Statistical issues in quality control of proteomic analyses: good experimental design and planning

Deep insights into the mechanism of isorhamnetin's anti-motion sickness effect based on photoshoproteomics

Isorhamnetin has recently been found to exhibit a remarkable anti-motion sickness effect, yet the underlying mechanism is still unclear. Herein, network pharmacology was employed to conduct a preliminary analysis on the possible biological processes involved. Results showed that common targets were localized in membranes, mitochondria, and glutamatergic synapses. In particular, protein phosphorylation, protein serine/threonine/tyrosinase activity and signal transduction might play a role in isorhamnetin's anti-motion sickness effect. Thus, mice phosphoproteomics analysis was further performed to explore the phosphorylated protein changes in the motion sickness process. Results showed that differentially phosphorylated proteins have an effect on postsynaptic density, glutamatergic synapses and other sites and are involved in various neurodegenerative disease pathways, endocytic pathways, cAMP signaling pathways and MAPK signaling pathways. Two key differentially phosphorylated proteins in glutamatergic synapses, namely, DLGAP and EPS8, might play key roles in isorhamnetin's anti-motion sickness process. The final molecular experimental verification results from qRT-PCR and western blot analyses indicated that isorhamnetin firstly regulates glutamatergic synapses and then reduces the excitability of the vestibular nucleus through inhibiting the NMDAR1/CaMKII/CREB signaling pathway, ultimately alleviating a series of symptoms of motion sickness in mice. The findings of this study provide valuable insights and a useful theoretical basis for the application of isorhamnetin as a new anti-motion sickness food ingredient.

Statistical analysis of high-dimensional biomedical data: a gentle introduction to analytical goals, common approaches and challenges

BACKGROUND

In high-dimensional data (HDD) settings, the number of variables associated with each observation is very large. Prominent examples of HDD in biomedical research include omics data with a large number of variables such as many measurements across the genome, proteome, or metabolome, as well as electronic health records data that have large numbers of variables recorded for each patient. The statistical analysis of such data requires knowledge and experience, sometimes of complex methods adapted to the respective research questions.

METHODS

Advances in statistical methodology and machine learning methods offer new opportunities for innovative analyses of HDD, but at the same time require a deeper understanding of some fundamental statistical concepts. Topic group TG9 "High-dimensional data" of the STRATOS (STRengthening Analytical Thinking for Observational Studies) initiative provides guidance for the analysis of observational studies, addressing particular statistical challenges and opportunities for the analysis of studies involving HDD. In this overview, we discuss key aspects of HDD analysis to provide a gentle introduction for non-statisticians and for classically trained statisticians with little experience specific to HDD.

RESULTS

The paper is organized with respect to subtopics that are most relevant for the analysis of HDD, in particular initial data analysis, exploratory data analysis, multiple testing, and prediction. For each subtopic, main analytical goals in HDD settings are outlined. For each of these goals, basic explanations for some commonly used analysis methods are provided. Situations are identified where traditional statistical methods cannot, or should not, be used in the HDD setting, or where adequate analytic tools are still lacking. Many key references are provided.

CONCLUSIONS

This review aims to provide a solid statistical foundation for researchers, including statisticians and non-statisticians, who are new to research with HDD or simply want to better evaluate and understand the results of HDD analyses.

A companion to the preclinical common data elements for proteomics, lipidomics, and metabolomics data in rodent epilepsy models. A report of the TASK3-WG4 omics working group of the ILAE/AES joint translational TASK force

The International League Against Epilepsy/American Epilepsy Society (ILAE/AES) Joint Translational Task Force established the TASK3 working groups to create common data elements (CDEs) for various preclinical epilepsy research disciplines. This is the second in a two-part series of omics papers, with the other including genomics, transcriptomics, and epigenomics. The aim of the CDEs was to improve the standardization of experimental designs across a range of epilepsy research-related methods. We have generated CDE tables with key parameters and case report forms (CRFs) containing the essential contents of the study protocols for proteomics, lipidomics, and metabolomics of samples from rodent models and people with epilepsy. We discuss the important elements that need to be considered for the proteomics, lipidomics, and metabolomics methodologies, providing a rationale for the parameters that should be documented.

Phosphoproteomic analysis reveals the effects of sleep deprivation on the hippocampus in mice

Sleep is essential for brain physiology, including nerve repair, neuronal activity, and metabolite clearance. The hippocampus is responsible for short-term memory, long-term memory, and spatial positioning. Herein, we investigated the effects of sleep deprivation on protein phosphorylation and related signaling pathways in the mouse hippocampus. The treatment group was sleep deprived for nine hours a day, and at the end of sleep deprivation, we removed the hippocampus for phosphoproteomic analysis. Through this analysis, we identified 65 sites and 27 proteins whose phosphorylation was significantly different between sleep-deprived animals and control animals. Differentially phosphorylated proteins (DPPs) were mainly distributed in the postsynaptic density, cytoplasm, and synapse. They participated in metabolic pathways, endocytosis, oxidative phosphorylation and other processes, and they were associated with Huntington's disease, Parkinson's disease, Alzheimer's disease, etc. Functional analysis of the phosphoproteome shows that sleep deprivation significantly affects the level of protein phosphorylation in the hippocampus of mice. This is the first reported study that has used phosphoproteomics to investigate the effects of sleep deprivation on hypothalamic regions. This study provides data resources that can serve as a valuable reference for sleep mechanism research, sleep disorder treatment, and drug development.

Important Issues in Planning a Proteomics Experiment: Statistical Considerations of Quantitative Proteomic Data

Mass spectrometry is frequently used in quantitative proteomics to detect differentially regulated proteins. A very important but unfortunately oftentimes neglected part in detecting differential proteins is the statistical analysis. Data from proteomics experiments are usually high-dimensional and hence require profound statistical methods. It is especially important to already correctly design a proteomic experiment before it is conducted in the laboratory. Only this can ensure that the statistical analysis is capable of detecting truly differential proteins afterward. This chapter thus covers aspects of both statistical planning as well as the actual analysis of quantitative proteomic experiments.

Determination of variability due to biological and technical variation in urinary extracellular vesicles as a crucial step in biomarker discovery studies

Urinary extracellular vesicles (EVs) are an attractive source of biomarkers for urological diseases. A crucial step in biomarker discovery studies is the determination of the variation parameters to perform a sample size calculation. In this way, a biomarker discovery study with sufficient statistical power can be performed to obtain biologically significant biomarkers. Here, a variation study was performed on both the protein and lipid content of urinary EVs of healthy individuals, aged between 52 and 69 years. Ultrafiltration (UF) in combination with size exclusion chromatography (SEC) was used to isolate the EVs from urine. Different experimental variation set-ups were used in this variation study. The calculated standard deviations (SDs) of the 90% least variable peptides and lipids did not exceed 2 and 1.2, respectively. These parameters can be used in a sample size calculation for a well-designed biomarker discovery study at the cargo of EVs.

Variance component analysis to assess protein quantification in biomarker discovery. Application to MALDI-TOF mass spectrometry

Controlling the technological variability on an analytical chain is critical for biomarker discovery. The sources of technological variability should be modeled, which calls for specific experimental design, signal processing, and statistical analysis. Furthermore, with unbalanced data, the various components of variability cannot be estimated with the sequential or adjusted sums of squares of usual software programs. We propose a novel approach to variance component analysis with application to the matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) technology and use this approach for protein quantification by a classical signal processing algorithm and two more recent ones (BHI-PRO 1 and 2). Given the high technological variability, the quantification failed to restitute the known quantities of five out of nine proteins present in a controlled solution. There was a linear relationship between protein quantities and peak intensities for four out of nine peaks with all algorithms. The biological component of the variance was higher with BHI-PRO than with the classical algorithm (80-95% with BHI-PRO 1, 79-95% with BHI-PRO 2 vs. 56-90%); thus, BHI-PRO were more efficient in protein quantification. The technological component of the variance was higher with the classical algorithm than with BHI-PRO (6-25% vs. 2.5-9.6% with BHI-PRO 1 and 3.5-11.9% with BHI-PRO 2). The chemical component was also higher with the classical algorithm (3.6-18.7% vs. < 3.5%). Thus, BHI-PRO were better in removing noise from signal when the expected peaks are detected. Overall, either BHI-PRO algorithm may reduce the technological variance from 25 to 10% and thus improve protein quantification and biomarker validation.

Designing Successful Proteomics Experiments

Because proteomics experiments are so complex they can readily fail, and do so without clear cause. Using standard experimental design techniques and incorporating quality control can greatly increase the chances of success. This chapter introduces the relevant concepts and provides examples specific to proteomic workflows. Applying these notions to design successful proteomics experiments is straightforward. It can help identify failure causes and greatly increase the likelihood of inter-laboratory reproducibility.

Studying Cellular Signal Transduction with OMIC Technologies

In the gulf between genotype and phenotype exists proteins and, in particular, protein signal transduction systems. These systems use a relatively limited parts list to respond to a much longer list of extracellular, environmental, and/or mechanical cues with rapidity and specificity. Most signaling networks function in a highly non-linear and often contextual manner. Furthermore, these processes occur dynamically across space and time. Because of these complexities, systems and "OMIC" approaches are essential for the study of signal transduction. One challenge in using OMIC-scale approaches to study signaling is that the "signal" can take different forms in different situations. Signals are encoded in diverse ways such as protein-protein interactions, enzyme activities, localizations, or post-translational modifications to proteins. Furthermore, in some cases, signals may be encoded only in the dynamics, duration, or rates of change of these features. Accordingly, systems-level analyses of signaling may need to integrate multiple experimental and/or computational approaches. As the field has progressed, the non-triviality of integrating experimental and computational analyses has become apparent. Successful use of OMIC methods to study signaling will require the "right" experiments and the "right" modeling approaches, and it is critical to consider both in the design phase of the project. In this review, we discuss common OMIC and modeling approaches for studying signaling, emphasizing the philosophical and practical considerations for effectively merging these two types of approaches to maximize the probability of obtaining reliable and novel insights into signaling biology.

Proteomics of pulmonary hypertension: could personalized profiles lead to personalized medicine?

Pulmonary hypertension (PH) is a fatal syndrome that arises from a multifactorial and complex background, is characterized by increased pulmonary vascular resistance and right heart afterload, and often leads to cor pulmonale. Over the past decades, remarkable progress has been made in reducing patient symptoms and delaying the progression of the disease. Unfortunately, PH remains a disease with no cure. The substantial heterogeneity of PH continues to be a major limitation to the development of newer and more efficacious therapies. New advances in our understanding of the biological pathways leading to such a complex pathogenesis will require the identification of the important proteins and protein networks that differ between a healthy lung (or right ventricle) and a remodeled lung in an individual with PH. In this article, we present the case for the increased use of proteomics--the study of proteins and protein networks--as a discovery tool for key proteins and protein networks operational in the PH lung. We review recent applications of proteomics in PH, and summarize the biological pathways identified. Finally, we attempt to presage what the future will bring with regard to proteomics in PH and offer our perspectives on the prospects of developing personalized proteomics and custom-tailored therapies.

Proteomics in cancer research: Are we ready for clinical practice?

Although genomics has delivered major advances in cancer prognostics, treatment and diagnostics, it still only provides a static image of the situation. To study more dynamic molecular entities, proteomics has been introduced into the cancer research field more than a decade ago. Currently, however, the impact of clinical proteomics on patient management and clinical decision-making is low and the implementations of scientific results in the clinic appear to be scarce. The search for cancer-related biomarkers with proteomics however, has major potential to improve risk assessment, early detection, diagnosis, prognosis, treatment selection and monitoring. In this review, we provide an overview of the transition of oncoproteomics towards translational oncology. We describe which lessons are learned from currently approved protein biomarkers and previous proteomic studies, what the pitfalls and challenges are in clinical proteomics applications, and how proteomic research can be successfully translated into medical practice.

Translating clinical proteomics: the importance of study design

Mass spectrometry-based clinical proteomics approaches were introduced into the biomedical field more than two decades ago. Despite recent developments both in the field of mass spectrometry and bioinformatics, the gap between proteomics results and their translation into clinical practice still needs to be closed, as implementation of proteomics results in the clinic appears to be scarce. An extra focus on the importance of the experimental design is therefore of crucial importance.

Assessment of tandem mass spectrometry and high-resolution mass spectrometry for the analysis of bupivacaine in plasma

Triple quadrupole mass spectrometers coupled with high performance liquid chromatography are workhorses in quantitative bioanalyses. They provide substantial benefits including reproducibility, sensitivity and selectivity for trace analysis. Selected reaction monitoring allows targeted assay development but datasets generated contain very limited information. Data mining and analysis of nontargeted high-resolution mass spectrometry profiles of biological samples offer the opportunity to perform more exhaustive assessments, including quantitative and qualitative analysis. The objectives of this study were to test method precision and accuracy, to statistically compare bupivacaine drug concentration in real study samples and to verify if high-resolution and accurate mass data collected in scan mode can actually permit retrospective data analysis, more specifically, extract metabolite related information. The precision and accuracy data presented using both instruments provided equivalent results. Overall, the accuracy ranged from 106.2 to 113.2% and the precision observed was from 1.0 to 3.7%. Statistical comparisons using a linear regression between both methods revealed a coefficient of determination (R(2)) of 0.9996 and a slope of 1.02, demonstrating a very strong correlation between the two methods. Individual sample comparison showed differences from -4.5 to 1.6%, well within the accepted analytical error. Moreover, post-acquisition extracted ion chromatograms at m/z 233.1648 ± 5 ppm (M - 56) and m/z 305.2224 ± 5 ppm (M + 16) revealed the presence of desbutyl-bupivacaine and three distinct hydroxylated bupivacaine metabolites. Post-acquisition analysis allowed us to produce semi-quantitative evaluations of the concentration-time profiles for bupicavaine metabolites.

Epidemiologic design and analysis for proteomic studies: a primer on -omic technologies

Proteome analysis is increasingly being used in investigations elucidating the molecular basis of disease, identifying diagnostic and prognostic markers, and ultimately improving patient care. We appraised the current status of proteomic investigations using human samples, including the state of the art in proteomic technologies, from sample preparation to data evaluation approaches, as well as key epidemiologic, statistical, and translational issues. We systematically reviewed the most highly cited clinical proteomic studies published between January 2009 and March 2014 that included a minimum of 100 samples, as well as strategies that have been successfully implemented to enhance the translational relevance of proteomic investigations. Limited comparability between studies and lack of specification of biomarker context of use are frequently observed. Nevertheless, there are initial examples of successful biomarker discovery in cross-sectional studies followed by validation in high-risk longitudinal cohorts. Translational potential is currently hindered, as limitations in proteomic investigations are not accounted for. Interdisciplinary communication between proteomics experts, basic researchers, epidemiologists, and clinicians, an orchestrated assimilation of required resources, and a more systematic translational outlook for accumulation of evidence may augment the public health impact of proteomic investigations.

Fourteen years of plant proteomics reflected in Proteomics: moving from model species and 2DE-based approaches to orphan species and gel-free platforms

In this article, the topic of plant proteomics is reviewed based on related papers published in the journal Proteomics since publication of the first issue in 2001. In total, around 300 original papers and 41 reviews published in Proteomics between 2000 and 2014 have been surveyed. Our main objective for this review is to help bridge the gap between plant biologists and proteomics technologists, two often very separate groups. Over the past years a number of reviews on plant proteomics have been published . To avoid repetition we have focused on more recent literature published after 2010, and have chosen to rather make continuous reference to older publications. The use of the latest proteomics techniques and their integration with other approaches in the "systems biology" direction are discussed more in detail. Finally we comment on the recent history, state of the art, and future directions of plant proteomics, using publications in Proteomics to illustrate the progress in the field. The review is organized into two major blocks, the first devoted to provide an overview of experimental systems (plants, plant organs, biological processes) and the second one to the methodology.

Phosphoproteomics in photosynthetic organisms

As primarily sessile organisms, photosynthetic species survive in dynamic environments by using elegant signaling pathways to manifest molecular responses to extracellular cues. These pathways exploit phosphorylation of specific amino acids (e.g. serine, threonine, tyrosine), which impact protein structure, function, and localization. Despite substantial progress in implementation of phosphoproteomics to understand photosynthetic organisms, researchers still struggle to translate a biological question into an experimental strategy and vice versa. This review evaluates the current status of phosphoproteomics in photosynthetic organisms and concludes with recommendations based on current knowledge.

A proteomic approach to investigate AuNPs effects in Balb/3T3 cells

Although gold nanoparticles (AuNPs) are currently used in several industrial products and biomedical applications, information about their biological effects is very limited. Thus, it is becoming crucial to assess their safety and adequately investigate the complexity of cell-nanoparticles interactions. In this work, the Balb/3T3 mouse fibroblast cell line was selected as an in vitro model to study the effects of AuNPs. Alteration of cellular processes and biochemical pathways caused by AuNPs exposure was investigated by analysing the differentially expressed proteome. Of interest was the difference observed in the protein pattern expression of cells exposed to AuNPs. It was found that 88 and 83 proteins were de-regulated after exposure to 5 and 15nm AuNPs, respectively. Analysis of the proteome revealed that AuNPs triggers several pathways related to cellular growth and proliferation, cell morphology, cell cycle regulation, cellular function and maintenance, oxidative stress, and inflammatory response. Moreover, SPR analysis showed an increase of ECM proteins biosynthesis in cells exposed to AuNPs. We observed by TEM analysis that NPs are internalized and confined mainly in autophagosomes. Endoplasmic reticulum stressed and modification at mitochondrial level occurred. This study aims to improve existing knowledge necessary for a correct assessment of the balance between AuNPs potential adverse and beneficial effects and might have important implications for biomedical applications (e.g. nanomedicine). To conclude proteomics link to system biology analysis is a valuable tool to understand and predict nanoparticles' toxicity, furthermore it has the potential to reveal pathways that may not be immediately evident with classical toxicological assays.

Phosphatidylinositol-3,4,5-trisphosphate 5-phosphatase 1: a meaningful and independent marker to predict stroke in the Chinese population

Label-free liquid chromatography-mass spectrometry (LC-MS) quantification methods have been described to determine serum proteins biomarkers in many diseases. Thus, the purpose of this study was to investigate the serum proteins biomarkers in the Chinese patients with acute ischemic stroke (AIS). In the study period, sera from 40 AIS patients and 40 normal cases were selected for screening study. Immunoaffinity subtraction was used to deplete the top most abundant serum proteins; the remaining serum proteins were subjected to trypsin digestion and analyzed in triplicate by label-free LC-MS/MS. The selected protein associations with disease risk were further evaluated by enzyme-linked immunosorbent assay (ELISA) testing of the remaining stroke cases and controls. Its value for biomarkers diagnosis was appreciated through receiver operating curve (ROC). Patients versus control levels differences were suggested for 19 proteins (nominal P < 0.05) for stroke, with three proteins having a false discovery rate <0.05. The association of Phosphatidylinositol-3,4,5-trisphosphate 5-phosphatase 1 (SHIP-1) with stroke (P < 0.001) was confirmed using ELISA in replication studies. Based on the ROC curve, the optimal cut-off value of serum SHIP-1 levels for diagnosis of stroke was projected to be 1,550 pg/ml, which yielded a sensitivity of 77.5% and a specificity of 88.3%. In multivariate analysis, there was an increased risk of AIS associated with SHIP-1 levels ≥ 1,550 pg/ml (OR 4.28, 95% CI: 1.97-8.96) after adjusting for possible confounders.

CONCLUSION

The discovery and replication studies presented here show SHIP-1 to be a risk marker for AIS in the Chinese population, which appears to be a novel finding.

Criteria for the use of omics-based predictors in clinical trials: explanation and elaboration

High-throughput 'omics' technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.

Interlaboratory study on differential analysis of protein glycosylation by mass spectrometry: the ABRF glycoprotein research multi-institutional study 2012

One of the principal goals of glycoprotein research is to correlate glycan structure and function. Such correlation is necessary in order for one to understand the mechanisms whereby glycoprotein structure elaborates the functions of myriad proteins. The accurate comparison of glycoforms and quantification of glycosites are essential steps in this direction. Mass spectrometry has emerged as a powerful analytical technique in the field of glycoprotein characterization. Its sensitivity, high dynamic range, and mass accuracy provide both quantitative and sequence/structural information. As part of the 2012 ABRF Glycoprotein Research Group study, we explored the use of mass spectrometry and ancillary methodologies to characterize the glycoforms of two sources of human prostate specific antigen (PSA). PSA is used as a tumor marker for prostate cancer, with increasing blood levels used to distinguish between normal and cancer states. The glycans on PSA are believed to be biantennary N-linked, and it has been observed that prostate cancer tissues and cell lines contain more antennae than their benign counterparts. Thus, the ability to quantify differences in glycosylation associated with cancer has the potential to positively impact the use of PSA as a biomarker. We studied standard peptide-based proteomics/glycomics methodologies, including LC-MS/MS for peptide/glycopeptide sequencing and label-free approaches for differential quantification. We performed an interlaboratory study to determine the ability of different laboratories to correctly characterize the differences between glycoforms from two different sources using mass spectrometry methods. We used clustering analysis and ancillary statistical data treatment on the data sets submitted by participating laboratories to obtain a consensus of the glycoforms and abundances. The results demonstrate the relative strengths and weaknesses of top-down glycoproteomics, bottom-up glycoproteomics, and glycomics methods.

Proteomic Workflows for Biomarker Identification Using Mass Spectrometry - Technical and Statistical Considerations during Initial Discovery

Identification of biomarkers capable of differentiating between pathophysiological states of an individual is a laudable goal in the field of proteomics. Protein biomarker discovery generally employs high throughput sample characterization by mass spectrometry (MS), being capable of identifying and quantifying thousands of proteins per sample. While MS-based technologies have rapidly matured, the identification of truly informative biomarkers remains elusive, with only a handful of clinically applicable tests stemming from proteomic workflows. This underlying lack of progress is attributed in large part to erroneous experimental design, biased sample handling, as well as improper statistical analysis of the resulting data. This review will discuss in detail the importance of experimental design and provide some insight into the overall workflow required for biomarker identification experiments. Proper balance between the degree of biological vs. technical replication is required for confident biomarker identification.

Biomarker discovery and applications for foods and beverages: proteomics to nanoproteomics

Foods and beverages have been at the heart of our society for centuries, sustaining humankind - health, life, and the pleasures that go with it. The more we grow and develop as a civilization, the more we feel the need to know about the food we eat and beverages we drink. Moreover, with an ever increasing demand for food due to the growing human population food security remains a major concern. Food safety is another growing concern as the consumers prefer varied foods and beverages that are not only traded nationally but also globally. The 21st century science and technology is at a new high, especially in the field of biological sciences. The availability of genome sequences and associated high-throughput sensitive technologies means that foods are being analyzed at various levels. For example and in particular, high-throughput omics approaches are being applied to develop suitable biomarkers for foods and beverages and their applications in addressing quality, technology, authenticity, and safety issues. Proteomics are one of those technologies that are increasingly being utilized to profile expressed proteins in different foods and beverages. Acquired knowledge and protein information have now been translated to address safety of foods and beverages. Very recently, the power of proteomic technology has been integrated with another highly sensitive and miniaturized technology called nanotechnology, yielding a new term nanoproteomics. Nanoproteomics offer a real-time multiplexed analysis performed in a miniaturized assay, with low-sample consumption and high sensitivity. To name a few, nanomaterials - quantum dots, gold nanoparticles, carbon nanotubes, and nanowires - have demonstrated potential to overcome the challenges of sensitivity faced by proteomics for biomarker detection, discovery, and application. In this review, we will discuss the importance of biomarker discovery and applications for foods and beverages, the contribution of proteomic technology in this process, and a shift towards nanoproteomics to suitably address associated issues. This article is part of a Special Issue entitled: Translational plant proteomics.

Interindividual variation in the proteome of human peripheral blood mononuclear cells

Peripheral blood mononuclear cells (PBMCs) are main actors in inflammatory processes and linked to many diseases, including rheumatoid arthritis, atherosclerosis, asthma, HIV and cancer. Moreover, they seem an interesting ‘surrogate tissue’ that can be used in biomarker discovery. In order to get a good experimental design for quantitative expression studies, the knowledge of the interindividual variation is an essential part. Therefore, PBMCs were isolated from 24 healthy volunteers (15 males, 9 females, ages 63–86) with no clinical signs of inflammation. The extracted proteins were separated using the two dimensional difference in gel electrophoresis technology (2D-DIGE), and the gel images were processed with the DeCyder 2D software. Protein spots present in at least 22 out of 24 healthy volunteers were selected for further statistical analysis. Determination of the coefficient of variation (CV) of the normalized spot volume values of these proteins, reveals that the total variation of the PBMC proteome varies between 12,99% to 148,45%, with a mean value of 28%. A supplemental look at the causes of technical variation showed that the isolation of PBMCs from whole blood is the factor which influences the experimental variance the most. This isolation should be handled with extra care and an additional washing step would be beneficial. Knowing the extent of variation, we show that at least 10 independent samples per group are needed to obtain statistical powerful data. This study demonstrates the importance of considering variance of a human population for a good experimental design for future protein profiling or biomarker studies.

Data processing methods and quality control strategies for label-free LC-MS protein quantification

Protein quantification using different LC-MS techniques is becoming a standard practice. However, with a multitude of experimental setups to choose from, as well as a wide array of software solutions for subsequent data processing, it is non-trivial to select the most appropriate workflow for a given biological question. In this review, we highlight different issues that need to be addressed by software for quantitative LC-MS experiments and describe different approaches that are available. With focus on label-free quantification, examples are discussed both for LC-MS/MS and LC-SRM data processing. We further elaborate on current quality control methodology for performing accurate protein quantification experiments. This article is part of a Special Issue entitled: Computational Proteomics in the Post-Identification Era. Guest Editors: Martin Eisenacher and Christian Stephan.

Additions to the Human Plasma Proteome via a Tandem MARS Depletion iTRAQ-Based Workflow

Robust platforms for determining differentially expressed proteins in biomarker and discovery studies using human plasma are of great interest. While increased depth in proteome coverage is desirable, it is associated with costs of experimental time due to necessary sample fractionation. We evaluated a robust quantitative proteomics workflow for its ability (1) to provide increased depth in plasma proteome coverage and (2) to give statistical insight useful for establishing differentially expressed plasma proteins. The workflow involves dual-stage immunodepletion on a multiple affinity removal system (MARS) column, iTRAQ tagging, offline strong-cation exchange chromatography, and liquid chromatography tandem mass spectrometry (LC-MS/MS). Independent workflow experiments were performed in triplicate on four plasma samples tagged with iTRAQ 4-plex reagents. After stringent criteria were applied to database searched results, 689 proteins with at least two spectral counts (SC) were identified. Depth in proteome coverage was assessed by comparison to the 2010 Human Plasma Proteome Reference Database in which our studies reveal 399 additional proteins which have not been previously reported. Additionally, we report on the technical variation of this quantitative workflow which ranges from ±11 to 30%.

Proteomics: from single molecules to biological pathways

The conventional reductionist approach to cardiovascular research investigates individual candidate factors or linear signalling pathways but ignores more complex interactions in biological systems. The advent of molecular profiling technologies that focus on a global characterization of whole complements allows an exploration of the interconnectivity of pathways during pathophysiologically relevant processes, but has brought about the issue of statistical analysis and data integration. Proteins identified by differential expression as well as those in protein–protein interaction networks identified through experiments and through computational modelling techniques can be used as an initial starting point for functional analyses. In combination with other ‘-omics’ technologies, such as transcriptomics and metabolomics, proteomics explores different aspects of disease, and the different pillars of observations facilitate the data integration in disease-specific networks. Ultimately, a systems biology approach may advance our understanding of cardiovascular disease processes at a ‘biological pathway’ instead of a ‘single molecule’ level and accelerate progress towards disease-modifying interventions.

Marine proteomics: a critical assessment of an emerging technology

The application of proteomics to marine sciences has increased in recent years because the proteome represents the interface between genotypic and phenotypic variability and, thus, corresponds to the broadest possible biomarker for eco-physiological responses and adaptations. Likewise, proteomics can provide important functional information regarding biosynthetic pathways, as well as insights into mechanism of action, of novel marine natural products. The goal of this review is to (1) explore the application of proteomics methodologies to marine systems, (2) assess the technical approaches that have been used, and (3) evaluate the pros and cons of this proteomic research, with the intent of providing a critical analysis of its future roles in marine sciences. To date, proteomics techniques have been utilized to investigate marine microbe, plant, invertebrate, and vertebrate physiology, developmental biology, seafood safety, susceptibility to disease, and responses to environmental change. However, marine proteomics studies often suffer from poor experimental design, sample processing/optimization difficulties, and data analysis/interpretation issues. Moreover, a major limitation is the lack of available annotated genomes and proteomes for most marine organisms, including several "model species". Even with these challenges in mind, there is no doubt that marine proteomics is a rapidly expanding and powerful integrative molecular research tool from which our knowledge of the marine environment, and the natural products from this resource, will be significantly expanded.

ROCS: a reproducibility index and confidence score for interaction proteomics studies

Background

Affinity-Purification Mass-Spectrometry (AP-MS) provides a powerful means of identifying protein complexes and interactions. Several important challenges exist in interpreting the results of AP-MS experiments. First, the reproducibility of AP-MS experimental replicates can be low, due both to technical variability and the dynamic nature of protein interactions in the cell. Second, the identification of true protein-protein interactions in AP-MS experiments is subject to inaccuracy due to high false negative and false positive rates. Several experimental approaches can be used to mitigate these drawbacks, including the use of replicated and control experiments and relative quantification to sensitively distinguish true interacting proteins from false ones.

Methods

To address the issues of reproducibility and accuracy of protein-protein interactions, we introduce a two-step method, called ROCS, which makes use of Indicator Prey Proteins to select reproducible AP-MS experiments, and of Confidence Scores to select specific protein-protein interactions. The Indicator Prey Proteins account for measures of protein identifiability as well as protein reproducibility, effectively allowing removal of outlier experiments that contribute noise and affect downstream inferences. The filtered set of experiments is then used in the Protein-Protein Interaction (PPI) scoring step. Prey protein scoring is done by computing a Confidence Score, which accounts for the probability of occurrence of prey proteins in the bait experiments relative to the control experiment, where the significance cutoff parameter is estimated by simultaneously controlling false positives and false negatives against metrics of false discovery rate and biological coherence respectively. In summary, the ROCS method relies on automatic objective criterions for parameter estimation and error-controlled procedures.

Results

We illustrate the performance of our method by applying it to five previously published AP-MS experiments, each containing well characterized protein interactions, allowing for systematic benchmarking of ROCS. We show that our method may be used on its own to make accurate identification of specific, biologically relevant protein-protein interactions, or in combination with other AP-MS scoring methods to significantly improve inferences.

Conclusions

Our method addresses important issues encountered in AP-MS datasets, making ROCS a very promising tool for this purpose, either on its own or in conjunction with other methods. We anticipate that our methodology may be used more generally in proteomics studies and databases, where experimental reproducibility issues arise. The method is implemented in the R language, and is available as an R package called “ROCS”, freely available from the CRAN repository http://cran.r-project.org/.

Gel-based and gel-free quantitative proteomics approaches at a glance

Two-dimensional gel electrophoresis (2-DE) is widely applied and remains the method of choice in proteomics; however, pervasive 2-DE-related concerns undermine its prospects as a dominant separation technique in proteome research. Consequently, the state-of-the-art shotgun techniques are slowly taking over and utilising the rapid expansion and advancement of mass spectrometry (MS) to provide a new toolbox of gel-free quantitative techniques. When coupled to MS, the shotgun proteomic pipeline can fuel new routes in sensitive and high-throughput profiling of proteins, leading to a high accuracy in quantification. Although label-based approaches, either chemical or metabolic, gained popularity in quantitative proteomics because of the multiplexing capacity, these approaches are not without drawbacks. The burgeoning label-free methods are tag independent and suitable for all kinds of samples. The challenges in quantitative proteomics are more prominent in plants due to difficulties in protein extraction, some protein abundance in green tissue, and the absence of well-annotated and completed genome sequences. The goal of this perspective assay is to present the balance between the strengths and weaknesses of the available gel-based and -free methods and their application to plants. The latest trends in peptide fractionation amenable to MS analysis are as well discussed.

Changes in the plasma proteome follows chronic opiate administration in simian immunodeficiency virus infected rhesus macaques

BACKGROUND

Substantive plasma proteomic changes follow lentiviral infection and disease pathobiology. We posit that such protein alterations are modified during drug abuse, further serving to affect the disease. To this end, we investigated the effect of opiate administration on the plasma proteome of Indian-strain rhesus monkeys infected with simian immunodeficiency virus (SIV) strain smm9.

METHODS

Whole blood was collected at 7 weeks prior to and 1.4 and 49 weeks after viral infection. Viral load, CD4(+) T cell subsets, and plasma protein content were measured from monkeys that did or did not receive continuous opiate administrations. The plasma proteome was identified and quantified by isobaric tags for relative and absolute quantitation labeling (iTRAQ) and mass spectrometry.

RESULTS

While substantive changes in plasma proteins were seen during SIV infection, the addition of opiates led to suppression of these changes as well as increased variance of the proteome. These changes demonstrate that opiates induce broad but variant immune suppression in SIV-infected monkeys.

CONCLUSION

The broad suppressive changes seen in plasma of SIV-infected monkeys likely reflect reduced multisystem immune homeostatic responses induced by opiates. Such occur as a consequence of complex cell-to-cell interactions operative between the virus and the host. We conclude that such changes in plasma proteomic profiling may be underappreciated and as such supports the need for improved clinical definitions.