Bioinformatics in proteomics

Several genome sequencing projects have recently been completed and the majority of human coding regions have been sequenced. In the next step many of the further studies will concentrate on proteins. Proteomics methods are essential for studying protein expression, activity, regulation and modifications. Bioinformatics is an integral part of proteomics research. The recent developments and applications in proteomics are discussed including mass spectrometry data analysis and interpretation, analysis and storage of the gel images to databases, gel comparison, and advanced methods to study e.g. protein co-expression, protein-protein interactions, as well as metabolic and cellular pathways. The significance of informatics in proteomics will gradually increase because of the advent of high-throughput methods relying on powerful data analysis.     

生物质谱技术在蛋白质组学研究中的应用

随着技术的进步,蛋白质组学的研究重心由最初旨在鉴定细胞或组织内基因组所表达的全部蛋白质转移到从整个蛋白质组水平上阐述包括蛋白翻译后修饰、生物大分子相互作用等反映蛋白质功能的层次。多种质谱离子化技术的突破使质谱技术成为蛋白质组学研究必不可少的手段。质谱技术联合蛋白质组学多角度、深层次探索生命系统分子本质成为现阶段生命科学研究领域的主旋律之一。本文简要综述了肽和蛋白质等生物大分子质谱分析的原理、方式和应用,并对其发展前景做出展望。     

Driving biochemical discovery with quantitative proteomics

Proteomic analysis of biological samples plays an increasing role in modern research. Although the application of proteomics technologies varies across many disciplines, proteomics largely is a tool for discovery that then leads to novel hypotheses. In recent years, new methods and technologies have been developed and applied in many areas of proteomics, and there is a strong push towards using proteomics in a quantitative manner. Indeed, mass spectrometry-based, quantitative proteomics approaches have been applied to great success in a variety of biochemical studies. In particular, the use of quantitative proteomics provides new insights into protein complexes and post-translational modifications and leads to the generation of novel insights into these important biochemical systems.     

Affinity prefractionation for MS‐based plasma proteomics

The plasma proteome has proven to be one of the most challenging proteomes to profile using currently available proteomics technologies. A plethora of methodologies have been used to profile human plasma in order to discover potential biomarkers for disease and for therapy optimization. Affinity‐based prefractionation coupled to MS has been shown to be one of the most successful ways to dig deeper into the plasma proteome. Depletion of high abundant plasma proteins is becoming an initial method of choice in any plasma profiling project. However, several other affinity‐based enrichment methods have been published in recent years. Here we review both protein and peptide affinity prefractionation methods coupled with MS‐based proteomics. Analysis of the proportion of cellular and extracellular annotated proteins of publicly available MS plasma proteomics data is performed to estimate the analytical depth of various prefractionation methods.     

Proteomics analysis of human cerebrospinal fluid

Cerebrospinal fluid (CSF) is secreted from several different central nervous system (CNS) structures, and any changes in the CSF composition will accurately reflect pathological processes. Proteomics offers a comprehensive bird's eye view to analyze CSF proteins at a systems level. This paper reviews the variety of analytical methods that have been used for proteomics analysis of CSF, including sample preparation, two-dimensional liquid and gel electrophoresis, mass spectrometry, bioinformatics, and non-gel methods. The differentially expressed CSF proteins that have been identified by proteomics methods are discussed.     

Challenges in mass spectrometry-based proteomics

During the last decade, protein analysis and proteomics have been established as new tools for understanding various biological problems. As the identification of proteins after classical separation techniques, such as two-dimensional gel electrophoresis, have become standard methods, new challenges arise in the field of proteomics. The development of "functional proteomics" combines functional characterization, like regulation, localization and modification, with the identification of proteins for deeper insight into cellular functions. Therefore, different mass spectrometric techniques for the analysis of post-translational modifications, such as phosphorylation and glycosylation, have been established as well as isolation and separation methods for the analysis of highly complex samples, e.g. protein complexes or cell organelles. Furthermore, quantification of protein levels within cells is becoming a focus of interest as mass spectrometric methods for relative or even absolute quantification have currently not been available. Protein or genome databases have been an essential part of protein identification up to now. Thus, de novo sequencing offers new possibilities in protein analytical studies of organisms not yet completely sequenced. The intention of this review is to provide a short overview about the current capabilities of protein analysis when addressing various biological problems.     

Mass spectrometry-based plasma proteomics: state of the art and future outlook

Mass spectrometry-based plasma proteomics is a field where intense research has been performed during the last decade. Being closely linked to biomarker discovery, the field has received a fair amount of criticism, mostly due to the low number of novel biomarkers reaching the clinic. However, plasma proteomics is under gradual development with improvements on fractionation methods, mass spectrometry instrumentation and analytical approaches. These recent developments have contributed to the revival of plasma proteomics. The goal of this review is to summarize these advances, focusing in particular on fractionation methods, both for targeted and global mass spectrometry-based plasma analysis.     

Asthma, the ugly duckling of lung disease proteomics?

The human respiratory system represents a vital but vulnerable system. It is a major target for many diseases such as cancer and asthma. The incidence of these diseases has increased dramatically in the last 40-50 years. In the search for possible new therapies, many experimental tools and methods have been developed to study these diseases, ranging from animal models to in vitro studies. In the last decades, genomic and proteomic approaches have gained a lot of attention. After the major scientific breakthroughs in the field of genomics, it is now widely accepted that to understand biological processes, large-scale protein studies through proteomics techniques are required. In the battle against lung cancer, the proteomics approach has already been successfully implemented. Surprisingly, only a few proteomics studies on the ever-increasing global asthma problem have been published so far. And although proteomics also has its limitations and experimental difficulties, in our opinion, proteomics can definitely contribute to the understanding of a complex disease such as asthma. Therefore, the additional values and possibilities of proteomics in asthma research should be thoroughly investigated. A close collaboration between the different scientific disciplines may eventually lead to the development of new therapeutic strategies against asthma.     

Advances in shotgun proteomics and the analysis of membrane proteomes

The emergence of shotgun proteomics has facilitated the numerous biological discoveries made by proteomic studies. However, comprehensive proteomic analysis remains challenging and shotgun proteomics is a continually changing field. This review details the recent developments in shotgun proteomics and describes emerging technologies that will influence shotgun proteomics going forward. In addition, proteomic studies of integral membrane proteins remain challenging due to the hydrophobic nature in integral membrane proteins and their general low abundance levels. However, there have been many strategies developed for enriching, isolating and separating membrane proteins for proteomic analysis that have moved this field forward. In summary, while shotgun proteomics is a widely used and mature technology, the continued pace of improvements in mass spectrometry and proteomic technology and methods indicate that future studies will have an even greater impact on biological discovery.     

Organellar proteomics: turning inventories into insights

Subcellular organization is yielding to large-scale analysis. Researchers are now applying robust mass-spectrometry-based proteomics methods to obtain an inventory of biochemically isolated organelles that contain hundreds of proteins. High-resolution methods allow accurate protein identification, and novel algorithms can distinguish genuine from co-purifying components. Organellar proteomes have been analysed by bioinformatic methods and integrated with other large-scale data sets. The dynamics of organelles can also be studied by quantitative proteomics, which offers powerful methods that are complementary to fluorescence-based microscopy. Here, we review the emerging trends in this rapidly expanding area and discuss the role of organellar proteomics in the context of functional genomics and systems biology.     

快速发展的亚细胞蛋白质组学

亚细胞蛋白质组是蛋白质组学领域中的一支新生力量 ,已成为蛋白质组学新的主流方向 ,通过多种策略和技术方法 ,一些重要的亚细胞结构的蛋白质组不断的得到分析 ,到目前为止 ,几乎所有亚细胞结构的蛋白质组学研究都有报道 ,而且已经深入到亚细胞器和复合体水平 ;另外 ,不仅局限于对亚细胞结构的蛋白组成进行简单分析 ,而且更注重功能性分析 ,将定量技术和差异分析引入亚细胞蛋白质组学 ,来观察此亚细胞结构的蛋白质组在某些生理或病理条件下的变化 ,这已经成为亚细胞蛋白质组学新的发展方向 .亚细胞蛋白质组学最大的困难在于怎样确认鉴定出来蛋白质的定位 ,是在提取过程中的污染还是真正在此亚细胞结构中有定位 ?这将是亚细胞蛋白质组学需要努力解决的挑战 .文章全面介绍了亚细胞蛋白质组学的最新研究进展 ,阐述了亚细胞蛋白质组学面临的挑战 ,并对亚细胞蛋白质组学的发展方向作了展望 .     

How to distinguish healthy from diseased? Classification strategy for mass spectrometry‐based clinical proteomics

SELDI-TOF-MS is rapidly gaining popularity as a screening tool for clinical applications of proteomics. Application of adequate statistical techniques in all the stages from measurement to information is obligatory. One of the statistical methods often used in proteomics is classification: the assignment of subjects to discrete categories, for example healthy or diseased. Lately, many new classification methods have been developed, often specifically for the analysis of X-omics data. For proteomics studies a good strategy for evaluating classification results is of prime importance, because usually the number of objects will be small and it would be wasteful to set aside part of these as a 'mere' test set. The present paper offers such a strategy in the form of a protocol which can be used for choosing among different statistical classification methods and obtaining figures of merit of their performance. This paper also illustrates the usefulness of proteomics in a clinical setting, serum samples from Gaucher disease patients, when used in combination with an appropriate classification method.     

Scalable Data Analysis in Proteomics and Metabolomics Using BioContainers and Workflows Engines

The recent improvements in mass spectrometry instruments and new analytical methods are increasing the intersection between proteomics and big data science. In addition, bioinformatics analysis is becoming increasingly complex and convoluted, involving multiple algorithms and tools. A wide variety of methods and software tools have been developed for computational proteomics and metabolomics during recent years, and this trend is likely to continue. However, most of the computational proteomics and metabolomics tools are designed as single‐tiered software application where the analytics tasks cannot be distributed, limiting the scalability and reproducibility of the data analysis. In this paper the key steps of metabolomics and proteomics data processing, including the main tools and software used to perform the data analysis, are summarized. The combination of software containers with workflows environments for large‐scale metabolomics and proteomics analysis is discussed. Finally, a new approach for reproducible and large‐scale data analysis based on BioContainers and two of the most popular workflow environments, Galaxy and Nextflow, is introduced to the proteomics and metabolomics communities.     

Multiple hypothesis testing in proteomics: a strategy for experimental work

In quantitative proteomics work, the differences in expression of many separate proteins are routinely examined to test for significant differences between treatments. This leads to the multiple hypothesis testing problem: when many separate tests are performed many will be significant by chance and be false positive results. Statistical methods such as the false discovery rate method that deal with this problem have been disseminated for more than one decade. However a survey of proteomics journals shows that such tests are not widely implemented in one commonly used technique, quantitative proteomics using two-dimensional electrophoresis. We outline a selection of multiple hypothesis testing methods, including some that are well known and some lesser known, and present a simple strategy for their use by the experimental scientist in quantitative proteomics work generally. The strategy focuses on the desirability of simultaneous use of several different methods, the choice and emphasis dependent on research priorities and the results in hand. This approach is demonstrated using case scenarios with experimental and simulated model data.     

Label-free quantification in clinical proteomics

Nowadays, proteomic studies no longer focus only on identifying as many proteins as possible in a given sample, but aiming for an accurate quantification of them. Especially in clinical proteomics, the investigation of variable protein expression profiles can yield useful information on pathological pathways or biomarkers and drug targets related to a particular disease. Over the time, many quantitative proteomic approaches have been established allowing researchers in the field of proteomics to refer to a comprehensive toolbox of different methodologies. In this review we will give an overview of different methods of quantitative proteomics with focus on label-free proteomics and its use in clinical proteomics.     

Cutting-edge technology. II. Proteomics: core technologies and applications in physiology

Technologies for proteomics, e.g., studies examining the protein complement of the genome, have been in development for over 20 years. More recently, proteomics has become formalized by combining techniques for large-scale protein separation with very precise, high-fidelity approaches that analyze, identify, and characterize the separated proteins. These methods bring to reality the powerful scope of proteomics, enabling researchers to investigate cellular function at the protein level and thus representing one of proteomics' most fitting applications. In this review, we take a brief and concise look at some of the current, physiologically relevant technologies that comprise proteomics and report specific applications in which proteomics has provided valuable biological insight.     

蛋白质组学的研究方法

近年来,蛋白质组学的研究在生物医学领域正逐步深入,研究方法也正逐步完善。本文综述了蛋白质组学的几种研究方法,指出了各种方法的优点及局限性,并且对蛋白质组学的研究方法和前景进行了展望。     

The methods of quantitative proteomics

In modern science proteomic analysis is inseparable from other fields of systemic biology. Possessing huge resources quantitative proteomics operates colossal information on molecular mechanisms of life. Advances in proteomics help researchers to solve complex problems of cell signaling, posttranslational modification, structure and funciotnal homology of proteins, molecular diagnostics etc. More than 40 various methods have been developed in proteomics for quantitative analysis of proteins. Although each method is unique and has certain advantages and disadvantages all these use various isotope labels (tags). In this review we will consider the most popular and effective methods employing both chemical modifications of proteins and also metabolic and enzymatic methods of isotope labeling.     

非酒精性脂肪性肝病蛋白质组学研究进展

非酒精性脂肪性肝病(nonalcoholic fatty liver disease,NAFLD)是一种常见慢性肝脏疾病,其发病率呈逐年上升趋势,但发病机制尚未明确,诊疗手段仍不完善.蛋白质组学(proteomics)的出现使NAFLD研究有了进一步的发展,相关研究已达21个.目前,蛋白质组学技术可以研究疾病相关的分子改变,从而寻找新的生物标志物和治疗靶标.在此,对蛋白质组学在NAFLD诊断及分期、发病机制和其他相关领域研究进展作一个较为全面的综述.首先,对研究中遇到的研究对象、样本种类、实验方法和标志物特征选择进行经验性总结.其次,除了介绍如何运用蛋白质组学研究病因、危险因素和重要分子在NAFLD发病机制中的作用,还介绍NAFLD发病机制的亚细胞蛋白质组学、修饰蛋白质组学以及蛋白质组学与转录组学相结合的研究实例.此外,对差异蛋白质的分析策略和价值作了重点阐述,收集到一些有望成为NAFLD治疗靶标的候选分子.最后,结合新技术展望研究新空间,以期能够有助于推动蛋白质组学在寻找新的疾病标志物、探索疾病分子机制和治疗靶标中开辟新的途径.     

Investigation of post-translational modifications in type 2 diabetes

The investigation of post-translational modifications (PTMs) plays an important role for the study of type 2 diabetes. The importance of PTMs has been realized with the advancement of analytical techniques. The challenging detection and analysis of post-translational modifications is eased by different enrichment methods and by high throughput mass spectrometry based proteomics studies. This technology along with different quantitation methods provide accurate knowledge about the changes happening in disease conditions as well as in normal conditions. In this review, we have discussed PTMs such as phosphorylation, N-glycosylation, O-GlcNAcylation, acetylation and advanced glycation end products in type 2 diabetes which have been characterized by high throughput mass spectrometry based proteomics analysis.