Labile Metabolite Profiling in Human Blood Using Phosphorus NMR Spectroscopy

Phosphorus metabolites occupy a unique place in cellular function as critical intermediates and products of cellular metabolism. Human blood is the most widely used biospecimen in the clinic and in the metabolomics field, and hence an ability to profile phosphorus metabolites in blood, quantitatively, would benefit a wide variety of investigations of cellular functions in health and diseases. Mass spectrometry (MS) and nuclear magnetic resonance (NMR) spectroscopy are the two premier analytical platforms used in the metabolomics field. However, detection and quantitation of phosphorus metabolites by MS can be challenging due to their lability, high polarity, structural isomerism, and interaction with chromatographic columns. The conventionally used ¹H NMR, on the other hand, suffers from poor resolution of these compounds. As a remedy, ³¹P NMR promises an important alternative to both MS and ¹H NMR. However, numerous challenges including the instability of phosphorus metabolites, their chemical shift sensitivity to solvent composition, pH, salt, and temperature, and the lack of identified metabolites have so far restricted the scope of ³¹P NMR. In the current study, we describe a method to analyze nearly 25 phosphorus metabolites in blood using a simple one-dimensional (1D) NMR spectrum. Establishment of the identity of unknown metabolites involved a combination of (a) comprehensively analyzing an array of 1D and two-dimensional (2D) ¹H/³¹P homonuclear and heteronuclear NMR spectra of blood; (b) mapping the central carbon metabolic pathway; (c) developing and using ¹H and ³¹P spectral and chemical shift databases; and finally (d) confirming the putative metabolite peaks with spiking using authentic compounds. The resulting simple 1D ³¹P NMR-based method offers an ability to visualize and quantify the levels of intermediates and products of multiple metabolic pathways, including central carbon metabolism, in one step. Overall, the findings represent a new dimension for blood metabolite analysis and are anticipated to greatly impact the blood metabolomics field.