Proteomic Signaling of Dual-Specificity Phosphatase 4 (DUSP4) in Alzheimer's Disease

DUSP4 is a member of the DUSP (dual-specificity phosphatase) subfamily that is selective to the mitogen-activated protein kinases (MAPK) and has been implicated in a range of biological processes and functions in Alzheimer's disease (AD). In this study, we utilized the stereotactic delivery of adeno-associated virus (AAV)-DUSP4 to overexpress DUSP4 in the dorsal hippocampus of 5xFAD and wildtype (WT) mice, then used mass spectrometry (MS)-based proteomics along with the label-free quantification to profile the proteome and phosphoproteome in the hippocampus. We identified protein expression and phosphorylation patterns modulated in 5xFAD mice and examined the sex-specific impact of DUSP4 overexpression on the 5xFAD proteome/phosphoproteome. In 5xFAD mice, a substantial number of proteins were up- or down-regulated in both male and female mice in comparison to age and sex-matched WT mice, many of which are involved in AD-related biological processes, such as activated immune response or suppressed synaptic activities. Many proteins in pathways, such as immune response were found to be suppressed in response to DUSP4 overexpression in male 5xFAD mice. In contrast, such a shift was absent in female mice. For the phosphoproteome, we detected an array of phosphorylation sites regulated in 5xFAD compared to WT and modulated via DUSP4 overexpression in each sex. Interestingly, 5xFAD- and DUSP4-associated phosphorylation changes occurred in opposite directions. Strikingly, both the 5xFAD- and DUSP4-associated phosphorylation changes were found to be mostly in neurons and play key roles in neuronal processes and synaptic functions. Site-centric pathway analysis revealed that both the 5xFAD- and DUSP4-associated phosphorylation sites were enriched for a number of kinase sets in females but only a limited number of sets of kinases in male mice. Taken together, our results suggest that male and female 5xFAD mice responded to DUSP4 overexpression via shared and sex-specific molecular mechanisms, which might underly similar reductions in amyloid pathology in both sexes while learning deficits were reduced in only females with DUSP4 overexpression. Finally, we validated our findings with the sex-specific AD-associated proteomes in human cohorts and further developed DUSP4-centric proteomic network models and signaling maps for each sex.

Proteomic signaling of dual specificity phosphatase 4 (DUSP4) in Alzheimer's disease

DUSP4 is a member of the DUSP (Dual-Specificity Phosphatase) subfamily that is selective to the mitogen-activated protein kinases (MAPK) and has been implicated in a range of biological processes and functions in Alzheimer's disease (AD). In this study, we utilized stereotactic delivery of adeno-associated virus (AAV)-DUSP4 to overexpress DUSP4 in the dorsal hippocampus of 5xFAD and wildtype (WT) mice, then used mass spectrometry (MS)-based proteomics along with label-free quantification to profile the proteome and phosphoproteome in the hippocampus. We identified patterns of protein expression and phosphorylation that are modulated in 5xFAD mice and examined the sex-specific impact of DUSP4 overexpression on the 5xFAD proteome/phosphoproteome. In 5xFAD mice, a substantial number of proteins were up- or down-regulated in both male and female mice in comparison to age and sex-matched WT mice, many of which are involved in AD-related biological processes, such as the activated immune response or suppression of synaptic activities. Upon DUSP4 overexpression, significantly regulated proteins were found in pathways that were suppressed, such as the immune response, in male 5xFAD mice. In contrast, such a shift was absent in female mice. For the phosphoproteome, we detected an array of phosphorylation sites that are regulated in 5xFAD compared to WT, and are modulated by DUSP4 overexpression in each sex. Interestingly, the changes in 5xFAD- and DUSP4-associated phosphorylation occurred in opposite directions. Strikingly, both the 5xFAD- and DUSP4-associated phosphorylation changes were found for the most part in neurons, and play key roles in neuronal processes and synaptic function. Site-centric pathway analysis revealed that both the 5xFAD- and DUSP4-associated phosphorylation sites were enriched for a number of kinase sets in female, but only a limited number of sets of kinases in male mice. Taken together, our results suggest that male and female 5xFAD mice respond to DUSP4 overexpression via shared and sex-specific molecular mechanisms, which might underly similar reductions in amyloid pathology in both sexes, while learning deficits were reduced in only females with DUSP4 overexpression. Finally, we validated our findings with the sex-specific AD-associated proteomes in human cohorts and further developed DUSP4-centric proteomic network models and signaling maps for each sex.

Spatial Centrosome Proteomic Profiling of Human iPSC-derived Neural Cells

The centrosome governs many pan-cellular processes including cell division, migration, and cilium formation. However, very little is known about its cell type-specific protein composition and the sub-organellar domains where these protein interactions take place. Here, we outline a protocol for the spatial interrogation of the centrosome proteome in human cells, such as those differentiated from induced pluripotent stem cells (iPSCs), through co-immunoprecipitation of protein complexes around selected baits that are known to reside at different structural parts of the centrosome, followed by mass spectrometry. The protocol describes expansion and differentiation of human iPSCs to dorsal forebrain neural progenitors and cortical projection neurons, harvesting and lysis of cells for protein isolation, co-immunoprecipitation with antibodies against selected bait proteins, preparation for mass spectrometry, processing the mass spectrometry output files using MaxQuant software, and statistical analysis using Perseus software to identify the enriched proteins by each bait. Given the large number of cells needed for the isolation of centrosome proteins, this protocol can be scaled up or down by modifying the number of bait proteins and can also be carried out in batches. It can potentially be adapted for other cell types, organelles, and species as well.

Identification and Quantification of Affinity-Purified Proteins with MaxQuant, Followed by the Discrimination of Nonspecific Interactions with the CRAPome Interface

Affinity purification coupled to mass spectrometry (AP-MS) is a powerful method to analyze protein-protein interactions (PPIs). The AP-MS approach provides an unbiased analysis of the entire protein complex and is useful to identify indirect interactors. However, reliable protein identification from the complex AP-MS experiments requires appropriate control of false identifications and rigorous statistical analysis. Another challenge that can arise from AP-MS analysis is to distinguish bona fide interacting proteins from the non-specifically bound endogenous proteins or the "background contaminants" that co-purified by the bait experiments. In this chapter, we will first describe the protocol for performing in-solution trypsinization for the samples from the AP experiment followed by LC-MS/MS analysis. We will then detail the MaxQuant workflow for protein identification and quantification for the PPI data derived from the AP-MS experiment. Finally, we describe the CRAPome interface to process the data by filtering against contaminant lists, score the interactions and visualize the protein interaction networks.

MaxQuant Module for the Identification of Genomic Variants Propagated into Peptides

Standard shotgun proteomics data analysis pipelines usually only identify peptides that are encoded in the reference genome. In many situations, it is desirable to identify peptides resulting from non-synonymous variations as well. Here, we present a new module in the MaxQuant software that takes both DNA and RNA based next-generation sequencing (NGS) data as well as raw proteomics data as input. This allows for the identification of variant peptides that are otherwise missed.

Mass Dynamics 1.0: A Streamlined, Web-Based Environment for Analyzing, Sharing, and Integrating Label-Free Data

Label-free quantification (LFQ) of shotgun proteomics data is a popular and robust method for the characterization of relative protein abundance between samples. Many analytical pipelines exist for the automation of this analysis, and some tools exist for the subsequent representation and inspection of the results of these pipelines. Mass Dynamics 1.0 (MD 1.0) is a web-based analysis environment that can analyze and visualize LFQ data produced by software such as MaxQuant. Unlike other tools, MD 1.0 utilizes a cloud-based architecture to enable researchers to store their data, enabling researchers to not only automatically process and visualize their LFQ data but also annotate and share their findings with collaborators and, if chosen, to easily publish results to the community. With a view toward increased reproducibility and standardization in proteomics data analysis and streamlining collaboration between researchers, MD 1.0 requires minimal parameter choices and automatically generates quality control reports to verify experiment integrity. Here, we demonstrate that MD 1.0 provides reliable results for protein expression quantification, emulating Perseus on benchmark datasets over a wide dynamic range. The MD 1.0 platform is available globally via: https://app.massdynamics.com/.

Mass Spectrometry-Based Quantitative Cysteine Redox Proteome Profiling of Isolated Mitochondria Using Differential iodoTMT Labeling

Mitochondria are central hubs of redox biochemistry in the cell. An important role of mitochondrial carbon metabolism is to oxidize respiratory substrates and to pass the electrons down the mitochondrial electron transport chain to reduce oxygen and to drive oxidative phosphorylation. During respiration, reactive oxygen species are produced as a side reaction, some of which in turn oxidize cysteine thiols in proteins. Hence, the redox status of cysteine-containing mitochondrial proteins has to be controlled by the mitochondrial glutathione and thioredoxin systems, which draw electrons from metabolically derived NADPH. The redox status of mitochondrial cysteines can undergo fast transitions depending on the metabolic status of the cell, as for instance at early seed germination. Here, we describe a state-of-the-art method to quantify redox state of protein cysteines in isolated Arabidopsis seedling mitochondria of controlled metabolic and respiratory state by MS²-based redox proteomics using the isobaric thiol labeling reagent Iodoacetyl Tandem Mass Tag™ (iodoTMT). The procedure is also applicable to isolated mitochondria of other plant and nonplant systems.

An Integrated Approach for the Efficient Extraction and Solubilization of Rice Microsomal Membrane Proteins for High-Throughput Proteomics

The preparation of microsomal membrane proteins (MPs) is critically important to microsomal proteomics. To date most research studies have utilized an ultracentrifugation-based approach for the isolation and solubilization of plant MPs. However, these approaches are labor-intensive, time-consuming, and unaffordable in certain cases. Furthermore, the use of sodium dodecyl sulfate (SDS) and its removal prior to a mass spectrometry (MS) analysis through multiple washing steps result in the loss of proteins. To address these limitations, this study introduced a simple micro-centrifugation-based MP extraction (MME) method from rice leaves, with the efficacy of this approach being compared with a commercially available plasma membrane extraction kit (PME). Moreover, this study assessed the subsequent solubilization of isolated MPs in an MS-compatible surfactant, namely, 4-hexylphenylazosulfonate (Azo) and SDS using a label-free proteomic approach. The results validated the effectiveness of the MME method, specifically in the enrichment of plasma membrane proteins as compared with the PME method. Furthermore, the findings showed that Azo demonstrated several advantages over SDS in solubilizing the MPs, which was reflected through a label-free quantitative proteome analysis. Altogether, this study provided a relatively simple and rapid workflow for the efficient extraction of MPs with an Azo-integrated MME approach for bottom-up proteomics.

Expanding the Perseus Software for Omics Data Analysis With Custom Plugins

The Perseus software provides a comprehensive framework for the statistical analysis of large-scale quantitative proteomics data, also in combination with other omics dimensions. Rapid developments in proteomics technology and the ever-growing diversity of biological studies increasingly require the flexibility to incorporate computational methods designed by the user. Here, we present the new functionality of Perseus to integrate self-made plugins written in C#, R, or Python. The user-written codes will be fully integrated into the Perseus data analysis workflow as custom activities. This also makes language-specific R and Python libraries from CRAN (cran.r-project.org), Bioconductor (bioconductor.org), PyPI (pypi.org), and Anaconda (anaconda.org) accessible in Perseus. The different available approaches are explained in detail in this article. To facilitate the distribution of user-developed plugins among users, we have created a plugin repository for community sharing and filled it with the examples provided in this article and a collection of already existing and more extensive plugins. © 2020 The Authors. Basic Protocol 1: Basic steps for R plugins Support Protocol 1: R plugins with additional arguments Basic Protocol 2: Basic steps for python plugins Support Protocol 2: Python plugins with additional arguments Basic Protocol 3: Basic steps and construction of C# plugins Basic Protocol 4: Basic steps of construction and connection for R plugins with C# interface Support Protocol 4: Advanced example of R Plugin with C# interface: UMAP Basic Protocol 5: Basic steps of construction and connection for python plugins with C# interface Support Protocol 5: Advanced example of python plugin with C# interface: UMAP Support Protocol 6: A basic workflow for the analysis of label-free quantification proteomics data using perseus.

Plant Phosphopeptide Identification and Label-Free Quantification by MaxQuant and Proteome Discoverer Software

Both the phosphorylation and dephosphorylation of plant proteins is involved in multiple biological processes, especially in regard to signal transduction. The identification of phosphopeptides from MS (mass spectrometry)-based methods and their subsequent quantification play an important role in plant phosphoproteomics analysis. Phosphopeptide(s) identification and label-free quantification can determine dynamic changes of phosphorylation events in plants. Both MaxQuant and Proteome Discoverer are professional software tools used to identify and quantify large-scale MS-based phosphoproteomic data. This chapter gives a detailed workflow of MaxQuant and Proteome Discoverer software to analyze large amounts of phosphoproteomic-related MS data for the identification and quantification of label-free plant phosphopeptides.

Dataset of a comparative proteomics experiment in a methylmalonyl-CoA mutase knockout HEK 293 cell model

Methylmalonic acidemia is a rare inborn error of metabolism with severe clinical complications and poor outcome. The present data article is related to a proteomic investigation conducted on a HEK 293 cell line which has been genetically modified using CRISPR-CAS9 system to knockout the methylmalonyl-CoA mutase enzyme (MUT-KO). Thus, the generated cell model for methylmalonic acidemia was used for a proteomic comparison with respect to HEK 293 wild type cells performing a label-free quantification (LFQ) experiment. A comparison between FASP and S-Trap digestion methods was performed on protein extracts before to proceed with the proteomic analysis of the samples. Four biological replicates were employed for LC-MS/MS analysis and each was run in technical triplicates. MaxQuant and Perseus platforms were used to perform the LFQ of the proteomes and carry out statistical analysis, respectively. Globally, 4341 proteins were identified, and 243 as differentially regulated, of which 150 down-regulated and 93 up-regulated in the MUT-KO condition. MS proteomics data have been deposited to the ProteomeXchange Consortium with the dataset identifier PXD017977. The information provided in this dataset shed new light on the cellular mechanisms altered in this rare metabolic disorder, highlighting quantitative unbalances in proteins acting in cell structure and architecture organization and response to the stress. This article can be used as a new source of protein actors to be validated and a starting point for the identification of clinically relevant therapeutic targets.

Isobaric Matching between Runs and Novel PSM-Level Normalization in MaxQuant Strongly Improve Reporter Ion-Based Quantification

Isobaric labeling has the promise of combining high sample multiplexing with precise quantification. However, normalization issues and the missing value problem of complete n-plexes hamper quantification across more than one n-plex. Here, we introduce two novel algorithms implemented in MaxQuant that substantially improve the data analysis with multiple n-plexes. First, isobaric matching between runs makes use of the three-dimensional MS1 features to transfer identifications from identified to unidentified MS/MS spectra between liquid chromatography–mass spectrometry runs in order to utilize reporter ion intensities in unidentified spectra for quantification. On typical datasets, we observe a significant gain in MS/MS spectra that can be used for quantification. Second, we introduce a novel PSM-level normalization, applicable to data with and without the common reference channel. It is a weighted median-based method, in which the weights reflect the number of ions that were used for fragmentation. On a typical dataset, we observe complete removal of batch effects and dominance of the biological sample grouping after normalization. Furthermore, we provide many novel processing and normalization options in Perseus, the companion software for the downstream analysis of quantitative proteomics results. All novel tools and algorithms are available with the regular MaxQuant and Perseus releases, which are downloadable at http://maxquant.org.

Shotgun Proteomics of Ascidians Tunic Gives New Insights on Host-Microbe Interactions by Revealing Diverse Antimicrobial Peptides

Ascidians are marine invertebrates associated with diverse microbial communities, embedded in their tunic, conferring special ecological and biotechnological relevance to these model organisms used in evolutionary and developmental studies. Next-generation sequencing tools have increased the knowledge of ascidians’ associated organisms and their products, but proteomic studies are still scarce. Hence, we explored the tunic of three ascidian species using a shotgun proteomics approach. Proteins extracted from the tunic of Ciona sp., Molgula sp., and Microcosmus sp. were processed using a nano LC-MS/MS system (Ultimate 3000 liquid chromatography system coupled to a Q-Exactive Hybrid Quadrupole-Orbitrap mass spectrometer). Raw data was searched against UniProtKB – the Universal Protein Resource Knowledgebase (Bacteria and Metazoa section) using Proteome Discoverer software. The resulting proteins were merged with a non-redundant Antimicrobial Peptides (AMPs) database and analysed with MaxQuant freeware. Overall, 337 metazoan and 106 bacterial proteins were identified being mainly involved in basal metabolism, cytoskeletal and catalytic functions. 37 AMPs were identified, most of them attributed to eukaryotic origin apart from bacteriocins. These results and the presence of “Biosynthesis of antibiotics” as one of the most highlighted pathways revealed the tunic as a very active tissue in terms of bioactive compounds production, giving insights on the interactions between host and associated organisms. Although the present work constitutes an exploratory study, the approach employed revealed high potential for high-throughput characterization and biodiscovery of the ascidians’ tunic and its microbiome.

The Quantitative Proteome of the Cement and Adhesive Gland of the Pedunculate Barnacle, Pollicipes pollicipes

Adhesive secretion has a fundamental role in barnacles’ survival, keeping them in an adequate position on the substrate under a variety of hydrologic regimes. It arouses special interest for industrial applications, such as antifouling strategies, underwater industrial and surgical glues, and dental composites. This study was focused on the goose barnacle Pollicipes pollicipes adhesion system, a species that lives in the Eastern Atlantic strongly exposed intertidal rocky shores and cliffs. The protein composition of P. pollicipes cement multicomplex and cement gland was quantitatively studied using a label-free LC-MS high-throughput proteomic analysis, searched against a custom transcriptome-derived database. Overall, 11,755 peptide sequences were identified in the gland while 2880 peptide sequences were detected in the cement, clustered in 1616 and 1568 protein groups, respectively. The gland proteome was dominated by proteins of the muscle, cytoskeleton, and some uncharacterized proteins, while the cement was, for the first time, reported to be composed by nearly 50% of proteins that are not canonical cement proteins, mainly unannotated proteins, chemical cues, and protease inhibitors, among others. Bulk adhesive proteins accounted for one-third of the cement proteome, with CP52k being the most abundant. Some unannotated proteins highly expressed in the proteomes, as well as at the transcriptomic level, showed similar physicochemical properties to the known surface-coupling barnacle adhesive proteins while the function of the others remains to be discovered. New quantitative and qualitative clues are provided to understand the diversity and function of proteins in the cement of stalked barnacles, contributing to the whole adhesion model in Cirripedia.

Comparing 22 Popular Phosphoproteomics Pipelines for Peptide Identification and Site Localization

Phosphorylation-driven cell signaling governs most biological functions and is widely studied using mass-spectrometry-based phosphoproteomics. Identifying the peptides and localizing the phosphorylation sites within them from the raw data is challenging and can be performed by several algorithms that return scores that are not directly comparable. This increases the heterogeneity among published phosphoproteomics data sets and prevents their direct integration. Here we compare 22 pipelines implemented in the main software tools used for bottom-up phosphoproteomics analysis (MaxQuant, Proteome Discoverer, PeptideShaker). We test six search engines (Andromeda, Comet, Mascot, MS Amanda, SequestHT, and X!Tandem) in combination with several localization scoring algorithms (delta score, D-score, PTM-score, phosphoRS, and Ascore). We show that these follow very different score distributions, which can lead to different false localization rates for the same threshold. We provide a strategy to discriminate correctly from incorrectly localized phosphorylation sites in a consistent manner across the tested pipelines. The results presented here can help users choose the most appropriate pipeline and cutoffs for their phosphoproteomics analysis.

Sorting the Muck from the Brass: Analysis of Protein Complexes and Cell Lysates

Reliable determination of protein complex composition or changes to protein levels in whole cells is challenging. Despite the multitude of methods now available for labeling, analysis, and the statistical processing of data, this large variety is of itself an issue: Which approach is most appropriate, where do you set cutoffs, and what is the most cost-effective strategy? One size does not fit all for such work, but some guidelines can help in terms of reducing cost, improving data quality, and ultimately advancing investigations. Here we describe two protocols and algorithms for facile sample preparation for mass spectrometric analysis, robust data processing, and considerations of how to interpret large proteomic datasets in a productive and robust manner.

High-Resolution Lysine Acetylome Profiling by Offline Fractionation and Immunoprecipitation

Acetylation of lysine side chains at their ε-amino group is a reversible posttranslational modification (PTM), which can affect diverse protein functions. Lysine acetylation was first described on histones, and nowadays gains more and more attention due to its more general occurrence in proteomes, and its possible crosstalk with other protein modifications. Here we describe a workflow to investigate the acetylation of lysine-containing peptides on a large scale. For this high-resolution lysine acetylome analysis, dimethyl-labeled peptide samples are pooled and offline-fractionated using hydrophilic interaction liquid chromatography (HILIC). The offline fractionation is followed by an immunoprecipitation and liquid chromatography-tandem mass spectrometry (LC-MS/MS) for data acquisition and subsequent data analysis.

LFQ-Analyst: An Easy-To-Use Interactive Web Platform To Analyze and Visualize Label-Free Proteomics Data Preprocessed with MaxQuant

Relative label-free quantification (LFQ) of shotgun proteomics data using precursor (MS1) signal intensities is one of the most commonly used applications to comprehensively and globally quantify proteins across biological samples and conditions. Due to the popularity of this technique, several software packages, such as the popular software suite MaxQuant, have been developed to extract, analyze, and compare spectral features and to report quantitative information of peptides, proteins, and even post-translationally modified sites. However, there is still a lack of accessible tools for the interpretation and downstream statistical analysis of these complex data sets, in particular for researchers and biologists with no or only limited experience in proteomics, bioinformatics, and statistics. We have therefore created LFQ-Analyst, which is an easy-to-use, interactive web application developed to perform differential expression analysis with "one click" and to visualize label-free quantitative proteomic data sets preprocessed with MaxQuant. LFQ-Analyst provides a wealth of user-analytic features and offers numerous publication-quality result graphics to facilitate statistical and exploratory analysis of label-free quantitative data sets. LFQ-Analyst, including an in-depth user manual, is freely available at https://bioinformatics.erc.monash.edu/apps/LFQ-Analyst .

Concepts and strategies of soybean seed proteomics using the shotgun proteomics approach

Introduction: The last decade has yielded significant developments in the field of proteomics, especially in mass spectrometry (MS) and data analysis tools. In particular, a shift from gel-based to MS-based proteomics has been observed, thereby providing a platform with which to construct proteome atlases for all life forms. Nevertheless, the analysis of plant proteomes, especially those of samples that contain high-abundance proteins (HAPs), such as soybean seeds, remains challenging. Areas covered: Here, we review recent progress in soybean seed proteomics and highlight advances in HAPs depletion methods and peptide pre-fractionation, identification, and quantification methods. We also suggest a pipeline for future proteomic analysis, in order to increase the dynamic coverage of the soybean seed proteome. Expert opinion: Because HAPs limit the dynamic resolution of the soybean seed proteome, the depletion of HAPs is a prerequisite of high-throughput proteome analysis, and owing to the use of two-dimensional gel electrophoresis-based proteomic approaches, few soybean seed proteins have been identified or characterized. Recent advances in proteomic technologies, which have significantly increased the proteome coverage of other plants, could be used to overcome the current complexity and limitation of soybean seed proteomics.

Novel interconnections of HOG signaling revealed by combined use of two proteomic software packages

Modern quantitative mass spectrometry (MS)-based proteomics enables researchers to unravel signaling networks by monitoring proteome-wide cellular responses to different stimuli. MS-based analysis of signaling systems usually requires an integration of multiple quantitative MS experiments, which remains challenging, given that the overlap between these datasets is not necessarily comprehensive. In a previous study we analyzed the impact of the yeast mitogen-activated protein kinase (MAPK) Hog1 on the hyperosmotic stress-affected phosphorylome. Using a combination of a series of hyperosmotic stress and kinase inhibition experiments, we identified a broad range of direct and indirect substrates of the MAPK. Here we re-evaluate this extensive MS dataset and demonstrate that a combined analysis based on two software packages, MaxQuant and Proteome Discoverer, increases the coverage of Hog1-target proteins by 30%. Using protein-protein proximity assays we show that the majority of new targets gained by this analysis are indeed Hog1-interactors. Additionally, kinetic profiles indicate differential trends of Hog1-dependent versus Hog1-independent phosphorylation sites. Our findings highlight a previously unrecognized interconnection between Hog1 signaling and the RAM signaling network, as well as sphingolipid homeostasis.

DO-MS: Data-Driven Optimization of Mass Spectrometry Methods

The performance of ultrasensitive liquid chromatography and tandem mass spectrometry (LC-MS/MS) methods, such as single-cell proteomics by mass spectrometry (SCoPE-MS), depends on multiple interdependent parameters. This interdependence makes it challenging to specifically pinpoint the sources of problems in the LC-MS/MS methods and approaches for resolving them. For example, a low signal at the MS2 level can be due to poor LC separation, ionization, apex targeting, ion transfer, or ion detection. We sought to specifically diagnose such problems by interactively visualizing data from all levels of bottom-up LC-MS/MS analysis. Many software packages, such as MaxQuant, already provide such data, and we developed an open source platform for their interactive visualization and analysis: Data-driven Optimization of MS (DO-MS). We found that in many cases DO-MS not only specifically diagnosed LC-MS/MS problems but also enabled us to rationally optimize them. For example, by using DO-MS to optimize the sampling of the elution peak apexes, we increased ion accumulation times and apex sampling, which resulted in a 370% more efficient delivery of ions for MS2 analysis. DO-MS is easy to install and use, and its GUI allows for interactive data subsetting and high-quality figure generation. The modular design of DO-MS facilitates customization and expansion. DO-MS v1.0.8 is available for download from GitHub: https://github.com/SlavovLab/DO-MS . Additional documentation is available at https://do-ms.slavovlab.net .

Purification and Proteomics of Influenza Virions

This chapter describes a basic workflow for analyzing the protein composition of influenza virions. In order to obtain suitable material, the chapter describes how to concentrate influenza virions from the growth media of infected cells and to purify them by ultracentrifugation through a density gradient. This approach is also suitable for purifying influenza virions from the allantoic fluid of embryonated chicken eggs. As a small quantity of microvesicles are co-purified with virions, optional steps are included to increase the stringency of purification by enriching material with viral receptor binding and cleaving activity. Material purified in this way can be used for a variety of downstream applications, including proteomics. As a detailed example of this, the chapter also describes a standard workflow for analyzing the protein composition of concentrated virions by liquid chromatography and tandem mass spectrometry.

Label-Free LC-MS/MS Strategy for Comprehensive Proteomic Profiling of Human Islets Collected Using Laser Capture Microdissection from Frozen Pancreata

Diabetes mellitus is caused by either loss of pancreatic islets β-cells (Type 1 Diabetes, T1D), insufficient insulin release in the islet β-cells coupled with insulin resistance in target tissues (Type 2 Diabetes, T2D), or impaired insulin release (genetic forms of diabetes and, possibly, T1D subtypes). The investigation of the islet proteome could elucidate facets of the pathogenesis of diabetes. Enzymatically isolated and cultured (EIC) islets are frequently used to investigate biochemical signaling pathways that could trigger β-cell changes and death in diabetes. However, they cannot fully reflect the natural protein composition and disease process of in vivo islets due to the stress from isolation procedures and in vitro culture. The laser capture microdissection method employs a high-energy laser source to separate the desired cells from the remaining tissue section in an environment which is well conserved and close to the natural condition. Here, we describe a label-free proteomic workflow of laser capture microdissected (LCM) human islets from fresh-frozen pancreas sections of cadaveric donors to obtain an accurate and unbiased profile of the pancreatic islet proteome. The workflow includes preparation of frozen tissue section, staining and dehydration, LCM islets collection, islet protein digestion, label-free Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS), database search, and statistical analysis.

Proteomic Profiling of Hsp90 Inhibitors

Mass spectrometry assays demonstrate that Hsp90 inhibitors alter the expression of approximately one-quarter of the assayable proteome in mammalian cells. These changes are extraordinarily robust and reproducible, making "proteomics profiling" the gold standard for validating the effects of new Hsp90 inhibitors on cultured cells. Proteomics assays can also suggest novel hypotheses regarding drug mechanisms. To assist investigators in adopting this approach, this Chapter provides detailed protocols for conducting simple proteomics assays of Hsp90 inhibition. The protocols present a robust label-free approach that utilizes pre-fractionation of protein samples by SDS-PAGE, thereby providing reasonably good penetration into the proteome while addressing common issues with sample quality. The actual programming and operation of liquid chromatography-tandem mass spectrometers is not covered, but expectations for achievable performance are discussed, as are alternative approaches, common challenges, and software for data analysis.

Proteome Analysis of Arabidopsis Roots

Plant health and development are directly depending on a plant's ability to react to a constantly changing environment. Sensing of water and nutrition levels and of the biotic environment is vital for a plant, making the root one of the key plant organs. Proteins are the key molecules that play numerous roles in a cell's everyday life. Quantitative proteome profiling of roots can provide a global overview on the molecular regulatory mechanisms and networks involved in plant growth and development and abiotic and biotic stress responses. Here, we provide a detailed proteomics workflow on Arabidopsis thaliana roots from plant growth up to proteomics data analysis.

Quantitative Proteomics for Xenopus Embryos II, Data Analysis

The oocytes, embryos, and cell-free lysates of the frog Xenopus laevis have emerged as powerful models for quantitative proteomic experiments. In the accompanying paper (Chapter 13) we describe how to prepare samples and acquire multiplexed proteomics spectra from those. As an illustrative example we use a 10-stage developmental time series from the egg to stage 35 (just before hatching). Here, we outline how to convert the ~700,000 acquired mass spectra from this time series into protein expression dynamics for ~9000 proteins. We first outline a preliminary quality-control analysis to discover any errors that occurred during sample preparation. We discuss how peptide and protein identification error rates are controlled, and how peptide and protein species are quantified. Our analysis relies on the freely available MaxQuant proteomics pipeline. Finally, we demonstrate how to start interpreting this large dataset by clustering and gene-set enrichment analysis.

Sample Preparation for Relative Quantitation of Proteins Using Tandem Mass Tags (TMT) and Mass Spectrometry (MS)

Quantitative proteome analysis allows comparisons of protein or phosphoprotein levels across multiple cell types or conditions. A number of experimental approaches have been described toward quantitative proteomics. In this chapter, we focus on Tandem Mass Tag (TMT) isobaric labeling of peptides for global, relative quantitation of proteins and phosphopeptides. To date, there has been no published protocol describing chemical labeling of small amounts of peptides specifically extracted from small tumor samples, for which rigorous sample preparation is necessary to ensure reproducible TMT labeling.

Proteomic profiling of early degenerative retina of RCS rats

AIM

To identify the underlying cellular and molecular changes in retinitis pigmentosa (RP).

METHODS

Label-free quantification-based proteomics analysis, with its advantages of being more economic and consisting of simpler procedures, has been used with increasing frequency in modern biological research. Dystrophic RCS rats, the first laboratory animal model for the study of RP, possess a similar pathological course as human beings with the diseases. Thus, we employed a comparative proteomics analysis approach for in-depth proteome profiling of retinas from dystrophic RCS rats and non-dystrophic congenic controls through Linear Trap Quadrupole - orbitrap MS/MS, to identify the significant differentially expressed proteins (DEPs). Bioinformatics analyses, including Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation and upstream regulatory analysis, were then performed on these retina proteins. Finally, a Western blotting experiment was carried out to verify the difference in the abundance of transcript factor E2F1.

RESULTS

In this study, we identified a total of 2375 protein groups from the retinal protein samples of RCS rats and non-dystrophic congenic controls. Four hundred thirty-four significantly DEPs were selected by Student's t-test. Based on the results of the bioinformatics analysis, we identified mitochondrial dysfunction and transcription factor E2F1 as the key initiation factors in early retinal degenerative process.

CONCLUSION

We showed that the mitochondrial dysfunction and the transcription factor E2F1 substantially contribute to the disease etiology of RP. The results provide a new potential therapeutic approach for this retinal degenerative disease.

Assessment of Label-Free Quantification in Discovery Proteomics and Impact of Technological Factors and Natural Variability of Protein Abundance

We evaluated the state of label-free discovery proteomics focusing especially on technological contributions and contributions of naturally occurring differences in protein abundance to the intersample variability in protein abundance estimates in this highly peptide-centric technology. First, the performance of popular quantitative proteomics software, Proteome Discoverer, Scaffold, MaxQuant, and Progenesis QIP, was benchmarked using their default parameters and some modified settings. Beyond this, the intersample variability in protein abundance estimates was decomposed into variability introduced by the entire technology itself and variable protein amounts inherent to individual plants of the Arabidopsis thaliana Col-0 accession. The technical component was considerably higher than the biological intersample variability, suggesting an effect on the degree and validity of reported biological changes in protein abundance. Surprisingly, the biological variability, protein abundance estimates, and protein fold changes were recorded differently by the software used to quantify the proteins, warranting caution in the comparison of discovery proteomics results. As expected, ∼99% of the proteome was invariant in the isogenic plants in the absence of environmental factors; however, few proteins showed substantial quantitative variability. This naturally occurring variation between individual organisms can have an impact on the causality of reported protein fold changes.

Isobaric Labeling-Based LC-MS/MS Strategy for Comprehensive Profiling of Human Pancreatic Tissue Proteome

The pancreas is an organ with both endocrine and exocrine functions, and various pathologies, such as pancreatic cancer and diabetes are associated with this organ. Owing to the limited pancreatic biopsy samples available for research, it is critical to make the best use of cadaveric pancreatic tissue for biomarker studies and mechanistic understanding of pancreas-related pathologies. Discovery-phase quantitative proteomics has attracted a lot of attention for its capabilities in large-scale protein identification and accurate protein quantification. Here, we describe a workflow using isobaric labeling (tandem mass tag or TMT) based quantitative proteomics to confidently identify and quantify human pancreatic tissue proteome, including sample preparation, isobaric tag labeling, peptide level fractionation, LC-MS/MS, database search, and statistical analysis.

Dimethyl-Labeling-Based Quantification of the Lysine Acetylome and Proteome of Plants

Photorespiratory enzymes in different cellular compartments have been reported to be posttranslational modified by phosphorylation, disulfide formation, S-nitrosylation, glutathionylation, and lysine acetylation. However, not much is known yet about the function of these modifications to regulate the activities, localizations, or interactions of the proteins in this metabolic pathway. Hence, it will be of great importance to study these modifications and their temporal and conditional occurrence in more detail. Here, we focus on the analysis of lysine acetylation as a relatively newly discovered modification on plant metabolic enzymes. The acetylation of lysine residues within proteins is a highly conserved and reversible posttranslational modification which occurs in all living organisms. First discovered on histones and implied in the regulation of gene expression, lysine acetylation also occurs on a diverse set of cellular proteins in different subcellular compartments and is particularly abundant on metabolic enzymes. Upon lysine acetylation, the function of proteins can be modulated due to the loss of the positive charge of the lysine residue. Lysine acetylation was also discovered on proteins involved in photosynthesis and novel tools are needed to study the regulation of this modification in dependence on the environmental conditions, tissues, or plant genotype. This chapter describes a method for the identification and relative quantification of lysine-acetylated proteins in plant tissues using a dimethyl labeling technique combined with an anti-acetyl lysine antibody enrichment strategy. Here, we describe the protein purification, labeling of trypsinated peptides, as well as immuno-enrichment of lysine-acetylated peptides followed by liquid chromatography tandem mass spectrometry (LC-MS/MS) data acquisition and analysis.

Efficient Quantitative Comparisons of Plasma Proteomes Using Label-Free Analysis with MaxQuant

Mass spectrometry (MS)-based quantitation of plasma proteomes is challenging due to the extremely wide dynamic range and molecular heterogeneity of plasma samples. However, recent advances in technology, MS instrumentation, and bioinformatics have enabled in-depth quantitative analyses of very complex proteomes, including plasma. Specifically, recent improvements in both label-based and label-free quantitation strategies have allowed highly accurate quantitative comparisons of expansive proteome datasets. Here we present a method for in-depth label-free analysis of human plasma samples using MaxQuant.

Label-Free Quantitative Proteomic Analysis of Harmless and Pathogenic Strains of Infectious Microalgae, Prototheca spp

Microalgae of the genus Prototheca (P.) spp are associated with rare algal infections of invertebrates termed protothecosis. Among the seven generally accepted species, P. zopfii genotype 2 (GT2) is associated with a severe form of bovine mastitis while P. blaschkeae causes the mild and sub-clinical form of mastitis. The reason behind the infectious nature of P. zopfii GT2, while genotype 1 (GT1) remains non-infectious, is not known. Therefore, in the present study we investigated the protein expression level difference between the genotypes of P. zopfii and P. blaschkeae. Cells were cultured to the mid-exponential phase, harvested, and processed for LC-MS analysis. Peptide data was acquired on an LTQ Orbitrap Velos, raw spectra were quantitatively analyzed with MaxQuant software and matching with the reference database of Chlorella variabilis and Auxenochlorella protothecoides resulted in the identification of 226 proteins. Comparison of an environmental strain with infectious strains resulted in the identification of 51 differentially expressed proteins related to carbohydrate metabolism, energy production and protein translation. The expression level of Hsp70 proteins and their role in the infectious process is worth further investigation. All mass spectrometry data are available via ProteomeXchange with identifier PXD005305.

Comparative Analysis of Two Helicobacter pylori Strains using Genomics and Mass Spectrometry-Based Proteomics

Helicobacter pylori, a gastroenteric pathogen believed to have co-evolved with humans over 100,000 years, shows significant genetic variability. This motivates the study of different H. pylori strains and the diseases they cause in order to identify determinants for disease evolution. In this study, we used proteomics tools to compare two H. pylori strains. Nic25_A was isolated in Nicaragua from a patient with intestinal metaplasia, and P12 was isolated in Europe from a patient with duodenal ulcers. Differences in the abundance of surface proteins between the two strains were determined with two mass spectrometry-based methods, label-free quantification (MaxQuant) or the use of tandem mass tags (TMT). Each approach used a lipid-based protein immobilization (LPI^TM) technique to enrich peptides of surface proteins. Using the MaxQuant software, we found 52 proteins that differed significantly in abundance between the two strains (up- or downregulated by a factor of 1.5); with TMT, we found 18 proteins that differed in abundance between the strains. Strain P12 had a higher abundance of proteins encoded by the cag pathogenicity island, while levels of the acid response regulator ArsR and its regulatory targets (KatA, AmiE, and proteins involved in urease production) were higher in strain Nic25_A. Our results show that differences in protein abundance between H. pylori strains can be detected with proteomic approaches; this could have important implications for the study of disease progression.

Analysis of Mass Spectrometry Data for Nucleolar Proteomics Experiments

With recent advances in experiment design, sample preparation, separation and instruments, mass spectrometry (MS)-based quantitative proteomics is becoming increasingly more popular. This has the potential to usher a new revolution in biology, in which the protein complement of cell populations can be described not only with increasing coverage, but also in all of its dimensions with unprecedented precision. Indeed, while earlier proteomics studies aimed solely at identifying as many as possible of the proteins present in the sample, newer, so-called Next Generation Proteomics studies add to this the aim of determining and quantifying the protein variants present in the sample, their mutual associations within complexes, their posttranslational modifications, their variation across the cell-cycle or in response to stimuli or perturbations, and their subcellular distribution. This has the potential to make MS proteomics much more useful for researchers, but will also mean that researchers with no background in MS will increasingly be confronted with the less-than trivial challenges of preparing samples for MS analysis, then processing and interpreting the results. In Chapter 20 , we described a workflow for isolating the protein contents of a specific SILAC-labeled organelle sample (the nucleolus) and processing it into peptides suitable for bottom-up MS analysis. Here, we complete this workflow by describing how to use the freely available MaxQuant software to convert the spectra stored in the Raw files into peptide- and protein-level information. We also briefly describe how to visualize the data using the free R scripting language.

Search Databases and Statistics: Pitfalls and Best Practices in Phosphoproteomics

Advances in mass spectrometric instrumentation in the past 15 years have resulted in an explosion in the raw data yield from typical phosphoproteomics workflows. This poses the challenge of confidently identifying peptide sequences, localizing phosphosites to proteins and quantifying these from the vast amounts of raw data. This task is tackled by computational tools implementing algorithms that match the experimental data to databases, providing the user with lists for downstream analysis. Several platforms for such automated interpretation of mass spectrometric data have been developed, each having strengths and weaknesses that must be considered for the individual needs. These are reviewed in this chapter. Equally critical for generating highly confident output datasets is the application of sound statistical criteria to limit the inclusion of incorrect peptide identifications from database searches. Additionally, careful filtering and use of appropriate statistical tests on the output datasets affects the quality of all downstream analyses and interpretation of the data. Our considerations and general practices on these aspects of phosphoproteomics data processing are presented here.

Resources for Assignment of Phosphorylation Sites on Peptides and Proteins

Reversible protein phosphorylation is a key regulatory posttranslational modification that plays a significant role in major cellular signaling processes. Phosphorylation events can be systematically identified, quantified, and localized on protein sequence using publicly available bioinformatic tools. Here we present the software tools commonly used by the phosphoproteomics community, discuss their underlying principles of operation, and provide a protocol for large-scale phosphoproteome data analysis using the MaxQuant software suite.

Proteomics Quality Control: Quality Control Software for MaxQuant Results

Mass spectrometry-based proteomics coupled to liquid chromatography has matured into an automatized, high-throughput technology, producing data on the scale of multiple gigabytes per instrument per day. Consequently, an automated quality control (QC) and quality analysis (QA) capable of detecting measurement bias, verifying consistency, and avoiding propagation of error is paramount for instrument operators and scientists in charge of downstream analysis. We have developed an R-based QC pipeline called Proteomics Quality Control (PTXQC) for bottom-up LC-MS data generated by the MaxQuant software pipeline. PTXQC creates a QC report containing a comprehensive and powerful set of QC metrics, augmented with automated scoring functions. The automated scores are collated to create an overview heatmap at the beginning of the report, giving valuable guidance also to nonspecialists. Our software supports a wide range of experimental designs, including stable isotope labeling by amino acids in cell culture (SILAC), tandem mass tags (TMT), and label-free data. Furthermore, we introduce new metrics to score MaxQuant's Match-between-runs (MBR) functionality by which peptide identifications can be transferred across Raw files based on accurate retention time and m/z. Last but not least, PTXQC is easy to install and use and represents the first QC software capable of processing MaxQuant result tables. PTXQC is freely available at https://github.com/cbielow/PTXQC .