Designing optimal experiments in metabolomics.

IF 3.5 3区医学 Q2 ENDOCRINOLOGY & METABOLISM

Metabolomics Pub Date : 2024-06-28 DOI:10.1007/s11306-024-02122-1

Mathies Brinks Sørensen, Jan Kloppenborg Møller, Mikael Lenz Strube, Charlotte Held Gotfredsen

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Abstract

Background: Metabolomics data is often complex due to the high number of metabolites, chemical diversity, and dependence on sample preparation. This makes it challenging to detect significant differences between factor levels and to obtain accurate and reliable data. To address these challenges, the use of Design of Experiments (DoE) techniques in the setup of metabolomic experiments is crucial. DoE techniques can be used to optimize the experimental design space, ensuring that the maximum amount of information is obtained from a limited sample space.

Aim of review: This review aims at providing a baseline workflow for applying DoE when generating metabolomics data.

Key scientific concepts of review: The review provides insights into the theory of DoE. The review showcases the theory being put into practice by highlighting different examples DoE being applied in metabolomics throughout the literature, considering both targeted and untargeted metabolomic studies in which the data was acquired using both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry techniques. In addition, the review presents DoE concepts not currently being applied in metabolomics, highlighting these as potential future prospects.

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设计代谢组学的最佳实验。

背景：代谢组学数据通常比较复杂，原因在于代谢物数量多、化学多样性以及对样品制备的依赖性。这就给检测因子水平之间的显著差异以及获得准确可靠的数据带来了挑战。为了应对这些挑战，在设置代谢组实验时使用实验设计（DoE）技术至关重要。实验设计技术可用于优化实验设计空间，确保从有限的样本空间中获得最大的信息量：本综述旨在提供在生成代谢组学数据时应用 DoE 的基本工作流程：综述提供了对 DoE 理论的见解。该综述通过重点介绍不同文献中将 DoE 应用于代谢组学的不同实例，展示了该理论在实践中的应用，其中既考虑了靶向性代谢组学研究，也考虑了非靶向性代谢组学研究，这些研究中的数据都是通过核磁共振 (NMR) 光谱和质谱技术获得的。此外，该综述还介绍了目前尚未应用于代谢组学的 DoE 概念，并强调了这些概念的潜在未来前景。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Metabolomics 医学-内分泌学与代谢

CiteScore

6.60

自引率

2.80%

发文量

审稿时长

2 months

期刊介绍： Metabolomics publishes current research regarding the development of technology platforms for metabolomics. This includes, but is not limited to: metabolomic applications within man, including pre-clinical and clinical pharmacometabolomics for precision medicine metabolic profiling and fingerprinting metabolite target analysis metabolomic applications within animals, plants and microbes transcriptomics and proteomics in systems biology Metabolomics is an indispensable platform for researchers using new post-genomics approaches, to discover networks and interactions between metabolites, pharmaceuticals, SNPs, proteins and more. Its articles go beyond the genome and metabolome, by including original clinical study material together with big data from new emerging technologies.