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用于评估神经组织中中心碳代谢变化的代谢组学分析方案。

Protocols for Metabolomic Analyses to Assess Changes in Central Carbon Metabolism from Neural Tissue.

作者信息

Noureldein Mai, Cochran Darcy, Bhinderwala Fatema, Lei Shulei, Woods Jade, Rose Jordan, Marshall Darrell D, Riekeberg Eli, Leite Aline De Lima, Morton Martha, Dodds Eric D, Franco Rodrigo, Powers Robert

机构信息

Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE, USA.

Nebraska Center for Integrated Biomolecular Communication, University of Nebraska-Lincoln, Lincoln, NE, USA.

出版信息

Methods Mol Biol. 2025;2925:329-382. doi: 10.1007/978-1-0716-4534-5_24.

Abstract

Metabolomics has been successfully applied to study neurological and neurodegenerative disorders, including Parkinson's disease for the following purposes: (1) identifying potential biomarkers of onset and disease progression; (2) identifying novel mechanisms of disease progression; and (3) assessing treatment prognosis and outcome. Reproducible and efficient extraction of metabolites is imperative to the success of any metabolomics investigation. Unlike other OMICS techniques, the composition of the metabolome can be negatively impacted by the preparation, processing, and handling of these samples. The proper choice of data collection, preprocessing, and processing protocols are similarly important to the design of an effective metabolomics experiment. Likewise, correctly applying univariate and multivariate statistical methods is essential for providing biologically relevant insights. In this chapter, we have outlined a detailed metabolomics workflow that addresses these issues. A step-by-step protocol from preparing neuronal cells and metabolomic tissue samples to their metabolic analyses using nuclear magnetic resonance, mass spectrometry, matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and chemometrics is presented.

摘要

代谢组学已成功应用于研究神经和神经退行性疾病,包括帕金森病,用于以下目的:(1)识别发病和疾病进展的潜在生物标志物;(2)识别疾病进展的新机制;(3)评估治疗预后和结果。可重复且高效地提取代谢物对于任何代谢组学研究的成功至关重要。与其他组学技术不同,代谢组的组成可能会受到这些样品的制备、处理和操作的负面影响。正确选择数据收集、预处理和处理方案对于设计有效的代谢组学实验同样重要。同样,正确应用单变量和多变量统计方法对于提供生物学相关见解至关重要。在本章中,我们概述了一个详细的代谢组学工作流程,以解决这些问题。本文介绍了一个从制备神经元细胞和代谢组学组织样品到使用核磁共振、质谱、基质辅助激光解吸/电离质谱成像(MALDI-MSI)和化学计量学进行代谢分析的分步方案。

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