Rischke S, Hahnefeld L, Burla B, Behrens F, Gurke R, Garrett T J
pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany.
Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany.
J Mass Spectrom Adv Clin Lab. 2023 Feb 17;28:47-55. doi: 10.1016/j.jmsacl.2023.02.003. eCollection 2023 Apr.
Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.
专注于内源性小分子的质谱分析已成为生物标志物发现不可或缺的一部分，有助于深入了解各种疾病的病理生理学，最终推动个性化医疗的应用。虽然液相色谱 - 质谱联用（LC-MS）方法使研究人员能够从数百或数千个样本中收集大量数据，但作为临床研究一部分的研究项目要成功实施，还需要与临床医生进行知识转移，数据科学家的参与以及与各利益相关者的互动。临床研究项目的初始规划阶段包括明确范围和设计，并邀请不同领域的相关专家参与。招募受试者和设计试验在很大程度上依赖于研究的总体目标和流行病学考量，而恰当的分析前样本处理对分析数据的质量有着直接影响。随后的LC-MS测量可以有针对性、半针对性或非针对性地进行，从而产生大小和准确性各异的数据集。数据处理进一步提高了数据质量，并且是计算机分析的先决条件。如今，对如此复杂的数据集进行评估依赖于经典统计学和机器学习应用的结合，以及其他工具，如通路分析和基因集富集。最后，在生物标志物可用作预后或诊断决策工具之前，必须对结果进行验证。在整个研究过程中，应采用质量控制措施来提高数据的可靠性并增强对结果的信心。本图文综述的目的是概述在开展基于LC-MS的临床研究项目以寻找小分子生物标志物时应采取的步骤。
