High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism

TLDR

This holistic NMR approach, combined with integrated computational methods, offers a data‑driven systems biology perspective that contrasts with single biomarker analyses. The study introduces a high‑throughput 1H NMR metabonomics method to characterize systemic metabolic phenotypes. The method uses two 1H NMR spectral windows—serum lipids, lipoprotein subclasses, and low‑molecular‑weight metabolites—analyzed via robotics‑controlled, fully automated instrumentation capable of 150–180 samples per 24 h, providing unique high‑throughput, cost‑effective, multi‑metabolic data analysis. The approach revealed associations between systemic metabolic phenotypes and metabolic syndrome in 4,407 participants and enables processing of up to 50,000 serum samples per year, facilitating large‑scale clinical and epidemiological studies.

Abstract

A high-throughput proton (1H) nuclear magnetic resonance (NMR) metabonomics approach is introduced to characterise systemic metabolic phenotypes. The methodology combines two molecular windows that contain the majority of the metabolic information available by 1H NMR from native serum, e.g. serum lipids, lipoprotein subclasses as well as various low-molecular-weight metabolites. The experimentation is robotics-controlled and fully automated with a capacity of about 150-180 samples in 24 h. To the best of our knowledge, the presented set-up is unique in the sense of experimental high-throughput, cost-effectiveness, and automated multi-metabolic data analyses. As an example, we demonstrate that the NMR data as such reveal associations between systemic metabolic phenotypes and the metabolic syndrome (n = 4407). The high-throughput of up to 50,000 serum samples per year is also paving the way for this technology in large-scale clinical and epidemiological studies. In contradiction to single 'biomarkers', the application of this holistic NMR approach and the integrated computational methods provides a data-driven systems biology approach to biomedical research.