Gunma University Initiative for Advanced Research (GIAR), Gunma University, 3-39-22 Showa-machi, Maebashi, Gunma 371-8511, Japan.
Division of Physiological Chemistry 2, Department of Medical Biochemistry and Biophysics, Karolinska Institutet, Biomedicum Quartier 9A, Stockholm 171-77, Sweden.
Anal Chem. 2021 Mar 30;93(12):5248-5258. doi: 10.1021/acs.analchem.1c00203. Epub 2021 Mar 19.
Urine is a noninvasive biofluid that is rich in polar metabolites and well suited for metabolomic epidemiology. However, because of individual variability in health and hydration status, the physiological concentration of urine can differ >15-fold, which can pose major challenges in untargeted liquid chromatography-mass spectrometry (LC-MS) metabolomics. Although numerous urine normalization methods have been implemented (e.g., creatinine, specific gravity-SG), most are manual and, therefore, not practical for population-based studies. To address this issue, we developed a method to measure SG in 96-well-plates using a refractive index detector (RID), which exhibited accuracy within 85-115% and <3.4% precision. Bland-Altman statistics showed a mean deviation of -0.0001 SG units (limits of agreement: -0.0014 to 0.0011) relative to a hand-held refractometer. Using this RID-based SG normalization, we developed an automated LC-MS workflow for untargeted urinary metabolomics in a 96-well-plate format. The workflow uses positive and negative ionization HILIC chromatography and acquires mass spectra in data-independent acquisition (DIA) mode at three collision energies. Five technical internal standards (tISs) were used to monitor data quality in each method, all of which demonstrated raw coefficients of variation (CVs) < 10% in the quality controls (QCs) and < 20% in the samples for a small cohort ( = 87 urine samples, = 22 QCs). Application in a large cohort ( = 842 urine samples, = 248 QCs) demonstrated CV < 5% and CV < 16% for 4/5 tISs after signal drift correction by cubic spline regression. The workflow identified >540 urinary metabolites including endogenous and exogenous compounds. This platform is suitable for performing urinary untargeted metabolomic epidemiology and will be useful for applications in population-based molecular phenotyping.
尿液是一种非侵入性生物体液，富含极性代谢物，非常适合代谢组学流行病学研究。然而，由于个体健康和水合状态的差异，尿液的生理浓度可能相差 15 倍以上，这给非靶向液相色谱-质谱（LC-MS）代谢组学带来了重大挑战。尽管已经实施了许多尿液标准化方法（例如肌酐、比重-SG），但大多数方法都是手动的，因此不适合基于人群的研究。为了解决这个问题，我们开发了一种在 96 孔板中使用折射率检测器（RID）测量比重（SG）的方法，该方法的准确性在 85-115%之间，精密度小于 3.4%。Bland-Altman 统计显示，与手持折射仪相比，SG 平均偏差为-0.0001 SG 单位（一致性范围：-0.0014 至 0.0011）。使用这种基于 RID 的 SG 标准化方法，我们开发了一种自动化的 LC-MS 工作流程，用于在 96 孔板格式中进行非靶向尿液代谢组学分析。该工作流程使用正负离子亲水色谱，并在三种碰撞能量下以数据非依赖性采集（DIA）模式获取质谱。使用 5 个技术内标（tISs）来监测每种方法中的数据质量，所有 tISs 的 QC 中的原始变异系数（CV）均小于 10%，样本中的 CV 均小于 20%（对于一个小队列，=87 个尿液样本，=22 个 QC）。在一个大队列（=842 个尿液样本，=248 个 QC）中的应用表明，经过三次样条回归信号漂移校正后，4/5 个 tISs 的 CV 小于 5%，CV 小于 16%。该工作流程鉴定出了 540 多种尿液代谢物，包括内源性和外源性化合物。该平台适用于进行非靶向尿液代谢组学流行病学研究，并且将有助于基于人群的分子表型研究。
