Problem
Research questions and friction points this paper is trying to address.
overlapping peaks
chemical shift variability
metabolite recovery
1D 1H NMR
spectral deconvolution
Exploiting chemical shift variability enables recovery of overlapping metabolites from 1H nuclear magnetic resonance spectra
🤖 AI Summary
One-dimensional ¹H NMR spectra of complex biological samples often suffer from severe peak overlap and chemical shift variability, which hinder accurate metabolite resolution. This work proposes Bayesian Shift-Invariant Non-negative Matrix Factorization (BSI-NMF), a novel approach that uniquely treats chemical shift variability—traditionally regarded as a nuisance—not as interference but as informative structure. By leveraging experimentally induced shift perturbations, BSI-NMF achieves identifiability of overlapping metabolite signals that are otherwise unrecoverable. Evaluated on simulated data, laboratory-controlled mixtures, and a large-scale real-world dataset comprising 2,439 urine samples from a European cohort, BSI-NMF consistently outperforms existing methods and successfully recovers metabolite signals previously missed by conventional analyses.
📝 Abstract
Overlapping peaks and sample-dependent chemical shift variability prevent reliable metabolite recovery from complex biological spectra. This problem is critical in one-dimensional proton (1D 1H) NMR which has become the standard method providing fast acquisition and information-rich spectra in metabolomics and foodomics. This study demonstrates how chemical shifts can be utilised as a strength in 1D 1H NMR, when suitably modeled through the proposed Bayesian Shift-Invariant Non-negative Matrix Factorization (BSI-NMF) procedure. We find that BSI-NMF accurately recovers the underlying chemical signals in 1D 1H NMR spectra missed by existing analyses approaches across simulations, laboratory created datasets, and a large urine dataset obtained from 2439 people across Europe. Our study highlights how shifts in the chemical signatures - until now perceived as a nuisance - can in fact when suitably modelled be instrumental for unique recovery of metabolites. This creates an opportunity to experimentally induce chemical shifts changes to facilitate unique recovery of spectra.
Innovation
Methods, ideas, or system contributions that make the work stand out.
chemical shift variability
Bayesian Shift-Invariant NMF
1D 1H NMR
metabolite recovery
non-negative matrix factorization
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