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Updated: Jan 25, 2026

Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
Published on: March 14, 2013
Advanced liquid chromatography-mass spectrometry enables merging widely targeted metabolomics and proteomics
Wenjing Liu1, Qingqing Song1, Yan Cao1
1Modern Research Center for Traditional Chinese Medicine, School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
Abstract:
Either widely targeted metabolomics or quantitative proteomics usually requires unique analytical platform. However, cross-platform omics studies entail higher levels of complexity and uncertainty, and result in a significant obstacle for high throughput assay as well. It is thereby urgent to pursue an integrative approach being capable of merging these two omics terms, namely widely targeted bi-omics. As an eligible analytical tool for large-scale targeted metabolomics, reversed phase liquid chromatography-hydrophilic interaction liquid chromatography-tailored selected reaction monitoring (RPLC-HILIC-tailored SRM) was deployed here to further receive the tryptic peptides as the analytes. Comparative evaluation of metabolites and tryptic peptides, 101 ones in total, between HepG2 and SK-Hep1 cells was conducted as a proof-of-concept. All analytes, regardless of metabolites or peptides, exhibited satisfactory chromatographic behaviors on RPLC-HILIC. Quantitative MS parameters, such as SRM transitions and collision energies (CEs), of either tryptic peptides or metabolites were online optimized in a standard compound-independent manner. It was worthwhile to mention that the signal responses of the peptides-of-choice generated by the optimized CEs were significantly superior to those values suggested by Skyline software. Calibration curves of both metabolites and peptides were constructed by serially diluting a so-called universal metabolome standard (UMS) sample. The quasi-content of each peptide or metabolite was gained according to applying those regressive calibration curves. After subjecting the quasi-content dataset into SIMCA-P software, significant differences took place between the two hepatic cell lines, and not only metabolites but tryptic peptides contributed to the discrimination. Above all, RPLC-HILIC-tailored SRM offered a promising choice towards widely targeted bi-omics attributing to the advantage of simultaneous monitoring metabolites and tryptic peptides.
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