细胞质膜蛋白质组学

随着基于质谱的大规模蛋白质鉴定技术的建立,蛋白质组学得到迅速发展。同时由于质膜在细胞生命活动中的重要作用,质膜蛋白质组学逐渐兴起,并发展成为蛋白质组学研究中的重要组成部分。但由于膜蛋白尤其是内在膜蛋白的强疏水性、低丰度,造成蛋白质提取、分离和鉴定相对困难,使质膜蛋白质组成为蛋白质组研究中的一个技术难点。     

癌细胞分泌蛋白质组富集与鉴定策略研究进展

分泌蛋白质中包含细胞因子、生长因子和激素等具有重要功能的生物活性分子,广泛参与细胞信号传导,细胞增殖、分化和凋亡的调控等多项重要生命过程。因此,从分泌体系中发掘生物标志物和治疗靶标,是癌症蛋白质组研究的一个重要方向。传统的分泌蛋白质体系主要包括血浆、尿液、脑脊液和组织间隙液等,这些系统的一个主要特点是其所包含的蛋白质浓度范围跨度很大,对蛋白质组分离和鉴定提出巨大挑战。而收集肿瘤细胞在无血清体系中培养时所分泌的蛋白质,开展蛋白质组研究,则可以避开常规分泌系统中大量高丰度蛋白质的干扰,获得肿瘤特异性的细胞分泌蛋白谱。近年来,随着蛋白质组学技术的发展和质谱仪的进步,癌细胞分泌蛋白质组学的研究进展很快。我们系统回顾了癌细胞分泌蛋白质组富集方法和鉴定策略的发展,概括了癌细胞分泌蛋白质组研究现状和规模,为该领域的进一步研究奠定基础。     

膜整合蛋白质的“鸟枪法”蛋白质组学分析策略

疾病状态下生物膜表面蛋白质分子标记的表达量和修饰状态会发生改变。但由于其低丰度和不易溶解等特性,制约了膜蛋白质组学的研究,同时也制约了相关药物靶标的设计。近年来,为克服这些困难,学者们提出了"鸟枪法"的膜蛋白质组学研究策略。基于此,本文论述了"鸟枪法"的蛋白质组学分析的基本过程及其后续的部分改进工作。随着新的策略不断被采用,更多膜蛋白质的拓扑学特征和功能的相关研究一定会走上新的台阶。     

蛋白质组研究中低丰度蛋白质的富集与鉴定技术

随着分离技术、质谱设备与生物信息学的发展,对复杂蛋白质组的深度覆盖研究已不再是遥不可及,对8 000个基因产物的规模化鉴定已经趋于常规化。然而,更深一步的蛋白质组覆盖,就遇到了低丰度蛋白质分离、鉴定等更高的挑战。因此,针对低丰度蛋白质鉴定方法与相关分离分析技术的发展,本文综述了改善低丰度蛋白质鉴定效率的三种方法的最新进展,包括蛋白质的高分辨分离与质谱分析策略、低丰度蛋白质的选择性富集方法和高丰度蛋白质的消减策略等。针对生物体系蛋白质组成的深度鉴定研究,逐步用于探索生命体在不同条件下蛋白质组的表达与修饰的动态变化,并最终发现具备重要生理与病理功能的调控性蛋白质、寻找候选药物靶标。     

血液蛋白质组学的研究技术及进展

人血液含有来源于几乎所有细胞、组织、器官的蛋白质,可以直接反映病理、生理状态,是各种疾病诊断、生物标志物发现的最有价值的标本。因此,长期以来,血浆蛋白质组一直是人们研究的热点,并被人类蛋白质组组织(HUPO)列为首批启动的重大国际合作研究项目。血浆蛋白质含量动态范围非常广、成分极其复杂,血浆蛋白质组的研究极富挑战性。近年来,血浆高丰度蛋白质去除、蛋白质/肽段分离、质谱鉴定、数据处理等多种相关技术都取得了很大的进展。本文简要综述了上述技术领域的研究和应用进展。     

尿液蛋白富集方法的比较

人尿液中蛋白含量低,在进行质谱分析时易被高丰度蛋白掩盖。因此,发展高效和高选择性的富集方法,是实现尿蛋白标记物深度覆盖的必要前提。探究不同实验方法对尿液蛋白富集和尿蛋白质组的影响尤为重要。本研究采用超滤法、硝酸纤维素膜富集法和饱和硫酸铵沉淀法,等体积各处理5例健康志愿者和膀胱癌患者10 mL尿液样本,富集尿液蛋白,SDS-PAGE分离尿蛋白,比较不同方法纯化的效率;通过质谱分析,比较不同纯化方法的肽段鉴定效果,确定针对尿液蛋白质组蛋白的最佳富集方法。相对于超滤和硝酸纤维素膜富集法,饱和硫酸铵沉淀法成功地应用于健康人尿蛋白的富集和质谱检测,在保证回收蛋白质量的前提下,可减少高丰度白蛋白的干扰,富集更多低丰度蛋白,提高了质谱鉴定的灵敏度。综上所述,饱和硫酸铵提取尿蛋白的效果较好,该方法具有大规模处理尿液、提高蛋白质组学筛选临床诊断标记物研究的应用潜力。     

蛋白质组研究新前沿：定量蛋白质组学

在过去几年里,蛋白质组研究取得了令人鼓舞的进展,2DE-MS途径的自动化,多维色谱整合串联质谱的使用,弥补了一些用双向凝胶电泳分离蛋白质的技术缺陷;从稳定同位素标记到ICAT战略的提出,为准确定量在细胞或组织中发挥重要调节功能的低丰度蛋白质提供了一个较为理想的方法。同时,蛋白质芯片技术的不断发展,也极大的丰富了定量蛋白质组学的研究。就定量蛋白质组学及其相关技术研究进展作一简要综述。     

尿蛋白质组学在疾病标志物研究中的应用

尿液是重要的疾病标志物来源. 本文介绍了当前尿蛋白质组学的研究进展和尿液中疾病标志物研究的主要问题, 并对未来的发展进行了展望. 由于实际的临床问题通常是对症状相似的多种疾病进行鉴别诊断, 仅仅比较某一种疾病组和健康人对照组的尿蛋白质组差异不足以找到具有诊断能力的标志物. 另外, 尿蛋白质组在个体间和同一个体的不同生理条件下的变化也为疾病标志物的寻找带来了困难. 本文提出, 进行正常人群个体间和不同生理条件下尿蛋白质变化范围的研究可以为鉴定疾病标志物提供参考标准, 从而帮助研究者发现由疾病、而不是生理学差异引起的蛋白的变化. 比较蛋白在血浆和尿液中丰度的变化可以揭示肾脏的生理学功能和发现疾病标志物. 最后提出, 建立一个数据共享平台, 收集和整合已有的疾病标志物研发成果, 将大大推动尿蛋白质组研究的发展.     

线粒体蛋白质组学研究进展

线粒体是真核生物中重要的细胞器,其包含的全部蛋白质称为线粒体蛋白质组。人类线粒体大约包含1500多种蛋白质,由核基因和线粒体基因共同编码。线粒体是细胞能量合成和物质代谢的中心,其功能障碍将直接或问接引起许多疾病。目前线粒体蛋白质组学正是系统性地研究线粒体在生理、病理过程中的功能变化以及研究疾病发生机制的重要方法。将线粒体蛋白质组的研究方法、研究进展、线粒体蛋白质组的性质及其在相关疾病研究中的作用进行综述,并对线粒体蛋白质组学在疾病发生机制和诊断治疗中的发展前景进行展望。     

差异蛋白质组学的研究进展

差异蛋白质组是蛋白质组学研究的一个主要内容,其核心在于寻找某种特定臣寸素引起样本之间蛋白质组的差异,揭示并验证蛋白质组在生理或病理过程中的变化。进一步对蛋白质组差异信息分析后,理论上可以推断造成这种变化的原因。因此,对于临床上肿瘤预诊、药物靶标寻找、细胞调控分子的鉴别等有着极大的实际意义。差异蛋白质组研究要求可靠性和可重复性。因此,对于样本处理要求较高,激光微切割技术和高丰度蛋白去除技术的应用优化了样本处理方法。目前差异蛋白质组的主要研究方法仍是2-DE分离和MS鉴定联合应用,基于2-DE的2-DDIGE方法弥补了2-DE的弱点,更适用于差异蛋白质组研究。除2-DE技术外的其他几种技术手段,如多维液相色谱分离技术、ICAT技术、蛋白芯片技术等差异蛋白质组学研究技术可以作为2-DE技术的补充,甚至或替代技术。     

IgY14 and SuperMix immunoaffinity separations coupled with liquid chromatography-mass spectrometry for human plasma proteomics biomarker discovery

Interest in the application of advanced proteomics technologies to human blood plasma- or serum-based clinical samples for the purpose of discovering disease biomarkers continues to grow; however, the enormous dynamic range of protein concentrations in these types of samples (often >10 orders of magnitude) represents a significant analytical challenge, particularly for detecting low-abundance candidate biomarkers. In response, immunoaffinity separation methods for depleting multiple high- and moderate-abundance proteins have become key tools for enriching low-abundance proteins and enhancing detection of these proteins in plasma proteomics. Herein, we describe IgY14 and tandem IgY14-Supermix separation methods for removing 14 high-abundance and up to 60 moderate-abundance proteins, respectively, from human blood plasma and highlight their utility when combined with liquid chromatography-tandem mass spectrometry for interrogating the human plasma proteome.     

Mouse models for neurodegenerative diseases and a theory of proteomics

Proteome analysis is usually performed by separating complex cellular protein extracts by two‐dimensional‐electrophoresis followed by protein identification using mass spectrometry. In this way proteins are compared from normal and diseased tissue in order to detect disease related protein changes. In a strict sense, however, this procedure cannot be called proteome analysis: the tools of proteomics are used just to detect some interesting proteins which are then investigated by protein chemistry as usual. Real proteome research would be studying the cellular proteome as a whole, its composition, organization and its kind of action. At present however, we have no idea how a proteome works as a whole; we have not even a theory about that. If we would know how the proteome of a cell type is arranged, we probably would alter our strategy to detect and analyze disease‐related proteins. I will present a theory of proteomics and show some results from our laboratory which support this theory. The results come from investigations of the mouse brain proteome and include mouse models for neurodegenerative diseases.     

Gene therapy approaches for Parkinson's disease

Proteome analysis is usually performed by separating complex cellular protein extracts by two-dimensional-electrophoresis followed by protein identification using mass spectrometry. In this way proteins are compared from normal and diseased tissue in order to detect disease related protein changes. In a strict sense, however, this procedure cannot be called proteome analysis: the tools of proteomics are used just to detect some interesting proteins which are then investigated by protein chemistry as usual. Real proteome research would be studying the cellular proteome as a whole, its composition, organization and its kind of action. At present however, we have no idea how a proteome works as a whole; we have not even a theory about that. If we would know how the proteome of a cell type is arranged, we probably would alter our strategy to detect and analyze disease-related proteins. I will present a theory of proteomics and show some results from our laboratory which support this theory. The results come from investigations of the mouse brain proteome and include mouse models for neurodegenerative diseases.     

Proteomic technologies for the identification of disease biomarkers in serum: advances and challenges ahead

Serum is an ideal biological sample that contains an archive of information due to the presence of a variety of proteins released by diseased tissue, and serum proteomics has gained considerable interest for the disease biomarker discovery. Easy accessibility and rapid protein changes in response to disease pathogenesis makes serum an attractive sample for clinical research. Despite these advantages, the analysis of serum proteome is very challenging due to the wide dynamic range of proteins, difficulty in finding low-abundance target analytes due to the presence of high-abundance serum proteins, high levels of salts and other interfering compounds, variations among individuals and paucity of reproducibility. Sample preparation introduces pre-analytical variations and poses major challenges to analyze the serum proteome. The label-free detection techniques such as surface plasmon resonance, microcantilever, few nanotechniques and different resonators are rapidly emerging for the analysis of serum proteome and they have exhibited potential to overcome few limitations of the conventional techniques. In this article, we will discuss the current status of serum proteome analysis for the biomarker discovery and address key technological advancements, with a focus on challenges and amenable solutions.     

Immunoisolation and subfractionation of synaptic vesicle proteins

Within recent years, the advances in proteomics techniques have resulted in considerable novel insights into the protein expression patterns of specific tissues, cells, and organelles. The information acquired from large-scale proteomics approaches indicated, however, that the proteomic analysis of whole cells or tissues is often not suited to fully unravel the proteomes of individual organellar constituents or to identify proteins that are present at low copy numbers. In addition, the identification of hydrophobic proteins is still a challenge. Therefore, the development of techniques applicable for the enrichment of low-abundance membrane proteins is essential for a comprehensive proteomic analysis. In addition to the enrichment of particular subcellular structures by subcellular fractionation, the spectrum of techniques applicable for proteomics research can be extended toward the separation of integral and peripheral membrane proteins using organic solvents, detergents, and detergent-based aqueous two-phase systems with water-soluble polymers. Here, we discuss the efficacy of a number of experimental protocols. We demonstrate that the appropriate selection of physicochemical conditions results in the isolation of synaptic vesicles of high purity whose proteome can be subfractionated into integral membrane proteins and soluble proteins by several phase separation techniques.     

Enhanced analysis of the mouse plasma proteome using cysteine-containing tryptic glycopeptides

A comprehensive understanding of the mouse plasma proteome is important for studies using mouse models to identify protein markers of human disease. To enhance our analysis of the mouse plasma proteome, we have developed a method for isolating low-abundance proteins using a cysteine-containing glycopeptide strategy. This method involves two orthogonal affinity capture steps. First, glycoproteins are coupled to an azlactone copolymer gel using hydrazide chemistry and cysteine residues are then biotinylated. After trypsinization and extensive washing, tethered N-glycosylated tryptic peptides are released from the gel using PNGase F. Biotinylated cysteinyl-containing glycopeptides are then affinity selected using a monomeric avidin gel and analyzed by LC-MS/MS. We have applied the method to a proteome analysis of mouse plasma. In two independent analyses using 200 muL each of C57BL mouse plasma, 51 proteins were detected. Only 42 proteins were seen when the same plasma sample was analyzed by glycopeptides only. A total of 104 N-glycosylation sites were identified. Of these, 17 sites have hitherto not been annotated in the Swiss-Prot database whereas 48 were considered probable, potential, or by similarity - i.e., based on little or no experimental evidence. We show that analysis by cysteine-containing glycopeptides allows detection of low-abundance proteins such as the epidermal growth factor receptor, the Vitamin K-dependent protein Z, the hepatocyte growth factor activator, and the lymphatic endothelium-specific hyaluronan receptor as these proteins were not detected in the glycopeptide control analysis.     

细胞质膜蛋白质组学研究技术进展

质膜蛋白在细胞中执行着非常重要的功能。随着蛋白质组学的发展,细胞质膜蛋白质组学成为蛋白质组学研究的重要组成部分,它为质膜蛋白的生物功能研究及药物靶标的发现提供了新的途径。然而,质膜蛋白丰度低、疏水性强,对现有蛋白质组学研究技术提出了挑战。简要综述了近年来质膜蛋白质组研究的相关技术进展,包括富集、提取分离鉴定方法及定量和生物信息学研究方法等。     

A high-confidence human plasma proteome reference set with estimated concentrations in PeptideAtlas

Human blood plasma can be obtained relatively noninvasively and contains proteins from most, if not all, tissues of the body. Therefore, an extensive, quantitative catalog of plasma proteins is an important starting point for the discovery of disease biomarkers. In 2005, we showed that different proteomics measurements using different sample preparation and analysis techniques identify significantly different sets of proteins, and that a comprehensive plasma proteome can be compiled only by combining data from many different experiments. Applying advanced computational methods developed for the analysis and integration of very large and diverse data sets generated by tandem MS measurements of tryptic peptides, we have now compiled a high-confidence human plasma proteome reference set with well over twice the identified proteins of previous high-confidence sets. It includes a hierarchy of protein identifications at different levels of redundancy following a clearly defined scheme, which we propose as a standard that can be applied to any proteomics data set to facilitate cross-proteome analyses. Further, to aid in development of blood-based diagnostics using techniques such as selected reaction monitoring, we provide a rough estimate of protein concentrations using spectral counting. We identified 20,433 distinct peptides, from which we inferred a highly nonredundant set of 1929 protein sequences at a false discovery rate of 1%. We have made this resource available via PeptideAtlas, a large, multiorganism, publicly accessible compendium of peptides identified in tandem MS experiments conducted by laboratories around the world.     

The human plasma proteome: history,character, and diagnostic prospects

The human plasma proteome holds the promise of a revolution in disease diagnosis and therapeutic monitoring provided that major challenges in proteomics and related disciplines can be addressed. Plasma is not only the primary clinical specimen but also represents the largest and deepest version of the human proteome present in any sample: in addition to the classical "plasma proteins," it contains all tissue proteins (as leakage markers) plus very numerous distinct immunoglobulin sequences, and it has an extraordinary dynamic range in that more than 10 orders of magnitude in concentration separate albumin and the rarest proteins now measured clinically. Although the restricted dynamic range of conventional proteomic technology (two-dimensional gels and mass spectrometry) has limited its contribution to the list of 289 proteins (tabulated here) that have been reported in plasma to date, very recent advances in multidimensional survey techniques promise at least double this number in the near future. Abundant scientific evidence, from proteomics and other disciplines, suggests that among these are proteins whose abundances and structures change in ways indicative of many, if not most, human diseases. Nevertheless, only a handful of proteins are currently used in routine clinical diagnosis, and the rate of introduction of new protein tests approved by the United States Food and Drug Administration (FDA) has paradoxically declined over the last decade to less than one new protein diagnostic marker per year. We speculate on the reasons behind this large discrepancy between the expectations arising from proteomics and the realities of clinical diagnostics and suggest approaches by which protein-disease associations may be more effectively translated into diagnostic tools in the future.     

Magnetic nanoparticles-based digestion and enrichment methods in proteomics analysis

In proteome research, rapid and effective proteolysis and enrichment strategies are essential for successful protein identification. Functionalized magnetic microspheres of micro- and nano-meter size are gaining increasing attention due to their easy manipulation and recovery, great specific surface areas and high surface activity. The introduction of magnetic nanoparticles into the field of proteomics study has accelerated the development of digestion and enrichment methods. In this article, we mainly focus on recent developments of using different functionalized magnetic nanoparticles for rapid digestion and preconcentration of low-abundance peptides/proteins, including those containing post-translational modifications, such as phosphorylation and glycosylation, prior to mass spectrometric analysis.