Detecting differential and correlated protein expression in label-free shotgun proteomics

Recent studies have revealed a relationship between protein abundance and sampling statistics, such as sequence coverage, peptide count, and spectral count, in label-free liquid chromatography-tandem mass spectrometry (LC-MS/MS) shotgun proteomics. The use of sampling statistics offers a promising method of measuring relative protein abundance and detecting differentially expressed or coexpressed proteins. We performed a systematic analysis of various approaches to quantifying differential protein expression in eukaryotic Saccharomyces cerevisiae and prokaryotic Rhodopseudomonas palustris label-free LC-MS/MS data. First, we showed that, among three sampling statistics, the spectral count has the highest technical reproducibility, followed by the less-reproducible peptide count and relatively nonreproducible sequence coverage. Second, we used spectral count statistics to measure differential protein expression in pairwise experiments using five statistical tests: Fisher's exact test, G-test, AC test, t-test, and LPE test. Given the S. cerevisiae data set with spiked proteins as a benchmark and the false positive rate as a metric, our evaluation suggested that the Fisher's exact test, G-test, and AC test can be used when the number of replications is limited (one or two), whereas the t-test is useful with three or more replicates available. Third, we generalized the G-test to increase the sensitivity of detecting differential protein expression under multiple experimental conditions. Out of 1622 identified R. palustris proteins in the LC-MS/MS experiment, the generalized G-test detected 1119 differentially expressed proteins under six growth conditions. Finally, we studied correlated expression of these 1119 proteins by analyzing pairwise expression correlations and by delineating protein clusters according to expression patterns. Through pairwise expression correlation analysis, we demonstrated that proteins co-located in the same operon were much more strongly coexpressed than those from different operons. Combining cluster analysis with existing protein functional annotations, we identified six protein clusters with known biological significance. In summary, the proposed generalized G-test using spectral count sampling statistics is a viable methodology for robust quantification of relative protein abundance and for sensitive detection of biologically significant differential protein expression under multiple experimental conditions in label-free shotgun proteomics.     

Global relative and absolute quantitation in microbial proteomics

Proteomic studies are designed to yield either qualitative information on proteins (identification, distribution, posttranslational modifications, interactions, structure and function) or quantitative information (abundance, distribution within different localizations, temporal changes in abundance due to synthesis and degradation or both). To this end these studies can draw upon a wide range of qualitative and quantitative gel-based and gel-free techniques. This review summarizes current proteomic workflows for global relative or absolute protein quantitation and their application in microbial physiology.     

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.     

Quantifying raft proteins in neonatal mouse brain by 'tube-gel' protein digestion label-free shotgun proteomics

Background  

The low concentration and highly hydrophobic nature of proteins in lipid raft samples present significant challenges for the sensitive and accurate proteomic analyses of lipid raft proteins. Elimination of highly enriched lipids and interfering substances from raft samples is generally required before mass spectrometric analyses can be performed, but these procedures often lead to excessive protein loss and increased sample variability. For accurate analyses of the raft proteome, simplified protocols are needed to avoid excessive sample handling and purification steps.     

The importance of the digest: proteolysis and absolute quantification in proteomics

Virtually all mass spectrometric-based methods for quantitative proteomics are at the peptide level, whether label-mediated or label-free. Absolute quantification in particular is based on the measurement of limit peptides, defined as those peptides that cannot be further fragmented by the protease in use. Complete release of analyte and (stable isotope labelled) standard ensures that the most reliable quantification data are recovered, especially when the standard peptides are in a different primary sequence context, such as sometimes occurs in the QconCAT methodology. Moreover, in label-free methods, incomplete digestion would diminish the ion current attributable to limit peptides and lead to artifactually low quantification data. It follows that an essential requirement for peptide-based absolute quantification in proteomics is complete and consistent proteolysis to limit peptides. In this paper we describe strategies to assess completeness of proteolysis and discuss the potential for variance in digestion efficiency to compromise the ensuing quantification data. We examine the potential for kinetically favoured routes of proteolysis, particularly at the last stages of the digestion, to direct products into ‘dead-end’ mis-cleaved products.     

Characterization of the human myocardial proteome in inflammatory dilated cardiomyopathy by label-free quantitative shotgun proteomics of heart biopsies

Dilated cardiomyopathy (DCM) is characterized by contractile dysfunction leading to heart failure. The molecular changes in the human heart associated with this disease have so far mostly been addressed at the gene expression level and only a few studies have analyzed global changes in the myocardial proteome. Therefore, our objective was to investigate the changes in the proteome in patients suffering from inflammatory DCM (iDCM) and chronic viral infection by a comprehensive quantitative approach. Comparative proteomic profiling of endomyocardial biopsies (EMB) from 10 patients with iDCM (left ventricular ejection fraction <40%, symptoms of heart failure) as well as 7 controls with normal left ventricular function and histology was performed by label-free proteome analysis (LC-MS/MS). Mass spectrometric data were analyzed with the Rosetta Elucidator software package. The analysis covered a total of 485 proteins. Among the 174 proteins displaying at least a 1.3-fold change in intensity (p < 0.05), major changes were observed for mitochondrial and cytoskeletal proteins, but also metabolic pathways were affected in iDCM compared to controls. In iDCM patients, we observed decreased levels of mitochondrial proteins involved in oxidative phosphorylation and tricarboxylic acid cycle. Furthermore, deregulation of proteins of carbohydrate metabolism, the actin cytoskeleton, and extracellular matrix remodeling was observed. Proteomic observations were confirmed by gene expression data and immunohistochemistry (e.g. collagen I and VI). This study demonstrates that label-free, mass spectrometry-centered approaches can identify disease dependent alterations in the proteome from small tissue samples such as endomyocardial biopsies. Thus, this technique might allow better disease characterization and may be a valuable tool in potential clinical proteomic studies.     

Comprehensive label-free method for the relative quantification of proteins from biological samples

Pharmaceutical companies and regulatory agencies are broadly pursuing biomarkers as a means to increase the productivity of drug development. Quantifying differential levels of proteins from complex biological samples such as plasma or cerebrospinal fluid is one specific approach being used to identify markers of drug action, efficacy, toxicity, etc. We have developed a comprehensive, fully automated, and label-free approach to relative protein quantification from LC-MS/MS experiments of proteolytic protein digests including: de-noising, mass and charge state estimation, chromatographic alignment, and peptide quantification via integration of extracted ion chromatograms. Results from a variance components study of the entire method indicate that most of the variability is attributable to the LC-MS injection, with a median peptide LC-MS injection coefficient of variation of 8% on a ThermoFinnigan LTQ mass spectrometer. Spiked recovery results suggest a quantifiable range of approximately 32-fold for a sample protein.     

Multiplexed absolute quantification in proteomics using artificial QCAT proteins of concatenated signature peptides

Absolute quantification in proteomics usually involves simultaneous determination of representative proteolytic peptides and stable isotope-labeled analogs. The principal limitation to widespread implementation of this approach is the availability of standard signature peptides in accurately known amounts. We report the successful design and construction of an artificial gene encoding a concatenation of tryptic peptides (QCAT protein) from several chick (Gallus gallus) skeletal muscle proteins and features for quantification and purification.     

Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments

Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.     

RIBAR and xRIBAR: Methods for reproducible relative MS/MS-based label-free protein quantification

Mass spectrometry-driven proteomics is increasingly relying on quantitative analyses for biological discoveries. As a result, different methods and algorithms have been developed to perform relative or absolute quantification based on mass spectrometry data. One of the most popular quantification methods are the so-called label-free approaches, which require no special sample processing, and can even be applied retroactively to existing data sets. Of these label-free methods, the MS/MS-based approaches are most often applied, mainly because of their inherent simplicity as compared to MS-based methods. The main application of these approaches is the determination of relative protein amounts between different samples, expressed as protein ratios. However, as we demonstrate here, there are some issues with the reproducibility across replicates of these protein ratio sets obtained from the various MS/MS-based label-free methods, indicating that the existing methods are not optimally robust. We therefore present two new methods (called RIBAR and xRIBAR) that use the available MS/MS data more effectively, achieving increased robustness. Both the accuracy and the precision of our novel methods are analyzed and compared to the existing methods to illustrate the increased robustness of our new methods over existing ones.     

Analyzing marginal cases in differential shotgun proteomics

SUMMARY: We present an approach to statistically pinpoint differentially expressed proteins that have quantitation values near the quantitation threshold and are not identified in all replicates (marginal cases). Our method uses a Bayesian strategy to combine parametric statistics with an empirical distribution built from the reproducibility quality of the technical replicates. AVAILABILITY: The software is freely available for academic use at http://pcarvalho.com/patternlab.     

Concepts and strategies of soybean seed proteomics using the shotgun proteomics approach

ABSTRACT

Introduction: The last decade has yielded significant developments in the field of proteomics, especially in mass spectrometry (MS) and data analysis tools. In particular, a shift from gel-based to MS-based proteomics has been observed, thereby providing a platform with which to construct proteome atlases for all life forms. Nevertheless, the analysis of plant proteomes, especially those of samples that contain high-abundance proteins (HAPs), such as soybean seeds, remains challenging.

Areas covered: Here, we review recent progress in soybean seed proteomics and highlight advances in HAPs depletion methods and peptide pre-fractionation, identification, and quantification methods. We also suggest a pipeline for future proteomic analysis, in order to increase the dynamic coverage of the soybean seed proteome.

Expert opinion: Because HAPs limit the dynamic resolution of the soybean seed proteome, the depletion of HAPs is a prerequisite of high-throughput proteome analysis, and owing to the use of two-dimensional gel electrophoresis-based proteomic approaches, few soybean seed proteins have been identified or characterized. Recent advances in proteomic technologies, which have significantly increased the proteome coverage of other plants, could be used to overcome the current complexity and limitation of soybean seed proteomics.     

Quality assessments of peptide-spectrum matches in shotgun proteomics

The peptide identification process in shotgun proteomics is most frequently solved with search engines. Such search engines assign scores that reflect similarity between the measured fragmentation spectrum and the theoretical spectra of the peptides of a given database. However, the scores from most search engines do not have a direct statistical interpretation. To understand and make use of the significance of peptide identifications, one must thus be familiar with some statistical concepts. Here, we discuss different statistical scores used to show the confidence of an identification and a set of methods to estimate these scores. We also describe the variance of statistical scores and imperfections of scoring functions of peptide-spectrum matches.     

Multiplexed absolute quantification for proteomics using concatenated signature peptides encoded by QconCAT genes

An important area of proteomics involves the need for quantification, whether relative or absolute. Many methods now exist for relative quantification, but to support biomarker proteomics and systems biology, absolute quantification rather than relative quantification is required. Absolute quantification usually involves the concomitant mass spectrometric determination of signature proteotypic peptides and stable isotope-labeled analogs. However, the availability of standard labeled signature peptides in accurately known amounts is a limitation to the widespread adoption of this approach. We describe the design and synthesis of artificial QconCAT proteins that are concatamers of tryptic peptides for several proteins. This protocol details the methods for the design, expression, labeling, purification, characterization and use of the QconCATs in the absolute quantification of complex protein mixtures. The total time required to complete this protocol (from the receipt of the QconCAT expression plasmid to the absolute quantification of the set of proteins encoded by the QconCAT protein in an analyte sample) is approximately 29 d.