Triple labeling of metabolites for metabolome analysis (TLEMMA): a stable isotope labeling approach for metabolite identification and network reconstruction

Abstract

Metabolite identification remains a significant challenge in mass spectrometry (MS)-based metabolomics research. To address this issue, we combined a triple-labeled precursor-based isotope tracing approach (TLEMMA) with high-resolution liquid chromatography-MS for metabolite identification and metabolic network construction. As a demonstration, we fed duckweed (Spirodela polyrhiza) with four forms of phenylalanine (Phe) including unlabeled Phe, Phe-⁵H₂, Phe-⁸H₂, and Phe-¹³C₉¹⁵N₁. The distinctive isotopic pattern obtained from MS spectra greatly facilitated data processing, enabling comprehensive extraction of all Phe-derived metabolites. Importantly, the labeling pattern allowed efficient metabolite identification by significantly reducing the number of structural and positional isomers. Using this approach, 47 phenylalanine-derived metabolites were putatively identified. To further evaluate the efficiency of metabolite identification in relation to the number of differently labeled precursors used, we compared the number of filtered candidates based solely on the labeling patterns obtained from unlabeled, single, dual, and triple isotope-labeled precursor tracing experiments. On average, TLEMMA eliminates the number of false candidates by 99.1% compared with unlabeled samples, 95% compared with single isotope-labeled samples, and 66.7% compared with dual isotope-labeled samples. This significant reduction in the number of false positives, along with the ability to identify previously unreported metabolites, demonstrates the power of TLEMMA in advancing the field of metabolomics and metabolic network reconstruction.