A computational tool to detect and avoid redundancy in selected reaction monitoring

A workflow for targeted proteomics assay development using a versatile linear ion trap

Advances in proteomics and mass spectrometry have enabled the study of limited cell populations, such as single-cell proteomics, where high-mass accuracy instruments are typically required. While triple quadrupoles offer fast and sensitive nominal resolution measurements, these instruments are effectively limited to targeted proteomics. Linear ion traps (LITs) offer a versatile, cost-effective alternative capable of both targeted and global proteomics. We demonstrate a workflow using a newly released, hybrid quadrupole-LIT instrument for developing targeted proteomics assays from global data-independent acquisition (DIA) measurements without needing high-mass accuracy. Gas-phase fraction-based DIA enables rapid target library generation in the same background chemical matrix as each quantitative injection. Using a new software tool embedded within EncyclopeDIA for scheduling parallel reaction monitoring assays, we show consistent quantification across three orders of magnitude of input material. Using this approach, we demonstrate measuring peptide quantitative linearity down to 25x dilution in a background of only a 1 ng proteome without requiring stable isotope labeled standards. At 1 ng total protein on column, we found clear consistency between immune cell populations measured using flow cytometry and immune markers measured using LIT-based proteomics. We believe hybrid quadrupole-LIT instruments represent an economic solution to democratizing mass spectrometry in a wide variety of laboratory settings.

A protein-protein interaction analysis tool for targeted cross-linking mass spectrometry

Protein networking is critical to understanding the biological functions of proteins and the underlying mechanisms of disease. However, identifying physical protein-protein interactions (PPIs) can be challenging. To gain insights into target proteins that interact with a particular disease, we need to profile all the proteins involved in the disease beforehand. Although the cross-linking mass spectrometry (XL-MS) method is a representative approach to identify physical interactions between proteins, calculating theoretical mass values for application to targeted mass spectrometry can be difficult. To address this challenge, our research team developed PPIAT, a web application that integrates information on reviewed human proteins, protein-protein interactions, cross-linkers, enzymes, and modifications. PPIAT leverages publicly accessible databases such as STRING to identify interactomes associated with target proteins. Moreover, it autonomously computes the theoretical mass value, accounting for all potential cross-linking scenarios pertinent to the application of XL-MS in SRM analysis. The outputs generated by PPIAT can be concisely represented in terms of protein interaction probabilities, complemented by findings from alternative analytical tools like Prego. These comprehensive summaries enable researchers to customize the results according to specific experimental conditions. All functions of PPIAT are available for free on the web application, making it a valuable tool for researchers studying protein-protein interactions.

Reduction of multiple reaction monitoring protein target list using correlation analysis

High mass resolution mass spectrometry provides hundreds to thousands of protein identifications per sample, and quantification is typically performed using label-free quantification. However, the gold standard of quantitative proteomics is multiple reaction monitoring (MRM) using triple quadrupole mass spectrometers and stable isotope reference peptides. This raises the question how to reduce a large data set to a small one without losing essential information. Here we present the reduction of such a data set using correlation analysis of bovine dairy ingredients and derived products. We were able to explain the variance in the proteomics data set using only 9 proteins across all major dairy protein classes: caseins, whey, and milk fat globule membrane proteins. We term this method Trinity-MRM. The reproducibility of the protein extraction and Trinity-MRM methods was shown to be below 5% in independent experiments (multi-day single-user and single-day multi-user) using double cream. Further application of this reductionist approach might include screening of large sample cohorts for biologically interesting samples before analysis by high-resolution mass spectrometry or other omics methodologies.

The Use of Bacteriophages in Biotechnology and Recent Insights into Proteomics

Phages have certain features, such as their ability to form protein-protein interactions, that make them good candidates for use in a variety of beneficial applications, such as in human or animal health, industry, food science, food safety, and agriculture. It is essential to identify and characterize the proteins produced by particular phages in order to use these viruses in a variety of functional processes, such as bacterial detection, as vehicles for drug delivery, in vaccine development, and to combat multidrug resistant bacterial infections. Furthermore, phages can also play a major role in the design of a variety of cheap and stable sensors as well as in diagnostic assays that can either specifically identify specific compounds or detect bacteria. This article reviews recently developed phage-based techniques, such as the use of recombinant tempered phages, phage display and phage amplification-based detection. It also encompasses the application of phages as capture elements, biosensors and bioreceptors, with a special emphasis on novel bacteriophage-based mass spectrometry (MS) applications.

Alpha-Tri: a deep neural network for scoring the similarity between predicted and measured spectra improves peptide identification of DIA data

MOTIVATION

Peptide identification of data-independent acquisition (DIA) mass spectrometry applying the peptide-centric approach heavily relies on the spectral library matching, such as the fragment intensity similarity. If the intensity similarity is calculated through all possible fragment ions of a targeted peptide instead of just a few fragment ions provided by the spectral library, the matching will be more comprehensive and reliable, and thus the identification will be more confident. In addition, the emergence of high precision spectrum predictors, like Prosit, also makes it possible to capitalize on the predicted spectrum, which contains all possible fragment ion intensities, to calculate the intensity similarity for DIA data.

RESULTS

In this work, we propose Alpha-Tri, a neural-network-based model to calculate intensity similarity as a post-processing score using the predicted spectrum, measured spectrum and correlation spectrum (triple-spectrum). The predicted spectrum is generated by Prosit, the measured spectrum is retrieved from the apex of the chromatograms of all possible fragment ions and the correlation spectrum is used to indicate the present probabilities of these fragment ions as the link between the precursor and its fragment ions is lost in DIA. By adopting a data-driven method, Alpha-Tri is able to learn the intensity similarity from the triple-spectrum. This learned value is appended to initial scores from DIA-NN, allowing the ensuing statistical validation tool to report more peptides at the same false discovery rate (FDR). In our evaluation of the HeLa dataset with gradient lengths ranging from 0.5 to 2 h, Alpha-Tri delivered 3.0-7.2% gains in peptide detections at 1% FDR. On LFQbench dataset, a mixed-species dataset with known ratios, Alpha-Tri identified more peptides and proteins fell within the valid ratio ranges by up to 8.6% and 7.6%, respectively, compared with DIA-NN solely.

AVAILABILITY AND IMPLEMENTATION

The original datasets for benchmarks are downloaded from the ProteomeXchange with the identifiers PXD005573, PXD000954 and PXD002952. Source code is available at https://github.com/YuAirLab/Alpha-Tri.

Analyzing Assay Specificity in Metabolomics Using Unique Ion Signature Simulations

Targeted, untargeted, and data-independent acquisition (DIA) metabolomics workflows are often hampered by ambiguous identification based on either MS1 information alone or relatively few MS2 fragment ions. While DIA methods have been popularized in proteomics, it is less clear whether they are suitable for metabolomics workflows due to their large precursor isolation windows and complex coisolation patterns. Here, we quantitatively investigate the conditions necessary for unique metabolite detection in complex backgrounds using precursor and fragment ion mass-to-charge (m/z) separation, comparing three benchmarked mass spectrometry (MS) methods [MS1, MRM (multiple reaction monitoring), and DIA]. Our simulations show that DIA outperformed MS1-only and MRM-based methods with regards to specificity by factors of ∼2.8-fold and ∼1.8-fold, respectively. Additionally, we show that our results are not dependent on the number of transitions used or the complexity of the background matrix. Finally, we show that collision energy is an important factor in unambiguous detection and that a single collision energy setting per compound cannot achieve optimal pairwise differentiation of compounds. Our analysis demonstrates the power of using both high-resolution precursor and high-resolution fragment ion m/z for unambiguous compound detection. This work also establishes DIA as an emerging MS acquisition method with high selectivity for metabolomics, outperforming both data-dependent acquisition (DDA) and MRM with regards to unique compound identification potential.

Tutorial: best practices and considerations for mass-spectrometry-based protein biomarker discovery and validation

Mass-spectrometry-based proteomic analysis is a powerful approach for discovering new disease biomarkers. However, certain critical steps of study design such as cohort selection, evaluation of statistical power, sample blinding and randomization, and sample/data quality control are often neglected or underappreciated during experimental design and execution. This tutorial discusses important steps for designing and implementing a liquid-chromatography-mass-spectrometry-based biomarker discovery study. We describe the rationale, considerations and possible failures in each step of such studies, including experimental design, sample collection and processing, and data collection. We also provide guidance for major steps of data processing and final statistical analysis for meaningful biological interpretations along with highlights of several successful biomarker studies. The provided guidelines from study design to implementation to data interpretation serve as a reference for improving rigor and reproducibility of biomarker development studies.

CIDer: A Statistical Framework for Interpreting Differences in CID and HCD Fragmentation

Library searching is a powerful technique for detecting peptides using either data independent or data dependent acquisition. While both large-scale spectrum library curators and deep learning prediction approaches have focused on beam-type CID fragmentation (HCD), resonance CID fragmentation remains a popular technique. Here we demonstrate an approach to model the differences between HCD and CID spectra, and present a software tool, CIDer, for converting libraries between the two fragmentation methods. We demonstrate that just using a combination of simple linear models and basic principles of peptide fragmentation, we can explain up to 43% of the variation between ions fragmented by HCD and CID across an array of collision energy settings. We further show that in some circumstances, searching converted CID libraries can detect more peptides than searching existing CID libraries or libraries of machine learning predictions from FASTA databases. These results suggest that leveraging information in existing libraries by converting between HCD and CID libraries may be an effective interim solution while large-scale CID libraries are being developed.

Avant-garde: an automated data-driven DIA data curation tool

Several challenges remain in data-independent acquisition (DIA) data analysis, such as to confidently identify peptides, define integration boundaries, remove interferences, and control false discovery rates. In practice, a visual inspection of the signals is still required, which is impractical with large datasets. We present Avant-garde as a tool to refine DIA (and parallel reaction monitoring) data. Avant-garde uses a novel data-driven scoring strategy: signals are refined by learning from the dataset itself, using all measurements in all samples to achieve the best optimization. We evaluate the performance of Avant-garde using benchmark DIA datasets and show that it can determine the quantitative suitability of a peptide peak, and reach the same levels of selectivity, accuracy, and reproducibility as manual validation. Avant-garde is complementary to existing DIA analysis engines and aims to establish a strong foundation for subsequent analysis of quantitative mass spectrometry data.

Proteomics-Based Methodologies for the Detection and Quantification of Seafood Allergens

Seafood is considered one of the main food allergen sources by the European Food Safety Authority (EFSA). It comprises several distinct groups of edible aquatic animals, including fish and shellfish, such as crustacean and mollusks. Recently, the EFSA recognized the high risk of food allergy over the world and established the necessity of developing new methodologies for its control. Consequently, accurate, sensitive, and fast detection methods for seafood allergy control and detection in food products are highly recommended. In this work, we present a comprehensive review of the applications of the proteomics methodologies for the detection and quantification of seafood allergens. For this purpose, two consecutive proteomics strategies (discovery and targeted proteomics) that are applied to the study and control of seafood allergies are reviewed in detail. In addition, future directions and new perspectives are also provided.

Proteomic Strategies to Evaluate the Impact of Farming Conditions on Food Quality and Safety in Aquaculture Products

This review presents the primary applications of various proteomic strategies to evaluate the impact of farming conditions on food quality and safety in aquaculture products. Aquaculture is a quickly growing sector that represents 47% of total fish production. Food quality, dietary management, fish welfare, the stress response, food safety, and antibiotic resistance, which are covered by this review, are among the primary topics in which proteomic techniques and strategies are being successfully applied. The review concludes by outlining future directions and potential perspectives.

The Skyline ecosystem: Informatics for quantitative mass spectrometry proteomics

Skyline is a freely available, open-source Windows client application for accelerating targeted proteomics experimentation, with an emphasis on the proteomics and mass spectrometry community as users and as contributors. This review covers the informatics encompassed by the Skyline ecosystem, from computationally assisted targeted mass spectrometry method development, to raw acquisition file data processing, and quantitative analysis and results sharing.

Prediction and Consequences of Cofragmentation in Metaproteomics

Metaproteomics can provide critical information about biological systems, but peptides are found within a complex background of other peptides. This complex background can change across samples, in some cases drastically. Cofragmentation, the coelution of peptides with similar mass to charge ratios, is one factor that influences which peptides are identified in an LC-MS/MS experiment: it is dependent on the nature and complexity of this dynamic background. Metaproteomics applications are particularly susceptible to cofragmentation-induced bias; they have vast protein sequence diversity and the abundance of those proteins can span many orders of magnitude. We have developed a mechanistic model that determines the number of potentially cofragmenting peptides in a given sample (called cobia, https://github.com/bertrand-lab/cobia ). We then used previously published data sets to validate our model, showing that the resulting peptide-specific score reflects the cofragmentation "risk" of peptides. Using an Antarctic sea ice edge metatranscriptome case study, we found that more rare taxonomic and functional groups are associated with higher cofragmentation bias. We also demonstrate how cofragmentation scores can be used to guide the selection of protein- or peptide-based biomarkers. We illustrate potential consequences of cofragmentation for multiple metaproteomic approaches, and suggest practical paths forward to cope with cofragmentation-induced bias.

Application of immobilized ATP to the study of NLRP inflammasomes

The NLRP proteins are a subfamily of the NOD-like receptor (NLR) innate immune sensors that possess an ATP-binding NACHT domain. As the most well studied member, NLRP3 can initiate the assembly process of a multiprotein complex, termed the inflammasome, upon detection of a wide range of microbial products and endogenous danger signals and results in the activation of pro-caspase-1, a cysteine protease that regulates multiple host defense pathways including cytokine maturation. Dysregulated NLRP3 activation contributes to inflammation and the pathogenesis of several chronic diseases, and the ATP-binding properties of NLRPs are thought to be critical for inflammasome activation. In light of this, we examined the utility of immobilized ATP matrices in the study of NLRP inflammasomes. Using NLRP3 as the prototypical member of the family, P-linked ATP Sepharose was determined to be a highly-effective capture agent. In subsequent examinations, P-linked ATP Sepharose was used as an enrichment tool to enable the effective profiling of NLRP3-biomarker signatures with selected reaction monitoring-mass spectrometry (SRM-MS). Finally, ATP Sepharose was used in combination with a fluorescence-linked enzyme chemoproteomic strategy (FLECS) screen to identify potential competitive inhibitors of NLRP3. The identification of a novel benzo[d]imidazol-2-one inhibitor that specifically targets the ATP-binding and hydrolysis properties of the NLRP3 protein implies that ATP Sepharose and FLECS could be applied other NLRPs as well.

Expanding the Use of Spectral Libraries in Proteomics

The 2017 Dagstuhl Seminar on Computational Proteomics provided an opportunity for a broad discussion on the current state and future directions of the generation and use of peptide tandem mass spectrometry spectral libraries. Their use in proteomics is growing slowly, but there are multiple challenges in the field that must be addressed to further increase the adoption of spectral libraries and related techniques. The primary bottlenecks are the paucity of high quality and comprehensive libraries and the general difficulty of adopting spectral library searching into existing workflows. There are several existing spectral library formats, but none captures a satisfactory level of metadata; therefore, a logical next improvement is to design a more advanced, Proteomics Standards Initiative-approved spectral library format that can encode all of the desired metadata. The group discussed a series of metadata requirements organized into three designations of completeness or quality, tentatively dubbed bronze, silver, and gold. The metadata can be organized at four different levels of granularity: at the collection (library) level, at the individual entry (peptide ion) level, at the peak (fragment ion) level, and at the peak annotation level. Strategies for encoding mass modifications in a consistent manner and the requirement for encoding high-quality and commonly seen but as-yet-unidentified spectra were discussed. The group also discussed related topics, including strategies for comparing two spectra, techniques for generating representative spectra for a library, approaches for selection of optimal signature ions for targeted workflows, and issues surrounding the merging of two or more libraries into one. We present here a review of this field and the challenges that the community must address in order to accelerate the adoption of spectral libraries in routine analysis of proteomics datasets.

Predictive chromatography of peptides and proteins as a complementary tool for proteomics

In the last couple of decades, considerable effort has been focused on developing methods for quantitative and qualitative proteome characterization. The method of choice in this characterization is mass spectrometry used in combination with sample separation. One of the most widely used separation techniques at the front end of a mass spectrometer is high performance liquid chromatography (HPLC). A unique feature of HPLC is its specificity to the amino acid sequence of separated peptides and proteins. This specificity may provide additional information about the peptides or proteins under study which is complementary to the mass spectrometry data. The value of this information for proteomics has been recognized in the past few decades, which has stimulated significant effort in the development and implementation of computational and theoretical models for the prediction of peptide retention time for a given sequence. Here we review the advances in this area and the utility of predicted retention times for proteomic applications.

Data-independent acquisition-based SWATH-MS for quantitative proteomics: a tutorial

Many research questions in fields such as personalized medicine, drug screens or systems biology depend on obtaining consistent and quantitatively accurate proteomics data from many samples. SWATH-MS is a specific variant of data-independent acquisition (DIA) methods and is emerging as a technology that combines deep proteome coverage capabilities with quantitative consistency and accuracy. In a SWATH-MS measurement, all ionized peptides of a given sample that fall within a specified mass range are fragmented in a systematic and unbiased fashion using rather large precursor isolation windows. To analyse SWATH-MS data, a strategy based on peptide-centric scoring has been established, which typically requires prior knowledge about the chromatographic and mass spectrometric behaviour of peptides of interest in the form of spectral libraries and peptide query parameters. This tutorial provides guidelines on how to set up and plan a SWATH-MS experiment, how to perform the mass spectrometric measurement and how to analyse SWATH-MS data using peptide-centric scoring. Furthermore, concepts on how to improve SWATH-MS data acquisition, potential trade-offs of parameter settings and alternative data analysis strategies are discussed.

An HS-MRM Assay for the Quantification of Host-cell Proteins in Protein Biopharmaceuticals by Liquid Chromatography Ion Mobility QTOF Mass Spectrometry

The analysis of low-level (1-100 ppm) protein impurities (e.g., host-cell proteins (HCPs)) in protein biotherapeutics is a challenging assay requiring high sensitivity and a wide dynamic range. Mass spectrometry-based quantification assays for proteins typically involve protein digestion followed by the selective reaction monitoring/multiple reaction monitoring (SRM/MRM) quantification of peptides using a low-resolution (Rs ~1,000) tandem quadrupole mass spectrometer. One of the limitations of this approach is the interference phenomenon observed when the peptide of interest has the "same" precursor and fragment mass (in terms of m/z values) as other co-eluting peptides present in the sample (within a 1-Da window). To avoid this phenomenon, we propose an alternative mass spectrometric approach, a high selectivity (HS) MRM assay that combines the ion mobility separation of peptide precursors with the high-resolution (Rs ~30,000) MS detection of peptide fragments. We explored the capabilities of this approach to quantify low-abundance peptide standards spiked in a monoclonal antibody (mAb) digest and demonstrated that it has the sensitivity and dynamic range (at least 3 orders of magnitude) typically achieved in HCP analysis. All six peptide standards were detected at concentrations as low as 0.1 nM (1 femtomole loaded on a 2.1-mm ID chromatographic column) in the presence of a high-abundance peptide background (2 µg of a mAb digest loaded on-column). When considering the MW of rabbit phosphorylase (97.2 kDa), from which the spiked peptides were derived, the LOQ of this assay is lower than 50 ppm. Relative standard deviations (RSD) of peak areas (n = 4 replicates) were less than 15% across the entire concentration range investigated (0.1-100 nM or 1-1,000 ppm) in this study.

Manual method of visually identifying candidate signals for a targeted peptide

The purpose of this study is to improve peptide signal identification in groups of extracted ion chromatograms (XICs) obtained with the liquid chromatography-selected reaction monitoring (LC-SRM) technique and a triple quadrupole mass spectrometer (QqQ) operating in one of the supported multiple reaction monitoring (MRM) modes. The imperfection of quadrupole mass analyzers causes ion interference, which impedes the identification of peptide signals as chromatographic peak groups in relevant retention time intervals. To investigate this problem in depth, the QqQ conversion of the eluate into XIC groups was considered as the consecutive transformations of the particles' abundances as the corresponding functions of retention time. In this study, the hypothesis that, during this conversion, the same chromatographic profile should be preserved as an implicit sign in each chromatographic peak of the signal was confirmed for peptides. To examine chromatographic profiles, continuous transformations of XIC groups were derived and implemented in srm2prot Express software (s2pe, http://msr.ibmc.msk.ru/s2pe). Because of ion interference, several peptide-like signals may appear in one XIC group. Therefore, these signals must be considered candidates for a targeted peptide's signal and should be resolved after identification. The theoretical investigation of intensity functions as XICs that are not distorted by noise produced three rules for Identifying Candidate Signals for a targeted Peptide (ICSP, http://msr.ibmc.msk.ru/ICSP) that constitute the proposed manual visual method. We theoretically and experimentally compared this method with the conventional semiempirical intuitive technique and found that the former significantly streamlines peptide signal identification and avoids typical errors.

Quantitation of myosin regulatory light chain phosphorylation in biological samples with multiple reaction monitoring mass spectrometry

The 20-kDa regulatory light chain of myosin II plays an important role in regulating smooth muscle contractile force. LC20 is phosphorylated canonically by myosin light chain kinase in a Ca²⁺/calmodulin-dependent manner at S19. The diphosphorylation of LC20 at T18 and S19 has been observed in smooth muscle tissues. Given that the phosphorylation of LC20 is positively correlated with tension development, the molar stoichiometry of LC20 phosphorylation is commonly profiled as a measure of smooth muscle contractility. Herein, we describe a novel multiple reaction monitoring (MRM)-mass spectrometry (MS) approach for the quantification of LC20 phosphorylation at T18 and S19. Unique precursor as well as y- and b-ion transitions were identified for unphosphorylated LC20-(TS), monophosphorylated LC20-(TpS) and diphosphorylated LC20-(pTpS) peptides. The MRM-MS assay could accurately define molar phosphorylation stoichiometries of S19 and T18 over a broad range (i.e., 0-2 mol P/mol LC20). Correlations of the results for two quantification techniques indicate that the MRM-MS assay performs equally to Phos-tag SDS-PAGE for the determination of LC20 phosphorylation stoichiometry in arterial tissue samples. The MRM-MS technique provides a robust alternative to antibody-based detection systems for the quantification of LC20 phosphorylation.

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).

Reproducible quantitative proteotype data matrices for systems biology

Historically, many mass spectrometry–based proteomic studies have aimed at compiling an inventory of protein compounds present in a biological sample, with the long-term objective of creating a proteome map of a species. However, to answer fundamental questions about the behavior of biological systems at the protein level, accurate and unbiased quantitative data are required in addition to a list of all protein components. Fueled by advances in mass spectrometry, the proteomics field has thus recently shifted focus toward the reproducible quantification of proteins across a large number of biological samples. This provides the foundation to move away from pure enumeration of identified proteins toward quantitative matrices of many proteins measured across multiple samples. It is argued here that data matrices consisting of highly reproducible, quantitative, and unbiased proteomic measurements across a high number of conditions, referred to here as quantitative proteotype maps, will become the fundamental currency in the field and provide the starting point for downstream biological analysis. Such proteotype data matrices, for example, are generated by the measurement of large patient cohorts, time series, or multiple experimental perturbations. They are expected to have a large effect on systems biology and personalized medicine approaches that investigate the dynamic behavior of biological systems across multiple perturbations, time points, and individuals.

Methods for the Analysis of Protein Phosphorylation-Mediated Cellular Signaling Networks

Protein phosphorylation-mediated cellular signaling networks regulate almost all aspects of cell biology, including the responses to cellular stimulation and environmental alterations. These networks are highly complex and comprise hundreds of proteins and potentially thousands of phosphorylation sites. Multiple analytical methods have been developed over the past several decades to identify proteins and protein phosphorylation sites regulating cellular signaling, and to quantify the dynamic response of these sites to different cellular stimulation. Here we provide an overview of these methods, including the fundamental principles governing each method, their relative strengths and weaknesses, and some examples of how each method has been applied to the analysis of complex signaling networks. When applied correctly, each of these techniques can provide insight into the topology, dynamics, and regulation of protein phosphorylation signaling networks.

Neucode Labels for Multiplexed, Absolute Protein Quantification

We describe a new method to accomplish multiplexed, absolute protein quantification in a targeted fashion. The approach draws upon the recently developed neutron encoding (NeuCode) metabolic labeling strategy and parallel reaction monitoring (PRM). Since PRM scanning relies upon high-resolution tandem mass spectra for targeted protein quantification, incorporation of multiple NeuCode labeled peptides permits high levels of multiplexing that can be accessed from high-resolution tandem mass spectra. Here we demonstrate this approach in cultured cells by monitoring a viral infection and the corresponding viral protein production over many infection time points in a single experiment. In this context the NeuCode PRM combination affords up to 30 channels of quantitative information in a single MS experiment.

Mycobacterium tuberculosis in the Proteomics Era

The emerging field of proteomics has contributed greatly to improving our understanding of the human pathogen Mycobacterium tuberculosis over the last two decades. In this chapter we provide a comprehensive overview of mycobacterial proteome research and highlight key findings. First, studies employing a combination of two-dimensional gel electrophoresis and mass spectrometry (MS) provided insights into the proteomic composition, initially of the whole bacillus and subsequently of subfractions, such as the cell wall, cytosol, and secreted proteins. Comparison of results obtained under various culture conditions, i.e., acidic pH, nutrient starvation, and low oxygen tension, aiming to mimic facets of the intracellular lifestyle of M. tuberculosis, provided initial clues to proteins relevant for intracellular survival and manipulation of the host cell. Further attempts were aimed at identifying the biological functions of the hypothetical M. tuberculosis proteins, which still make up a quarter of the gene products of M. tuberculosis, and at characterizing posttranslational modifications. Recent technological advances in MS have given rise to new methods such as selected reaction monitoring (SRM) and data-independent acquisition (DIA). These targeted, cutting-edge techniques combined with a public database of specific MS assays covering the entire proteome of M. tuberculosis allow the simple and reliable detection of any mycobacterial protein. Most recent studies attempt not only to identify but also to quantify absolute amounts of single proteins in the complex background of host cells without prior sample fractionation or enrichment. Finally, we will discuss the potential of proteomics to advance vaccinology, drug discovery, and biomarker identification to improve intervention and prevention measures for tuberculosis.

An Integrated Multiomics Approach to Identify Candidate Antigens for Serodiagnosis of Human Onchocerciasis

Improved diagnostic methods are needed to support ongoing efforts to eliminate onchocerciasis (river blindness). This study used an integrated approach to identify adult female Onchocerca volvulus antigens that can be explored for developing serodiagnostic tests. The first step was to develop a detailed multi-omics database of all O. volvulus proteins deduced from the genome, gene transcription data for different stages of the parasite including eight individual female worms (providing gene expression information for 94.8% of all protein coding genes), and the adult female worm proteome (detecting 2126 proteins). Next, female worm proteins were purified with IgG antibodies from onchocerciasis patients and identified using LC-MS with a high-resolution hybrid quadrupole-time-of-flight mass spectrometer. A total of 241 immunoreactive proteins were identified among those bound by IgG from infected individuals but not IgG from uninfected controls. These included most of the major diagnostic antigens described over the past 25 years plus many new candidates. Proteins of interest were prioritized for further study based on a lack of conservation with orthologs in the human host and other helminthes, their expression pattern across the life cycle, and their consistent expression among individual female worms. Based on these criteria, we selected 33 proteins that should be carried forward for testing as serodiagnostic antigens to supplement existing diagnostic tools. These candidates, together with the extensive pan-omics dataset generated in this study are available to the community (http://nematode.net) to facilitate basic and translational research on onchocerciasis.

In silico verification and parallel reaction monitoring prevalidation of potential prostate cancer biomarkers

PURPOSE

Targeted proteomics of potential biomarkers is often challenging. Hence, we developed an intermediate workflow to streamline potential urinary biomarkers of prostate cancer (PCa).

MATERIALS & METHODS

Using previously discovered potential PCa biomarkers; we selected proteotypic peptides for targeted validation. Preliminary in silico immunohistochemical and single reaction monitoring (SRM) verification was performed. Successful PTPs were then prevalidated using parallel reaction monitoring (PRM) and reconfirmed in 15 publicly available databases.

RESULTS

Stringency-based targetable potential biomarkers were shortlisted following in silico screening. PRM reveals top 12 potential biomarkers including the top ranking seven in silico verification-based biomarkers. Database reconfirmation showed differential expression between PCa and benign/normal prostatic urine samples.

CONCLUSION

The pragmatic penultimate screening step, described herein, would immensely improve targeted proteomics validation of potential disease biomarkers.

Data-independent-acquisition mass spectrometry for identification of targeted-peptide site-specific modifications

We present a novel strategy based on data-independent acquisition coupled to targeted data extraction for the detection and identification of site-specific modifications of targeted peptides in a completely unbiased manner. This method requires prior knowledge of the site of the modification along the peptide backbone from the protein of interest, but not the mass of the modification. The procedure, named multiplex adduct peptide profiling (MAPP), consists of three steps: 1) A fragment-ion tag is extracted from the data, consisting of the b-type and y-type ion series from the N and C-terminus, respectively, up to the amino-acid position that is believed to be modified; 2) MS1 features are matched to the fragment-ion tag in retention-time space, using the isolation window as a pre-filter to enable calculation of the mass of the modification; and 3) modified fragment ions are overlaid with the unmodified fragment ions to verify the mass calculated in step 2. We discuss the development, applications, and limitations of this new method for detection of unknown peptide modifications. We present an application of the method in profiling adducted peptides derived from abundant proteins in biological fluids with the ultimate objective of detecting biomarkers of exposure to reactive species.

Mining Missing Membrane Proteins by High-pH Reverse-Phase StageTip Fractionation and Multiple Reaction Monitoring Mass Spectrometry

Despite significant efforts in the past decade toward complete mapping of the human proteome, 3564 proteins (neXtProt, 09-2014) are still "missing proteins". Over one-third of these missing proteins are annotated as membrane proteins, owing to their relatively challenging accessibility with standard shotgun proteomics. Using nonsmall cell lung cancer (NSCLC) as a model study, we aim to mine missing proteins from disease-associated membrane proteome, which may be still largely under-represented. To increase identification coverage, we employed Hp-RP StageTip prefractionation of membrane-enriched samples from 11 NSCLC cell lines. Analysis of membrane samples from 20 pairs of tumor and adjacent normal lung tissue was incorporated to include physiologically expressed membrane proteins. Using multiple search engines (X!Tandem, Comet, and Mascot) and stringent evaluation of FDR (MAYU and PeptideShaker), we identified 7702 proteins (66% membrane proteins) and 178 missing proteins (74 membrane proteins) with PSM-, peptide-, and protein-level FDR of 1%. Through multiple reaction monitoring using synthetic peptides, we provided additional evidence of eight missing proteins including seven with transmembrane helix domains. This study demonstrates that mining missing proteins focused on cancer membrane subproteome can greatly contribute to map the whole human proteome. All data were deposited into ProteomeXchange with the identifier PXD002224.

LC–MS-based quantification of intact proteins: perspective for clinical and bioanalytical applications

Bioanalytical LC–MS for protein quantification is traditionally based on enzymatic digestion of the target protein followed by absolute quantification of a specific signature peptide relative to a stable-isotope labeled analog. The enzymatic digestion, nonetheless, limits rapid method development, sample throughput and turnaround time, and, moreover, makes that essential information regarding the biological function of the intact protein is lost. The recent advancements in high-resolution MS instrumentation and improved sample preparation techniques dedicated to protein clean-up raise the question to what extent LC–MS can be applied for quantitative bioanalysis of intact proteins. This review provides an overview of current and potential applications of LC–MS for intact protein quantification as well as the main limitations and challenges for broad application.

Identification of a Set of Conserved Eukaryotic Internal Retention Time Standards for Data-independent Acquisition Mass Spectrometry

Accurate knowledge of retention time (RT) in liquid chromatography-based mass spectrometry data facilitates peptide identification, quantification, and multiplexing in targeted and discovery-based workflows. Retention time prediction is particularly important for peptide analysis in emerging data-independent acquisition (DIA) experiments such as SWATH-MS. The indexed RT approach, iRT, uses synthetic spiked-in peptide standards (SiRT) to set RT to a unit-less scale, allowing for normalization of peptide RT between different samples and chromatographic set-ups. The obligatory use of SiRTs can be costly and complicates comparisons and data integration if standards are not included in every sample. Reliance on SiRTs also prevents the inclusion of archived mass spectrometry data for generation of the peptide assay libraries central to targeted DIA-MS data analysis. We have identified a set of peptide sequences that are conserved across most eukaryotic species, termed Common internal Retention Time standards (CiRT). In a series of tests to support the appropriateness of the CiRT-based method, we show: (1) the CiRT peptides normalized RT in human, yeast, and mouse cell lysate derived peptide assay libraries and enabled merging of archived libraries for expanded DIA-MS quantitative applications; (2) CiRTs predicted RT in SWATH-MS data within a 2-min margin of error for the majority of peptides; and (3) normalization of RT using the CiRT peptides enabled the accurate SWATH-MS-based quantification of 340 synthetic isotopically labeled peptides that were spiked into either human or yeast cell lysate. To automate and facilitate the use of these CiRT peptide lists or other custom user-defined internal RT reference peptides in DIA workflows, an algorithm was designed to automatically select a high-quality subset of datapoints for robust linear alignment of RT for use. Implementations of this algorithm are available for the OpenSWATH and Skyline platforms. Thus, CiRT peptides can be used alone or as a complement to SiRTs for RT normalization across peptide spectral libraries and in quantitative DIA-MS studies.

Automated Validation of Results and Removal of Fragment Ion Interferences in Targeted Analysis of Data-independent Acquisition Mass Spectrometry (MS) using SWATHProphet

Proteomics by mass spectrometry technology is widely used for identifying and quantifying peptides and proteins. The breadth and sensitivity of peptide detection have been advanced by the advent of data-independent acquisition mass spectrometry. Analysis of such data, however, is challenging due to the complexity of fragment ion spectra that have contributions from multiple co-eluting precursor ions. We present SWATHProphet software that identifies and quantifies peptide fragment ion traces in data-independent acquisition data, provides accurate probabilities to ensure results are correct, and automatically detects and removes contributions to quantitation originating from interfering precursor ions. Integration in the widely used open source Trans-Proteomic Pipeline facilitates subsequent analyses such as combining results of multiple data sets together for improved discrimination using iProphet and inferring sample proteins using ProteinProphet. This novel development should greatly help make data-independent acquisition mass spectrometry accessible to large numbers of users.

Building high-quality assay libraries for targeted analysis of SWATH MS data

Targeted proteomics by selected/multiple reaction monitoring (S/MRM) or, on a larger scale, by SWATH (sequential window acquisition of all theoretical spectra) MS (mass spectrometry) typically relies on spectral reference libraries for peptide identification. Quality and coverage of these libraries are therefore of crucial importance for the performance of the methods. Here we present a detailed protocol that has been successfully used to build high-quality, extensive reference libraries supporting targeted proteomics by SWATH MS. We describe each step of the process, including data acquisition by discovery proteomics, assertion of peptide-spectrum matches (PSMs), generation of consensus spectra and compilation of MS coordinates that uniquely define each targeted peptide. Crucial steps such as false discovery rate (FDR) control, retention time normalization and handling of post-translationally modified peptides are detailed. Finally, we show how to use the library to extract SWATH data with the open-source software Skyline. The protocol takes 2-3 d to complete, depending on the extent of the library and the computational resources available.

Targeted proteomics: a bridge between discovery and validation

New technologies in mass spectrometry are beginning to mature and show unique advantages for the identification and quantitation of proteins. In recent years, one of the significant goals of clinical proteomics has been to identify biomarkers that can be used for clinical diagnosis. As technology has progressed, the list of potential biomarkers has grown. However, the verification and validation of these potential biomarkers is increasingly challenging and require high-throughput quantitative assays, targeting specific candidates. Targeted proteomics bridges the gap between biomarker discovery and the development of clinically applicable biomarker assays.

A repository of assays to quantify 10,000 human proteins by SWATH-MS

Mass spectrometry is the method of choice for deep and reliable exploration of the (human) proteome. Targeted mass spectrometry reliably detects and quantifies pre-determined sets of proteins in a complex biological matrix and is used in studies that rely on the quantitatively accurate and reproducible measurement of proteins across multiple samples. It requires the one-time, a priori generation of a specific measurement assay for each targeted protein. SWATH-MS is a mass spectrometric method that combines data-independent acquisition (DIA) and targeted data analysis and vastly extends the throughput of proteins that can be targeted in a sample compared to selected reaction monitoring (SRM). Here we present a compendium of highly specific assays covering more than 10,000 human proteins and enabling their targeted analysis in SWATH-MS datasets acquired from research or clinical specimens. This resource supports the confident detection and quantification of 50.9% of all human proteins annotated by UniProtKB/Swiss-Prot and is therefore expected to find wide application in basic and clinical research. Data are available via ProteomeXchange (PXD000953-954) and SWATHAtlas (SAL00016-35).

A targeted proteomics toolkit for high-throughput absolute quantification of Escherichia coli proteins

Transformation of engineered Escherichia coli into a robust microbial factory is contingent on precise control of metabolism. Yet, the throughput of omics technologies used to characterize cell components has lagged far behind our ability to engineer novel strains. To expand the utility of quantitative proteomics for metabolic engineering, we validated and optimized targeted proteomics methods for over 400 proteins from more than 20 major pathways in E. coli metabolism. Complementing these methods, we constructed a series of synthetic genes to produce concatenated peptides (QconCAT) for absolute quantification of the proteins and made them available through the Addgene plasmid repository (www.addgene.org). To facilitate high sample throughput, we developed a fast, analytical-flow chromatography method using a 5.5-min gradient (10 min total run time). Overall this toolkit provides an invaluable resource for metabolic engineering by increasing sample throughput, minimizing development time and providing peptide standards for absolute quantification of E. coli proteins.

A framework for installable external tools in Skyline

Skyline is a Windows client application for targeted proteomics method creation and quantitative data analysis. The Skyline document model contains extensive mass spectrometry data from targeted proteomics experiments performed using selected reaction monitoring, parallel reaction monitoring and data-independent and data-dependent acquisition methods. Researchers have developed software tools that perform statistical analysis of the experimental data contained within Skyline documents. The new external tools framework allows researchers to integrate their tools into Skyline without modifying the Skyline codebase. Installed tools provide point-and-click access to downstream statistical analysis of data processed in Skyline. The framework also specifies a uniform interface to format tools for installation into Skyline. Tool developers can now easily share their tools with proteomics researchers using Skyline.

AVAILABILITY AND IMPLEMENTATION

Skyline is available as a single-click self-updating web installation at http://skyline.maccosslab.org. This Web site also provides access to installable external tools and documentation.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Levels of the retinoic acid synthesizing enzyme aldehyde dehydrogenase-1A2 are lower in testicular tissue from men with infertility

OBJECTIVE

To determine whether decreased testicular levels of enzymes necessary for retinoic acid biosynthesis were associated with male infertility, as retinoic acid is known to be necessary for spermatogenesis.

DESIGN

Observational analysis of testicular tissue samples, sperm indices, and serum hormone concentrations.

SETTING

Two infertility centers in Chile.

PATIENT(S)

32 infertile men and 11 control men.

INTERVENTION(S)

Measurement of the three enzymes necessary for retinoic acid biosynthesis, aldehyde dehydrogenase (ALDH) 1A1, 1A2, and 1A3, in testicular tissue by a novel liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) peptide assay.

MAIN OUTCOME MEASURE(S)

ALDH isozyme levels compared by type of infertility and correlated with testicular germ cell numbers, sperm parameters, and serum and intratesticular hormone concentrations.

RESULT(S)

Men with infertility had statistically significantly reduced levels of ALDH1A2 but not ALDH1A1 or ALDH1A3 in their testicular tissue compared with men with normal spermatogenesis. The ALDH1A2 protein levels were strongly correlated with the number of germ cells found via testicular biopsy.

CONCLUSION(S)

These findings suggest that ALDH1A2 is the enzyme involved in retinoic acid biosynthesis in human germ cells. Further study of the relationship between intratesticular ALDH1A2 and male infertility is warranted to determine whether men with infertility have a reduced ability to synthesize retinoic acid within their germ cells that could impair spermatogenesis.

Screening of missing proteins in the human liver proteome by improved MRM-approach-based targeted proteomics

To completely annotate the human genome, the task of identifying and characterizing proteins that currently lack mass spectrometry (MS) evidence is inevitable and urgent. In this study, as the first effort to screen missing proteins in large scale, we developed an approach based on SDS-PAGE followed by liquid chromatography-multiple reaction monitoring (LC-MRM), for screening of those missing proteins with only a single peptide hit in the previous liver proteome data set. Proteins extracted from normal human liver were separated in SDS-PAGE and digested in split gel slice, and the resulting digests were then subjected to LC-schedule MRM analysis. The MRM assays were developed through synthesized crude peptides for target peptides. In total, the expressions of 57 target proteins were confirmed from 185 MRM assays in normal human liver tissues. Among the proved 57 one-hit wonders, 50 proteins are of the minimally redundant set in the PeptideAtlas database, 7 proteins even have none MS-based information previously in various biological processes. We conclude that our SDS-PAGE-MRM workflow can be a powerful approach to screen missing or poorly characterized proteins in different samples and to provide their quantity if detected. The MRM raw data have been uploaded to ISB/SRM Atlas/PASSEL (PXD000648).