Mass Spectrometry-based Quantitative Proteomics Research Articles

Quantitative Proteomic Approach Identifies Vpr Binding Protein as Novel Host Factor Supporting Influenza A Virus Infections in Human Cells

Influenza A virus (IAV) infections are a major cause for respiratory disease in humans, which affects all age groups and contributes substantially to global morbidity and mortality. IAV have a large natural host reservoir in avian species. However, many avian IAV strains lack adaptation to other hosts and hardly propagate in humans. While seasonal or pandemic IAV strains replicate efficiently in permissive human cells, many avian IAV cause abortive nonproductive infections in these hosts despite successful cell entry. However, the precise reasons for these differential outcomes are poorly defined. We hypothesized that the distinct course of an IAV infection with a given virus strain is determined by the differential interplay between specific host and viral factors. By using Spike-in SILAC mass spectrometry-based quantitative proteomics we characterized sets of cellular factors whose abundance is specifically up- or downregulated in the course of permissive versus nonpermissive IAV infection, respectively. This approach allowed for the definition and quantitative comparison of about 3500 proteins in human lung epithelial cells in response to seasonal or low-pathogenic avian H3N2 IAV. Many identified proteins were similarly regulated by both virus strains, but also 16 candidates with distinct changes in permissive versus nonpermissive infection were found. RNAi-mediated knockdown of these differentially regulated host factors identified Vpr binding protein (VprBP) as proviral host factor because its downregulation inhibited efficient propagation of seasonal IAV whereas overexpression increased viral replication of both seasonal and avian IAV. These results not only show that there are similar differences in the overall changes during permissive and nonpermissive influenza virus infections, but also provide a basis to evaluate VprBP as novel anti-IAV drug target.

Insights into autosomal dominant polycystic kidney disease by quantitative mass spectrometry-based proteomics.

Autosomal dominant polycystic kidney disease (ADPKD) is a common monogenetic disorder that is caused by mutations in the genes PKD1 and PKD2 encoding polycystin-1 and polycystin-2, respectively. Polycystin-1 and -2 form a complex, interact with several proteins involved in signal transduction and localize to discrete subcellular positions, most importantly the primary cilium. Whereas the causative mutations leading to ADPKD are known, the underlying deregulated cellular pathways are not well understood. In the current review, we introduce state-of-the-art mass spectrometry (MS)-based proteomic techniques and summarize their use in kidney and ADPKD research. Proteomic profiling approaches, the elucidation of ADPKD-relevant protein-protein interactions and the regulation of posttranslational modifications are included. We also discuss the use of MS-based methods for ADPKD prognosis, diagnosis and disease monitoring by using protein- and peptide-based biomarkers.

Characterization of the human aqueous humour proteome: A comparison of the genders

Aqueous humour (AH) is an important biologic fluid that maintains normal intraocular pressure and contains proteins that regulate the homeostasis of ocular tissues. Any alterations in the protein compositions are correlated to the pathogenesis of various ocular disorders. In recent years, gender-based medicine has emerged as an important research focus considering the prevalence of certain diseases, which are higher in a particular sex. Nevertheless, the inter-gender variations in the AH proteome are unknown. Therefore, this study endeavoured to characterize the AH proteome to assess the differences between genders. Thirty AH samples of patients who underwent cataract surgery were categorized according to their gender. Label-free quantitative discovery mass spectrometry-based proteomics strategy was employed to characterize the AH proteome. A total of 147 proteins were identified with a false discovery rate of less than 1% and only the top 10 major AH proteins make up almost 90% of the total identified proteins. A large number of proteins identified were correlated to defence, immune and inflammatory mechanisms, and response to wounding. Four proteins were found to be differentially abundant between the genders, comprising SERPINF1, SERPINA3, SERPING1 and PTGDS. The findings emerging from our study provide the first insight into the gender-based proteome differences in the AH and also highlight the importance in considering potential sex-dependent changes in the proteome of ocular pathologies in future studies employing the AH.

A temporal proteome dynamics study reveals the molecular basis of induced phenotypic resistance in Mycobacterium smegmatis at sub-lethal rifampicin concentrations

In the last 40 years only one new antitubercular drug has been approved, whilst resistance to current drugs, including rifampicin, is spreading. Here, we used the model organism Mycobacterium smegmatis to study mechanisms of phenotypic mycobacterial resistance, employing quantitative mass spectrometry-based proteomics to investigate the temporal effects of sub-lethal concentrations of rifampicin on the mycobacterial proteome at time-points corresponding to early response, onset of bacteriostasis and early recovery. Across 18 samples, a total of 3,218 proteins were identified from 31,846 distinct peptides averaging 16,250 identified peptides per sample. We found evidence that two component signal transduction systems (e.g. MprA/MprB) play a major role during initial mycobacterial adaptive responses to sub-lethal rifampicin and that, after dampening an initial SOS response, the bacteria supress the DevR (DosR) regulon and also upregulate their transcriptional and translational machineries. Furthermore, we found a co-ordinated dysregulation in haeme and mycobactin synthesis. Finally, gradual upregulation of the M. smegmatis-specific rifampin ADP-ribosyl transferase was observed which, together with upregulation of transcriptional and translational machinery, likely explains recovery of normal growth. Overall, our data indicates that in mycobacteria, sub-lethal rifampicin triggers a concerted phenotypic response that contrasts significantly with that observed at higher antimicrobial doses.

A proteomic approach to identify endosomal cargoes controlling cancer invasiveness.

ABSTRACTWe have previously shown that Rab17, a small GTPase associated with epithelial polarity, is specifically suppressed by ERK2 (also known as MAPK1) signalling to promote an invasive phenotype. However, the mechanisms through which Rab17 loss permits invasiveness, and the endosomal cargoes that are responsible for mediating this, are unknown. Using quantitative mass spectrometry-based proteomics, we have found that knockdown of Rab17 leads to a highly selective reduction in the cellular levels of a v-SNARE (Vamp8). Moreover, proteomics and immunofluorescence indicate that Vamp8 is associated with Rab17 at late endosomes. Reduced levels of Vamp8 promote transition between ductal carcinoma in situ (DCIS) and a more invasive phenotype. We developed an unbiased proteomic approach to elucidate the complement of receptors that redistributes between endosomes and the plasma membrane, and have pin-pointed neuropilin-2 (NRP2) as a key pro-invasive cargo of Rab17- and Vamp8-regulated trafficking. Indeed, reduced Rab17 or Vamp8 levels lead to increased mobilisation of NRP2-containing late endosomes and upregulated cell surface expression of NRP2. Finally, we show that NRP2 is required for the basement membrane disruption that accompanies the transition between DCIS and a more invasive phenotype.

Time-Resolved Analysis of Matrix Metalloproteinase Substrates in Complex Samples.

Identification of physiological substrates is the key to understanding the pleiotropic functions of matrix metalloproteinases (MMPs) in health and disease. Quantitative mass spectrometry-based proteomics has revolutionized current approaches in protease substrate discovery and helped to unravel many new MMP activities in complex biological systems. Multiplexing further extended the capabilities of these techniques and facilitated more complicated experimental designs that include multiple proteases or monitoring the activity of a single protease at more than one concentration or at multiple time points with a complex test proteome. In this chapter, we provide a protocol for time-resolved iTRAQ-based Terminal Amine Isotopic Labeling of Substrates (TAILS), with the focus on MMP substrate identification and characterization in cell culture supernatants and introduce an automated procedure for the interpretation of time-resolved iTRAQ-TAILS datasets.

A Bioconductor workflow for processing and analysing spatial proteomics data

Spatial proteomics is the systematic study of protein sub-cellular localisation. In this workflow, we describe the analysis of a typical quantitative mass spectrometry-based spatial proteomics experiment using the MSnbase and pRoloc Bioconductor package suite. To walk the user through the computational pipeline, we use a recently published experiment predicting protein sub-cellular localisation in pluripotent embryonic mouse stem cells. We describe the software infrastructure at hand, importing and processing data, quality control, sub-cellular marker definition, visualisation and interactive exploration. We then demonstrate the application and interpretation of statistical learning methods, including novelty detection using semi-supervised learning, classification, clustering and transfer learning and conclude the pipeline with data export. The workflow is aimed at beginners who are familiar with proteomics in general and spatial proteomics in particular.

The Promises of Quantitative Proteomics in Precision Medicine

Precision medicine approach has a potential to ensure optimum efficacy and safety of drugs at individual patient level. Physiologically based pharmacokinetic and pharmacodynamic (PBPK/PD) models could play a significant role in precision medicine by predicting interindividual variability in drug disposition and response. In order to develop robust PBPK/PD models, it is imperative that the critical physiological parameters affecting drug disposition and response and their variability are precisely characterized. Currently used PBPK/PD modeling software, for example, Simcyp and Gastroplus, encompass information such as organ volumes, blood flows to organs, body fat composition, glomerular filtration rate, etc. However, the information on the interindividual variability of the majority of the proteins associated with PK and PD, for example, drug metabolizing enzymes, transporters, and receptors, are not fully incorporated into these PBPK modeling platforms. Such information is significant because the population factors such as age, genotype, disease, and gender can affect abundance or activity of these proteins. To fill this critical knowledge gap, mass spectrometry-based quantitative proteomics has emerged as an important technique to characterize interindividual variability in the protein abundance of drug metabolizing enzymes, transporters, and receptors. Integration of these quantitative proteomics data into in silico PBPK/PD modeling tools will be crucial toward precision medicine.

Temperature-dependent regulation of the Ochrobactrum anthropi proteome.

Ochrobactrum anthropi is a Gram-negative rod belonging to the Brucellaceae family, able to colonize a variety of environments, and actually reported as a human opportunistic pathogen. Despite its low virulence, the bacterium causes a growing number of hospital-acquired infections mainly, but not exclusively, in immunocompromised patients. The aim of this study was to obtain an overview of the global proteome changes occurring in O. anthropi in response to different growth temperatures, in order to achieve a major understanding of the mechanisms by which the bacterium adapts to different habitats and to identify some potential virulence factors. Combined quantitative mass spectrometry-based proteomics and bioinformatics approaches were carried out on two O. anthropi strains grown at temperatures miming soil/plants habitat (25°C) and human host environment (37°C), respectively. Proteomic analysis led to the identification of over 150 differentially expressed proteins in both strains, out of over 1200 total protein identifications. Among them, proteins responsible for heat shock response (DnaK, GrpE), motility (FliC, FlgG, FlgE), and putative virulence factors (TolB) were identified. The study represents the first quantitative proteomic analysis of O. anthropi performed by high-resolution quantitative mass spectrometry.

Deciphering lymphoma pathogenesis via state-of-the-art mass spectrometry-based quantitative proteomics.

Mass spectrometry-based quantitative proteomics specifically applied to comprehend the pathogenesis of lymphoma has incremental value in deciphering the heterogeneity in complex deregulated molecular mechanisms/pathways of the lymphoma entities, implementing the current diagnostic and therapeutic strategies. Essential global, targeted and functional differential proteomics analyses although still evolving, have been successfully implemented to shed light on lymphoma pathogenesis to discover and explore the role of potential lymphoma biomarkers and drug targets. This review aims to outline and appraise the present status of MS-based quantitative proteomic approaches in lymphoma research, introducing the current state-of-the-art MS-based proteomic technologies, the opportunities they offer in biological discovery in human lymphomas and the related limitation issues arising from sample preparation to data evaluation. It is a synopsis containing information obtained from recent research articles, reviews and public proteomics repositories (PRIDE). We hope that this review article will aid, assimilate and assess all the information aiming to accelerate the development and validation of diagnostic, prognostic or therapeutic targets for an improved and empowered clinical proteomics application in lymphomas in the nearby future.

Neuronal GPCR OCTR-1 regulates innate immunity by controlling protein synthesis in Caenorhabditis elegans

Upon pathogen infection, microbial killing pathways and cellular stress pathways are rapidly activated by the host innate immune system. These pathways must be tightly regulated because insufficient or excessive immune responses have deleterious consequences. Increasing evidence indicates that the nervous system regulates the immune system to confer coordinated protection to the host. However, the precise mechanisms of neural-immune communication remain unclear. Previously we have demonstrated that OCTR-1, a neuronal G protein-coupled receptor, functions in the sensory neurons ASH and ASI to suppress innate immune responses in non-neural tissues against Pseudomonas aeruginosa in Caenorhabditis elegans. In the current study, by using a mass spectrometry-based quantitative proteomics approach, we discovered that OCTR-1 regulates innate immunity by suppressing translation and the unfolded protein response (UPR) pathways at the protein level. Functional assays revealed that OCTR-1 inhibits specific protein synthesis factors such as ribosomal protein RPS-1 and translation initiation factor EIF-3.J to reduce infection-triggered protein synthesis and UPR. Translational inhibition by chemicals abolishes the OCTR-1-controlled innate immune responses, indicating that activation of the OCTR-1 pathway is dependent on translation upregulation such as that induced by pathogen infection. Because OCTR-1 downregulates protein translation activities, the OCTR-1 pathway could function to suppress excessive responses to infection or to restore protein homeostasis after infection.

Proteomic profiling of retinoblastoma by high resolution mass spectrometry.

BackgroundRetinoblastoma is an ocular neoplastic cancer caused primarily due to the mutation/deletion of RB1 gene. Due to the rarity of the disease very limited information is available on molecular changes in primary retinoblastoma. High throughput analysis of retinoblastoma transcriptome is available however the proteomic landscape of retinoblastoma remains unexplored. In the present study we used high resolution mass spectrometry-based quantitative proteomics to identify proteins associated with pathogenesis of retinoblastoma.MethodsWe used five pooled normal retina and five pooled retinoblastoma tissues to prepare tissue lysates. Equivalent amount of proteins from each group was trypsin digested and labeled with iTRAQ tags. The samples were analyzed on Orbitrap Velos mass spectrometer. We further validated few of the differentially expressed proteins by immunohistochemistry on primary tumors.ResultsWe identified and quantified a total of 3587 proteins in retinoblastoma when compared with normal adult retina. In total, we identified 899 proteins that were differentially expressed in retinoblastoma with a fold change of ≥2 of which 402 proteins were upregulated and 497 were down regulated. Insulin growth factor 2 mRNA binding protein 1 (IGF2BP1), chromogranin A, fetuin A (ASHG), Rac GTPase-activating protein 1 and midkine that were found to be overexpressed in retinoblastoma were further confirmed by immunohistochemistry by staining 15 independent retinoblastoma tissue sections. We further verified the effect of IGF2BP1 on cell proliferation and migration capability of a retinoblastoma cell line using knockdown studies.ConclusionsIn the present study mass spectrometry-based quantitative proteomic approach was applied to identify proteins differentially expressed in retinoblastoma tumor. This study identified the mitochondrial dysfunction and lipid metabolism pathways as the major pathways to be deregulated in retinoblastoma. Further knockdown studies of IGF2BP1 in retinoblastoma cell lines revealed it as a prospective therapeutic target for retinoblastoma.Electronic supplementary materialThe online version of this article (doi:10.1186/s12014-016-9128-7) contains supplementary material, which is available to authorized users.

Analysis of Intrinsic Peptide Detectability via Integrated Label-Free and SRM-Based Absolute Quantitative Proteomics.

Quantitative mass spectrometry-based proteomics of complex biological samples remains challenging in part due to the variability and charge competition arising during electrospray ionization (ESI) of peptides and the subsequent transfer and detection of ions. These issues preclude direct quantification from signal intensity alone in the absence of a standard. A deeper understanding of the governing principles of peptide ionization and exploitation of the inherent ionization and detection parameters of individual peptides is thus of great value. Here, using the yeast proteome as a model system, we establish the concept of peptide F-factor as a measure of detectability, closely related to ionization efficiency. F-factor is calculated by normalizing peptide precursor ion intensity by absolute abundance of the parent protein. We investigated F-factor characteristics in different shotgun proteomics experiments, including across multiple ESI-based LC-MS platforms. We show that F-factors mirror previously observed physicochemical predictors as peptide detectability but demonstrate a nonlinear relationship between hydrophobicity and peptide detectability. Similarly, we use F-factors to show how peptide ion coelution adversely affects detectability and ionization. We suggest that F-factors have great utility for understanding peptide detectability and gas-phase ion chemistry in complex peptide mixtures, selection of surrogate peptides in targeted MS studies, and for calibration of peptide ion signal in label-free workflows. Data are available via ProteomeXchange with identifier PXD003472.

Biomarker discovery: success as a function of risk mitigation

Protein biomarker discovery is a fascinating enterprise; however, success in terms of products for in vitro diagnostic use is sparse. New developments in mass spectrometry-based quantitative proteomics as discovery technology have opened up new avenues for this endeavor. In addition to choice of technology, sample properties, study design and validation strategy are potent pillars required for project success. The challenge for successful biomarker discovery can be described by a series of risks that need to be mitigated. This article intends to describe the major risks along with possible solutions.

A Novel Quantitative Mass Spectrometry Platform for Determining Protein O-GlcNAcylation Dynamics

Over the past decades, protein O-GlcNAcylation has been found to play a fundamental role in cell cycle control, metabolism, transcriptional regulation, and cellular signaling. Nevertheless, quantitative approaches to determine in vivo GlcNAc dynamics at a large-scale are still not readily available. Here, we have developed an approach to isotopically label O-GlcNAc modifications on proteins by producing (13)C-labeled UDP-GlcNAc from (13)C6-glucose via the hexosamine biosynthetic pathway. This metabolic labeling was combined with quantitative mass spectrometry-based proteomics to determine protein O-GlcNAcylation turnover rates. First, an efficient enrichment method for O-GlcNAc peptides was developed with the use of phenylboronic acid solid-phase extraction and anhydrous DMSO. The near stoichiometry reaction between the diol of GlcNAc and boronic acid dramatically improved the enrichment efficiency. Additionally, our kinetic model for turnover rates integrates both metabolomic and proteomic data, which increase the accuracy of the turnover rate estimation. Other advantages of this metabolic labeling method include in vivo application, direct labeling of the O-GlcNAc sites and higher confidence for site identification. Concentrating only on nuclear localized GlcNAc modified proteins, we are able to identify 105 O-GlcNAc peptides on 42 proteins and determine turnover rates of 20 O-GlcNAc peptides from 14 proteins extracted from HeLa nuclei. In general, we found O-GlcNAcylation turnover rates are slower than those published for phosphorylation or acetylation. Nevertheless, the rates widely varied depending on both the protein and the residue modified. We believe this methodology can be broadly applied to reveal turnovers/dynamics of protein O-GlcNAcylation from different biological states and will provide more information on the significance of O-GlcNAcylation, enabling us to study the temporal dynamics of this critical modification for the first time.

Endurance Exercise Improves Molecular Pathways of Aerobic Metabolism in Patients With Myositis.

Endurance exercise demonstrates beneficial effects in polymyositis/dermatomyositis (PM/DM); however, the molecular effects of exercise on skeletal muscle are incompletely understood. We undertook this controlled pilot study to investigate the effects of a 12-week endurance exercise training program on the molecular profile of skeletal muscle in patients with established PM/DM compared to a nonexercised control group of patients with established PM/DM. Fifteen patients (7 in the exercise group and 8 in the control group) with paired baseline and 12-week follow-up muscle biopsy samples were included. Messenger RNA expression profiling, mass spectrometry-based quantitative proteomics, and immunohistochemical analyses were performed on muscle biopsy samples to determine molecular adaptations associated with changes in clinical measurements induced by endurance exercise. Compared to the control group, the exercise group improved in minutes of cycling time (P < 0.01) and Vo2 max (P < 0.05). The exercise group also had reduced disease activity (P < 0.05) and reduced lactate levels at exhaustion (P < 0.05). Genes related to capillary growth, mitochondrial biogenesis, protein synthesis, cytoskeletal remodeling, and muscle hypertrophy were up-regulated in the exercise group, while genes related to inflammation/immune response and endoplasmic reticulum stress were down-regulated. Mitochondrial pathways including the oxidative phosphorylation metabolic pathway were most affected by the endurance exercise, as demonstrated by proteomics analysis. The exercise group also showed a higher number of capillaries per mm(2) in follow-up biopsy samples (P < 0.05). Our data indicate that endurance exercise in patients with established PM and DM may activate an aerobic phenotype and promote muscle growth and simultaneously suppress the inflammatory response in these patients' muscles, as supported by a combination of data on gene expression, proteomics, and capillary density in repeated muscle biopsies.

Towards Understanding Male Infertility After Spinal Cord Injury Using Quantitative Proteomics

The study of male infertility after spinal cord injury (SCI) has enhanced the understanding of seminal plasma (SP) as an important regulator of spermatozoa function. However, the most important factors leading to the diminished sperm motility and viability observed in semen of men with SCI remained unknown. Thus, to explore SP related molecular mechanisms underlying infertility after SCI, we used mass spectrometry-based quantitative proteomics to compare SP retrieved from SCI patients to normal controls. As a result, we present an in-depth characterization of the human SP proteome, identifying ∼2,800 individual proteins, and describe, in detail, the differential proteome observed in SCI. Our analysis demonstrates that a hyper-activation of the immune system may influence some seminal processes, which likely are not triggered by microbial infection. Moreover, we show evidence of an important prostate gland functional failure,i.e.diminished abundance of metabolic enzymes related to ATP turnover and those secreted via prostasomes. Further we identify the main outcome related to this fact and that it is intrinsically linked to the low sperm motility in SCI. Together, our data highlights the molecular pathways hindering fertility in SCI and shed new light on other causes of male infertility.

Mapping and Exploring the Collagen-I Proteostasis Network.

Collagen-I is the most abundant protein in the human body, yet our understanding of how the endoplasmic reticulum regulates collagen-I proteostasis (folding, quality control, and secretion) remains immature. Of particular importance, interactomic studies to map the collagen-I proteostasis network have never been performed. Such studies would provide insight into mechanisms of collagen-I folding and misfolding in cells, an area that is particularly important owing to the prominence of the collagen misfolding-related diseases. Here, we overcome key roadblocks to progress in this area by generating stable fibrosarcoma cells that inducibly express properly folded and modified collagen-I strands tagged with distinctive antibody epitopes. Selective immunoprecipitation of collagen-I from these cells integrated with quantitative mass spectrometry-based proteomics permits the first mapping of the collagen-I proteostasis network. Biochemical validation of the resulting map leads to the assignment of numerous new players in collagen-I proteostasis, and the unanticipated discovery of apparent aspartyl-hydroxylation as a new post-translational modification in the N-propeptide of collagen-I. Furthermore, quantitative analyses reveal that Erp29, an abundant endoplasmic reticulum proteostasis machinery component with few known functions, plays a key role in collagen-I retention under ascorbate-deficient conditions. In summary, the work here provides fresh insights into the molecular mechanisms of collagen-I proteostasis, yielding a detailed roadmap for future investigations. Straightforward adaptations of the cellular platform developed will also enable hypothesis-driven, comparative research on the likely distinctive proteostasis mechanisms engaged by normal and disease-causing, misfolding collagen-I variants, potentially motivating new therapeutic strategies for currently incurable collagenopathies.

Structural Basis for Nucleotide Hydrolysis by the Acid Sphingomyelinase-like Phosphodiesterase SMPDL3A

Sphingomyelin phosphodiesterase, acid-like 3A (SMPDL3A) is a member of a small family of proteins founded by the well characterized lysosomal enzyme, acid sphingomyelinase (ASMase). ASMase converts sphingomyelin into the signaling lipid, ceramide. It was recently discovered that, in contrast to ASMase, SMPDL3A is inactive against sphingomyelin and, surprisingly, can instead hydrolyze nucleoside diphosphates and triphosphates, which may play a role in purinergic signaling. As none of the ASMase-like proteins has been structurally characterized to date, the molecular basis for their substrate preferences is unknown. Here we report crystal structures of murine SMPDL3A, which represent the first structures of an ASMase-like protein. The catalytic domain consists of a central mixed β-sandwich surrounded by α-helices. Additionally, SMPDL3A possesses a unique C-terminal domain formed from a cluster of four α-helices that appears to distinguish this protein family from other phosphoesterases. We show that SMDPL3A is a di-zinc-dependent enzyme with an active site configuration that suggests a mechanism of phosphodiester hydrolysis by a metal-activated water molecule and protonation of the leaving group by a histidine residue. Co-crystal structures of SMPDL3A with AMP and α,β-methylene ADP (AMPCP) reveal that the substrate binding site accommodates nucleotides by establishing interactions with their base, sugar, and phosphate moieties, with the latter the major contributor to binding affinity. Our study provides the structural basis for SMPDL3A substrate specificity and sheds new light on the function of ASMase-like proteins.

Using PSEA-Quant for Protein Set Enrichment Analysis of Quantitative Mass Spectrometry-Based Proteomics.

PSEA-Quant analyzes quantitative mass spectrometry-based proteomics datasets to identify enrichments of annotations contained in repositories such as the Gene Ontology and Molecular Signature databases. It allows users to identify the annotations that are significantly enriched for reproducibly quantified high abundance proteins. PSEA-Quant is available on the Web and as a command-line tool. It is compatible with all label-free and isotopic labeling-based quantitative proteomics methods. This protocol describes how to use PSEA-Quant and interpret its output. The importance of each parameter as well as troubleshooting approaches are also discussed. © 2016 by John Wiley & Sons, Inc.