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代谢组学中质谱技术的进展:疾病生物标志物发现中的策略、挑战与创新。

Advances in mass spectrometry for metabolomics: Strategies, challenges, and innovations in disease biomarker discovery.

机构信息

Department of Pharmaceutical Analysis, National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Gandhinagar, Gujarat, India.

Department of Biotechnology, National Institute of Pharmaceutical Education and Research-Ahmedabad (NIPER-A), An Institute of National Importance, Government of India, Gandhinagar, Gujarat, India.

出版信息

Biomed Chromatogr. 2024 Dec;38(12):e6019. doi: 10.1002/bmc.6019. Epub 2024 Oct 7.

Abstract

Mass spectrometry (MS) plays a crucial role in metabolomics, especially in the discovery of disease biomarkers. This review outlines strategies for identifying metabolites, emphasizing precise and detailed use of MS techniques. It explores various methods for quantification, discusses challenges encountered, and examines recent breakthroughs in biomarker discovery. In the field of diagnostics, MS has revolutionized approaches by enabling a deeper understanding of tissue-specific metabolic changes associated with disease. The reliability of results is ensured through robust experimental design and stringent system suitability criteria. In the past, data quality, standardization, and reproducibility were often overlooked despite their significant impact on MS-based metabolomics. Progress in this field heavily depends on continuous training and education. The review also highlights the emergence of innovative MS technologies and methodologies. MS has the potential to transform our understanding of metabolic landscapes, which is crucial for disease biomarker discovery. This article serves as an invaluable resource for researchers in metabolomics, presenting fresh perspectives and advancements that propels the field forward.

摘要

质谱(MS)在代谢组学中起着至关重要的作用,特别是在发现疾病生物标志物方面。本文概述了鉴定代谢物的策略,强调了精确和详细使用 MS 技术。它探讨了各种定量方法,讨论了遇到的挑战,并研究了生物标志物发现的最新突破。在诊断领域,MS 通过深入了解与疾病相关的组织特异性代谢变化,彻底改变了方法。通过稳健的实验设计和严格的系统适用性标准,确保了结果的可靠性。过去,尽管数据质量、标准化和可重复性对基于 MS 的代谢组学有重大影响,但往往被忽视。该领域的进展在很大程度上取决于持续的培训和教育。本文还重点介绍了创新的 MS 技术和方法的出现。MS 有可能改变我们对代谢景观的理解,这对发现疾病生物标志物至关重要。本文为代谢组学研究人员提供了宝贵的资源,提出了新的观点和进展,推动了该领域的发展。

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