Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration
by
Yoshihiro Izumi 1
, Fumio Matsuda 2,*, Akiyoshi Hirayama 3, Kazutaka Ikeda 4, Yoshihiro Kita 5, Kanta Horie 6, Daisuke Saigusa 7, Kosuke Saito 8, Yuji Sawada 9, Hiroki Nakanishi 10, Nobuyuki Okahashi 2, Masatomo Takahashi 1, Motonao Nakao 1, Kosuke Hata 1, Yutaro Hoshi 11, Motohiko Morihara 12, Kazuhiro Tanabe 13, Takeshi Bamba 1,* and Yoshiya Oda 5
Yoshihiro Izumi 1, Fumio Matsuda 2,*, Akiyoshi Hirayama 3, Kazutaka Ikeda 4, Yoshihiro Kita 5, Kanta Horie 6, Daisuke Saigusa 7, Kosuke Saito 8, Yuji Sawada 9, Hiroki Nakanishi 10, Nobuyuki Okahashi 2, Masatomo Takahashi 1, Motonao Nakao 1, Kosuke Hata 1, Yutaro Hoshi 11, Motohiko Morihara 12, Kazuhiro Tanabe 13, Takeshi Bamba 1,* and Yoshiya Oda 5
1
Division of Metabolomics, Medical Institute of Bioregulation, Kyushu University, 3-1-1 Maidashi, Higashi-ku, Fukuoka 812-8582, Japan
2
Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-5 Yamadaoka, Suita, Osaka 565-0871, Japan
3
Institute for Advanced Biosciences, Keio University, 246-2 Mizukami, Kakuganji, Tsuruoka, Yamagata 997-0052, Japan
4
Laboratory for Metabolomics, RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-Ku, Yokohama, Kanagawa 230-0045, Japan
5
Department of Lipidomics, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
6
Translational Science, Neurology Business Group, Eisai Co., Ltd., 5-1-3 Tokodai, Tsukuba, Ibaraki 300-2635, Japan
7
Tohoku Medical Megabank Organization, Tohoku University, 2-1 Seiryo-machi, Aoba-ku, Sendai, Miyagi 980-8573, Japan
8
Division of Medical Safety Science, National Institute of Health Science, 3-25-26 Tonomachi, Kawasaki-ku, Kawasaki, Kanagawa 210-9501, Japan
9
RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan
10
Research Center for Biosignal, Akita University, 1-1-1 Hondo, Akita-city, Akita 010-8543, Japan
11
Pharmacokinetic Research Laboratories, Ono Pharmaceutical Co., Ltd., 17-2 Wadai, Tsukuba, Ibaraki 300-4247, Japan
12
Translational Research Laboratories, Ono Pharmaceutical Co., Ltd., 3-1-1 Sakurai Shimamoto-cho, Mishima-gun, Osaka 618-8585, Japan
13
Medical Solution Promotion Department, Medical Solution Segment, LSI Medience Corporation, 3-30-1, Shimura, Itabashi-ku, Tokyo 174-8555, Japan
*
Authors to whom correspondence should be addressed.
Metabolites 2019, 9(11), 257; https://doi.org/10.3390/metabo9110257
Received: 29 September 2019 / Revised: 26 October 2019 / Accepted: 28 October 2019 / Published: 31 October 2019
(This article belongs to the Special Issue Metabolomics-Driven Biotechnology)
Background: 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. Methods: 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. Results: 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. Conclusion: 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.
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Keywords:
metabolomics; relative quantification; method validation; inter-laboratory comparison; data integration; quality control sample
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MDPI and ACS Style
Izumi, Y.; Matsuda, F.; Hirayama, A.; Ikeda, K.; Kita, Y.; Horie, K.; Saigusa, D.; Saito, K.; Sawada, Y.; Nakanishi, H.; Okahashi, N.; Takahashi, M.; Nakao, M.; Hata, K.; Hoshi, Y.; Morihara, M.; Tanabe, K.; Bamba, T.; Oda, Y. Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration. Metabolites 2019, 9, 257.
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