Calibration-curve-locking database for semi-quantitative metabolomics by gas chromatography/mass spectrometry

Kosuke Hata, Yuki Soma, Toshiyuki Yamashita, Masatomo Takahashi, Kuniyo Sugitate, Takeshi Serino, Hiromi Miyagawa, Kenichi Suzuki, Kayoko Yamada, Takatomo Kawamukai, Teruhisa Shiota, Yoshihiro Izumi, Takeshi Bamba

Research output: Contribution to journalArticlepeer-review

Abstract

Calibration-Curve-Locking Databases (CCLDs) have been constructed for automatic compound search and semi-quantitative screening by gas chromatography/mass spectrometry (GC/MS) in several fields. CCLD felicitates the semi-quantification of target compounds without calibration curve preparation because it contains the retention time (RT), calibration curves, and electron ioniza-tion (EI) mass spectra, which are obtained under stable apparatus conditions. Despite its usefulness, there is no CCLD for metabolomics. Herein, we developed a novel CCLD and semi-quantification framework for GC/MS-based metabolomics. All analytes were subjected to GC/MS after deriva-tization under stable apparatus conditions using (1) target tuning, (2) RT locking technique, and (3) automatic derivatization and injection by a robotic platform. The RTs and EI mass spectra were obtained from an existing authorized database. A quantifier ion and one or two qualifier ions were selected for each target metabolite. The calibration curves were obtained as plots of the peak area ratio of the target compounds to an internal standard versus the target compound concentration. These data were registered in a database as a novel CCLD. We examined the applicability of CCLD for analyzing human plasma, resulting in time-saving and labor-saving semi-qualitative screening without the need for standard substances.

Original languageEnglish
Article number207
JournalMetabolites
Volume11
Issue number4
DOIs
Publication statusPublished - Apr 2021

All Science Journal Classification (ASJC) codes

  • Endocrinology, Diabetes and Metabolism
  • Biochemistry
  • Molecular Biology

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