Proteomics approaches to biomarker detection.

The development of mass spectrometry (MS) technologies has brought the ability to gather massive amounts of data characterising the proteomes of complex mixtures. A major focus in proteomics is to leverage this data-gathering capability to conduct comparative analyses of biofluids from healthy and disease-affected patients for the identification of highly specific biomarkers and/or the development of MS-based diagnostic platforms. Much effort has gone into optimising the biofluid proteome coverage that can be obtained using these technologies, leaving proteomics poised to make an important impact in disease diagnostics in the future.     

基于质谱的蛋白质生物标志物发现中的特征选择与机器学习方法研究进展

随着质谱技术的进步以及生物信息学与统计学算法的发展,以疾病研究为主要目的之一的人类蛋白质组计划正快速推进。蛋白质生物标志物在疾病早期诊断和临床治疗等方面有着非常重要的意义,其发现策略和方法的研究已成为一个重要的热点领域。特征选择与机器学习对于解决蛋白质组数据"高维度"及"稀疏性"问题有较好的效果,因而逐渐被广泛地应用于发现蛋白质生物标志物的研究中。文中主要阐述蛋白质生物标志物的发现策略以及其中特征选择与机器学习方法的原理、应用实例和适用范围,并讨论深度学习方法在本领域的应用前景及局限性,以期为相关研究提供参考。     

Serum biomarker discovery related to pathogenesis in acute coronary syndrome by proteomic approach

Acute coronary syndrome (ACS) results from inadequate supply of blood flow from the coronary arteries to the heart or ischemia. ACS has an extremely high morbidity and mortality. The levels of biomarkers currently used for detection of ACS also increase in response to myocardial necrosis and other diseases and are not elevated immediately after symptoms appear, thus limiting their diagnostic capacity. Therefore, we aimed to discover new ACS diagnostic biomarkers with high sensitivity and specificity that are specifically related to ACS pathogenesis. Sera from 50 patients with ACS and healthy controls (discovery cohort) each were analyzed using mass spectrometry (MS) to identify differentially expressed proteins, and protein candidates were evaluated as ACS biomarkers in 120 people in each group (validation cohort). α-1-acid glycoprotein 1 (AGP1), complement C5 (C5), leucine-rich α-2-glycoprotein (LRG), and vitronectin (VN) were identified as biomarkers whose levels increase and gelsolin (GSN) as a biomarker whose levels decrease in patients with ACS. We concluded that these biomarkers are associated with the pathogenesis of ACS and can predict the onset of ACS prior to the appearance of necrotic biomarkers.     

Solvent interaction analysis as a proteomic approach to structure-based biomarker discovery and clinical diagnostics

Proteins have several measurable features in biological fluids that may change under pathological conditions. The current disease biomarker discovery is mostly based on protein concentration in the sample as the measurable feature. Changes in protein structures, such as post-translational modifications and in protein–partner interactions are known to accompany pathological processes. Changes in glycosylation profiles are well-established for many plasma proteins in various types of cancer and other diseases. The solvent interaction analysis method is based on protein partitioning in aqueous two-phase systems and is highly sensitive to changes in protein structure and protein–protein- and protein–partner interactions while independent of the protein concentration in the biological sample. It provides quantitative index: partition coefficient representing changes in protein structure and interactions with partners. The fundamentals of the method are presented with multiple examples of applications of the method to discover and monitor structural protein biomarkers as disease-specific diagnostic indicators.     

Bridging neuronal correlations and dimensionality reduction

1. Download : Download high-res image (119KB)
2. Download : Download full-size image

     

Studying multiple protein profiles over time to assess biomarker validity

Protein profile analysis is increasingly used for identification of disease biomarkers. The approaches vary from surface-enhanced laser desorption/ionization to protein arrays. Newer platforms are constantly being developed. Almost all are based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry and are often coupled with sophisticated software tools. Protein profiling has been applied to a variety of samples including plasma, urine, cerebrospinal fluid, saliva and solid tissue. This article focuses on those instances where it is possible to obtain sequential samples from the same individual. In the authors use of a profile method, many protein changes with highly significant correlations to disease have been found. The main challenge lies in the validation of the marker to demonstrate its adequacy for use in the clinical setting. The latter requires a methodology that is robust and amenable to high-throughput. One problem is that interindividual variability among the healthy population can mask major changes that occur on an intraindividual basis. Often, a large change for an individual may remain within the range of healthy individuals. Thus, one strategy to optimize biomarker discovery is to examine serial samples from a given individual, where a disease biomarker is established by comparison with the individual’s own baseline sample. The focus of this review is to illustrate the principle and value of serial protein profiling using a rapid protein extraction method.     

Revisiting biomarker discovery by plasma proteomics

Clinical analysis of blood is the most widespread diagnostic procedure in medicine, and blood biomarkers are used to categorize patients and to support treatment decisions. However, existing biomarkers are far from comprehensive and often lack specificity and new ones are being developed at a very slow rate. As described in this review, mass spectrometry (MS)‐based proteomics has become a powerful technology in biological research and it is now poised to allow the characterization of the plasma proteome in great depth. Previous “triangular strategies” aimed at discovering single biomarker candidates in small cohorts, followed by classical immunoassays in much larger validation cohorts. We propose a “rectangular” plasma proteome profiling strategy, in which the proteome patterns of large cohorts are correlated with their phenotypes in health and disease. Translating such concepts into clinical practice will require restructuring several aspects of diagnostic decision‐making, and we discuss some first steps in this direction.     

Euclidean sections of protein conformation space and their implications in dimensionality reduction

Dimensionality reduction is widely used in searching for the intrinsic reaction coordinates for protein conformational changes. We find the dimensionality?reduction methods using the pairwise root?mean?square deviation (RMSD) as the local distance metric face a challenge. We use Isomap as an example to illustrate the problem. We believe that there is an implied assumption for the dimensionality‐reduction approaches that aim to preserve the geometric relations between the objects: both the original space and the reduced space have the same kind of geometry, such as Euclidean geometry vs. Euclidean geometry or spherical geometry vs. spherical geometry. When the protein free energy landscape is mapped onto a 2D plane or 3D space, the reduced space is Euclidean, thus the original space should also be Euclidean. For a protein with N atoms, its conformation space is a subset of the 3N‐dimensional Euclidean space R^3N. We formally define the protein conformation space as the quotient space of R^3N by the equivalence relation of rigid motions. Whether the quotient space is Euclidean or not depends on how it is parameterized. When the pairwise RMSD is employed as the local distance metric, implicit representations are used for the protein conformation space, leading to no direct correspondence to a Euclidean set. We have demonstrated that an explicit Euclidean‐based representation of protein conformation space and the local distance metric associated to it improve the quality of dimensionality reduction in the tetra‐peptide and β‐hairpin systems. Proteins 2014; 82:2585–2596. © 2014 Wiley Periodicals, Inc.     

Advanced proteomic technologies for cancer biomarker discovery

Proteomic technologies have experienced major improvements in recent years. Such advances have facilitated the discovery of potential tumor markers with improved sensitivities and specificities for the diagnosis, prognosis and treatment monitoring of cancer patients. This review will focus on four state-of-the-art proteomic technologies, namely 2D difference gel electrophoresis, MALDI imaging mass spectrometry, electron transfer dissociation mass spectrometry and reverse-phase protein array. The major advancements these techniques have brought about and examples of their applications in cancer biomarker discovery will be presented in this review, so that readers can appreciate the immense progress in proteomic technologies from 1997 to 2008. Finally, a summary will be presented that discusses current hurdles faced by proteomic researchers, such as the wide dynamic range of protein abundance, standardization of protocols and validation of cancer biomarkers, and a 5-year view of potential solutions to such problems will be provided.     

The ABRF Proteomics Research Group studies: educational exercises for qualitative and quantitative proteomic analyses

Resource (core) facilities have played an ever-increasing role in furnishing the scientific community with specialized instrumentation and expertise for proteomics experiments in a cost-effective manner. The Proteomics Research Group (PRG) of the Association of Biomolecular Resource Facilities (ABRF) has sponsored a number of research studies designed to enable participants to try new techniques and assess their capabilities relative to other laboratories analyzing the same samples. Presented here are results from three PRG studies representing different samples that are typically analyzed in a core facility, ranging from simple protein identification to targeted analyses, and include intentional challenges to reflect realistic studies. The PRG2008 study compares different strategies for the qualitative characterization of proteins, particularly the utility of complementary methods for characterizing truncated protein forms. The use of different approaches for determining quantitative differences for several target proteins in human plasma was the focus of the PRG2009 study. The PRG2010 study explored different methods for determining specific constituents while identifying unforeseen problems that could account for unanticipated results associated with the different samples, and included (15) N-labeled proteins as an additional challenge. These studies provide a valuable educational resource to research laboratories and core facilities, as well as a mechanism for establishing good laboratory practices.     

A proteomic approach to obesity and type 2 diabetes

The incidence of obesity and type diabetes 2 has increased dramatically resulting in an increased interest in its biomedical relevance. However, the mechanisms that trigger the development of diabetes type 2 in obese patients remain largely unknown. Scientific, clinical and pharmaceutical communities are dedicating vast resources to unravel this issue by applying different omics tools. During the last decade, the advances in proteomic approaches and the Human Proteome Organization have opened and are opening a new door that may be helpful in the identification of patients at risk and to improve current therapies. Here, we briefly review some of the advances in our understanding of type 2 diabetes that have occurred through the application of proteomics. We also review, in detail, the current improvements in proteomic methodologies and new strategies that could be employed to further advance our understanding of this pathology. By applying these new proteomic advances, novel therapeutic and/or diagnostic protein targets will be discovered in the obesity/Type 2 diabetes area.     

Neuroproteomics and microRNAs studies in multiple sclerosis: transforming research and clinical knowledge in biomarker research

Multiple sclerosis (MS) is a complex disease characterized by extensive phenotypic variability. Biomarkers to capture the different aspects of MS heterogeneity, and to help make a diagnosis and monitor disease progression, while providing insights into etiopathogenesis and response to treatment, are urgently needed. Omics technologies and research efforts with microRNAs have provide unparalleled opportunities for exploring altered protein profiles associated with molecular mechanisms of disease, substantially expanding the list of candidate biomarkers for MS. This review presents evidence from proteomic studies that have focused on identification of biomarkers released in biofluids as a result of the different pathophysiological processes of MS. Also discussed is the emerging role of miRNAs as complementary biomarkers related to cellular processes occurring in MS patients. Also provided is an overview of candidate biomarkers that have been proposed for elucidating pathophysiological processes and disease activity and for guiding clinical diagnosis and/or therapeutic interventions in MS.     

污染胁迫下的蚯蚓蛋白质组学研究进展

随着蛋白质组学的发展和每年有大量环境污染物进入土壤环境中,污染胁迫模式动物的相关生物标志物受到日益关注。蚯蚓,作为土壤中最大的无脊椎动物,是研究和评价土壤生态污染良好的模式动物。研究蚯蚓的蛋白质组学,对于寻找环境生态污染相关生物标志物和阐明生态毒理学机制有着十分重要的现实意义。目前已知的污染胁迫下蚯蚓蛋白质组学研究,提供了几个特定污染物胁迫蚯蚓的蛋白表达谱。这些蛋白涉及许多生物学过程,例如信号传导、糖酵解、能量代谢、分子伴侣和转录调节,提示了相关污染物可能的生态毒理学机制,有望成为潜在的生物标志物,用于有毒污染物的监测,但其特异性需要进一步试验的验证。对蚯蚓受污染胁迫的蛋白质组表达谱及潜在生物标志物进行简要综述。     

Update on proteomic studies of formalin-fixed paraffin-embedded tissues

Introduction: This review is an update on recent progress in proteomic studies of formalin-fixed paraffin-embedded (FFPE) tissues, which open the opportunity to investigate diseases and research potential biomarkers, particularly when availability of fresh/frozen tissues is low.

Areas covered: We described improvement of existing protocols or the new ones regarding deparaffinization and protein extraction of FFPE samples published from 2014 to today. Moreover, the growing interest to use FFPE tissues for mass spectrometry imaging approach is presented together with the search of post-translational modifications.

Expert opinion: In the last few years, the number of papers using FFPE tissues in proteomic analysis is growing. The interest to apply proteomic analysis to FFPE tissues lies in the easy accessibility of a great number of samples from archives. Nevertheless, standardization in the approach among the different researchers is not achieved, making essentially incomparable the results obtained. This limit should be overcome.     

Clinical implications from proteomic studies in neurodegenerative diseases: lessons from mitochondrial proteins

Mitochondria play a key role in eukaryotic cells, being mediators of energy, biosynthetic and regulatory requirements of these cells. Emerging proteomics techniques have allowed scientists to obtain the differentially expressed proteome or the proteomic redox status in mitochondria. This has unmasked the diversity of proteins with respect to subcellular location, expression and interactions. Mitochondria have become a research ‘hot spot’ in subcellular proteomics, leading to identification of candidate clinical targets in neurodegenerative diseases in which mitochondria are known to play pathological roles. The extensive efforts to rapidly obtain differentially expressed proteomes and unravel the redox proteomic status in mitochondria have yielded clinical insights into the neuropathological mechanisms of disease, identification of disease early stage and evaluation of disease progression. Although current technical limitations hamper full exploitation of the mitochondrial proteome in neurosciences, future advances are predicted to provide identification of specific therapeutic targets for neurodegenerative disorders.     

Mass spectrometry is only one piece of the puzzle in clinical proteomics.

Biomarker discovery in clinical proteomics is being performed on relatively large patient cohorts by utilizing the high throughput of laser desorption/ionization mass spectrometry (MALDI- and SELDI-TOF-MS). Dealing directly with patient samples as opposed to working in cell or animal systems requires a host of considerations both before and after mass spectrometric analysis to obtain robust biomarker candidates. The challenges associated with the heterogeneity of typical samples are amplified by the ability to detect hundreds to thousands of proteins simultaneously. Adherence to protocols and consistency, however, can ensure optimal results. A study starts necessarily with a relevant clinical question and proceeds to a planning phase where sample availability, statistical test selection, logistics and bias reduction are key points. The physical analysis requires consistency and standardized protocols that are helped significantly through automation. Data analysis is broken into two stages, screening and final testing, which can detect either single candidates or a pattern of proteins. Biomarker identification can be performed at this point and will help significantly in the last stage, interpretation. Replication should be performed in an independent sample set in a separate study. The candidate biomarkers from an initial study give a wealth of information that can help to pinpoint patient subpopulations for a more exhaustive proteomic study using complementary platforms with limited capacity but extremely high information content. A clinical proteomics pilot project can also lead to better selection of model systems by providing a direct link with patient samples.     

Human peripheral blood mononuclear cells: A review of recent proteomic applications

Human peripheral blood mononuclear cells (PBMCs) represent a sentinel blood sample which reacts to different pathophysiological stimuli in the form of immunological responses/immunophenotypic changes. The study of molecular content of PBMCs can provide better understanding of immune processes giving the possibility of monitoring the health conditions of the host organism. Proteomic analysis of PBMCs can achieve mentioned goal as important immune-related biomarkers are easily accessible for analysis. PBMCs have been gaining attention in different research areas including preclinical or clinical investigations. In this review, recent applications of proteomic analysis of PBMCs are described and discussed. Approaches are divided based on different proteomic workflows such as in-gel, in-solution and on-filter modes. The effect of various diseases such as autoimmune, cancer, neurodegenerative, viral, metabolic, and various immune stimulations such as radiation, vaccine, corticosteroids over PBMCs proteome, are described with emphasis on promising protein biomarker candidates.     

A two-dimensional liquid-phase separation method coupled with mass spectrometry for proteomic studies of breast cancer and biomarker identification

A two-dimensional liquid-phase separation scheme coupled with mass spectrometry (MS) is presented for proteomic analysis of cell lysates from normal and malignant breast epithelial cell lines. Liquid-phase separations consist of isoelectric focusing as the first dimension and nonporous silica reverse-phase high-performance liquid chromatography (NPS-RP-HPLC) as the second dimension. Protein quantitation and mass measurement are performed using electrospray ionization-time of flight MS (ESI-TOF MS). Proteins are identified by peptide mass fingerprinting using matrix-assisted laser desorption ionization-time of flight MS (MALDI-TOF MS) and MALDI-quadrupole time of flight (QTOF)-tandem mass spectrometry (MS/MS). Two pH regions with 50-60 unique proteins in each pH range were chosen for analysis. Mass maps were created that allowed visualization of protein quantitation differences between normal and malignant breast epithelial cells. Of the approximately 110 unique proteins observed from mass mapping experiments over the limited pH range, 40 (36%) were positively identified by peptide mass fingerprinting and assigned to bands in the mass maps. Of these 40 proteins, 22 were more highly expressed in one or more of the malignant cell lines. These proteins represent potential breast cancer biomarkers that could aid in diagnosis, therapy, or drug development.