LC-MS Orbitrap-based metabolomics using a novel hybrid zwitterionic hydrophilic interaction liquid chromatography and rigorous metabolite identification reveals doxorubicin-induced metabolic perturbations in breast cancer cells

Abstract

The identification of metabolites in biological samples presents a challenge in untargeted metabolomics, mainly due to limited databases and inadequate chromatography. Current LC columns suffer from high pH instability (silica-based), low efficiencies and pressure limitations (polymer-based), or inadequate retention of polar/semi-polar metabolites (reverse-phase). In this study, a comprehensive LC-MS workflow was developed to address these limitations using a novel zwitterionic HILIC (Z-HILIC), high-resolution MS, deep-scan data-dependent acquisition (DDA), and a large chemical library comprising 990 standards. The method performance was evaluated and compared with a widely-used ZIC-pHILIC method. Z-HILIC detected 707 (71%) of the standards compared to 543 (55%) standards with the ZIC-pHILIC showing enhanced resolution, sensitivity, selectivity and retention time (RT) distribution. In triple-negative Hs578T breast cancer cell extracts spiked with the standards, Z-HILIC annotated 79.1% of the detected standards versus 66.6% with ZIC-pHILIC, demonstrating improved sensitivity, stability, and reduced matrix effects for metabolite profiling. Deep-scan DDA of the spiked cell extracts increased the number of the identified metabolites using RT, m/z and MS/MS by more than 80% compared to standard DDA. The workflow was used to investigate the metabolic signature of doxorubicin-treated Hs578T cells (n = 15). The analysis resulted in identifying 173 metabolites, of which 26 metabolites and 20 metabolic pathways were significantly altered in doxorubicin treated cells compared to controls. These pathways were associated with oxidative stress, mitochondrial dysfunction, and impaired biosynthesis, consistent with prior knowledge about the action of doxorubicin. This comprehensive workflow promises to enhance metabolite profiling across diverse metabolomics studies.