Fully automated online multi-dimensional protein profiling system for complex mixtures.

For high throughput proteome analysis of highly complex protein mixtures, we have constructed a fully automated online system for multi-dimensional protein profiling, which utilizes a combination of two-dimensional liquid chromatography and tandem mass spectrometry (2D-LC-MS-MS), based on our well-established offline system described previously [K. Fujii, T. Nakano, T. Kawamura, F. Usui, Y. Bando, R. Wang, T. Nishimura, J. Proteome Res. 3 (2004) 712]. A two-valve switching system on a programmable auto sample injector is utilized for online two-dimensional chromatography with strong cation-exchange (SCX) and reversed-phase (RP) separations. The SCX separation is carried out during the equilibration of RP chromatography and the entire sequence of analysis was performed under fully automated conditions within 4 h, based on six SCX fractionations, and 40 min running time for the two-dimensional RP chromatography. In order to evaluate its performance in the detection and identification of proteins, digests of six standard proteins and yeast 20S proteasome have been analyzed and their results were compared to those obtained by the one-dimensional reversed-phase chromatography system (ID-LC-MS-MS). The 2D-LC-MS-MS system demonstrated that both the number of peptide fragments detected and the protein coverage had more than doubled. Furthermore, this multi-dimensional protein profiling system was also applied to the human 26S proteasome, which is one of the highly complex protein mixtures. Consequently, 723 peptide fragments were identified as 31 proteasome components, together with other coexisting proteins in the sample. The identification could be comprehensively performed with a 63% sequence coverage on an average, and additionally, with modifications at the N-terminus. These results indicated that the online 2D-LC-MS-MS system being described here is capable of analyzing highly complex protein mixtures in a high throughput manner, and that it would be applicable to dynamic proteomics.