Altmann Heidi, Barovic Marko, Straßburger Katrin, Tschäpel Maximilian, Jonas Sophie, Poitz David M, Belavgeni Alexia, Chavakis Triantafyllos, Mirtschink Peter, Funk Alexander M
Medical Clinic & Polyclinic 1, University Hospital and Faculty of Medicine Carl Gustav Carus of TU Dresden, Dresden 01307, Germany.
National Center for Tumor Diseases (NCT/UCC) Partner Site Dresden, Dresden 01307, Germany.
Anal Chem. 2025 Feb 11;97(5):2762-2769. doi: 10.1021/acs.analchem.4c04938. Epub 2025 Jan 28.
The quality of biological samples used in metabolomics research is significantly influenced by preanalytical factors, such as the timing of centrifugation and freezing. This study aimed to evaluate how preanalytical factors, like delays in centrifugation and freezing, affect metabolomics research. Blood samples, collected in various tube types, were subjected to controlled pre- and postcentrifugation delays. Metabolite levels were quantified using NMR spectroscopy and fitted in linear mixed models used to predict changes in metabolite concentrations over time. The results showed that some metabolites, such as lactic acid, were significantly affected by even short delays, while others remained stable for longer. The study introduced the concept of a "stability time point", marking when a metabolite's concentration changes by 20%. These predictive models were validated in a separate cohort. To apply these findings, the authors developed the PRIMA Panel, an open-source R Shiny tool. This tool allows researchers to assess the impact of preanalytical variations on their samples, predict metabolite stability, and generate performance reports. The PRIMA Panel was tested using samples from the Dresden Integrated Liquid Biobank, proving its utility in a real-world biobank setting. The study emphasizes the importance of tracking preanalytical factors to improve the reliability of metabolomics analyses. The PRIMA Panel is available online and for local deployment, providing a practical solution for quality control in metabolomics research. The results of the study underscore the importance of tracking preanalytical factors in biobanking. A versatile tool for assessing their impact on metabolic data is introduced, improving the reliability of future analyses.
代谢组学研究中使用的生物样本质量受离心和冷冻时间等分析前因素的显著影响。本研究旨在评估离心和冷冻延迟等分析前因素如何影响代谢组学研究。采集于各种类型试管中的血样经历了可控的离心前和离心后延迟。使用核磁共振波谱法定量代谢物水平,并将其拟合到线性混合模型中,用于预测代谢物浓度随时间的变化。结果表明,一些代谢物,如乳酸,即使是短暂的延迟也会受到显著影响,而其他代谢物在更长时间内保持稳定。该研究引入了“稳定时间点”的概念,即代谢物浓度变化20%的时间点。这些预测模型在一个独立的队列中得到了验证。为了应用这些发现,作者开发了PRIMA Panel,这是一个开源的R Shiny工具。该工具允许研究人员评估分析前变化对其样本的影响,预测代谢物稳定性,并生成性能报告。使用德累斯顿综合液体生物样本库的样本对PRIMA Panel进行了测试,证明了其在实际生物样本库环境中的实用性。该研究强调了追踪分析前因素对提高代谢组学分析可靠性的重要性。PRIMA Panel可在线获取并用于本地部署,为代谢组学研究中的质量控制提供了一个实用的解决方案。该研究结果强调了在生物样本库中追踪分析前因素的重要性。引入了一种评估其对代谢数据影响的通用工具,提高了未来分析的可靠性。