蛋白质质谱分析的无标记定量算法研究进展

作为发现疾病相关生物标志物的重要途径,定量研究已成为蛋白质组学的热点问题.随着实验方法的发展和改进,定量数据处理算法也在不断更新和完善.将现有的无标记定量方法归纳为需要/不需要鉴定结果两类方法,分析比较了两类方法的异同及优缺点,详细讨论了所涉及的主要算法,总结了一些常用的无标记定量软件及对应的网络资源.展望了无标记定量数据分析的未来研究方向.     

同位素标记相对和绝对定量技术研究进展

定量蛋白质组学是蛋白质研究的前沿学科。目前常用的定量蛋白质组学研究技术有荧光差异凝胶电泳（DIGE）、同位素亲和标记（ICAT）等。同位素标记相对和绝对定量（iTRAQ）技术是近年来最新开发的一种新的蛋白质组学定量研究技术。结合非凝胶串联质谱技术,该技术可对复杂样本、细胞器、细胞裂解液等样本进行相对和绝对定量研究,具有较好的定量效果、较高的重复性,并可对多达四种不同样本同时进行定量分析。本文对 iTRAQ 技术的原理、实验方法及应用进展进行了综述。     

基于质谱的选择反应监测技术相关策略和方法的研究进展

随着蛋白质组学研究的不断深入,基于质谱的选择反应监测技术(SRM)已经成为以发现生物标志物为代表的定向蛋白质组学研究的重要手段.SRM技术根据假设信息,特异性地获取符合假设条件的质谱信号,去除不符合条件的离子信号干扰,从而得到特定蛋白质的定量信息.SRM技术具有更高的灵敏度和精确性、更大的动态范围等优势.该技术可分为实验设计、数据获取和数据分析三个步骤.在这几个步骤中,最重要的是利用生物信息学手段总结当前实验数据的结果,并用机器学习方法和总结的经验规则进行SRM实验的母离子和子离子对的预测.针对数据质控和定量的生物信息学方法研究在提高SRM数据可靠性方面具有重要作用.此外,为方便SRM的研究,本文还收集、汇总了SRM技术相关的软件、工具和数据库资源.随着质谱仪器的不断发展,新的SRM实验策略以及分析方法、计算工具也应运而生.结合更优化的实验策略、方法,采用更精准的生物信息学算法和工具,SRM在未来蛋白质组学的发展中将发挥更加重要的作用.     

iTRAQ技术及其在蛋白质组学中的应用

近年来随着蛋白质组学的迅速发展,其相应的方法学研究也取得了巨大的进步, 一系列新技术融入了蛋白质组学研究中,极大地促进了这门学科的发展.相对和绝对定量同位素标记(iTRAQ)技术与高度敏感性和准确性的串联质谱及多维液相色谱联用技术已成为蛋白质定性和定量研究的主要工具之一. 该技术可对复杂样本、细胞器、细 胞裂解液等样本进行相对和绝对定量研究,具有较好的定量效果、较高的重复性.由于其能够同时对多达8种样品进行标记分析,故在生命科学的各个领域得到了广泛的应用.本文对iTRAQ的原理、实验流程、优缺点及近几年的应用进展进行综述.     

同位素标记相对和绝对定量蛋白组技术研究进展

近年来定量蛋白组学技术迅速发展,目前常用的有双向荧光差异凝胶电泳、同位素亲和标记、15N同位素标记、同位素标记相对和绝对定量和细胞培养条件下稳定同位素标记技术等。同位素标记相对和绝对定量技术以其高通量、高灵敏度、高重复性、高动态检测限和能对各种复杂样品进行相对和绝对定量研究等优势而备受研究者青睐,目前已发展到在同一实验中分析8组样品,增加了实验设计的灵活性。我们就同位素标记相对和绝对定量技术在定量蛋白组史中的地位作用、研究策略,以及在病毒致病机制研究和医药临床相关问题中的应用做简要综述。     

同位素稀释法在绝对定量蛋白质组中的研究进展

绝对定量蛋白质组是指基于蛋白质组学方法对细胞、组织或体液中的蛋白质进行绝对量或浓度测定．目前,常用的绝对定量方法主要有基于同位素稀释法的蛋白质组学绝对定量方法和基于质谱数据统计分析的非标记方法．基于同位素稀释法的绝对定量方法是用已知量的同位素标记物对与其混合的样本蛋白质浓度进行测定．常见的同位素标记物包括：由AQUA法、QconCAT法产生的特异性水解肽段,由PSAQ法、Absolute SILAC法产生的标记蛋白和由PrESTs-SILAC法产生的蛋白抗原表位标签．由于同位素稀释法可以对蛋白质进行准确和精确定量,对于临床疾病的诊断和治疗具有明显的现实意义．本文对同位素稀释法在绝对定量蛋白质组中的研究进展及其优缺点和最新应用进行了评述．     

高精度相对和绝对定量的等量异位标签在定量蛋白质组学研究中的新进展

蛋白质组学逐渐从定性研究转向定量研究。在定量蛋白质组学技术中,相对和绝对定量的等量异位标签(Isobaric tags for relative and absolute quantitation,iTRAQ)是应用最广泛的技术之一,具有通量高、稳定性强及不受样品来源制约等优点,几乎可以对任意样品进行标记,而且可以同时对多达8个样品进行定量分析,有效地提高了通量。iTRAQ技术不断改进,其定量准确性显著提高,适用的平台越来越多,为微生物、动物、植物、生物医学领域蛋白质及其翻译后修饰组研究创造了条件。文中综述了高精度iTRAQ技术在定量蛋白质组学研究中的最新发展及其应用。     

基于质谱的蛋白质N末端乙酰化程度定量方法的研究

随着质谱技术及各种定量方法的不断完善和发展,定量蛋白质组学的方法不断地被应用到各类生物学研究中。蛋白质组学定性定量数据的处理主要通过一些多功能的商业化或者开源软件来进行,如常用的数据分析软件Proteome Discoverer和Maxquant。但是在通过化学标记对蛋白质N末端乙酰化程度进行定量这一方面,Proteome Discoverer和Maxquant在一定程度上存在准确性不高和完整度不够的问题。于是本研究针对自己的实验特点,通过Java算法编写了相应的定量程序Acequant来完成N末端乙酰化程度的相对定量。本研究将该程序在已有相关报道的He La cell上进行了验证,Acequant共定量到1 587个蛋白质N末端,而Proteome Discoverer和Maxquant分别只定量到42个和306个N末端。同时,手动验证原始图谱也证实了Acequant定量的准确性更好。于是,本研究将此方法进一步应用到秀丽隐杆线虫N末端乙酰化的研究中,并初步发现了线虫整体的N末端乙酰化状态,为进一步的N末端研究提供了支持。     

植物蛋白质组学研究若干重要进展

植物蛋白质组学近年来正从定性向精确定量蛋白质组学的方向发展。国际上近两年发表的约160篇研究论文报道了利用不断改进的双向电泳结合生物质谱技术、多维蛋白质鉴定技术,以及包括双向荧光差异凝胶电泳、幅N体内代谢标记、同位素标记的亲和标签、同位素标记相对和绝对定量等在内的第2代蛋白质组学技术,对植物组织（器官）与细胞器、植物发育过程和植物响应环境胁迫的蛋白质组特征,以及植物蛋白质翻译后修饰和蛋白质相互作用等方面的研究成果。该文对上述报道进行总结,综述了2007年以来植物蛋白质组学若干重要问题研究的新进展。     

植物蛋白质组学研究若干重要进展

植物蛋白质组学近年来正从定性向精确定量蛋白质组学的方向发展。国际上近两年发表的约160篇研究论文报道了利用不断改进的双向电泳结合生物质谱技术、多维蛋白质鉴定技术, 以及包括双向荧光差异凝胶电泳、15N体内代谢标记、同位素标记的亲和标签、同位素标记相对和绝对定量等在内的第2代蛋白质组学技术, 对植物组织(器官)与细胞器、植物发育过程和植物响应环境胁迫的蛋白质组特征, 以及植物蛋白质翻译后修饰和蛋白质相互作用等方面的研究成果。该文对上述报道进行总结, 综述了2007年以来植物蛋白质组学若干重要问题研究的新进展。     

Computational methods for the comparative quantification of proteins in label-free LCn-MS experiments

Liquid chromatography (LC) coupled to electrospray mass spectrometry (MS) is well established in high-throughput proteomics. The technology enables rapid identification of large numbers of proteins in a relatively short time. Comparative quantification of identified proteins from different samples is often regarded as the next step in proteomics experiments enabling the comparison of protein expression in different proteomes. Differential labeling of samples using stable isotope incorporation or conjugation is commonly used to compare protein levels between samples but these procedures are difficult to carry out in the laboratory and for large numbers of samples. Recently, comparative quantification of label-free LC(n)-MS proteomics data has emerged as an alternative approach. In this review, we discuss different computational approaches for extracting comparative quantitative information from label-free LC(n)-MS proteomics data. The procedure for computationally recovering the quantitative information is described. Furthermore, statistical tests used to evaluate the relevance of results will also be discussed.     

定量蛋白质组学无标记定量方法的研究进展

依靠质谱技术的蛋白质组学快速发展,寻求速度快、重复性好以及准确度高的定量方法是该领域的一项艰巨任务,定量蛋白质组学分支领域应运而生．其中,无标记定量方法以其样品制备简单、耗材费用低廉以及结果数据分析便捷等优点渐露锋芒．无标记定量方法通常分为信号强度法和谱图计数法两大类．本文在这两种无标记定量方法计算原理的基础上,针对各种常用的无标记定量方法及最新进展做一个较为全面的介绍,并将详细讨论两类方法的异同点,以及目前蛋白质组学中无标记定量方法所面临的主要挑战,希望能为这一领域的研究人员在选择无标记定量方法时提供一个合理的参考．     

Mass Spectrometry-Based Approaches Toward Absolute Quantitative Proteomics

Mass spectrometry has served as a major tool for the discipline of proteomics to catalogue proteins in an unprecedented scale. With chemical and metabolic techniques for stable isotope labeling developed over the past decade, it is now routinely used as a method for relative quantification to provide valuable information on alteration of protein abundance in a proteome-wide scale. More recently, absolute or stoichiometric quantification of proteome is becoming feasible, in particular, with the development of strategies with isotope-labeled standards composed of concatenated peptides. On the other hand, remarkable progress has been also made in label-free quantification methods based on the number of identified peptides. Here we review these mass spectrometry-based approaches for absolute quantification of proteome and discuss their implications.Key Words: Quantitative proteomics, mass spectrometry, absolute quantification, stable isotope labeling, label-free.     

Recent progress in liquid chromatography-based separation and label-free quantitative plant proteomics

Recent innovations in liquid chromatography-mass spectrometry (LC-MS)-based methods have facilitated quantitative and functional proteomic analyses of large numbers of proteins derived from complex samples without any need for protein or peptide labelling. Regardless of its great potential, the application of these proteomics techniques to plant science started only recently. Here we present an overview of label-free quantitative proteomics features and their employment for analysing plants. Recent methods used for quantitative protein analyses by MS techniques are summarized and major challenges associated with label-free LC-MS-based approaches, including sample preparation, peptide separation, quantification and kinetic studies, are discussed. Database search algorithms and specific aspects regarding protein identification of non-sequenced organisms are also addressed. So far, label-free LC-MS in plant science has been used to establish cellular or subcellular proteome maps, characterize plant-pathogen interactions or stress defence reactions, and for profiling protein patterns during developmental processes. Improvements in both, analytical platforms (separation technology and bioinformatics/statistical analysis) and high throughput nucleotide sequencing technologies will enhance the power of this method.     

The importance of the digest: proteolysis and absolute quantification in proteomics

Virtually all mass spectrometric-based methods for quantitative proteomics are at the peptide level, whether label-mediated or label-free. Absolute quantification in particular is based on the measurement of limit peptides, defined as those peptides that cannot be further fragmented by the protease in use. Complete release of analyte and (stable isotope labelled) standard ensures that the most reliable quantification data are recovered, especially when the standard peptides are in a different primary sequence context, such as sometimes occurs in the QconCAT methodology. Moreover, in label-free methods, incomplete digestion would diminish the ion current attributable to limit peptides and lead to artifactually low quantification data. It follows that an essential requirement for peptide-based absolute quantification in proteomics is complete and consistent proteolysis to limit peptides. In this paper we describe strategies to assess completeness of proteolysis and discuss the potential for variance in digestion efficiency to compromise the ensuing quantification data. We examine the potential for kinetically favoured routes of proteolysis, particularly at the last stages of the digestion, to direct products into ‘dead-end’ mis-cleaved products.     

Advances in quantitative proteomics

Large-scale protein quantification has become a major proteomics application in many areas of biological and medical research. During the past years, different techniques have been developed, including gel-based such as differential in-gel electrophoresis (DIGE) and liquid chromatography-based such as isotope labeling and label-free quantification. These quantitative proteomics tools hold significant promise for biomarker discovery, diagnostic and therapeutic applications. They are also important for research in functional genomics and systems biology towards basic understanding of molecular networks and pathway interactions. In this review, we summarize current technologies in quantitative proteomics and discuss recent applications of the technologies.     

Computational and informatics strategies for identification of specific protein interaction partners in affinity purification mass spectrometry experiments

Analysis of protein interaction networks and protein complexes using affinity purification and mass spectrometry (AP/MS) is among most commonly used and successful applications of proteomics technologies. One of the foremost challenges of AP/MS data is a large number of false-positive protein interactions present in unfiltered data sets. Here we review computational and informatics strategies for detecting specific protein interaction partners in AP/MS experiments, with a focus on incomplete (as opposite to genome wide) interactome mapping studies. These strategies range from standard statistical approaches, to empirical scoring schemes optimized for a particular type of data, to advanced computational frameworks. The common denominator among these methods is the use of label-free quantitative information such as spectral counts or integrated peptide intensities that can be extracted from AP/MS data. We also discuss related issues such as combining multiple biological or technical replicates, and dealing with data generated using different tagging strategies. Computational approaches for benchmarking of scoring methods are discussed, and the need for generation of reference AP/MS data sets is highlighted. Finally, we discuss the possibility of more extended modeling of experimental AP/MS data, including integration with external information such as protein interaction predictions based on functional genomics data.     

Quantitative proteomic analyses of crop seedlings subjected to stress conditions; a commentary

Quantitative proteomics is one of the analytical approaches used to clarify crop responses to stress conditions. Recent remarkable advances in proteomics technologies allow for the identification of a wider range of proteins than was previously possible. Current proteomic methods fall into roughly two categories: gel-based quantification methods, including conventional two-dimensional gel electrophoresis and two-dimensional fluorescence difference gel electrophoresis, and MS-based quantification methods consists of label-based and label-free protein quantification approaches. Although MS-based quantification methods have become mainstream in recent years, gel-based quantification methods are still useful for proteomic analyses. Previous studies examining crop responses to stress conditions reveal that each method has both advantages and disadvantages in regard to protein quantification in comparative proteomic analyses. Furthermore, one proteomics approach cannot be fully substituted by another technique. In this review, we discuss and highlight the basis and applications of quantitative proteomic analysis approaches in crop seedlings in response to flooding and osmotic stress as two environmental stresses.