Using neural networks to obtain NMR spectra of both small and macromolecules from blood samples in a single experiment

Abstract

Metabolomics plays a crucial role in understanding metabolic processes within biological systems. Using specific pulse sequences, NMR-based metabolomics detects small and macromolecular metabolites that are altered in blood samples. Here we proposed a method called spectral editing neural network, which can effectively edit and separate the spectral signals of small and macromolecules in ¹H NMR spectra of serum and plasma based on the linewidth of the peaks. We applied the model to process the ¹H NMR spectra of plasma and serum. The extracted small and macromolecular spectra were then compared with experimentally obtained relaxation-edited and diffusion-edited spectra. Correlation analysis demonstrated the quantitative capability of the model in the extracted small molecule signals from ¹H NMR spectra. The principal component analysis showed that the spectra extracted by the model and those obtained by NMR spectral editing methods reveal similar group information, demonstrating the effectiveness of the model in signal extraction.

¹H NMR-based metabolomics can detect small and macromolecular metabolites simultaneously from complex biological samples, however, signaling overlap remains a challenge for accurate molecular identification and quantification. Here, the authors develop a spectral editing neural network to effectively edit and separate the spectral signals of small and macromolecules in the 1H NMR spectra of serum and plasma based on the linewidth of the peaks.