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Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration

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 exa...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6918145/
https://www.ncbi.nlm.nih.gov/pubmed/31683650
http://dx.doi.org/10.3390/metabo9110257
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author 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
author_facet 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
author_sort Izumi, Yoshihiro
collection PubMed
description 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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spelling pubmed-69181452019-12-24 Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration 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 Metabolites Article 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. MDPI 2019-10-31 /pmc/articles/PMC6918145/ /pubmed/31683650 http://dx.doi.org/10.3390/metabo9110257 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
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
Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration
title Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration
title_full Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration
title_fullStr Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration
title_full_unstemmed Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration
title_short Inter-Laboratory Comparison of Metabolite Measurements for Metabolomics Data Integration
title_sort inter-laboratory comparison of metabolite measurements for metabolomics data integration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6918145/
https://www.ncbi.nlm.nih.gov/pubmed/31683650
http://dx.doi.org/10.3390/metabo9110257
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