Automated parallel derivatization of metabolites with SWATH-MS data acquisition for qualitative and quantitative analysis

For metabolite profiling chemical derivatization has been used to improve MS sensitivity and LC retention. However, for multi-analytes quantification, the number of commercially available isotopically labelled internal standards is limited. Besides, there is no single workflow which can provide large-scale metabolomics coverage in particular for polar metabolites. To overcome these limitations and to improve reproducibility a fully automated dual derivatization approach was developed. Differential Isotope Labeling (DIL) was adopted by derivatizing carbonyl, amino and phenol metabolites with two isotopic forms. Urine samples were derivatized with C-12-dansyl chloride (DnsCl) and C-12-dansylhydrazine (DnsHz). Suitable quantification standards were generated by derivatized 40 standards including amino acids, sex hormones and other highly polar metabolites with labelled C-13(2)-dansyl chloride and C-13(2)-dansylhydrazine. The derivatization of the standards and the urine sample was performed using a PAL RTC autosampler in-line to column-switching LC-HRMS analysis with data independent acquisition (SWATH-MS). The parallel reactions were completed in 15 min inside of two agitators at different conditions overlapping with the LC-MS analysis time which was of 25 min. The column switching setup is critical to remove the excess of reagents which can negatively affect the ionization efficiency and deteriorate the chromatographic performance. The combination of dual DIL with SWATH-MS acquisition enables post-identification of unknown metabolites and quantitation at precursor (MS1) and specific tag fragment (MS2) levels. The inter- and intra-batch accuracy and precision of the method fall in the range +/- 15% using single point calibration, and at MS1 or MS2 level providing full flexibility. The method was successfully applied to the analysis of human urine samples. (C) 2020 Elsevier B.V. All rights reserved.