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.     

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.     

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.     

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。     

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.     

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.     

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.     

The proteomic landscape of renal tumors

Introduction: Renal cell carcinoma (RCC) is the most fatal of the common urologic cancers, with approximately 35% of patients dying within 5 years following diagnosis. Therefore, there is a need for non-invasive markers that are capable of detecting and determining the severity of small renal masses at an early stage in order to tailor treatment and follow-up. Proteomic studies have proved to be very useful in the study of tumors.

Areas covered: In this review, we will detail the current knowledge obtained by the different proteomic approaches, focusing on MS-based strategies, used to investigate RCC biology in order to identify diagnostic, prognostic and predictive biomarkers on tissue, cultured cells and biological fluids.

Expert commentary: Currently, no reliable biomarkers or targets for RCC have been translated into the clinical setting. Moreover, despite the efforts of proteomics and other -omics disciplines, only a small number of them have been observed as shared targets between the different analytical platforms and biological specimens. The difficulty to define a specific molecular pattern for RCC and its subtypes highlights a peculiar profile and a heterogeneity that must be taken into account in future studies.     

iTRAQ标记技术与差异蛋白质组学的生物标志物研究

结合多维液相色谱和串联质谱分析,iTRAQ技术已成为差异蛋白质组学定量研究的主要工具之一。而寻找和发现区别于正常生理状态下的疾病特异表达蛋白质,有利于阐明疾病的发病机理,对疾病的预防、诊断、预后和疗效监测具有重要作用,并有助于用作新靶点来开发临床治疗药物。本文重点就该技术在医学领域中进行差异蛋白质组分析并寻找标记蛋白质的研究进行综述。     

The potential impact of recent insights into proteomic changes associated with glaucoma

Introduction: Glaucoma, a major ocular neuropathy, is still far from being understood on a molecular scale. Proteomic workflows revealed glaucoma associated alterations in different eye components. By using state-of-the-art mass spectrometric (MS) based discovery approaches large proteome datasets providing important information about glaucoma related proteins and pathways could be generated. Corresponding proteomic information could be retrieved from various ocular sample species derived from glaucoma experimental models or from original human material (e.g. optic nerve head or aqueous humor). However, particular eye tissues with the potential for understanding the disease’s molecular pathomechanism remains underrepresented.

Areas covered: The present review provides an overview of the analysis depth achieved for the glaucomatous eye proteome. With respect to different eye regions and biofluids, proteomics related literature was found using PubMed, Scholar and UniProtKB. Thereby, the review explores the potential of clinical proteomics for glaucoma research.

Expert commentary: Proteomics will provide important contributions to understanding the molecular processes associated with glaucoma. Sensitive discovery and targeted MS approaches will assist understanding of the molecular interplay of different eye components and biofluids in glaucoma. Proteomic results will drive the comprehension of glaucoma, allowing a more stringent disease hypothesis within the coming years.     

Validation of a proteomic biomarker panel to diagnose minor-stroke and transient ischaemic attack: phase 2 of SpecTRA,a large scale translational study

Abstract

OBJECTIVE: To validate our previously developed 16 plasma-protein biomarker panel to differentiate between transient ischaemic attack (TIA) and non-cerebrovascular emergency department (ED) patients.

METHOD: Two consecutive cohorts of ED patients prospectively enrolled at two urban medical centers into the second phase of SpecTRA study (training, cohort 2A, n?=?575; test, cohort 2B, n?=?528). Plasma samples were analyzed using liquid chromatography/multiple reaction monitoring-mass spectrometry. Logistic regression models which fit cohort 2A were validated on cohort 2B.

RESULTS: Three of the panel proteins failed quality control and were removed from the panel. During validation, panel models did not outperform a simple motor/speech (M/S) deficit variable. Post-hoc analyses suggested the measured behaviour of L-selectin and coagulation factor V contributed to poor model performance. Removal of these proteins increased the external performance of a model containing the panel and the M/S variable.

CONCLUSIONS: Univariate analyses suggest insulin-like growth factor-binding protein 3 and serum paraoxonase/lactonase 3 are reliable and reproducible biomarkers for TIA status. Logistic regression models indicated L-selectin, apolipoprotein B-100, coagulation factor IX, and thrombospondin-1 to be significant multivariate predictors of TIA. We discuss multivariate feature subset analyses as an exploratory technique to better understand a panel’s full predictive potential.     

CSF analysis for protein biomarker identification in patients with leptomeningeal metastases from CNS lymphoma

Introduction: Leptomeningeal metastases (LM) from lymphoma remain a difficult complication for oncologist due to the high incidence in morbidity and mortality. Early diagnostic and initiation of treatment are essential to prevent neurological deterioration.

Areas covered: In this review, several proteomic approaches are described in order to help and provide the basis for the identification of biomarkers useful in early diagnosis, also in discovery novel targets for therapeutic agents. In fact, the identification of biomarkers will have a high potential to detect leptomeningeal lymphoma, as well as to predict its progression and treatment response.

Expert commentary: In the case of LM by Central nervous system (CNS) lymphoma, these studies generated the first insights into the utility of proteomic analysis for biomarker identification and will be demonstrated that identifying specific proteins in cerebrospinal fluid (CSF) had much greater sensitivity for detecting LM in comparison to standard cytological protocols.     

Verification of a proteomic biomarker panel to diagnose minor stroke and transient ischaemic attack: phase 1 of SpecTRA,a large scale translational study

Objective: To derive a plasma biomarker protein panel from a list of 141 candidate proteins which can differentiate transient ischaemic attack (TIA)/minor stroke from non-cerebrovascular (mimic) conditions in emergency department (ED) settings.

Design: Prospective clinical study (#NCT03050099) with up to three timed blood draws no more than 36?h following symptom onset. Plasma samples analysed by multiple reaction monitoring-mass spectrometry (MRM-MS).

Participants: Totally 545 participants suspected of TIA enrolled in the EDs of two urban medical centres.

Outcomes: 90-day, neurologist-adjudicated diagnosis of TIA informed by clinical and radiological investigations.

Results: The final protein panel consists of 16 proteins whose patterns show differential abundance between TIA and mimic patients. Nine of the proteins were significant univariate predictors of TIA [odds ratio (95% confidence interval)]: L-selectin [0.726 (0.596–0.883)]; Insulin-like growth factor-binding protein 3 [0.727 (0.594–0.889)]; Coagulation factor X [0.740 (0.603–0.908)]; Serum paraoxonase/lactonase 3 [0.763 (0.630–0.924)]; Thrombospondin-1 [1.313 (1.081–1.595)]; Hyaluronan-binding protein 2 [0.776 (0.637–0.945)]; Heparin cofactor 2 [0.775 (0.634–0.947)]; Apolipoprotein B-100 [1.249 (1.037–1.503)]; and von Willebrand factor [1.256 (1.034–1.527)]. The scientific plausibility of the panel proteins is discussed.

Conclusions: Our panel has the potential to assist ED physicians in distinguishing TIA from mimic patients.     

TrioMDR: Detecting SNP interactions in trio families with model-based multifactor dimensionality reduction

Single nucleotide polymorphism (SNP) interactions can explain the missing heritability of common complex diseases. Many interaction detection methods have been proposed in genome-wide association studies, and they can be divided into two types: population-based and family-based. Compared with population-based methods, family-based methods are robust vs. population stratification. Several family-based methods have been proposed, among which Multifactor Dimensionality Reduction (MDR)-based methods are popular and powerful. However, current MDR-based methods suffer from heavy computational burden. Furthermore, they do not allow for main effect adjustment. In this work we develop a two-stage model-based MDR approach (TrioMDR) to detect multi-locus interaction in trio families (i.e., two parents and one affected child). TrioMDR combines the MDR framework with logistic regression models to check interactions, so TrioMDR can adjust main effects. In addition, unlike consuming permutation procedures used in traditional MDR-based methods, TrioMDR utilizes a simple semi-parameter P-values correction procedure to control type I error rate, this procedure only uses a few permutations to achieve the significance of a multi-locus model and significantly speeds up TrioMDR. We performed extensive experiments on simulated data to compare the type I error and power of TrioMDR under different scenarios. The results demonstrate that TrioMDR is fast and more powerful in general than some recently proposed methods for interaction detection in trios. The R codes of TrioMDR are available at: https://github.com/TrioMDR/TrioMDR.     

蛋白质组学技术在感染研究中的应用

蛋白质组学的目标在于阐明特定生物体、组织、细胞或亚细胞结构中全部蛋白质的表达模式和功能模式,其技术平台由高通量的蛋白质分离技术、鉴定技术和生物信息学组成。在许多研究领域,蛋白质组学技术为阐明疾病过程和生命现象的分子机制提供了全面、网络和动态的蛋白质组信息。感染是重要的基本致病因素之一,蛋白质组学的研究策略和技术方法有利于快速分离鉴定病原体蛋白质组、宿主免疫细胞蛋白质组、感染相关蛋白、疫苗候：选抗原蛋白、生物标志物和药物靶标,从而明显加快病原体、宿主反应、感染发病机制以及感染预防、诊断和治疗等相关研究的进程。     

Quantification of cotinine in dried blood spots as a biomarker of exposure to tobacco smoke

Objective: We present an ultra-sensitive, minimally-invasive method for quantifying cotinine in dried blood spot (DBS) samples as a biomarker of exposure to tobacco smoke that can be collected using a simple heel or finger prick to obtain blood samples.

Methods: Cotinine levels were measured in matched plasma and reconstituted DBS samples from smokers and nonsmokers to evaluate assay parameters. In addition, we applied this new method to finger-prick DBS samples that were collected from infants, children and young adults ages 1–21 to estimate exposure to tobacco smoke. Partitioning of cotinine across red blood cells and haematocrit effects were investigated.

Results: Cotinine levels measured in matched plasma and reconstituted DBS samples from smokers and nonsmokers were found to be highly correlated (R²=0.94), with 100% sensitivity and 94% specificity to differentiate reported smokers from nonsmokers. With this method, the LOQ is <0.25?ng/mL using a single 3.2?mm punch of a DBS, and haematocrit effects are negligible.

Conclusions: This sensitive, high-throughput and minimally-invasive method for quantifying cotinine in DBS samples provides a simple and cost effective means for estimating exposure to tobacco smoke in population based studies, and has particular advantages in studies involving infants and children.     

Preprocessing of tandem mass spectrometric data based on decision tree classification

In this study, we present a preprocessing method for quadrupole time-of-flight （Q-TOF） tandem mass spectra to increase the accuracy of database searching for peptide （protein） identification. Based on the natural isotopic information inherent in tandem mass spectra, we construct a decision tree after feature selection to classify the noise and ion peaks in tandem spectra. Furthermore, we recognize overlapping peaks to find the monoisotopic masses of ions for the following identification process. The experimental results show that this preprocessing method increases the search speed and the reliability of peptide identification.