Discussion on common data analysis strategies used in MS-based proteomics

A Computational Tool for Analysis of Mass Spectrometry Data of Ubiquitin-Enriched Samples

Mass spectrometry data on ubiquitin and ubiquitin-like modifiers are becoming increasingly more accessible, and the coverage progressively deepen as methodologies mature. This type of mass spectrometry data is linked to specific data analysis pipelines for ubiquitin. This chapter describes a computational tool to facilitate analysis of mass spectrometry data obtained on ubiquitin-enriched samples. For example, the analysis of ubiquitin branch site statistics and functional enrichment analysis against ubiquitin proteasome system protein sets are completed with a few functional calls. We foresee that the proposed computational methodology can aid in proximity drug design by, for example, elucidating the expression of E3 ligases and other factors related to the ubiquitin proteasome system.

TMT-Based Quantitative Proteomic Analysis Identified Proteins and Signaling Pathways Involved in the Response to Xanthatin Treatment in Human HT-29 Colon Cancer Cells

BACKGROUND

Xanthatin is a plant-derived bioactive sesquiterpene lactone from the Xanthium strumarium L., and it has been used as a traditional Chinese medicine. Recently, many studies have reported that xanthatin has anticancer activity. However, a comprehensive understanding of the mechanism underlying the antitumor effects of xanthatin is still lacking.

OBJECTIVE

To systematically and comprehensively identify the underlying mechanisms of xanthatin on cancer cells, quantitative proteomic techniques were performed.

METHODS

Xanthatin induced HT-29 colon cancer cells death was detected by lactate dehydrogenase (LDH) release cell death assay. Differentially abundant proteins in two groups (control groups and xanthatin treatment groups) of human HT-29 colon cancer cells were identified using tandem mass tag (TMT) quantitative proteomic techniques. All the significant differentially abundant proteins were generally characterized by performing hierarchical clustering, Gene Ontology (GO) enrichment analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. We chose Western blot analysis to validate the candidate proteins in the proteomics results.

RESULTS

A total of 5637 proteins were identified, of which 397 significantly differentially abundant proteins in the groups were quantified. Based on the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses, we found that p53-related signaling played an important role in xanthatin-treated HT-29 colon cancer cells. p53-upregulated modulator of apoptosis (Puma), Sestrin-2 and p14ARF, which were selected from among p53-related signaling proteins, were further validated, and the results were consistent with the tandem mass tag quantitative proteomic results.

CONCLUSION

We first investigated the molecular mechanism underlying the effects of xanthatin treatment on HT-29 colon cancer cells using tandem mass tag quantitative proteomic methods and provided a global comprehensive understanding of the antitumor effects of xanthatin. However, it is necessary to further confirm the function of the differentially abundant proteins and the potentially associated signaling pathways.

Quick microbial molecular phenotyping by differential shotgun proteomics

Differential shotgun proteomics identifies proteins that discriminate between sets of samples based on differences in abundance. This methodology can be easily applied to study (i) specific microorganisms subjected to a variety of growth or stress conditions or (ii) different microorganisms sampled in the same condition. In microbiology, this comparison is particularly successful because differing microorganism phenotypes are explained by clearly altered abundances of key protein players. The extensive description and quantification of proteins from any given microorganism can be routinely obtained for several conditions within a few days by tandem mass spectrometry. Such protein-centred microbial molecular phenotyping is rich in information. However, well-designed experimental strategies, carefully parameterized analytical pipelines, and sound statistical approaches must be applied if the shotgun proteomic data are to be correctly interpreted. This minireview describes these key items for a quick molecular phenotyping based on label-free quantification shotgun proteomics.

Methods and Algorithms for Quantitative Proteomics by Mass Spectrometry

Protein quantitation by mass spectrometry has always been a resourceful technique in protein discovery, and more recently it has leveraged the advent of clinical proteomics. A single mass spectrometry analysis experiment provides identification and quantitation of proteins as well as information on posttranslational modifications landscape. By contrast, protein array technologies are restricted to quantitation of targeted proteins and their modifications. Currently, there are an overwhelming number of quantitative mass spectrometry methods for protein and peptide quantitation. The aim here is to provide an overview of the most common mass spectrometry methods and algorithms used in quantitative proteomics and discuss the computational aspects to obtain reliable quantitative measures of proteins, peptides and their posttranslational modifications. The development of a pipeline using commercial or freely available software is one of the main challenges in data analysis of many experimental projects. Recent developments of R statistical programming language make it attractive to fully develop pipelines for quantitative proteomics. We discuss concepts of quantitative proteomics that together with current R packages can be used to build highly customizable pipelines.

Introduction to Mass Spectrometry-Based Proteomics

Mass spectrometry, a technology to determine the mass of ionized molecules and biomolecules, is increasingly applied for the global identification and quantification of proteins. Proteomics applies mass spectrometry in many applications, and each application requires consideration of analytical choices, instrumental limitations and data processing steps. These depend on the aim of the study and means of conducting it. Choosing the right combination of sample preparation, MS instrumentation, and data processing allows exploration of different aspects of the proteome. This chapter gives an outline for some of these commonly used setups and some of the key concepts, many of which later chapters discuss in greater depth. Understanding and handling mass spectrometry data is a multifaceted task that requires many user decisions to obtain the most comprehensive information from an MS experiment. Later chapters in this book deal in-depth with various aspects of the process and how different tools addresses the many analytical challenges. This chapter revises the basic concept in mass spectrometry (MS)-based proteomics.

Clustered, Regularly Interspaced Short Palindromic Repeats (CRISPR)/Cas9-coupled Affinity Purification/Mass Spectrometry Analysis Revealed a Novel Role of Neurofibromin in mTOR Signaling

Neurofibromin (NF1) is a well known tumor suppressor that is commonly mutated in cancer patients. It physically interacts with RAS and negatively regulates RAS GTPase activity. Despite the importance of NF1 in cancer, a high quality endogenous NF1 interactome has yet to be established. In this study, we combined clustered, regularly interspaced short palindromic repeats (CRISPR)/Cas9-mediated gene knock-out technology with affinity purification using antibodies against endogenous proteins, followed by mass spectrometry analysis, to sensitively and accurately detect NF1 protein-protein interactions in unaltered in vivo settings. Using this system, we analyzed endogenous NF1-associated protein complexes and identified 49 high-confidence candidate interaction proteins, including RAS and other functionally relevant proteins. Through functional validation, we found that NF1 negatively regulates mechanistic target of rapamycin signaling (mTOR) in a LAMTOR1-dependent manner. In addition, the cell growth and survival of NF1-deficient cells have become dependent on hyperactivation of the mTOR pathway, and the tumorigenic properties of these cells have become dependent on LAMTOR1. Taken together, our findings may provide novel insights into therapeutic approaches targeting NF1-deficient tumors.

Bronchoalveolar Lavage: Quantitative Mass Spectrometry-Based Proteomics Analysis in Lung Diseases

Bronchoalveolar lavage (BAL) fluid, obtained by a relatively noninvasive procedure, is used as a practice for diagnosis of various lung diseases as source of cells for cytology analysis. The acellular component of BAL potentially can complement and be a key for the establishment of diagnostic or as a prognostic indicator. This chapter discusses the aspects of standardization of BAL sample preparation and processing and its implications on the BAL fluid proteome quantitative analysis by high-throughput mass spectrometry. The detailed conditions for quantitative analysis of BAL proteome in the context of biomarker discovery are introduced.

Proteomics in the Assessment of the Therapeutic Response of Antineoplastic Drugs: Strategies and Practical Applications

Uncovering unknown pathological mechanisms and body response to applied medication are the driving forces toward personalized medicine. In this post-genomic era, all eyes are turned to the proteomics field, searching for answers and explanations by investigating the gene end point functional units-proteins and their proteoforms. The development of cutting-edge mass spectrometric technologies and bioinformatics tools have allowed the life-science community to discover disease-specific proteins as biomarkers, which are often concealed by high sample complexity and dynamic range of abundance. Currently, there are several proteomics-based approaches to investigate the proteome. This chapter focuses on gold standard proteomics strategies and related issues toward candidate biomarker discovery, which may have diagnostic/prognostic as well as mechanistic utility in cancer drug resistance.

Mass Spectrometry-Based Protein Quantification

Quantification of individual proteins and even entire proteomes is an important theme in proteomics research. Quantitative proteomics is an approach to obtain quantitative information about proteins in a sample. Compared to qualitative or semi-quantitative proteomics, this approach can provide more insight into the effects of a specific stimulus, such as a change in the expression level of a protein and its posttranslational modifications, or to a panel of proposed biomarkers in a given disease state. Proteomics methodologies, along with a variety of bioinformatics approaches, are a major tool in quantitative proteomics. As the theory and technological aspects underlying the proteomics methodologies will be extensively described in Chap. 20 , and protein identification as a prerequisite of quantification has been discussed in Chap. 17 , we will focus on the quantitative proteomics bioinformatics algorithms and software tools in this chapter. Our goal is to provide researchers and newcomers a rational framework to select suitable bioinformatics tools for data analysis, interpretation, and integration in protein quantification. Before doing so, a brief overview of quantitative proteomics is provided.

Bioinformatics approaches for the functional interpretation of protein lists: from ontology term enrichment to network analysis

The main result of a great deal of the published proteomics studies is a list of identified proteins, which then needs to be interpreted in relation to the research question and existing knowledge. In the early days of proteomics this interpretation was only based on expert insights, acquired by digesting a large amount of relevant literature. With the growing size and complexity of the experimental datasets, many computational techniques, databases, and tools have claimed a central role in this task. In this review we discuss commonly and less commonly used methods to functionally interpret experimental proteome lists and compare them with available knowledge. We first address several functional analysis and enrichment techniques based on ontologies and literature. Then we outline how various types of network and pathway information can be used. While the problem of functional interpretation of proteome data is to an extent equivalent to the interpretation of transcriptome or other ''omics'' data, this paper addresses some of the specific challenges and solutions of the proteomics field.

Overview of proteomics studies in obstructive sleep apnea

Obstructive sleep apnea (OSA) is an underdiagnosed common public health concern causing deleterious effects on metabolic and cardiovascular health. Although much has been learned regarding the pathophysiology and consequences of OSA in the past decades, the molecular mechanisms associated with such processes remain poorly defined. The advanced high-throughput proteomics-based technologies have become a fundamental approach for identifying novel disease mediators as potential diagnostic and therapeutic targets for many diseases, including OSA. Here, we briefly review OSA pathophysiology and the technological advances in proteomics and the first results of its application to address critical issues in the OSA field.

From pathways to networks: connecting dots by establishing protein-protein interaction networks in signaling pathways using affinity purification and mass spectrometry

Signal transductions are the basis of biological activities in all living organisms. Studying the signaling pathways, especially under physiological conditions, has become one of the most important facets of modern biological research. During the last decade, MS has been used extensively in biological research and is proven to be effective in addressing important biological questions. Here, we review the current progress in the understanding of signaling networks using MS approaches. We will focus on studies of protein-protein interactions that use affinity purification followed by MS approach. We discuss obstacles to affinity purification, data processing, functional validation, and identification of transient interactions and provide potential solutions for pathway-specific proteomics analysis, which we hope one day will lead to a comprehensive understanding of signaling networks in humans.

Condenser: a statistical aggregation tool for multi-sample quantitative proteomic data from Matrix Science Mascot Distiller™

We describe Condenser, a freely available, comprehensive open-source tool for merging multidimensional quantitative proteomics data from the Matrix Science Mascot Distiller Quantitation Toolbox into a common format ready for subsequent bioinformatic analysis. A number of different relative quantitation technologies, such as metabolic (15)N and amino acid stable isotope incorporation, label-free and chemical-label quantitation are supported. The program features multiple options for curative filtering of the quantified peptides, allowing the user to choose data quality thresholds appropriate for the current dataset, and ensure the quality of the calculated relative protein abundances. Condenser also features optional global normalization, peptide outlier removal, multiple testing and calculation of t-test statistics for highlighting and evaluating proteins with significantly altered relative protein abundances. Condenser provides an attractive addition to the gold-standard quantitative workflow of Mascot Distiller, allowing easy handling of larger multi-dimensional experiments. Source code, binaries, test data set and documentation are available at http://condenser.googlecode.com/.

Guidelines for the design, analysis and interpretation of 'omics' data: focus on human endometrium

BACKGROUND

'Omics' high-throughput analyses, including genomics, epigenomics, transcriptomics, proteomics and metabolomics, are widely applied in human endometrial studies. Analysis of endometrial transcriptome patterns in physiological and pathophysiological conditions has been to date the most commonly applied 'omics' technique in human endometrium. As the technologies improve, proteomics holds the next big promise for this field. The 'omics' technologies have undoubtedly advanced our knowledge of human endometrium in relation to fertility and different diseases. Nevertheless, the challenges arising from the vast amount of data generated and the broad variation of 'omics' profiling according to different environments and stimuli make it difficult to assess the validity, reproducibility and interpretation of such 'omics' data. With the expansion of 'omics' analyses in the study of the endometrium, there is a growing need to develop guidelines for the design of studies, and the analysis and interpretation of 'omics' data.

METHODS

Systematic review of the literature in PubMed, and references from relevant articles were investigated up to March 2013.

RESULTS

The current review aims to provide guidelines for future 'omics' studies on human endometrium, together with a summary of the status and trends, promise and shortcomings in the high-throughput technologies. In addition, the approaches presented here can be adapted to other areas of high-throughput 'omics' studies.

CONCLUSION

A highly rigorous approach to future studies, based on the guidelines provided here, is a prerequisite for obtaining data on biological systems which can be shared among researchers worldwide and will ultimately be of clinical benefit.

Deciphering post-translational modification codes

Post-translational modifications (PTMs) occur on nearly all proteins. Many domains within proteins are modified on multiple amino acid sidechains by diverse enzymes to create a myriad of possible protein species. How these combinations of PTMs lead to distinct biological outcomes is only beginning to be understood. This manuscript highlights several examples of combinatorial PTMs in proteins, and describes recent technological developments, which are driving our ability to understand how PTM patterns may "code" for biological outcomes.

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

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

Methods and algorithms for quantitative proteomics by mass spectrometry

Protein quantitation by mass spectrometry (MS) is attractive since it is possible to obtain both identification and quantitative values of proteins and their posttranslational modifications in a single experiment. In contrast, protein arrays only provide quantitative values of targeted proteins and their modifications. There are an overwhelming number of quantitative MS methods for protein and peptide quantitation. The aim here is to provide an overview of the most common MS methods and algorithms used in quantitative proteomics and discuss the computational algorithms needed to reliably quantitate proteins, peptides, and their posttranslational modifications. One of the main challenges in data analysis of many experimental projects is to pipe together a number of software solutions that are either commercial or freely available. The aim of this chapter is to provide a good set of algorithms, ideas, and resources that can easily be implemented in scripting language like R, Python, or Perl. By understanding the algorithmic ideas presented here, data from any instrument or modified experimental protocol can be analyzed and is therefore in the authors' opinion more valuable than a black box concept.

Algorithms for database-dependent search of MS/MS data

The frequent used bottom-up strategy for identification of proteins and their associated modifications generate nowadays typically thousands of MS/MS spectra that normally are matched automatically against a protein sequence database. Search engines that take as input MS/MS spectra and a protein sequence database are referred as database-dependent search engines. Many programs both commercial and freely available exist for database-dependent search of MS/MS spectra and most of the programs have excellent user documentation. The aim here is therefore to outline the algorithm strategy behind different search engines rather than providing software user manuals. The process of database-dependent search can be divided into search strategy, peptide scoring, protein scoring, and finally protein inference. Most efforts in the literature have been put in to comparing results from different software rather than discussing the underlining algorithms. Such practical comparisons can be cluttered by suboptimal implementation and the observed differences are frequently caused by software parameters settings which have not been set proper to allow even comparison. In other words an algorithmic idea can still be worth considering even if the software implementation has been demonstrated to be suboptimal. The aim in this chapter is therefore to split the algorithms for database-dependent searching of MS/MS data into the above steps so that the different algorithmic ideas become more transparent and comparable. Most search engines provide good implementations of the first three data analysis steps mentioned above, whereas the final step of protein inference are much less developed for most search engines and is in many cases performed by an external software. The final part of this chapter illustrates how protein inference is built into the VEMS search engine and discusses a stand-alone program SIR for protein inference that can import a Mascot search result.

Introduction to mass spectrometry-based proteomics

Mass spectrometry has been widely applied to study biomolecules and one rapidly developing field is the global analysis of proteins, proteomics. Understanding and handling mass spectrometry data is a multifaceted task that requires many decisions to be made to get the most comprehensive information from an experiment. Later chapters in this book deal in-depth with various aspects of the process and how different tools can be applied to the many analytical challenges. This introductory chapter is intended as a basic introduction to mass spectrometry (MS)-based proteomics to set the scene for newcomers and give pointers to reference material. There are many applications of mass spectrometry in proteomics and each application is associated with some analytical choices, instrumental limitations and data processing steps that depend on the aim of the study and means of conducting it. Different aspects of the proteome can be explored by choosing the right combination of sample preparation, MS instrumentation and data processing. This chapter gives an outline for some of these commonly used setups and some of the key concepts, many of which are explored in greater depth in later chapters.

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.

Proteomics and systems biology: current and future applications in the nutritional sciences

In the last decade, advances in genomics, proteomics, and metabolomics have yielded large-scale datasets that have driven an interest in global analyses, with the objective of understanding biological systems as a whole. Systems biology integrates computational modeling and experimental biology to predict and characterize the dynamic properties of biological systems, which are viewed as complex signaling networks. Whereas the systems analysis of disease-perturbed networks holds promise for identification of drug targets for therapy, equally the identified critical network nodes may be targeted through nutritional intervention in either a preventative or therapeutic fashion. As such, in the context of the nutritional sciences, it is envisioned that systems analysis of normal and nutrient-perturbed signaling networks in combination with knowledge of underlying genetic polymorphisms will lead to a future in which the health of individuals will be improved through predictive and preventative nutrition. Although high-throughput transcriptomic microarray data were initially most readily available and amenable to systems analysis, recent technological and methodological advances in MS have contributed to a linear increase in proteomic investigations. It is now commonplace for combined proteomic technologies to generate complex, multi-faceted datasets, and these will be the keystone of future systems biology research. This review will define systems biology, outline current proteomic methodologies, highlight successful applications of proteomics in nutrition research, and discuss the challenges for future applications of systems biology approaches in the nutritional sciences.