Sanofi, Waltham, Massachusetts 02451, United States.
Barnett Institute of Chemical and Biological Analysis, Department of Chemistry and Chemical Biology, Northeastern University, Boston, Massachusetts 02115, United States.
J Proteome Res. 2022 Jan 7;21(1):151-163. doi: 10.1021/acs.jproteome.1c00628. Epub 2021 Nov 29.
Microscale-based separations are increasingly being applied in the field of metabolomics for the analysis of small-molecule metabolites. These methods have the potential to provide improved sensitivity, less solvent waste, and reduced sample-size requirements. Ion-pair free microflow-based global metabolomics methods, which we recently reported, were further compared to analytical flow ion-pairing reagent containing methods using a sample set from a urea cycle disorder (UCD) mouse model. Mouse urine and brain homogenate samples representing healthy, diseased, and disease-treated animals were analyzed by both methods. Data processing was performed using univariate and multivariate techniques followed by analyte trend analysis. The microflow methods performed comparably to the analytical flow ion-pairing methods with the ability to separate the three sample groups when analyzed by partial least-squares analysis. The number of detected metabolic features present after each data processing step was similar between the microflow-based methods and the ion-pairing methods in the negative ionization mode. The observed analyte trend and coverage of known UCD biomarkers were the same for both evaluated approaches. The 12.5-fold reduction in sample injection volume required for the microflow-based separations highlights the potential of this method to support studies with sample-size limitations.
基于微尺度的分离技术在代谢组学领域中越来越多地被应用于小分子代谢物的分析。这些方法具有提高灵敏度、减少溶剂浪费和减少样品量需求的潜力。我们最近报道的无离子对微流基全局代谢组学方法与含有分析流离子对试剂的方法进行了进一步比较,使用来自尿素循环障碍 (UCD) 小鼠模型的样品集。通过两种方法分析了代表健康、患病和疾病治疗动物的小鼠尿液和脑匀浆样品。使用单变量和多变量技术以及分析物趋势分析进行数据处理。偏最小二乘分析表明,微流方法与分析流离子对方法的性能相当,能够分离三个样品组。在负离子模式下,经过每个数据处理步骤后,微流方法和离子对方法检测到的代谢特征数量相似。两种评估方法观察到的分析物趋势和已知 UCD 生物标志物的覆盖率相同。微流基分离所需的样品注入体积减少了 12.5 倍,突出了该方法在支持具有样品量限制的研究方面的潜力。