Haslauer Kristina E, Schmitt-Kopplin Philippe, Heinzmann Silke S
Research Unit Analytical BioGeoChemistry, Helmholtz Zentrum München, German Research Center for Environmental Health, D-85764 Neuherberg, Germany.
Chair of Analytical Food Chemistry, Technical University Munich, D-85354 Freising-Weihenstephan, Germany.
Metabolites. 2021 Apr 29;11(5):285. doi: 10.3390/metabo11050285.
Nuclear magnetic resonance (NMR) spectroscopy is well-established to address questions in large-scale untargeted metabolomics. Although several approaches in data processing and analysis are available, significant issues remain. NMR spectroscopy of urine generates information-rich but complex spectra in which signals often overlap. Furthermore, slight changes in pH and salt concentrations cause peak shifting, which introduces, in combination with baseline irregularities, un-informative noise in statistical analysis. Within this work, a straight-forward data processing tool addresses these problems by applying a non-linear curve fitting model based on Voigt function line shape and integration of the underlying peak areas. This method allows a rapid untargeted analysis of urine metabolomics datasets without relying on time-consuming 2D-spectra based deconvolution or information from spectral libraries. The approach is validated with spiking experiments and tested on a human urine H dataset compared to conventionally used methods and aims to facilitate metabolomics data analysis.
核磁共振（NMR）光谱技术在大规模非靶向代谢组学问题研究中已得到广泛应用。尽管在数据处理和分析方面有多种方法可用，但仍存在重大问题。尿液的核磁共振光谱会产生信息丰富但复杂的光谱，其中信号常常重叠。此外，pH值和盐浓度的微小变化会导致峰位移动，这与基线不规则性相结合，在统计分析中引入了无信息噪声。在这项工作中，一个简单的数据处理工具通过应用基于Voigt函数线形的非线性曲线拟合模型并对潜在峰面积进行积分来解决这些问题。该方法允许对尿液代谢组学数据集进行快速非靶向分析，而无需依赖耗时的基于二维光谱的去卷积或光谱库信息。该方法通过加标实验进行了验证，并与传统使用的方法相比在人类尿液H数据集上进行了测试，旨在促进代谢组学数据分析。
