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.     

Urinary proteomics: towards biomarker discovery, diagnostics and prognostics

During recent years, the proteomics field has moved onward to clinical applications, particularly for biomarker discovery, diagnostics and prognostics of human diseases. The urine is one of the ideal clinical samples for such applications because it is readily available in almost all patients, and its collection is very simple and non-invasive. Urinary proteomics thus becomes one of the most interesting subdisciplines in the clinical proteomics area. This article highlights and updates recent progress in the urinary proteomics field for clinical applications.     

Mitochondrial proteomics in free radical research

The importance of mitochondrial dysfunction in numerous diseases has long been appreciated. The impact of oxidative and nitrosative stress on mitochondrial function is complex; however, recent progress in the field using proteomics technologies has begun to shed light on the molecular defects responsible for mitochondrial and cellular dysfunction. This review focuses on the state-of-the-art technologies being used and current research endeavors in the field of mitochondrial proteomics with emphasis on those advancements being made in the field of free radical biology to identify the importance of alterations to the mitochondrial proteome in the development of disease.     

Saliva proteome research: current status and future outlook

Human saliva harbours proteins of clinical relevance and about 30% of blood proteins are also present in saliva. This highlights that saliva can be used for clinical applications just as urine or blood. However, the translation of salivary biomarker discoveries into clinical settings is hampered by the dynamics and complexity of the salivary proteome. This review focuses on the current status of technological developments and achievements relating to approaches for unravelling the human salivary proteome. We discuss the dynamics of the salivary proteome, as well as the importance of sample preparation and processing techniques and their influence on downstream protein applications; post-translational modifications of salivary proteome and protein: protein interactions. In addition, we describe possible enrichment strategies for discerning post-translational modifications of salivary proteins, the potential utility of selected-reaction-monitoring techniques for biomarker discovery and validation, limitations to proteomics and the biomarker challenge and future perspectives. In summary, we provide recommendations for practical saliva sampling, processing and storage conditions to increase the quality of future studies in an emerging field of saliva clinical proteomics. We propose that the advent of technologies allowing sensitive and high throughput proteome-wide analyses, coupled to well-controlled study design, will allow saliva to enter clinical practice as an alternative to blood-based methods due to its simplistic nature of sampling, non-invasiveness, easy of collection and multiple collections by untrained professionals and cost-effective advantages.     

Environmental proteomics: applications of proteome profiling in environmental microbiology and biotechnology

In this review, we present the use of proteomics to advanceknowledge in the field of environmental biotechnology, includingstudies of bacterial physiology, metabolism and ecology. Bacteriaare widely applied in environmental biotechnology for theirability to catalyze dehalogenation, methanogenesis, denitrificationand sulfate reduction, among others. Their tolerance to radiationand toxic compounds is also of importance. Proteomics has animportant role in helping uncover the pathways behind thesecellular processes. Environmental samples are often highly complex,which makes proteome studies in this field especially challenging.Some of these challenges are the lack of genome sequences forthe vast majority of environmental bacteria, difficulties inisolating bacteria and proteins from certain environments, andthe presence of complex microbial communities. Despite thesechallenges, proteomics offers a unique dynamic view into cellularfunction. We present examples of environmental proteomics ofmodel organisms, and then discuss metaproteomics (microbialcommunity proteomics), which has the potential to provide insightsinto the function of a community without isolating organisms.Finally, the environmental proteomics literature is summarizedas it pertains to the specific application areas of wastewatertreatment, metabolic engineering, microbial ecology and environmentalstress responses.      

A Bioinformatics Perspective on Proteomics: Data Storage, Analysis, and Integration

The field of proteomics is advancing rapidly as a result of powerful new technologies and proteomics experiments yield a vast and increasing amount of information. Data regarding protein occurrence, abundance, identity, sequence, structure, properties, and interactions need to be stored. Currently, a common standard has not yet been established and open access to results is needed for further development of robust analysis algorithms. Databases for proteomics will evolve from pure storage into knowledge resources, providing a repository for information (meta-data) which is mainly not stored in simple flat files. This review will shed light on recent steps towards the generation of a common standard in proteomics data storage and integration, but is not meant to be a comprehensive overview of all available databases and tools in the proteomics community.     

蛋白质组学在神经科学中的应用

蛋白质组学是指对基因组编码的所有蛋白质进行大规模分析的一门学科,它分为表达蛋白质组学和功能蛋白质组学。新的蛋白质组学工具将为高度复杂的神经科学的研究提供便利。作者简述了表达蛋白质组学和功能蛋白质组学在这一领域的应用。     

Urine as a source for clinical proteome analysis: From discovery to clinical application

The success of clinical proteome analysis should be assessed based on the clinical impact following implementation of findings. Although there have been several technological advancements in mass spectrometry in the last years, these have not resulted in similar advancements in clinical proteomics. In addition, application of proteomic biomarkers in clinical diagnostics and practical improvement in the disease management is extremely rare. In this review, we discuss the relevant issues associated with identification of robust biomarkers of clinical value. Urine appears to be an ideal source of biomarkers, for theoretical, methodological, and practical reasons. Therefore, this review is focused on the search for biomarkers in urine within the last decade. Urine can be used for non-invasive assessment of a variety of diseases including those affecting the urogenital tract and also other pathologies such as cardiovascular disease or appendicitis. We also discuss the importance of data validation, an essential step in translating biomarkers into the clinical practice. Furthermore, we examine several examples of apparently successful proteomic biomarker discovery studies and their implications for disease diagnosis, prognosis, and therapy evaluation. We also discuss some current challenges in this field and reflect on future research prospects. This article is part of a Special Issue entitled: Biomarkers: A Proteomic Challenge.     

蛋白质组学技术在精子蛋白研究中的应用

从蛋白质组学研究的技术手段、蛋白质组学在人类不育及精卵相互识别并结合的机理研究、免疫法开展男性避孕方法的研究及蛋白质组学研究方法在家畜繁殖环节中的应用等几个方面阐述了蛋白质组学在人类生殖及动物繁殖环节相关研究中的重要作用。说明蛋白质组学已经成为生命科学未来发展的主要分支之一,为揭示生命个体的蛋白质动态变化提供了技术手段和理论基础,并将在药物开发,生命活动机理研究等方面发挥巨大作用,也必将会在家畜繁殖学领域发挥其应有的作用。     

Proteomics in clinical trials and practice: present uses and future promise

The study of clinical proteomics is a promising new field that has the potential to have many applications, including the identification of biomarkers and monitoring of disease, especially in the field of oncology. Expression proteomics evaluates the cellular production of proteins encoded by a particular gene and exploits the differential expression and post-translational modifications of proteins between healthy and diseased states. These biomarkers may be applied towards early diagnosis, prognosis, and prediction of response to therapy. Functional proteomics seeks to decipher protein-protein interactions and biochemical pathways involved in disease biology and targeted by newer molecular therapeutics. Advanced spectrometry technologies and new protein array formats have improved these analyses and are now being applied prospectively in clinical trials. Further advancement of proteomics technology could usher in an era of personalized molecular medicine, where diseases are diagnosed at earlier stages and where therapies are more effective because they are tailored to the protein expression of a patient's malignancy.     

Proteomics in cancer vaccine development

Proteomics is a new scientific field aimed at the large-scale characterization of the protein constituents of biologic systems. It facilitates comparisons between different protein preparations by searching for minute differences in their protein expression repertoires and the patterns of their post-translational modifications. These attributes make proteomics perfectly suited for searching for proteins and peptides expressed exclusively or preferentially in cancer cells as candidates for cancer vaccines. The main proteomics technologies include 2D polyacrylamide gel electrophoresis, multidimensional high-performance liquid chromatography, mass spectrometry and protein arrays. Proteomics technologies used to analyze cancer culture cells, fresh tumor specimens, human leukocyte antigen peptides, serum and serum antibodies (serologic proteomics) have successfully identified tumor markers. Turning the potential vaccine candidates identified by proteomics technologies into clinical treatments awaits demonstration.     

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.     

Visualize: a free and open source multifunction tool for proteomics data analysis

A major challenge in the field of high-throughput proteomics is the conversion of the large volume of experimental data that is generated into biological knowledge. Typically, proteomics experiments involve the combination and comparison of multiple data sets and the analysis and annotation of these combined results. Although there are some commercial applications that provide some of these functions, there is a need for a free, open source, multifunction tool for advanced proteomics data analysis. We have developed the Visualize program that provides users with the abilities to visualize, analyze, and annotate proteomics data; combine data from multiple runs, and quantitate differences between individual runs and combined data sets. Visualize is licensed under GNU GPL and can be downloaded from http://proteomics.mcw.edu/visualize. It is available as compiled client-based executable files for both Windows and Mac OS X platforms as well as PERL source code.     

Proteomic biomarker discovery for the monogenic disease cystic fibrosis

Proteomics was initially viewed as a promising new scientific discipline to study complex disorders such as polygenic, infectious and environment-related diseases. However, the first attempts to understand a monogenic disease such as cystic fibrosis (CF) by proteomics-based approaches have proved quite rewarding. In CF, the impairment of a unique protein, the CF transmembrane conductance regulator, does not completely explain the complex and variable CF clinical phenotype. The great advances in our knowledge about the molecular and cellular consequences of such impairment have not been sufficient to be translated into effective treatments, and CF patients are still dying due to chronic progressive lung dysfunction. The progression of proteomics application in CF will certainly unravel new proteins that could be useful as biomarkers either to elucidate CF basic mechanisms and to better monitor the disease progression, or to promote the development of novel therapeutic strategies against CF. This review will summarize the recent technological advances in proteomics and the first results of its application to address the most important issues in the CF field.     

Proteomic biomarker discovery for the monogenic disease cystic fibrosis

Proteomics was initially viewed as a promising new scientific discipline to study complex disorders such as polygenic, infectious and environment-related diseases. However, the first attempts to understand a monogenic disease such as cystic fibrosis (CF) by proteomics-based approaches have proved quite rewarding. In CF, the impairment of a unique protein, the CF transmembrane conductance regulator, does not completely explain the complex and variable CF clinical phenotype. The great advances in our knowledge about the molecular and cellular consequences of such impairment have not been sufficient to be translated into effective treatments, and CF patients are still dying due to chronic progressive lung dysfunction. The progression of proteomics application in CF will certainly unravel new proteins that could be useful as biomarkers either to elucidate CF basic mechanisms and to better monitor the disease progression, or to promote the development of novel therapeutic strategies against CF. This review will summarize the recent technological advances in proteomics and the first results of its application to address the most important issues in the CF field.     

Miniaturisation is mandatory unravelling the human proteome

An insight into the proteome status of today is presented, covering aspects of market values, new scientific literature sources, new technology initiatives and future developments. The authors give a personal view of the importance and the expected progress of microtechnology in proteome research. The background to some of the fundamental analytical technologies where for example, chromatography has played and still plays a major role in the protein research area is emphasized. From this field we are currently harvesting decades of fundamental research in separation science. For further progress in the chromatography field, downscaling has proven to be mandatory. Likewise, in the proteomics era ahead miniaturisation and microchip techniques are foreseen to play a fundamental role for finalising the human proteome, an essential milestone for elucidating disease pathophysiology.     

Proteomics in the post-genome age.

The genome sequencing effort has helped spawn the burgeoning field of proteomics. This review article examines state-of-the-art proteomics methods that are helping change the discovery paradigm in a variety of biological disciplines and, in particular, protein biochemistry. The review discusses both classical and novel methods to perform high-throughput qualitative and quantitative "global" as well as targeted proteome analysis of complex biological systems. From a drug discovery standpoint, the synergy between genomics and proteomics will help elucidate disease mechanisms, identify novel drug targets, and identify surrogate biomarkers that could be used to conduct clinical trials.     

Multivariate approaches in plant science

The objective of proteomics is to get an overview of the proteins expressed at a given point in time in a given tissue and to identify the connection to the biochemical status of that tissue. Therefore sample throughput and analysis time are important issues in proteomics. The concept of proteomics is to encircle the identity of proteins of interest. However, the overall relation between proteins must also be explained. Classical proteomics consist of separation and characterization, based on two-dimensional electrophoresis, trypsin digestion, mass spectrometry and database searching. Characterization includes labor intensive work in order to manage, handle and analyze data. The field of classical proteomics should therefore be extended to also include handling of large datasets in an objective way. The separation obtained by two-dimensional electrophoresis and mass spectrometry gives rise to huge amount of data. We present a multivariate approach to the handling of data in proteomics with the advantage that protein patterns can be spotted at an early stage and consequently the proteins selected for sequencing can be selected intelligently. These methods can also be applied to other data generating protein analysis methods like mass spectrometry and near infrared spectroscopy and examples of application to these techniques are also presented. Multivariate data analysis can unravel complicated data structures and may thereby relieve the characterization phase in classical proteomics. Traditionally statistical methods are not suitable for analysis of the huge amounts of data, where the number of variables exceed the number of objects. Multivariate data analysis, on the other hand, may uncover the hidden structures present in these data. This study takes its starting point in the field of classical proteomics and shows how multivariate data analysis can lead to faster ways of finding interesting proteins. Multivariate analysis has shown interesting results as a supplement to classical proteomics and added a new dimension to the field of proteomics.     

Analytical nanobiotechnology for medicine diagnostics

The review is concerned with the state-of-the-art and the prospects of development of nanotechnologies in clinical proteomics. Nanotechnology in clinical proteomics is a new medical research direction, dealing with the creation and application of nanodevices for performing proteomic analyses in the clinic. Nanotechnological progress in the field of atomic force microscopy makes it possible to perform clinical studies on the revelation, visualization and identification of protein disease markers, in particular of those with the sensitivity of 10(-17) M that surpasses by several orders the sensitivity of commonly adopted clinical methods. At the same time, implementation of nanotechnological approaches into diagnostics allows for the creation of new diagnostic systems based on the optical, electro-optical, electromechanical and electrochemical nanosensoric elements with high operating speed. The application of nanotechnological approaches to creating nanopore-based devices for express sequencing of the genome is discussed.