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.Expected final online publication date for the Annual Review of Analytical Chemistry, Volume 17 is May 2024. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

中文翻译：

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利用 LC-MS 最大限度地提高生物分子发现的分析性能：关注精神疾病

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，这些发展为识别新的基于疾病的生物标志物带来了改进。特别关注精神疾病，例如精神分裂症，因为它们被认为不是单一的疾病实体，而是具有许多重叠症状的一系列疾病。本综述包括对用于生物标志物研究的各种类型的常用质谱平台的描述，以及最大限度地提高数据覆盖范围、减少样本异质性和解决潜在混杂因素的补充技术。最后，我们总结了可用于提高数据质量的不同统计方法，以帮助蛋白质组学研究结果的可靠性和解释，并增强其临床应用的可转化性和新数据集的普遍性。预计最终在线发布日期《分析化学年度评论》第 17 卷将于 2024 年 5 月发布。请参阅 http://www.annualreviews.org/page/journal/pubdates 了解修订后的估计值。