High-resolution accurate mass- mass spectrometry based- untargeted metabolomics: Reproducibility and detection power across data-dependent acquisition, data-independent acquisition, and AcquireX

Abstract

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.

Keywords: AcquireX; DDA; DIA; detection power; reproducibility.

Graphical abstract

Fig. 1

Retention times (min) of 14 eicosanoid standards measured in StdMix samples over 21 days. The standards, present at 50 ng/mL each with 50 µg/mL BHT in 1:1H₂O:MeOH (v/v), were analyzed in quadruplicate on each day. Chromatographic separation was performed using a Kinetex® C18 (100 × 2.1 mm, 2.6 µm) column with a flow rate of 0.5 mL/min. The mobile phases consisted of H₂O/ACN (95:5, v/v, 0.05 % FA) as phase A and ACN/IPA (50:50, v/v, 0.05 % FA) as phase B. The gradient was 0 min 100 % A, 35.9 min 0 % A, 40.9 min 0 % A, and 41 min 100 % A, followed by 7.5 min re-equilibration. The column and autosampler were maintained at 50 °C and 10 °C, respectively.

Fig. 2

Comparison of the number of detected features (left), MS¹-annotated features (middle; based on mass list and ChemSpider), and MS²-matched features (right; based on mzCloud) across three acquisition modes: AcquireX, DDA, and DIA. Analyses were performed in negative-ion mode on unspiked total lipid extract (TLE) samples. Each data point represents the mean of three replicate injections per mode per week. Values shown are after background subtraction. The calculated MS²-based identification rate relative to total detected features is 7.6 % for AcquireX and 5 % for DDA and DIA.

Fig. 3

Overlapped MS¹ and MS² features in TLE samples across three independent measurements acquired using AcquireX, DDA, and DIA modes. The overlap between all three weeks was calculated as the proportion of features shared across all datasets relative to the total number of unique (non-redundant) features detected in the three measurements. Similarly, pairwise overlaps between two weeks were calculated as the proportion of features shared between the respective datasets relative to the total number of unique features observed in those two weeks.

Fig. 4

Identification accuracy and reproducibility of eicosanoid spiking standards analyzed using AcquireX, DDA, and DIA across three independent weekly measurements. The figure includes three panels, one per acquisition mode, with the 13 eicosanoid standards plotted on the y-axis and the weekly measurements on the x-axis. MS¹ and MS² data were acquired from TLE samples spiked at 10 ng/mL for DDA and DIA. For AcquireX, MS¹ data were acquired at 10 ng/mL spiking, whereas ID-TLE samples used for MS² acquisition were spiked at 0.01 ng/mL. Data were processed using an untargeted workflow, with feature annotation based on publicly available libraries (MS¹: ChemSpider and mass lists; MS²: mzCloud; see Figs. S2 and S3 in SI). Detection was defined as the presence of a corresponding feature with a mass error ≤ 5 ppm and consistent retention time. The 14 eicosanoid standards were grouped into two panels: the top panel includes standards with available reference spectra in mzCloud used for MS²-based matching, while the bottom panel contains those without mzCloud reference spectra. Correct annotations are indicated with green circles and incorrect annotations with orange triangles.

Fig. 5

Sensitivity assessment of AcquireX, DDA, and DIA for detecting and differentiating low-abundance eicosanoid standards across four decreasing spiking levels. The figure includes three panels, one per acquisition mode, with the 13 eicosanoid standards plotted on the y-axis and the spiking levels on the x-axis. For each concentration level, a spiked sample was compared to an unspiked TLE sample to mimic a biomarker discovery setup. Detection was defined as the presence of a corresponding feature with a mass error ≤ 5 ppm and consistent retention time. Color coding reflects the outcome for each compound: lilac indicates not detected, orange indicates detected but not statistically significant, and green indicates detection with significant upregulation (p < 0.05 and fold-change ≥ 2). Results are based on data acquired during the second weekly measurement.