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Pseudomonas aeruginosa is a complex, versatile and medically significant Gram-negative pathogen. The lipopolysaccharide-rich outer membrane (OM) of P. aeruginosa provides a protective barrier against the immune system, but also contains a small proteome. According to predictive algorithms, the OM may contain 150 components. Its most prominent components are porins, which selectively admit nutrients required by the cell. Targeting the OM with therapeutic antibodies requires knowing not only what proteins are present under particular conditions, but would also be facilitated by topographical information indicating how exposed and accessible these proteins are for attack. We have explored three different technologies for comparative proteomic studies of the OM proteome, each of which might offer particular insights. (i) SILAC studies in which OM proteins from standard-medium cells were compared with those from cells grown on 13C-substituted lysine and arginine. A multiplexed MS/MS method (G. Zhang and T. Neubert (2006) Mol. Cell. Proteom. 5:401-411) was used to extract protein ratios. This method was compromised by the metabolic versatility of P. aeruginosa, through which 13C label was distributed into other amino acids and thereby made ineffective as a basis for quantitation. (ii) Proteome sampling by heavy treatment of intact cells with a succinimidyl ester biotinylating agent, followed by tryptic digestion and isolation and identification of only biotinylated peptides. This method convincingly favored exposed amino groups of surface proteins, and detected major shifts in expression that followed changes to growth conditions. For example, cells grown in the presence of excess iron expressed high levels of nitrite reductase. (iii) As a further approach to quantitative comparative proteomics, digests of separate OM preps from differently treated cells were reductively methylated using sodium cyanoborohydride and either standard or deuterated formaldehyde. Initial comparisons of light and heavy controls suggested that this approach would be superior to metabolic labeling by SILAC.