To label or not to label: Applications of quantitative proteomics in neuroscience research

Sex-specific cortical, hippocampal and thalamic whole genome transcriptome data from controls and a G72 schizophrenia mouse model

OBJECTIVES

The G72 mouse model of schizophrenia represents a well-known model that was generated to meet the main translational criteria of isomorphism, homology and predictability of schizophrenia to a maximum extent. In order to get a more detailed view of the complex etiopathogenesis of schizophrenia, whole genome transcriptome studies turn out to be indispensable. Here we carried out microarray data collection based on RNA extracted from the retrosplenial cortex, hippocampus and thalamus of G72 transgenic and wild-type control mice. Experimental animals were age-matched and importantly, both sexes were considered separately.

DATA DESCRIPTION

The isolated RNA from all three brain regions was purified, quantified und quality controlled before initiation of the hybridization procedure with SurePrint G3 Mouse Gene Expression v2 8  ×  60 K microarrays. Following immunofluorescent measurement und preprocessing of image data, raw transcriptome data from G72 mice and control animals were extracted and uploaded in a public database. Our data allow insight into significant alterations in gene transcript levels in G72 mice and enable the reader/user to perform further complex analyses to identify potential age-, sex- and brain-region-specific alterations in transcription profiles and related pathways. The latter could facilitate biomarker identification and drug research and development in schizophrenia research.

Double-Edged Sword Effect of Pyroptosis: The Role of Caspase-1/-4/-5/-11 in Different Levels of Apical Periodontitis

The study was to investigate the effect of canonical and noncanonical pyroptosis in apical periodontitis. Proteins' profiles of human apical periodontitis tissue were analyzed by label-free proteomics. Immunofluorescence was used to detect proteins related to pyroptosis in human apical periodontitis tissues and experimental apical periodontitis models. A dual experimental apical periodontitis model with both smaller (mandible) and larger (maxilla) bone lesions was established. THP-1-derived macrophages were stimulated with P. gingivalis lipopolysaccharide in vitro with or without the caspase-1/-4/-5 inhibitor Ac-FTDL-CMK. Propidium iodide staining, lactic dehydrogenase release and Western blot were applied to evaluate cell death and the protein expression. Caspase-1/-4/-5 were expressed in human apical periodontitis tissues. Caspase-1/-11 were involved in bone loss in experimental apical periodontitis. Caspase-1/-11 inhibitors reduced bone loss in larger lesions (maxilla) but accelerated bone loss in smaller lesions (mandible). Caspase-1/-4/-5 inhibitors also showed double-edged sword effects on propidium iodide staining and lactic dehydrogenase release in vitro. The expression of cleaved-caspase-1/-4/-5, mature interluekin-1β and gasdermin D N-terminal domain increased in THP-1-derived macrophages after lipopolysaccharide stimulation but decreased after treatment with Ac-FTDL-CMK. Pyroptosis contributed to apical periodontitis and excited a double-edged sword effect in inducing bone loss in vivo and cell death in vitro.

Assessment of label-free quantification and missing value imputation for proteomics in non-human primates

BACKGROUND

Reliable and effective label-free quantification (LFQ) analyses are dependent not only on the method of data acquisition in the mass spectrometer, but also on the downstream data processing, including software tools, query database, data normalization and imputation. In non-human primates (NHP), LFQ is challenging because the query databases for NHP are limited since the genomes of these species are not comprehensively annotated. This invariably results in limited discovery of proteins and associated Post Translational Modifications (PTMs) and a higher fraction of missing data points. While identification of fewer proteins and PTMs due to database limitations can negatively impact uncovering important and meaningful biological information, missing data also limits downstream analyses (e.g., multivariate analyses), decreases statistical power, biases statistical inference, and makes biological interpretation of the data more challenging. In this study we attempted to address both issues: first, we used the MetaMorphues proteomics search engine to counter the limits of NHP query databases and maximize the discovery of proteins and associated PTMs, and second, we evaluated different imputation methods for accurate data inference. We used a generic approach for missing data imputation analysis without distinguising the potential source of missing data (either non-assigned m/z or missing values across runs).

RESULTS

Using the MetaMorpheus proteomics search engine we obtained quantitative data for 1622 proteins and 10,634 peptides including 58 different PTMs (biological, metal and artifacts) across a diverse age range of NHP brain frontal cortex. However, among the 1622 proteins identified, only 293 proteins were quantified across all samples with no missing values, emphasizing the importance of implementing an accurate and statiscaly valid imputation method to fill in missing data. In our imputation analysis we demonstrate that Single Imputation methods that borrow information from correlated proteins such as Generalized Ridge Regression (GRR), Random Forest (RF), local least squares (LLS), and a Bayesian Principal Component Analysis methods (BPCA), are able to estimate missing protein abundance values with great accuracy.

CONCLUSIONS

Overall, this study offers a detailed comparative analysis of LFQ data generated in NHP and proposes strategies for improved LFQ in NHP proteomics data.

Benchmarking differential expression, imputation and quantification methods for proteomics data

Data analysis is a critical part of quantitative proteomics studies in interpreting biological questions. Numerous computational tools for protein quantification, imputation and differential expression (DE) analysis were generated in the past decade and the search for optimal tools is still going on. Moreover, due to the rapid development of RNA sequencing (RNA-seq) technology, a vast number of DE analysis methods were created for that purpose. The applicability of these newly developed RNA-seq-oriented tools to proteomics data remains in doubt. In order to benchmark these analysis methods, a proteomics dataset consisting of proteins derived from humans, yeast and drosophila, in defined ratios, was generated in this study. Based on this dataset, DE analysis tools, including microarray- and RNA-seq-based ones, imputation algorithms and protein quantification methods were compared and benchmarked. Furthermore, applying these approaches to two public datasets showed that RNA-seq-based DE tools achieved higher accuracy (ACC) in identifying DEPs. This study provides useful guidelines for analyzing quantitative proteomics datasets. All the methods used in this study were integrated into the Perseus software, version 2.0.3.0, which is available at https://www.maxquant.org/perseus.

Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers

Post-translational modifications (PTMs) add a layer of complexity to the proteome through the addition of biochemical moieties to specific residues of proteins, altering their structure, function and/or localization. Mass spectrometry (MS)-based techniques are at the forefront of PTM analysis due to their ability to detect large numbers of modified proteins with a high level of sensitivity and specificity. The low stoichiometry of modified peptides means fractionation and enrichment techniques are often performed prior to MS to improve detection yields. Immuno-based techniques remain popular, with improvements in the quality of commercially available modification-specific antibodies facilitating the detection of modified proteins with high affinity. PTM-focused studies on blood cancers have provided information on altered cellular processes, including cell signaling, apoptosis and transcriptional regulation, that contribute to the malignant phenotype. Furthermore, the mechanism of action of many blood cancer therapies, such as kinase inhibitors, involves inhibiting or modulating protein modifications. Continued optimization of protocols and techniques for PTM analysis in blood cancer will undoubtedly lead to novel insights into mechanisms of malignant transformation, proliferation, and survival, in addition to the identification of novel biomarkers and therapeutic targets. This review discusses techniques used for PTM analysis and their applications in blood cancer research.

Proteomic Analysis of Brain Regions Reveals Brain Regional Differences and the Involvement of Multiple Keratins in Chronic Alcohol Neurotoxicity

AIMS

Alcohol abuse has attracted public attention and chronic alcohol exposure can result in irreversible structural changes in the brain. The molecular mechanisms underlying alcohol neurotoxicity are complex, mandating comprehensive mining of spatial protein expression profile.

METHODS

In this study, mice models of chronic alcohol intoxication were established after 95% alcohol vapor administration for 30 consecutive days. On Day 30, striatum (the dorsal and ventral striatum) and hippocampus, the two major brain regions responsible for learning and memorizing while being sensitive to alcohol toxicity, were collected. After that, isobaric tags for relative and absolute quantitation -based quantitative proteomic analysis were carried out for further exploration of the novel mechanisms underlying alcohol neurotoxicity.

RESULTS

Proteomic results showed that in the striatum, 29 proteins were significantly up-regulated and 17 proteins were significantly down-regulated. In the hippocampus, 72 proteins were significantly up-regulated, while 2 proteins were significantly down-regulated. Analysis of the overlay proteins revealed that a total of 102 proteins were consistently altered (P < 0.05) in both hippocampus and striatum regions, including multiple keratins such as Krt6a, Krt17 and Krt5. Ingenuity pathway analysis revealed that previously reported diseases/biofunctions such as dermatological diseases and developmental disorders were enriched in those proteins. Interestingly, the glucocorticoid receptor (GR) signaling was among the top enriched pathways in both brain regions, while multiple keratins from the GR signaling such as Krt1 and Krt17 exhibited significantly opposite expression patterns in the two brain nuclei. Moreover, there are several other involved pathways significantly differed between the hippocampus and striatum.

CONCLUSIONS

Our data revealed brain regional differences upon alcohol consumption and indicated the critical involvement of keratins from GR signaling in alcohol neurotoxicity. The differences in proteomic results between the striatum and hippocampus suggested a necessity of taking into consideration brain regional differences and intertwined signaling pathways rather than merely focusing on single nuclei or molecule during the study of drug-induced neurotoxicity in the future.

Characterization of Odontogenic Differentiation from Human Dental Pulp Stem Cells Using TMT-Based Proteomic Analysis

Background

The repair of dental pulp injury relies on the odontogenic differentiation of dental pulp stem cells (DPSCs). To better understand the odontogenic differentiation of DPSCs and identify proteins involved in this process, tandem mass tags (TMTs) coupled with liquid chromatography-tandem mass spectrometry (LC-MS/MS) were applied to compare the proteomic profiles of induced and control DPSCs.

Methods

The proteins expressed during osteogenic differentiation of human DPSCs were profiled using the TMT method combined with LC-MS/MS analysis. The identified proteins were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. Then, a protein-protein interaction (PPI) network was constructed. Two selected proteins were confirmed by western blotting (WB) analysis.

Results

A total of 223 proteins that were differentially expressed were identified. Among them, 152 proteins were significantly upregulated and 71 were downregulated in the odontogenic differentiation group compared with the control group. On the basis of biological processes in GO, the identified proteins were mainly involved in cellular processes, metabolic processes, and biological regulation, which are connected with the signaling pathways highlighted by KEGG pathway analysis. PPI networks showed that most of the differentially expressed proteins were implicated in physical or functional interaction. The protein expression levels of FBN1 and TGF-β2 validated by WB were consistent with the proteomic analysis.

Conclusions

This is the first proteomic analysis of human DPSC odontogenesis using a TMT method. We identified many new differentially expressed proteins that are potential targets for pulp-dentin complex regeneration and repair.

THE PRESENT AND FUTURE OF THE MASS SPECTROMETRY-BASED INVESTIGATION OF THE EXOSOME LANDSCAPE

Exosomes are critical intercellular messengers released upon the fusion of multivesicular bodies with the cellular plasma membrane that deliver their cargo in the form of extracellular vesicles. Containing numerous nonrandomly packed functional proteins, lipids, and RNAs, exosomes are vital intercellular messengers that contribute to the physiologic processes of the healthy organism. During the post-genome era, exosome-oriented proteomics have garnered great interest. Since its establishment, mass spectrometry (MS) has been indispensable for the field of proteomics research and has advanced rapidly to interrogate biological samples at a higher resolution and sensitivity. Driven by new methodologies and more advanced instrumentation, MS-based approaches have revolutionized our understanding of protein biology. As the access to online proteomics database platforms has blossomed, experimental data processing occurs with more speed and accuracy. Here, we review recent advances in the technological progress of MS-based proteomics and several new detection strategies for MS-based proteomics research. We also summarize the use of integrated online databases for proteomics research in the era of big data. © 2020 John Wiley & Sons Ltd. Mass Spec Rev.

Brain IGFBP-5 modifies the relation of depressive symptoms to decline in cognition in older persons

BACKGROUND

Brain proteins, including Insulin-like Growth Factor Binding Protein 5 (IGFBP-5), have been associated with cognitive dysfunction in aging. Mechanisms linking depression with cognition are poorly understood. We hypothesize that the association of depressive symptoms with cognition is mediated or modified by brain proteins.

METHODS

IGFBP-5, HSPB2, AK4, ITPK1 and PLXNB1 were measured in dorsolateral prefrontal cortex in 1057 deceased participants, who underwent annual assessments of depressive symptoms and cognition for a mean of 8.9 years. The average number of depressive symptoms per year before a dementia diagnosis was calculated for each person.

RESULTS

A one standard deviation above the mean IGFBP-5 was associated with a 14% higher odds of having more depressive symptoms (p < 0.031). Higher IGFBP-5 was associated with faster decline in global cognition (p < 0.001) and five cognitive domains (p < 0.008), controlling for depressive symptoms. IGFBP-5 moderated the association of depressive symptoms with decline in global cognition (p = 0.045). IGFBP-5 mediated ten percent or less of the total effect of depressive symptoms on decline in global cognition and the cognitive domains (p > 0.070).

LIMITATIONS

Participants were volunteers and self-selection bias limits the generalizability of our findings. In addition, we used self-reported data on depressive symptoms. However, we also used data on depression medications as sensitivity analyses to confirm findings.

CONCLUSIONS

In old age, brain IGFBP-5 is associated with depressive symptoms and cognition. The association of depressive symptoms with cognitive decline is conditional on IGFBP-5.

NHS-based Tandem Mass Tagging of Proteins at the Level of Whole Cells: A Critical Evaluation in Comparison to Conventional TMT-Labeling Approaches for Quantitative Proteome Analysis

Tandem mass tags (TMT) are usually introduced at the levels of isolated proteins or peptides. Here, for the first time, we report the labeling of whole cells and a critical evaluation of its performance in comparison to conventional labeling approaches. The obtained results indicated that TMT protein labeling using intact cells is generally possible, if it is coupled to a subsequent enrichment using anti-TMT antibody. The quantitative results were similar to those obtained after labeling of isolated proteins and both were found to be slightly complementary to peptide labeling. Furthermore, when using NHS-based TMT, no specificity towards cell surface proteins was observed in the case of cell labeling. In summary, the conducted study revealed first evidence for the general possibility of TMT cell labeling and highlighted limitations of NHS-based labeling reagents. Future studies should therefore focus on the synthesis and investigation of membrane impermeable TMTs to increase specificity towards cell surface proteins.

Olfactory bulb neuroproteomics reveals a chronological perturbation of survival routes and a disruption of prohibitin complex during Alzheimer's disease progression

Olfactory dysfunction is among the earliest features of Alzheimer’s disease (AD). Although neuropathological abnormalities have been detected in the olfactory bulb (OB), little is known about its dynamic biology. Here, OB- proteome analysis showed a stage-dependent synaptic proteostasis impairment during AD evolution. In addition to progressive modulation of tau and amyloid precursor protein (APP) interactomes, network-driven proteomics revealed an early disruption of upstream and downstream p38 MAPK pathway and a subsequent impairment of Phosphoinositide-dependent protein kinase 1 (PDK1)/Protein kinase C (PKC) signaling axis in the OB from AD subjects. Moreover, a mitochondrial imbalance was evidenced by a depletion of Prohibitin-2 (Phb2) levels and a specific decrease in the phosphorylated isoforms of Phb1 in intermediate and advanced AD stages. Interestingly, olfactory Phb subunits were also deregulated across different types of dementia. Phb2 showed a specific up-regulation in mixed dementia, while Phb1 isoforms were down-regulated in frontotemporal lobar degeneration (FTLD). However, no differences were observed in the olfactory expression of Phb subunits in progressive supranuclear palsy (PSP). To sum up, our data reflect, in part, the missing links in the biochemical understanding of olfactory dysfunction in AD, unveiling Phb complex as a differential driver of neurodegeneration at olfactory level.

Using 15N-Metabolic Labeling for Quantitative Proteomic Analyses

Quantitative proteomics has benefited from the application of stable isotope labeling-based approaches. Using stable isotopically labeled material as an internal standard in proteomic comparisons allows an unbiased and accurate quantification of protein expression level changes. Here, we describe the use of in vivo ¹⁵N metabolic labeling to generate labeled protein standards from mice. We then present a protocol including sample preparation, mass spectrometry, and data analysis workflows using these standards to compare unlabeled proteomes. We focus on mouse brain tissue and plasma samples, although this conceptual framework can be applied to most organisms.

Stable isotope metabolic labeling suggests differential turnover of the DPYSL protein family

PURPOSE

In this work, we discuss how in vivo ¹⁵ N metabolic labeling in combination with MS simultaneously provides information on protein expression and protein turnover.

EXPERIMENTAL DESIGN

We metabolically labeled mice with the stable nitrogen isotope ¹⁵ N using a ¹⁵ N-enriched diet and analyzed unlabeled (¹⁴ N) versus ¹⁵ N-labeled brain tissue with LC-MS/MS. We then compared the ¹⁴ N versus ¹⁵ N peptide isotopologue clusters of ¹⁴ N and ¹⁵ N-labeled dihydropyrimidinase-related (DPYSL) proteins.

RESULTS

We present a workflow assessing protein expression and turnover at different time points of mouse brain development. Our data demonstrate distinct protein turnover patterns of DPYSL3 and DPYSL5 compared to other quantified proteins. We report the presence of two DPYSL3 and DPYSL5 populations with different ¹⁵ N incorporation rates, indicating altered protein turnover during development.

CONCLUSIONS AND CLINICAL RELEVANCE

In vivo ¹⁵ N metabolic labeling allows the simultaneous investigation of protein expression and turnover, enabling detailed protein dynamics studies. We report for the first time protein turnover data for the DPYSL2, DPYSL3, and DPYSL5 protein family members. As DPYSL proteins have important functions for nervous system maturation, our data provide useful information on their molecular fate during brain development.

Mass Spectrometry-Based Approaches to Understand the Molecular Basis of Memory

The central nervous system is responsible for an array of cognitive functions such as memory, learning, language, and attention. These processes tend to take place in distinct brain regions; yet, they need to be integrated to give rise to adaptive or meaningful behavior. Since cognitive processes result from underlying cellular and molecular changes, genomics and transcriptomics assays have been applied to human and animal models to understand such events. Nevertheless, genes and RNAs are not the end products of most biological functions. In order to gain further insights toward the understanding of brain processes, the field of proteomics has been of increasing importance in the past years. Advancements in liquid chromatography-tandem mass spectrometry (LC-MS/MS) have enabled the identification and quantification of thousands of proteins with high accuracy and sensitivity, fostering a revolution in the neurosciences. Herein, we review the molecular bases of explicit memory in the hippocampus. We outline the principles of mass spectrometry (MS)-based proteomics, highlighting the use of this analytical tool to study memory formation. In addition, we discuss MS-based targeted approaches as the future of protein analysis.

Innate and adaptive immunity in the development of depression: An update on current knowledge and technological advances

The inflammation theory of depression, proposed over 20years ago, was influenced by early studies on T cell responses and since then has been a stimulus for numerous research projects aimed at understanding the relationship between immune function and depression. Observational studies have shown that indicators of immunity, especially C reactive protein and proinflammatory cytokines, such as interleukin 6, are associated with an increased risk of depressive disorders, although the evidence from randomized trials remains limited and only few studies have assessed the interplay between innate and adaptive immunity in depression. In this paper, we review current knowledge on the interactions between central and peripheral innate and adaptive immune molecules and the potential role of immune-related activation of microglia, inflammasomes and indoleamine-2,3-dioxygenase in the development of depressive symptoms. We highlight how combining basic immune methods with more advanced 'omics' technologies would help us to make progress in unravelling the complex associations between altered immune function and depressive disorders, in the identification of depression-specific biomarkers and in developing immunotherapeutic treatment strategies that take individual variability into account.

Drafting the proteome landscape of myeloid-derived suppressor cells

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of cells that are defined by their myeloid origin, immature state, and ability to potently suppress T-cell responses. They regulate immune responses and the population significantly increases in the tumor microenvironment of patients with glioma and other malignant tumors. For their study, MDSCs are usually isolated from the spleen or directly of tumors from a large number of tumor-bearing mice although promising ex vivo differentiated MDSC production systems have been recently developed. During the last years, proteomics has emerged as a powerful approach to analyze MDSCs proteomes using shotgun-based mass spectrometry (MS), providing functional information about cellular homeostasis and metabolic state at a global level. Here, we will revise recent proteome profiling studies performed in MDSCs from different origins. Moreover, we will perform an integrative functional analysis of the protein compilation derived from these large-scale proteomic studies in order to obtain a comprehensive view of MDSCs biology. Finally, we will also discuss the potential application of high-throughput proteomic approaches to study global proteome dynamics and post-translational modifications (PTMs) during the differentiation process of MDSCs that will greatly boost the identification of novel MDSC-specific therapeutic targets to apply in cancer immunotherapy.

Analytical platforms in vitreoretinal proteomics

Current proteomic technologies can effectively be used to study the proteins of the vitreous body and retina in health and disease. The use of appropriate samples, analytical platform and bioinformatic method are essential factors to consider when undertaking such studies. Certain proteins may hinder the detection and evaluation of more relevant proteins associated with pathological processes if not carefully considered, particularly in the sample preparation and data analysis stages. The utilization of more than one quantification technique and database search program to expand the level of proteome coverage and analysis will help to generate more robust and worthwhile results. This review discusses important aspects of sample processing and the use of label and label-free quantitative proteomics strategies applied to the vitreous and retina.

What Have Mass Spectrometry-Based Proteomics and Metabolomics (Not) Taught Us about Psychiatric Disorders?

Understanding the molecular causes and finding appropriate therapies for psychiatric disorders are challenging tasks for research; -omics technologies are used to elucidate the molecular mechanisms underlying brain dysfunction in a hypothesis-free manner. In this review, we will focus on mass spectrometry-based proteomics and metabolomics and address how these approaches have contributed to our understanding of psychiatric disorders. Specifically, we will discuss what we have learned from mass spectrometry-based proteomics and metabolomics studies in rodent models and human cohorts, outline current limitations and discuss the potential of these methods for future applications in psychiatry.

Can proteomics-based diagnostics aid clinical psychiatry?

Despite major advances in infrastructure and instrumentation, proteomics-driven translational applications have not yet yielded the results that the scientific community has envisaged. In this viewpoint, the perspective of proteomics-based diagnostics in the field of clinical psychiatry is explored. The challenges that proteomics faces in the context of translational approaches are outlined and directions toward a successful clinical implementation are provided. Additional challenges that psychiatric disorders pose for clinical proteomics are highlighted and the potential of proteomics-based, blood tests for psychiatric disorders is being assessed. Proteomics offers a valuable toolkit for clinical translation that needs to be handled in a pragmatic manner and with realistic expectations.

In-depth comparative proteomic analysis of yeast proteome using iTRAQ and SWATH based MS

SWATH is capable of quantifying proteins of lower abundance as compared to iTRAQ.

Resonant waveguide grating for monitoring biomolecular interactions

Label-free detection technologies have been widely used to characterize biomolecular interactions without having to label the target molecules. These technologies exhibit considerable potential in facilitating assay development and enabling new integrated readouts. When combined with high-throughput capability, label-free detection may be applied to small molecule screens for drug candidates. Based on the resonant waveguide grating biosensors, a label-free high-throughput detection system, the Epic(®) System, has been applied to monitor molecular interactions. Here we describe a generic label-free assay to quantitatively measure phospho-specific interactions between a trafficking signal-phosphorylated SWTY peptide and 14-3-3 proteins or anti-phosphopeptide antibodies. Compared with the solution-based fluorescence anisotropy assay, our results support that the high-throughput resonant waveguide grating biosensor system has shown the capability not only for high-throughput characterization of binding rank and affinity but also for the exploration of potential interacting kinases for the substrates. Hence, it provides a new generic HTS platform for phospho-detection.

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

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

Proteomics, metabolomics, and protein interactomics in the characterization of the molecular features of major depressive disorder

Omics technologies emerged as complementary strategies to genomics in the attempt to understand human illnesses. In general, proteomics technologies emerged earlier than those of metabolomics for major depressive disorder (MDD) research, but both are driven by the identification of proteins and/or metabolites that can delineate a comprehensive characterization of MDD's molecular mechanisms, as well as lead to the identification of biomarker candidates of all types—prognosis, diagnosis, treatment, and patient stratification. Also, one can explore protein and metabolite interactomes in order to pinpoint additional molecules associated with the disease that had not been picked up initially. Here, results and methodological aspects of MDD research using proteomics, metabolomics, and protein interactomics are reviewed, focusing on human samples.

Maternal serum serpin B7 is associated with early spontaneous preterm birth

OBJECTIVE

We sought to identify serum biomarkers of early spontaneous preterm birth (SPTB) using semiquantitative proteomic analyses.

STUDY DESIGN

This was a nested case-control study of pregnant women with previous SPTB. Maternal serum was collected at 19-24 and 28-32 weeks' gestation, and analyzed by liquid chromatography-multiple reaction monitoring/mass spectrometry. Targeted and shotgun proteomics identified 31 candidate proteins that were differentially expressed in pooled serum samples from spontaneous preterm (cases [<34 weeks]) and term (controls) deliveries. Candidate protein expression was compared in individual serum samples between cases and controls matched by age and race groups, and clinical site. Protein expression was verified by Western blot in the placenta and fetal membranes from cases and controls.

RESULTS

Serum samples were available for 35 cases and 35 controls at 19-24 weeks, and 16 cases and 16 controls at 28-32 weeks. One protein, serpin B7, yielded serum concentrations that differed between cases and controls. The mean concentration of serpin B7 at 28-32 weeks was 1.5-fold higher in women with subsequent preterm deliveries compared to controls; there was no difference at 19-24 weeks. Higher levels of serpin B7 at both gestational age windows were associated with a shorter interval to delivery, and higher levels of serpin B7 in samples from 28-32 weeks were associated with a lower gestational age at delivery. Western blotting identified serpin B7 protein in placenta, amnion, and chorion from cases and controls.

CONCLUSION

Targeted and shotgun serum proteomics analyses associated 1 protein, serpin B7, with early SPTB. Our results require validation in other cohorts and analysis of the possible mechanistic role of serpin B7 in parturition.

Quantitative proteomics in cardiovascular research: global and targeted strategies

Extensive technical advances in the past decade have substantially expanded quantitative proteomics in cardiovascular research. This has great promise for elucidating the mechanisms of cardiovascular diseases and the discovery of cardiac biomarkers used for diagnosis and treatment evaluation. Global and targeted proteomics are the two major avenues of quantitative proteomics. While global approaches enable unbiased discovery of altered proteins via relative quantification at the proteome level, targeted techniques provide higher sensitivity and accuracy, and are capable of multiplexed absolute quantification in numerous clinical/biological samples. While promising, technical challenges need to be overcome to enable full utilization of these techniques in cardiovascular medicine. Here, we discuss recent advances in quantitative proteomics and summarize applications in cardiovascular research with an emphasis on biomarker discovery and elucidating molecular mechanisms of disease. We propose the integration of global and targeted strategies as a high-throughput pipeline for cardiovascular proteomics. Targeted approaches enable rapid, extensive validation of biomarker candidates discovered by global proteomics. These approaches provide a promising alternative to immunoassays and other low-throughput means currently used for limited validation.

2DE: the phoenix of proteomics

Given the rapid developments in mass spectrometry (MS) in terms of sensitivity, mass accuracy, and throughput, some have suggested that two-dimensional gel electrophoresis (2DE) may no longer be a method of choice for proteomic analyses. However, as recognition of issues with these newer shotgun-MS approaches grows, there is a fresh and growing regard for the maturity of 2DE-MS as a genuine top-down analytical approach, particularly as it resolves thousands of intact protein species in a single run, enabling the simultaneous analysis of total protein complement, including isoforms and post-translational modifications. Given the strengths of both, it is most appropriate to view these as complementary or at least parallel approaches: as proteins encompass a myriad of physico-chemical properties, and the real aim is to explore proteomes as deeply as possible, all available resolving strategies must be considered in terms of the complexity encountered. It is time to critically and constructively focus on the optimization and integration of existing techniques rather than simplistically suggesting that one should replace the other. Our intention here is thus to present an overview of protein resolving techniques, focusing on milestones associated with 2DE, including pros, cons, advances and variations, in particular relative to shotgun proteomic approaches.

BIOLOGICAL SIGNIFICANCE

Proteomic researchers recognize the importance of 2DE in the history of proteomics. But the latest developments in mass spectrometry-based techniques have led some researchers to retire 2DE in their labs. However, we argue here that 2DE-MS is a genuine top-down analytical approach. The significance of this discussion is to make proteomic researchers aware of the importance of this technique in a proteomic pipeline. This article is part of a Special Issue entitled: Environmental and structural proteomics.

Trimethylation enhancement using diazomethane (TrEnDi): rapid on-column quaternization of peptide amino groups via reaction with diazomethane significantly enhances sensitivity in mass spectrometry analyses via a fixed, permanent positive charge

Defining cellular processes relies heavily on elucidating the temporal dynamics of proteins. To this end, mass spectrometry (MS) is an extremely valuable tool; different MS-based quantitative proteomics strategies have emerged to map protein dynamics over the course of stimuli. Herein, we disclose our novel MS-based quantitative proteomics strategy with unique analytical characteristics. By passing ethereal diazomethane over peptides on strong cation exchange resin within a microfluidic device, peptides react to contain fixed, permanent positive charges. Modified peptides display improved ionization characteristics and dissociate via tandem mass spectrometry (MS(2)) to form strong a2 fragment ion peaks. Process optimization and determination of reactive functional groups enabled a priori prediction of MS(2) fragmentation patterns for modified peptides. The strategy was tested on digested bovine serum albumin (BSA) and successfully quantified a peptide that was not observable prior to modification. Our method ionizes peptides regardless of proton affinity, thus decreasing ion suppression and permitting predictable multiple reaction monitoring (MRM)-based quantitation with improved sensitivity.

The yeast Sks1p kinase signaling network regulates pseudohyphal growth and glucose response

The yeast Saccharomyces cerevisiae undergoes a dramatic growth transition from its unicellular form to a filamentous state, marked by the formation of pseudohyphal filaments of elongated and connected cells. Yeast pseudohyphal growth is regulated by signaling pathways responsive to reductions in the availability of nitrogen and glucose, but the molecular link between pseudohyphal filamentation and glucose signaling is not fully understood. Here, we identify the glucose-responsive Sks1p kinase as a signaling protein required for pseudohyphal growth induced by nitrogen limitation and coupled nitrogen/glucose limitation. To identify the Sks1p signaling network, we applied mass spectrometry-based quantitative phosphoproteomics, profiling over 900 phosphosites for phosphorylation changes dependent upon Sks1p kinase activity. From this analysis, we report a set of novel phosphorylation sites and highlight Sks1p-dependent phosphorylation in Bud6p, Itr1p, Lrg1p, Npr3p, and Pda1p. In particular, we analyzed the Y309 and S313 phosphosites in the pyruvate dehydrogenase subunit Pda1p; these residues are required for pseudohyphal growth, and Y309A mutants exhibit phenotypes indicative of impaired aerobic respiration and decreased mitochondrial number. Epistasis studies place SKS1 downstream of the G-protein coupled receptor GPR1 and the G-protein RAS2 but upstream of or at the level of cAMP-dependent PKA. The pseudohyphal growth and glucose signaling transcription factors Flo8p, Mss11p, and Rgt1p are required to achieve wild-type SKS1 transcript levels. SKS1 is conserved, and deletion of the SKS1 ortholog SHA3 in the pathogenic fungus Candida albicans results in abnormal colony morphology. Collectively, these results identify Sks1p as an important regulator of filamentation and glucose signaling, with additional relevance towards understanding stress-responsive signaling in C. albicans.

Eukaryotic cells respond to nutritional and environmental stress through complex regulatory programs controlling cell metabolism, growth, and morphology. In the budding yeast Saccharomyces cerevisiae, conditions of limited nitrogen and/or glucose can initiate a dramatic growth transition wherein the yeast cells form extended multicellular filaments resembling the true hyphal tubes of filamentous fungi. The formation of these pseudohyphal filaments is governed by core regulatory pathways that have been studied for decades; however, the mechanism by which these signaling systems are integrated is less well understood. We find that the protein kinase Sks1p contributes to the integration of signals for nitrogen and/or glucose limitation, resulting in pseudohyphal growth. We implemented a mass spectrometry-based approach to profile phosphorylation events across the proteome dependent upon Sks1p kinase activity and identified phosphorylation sites important for mitochondrial function and pseudohyphal growth. Our studies place Sks1p in the regulatory context of a well-known pseudohyphal growth signaling pathway. We further find that SKS1 is conserved and required for stress-responsive colony morphology in the principal opportunistic human fungal pathogen Candida albicans. Thus, Sks1p is part of the mechanism integrating glucose-responsive cell signaling and pseudohyphal growth, and its function is required for colony morphology linked with virulence in C. albicans.

A decade of proteomics studies of glaucomatous neurodegeneration

Glaucoma is a leading cause of blindness; however, limited understanding of the molecular mechanisms involved in optic nerve degeneration hinders the development of improved treatment strategies. Proteomics techniques that combine the protein chemistry, MS, and bioinformatics offer the opportunity to shed light on molecular mechanisms so that new treatment strategies can be developed for immunomodulation, neuroprotection, neurorescue, neuroregeneration, and function gain in glaucoma. The proteomics technologies also hold great promise for biomarker discovery, another important goal of glaucoma research. As much as developing new treatment strategies, molecular biomarkers are strongly needed for early diagnosis of glaucoma, prediction of its prognosis, and monitoring the responses to new treatments. It is now a decade that the proteomics analysis techniques have been using to move glaucoma research forward. This review will focus on valuable applications of proteomics in the field of glaucoma research and highlight the power of this analytical toolbox in translational and clinical research toward better characterization and improved treatment of glaucomatous neurodegeneration and discovery of glaucoma-related molecular biomarkers.

Recent advances in molecular imaging of premalignant gastrointestinal lesions and future application for early detection of barrett esophagus

Recent advances in optical molecular imaging allow identification of morphologic and biochemical changes in tissues associated with gastrointestinal (GI) premalignant lesions earlier and in real-time. This focused review series introduces high-resolution imaging modalities that are being evaluated preclinically and clinically for the detection of early GI cancers, especially Barrett esophagus and esophageal adenocarcinoma. Although narrow band imaging, autofluorescence imaging, and chromoendoscopy are currently applied for this purpose in the clinic, further adoptions of probe-based confocal laser endomicroscopy, high-resolution microendoscopy, optical coherence tomography, and metabolomic imaging, as well as imaging mass spectrometry, will lead to detection at the earliest and will guide predictions of the clinical course in the near future in a manner that is beyond current advancements in optical imaging. In this review article, the readers will be introduced to sufficient information regarding this matter with which to enjoy this new era of high technology and to confront science in the field of molecular medical imaging.

Integrative biological analysis for neuropsychopharmacology

Although advances in psychotherapy have been made in recent years, drug discovery for brain diseases such as schizophrenia and mood disorders has stagnated. The need for new biomarkers and validated therapeutic targets in the field of neuropsychopharmacology is widely unmet. The brain is the most complex part of human anatomy from the standpoint of number and types of cells, their interconnections, and circuitry. To better meet patient needs, improved methods to approach brain studies by understanding functional networks that interact with the genome are being developed. The integrated biological approaches--proteomics, transcriptomics, metabolomics, and glycomics--have a strong record in several areas of biomedicine, including neurochemistry and neuro-oncology. Published applications of an integrated approach to projects of neurological, psychiatric, and pharmacological natures are still few but show promise to provide deep biological knowledge derived from cells, animal models, and clinical materials. Future studies that yield insights based on integrated analyses promise to deliver new therapeutic targets and biomarkers for personalized medicine.

Protein-protein interactions: switch from classical methods to proteomics and bioinformatics-based approaches

Following the sequencing of the human genome and many other organisms, research on protein-coding genes and their functions (functional genomics) has intensified. Subsequently, with the observation that proteins are indeed the molecular effectors of most cellular processes, the discipline of proteomics was born. Clearly, proteins do not function as single entities but rather as a dynamic network of team players that have to communicate. Though genetic (yeast two-hybrid Y2H) and biochemical methods (co-immunoprecipitation Co-IP, affinity purification AP) were the methods of choice at the beginning of the study of protein-protein interactions (PPI), in more recent years there has been a shift towards proteomics-based methods and bioinformatics-based approaches. In this review, we first describe in depth PPIs and we make a strong case as to why unraveling the interactome is the next challenge in the field of proteomics. Furthermore, classical methods of investigation of PPIs and structure-based bioinformatics approaches are presented. The greatest emphasis is placed on proteomic methods, especially native techniques that were recently developed and that have been shown to be reliable. Finally, we point out the limitations of these methods and the need to set up a standard for the validation of PPI experiments.

Comparison of label-free and label-based strategies for proteome analysis of hepatoma cell lines

Within the past decade numerous methods for quantitative proteome analysis have been developed of which all exhibit particular advantages and disadvantages. Here, we present the results of a study aiming for a comprehensive comparison of ion-intensity based label-free proteomics and two label-based approaches using isobaric tags incorporated at the peptide and protein levels, respectively. As model system for our quantitative analysis we used the three hepatoma cell lines HepG2, Hep3B and SK-Hep-1. Four biological replicates of each cell line were quantitatively analyzed using an RPLC-MS/MS setup. Each quantification experiment was performed twice to determine technical variances of the different quantification techniques. We were able to show that the label-free approach by far outperforms both TMT methods regarding proteome coverage, as up to threefold more proteins were reproducibly identified in replicate measurements. Furthermore, we could demonstrate that all three methods show comparable reproducibility concerning protein quantification, but slightly differ in terms of accuracy. Here, label-free was found to be less accurate than both TMT approaches. It was also observed that the introduction of TMT labels at the protein level reduces the effect of underestimation of protein ratios, which is commonly monitored in case of TMT peptide labeling. Previously reported differences in protein expression between the particular cell lines were furthermore reproduced, which confirms the applicability of each investigated quantification method to study proteomic differences in such biological systems. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.

Quantitation of endogenous peptides using mass spectrometry based methods

The mass spectrometry-based 'omics' sub-discipline that focuses on comprehensive, often exploratory, analyses of endogenous peptides involved in cell-to-cell communication is oftentimes referred to as peptidomics. Although the progress in bioanalytical technology development for peptide discovery has been tremendous, perhaps the largest advances have involved robust quantitative mass spectrometric approaches and data mining algorithms. These efforts have accelerated the discovery and validation of biomarkers, functionally important posttranslational modifications, and unexpected molecular interactions, information that aids drug development. In this article we outline the current approaches used in quantitative peptidomics and the technical challenges that stimulate new advances in the field, while also reviewing the newest literature on functional characterizations of endogenous peptides using quantitative mass spectrometry.

Recent advances in quantitative neuroproteomics

The field of proteomics is undergoing rapid development in a number of different areas including improvements in mass spectrometric platforms, peptide identification algorithms and bioinformatics. In particular, new and/or improved approaches have established robust methods that not only allow for in-depth and accurate peptide and protein identification and modification, but also allow for sensitive measurement of relative or absolute quantitation. These methods are beginning to be applied to the area of neuroproteomics, but the central nervous system poses many specific challenges in terms of quantitative proteomics, given the large number of different neuronal cell types that are intermixed and that exhibit distinct patterns of gene and protein expression. This review highlights the recent advances that have been made in quantitative neuroproteomics, with a focus on work published over the last five years that applies emerging methods to normal brain function as well as to various neuropsychiatric disorders including schizophrenia and drug addiction as well as of neurodegenerative diseases including Parkinson's disease and Alzheimer's disease. While older methods such as two-dimensional polyacrylamide electrophoresis continued to be used, a variety of more in-depth MS-based approaches including both label (ICAT, iTRAQ, TMT, SILAC, SILAM), label-free (label-free, MRM, SWATH) and absolute quantification methods, are rapidly being applied to neurobiological investigations of normal and diseased brain tissue as well as of cerebrospinal fluid (CSF). While the biological implications of many of these studies remain to be clearly established, that there is a clear need for standardization of experimental design and data analysis, and that the analysis of protein changes in specific neuronal cell types in the central nervous system remains a serious challenge, it appears that the quality and depth of the more recent quantitative proteomics studies is beginning to shed light on a number of aspects of neuroscience that relates to normal brain function as well as of the changes in protein expression and regulation that occurs in neuropsychiatric and neurodegenerative disorders.

Proteomic profiling in schizophrenia: enabling stratification for more effective treatment

Schizophrenia is a heterogeneous psychiatric disorder characterized by an array of clinical manifestations. Although the best known manifestations include serious effects on mood and behavior, patients can also display co-morbidities, including immune system or metabolic abnormalities. Thorough characterization of these conditions using proteomic profiling methods has increased our knowledge of these molecular differences and has helped to unravel the complexity and heterogeneity of this debilitating condition. This could lead to patient stratification through characterization of biochemically different subtypes of the disease. In addition, proteomic methods have recently been used for molecular characterization of the mechanism of action of antipsychotic medications in both preclinical models and patients. This has resulted in identification of molecular panels that show some promise for prediction of response or for monitoring treatment outcome. This review describes how proteomic profiling methods can impact the future of schizophrenia diagnosis and therapeutics, and facilitate personalized medicine approaches for more effective treatment management of schizophrenia patients.

A proteomics view of the molecular mechanisms and biomarkers of glaucomatous neurodegeneration

Despite improving understanding of glaucoma, key molecular players of neurodegeneration that can be targeted for treatment of glaucoma, or molecular biomarkers that can be useful for clinical testing, remain unclear. Proteomics technology offers a powerful toolbox to accomplish these important goals of the glaucoma research and is increasingly being applied to identify molecular mechanisms and biomarkers of glaucoma. Recent studies of glaucoma using proteomics analysis techniques have resulted in the lists of differentially expressed proteins in human glaucoma and animal models. The global analysis of protein expression in glaucoma has been followed by cell-specific proteome analysis of retinal ganglion cells and astrocytes. The proteomics data have also guided targeted studies to identify post-translational modifications and protein-protein interactions during glaucomatous neurodegeneration. In addition, recent applications of proteomics have provided a number of potential biomarker candidates. Proteomics technology holds great promise to move glaucoma research forward toward new treatment strategies and biomarker discovery. By reviewing the major proteomics approaches and their applications in the field of glaucoma, this article highlights the power of proteomics in translational and clinical research related to glaucoma and also provides a framework for future research to functionally test the importance of specific molecular pathways and validate candidate biomarkers.

O potencial da rotulação metabólica de 15N para a pesquisa de esquizofrenia

Pesquisas em psiquiatria ainda necessitam de estudos não dirigidos por hipóteses para revelar fundamentos neurobiológicos e biomarcadores moleculares para distúrbios psiquiátricos. Metodologias proteômicas disponibilizam uma série de ferramentas para esses fins. Apresentamos o princípio de rotulação metabólica utilizando 15N para proteômica quantitativa e suas aplicações em modelos animais de fenótipos psiquiátricos com um foco particular em esquizofrenia. Exploramos o potencial de rotulação metabólica por 15N em diferentes tipos de experimentos, bem como suas considerações metodológicas.

Mass Spectrometry-based Proteomics and Peptidomics for Systems Biology and Biomarker Discovery

The scientific community has shown great interest in the field of mass spectrometry-based proteomics and peptidomics for its applications in biology. Proteomics technologies have evolved to produce large datasets of proteins or peptides involved in various biological and disease progression processes producing testable hypothesis for complex biological questions. This review provides an introduction and insight to relevant topics in proteomics and peptidomics including biological material selection, sample preparation, separation techniques, peptide fragmentation, post-translation modifications, quantification, bioinformatics, and biomarker discovery and validation. In addition, current literature and remaining challenges and emerging technologies for proteomics and peptidomics are presented.

An insight into iTRAQ: where do we stand now?

The iTRAQ (isobaric tags for relative and absolute quantification) technique is widely employed in proteomic workflows requiring relative quantification. Here, we review the iTRAQ literature; in particular, we focus on iTRAQ usage in relation to other commonly used quantitative techniques e.g. stable isotope labelling in culture (SILAC), label-free methods and selected reaction monitoring (SRM). As a result, we identify several issues arising with respect to iTRAQ. Perhaps frustratingly, iTRAQ's attractiveness has been undermined by a number of technical and analytical limitations: it may not be truly quantitative, as the changes in abundance reported will generally be underestimated. We discuss weaknesses and strengths of iTRAQ as a methodology for relative quantification in the light of this and other technical issues. We focus on technical developments targeted at iTRAQ accuracy and precision, use of 4-plex over 8-plex reagents and application of iTRAQ to post-translational modification (PTM) workflows. We also discuss iTRAQ in relation to label-free approaches, to which iTRAQ is losing ground.