Optimisation of LCMS QTOF settings for untargeted serum metabolomic profiling of post COVID-19 syndrome

Introduction: Post-COVID-19 Syndrome (PCS) is a complex condition characterized by prolonged symptoms and metabolic disturbances following SARS-CoV-2 infection. Metabolomics, an approach to examine metabolites modifications, has potential in clarifying the underlying mechanisms of PCS and discovering disease biomarkers. This study aimed to optimize the setting of Liquid chromatography mass spectrometry quadrupole-time-of-flight (LCMS-QTOF) to achieve the most efficient and highly reproducible serum metabolomic profiling in PCS patients. Materials and method: Serum samples were collected from PCS patients who were recruited at post-COVID-19 clinic and the diagnosis was confirmed by physicians. Inclusion criteria included persistent symptoms such as cough, dyspnea or/and fatigue more than three months post infection with exclusion of other differential diagnoses. The serum samples were prepared using methanol as the solvent. Metabolomic analysis was performed using Agilent Technologies 6550 iFunnel Q-TOF LC/MS machine, operated in both positive and negative ionization. The mobile phase gradient was adjusted to optimize the separation and detection of metabolites, according to the graph of peak intensities versus retention time. The graph peak quality and total number of identified compounds yielded for each setting were documented. Results: Positive ionization showed better graph peak distribution over time and yielded a greater number of detectable and identified compounds than negative ionization, (132 compounds, 38 identified vs. 122 compounds, 31 identified). Consistent, equally distributed resolved peaks and more detectable compounds were found when the mobile phase A gradient percentage was adjusted to 100%, 70%, 58%, 30% and 0%, while mobile phase B gradient percentage was adjusted to 0%, 30%, 42%, 70%, 100% at 0, 3, 9, 14 and 16 minutes. Conclusion: Positive ionization and adjustments to the mobile phase gradient improved detection of metabolites. This optimization phase is crucial for identifying key metabolic changes in PCS and useful as a reference for future metabolomics studies.