Normalization approaches for removing systematic biases associated with mass spectrometry and label-free proteomics

SILVER: an efficient tool for stable isotope labeling LC-MS data quantitative analysis with quality control methods

SUMMARY

With the advance of experimental technologies, different stable isotope labeling methods have been widely applied to quantitative proteomics. Here, we present an efficient tool named SILVER for processing the stable isotope labeling mass spectrometry data. SILVER implements novel methods for quality control of quantification at spectrum, peptide and protein levels, respectively. Several new quantification confidence filters and indices are used to improve the accuracy of quantification results. The performance of SILVER was verified and compared with MaxQuant and Proteome Discoverer using a large-scale dataset and two standard datasets. The results suggest that SILVER shows high accuracy and robustness while consuming much less processing time. Additionally, SILVER provides user-friendly interfaces for parameter setting, result visualization, manual validation and some useful statistics analyses.

AVAILABILITY AND IMPLEMENTATION

SILVER and its source codes are freely available under the GNU General Public License v3.0 at http://bioinfo.hupo.org.cn/silver.

Proteomics quality and standard: from a regulatory perspective

Proteomics has emerged as a rapidly expanding field dealing with large-scale protein analyses. It is anticipated that proteomics data will be increasingly submitted to the U.S. Food and Drug Administration (FDA) for biomarker qualification or in conjunction with applications for the approval of drugs, medical devices, and other FDA-regulated consumer products. To date, however, no established guideline has been available regarding the generation, submission and assessment of the quality of proteomics data that will be reviewed by regulatory agencies for decision making. Therefore, this commentary is aimed at provoking some thoughts and debates towards developing a framework which can guide future proteomics data submission. The ultimate goal is to establish quality control standards for proteomics data generation and evaluation, and to prepare government agencies such as the FDA to meet future obligations utilizing proteomics data to support regulatory decision.

Gaussian process regression model for normalization of LC-MS data using scan-level information

Background

Differences in sample collection, biomolecule extraction, and instrument variability introduce bias to data generated by liquid chromatography coupled with mass spectrometry (LC-MS). Normalization is used to address these issues. In this paper, we introduce a new normalization method using the Gaussian process regression model (GPRM) that utilizes information from individual scans within an extracted ion chromatogram (EIC) of a peak. The proposed method is particularly applicable for normalization based on analysis order of LC-MS runs. Our method uses measurement variabilities estimated through LC-MS data acquired from quality control samples to correct for bias caused by instrument drift. Maximum likelihood approach is used to find the optimal parameters for the fitted GPRM. We review several normalization methods and compare their performance with GPRM.

Results

To evaluate the performance of different normalization methods, we consider LC-MS data from a study where metabolomic approach is utilized to discover biomarkers for liver cancer. The LC-MS data were acquired by analysis of sera from liver cancer patients and cirrhotic controls. In addition, LC-MS runs from a quality control (QC) sample are included to assess the run to run variability and to evaluate the ability of various normalization method in reducing this undesired variability. Also, ANOVA models are applied to the normalized LC-MS data to identify ions with intensity measurements that are significantly different between cases and controls.

Conclusions

One of the challenges in using label-free LC-MS for quantitation of biomolecules is systematic bias in measurements. Several normalization methods have been introduced to overcome this issue, but there is no universally applicable approach at the present time. Each data set should be carefully examined to determine the most appropriate normalization method. We review here several existing methods and introduce the GPRM for normalization of LC-MS data. Through our in-house data set, we show that the GPRM outperforms other normalization methods considered here, in terms of decreasing the variability of ion intensities among quality control runs.

Inhibition of transketolase by oxythiamine altered dynamics of protein signals in pancreatic cancer cells

Oxythiamine (OT), an analogue of anti-metabolite, can suppress the nonoxidative synthesis of ribose and induce cell apoptosis by causing a G1 phase arrest in vitro and in vivo. However, the molecular mechanism remains unclear yet. In the present study, a quantitative proteomic analysis using the modified SILAC method (mSILAC) was performed to determine the effect of metabolic inhibition on dynamic changes of protein expression in MIA PaCa-2 cancer cells treated with OT at various doses (0 μM, 5 μM, 50 μM and 500 μM) and time points (0 h, 12 h and 48 h). A total of 52 differential proteins in MIA PaCa-2 cells treated with OT were identified, including 14 phosphorylated proteins. Based on the dynamic expression pattern, these proteins were categorized in three clusters, straight down-regulation (cluster 1, 37% of total proteins), upright "V" shape expression pattern (cluster 2, 47.8% total), and downright "V" shape pattern (cluster 3, 15.2% total). Among them, Annexin A1 expression was significantly down-regulated by OT treatment in time-dependent manner, while no change of this protein was observed in OT dose-dependent fashion. Pathway analysis suggested that inhibition of transketolase resulted in changes of multiple cellular signaling pathways associated with cell apoptosis. The temporal expression patterns of proteins revealed that OT altered dynamics of protein expression in time-dependent fashion by suppressing phosphor kinase expression, resulting in cancer cell apoptosis. Results from this study suggest that interference of single metabolic enzyme activity altered multiple cellular signaling pathways.

Determination of burn patient outcome by large-scale quantitative discovery proteomics

OBJECTIVES

Emerging proteomics techniques can be used to establish proteomic outcome signatures and to identify candidate biomarkers for survival following traumatic injury. We applied high-resolution liquid chromatography-mass spectrometry and multiplex cytokine analysis to profile the plasma proteome of survivors and nonsurvivors of massive burn injury to determine the proteomic survival signature following a major burn injury.

DESIGN

Proteomic discovery study.

SETTING

Five burn hospitals across the United States.

PATIENTS

Thirty-two burn patients (16 nonsurvivors and 16 survivors), 19-89 years old, were admitted within 96 hours of injury to the participating hospitals with burns covering more than 20% of the total body surface area and required at least one surgical intervention.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We found differences in circulating levels of 43 proteins involved in the acute-phase response, hepatic signaling, the complement cascade, inflammation, and insulin resistance. Thirty-two of the proteins identified were not previously known to play a role in the response to burn. Interleukin-4, interleukin-8, granulocyte macrophage colony-stimulating factor, monocyte chemotactic protein-1, and β2-microglobulin correlated well with survival and may serve as clinical biomarkers.

CONCLUSIONS

These results demonstrate the utility of these techniques for establishing proteomic survival signatures and for use as a discovery tool to identify candidate biomarkers for survival. This is the first clinical application of a high-throughput, large-scale liquid chromatography-mass spectrometry-based quantitative plasma proteomic approach for biomarker discovery for the prediction of patient outcome following burn, trauma, or critical illness.

Sources of technical variability in quantitative LC-MS proteomics: human brain tissue sample analysis

To design a robust quantitative proteomics study, an understanding of both the inherent heterogeneity of the biological samples being studied as well as the technical variability of the proteomics methods and platform is needed. Additionally, accurately identifying the technical steps associated with the largest variability would provide valuable information for the improvement and design of future processing pipelines. We present an experimental strategy that allows for a detailed examination of the variability of the quantitative LC-MS proteomics measurements. By replicating analyses at different stages of processing, various technical components can be estimated and their individual contribution to technical variability can be dissected. This design can be easily adapted to other quantitative proteomics pipelines. Herein, we applied this methodology to our label-free workflow for the processing of human brain tissue. For this application, the pipeline was divided into four critical components: Tissue dissection and homogenization (extraction), protein denaturation followed by trypsin digestion and SPE cleanup (digestion), short-term run-to-run instrumental response fluctuation (instrumental variance), and long-term drift of the quantitative response of the LC-MS/MS platform over the 2 week period of continuous analysis (instrumental stability). From this analysis, we found the following contributions to variability: extraction (72%) >> instrumental variance (16%) > instrumental stability (8.4%) > digestion (3.1%). Furthermore, the stability of the platform and its suitability for discovery proteomics studies is demonstrated.

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.

Profiling of the human monocytic cell secretome by quantitative label-free mass spectrometry identifies stimulus-specific cytokines and proinflammatory proteins

The immune response to pathogens or injury relies on the concerted release of cytokines and proteins with biological activity important for host protection, host defense, and wound healing. Consequently, the secretome of immune cells provides a promising resource for discovery of specific molecular markers and targets for pharmacological intervention. Here, we employ label-free MS for unbiased, quantitative profiling of the human monocytic cell secretome under different proinflammatory stimuli. The quantitative secretome profiles reveal the highly stimulus-dependent cellular response and differential, specific secretion of more than 200 proteins, including important proinflammatory proteins and cytokines.

Issues and applications in label-free quantitative mass spectrometry

To address the challenges associated with differential expression proteomics, label-free mass spectrometric protein quantification methods have been developed as alternatives to array-based, gel-based, and stable isotope tag or label-based approaches. In this paper, we focus on the issues associated with label-free methods that rely on quantitation based on peptide ion peak area measurement. These issues include chromatographic alignment, peptide qualification for quantitation, and normalization. In addressing these issues, we present various approaches, assembled in a recently developed label-free quantitative mass spectrometry platform, that overcome these difficulties and enable comprehensive, accurate, and reproducible protein quantitation in highly complex protein mixtures from experiments with many sample groups. As examples of the utility of this approach, we present a variety of cases where the platform was applied successfully to assess differential protein expression or abundance in body fluids, in vitro nanotoxicology models, tissue proteomics in genetic knock-in mice, and cell membrane proteomics.

Two-dimensional gel electrophoresis revealed antipsychotic drugs induced protein expression modulations in C6 glioma cells

The efficacy and side effects of long-term administration of antipsychotic drugs (APDs) may be attributed to drug-induced change in protein expression in brain cells. Glial cells are non-neuronal cells that can provide nutrients and physiological support to neuronal cells. Glial cells are believed to participate in neurotransmission, neurons' early development, and guiding migration of neurons. Accumulated clinical data also indicate relationships between disturbance of glial cells' function and various psychotic diseases including schizophrenia. We used two-dimensional gel electrophoresis coupled with MALDI-TOF/TOF mass spectrometry protein identification to analyze differentially expressed proteins in haloperidol-, risperidone-, and clozapine-treated C6 glioma cells. We found that the expression of pericentrin, glial fibrillary acidic protein, Rho GDP-dissociation inhibitor 1, anionic trypsin-1, peroxiredoxin-1, and parvalbumin were regulated by each of the three APDs. Western blot analysis supported the findings. Real-time quantitative PCR detected changed transcriptions of those proteins. Protein and gene expression of N-cadherin in C6 cells were affected by haloperidol and clozapine but not risperidone. In addition, regulatory effects of clozapine on the glyceraldehyde 3-phosphate dehydrogenase gene were observed in C6 cells. This may be the first study to uncover how APD-modulated genes may cause protein expression changes and affect ARHGDIA-mediated regulation of Rho GTPase family proteins in glial cells.

Serum proteomics reveals systemic dysregulation of innate immunity in type 1 diabetes

Proteomics analysis identifies human serum proteins involved with innate immune responses, complement activation, and blood coagulation that are diagnostic for type 1 diabetes.

Using global liquid chromatography-mass spectrometry (LC-MS)–based proteomics analyses, we identified 24 serum proteins that were significantly variant between those with type 1 diabetes (T1D) and healthy controls. Functionally, these proteins represent innate immune responses, the activation cascade of complement, inflammatory responses, and blood coagulation. Targeted verification analyses were performed on 52 surrogate peptides representing these proteins, with serum samples from an antibody standardization program cohort of 100 healthy control and 50 type 1 diabetic subjects. 16 peptides were verified as having very good discriminating power, with areas under the receiver operating characteristic curve ≥0.8. Further validation with blinded serum samples from an independent cohort (10 healthy control and 10 type 1 diabetics) demonstrated that peptides from platelet basic protein and C1 inhibitor achieved both 100% sensitivity and 100% specificity for classification of samples. The disease specificity of these proteins was assessed using sera from 50 age-matched type 2 diabetic individuals, and a subset of proteins, C1 inhibitor in particular, were exceptionally good discriminators between these two forms of diabetes. The panel of biomarkers distinguishing those with T1D from healthy controls and those with type 2 diabetes suggests that dysregulated innate immune responses may be associated with the development of this disorder.

Tools for label-free peptide quantification

The increasing scale and complexity of quantitative proteomics studies complicate subsequent analysis of the acquired data. Untargeted label-free quantification, based either on feature intensities or on spectral counting, is a method that scales particularly well with respect to the number of samples. It is thus an excellent alternative to labeling techniques. In order to profit from this scalability, however, data analysis has to cope with large amounts of data, process them automatically, and do a thorough statistical analysis in order to achieve reliable results. We review the state of the art with respect to computational tools for label-free quantification in untargeted proteomics. The two fundamental approaches are feature-based quantification, relying on the summed-up mass spectrometric intensity of peptides, and spectral counting, which relies on the number of MS/MS spectra acquired for a certain protein. We review the current algorithmic approaches underlying some widely used software packages and briefly discuss the statistical strategies for analyzing the data.

DataPipeline: automated importing and fitting of large amounts of biophysical data

Raw data from experiments across the biological sciences comes in a large variety of text formats. In small or medium sized laboratories researchers often use an assorted collection of software to interpret, fit, and visualize their data. The spreadsheet is commonly the core component of such a workflow. The limitations of such programs for large amounts of heterogeneous data can be frustrating. We report the construction of DataPipeline, a desktop and command-line application that automates the tasks of importing, fitting, and plotting of text-based data. The software is designed to simplify the process of importing text data from various sources using simple configuration files to describe raw file formats. Once imported, curve fitting can be performed using custom fitting models designed by the user inside the application. Fitted parameters can be grouped together as new datasets to be fitted to other models and experimental uncertainties propagated to give error estimates. This software will be useful for processing of data from high through-put biological experiments or for rapid visualization of pilot data without the need for a chain of different programs to carry out each step. DataPipeline and source code is available under an open source license. The software can be freely downloaded at http://code.google.com/p/peat/downloads/list.

Normalization and missing value imputation for label-free LC-MS analysis

Shotgun proteomic data are affected by a variety of known and unknown systematic biases as well as high proportions of missing values. Typically, normalization is performed in an attempt to remove systematic biases from the data before statistical inference, sometimes followed by missing value imputation to obtain a complete matrix of intensities. Here we discuss several approaches to normalization and dealing with missing values, some initially developed for microarray data and some developed specifically for mass spectrometry-based data.

Evaluation of a genome-scale in silico metabolic model for Geobacter metallireducens by using proteomic data from a field biostimulation experiment

Accurately predicting the interactions between microbial metabolism and the physical subsurface environment is necessary to enhance subsurface energy development, soil and groundwater cleanup, and carbon management. This study was an initial attempt to confirm the metabolic functional roles within an in silico model using environmental proteomic data collected during field experiments. Shotgun global proteomics data collected during a subsurface biostimulation experiment were used to validate a genome-scale metabolic model of Geobacter metallireducens-specifically, the ability of the metabolic model to predict metal reduction, biomass yield, and growth rate under dynamic field conditions. The constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes. Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low abundances of proteins associated with amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.

Proteomics wants cRacker: automated standardized data analysis of LC-MS derived proteomic data

The large-scale analysis of thousands of proteins under various experimental conditions or in mutant lines has gained more and more importance in hypothesis-driven scientific research and systems biology in the past years. Quantitative analysis by large scale proteomics using modern mass spectrometry usually results in long lists of peptide ion intensities. The main interest for most researchers, however, is to draw conclusions on the protein level. Postprocessing and combining peptide intensities of a proteomic data set requires expert knowledge, and the often repetitive and standardized manual calculations can be time-consuming. The analysis of complex samples can result in very large data sets (lists with several 1000s to 100,000 entries of different peptides) that cannot easily be analyzed using standard spreadsheet programs. To improve speed and consistency of the data analysis of LC-MS derived proteomic data, we developed cRacker. cRacker is an R-based program for automated downstream proteomic data analysis including data normalization strategies for metabolic labeling and label free quantitation. In addition, cRacker includes basic statistical analysis, such as clustering of data, or ANOVA and t tests for comparison between treatments. Results are presented in editable graphic formats and in list files.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

Current challenges in software solutions for mass spectrometry-based quantitative proteomics

Mass spectrometry-based proteomics has evolved as a high-throughput research field over the past decade. Significant advances in instrumentation, and the ability to produce huge volumes of data, have emphasized the need for adequate data analysis tools, which are nowadays often considered the main bottleneck for proteomics development. This review highlights important issues that directly impact the effectiveness of proteomic quantitation and educates software developers and end-users on available computational solutions to correct for the occurrence of these factors. Potential sources of errors specific for stable isotope-based methods or label-free approaches are explicitly outlined. The overall aim focuses on a generic proteomic workflow.

A probabilistic framework for peptide and protein quantification from data-dependent and data-independent LC-MS proteomics experiments

A probability-based quantification framework is presented for the calculation of relative peptide and protein abundance in label-free and label-dependent LC-MS proteomics data. The results are accompanied by credible intervals and regulation probabilities. The algorithm takes into account data uncertainties via Poisson statistics modified by a noise contribution that is determined automatically during an initial normalization stage. Protein quantification relies on assignments of component peptides to the acquired data. These assignments are generally of variable reliability and may not be present across all of the experiments comprising an analysis. It is also possible for a peptide to be identified to more than one protein in a given mixture. For these reasons the algorithm accepts a prior probability of peptide assignment for each intensity measurement. The model is constructed in such a way that outliers of any type can be automatically reweighted. Two discrete normalization methods can be employed. The first method is based on a user-defined subset of peptides, while the second method relies on the presence of a dominant background of endogenous peptides for which the concentration is assumed to be unaffected. Normalization is performed using the same computational and statistical procedures employed by the main quantification algorithm. The performance of the algorithm will be illustrated on example data sets, and its utility demonstrated for typical proteomics applications. The quantification algorithm supports relative protein quantification based on precursor and product ion intensities acquired by means of data-dependent methods, originating from all common isotopically-labeled approaches, as well as label-free ion intensity-based data-independent methods.

Strategy for identifying dendritic cell-processed CD4+ T cell epitopes from the HIV gag p24 protein

Mass Spectrometry (MS) is becoming a preferred method to identify class I and class II peptides presented on major histocompability complexes (MHC) on antigen presenting cells (APC). We describe a combined computational and MS approach to identify exogenous MHC II peptides presented on mouse spleen dendritic cells (DCs). This approach enables rapid, effective screening of a large number of possible peptides by a computer-assisted strategy that utilizes the extraordinary human ability for pattern recognition. To test the efficacy of the approach, a mixture of epitope peptide mimics (mimetopes) from HIV gag p24 sequence were added exogenously to Fms-like tyrosine kinase 3 ligand (Flt3L)-mobilized splenic DCs. We identified the exogenously added peptide, VDRFYKTLRAEQASQ, and a second peptide, DRFYKLTRAEQASQ, derived from the original exogenously added 15-mer peptide. Furthermore, we demonstrated that our strategy works efficiently with HIV gag p24 protein when delivered, as vaccine protein, to Flt3L expanded mouse splenic DCs in vitro through the DEC-205 receptor. We found that the same MHC II-bound HIV gag p24 peptides, VDRFYKTLRAEQASQ and DRFYKLTRAEQASQ, were naturally processed from anti-DEC-205 HIV gag p24 protein and presented on DCs. The two identified VDRFYKTLRAEQASQ and DRFYKLTRAEQASQ MHC II-bound HIV gag p24 peptides elicited CD4⁺ T-cell mediated responses in vitro. Their presentation by DCs to antigen-specific T cells was inhibited by chloroquine (CQ), indicating that optimal presentation of these exogenously added peptides required uptake and vesicular trafficking in mature DCs. These results support the application of our strategy to identify and characterize peptide epitopes derived from vaccine proteins processed by DCs and thus has the potential to greatly accelerate DC-based vaccine development.

Key issues in the acquisition and analysis of qualitative and quantitative mass spectrometry data for peptide-centric proteomic experiments

Proteomic technologies have matured to a level enabling accurate and reproducible quantitation of peptides and proteins from complex biological matrices. Analysis of samples as diverse as assembled protein complexes, whole cell lysates or sub-cellular proteomes from cell cultures, and direct analysis of animal and human tissues and fluids demonstrate the incredible versatility of the fundamental nature of the technique that forms the basis of most proteomic applications today (mass spectrometry). Determining the mass of biomolecules and their fragments or related products with high accuracy can convey a highly specific assay for detection and identification. Importantly, ion currents representative of these specifically identified analytes can be accurately quantified with the correct application of smart isobaric tagging chemistries, heavy and light isotopically derivatised samples or standards, or by careful application of workflows to compare unlabelled samples in so-called 'label-free' and targeted selected reaction monitoring experiments. In terms of exploring biology, a myriad of protein changes and modifications are being increasingly probed and quantified, including diverse chemical changes from relatively decisive modifications such as protein splicing and truncation, to more transient dynamic modifications such as phosphorylation, acetylation and ubiquitination. Proteomic workflows can be complex beasts and several key considerations to ensure effective applications have been outlined in the recent literature. The past year has witnessed the publication of several excellent reviews that thoroughly describe the fundamental principles underlying the state of the art. This review further elaborates on specific critical issues introduced by these publications and raises other important unaddressed considerations and new developments that directly impact on the effectiveness of proteomic technologies, in particular for, but not necessarily exclusive to peptide-centric experiments. These factors are discussed both in terms of qualitative analyses, including dynamic range and sampling issues, and developments to improve the translation of peptide fragmentation data into peptide and protein identities, as well as quantitative analyses, including data normalisation and the utility of ontology or functional annotation, the effects of modified peptides, and considered experimental design to facilitate the use of robust statistical methods.

DanteR: an extensible R-based tool for quantitative analysis of -omics data

MOTIVATION

The size and complex nature of mass spectrometry-based proteomics datasets motivate development of specialized software for statistical data analysis and exploration. We present DanteR, a graphical R package that features extensive statistical and diagnostic functions for quantitative proteomics data analysis, including normalization, imputation, hypothesis testing, interactive visualization and peptide-to-protein rollup. More importantly, users can easily extend the existing functionality by including their own algorithms under the Add-On tab.

AVAILABILITY

DanteR and its associated user guide are available for download free of charge at http://omics.pnl.gov/software/. We have an updated binary source for the DanteR package up on our website together with a vignettes document. For Windows, a single click automatically installs DanteR along with the R programming environment. For Linux and Mac OS X, users must install R and then follow instructions on the DanteR website for package installation.

CONTACT

rds@pnnl.gov.

Smoke-induced signal molecules in bone marrow cells from altered low-density lipoprotein receptor-related protein 5 mice

Mechanism underlying smoke-induced loss of bone mass is unknown. In this study, we hypothesized that protein signals induced by smoking in bone marrow may be associated with the loss of bone mass. Using a proteomics approach, we identified 38 proteins differentially expressed in bone marrow cells from low-density lipoprotein receptor-related protein 5 (Lrp5) mice exposed to cigarette smoking. Smoking effects on protein expression in bone marrow among three genotypes (Lrp5(+/+), Lrp5(G171V), and Lrp5(-/-)) varied. On the basis of the ratio of protein expression induced by smoking versus nonsmoking, smoke induced protein expression significantly in wild-type mice compared to the other two genotypes (Lrp5(G171V) and Lrp5(-/-)). These proteins include inhibitors of β-catenin and proteins associated with differentiation of osteoclasts. We observed that S100A8 and S100A9 were overexpressed in human smokers compared to nonsmokers, which confirmed the effect of smoking on the expression of two proteins in Lrp5 mice, suggesting the role of these proteins in bone remodeling. Smoke induced expression of S100A8 and S100A9 in a time-dependent fashion, which was opposite of the changes in the ratio of OPG/RANKL in bone marrow cells, suggesting that the high levels of S100A8 and S100A9 may be associated with smoke-induced bone loss by increasing bone resorption.

Tissue proteomics by one-dimensional gel electrophoresis combined with label-free protein quantification

Label-free methods streamline quantitative proteomics of tissues by alleviating the need for metabolic labeling of proteins with stable isotopes. Here we detail and implement solutions to common problems in label-free data processing geared toward tissue proteomics by one-dimensional gel electrophoresis followed by liquid chromatography tandem mass spectrometry (geLC MS/MS). Our quantification pipeline showed high levels of performance in terms of duplicate reproducibility, linear dynamic range, and number of proteins identified and quantified. When applied to the liver of an adenomatous polyposis coli (APC) knockout mouse, we demonstrated an 8-fold increase in the number of statistically significant changing proteins compared to alternative approaches, including many more previously unidentified hydrophobic proteins. Better proteome coverage and quantification accuracy revealed molecular details of the perturbed energy metabolism.

Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data

MOTIVATION

Protein abundance in quantitative proteomics is often based on observed spectral features derived from liquid chromatography mass spectrometry (LC-MS) or LC-MS/MS experiments. Peak intensities are largely non-normal in distribution. Furthermore, LC-MS-based proteomics data frequently have large proportions of missing peak intensities due to censoring mechanisms on low-abundance spectral features. Recognizing that the observed peak intensities detected with the LC-MS method are all positive, skewed and often left-censored, we propose using survival methodology to carry out differential expression analysis of proteins. Various standard statistical techniques including non-parametric tests such as the Kolmogorov-Smirnov and Wilcoxon-Mann-Whitney rank sum tests, and the parametric survival model and accelerated failure time-model with log-normal, log-logistic and Weibull distributions were used to detect any differentially expressed proteins. The statistical operating characteristics of each method are explored using both real and simulated datasets.

RESULTS

Survival methods generally have greater statistical power than standard differential expression methods when the proportion of missing protein level data is 5% or more. In particular, the AFT models we consider consistently achieve greater statistical power than standard testing procedures, with the discrepancy widening with increasing missingness in the proportions.

AVAILABILITY

The testing procedures discussed in this article can all be performed using readily available software such as R. The R codes are provided as supplemental materials.

CONTACT

ctekwe@stat.tamu.edu.

Batch effects correction improves the sensitivity of significance tests in spectral counting-based comparative discovery proteomics

Shotgun proteomics has become the standard proteomics technique for the large-scale measurement of protein abundances in biological samples. Despite quantitative proteomics has been usually performed using label-based approaches, label-free quantitation offers advantages related to the avoidance of labeling steps, no limitation in the number of samples to be compared, and the gain in protein detection sensitivity. However, since samples are analyzed separately, experimental design becomes critical. The exploration of spectral counting quantitation based on LC-MS presented here gathers experimental evidence of the influence of batch effects on comparative proteomics. The batch effects shown with spiking experiments clearly interfere with the biological signal. In order to minimize the interferences from batch effects, a statistical correction is proposed and implemented. Our results show that batch effects can be attenuated statistically when proper experimental design is used. Furthermore, the batch effect correction implemented leads to a substantial increase in the sensitivity of statistical tests. Finally, the applicability of our batch effects correction is shown on two different biomarker discovery projects involving cancer secretomes. We think that our findings will allow designing and executing better comparative proteomics projects and will help to avoid reaching false conclusions in the field of proteomics biomarker discovery.

Dengue virus infection perturbs lipid homeostasis in infected mosquito cells

Dengue virus causes ∼50-100 million infections per year and thus is considered one of the most aggressive arthropod-borne human pathogen worldwide. During its replication, dengue virus induces dramatic alterations in the intracellular membranes of infected cells. This phenomenon is observed both in human and vector-derived cells. Using high-resolution mass spectrometry of mosquito cells, we show that this membrane remodeling is directly linked to a unique lipid repertoire induced by dengue virus infection. Specifically, 15% of the metabolites detected were significantly different between DENV infected and uninfected cells while 85% of the metabolites detected were significantly different in isolated replication complex membranes. Furthermore, we demonstrate that intracellular lipid redistribution induced by the inhibition of fatty acid synthase, the rate-limiting enzyme in lipid biosynthesis, is sufficient for cell survival but is inhibitory to dengue virus replication. Lipids that have the capacity to destabilize and change the curvature of membranes as well as lipids that change the permeability of membranes are enriched in dengue virus infected cells. Several sphingolipids and other bioactive signaling molecules that are involved in controlling membrane fusion, fission, and trafficking as well as molecules that influence cytoskeletal reorganization are also up regulated during dengue infection. These observations shed light on the emerging role of lipids in shaping the membrane and protein environments during viral infections and suggest membrane-organizing principles that may influence virus-induced intracellular membrane architecture.

Comparison of Chloroflexus aurantiacus strain J-10-fl proteomes of cells grown chemoheterotrophically and photoheterotrophically

Chloroflexus aurantiacus J-10-fl is a thermophilic green bacterium, a filamentous anoxygenic phototroph, and the model organism of the phylum Chloroflexi. We applied high-throughput, liquid chromatography-mass spectrometry in a global quantitative proteomics investigation of C. aurantiacus cells grown under oxic (chemoorganoheterotrophically) and anoxic (photoorganoheterotrophically) redox states. Our global analysis identified 13,524 high-confidence peptides that matched to 1,286 annotated proteins, 242 of which were either uniquely identified or significantly increased in abundance under photoheterotrophic culture condition. Fifty-four of the 242 proteins are previously characterized photosynthesis-related proteins, including chlorosome proteins, proteins involved in the bacteriochlorophyll biosynthesis, 3-hydroxypropionate (3-OHP) CO(2) fixation pathway, and components of electron transport chains. The remaining 188 proteins have not previously been reported. Of these, five proteins were found to be encoded by genes from a novel operon and observed only in photoheterotrophically grown cells. These proteins candidates may prove useful in further deciphering the phototrophic physiology of C. aurantiacus and other filamentous anoxygenic phototrophs.

Development of a biomarker for Geobacter activity and strain composition; proteogenomic analysis of the citrate synthase protein during bioremediation of U(VI)

Monitoring the activity of target microorganisms during stimulated bioremediation is a key problem for the development of effective remediation strategies. At the US Department of Energy's Integrated Field Research Challenge (IFRC) site in Rifle, CO, the stimulation of Geobacter growth and activity via subsurface acetate addition leads to precipitation of U(VI) from groundwater as U(IV). Citrate synthase (gltA) is a key enzyme in Geobacter central metabolism that controls flux into the TCA cycle. Here, we utilize shotgun proteomic methods to demonstrate that the measurement of gltA peptides can be used to track Geobacter activity and strain evolution during in situ biostimulation. Abundances of conserved gltA peptides tracked Fe(III) reduction and changes in U(VI) concentrations during biostimulation, whereas changing patterns of unique peptide abundances between samples suggested sample-specific strain shifts within the Geobacter population. Abundances of unique peptides indicated potential differences at the strain level between Fe(III)-reducing populations stimulated during in situ biostimulation experiments conducted a year apart at the Rifle IFRC. These results offer a novel technique for the rapid screening of large numbers of proteomic samples for Geobacter species and will aid monitoring of subsurface bioremediation efforts that rely on metal reduction for desired outcomes.

The age of the "ome": genome, transcriptome and proteome data set collection and analysis

The current state of human genetic studies is both a marvel and a morass. A marvel in that with the completion of the human genome sequence, projects that used to take years now take months or weeks; however, this creates a wealth of data concomitant to a black hole of meaning. In terms of the well used analogy: the human genome sequence is a library in an ancient language with no Rosetta stone. Researchers have readily exploited the human genome map and thousands of candidate gene studies for a multitude of diseases have been performed. However, many of those studies have found that the variants associated with disease risk are not obvious coding changes. The question now becomes: what do these associations mean? One approach to the downstream mapping of associations is to use additional information to map which variant might truly be causative of risk and what that risk variant is doing. This review will summarize the current state of both data set collection and analysis for the understanding of DNA variants and their downstream effects on transcripts and proteins. This article is part of a Special Issue entitled 'Transcriptome'.

A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors

Quantification of LC-MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run-to-run of the instrument due to both technical and biological variation. Thus, normalization of peak intensities across an LC-MS proteomics dataset is a fundamental step in pre-processing. However, the downstream analysis of LC-MS proteomics data can be dramatically affected by the normalization method selected. Current normalization procedures for LC-MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If they are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, possibly affecting downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, which includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between-group variance structure in order to identify the most appropriate normalization methods that improve the structure of the data without introducing bias into the normalized peak intensities.

Aging enhances the production of reactive oxygen species and bactericidal activity in peritoneal macrophages by upregulating classical activation pathways

Maintenance of macrophages in their basal state and their rapid activation in response to pathogen detection are central to the innate immune system, acting to limit nonspecific oxidative damage and promote pathogen killing following infection. To identify possible age-related alterations in macrophage function, we have assayed the function of peritoneal macrophages from young (3-4 months) and aged (14-15 months) Balb/c mice. In agreement with prior suggestions, we observe age-dependent increases in the extent of recruitment of macrophages into the peritoneum, as well as ex vivo functional changes involving enhanced nitric oxide production under resting conditions that contribute to a reduction in the time needed for full activation of senescent macrophages following exposure to lipopolysaccharides (LPS). Further, we observe enhanced bactericidal activity following Salmonella uptake by macrophages isolated from aged Balb/c mice in comparison with those isolated from young animals. Pathways responsible for observed phenotypic changes were interrogated using tandem mass spectrometry, which identified age-dependent increases in levels of proteins linked to immune cell pathways under basal conditions and following LPS activation. Immune pathways upregulated in macrophages isolated from aged mice include proteins critical to the formation of the immunoproteasome. Detection of these latter proteins is dramatically enhanced following LPS exposure for macrophages isolated from aged animals; in comparison, the identification of immunoproteasome subunits is insensitive to LPS exposure for macrophages isolated from young animals. Consistent with observed global changes in the proteome, quantitative proteomic measurements indicate that there are age-dependent abundance changes involving specific proteins linked to immune cell function under basal conditions. LPS exposure selectively increases the levels of many proteins involved in immune cell function in aged Balb/c mice. Collectively, these results indicate that macrophages isolated from old mice are in a preactivated state that enhances their sensitivities to LPS exposure. The hyper-responsive activation of macrophages in aged animals may act to minimize infection by general bacterial threats that arise due to age-dependent declines in adaptive immunity. However, this hypersensitivity and the associated increase in the level of formation of reactive oxygen species are likely to contribute to observed age-dependent increases in the level of oxidative damage that underlie many diseases of the elderly.

Processing methods for signal suppression of FTMS data

Background

Fourier Transform Mass Spectrometry coupled with Liquid Chromatography(LC-FTMS) has been widely used in proteomics. Past investigation has revealed that there exists an intensity dependent random suppression in peptide elution profiles in LC-FTMS data. The suppression is homogenous for the same peptide but non-homogenous for different peptides. The correction of suppressed profiles and an estimation on the range of suppression are necessary for accurate and reliable quantification using FTMS data.

Results

A software package, Gcorr, is presented. The software corrects peptide profiles that satisfy correction conditions, and it can predict fold change null distributions at different intensity levels. Subsequently, the significance P-values of measured fold changes can be estimated based on the predicted null distributions. We have used an 1:1 LC-FTMS label-free dataset pair collected based on the same sample to verify that our predicted null distributions conforms to that of the observed null distribution.

Conclusions

This software is able to provide suppression correction for peptide profiles, suppression distribution analysis and peptide differential expression analysis in terms of its fold change significance. The software is freely available at http://compgenomics.utsa.edu/Suppression_Study.html.

Software for quantitative proteomic analysis using stable isotope labeling and data independent acquisition

Many software tools have been developed for analyzing stable isotope labeling (SIL)-based quantitative proteomic data using data dependent acquisition (DDA). However, programs for analyzing SIL-based quantitative proteomics data obtained with data independent acquisition (DIA) have yet to be reported. Here, we demonstrated the development of a new software for analyzing SIL data using the DIA method. Performance of the DIA on SYNAPT G2MS was evaluated using SIL-labeled complex proteome mixtures with known heavy/light ratios (H/L = 1:1, 1:5, and 1:10) and compared with the DDA on linear ion trap (LTQ)-Orbitrap MS. The DIA displays relatively high quantitation accuracy for peptides cross all intensity regions, while the DDA shows an intensity dependent distribution of H/L ratios. For the three proteome mixtures, the number of detected SIL-peptide pairs and dynamic range of protein intensities using DIA drop stepwise, whereas no significant changes in these aspects using DDA were observed. The new software was applied to investigate the proteome difference between mouse embryonic fibroblasts (MEFs) and MEF-derived induced pluripotent stem cells (iPSCs) using (16)O/(18)O labeling. Our study expanded the capacities of our UNiquant software pipeline and provided valuable insight into the performance of the two cutting-edge MS platforms for SIL-based quantitative proteomic analysis today.

Insulin-like growth factor binding protein-6 delays replicative senescence of human fibroblasts

Cellular senescence can be induced by a variety of mechanisms, and recent data suggest a key role for cytokine networks to maintain the senescent state. Here, we have used a proteomic LC-MS/MS approach to identify new extracellular regulators of senescence in human fibroblasts. We identified 26 extracellular proteins with significantly different abundance in conditioned media from young and senescent fibroblasts. Among these was insulin-like growth factor binding protein-6 (IGFBP-6), which was chosen for further analysis. When IGFBP-6 gene expression was downregulated, cell proliferation was inhibited and apoptotic cell death was increased. Furthermore, downregulation of IGFBP-6 led to premature entry into cellular senescence. Since IGFBP-6 overexpression increased cellular lifespan, the data suggest that IGFBP-6, in contrast to other IGF binding proteins, is a negative regulator of cellular senescence in human fibroblasts.

Bioinformatics Tools for Mass Spectrometry-Based High-Throughput Quantitative Proteomics Platforms

Determining global proteome changes is important for advancing a systems biology view of cellular processes and for discovering biomarkers. Liquid chromatography, coupled to mass spectrometry, has been widely used as a proteomics technique for discovering differentially expressed proteins in biological samples. However, although a large number of high-throughput studies have identified differentially regulated proteins, only a small fraction of these results have been reproduced and independently verified. The use of different approaches to data processing and analyses is among the factors which contribute to inconsistent conclusions. This perspective provides a comprehensive and critical overview of bioinformatics methods for commonly used mass spectrometry-based quantitative proteomics, employing both stable isotope labeling and label-free approaches. We evaluate the challenges associated with current quantitative proteomics techniques, placing particular emphasis on data analyses. The complexity of processing and interpreting proteomics datasets has become a central issue as sensitivity, mass resolution, mass accuracy and throughput of mass spectrometers have improved. A number of computer programs are available to address these challenges, and are reviewed here. We focus on approaches for signal processing, noise reduction, and methods for protein abundance estimation.

Quantitative analysis of p53 expression in human normal and cancer tissue microarray with global normalization method

Tissue microarray based immunohistochemical staining and proteomics are important tools to create and validate clinically relevant cancer biomarkers. Immunohistochemical stains using formalin-fixed tissue microarray sections for protein expression are scored manually and semi-quantitatively. Digital image analysis methods remove some of the drawbacks of manual scoring but may need other methods such as normalization to provide across the board utility. In the present study, quantitative proteomics-based global normalization method was used to evaluate its utility in the analysis of p53 protein expression in mixed human normal and cancer tissue microarray. Global normalization used the mean or median of β-actin to calculate ratios of individual core stain intensities, then log transformed the ratios, calculate a mean or median and subtracted the value from the log of ratios. In the absence of global normalization of p53 protein expression, 44% (42 of 95) of tissue cores were positive using the median of intensity values and 40% (38 of 95) using the mean of intensities as cut-off points. With global normalization, p53 positive cores changed to 20% (19 of 95) when using median of intensities and 15.8%(15 of 95) when the mean of intensities were used. In conclusion, the global normalization method helped to define positive p53 staining in the tissue microarray set used. The method used helped to define clear cut-off points and confirmed all negatively stained tissue cores. Such normalization methods should help to better define clinically useful biomarkers.

Liquid chromatography-mass spectrometry-based quantitative proteomics

LC-MS-based quantitative proteomics has become increasingly applied to a wide range of biological applications due to growing capabilities for broad proteome coverage and good accuracy and precision in quantification. Herein, we review the current LC-MS-based quantification methods with respect to their advantages and limitations and highlight their potential applications.

Proteins and an inflammatory network expressed in colon tumors

The adenomatous polyposis coli (APC) protein is crucial to homeostasis of normal intestinal epithelia because it suppresses the β-catenin/TCF pathway. Consequently, loss or mutation of the APC gene causes colorectal tumors in humans and mice. Here, we describe our use of multidimensional protein identification technology (MudPIT) to compare protein expression in colon tumors to that of adjacent healthy colon tissue from Apc(Min/+) mice. Twenty-seven proteins were found to be up-regulated in colon tumors and 25 were down-regulated. As an extension of the proteomic analysis, the differentially expressed proteins were used as "seeds" to search for coexpressed genes. This approach revealed a coexpression network of 45 genes that is up-regulated in colon tumors. Members of the network include the antibacterial peptide cathelicidin (CAMP), Toll-like receptors (TLRs), IL-8, and triggering receptor expressed on myeloid cells 1 (TREM1). The coexpression network is associated with innate immunity and inflammation, and there is significant concordance between its connectivity in humans versus mice (Friedman: p value = 0.0056). This study provides new insights into the proteins and networks that are likely to drive the onset and progression of colon cancer.