid, multiplex, and precise quantification of proteins in complex biological samples such as plasma. We used liquid chromatography coupled to multiple reaction monitoring (MRM) mass spectrometry to evaluate the concentrations of 31 coagulation- and fibrinolysis-related proteins in plasma from 25 healthy controls, 25 patients with VT, and 25 patients with VT who were also diagnosed with cancer. The concentration level of 1 to 3 proteotypic peptides per protein was determined, and all samples were previously characterized using traditional antibody- or activity-based methods. When comparing the conventional and the MRM strategies, the mean Pearson correlation for the 13 proteins (covered by 36 target peptides) shared between the 2 approaches was 0.77, indicating a good correlation. Additionally, MRM offers higher sensitivity (mean regression slope, 0.81), higher multiplicity in a single run, and good ability to leverage all measurements to discriminate groups using unsupervised clustering, which identified vitamin K antagonist users as well as patients with VT and cancer. The data collected using MRM show that the combination of coagulation factor levels yields signature information on VT and cancer, which was not obvious from a single measurement. These results encourage the further validation and investigation of MRM in profiling protein signature of disease.

Multiplexed MRM-Based Protein Quantitation Using Two Different Stable Isotope-Labeled Peptide Isotopologues for Calibration

When quantifying endogenous plasma proteins for fundamental and biomedical research - as well as for clinical applications - precise, reproducible, and robust assays are required. Targeted detection of peptides in a bottom-up strategy is the most common and precise mass spectrometry-based quantitation approach when combined with the use of stable isotope-labeled peptides. However, when measuring protein in plasma, the unknown endogenous levels prevent the implementation of the best calibration strategies, since no blank matrix is available. Consequently, several alternative calibration strategies are employed by different laboratories. In this study, these methods were compared to a new approach using two different stable isotope-labeled standard (SIS) peptide isotopologues for each endogenous peptide to be quantified, enabling an external calibration curve as well as the quality control samples to be prepared in pooled human plasma without interference from endogenous peptides. This strategy improves the analytical performance of the assay and enables the accuracy of the assay to be monitored, which can also facilitate method development and validation.

Prediction of Hopeless Peptides Unlikely to be Selected for Targeted Proteome Analysis

In targeted proteomics using liquid chromatography-tandem triple quadrupole mass spectrometry (LC/MS/MS) in the selected reaction monitoring (SRM) mode, selecting the best observable or visible peptides is a key step in the development of SRM assay methods of target proteins. A direct comparison of signal intensities among all candidate peptides by brute-force LC/MS/MS analysis is a concrete approach for peptide selection. However, the analysis requires an SRM method with hundreds of transitions. This study reports on the development of a method for predicting and identifying hopeless peptides to reduce the number of candidate peptides needed for brute-force experiments. Hopeless peptides are proteotypic peptides that are unlikely to be selected for targets in SRM analysis owing to their poor ionization characteristics. Targeted proteomics data from Escherichia coli demonstrated that the relative ionization efficiency between two peptides could be predicted from sequences of two peptides, when a multivariate regression model is used. Validation of the method showed that >20% of the candidate peptides could be successfully eliminated as hopeless peptides with a false positive rate of less than 2%.

Current trends in quantitative proteomics - an update

Proteins can provide insights into biological processes at the functional level, so they are very promising biomarker candidates. The quantification of proteins in biological samples has been routinely used for the diagnosis of diseases and monitoring the treatment. Although large-scale protein quantification in complex samples is still a challenging task, a great amount of effort has been made to advance the technologies that enable quantitative proteomics. Seven years ago, in 2009, we wrote an article about the current trends in quantitative proteomics. In writing this current paper, we realized that, today, we have an even wider selection of potential tools for quantitative proteomics. These tools include new derivatization reagents, novel sampling formats, new types of analyzers and scanning techniques, and recently developed software to assist in assay development and data analysis. In this review article, we will discuss these innovative methods, and their current and potential applications in proteomics. Copyright © 2017 John Wiley & Sons, Ltd.

Towards reproducible MRM based biomarker discovery using dried blood spots

There is an increasing interest in the use of dried blood spot (DBS) sampling and multiple reaction monitoring in proteomics. Although several groups have explored the utility of DBS by focusing on protein detection, the reproducibility of the approach and whether it can be used for biomarker discovery in high throughput studies is yet to be determined. We assessed the reproducibility of multiplexed targeted protein measurements in DBS compared to serum. Eighty-two medium to high abundance proteins were monitored in a number of technical and biological replicates. Importantly, as part of the data analysis, several statistical quality control approaches were evaluated to detect inaccurate transitions. After implementing statistical quality control measures, the median CV on the original scale for all detected peptides in DBS was 13.2% and in Serum 8.8%. We also found a strong correlation (r = 0.72) between relative peptide abundance measured in DBS and serum. The combination of minimally invasive sample collection with a highly specific and sensitive mass spectrometry (MS) technique allows for targeted quantification of multiple proteins in a single MS run. This approach has the potential to fundamentally change clinical proteomics and personalized medicine by facilitating large-scale studies.

Large-Scale SRM Screen of Urothelial Bladder Cancer Candidate Biomarkers in Urine

Urothelial bladder cancer is a condition associated with high recurrence and substantial morbidity and mortality. Noninvasive urinary tests that would detect bladder cancer and tumor recurrence are required to significantly improve patient care. Over the past decade, numerous bladder cancer candidate biomarkers have been identified in the context of extensive proteomics or transcriptomics studies. To translate these findings in clinically useful biomarkers, the systematic evaluation of these candidates remains the bottleneck. Such evaluation involves large-scale quantitative LC-SRM (liquid chromatography-selected reaction monitoring) measurements, targeting hundreds of signature peptides by monitoring thousands of transitions in a single analysis. The design of highly multiplexed SRM analyses is driven by several factors: throughput, robustness, selectivity and sensitivity. Because of the complexity of the samples to be analyzed, some measurements (transitions) can be interfered by coeluting isobaric species resulting in biased or inconsistent estimated peptide/protein levels. Thus the assessment of the quality of SRM data is critical to allow flagging these inconsistent data. We describe an efficient and robust method to process large SRM data sets, including the processing of the raw data, the detection of low-quality measurements, the normalization of the signals for each protein, and the estimation of protein levels. Using this methodology, a variety of proteins previously associated with bladder cancer have been assessed through the analysis of urine samples from a large cohort of cancer patients and corresponding controls in an effort to establish a priority list of most promising candidates to guide subsequent clinical validation studies.

Multiple Reaction Monitoring Using Double Isotopologue Peptide Standards for Protein Quantification

Multiple reaction monitoring (MRM) is a technique used in tandem mass spectrometry where the first mass analyzer preselects parent ions for fragmentation and the second mass analyzer transmits selected product ions to the detector. This targeted technique has found widespread application in bottom-up proteomics for monitoring target peptides in a complex enzymatic digest. Quantitative MRM can be performed on enzymatically digested samples using spiked-in synthetic peptide standards, providing unsurpassed quantitative accuracy and a dynamic range of four orders of magnitude, often eliminating the need for prior depletion of high-abundance proteins. The development of MRM assays requires technical rigor, and this chapter details a methodology for sample preparation, data acquisition, and analyses to successfully perform quantitative MRM assays using two distinct isotopologue peptide standards to quantify proteins in mouse plasma and heart tissue.

Multiplexed panel of precisely quantified salivary proteins for biomarker assessment

An increasingly popular "absolute" quantitative technique involves the SRM or MRM approach with stable isotope-labeled standards (SIS). Using this approach, many proteins in human plasma/serum have been quantified for biomarker assessment and disease stratification. Due to the complexity of plasma and the invasive nature of its collection, alternative biosamples are currently being explored. Here, we present the broadest panel of multiplexed MRM assays with SIS peptides for saliva proteins developed to date. The validated panel consists of 158 candidate human saliva protein biomarkers, inferred from 244 interference-free peptides. The resulting concentrations were reproducibly quantified over a 6 order-of-magnitude concentration range (from 218 μg/mL to 88 pg/mL; average CVs of 12% over analytical triplicates). All concentrations were determined from reverse standard curves, which were generated using a constant concentration of endogenous material with varying concentrations of spiked-in SIS peptides. The large-scale screening of the soluble and membrane-associated proteins contained within the 158-plex assay could present new opportunities for biomarker assessment and clinical diagnostics.

A Multiplexed Cytokeratin Analysis Using Targeted Mass Spectrometry Reveals Specific Profiles in Cancer-Related Pleural Effusions

Pleural effusion (PE), excess fluid in the pleural space, is often observed in lung cancer patients and also forms due to many benign ailments. Classifying it quickly is critical, but this remains an analytical challenge often lengthening the diagnosis process or exposing patients to unnecessary risky invasive procedures. We tested the analysis of PE using a multiplexed cytokeratin (CK) panel with targeted mass spectrometry–based quantitation for its rapid classification. CK markers are often assessed in pathological examinations for cancer diagnosis and guiding treatment course. We developed methods to simultaneously quantify 33 CKs in PE using peptide standards for increased analytical specificity and a simple CK enrichment method to detect their low amounts. Analyzing 121 PEs associated with a variety of lung cancers and noncancerous causes, we show that abundance levels of 10 CKs can be related to PE etiology. CK-6, CK-7, CK-8, CK-18, and CK-19 were found at significantly higher levels in cancer-related PEs. Additionally, elevated levels of vimentin and actin differentiated PEs associated with bacterial infections. A classifier algorithm effectively grouped PEs into cancer-related or benign PEs with 81% sensitivity and 79% specificity. A set of undiagnosed PEs showed that our method has potential to shorten PE diagnosis time. For the first time, we show that a cancer-relevant panel of simple-epithelial CK markers currently used in clinical assessment can also be quantitated in PEs. Additionally, while requiring less invasive sampling, our methodology demonstrated a significant ability to identify cancer-related PEs in clinical samples and thus could improve patient care in the future.

Peptide Biosynthesis with Stable Isotope Labeling from a Cell-free Expression System for Targeted Proteomics with Absolute Quantification

Because of its specificity and sensitivity, targeted proteomics using mass spectrometry for multiple reaction monitoring is a powerful tool to detect and quantify pre-selected peptides from a complex background and facilitates the absolute quantification of peptides using isotope-labeled forms as internal standards. How to generate isotope-labeled peptides remains an urgent challenge for accurately quantitative targeted proteomics on a large scale. Herein, we propose that isotope-labeled peptides fused with a quantitative tag could be synthesized through an expression system in vitro, and the homemade peptides could be enriched by magnetic beads with tag-affinity and globally quantified based on the corresponding multiple reaction monitoring signals provided by the fused tag. An Escherichia coli cell-free protein expression system, protein synthesis using recombinant elements, was adopted for the synthesis of isotope-labeled peptides fused with Strep-tag. Through a series of optimizations, we enabled efficient expression of the labeled peptides such that, after Strep-Tactin affinity enrichment, the peptide yield was acceptable in scale for quantification, and the peptides could be completely digested by trypsin to release the Strep-tag for quantification. Moreover, these recombinant peptides could be employed in the same way as synthetic peptides for multiple reaction monitoring applications and are likely more economical and useful in a laboratory for the scale of targeted proteomics. As an application, we synthesized four isotope-labeled glutathione S-transferase (GST) peptides and added them to mouse sera pre-treated with GST affinity resin as internal standards. A quantitative assay of the synthesized GST peptides confirmed the absolute GST quantification in mouse sera to be measurable and reproducible.

2016 update of the PRIDE database and its related tools

The PRoteomics IDEntifications (PRIDE) database is one of the world-leading data repositories of mass spectrometry (MS)-based proteomics data. Since the beginning of 2014, PRIDE Archive (http://www.ebi.ac.uk/pride/archive/) is the new PRIDE archival system, replacing the original PRIDE database. Here we summarize the developments in PRIDE resources and related tools since the previous update manuscript in the Database Issue in 2013. PRIDE Archive constitutes a complete redevelopment of the original PRIDE, comprising a new storage backend, data submission system and web interface, among other components. PRIDE Archive supports the most-widely used PSI (Proteomics Standards Initiative) data standard formats (mzML and mzIdentML) and implements the data requirements and guidelines of the ProteomeXchange Consortium. The wide adoption of ProteomeXchange within the community has triggered an unprecedented increase in the number of submitted data sets (around 150 data sets per month). We outline some statistics on the current PRIDE Archive data contents. We also report on the status of the PRIDE related stand-alone tools: PRIDE Inspector, PRIDE Converter 2 and the ProteomeXchange submission tool. Finally, we will give a brief update on the resources under development 'PRIDE Cluster' and 'PRIDE Proteomes', which provide a complementary view and quality-scored information of the peptide and protein identification data available in PRIDE Archive.

Exploring the potential of public proteomics data

In a global effort for scientific transparency, it has become feasible and good practice to share experimental data supporting novel findings. Consequently, the amount of publicly available MS-based proteomics data has grown substantially in recent years. With some notable exceptions, this extensive material has however largely been left untouched. The time has now come for the proteomics community to utilize this potential gold mine for new discoveries, and uncover its untapped potential. In this review, we provide a brief history of the sharing of proteomics data, showing ways in which publicly available proteomics data are already being (re-)used, and outline potential future opportunities based on four different usage types: use, reuse, reprocess, and repurpose. We thus aim to assist the proteomics community in stepping up to the challenge, and to make the most of the rapidly increasing amount of public proteomics data.

Making proteomics data accessible and reusable: current state of proteomics databases and repositories

Compared to other data-intensive disciplines such as genomics, public deposition and storage of MS-based proteomics, data are still less developed due to, among other reasons, the inherent complexity of the data and the variety of data types and experimental workflows. In order to address this need, several public repositories for MS proteomics experiments have been developed, each with different purposes in mind. The most established resources are the Global Proteome Machine Database (GPMDB), PeptideAtlas, and the PRIDE database. Additionally, there are other useful (in many cases recently developed) resources such as ProteomicsDB, Mass Spectrometry Interactive Virtual Environment (MassIVE), Chorus, MaxQB, PeptideAtlas SRM Experiment Library (PASSEL), Model Organism Protein Expression Database (MOPED), and the Human Proteinpedia. In addition, the ProteomeXchange consortium has been recently developed to enable better integration of public repositories and the coordinated sharing of proteomics information, maximizing its benefit to the scientific community. Here, we will review each of the major proteomics resources independently and some tools that enable the integration, mining and reuse of the data. We will also discuss some of the major challenges and current pitfalls in the integration and sharing of the data.

An evolving computational platform for biological mass spectrometry: workflows, statistics and data mining with MASSyPup64

In biological mass spectrometry, crude instrumental data need to be converted into meaningful theoretical models. Several data processing and data evaluation steps are required to come to the final results. These operations are often difficult to reproduce, because of too specific computing platforms. This effect, known as 'workflow decay', can be diminished by using a standardized informatic infrastructure. Thus, we compiled an integrated platform, which contains ready-to-use tools and workflows for mass spectrometry data analysis. Apart from general unit operations, such as peak picking and identification of proteins and metabolites, we put a strong emphasis on the statistical validation of results and Data Mining. MASSyPup64 includes e.g., the OpenMS/TOPPAS framework, the Trans-Proteomic-Pipeline programs, the ProteoWizard tools, X!Tandem, Comet and SpiderMass. The statistical computing language R is installed with packages for MS data analyses, such as XCMS/metaXCMS and MetabR. The R package Rattle provides a user-friendly access to multiple Data Mining methods. Further, we added the non-conventional spreadsheet program teapot for editing large data sets and a command line tool for transposing large matrices. Individual programs, console commands and modules can be integrated using the Workflow Management System (WMS) taverna. We explain the useful combination of the tools by practical examples: (1) A workflow for protein identification and validation, with subsequent Association Analysis of peptides, (2) Cluster analysis and Data Mining in targeted Metabolomics, and (3) Raw data processing, Data Mining and identification of metabolites in untargeted Metabolomics. Association Analyses reveal relationships between variables across different sample sets. We present its application for finding co-occurring peptides, which can be used for target proteomics, the discovery of alternative biomarkers and protein-protein interactions. Data Mining derived models displayed a higher robustness and accuracy for classifying sample groups in targeted Metabolomics than cluster analyses. Random Forest models do not only provide predictive models, which can be deployed for new data sets, but also the variable importance. We demonstrate that the later is especially useful for tracking down significant signals and affected pathways in untargeted Metabolomics. Thus, Random Forest modeling supports the unbiased search for relevant biological features in Metabolomics. Our results clearly manifest the importance of Data Mining methods to disclose non-obvious information in biological mass spectrometry . The application of a Workflow Management System and the integration of all required programs and data in a consistent platform makes the presented data analyses strategies reproducible for non-expert users. The simple remastering process and the Open Source licenses of MASSyPup64 (http://www.bioprocess.org/massypup/) enable the continuous improvement of the system.

Multiple Reaction Monitoring Enables Precise Quantification of 97 Proteins in Dried Blood Spots

The dried blood spot (DBS) methodology provides a minimally invasive approach to sample collection and enables room-temperature storage for most analytes. DBS samples have successfully been analyzed by liquid chromatography multiple reaction monitoring mass spectrometry (LC/MRM-MS) to quantify a large range of small molecule biomarkers and drugs; however, this strategy has only recently been explored for MS-based proteomics applications. Here we report the development of a highly multiplexed MRM assay to quantify endogenous proteins in human DBS samples. This assay uses matching stable isotope-labeled standard peptides for precise, relative quantification, and standard curves to characterize the analytical performance. A total of 169 peptides, corresponding to 97 proteins, were quantified in the final assay with an average linear dynamic range of 207-fold and an average R(2) value of 0.987. The total range of this assay spanned almost 5 orders of magnitude from serum albumin (P02768) at 18.0 mg/ml down to cholinesterase (P06276) at 190 ng/ml. The average intra-assay and inter-assay precision for 6 biological samples ranged from 6.1-7.5% CV and 9.5-11.0% CV, respectively. The majority of peptide targets were stable after 154 days at storage temperatures from -20 °C to 37 °C. Furthermore, protein concentration ratios between matching DBS and whole blood samples were largely constant (<20% CV) across six biological samples. This assay represents the highest multiplexing yet achieved for targeted protein quantification in DBS samples and is suitable for biomedical research applications.

Quest for Missing Proteins: Update 2015 on Chromosome-Centric Human Proteome Project

This paper summarizes the recent activities of the Chromosome-Centric Human Proteome Project (C-HPP) consortium, which develops new technologies to identify yet-to-be annotated proteins (termed "missing proteins") in biological samples that lack sufficient experimental evidence at the protein level for confident protein identification. The C-HPP also aims to identify new protein forms that may be caused by genetic variability, post-translational modifications, and alternative splicing. Proteogenomic data integration forms the basis of the C-HPP's activities; therefore, we have summarized some of the key approaches and their roles in the project. We present new analytical technologies that improve the chemical space and lower detection limits coupled to bioinformatics tools and some publicly available resources that can be used to improve data analysis or support the development of analytical assays. Most of this paper's content has been compiled from posters, slides, and discussions presented in the series of C-HPP workshops held during 2014. All data (posters, presentations) used are available at the C-HPP Wiki (http://c-hpp.webhosting.rug.nl/) and in the Supporting Information.

Selected Reaction Monitoring Mass Spectrometry for Absolute Protein Quantification

Absolute quantification of target proteins within complex biological samples is critical to a wide range of research and clinical applications. This protocol provides step-by-step instructions for the development and application of quantitative assays using selected reaction monitoring (SRM) mass spectrometry (MS). First, likely quantotypic target peptides are identified based on numerous criteria. This includes identifying proteotypic peptides, avoiding sites of posttranslational modification, and analyzing the uniqueness of the target peptide to the target protein. Next, crude external peptide standards are synthesized and used to develop SRM assays, and the resulting assays are used to perform qualitative analyses of the biological samples. Finally, purified, quantified, heavy isotope labeled internal peptide standards are prepared and used to perform isotope dilution series SRM assays. Analysis of all of the resulting MS data is presented. This protocol was used to accurately assay the absolute abundance of proteins of the chemotaxis signaling pathway within RAW 264.7 cells (a mouse monocyte/macrophage cell line). The quantification of Gi2 (a heterotrimeric G-protein α-subunit) is described in detail.

An extensive library of surrogate peptides for all human proteins

Selecting the most appropriate surrogate peptides to represent a target protein is a major component of experimental design in Multiple Reaction Monitoring (MRM). Our software PeptidePicker with its v-score remains distinctive in its approach of integrating information about the proteins, their tryptic peptides, and the suitability of these peptides for MRM that is available online in UniProtKB, NCBI's dbSNP, ExPASy, PeptideAtlas, PRIDE, and GPMDB. The scoring algorithm reflects our "best knowledge" for selecting candidate peptides for MRM, based on the uniqueness of the peptide in the targeted proteome, its physiochemical properties, and whether it has previously been observed. Here we present an updated approach where we have already compiled a list of all possible surrogate peptides of the human proteome. Using our stringent selection criteria, the list includes 165k suitable MRM peptides covering 17k proteins of the human reviewed proteins in UniProtKB. Compared to average of 2-4min per protein for retrieving and integrating the information, the precompiled list includes all peptides available instantly. This allows a more cohesive and faster design of a multiplexed MRM experiment and provides insights into evidence for a protein's existence. We will keep this list up-to-date as proteomics data repositories continue to grow. This article is part of a Special Issue entitled: Computational Proteomics.

Current strategies and findings in clinically relevant post-translational modification-specific proteomics

Mass spectrometry-based proteomics has considerably extended our knowledge about the occurrence and dynamics of protein post-translational modifications (PTMs). So far, quantitative proteomics has been mainly used to study PTM regulation in cell culture models, providing new insights into the role of aberrant PTM patterns in human disease. However, continuous technological and methodical developments have paved the way for an increasing number of PTM-specific proteomic studies using clinical samples, often limited in sample amount. Thus, quantitative proteomics holds a great potential to discover, validate and accurately quantify biomarkers in body fluids and primary tissues. A major effort will be to improve the complete integration of robust but sensitive proteomics technology to clinical environments. Here, we discuss PTMs that are relevant for clinical research, with a focus on phosphorylation, glycosylation and proteolytic cleavage; furthermore, we give an overview on the current developments and novel findings in mass spectrometry-based PTM research.

The complexity of protein semiochemistry in mammals

The high degree of protein sequence similarity in the MUPs (major urinary proteins) poses considerable challenges for their individual differentiation, analysis and quantification. In the present review, we discuss MS approaches for MUP quantification, at either the protein or the peptide level. In particular, we describe an approach to multiplexed quantification based on the design and synthesis of novel proteins (QconCATs) that are concatamers of quantification standards, providing a simple route to the generation of a set of stable-isotope-labelled peptide standards. The MUPs pose a particular challenge to QconCAT design, because of their sequence similarity and the limited number of peptides that can be used to construct the standards. Such difficulties can be overcome by careful attention to the analytical workflow.

Quantitative mass spectrometry of urinary biomarkers

The effectiveness of treatment of renal diseases is limited because the lack of diagnostic, prognostic and therapeutic markers. Despite the more than a decade of intensive investigation of urinary biomarkers, no new clinical biomarkers were approved. This is in part because the early expectations toward proteomics in biomarkers discovery were significantly higher than the capability of technology at the time. However, during the last decade, proteomic technology has made dramatic progress in both the hardware and software methods. In this review we are discussing modern quantitative methods of mass-spectrometry and providing several examples of their applications for discovery and validation of renal disease biomarkers. We are optimistic about future prospects for the development of novel of specific clinical urinary biomarkers.

Sample preparation strategies for targeted proteomics via proteotypic peptides in human blood using liquid chromatography tandem mass spectrometry

The simultaneous quantification of protein concentrations via proteotypic peptides in human blood by liquid chromatography coupled to quadrupole MS/MS is an important field of bioanalytical research with a high potential for routine diagnostic applications. This review summarizes currently available sample preparation procedures and trends for absolute protein quantification in blood using LC-MS/MS. It discusses approaches of transferring established qualitative protocols to a quantitative analysis regarding their reliability and reproducibility. Techniques used to enhance method sensitivity such as the depletion of high-abundant proteins or the immunoaffinity enrichment of proteins and peptides are described. Furthermore, workflows for (i) protein denaturation, (ii) disulfide bridge reduction and (iii) thiol alkylation as well as (iv) enzymatic digestion for absolute protein quantification are presented. The main focus is on the tryptic digestion as a bottleneck of protein quantification via proteotypic peptides. Conclusively, requirements for a high-throughput application are discussed.

Quantitative analysis of single amino acid variant peptides associated with pancreatic cancer in serum by an isobaric labeling quantitative method

Single amino acid variations are highly associated with many human diseases. The direct detection of peptides containing single amino acid variants (SAAVs) derived from nonsynonymous single nucleotide polymorphisms (SNPs) in serum can provide unique opportunities for SAAV associated biomarker discovery. In the present study, an isobaric labeling quantitative strategy was applied to identify and quantify variant peptides in serum samples of pancreatic cancer patients and other benign controls. The largest number of SAAV peptides to date in serum including 96 unique variant peptides were quantified in this quantitative analysis, of which five variant peptides showed a statistically significant difference between pancreatic cancer and other controls (p-value < 0.05). Significant differences in the variant peptide SDNCEDTPEAGYFAVAVVK from serotransferrin were detected between pancreatic cancer and controls, which was further validated by selected reaction monitoring (SRM) analysis. The novel biomarker panel obtained by combining α-1-antichymotrypsin (AACT), Thrombospondin-1 (THBS1) and this variant peptide showed an excellent diagnostic performance in discriminating pancreatic cancer from healthy controls (AUC = 0.98) and chronic pancreatitis (AUC = 0.90). These results suggest that large-scale analysis of SAAV peptides in serum may provide a new direction for biomarker discovery research.