Analysis Of Quantitative Proteomics Data Research Articles

Proteomic Profiling of Colorectal Adenomas Identifies a Predictive Risk Signature for Development of Metachronous Advanced Colorectal Neoplasia

Colonic adenomatous polyps, or adenomas, are frequent precancerous lesions and the origin of most cases of colorectal adenocarcinoma. However, we know from epidemiologic studies that although most colorectal cancers (CRCs) originate from adenomas, only a small fraction of adenomas (3%-5%) ever progress to cancer. At present, there are no molecular markers to guide follow-up surveillance programs. We profiled, by mass spectrometry-based proteomics combined with machine learning analysis, a selected cohort of formalin-fixed, paraffin-embedded high-grade (HG) adenomas with long clinical follow-up, collected as part of the Danish national screening program. We grouped subjects in the cohort according to their subsequent history of findings: a nonmetachronous advanced neoplasia group (G0), with no new HG adenomas or CRCs up to 10 years after polypectomy, and a metachronous advanced neoplasia group (G1) where individuals developed a new HG adenoma or CRC within 5 years of diagnosis. We generated a proteome dataset from 98 selected HG adenoma samples, including 20 technical replicates, of which 45 samples belonged to the nonmetachronous advanced neoplasia group and 53 to the metachronous advanced neoplasia group. The clear distinction of these 2 groups seen in a Uniform Manifold Approximation and Projection plot indicated that the information contained within the abundance of the ∼5000 proteins was sufficient to predict the future occurrence of HG adenomas or development of CRC. We performed an in-depth analysis of quantitative proteomic data from 98 resected adenoma samples using various novel algorithms and statistical packages and found that their proteome can predict development of metachronous advanced lesions and progression several years in advance.

SQuAPP—simple quantitative analysis of proteins and PTMs

The comprehensive analysis of the proteome and its modulation by post-translational modification (PTM) is increasingly used in biological and biomedical studies. As a result, proteomics data analysis is ever more carried out by scientists with limited expertise in this type of data. While excellent software solutions for comprehensive and rigorous analysis of quantitative proteomic data exist, most are complex and not well suited for non-proteomics scientists. Integrative analysis of multi-level proteomics data on protein and diverse PTMs, like phosphorylation or proteolytic processing, remains particularly challenging and inaccessible to most biologists. To fill this void, we developed SQuAPP, an R-Shiny web-based analysis pipeline for the quantitative analysis of proteomic data. SQuAPP uses a streamlined workflow model to guide expert and novice users through quality control, data pre-processing, statistical analysis and visualization steps. Processing the protein, peptide and PTM datasets in parallel and their quantitative integration enable rapid identification of protein-level-independent modulation of protein modifications and intuitive interpretation of dynamic dependencies between different protein modifications. SQuAPP is available at http://squapp.langelab.org/. The source code and local setup instructions can be accessed from https://github.com/LangeLab/SQuAPP.

Label-Free Mass Spectrometry-Based Quantitative Proteomics to Evaluate the Effects of the Calcium-Sensing Receptor Agonist Cinacalcet on Protein Expression in Rat Brains and Livers.

BackgroundCinacalcet is a calcium-sensing receptor agonist that is clinically approved for the treatment of secondary hyperparathyroidism in chronic kidney disease and hypercalcemia in patients with parathyroid carcinoma. This study aimed to use quantitative mass spectrometry-based label-free proteomics to evaluate the effects of cinacalcet on protein expression in rat brains and livers.Material/MethodsWe randomly assigned 18 Wistar rats to 2 groups: an untreated control group (n=6) and a group treated with cinacalcet at a dose corresponding to the maximum dose used in humans (2 mg/kg/body weight, 5 days/week) divided into 7-day (n=6) and 21-day (n=6) treatment subgroups. A mass-spectrometry-based label-free quantitative proteomics approach using peptides peak area calculation was used to evaluate the changes in protein expression in examined tissues. Bioinformatics analysis of quantitative proteomics data was done using MaxQuant and Perseus environment.ResultsNo changes in protein expression were revealed in the 7-day treatment subgroup. We detected 10 upregulated and 3 downregulated proteins in the liver and 1 upregulated protein in the brain in the 21-day treatment subgroup compared to the control group. Based on Gene Ontology classification, all identified differentially expressed proteins were indicated as molecular functions involved in the enzyme regulator activity (36%), binding (31%), and catalytic activity (19%).ConclusionsThese findings indicate that long-term cinacalcet therapy can impair phase II of enzymatic detoxication and can cause disturbances in blood hemostasis, lipid metabolism, and inflammatory mediators or contribute to the acceleration of cognitive dysfunction; therefore, appropriate patient monitoring should be considered.

PT109, a novel multi-kinase inhibitor suppresses glioblastoma multiforme through cell reprogramming: Involvement of PTBP1/PKM1/2 pathway

Glioblastoma multiforme (GBM) is the most prevalent type and lethal form of primary malignant brain tumor, accounting for about 40–50% of intracranial tumors and without effective treatments now. Cell reprogramming is one of the emerging treatment approaches for GBM, which can reprogram glioblastomas into non-tumor cells to achieve therapeutic effects. However, anti-GBM drugs through reprogramming can only provide limited symptom relief, and cannot completely cure GBM. Here we showed that PT109, a novel multi-kinase inhibitor, suppressed GBM's proliferation, colony formation, migration and reprogramed GBM into oligodendrocytes. Analysis of quantitative proteomics data after PT109 administration of human GBM cells showed significant influence of energy metabolism, cell cycle, and immune system processes of GBM-associated protein. Metabolomics analysis showed that PT109 improved the aerobic respiration process in glioma cells. Meanwhile, we found that PT109 could significantly increase the ratio of Pyruvate kinase M1/2 (PKM1/2) by reducing the level of polypyrimidine tract-binding protein 1 (PTBP1). Altogether, this work developed a novel anti-GBM small molecule PT109, which reprogramed GBM into oligodendrocytes and changed the metabolic pattern of GBM through the PTBP1/PKM1/2 pathway, providing a new strategy for the development of anti-glioma drugs.

ComplexBrowser: A Tool for Identification and Quantification of Protein Complexes in Large-scale Proteomics Datasets

We have developed ComplexBrowser, an open source, online platform for supervised analysis of quantitative proteomic data (label free and isobaric mass tag based) that focuses on protein complexes. The software uses manually curated information from CORUM and Complex Portal databases to identify protein complex components. For the first time, we provide a Complex Fold Change (CFC) factor that identifies up- and downregulated complexes based on the level of complex subunits coregulation. The software provides interactive visualization of protein complexes' composition and expression for exploratory analysis and incorporates a quality control step that includes normalization and statistical analysis based on the limma package. ComplexBrowser was tested on two published studies identifying changes in protein expression within either human adenocarcinoma tissue or activated mouse T-cells. The analysis revealed 1519 and 332 protein complexes, of which 233 and 41 were found coordinately regulated in the respective studies. The adopted approach provided evidence for a shift to glucose-based metabolism and high proliferation in adenocarcinoma tissues, and the identification of chromatin remodeling complexes involved in mouse T-cell activation. The results correlate with the original interpretation of the experiments and provide novel biological details about the protein complexes affected. ComplexBrowser is, to our knowledge, the first tool to automate quantitative protein complex analysis for high-throughput studies, providing insights into protein complex regulation within minutes of analysis.

Abstract 2492: BRAF inhibitors synergize with BH3 mimetics to induce apoptosis in BRAF mutant colorectal cancer cells

Abstract Activating mutations in BRAF (BRAFV600E) occur in ~10% of colorectal cancers (CRCs) and drive tumorigenesis through constitutive activation of MAPK signaling. In metastatic CRC, BRAF mutations are associated with poorer prognosis and resistance to conventional therapies, necessitating an urgent need to develop new treatments for these patients. BRAF inhibitors such as vemurafenib and dabrafenib have significant clinical activity in BRAF-mutant melanoma, however BRAF-mutant CRCs are largely refractory to these agents, due at least in part to feedback-relief mediated reactivation of MAPK signaling or alternate signaling pathway activation. Strategies to enhance the activity of BRAF inhibitors in BRAF-mutant CRC are therefore needed. Consistent with clinical observations, treatment of a panel of BRAF-mutant melanoma and CRC cell lines with vemurafenib resulted in significantly increased apoptosis in melanoma cell lines compared to CRC cell lines, where effects were largely cytostatic. To determine the mechanisms for this differential response we interrogated vemurafenib-induced gene expression changes in the two tumor types, focusing on altered expression of components of the intrinsic apoptotic pathway. Vemurafenib induced a more pronounced increase in expression of the pro-apoptotic genes BIM, BMF and PUMA and suppression of pro-survival gene MCL1 in melanoma cells compared to CRC cells. These findings suggested that the extent to which expression of pro and anti-apoptotic genes are altered by vemurafenib in CRC cells may be insufficient to reach the threshold required for apoptosis initiation. We therefore postulated that BH3-mimetics may synergize with vemurafenib to induce apoptosis in BRAF-mutant CRC cells. Analysis of quantitative proteomic data of BRAF-mutant CRC cell lines revealed significantly higher basal expression of the pro-survival proteins Bcl-xL and MCL1 compared to BCL2 and BCLW, suggesting CRC cells may be particularly dependent on Bcl-xL and MCL1 for survival. Indeed, combination treatment of BRAF-mutant CRC cells with the Bcl-xL inhibitor A-1331852 significantly enhanced apoptosis in the majority of BRAF-mutant CRC lines. Comparatively, combination treatment of vemurafenib with the MCL1 inhibitor S63845 induced a modest increase in apoptosis, while combination treatment with the BCL2 inhibitor ABT-199 had no effect on apoptosis, consistent with the low levels of BCL2 expression in these lines. Finally, we investigated the effect of combination treatment of vemurafenib with inhibitors of both Bcl-xL and MCL1. The triple combination further enhanced apoptosis in 3/5 cell lines, suggesting these cell lines are likely dependent on both Bcl-xL and MCL1 for survival. Collectively, these findings demonstrate that combining BRAF-inhibitors with Bcl-xL and/or MCL1 inhibitors may represent a novel strategy for treating BRAF-mutant CRC. Citation Format: Laura J. Jenkins, Fiona Chionh, Ian Y. Luk, Erinna F. Lee, Amardeep S. Dhillon, Niall Tebbutt, Walter D. Fairlie, John M. Mariadason. BRAF inhibitors synergize with BH3 mimetics to induce apoptosis in BRAF mutant colorectal cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2492.

Experimental design and data-analysis in label-free quantitative LC/MS proteomics: A tutorial with MSqRob

Label-free shotgun proteomics is routinely used to assess proteomes. However, extracting relevant information from the massive amounts of generated data remains difficult. This tutorial provides a strong foundation on analysis of quantitative proteomics data. We provide key statistical concepts that help researchers to design proteomics experiments and we showcase how to analyze quantitative proteomics data using our recent free and open-source R package MSqRob, which was developed to implement the peptide-level robust ridge regression method for relative protein quantification described by Goeminne et al. MSqRob can handle virtually any experimental proteomics design and outputs proteins ordered by statistical significance. Moreover, its graphical user interface and interactive diagnostic plots provide easy inspection and also detection of anomalies in the data and flaws in the data analysis, allowing deeper assessment of the validity of results and a critical review of the experimental design. Our tutorial discusses interactive preprocessing, data analysis and visualization of label-free MS-based quantitative proteomics experiments with simple and more complex designs. We provide well-documented scripts to run analyses in bash mode on GitHub, enabling the integration of MSqRob in automated pipelines on cluster environments (https://github.com/statOmics/MSqRob). SignificanceThe concepts outlined in this tutorial aid in designing better experiments and analyzing the resulting data more appropriately. The two case studies using the MSqRob graphical user interface will contribute to a wider adaptation of advanced peptide-based models, resulting in higher quality data analysis workflows and more reproducible results in the proteomics community. We also provide well-documented scripts for experienced users that aim at automating MSqRob on cluster environments.

MapDIA: Preprocessing and statistical analysis of quantitative proteomics data from data independent acquisition mass spectrometry

Data independent acquisition (DIA) mass spectrometry is an emerging technique that offers more complete detection and quantification of peptides and proteins across multiple samples. DIA allows fragment-level quantification, which can be considered as repeated measurements of the abundance of the corresponding peptides and proteins in the downstream statistical analysis. However, few statistical approaches are available for aggregating these complex fragment-level data into peptide- or protein-level statistical summaries. In this work, we describe a software package, mapDIA, for statistical analysis of differential protein expression using DIA fragment-level intensities. The workflow consists of three major steps: intensity normalization, peptide/fragment selection, and statistical analysis. First, mapDIA offers normalization of fragment-level intensities by total intensity sums as well as a novel alternative normalization by local intensity sums in retention time space. Second, mapDIA removes outlier observations and selects peptides/fragments that preserve the major quantitative patterns across all samples for each protein. Last, using the selected fragments and peptides, mapDIA performs model-based statistical significance analysis of protein-level differential expression between specified groups of samples. Using a comprehensive set of simulation datasets, we show that mapDIA detects differentially expressed proteins with accurate control of the false discovery rates. We also describe the analysis procedure in detail using two recently published DIA datasets generated for 14–3-3β dynamic interaction network and prostate cancer glycoproteome. AvailabilityThe software was written in C++ language and the source code is available for free through SourceForge website http://sourceforge.net/projects/mapdia/.This article is part of a Special Issue entitled: Computational Proteomics.

Deep proteomic profiling of vasopressin-sensitive collecting duct cells. II. Bioinformatic analysis of vasopressin signaling.

Vasopressin controls osmotic water transport in the renal collecting duct through regulation of aquaporin-2 (AQP2). We carried out bioinformatic analysis of quantitative proteomic data from the accompanying article to investigate the mechanisms involved. The experiments used stable isotope labeling by amino acids in cell culture in cultured mpkCCD cells to quantify each protein species in each of five differential-centrifugation (DC) fractions with or without the vasopressin analog 1-desamino-8-d-arginine-vasopressin (dDAVP). The mass spectrometry data and parallel Western blot experiments confirmed that dDAVP addition is associated with an increase in AQP2 abundance in the 17,000-g pellet and a corresponding decrease in the 200,000-g pellet. Remarkably, all subunits of the cytoplasmic ribosome also increased in the 17,000-g pellet in response to dDAVP (P < 10(-34)), with a concomitant decrease in the 200,000-g pellet. Eukaryotic translation initiation complex 3 (eIF3) subunits underwent parallel changes (P < 10(-6)). These findings are consistent with translocation of assembled ribosomes and eIF3 complexes into the rough endoplasmic reticulum in response to dDAVP. Conversely, there was a systematic decrease in small GTPase abundances in the 17,000-g fraction. In contrast, most proteins, including protein kinases, showed no systematic redistribution among DC fractions. Of the 521 protein kinases coded by the mouse genome, 246 were identified, but many fewer were found to colocalize with AQP2 among DC fractions. Bayes' rule was used to integrate the new colocalization data with prior data to identify protein kinases most likely to phosphorylate aquaporin-2 at Ser(256) (Camk2b > Camk2d > Prkaca) and Ser(261) (Mapk1 = Mapk3 > Mapk14).

The characterization of microRNA-mediated gene regulation as impacted by both target site location and seed match type.

MicroRNAs (miRNAs) are small RNA molecules that play important roles in gene regulation and translational repression. The mechanisms that facilitate miRNA target binding and recognition have been extensively studied in recent years. However, it is still not well known how the miRNA regulation is affected by the location and the flanking sequences of miRNA target sites. In this study, we systematically quantify the contribution of a wide spectrum of target sites on miRNA-mediated gene expression regulation. Our study investigates target sites located in four different gene regions, including 3' untranslated regions, coding sequences, 5′ untranslated regions and promoter regions. We have also introduced four additional non-canonical types of seed matches beyond the canonical seed matches, and included them in our study. Computational analysis of quantitative proteomic data has demonstrated that target sites located in different regions impact the miRNA-mediated repression differently but synergistically. In addition, we have shown the synergistic effects among non-canonical seed matches and canonical ones that enhance the miRNA regulatory effects. Further systematic analysis on the site accessibility near the target regions and the secondary structure of the mRNA sequences have demonstrated substantial variations among target sites of different locations and of different types of seed matches, suggesting the mRNA secondary structure could explain some of the difference in the miRNA regulatory effects impacted by these different target sites. Our study implies miRNAs might regulate their targets under different mechanisms when target sites vary in both their locations and the types of seed matches they contain.

Improved Normalization of Systematic Biases Affecting Ion Current Measurements in Label-free Proteomics Data

Normalization is an important step in the analysis of quantitative proteomics data. If this step is ignored, systematic biases can lead to incorrect assumptions about regulation. Most statistical procedures for normalizing proteomics data have been borrowed from genomics where their development has focused on the removal of so-called 'batch effects.' In general, a typical normalization step in proteomics works under the assumption that most peptides/proteins do not change; scaling is then used to give a median log-ratio of 0. The focus of this work was to identify other factors, derived from knowledge of the variables in proteomics, which might be used to improve normalization. Here we have examined the multi-laboratory data sets from Phase I of the NCI's CPTAC program. Surprisingly, the most important bias variables affecting peptide intensities within labs were retention time and charge state. The magnitude of these observations was exaggerated in samples of unequal concentrations or "spike-in" levels, presumably because the average precursor charge for peptides with higher charge state potentials is lower at higher relative sample concentrations. These effects are consistent with reduced protonation during electrospray and demonstrate that the physical properties of the peptides themselves can serve as good reporters of systematic biases. Between labs, retention time, precursor m/z, and peptide length were most commonly the top-ranked bias variables, over the standardly used average intensity (A). A larger set of variables was then used to develop a stepwise normalization procedure. This statistical model was found to perform as well or better on the CPTAC mock biomarker data than other commonly used methods. Furthermore, the method described here does not require a priori knowledge of the systematic biases in a given data set. These improvements can be attributed to the inclusion of variables other than average intensity during normalization.

Identification of Potential Mediators of Retinotopic Mapping: A Comparative Proteomic Analysis of Optic Nerve from WT and Phr1 Retinal Knockout Mice

Retinal ganglion cells (RGCs) transmit visual information topographically from the eye to the brain, creating a map of visual space in retino-recipient nuclei (retinotopy). This process is affected by retinal activity and by activity-independent molecular cues. Phr1, which encodes a presumed E3 ubiquitin ligase (PHR1), is required presynaptically for proper placement of RGC axons in the lateral geniculate nucleus and the superior colliculus, suggesting that increased levels of PHR1 target proteins may be instructive for retinotopic mapping of retinofugal projections. To identify potential target proteins, we conducted a proteomic analysis of optic nerve to identify differentially abundant proteins in the presence or absence of Phr1 in RGCs. 1D gel electrophoresis identified a specific band in controls that was absent in mutants. Targeted proteomic analysis of this band demonstrated the presence of PHR1. Additionally, we conducted an unbiased proteomic analysis that identified 30 proteins as being significantly different between the two genotypes. One of these, heterogeneous nuclear ribonucleoprotein M (hnRNP-M), regulates antero-posterior patterning in invertebrates and can function as a cell surface adhesion receptor in vertebrates. Thus, we have demonstrated that network analysis of quantitative proteomic data is a useful approach for hypothesis generation and for identifying biologically relevant targets in genetically altered biological models.

A Critical Appraisal of Techniques, Software Packages, and Standards for Quantitative Proteomic Analysis

New methods for performing quantitative proteome analyses based on differential labeling protocols or label-free techniques are reported in the literature on an almost monthly basis. In parallel, a correspondingly vast number of software tools for the analysis of quantitative proteomics data has also been described in the literature and produced by private companies. In this article we focus on the review of some of the most popular techniques in the field and present a critical appraisal of several software packages available to process and analyze the data produced. We also describe the importance of community standards to support the wide range of software, which may assist researchers in the analysis of data using different platforms and protocols. It is intended that this review will serve bench scientists both as a useful reference and a guide to the selection and use of different pipelines to perform quantitative proteomics data analysis. We have produced a web-based tool ( http://www.proteosuite.org/?q=other_resources ) to help researchers find appropriate software for their local instrumentation, available file formats, and quantitative methodology.

GProX, a User-Friendly Platform for Bioinformatics Analysis and Visualization of Quantitative Proteomics Data

Recent technological advances have made it possible to identify and quantify thousands of proteins in a single proteomics experiment. As a result of these developments, the analysis of data has become the bottleneck of proteomics experiment. To provide the proteomics community with a user-friendly platform for comprehensive analysis, inspection and visualization of quantitative proteomics data we developed the Graphical Proteomics Data Explorer (GProX)(1). The program requires no special bioinformatics training, as all functions of GProX are accessible within its graphical user-friendly interface which will be intuitive to most users. Basic features facilitate the uncomplicated management and organization of large data sets and complex experimental setups as well as the inspection and graphical plotting of quantitative data. These are complemented by readily available high-level analysis options such as database querying, clustering based on abundance ratios, feature enrichment tests for e.g. GO terms and pathway analysis tools. A number of plotting options for visualization of quantitative proteomics data is available and most analysis functions in GProX create customizable high quality graphical displays in both vector and bitmap formats. The generic import requirements allow data originating from essentially all mass spectrometry platforms, quantitation strategies and software to be analyzed in the program. GProX represents a powerful approach to proteomics data analysis providing proteomics experimenters with a toolbox for bioinformatics analysis of quantitative proteomics data. The program is released as open-source and can be freely downloaded from the project webpage at http://gprox.sourceforge.net.

In Situ Proteomic Analysis of Human Breast Cancer Epithelial Cells Using Laser Capture Microdissection: Annotation by Protein Set Enrichment Analysis and Gene Ontology

Identification of molecular signatures that allow detection of the transition from normal breast epithelial cells to malignant invasive cells is a critical component in the development of diagnostic, therapeutic, and preventative strategies for human breast cancer. Substantial efforts have been devoted to deciphering breast cancer etiology at the genome level, but only a limited number of studies have appeared at the proteome level. In this work, we compared individual in situ proteome profiles of nonpatient matched nine noncancerous, normal breast epithelial (NBE) samples with nine estrogen receptor (ER)-positive (luminal subtype), invasive malignant breast epithelial (MBE) samples by combining laser capture microdissection (LCM) and quantitative shotgun proteomics. A total of 12,970 unique peptides were identified from the 18 samples, and 1623 proteins were selected for quantitative analysis using spectral index (SpI) as a measure of protein abundance. A total of 298 proteins were differentially expressed between NBE and MBE at 95% confidence level, and this differential expression correlated well with immunohistochemistry (IHC) results reported in the Human Protein Atlas (HPA) database. To assess pathway level patterns in the observed expression changes, we developed protein set enrichment analysis (PSEA), a modification of a well-known approach in gene expression analysis, Gene Set Enrichment Analysis (GSEA). Unlike single gene-based functional term enrichment analyses that only examines pathway overrepresentation of proteins above a given significance threshold, PSEA applies a weighted running sum statistic to the entire expression data to discover significantly enriched protein groups. Application of PSEA to the expression data in this study revealed not only well-known ER-dependent and cellular morphology-dependent protein abundance changes, but also significant alterations of downstream targets for multiple transcription factors (TFs), suggesting a role for specific gene regulatory pathways in breast tumorigenesis. A parallel GOMiner analysis revealed both confirmatory and complementary data to PSEA. The combination of the two annotation approaches yielded extensive biological feature mapping for in depth analysis of the quantitative proteomic data.

Normalization and Statistical Analysis of Quantitative Proteomics Data Generated by Metabolic Labeling

Comparative proteomics is a powerful analytical method for learning about the responses of biological systems to changes in growth parameters. To make confident inferences about biological responses, proteomics approaches must incorporate appropriate statistical measures of quantitative data. In the present work we applied microarray-based normalization and statistical analysis (significance testing) methods to analyze quantitative proteomics data generated from the metabolic labeling of a marine bacterium (Sphingopyxis alaskensis). Quantitative data were generated for 1,172 proteins, representing 1,736 high confidence protein identifications (54% genome coverage). To test approaches for normalization, cells were grown at a single temperature, metabolically labeled with (14)N or (15)N, and combined in different ratios to give an artificially skewed data set. Inspection of ratio versus average (MA) plots determined that a fixed value median normalization was most suitable for the data. To determine an appropriate statistical method for assessing differential abundance, a -fold change approach, Student's t test, unmoderated t test, and empirical Bayes moderated t test were applied to proteomics data from cells grown at two temperatures. Inverse metabolic labeling was used with multiple technical and biological replicates, and proteomics was performed on cells that were combined based on equal optical density of cultures (providing skewed data) or on cell extracts that were combined to give equal amounts of protein (no skew). To account for arbitrarily complex experiment-specific parameters, a linear modeling approach was used to analyze the data using the limma package in R/Bioconductor. A high quality list of statistically significant differentially abundant proteins was obtained by using lowess normalization (after inspection of MA plots) and applying the empirical Bayes moderated t test. The approach also effectively controlled for the number of false discoveries and corrected for the multiple testing problem using the Storey-Tibshirani false discovery rate (Storey, J. D., and Tibshirani, R. (2003) Statistical significance for genomewide studies. Proc. Natl. Acad. Sci. U.S.A. 100, 9440-9445). The approach we have developed is generally applicable to quantitative proteomics analyses of diverse biological systems.

Capture and analysis of quantitative proteomic data.

Whilst the array of techniques available for quantitative proteomics continues to grow, the attendant bioinformatic software tools are similarly expanding in number. The data capture and analysis of such quantitative data is obviously crucial to the experiment and the methods used to process it will critically affect the quality of the data obtained. These tools must deal with a variety of issues, including identification of labelled and unlabelled peptide species, location of the corresponding MS scans in the experiment, construction of representative ion chromatograms, location of the true peptide ion chromatogram start and end, elimination of background signal in the mass spectrum and chromatogram and calculation of both peptide and protein ratios/abundances. A variety of tools and approaches are available, in part restricted by the nature of the experiment to be performed and available instrumentation. Currently, although there is no single consensus on precisely how to calculate protein and peptide abundances, many common themes have emerged which identify and reduce many of the key sources of error. These issues will be discussed, along with those relating to deposition of quantitative data. At present, mature data standards for quantitative proteomics are not yet available, although formats are beginning to emerge.

2DDB - a bioinformatics solution for analysis of quantitative proteomics data.

BackgroundWe present 2DDB, a bioinformatics solution for storage, integration and analysis of quantitative proteomics data. As the data complexity and the rate with which it is produced increases in the proteomics field, the need for flexible analysis software increases.Results2DDB is based on a core data model describing fundamentals such as experiment description and identified proteins. The extended data models are built on top of the core data model to capture more specific aspects of the data. A number of public databases and bioinformatical tools have been integrated giving the user access to large amounts of relevant data. A statistical and graphical package, R, is used for statistical and graphical analysis. The current implementation handles quantitative data from 2D gel electrophoresis and multidimensional liquid chromatography/mass spectrometry experiments.ConclusionThe software has successfully been employed in a number of projects ranging from quantitative liquid-chromatography-mass spectrometry based analysis of transforming growth factor-beta stimulated fi-broblasts to 2D gel electrophoresis/mass spectrometry analysis of biopsies from human cervix. The software is available for download at SourceForge.

Differential Label-free Quantitative Proteomic Analysis of Shewanella oneidensis Cultured under Aerobic and Suboxic Conditions by Accurate Mass and Time Tag Approach

We describe the application of LC-MS without the use of stable isotope labeling for differential quantitative proteomic analysis of whole cell lysates of Shewanella oneidensis MR-1 cultured under aerobic and suboxic conditions. LC-MS/MS was used to initially identify peptide sequences, and LC-FTICR was used to confirm these identifications as well as measure relative peptide abundances. 2343 peptides covering 668 proteins were identified with high confidence and quantified. Among these proteins, a subset of 56 changed significantly using statistical approaches such as statistical analysis of microarrays, whereas another subset of 56 that were annotated as performing housekeeping functions remained essentially unchanged in relative abundance. Numerous proteins involved in anaerobic energy metabolism exhibited up to a 10-fold increase in relative abundance when S. oneidensis was transitioned from aerobic to suboxic conditions.