Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia 30322, United States.
Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States.
Anal Chem. 2020 Jul 7;92(13):8836-8844. doi: 10.1021/acs.analchem.0c00338. Epub 2020 Jun 15.
Reference standardization was developed to address quantification and harmonization challenges for high-resolution metabolomics (HRM) data collected across different studies or analytical methods. Reference standardization relies on the concurrent analysis of calibrated pooled reference samples at predefined intervals and enables a single-step batch correction and quantification for high-throughput metabolomics. Here, we provide quantitative measures of approximately 200 metabolites for each of three pooled reference materials (220 metabolites for Qstd3, 211 metabolites for CHEAR, 204 metabolites for NIST1950) and show application of this approach for quantification supports harmonization of metabolomics data collected from 3677 human samples in 17 separate studies analyzed by two complementary HRM methods over a 17-month period. The results establish reference standardization as a method suitable for harmonizing large-scale metabolomics data and extending capabilities to quantify large numbers of known and unidentified metabolites detected by high-resolution mass spectrometry methods.
参考标准化旨在解决高分辨率代谢组学（HRM）数据在不同研究或分析方法中收集时的量化和协调挑战。参考标准化依赖于在预定义的时间间隔内同时分析经过校准的 pooled 参考样本，并且能够为高通量代谢组学提供单一步骤的批量校正和定量。在这里，我们提供了三种 pooled 参考材料（Qstd3 中的大约 200 种代谢物，CHEAR 中的 211 种代谢物，NIST1950 中的 204 种代谢物）的每种大约 200 种代谢物的定量测量值，并展示了这种方法的应用，支持了通过两种互补的 HRM 方法分析的 17 项独立研究中从 3677 个人类样本中收集的代谢组学数据的协调。结果表明，参考标准化是一种适合协调大规模代谢组学数据的方法，并扩展了通过高分辨率质谱方法检测到的大量已知和未知代谢物的定量能力。
