Absolute Minimal Sampling of Homonuclear 2D NMR TOCSY Spectra for High‐Throughput Applications of Complex Mixtures

Modern applications of 2D NMR spectroscopy to diagnostic screening, metabolomics, quality control, and other high‐throughput applications are often limited by the time‐consuming sampling requirements along the indirect time domain t ₁. 2D total correlation spectroscopy (TOCSY) provides unique spin connectivity information for the analysis of a large number of compounds in complex mixtures, but standard methods typically require >100 t ₁ increments for an accurate spectral reconstruction, rendering these experiments ineffective for high‐throughput applications. For a complex metabolite mixture it is demonstrated that absolute minimal sampling (AMS), based on direct fitting of resonance frequencies and amplitudes in the time domain, yields an accurate spectral reconstruction of TOCSY spectra using as few as 16 t ₁ points. This permits the rapid collection of homonuclear 2D NMR experiments at high resolution with measurement times that previously were only the realm of 1D experiments.     

Untargeted Tumor Metabolomics with Liquid Chromatography–Surface-Enhanced Raman Spectroscopy

Metabolomics is a powerful systems biology approach that monitors changes in biomolecule concentrations to diagnose and monitor health and disease. However, leading metabolomics technologies, such as NMR and mass spectrometry (MS), access only a small portion of the metabolome. Now an approach is presented that uses the high sensitivity and chemical specificity of surface-enhanced Raman scattering (SERS) for online detection of metabolites from tumor lysates following liquid chromatography (LC). The results demonstrate that this LC-SERS approach has metabolite detection capabilities comparable to the state-of-art LC-MS but suggest a selectivity for the detection of a different subset of metabolites. Analysis of replicate LC-SERS experiments exhibit reproducible metabolite patterns that can be converted into barcodes, which can differentiate different tumor models. Our work demonstrates the potential of LC-SERS technology for metabolomics-based diagnosis and treatment of cancer.