A Fully Automated System with Online Sample Loading, Isotope Dimethyl Labeling and Multidimensional Separation for High-Throughput Quantitative Proteome Analysis Fangjun Wang, Rui Chen, Jun Zhu, Deguang Sun, Chunxia Song, Yifeng Wu, Mingliang Ye, Liming Wang, and Hanfa Zou* ,† CAS Key Lab of Separation Sciences for Analytical Chemistry, National Chromatographic Research and Analysis Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, China, and The Second Affiliated Hospital of Dalian Medical University, Dalian 116027, China Multidimensional separation is often applied for large- scale qualitative and quantitative proteome analysis. A fully automated system with integration of a reversed phase-strong cation exchange (RP-SCX) biphasic trap column into vented sample injection system was devel- oped to realize online sample loading, isotope dimethyl labeling and online multidimensional separation of the proteome samples. Comparing to conventionally manual isotope labeling and off-line fractionation technologies, this system is fully automated and time-saving, which is benefit for improving the quantification reproducibility and accuracy. As phosphate SCX monolith was integrated into the biphasic trap column, high sample injection flow rate and high-resolution stepwise fractionation could be easily achieved. 1000 proteins could be quantified in 30 h proteome analysis, and the proteome coverage of quantitative analysis can be further greatly improved by prolong the multidimensional separation time. This sys- tem was applied to analyze the different protein expression level of HCC and normal human liver tissues. After three times replicated analysis, finally 94 up-regulated and 249 down-regulated (HCC/Normal) proteins were successfully obtained. These significantly regulated proteins are widely validated by both gene and proteins expression studies previously. Such as some enzymes involved in urea cycle, methylation cycle and fatty acids catabolism in liver were all observed down-regulated. Mass spectrometry (MS) has been widely applied in protein quantification of various biological samples. 1-3 And it is emerging as a powerful tool for elucidation of different physiological and pathological processes in biological systems as well as discovery of useful protein biomarkers for early detection of serious diseases such as cancers. 4-8 Up to now, a number of technologies were applied to qualitative and quantitative proteome analysis of different biological samples, such as cell lines, tissues, and serum. Two-dimensional electrophoresis (2DE) is the most frequently used technology for protein quantifications of clinical samples. 9-11 As 2DE is unable to quantify very acidic or basic proteins, extremely large or small proteins and membrane proteins, MS is a good alternative without these limits for high throughput proteome analysis. 1 In order to accurately quantify proteins by MS in shotgun proteome technology, two strategies are usually applied. The first one is label-free approach, which obtains the relative quantity of each peptide among samples by comparing the corresponding peak intensity in parallel nanoflow liquid chromatography coupled with tandem mass spectrometry (µLC-MS/MS) analyses. 12-14 The advantage of label-free approach is that no chemical labeling is required and several samples can be compared simultaneously. However, the poor reproducibility of µLC-MS/MS analysis might compromise the accuracy of quantification. The other one is stable isotope labeling, which usually codes different samples with different isotope reagents at first, and then the samples are mixed and analyzed by µLC-MS/MS. The relative quantity of each peptide among different samples is obtained by comparing the * To whom correspondence should be addressed. Phone: +86-411-84379610. Fax: +86-411-84379620. E-mail: hanfazou@dicp.ac.cn. Chinese Academy of Sciences. The Second Affiliated Hospital of Dalian Medical University. (1) Aebersold, R.; Mann, M. Nature 2003, 422, 198–207. (2) Ong, S. E.; Mann, M. Nat. Chem. Biol. 2005, 1, 252–262. (3) Abu-Farha, M.; Elisma, F.; Zhou, H. J.; Tian, R. J.; Zhou, H.; Asmer, M. S.; Figeys, D. Anal. Chem. 2009, 81, 4585–4599. (4) Chen, R.; Pan, S.; Brentnall, T. A.; Aebersold, R. Mol. Cell. Proteomics 2005, 4, 523–533. (5) Kuramitsu, Y.; Nakamura, K. Proteomics 2006, 6, 5650–5661. (6) Bertucci, F.; Birnbaum, D.; Goncalves, A. Mol. Cell. Proteomics 2006, 5, 1772–1786. (7) Zheng, J. J.; Gao, X.; Beretta, L.; He, F. C. Proteomics 2006, 6, 1716–1718. (8) Seow, T. K.; Liang, R.; Leow, C. K.; Chung, M. C. M. Proteomics 2001, 1, 1249–1263. (9) Seow, T. K.; Ong, S. E.; Liang, R.; Ren, E. C.; Chan, L.; Ou, K.; Chung, M. C. M. Electrophoresis 2000, 21, 1787–1813. (10) Liang, C.; Leow, C. K.; Neo, J. C. H.; Tan, G. S.; Lo, S. L.; Lim, J. W. E.; Seow, T. K.; Lai, P. B. S.; Chung, M. C. M. Proteomics 2005, 5, 2258– 2271. (11) Sun, W.; Xing, B. C.; Sun, Y.; Du, X. J.; Lu, M.; Hao, C. Y.; Lu, Z. A.; Mi, W.; Wu, S. F.; Wei, H. D.; Gao, X.; Zhu, Y. P.; Jiang, Y.; Qian, X. H.; He, F. C. Mol. Cell. Proteomics 2007, 6, 1798–1808. (12) Wang, W.; Zhou, H.; Lin, H.; Roy, S.; Shaler, T. A.; Hill, L. R.; Norton, S.; Kumar, P.; Anderle, M.; Becker, C. H. Anal. Chem. 2003, 75, 4818–4826. (13) Radulovic, D.; Jelveh, S.; Ryu, S.; Hamilton, T. G.; Foss, E.; Mao, Y.; Emili, A. Mol. Cell. Proteomics 2004, 3, 984–997. (14) Wang, F. J.; Ye, M. L.; Dong, J.; Tian, R. J.; Hu, L. H.; Han, G. H.; Jiang, X. N.; Wu, R. A.; Zou, H. F. J. Sep. Sci. 2008, 31, 2589–2597. Anal. Chem. 2010, 82, 3007–3015 10.1021/ac100075y 2010 American Chemical Society 3007 Analytical Chemistry, Vol. 82, No. 7, April 1, 2010 Published on Web 03/15/2010