Bioinformatics approaches in clinical proteomics

Protein expression profiling is increasingly being used to discover, validate and characterize biomarkers that can potentially be used for diagnostic purposes and to aid in pharmaceutical development. Correct analysis of data obtained from these experiments requires an understanding of the underlying analytic procedures used to obtain the data, statistical principles underlying high-dimensional data and clinical statistical tools used to determine the utility of the interpreted data. This review summarizes each of these steps, with the goal of providing the nonstatistician proteomics researcher with a working understanding of the various approaches that may be used by statisticians. Emphasis is placed on the process of mining high-dimensional data to identify a specific set of biomarkers that may be used in a diagnostic or other assay setting.     

质谱流式技术用于单细胞检测

质谱流式技术(mass cytometry)是利用质谱原理对单细胞进行多参数检测的流式技术,能够在单细胞水平实现超过50种标志物的同时测量,显著增强了对细胞生长进程和复杂细胞系统的评估能力。该文简要介绍了质谱流式技术的基本工作原理,并从金属元素标记、质量分析器、高维单细胞数据处理等方面展开论述,阐明设计新型金属元素标签和选择飞行时间质谱的必要性,归纳分析高维单细胞数据的算法并总结各种算法的优点和局限性。     

Online coupling of capillary electrophoresis with mass spectrometry for the identification of biomarkers for clinical diagnosis

Proteomic screening of complex biologic samples is of increasing importance in clinical research and diagnosis. In the postgenomic area it is evident that changes of the composition of body fluids, as well as post-translational modifications of proteins and peptides, provide more information than genetic typing. The study of these changes allows the state of health or disease of particular organs, and consequently, the whole organism, to be described. This review describes the application of capillary electrophoresis coupled online to an electrospray ionization time-of-flight mass spectrometer to the analysis of body fluids obtained from patients for the identification of biomarkers for diagnostic purposes.     

Targeted and Untargeted Proteomics Approaches in Biomarker Development

An enormous amount of research effort has been devoted to biomarker discovery and validation. With the completion of the human genome, proteomics is now playing an increasing role in this search for new and better biomarkers. Here, what leads to successful biomarker development is reviewed and how these features may be applied in the context of proteomic biomarker research is considered. The “fit‐for‐purpose” approach to biomarker development suggests that untargeted proteomic approaches may be better suited for early stages of biomarker discovery, while targeted approaches are preferred for validation and implementation. A systematic screening of published biomarker articles using MS‐based proteomics reveals that while both targeted and untargeted technologies are used in proteomic biomarker development, most researchers do not combine these approaches. i) The reasons for this discrepancy, (ii) how proteomic technologies can overcome technical challenges that seem to limit their translation into the clinic, and (iii) how MS can improve, complement, or replace existing clinically important assays in the future are discussed.     

The discovery of protein biomarkers in pre-eclampsia: the promising role of mass spectrometry

Abstract

Context: Pre-eclampsia (PE) is a common hypertensive disorder of pregnancy that substantially affects maternal and neonatal morbidity and mortality worldwide. The aetiology of the disease remains poorly understood with lack of reliable diagnostic tests. PE is a multisystem disorder so it is very unlikely that a single or a small group of biomarkers will accurately predict the disease. Mass spectrometry (MS) is indispensable analytical tool in protein analysis studies. MS-based proteomics have the ability to detect the entire protein complement to provide a useful window into a range of biological processes and allow the identification of differentially expressed proteins between samples.

Objective: The aim of this review is to summarise, discuss and evaluate the current predominant MS-based approaches applied for protein biomarker discovery. The paper also seeks to evaluate the current potential PE biomarkers described in the literature and identify issues that can guide future research.

Conclusion: MS-based proteomics studies are promising alternatives to classical hypothesis-driven approaches to discover novel biomarkers and provide new insights into the underlying phathophysiological mechanisms of PE. This should aid in the early diagnosis of PE and the understanding of the aetiology of the disease.     

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.     

Identification of proteins released by follicular lymphoma-derived cells using a mass spectrometry-based approach

The recent advent of mass spectrometry-based methodologies for the analysis of complex protein mixtures opens new opportunities for the discovery of biomarkers that may aid in the diagnostic work-up of cancer. Follicular lymphoma (FL) is the most common form of low-grade non-Hodgkin lymphoma in the Western Hemisphere. Identification of tumor markers that facilitate early disease detection would be a significant advance in the management of FL. We have employed a strategy that entailed propagation of a follicular-derived cell line in serum-free media, protein extraction, and reverse-phase liquid chromatography, with subsequent electrospray ionization and tandem mass spectrometry analysis for the identification of proteins that are released by FL. Using a two-peptide minimum per protein and standard criteria, 209 proteins (5.6% maximum predicted error rate) released from the FL cells were identified. The released proteins included several growth factors, cytokines, acute phase reactants and cellular components previously known to be present in FL cells. Importantly, a greater proportion of proteins previously unassociated with FL were identified with high statistical confidence. Our studies provide a list of proteins, which may be candidates for early screening, diagnosis and therapeutic monitoring of patients with a suspected or biopsy-confirmed diagnosis of FL.     

Inference for reclassification statistics under nested and non-nested models for biomarker evaluation

Abstract

The Net Reclassification Improvement (NRI) and the Integrated Discrimination Improvement (IDI) are used to evaluate the diagnostic accuracy improvement for biomarkers in a wide range of applications. Most applications for these reclassification metrics are confined to nested model comparison. We emphasize the important extensions of these metrics to the non-nested comparison. Non-nested models are important in practice, in particular, in high-dimensional data analysis and in sophisticated semiparametric modeling. We demonstrate that the assessment of accuracy improvement may follow the familiar NRI and IDI evaluation. While the statistical properties of the estimators for NRI and IDI have been well studied in the nested setting, one cannot always rely on these asymptotic results to implement the inference procedure for practical data, especially for testing the null hypothesis of no improvement, and these properties have not been established for the non-nested setting. We propose a generic bootstrap re-sampling procedure for the construction of confidence intervals and hypothesis tests. Extensive simulations and real biomedical data examples illustrate the applicability of the proposed inference methods for both nested and non-nested models.     

Clinical application of urinary proteomics/peptidomics

The technology platforms for proteome analysis have advanced considerably over the last few years. Driven by these advancements in technology, the number of studies on the analysis of the proteome/peptidome, with the aim of defining clinically relevant biomarkers, has substantially risen. Urine has become an increasingly relevant target for clinically oriented proteome analysis; the first clinical trials based on urinary proteomics have been initiated, and studies including several hundred patients have been published. In this article, we summarize the relevant technical aspects in biomarkers discovery and the course from biomarker discovery or ‘potential’ biomarkers to those that have been validated and are clinically important. We discuss experimental design based on the statistics calculated to produce a clinically important end point. We present several examples of proteomic studies that have defined urinary biomarkers for clinical applications, focusing on capillary electrophoresis coupled to mass spectrometry as a technology. Finally, current challenges and considerations for future studies will be discussed.     

Recent advances in platelet proteomics

Platelets are the fundamental players in primary hemostasis, but are also involved in several pathological conditions. The remarkable advances in proteomic methodologies have allowed a better understanding of the basic physiological pathways underlying platelet biology. In addition, recent platelet proteomics focused on disease conditions, helping to elucidate the molecular mechanisms of complex and/or unknown human disorders and to find novel biomarkers for early diagnosis and drug targets. The most common and innovative proteomic techniques, both gel-based and gel-free, used in platelet proteomics will be reviewed here. A particular focus will be given to studies that used a subproteomic strategy to analyze specific platelet conditions (resting or activated), compartments (membrane, granules and microparticles) or fractions (phosphoproteome or glycoproteome). The thousands of platelet proteins and interactions discovered so far by these different powerful proteomic approaches represent a precious source of information for both basic science and clinical applications in the field of platelet biology.     

An overview of techniques for linking high-dimensional molecular data to time-to-event endpoints by risk prediction models

Analysis of molecular data promises identification of biomarkers for improving prognostic models, thus potentially enabling better patient management. For identifying such biomarkers, risk prediction models can be employed that link high-dimensional molecular covariate data to a clinical endpoint. In low-dimensional settings, a multitude of statistical techniques already exists for building such models, e.g. allowing for variable selection or for quantifying the added value of a new biomarker. We provide an overview of techniques for regularized estimation that transfer this toward high-dimensional settings, with a focus on models for time-to-event endpoints. Techniques for incorporating specific covariate structure are discussed, as well as techniques for dealing with more complex endpoints. Employing gene expression data from patients with diffuse large B-cell lymphoma, some typical modeling issues from low-dimensional settings are illustrated in a high-dimensional application. First, the performance of classical stepwise regression is compared to stage-wise regression, as implemented by a component-wise likelihood-based boosting approach. A second issues arises, when artificially transforming the response into a binary variable. The effects of the resulting loss of efficiency and potential bias in a high-dimensional setting are illustrated, and a link to competing risks models is provided. Finally, we discuss conditions for adequately quantifying the added value of high-dimensional gene expression measurements, both at the stage of model fitting and when performing evaluation.     

Review of Matrix-Assisted Laser Desorption Ionization-Imaging Mass Spectrometry for Lipid Biochemical Histopathology

Matrix-Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) is a rapidly evolving method used for the in situ visualization and localization of molecules such as drugs, lipids, peptides, and proteins in tissue sections. Therefore, molecules such as lipids, for which antibodies and other convenient detection reagents do not exist, can be detected, quantified, and correlated with histopathology and disease mechanisms. Furthermore, MALDI-IMS has the potential to enhance our understanding of disease pathogenesis through the use of “biochemical histopathology”. Herein, we review the underlying concepts, basic methods, and practical applications of MALDI-IMS, including post-processing steps such as data analysis and identification of molecules. The potential utility of MALDI-IMS as a companion diagnostic aid for lipid-related pathological states is discussed.     

Current knowledge of the human sperm proteome

The knowledge of the mature sperm proteome is undoubtedly the basis for understanding sperm function, the mechanisms responsible for fertilization, the reasons for infertility and possible treatments. The methods of sperm protein extraction depend mainly on the proteins of interest and the protein separation techniques that will be employed. The isolation of the membrane proteins appears to be most problematic step. Nevertheless, two-dimensional electrophoresis and mass spectrometry have become the main techniques used in human sperm protein analysis. We outline the present techniques used to examine the sperm proteome and data generated from studies on the human sperm and different types of male infertility. We present the most characteristic proteins that are involved in sperm function. Their value as biomarkers for diagnosis and treatment of infertility would require further validation. We focus on selected and critical studies of the human sperm proteome to present our subjective view of this fast-moving field.     

Genome annotating proteomics pipelines: available tools

Proteomics based on tandem mass spectrometry is a powerful tool for identifying novel biomarkers and drug targets. Previously, a major bottleneck in high-throughput proteomics has been that the computational techniques needed to reliably identify proteins from proteomic data lagged behind the ability to collect the immense quantity of data generated. This is no longer the case, as fully automated pipelines for peptide and protein identification exist, and these are publicly and privately accessible. Such pipelines can automatically and rapidly generate high-confidence protein identifications from large datasets in a searchable format covering multiple experimental runs. However, the main challenge for the community now is to use these resources as they are, by taking full advantage of the pooling of information, so that the next barrier in our understanding of biology may be broken. There are currently two pipelines in the public domain that provide such potential: PeptideAtlas and the Genome Annotating Proteomic Pipeline. This review will introduce their features in the context of high-throughput proteomics, and provide indicative results as to their usefulness and usability through a side-by-side comparison of results obtained when processing a set of human plasma samples.     

Amyloid-β as a biomarker for Alzheimer’s disease: quantification methods in body fluids

Alzheimer’s disease (AD) is the most common neurodegenerative disorder, characterized by neuronal impairment leading to dramatic changes in brain. Amyloid-β peptides and tau protein are the most promising biomarkers for AD. Cerebrospinal fluid and plasma are used to determine the concentration of these species. Since the pathological processes of AD start decades before the first symptoms, biomarkers may provide the possibility of early disease detection. The application of rapidly emerging technology, such as mass spectrometry, has opened new avenues to accelerate biomarker discovery, both for diagnostic as well as for prognostic purposes. This review summarizes AD biomarker studies with focus on amyloid-β peptides in biological fluids and their quantification with immunoassays as well as the latest mass spectrometry-based methods.     

Discovering plant metabolic biomarkers for phenotype prediction using an untargeted approach

Biomarkers are used to predict phenotypical properties before these features become apparent and, therefore, are valuable tools for both fundamental and applied research. Diagnostic biomarkers have been discovered in medicine many decades ago and are now commonly applied. While this is routine in the field of medicine, it is of surprise that in agriculture this approach has never been investigated. Up to now, the prediction of phenotypes in plants was based on growing plants and assaying the organs of interest in a time intensive process. For the first time, we demonstrate in this study the application of metabolomics to predict agronomic important phenotypes of a crop plant that was grown in different environments. Our procedure consists of established techniques to screen untargeted for a large amount of metabolites in parallel, in combination with machine learning methods. By using this combination of metabolomics and biomathematical tools metabolites were identified that can be used as biomarkers to improve the prediction of traits. The predictive metabolites can be selected and used subsequently to develop fast, targeted and low‐cost diagnostic biomarker assays that can be implemented in breeding programs or quality assessment analysis. The identified metabolic biomarkers allow for the prediction of crop product quality. Furthermore, marker‐assisted selection can benefit from the discovery of metabolic biomarkers when other molecular markers come to its limitation. The described marker selection method was developed for potato tubers, but is generally applicable to any crop and trait as it functions independently of genomic information.     

Declining plasma fibrinogen alpha fragment identifies HER2-positive breast cancer patients and reverts to normal levels after surgery

Breast cancer is the most common nonskin malignancy affecting women. Currently, no simple, blood-based diagnostic test exists to complement radiological screening and increase sensitivity of detection. To screen plasma specimens and identify biomarkers that detect HER2-positive breast cancer, automated robotic sample processing followed by surface-enhanced laser desorption ionization time-of-flight (SELDI-TOF) mass spectroscopy was used. Multiple statistical algorithms were used to select biomarkers that segregate cancer patients versus controls and produced average CV rates ranging from 20% to 29%. A set of seven biomarkers were validated on an independent test data set and achieved the best error rate of 19.1%. A permutation test indicated a p-value for CV error less than 0.002. Moreover, a ROC curve using these biomarkers achieved an area-under-the-curve value of 0.95 on an independent test data set. The marker responsible for most of the resolving power was identified as a fragment of Fibrinogen Alpha (FGA) encompassing residues 605-629. This marker was present at lower levels in cancer patients as compared to controls. The importance of this biomarker was validated in a longitudinal study comparing pre- and post-operative levels and was shown to revert to normal levels after surgery. This fragment may serve as a useful diagnostic and treatment-monitoring marker.     

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.     

SELDI-TOF-MS筛选慢性阻塞性肺疾病血清标志蛋白的初步研究

目的:利用表面增强激光解吸电离飞行时间质谱技术(SELDI-TOF-MS)筛选慢性阻塞性肺疾病(COPD)血清特异标志物。方法:应用SELDI-TOF-MS技术检测30例COPD稳定期患者和30例健康对照者血清蛋白指纹图谱,采用Biomarker pattern软件进行分析,建立COPD的诊断模型。结果:COPD患者血清蛋白图谱与对照组相比,在相对分子质量2000-15 000范围内共检测到75个蛋白峰,发现19个有统计学差异的蛋白峰(P0.05)。通过对COPD组与对照组间的数据作进一步分析,经BPS软件分析,建立质荷比(M/Z)3 167、4 645的差异蛋白组成的诊断模型,其诊断敏感度为96.67%,特异度为96.67%。结论:SELDI-TOF-MS技术是一种快速、简单易行、用量少和高通量的分析方法。能直接筛选出COPD血清中特异表达标志物,用特异表达标志物建立的诊断模型能有效区分COPD患者与健康对照者,有望成为COPD诊断的辅助指标。