Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey

Abstract REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) is a global strategy and regulation policy of the EU that aims to improve the protection of human health and the environment through the better and earlier identification of the intrinsic properties of chemical sub...

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Autores principales:	Marta Moreno-Torres, Guillem García-Llorens, Erika Moro, Rebeca Méndez, Guillermo Quintás, José Vicente Castell
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/4dee4ab973534549828fbc123ace29d7
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spelling	oai:doaj.org-article:4dee4ab973534549828fbc123ace29d72021-11-14T12:17:59ZFactors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey10.1038/s41598-021-01652-12045-2322https://doaj.org/article/4dee4ab973534549828fbc123ace29d72021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-01652-1https://doaj.org/toc/2045-2322Abstract REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) is a global strategy and regulation policy of the EU that aims to improve the protection of human health and the environment through the better and earlier identification of the intrinsic properties of chemical substances. It entered into force on 1st June 2007 (EC 1907/2006). REACH and EU policies plead for the use of robust high-throughput "omic" techniques for the in vitro investigation of the toxicity of chemicals that can provide an estimation of their hazards as well as information regarding the underlying mechanisms of toxicity. In agreement with the 3R’s principles, cultured cells are nowadays widely used for this purpose, where metabolomics can provide a real-time picture of the metabolic effects caused by exposure of cells to xenobiotics, enabling the estimations about their toxicological hazards. High quality and robust metabolomics data sets are essential for precise and accurate hazard predictions. Currently, the acquisition of consistent and representative metabolomic data is hampered by experimental drawbacks that hinder reproducibility and difficult robust hazard interpretation. Using the differentiated human liver HepG2 cells as model system, and incubating with hepatotoxic (acetaminophen and valproic acid) and non-hepatotoxic compounds (citric acid), we evaluated in-depth the impact of several key experimental factors (namely, cell passage, processing day and storage time, and compound treatment) and instrumental factors (batch effect) on the outcome of an UPLC-MS metabolomic analysis data set. Results showed that processing day and storage time had a significant impact on the retrieved cell's metabolome, while the effect of cell passage was minor. Meta-analysis of results from pathway analysis showed that batch effect corrections and quality control (QC) measures are critical to enable consistent and meaningful estimations of the effects caused by compounds on cells. The quantitative analysis of the changes in metabolic pathways upon bioactive compound treatment remained consistent despite the concurrent causes of metabolomic data variation. Thus, upon appropriate data retrieval and correction and by an innovative metabolic pathway analysis, the metabolic alteration predictions remained conclusive despite the acknowledged sources of variability.Marta Moreno-TorresGuillem García-LlorensErika MoroRebeca MéndezGuillermo QuintásJosé Vicente CastellNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Marta Moreno-Torres Guillem García-Llorens Erika Moro Rebeca Méndez Guillermo Quintás José Vicente Castell Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey
description	Abstract REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) is a global strategy and regulation policy of the EU that aims to improve the protection of human health and the environment through the better and earlier identification of the intrinsic properties of chemical substances. It entered into force on 1st June 2007 (EC 1907/2006). REACH and EU policies plead for the use of robust high-throughput "omic" techniques for the in vitro investigation of the toxicity of chemicals that can provide an estimation of their hazards as well as information regarding the underlying mechanisms of toxicity. In agreement with the 3R’s principles, cultured cells are nowadays widely used for this purpose, where metabolomics can provide a real-time picture of the metabolic effects caused by exposure of cells to xenobiotics, enabling the estimations about their toxicological hazards. High quality and robust metabolomics data sets are essential for precise and accurate hazard predictions. Currently, the acquisition of consistent and representative metabolomic data is hampered by experimental drawbacks that hinder reproducibility and difficult robust hazard interpretation. Using the differentiated human liver HepG2 cells as model system, and incubating with hepatotoxic (acetaminophen and valproic acid) and non-hepatotoxic compounds (citric acid), we evaluated in-depth the impact of several key experimental factors (namely, cell passage, processing day and storage time, and compound treatment) and instrumental factors (batch effect) on the outcome of an UPLC-MS metabolomic analysis data set. Results showed that processing day and storage time had a significant impact on the retrieved cell's metabolome, while the effect of cell passage was minor. Meta-analysis of results from pathway analysis showed that batch effect corrections and quality control (QC) measures are critical to enable consistent and meaningful estimations of the effects caused by compounds on cells. The quantitative analysis of the changes in metabolic pathways upon bioactive compound treatment remained consistent despite the concurrent causes of metabolomic data variation. Thus, upon appropriate data retrieval and correction and by an innovative metabolic pathway analysis, the metabolic alteration predictions remained conclusive despite the acknowledged sources of variability.
format	article
author	Marta Moreno-Torres Guillem García-Llorens Erika Moro Rebeca Méndez Guillermo Quintás José Vicente Castell
author_facet	Marta Moreno-Torres Guillem García-Llorens Erika Moro Rebeca Méndez Guillermo Quintás José Vicente Castell
author_sort	Marta Moreno-Torres
title	Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey
title_short	Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey
title_full	Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey
title_fullStr	Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey
title_full_unstemmed	Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey
title_sort	factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4dee4ab973534549828fbc123ace29d7
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Factors that influence the quality of metabolomics data in in vitro cell toxicity studies: a systematic survey

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