Multiparametric Optimization of Data-Dependent Acquisition Towards More Holistic Bacterial Metabolite Coverage Through Molecular Networking.

Abstract

Prokaryotic organisms rely on a limited array of metabolites for survival, which varies according to their natural environment. For example, soil-borne bacteria produce diverse metabolites, such as antibiotics, to thrive in their competitive surroundings, inhibiting the growth of nearby competing bacteria. The structural diversity of these compounds offers great analytical challenges, since there is no universal acquisition setting that can be applied to achieve their comprehensive coverage. Therefore, the use of a single experimental setup inevitably hinders the comprehensive metabolite coverage, which would affect the outputs. To address this, we propose employing a design of experiment (DoE) approach through the central composite design (CCD) to enhance the metabolite detection and broaden the coverage of the data-dependent acquisition (DDA) mode of the UHPLC-qTOF-MS technique. Our study reveals that altering collision energy significantly enhances metabolite coverage compared to adjusting the DDA threshold of detection. Furthermore, the ability of global natural product social (GNPS)-based molecular network models to annotate metabolites is greatly influenced by data acquisition settings, particularly affecting MS² data. Interestingly, molecular networks constructed from averaged spectral data obtained through randomly selected DDA settings outperform those generated using customized settings through DoE modeling. This study demonstrates that in untargeted LC-MS metabolomics, both collision energy and intensity threshold independently enhance metabolite coverage in untargeted metabolomics. However, their combined use results in even greater coverage. Consequently, we recommend adopting group-based optimization over single-point optimization for more comprehensive metabolite coverage and in-depth exploration. However, caution should be taken in order to balance between robust data and redundancy.