~（18）O标记技术改良和分析工具开发及在鼻咽癌转移相关膜蛋白组研究中的应用
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摘要
蛋白质组学已经成为当前生命科学研究的重要内容,已广泛应用于生命科学各个领域。由于基因在转录、翻译后产生蛋白质的过程中,存在着转录水平、翻译水平的调控,同时还存在着蛋白质的翻译后修饰,因而蛋白质组研究要比基因组研究复杂得多,蛋白质组学对技术的依赖性和要求也远超过基因组学。因此,发展高通量、高灵敏度、高准确性的方法和技术一直是蛋白质组学研究面临的挑战。
随着蛋白质研究技术和方法的进步,蛋白质组学研究呈现出新的特点：即由原来以蛋白质表达谱研究为中心逐步向蛋白质精确定量和功能研究转变。目前以稳定同位素标记技术为代表的定量蛋白质组学技术己逐渐成为了蛋白质组研究的新技术之一。但定量蛋白质组学技术还处于发展阶段,其研究技术和相关分析工具有待改进和完善。
~(18)O标记技术是较早应用于定量蛋白质组学研究的技术之一。该技术以酶解反应动力学原理为基础,通过酶催化反应进行蛋白质的~(18)O标记。~(18)O标记技术具有操作简单、条件温和、没有副产物等独特优势。但~(18)O技术也存在如下不足：(1)在酶存在的情况下标记容易丢失,从而影响定量分析的准确性；(2)获得的数据分析相对复杂；(3)定量分析工具不足。
膜蛋白在细胞与细胞识别、信号转导、物质转运等方面发挥重要功能。膜蛋白异常表达常导致细胞信号转导、细胞粘附和运动等方面的改变,并与肿瘤侵袭和转移密切相关。因此,膜蛋白质组研究已成为肿瘤蛋白质组研究的重要内容。
基于上述考虑,我们首先对~(18)O标记方法进行了优化和改进。~(18)O标记中标记丢失是该方法的主要的弱点。针对这一点,我们采用固相胰酶作为标记酶,标记反应结束后,通过Ziptip对固相胰酶进行过滤去除,分析过滤后的样本在不同pH条件下标记的稳定性。结果显示,与热处理法比较,Ziptip过滤法能显著提高~(18)O标记的稳定性。在连续三天的检测中未发现明显的标记丢失现象。进一步分析了Ziptip过滤法对定量分析结果的的影响,结果显示在连续三天的检测中肽段的定量差异均小于5%。对过滤样品进行等电聚焦(IEF)分离后也显示~(18)O标记无明显丢失,进一步的分析也显示肽段标记效率与肽段分子量和理论等电点无明显相关性。另外,Ziptip过滤法所处理样品均为10-20μl,因此,我们优化和改进的~(18)O标记方法能够用于微量蛋白样本的定量分析,这对临床稀缺样本,如激光捕获显微切割(LCM)纯化的组织样本的分析具有重要意义。
我们针对目前~(18)0蛋白定量分析工具不足的现状开发了OxyQuantToolkit工具。该工具采用java语言开发,具有跨平台的优势。分析工具的功能涵盖了~(18)0标记技术中数据处理的多个环节,通过该工具可自动完成~(18)0标记定量分析中的大量计算任务,分析工具可输出定量分析结果。同时分析工具还具有数据整理,转存方面的功能,这对有效管理数据也有积极作用。另外,该工具还包含了质谱数据的可视化编程包,可进行质谱通用格式文件mzxml的可视化查看。OxyQuantToolkit分析工具的开发为基于~(18)0标记定量蛋白质组学技术的推广与应用奠定了基础。
在标记方法改进和分析工具开发的基础上,我们利用改进的~(18)0标记技术对高转移鼻咽癌细胞5-8F与不转移鼻咽癌细胞6-10B的膜蛋白组进行了定量分析。结果共鉴定了503种蛋白质,其中44%为膜蛋白,89.7%的蛋白质定量结果在0.5-2之间(均值为1.13)。对重复鉴定的395个蛋白的定量分析结果进行统计学分析,结果显示其标准差均值为0.06,具有较高的重复性。结果表明,改进的18O标记技术具有重要的应用价值。另外,参照文献标准(差异变化两倍以上、在三次重复实验中均被鉴定的蛋白质),我们对5-8F与6-10B细胞的膜蛋白组进行了比较,获得了31个差异表达的膜蛋白,其中在5-8F中表达下调的6个,表达上调的25个。生物信息分析显示,其中一些差异蛋白如ITGB1、EphA2与细胞粘附、肿瘤侵袭和转移密切相关。这些差异蛋白质的发现为研究鼻咽癌转移机制以及鼻咽癌转移的防治提供了靶标。进一步比较5-8F与6-10B细胞的膜磷酸化蛋白组的差异,发现EphA2促转移的关键位点$897磷酸化水平在两株细胞存在差异,且EGFR能够磷酸化该位点,这提示EGFR可能通过活化EphA2促进鼻咽癌转移。
本研究建立了基于Ziptip过滤去除固相胰酶的改良18O标记方法。该方法具有标记稳定、定量分析准确结果以及适合微量样本分析的特点；开发了与平台无关的基于~(18)0标记技术的定量蛋白质组分析工具OxyQuantToolkit,为~(18)0标记技术的推广和应用打下了基础,并开发了基于java的质谱数据可视化编程包,实现了mzxml文件的可视化浏览；采用改良的~(18)0标记定量蛋白质组学技术比较不同转移潜能NPC细胞株的膜蛋白质组的差异,鉴定了31个与NPC转移相关的膜蛋白质,为NPC转移机制研究及其防治提供了靶标；应用定量磷酸化蛋白技术鉴定到EphA2促肿瘤关键位点S897的差异表达,证实EGFR活化能诱导EphA2促肿瘤转移的关键位点S897磷酸化,为揭示EGFR促鼻咽癌转移机制的研究提供了重要线索。
Proteomics has become an important part of life science research and has been widely used in life science fields. Because there are transcriptional and translational regμlation of gene expression and post-translational modification of proteins, proteomics is much more complicated than genomics. Therefore, proteomics research is much more dependent on technology than genomic research. Therefore, the development of methods and techniques with high throughput, high sensitivity and high accuracy has been the challenge for proteomics research.
With advances in the proteomics techniques and methods, proteomics research has new feature:a research focus has moved from the protein expression profile to precise quantitative and functional studies of proteins. Currently quantitative proteomics technology based on stable isotope labeling have become a new proteomics technology. However, quantitative proteomics technology is still in development stage and the research techniques need to be improved and analysis tools need to be developed.
~(18)O labeling technique is one of the earlier techniques for quantitative proteomics. The technique based on the principle of enzyme kinetics, where proteins are labeled with ~(18)O by enzyme catalytic reaction.~(18)O labeling technique is simple, mild technique with no by-products. However, there are some disadvantages of ~(18)O labeling technique:(1) ~(18)O-label is easily lost at the presence of the enzyme, thus affecting the accuracy of quantitation; (2) Data analysis is relatively complex; (3) Lack of computational tools for quantitative analysis.
Membrane proteins play important roles in cell recognition, signal transduction material transport and etc. Abnormal expression of membrane proteins often lead to the changes of signal transduction, cell adhesion, motility and etc. which are related with tumor invasion and metastasis. Therefore, the study on tumors' membrane proteome has become an important part of proteomic research.
Based on the above considerations, we first optimized and improved ~(18)O labeling method. The loss of ~(18)O-label is the major weakness of this method. For this, We use solid-phase trypsin as a labeling enzyme and, after the reaction, the samples were filtered using Ziptip to remove solid-phase trypsin. The stability of ~(18)O-label of the filtered samples stored under different pH conditions was analyzed. The resμlts showed that Ziptip filtration can significantly improve the stability of ~(18)O-label, compared with the heat treatment method. There was no obvious loss of 180-label during continuous monitoring of three days. Further analysis of the impact of the Ziptip filtration on the results of quantitative analysis showed that the differences of quantitation are less than 5% during continuous monitoring of three days. There were no significant loss of ~(18)O-label during the filter samples were separated by isoelectric focusing (IEF). Further analysis also showed that the ~(18)O labeled efficiency of peptides was no significant correlation with their molecμlar weight and theoretical isoelectric point. In addition, the volume of the samples handled by Ziptip was merely 10-20μl in the present study. Therefore, the optimized and improved ~(18)O labeling method provided by us can be used for quantitative analysis of trace samples, which is of great significance for scarce clinical samples, such as laser capture microdissection (LCM) purified tissue samples and other limited samples.
Considering the lack of computational tools for ~(18)O labeling data anlaysis, We developed an computer tool named OxyQuantToolkit. The tool is developed using java language and therefore has cross-platform property. The tool has functions covering mμltiple aspects of ~(18)O labeling data processing. A large number of computing tasks can be done automatically by the tool during ~(18)O labeling data anslysis. The results of quantitative analysis can be output automatically. At the same time,the tool has functions for data arrangement and data dump, which has a positive effect on effective data management. In addition, the tool contains a data visualization programming package, which can be used for viewing mass spectrometry data in mzxml format. The development of OxyQuantToolkit provided the foundation for facilitating and broadening the application of ~(18)O labeling quantitative proteomics technology.
On the basis of the improvement of ~(18)O labeling method and the development of analysis tool for ~(18)O labeling data analysis, We use the improved ~(18)O labeling method for the quantitative analysis of the membrane proteins from high metastatic nasopharyngeal carcinoma(NPC) 5-8F cells and non metastatic NPC 6-10B. A total of 503 proteins were identified, with 44% of membrane protein and 89.7% of the proteins' expression ratio in 5-8F versus 6-10B were between 0.5-2 (mean 1.13). Statistic analysis of the quantitative results of 395 proteins identified in three repeated experiments demonstrated that the average standard deviation was 0.06, showing high repeatability. The results showed that the improved ~(18)O labeling has great applicable value. In addition, according to the standard provided in literatures (changing more than twice and identified in three repeated experiments),31 differential expressed membrane proteins between 5-8F and 6-10B were found, of which 6 were downregμlated and 25 were upregμlated in 5-8F. Bioinformatics analysis showed that some differential expressed proteins, such as ITGB1 and EphA2, are related to cell adhesion, tumor invasion and metastasis. The discovery of these proteins provided the targets for the study of metastatic mechanisms and prevention and treatment of nasopharyngeal carcinoma. Further comparative analysis of the phosphoproteins in the membrane fraction of 5-8F and 6-10b cells found that EphA2 S897 phosphorylation site which is key to promoting metastasis are differentially expressed, and activated EGFR can phosphorylate the site, indicating that EGFR promotes NPC metastasis possibly by phosphorylating the site.
An improved ~(18)O labeling method based on removal of solid-phase trypsin using Ziptip has been established. The method has features such as high stability of label, high accuracy of quantitation and being suited to the analysis of trace samples; the platform-independent analysis tool, OxyQuantToolkit, for ~(18)O labeling-based quantitative proteomics was developed, which provide the foundation for facilitating and broadening the application of 180 labeling technique. A java-based programming package for mass spectrometry data visualization was developed and a viewer for viewing mass spcetrometry data in mzxml format was also implemented; Comparative proteomic analysis of membrane proteins between NPC cell lines with different metastatic potential are performed using improved 180 labeling method, and 31 metastasis-related membrane proteins were identified, which provided the targets for the study of metastatic mechanism and the prevention of NPC. Comparative phsosphoproteomic analysis of membrane proteins between NPC cell lines with different metastatic potential are performed and the differential phsosphorylation level of EphA2 S897 site was identified, which is key to promoting metastasis. Further analysis confirms that activation of the EGFR can phosphorylate the site, which provide a important clue for studying the mechanism of EGFR that promoting NPC metastasis.
随着蛋白质研究技术和方法的进步,蛋白质组学研究呈现出新的特点：即由原来以蛋白质表达谱研究为中心逐步向蛋白质精确定量和功能研究转变。目前以稳定同位素标记技术为代表的定量蛋白质组学技术己逐渐成为了蛋白质组研究的新技术之一。但定量蛋白质组学技术还处于发展阶段,其研究技术和相关分析工具有待改进和完善。
~(18)O标记技术是较早应用于定量蛋白质组学研究的技术之一。该技术以酶解反应动力学原理为基础,通过酶催化反应进行蛋白质的~(18)O标记。~(18)O标记技术具有操作简单、条件温和、没有副产物等独特优势。但~(18)O技术也存在如下不足：(1)在酶存在的情况下标记容易丢失,从而影响定量分析的准确性；(2)获得的数据分析相对复杂；(3)定量分析工具不足。
膜蛋白在细胞与细胞识别、信号转导、物质转运等方面发挥重要功能。膜蛋白异常表达常导致细胞信号转导、细胞粘附和运动等方面的改变,并与肿瘤侵袭和转移密切相关。因此,膜蛋白质组研究已成为肿瘤蛋白质组研究的重要内容。
基于上述考虑,我们首先对~(18)O标记方法进行了优化和改进。~(18)O标记中标记丢失是该方法的主要的弱点。针对这一点,我们采用固相胰酶作为标记酶,标记反应结束后,通过Ziptip对固相胰酶进行过滤去除,分析过滤后的样本在不同pH条件下标记的稳定性。结果显示,与热处理法比较,Ziptip过滤法能显著提高~(18)O标记的稳定性。在连续三天的检测中未发现明显的标记丢失现象。进一步分析了Ziptip过滤法对定量分析结果的的影响,结果显示在连续三天的检测中肽段的定量差异均小于5%。对过滤样品进行等电聚焦(IEF)分离后也显示~(18)O标记无明显丢失,进一步的分析也显示肽段标记效率与肽段分子量和理论等电点无明显相关性。另外,Ziptip过滤法所处理样品均为10-20μl,因此,我们优化和改进的~(18)O标记方法能够用于微量蛋白样本的定量分析,这对临床稀缺样本,如激光捕获显微切割(LCM)纯化的组织样本的分析具有重要意义。
我们针对目前~(18)0蛋白定量分析工具不足的现状开发了OxyQuantToolkit工具。该工具采用java语言开发,具有跨平台的优势。分析工具的功能涵盖了~(18)0标记技术中数据处理的多个环节,通过该工具可自动完成~(18)0标记定量分析中的大量计算任务,分析工具可输出定量分析结果。同时分析工具还具有数据整理,转存方面的功能,这对有效管理数据也有积极作用。另外,该工具还包含了质谱数据的可视化编程包,可进行质谱通用格式文件mzxml的可视化查看。OxyQuantToolkit分析工具的开发为基于~(18)0标记定量蛋白质组学技术的推广与应用奠定了基础。
在标记方法改进和分析工具开发的基础上,我们利用改进的~(18)0标记技术对高转移鼻咽癌细胞5-8F与不转移鼻咽癌细胞6-10B的膜蛋白组进行了定量分析。结果共鉴定了503种蛋白质,其中44%为膜蛋白,89.7%的蛋白质定量结果在0.5-2之间(均值为1.13)。对重复鉴定的395个蛋白的定量分析结果进行统计学分析,结果显示其标准差均值为0.06,具有较高的重复性。结果表明,改进的18O标记技术具有重要的应用价值。另外,参照文献标准(差异变化两倍以上、在三次重复实验中均被鉴定的蛋白质),我们对5-8F与6-10B细胞的膜蛋白组进行了比较,获得了31个差异表达的膜蛋白,其中在5-8F中表达下调的6个,表达上调的25个。生物信息分析显示,其中一些差异蛋白如ITGB1、EphA2与细胞粘附、肿瘤侵袭和转移密切相关。这些差异蛋白质的发现为研究鼻咽癌转移机制以及鼻咽癌转移的防治提供了靶标。进一步比较5-8F与6-10B细胞的膜磷酸化蛋白组的差异,发现EphA2促转移的关键位点$897磷酸化水平在两株细胞存在差异,且EGFR能够磷酸化该位点,这提示EGFR可能通过活化EphA2促进鼻咽癌转移。
本研究建立了基于Ziptip过滤去除固相胰酶的改良18O标记方法。该方法具有标记稳定、定量分析准确结果以及适合微量样本分析的特点；开发了与平台无关的基于~(18)0标记技术的定量蛋白质组分析工具OxyQuantToolkit,为~(18)0标记技术的推广和应用打下了基础,并开发了基于java的质谱数据可视化编程包,实现了mzxml文件的可视化浏览；采用改良的~(18)0标记定量蛋白质组学技术比较不同转移潜能NPC细胞株的膜蛋白质组的差异,鉴定了31个与NPC转移相关的膜蛋白质,为NPC转移机制研究及其防治提供了靶标；应用定量磷酸化蛋白技术鉴定到EphA2促肿瘤关键位点S897的差异表达,证实EGFR活化能诱导EphA2促肿瘤转移的关键位点S897磷酸化,为揭示EGFR促鼻咽癌转移机制的研究提供了重要线索。
Proteomics has become an important part of life science research and has been widely used in life science fields. Because there are transcriptional and translational regμlation of gene expression and post-translational modification of proteins, proteomics is much more complicated than genomics. Therefore, proteomics research is much more dependent on technology than genomic research. Therefore, the development of methods and techniques with high throughput, high sensitivity and high accuracy has been the challenge for proteomics research.
With advances in the proteomics techniques and methods, proteomics research has new feature:a research focus has moved from the protein expression profile to precise quantitative and functional studies of proteins. Currently quantitative proteomics technology based on stable isotope labeling have become a new proteomics technology. However, quantitative proteomics technology is still in development stage and the research techniques need to be improved and analysis tools need to be developed.
~(18)O labeling technique is one of the earlier techniques for quantitative proteomics. The technique based on the principle of enzyme kinetics, where proteins are labeled with ~(18)O by enzyme catalytic reaction.~(18)O labeling technique is simple, mild technique with no by-products. However, there are some disadvantages of ~(18)O labeling technique:(1) ~(18)O-label is easily lost at the presence of the enzyme, thus affecting the accuracy of quantitation; (2) Data analysis is relatively complex; (3) Lack of computational tools for quantitative analysis.
Membrane proteins play important roles in cell recognition, signal transduction material transport and etc. Abnormal expression of membrane proteins often lead to the changes of signal transduction, cell adhesion, motility and etc. which are related with tumor invasion and metastasis. Therefore, the study on tumors' membrane proteome has become an important part of proteomic research.
Based on the above considerations, we first optimized and improved ~(18)O labeling method. The loss of ~(18)O-label is the major weakness of this method. For this, We use solid-phase trypsin as a labeling enzyme and, after the reaction, the samples were filtered using Ziptip to remove solid-phase trypsin. The stability of ~(18)O-label of the filtered samples stored under different pH conditions was analyzed. The resμlts showed that Ziptip filtration can significantly improve the stability of ~(18)O-label, compared with the heat treatment method. There was no obvious loss of 180-label during continuous monitoring of three days. Further analysis of the impact of the Ziptip filtration on the results of quantitative analysis showed that the differences of quantitation are less than 5% during continuous monitoring of three days. There were no significant loss of ~(18)O-label during the filter samples were separated by isoelectric focusing (IEF). Further analysis also showed that the ~(18)O labeled efficiency of peptides was no significant correlation with their molecμlar weight and theoretical isoelectric point. In addition, the volume of the samples handled by Ziptip was merely 10-20μl in the present study. Therefore, the optimized and improved ~(18)O labeling method provided by us can be used for quantitative analysis of trace samples, which is of great significance for scarce clinical samples, such as laser capture microdissection (LCM) purified tissue samples and other limited samples.
Considering the lack of computational tools for ~(18)O labeling data anlaysis, We developed an computer tool named OxyQuantToolkit. The tool is developed using java language and therefore has cross-platform property. The tool has functions covering mμltiple aspects of ~(18)O labeling data processing. A large number of computing tasks can be done automatically by the tool during ~(18)O labeling data anslysis. The results of quantitative analysis can be output automatically. At the same time,the tool has functions for data arrangement and data dump, which has a positive effect on effective data management. In addition, the tool contains a data visualization programming package, which can be used for viewing mass spectrometry data in mzxml format. The development of OxyQuantToolkit provided the foundation for facilitating and broadening the application of ~(18)O labeling quantitative proteomics technology.
On the basis of the improvement of ~(18)O labeling method and the development of analysis tool for ~(18)O labeling data analysis, We use the improved ~(18)O labeling method for the quantitative analysis of the membrane proteins from high metastatic nasopharyngeal carcinoma(NPC) 5-8F cells and non metastatic NPC 6-10B. A total of 503 proteins were identified, with 44% of membrane protein and 89.7% of the proteins' expression ratio in 5-8F versus 6-10B were between 0.5-2 (mean 1.13). Statistic analysis of the quantitative results of 395 proteins identified in three repeated experiments demonstrated that the average standard deviation was 0.06, showing high repeatability. The results showed that the improved ~(18)O labeling has great applicable value. In addition, according to the standard provided in literatures (changing more than twice and identified in three repeated experiments),31 differential expressed membrane proteins between 5-8F and 6-10B were found, of which 6 were downregμlated and 25 were upregμlated in 5-8F. Bioinformatics analysis showed that some differential expressed proteins, such as ITGB1 and EphA2, are related to cell adhesion, tumor invasion and metastasis. The discovery of these proteins provided the targets for the study of metastatic mechanisms and prevention and treatment of nasopharyngeal carcinoma. Further comparative analysis of the phosphoproteins in the membrane fraction of 5-8F and 6-10b cells found that EphA2 S897 phosphorylation site which is key to promoting metastasis are differentially expressed, and activated EGFR can phosphorylate the site, indicating that EGFR promotes NPC metastasis possibly by phosphorylating the site.
An improved ~(18)O labeling method based on removal of solid-phase trypsin using Ziptip has been established. The method has features such as high stability of label, high accuracy of quantitation and being suited to the analysis of trace samples; the platform-independent analysis tool, OxyQuantToolkit, for ~(18)O labeling-based quantitative proteomics was developed, which provide the foundation for facilitating and broadening the application of 180 labeling technique. A java-based programming package for mass spectrometry data visualization was developed and a viewer for viewing mass spcetrometry data in mzxml format was also implemented; Comparative proteomic analysis of membrane proteins between NPC cell lines with different metastatic potential are performed using improved 180 labeling method, and 31 metastasis-related membrane proteins were identified, which provided the targets for the study of metastatic mechanism and the prevention of NPC. Comparative phsosphoproteomic analysis of membrane proteins between NPC cell lines with different metastatic potential are performed and the differential phsosphorylation level of EphA2 S897 site was identified, which is key to promoting metastasis. Further analysis confirms that activation of the EGFR can phosphorylate the site, which provide a important clue for studying the mechanism of EGFR that promoting NPC metastasis.
引文
[1]. Murphy, R.C. and K.L. Clay, Synthesis and back exchange of 180 labeled amino acids for use as internal standards with mass spectrometry. Biomed Mass Spectrom, 1979.6(7):p.309-14.
[2]. Yao, X., C. Afonso, and C. Fenselau, Dissection of proteolytic 180 labeling: endoprotease-catalyzed 16O-to-18O exchange of truncated peptide substrates. J Proteome Res,2003.2(2):p.147-52.
[3]. Angel, P.M. and R. Orlando, Trypsin is the primary mechanism by which the (18)O isotopic label is lost in quantitative proteomic studies. Anal Biochem,2006.359(1):p. 26-34.
[4]. Elias, J.E. and S.P. Gygi, Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods,2007.4(3):p. 207-14.
[5]. Yao, X., A. Freas, and J. Ramirezet.al., Proteolytic 180 labeling for comparative proteomics:model studies with two serotypes of adenovirus. Anal Chem,2001. 73(13):p.2836-42.
[6]. Stewart, I.I., T. Thomson, and D. Figeys,180 labeling:a tool for proteomics. Rapid Commun Mass Spectrom,2001.15(24):p.2456-65.
[7]. Cargile, B.J., D.L. Talley, and J.L. Jr Stephenson, Immobilized pH gradients as a first dimension in shotgun proteomics and analysis of the accuracy of pI predictability of peptides. Electrophoresis,2004.25(6):p.936-45.
[8]. Lopez, M.F., K. Berggren, and E. Chernokalskayaet.al., A comparison of silver stain and SYPRO Ruby Protein Gel Stain with respect to protein detection in two-dimensional gels and identification by peptide mass profiling. Electrophoresis, 2000.21(17):p.3673-83.
[9]. Gorg, A., O. Drews, and C. Lucket.al.,2-DE with IPGs. Electrophoresis,2009.30 Suppl 1:p. S122-32.
[10]. Smolka, M., H. Zhou, and R. Aebersold, Quantitative protein profiling using two-dimensional gel electrophoresis, isotope-coded affinity tag labeling, and mass spectrometry. Mol Cell Proteomics,2002.1(1):p.19-29.
[11]. Schnolzer, M., P. Jedrzejewski, and W.D. Lehmann, Protease-catalyzed incorporation of 180 into peptide fragments and its application for protein sequencing by electrospray and matrix-assisted laser desorption/ionization mass spectrometry. Electrophoresis,1996.17(5):p.945-53.
[12]. Sakai, J., S. Kojima, and K. Yanagiet.al.,18O-labeling quantitative proteomics using an ion trap mass spectrometer. Proteomics,2005.5(1):p.16-23.
[13]. Mirza, S.P., A.S. Greene, and M. Olivier,18O labeling over a coffee break:a rapid strategy for quantitative proteomics. J Proteome Res,2008.7(7):p.3042-8.
[14]. Staes, A., H. Demol, and J. Van Dammeet.al., Global differential non-gel proteomics by quantitative and stable labeling of tryptic peptides with oxygen-18. J Proteome Res,2004.3(4):p.786-91.
[15]. Petritis, B., W.J. Qian, and D. Campet.al., A Simple Procedure for Effective Quenching of Trypsin Activity and Prevention of 18O-Labeling Back-Exchange. J Proteome Res,2009.8(5):p.2157-2163.
[16]. Sevinsky, J.R., K.J. Brown, and B.J. Cargileet.al., Minimizing back exchange in 180/160 quantitative proteomics experiments by incorporation of immobilized trypsin into the initial digestion step. Anal Chem,2007.79(5):p.2158-62.
[17]. Brown, K.J. and C. Fenselau, Investigation of doxorubicin resistance in MCF-7 breast cancer cells using shot-gun comparative proteomics with proteolytic 180 labeling. J Proteome Res,2004.3(3):p.455-62.
[18]. Blonder, J., M.L. Hale, and K.C. Chanet.al., Quantitative profiling of the detergent-resistant membrane proteome of iota-b toxin induced vero cells. J Proteome Res,2005.4(2):p.523-31.
[19]. Nelson, C.J., A.D. Hegeman, and A.C. Harmset.al., A quantitative analysis of Arabidopsis plasma membrane using trypsin-catalyzed (18)O labeling. Mol Cell Proteomics,2006.5(8):p.1382-95.
[20]. Qian, W.J., M.E. Monroe, and T. Liuet.al., Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 160/180 labeling and the accurate mass and time tag approach. Mol Cell Proteomics,2005.4(5):p. 700-9.
[21]. Chen, X., S.W. Cushman, and L.K. Pannellet.al., Quantitative proteomic analysis of the secretory proteins from rat adipose cells using a 2D liquid chromatography-MS/MS approach. J Proteome Res,2005.4(2):p.570-7.
[22]. Hood, B.L., D.A. Lucas, and G. Kimet.al., Quantitative analysis of the low molecular weight serum proteome using 18O stable isotope labeling in a lung tumor xenograft mouse model. J Am Soc Mass Spectrom,2005.16(8):p.1221-30.
[23]. Cristoni, S. and L.R. Bernardi, Bioinformatics in mass spectrometry data analysis for proteomics studies. Expert Rev Proteomics,2004.1(4):p.469-83.
[24]. Haoudi, A. and H. Bensmail, Bioinformatics and data mining in proteomics. Expert Rev Proteomics,2006.3(3):p.333-43.
[25]. Ivakhno, S. and A. Kornelyuk, Quantitative proteomics and its applications for systems biology. Biochemistry (Mosc),2006.71(10):p.1060-72.
[26]. Gygi, S.P., B. Rist, and S.A. Gerberet.al., Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol,1999.17(10):p.994-9.
[27]. Koehler, C.J., M. Strozynski, and F. Kozielskiet.al., Isobaric peptide termini labeling for MS/MS-based quantitative proteomics. J Proteome Res,2009.8(9):p.4333-41.
[28]. Cox, J. and M. Mann, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol,2008.26(12):p.1367-72.
[29]. Lin, S.M., L. Zhu, and A.Q. Winteret.al., What is mzXML good for?. Expert Rev Proteomics,2005.2(6):p.839-45.
[30]. Braisted, J.C., S. Kuntumalla, and C. Vogelet.al., The APEX Quantitative Proteomics Tool:generating protein quantitation estimates from LC-MS/MS proteomics results. BMC Bioinformatics,2008.9:p.529.
[31]. Li, X.J., H. Zhang, and J.A. Ranishet.al., Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. Anal Chem,2003.75(23):p.6648-57.
[32]. Arntzen, M.O., C.J. Koehler, and H. Barsneset.al., IsobariQ:software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT. J Proteome Res,2011.10(2): p.913-20.
[33]. Park, S.K., J.D. Venable, and T. Xuet.al., A quantitative analysis software tool for mass spectrometry-based proteomics. Nat Methods,2008.5(4):p.319-22.
[34]. Bouyssie, D., P.A. de Gonzalez, and E. Moutonet.al., 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. Mol Cell Proteomics,2007.6(9):p.1621-37.
[35]. Mortensen, P., J.W. Gouw, and J.V. Olsenet.al., MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J Proteome Res,2010.9(1):p. 393-403.
[36]. Lin, W.T., W.N. Hung, and Y.H. Yianet.al., Multi-Q:a fully automated tool for multiplexed protein quantitation. J Proteome Res,2006.5(9):p.2328-38.
[37]. Khan, Z., J.S. Bloom, and B.A. Garciaet.al., Protein quantification across hundreds of experimental conditions. Proc Natl Acad Sci U S A,2009.106(37):p.15544-8.
[38]. Boehm, A.M., S. Putz, and D. Altenhoferet.al., Precise protein quantification based on peptide quantification using iTRAQ. BMC Bioinformatics,2007.8:p.214.
[39]. Mason, C.J., T.M. Therneau, and J.E. Eckel-Passowet.al., A method for automatically interpreting mass spectra of 18O-labeled isotopic clusters. Mol Cell Proteomics,2007. 6(2):p.305-18.
[40]. Halligan, B.D., R.Y. Slyper, and S.N. Twiggeret.al., ZoomQuant:an application for the quantitation of stable isotope labeled peptides. J Am Soc Mass Spectrom,2005. 16(3):p.302-6.
[41]. Han, D.K., J. Eng, and H. Zhouet.al., Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat Biotechnol,2001.19(10):p.946-51.
[42]. Patwardhan, A.J., E.F. Strittmatter, and D.N. Campet.al., Quantitative proteome analysis of breast cancer cell lines using 18O-labeling and an accurate mass and time tag strategy. Proteomics,2006.6(9):p.2903-15.
[43]. Wang, J., P. Gutierrez, and N. Edwardset.al., Integration of 18O labeling and solution isoelectric focusing in a shotgun analysis of mitochondrial proteins. J Proteome Res, 2007.6(12):p.4601-7.
[44]. Ding, S.J., Y. Wang, and J.M. Jacobset.al., Quantitative phosphoproteome analysis of lysophosphatidic acid induced chemotaxis applying dual-step (1.8)O labeling coupled with immobilized metal-ion affinity chromatography. J Proteome Res,2008.7(10):p. 4215-24.
[45]. Fenselau, C. and X. Yao,18O2-labeling in quantitative proteomic strategies:a status report. J Proteome Res,2009.8(5):p.2140-3.
[46]. Helsens, K., L. Martens, and J. Vandekerckhoveet.al., MascotDatfile:an open-source library to fully parse and analyse MASCOT MS/MS search results. Proteomics,2007. 7(3):p.364-6.
[47]. Ong, S.E., B. Blagoev, and I. Kratchmarovaet.al., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics,2002.1(5):p.376-86.
[48]. Zhong, H., S.L. Marcus, and L. Li, Two-dimensional mass spectra generated from the analysis of 15N-labeled and unlabeled peptides for efficient protein identification and de novo peptide sequencing. J Proteome Res,2004.3(6):p.1155-63.
[49]. Hardt, M., H.E. Witkowska, and S. Webbet.al., Assessing the effects of diurnal variation on the composition of human parotid saliva:quantitative analysis of native peptides using iTRAQ reagents. Anal Chem,2005.77(15):p.4947-54.
[50]. Rao, K.C., V. Palamalai, and J.R. Dunlevyet.al., Peptidyl-Lys metalloendopeptidase-catalyzed 180 labeling for comparative proteomics: application to cytokine/lipolysaccharide-treated human retinal pigment epithelium cell line. Mol Cell Proteomics,2005.4(10):p.1550-7.
[51]. Fernandez-de-Cossio, J., L.J. Gonzalez, and Y. Satomiet.al., Automated interpretation of mass spectra of complex mixtures by matching of isotope peak distributions. Rapid Commun Mass Spectrom,2004.18(20):p.2465-72.
[52]. Pedrioli, P.G., J.K. Eng, and R. Hubleyet.al., A common open representation of mass spectrometry data and its application to proteomics research. Nat Biotechnol,2004. 22(11):p.1459-66.
[53]. Veenstra, T.D., Global and targeted quantitative proteomics for biomarker discovery. J Chromatogr B Analyt Technol Biomed Life Sci,2007.847(1):p.3-11.
[54]. Leth-Larsen, R., R. Lund, and H.V. Hansenet.al., Metastasis-related plasma membrane proteins of human breast cancer cells identified by comparative quantitative mass spectrometry. Mol Cell Proteomics,2009.8(6):p.1436-49.
[55]. Roesli, C., B. Borgia, and C. Schliemannet.al., Comparative analysis of the membrane proteome of closely related metastatic and nonmetastatic tumor cells. Cancer Res,2009.69(13):p.5406-14.
[56]. Hsu, J.L., L.Y. Wang, and S.Y. Wanget.al., Functional phosphoproteomic profiling of phosphorylation sites in membrane fractions of salt-stressed Arabidopsis thaliana. Proteome Sci,2009.7:p.42.
[57]. Miao, H., D.Q. Li, and A. Mukherjeeet.al., EphA2 mediates ligand-dependent inhibition and ligand-independent promotion of cell migration and invasion via a reciprocal regulatory loop with Akt. Cancer Cell,2009.16(1):p.9-20.
[58]. Taddei, M.L., M. Parri, and A. Angelucciet.al., Kinase-dependent and -independent roles of EphA2 in the regulation of prostate cancer invasion and metastasis. Am J Pathol,2009.174(4):p.1492-503.
[59]. Blueggel, M., D. Chamrad, and H.E. Meyer, Bioinformatics in proteomics. Curr Pharm Biotechnol,2004.5(1):p.79-88.
[60]. Wallin, E. and G. von Heijne, Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci,1998.7(4):p. 1029-38.
[61]. Hopkins, A.L. and C.R. Groom, The druggable genome. Nat Rev Drug Discov,2002. 1(9):p.727-30.
[62]. Lund, R., R. Leth-Larsen, and O.N. Jensenet.al., Efficient isolation and quantitative proteomic analysis of cancer cell plasma membrane proteins for identification of metastasis-associated cell surface markers. J Proteome Res,2009.8(6):p.3078-90.
[63]. Tan, S., H.T. Tan, and M.C. Chung, Membrane proteins and membrane proteomics. Proteomics,2008.8(19):p.3924-32.
[64]. Ramus, C., P.A. de Gonzalez, and C. Dahoutet.al., An optimized strategy for ICAT quantification of membrane proteins. Mol Cell Proteomics,2006.5(1):p.68-78.
[65]. Molloy, M.P., B.R. Herbert, and B.J. Walshet.al., Extraction of membrane proteins by differential solubilization for separation using two-dimensional gel electrophoresis. Electrophoresis,1998.19(5):p.837-44.
[66]. Santoni, V., M. Molloy, and T. Rabilloud, Membrane proteins and proteomics:un amour impossible?. Electrophoresis,2000.21(6):p.1054-70.
[67]. Gygi, S.P., G.L. Corthals, and Y. Zhanget.al., Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci U S A,2000. 97(17):p.9390-5.
[68]. Wu, C.C. and R. Jr Yates, The application of mass spectrometry to membrane proteomics. Nat Biotechnol,2003.21(3):p.262-7.
[69]. Link, A.J., J. Eng, and D.M. Schieltzet.al., Direct analysis of protein complexes using mass spectrometry. Nat Biotechnol,1999.17(7):p.676-82.
[70]. Chenau, J., S. Michelland, and J. Sidibeet.al., Peptides OFFGEL electrophoresis:a suitable pre-analytical step for complex eukaryotic samples fractionation compatible with quantitative iTRAQ labeling. Proteome Sci,2008.6:p.9.
[71]. Pinkse, M.W., P.M. Uitto, and M.J. Hilhorstet.al., Selective isolation at the femtomole level of phosphopeptides from proteolytic digests using 2D-NanoLC-ESI-MS/MS and titanium oxide precolumns. Anal Chem,2004.76(14): p.3935-43.
[72]. Nita-Lazar, A., H. Saito-Benz, and F.M. White, Quantitative phosphoproteomics by mass spectrometry:past, present, and future. Proteomics,2008.8(21):p.4433-43.
[73]. Lee, H.J., K. Na, and M.S. Kwonet.al., Quantitative analysis of phosphopeptides in search of the disease biomarker from the hepatocellular carcinoma specimen. Proteomics,2009.9(12):p.3395-408.
[74]. Leroy, C., C. Fialin, and A. Sirventet.al., Quantitative phosphoproteomics reveals a cluster of tyrosine kinases that mediates SRC invasive activity in advanced colon carcinoma cells. Cancer Res,2009.69(6):p.2279-86.
[75]. Chaerkady, R. and A. Pandey, Quantitative proteomics for identification of cancer biomarkers. Proteomics Clinical Applications,2007.1(9):p.1080-1089.
[76].谭颖,王玉霞,and李晶et.al.,～(18)O标记法在定量蛋白质组学中的应用.细胞生物学杂志,2006(06).
[77]. Stewart, I.I., T. Thomson, and D. Figeys,18O labeling:a tool for proteomics. Rapid Commun Mass Spectrom,2001.15(24):p.2456-65.
[78]. Niles, R., H.E. Witkowska, and S. Allenet.al., Acid-catalyzed oxygen-18 labeling of peptides. Anal Chem,2009.81(7):p.2804-9.
[79]. Hajkova, D., K.C. Rao, and M. Miyagi, pH dependency of the carboxyl oxygen exchange reaction catalyzed by lysyl endopeptidase and trypsin. J Proteome Res, 2006.5(7):p.1667-73.
[80]. Miyagi, M. and K.C. Rao, Proteolytic 18O-labeling strategies for quantitative proteomics. Mass Spectrom Rev,2007.26(1):p.121-36.
[81]. Kristiansen, T.Z., H.C. Harsha, and M. Gronborget.al., Differential membrane proteomics using 18O-labeling to identify biomarkers for cholangiocarcinoma. J Proteome Res,2008.7(11):p.4670-7.
[82]. Aikawa, T., C.A. Whipple, and M.E. Lopezet.al., Glypican-1 modulates the angiogenic and metastatic potential of human and mouse cancer cells. J Clin Invest, 2008.118(1):p.89-99.
[83]. Ushiku, T., H. Uozaki, and A. Shinozakiet.al., Glypican 3-expressing gastric carcinoma:distinct subgroup unifying hepatoid, clear-cell, and alpha-fetoprotein-producing gastric carcinomas. Cancer Sci,2009.100(4):p.626-32.
[84]. Vaught, D., D.M. Brantley-Sieders, and J. Chen, Eph receptors in breast cancer:roles in tumor promotion and tumor suppression. Breast Cancer Res,2008.10(6):p.217.
[85]. Faoro, L., P.A. Singleton, and G.M. Cervanteset.al., EphA2 mutation in lung squamous cell carcinoma promotes increased cell survival, cell invasion, focal adhesions, and mammalian target of rapamycin activation. J Biol Chem,2010. 285(24):p.18575-85.
[86]. Brantley-Sieders, D.M., G. Zhuang, and D. Hickset.al., The receptor tyrosine kinase EphA2 promotes mammary adenocarcinoma tumorigenesis and metastatic progression in mice by amplifying ErbB2 signaling. J Clin Invest,2008.118(1):p. 64-78.
[87]. Lu, C., M.M. Shahzad, and H. Wanget.al., EphA2 overexpression promotes ovarian cancer growth. Cancer Biol Ther,2008.7(7):p.1098-103.
[88]. Zelinski, D.P., N.D. Zantek, and J.C. Stewartet.al., EphA2 overexpression causes tumorigenesis of mammary epithelial cells. Cancer Res,2001.61(5):p.2301-6.
[89]. Chen, J., G. Zhuang, and L. Friedenet.al., Eph receptors and Ephrins in cancer: common themes and controversies. Cancer Res,2008.68(24):p.10031-3.
[90]. Liu, Y., X. Zhang, and Y. Qiuet.al., Clinical significance of EphA2 expression in squamous-cell carcinoma of the head and neck. J Cancer Res Clin Oncol,2011. 137(5):p.761-9.
[1]Yates J R, Ruse C I, Nakorchevsky A. Proteomics by mass spectrometry:approaches, advances, and applications. Annu Rev Biomed Eng,2009,11:49-79.
[2]Vermeμlen M, Selbach M. Quantitative proteomics:a tool to assess cell differentiation. Curr Opin Cell Biol,2009,21(6):761-766.
[3]Teckchandani A, Toida N, Goodchild J, et al. Quantitative proteomics identifies a Dab2/integrin modμle regμlating cell migration. J Cell Biol,2009,186(1):99-111.
[4]Lee K H. Proteomics:a technology-driven and technology-limited discovery science. Trends Biotechnol,2001,19(6):217-222.
[5]Blackstock W P, Weir M P. Proteomics:quantitative and physical mapping of cellular proteins. Trends Biotechnol,1999,17(3):121-127.
[6]Ong S E, Mann M. Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol,2005,1(5):252-262.
[7]Ivakhno S, Kornelyuk A. Quantitative proteomics and its applications for systems biology. Biochemistry (Mosc),2006,71(10):1060-1072.
[8]Elliott M H, Smith D S, Parker C E, et al. Current trends in quantitative proteomics. J Mass Spectrom,2009,44(12):1637-1660.
[9]Mueller L N, Brusniak M Y, Mani D R, et al. An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J Proteome Res,2008,7(1):51-61.
[10]Gorg A, Weiss W, Dunn M J. Current two-dimensional electrophoresis technology for proteomics. Proteomics,2004,4(12):3665-3685.
[11]Patton W F. Detection technologies in proteome analysis. J Chromatogr B Analyt Technol Biomed Life Sci,2002,771(1-2):3-31.
[12]Smales C M, Birch J R, Racher A J, et al. Evaluation of individual protein errors in silver-stained two-dimensional gels. Biochem Biophys Res Commun,2003,306(4):1050-1055.
[13]Smolka M, Zhou H, Aebersold R. Quantitative protein profiling using two-dimensional gel electrophoresis, isotope-coded affinity tag labeling, and mass spectrometry. Mol Cell Proteomics,2002,1(1):19-29.
[14]Camacho-Carvajal M M, Wollscheid B, Aebersold R, et al. Two-dimensional Blue native/SDS gel electrophoresis of mμlti-protein complexes from whole cellμlar lysates: a proteomics approach. Mol Cell Proteomics,2004,3(2):176-182.
[15]Sinha P, Poland J, Schnolzer M, et al. A new silver staining apparatus and procedure for matrix-assisted laser desorption/ionization-time of flight analysis of proteins after two-dimensional electrophoresis. Proteomics,2001,1(7):835-840.
[16]Yan J X, Wait R, Berkelman T, et al. A modified silver staining protocol for visualization of proteins compatible with matrix-assisted laser desorption/ionization and electrospray ionization-mass spectrometry. Electrophoresis,2000,21(17):3666-3672.
[17]Candiano G, Bruschi M, Musante L, et al. Blue silver:a very sensitive colloidal Coomassie G-250 staining for proteome analysis. Electrophoresis,2004,25(9):1327-1333.
[18]Marshall T. Detection of protein in polyacrylamide gels using an improved silver stain. Anal Biochem,1984,136(2):340-346.
[19]Nock C M, Ball M S, White I R, et al. Mass spectrometric compatibility of Deep Purple and SYPRO Ruby total protein stains for high-throughput proteomics using large-format two-dimensional gel electrophoresis. Rapid Commun Mass Spectrom,2008,22(6):881-886.
[20]Chakravarti B, Fathy P, Sindicich M, et al. Comparison of SYPRO Ruby and Flamingo fluorescent stains for application in proteomic research. Anal Biochem,2010,398(1):1-6.
[21]Lopez M F, Berggren K, Chernokalskaya E, et al. A comparison of silver stain and SYPRO Ruby Protein Gel Stain with respect to protein detection in two-dimensional gels and identification by peptide mass profiling. Electrophoresis,2000,21(17):3673-3683.
[22]Minden J S, Dowd S R, Meyer H E, et al. Difference gel electrophoresis. Electrophoresis,2009,30 Suppl 1:S156-S161.
[23]Timms J F, Cramer R. Difference gel electrophoresis. Proteomics,2008,8(23-24): 4886-4897.
[24]Santoni V, Molloy M, Rabilloud T. Membrane proteins and proteomics:un amour impossible?Electrophoresis,2000,21(6):1054-1070.
[25]Westermeier R, Schickle H. The current state of the art in high-resolution two-dimensional electrophoresis. Arch Physiol Biochem,2009,115(5):279-285.
[26]Rabilloud T. Membrane proteins and proteomics:love is possible, but so difficult. Electrophoresis,2009,30 Suppl 1:S174-S180.
[27]Vercauteren F G, Arckens L, Quirion R. Applications and current challenges of proteomic approaches, focusing on two-dimensional electrophoresis. Amino Acids, 2007,33(3):405-414.
[28]Flory M R, Griffin T J, Martin D, et al. Advances in quantitative proteomics using stable isotope tags. Trends Biotechnol,2002,20(12 Suppl):S23-S29.
[29]Ong S E, Blagoev B, Kratchmarova I, et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics,2002,1(5):376-386.
[30]Lafaye A, Labarre J, Tabet J C, et al. Liquid chromatography-mass spectrometry and 15N metabolic labeling for quantitative metabolic profiling. Anal Chem, 2005,77(7):2026-2033.
[31]Gygi S P, Rist B, Gerber S A, et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol,1999,17(10):994-999.
[32]Shadforth I P, Dunkley T P, Lilley K S, et al. i-Tracker:for quantitative proteomics using iTRAQ. BMC Genomics,2005,6:145.
[33]Mirgorodskaya O A, Kozmin Y P, Titov M I, et al. Quantitation of peptides and proteins by matrix-assisted laser desorption/ionization mass spectrometry using (18)O-labeled internal standards. Rapid Commun Mass Spectrom,2000,14 (14):1226-1232.
[34]Marcilla M, Alpizar A, Paradela A, et al. A systematic approach to assess amino acid conversions in SILAC experiments. Talanta,2011,84(2):430-436.
[35]Tao W A. Soluble polymer-based isotopic labeling (SoPIL):a new strategy to discover protein biomarkers?Expert Rev Proteomics,2007,4(5):603-607.
[36]Zhang H, Yan W, Aebersold R. Chemical probes and tandem mass spectrometry:a strategy for the quantitative analysis of proteomes and subproteomes. Curr Opin Chem Biol,2004,8(1):66-75.
[37]Aggarwal K, Choe L H, Lee K H. Shotgun proteomics using the iTRAQ isobaric tags. Brief Funct Genomic Proteomic,2006,5(2):112-120.
[38]Anderson N L, Anderson N G. The human plasma proteome:history, character, and diagnostic prospects. Mol Cell Proteomics,2002,1(11):845-867.
[39]Wu L, Han D K. Overcoming the dynamic range problem in mass spectrometry- based shotgun proteomics. Expert Rev Proteomics,2006,3(6):611-619.
[40]Righetti P G, Castagna A, Antonucci F, et al. Proteome analysis in the clinical chemistry laboratory:myth or reality?Clin Chim Acta,2005,357(2):123-139.
[41]Omenn G S. Strategies for plasma proteomic profiling of cancers. Proteomics, 2006,6(20):5662-5673.
[42]Cordwell S J, Nouwens A S, Verrills N M, et al. Subproteomics based upon protein cellμlar location and relative solubilities in conjunction with composite two-dimensional electrophoresis gels. Electrophoresis,2000,21(6):1094-1103.
[43]Murphy R C, Clay K L. Synthesis and back exchange of 180 labeled amino acids for use as internal standards with mass spectrometry. Biomed Mass Spectrom, 1979,6(7):309-314.
[44]Schnolzer M, Jedrzejewski P, Lehmann W D. Protease-catalyzed incorporation of 180 into peptide fragments and its application for protein sequencing by electrospray and matrix-assisted laser desorption/ionization mass spectrometry. Electrophoresis,1996,17(5):945-953.
[45]Shevchenko A, Chernushevich I, Ens W, et al. Rapid 'de novo' peptide sequencing by a combination of nanoelectrospray, isotopic labeling and a quadrupole/time-of-flight mass spectrometer. Rapid Commun Mass Spectrom,1997,11(9):1015-1024.
[46]Qin J, Herring C J, Zhang X. De novo peptide sequencing in an ion trap mass spectrometer with 180 labeling. Rapid Commun Mass Spectrom,1998,12(5): 209-216.
[47]Stewart I I, Thomson T, Figeys D.180 labeling:a tool for proteomics. Rapid Commun Mass Spectrom,2001,15(24):2456-2465.
[48]Broedel O, Krause E, Stephanowitz H, et al. In-Gel (18)O Labeling for Improved Identification of Proteins from 2-DE Gel Spots in Comparative Proteomic Experiments. J Proteome Res,2009,8(7):3771-3777.
[49]Lane C S, Wang Y, Betts R, et al. Comparative cytochrome P450 proteomics in the livers of immunodeficient mice using 180 stable isotope labeling. Mol Cell Proteomics,2007,6(6):953-962.
[50]Sakai J, Kojima S, Yanagi K, et al.180-labeling quantitative proteomics using an ion trap mass spectrometer. Proteomics,2005,5(1):16-23.
[51]Halligan B D, Slyper R Y, Twigger S N, et al. ZoomQuant:an application for the quantitation of stable isotope labeled peptides. J Am Soc Mass Spectrom,2005, 16(3):302-306.
[52]Mason C J, Therneau T M, Eckel-Passow J E, et al. A method for automatically interpreting mass spectra of 180-labeled isotopic clusters. Mol Cell Proteomics,2007, 6(2):305-318.
[53]Fenselau C, Yao X.1802-labeling in quantitative proteomic strategies:a status report. J Proteome Res,2009,8(5):2140-2143.
[54]Yao X, Afonso C, Fenselau C. Dissection of proteolytic 180 labeling: endoprotease-catalyzed 16O-to-18O exchange of truncated peptide substrates. J Proteome Res,2003,2(2):147-152.
[55]Angel P M, Orlando R. Trypsin is the primary mechanism by which the (18)O isotopic label is lost in quantitative proteomic studies. Anal Biochem,2006,359(1):26-34.
[56]Qian W J, Monroe M E, Liu T, et al. Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 160/180 labeling and the accurate mass and time tag approach. Mol Cell Proteomics,2005,4(5):700-709.
[57]Liu T, Qian W J, Strittmatter E F, et al. High-throμghput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology. Anal Chem, 2004,76(18):5345-5353.
[58]Storms H F, Van Der Heijden R, Tjaden U R, et al. Considerations for proteolytic labeling-optimization of 180 incorporation and prohibition of back-exchange. Rapid Commun Mass Spectrom,2006,20(23):3491-3497.
[59]Petritis B, Qian W J, Camp D, et al. A Simple Procedure for Effective Quenching of Trypsin Activity and Prevention of 180-Labeling Back-Exchange. J Proteome Res,2009,8(5):2157-2163.
[60]Brown K J, Fenselau C. Investigation of doxorubicin resistance in MCF-7 breast cancer cells using shot-gun comparative proteomics with proteolytic 180 labeling. J Proteome Res,2004,3(3):455-462.
[61]Blonder J, Hale M L, Chan K C, et al. Quantitative profiling of the detergent-resistant membrane proteome of iota-b toxin induced vero cells. J Proteome Res,2005,4(2):523-531.
[62]Nelson C J, Hegeman A D, Harms A C, et al. A quantitative analysis of Arabidopsis plasma membrane using trypsin-catalyzed (18)O labeling. Mol Cell Proteomics,2006,5(8):1382-1395.
[63]Staes A, Demol H, Van Damme J, et al. Global differential non-gel proteomics by quantitative and stable labeling of tryptic peptides with oxygen-18. J Proteome Res,2004,3(4):786-791.
[64]Sevinsky J R, Brown K J, Cargile B J, et al. Minimizing back exchange in 180/160 quantitative proteomics experiments by incorporation of immobilized trypsin into the initial digestion step. Anal Chem,2007,79(5):2158-2162.
[65]Niles R, Witkowska H E, Allen S, et al. Acid-catalyzed oxygen-18 labeling of peptides. Anal Chem,2009,81(7):2804-2809.
[66]Patwardhan A J, Strittmatter E F, Camp D N, et al. Quantitative proteome analysis of breast cancer cell lines using 180-labeling and an accurate mass and time tag strategy. Proteomics,2006,6(9):2903-2915.
[67]Hathout Y, Riordan K, Gehrmann M, et al. Differential protein expression in the cytosol fraction of an MCF-7 breast cancer cell line selected for resistance toward melphalan. J Proteome Res,2002,1(5):435-442.
[68]Brown K J, Fenselau C. Investigation of doxorubicin resistance in MCF-7 breast cancer cells using shot-gun comparative proteomics with proteolytic 180 labeling. J Proteome Res,2004,3(3):455-462.
[69]Bunai K, Yamane K. Effectiveness and limitation of two-dimensional gel electrophoresis in bacterial membrane protein proteomics and perspectives. J Chromatogr B Analyt Technol Biomed Life Sci,2005,815(1-2):227-236.
[70]Chi L M, Lee C W, Chang K P, et al. Enhanced interferon signaling pathway in oral cancer revealed by quantitative proteome analysis of microdissected specimens using 160/180 labeling and integrated two-dimensional LC-ESI-MALDI tandem MS. Mol Cell Proteomics,2009,8(7):1453-1474.
[71]Bantscheff M, Dumpelfeld B, Kuster B. Femtomol sensitivity post-digest(18)O labeling for relative quantification of differential protein complex composition. Rapid Commun Mass Spectrom,2004,18(8):869-876.
[72]Jorge I, Navarro P, Martinez-Acedo P, et al. Statistical model to analyze quantitative proteomics data obtained by 180/160 labeling and linear ion trap mass spectrometry: application to the study of vascμlar endothelial growth factor-induced angiogenesis in endothelial cells. Mol Cell Proteomics,2009,8(5):1130-1149.
[73]Lundstrom K. Structural genomics for membrane proteins. Cell Mol Life Sci,2006, 63(22):2597-2607.
[74]Rais I, Karas M, Schagger H. Two-dimensional electrophoresis for the isolation of integral membrane proteins and mass spectrometric identification. Proteomics, 2004,4(9):2567-2571.
[75]Rabilloud T. Membrane proteins ride shotgun. Nat Biotechnol,2003,21(5):508-510.
[76]Kristiansen T Z, Harsha H C, Gronborg M, et al. Differential membrane proteomics using 180-labeling to identify biomarkers for cholangiocarcinoma. J Proteome Res,2008,7(11):4670-4677.
[77]Chong P K, Lee H, Kong J W, et al. Phosphoproteomics, oncogenic signaling and cancer research. Proteomics,2008,8(21):4370-4382.
[78]Thingholm T E, Jensen O N, Larsen M R. Analytical strategies for phosphoproteomics. Proteomics,2009,9(6):1451-1468.
[79]Reinders J, Sickmann A. State-of-the-art in phosphoproteomics. Proteomics,2005, 5(16):4052-4061.
[80]Johnson S A, Hunter T. Phosphoproteomics finds its timing. Nat Biotechnol, 2004,22(9):1093-1094.
[81]Ding S, Bond A E, Lemiere F, et al. Online immobilized metal affinity chromatography /mass spectrometric analysis of changes elicited by cCMP in the murine brain phosphoproteome. Rapid Commun Mass Spectrom,2008,22(24):4129-4138.
[82]Wei J, Zhang Y, Wang J, et al. Highly efficient enrichment of phosphopeptides by magnetic nanoparticles coated with zirconium phosphonate for phosphoproteome analysis. Rapid Commun Mass Spectrom,2008,22(7):1069-1080.
[83]Sui S, Wang J, Yang B, et al. Phosphoproteome analysis of the human Chang liver cells using SCX and a complementary mass spectrometric strategy. Proteomics,2008,8(10):2024-2034.
[84]Ahn Y H, Ji E S, Lee J Y, et al. Coupling of TiO(2)-mediated enrichment and on-bead guanidinoethanethiol labeling for effective phosphopeptide analysis by matrix-assisted laser desorption/ionization mass spectrometry. Rapid Commun Mass Spectrom,2007,21(24):3987-3994.
[85]Feng S, Pan C, Jiang X, et al. Fe3+ immobilized metal affinity chromatography with silica monolithic capillary column for phosphoproteome analysis. Proteomics,2007,7(3):351-360.
[86]Pinkse M W, Uitto P M, Hilhorst M J, et al. Selective isolation at the femtomole level of phosphopeptides from proteolytic digests using 2D-NanoLC-ESI-MS/MS and titanium oxide precolumns. Anal Chem,2004,76(14):3935-3943.
[87]Liang X, Fonnum G, Hajivandi M, et al. Quantitative comparison of IMAC and TiO2 surfaces used in the study of regμlated, dynamic protein phosphorylation. J Am Soc Mass Spectrom,2007,18(11):1932-1944.
[88]Lee H J, Na K, Kwon M S, et al. Quantitative analysis of phosphopeptides in search of the disease biomarker from the hepatocellμlar carcinoma specimen. Proteomics,2009,9(12):3395-3408.
[89]Leroy C, Fialin C, Sirvent A, et al. Quantitative phosphoproteomics reveals a cluster of tyrosine kinases that mediates SRC invasive activity in advanced colon carcinoma cells. Cancer Res,2009,69(6):2279-2286.
[90]Yu L R, Zhu Z, Chan K C, et al. Improved titanium dioxide enrichment of phosphopeptides from HeLa cells and high confident phosphopeptide identification by cross-validation of MS/MS and MS/MS/MS spectra. J Proteome Res, 2007,6(11):4150-4162.
[91]Gevaert K, Staes A, Van Damme J, et al. Global phosphoproteome analysis on human HepG2 hepatocytes using reversed-phase diagonal LC. Proteomics,2005, 5(14):3589-3599.
[92]Piggee C. Phosphoproteomics:miles to go before it's routine. Anal Chem,2009, 81(7):2418-2420.
[2]. Yao, X., C. Afonso, and C. Fenselau, Dissection of proteolytic 180 labeling: endoprotease-catalyzed 16O-to-18O exchange of truncated peptide substrates. J Proteome Res,2003.2(2):p.147-52.
[3]. Angel, P.M. and R. Orlando, Trypsin is the primary mechanism by which the (18)O isotopic label is lost in quantitative proteomic studies. Anal Biochem,2006.359(1):p. 26-34.
[4]. Elias, J.E. and S.P. Gygi, Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods,2007.4(3):p. 207-14.
[5]. Yao, X., A. Freas, and J. Ramirezet.al., Proteolytic 180 labeling for comparative proteomics:model studies with two serotypes of adenovirus. Anal Chem,2001. 73(13):p.2836-42.
[6]. Stewart, I.I., T. Thomson, and D. Figeys,180 labeling:a tool for proteomics. Rapid Commun Mass Spectrom,2001.15(24):p.2456-65.
[7]. Cargile, B.J., D.L. Talley, and J.L. Jr Stephenson, Immobilized pH gradients as a first dimension in shotgun proteomics and analysis of the accuracy of pI predictability of peptides. Electrophoresis,2004.25(6):p.936-45.
[8]. Lopez, M.F., K. Berggren, and E. Chernokalskayaet.al., A comparison of silver stain and SYPRO Ruby Protein Gel Stain with respect to protein detection in two-dimensional gels and identification by peptide mass profiling. Electrophoresis, 2000.21(17):p.3673-83.
[9]. Gorg, A., O. Drews, and C. Lucket.al.,2-DE with IPGs. Electrophoresis,2009.30 Suppl 1:p. S122-32.
[10]. Smolka, M., H. Zhou, and R. Aebersold, Quantitative protein profiling using two-dimensional gel electrophoresis, isotope-coded affinity tag labeling, and mass spectrometry. Mol Cell Proteomics,2002.1(1):p.19-29.
[11]. Schnolzer, M., P. Jedrzejewski, and W.D. Lehmann, Protease-catalyzed incorporation of 180 into peptide fragments and its application for protein sequencing by electrospray and matrix-assisted laser desorption/ionization mass spectrometry. Electrophoresis,1996.17(5):p.945-53.
[12]. Sakai, J., S. Kojima, and K. Yanagiet.al.,18O-labeling quantitative proteomics using an ion trap mass spectrometer. Proteomics,2005.5(1):p.16-23.
[13]. Mirza, S.P., A.S. Greene, and M. Olivier,18O labeling over a coffee break:a rapid strategy for quantitative proteomics. J Proteome Res,2008.7(7):p.3042-8.
[14]. Staes, A., H. Demol, and J. Van Dammeet.al., Global differential non-gel proteomics by quantitative and stable labeling of tryptic peptides with oxygen-18. J Proteome Res,2004.3(4):p.786-91.
[15]. Petritis, B., W.J. Qian, and D. Campet.al., A Simple Procedure for Effective Quenching of Trypsin Activity and Prevention of 18O-Labeling Back-Exchange. J Proteome Res,2009.8(5):p.2157-2163.
[16]. Sevinsky, J.R., K.J. Brown, and B.J. Cargileet.al., Minimizing back exchange in 180/160 quantitative proteomics experiments by incorporation of immobilized trypsin into the initial digestion step. Anal Chem,2007.79(5):p.2158-62.
[17]. Brown, K.J. and C. Fenselau, Investigation of doxorubicin resistance in MCF-7 breast cancer cells using shot-gun comparative proteomics with proteolytic 180 labeling. J Proteome Res,2004.3(3):p.455-62.
[18]. Blonder, J., M.L. Hale, and K.C. Chanet.al., Quantitative profiling of the detergent-resistant membrane proteome of iota-b toxin induced vero cells. J Proteome Res,2005.4(2):p.523-31.
[19]. Nelson, C.J., A.D. Hegeman, and A.C. Harmset.al., A quantitative analysis of Arabidopsis plasma membrane using trypsin-catalyzed (18)O labeling. Mol Cell Proteomics,2006.5(8):p.1382-95.
[20]. Qian, W.J., M.E. Monroe, and T. Liuet.al., Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 160/180 labeling and the accurate mass and time tag approach. Mol Cell Proteomics,2005.4(5):p. 700-9.
[21]. Chen, X., S.W. Cushman, and L.K. Pannellet.al., Quantitative proteomic analysis of the secretory proteins from rat adipose cells using a 2D liquid chromatography-MS/MS approach. J Proteome Res,2005.4(2):p.570-7.
[22]. Hood, B.L., D.A. Lucas, and G. Kimet.al., Quantitative analysis of the low molecular weight serum proteome using 18O stable isotope labeling in a lung tumor xenograft mouse model. J Am Soc Mass Spectrom,2005.16(8):p.1221-30.
[23]. Cristoni, S. and L.R. Bernardi, Bioinformatics in mass spectrometry data analysis for proteomics studies. Expert Rev Proteomics,2004.1(4):p.469-83.
[24]. Haoudi, A. and H. Bensmail, Bioinformatics and data mining in proteomics. Expert Rev Proteomics,2006.3(3):p.333-43.
[25]. Ivakhno, S. and A. Kornelyuk, Quantitative proteomics and its applications for systems biology. Biochemistry (Mosc),2006.71(10):p.1060-72.
[26]. Gygi, S.P., B. Rist, and S.A. Gerberet.al., Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol,1999.17(10):p.994-9.
[27]. Koehler, C.J., M. Strozynski, and F. Kozielskiet.al., Isobaric peptide termini labeling for MS/MS-based quantitative proteomics. J Proteome Res,2009.8(9):p.4333-41.
[28]. Cox, J. and M. Mann, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol,2008.26(12):p.1367-72.
[29]. Lin, S.M., L. Zhu, and A.Q. Winteret.al., What is mzXML good for?. Expert Rev Proteomics,2005.2(6):p.839-45.
[30]. Braisted, J.C., S. Kuntumalla, and C. Vogelet.al., The APEX Quantitative Proteomics Tool:generating protein quantitation estimates from LC-MS/MS proteomics results. BMC Bioinformatics,2008.9:p.529.
[31]. Li, X.J., H. Zhang, and J.A. Ranishet.al., Automated statistical analysis of protein abundance ratios from data generated by stable-isotope dilution and tandem mass spectrometry. Anal Chem,2003.75(23):p.6648-57.
[32]. Arntzen, M.O., C.J. Koehler, and H. Barsneset.al., IsobariQ:software for isobaric quantitative proteomics using IPTL, iTRAQ, and TMT. J Proteome Res,2011.10(2): p.913-20.
[33]. Park, S.K., J.D. Venable, and T. Xuet.al., A quantitative analysis software tool for mass spectrometry-based proteomics. Nat Methods,2008.5(4):p.319-22.
[34]. Bouyssie, D., P.A. de Gonzalez, and E. Moutonet.al., 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. Mol Cell Proteomics,2007.6(9):p.1621-37.
[35]. Mortensen, P., J.W. Gouw, and J.V. Olsenet.al., MSQuant, an open source platform for mass spectrometry-based quantitative proteomics. J Proteome Res,2010.9(1):p. 393-403.
[36]. Lin, W.T., W.N. Hung, and Y.H. Yianet.al., Multi-Q:a fully automated tool for multiplexed protein quantitation. J Proteome Res,2006.5(9):p.2328-38.
[37]. Khan, Z., J.S. Bloom, and B.A. Garciaet.al., Protein quantification across hundreds of experimental conditions. Proc Natl Acad Sci U S A,2009.106(37):p.15544-8.
[38]. Boehm, A.M., S. Putz, and D. Altenhoferet.al., Precise protein quantification based on peptide quantification using iTRAQ. BMC Bioinformatics,2007.8:p.214.
[39]. Mason, C.J., T.M. Therneau, and J.E. Eckel-Passowet.al., A method for automatically interpreting mass spectra of 18O-labeled isotopic clusters. Mol Cell Proteomics,2007. 6(2):p.305-18.
[40]. Halligan, B.D., R.Y. Slyper, and S.N. Twiggeret.al., ZoomQuant:an application for the quantitation of stable isotope labeled peptides. J Am Soc Mass Spectrom,2005. 16(3):p.302-6.
[41]. Han, D.K., J. Eng, and H. Zhouet.al., Quantitative profiling of differentiation-induced microsomal proteins using isotope-coded affinity tags and mass spectrometry. Nat Biotechnol,2001.19(10):p.946-51.
[42]. Patwardhan, A.J., E.F. Strittmatter, and D.N. Campet.al., Quantitative proteome analysis of breast cancer cell lines using 18O-labeling and an accurate mass and time tag strategy. Proteomics,2006.6(9):p.2903-15.
[43]. Wang, J., P. Gutierrez, and N. Edwardset.al., Integration of 18O labeling and solution isoelectric focusing in a shotgun analysis of mitochondrial proteins. J Proteome Res, 2007.6(12):p.4601-7.
[44]. Ding, S.J., Y. Wang, and J.M. Jacobset.al., Quantitative phosphoproteome analysis of lysophosphatidic acid induced chemotaxis applying dual-step (1.8)O labeling coupled with immobilized metal-ion affinity chromatography. J Proteome Res,2008.7(10):p. 4215-24.
[45]. Fenselau, C. and X. Yao,18O2-labeling in quantitative proteomic strategies:a status report. J Proteome Res,2009.8(5):p.2140-3.
[46]. Helsens, K., L. Martens, and J. Vandekerckhoveet.al., MascotDatfile:an open-source library to fully parse and analyse MASCOT MS/MS search results. Proteomics,2007. 7(3):p.364-6.
[47]. Ong, S.E., B. Blagoev, and I. Kratchmarovaet.al., Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics,2002.1(5):p.376-86.
[48]. Zhong, H., S.L. Marcus, and L. Li, Two-dimensional mass spectra generated from the analysis of 15N-labeled and unlabeled peptides for efficient protein identification and de novo peptide sequencing. J Proteome Res,2004.3(6):p.1155-63.
[49]. Hardt, M., H.E. Witkowska, and S. Webbet.al., Assessing the effects of diurnal variation on the composition of human parotid saliva:quantitative analysis of native peptides using iTRAQ reagents. Anal Chem,2005.77(15):p.4947-54.
[50]. Rao, K.C., V. Palamalai, and J.R. Dunlevyet.al., Peptidyl-Lys metalloendopeptidase-catalyzed 180 labeling for comparative proteomics: application to cytokine/lipolysaccharide-treated human retinal pigment epithelium cell line. Mol Cell Proteomics,2005.4(10):p.1550-7.
[51]. Fernandez-de-Cossio, J., L.J. Gonzalez, and Y. Satomiet.al., Automated interpretation of mass spectra of complex mixtures by matching of isotope peak distributions. Rapid Commun Mass Spectrom,2004.18(20):p.2465-72.
[52]. Pedrioli, P.G., J.K. Eng, and R. Hubleyet.al., A common open representation of mass spectrometry data and its application to proteomics research. Nat Biotechnol,2004. 22(11):p.1459-66.
[53]. Veenstra, T.D., Global and targeted quantitative proteomics for biomarker discovery. J Chromatogr B Analyt Technol Biomed Life Sci,2007.847(1):p.3-11.
[54]. Leth-Larsen, R., R. Lund, and H.V. Hansenet.al., Metastasis-related plasma membrane proteins of human breast cancer cells identified by comparative quantitative mass spectrometry. Mol Cell Proteomics,2009.8(6):p.1436-49.
[55]. Roesli, C., B. Borgia, and C. Schliemannet.al., Comparative analysis of the membrane proteome of closely related metastatic and nonmetastatic tumor cells. Cancer Res,2009.69(13):p.5406-14.
[56]. Hsu, J.L., L.Y. Wang, and S.Y. Wanget.al., Functional phosphoproteomic profiling of phosphorylation sites in membrane fractions of salt-stressed Arabidopsis thaliana. Proteome Sci,2009.7:p.42.
[57]. Miao, H., D.Q. Li, and A. Mukherjeeet.al., EphA2 mediates ligand-dependent inhibition and ligand-independent promotion of cell migration and invasion via a reciprocal regulatory loop with Akt. Cancer Cell,2009.16(1):p.9-20.
[58]. Taddei, M.L., M. Parri, and A. Angelucciet.al., Kinase-dependent and -independent roles of EphA2 in the regulation of prostate cancer invasion and metastasis. Am J Pathol,2009.174(4):p.1492-503.
[59]. Blueggel, M., D. Chamrad, and H.E. Meyer, Bioinformatics in proteomics. Curr Pharm Biotechnol,2004.5(1):p.79-88.
[60]. Wallin, E. and G. von Heijne, Genome-wide analysis of integral membrane proteins from eubacterial, archaean, and eukaryotic organisms. Protein Sci,1998.7(4):p. 1029-38.
[61]. Hopkins, A.L. and C.R. Groom, The druggable genome. Nat Rev Drug Discov,2002. 1(9):p.727-30.
[62]. Lund, R., R. Leth-Larsen, and O.N. Jensenet.al., Efficient isolation and quantitative proteomic analysis of cancer cell plasma membrane proteins for identification of metastasis-associated cell surface markers. J Proteome Res,2009.8(6):p.3078-90.
[63]. Tan, S., H.T. Tan, and M.C. Chung, Membrane proteins and membrane proteomics. Proteomics,2008.8(19):p.3924-32.
[64]. Ramus, C., P.A. de Gonzalez, and C. Dahoutet.al., An optimized strategy for ICAT quantification of membrane proteins. Mol Cell Proteomics,2006.5(1):p.68-78.
[65]. Molloy, M.P., B.R. Herbert, and B.J. Walshet.al., Extraction of membrane proteins by differential solubilization for separation using two-dimensional gel electrophoresis. Electrophoresis,1998.19(5):p.837-44.
[66]. Santoni, V., M. Molloy, and T. Rabilloud, Membrane proteins and proteomics:un amour impossible?. Electrophoresis,2000.21(6):p.1054-70.
[67]. Gygi, S.P., G.L. Corthals, and Y. Zhanget.al., Evaluation of two-dimensional gel electrophoresis-based proteome analysis technology. Proc Natl Acad Sci U S A,2000. 97(17):p.9390-5.
[68]. Wu, C.C. and R. Jr Yates, The application of mass spectrometry to membrane proteomics. Nat Biotechnol,2003.21(3):p.262-7.
[69]. Link, A.J., J. Eng, and D.M. Schieltzet.al., Direct analysis of protein complexes using mass spectrometry. Nat Biotechnol,1999.17(7):p.676-82.
[70]. Chenau, J., S. Michelland, and J. Sidibeet.al., Peptides OFFGEL electrophoresis:a suitable pre-analytical step for complex eukaryotic samples fractionation compatible with quantitative iTRAQ labeling. Proteome Sci,2008.6:p.9.
[71]. Pinkse, M.W., P.M. Uitto, and M.J. Hilhorstet.al., Selective isolation at the femtomole level of phosphopeptides from proteolytic digests using 2D-NanoLC-ESI-MS/MS and titanium oxide precolumns. Anal Chem,2004.76(14): p.3935-43.
[72]. Nita-Lazar, A., H. Saito-Benz, and F.M. White, Quantitative phosphoproteomics by mass spectrometry:past, present, and future. Proteomics,2008.8(21):p.4433-43.
[73]. Lee, H.J., K. Na, and M.S. Kwonet.al., Quantitative analysis of phosphopeptides in search of the disease biomarker from the hepatocellular carcinoma specimen. Proteomics,2009.9(12):p.3395-408.
[74]. Leroy, C., C. Fialin, and A. Sirventet.al., Quantitative phosphoproteomics reveals a cluster of tyrosine kinases that mediates SRC invasive activity in advanced colon carcinoma cells. Cancer Res,2009.69(6):p.2279-86.
[75]. Chaerkady, R. and A. Pandey, Quantitative proteomics for identification of cancer biomarkers. Proteomics Clinical Applications,2007.1(9):p.1080-1089.
[76].谭颖,王玉霞,and李晶et.al.,～(18)O标记法在定量蛋白质组学中的应用.细胞生物学杂志,2006(06).
[77]. Stewart, I.I., T. Thomson, and D. Figeys,18O labeling:a tool for proteomics. Rapid Commun Mass Spectrom,2001.15(24):p.2456-65.
[78]. Niles, R., H.E. Witkowska, and S. Allenet.al., Acid-catalyzed oxygen-18 labeling of peptides. Anal Chem,2009.81(7):p.2804-9.
[79]. Hajkova, D., K.C. Rao, and M. Miyagi, pH dependency of the carboxyl oxygen exchange reaction catalyzed by lysyl endopeptidase and trypsin. J Proteome Res, 2006.5(7):p.1667-73.
[80]. Miyagi, M. and K.C. Rao, Proteolytic 18O-labeling strategies for quantitative proteomics. Mass Spectrom Rev,2007.26(1):p.121-36.
[81]. Kristiansen, T.Z., H.C. Harsha, and M. Gronborget.al., Differential membrane proteomics using 18O-labeling to identify biomarkers for cholangiocarcinoma. J Proteome Res,2008.7(11):p.4670-7.
[82]. Aikawa, T., C.A. Whipple, and M.E. Lopezet.al., Glypican-1 modulates the angiogenic and metastatic potential of human and mouse cancer cells. J Clin Invest, 2008.118(1):p.89-99.
[83]. Ushiku, T., H. Uozaki, and A. Shinozakiet.al., Glypican 3-expressing gastric carcinoma:distinct subgroup unifying hepatoid, clear-cell, and alpha-fetoprotein-producing gastric carcinomas. Cancer Sci,2009.100(4):p.626-32.
[84]. Vaught, D., D.M. Brantley-Sieders, and J. Chen, Eph receptors in breast cancer:roles in tumor promotion and tumor suppression. Breast Cancer Res,2008.10(6):p.217.
[85]. Faoro, L., P.A. Singleton, and G.M. Cervanteset.al., EphA2 mutation in lung squamous cell carcinoma promotes increased cell survival, cell invasion, focal adhesions, and mammalian target of rapamycin activation. J Biol Chem,2010. 285(24):p.18575-85.
[86]. Brantley-Sieders, D.M., G. Zhuang, and D. Hickset.al., The receptor tyrosine kinase EphA2 promotes mammary adenocarcinoma tumorigenesis and metastatic progression in mice by amplifying ErbB2 signaling. J Clin Invest,2008.118(1):p. 64-78.
[87]. Lu, C., M.M. Shahzad, and H. Wanget.al., EphA2 overexpression promotes ovarian cancer growth. Cancer Biol Ther,2008.7(7):p.1098-103.
[88]. Zelinski, D.P., N.D. Zantek, and J.C. Stewartet.al., EphA2 overexpression causes tumorigenesis of mammary epithelial cells. Cancer Res,2001.61(5):p.2301-6.
[89]. Chen, J., G. Zhuang, and L. Friedenet.al., Eph receptors and Ephrins in cancer: common themes and controversies. Cancer Res,2008.68(24):p.10031-3.
[90]. Liu, Y., X. Zhang, and Y. Qiuet.al., Clinical significance of EphA2 expression in squamous-cell carcinoma of the head and neck. J Cancer Res Clin Oncol,2011. 137(5):p.761-9.
[1]Yates J R, Ruse C I, Nakorchevsky A. Proteomics by mass spectrometry:approaches, advances, and applications. Annu Rev Biomed Eng,2009,11:49-79.
[2]Vermeμlen M, Selbach M. Quantitative proteomics:a tool to assess cell differentiation. Curr Opin Cell Biol,2009,21(6):761-766.
[3]Teckchandani A, Toida N, Goodchild J, et al. Quantitative proteomics identifies a Dab2/integrin modμle regμlating cell migration. J Cell Biol,2009,186(1):99-111.
[4]Lee K H. Proteomics:a technology-driven and technology-limited discovery science. Trends Biotechnol,2001,19(6):217-222.
[5]Blackstock W P, Weir M P. Proteomics:quantitative and physical mapping of cellular proteins. Trends Biotechnol,1999,17(3):121-127.
[6]Ong S E, Mann M. Mass spectrometry-based proteomics turns quantitative. Nat Chem Biol,2005,1(5):252-262.
[7]Ivakhno S, Kornelyuk A. Quantitative proteomics and its applications for systems biology. Biochemistry (Mosc),2006,71(10):1060-1072.
[8]Elliott M H, Smith D S, Parker C E, et al. Current trends in quantitative proteomics. J Mass Spectrom,2009,44(12):1637-1660.
[9]Mueller L N, Brusniak M Y, Mani D R, et al. An assessment of software solutions for the analysis of mass spectrometry based quantitative proteomics data. J Proteome Res,2008,7(1):51-61.
[10]Gorg A, Weiss W, Dunn M J. Current two-dimensional electrophoresis technology for proteomics. Proteomics,2004,4(12):3665-3685.
[11]Patton W F. Detection technologies in proteome analysis. J Chromatogr B Analyt Technol Biomed Life Sci,2002,771(1-2):3-31.
[12]Smales C M, Birch J R, Racher A J, et al. Evaluation of individual protein errors in silver-stained two-dimensional gels. Biochem Biophys Res Commun,2003,306(4):1050-1055.
[13]Smolka M, Zhou H, Aebersold R. Quantitative protein profiling using two-dimensional gel electrophoresis, isotope-coded affinity tag labeling, and mass spectrometry. Mol Cell Proteomics,2002,1(1):19-29.
[14]Camacho-Carvajal M M, Wollscheid B, Aebersold R, et al. Two-dimensional Blue native/SDS gel electrophoresis of mμlti-protein complexes from whole cellμlar lysates: a proteomics approach. Mol Cell Proteomics,2004,3(2):176-182.
[15]Sinha P, Poland J, Schnolzer M, et al. A new silver staining apparatus and procedure for matrix-assisted laser desorption/ionization-time of flight analysis of proteins after two-dimensional electrophoresis. Proteomics,2001,1(7):835-840.
[16]Yan J X, Wait R, Berkelman T, et al. A modified silver staining protocol for visualization of proteins compatible with matrix-assisted laser desorption/ionization and electrospray ionization-mass spectrometry. Electrophoresis,2000,21(17):3666-3672.
[17]Candiano G, Bruschi M, Musante L, et al. Blue silver:a very sensitive colloidal Coomassie G-250 staining for proteome analysis. Electrophoresis,2004,25(9):1327-1333.
[18]Marshall T. Detection of protein in polyacrylamide gels using an improved silver stain. Anal Biochem,1984,136(2):340-346.
[19]Nock C M, Ball M S, White I R, et al. Mass spectrometric compatibility of Deep Purple and SYPRO Ruby total protein stains for high-throughput proteomics using large-format two-dimensional gel electrophoresis. Rapid Commun Mass Spectrom,2008,22(6):881-886.
[20]Chakravarti B, Fathy P, Sindicich M, et al. Comparison of SYPRO Ruby and Flamingo fluorescent stains for application in proteomic research. Anal Biochem,2010,398(1):1-6.
[21]Lopez M F, Berggren K, Chernokalskaya E, et al. A comparison of silver stain and SYPRO Ruby Protein Gel Stain with respect to protein detection in two-dimensional gels and identification by peptide mass profiling. Electrophoresis,2000,21(17):3673-3683.
[22]Minden J S, Dowd S R, Meyer H E, et al. Difference gel electrophoresis. Electrophoresis,2009,30 Suppl 1:S156-S161.
[23]Timms J F, Cramer R. Difference gel electrophoresis. Proteomics,2008,8(23-24): 4886-4897.
[24]Santoni V, Molloy M, Rabilloud T. Membrane proteins and proteomics:un amour impossible?Electrophoresis,2000,21(6):1054-1070.
[25]Westermeier R, Schickle H. The current state of the art in high-resolution two-dimensional electrophoresis. Arch Physiol Biochem,2009,115(5):279-285.
[26]Rabilloud T. Membrane proteins and proteomics:love is possible, but so difficult. Electrophoresis,2009,30 Suppl 1:S174-S180.
[27]Vercauteren F G, Arckens L, Quirion R. Applications and current challenges of proteomic approaches, focusing on two-dimensional electrophoresis. Amino Acids, 2007,33(3):405-414.
[28]Flory M R, Griffin T J, Martin D, et al. Advances in quantitative proteomics using stable isotope tags. Trends Biotechnol,2002,20(12 Suppl):S23-S29.
[29]Ong S E, Blagoev B, Kratchmarova I, et al. Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Mol Cell Proteomics,2002,1(5):376-386.
[30]Lafaye A, Labarre J, Tabet J C, et al. Liquid chromatography-mass spectrometry and 15N metabolic labeling for quantitative metabolic profiling. Anal Chem, 2005,77(7):2026-2033.
[31]Gygi S P, Rist B, Gerber S A, et al. Quantitative analysis of complex protein mixtures using isotope-coded affinity tags. Nat Biotechnol,1999,17(10):994-999.
[32]Shadforth I P, Dunkley T P, Lilley K S, et al. i-Tracker:for quantitative proteomics using iTRAQ. BMC Genomics,2005,6:145.
[33]Mirgorodskaya O A, Kozmin Y P, Titov M I, et al. Quantitation of peptides and proteins by matrix-assisted laser desorption/ionization mass spectrometry using (18)O-labeled internal standards. Rapid Commun Mass Spectrom,2000,14 (14):1226-1232.
[34]Marcilla M, Alpizar A, Paradela A, et al. A systematic approach to assess amino acid conversions in SILAC experiments. Talanta,2011,84(2):430-436.
[35]Tao W A. Soluble polymer-based isotopic labeling (SoPIL):a new strategy to discover protein biomarkers?Expert Rev Proteomics,2007,4(5):603-607.
[36]Zhang H, Yan W, Aebersold R. Chemical probes and tandem mass spectrometry:a strategy for the quantitative analysis of proteomes and subproteomes. Curr Opin Chem Biol,2004,8(1):66-75.
[37]Aggarwal K, Choe L H, Lee K H. Shotgun proteomics using the iTRAQ isobaric tags. Brief Funct Genomic Proteomic,2006,5(2):112-120.
[38]Anderson N L, Anderson N G. The human plasma proteome:history, character, and diagnostic prospects. Mol Cell Proteomics,2002,1(11):845-867.
[39]Wu L, Han D K. Overcoming the dynamic range problem in mass spectrometry- based shotgun proteomics. Expert Rev Proteomics,2006,3(6):611-619.
[40]Righetti P G, Castagna A, Antonucci F, et al. Proteome analysis in the clinical chemistry laboratory:myth or reality?Clin Chim Acta,2005,357(2):123-139.
[41]Omenn G S. Strategies for plasma proteomic profiling of cancers. Proteomics, 2006,6(20):5662-5673.
[42]Cordwell S J, Nouwens A S, Verrills N M, et al. Subproteomics based upon protein cellμlar location and relative solubilities in conjunction with composite two-dimensional electrophoresis gels. Electrophoresis,2000,21(6):1094-1103.
[43]Murphy R C, Clay K L. Synthesis and back exchange of 180 labeled amino acids for use as internal standards with mass spectrometry. Biomed Mass Spectrom, 1979,6(7):309-314.
[44]Schnolzer M, Jedrzejewski P, Lehmann W D. Protease-catalyzed incorporation of 180 into peptide fragments and its application for protein sequencing by electrospray and matrix-assisted laser desorption/ionization mass spectrometry. Electrophoresis,1996,17(5):945-953.
[45]Shevchenko A, Chernushevich I, Ens W, et al. Rapid 'de novo' peptide sequencing by a combination of nanoelectrospray, isotopic labeling and a quadrupole/time-of-flight mass spectrometer. Rapid Commun Mass Spectrom,1997,11(9):1015-1024.
[46]Qin J, Herring C J, Zhang X. De novo peptide sequencing in an ion trap mass spectrometer with 180 labeling. Rapid Commun Mass Spectrom,1998,12(5): 209-216.
[47]Stewart I I, Thomson T, Figeys D.180 labeling:a tool for proteomics. Rapid Commun Mass Spectrom,2001,15(24):2456-2465.
[48]Broedel O, Krause E, Stephanowitz H, et al. In-Gel (18)O Labeling for Improved Identification of Proteins from 2-DE Gel Spots in Comparative Proteomic Experiments. J Proteome Res,2009,8(7):3771-3777.
[49]Lane C S, Wang Y, Betts R, et al. Comparative cytochrome P450 proteomics in the livers of immunodeficient mice using 180 stable isotope labeling. Mol Cell Proteomics,2007,6(6):953-962.
[50]Sakai J, Kojima S, Yanagi K, et al.180-labeling quantitative proteomics using an ion trap mass spectrometer. Proteomics,2005,5(1):16-23.
[51]Halligan B D, Slyper R Y, Twigger S N, et al. ZoomQuant:an application for the quantitation of stable isotope labeled peptides. J Am Soc Mass Spectrom,2005, 16(3):302-306.
[52]Mason C J, Therneau T M, Eckel-Passow J E, et al. A method for automatically interpreting mass spectra of 180-labeled isotopic clusters. Mol Cell Proteomics,2007, 6(2):305-318.
[53]Fenselau C, Yao X.1802-labeling in quantitative proteomic strategies:a status report. J Proteome Res,2009,8(5):2140-2143.
[54]Yao X, Afonso C, Fenselau C. Dissection of proteolytic 180 labeling: endoprotease-catalyzed 16O-to-18O exchange of truncated peptide substrates. J Proteome Res,2003,2(2):147-152.
[55]Angel P M, Orlando R. Trypsin is the primary mechanism by which the (18)O isotopic label is lost in quantitative proteomic studies. Anal Biochem,2006,359(1):26-34.
[56]Qian W J, Monroe M E, Liu T, et al. Quantitative proteome analysis of human plasma following in vivo lipopolysaccharide administration using 160/180 labeling and the accurate mass and time tag approach. Mol Cell Proteomics,2005,4(5):700-709.
[57]Liu T, Qian W J, Strittmatter E F, et al. High-throμghput comparative proteome analysis using a quantitative cysteinyl-peptide enrichment technology. Anal Chem, 2004,76(18):5345-5353.
[58]Storms H F, Van Der Heijden R, Tjaden U R, et al. Considerations for proteolytic labeling-optimization of 180 incorporation and prohibition of back-exchange. Rapid Commun Mass Spectrom,2006,20(23):3491-3497.
[59]Petritis B, Qian W J, Camp D, et al. A Simple Procedure for Effective Quenching of Trypsin Activity and Prevention of 180-Labeling Back-Exchange. J Proteome Res,2009,8(5):2157-2163.
[60]Brown K J, Fenselau C. Investigation of doxorubicin resistance in MCF-7 breast cancer cells using shot-gun comparative proteomics with proteolytic 180 labeling. J Proteome Res,2004,3(3):455-462.
[61]Blonder J, Hale M L, Chan K C, et al. Quantitative profiling of the detergent-resistant membrane proteome of iota-b toxin induced vero cells. J Proteome Res,2005,4(2):523-531.
[62]Nelson C J, Hegeman A D, Harms A C, et al. A quantitative analysis of Arabidopsis plasma membrane using trypsin-catalyzed (18)O labeling. Mol Cell Proteomics,2006,5(8):1382-1395.
[63]Staes A, Demol H, Van Damme J, et al. Global differential non-gel proteomics by quantitative and stable labeling of tryptic peptides with oxygen-18. J Proteome Res,2004,3(4):786-791.
[64]Sevinsky J R, Brown K J, Cargile B J, et al. Minimizing back exchange in 180/160 quantitative proteomics experiments by incorporation of immobilized trypsin into the initial digestion step. Anal Chem,2007,79(5):2158-2162.
[65]Niles R, Witkowska H E, Allen S, et al. Acid-catalyzed oxygen-18 labeling of peptides. Anal Chem,2009,81(7):2804-2809.
[66]Patwardhan A J, Strittmatter E F, Camp D N, et al. Quantitative proteome analysis of breast cancer cell lines using 180-labeling and an accurate mass and time tag strategy. Proteomics,2006,6(9):2903-2915.
[67]Hathout Y, Riordan K, Gehrmann M, et al. Differential protein expression in the cytosol fraction of an MCF-7 breast cancer cell line selected for resistance toward melphalan. J Proteome Res,2002,1(5):435-442.
[68]Brown K J, Fenselau C. Investigation of doxorubicin resistance in MCF-7 breast cancer cells using shot-gun comparative proteomics with proteolytic 180 labeling. J Proteome Res,2004,3(3):455-462.
[69]Bunai K, Yamane K. Effectiveness and limitation of two-dimensional gel electrophoresis in bacterial membrane protein proteomics and perspectives. J Chromatogr B Analyt Technol Biomed Life Sci,2005,815(1-2):227-236.
[70]Chi L M, Lee C W, Chang K P, et al. Enhanced interferon signaling pathway in oral cancer revealed by quantitative proteome analysis of microdissected specimens using 160/180 labeling and integrated two-dimensional LC-ESI-MALDI tandem MS. Mol Cell Proteomics,2009,8(7):1453-1474.
[71]Bantscheff M, Dumpelfeld B, Kuster B. Femtomol sensitivity post-digest(18)O labeling for relative quantification of differential protein complex composition. Rapid Commun Mass Spectrom,2004,18(8):869-876.
[72]Jorge I, Navarro P, Martinez-Acedo P, et al. Statistical model to analyze quantitative proteomics data obtained by 180/160 labeling and linear ion trap mass spectrometry: application to the study of vascμlar endothelial growth factor-induced angiogenesis in endothelial cells. Mol Cell Proteomics,2009,8(5):1130-1149.
[73]Lundstrom K. Structural genomics for membrane proteins. Cell Mol Life Sci,2006, 63(22):2597-2607.
[74]Rais I, Karas M, Schagger H. Two-dimensional electrophoresis for the isolation of integral membrane proteins and mass spectrometric identification. Proteomics, 2004,4(9):2567-2571.
[75]Rabilloud T. Membrane proteins ride shotgun. Nat Biotechnol,2003,21(5):508-510.
[76]Kristiansen T Z, Harsha H C, Gronborg M, et al. Differential membrane proteomics using 180-labeling to identify biomarkers for cholangiocarcinoma. J Proteome Res,2008,7(11):4670-4677.
[77]Chong P K, Lee H, Kong J W, et al. Phosphoproteomics, oncogenic signaling and cancer research. Proteomics,2008,8(21):4370-4382.
[78]Thingholm T E, Jensen O N, Larsen M R. Analytical strategies for phosphoproteomics. Proteomics,2009,9(6):1451-1468.
[79]Reinders J, Sickmann A. State-of-the-art in phosphoproteomics. Proteomics,2005, 5(16):4052-4061.
[80]Johnson S A, Hunter T. Phosphoproteomics finds its timing. Nat Biotechnol, 2004,22(9):1093-1094.
[81]Ding S, Bond A E, Lemiere F, et al. Online immobilized metal affinity chromatography /mass spectrometric analysis of changes elicited by cCMP in the murine brain phosphoproteome. Rapid Commun Mass Spectrom,2008,22(24):4129-4138.
[82]Wei J, Zhang Y, Wang J, et al. Highly efficient enrichment of phosphopeptides by magnetic nanoparticles coated with zirconium phosphonate for phosphoproteome analysis. Rapid Commun Mass Spectrom,2008,22(7):1069-1080.
[83]Sui S, Wang J, Yang B, et al. Phosphoproteome analysis of the human Chang liver cells using SCX and a complementary mass spectrometric strategy. Proteomics,2008,8(10):2024-2034.
[84]Ahn Y H, Ji E S, Lee J Y, et al. Coupling of TiO(2)-mediated enrichment and on-bead guanidinoethanethiol labeling for effective phosphopeptide analysis by matrix-assisted laser desorption/ionization mass spectrometry. Rapid Commun Mass Spectrom,2007,21(24):3987-3994.
[85]Feng S, Pan C, Jiang X, et al. Fe3+ immobilized metal affinity chromatography with silica monolithic capillary column for phosphoproteome analysis. Proteomics,2007,7(3):351-360.
[86]Pinkse M W, Uitto P M, Hilhorst M J, et al. Selective isolation at the femtomole level of phosphopeptides from proteolytic digests using 2D-NanoLC-ESI-MS/MS and titanium oxide precolumns. Anal Chem,2004,76(14):3935-3943.
[87]Liang X, Fonnum G, Hajivandi M, et al. Quantitative comparison of IMAC and TiO2 surfaces used in the study of regμlated, dynamic protein phosphorylation. J Am Soc Mass Spectrom,2007,18(11):1932-1944.
[88]Lee H J, Na K, Kwon M S, et al. Quantitative analysis of phosphopeptides in search of the disease biomarker from the hepatocellμlar carcinoma specimen. Proteomics,2009,9(12):3395-3408.
[89]Leroy C, Fialin C, Sirvent A, et al. Quantitative phosphoproteomics reveals a cluster of tyrosine kinases that mediates SRC invasive activity in advanced colon carcinoma cells. Cancer Res,2009,69(6):2279-2286.
[90]Yu L R, Zhu Z, Chan K C, et al. Improved titanium dioxide enrichment of phosphopeptides from HeLa cells and high confident phosphopeptide identification by cross-validation of MS/MS and MS/MS/MS spectra. J Proteome Res, 2007,6(11):4150-4162.
[91]Gevaert K, Staes A, Van Damme J, et al. Global phosphoproteome analysis on human HepG2 hepatocytes using reversed-phase diagonal LC. Proteomics,2005, 5(14):3589-3599.
[92]Piggee C. Phosphoproteomics:miles to go before it's routine. Anal Chem,2009, 81(7):2418-2420.