Each sample was injected into the GC-MS immediately after derivatization. The recovery was calculated by comparing the GC-peak area of each metabolite derivative when analyzed in a standard mixture alone or spiked on spent microbial http://www.selleckchem.com/products/CHIR-258.html culture medium. The contribution by metabolites genuinely present

in the spent culture medium was subtracted from the final results. Derivatization of biological samples To evaluate the performance of each derivatization technique on real biological samples we derivatized spent Inhibitors,research,lifescience,medical culture medium samples (n = 9) of five different strains of Acidovorax temperans using both derivatization techniques. The methods were compared based on the number of metabolites detected and identified as well as on their ability to discriminate the different A. temperans strains. GeneSpring MS 1.2 software (Agilent Technologies, Santa Clara CA, USA) was used for data mining Inhibitors,research,lifescience,medical and multivariate data analysis. Results Repeatability of GC-MS analysis As a baseline for comparing the two derivatization techniques, we first determined the repeatability of our measurements with our GC-MS equipment, including factors such as variation in injection volumes, Inhibitors,research,lifescience,medical performance, etc. Samples containing a mixture of compounds that produce stable derivatives

by both silylation and alkylation were derivatized and injected six times into the GC-MS. Table 2 presents the variability observed between the six analyses. Excellent performance of the instrument was clearly demonstrated for both silylated and alkylated derivatives with relative variability below 10% (except for cysteine 50 pmol, MCF, 11.5%). Table 2. Repeatability Inhibitors,research,lifescience,medical Inhibitors,research,lifescience,medical (RSD) of the GC-MS

instrument for some stable metabolite derivatives. Stability of different derivatives The stability of metabolite derivatives is an important parameter for derivatized samples that may have to wait hours in a queue before injection. Figure 3 presents the variability of metabolite level data within 72 hours for both derivatization techniques tested. Except the amino acid alanine, all silylated derivatives presented a pronounced variability within 72 hours Batimastat compared to alkylated compounds (Figure 3A). For all compounds the yield of the derivative increased (Figure 4) indicating the silylation reaction was not driven to completion. With only one exception in the lower concentration mixture, all MCF derivatives were found to be remarkably stable over 72 hours (3 days) at room temperature (RSD < 10%) (Figure 3B). The internal standard in the samples was an isotope-labeled alanine, and evidently this could correct for the variation of silylated alanine levels. However, other silylated derivatives showed variable degrees of instability.