Abstract
Small Molecule Enhancement SpectroscopY (SMolESY) was employed to develop a unique and fully automated computational solution for the assignment and integration of 1H 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 1H 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.
Highlights
Nuclear magnetic resonance (NMR) signals from metabolites in challenging matrices containing macromolecules
All spectra are automatically calibrated to the 1H nuclear magnetic resonance (NMR) signals of glucose, in particular, to the anomeric proton of glucose (Figure 2a) resonating at 5.233 ppm as routinely done for the analysis of blood 1H NMR profiles.[14]
The automatic identification of the 1H NMR singlets is more challenging, including those belonging to particular chemical groups of glycine, creatine/creatinine, choline, dimethyl-sulfone (DMSO2), acetone, and acetic acid (Figure 2b)
Summary
When compared with a biomarker discovery approach utilizing the whole 1H NMR profile, principal component analysis (PCA) of the 22 plasma metabolites’ relative concentration values from SMolESY-select in a cohort of 361 plasma samples consisting of two independent NMR data sets efficiently reveals the same biomarkers (Figure S8). Together, these analyses demonstrate the direct applicability and reliability of the algorithm present for the 22 metabolites’ panel measurements in the serum/plasma matrices
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