National Phenome Centre, Imperial College London, Hammer-smith Campus, IRDB Building, London W12 0NN, United Kingdom.
Section of Bioanalytical Chemistry, Division of Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom.
Anal Chem. 2021 Mar 30;93(12):4995-5000. doi: 10.1021/acs.analchem.1c00113. Epub 2021 Mar 18.
Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of H nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules (herein blood products). Sensitive and reliable quantitation is provided by instant signal deconvolution and straightforward integration bolstered by spectral resolution enhancement and macromolecular signal suppression. The approach is highly efficient, requiring only standard one-dimensional H NMR spectra and avoiding the need for sample preprocessing, complex deconvolution, and spectral baseline fitting. The performance of the algorithm, developed using >4000 NMR serum and plasma spectra, was evaluated using an additional >8800 spectra, yielding an assignment accuracy greater than 99.5% for all 22 metabolites targeted. Further validation of its quantitation capabilities illustrated a reliable performance among challenging phenotypes. The simplicity and complete automation of the approach support the application of NMR-based metabolite panel measurements in clinical and population screening applications.
小分子增强磁共振波谱学 (SMolESY) 被用于开发一种独特且完全自动化的计算解决方案,用于分配和整合来自含有大分子(本文中的血液制品)的挑战性基质中代谢物的 H 核磁共振 (NMR) 信号。通过即时信号解卷积和简单的积分,加上光谱分辨率增强和大分子信号抑制,提供了灵敏可靠的定量分析。该方法非常高效,仅需要标准的一维 H NMR 光谱,并且避免了样品预处理、复杂的解卷积和光谱基线拟合的需要。该算法的性能是使用超过 4000 个 NMR 血清和血浆光谱进行开发的,并使用另外超过 8800 个光谱进行了评估,对于所有 22 种靶向代谢物,其分配准确性均大于 99.5%。进一步验证其定量能力表明,在具有挑战性的表型中具有可靠的性能。该方法的简单性和完全自动化支持将基于 NMR 的代谢物组测量应用于临床和人群筛查应用。