稳定同位素标记试剂DiART在蛋白质组中的定量特征研究

等重同位素标记方法,如同位素标记相对和绝对定量(iTRAQ),可以对肽段进行化学标记,之后通过质谱分析得到的报告离子的强度信息而实现对肽段的定量.目前,这种标记技术在定量蛋白质组学研究中有广泛应用.DiART也是一种等重同位素标记方法,且与iTRAQ的定量原理类似,但是其化学结构组成与iTRAQ有所不同,因而其定量特征有其独特表现.本研究通过对DiART标记的简单蛋白样品、复杂蛋白样品以及复杂样品中目标蛋白的定量情况进行了分析,从而对DiART的定量特征进行了研究,并同时与iTRAQ进行了比较.结果表明,DiART方法在定量稳定性、准确性及动态范围方面均更具优势.     

基于液相色谱质谱联用的蛋白质组非标记定量研究策略的建立及应用

定量蛋白质组研究是蛋白质组研究的热点和难点,而液相色谱质谱技术已经被广泛地应用于蛋白质的定性和定量研究.该研究建立和优化了一种基于液相色谱质谱联用技术的蛋白质组非标记定量方法,并对两种肽段质谱检测计数的归一化算法进行了比较,结果发现ASC法要优于Rsc法.最后,将建立的方法应用于肝癌细胞模型HepG2和HepG2-HBx细胞系的差异蛋白质组表达研究.质谱鉴定结果用聚类分析软件cluster3.0进行分析,最后鉴定出107个重叠蛋白,其中9个蛋白质表达上调(Ratio>1.75),6个蛋白质表达下调(Ratio<0.5),这些蛋白质均与肝癌发生和恶化密切相关.结果表明,该技术操作简单、方便,具有较高的灵敏度和动态范围,利用该方法进行差异蛋白质组研究和发现生物标志物在理论和临床上具有十分重要的意义.     

利用TMT标记结合2DLC-MS/MS技术对水牛卵母细胞体外成熟前后差异蛋白质组的分析

本试验利用TMT标记并结合二维高效液相色谱/串联质谱联用的研究策略对水牛卵母细胞成熟前后差异蛋白质组进行分析。试验首先收集水牛成熟前卵母细胞和成熟后卵母细胞,分别提取卵母细胞这两个时期的蛋白质,酶解蛋白后进行TMT标记,其中TMT-126标记成熟后的肽段,TMT-129标记成熟前的肽段,标记后采用强阳离子交换柱对酶解得到的肽段进行分离,接着进行nano LC分离,质谱分析采用在线连接电喷雾串联Orbitrap的方法,最后使用SEQUEST软件进行数据库搜索,采用生物信息学方法对鉴定得到的差异蛋白质进行初步分析。根据定量差异倍数≥2即认为蛋白表达存在差异,鉴定出卵母细胞成熟前高表达的蛋白有18种,成熟后高表达的蛋白有26种。对这些差异蛋白进行生物信息学分析表明,利用TMT标记并结合二维高效液相色谱/串联质谱联用的研究策略可以有效地分离和鉴定水牛卵母细胞成熟前后的蛋白质。本试验发现可能与水牛卵母细胞成熟相关的标志性蛋白:调控细胞凋亡蛋白(BCL2L10)、胎球蛋白(AHSG)、伴侣蛋白(Erp29),这可能为今后研究水牛卵母细胞成熟前后的蛋白质表达变化规律提供了试验依据。     

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．     

一种结合生物医学知识的蛋白质组非标记定量分析方法及其应用

基于质谱的非标记定量方法能够对复杂蛋白质组进行规模化分析,同时,在定量分析的基础上理解和解释蛋白质组的功能和相互作用关系更有意义.这需要建立一种有效的兼容定量和定性分析结果的方法.针对这一需求,本文首先借鉴了NSAF(normalized spectral abundance factor)算法采用肽段计数对蛋白质组数据进行定量,进一步结合共享肽对该方法进行优化.以此为基础,通过g:Profiler获取海量蛋白质组的功能注释信息,在定量分析的过程中,同步实现了对蛋白质组数据的功能性分析.本文选择来自人心脏、小鼠心脏、小鼠肝脏的三组线粒体蛋白质组数据对该方法进行验证,按照功能性分析将三组数据划分为若干功能组或信号通路,并进行相关性、功能聚类以及电子传递链分析.结果表明,结合共享肽的优化算法克服了对低丰度蛋白质的错误估计,提高了非标记定量的准确性.同时,结合生物医学知识的分析方法解释了蛋白质组的功能和相互作用关系,为差异比较蛋白质组学、疾病蛋白质组学以及功能蛋白质组学等组学研究提供了新的方法.     

基于电感耦合等离子体质谱的蛋白质定量技术研究进展

综述了ICP-MS法应用于蛋白质定量技术方面的研究进展.蛋白质定量研究已成为蛋白质组学研究领域的热点,它是解析生物体蛋白质功能的重要途径.基于同位素标记和生物质谱分析技术是蛋白质定量最常用的方法之一,近年来,随着质谱技术的发展,电感耦合等离子体质谱(ICP-MS)技术成为元素测量的重要手段,这使其在蛋白质定量中具一定的应用前景.     

基于数据非依赖采集的以肽为中心分析算法和软件的研究进展

数据非依赖采集(data-independent acquisition,DIA)是一种高通量、无偏性的质谱数据采集方法,具有定量结果重现性好,对低丰度蛋白质友好的特点,是近年来进行大队列蛋白质组研究的首选方法之一。由于DIA产生的二级谱是混合谱,包含了多个肽段的碎片离子信息,使得蛋白质鉴定和定量更加困难。目前,DIA数据分析方法分为两大类,即以肽为中心和以谱图为中心。其中,以肽为中心的分析方法鉴定更灵敏,定量更准确,已成为DIA数据解析的主流方法。其分析流程包括构建谱图库、提取色谱峰群、特征打分和结果质控4个关键步骤。本文综述了以肽为中心的DIA数据分析流程,介绍了基于此流程的数据分析软件及相关比较评估工作,进一步总结了已有的算法改进工作,最后对未来发展方向进行了展望。     

iTRAQ结合2D LC-MS/MS技术在草菇不同生长发育时期蛋白质组分析中的应用

【目的】旨在采用iTRAQ标记结合二维液相色谱串联质谱技术对草菇不同生长发育阶段的差异蛋白质组进行研究。【方法】首先将提取的草菇不同生长阶段蛋白样品进行SDS-PAGE分析,其次将经二维液相色谱串联质谱技术获取的串联质谱数据通过MASCOT软件搜库,之后对鉴定蛋白质数据进行了主成分分析(Principal componentanalysis,PCA)、层次聚类(Hierarchy clustering)分析、K-均值(K-means)聚类和GeneOntology(GO)注释分析。【结果】试验结果显示,共计获得2 335个不同肽段,鉴定到1 039个蛋白质,其中1 030个蛋白质具有定量信息。在子实体阶段中显著上调蛋白质64个,下调蛋白质150个。生物信息学分析表明,iTRAQ标记技术结合二维液相色谱串联质谱可对不同生长发育时期的草菇蛋白样品进行有效地分离和鉴定。【结论】这一研究结果为深入研究草菇乃至其他大型担子菌子实体形成和发育的分子机制提供借鉴。     

生物活性肽蛋白质组分分离与鉴定方法的建立

目的:评估大规模蛋白质组分离与鉴定技术策略在生物活性肽研究中的应用价值。方法:采用全溶液酶解及胶内分离与酶解方法分离生物活性肽;肽段离子阱串联质谱鉴定时,采用碰撞诱导解离与电子转移解离2种互补的肽段碎裂模式。结果:几种方法联用,鉴定到复杂生物活性肽样品中236个多肽大分子;不同分离和鉴定策略显示出良好的互补性,基于凝胶电泳分离的策略提供了最好的鉴定效果。结论:大规模的蛋白质组学分离与鉴定技术策略可以有效应用于生物活性肽组分的表征。     

N-糖肽的规模化质谱解析方法进展

蛋白质糖基化修饰的鉴定是蛋白质翻译后修饰分析中最具挑战性的任务之一,近几年尤其受到关注.快速发展的质谱技术为规模化的蛋白质糖基化修饰研究提供了有效的手段.与其他基于质谱技术的翻译后修饰鉴定相比,糖基化鉴定的难点在于糖链是大分子而且存在微观不均一性,另外糖链本身可以在串联质谱中碎裂且与肽段的碎裂规律不同,导致蛋白质组学的质谱解析方法和软件难以完整地鉴定肽段序列和糖链结构.完整N-糖肽的鉴定是糖基化分析的热点内容之一,针对N-糖肽的鉴定,近年来,人们开发了多种多样的质谱解析方法,其中包括用N-糖酰胺酶切除糖链后鉴定N-糖基化位点的方法、基于电子转运裂解的糖肽肽段鉴定、基于高能碰撞裂解与电子转运裂解联用或碰撞诱导裂解与三级谱联用的完整N-糖肽鉴定等等.本文对这些质谱解析方法进行了整理和综述,简要指出了目前完整糖肽鉴定软件存在的一些不足,展望了未来的发展方向.     

Critical assessment of proteome‐wide label‐free absolute abundance estimation strategies

There is a great interest in reliable ways to obtain absolute protein abundances at a proteome‐wide scale. To this end, label‐free LC‐MS/MS quantification methods have been proposed where all identified proteins are assigned an estimated abundance. Several variants of this quantification approach have been presented, based on either the number of spectral counts per protein or MS1 peak intensities. Equipped with several datasets representing real biological environments, containing a high number of accurately quantified reference proteins, we evaluate five popular low‐cost and easily implemented quantification methods (Absolute Protein Expression, Exponentially Modified Protein Abundance Index, Intensity‐Based Absolute Quantification Index, Top3, and MeanInt). Our results demonstrate considerably improved abundance estimates upon implementing accurately quantified reference proteins; that is, using spiked in stable isotope labeled standard peptides or a standard protein mix, to generate a properly calibrated quantification model. We show that only the Top3 method is directly proportional to protein abundance over the full quantification range and is the preferred method in the absence of reference protein measurements. Additionally, we demonstrate that spectral count based quantification methods are associated with higher errors than MS1 peak intensity based methods. Furthermore, we investigate the impact of miscleaved, modified, and shared peptides as well as protein size and the number of employed reference proteins on quantification accuracy.     

Comparison of label-free methods for quantifying human proteins by shotgun proteomics

Measurements of mass spectral peak intensities and spectral counts are promising methods for quantifying protein abundance changes in shotgun proteomic analyses. We describe Serac, software developed to evaluate the ability of each method to quantify relative changes in protein abundance. Dynamic range and linearity using a three-dimensional ion trap were tested using standard proteins spiked into a complex sample. Linearity and good agreement between observed versus expected protein ratios were obtained after normalization and background subtraction of peak area intensity measurements and correction of spectral counts to eliminate discontinuity in ratio estimates. Peak intensity values useful for protein quantitation ranged from 10(7) to 10(11) counts with no obvious saturation effect, and proteins in replicate samples showed variations of less than 2-fold within the 95% range (+/-2sigma) when >or=3 peptides/protein were shared between samples. Protein ratios were determined with high confidence from spectral counts when maximum spectral counts were >or=4 spectra/protein, and replicates showed equivalent measurements well within 95% confidence limits. In further tests, complex samples were separated by gel exclusion chromatography, quantifying changes in protein abundance between different fractions. Linear behavior of peak area intensity measurements was obtained for peptides from proteins in different fractions. Protein ratios determined by spectral counting agreed well with those determined from peak area intensity measurements, and both agreed with independent measurements based on gel staining intensities. Overall spectral counting proved to be a more sensitive method for detecting proteins that undergo changes in abundance, whereas peak area intensity measurements yielded more accurate estimates of protein ratios. Finally these methods were used to analyze differential changes in protein expression in human erythroleukemia K562 cells stimulated under conditions that promote cell differentiation by mitogen-activated protein kinase pathway activation. Protein changes identified with p<0.1 showed good correlations with parallel measurements of changes in mRNA expression.     

Label-free quantitative analysis of one-dimensional PAGE LC/MS/MS proteome: application on angiotensin II-stimulated smooth muscle cells secretome

A widely used method for protein identification couples prefractionation of protein samples by one-dimensional (1D) PAGE with LC/MS/MS. We developed a new label-free quantitative algorithm by combining measurements of spectral counting, ion intensity, and peak area on 1D PAGE-based proteomics. This algorithm has several improvements over other label-free quantitative algorithms: (i) Errors in peak detection are reduced because the retention time is based on each LC/MS/MS run and actual precursor m/z. (ii) Detection sensitivity is increased because protein quantification is based on the combination of peptide count, ion intensity, and peak area. (iii) Peak intensity and peak area are calculated in each LC/MS/MS run for all slices from 1D PAGE for every single identified protein and visualized as a Western blot image. The sensitivity and accuracy of this algorithm were demonstrated by using standard curves (17.4 fmol to 8.7 pmol), complex protein mixtures (30 fmol to 1.16 pmol) of known composition, and spiked protein (34.8 fmol to 17.4 pmol) in complex proteins. We studied the feasibility of this approach using the secretome of angiotensin II (Ang II)-stimulated vascular smooth muscle cells (VSMCs). From the VSMC-conditioned medium, 629 proteins were identified including 212 putative secreted proteins. 26 proteins were differently expressed in control and Ang II-stimulated VSMCs, including 18 proteins not previously reported. Proteins related to cell growth (CYR61, protein NOV, and clusterin) were increased, whereas growth arrest-specific 6 (GAS6) and growth/differentiation factor 6 were decreased by Ang II stimulation. Ang II-stimulated changes of plasminogen activator inhibitor-1, GAS6, cathepsin B, and periostin were validated by Western blot. In conclusion, a novel label-free quantitative analysis of 1D PAGE-LC/MS/MS-based proteomics has been successfully applied to the identification of new potential mediators of Ang II action and may provide an alternative to traditional protein staining methods.     

Stable isotope labeling tandem mass spectrometry (SILT): integration with peptide identification and extension to data-dependent scans

Quantitation of relative or absolute amounts of proteins by mass spectrometry can be prone to large errors. The use of MS/MS ion intensities and stable isotope labeling, which we term stable isotope labeling tandem mass spectrometry (SILT), decreases the effects of contamination from unrelated compounds. We present a software package (SILTmass) that automates protein identification and quantification by the SILT method. SILTmass has the ability to analyze the kinetics of protein turnover, in addition to relative and absolute protein quantitation. Instead of extracting chromatograms to find elution peaks, SILTmass uses only scans in which a peptide is identified and that meet an ion intensity threshold. Using only scans with identified peptides, the accuracy and precision of SILT is shown to be superior to precursor ion intensities, particularly at high or low dilutions of the isotope labeled compounds or with low amounts of protein. Using example scans, we demonstrate likely reasons for the improvements in quantitation by SILT. The appropriate use of variable modifications in peptide identification is described for measurement of protein turnover kinetics. The combination of identification with SILT facilitates quantitation without peak detection and helps to ensure the appropriate use of variable modifications for kinetics experiments.     

Identification of treatment efficacy-related host factors in chronic hepatitis C by ProteinChip serum analysis

Recent development of proteomic array technology, including protein profiling coupling ProteinChip array with surface-enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF/MS), provides a potentially powerful tool for discovery of new biomarkers by comparison of its profiles according to patient phenotypes. We used this approach to identify the host factors associated with treatment response in patients with chronic hepatitis C (CHC) receiving a 48-wk course of pegylated interferon (PEG-IFN) alpha 2b plus ribavirin (RBV). Protein profiles of pretreatment serum samples from 32 patients with genotype 1b and high viral load were conducted by SELDI-TOF/MS by using the three different ProteinChip arrays (CM10, Q10, IMAC30). Proteins showed significantly different peak intensities between sustained virological responders (SVRs), and non-SVRs were identified by chromatography, SDS-PAGE, TOF/MS and tandem mass spectrometry (MS/MS) assay. Eleven peak intensities were significantly different between SVRs and non-SVRs. The three SVR-increased peaks could be identified as two apolipoprotein (Apo) fragments and albumin and, among the eight non-SVR-increased proteins, four peaks identified as two iron-related and two fibrogenesis-related protein fragments, respectively. Multivariate analysis showed that the serum ferritin and three peak intensity values (Apo A1, hemopexin and transferrin) were independent variables associated with SVRs, and the area under the receiver operating characteristic (ROC) curves for SVR prediction by using the Apo A1/hemopexin and hemopexin/transferrin were 0.964 and 0.936. In conclusion, pretreatment serum protein profiling by SELDI-TOF/MS is variable for identification of response-related host factors, which are useful for treatment efficacy prediction in CHC receiving PEG-IFN plus RBV. Our data also may help us understand the mechanism for treatment resistance and development of more effective antiviral therapy targeted toward the modulation of lipogenesis or iron homeostasis in CHC patients.     

Comparative evaluation of label-free SINQ normalized spectral index quantitation in the central proteomics facilities pipeline

Normalized spectral index quantification was recently presented as an accurate method of label‐free quantitation, which improved spectral counting by incorporating the intensities of peptide MS/MS fragment ions into the calculation of protein abundance. We present SINQ, a tool implementing this method within the framework of existing analysis software, our freely available central proteomics facilities pipeline (CPFP). We demonstrate, using data sets of protein standards acquired on a variety of mass spectrometers, that SINQ can rapidly provide useful estimates of the absolute quantity of proteins present in a medium‐complexity sample. In addition, relative quantitation of standard proteins spiked into a complex lysate background and run without pre‐fractionation produces accurate results at amounts above 1 fmol on column. We compare quantitation performance to various precursor intensity‐ and identification‐based methods, including the normalized spectral abundance factor (NSAF), exponentially modified protein abundance index (emPAI), MaxQuant, and Progenesis LC‐MS. We anticipate that the SINQ tool will be a useful asset for core facilities and individual laboratories that wish to produce quantitative MS data, but lack the necessary manpower to routinely support more complicated software workflows. SINQ is freely available to obtain and use as part of the central proteomics facilities pipeline, which is released under an open‐source license.     

SWATH enables precise label‐free quantification on proteome scale

MS‐based proteomics has emerged as a powerful tool in biological studies. The shotgun proteomics strategy, in which proteolytic peptides are analyzed in data‐dependent mode, enables a detection of the most comprehensive proteome (>10 000 proteins from whole‐cell lysate). The quantitative proteomics uses stable isotopes or label‐free method to measure relative protein abundance. The isotope labeling strategies are more precise and accurate compared to label‐free methods, but labeling procedures are complicated and expensive, and the sample number and types are also limited. Sequential window acquisition of all theoretical mass spectra (SWATH) is a recently developed technique, in which data‐independent acquisition is coupled with peptide spectral library match. In principle SWATH method is able to do label‐free quantification in an MRM‐like manner, which has higher quantification accuracy and precision. Previous data have demonstrated that SWATH can be used to quantify less complex systems, such as spiked‐in peptide mixture or protein complex. Our study first time assessed the quantification performance of SWATH method on proteome scale using a complex mouse‐cell lysate sample. In total 3600 proteins got identified and quantified without sample prefractionation. The SWATH method shows outstanding quantification precision, whereas the quantification accuracy becomes less perfect when protein abundances differ greatly. However, this inaccuracy does not prevent discovering biological correlates, because the measured signal intensities had linear relationship to the sample loading amounts; thus the SWATH method can predict precisely the significance of a protein. Our results prove that SWATH can provide precise label‐free quantification on proteome scale.     

A reproducibility-based evaluation procedure for quantifying the differences between MS/MS peak intensity normalization methods

The identification of peptides and proteins from fragmentation mass spectra is a very common approach in the field of proteomics. Contemporary high-throughput peptide identification pipelines can quickly produce large quantities of MS/MS data that contain valuable knowledge about the actual physicochemical processes involved in the peptide fragmentation process, which can be extracted through extensive data mining studies. As these studies attempt to exploit the intensity information contained in the MS/MS spectra, a critical step required for a meaningful comparison of this information between MS/MS spectra is peak intensity normalization. We here describe a procedure for quantifying the efficiency of different published normalization methods in terms of the quartile coefficient of dispersion (qcod) statistic. The quartile coefficient of dispersion is applied to measure the dispersion of the peak intensities between redundant MS/MS spectra, allowing the quantification of the differences in computed peak intensity reproducibility between the different normalization methods. We demonstrate that our results are independent of the data set used in the evaluation procedure, allowing us to provide generic guidance on the choice of normalization method to apply in a certain MS/MS pipeline application.     

Extending the dynamic range of label-free mass spectrometric quantification of affinity purifications

Affinity purification (AP) of protein complexes combined with LC-MS/MS analysis is the current method of choice for identification of protein-protein interactions. Their interpretation with respect to significance, specificity, and selectivity requires quantification methods coping with enrichment factors of more than 1000-fold, variable amounts of total protein, and low abundant, unlabeled samples. We used standardized samples (0.1-1000 fmol) measured on high resolution hybrid linear ion trap instruments (LTQ-FT/Orbitrap) to characterize and improve linearity and dynamic range of label-free approaches. Quantification based on spectral counts was limited by saturation and ion suppression effects with samples exceeding 100 ng of protein, depending on the instrument setup. In contrast, signal intensities of peptides (peak volumes) selected by a novel correlation-based method (TopCorr-PV) were linear over at least 4 orders of magnitude and allowed for accurate relative quantification of standard proteins spiked into a complex protein background. Application of this procedure to APs of the voltage-gated potassium channel Kv1.1 as a model membrane protein complex unambiguously identified the whole set of known interaction partners together with novel candidates. In addition to discriminating these proteins from background, we could determine efficiency, cross-reactivities, and selection biases of the used purification antibodies. The enhanced dynamic range of the developed quantification procedure appears well suited for sensitive identification of specific protein-protein interactions, detection of antibody-related artifacts, and optimization of AP conditions.     

An integrated, directed mass spectrometric approach for in-depth characterization of complex peptide mixtures

LC-MS/MS has emerged as the method of choice for the identification and quantification of protein sample mixtures. For very complex samples such as complete proteomes, the most commonly used LC-MS/MS method, data-dependent acquisition (DDA) precursor selection, is of limited utility. The limited scan speed of current mass spectrometers along with the highly redundant selection of the most intense precursor ions generates a bias in the pool of identified proteins toward those of higher abundance. A directed LC-MS/MS approach that alleviates the limitations of DDA precursor ion selection by decoupling peak detection and sequencing of selected precursor ions is presented. In the first stage of the strategy, all detectable peptide ion signals are extracted from high resolution LC-MS feature maps or aligned sets of feature maps. The selected features or a subset thereof are subsequently sequenced in sequential, non-redundant directed LC-MS/MS experiments, and the MS/MS data are mapped back to the original LC-MS feature map in a fully automated manner. The strategy, implemented on an LTQ-FT MS platform, allowed the specific sequencing of 2,000 features per analysis and enabled the identification of more than 1,600 phosphorylation sites using a single reversed phase separation dimension without the need for time-consuming prefractionation steps. Compared with conventional DDA LC-MS/MS experiments, a substantially higher number of peptides could be identified from a sample, and this increase was more pronounced for low intensity precursor ions.