Non-targeted liquid chromatography−tandem mass spectrometry (LC−MS/MS) is a widely used tool for metabolomics analysis, enabling the detection and annotation of small molecules in complex environmental samples. Data-dependent acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage, and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with classical and feature-based molecular networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality-related metrics such as annotation and singleton rates, MS/MS placement, and coverage. Our results show that automatic gain control, microscans, mass resolving power, and dynamic exclusion are the most critical parameters, whereas collision energy, TopN, and isolation width had moderate and apex trigger, monoisotopic selection, and isotopic exclusion minor effects. The insights into the data acquisition ergonomics of the Q Exactive platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.

No abstract

Non-targeted liquid chromatography tandem mass spectrometry (LC-MS/MS) is a widely used tool for the detection and annotation of small molecules in complex environmental samples. Data Dependent Acquisition (DDA) of product ion spectra is thereby currently one of the most frequently applied data acquisition strategies. The optimization of DDA parameters is central to ensuring high spectral quality, coverage and number of compound annotations. Here, we evaluated the influence of 10 central DDA settings of the Q Exactive mass spectrometer on natural organic matter samples from ocean, river, and soil environments. After data analysis with Classical and Feature-based Molecular Networking using MZmine and GNPS, we compared the total number of network nodes, multivariate clustering, and spectrum quality related metrics such as annotation and singleton rates, MS/MS placement and coverage. Our results show that Automatic Gain Control, Microscans, Mass Resolution, and Dynamic Exclusion are the most critical parameters, whereas Collision Energy, TopN, and Isolation Width had moderate and Apex Trigger, Monoisotopic Selection, and Isotopic Exclusion minor effects. The insights into the data acquisition ergonomics of the Orbitrap platform presented here can guide new users and provide them with initial method parameters, some of which may also be transferable to other sample types and MS platforms.

No abstract