High-resolution accurate mass- mass spectrometry based- untargeted metabolomics: Reproducibility and detection power...

Untargeted metabolomics aims at the unbiased metabolic profiling and biomarker discovery but requires methods with high sensitivity and reproducibility. Here, we compare three acquisition modes-Data-Dependent Acquisition (DDA), Data-Independent Acquisition (DIA), and AcquireX -to evaluate performance and reproducibility in detecting low-abundance metabolites in a complex matrix. A system suitability test (SST) based on 14 eicosanoid standards was implemented to evaluate the suitability of our instrumental setup prior to conducting untargeted metabolomics analyses and monitor long-term system performance. Bovine liver total Lipid Extract (TLE) was spiked with decreasing levels (10-0.01 ng/mL) of the eicosanoid standard mix (StdMix) to compare the detection power of each mode. Reproducibility was evaluated over three independent measurements, spaced one week apart. Chromatographic separation was performed on a C18-Kinetex Core-Shell column and HRAM-MS/MS data were acquired using an Orbitrap Exploris 480. DIA detected and identified the highest number of metabolic features, (averaging 1036 metabolic features over three measurements), followed by DDA (18 % fewer) and AcquireX (37 % fewer). Moreover, DIA demonstrated superior reproducibility, with a coefficient of variance of 10 % across detected compounds over three measurements, compared to 17 % for DDA and 15 % for AcquireX. DIA further exhibited better compound identification consistency, with 61 % overlap between two days, compared to DDA (43 %) and AcquireX (50 %). DIA reproduced fragmentation spectra patterns with high consistency, contributing to higher reproducibility in compound identification. DIA showed the best detection power for all spiking eicosanoids at 10 and 1 ng/mL in TLE matrix. At low spiking levels, 0.1 and 0.01 ng/mL, a general cut-off was observed for the three acquisition modes. None of this assessed acquisition modes was able to detect and/or identify eicosanoids at physiologically relevant concentrations, explaining their frequent omission in routine untargeted analyses.