Trimigno Alessia, Holderman Nicole R, Dong Chen, Boardman Kari D, Zhao Jifang, O'Day Elizabeth M
Olaris, Inc., Framingham, MA 01702, USA.
Metabolites. 2024 May 10;14(5):275. doi: 10.3390/metabo14050275.
Metabolomics, especially urine-based studies, offers incredible promise for the discovery and development of clinically impactful biomarkers. However, due to the unique challenges of urine, a highly precise and reproducible workflow for NMR-based urine metabolomics is lacking. Using 1D and 2D non-uniform sampled (NUS) H-C NMR spectroscopy, we systematically explored how changes in hydration or specific gravity (SG) and pH can impact biomarker discovery. Further, we examined additional sources of error in metabolomics studies and identified Navigator molecules that could monitor for those biases. Adjustment of SG to 1.002-1.02 coupled with a dynamic sum-based peak thresholding eliminates false positives associated with urine hydration and reduces variation in chemical shift. We identified Navigator molecules that can effectively monitor for inconsistencies in sample processing, SG, protein contamination, and pH. The workflow described provides quality assurance and quality control tools to generate high-quality urine metabolomics data, which is the first step in biomarker discovery.
代谢组学，尤其是基于尿液的研究，为发现和开发具有临床影响力的生物标志物带来了巨大希望。然而，由于尿液存在独特的挑战，目前缺乏一种用于基于核磁共振（NMR）的尿液代谢组学的高度精确且可重复的工作流程。我们使用一维和二维非均匀采样（NUS）氢碳核磁共振波谱，系统地探究了水合作用或比重（SG）以及pH值的变化如何影响生物标志物的发现。此外，我们检查了代谢组学研究中的其他误差来源，并识别出了能够监测这些偏差的导航分子。将SG调整至1.002 - 1.02并结合基于动态总和的峰阈值处理，可消除与尿液水合作用相关的假阳性，并减少化学位移的变化。我们识别出了能够有效监测样品处理、SG、蛋白质污染和pH值不一致情况的导航分子。所描述的工作流程提供了质量保证和质量控制工具，以生成高质量的尿液代谢组学数据，这是生物标志物发现的第一步。
