利用 LC-MS 最大限度提高生物分子发现的分析性能:聚焦精神疾病。

Bradley J Smith, Paul C Guest, Daniel Martins-de-Souza
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引用次数: 0

摘要

在这篇综述中,我们讨论了质谱蛋白质组学和代谢组学的前沿发展,这些发展为确定新的疾病生物标记物带来了改进。本综述特别关注精神疾病,例如精神分裂症,因为这些疾病被认为不是单一的疾病实体,而是具有多种重叠症状的疾病谱。本综述介绍了用于生物标记物研究的各类常用质谱平台,以及最大化数据覆盖范围、减少样本异质性和解决潜在混杂因素的辅助技术。最后,我们总结了可用于提高数据质量的不同统计方法,以帮助提高蛋白质组学研究结果的可靠性和解释性,并增强其临床应用的可转化性和对新数据集的通用性。预计《分析化学年度综述》第 17 卷的最终在线出版日期为 2024 年 5 月。修订后的预计日期请参见 http://www.annualreviews.org/page/journal/pubdates。
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Maximizing Analytical Performance in Biomolecular Discovery with LC-MS: Focus on Psychiatric Disorders.

In this review, we discuss the cutting-edge developments in mass spectrometry proteomics and metabolomics that have brought improvements for the identification of new disease-based biomarkers. A special focus is placed on psychiatric disorders, for example, schizophrenia, because they are considered to be not a single disease entity but rather a spectrum of disorders with many overlapping symptoms. This review includes descriptions of various types of commonly used mass spectrometry platforms for biomarker research, as well as complementary techniques to maximize data coverage, reduce sample heterogeneity, and work around potentially confounding factors. Finally, we summarize the different statistical methods that can be used for improving data quality to aid in reliability and interpretation of proteomics findings, as well as to enhance their translatability into clinical use and generalizability to new data sets.

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