4.5 Article

Large-scale μLC-MS/MS for silver- and Coomassie blue-stained polyacrylamide gels

期刊

ELECTROPHORESIS
卷 26, 期 23, 页码 4495-4507

出版社

WILEY
DOI: 10.1002/elps.200500093

关键词

automation; Coomassie Blue staining; mu LC-MS/MS; mass spectrometry; protein identification; SEQUEST; silver staining; two-dimensional gel electrophoresis

资金

  1. NCRR NIH HHS [P41 RR11823-09] Funding Source: Medline

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2-DE combined with LC-MS/MS has become a routine, reliable protein separation and identification technology for proteome analysis. The demand for large-scale protein identifications after 2-DE separation requires a sensitive and high-throughput LC-MS/MS method. In this report, a simple, splitless, fully automated capillary LC-MS/MS system was described for the large-scale identification of proteins from gels stained with either silver or CBB. The gel samples were digested and peptides were extracted using an in-gel digestion workstation. The peptides were automatically introduced into a capillary column by an autosampler connected to an HPLC pump. A nanoLC pump was then used to deliver the gradient and elute the peptides from the capillary column directly into an LCQ IT mass spectrometer. Neither a peptide trapping setting nor a flow split is needed in this simple setup. The collected MS/MS spectra were then automatically searched by SEQUEST, and filtered and organized by DTASelect. Hundreds of silver-stained or CBB-stained Shewanella oneidensis, Geobacter sulfurreducens, and Geobacter metallireducens proteins separated by denaturing or nondenaturing 2-DE were digested and routinely analyzed using this fully automated mu LC-MS/MS system. High peptide hits and sequence coverage were achieved for most CBB-stained gel spots. About 75% of the spots were found to contain multiple proteins. Although silver staining is not commonly thought to be optimal for MS analysis, protein identifications were successfully obtained from silver-stained 2-DE spots detected using methods with and without formaldehyde for protein fixation.

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