Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14-3-3 system

A proteomic and ultrastructural characterization of Aspergillus fumigatus' conidia adaptation at different culture ages

The airborne fungus Aspergillus fumigatus is one of the most common agents of human fungal infections with a remarkable impact on public health. However, A. fumigatus conidia atmospheric resistance and longevity mechanisms are still unknown. Therefore, in this work, the processes underlying conidial adaptation were studied by a time course evaluation of the proteomics and ultrastructural changes of A. fumigatus' conidia at three time-points selected according to relevant changes previously established in conidial survival rates. The proteomics characterization revealed that conidia change from a highly active metabolic to a dormant state, culminating in cell autolysis as revealed by the increased levels of hydrolytic enzymes. Structural characterization corroborates the proteomics data, with noticeable changes observed in mitochondria, nucleus and plasma membrane ultrastructure, accompanied by the formation of autophagic vacuoles. These changes are consistent with both apoptotic and autophagic processes, and indicate that the changes in protein levels may anticipate those in cell morphology.

SIGNIFICANCE

The findings presented in this work not only clarify the processes underlying conidial adaptation to nutrient limiting conditions but can also be exploited for improving infection control strategies and in the development of new therapeutical drugs. Additionally, the present study was deposited in a public database and thus, it may also be a valuable dataset to be used by the scientific community as a tool to understand and identified other potential targets associated with conidia resistance.

Cellular proteomic analysis of porcine circovirus type 2 and classical swine fever virus coinfection in porcine kidney-15 cells using isobaric tags for relative and absolute quantitation-coupled LC-MS/MS

Viral coinfection or superinfection in host has caused public health concern and huge economic losses of farming industry. The influence of viral coinfection on cellular protein abundance is essential for viral pathogenesis. Based on a coinfection model for porcine circovirus type 2 (PCV2) and classical swine fever virus (CSFV) developed previously by our laboratory, isobaric tags for relative and absolute quantitation (iTRAQ)-coupled LC-MS/MS proteomic profiling was performed to explore the host cell responses to PCV2-CSFV coinfection. Totally, 3932 proteins were identified in three independent mass spectrometry analyses. Compared with uninfected cells, 304 proteins increased (fold change >1.2) and 198 decreased (fold change <0.833) their abundance in PCV2-infected cells (p < 0.05), 60 and 61 were more and less abundant in CSFV-infected cells, and 196 and 158 were more and less abundant, respectively in cells coinfected with PCV2 and CSFV. Representative differentially abundant proteins were validated by quantitative real-time PCR, Western blotting and confocal laser scanning microscopy. Bioinformatic analyses confirmed the dominant role of PCV2, and indicated that mitochondrial dysfunction, nuclear factor erythroid 2-related factor 2 (Nrf2)-mediated oxidative stress response and apoptosis signaling pathways might be the specifical targets during PCV2-CSFV coinfection.

Optimization of Acquisition and Data-Processing Parameters for Improved Proteomic Quantification by Sequential Window Acquisition of All Theoretical Fragment Ion Mass Spectrometry

Proteomic analysis with data-independent acquisition (DIA) approaches represented by the sequential window acquisition of all theoretical fragment ion spectra (SWATH) technique has gained intense interest in recent years because DIA is able to overcome the intrinsic weakness of conventional data-dependent acquisition (DDA) methods and afford higher throughout and reproducibility for proteome-wide quantification. Although the raw mass spectrometry (MS) data quality and the data-mining workflow conceivably influence the throughput, accuracy and consistency of SWATH-based proteomic quantification, there lacks a systematic evaluation and optimization of the acquisition and data-processing parameters for SWATH MS analysis. Herein, we evaluated the impact of major acquisition parameters such as the precursor mass range, isolation window width and accumulation time as well as the data-processing variables including peak extraction criteria and spectra library selection on SWATH performance. Fine tuning these interdependent parameters can further improve the throughput and accuracy of SWATH quantification compared to the original setting adopted in most SWATH proteomic studies. Furthermore, we compared the effectiveness of two widely used peak extraction software PeakView and Spectronaut in discovery of differentially expressed proteins in a biological context. Our work is believed to contribute to a deeper understanding of the critical factors in SWATH MS experiments and help researchers optimize their SWATH parameters and workflows depending on the sample type, available instrument and software.

Generation of High-Quality SWATH® Acquisition Data for Label-free Quantitative Proteomics Studies Using TripleTOF® Mass Spectrometers

Data-independent acquisition is a powerful mass spectrometry technique that enables comprehensive MS and MS/MS analysis of all detectable species, providing an information rich data file that can be mined deeply. Here, we describe how to acquire high-quality SWATH^® Acquisition data to be used for large quantitative proteomic studies. We specifically focus on using variable sized Q1 windows for acquisition of MS/MS data for generating higher specificity quantitative data.

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.

The Cohesive Metastasis Phenotype in Human Prostate Cancer

A critical barrier for the successful prevention and treatment of recurrent prostate cancer is detection and eradication of metastatic and therapy-resistant disease. Despite the fall in diagnoses and mortality, the reported incidence of metastatic disease has increased 72% since 2004. Prostate cancer arises in cohesive groups as intraepithelial neoplasia, migrates through muscle and leaves the gland via perineural invasion for hematogenous dissemination. Current technological advances have shown cohesive-clusters of tumor (also known as microemboli) within the circulation. Circulating tumor cell (CTC) profiles are indicative of disseminated prostate cancer, and disseminated tumor cells (DTC) are found in cohesive-clusters, a phenotypic characteristic of both radiation- and drug-resistant tumors. Recent reports in cell biology and informatics, coupled with mass spectrometry, indicate that the integrin adhesome network provides an explanation for the biophysical ability of cohesive-clusters of tumor cells to invade thorough muscle and nerve microenvironments while maintaining adhesion-dependent therapeutic resistance. Targeting cohesive-clusters takes advantage of the known ability of extracellular matrix (ECM) adhesion to promote tumor cell survival and represents an approach that has the potential to avoid the progression to drug- and radiotherapy-resistance. In the following review we will examine the evidence for development and dissemination of cohesive-clusters in metastatic prostate cancer.

LC-MS/MS-based targeted proteomics quantitatively detects the interaction between p53 and MDM2 in breast cancer

In breast cancer, p53 could be functionally compromised by interaction with several proteins. Among those proteins, MDM2 serves as a pivotal negative regulator and counteracts p53 activation. Thus, the ability to quantitatively and accurately monitor the changes in level of p53-MDM2 interaction with disease state can enable an improved understanding of this protein-protein interaction (PPI), provide a better insight into cancer development and allow the emergence of advanced treatments. However, rare studies have evaluated the quantitative extent of PPI including p53-MDM2 interaction so far. In this study, a LC-MS/MS-based targeted proteomics assay was developed and coupled with co-immunoprecipitation (Co-IP) for the quantification of p53-MDM2 complex. A p53 antibody with the epitope residing at 156-214 residues achieved the greatest IP efficiency. 321KPLDGEYFTLQIR333 (p53) and 327ENWLPEDK334 (MDM2) were selected as surrogate peptides in the targeted analysis. Stable isotope-labeled synthetic peptides were used as internal standards. An LOQ (limit of quantification) of 2ng/mL was obtained. Then, the assay was applied to quantitatively detect total p53, total MDM2 and p53-MDM2 in breast cells and tissue samples. Western blotting was performed for a comparison. Finally, a quantitative time-course analysis in MCF-7 cells with the treatment of nutlin-3 as a PPI inhibitor was also monitored.

BIOLOGICAL SIGNIFICANCE

Proteins do not function as single entities but rather as a team player that has to communicate. Protein-protein interaction (PPI), normally by means of non-covalent contact among binary or large protein complex, is essential for many cellular processes including cancer progression. Thus, the ability to quantitatively and accurately monitor the changes in level of PPI with disease state can enable an improved understanding of PPI, provide a better insight into cancer development and allow the emergence of advanced treatments. However, rare studies have evaluated the quantitative extent of PPI so far. The major issue of current available approaches is the trade-off between sensitivity and specificity. Thus, techniques with the ability to quantify PPIs with both high sensitivity (low false-negative rate) and high specificity (low false-positive rate) are eagerly desired. Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based targeted proteomics has shown its potential to study biomolecules because of its high sensitivity, high selectivity and wide dynamic range. In this study, we made an effort to develop a LC-MS/MS-based targeted proteomics assay for the quantitative detection of p53-MDM2 interaction in breast cells and tissue samples.

Transitioning from Targeted to Comprehensive Mass Spectrometry Using Genetic Algorithms

Targeted proteomic assays are becoming increasingly popular because of their robust quantitative applications enabled by internal standardization, and they can be routinely executed on high performance mass spectrometry instrumentation. However, these assays are typically limited to 100s of analytes per experiment. Considerable time and effort are often expended in obtaining and preparing samples prior to targeted analyses. It would be highly desirable to detect and quantify 1000s of analytes in such samples using comprehensive mass spectrometry techniques (e.g., SWATH and DIA) while retaining a high degree of quantitative rigor for analytes with matched internal standards. Experimentally, it is facile to port a targeted assay to a comprehensive data acquisition technique. However, data analysis challenges arise from this strategy concerning agreement of results from the targeted and comprehensive approaches. Here, we present the use of genetic algorithms to overcome these challenges in order to configure hybrid targeted/comprehensive MS assays. The genetic algorithms are used to select precursor-to-fragment transitions that maximize the agreement in quantification between the targeted and the comprehensive methods. We find that the algorithm we used provided across-the-board improvement in the quantitative agreement between the targeted assay data and the hybrid comprehensive/targeted assay that we developed, as measured by parameters of linear models fitted to the results. We also found that the algorithm could perform at least as well as an independently-trained mass spectrometrist in accomplishing this task. We hope that this approach will be a useful tool in the development of quantitative approaches for comprehensive proteomics techniques. Graphical Abstract ᅟ.

Contribution of Mass Spectrometry-Based Proteomics to the Understanding of TNF-α Signaling

NF-κB is a family of ubiquitous dimeric transcription factors that play a role in a myriad of cellular processes, ranging from differentiation to stress response and immunity. In inflammation, activation of NF-κB is mediated by pro-inflammatory cytokines, in particular the prototypic cytokines IL-1β and TNF-α, which trigger the activation of complex signaling cascades. In spite of decades of research, the system level understanding of TNF-α signaling is still incomplete. This is partially due to the limited knowledge at the proteome level. The objective of this review is to summarize and critically evaluate the current status of the proteomic research on TNF-α signaling. We will discuss the merits and flaws of the existing studies as well as the insights that they have generated into the proteomic landscape and architecture connected to this signaling pathway. Besides delineating past and current trends in TNF-α proteomic research, we will identify research directions and new methodologies that can further contribute to characterize the TNF-α associated proteome in space and time.

Targeted In-Depth Quantification of Signaling Using Label-Free Mass Spectrometry

Protein phosphorylation encodes information on the activity of kinase-driven signaling pathways that regulate cell biology. This chapter discusses an approach, named TIQUAS (targeted in-depth quantification of signaling), to quantify cell signaling comprehensively and without bias. The workflow-based on mass spectrometry (MS) and computational science-consists of targeting the analysis of phosphopeptides previously identified by shotgun liquid chromatography tandem MS (LC-MS/MS) across the samples that are being compared. TIQUAS therefore takes advantage of concepts derived from both targeted (data-independent) and data-dependent acquisition methods; phosphorylation sites are quantified in all experimental samples regardless of whether or not these phosphopeptides were identified by MS/MS in all runs. As a result, datasets are obtained containing quantitative information on several thousand phosphorylation sites in as many samples and replicates as required in the experimental design, and these rich datasets are devoid of a significant number of missing data points. This chapter discussed the biochemical, analytical, and computational procedures required to apply the approach and for obtaining a biological interpretation of the data in the context of our understanding of cell signaling regulation and kinase-substrate relationships.

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.

Proteome-wide association studies identify biochemical modules associated with a wing-size phenotype in Drosophila melanogaster

The manner by which genetic diversity within a population generates individual phenotypes is a fundamental question of biology. To advance the understanding of the genotype–phenotype relationships towards the level of biochemical processes, we perform a proteome-wide association study (PWAS) of a complex quantitative phenotype. We quantify the variation of wing imaginal disc proteomes in Drosophila genetic reference panel (DGRP) lines using SWATH mass spectrometry. In spite of the very large genetic variation (1/36 bp) between the lines, proteome variability is surprisingly small, indicating strong molecular resilience of protein expression patterns. Proteins associated with adult wing size form tight co-variation clusters that are enriched in fundamental biochemical processes. Wing size correlates with some basic metabolic functions, positively with glucose metabolism but negatively with mitochondrial respiration and not with ribosome biogenesis. Our study highlights the power of PWAS to filter functional variants from the large genetic variability in natural populations.

How genetic diversity generates complex phenotypes along a continuum remains a fundamental question of biology. Here—applying the emerging SWATH proteomics technology—the authors describe a proteome wide association study (PWAS) of Drosophila wing size and identify functional protein clusters associated with this trait.

Network biology concepts in complex disease comorbidities

The co-occurrence of diseases can inform the underlying network biology of shared and multifunctional genes and pathways. In addition, comorbidities help to elucidate the effects of external exposures, such as diet, lifestyle and patient care. With worldwide health transaction data now often being collected electronically, disease co-occurrences are starting to be quantitatively characterized. Linking network dynamics to the real-life, non-ideal patient in whom diseases co-occur and interact provides a valuable basis for generating hypotheses on molecular disease mechanisms, and provides knowledge that can facilitate drug repurposing and the development of targeted therapeutic strategies.

SWATH acquisition mode for drug metabolism and metabolomics investigations

Aim: Sequential window acquisition of all theoretical fragment-ion spectra (SWATH) has recently emerged as a powerful high resolution mass spectrometric data independent acquisition technique. In the present work, the potential and challenges of an integrated strategy based on LC-SWATH/MS for simultaneous drug metabolism and metabolomics studies was investigated. Methodology: The richness of SWATH data allows numerous data analysis approaches, including: detection of metabolites by prediction; metabolite detection by mass defect filtering; quantification from high-resolution MS precursor chromatograms or fragment chromatograms. Multivariate analysis can be applied to the data from the full scan or SWATH windows and allows changes in endogenous metabolites as well as xenobiotic metabolites, to be detected. Principal component variable grouping detects intersample variable correlation and groups variables with similar profiles which simplifies interpretation and highlights related ions and fragments. Principal component variable grouping can extract product ion spectra from the data collected by fragmenting a wide precursor ion window. Conclusion: It was possible to characterize 28 vinpocetine metabolites in urine, mostly mono- and di-hydroxylated forms, and detect endogenous metabolite expression changes in urine after the administration of a single dose of a model drug (vinpocetine) to rats.

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.

Co-recruitment analysis of the CBL and CBLB signalosomes in primary T cells identifies CD5 as a key regulator of TCR-induced ubiquitylation

T-cell receptor (TCR) signaling is essential for the function of T cells and negatively regulated by the E3 ubiquitin-protein ligases CBL and CBLB Here, we combined mouse genetics and affinity purification coupled to quantitative mass spectrometry to monitor the dynamics of the CBL and CBLB signaling complexes that assemble in normal T cells over 600 seconds of TCR stimulation. We identify most previously known CBL and CBLB interacting partners, as well as a majority of proteins that have not yet been implicated in those signaling complexes. We exploit correlations in protein association with CBL and CBLB as a function of time of TCR stimulation for predicting the occurrence of direct physical association between them. By combining co-recruitment analysis with biochemical analysis, we demonstrated that the CD5 transmembrane receptor constitutes a key scaffold for CBL- and CBLB-mediated ubiquitylation following TCR engagement. Our results offer an integrated view of the CBL and CBLB signaling complexes induced by TCR stimulation and provide a molecular basis for their negative regulatory function in normal T cells.

NiCoMnO4: A Bifunctional Affinity Probe for His-Tagged Protein Purification and Phosphorylation Sites Recognition

A bifunctional affinity probe NiCoMnO4 was designed and prepared with controllable morphology and size using facile methods. It was observed that the probe could be applied in His-tagged proteins purification and phosphopeptides enrichment simply through the buffer modulation. NiCoMnO4 particles showed satisfactory cycling performance for His-tagged proteins purification and broad pH-tolerance of loading buffer for phosphopeptides affinity. Therefore, a high-throughput, cost-effective, and efficient protein/peptide purification method was developed within 10 min based on the novel bifunctional affinity probe.

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.

Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer

To provide a detailed analysis of the molecular components and underlying mechanisms associated with ovarian cancer, we performed a comprehensive mass-spectrometry-based proteomic characterization of 174 ovarian tumors previously analyzed by The Cancer Genome Atlas (TCGA), of which 169 were high-grade serous carcinomas (HGSCs). Integrating our proteomic measurements with the genomic data yielded a number of insights into disease, such as how different copy-number alternations influence the proteome, the proteins associated with chromosomal instability, the sets of signaling pathways that diverse genome rearrangements converge on, and the ones most associated with short overall survival. Specific protein acetylations associated with homologous recombination deficiency suggest a potential means for stratifying patients for therapy. In addition to providing a valuable resource, these findings provide a view of how the somatic genome drives the cancer proteome and associations between protein and post-translational modification levels and clinical outcomes in HGSC. VIDEO ABSTRACT.

Unveiling the Differences of Secretome of Human Bone Marrow Mesenchymal Stem Cells, Adipose Tissue-Derived Stem Cells, and Human Umbilical Cord Perivascular Cells: A Proteomic Analysis

The use of human mesenchymal stem cells (hMSCs) has emerged as a possible therapeutic strategy for CNS-related conditions. Research in the last decade strongly suggests that MSC-mediated benefits are closely related with their secretome. Studies published in recent years have shown that the secretome of hMSCs isolated from different tissue sources may present significant variation. With this in mind, the present work performed a comparative proteomic-based analysis through mass spectrometry on the secretome of hMSCs derived from bone marrow (BMSCs), adipose tissue (ASCs), and human umbilical cord perivascular cells (HUCPVCs). The results revealed that BMSCs, ASCs, and HUCPVCs differed in their secretion of neurotrophic, neurogenic, axon guidance, axon growth, and neurodifferentiative proteins, as well as proteins with neuroprotective actions against oxidative stress, apoptosis, and excitotoxicity, which have been shown to be involved in several CNS disorder/injury processes. Although important changes were observed within the secretome of the cell populations that were analyzed, all cell populations shared the capability of secreting important neuroregulatory molecules. The difference in their secretion pattern may indicate that their secretome is specific to a condition of the CNS. Nevertheless, the confirmation that the secretome of MSCs isolated from different tissue sources is rich in neuroregulatory molecules represents an important asset not only for the development of future neuroregenerative strategies but also for their use as a therapeutic option for human clinical trials.

Modulation of the Mesenchymal Stem Cell Secretome Using Computer-Controlled Bioreactors: Impact on Neuronal Cell Proliferation, Survival and Differentiation

In recent years it has been shown that the therapeutic benefits of human mesenchymal stem/stromal cells (hMSCs) in the Central Nervous System (CNS) are mainly attributed to their secretome. The implementation of computer-controlled suspension bioreactors has shown to be a viable route for the expansion of these cells to large numbers. As hMSCs actively respond to their culture environment, there is the hypothesis that one can modulate its secretome through their use. Herein, we present data indicating that the use of computer-controlled suspension bioreactors enhanced the neuroregulatory profile of hMSCs secretome. Indeed, higher levels of in vitro neuronal differentiation and NOTCH1 expression in human neural progenitor cells (hNPCs) were observed when these cells were incubated with the secretome of dynamically cultured hMSCs. A similar trend was also observed in the hippocampal dentate gyrus (DG) of rat brains where, upon injection, an enhanced neuronal and astrocytic survival and differentiation, was observed. Proteomic analysis also revealed that the dynamic culturing of hMSCs increased the secretion of several neuroregulatory molecules and miRNAs present in hMSCs secretome. In summary, the appropriate use of dynamic culture conditions can represent an important asset for the development of future neuro-regenerative strategies involving the use of hMSCs secretome.

SAINTq: Scoring protein-protein interactions in affinity purification - mass spectrometry experiments with fragment or peptide intensity data

SAINT (Significance Analysis of INTeractome) is a probabilistic method for scoring bait-prey interactions against negative controls in affinity purification - mass spectrometry (AP-MS) experiments. Our published SAINT algorithms use spectral counts or protein intensities as the input for calculating the probability of true interaction, which enables objective selection of high-confidence interactions with false discovery control. With the advent of new protein quantification methods such as Data Independent Acquisition (DIA), we redeveloped the scoring method to utilize the reproducibility information embedded in the peptide or fragment intensity data as a key scoring criterion, bypassing protein intensity summarization required in the previous SAINT workflow. The new software package, SAINTq, addresses key issues in the interaction scoring based on intensity data, including treatment of missing values and selection of peptides and fragments for scoring each prey protein. We applied SAINTq to two independent DIA AP-MS data sets profiling the interactome of MEPCE and EIF4A2 and that of 14-3-3β, and benchmarked the performance in terms of recovering previously reported literature interactions in the iRefIndex database. In both data sets, the SAINTq analysis using the fragment-level intensity data led to the most sensitive detection of literature interactions at the same level of specificity. This analysis outperforms the analysis using protein intensity data summed from fragment intensity data that is equivalent to the model in SAINTexpress.

NAD⁺ repletion improves mitochondrial and stem cell function and enhances life span in mice

Adult stem cells (SCs) are essential for tissue maintenance and regeneration yet are susceptible to senescence during aging. We demonstrate the importance of the amount of the oxidized form of cellular nicotinamide adenine dinucleotide (NAD(+)) and its effect on mitochondrial activity as a pivotal switch to modulate muscle SC (MuSC) senescence. Treatment with the NAD(+) precursor nicotinamide riboside (NR) induced the mitochondrial unfolded protein response and synthesis of prohibitin proteins, and this rejuvenated MuSCs in aged mice. NR also prevented MuSC senescence in the mdx (C57BL/10ScSn-Dmd(mdx)/J) mouse model of muscular dystrophy. We furthermore demonstrate that NR delays senescence of neural SCs and melanocyte SCs and increases mouse life span. Strategies that conserve cellular NAD(+) may reprogram dysfunctional SCs and improve life span in mammals.

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.

Proteomics-Based Analysis of Protein Complexes in Pluripotent Stem Cells and Cancer Biology

A protein complex consists of two or more proteins that are linked together through protein-protein interactions. The proteins show stable/transient and direct/indirect interactions within the protein complex or between the protein complexes. Protein complexes are involved in regulation of most of the cellular processes and molecular functions. The delineation of protein complexes is important to expand our knowledge on proteins functional roles in physiological and pathological conditions. The genetic yeast-2-hybrid method has been extensively used to characterize protein-protein interactions. Alternatively, a biochemical-based affinity purification coupled with mass spectrometry (AP-MS) approach has been widely used to characterize the protein complexes. In the AP-MS method, a protein complex of a target protein of interest is purified using a specific antibody or an affinity tag (e.g., DYKDDDDK peptide (FLAG) and polyhistidine (His)) and is subsequently analyzed by means of MS. Tandem affinity purification, a two-step purification system, coupled with MS has been widely used mainly to reduce the contaminants. We review here a general principle for AP-MS-based characterization of protein complexes and we explore several protein complexes identified in pluripotent stem cell biology and cancer biology as examples.

A first dataset toward a standardized community-driven global mapping of the human immunopeptidome

We present the first standardized HLA peptidomics dataset generated by the immunopeptidomics community. The dataset is composed of native HLA class I peptides as well as synthetic HLA class II peptides that were acquired in data-dependent acquisition mode using multiple types of mass spectrometers. All laboratories used the spiked-in landmark iRT peptides for retention time normalization and data analysis. The mass spectrometric data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD001872. The generated data were used to build HLA allele-specific peptide spectral and assay libraries, which were stored in the SWATHAtlas database. Data presented here are described in more detail in the original eLife article entitled ‘An open-source computational and data resource to analyze digital maps of immunopeptidomes’.

Determining the Composition and Stability of Protein Complexes Using an Integrated Label-Free and Stable Isotope Labeling Strategy

In biological systems, proteins catalyze the fundamental reactions that underlie all cellular functions, including metabolic processes and cell survival and death pathways. These biochemical reactions are rarely accomplished alone. Rather, they involve a concerted effect from many proteins that may operate in a directed signaling pathway and/or may physically associate in a complex to achieve a specific enzymatic activity. Therefore, defining the composition and regulation of protein complexes is critical for understanding cellular functions. In this chapter, we describe an approach that uses quantitative mass spectrometry (MS) to assess the specificity and the relative stability of protein interactions. Isolation of protein complexes from mammalian cells is performed by rapid immunoaffinity purification, and followed by in-solution digestion and high-resolution mass spectrometry analysis. We employ complementary quantitative MS workflows to assess the specificity of protein interactions using label-free MS and statistical analysis, and the relative stability of the interactions using a metabolic labeling technique. For each candidate protein interaction, scores from the two workflows can be correlated to minimize nonspecific background and profile protein complex composition and relative stability.

Unraveling Mesenchymal Stem Cells' Dynamic Secretome Through Nontargeted Proteomics Profiling

The modulatory and regenerative potential shown by the use of MSC secretomes has emphasized the importance of their proteomics profiling. Proteomic analysis, initially focused on the targeted analysis of some candidate proteins or the identification of the secreted proteins, has been changing to an untargeted profiling also based on the quantitative evaluation of the secreted proteins.The study of the secretome can be accomplished through several different proteomics-based approaches; however this analysis must overcome one key challenge of secretome analysis: the low amount of secreted proteins and usually their high dilution.In this chapter, a general workflow for the untargeted proteomic profile of MSC's secretome is presented, in combination with a comprehensive description of the major techniques/procedures that can be used. Special focus is given to the main procedures to obtain the secreted proteins, from secretome concentration by ultrafiltration to protein precipitation. Lastly, different proteomics-based approaches are presented, emphasizing alternative digestion techniques and available mass spectrometry-based quantitative methods.

SWATH enables precise label-free quantification on proteome scale

MS-based proteomics has emerged as a powerful tool in biological studies. The shotgun proteomics strategy, in which proteolytic peptides are analyzed in data-dependent mode, enables a detection of the most comprehensive proteome (>10 000 proteins from whole-cell lysate). The quantitative proteomics uses stable isotopes or label-free method to measure relative protein abundance. The isotope labeling strategies are more precise and accurate compared to label-free methods, but labeling procedures are complicated and expensive, and the sample number and types are also limited. Sequential window acquisition of all theoretical mass spectra (SWATH) is a recently developed technique, in which data-independent acquisition is coupled with peptide spectral library match. In principle SWATH method is able to do label-free quantification in an MRM-like manner, which has higher quantification accuracy and precision. Previous data have demonstrated that SWATH can be used to quantify less complex systems, such as spiked-in peptide mixture or protein complex. Our study first time assessed the quantification performance of SWATH method on proteome scale using a complex mouse-cell lysate sample. In total 3600 proteins got identified and quantified without sample prefractionation. The SWATH method shows outstanding quantification precision, whereas the quantification accuracy becomes less perfect when protein abundances differ greatly. However, this inaccuracy does not prevent discovering biological correlates, because the measured signal intensities had linear relationship to the sample loading amounts; thus the SWATH method can predict precisely the significance of a protein. Our results prove that SWATH can provide precise label-free quantification on proteome scale.

Fundamentals of protein interaction network mapping

Studying protein interaction networks of all proteins in an organism (“interactomes”) remains one of the major challenges in modern biomedicine. Such information is crucial to understanding cellular pathways and developing effective therapies for the treatment of human diseases. Over the past two decades, diverse biochemical, genetic, and cell biological methods have been developed to map interactomes. In this review, we highlight basic principles of interactome mapping. Specifically, we discuss the strengths and weaknesses of individual assays, how to select a method appropriate for the problem being studied, and provide general guidelines for carrying out the necessary follow‐up analyses. In addition, we discuss computational methods to predict, map, and visualize interactomes, and provide a summary of some of the most important interactome resources. We hope that this review serves as both a useful overview of the field and a guide to help more scientists actively employ these powerful approaches in their research.

Illuminating Spatial and Temporal Organization of Protein Interaction Networks by Mass Spectrometry-Based Proteomics

Protein–protein interactions are at the core of all cellular functions and dynamic alterations in protein interactions regulate cellular signaling. In the last decade, mass spectrometry (MS)-based proteomics has delivered unprecedented insights into human protein interaction networks. Affinity purification-MS (AP-MS) has been extensively employed for focused and high-throughput studies of steady state protein–protein interactions. Future challenges remain in mapping transient protein interactions after cellular perturbations as well as in resolving the spatial organization of protein interaction networks. AP-MS can be combined with quantitative proteomics approaches to determine the relative abundance of purified proteins in different conditions, thereby enabling the identification of transient protein interactions. In addition to affinity purification, methods based on protein co-fractionation have been combined with quantitative MS to map transient protein interactions during cellular signaling. More recently, approaches based on proximity tagging that preserve the spatial dimension of protein interaction networks have been introduced. Here, we provide an overview of MS-based methods for analyzing protein–protein interactions with a focus on approaches that aim to dissect the temporal and spatial aspects of protein interaction networks.

Enabling systematic interrogation of protein-protein interactions in live cells with a versatile ultra-high-throughput biosensor platform

Large-scale genomics studies have generated vast resources for in-depth understanding of vital biological and pathological processes. A rising challenge is to leverage such enormous information to rapidly decipher the intricate protein-protein interactions (PPIs) for functional characterization and therapeutic interventions. While a number of powerful technologies have been employed to detect PPIs, a singular PPI biosensor platform with both high sensitivity and robustness in a mammalian cell environment remains to be established. Here we describe the development and integration of a highly sensitive NanoLuc luciferase-based bioluminescence resonance energy transfer technology, termed BRET(n), which enables ultra-high-throughput (uHTS) PPI detection in live cells with streamlined co-expression of biosensors in a miniaturized format. We further demonstrate the application of BRET(n) in uHTS format in chemical biology research, including the discovery of chemical probes that disrupt PRAS40 dimerization and pathway connectivity profiling among core members of the Hippo signaling pathway. Such hippo pathway profiling not only confirmed previously reported PPIs, but also revealed two novel interactions, suggesting new mechanisms for regulation of Hippo signaling. Our BRET(n) biosensor platform with uHTS capability is expected to accelerate systematic PPI network mapping and PPI modulator-based drug discovery.

Analysis of colorectal cancer glyco-secretome identifies laminin β-1 (LAMB1) as a potential serological biomarker for colorectal cancer

The high mortality rate in colorectal cancer is mostly ascribed to metastasis, but the only clinical biomarker available for disease monitoring and prognosis is the carcinoembryonic antigen (CEA). However, the prognostic utility of CEA remains controversial. In an effort to identify novel biomarkers that could be potentially translated for clinical use, we collected the secretomes from the colon adenocarcinoma cell line HCT-116 and its metastatic derivative, E1, using the hollow fiber culture system, and utilized the multilectin affinity chromatography approach to enrich for the secreted glycoproteins (glyco-secretome). The HCT-116 and E1 glyco-secretomes were compared using the label-free quantitative SWATH-MS technology, and a total of 149 glycoproteins were differentially secreted in E1 cells. Among these glycoproteins, laminin β-1 (LAMB1), a glycoprotein not previously known to be secreted in colorectal cancer cells, was observed to be oversecreted in E1 cells. In addition, we showed that LAMB1 levels were significantly higher in colorectal cancer patient serum samples as compared to healthy controls when measured using ELISA. ROC analyses indicated that LAMB1 performed better than CEA at discriminating between colorectal cancer patients from controls. Moreover, the diagnostic performance was further improved when LAMB1 was used in combination with CEA.

A human interactome in three quantitative dimensions organized by stoichiometries and abundances

The organization of a cell emerges from the interactions in protein networks. The interactome is critically dependent on the strengths of interactions and the cellular abundances of the connected proteins, both of which span orders of magnitude. However, these aspects have not yet been analyzed globally. Here, we have generated a library of HeLa cell lines expressing 1,125 GFP-tagged proteins under near-endogenous control, which we used as input for a next-generation interaction survey. Using quantitative proteomics, we detect specific interactions, estimate interaction stoichiometries, and measure cellular abundances of interacting proteins. These three quantitative dimensions reveal that the protein network is dominated by weak, substoichiometric interactions that play a pivotal role in defining network topology. The minority of stable complexes can be identified by their unique stoichiometry signature. This study provides a rich interaction dataset connecting thousands of proteins and introduces a framework for quantitative network analysis.

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.

The Use of Variable Q1 Isolation Windows Improves Selectivity in LC-SWATH-MS Acquisition

As tryptic peptides and metabolites are not equally distributed along the mass range, the probability of cross fragment ion interference is higher in certain windows when fixed Q1 SWATH windows are applied. We evaluated the benefits of utilizing variable Q1 SWATH windows with regards to selectivity improvement. Variable windows based on equalizing the distribution of either the precursor ion population (PIP) or the total ion current (TIC) within each window were generated by an in-house software, swathTUNER. These two variable Q1 SWATH window strategies outperformed, with respect to quantification and identification, the basic approach using a fixed window width (FIX) for proteomic profiling of human monocyte-derived dendritic cells (MDDCs). Thus, 13.8 and 8.4% additional peptide precursors, which resulted in 13.1 and 10.0% more proteins, were confidently identified by SWATH using the strategy PIP and TIC, respectively, in the MDDC proteomic sample. On the basis of the spectral library purity score, some improvement warranted by variable Q1 windows was also observed, albeit to a lesser extent, in the metabolomic profiling of human urine. We show that the novel concept of "scheduled SWATH" proposed here, which incorporates (i) variable isolation windows and (ii) precursor retention time segmentation further improves both peptide and metabolite identifications.

Interacting Network of the Gap Junction (GJ) Protein Connexin43 (Cx43) is Modulated by Ischemia and Reperfusion in the Heart

The coordinated and synchronized cardiac muscle contraction relies on an efficient gap junction-mediated intercellular communication (GJIC) between cardiomyocytes, which involves the rapid anisotropic impulse propagation through connexin (Cx)-containing channels, namely of Cx43, the most abundant Cx in the heart. Expectedly, disturbing mechanisms that affect channel activity, localization and turnover of Cx43 have been implicated in several cardiomyopathies, such as myocardial ischemia. Besides gap junction-mediated intercellular communication, Cx43 has been associated with channel-independent functions, including modulation of cell adhesion, differentiation, proliferation and gene transcription. It has been suggested that the role played by Cx43 is dictated by the nature of the proteins that interact with Cx43. Therefore, the characterization of the Cx43-interacting network and its dynamics is vital to understand not only the molecular mechanisms underlying pathological malfunction of gap junction-mediated intercellular communication, but also to unveil novel and unanticipated biological functions of Cx43. In the present report, we applied a quantitative SWATH-MS approach to characterize the Cx43 interactome in rat hearts subjected to ischemia and ischemia-reperfusion. Our results demonstrate that, in the heart, Cx43 interacts with proteins related with various biological processes such as metabolism, signaling and trafficking. The interaction of Cx43 with proteins involved in gene transcription strengthens the emerging concept that Cx43 has a role in gene expression regulation. Importantly, our data shows that the interactome of Cx43 (Connexome) is differentially modulated in diseased hearts. Overall, the characterization of Cx43-interacting network may contribute to the establishment of new therapeutic targets to modulate cardiac function in physiological and pathological conditions. Data are available via ProteomeXchange with identifier PXD002331.

Implementing visible 473 nm photodissociation in a Q-Exactive mass spectrometer: towards specific detection of cysteine-containing peptides

Improvement of the fragmentation specificity may streamline data processing of bottom-up proteomic experiments by drastically reducing either the amount of MS/MS data to process in the discovery phase or the detection of interfering signals in targeted quantification. Photodissociation at appropriate wavelengths is a promising alternative technique to the non-discriminating conventional activation mode by collision. Here, we describe the implementation of visible LID at 473 nm in a Q-Exactive-Orbitrap mass spectrometer for the specific detection of cysteine-containing peptides tagged with a Dabcyl group. HCD cell DC offset and irradiation time were optimized to obtain high fragmentation yield and spectra free of contaminating CID product ions, while keeping the irradiation time scale compatible with chromatographic separation. With this optimized experimental set-up, the selective detection of cysteine-containing peptides in a whole tryptic hydrolysate of three combined proteins is demonstrated by comparing all ion fragmentation (AIF) spectra recorded online with and without laser irradiation.