Sugar phosphate analysis with baseline separation and soft ionization by gas chromatography-negative chemical ionization-mass spectrometry improves flux estimation of bidirectional reactions in cancer cells.

Precise measurement of sugar phosphates in glycolysis and the pentose phosphate (PP) pathway for 13C-metabolic flux analysis (13C-MFA) is needed to understand cancer-specific metabolism. Although various analytical methods have been proposed, analysis of sugar phosphates is challenging because of the structural similarity of various isomers and low intracellular abundance. In this study, gas chromatography-negative chemical ionization-mass spectrometry (GC-NCI-MS) is applied to sugar phosphate analysis with o-(2,3,4,5,6-pentafluorobenzyl) oxime (PFBO) and trimethylsilyl (TMS) derivatization. Optimization of the GC temperature gradient achieved baseline separation of sugar phosphates in 31 min. Mass spectra showed the predominant generation of fragment ions containing all carbon atoms in the sugar phosphate backbone. The limit of detection of pentose 5-phosphates and hexose 6-phosphates was 10 nM. The method was applied to 13C-labeling measurement of sugar phosphates for 13C-MFA of the MCF-7 human breast cancer cell line. 13C-labeling of sugar phosphates for 13C-MFA improved the estimation of the net flux and reversible flux of bidirectional reactions in glycolysis and the PP pathway.

Investigations in the possibility of early detection of colorectal cancer by gas chromatography/triple-quadrupole mass spectrometry.

In developed countries, the number of patients with colorectal cancer has been increasing, and colorectal cancer is one of the most common causes of cancer death. To improve the quality of life of colorectal cancer patients, it is necessary to establish novel screening methods that would allow early detection of colorectal cancer. We performed metabolome analysis of a plasma sample set from 282 stage 0/I/II colorectal cancer patients and 291 healthy volunteers using gas chromatography/triple-quadrupole mass spectrometry in an attempt to identify metabolite biomarkers of stage 0/I/II colorectal cancer. The colorectal cancer patients included patients with stage 0 (N=79), I (N=80), and II (N=123) in whom invasion and metastasis were absent. Our analytical system detected 64 metabolites in the plasma samples, and the levels of 29 metabolites differed significantly (Bonferroni-corrected p=0.000781) between the patients and healthy volunteers. Based on these results, a multiple logistic regression analysis of various metabolite biomarkers was carried out, and a stage 0/I/II colorectal cancer prediction model was established. The area under the curve, sensitivity, and specificity values of this model for detecting stage 0/I/II colorectal cancer were 0.996, 99.3%, and 93.8%, respectively. The model's sensitivity and specificity values for each disease stage were >90%, and surprisingly, its sensitivity for stage 0, specificity for stage 0, and sensitivity for stage II disease were all 100%. Our predictive model can aid early detection of colorectal cancer and has potential as a novel screening test for cases of colorectal cancer that do not involve lymph node or distant metastasis.

Improvement of sample throughput using fast gas chromatography mass-spectrometry for biochemical diagnosis of organic acid disorders.

BACKGROUND: Gas chromatograph mass-spectrometric (GC/MS) method of analysis for urinary organic acids is used for the diagnosis of a variety of metabolic disorders. The method is time-consuming and does not allow for improvements in sample throughput. Although the sample preparation and the data processing have been improved, the long GC/MS analysis time still remains to be problematic. METHODS: The fast-GC/MS method, which utilizes a short microbore capillary GC column and fast temperature programming, was applied to the analysis for urinary organic acids. Urine samples obtained from 15 patients with 9 different disorders and 16 healthy controls were analyzed using conventional GC/MS and fast-GC/MS. RESULTS: Analysis cycle time was shortened from 1 h to 15 min. The automated data system uses retention indices determined by conventional-GC/MS for the identification of 134 organic acids. These retention indices can also be used in data obtained by fast-GC/MS. New fast-GC/MS method with the automated data system gave the same diagnostic results as conventional-GC/MS except for 1 healthy control. CONCLUSIONS: The combined system of fast-GC/MS and the automated data system will be powerful tools in clinical laboratories due to increased sample throughput and reduced analysis costs.