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．     

定量蛋白质组学中的同位素标记技术

定量蛋白质组学的目的是对复杂的混合体系中所有的蛋白质进行鉴定,并对蛋白质的量及量的变化进行准确的测定,是当前系统生物科学研究的重要内容。近年来,由于质谱技术和生物信息学的进步,定量蛋白质组学在分析蛋白质组或亚蛋白质组方面已取得了令人瞩目的成就,但其最显著的成就应该归功于稳定同位素标记技术的应用。该技术使用针对某一类蛋白具有特异性的化学探针来标记目的蛋白质或肽段,同时化学探针要求含有用以精确定量的稳定同位素信号。在此基础上,实现了对表达的蛋白质差异和翻译后修饰的蛋白质差异进行精确定量分析。综述了在定量蛋白质组学中使用的各种同位素标记技术及其应用。     

稳定同位素化学标记结合质谱技术在定量蛋白质组学中的应用

传统的蛋白质组定量策略主要是通过双向凝胶电泳来进行相对定量。由于该方法不能对相对分子质量极高或极低、等电点极酸或极碱和含量低的蛋白质以及膜蛋白质等进行有效分离和检测,所以已不能适应目前蛋白质组研究深入发展的需要。近年来,定量蛋白质组学的发展主要是以同位素亲和标签试剂为代表的、以质谱检测为核心的稳定同位素化学标记方法。稳定同位素化学标记结合质谱技术,使定量蛋白质组的分析更趋简单、准确和快速,具有良好的发展前景。本文对稳定同位素化学标记结合质谱技术在定量蛋白质组学中的研究进展进行了评述。     

整合素α6在不同转移潜能膀胱癌细胞中的定量差异表达分析

整合素是一类跨膜糖蛋白,能够与细胞外基质结合参与许多生物学过程,尤其是与癌细胞的增殖、迁移等密切相关.目前在研究膀胱癌的发生发展过程中,对整合素的关注较少.本研究利用稳定同位素标记技术(SILAC)蛋白质定量技术结合质谱分析技术,对3株不同转移潜能的膀胱癌细胞株中的蛋白质组差异表达进行研究,并通过蛋白质免疫印迹技术、实时荧光定量PCR、细胞免疫荧光照相和流式细胞术等多种技术进行验证.同时使用临床组织基因芯片结果对比分析表明,与正常膀胱表皮细胞HCV29相比,整合素α6在膀胱癌细胞中表达量均有明显的上升,且在非侵染性膀胱癌KK47细胞中的表达量最高,侵染性的膀胱癌YTS1细胞中表达量次之.这一结果说明整合素α6与膀胱癌的发展具有密切关系,并为深入研究整合素α6在膀胱癌发生发展中的作用提供了前期基础.     

用SILAC技术研究感染H5N1禽流感病毒后A549肺癌细胞蛋白质组的表达变化

目的：鉴定高致病性H5N1禽流感病毒感染A549肺癌细胞后,细胞蛋白质组的表达变化,并鉴定特异分子通路的改变及其涉及的关键蛋白质分子。方法：利用稳定同位素标记氨基酸技术（SILAC）标记A549细胞,得到“重标”或“轻标”的A549细胞;“重标”细胞感染高致病性F15N1禽流感病毒24h后提取细胞总蛋白,与从未感染病毒的“轻标”细胞中提取的总蛋白等量混合,酶解肽段,经正交反相色谱分离后用质谱鉴定,对数据进行定性和定量分析。结果：共鉴定到3504个蛋白质,有定量信息的蛋白质为2469个,病毒感染后表达量升高的蛋白质为72个,表达量降低的蛋白质为66个,其中包括参与多个分子调控途径如RNA剪接体、干扰素诱导通路、泛素化通路、胰岛素通路等的蛋白质。结论：建立了利用SILAC技术研究宿主细胞一病毒相互作用的方法,发现了高致病性H5N1禽流感病毒感染宿主细胞相关的关键蛋白质,为探索H5N1病毒致病的分子机理提供了理论基础。     

小鼠饲养过程中蛋白质组的整体重稳定性同位素标记

文中探讨了稳定同位素代谢标记(SILAC)定量技术在哺乳动物模型上的应用,建立了可用于疾病模型比较蛋白质组学研究的定量内标。用含1%13C6-Lysine的SILAC鼠粮饲养C57BL/6J雌性小鼠,记为F0代,通过与雄性小鼠交配繁殖出F1代,相同方法繁殖出F2代标记小鼠。分离F2代小鼠的大脑、肺脏、心脏、胃、小肠、肝脏、脾脏、肾脏和肌肉组织,提取各组织的全蛋白,进行蛋白酶Lys-C酶切,利用高效液相色谱串联质谱联用技术(LC-MS/MS)进行鉴定和定量分析。对蛋白质定量结果进行高斯拟合确定均值及标准差,得出不同脏器和小鼠整体的标记效率,肝脏的标记效率最高,为96.34%±0.90%;大脑标记效率最低,为92.62%±1.98%,小鼠整体标记效率为95.80%±0.64%,符合SILAC标记效率不低于95%的国际标准。成功地将SILAC方法从微生物和细胞系水平拓展到哺乳动物模型,这为基于动物模型研究疾病发生、发展机制提供了有力工具。     

羟基喜树碱诱导肝癌细胞凋亡前后细胞核蛋白质组变化的定量分析

对羟基喜树碱诱导肝癌细胞凋亡前后的细胞核蛋白质组进行定量研究,获得凋亡前后细胞核蛋白质组中差异蛋白表达量相对变化的信息,为在亚细胞定量蛋白质组水平上深入探讨羟基喜树碱的作用机理提供实验依据。分离提取羟基喜树碱诱导肝癌细胞凋亡前后的细胞核并鉴定其纯度,用含稳定同位素的化学试剂c-ICAT标记细胞凋亡前后的细胞核蛋白,对细胞核标记蛋白进行消化和纯化,利用基于多维色谱-线性离子阱/静电场轨道阱质谱联用技术的鸟枪（Shotgun）法策略及c-ICAT定量策略分析鉴定蛋白质,获得同一种肝癌细胞核蛋白质在凋亡前后细胞中的表达量变化比值。分析鉴定了在细胞凋亡前后的表达量差异有显著统计学意义（P〈0.05）的肝癌细胞核蛋白42个,其中,12个蛋白的表达量在羟基喜树碱诱导细胞凋亡后下调,30个蛋白的表达量在凋亡细胞中上调。这些差异蛋白的分子功能主要与细胞增殖、凋亡和分化、能量代谢、核酸代谢以及细胞骨架相关。     

羟基喜树碱诱导肝癌细胞凋亡前后线粒体疏水蛋白质组的定量分析

抗癌剂羟基喜树碱可以通过线粒体途径诱导肝癌细胞凋亡.应用定量蛋白质组学技术分析羟 基喜树碱诱导肝癌细胞凋亡前后的线粒体疏水蛋白质差异表达,探讨癌细胞凋亡机制及羟基 喜树碱的抗癌机理.分离提取羟基喜树碱诱导肝癌细胞凋亡前后的线粒体,并采用顺序抽提法提取疏水蛋白质;用含稳定同位素亲和标签的c-ICAT试剂标记蛋白,利用基于多维色谱线性离子阱/静电场轨道阱质谱联用技术的鸟枪(shotgun)法策略分析鉴定了在肝癌细胞凋亡前后的线粒体中表达量差异有显著统计学意义(P<0.05)的疏水蛋白144种,其中, 12种蛋白的表达量在凋亡细胞中下调,而表达量在羟基喜树碱诱导细胞凋亡后上调10倍以上的蛋白43种.这些蛋白主要与细胞分裂增殖、分化凋亡、能量代谢、核酸代谢以及信号转导相关.该研究结果为在亚细胞定量蛋白质组水平上深入探讨羟基喜树碱的作用机理提供了新的实验依据,亦为研究肿瘤细胞凋亡机制提供了新的思路.     

稳定同位素标签技术在定量蛋白质组研究的应用

高通量的从蛋白质组水平进行整体的蛋白质鉴定和精确定量比较分析,在阐述生物功能以及疾病发生发展机制等方面非常重要。稳定同位素标签技术在过去的几年中获得了很大的发展,并形成了代谢引入类、化学合成类以及酶解引入类等三大类型。该文对稳定同位素标签技术的技术特点以及应用进行了简述。     

定量蛋白质组学研究的技术体系

从蛋白质组水平上对基因表达进行准确的定量分析,是比较蛋白质组学的重要内容,是研究重大疾病致病机制以及药理控制机制的必要手段。定量分析蛋白质组的方法主要包括:2DE结合串联质谱技术,稳定同位素标记技术等。     

Proteomic discovery of protease substrates

Elucidation of in vivo substrate degradomes of a protease is a daunting endeavor because of the large number of proteins in a proteome and often minute and transient amounts of key substrates. Proteomic substrate screens for proteases are currently experiencing impressive progress. Mass spectrometry-based global proteome analysis, interfaced with liquid-chromatography and together with stable isotope labeling strategies, has provided increased coverage and sensitivity for quantitative proteomics. ICAT and iTRAQ labeling have been used to identify a plethora of new matrix metalloproteinase substrates. Emerging techniques focus on the quantitative analysis of proteolytically generated neo amino-termini, which we call terminopes, on a system-wide basis. In vivo SILAC pulse-chase experiments have also enabled the study of individual protein turnover and global proteome dynamics in cells and whole organisms. Together with activity-based probes for the profiling of functional proteases, there is now in place an array of complementary technologies to dissect the 'protease web' and its distortion in pathology.     

Stable isotope labeling with amino acids in Drosophila for quantifying proteins and modifications

Drosophila melanogaster is a common animal model for genetics studies, and quantitative proteomics studies of the fly are emerging. Here, we present in detail the development of a procedure to incorporate stable isotope-labeled amino acids into the fly proteome. In the method of stable isotope labeling with amino acids in Drosophila melanogaster (SILAC fly), flies were fed with SILAC-labeled yeast grown with modified media, enabling near complete labeling in a single generation. Biological variation in the proteome among individual flies was evaluated in a series of null experiments. We further applied the SILAC fly method to profile proteins from a model of fragile X syndrome, the most common cause of inherited mental retardation in human. The analysis identified a number of altered proteins in the disease model, including actin-binding protein profilin and microtubulin-associated protein futsch. The change of both proteins was validated by immunoblotting analysis. Moreover, we extended the SILAC fly strategy to study the dynamics of protein ubiquitination during the fly life span (from day 1 to day 30), by measuring the level of ubiquitin along with two major polyubiquitin chains (K48 and K63 linkages). The results show that the abundance of protein ubiquitination and the two major linkages do not change significantly within the measured age range. Together, the data demonstrate the application of the SILAC principle in D. melanogaster, facilitating the integration of powerful fly genomics with emerging proteomics.     

Mascot file parsing and quantification (MFPaQ), a new software to parse, validate, and quantify proteomics data generated by ICAT and SILAC mass spectrometric analyses: application to the proteomics study of membrane proteins from primary human endothelial cells

Proteomics strategies based on nanoflow (nano-) LC-MS/MS allow the identification of hundreds to thousands of proteins in complex mixtures. When combined with protein isotopic labeling, quantitative comparison of the proteome from different samples can be achieved using these approaches. However, bioinformatics analysis of the data remains a bottleneck in large scale quantitative proteomics studies. Here we present a new software named Mascot File Parsing and Quantification (MFPaQ) that easily processes the results of the Mascot search engine and performs protein quantification in the case of isotopic labeling experiments using either the ICAT or SILAC (stable isotope labeling with amino acids in cell culture) method. This new tool provides a convenient interface to retrieve Mascot protein lists; sort them according to Mascot scoring or to user-defined criteria based on the number, the score, and the rank of identified peptides; and to validate the results. Moreover the software extracts quantitative data from raw files obtained by nano-LC-MS/MS, calculates peptide ratios, and generates a non-redundant list of proteins identified in a multisearch experiment with their calculated averaged and normalized ratio. Here we apply this software to the proteomics analysis of membrane proteins from primary human endothelial cells (ECs), a cell type involved in many physiological and pathological processes including chronic inflammatory diseases such as rheumatoid arthritis. We analyzed the EC membrane proteome and set up methods for quantitative analysis of this proteome by ICAT labeling. EC microsomal proteins were fractionated and analyzed by nano-LC-MS/MS, and database searches were performed with Mascot. Data validation and clustering of proteins were performed with MFPaQ, which allowed identification of more than 600 unique proteins. The software was also successfully used in a quantitative differential proteomics analysis of the EC membrane proteome after stimulation with a combination of proinflammatory mediators (tumor necrosis factor-alpha, interferon-gamma, and lymphotoxin alpha/beta) that resulted in the identification of a full spectrum of EC membrane proteins regulated by inflammation.     

Quantitative proteomics to study mitogen-activated protein kinases

In the last several years, the impact of mass spectrometry (MS)-based proteomics on cell signaling research has increased dramatically. This development has been driven both by better instrumentation and by the progression of proteomics from mainly qualitative measurements towards quantitative analyses. In this regard, Stable Isotope Labeling by Amino acids in Cell culture (SILAC) has established itself as one of the most popular and useful quantitative proteomic methodologies to study signaling networks. SILAC relies on the metabolic incorporation of non-radioactive heavy isotopes in the whole proteome of desired cell line, making all proteins from these cells easily distinguishable in the mass spectrometers from the proteins originating from control cells. The procedure does not involve any chemical derivatization steps and, importantly, allows mixing of the two cell populations for combined additional sample manipulation, thus leading to highly reliable results with minimal errors. In this chapter, we describe in detail the SILAC labeling procedure and explain how to design SILAC experiments to examine the level and duration of phosphorylation of endogenous MAP kinases and their substrates in cell culture systems.     

In-depth quantitative cardiac proteomics combining electron transfer dissociation and the metalloendopeptidase Lys-N with the SILAC mouse

In quantitative proteomics stable isotope labeling has progressed from cultured cells toward the total incorporation of labeled atoms or amino acids into whole multicellular organisms. For instance, the recently introduced (13)C(6)-lysine labeled SILAC mouse allows accurate comparison of protein expression directly in tissue. In this model, only lysine, but not arginine, residues are isotope labeled, as the latter may cause complications to the quantification by in vivo conversion of arginine to proline. The sole labeling of lysines discourages the use of trypsin, as not all peptides will be quantifiable. Therefore, in the initial work Lys-C was used for digestion. Here, we demonstrate that the lysine-directed protease metalloendopeptidase Lys-N is an excellent alternative. As lysine directed peptides generally yield longer and higher charged peptides, alongside the more traditional collision induced dissociation we also implemented electron transfer dissociation in a quantitative stable isotope labeling with amino acid in cell culture workflow for the first time. The utility of these two complementary approaches is highlighted by investigating the differences in protein expression between the left and right ventricle of a mouse heart. Using Lys-N and electron transfer dissociation yielded coverage to a depth of 3749 proteins, which is similar as earlier investigations into the murine heart proteome. In addition, this strategy yields quantitative information on ～ 2000 proteins with a median coverage of four peptides per protein in a single strong cation exchange-liquid chromatography-MS experiment, revealing that the left and right ventricle proteomes are very similar qualitatively as well as quantitatively.     

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。     

Find Pairs: The Module for Protein Quantification of the PeakQuant Software Suite

Abstract Accurate quantification of proteins is one of the major tasks in current proteomics research. To address this issue, a wide range of stable isotope labeling techniques have been developed, allowing one to quantitatively study thousands of proteins by means of mass spectrometry. In this article, the FindPairs module of the PeakQuant software suite is detailed. It facilitates the automatic determination of protein abundance ratios based on the automated analysis of stable isotope-coded mass spectrometric data. Furthermore, it implements statistical methods to determine outliers due to biological as well as technical variance of proteome data obtained in replicate experiments. This provides an important means to evaluate the significance in obtained protein expression data. For demonstrating the high applicability of FindPairs, we focused on the quantitative analysis of proteome data acquired in (14)N/(15)N labeling experiments. We further provide a comprehensive overview of the features of the FindPairs software, and compare these with existing quantification packages. The software presented here supports a wide range of proteomics applications, allowing one to quantitatively assess data derived from different stable isotope labeling approaches, such as (14)N/(15)N labeling, SILAC, and iTRAQ. The software is publicly available at http://www.medizinisches-proteom-center.de/software and free for academic use.