High-resolution accurate mass- mass spectrometry based- untargeted metabolomics: Reproducibility and detection power across data-dependent acquisition, data-independent acquisition, and AcquireX.
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引用次数: 0
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.
期刊介绍:
Computational and Structural Biotechnology Journal (CSBJ) is an online gold open access journal publishing research articles and reviews after full peer review. All articles are published, without barriers to access, immediately upon acceptance. The journal places a strong emphasis on functional and mechanistic understanding of how molecular components in a biological process work together through the application of computational methods. Structural data may provide such insights, but they are not a pre-requisite for publication in the journal. Specific areas of interest include, but are not limited to:
Structure and function of proteins, nucleic acids and other macromolecules
Structure and function of multi-component complexes
Protein folding, processing and degradation
Enzymology
Computational and structural studies of plant systems
Microbial Informatics
Genomics
Proteomics
Metabolomics
Algorithms and Hypothesis in Bioinformatics
Mathematical and Theoretical Biology
Computational Chemistry and Drug Discovery
Microscopy and Molecular Imaging
Nanotechnology
Systems and Synthetic Biology