Glycoproteomic analysis of prostate cancer tissues by SWATH mass spectrometry discovers N-acylethanolamine acid amidase and protein tyrosine kinase 7 as signatures for tumor aggressiveness

Feature selection in clinical proteomics: with great power comes great reproducibility

In clinical proteomics, reproducible feature selection is unattainable given the standard statistical hypothesis-testing framework. This leads to irreproducible signatures with no diagnostic power. Instability stems from high P-value variability (p_var), which is inevitable and insolvable. The impact of p_var can be reduced via power increment, for example increasing sample size and measurement accuracy. However, these are not realistic solutions in practice. Instead, workarounds using existing data such as signal boosting transformation techniques and network-based statistical testing is more practical. Furthermore, it is useful to consider other metrics alongside P-values including confidence intervals, effect sizes and cross-validation accuracies to make informed inferences.

Chemical Glycoproteomics

Chemical tools have accelerated progress in glycoscience, reducing experimental barriers to studying protein glycosylation, the most widespread and complex form of posttranslational modification. For example, chemical glycoproteomics technologies have enabled the identification of specific glycosylation sites and glycan structures that modulate protein function in a number of biological processes. This field is now entering a stage of logarithmic growth, during which chemical innovations combined with mass spectrometry advances could make it possible to fully characterize the human glycoproteome. In this review, we describe the important role that chemical glycoproteomics methods are playing in such efforts. We summarize developments in four key areas: enrichment of glycoproteins and glycopeptides from complex mixtures, emphasizing methods that exploit unique chemical properties of glycans or introduce unnatural functional groups through metabolic labeling and chemoenzymatic tagging; identification of sites of protein glycosylation; targeted glycoproteomics; and functional glycoproteomics, with a focus on probing interactions between glycoproteins and glycan-binding proteins. Our goal with this survey is to provide a foundation on which continued technological advancements can be made to promote further explorations of protein glycosylation.

Exploring novel paths towards protein signatures of chronic pain

Pain is a major symptom of many medical conditions and the worldwide number one reason for people to seek medical assistance. It affects the quality of life of patients and poses a heavy financial burden on society with high costs of treatment and lost productivity. Furthermore, the treatment of chronic pain presents a big challenge as pain therapeutics often lack efficacy and exhibit minimal safety profiles. The latter can be largely attributed to the fact that current therapies target molecules with key physiological functions throughout the body. In light of these difficulties, the identification of proteins specifically involved in chronic pain states is of paramount importance for designing selective interventions. Several profiling efforts have been employed with the aim to dissect the molecular underpinnings of chronic pain, both on the level of the transcriptome and proteome. However, generated results are often inconsistent and non-overlapping, which is largely due to inherent technical constraints. A potential solution may be offered by emerging strategies capable of performing standardized and reproducible proteome analysis, such as data-independent acquisition-mass spectrometry (DIA-MS). We have recently demonstrated the applicability of DIA-MS to interrogate chronic pain-related proteome alterations in mice. Based on our results, we aim to provide an overview on DIA-MS and its potential to contribute to the comprehensive characterization of molecular signatures underlying pain pathologies.

Characterization of Site-Specific N-Glycopeptide Isoforms of α-1-Acid Glycoprotein from an Interlaboratory Study Using LC-MS/MS

Glycoprotein conformations are complex and heterogeneous. Currently, site-specific characterization of glycopeptides is a challenge. We sought to establish an efficient method of N-glycoprotein characterization using mass spectrometry (MS). Using alpha-1-acid glycoprotein (AGP) as a model N-glycoprotein, we identified its tryptic N-glycopeptides and examined the data reproducibility in seven laboratories running different LC-MS/MS platforms. We used three test samples and one blind sample to evaluate instrument performance with entire sample preparation workflow. 165 site-specific N-glycopeptides representative of all N-glycosylation sites were identified from AGP 1 and AGP 2 isoforms. The glycopeptide fragmentations by collision-induced dissociation or higher-energy collisional dissociation (HCD) varied based on the MS analyzer. Orbitrap Elite identified the greatest number of AGP N-glycopeptides, followed by Triple TOF and Q-Exactive Plus. Reproducible generation of oxonium ions, glycan-cleaved glycopeptide fragment ions, and peptide backbone fragment ions was essential for successful identification. Laboratory proficiency affected the number of identified N-glycopeptides. The relative quantities of the 10 major N-glycopeptide isoforms of AGP detected in four laboratories were compared to assess reproducibility. Quantitative analysis showed that the coefficient of variation was <25% for all test samples. Our analytical protocol yielded identification and quantification of site-specific N-glycopeptide isoforms of AGP from control and disease plasma sample.

Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry

The myriad of peptides presented at the cell surface by class I and class II major histocompatibility complex (MHC) molecules are referred to as the immunopeptidome and are of great importance for basic and translational science. For basic science, the immunopeptidome is a critical component for understanding the immune system; for translational science, exact knowledge of the immunopeptidome can directly fuel and guide the development of next-generation vaccines and immunotherapies against autoimmunity, infectious diseases, and cancers. In this mini-review, we summarize established isolation techniques as well as emerging mass spectrometry-based platforms (i.e. SWATH-MS) to identify and quantify MHC-associated peptides. We also highlight selected biological applications and discuss important current technical limitations that need to be solved to accelerate the development of this field.

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.

Integral membrane proteins in proteomics. How to break open the black box?

Integral membrane proteins (IMPs) are coded by 20-30% of human genes and execute important functions - transmembrane transport, signal transduction, cell-cell communication, cell adhesion to the extracellular matrix, and many other processes. Due to their hydrophobicity, low expression and lack of trypsin cleavage sites in their transmembrane segments, IMPs have been generally under-represented in routine proteomic analyses. However, the field of membrane proteomics has changed markedly in the past decade, namely due to the introduction of filter assisted sample preparation (FASP), the establishment of cell surface capture (CSC) protocols, and the development of methods that enable analysis of the hydrophobic transmembrane segments. This review will summarize the recent developments in the field and outline the most successful strategies for the analysis of integral membrane proteins.

SIGNIFICANCE

Integral membrane proteins (IMPs) are attractive therapeutic targets mostly due to their many important functions. However, our knowledge of the membrane proteome is severely limited to effectively exploit their potential. This is mostly due to the lack of appropriate techniques or methods compatible with the typical features of IMPs, namely hydrophobicity, low expression and lack of trypsin cleavage sites. This review summarizes the most recent development in membrane proteomics and outlines the most successful strategies for their large-scale analysis.

TRIC: an automated alignment strategy for reproducible protein quantification in targeted proteomics

Large scale, quantitative proteomic studies have become essential for the analysis of clinical cohorts, large perturbation experiments and systems biology studies. While next-generation mass spectrometric techniques such as SWATH-MS have substantially increased throughput and reproducibility, ensuring consistent quantification of thousands of peptide analytes across multiple LC-MS/MS runs remains a challenging and laborious manual process. To produce highly consistent and quantitatively accurate proteomics data matrices in an automated fashion, we have developed the TRIC software which utilizes fragment ion data to perform cross-run alignment, consistent peak-picking and quantification for high throughput targeted proteomics. TRIC uses a graph-based alignment strategy based on non-linear retention time correction to integrate peak elution information from all LC-MS/MS runs acquired in a study. When compared to state-of-the-art SWATH-MS data analysis, the algorithm was able to reduce the identification error by more than 3-fold at constant recall, while correcting for highly non-linear chromatographic effects. On a pulsed-SILAC experiment performed on human induced pluripotent stem (iPS) cells, TRIC was able to automatically align and quantify thousands of light and heavy isotopic peak groups and substantially increased the quantitative completeness and biological information in the data, providing insights into protein dynamics of iPS cells. Overall, this study demonstrates the importance of consistent quantification in highly challenging experimental setups, and proposes an algorithm to automate this task, constituting the last missing piece in a pipeline for automated analysis of massively parallel targeted proteomics datasets.

Advances in targeted proteomics and applications to biomedical research

Targeted proteomics technique has emerged as a powerful protein quantification tool in systems biology, biomedical research, and increasing for clinical applications. The most widely used targeted proteomics approach, selected reaction monitoring (SRM), also known as multiple reaction monitoring (MRM), can be used for quantification of cellular signaling networks and preclinical verification of candidate protein biomarkers. As an extension to our previous review on advances in SRM sensitivity (Shi et al., Proteomics, 12, 1074-1092, 2012) herein we review recent advances in the method and technology for further enhancing SRM sensitivity (from 2012 to present), and highlighting its broad biomedical applications in human bodily fluids, tissue and cell lines. Furthermore, we also review two recently introduced targeted proteomics approaches, parallel reaction monitoring (PRM) and data-independent acquisition (DIA) with targeted data extraction on fast scanning high-resolution accurate-mass (HR/AM) instruments. Such HR/AM targeted quantification with monitoring all target product ions addresses SRM limitations effectively in specificity and multiplexing; whereas when compared to SRM, PRM and DIA are still in the infancy with a limited number of applications. Thus, for HR/AM targeted quantification we focus our discussion on method development, data processing and analysis, and its advantages and limitations in targeted proteomics. Finally, general perspectives on the potential of achieving both high sensitivity and high sample throughput for large-scale quantification of hundreds of target proteins are discussed.

Untargeted, spectral library-free analysis of data-independent acquisition proteomics data generated using Orbitrap mass spectrometers

We describe an improved version of the data-independent acquisition (DIA) computational analysis tool DIA-Umpire, and show that it enables highly sensitive, untargeted, and direct (spectral library-free) analysis of DIA data obtained using the Orbitrap family of mass spectrometers. DIA-Umpire v2 implements an improved feature detection algorithm with two additional filters based on the isotope pattern and fractional peptide mass analysis. The targeted re-extraction step of DIA-Umpire is updated with an improved scoring function and a more robust, semiparametric mixture modeling of the resulting scores for computing posterior probabilities of correct peptide identification in a targeted setting. Using two publicly available Q Exactive DIA datasets generated using HEK-293 cells and human liver microtissues, we demonstrate that DIA-Umpire can identify similar number of peptide ions, but with better identification reproducibility between replicates and samples, as with conventional data-dependent acquisition. We further demonstrate the utility of DIA-Umpire using a series of Orbitrap Fusion DIA experiments with HeLa cell lysates profiled using conventional data-dependent acquisition and using DIA with different isolation window widths.

The Role of Proteomics in Biomarker Development for Improved Patient Diagnosis and Clinical Decision Making in Prostate Cancer

Prostate Cancer (PCa) is the second most commonly diagnosed cancer in men worldwide. Although increased expression of prostate-specific antigen (PSA) is an effective indicator for the recurrence of PCa, its intended use as a screening marker for PCa is of considerable controversy. Recent research efforts in the field of PCa biomarkers have focused on the identification of tissue and fluid-based biomarkers that would be better able to stratify those individuals diagnosed with PCa who (i) might best receive no treatment (active surveillance of the disease); (ii) would benefit from existing treatments; or (iii) those who are likely to succumb to disease recurrence and/or have aggressive disease. The growing demand for better prostate cancer biomarkers has coincided with the development of improved discovery and evaluation technologies for multiplexed measurement of proteins in bio-fluids and tissues. This review aims to (i) provide an overview of these technologies as well as describe some of the candidate PCa protein biomarkers that have been discovered using them; (ii) address some of the general limitations in the clinical evaluation and validation of protein biomarkers; and (iii) make recommendations for strategies that could be adopted to improve the successful development of protein biomarkers to deliver improvements in personalized PCa patient decision making.

Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer

Biomarkers are rapidly gaining importance in personalized medicine. Although numerous molecular signatures have been developed over the past decade, there is a lack of overlap and many biomarkers fail to validate in independent patient cohorts and hence are not useful for clinical application. For these reasons, identification of novel and robust biomarkers remains a formidable challenge. We combine targeted proteomics with computational biology to discover robust proteomic signatures for prostate cancer. Quantitative proteomics conducted in expressed prostatic secretions from men with extraprostatic and organ-confined prostate cancers identified 133 differentially expressed proteins. Using synthetic peptides, we evaluate them by targeted proteomics in a 74-patient cohort of expressed prostatic secretions in urine. We quantify a panel of 34 candidates in an independent 207-patient cohort. We apply machine-learning approaches to develop clinical predictive models for prostate cancer diagnosis and prognosis. Our results demonstrate that computationally guided proteomics can discover highly accurate non-invasive biomarkers.

Proteomic technologies are capable of identifying thousands of proteins in biological samples, but biomarker applications are lagging. Here the authors use Multiple Reaction Monitoring Mass Spectrometry to delineate peptide signatures that accurately distinguish between defined prostate cancer patient risk groups.

Dual targeting of p53 and c-MYC selectively eliminates leukaemic stem cells

Chronic myeloid leukaemia (CML) arises after transformation of a haemopoietic stem cell (HSC) by the protein-tyrosine kinase BCR-ABL. Direct inhibition of BCR-ABL kinase has revolutionized disease management, but fails to eradicate leukaemic stem cells (LSCs), which maintain CML. LSCs are independent of BCR-ABL for survival, providing a rationale for identifying and targeting kinase-independent pathways. Here we show--using proteomics, transcriptomics and network analyses--that in human LSCs, aberrantly expressed proteins, in both imatinib-responder and non-responder patients, are modulated in concert with p53 (also known as TP53) and c-MYC regulation. Perturbation of both p53 and c-MYC, and not BCR-ABL itself, leads to synergistic cell kill, differentiation, and near elimination of transplantable human LSCs in mice, while sparing normal HSCs. This unbiased systems approach targeting connected nodes exemplifies a novel precision medicine strategy providing evidence that LSCs can be eradicated.

Fast MS/MS acquisition without dynamic exclusion enables precise and accurate quantification of proteome by MS/MS fragment intensity

Most currently proteomic studies use data-dependent acquisition with dynamic exclusion to identify and quantify the peptides generated by the digestion of biological sample. Although dynamic exclusion permits more identifications and higher possibility to find low abundant proteins, stochastic and irreproducible precursor ion selection caused by dynamic exclusion limit the quantification capabilities, especially for MS/MS based quantification. This is because a peptide is usually triggered for fragmentation only once due to dynamic exclusion. Therefore the fragment ions used for quantification only reflect the peptide abundances at that given time point. Here, we propose a strategy of fast MS/MS acquisition without dynamic exclusion to enable precise and accurate quantification of proteome by MS/MS fragment intensity. The results showed comparable proteome identification efficiency compared to the traditional data-dependent acquisition with dynamic exclusion, better quantitative accuracy and reproducibility regardless of label-free based quantification or isobaric labeling based quantification. It provides us with new insights to fully explore the potential of modern mass spectrometers. This strategy was applied to the relative quantification of two human disease cell lines, showing great promises for quantitative proteomic applications.

SWATH Mass Spectrometry Performance Using Extended Peptide MS/MS Assay Libraries

The use of data-independent acquisition methods such as SWATH for mass spectrometry based proteomics is usually performed with peptide MS/MS assay libraries which enable identification and quantitation of peptide peak areas. Reference assay libraries can be generated locally through information dependent acquisition, or obtained from community data repositories for commonly studied organisms. However, there have been no studies performed to systematically evaluate how locally generated or repository-based assay libraries affect SWATH performance for proteomic studies. To undertake this analysis, we developed a software workflow, SwathXtend, which generates extended peptide assay libraries by integration with a local seed library and delivers statistical analysis of SWATH-quantitative comparisons. We designed test samples using peptides from a yeast extract spiked into peptides from human K562 cell lysates at three different ratios to simulate protein abundance change comparisons. SWATH-MS performance was assessed using local and external assay libraries of varying complexities and proteome compositions. These experiments demonstrated that local seed libraries integrated with external assay libraries achieve better performance than local assay libraries alone, in terms of the number of identified peptides and proteins and the specificity to detect differentially abundant proteins. Our findings show that the performance of extended assay libraries is influenced by the MS/MS feature similarity of the seed and external libraries, while statistical analysis using multiple testing corrections increases the statistical rigor needed when searching against large extended assay libraries.

Standardized Profiling of The Membrane-Enriched Proteome of Mouse Dorsal Root Ganglia (DRG) Provides Novel Insights Into Chronic Pain

Chronic pain is a complex disease with limited treatment options. Several profiling efforts have been employed with the aim to dissect its molecular underpinnings. However, generated results are often inconsistent and nonoverlapping, which is largely because of inherent technical constraints. Emerging data-independent acquisition (DIA)-mass spectrometry (MS) has the potential to provide unbiased, reproducible and quantitative proteome maps - a prerequisite for standardization among experiments. Here, we designed a DIA-based proteomics workflow to profile changes in the abundance of dorsal root ganglia (DRG) proteins in two mouse models of chronic pain, inflammatory and neuropathic. We generated a DRG-specific spectral library containing 3067 DRG proteins, which enables their standardized quantification by means of DIA-MS in any laboratory. Using this resource, we profiled 2526 DRG proteins in each biological replicate of both chronic pain models and respective controls with unprecedented reproducibility. We detected numerous differentially regulated proteins, the majority of which exhibited pain model-specificity. Our approach recapitulates known biology and discovers dozens of proteins that have not been characterized in the somatosensory system before. Functional validation experiments and analysis of mouse pain behaviors demonstrate that indeed meaningful protein alterations were discovered. These results illustrate how the application of DIA-MS can open new avenues to achieve the long-awaited standardization in the molecular dissection of pathologies of the somatosensory system. Therefore, our findings provide a valuable framework to qualitatively extend our understanding of chronic pain and somatosensation.

Instant Integrated Ultradeep Quantitative-structural Membrane Proteomics Discovered Post-translational Modification Signatures for Human Cys-loop Receptor Subunit Bias

Neurotransmitter ligand-gated ion channels (LGICs) are widespread and pivotal in brain functions. Unveiling their structure-function mechanisms is crucial to drive drug discovery, and demands robust proteomic quantitation of expression, post-translational modifications (PTMs) and dynamic structures. Yet unbiased digestion of these modified transmembrane proteins-at high efficiency and peptide reproducibility-poses the obstacle. Targeting both enzyme-substrate contacts and PTMs for peptide formation and detection, we devised flow-and-detergent-facilitated protease and de-PTM digestions for deep sequencing (FDD) method that combined omni-compatible detergent, tandem immobilized protease/PNGase columns, and Cys-selective reduction/alkylation, to achieve streamlined ultradeep peptide preparation within minutes not days, at high peptide reproducibility and low abundance-bias. FDD transformed enzyme-protein contacts into equal catalytic travel paths through enzyme-excessive columns regardless of protein abundance, removed products instantly preventing inhibition, tackled intricate structures via sequential multiple micro-digestions along the flow, and precisely controlled peptide formation by flow rate. Peptide-stage reactions reduced steric bias; low contamination deepened MS/MS scan; distinguishing disulfide from M oxidation and avoiding gain/loss artifacts unmasked protein-endogenous oxidation states. Using a recent interactome of 285-kDa human GABA type A receptor, this pilot study validated FDD platform's applicability to deep sequencing (up to 99% coverage), H/D-exchange and TMT-based structural mapping. FDD discovered novel subunit-specific PTM signatures, including unusual nontop-surface N-glycosylations, that may drive subunit biases in human Cys-loop LGIC assembly and pharmacology, by redefining subunit/ligand interfaces and connecting function domains.

Image-based computational quantification and visualization of genetic alterations and tumour heterogeneity

Recent large-scale genome analyses of human tissue samples have uncovered a high degree of genetic alterations and tumour heterogeneity in most tumour entities, independent of morphological phenotypes and histopathological characteristics. Assessment of genetic copy-number variation (CNV) and tumour heterogeneity by fluorescence in situ hybridization (ISH) provides additional tissue morphology at single-cell resolution, but it is labour intensive with limited throughput and high inter-observer variability. We present an integrative method combining bright-field dual-colour chromogenic and silver ISH assays with an image-based computational workflow (ISHProfiler), for accurate detection of molecular signals, high-throughput evaluation of CNV, expressive visualization of multi-level heterogeneity (cellular, inter- and intra-tumour heterogeneity), and objective quantification of heterogeneous genetic deletions (PTEN) and amplifications (19q12, HER2) in diverse human tumours (prostate, endometrial, ovarian and gastric), using various tissue sizes and different scanners, with unprecedented throughput and reproducibility.

Advances in mass spectrometry-based clinical biomarker discovery

The greatest unmet needs in biomarker discovery are those discoveries that lead to the development of clinical diagnostic tests. These clinical diagnostic tests can provide early intervention when a patient would present otherwise healthy (e.g., cancer or cardiovascular disease) and aid clinical decision making with improved clinical outcomes. The past two decades have seen significant technological improvements in the analytical capabilities of mass spectrometers. Mass spectrometers are unique in that they can directly analyze any biological molecule susceptible to ionization. The biological studies of human metabolites and proteins using contemporary mass spectrometry technology (metabolomics and proteomics, respectively) has been ongoing for over a decade. Some of these studies have resulted in exciting insights into human biology. However, relatively few biomarkers have been translated into clinical tests. This review will discuss some key technological developments that have occurred over this time with an emphasis on technologies that will create new avenues for biomarker discovery.

The clinical impact of recent advances in LC-MS for cancer biomarker discovery and verification

Mass spectrometry (MS) -based proteomics has become an indispensable tool with broad applications in systems biology and biomedical research. With recent advances in liquid chromatography (LC) and MS instrumentation, LC-MS is making increasingly significant contributions to clinical applications, especially in the area of cancer biomarker discovery and verification. To overcome challenges associated with analyses of clinical samples (for example, a wide dynamic range of protein concentrations in bodily fluids and the need to perform high throughput and accurate quantification of candidate biomarker proteins), significant efforts have been devoted to improve the overall performance of LC-MS-based clinical proteomics platforms. Reviewed here are the recent advances in LC-MS and its applications in cancer biomarker discovery and quantification, along with the potentials, limitations and future perspectives.

Immunocapture strategies in translational proteomics

Aiming at clinical studies of human diseases, antibody-assisted assays have been applied to biomarker discovery and toward a streamlined translation from patient profiling to assays supporting personalized treatments. In recent years, integrated strategies to couple and combine antibodies with mass spectrometry-based proteomic efforts have emerged, allowing for novel possibilities in basic and clinical research. Described in this review are some of the field's current and emerging immunocapture approaches from an affinity proteomics perspective. Discussed are some of their advantages, pitfalls and opportunities for the next phase in clinical and translational proteomics.

Targeted proteomics of solid cancers: from quantification of known biomarkers towards reading the digital proteome maps

The concept of personalized medicine includes novel protein biomarkers that are expected to improve the early detection, diagnosis and therapy monitoring of malignant diseases. Tissues, biofluids, cell lines and xenograft models are the common sources of biomarker candidates that require verification of clinical value in independent patient cohorts. Targeted proteomics - based on selected reaction monitoring, or data extraction from data-independent acquisition based digital maps - now represents a promising mass spectrometry alternative to immunochemical methods. To date, it has been successfully used in a high number of studies answering clinical questions on solid malignancies: breast, colorectal, prostate, ovarian, endometrial, pancreatic, hepatocellular, lung, bladder and others. It plays an important role in functional proteomic experiments that include studying the role of post-translational modifications in cancer progression. This review summarizes verified biomarker candidates successfully quantified by targeted proteomics in this field and directs the readers who plan to design their own hypothesis-driven experiments to appropriate sources of methods and knowledge.

Activity-Based Probe for N-Acylethanolamine Acid Amidase

N-Acylethanolamine acid amidase (NAAA) is a lysosomal cysteine hydrolase involved in the degradation of saturated and monounsaturated fatty acid ethanolamides (FAEs), a family of endogenous lipid signaling molecules that includes oleoylethanolamide (OEA) and palmitoylethanolamide (PEA). Among the reported NAAA inhibitors, α-amino-β-lactone (3-aminooxetan-2-one) derivatives have been shown to prevent FAE hydrolysis in innate-immune and neural cells and to reduce reactions to inflammatory stimuli. Recently, we disclosed two potent and selective NAAA inhibitors, the compounds ARN077 (5-phenylpentyl-N-[(2S,3R)-2-methyl-4-oxo-oxetan-3-yl]carbamate) and ARN726 (4-cyclohexylbutyl-N-[(S)-2-oxoazetidin-3-yl]carbamate). The former is active in vivo by topical administration in rodent models of hyperalgesia and allodynia, while the latter exerts systemic anti-inflammatory effects in mouse models of lung inflammation. In the present study, we designed and validated a derivative of ARN726 as the first activity-based protein profiling (ABPP) probe for the in vivo detection of NAAA. The newly synthesized molecule 1 is an effective in vitro and in vivo click-chemistry activity based probe (ABP), which is able to capture the catalytically active form of NAAA in Human Embryonic Kidney 293 (HEK293) cells overexpressing human NAAA as well as in rat lung tissue. Competitive ABPP with 1 confirmed that ARN726 and ARN077 inhibit NAAA in vitro and in vivo. Compound 1 is a useful new tool to identify activated NAAA both in vitro and in vivo and to investigate the physiological and pathological roles of this enzyme.

mapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry

Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14-3-3β dynamic interaction network and prostate cancer glycoproteome.

AVAILABILITY

The software was written in C++ language and the source code is available for free through SourceForge website http://sourceforge.net/projects/mapdia/.This article is part of a Special Issue entitled: Computational Proteomics.

Applications of targeted proteomics in systems biology and translational medicine

Biological systems are composed of numerous components of which proteins are of particularly high functional significance. Network models are useful abstractions for studying these components in context. Network representations display molecules as nodes and their interactions as edges. Because they are difficult to directly measure, functional edges are frequently inferred from suitably structured datasets consisting of the accurate and consistent quantification of network nodes under a multitude of perturbed conditions. For the precise quantification of a finite list of proteins across a wide range of samples, targeted proteomics exemplified by selected/multiple reaction monitoring (SRM, MRM) mass spectrometry has proven useful and has been applied to a variety of questions in systems biology and clinical studies. Here, we survey the literature of studies using SRM-MS in systems biology and clinical proteomics. Systems biology studies frequently examine fundamental questions in network biology, whereas clinical studies frequently focus on biomarker discovery and validation in a variety of diseases including cardiovascular disease and cancer. Targeted proteomics promises to advance our understanding of biological networks and the phenotypic significance of specific network states and to advance biomarkers into clinical use.

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.

A pH-responsive soluble polymer-based homogeneous system for fast and highly efficient N-glycoprotein/glycopeptide enrichment and identification by mass spectrometry

A homogeneous reaction system was developed for facile and highly efficient enrichment of biomolecules by exploiting the reversible self-assembly of a stimuli-responsive polymer.

Liquid phase homogeneous reactions using soluble polymer supports have found numerous applications in homogeneous catalysis and organic synthesis because of their advantages of no interface mass transfer limitation and a high conversion rate. However, their application in analytical separation is limited by the inefficient/inconvenient recovery of the target molecules from the extremely complex biological samples. Here, we report a stimuli-responsive polymer system for facile and efficient enrichment of trace amounts of biomolecules from complex biological samples. The soluble polymer supports provide a homogeneous reaction system with fast mass transfer and facilitate interactions between the supports and the target molecules. More importantly, the stimuli-responsive polymers exhibit reversible self-assembly and phase separation under pH variations, which leads to facial sample recovery with a high yield of the target biomolecules. The stimuli-responsive polymer is successfully applied to the enrichment of low abundant N-glycoproteins/glycopeptides, which play crucial roles in various key biological processes in mammals and are closely correlated with the occurrence, progression and metastasis of cancer. N-Glycoprotein is coupled to the stimuli-responsive polymer using the reported hydrazide chemistry with pre-oxidation of the oligosaccharide structure. Highly efficient enrichment of N-glycoproteins/N-glycopeptides with >95% conversion rate is achieved within 1 h, which is eight times faster than using solid/insoluble hydrazide enrichment materials. Mass spectrometry analysis achieves low femtomolar identification sensitivity and obtained 1317 N-glycopeptides corresponding to 458 N-glycoproteins in mouse brain, which is more than twice the amount obtained after enrichment using commercial solid/insoluble materials. These results demonstrate the capability of this “smart” polymer system to combine stimuli-responsive and target-enrichment moieties to achieve improved identification of key biological and disease related biomolecules.

Online Peptide fractionation using a multiphasic microfluidic liquid chromatography chip improves reproducibility and detection limits for quantitation in discovery and targeted proteomics

Comprehensive proteomic profiling of biological specimens usually requires multidimensional chromatographic peptide fractionation prior to mass spectrometry. However, this approach can suffer from poor reproducibility because of the lack of standardization and automation of the entire workflow, thus compromising performance of quantitative proteomic investigations. To address these variables we developed an online peptide fractionation system comprising a multiphasic liquid chromatography (LC) chip that integrates reversed phase and strong cation exchange chromatography upstream of the mass spectrometer (MS). We showed superiority of this system for standardizing discovery and targeted proteomic workflows using cancer cell lysates and nondepleted human plasma. Five-step multiphase chip LC MS/MS acquisition showed clear advantages over analyses of unfractionated samples by identifying more peptides, consuming less sample and often improving the lower limits of quantitation, all in highly reproducible, automated, online configuration. We further showed that multiphase chip LC fractionation provided a facile means to detect many N- and C-terminal peptides (including acetylated N terminus) that are challenging to identify in complex tryptic peptide matrices because of less favorable ionization characteristics. Given as much as 95% of peptides were detected in only a single salt fraction from cell lysates we exploited this high reproducibility and coupled it with multiple reaction monitoring on a high-resolution MS instrument (MRM-HR). This approach increased target analyte peak area and improved lower limits of quantitation without negatively influencing variance or bias. Further, we showed a strategy to use multiphase LC chip fractionation LC-MS/MS for ion library generation to integrate with SWATH(TM) data-independent acquisition quantitative workflows. All MS data are available via ProteomeXchange with identifier PXD001464.

Evolutionary Engineering Improves Tolerance for Replacement Jet Fuels in Saccharomyces cerevisiae

Monoterpenes are liquid hydrocarbons with applications ranging from flavor and fragrance to replacement jet fuel. Their toxicity, however, presents a major challenge for microbial synthesis. Here we evolved limonene-tolerant Saccharomyces cerevisiae strains and sequenced six strains across the 200-generation evolutionary time course. Mutations were found in the tricalbin proteins Tcb2p and Tcb3p. Genomic reconstruction in the parent strain showed that truncation of a single protein (tTcb3p(1-989)), but not its complete deletion, was sufficient to recover the evolved phenotype improving limonene fitness 9-fold. tTcb3p(1-989) increased tolerance toward two other monoterpenes (β-pinene and myrcene) 11- and 8-fold, respectively, and tolerance toward the biojet fuel blend AMJ-700t (10% cymene, 50% limonene, 40% farnesene) 4-fold. tTcb3p(1-989) is the first example of successful engineering of phase tolerance and creates opportunities for production of the highly toxic C10 alkenes in yeast.

DIA-Umpire: comprehensive computational framework for data-independent acquisition proteomics

As a result of recent improvements in mass spectrometry (MS), there is increased interest in data-independent acquisition (DIA) strategies in which all peptides are systematically fragmented using wide mass-isolation windows ('multiplex fragmentation'). DIA-Umpire (http://diaumpire.sourceforge.net/), a comprehensive computational workflow and open-source software for DIA data, detects precursor and fragment chromatographic features and assembles them into pseudo-tandem MS spectra. These spectra can be identified with conventional database-searching and protein-inference tools, allowing sensitive, untargeted analysis of DIA data without the need for a spectral library. Quantification is done with both precursor- and fragment-ion intensities. Furthermore, DIA-Umpire enables targeted extraction of quantitative information based on peptides initially identified in only a subset of the samples, resulting in more consistent quantification across multiple samples. We demonstrated the performance of the method with control samples of varying complexity and publicly available glycoproteomics and affinity purification-MS data.

Using data-independent, high-resolution mass spectrometry in protein biomarker research: perspectives and clinical applications

In medicine, there is an urgent need for protein biomarkers in a range of applications that includes diagnostics, disease stratification, and therapeutic decisions. One of the main technologies to address this need is MS, used for protein biomarker discovery and, increasingly, also for protein biomarker validation. Currently, data-dependent analysis (also referred to as shotgun proteomics) and targeted MS, exemplified by SRM, are the most frequently used mass spectrometric methods. Recently developed data-independent acquisition techniques combine the strength of shotgun and targeted proteomics, while avoiding some of the limitations of the respective methods. They provide high-throughput, accurate quantification, and reproducible measurements within a single experimental setup. Here, we describe and review data-independent acquisition strategies and their recent use in clinically oriented studies. In addition, we also provide a detailed guide for the implementation of SWATH-MS (where SWATH is sequential window acquisition of all theoretical mass spectra)-one of the data-independent strategies that have gained wide application of late.

Altered glycosylation in prostate cancer

Prostate cancer is annually the most common newly diagnosed cancer in men. The prostate functions as a major secretory gland for the production of glycoproteins critical to sperm activation and reproduction. Prostate-specific antigen (PSA), produced by the prostate, is one of the most commonly assayed glycoproteins in blood, serving as a biomarker for early detection and progression of prostate cancer. The single site of N-glycosylation on PSA has been the target of multiple glycan characterization studies. In this review, the extensive number of studies that have characterized the changes in O-linked and N-linked glycosylations associated with prostate cancer development and progression will be summarized. This includes analysis of the glycosylation of PSA, and other prostate glycoproteins, in tissues, clinical biofluids, and cell line models. Other studies are summarized in the context of understanding the complexities of these glycan changes in order to address the many confounding questions associated with prostate cancer, as well as efforts to improve prostate cancer biomarker assays using targeted glycomic-based strategies.

Quantitative variability of 342 plasma proteins in a human twin population

The degree and the origins of quantitative variability of most human plasma proteins are largely unknown. Because the twin study design provides a natural opportunity to estimate the relative contribution of heritability and environment to different traits in human population, we applied here the highly accurate and reproducible SWATH mass spectrometry technique to quantify 1,904 peptides defining 342 unique plasma proteins in 232 plasma samples collected longitudinally from pairs of monozygotic and dizygotic twins at intervals of 2–7 years, and proportioned the observed total quantitative variability to its root causes, genes, and environmental and longitudinal factors. The data indicate that different proteins show vastly different patterns of abundance variability among humans and that genetic control and longitudinal variation affect protein levels and biological processes to different degrees. The data further strongly suggest that the plasma concentrations of clinical biomarkers need to be calibrated against genetic and temporal factors. Moreover, we identified 13 cis-SNPs significantly influencing the level of specific plasma proteins. These results therefore have immediate implications for the effective design of blood-based biomarker studies.

Coupling enrichment methods with proteomics for understanding and treating disease

Owing to recent advances in proteomics analytical methods and bioinformatics capabilities there is a growing trend toward using these capabilities for the development of drugs to treat human disease, including target and drug evaluation, understanding mechanisms of drug action, and biomarker discovery. Currently, the genetic sequences of many major organisms are available, which have helped greatly in characterizing proteomes in model animal systems and humans. Through proteomics, global profiles of different disease states can be characterized (e.g. changes in types and relative levels as well as changes in PTMs such as glycosylation or phosphorylation). Although intracellular proteomics can provide a broad overview of physiology of cells and tissues, it has been difficult to quantify the low abundance proteins which can be important for understanding the diseased states and treatment progression. For this reason, there is increasing interest in coupling comparative proteomics methods with subcellular fractionation and enrichment techniques for membranes, nucleus, phosphoproteome, glycoproteome as well as low abundance serum proteins. In this review, we will provide examples of where the utilization of different proteomics-coupled enrichment techniques has aided target and biomarker discovery, understanding the drug targeting mechanism, and mAb discovery. Taken together, these improvements will help to provide a better understanding of the pathophysiology of various diseases including cancer, autoimmunity, inflammation, cardiovascular disease, and neurological conditions, and in the design and development of better medicines for treating these afflictions.

Comparison of five methods for direct extraction of surface proteins from Listeria monocytogenes for proteomic analysis by orbitrap mass spectrometry

Extracts of surface proteins, with minimal artifacts from contaminating cytosolic components, are highly desirable for investigating surface factors involved in the attachment and formation of biofilms by bacteria that are problematic in commercial food processing facilities. In this study, we compared the protein profiles of the food pathogen, Listeria monocytogenes, recovered after applying different surface protein extraction methods compiled from the literature: trypsin-enzymatic shaving with BICAM/sucrose or Tris/sucrose buffers (Tryp B+S, Tryp T+S), Tris-buffered urea (UB), lithium chloride (LiCl) and Tris-buffered urea applied with hypotonic-stressed cells (UB-Ghost), and subjected them to liquid chromatography tandem mass spectrometry and protein identification. The data indicate that the UB-Ghost extraction method provides a cleaner extract of surface proteins including the predicted (this study and the literature) or validated members (literature) from L. monocytogenes. This was determined by an accumulative lower unique peptide number exhibited by mass spectrometry for total cytoplasmic proteins among different surface extracts, with a majority of proteins demonstrating hydrophilic properties. The extracted proteins were from different functional categories and have associations with the cell surface, intermediary metabolism, information pathways, or functionally unknown proteins as suggested by in silico analyses performed by other groups (Leger and ListiList). The utilization of an optimized method for surface protein extraction should greatly facilitate identification by LC-MS/MS that could be useful to anyone working on molecular proteomics of bacterial surfaces.

The role of protein and peptide separation before mass spectrometry analysis in clinical proteomics

The purpose of clinical proteomics is to characterise protein profiles of a plethora of diseases with the aim of finding specific biomarkers. These are particularly valuable for early diagnosis, and represent key molecules suitable to elucidate pathogenic mechanisms. Samples deriving from patients (i.e. blood, urine, cerebrospinal fluid, biopsies) are the sources for clinical proteomics. Due to the complexity of the extracted samples their direct analysis is unachievable. Any analytical clinical proteomics study should start with the choice of the optimal combination of strategies with respect to both sample preparations and MS approaches. Protein or peptide fractionation (off-line or on-line) is essential to reduce complexity of biological samples and to achieve the most complete and reproducible analysis. The aim of this review is to introduce the readers to a functional range of strategies to help scientists in their proteomics set up. In particular, the separation approaches of proteins or peptides (both gel-based and gel-free) are reviewed with special attention paid to their advantages and limitations, and to the different liquid chromatography techniques used to peptide fractionation after protein enzymatic digestion and before their detection. Finally, the role of mass spectrometry (MS) for protein identification and quantification is discussed including emerging MS data acquisition strategies.

Exploring the glycoproteomics landscape with advanced MS technologies

The advance of glycoproteomic technologies has offered unique insights into the importance of glycosylation in determining the functional roles of a protein within a cell. Biologically active glycoproteins include the categories of enzymes, hormones, proteins involved in cell proliferation, cell membrane proteins involved in cell-cell recognition, and communication events or secreted proteins, just to name a few. The recent progress in analytical instrumentation, methodologies, and computational approaches has enabled a detailed exploration of glycan structure, connectivity, and heterogeneity, underscoring the staggering complexity of the glycome repertoire in a cell. A variety of approaches involving the use of spectroscopy, MS, separation, microfluidic, and microarray technologies have been used alone or in combination to tackle the glycoproteome challenge, the research results of these efforts being captured in an overwhelming number of annual publications. This work is aimed at reviewing the major developments and accomplishments in the field of glycoproteomics, with focus on the most recent advancements (2012-2014) that involve the use of capillary separations and MS detection.

Applying 'Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra' (SWATH) for systematic toxicological analysis with liquid chromatography-high-resolution tandem mass spectrometry

Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become an indispensable analytical technique in clinical and forensic toxicology for detection and identification of potentially toxic or harmful compounds. Particularly, non-target LC-MS/MS assays enable extensive and universal screening requested in systematic toxicological analysis. An integral part of the identification process is the generation of information-rich product ion spectra which can be searched against libraries of reference mass spectra. Usually, 'data-dependent acquisition' (DDA) strategies are applied for automated data acquisition. In this study, the 'data-independent acquisition' (DIA) method 'Sequential Windowed Acquisition of All Theoretical Fragment Ion Mass Spectra' (SWATH) was combined with LC-MS/MS on a quadrupole-quadrupole-time-of-flight (QqTOF) instrument for acquiring informative high-resolution tandem mass spectra. SWATH performs data-independent fragmentation of all precursor ions entering the mass spectrometer in 21m/z isolation windows. The whole m/z range of interest is covered by continuous stepping of the isolation window. This allows numerous repeat analyses of each window during the elution of a single chromatographic peak and results in a complete fragment ion map of the sample. Compounds and samples typically encountered in forensic casework were used to assess performance characteristics of LC-MS/MS with SWATH. Our experiments clearly revealed that SWATH is a sensitive and specific identification technique. SWATH is capable of identifying more compounds at lower concentration levels than DDA does. The dynamic range of SWATH was estimated to be three orders of magnitude. Furthermore, the >600,000 SWATH spectra matched led to only 408 incorrect calls (false positive rate = 0.06 %). Deconvolution of generated ion maps was found to be essential for unravelling the full identification power of LC-MS/MS with SWATH. With the available software, however, only semi-automated deconvolution was enabled, which rendered data interpretation a laborious and time-consuming process.