Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration

• Published in: Metabolites 2019-10, Vol.9 (11), p.257
• Main Authors: Izumi, Yoshihiro, Matsuda, Fumio, Hirayama, Akiyoshi, Ikeda, Kazutaka, Kita, Yoshihiro, Horie, Kanta, Saigusa, Daisuke, Saito, Kosuke, Sawada, Yuji, Nakanishi, Hiroki, Okahashi, Nobuyuki, Takahashi, Masatomo, Nakao, Motonao, Hata, Kosuke, Hoshi, Yutaro, Morihara, Motohiko, Tanabe, Kazuhiro, Bamba, Takeshi, Oda, Yoshiya
• Format: Article
• Language: English
• Subjects: Amino acids; Cell lines; Chromatography; data integration; Datasets; Hydrophobicity; inter-laboratory comparison; Laboratories; Lipids; Mass spectrometry; Metabolites; Metabolomics; method validation; Methods; Plasma; quality control sample; Reference materials; relative quantification; Reproducibility; Scientific imaging
• ISSN: 2218-1989; 2218-1989
• DOI: 10.3390/metabo9110257

One of the current problems in the field of metabolomics is the difficulty in integrating data collected using different equipment at different facilities, because many metabolomic methods have been developed independently and are unique to each laboratory. In this study, we examined whether different analytical methods among 12 different laboratories provided comparable relative quantification data for certain metabolites. Identical samples extracted from two cell lines (HT-29 and AsPc-1) were distributed to each facility, and hydrophilic and hydrophobic metabolite analyses were performed using the daily routine protocols of each laboratory. The results indicate that there was no difference in the relative quantitative data (HT-29/AsPc-1) for about half of the measured metabolites among the laboratories and assay methods. Data review also revealed that errors in relative quantification were derived from issues such as erroneous peak identification, insufficient peak separation, a difference in detection sensitivity, derivatization reactions, and extraction solvent interference. The results indicated that relative quantification data obtained at different facilities and at different times would be integrated and compared by using a reference materials shared for data normalization.